Search results for: task offloading

Authors: Muhamad Hafiz Ismail, Ahmad H., Nelfianty M. R.

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The longitudinal study is conducted to identify predictive factors of soccer talent among male players aged 14 to 17 years. Convenience sampling involving elite respondents (n=20) and sub-elite respondents (n=20) male soccer players. Descriptive statistics were reported as frequencies and percentages. The inferential statistical analysis is used to report the status of reliability, independent samples t-test, paired samples t-test, and multiple regression analysis. Generally, there are differences in mean of height, muscular strength, muscular endurance, cardiovascular endurance, task orientation, cognitive anxiety, self-confidence, juggling skills, short pass skills, long pass skills, dribbling skills, and shooting skills for 20 elite players and sub-elite players. Accordingly, there was a significant difference between pre and post-test for thirteen variables of height, weight, fat percentage, muscle strength, muscle endurance, cardiovascular endurance, flexibility, BMI, task orientation, juggling skills, short pass skills, a long pass skills, and dribbling skills. Based on the first predictive factors (physical), second predictive factors (fitness), third predictive factors (psychological), and fourth predictive factors (skills in playing football) pledged to the soccer talent; four multiple regression models were produced. The first predictive factor (physical) contributed 53.5 percent, supported by height and percentage of fat in soccer talents. The second predictive factor (fitness) contributed 63.2 percent and the third predictive factors (psychology) contributed 66.4 percent of soccer talent. The fourth predictive factors (skills) contributed 59.0 percent of soccer talent. The four multiple regression models could be used as a guide for talent scouting for soccer players of the future.

Keywords: soccer talent identification, fitness and physical test, soccer skills test, psychological test

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Authors: Filippo Portera

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Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we present several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vextor Classification and a regression net for Multylayer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better.

Keywords: loss, binary-classification, MLP, weights, regression

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Authors: Chih-Jer Lin, Jian-Hong Hou

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Industry 4.0 is a vision of integrated industry implemented by artificial intelligent computing, software, and Internet technologies. The main goal of industry 4.0 is to deal with the difficulty owing to competitive pressures in the marketplace. For today’s manufacturing factories, the type of production is changed from mass production (high quantity production with low product variety) to medium quantity-high variety production. To offer flexibility, better quality control, and improved productivity, robot manipulators are used to combine material processing, material handling, and part positioning systems into an integrated manufacturing system. To implement the automated system for sheet metal bending operations, motion planning of a 7-degrees of freedom (DOF) robot is studied in this paper. A virtual reality (VR) environment of a bending cell, which consists of the robot and a bending machine, is established using the virtual robot experimentation platform (V-REP) simulator. For sheet metal bending operations, the robot only needs six DOFs for the pick-and-place or tracking tasks. Therefore, this 7 DOF robot has more DOFs than the required to execute a specified task; it can be called a redundant robot. Therefore, this robot has kinematic redundancies to deal with the task-priority problems. For redundant robots, Pseudo-inverse of the Jacobian is the most popular motion planning method, but the pseudo-inverse methods usually lead to a kind of chaotic motion with unpredictable arm configurations as the Jacobian matrix lose ranks. To overcome the above problem, we proposed a method to formulate the motion planning problems as optimization problem. Moreover, a genetic algorithm (GA) based method is proposed to deal with motion planning of the redundant robot. Simulation results validate the proposed method feasible for motion planning of the redundant robot in an automated sheet-metal bending operations.

Keywords: redundant robot, motion planning, genetic algorithm, obstacle avoidance

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Authors: Talha Ali Khan

Abstract:

Discovering different energy resources to full fill the world growing demand is now one of the society’s bigger challenge for the next half-century. The main task is to convert the sun radiation into electricity via photovoltaic solar cells which is suddenly decreasing $/watt of delivered solar electricity. Therefore, in this context, the sun trackers are those devices that can be used to ameliorate efficiency. In this paper, a variety of the sun tracking systems are evaluated and their merits and demerits are highlighted. The most adept and proficient sun-tracking devices are polar axis and azimuth-elevation types.

Keywords: dual axis, fixed axis, sun tracker, MPPT

Procedia PDF Downloads 578

Authors: Anna Rinaldi, Pierfrancesco Dellino

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COVID-19 vaccine hesitancy can be observed at different rates in different countries. In June 2021, 1,068 people were surveyed in France and Italy to inquire about individual potential acceptance, focusing on time preferences in a risk-return framework: having the vaccination today, in a month, and in 3 months; perceived risks of vaccination and COVID-19; and expected benefit of the vaccine. A randomized controlled trial was conducted to understand how everyday stimuli like fact-based news about vaccines impact an audience's acceptance of vaccination. The main experiment involved two groups of participants and two different articles about vaccine-related thrombosis taken from two Italian newspapers. One article used a more abstract description and language, and the other used a more anecdotal description and concrete language; each group read only one of these articles. Two other groups were assigned categorization tasks; one was asked to complete a concrete categorization task, and the other an abstract categorization task. Individual preferences for vaccination were found to be variable and unstable over time, and individual choices of accepting, refusing, or delaying could be affected by the way news is written. In order to understand these dynamic preferences, the present work proposes a new model based on seven categories of human behaviors that were validated by a neural network. A treatment effect was observed: participants who read the articles shifted to vaccine hesitancy categories more than participants assigned to other treatments and control. Furthermore, there was a significant gender effect, showing that the type of language leading to a lower hesitancy rate for men is correlated with a higher hesitancy rate for women and vice versa. This outcome should be taken into consideration for an appropriate gender-based communication campaign aimed at achieving herd immunity. The trial was registered at ClinicalTrials.gov NCT05582564 (17/10/2022).

Keywords: vaccine hesitancy, risk elicitation, neural network, covid19

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Authors: Km Tanu

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It has been widely accepted that the learning style of adults and children is different, the learning motivation among adults vary, and even their learning preferences cannot be predetermined. In India, where the population is widely diverse and socio-economic and cultural disparities are there, the learning strategies should also be according to their needs and preferences. The present study explores the concept of adult learners in India in order to understand their needs and styles better. The adult learning principles of Malcolm Knowles have been analyzed, and its presence in the different policies and programs has been traced. To what extent these principles and other such concepts would be beneficial for the Indian population and for effective learning strategies, and what contextual understanding is needed, has been argued in the study. Descriptive research methodology, along with content and thematic analyses, has been used for the paper. It has been argued that there are four areas that play crucial roles in making learning effective. These are the learner, the facilitator, the resources and the policy. The prior experiences of the learners, their motivation, the group to which they belong (i.e., the learning styles and the strategies can be varied for the group of farmers and migrant laborers), and their expected outcome play an important role in making any adult education program successful but along with this, the role of facilitator or the educator is also very important as it is not easy to deal with the adult learners, the understanding that the task is not to teach the adult learners but to make them learn and to use their prior knowledge is a task in itself, proper training is needed for that matter. Many times, it has been seen that adult education programs are poorly funded, or even if they are funded, the fund is not utilized well; the unavailability of the resources is one of the reasons for the failure of adult education programs, and if we see these four points as a triangle, at the bottom, there is a policy document. A well-stated and described doable policy document is also equally important.

Keywords: adult education, Indian adult learner, effective learning styles, Malcolm Knowles learning principles, adult education policies and program

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Authors: Madelaine F. Gatchalian, Mary Jane D. Tepora

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This study was designed to determine the relationship between learning environment and motivation of CNHS, SY 2012-2013. There were 376 respondents taken randomly. Frequency distribution, percentage, mean, standard deviation, Mann Whitney Test, Kruskall Wallis One-way ANOVA and Spearman Rank Correlational Coefficient were used in analyzing the data. As to age, most of the respondents were 13 years old while female students outnumbered the male students. Majority of parents’ educational attainment of CNHS students were high school/vocational graduates. Most fathers worked in the private sector, while majority of the mothers were unemployed whose family income range from Php 5,000.00 to Php 14,999.00. Most of the respondents were first child composed of five family members. Findings showed no significant differences in perceived learning environment when respondents were grouped in terms of age, sex, parents’ educational attainment, parents’ occupation, sibling order and number of family members. Only monthly family income showed significant differences in perceived learning environment. There are no significant differences in perceived learning motivation when respondents were grouped in terms of age, sex, parents’ educational attainment (father), parents’ occupation (father), sibling order, and number of family members. Parents’ educational attainment (mother), parents’ occupation (mother) and monthly family income showed significant differences in perceived learning motivation. There is significant relationship between the six subscales of perceived learning environment, namely: student cohesiveness, teacher support, involvement, task orientation, cooperation and equity and perceived learning motivation of CNHS students, SY, 2012-2013. The results of this study indicated that learning environment including student cohesiveness, teachers support, involvement, task orientation, cooperation and equity is significantly related to students’ learning motivation.

Keywords: learning environment, motivation, demographic profile, secondary students

Procedia PDF Downloads 376

Authors: Talha Ali Khan

Abstract:

Discovering different energy resources to fulfill the world's growing demand is now one of the society’s bigger challenges for the next half-century. The main task is to convert the sun radiation into electricity via photovoltaic solar cells which is suddenly decreasing $/watt of delivered solar electricity. Therefore, in this context the sun trackers are those devices that can be used to ameliorate efficiency. In this paper, a variety of the sun tracking systems are evaluated and their merits and demerits are highlighted. The most adept and proficient sun-tracking devices are polar axis and azimuth-elevation types.

Keywords: dual axis, fixed axis, sun tracker, sun radiation

Procedia PDF Downloads 454

Authors: Hadia Rafi

Abstract:

To make any decision in any work/task/project it involves many factors that needed to be looked. The analytic Hierarchy process (AHP) is based on the judgments of experts to derive the required results this technique measures the intangibles and then by the help of judgment and software analysis the comparisons are made which shows how much a certain element/unit leads another. AHP includes how an inconsistent judgment should be made consistent and how the judgment should be improved when possible. The Priority scales are obtained by multiplying them with the priority of their parent node and after that they are added.

Keywords: AHP, priority scales, parent node, software analysis

Procedia PDF Downloads 406

Authors: Elahe Kamari

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Research on translation process has been conducted for more than 20 years, investigating various issues and using different research methodologies. Most recently, researchers have started to use eye tracking to study translation processes. They believed that the observable, measurable data that can be gained from eye tracking are indicators of unobservable cognitive processes happening in the translators’ mind during translation tasks. The aim of this study was to investigate directionality in translation processes through using eye tracking. The following hypotheses were tested: 1) processing the target text requires more cognitive effort than processing the source text, in both directions of translation; 2) L2 translation tasks on the whole require more cognitive effort than L1 tasks; 3) cognitive resources allocated to the processing of the source text is higher in L1 translation than in L2 translation; 4) cognitive resources allocated to the processing of the target text is higher in L2 translation than in L1 translation; and 5) in both directions non-professional translators invest more cognitive effort in translation tasks than do professional translators. The performance of a group of 30 male professional translators was compared with that of a group of 30 male non-professional translators. All the participants translated two comparable texts one into their L1 (Persian) and the other into their L2 (English). The eye tracker measured gaze time, average fixation duration, total task length and pupil dilation. These variables are assumed to measure the cognitive effort allocated to the translation task. The data derived from eye tracking only confirmed the first hypothesis. This hypothesis was confirmed by all the relevant indicators: gaze time, average fixation duration and pupil dilation. The second hypothesis that L2 translation tasks requires allocation of more cognitive resources than L1 translation tasks has not been confirmed by all four indicators. The third hypothesis that source text processing requires more cognitive resources in L1 translation than in L2 translation and the fourth hypothesis that target text processing requires more cognitive effort in L2 translation than L1 translation were not confirmed. It seems that source text processing in L2 translation can be just as demanding as in L1 translation. The final hypothesis that non-professional translators allocate more cognitive resources for the same translation tasks than do the professionals was partially confirmed. One of the indicators, average fixation duration, indicated higher cognitive effort-related values for professionals.

Keywords: translation processes, eye tracking, cognitive resources, directionality

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Authors: Luis Alvarado, Victor Poblete, Isaac Gonzalez, Yetzabeth Gonzalez

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In MIREX 2016 (http://www.music-ir.org/mirex), the deep neural network (DNN)-Deep Chroma Extractor, proposed by Korzeniowski and Wiedmer, reached the highest score in an audio chord recognition task. In the present paper, this tool is assessed under acoustic reverberant environments and distinct source-microphone distances. The evaluation dataset comprises The Beatles and Queen datasets. These datasets are sequentially re-recorded with a single microphone in a real reverberant chamber at four reverberation times (0 -anechoic-, 1, 2, and 3 s, approximately), as well as four source-microphone distances (32, 64, 128, and 256 cm). It is expected that the performance of the trained DNN will dramatically decrease under these acoustic conditions with signals degraded by room reverberation and distance to the source. Recently, the effect of the bio-inspired Locally-Normalized Cepstral Coefficients (LNCC), has been assessed in a text independent speaker verification task using speech signals degraded by additive noise at different signal-to-noise ratios with variations of recording distance, and it has also been assessed under reverberant conditions with variations of recording distance. LNCC showed a performance so high as the state-of-the-art Mel Frequency Cepstral Coefficient filters. Based on these results, this paper proposes a variation of locally-normalized triangular filters called Locally-Normalized Quarter Tone (LNQT) filters. By using the LNQT spectrogram, robustness improvements of the trained Deep Chroma Extractor are expected, compared with classical triangular filters, and thus compensating the music signal degradation improving the accuracy of the chord recognition system.

Keywords: chord recognition, deep neural networks, feature extraction, music information retrieval

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Authors: Karen Jacob

Abstract:

Over the last decades, there has been a rise in the use of CLIL (content and language integrated learning) methodology as a way of reinforcing FL (foreign language) acquisition. CLIL techniques have also been transferred to the formal instruction-based FL classroom where through content-based lessons and project work it can very often say that teachers are ‘clilling’ in the FL classroom. When it comes to motivating students to acquire an FL, we have to take into account that English is not your run-of-the-mill FL: English is an international language (EIL). Consequently, this means that EFL students should be able to use English as an international medium of communication. This leads to the assumption that along with FL competence, speakers of EIL will need to become competent international citizens with knowledge of other societies, both contextually and geographically, and be flexible, open-minded, respectful and sensitive towards other world groups. Rather than ‘intercultural’ competence we should be referring to ‘transcultural’ competence. This paper reports the implementation of a content- and task-based approach to EFL teaching which was applied to two groups of 15 year-olds from two schools on the Spanish island of Mallorca during the school year 2015-2016. Students worked on three units of work that aimed at ‘internationalising’ the classroom by introducing topics that would encourage them to become transculturally aware of the world in which they live. In this paper we discuss the feedback given by the teachers and students on various aspects of the approach in order to answer the following research questions: 1) To what extent were the students motivated by the content and activities of the classes?; 2) Did this motivation have a positive effect on the students’ overall results for the subject; 3) Did the participants show any signs of becoming transculturally aware. Preliminary results from qualitative data show that the students enjoyed the move away from the more traditional EFL content and, as a result, they became more competent in speaking and writing. Students also appeared to become more knowledgeable and respectful towards the ‘other’. The EFL approach described in this paper takes a more qualitative approach to research by describing what is really going on in the EFL classroom and makes a conscious effort to provide real examples of not only the acquisition of linguistic competence but also the acquisition of other important communication skills that are of utmost importance in today's international arena.

Keywords: CLIL, content- and task-based learning, internationalisation, transcultural competence

Procedia PDF Downloads 241

Authors: Kwan Hee Han

Abstract:

External environment of enterprise is rapidly changing majorly by global competition, cost reduction pressures, and new technology. In these situations, production scheduling function plays a critical role to meet customer requirements and to attain the goal of operational efficiency. It deals with short-term decision making in the production process of the whole supply chain. The major task of production scheduling is to seek a balance between customer orders and limited resources. In manufacturing companies, this task is so difficult because it should efficiently utilize resource capacity under the careful consideration of many interacting constraints. At present, many computerized software solutions have been utilized in many enterprises to generate a realistic production schedule to overcome the complexity of schedule generation. However, most production scheduling systems do not provide sufficient information about the validity of the generated schedule except limited statistics. Process mining only recently emerged as a sub-discipline of both data mining and business process management. Process mining techniques enable the useful analysis of a wide variety of processes such as process discovery, conformance checking, and bottleneck analysis. In this study, the performance of generated production schedule is evaluated by mining event log data of production scheduling software system by using the process mining techniques since every software system generates event logs for the further use such as security investigation, auditing and error bugging. An application of process mining approach is proposed for the validation of the goodness of production schedule generated by scheduling software systems in this study. By using process mining techniques, major evaluation criteria such as utilization of workstation, existence of bottleneck workstations, critical process route patterns, and work load balance of each machine over time are measured, and finally, the goodness of production schedule is evaluated. By using the proposed process mining approach for evaluating the performance of generated production schedule, the quality of production schedule of manufacturing enterprises can be improved.

Keywords: data mining, event log, process mining, production scheduling

Procedia PDF Downloads 279

Authors: Jérémie Ochin

Abstract:

Soccer analytics are built on two data sources: the frame-by-frame position of each player on the terrain and the sequences of events, such as ball drive, pass, cross, shot, throw-in... With more than 2000 ball-events per soccer game, their precise and exhaustive annotation, based on a monocular video stream such as a TV broadcast, remains a tedious and costly manual task. State-of-the-art methods for spatio-temporal action detection from a monocular video stream, often based on 3D convolutional neural networks, are close to reach levels of performances in mean Average Precision (mAP) compatibles with the automation of such task. Nevertheless, to meet their expectation of exhaustiveness in the context of data analytics, such methods must be applied in a regime of high recall – low precision, using low confidence score thresholds. This setting unavoidably leads to the detection of false positives that are the product of the well documented overconfidence behaviour of neural networks and, in this case, their limited access to contextual information and understanding of the game: their predictions are highly unstructured. Based on the assumption that professional soccer players’ behaviour, pose, positions and velocity are highly interrelated and locally driven by the player performing a ball-action, it is hypothesized that the addition of information regarding surrounding player’s appearance, positions and velocity in the prediction methods can improve their metrics. Several methods are compared to build a proper representation of the game surrounding a player, from handcrafted features of the local graph, based on domain knowledge, to the use of Graph Neural Networks trained in an end-to-end fashion with existing state-of-the-art 3D convolutional neural networks. It is shown that the inclusion of information regarding surrounding players helps reaching higher metrics.

Keywords: fine-grained action recognition, human action recognition, convolutional neural networks, graph neural networks, spatio-temporal action recognition

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Authors: Rohit T. P., Sahil Athrij, Sasi Gopalan

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Currently, the content entertainment industry is dominated by mobile devices. As the trends slowly shift towards Augmented/Virtual Reality applications the computational demands on these devices are increasing exponentially and we are already reaching the limits of hardware optimizations. This paper proposes a software solution to this problem. By leveraging the capabilities of cloud computing we can offload the work from mobile devices to dedicated rendering servers that are way more powerful. But this introduces the problem of latency. This paper introduces a protocol that can achieve high-performance low latency Augmented/Virtual Reality experience. There are two parts to the protocol, 1) In-flight compression The main cause of latency in the system is the time required to transmit the camera frame from client to server. The round trip time is directly proportional to the amount of data transmitted. This can therefore be reduced by compressing the frames before sending. Using some standard compression algorithms like JPEG can result in minor size reduction only. Since the images to be compressed are consecutive camera frames there won't be a lot of changes between two consecutive images. So inter-frame compression is preferred. Inter-frame compression can be implemented efficiently using WebGL but the implementation of WebGL limits the precision of floating point numbers to 16bit in most devices. This can introduce noise to the image due to rounding errors, which will add up eventually. This can be solved using an improved interframe compression algorithm. The algorithm detects changes between frames and reuses unchanged pixels from the previous frame. This eliminates the need for floating point subtraction thereby cutting down on noise. The change detection is also improved drastically by taking the weighted average difference of pixels instead of the absolute difference. The kernel weights for this comparison can be fine-tuned to match the type of image to be compressed. 2) Dynamic Load distribution Conventional cloud computing architectures work by offloading as much work as possible to the servers, but this approach can cause a hit on bandwidth and server costs. The most optimal solution is obtained when the device utilizes 100% of its resources and the rest is done by the server. The protocol balances the load between the server and the client by doing a fraction of the computing on the device depending on the power of the device and network conditions. The protocol will be responsible for dynamically partitioning the tasks. Special flags will be used to communicate the workload fraction between the client and the server and will be updated in a constant interval of time ( or frames ). The whole of the protocol is designed so that it can be client agnostic. Flags are available to the client for resetting the frame, indicating latency, switching mode, etc. The server can react to client-side changes on the fly and adapt accordingly by switching to different pipelines. The server is designed to effectively spread the load and thereby scale horizontally. This is achieved by isolating client connections into different processes.

Keywords: 2D kernelling, augmented reality, cloud computing, dynamic load distribution, immersive experience, mobile computing, motion tracking, protocols, real-time systems, web-based augmented reality application

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Authors: Drew Commandeur, Ryan Brodie, Sandra Hundza, Marc Klimstra

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The amplitude of raw electromyography (EMG) is affected by recording conditions and often requires normalization to make meaningful comparisons. Bioelectric methods normalize with an EMG signal recorded during a standardized task or from the experimental protocol itself, while biomechanical methods often involve measurements with an additional sensor such as a force transducer. Common bioelectric normalization techniques for treadmill walking include maximum voluntary isometric contraction (MVIC), dynamic EMG peak (EMGPeak) or dynamic EMG mean (EMGMean). There are several concerns with using MVICs to normalize EMG, including poor reliability and potential discomfort. A limitation of bioelectric normalization techniques is that they could result in a misrepresentation of the absolute magnitude of force generated by the muscle and impact the interpretation of EMG between functionally disparate groups. Additionally, methods that normalize to EMG recorded during the task may eliminate some real inter-individual variability due to biological variation. This study compared biomechanical and bioelectric EMG normalization techniques during treadmill walking to assess the impact of the normalization method on the functional interpretation of EMG data. For the biomechanical method, we normalized EMG to a target torque (EMGTS) and the bioelectric methods used were normalization to the mean and peak of the signal during the walking task (EMGMean and EMGPeak). The effect of normalization on muscle activation pattern, EMG amplitude, and inter-individual variability were compared between disparate cohorts of OLD (76.6 yrs N=11) and YOUNG (26.6 yrs N=11) adults. Participants walked on a treadmill at a self-selected pace while EMG was recorded from the right lower limb. EMG data from the soleus (SOL), medial gastrocnemius (MG), tibialis anterior (TA), vastus lateralis (VL), and biceps femoris (BF) were phase averaged into 16 bins (phases) representing the gait cycle with bins 1-10 associated with right stance and bins 11-16 with right swing. Pearson’s correlations showed that activation patterns across the gait cycle were similar between all methods, ranging from r =0.86 to r=1.00 with p<0.05. This indicates that each method can characterize the muscle activation pattern during walking. Repeated measures ANOVA showed a main effect for age in MG for EMGPeak but no other main effects were observed. Interactions between age*phase of EMG amplitude between YOUNG and OLD with each method resulted in different statistical interpretation between methods. EMGTS normalization characterized the fewest differences (four phases across all 5 muscles) while EMGMean (11 phases) and EMGPeak (19 phases) showed considerably more differences between cohorts. The second notable finding was that coefficient of variation, the representation of inter-individual variability, was greatest for EMGTS and lowest for EMGMean while EMGPeak was slightly higher than EMGMean for all muscles. This finding supports our expectation that EMGTS normalization would retain inter-individual variability which may be desirable, however, it also suggests that even when large differences are expected, a larger sample size may be required to observe the differences. Our findings clearly indicate that interpretation of EMG is highly dependent on the normalization method used, and it is essential to consider the strengths and limitations of each method when drawing conclusions.

Keywords: electromyography, EMG normalization, functional EMG, older adults

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: A. V. Sokolov, S. V. Vorobyev, A. Yu. Efimtcev, V. Yu. Lobzin, I. A. Lupanov, O. A. Cherdakov, V. A. Fokin

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Alzheimer’s disease (AD) is the most common form of dementia. Different brain regions are involved to the pathological process of AD. The purpose of this study was to evaluate brain activation by visual memory task in patients with Alzheimer's disease and determine correlation between memory impairment and atrophy of memory specific brain regions of frontal and medial temporal lobes. To investigate the organization of memory and localize cortical areas activated by visual memory task we used functional magnetic resonance imaging and to evaluate brain atrophy of patients with Alzheimer's disease we used voxel-based morphometry. FMRI was performed on 1.5 T MR-scanner Siemens Magnetom Symphony with BOLD (Blood Oxygenation Level Dependent) technique, based on distinctions of magnetic properties of hemoglobin. For test stimuli we used series of 12 not related images for "Baseline" and 12 images with 6 presented before for "Active". Stimuli were presented 3 times with reduction of repeated images to 4 and 2. Patients with Alzheimer's disease showed less activation in hippocampal formation (HF) region and parahippocampal gyrus then healthy persons of control group (p<0.05). The study also showed reduced activation in posterior cingulate cortex (p<0.001). Voxel-based morphometry showed significant atrophy of grey matter in Alzheimer’s disease patients, especially of both temporal lobes (fusiform and parahippocampal gyri); frontal lobes (posterior cingulate and superior frontal gyri). The study showed correlation between memory impairment and atrophy of memory specific brain regions of frontal and medial temporal lobes. Thus, reduced activation in hippocampal formation and parahippocampal gyri, in posterior cingulate gyrus in patients with Alzheimer's disease correlates to significant atrophy of these regions, detected by voxel-based morphometry, and to deterioration of specific cognitive functions.

Keywords: Alzheimer’s disease, functional MRI, voxel-based morphometry

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Authors: Hannah Edwards, Femke T. A. Buisman-Pijlman, Adrian Esterman, Craig Phillips, Sandra Orgeig, Andrea Gordon

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Mother-infant bonding is a term which refers to the early emotional connectedness between a mother and her infant. Strong mother-infant bonding promotes higher quality mother and infant interactions including prolonged breastfeeding, secure attachment and increased sensitive parenting and maternal responsiveness. Strengthening of all such interactions leads to improved social behavior, and emotional and cognitive development throughout childhood, adolescence and adulthood. The positive outcomes observed following strong mother-infant bonding emphasize the need to screen new mothers for disrupted mother-infant bonding, and in turn the need for a robust, valid tool to assess mother-infant bonding. A recent scoping review conducted by the research team identified four tools to assess mother-infant bonding, all of which employed self-rating scales. Thus, whilst these tools demonstrated both adequate validity and reliability, they rely on self-reported information from the mother. As such this may reflect a mother’s perception of bonding with their infant, rather than their actual behavior. Therefore, a new tool to assess mother-infant bonding has been developed. The Recorded Interaction Task (RIT) addresses shortcomings of previous tools by employing observational methods to assess bonding. The RIT focusses on the common interaction between mother and infant of changing a nappy, at the target age of 2-6 months, which is visually recorded and then later assessed. Thirteen maternal and seven infant behaviors are scored on the RIT Observation Scoring Sheet, and a final combined score of mother-infant bonding is determined. The aim of the current study was to assess the content validity and inter-rater reliability of the RIT. A panel of six experts with specialized expertise in bonding and infant behavior were consulted. Experts were provided with the RIT Observation Scoring Sheet, a visual recording of a nappy change interaction, and a feedback form. Experts scored the mother and infant interaction on the RIT Observation Scoring Sheet and completed the feedback form which collected their opinions on the validity of each item on the RIT Observation Scoring Sheet and the RIT as a whole. Twelve of the 20 items on the RIT Observation Scoring Sheet were scored ‘Valid’ by all (n=6) or most (n=5) experts. Two items received a ‘Not valid’ score from one expert. The remainder of the items received a mixture of ‘Valid’ and ‘Potentially Valid’ scores. Few changes were made to the RIT Observation Scoring Sheet following expert feedback, including rewording of items for clarity and the exclusion of an item focusing on behavior deemed not relevant for the target infant age. The overall ICC for single rater absolute agreement was 0.48 (95% CI 0.28 – 0.71). Experts (n=6) ratings were less consistent for infant behavior (ICC 0.27 (-0.01 – 0.82)) compared to mother behavior (ICC 0.55 (0.28 – 0.80)). Whilst previous tools employ self-report methods to assess mother-infant bonding, the RIT utilizes observational methods. The current study highlights adequate content validity and moderate inter-rater reliability of the RIT, supporting its use in future research. A convergent validity study comparing the RIT against an existing tool is currently being undertaken to confirm these results.

Keywords: content validity, inter-rater reliability, mother-infant bonding, observational tool, recorded interaction task

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Authors: N. V. Kuznetsov, O. A. Kuznetsova, G. A. Leonov, M. V. Yuldashev, R. V. Yuldashev

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Nonlinear analysis of the phase locked loop (PLL)-based circuits is a challenging task. Thus, the simulation is widely used for their study. In this work, we consider a mathematical model of the optical Costas loop and demonstrate the limitations of simulation approach related to the existence of so-called hidden oscillations in the phase space of the model.

Keywords: optical Costas loop, mathematical model, simulation, hidden oscillation

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Authors: Jordan Gallant

Abstract:

In the compound word processing literature, much attention has been paid to the relationship between a compound’s denotational meaning and that of its morphological whole-word constituents, which is referred to as ‘semantic transparency’. However, the parallel relationship between a compound’s connotation and that of its constituents has not been addressed at all. For instance, while a compound like ‘painkiller’ might be semantically transparent, it is not ‘affectively transparent’. That is, both constituents have primarily negative connotations, while the whole compound has a positive one. This paper investigates the role of affective transparency on compound processing using two methodologies commonly employed in this field: a lexical decision task and a typing task. The critical stimuli used were 112 English bi-constituent compounds that differed in terms of the effective transparency of their constituents. Of these, 36 stimuli contained constituents with similar connotations to the compound (e.g., ‘dreamland’), 36 contained constituents with more positive connotations (e.g. ‘bedpan’), and 36 contained constituents with more negative connotations (e.g. ‘painkiller’). Connotation of whole-word constituents and compounds were operationalized via valence ratings taken from an off-line ratings database. In Experiment 1, compound stimuli and matched non-word controls were presented visually to participants, who were then asked to indicate whether it was a real word in English. Response times and accuracy were recorded. In Experiment 2, participants typed compound stimuli presented to them visually. Individual keystroke response times and typing accuracy were recorded. The results of both experiments provided positive evidence that compound processing is influenced by effective transparency. In Experiment 1, compounds in which both constituents had more negative connotations than the compound itself were responded to significantly more slowly than compounds in which the constituents had similar or more positive connotations. Typed responses from Experiment 2 showed that inter-keystroke intervals at the morphological constituent boundary were significantly longer when the connotation of the head constituent was either more positive or more negative than that of the compound. The interpretation of this finding is discussed in the context of previous compound typing research. Taken together, these findings suggest that affective transparency plays a role in the recognition, storage, and production of English compound words. This study provides a promising first step in a new direction for research on compound words.

Keywords: compound processing, semantic transparency, typed production, valence

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Authors: Lou J. Pino, Mark Campbell, Matthew J. Kennedy, Ashleigh C. Kennedy

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A concussion is complex and nuanced, with cognitive rest being a key component of recovery. Cognitive overexertion during rehabilitation from a concussion is associated with delayed recovery. However, daily living imposes cognitive demands that may be unavoidable and difficult to quantify. Therefore, a portable tool capable of alerting patients before cognitive overexertion occurs could allow patients to maintain their quality of life while preventing symptoms and recovery setbacks. EEG allows for a sensitive measure of cognitive exertion. Clinical 32-lead EEG headsets are not practical for day-to-day concussion rehabilitation management. However, there are now commercially available and affordable portable EEG headsets. Thus, these headsets can potentially be used to continuously monitor cognitive exertion during mental tasks to alert the wearer of overexertion, with the aim of preventing the occurrence of symptoms to speed recovery times. The objective of this study was to test an algorithm for predicting cognitive exertion from EEG data collected from a portable headset. EEG data were acquired from 10 participants (5 males, 5 females). Each participant wore a portable 4 channel EEG headband while completing 10 tasks: rest (eyes closed), rest (eyes open), three levels of the increasing difficulty of logic puzzles, three levels of increasing difficulty in multiplication questions, rest (eyes open), and rest (eyes closed). After each task, the participant was asked to report their perceived level of cognitive exertion using the NASA Task Load Index (TLX). Each participant then completed a second session on a different day. A customized machine learning model was created using data from the first session. The performance of each model was then tested using data from the second session. The mean correlation coefficient between TLX scores and predicted cognitive exertion was 0.75 ± 0.16. The results support the efficacy of the algorithm for predicting cognitive exertion. This demonstrates that the algorithms developed in this study used with portable EEG devices have the potential to aid in the concussion recovery process by monitoring and warning patients of cognitive overexertion. Preventing cognitive overexertion during recovery may reduce the number of symptoms a patient experiences and may help speed the recovery process.

Keywords: cognitive activity, EEG, machine learning, personalized recovery

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Authors: H. Pasternak, A. Chwastek

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The paper describes the results from a research project about repair of welds. The repair was carried out by grinding the flawed seams and re-welding them. The main task was to determine the FAT classes of original state and after repair of seams according to the assessment procedures, such as nominal, structural and effective notch stress approach. The first part shows the results of the tests, the second part encloses numerical analysis and evaluation of results to determine the fatigue strength classes according to three assessment procedures.

Keywords: cyclic loading, fatigue crack, post-weld treatment, seams’ repair

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Authors: Andrey V. Timofeev, Dmitry V. Egorov

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This paper introduces an original method of parametric optimization of the structure for multimodal decision-level fusion scheme which combines the results of the partial solution of the classification task obtained from assembly of the mono-modal classifiers. As a result, a multimodal fusion classifier which has the minimum value of the total error rate has been obtained.

Keywords: classification accuracy, fusion solution, total error rate, multimodal fusion classifier

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Authors: Suraina Sulong

Abstract:

Keyboard support is one of the main accessibility requirements for web pages and web applications for blind user. But it is not sufficient that the blind user can perform all actions on the page using the keyboard. In addition, designers of web sites or web applications have to make sure that keyboard users can use their pages with acceptable performance. We present GOMS models for navigation in web pages with specific task given to the blind user to accomplish. These models can be used to construct the user model for accessible website.

Keywords: GOMS analysis, usability factor, blind user, human computer interaction

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Authors: Kamila Irena Szpunar

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The central idea of the study is considered from two perspectives. The first perspective includes the interpersonal relationships formed by coach and athlete. Another perspective is connected with motivational environment which is created by the coach in team. This study will show the interplay between the perceived motivational climate created by the coach and the interpersonal dynamics between coaches and athletes. It is important because it will supply knowledge of the interpersonal conditions that can foster adaptive or maladaptive behavior in sport conditions. It also ensures implications for understanding how the perceived motivational atmosphere in a team is manifested at the level of coach – athlete relationship and interactions. The primary purpose of the study was to identify the association between coach-athlete relationship and athletes' perception of the motivational climate in team sports. The secondary purposes examined the differences between female and male athletes in perceiving of the motivational climate and the coach athlete-relationship. To check coach-athlete relationship Polish translation of The Coach-Athlete Relationship Questionnaire will be used. It measures athletes' perceptions of coach- athlete relationship defined by 3+1 Cs conceptual model of the coach-athlete relationship. From this model were used three constructs such as closeness (feelings of trust, respect etc.), commitment (thoughts about the future of the relationship), and complementarity (co-operative interactions during practice sessions). To check perceived motivational climate will be used Polish translation of The Perceived Motivational Climate in Sport Questionnaire-2 (PMCSQ-2). PMCSQ-2 was created to assess athletes' perceptions of the motivational climates in their teams. The questionnaire includes two general dimensions, the perceived task-involving climate and the perceived ego-involving climate; each contains three subscales. To check the associations between elements the motivational climate and coach-athlete relationship was used canonical correlation analysis. Student's t-test was used to check gender differences in athletes' perceptions of the motivational climate and the coach-athlete relationship. The findings suggest that in Polish athletes' perceptions of the coach-athlete relationship have motivational significance and that there are gender differences between female and male athletes in both variables – coach-athlete relationship and kind of motivational climate. According to the author's knowledge, such kind of study has not been conducted in Polish conditions before and is the first study on the subject of the motivational climate and the coach-athlete relationship in Poland. Information from this study can be useful for the development of interventions for enhancing the quality of coach- athlete relationship and its associated outcomes connected with motivational climate.

Keywords: coach-athlete relationship, ego-involving climate, motivational climate, task-involving climate

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Authors: Beata, Bury

Abstract:

Doge speak is an Internet variety with its own linguistic patterns and regularities. Doge meme contains some unconventional grammar rules which make it recognizable. With the use of doge corpus, certain characteristics of doge speak as well as reasons for its popularity are analyzed. The study concludes that doge memes can be applied to a variety of situations, for instance advertising or fashion industry. Doge users play with language and create surprising linguistic combinations. To sum up, doge meme making is a multiperson task. Doge users predict and comment on the world with the use of doge memes.

Keywords: dogespeak, internet language, language play, meme

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Authors: Nancy A. Delich, Stephen D. Roberts

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This case study explores using self-efficacy theory in counseling deaf and hard of hearing students in one California school district. Self-efficacy is described as the confidence a student has for performing a set of skills required to succeed at a specific task. When students need to learn a skill, self-efficacy can be a major factor in influencing behavioral change. Self-efficacy is domain specific, meaning that students can have high confidence in their abilities to accomplish a task in one domain, while at the same time having low confidence in their abilities to accomplish another task in a different domain. The communication isolation experienced by deaf and hard of hearing children and adolescents can negatively impact their belief about their ability to navigate life challenges. There is a need to address issues that impact deaf and hard of hearing students’ social-emotional development. Failure to address these needs may result in depression, suicidal ideation, and anxiety among other mental health concerns. Self-efficacy training can be used to address these socio-emotional developmental issues with this population. Four sources of experiences are applied during an intervention: (a) enactive mastery experience, (b) vicarious experience, (c) verbal persuasion, and (d) physiological and affective states. This case study describes the use of self-efficacy training with a coed group of 12 deaf and hard of hearing high school students who experienced bullying at school. Beginning with enactive mastery experience, the counselor introduced the topic of bullying to the group. The counselor educated the students about the different types of bullying while teaching them the terminology, signs and their meanings. The most effective way to increase self-efficacy is through extensive practice. To better understand these concepts, the students practiced through role-playing with the goal of developing self-advocacy skills. Vicarious experience is the perception that students have about their capabilities. Viewing other students advocating for themselves, cognitively rehearsing what actions they will and will not take, and teaching each other how to stand up against bullying can strengthen their belief in successfully overcoming bullying. The third source of self-efficacy beliefs is verbal persuasion. It occurs when others express belief in the capabilities of the student. Didactic training and pedagogic materials on bullying were employed as part of the group counseling sessions. The fourth source of self-efficacy appraisals is physiological and affective states. Students expect positive emotions to be associated with successful skilled performance. When students practice new skills, the counselor can apply several strategies to enhance self-efficacy while reducing and controlling emotional and physical states. The intervention plan incorporated all four sources of self-efficacy training during several interactive group sessions regarding bullying. There was an increased understanding around the issues of bullying, resulting in the students’ belief of their ability to perform protective behaviors and deter future occurrences. The outcome of the intervention plan resulted in a reduction of reported bullying incidents. In conclusion, self-efficacy training can be an effective counseling and teaching strategy in addressing and enhancing the social-emotional functioning with deaf and hard of hearing adolescents.

Keywords: counseling, self-efficacy, bullying, social-emotional development, mental health, deaf and hard of hearing students

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Authors: Sana Fatima, Paul Sadler, Jon Cooper, David Mendel, Ayesha Jameel

Abstract:

Background: Neurodiversity refers to intrinsic differences between human minds and encompasses dyspraxia, dyslexia, attention deficit hyperactivity disorder, dyscalculia, autism spectrum disorder, and Tourette syndrome. There is increasing recognition of neurodiversity in relation to disability/diversity in medical education and the associated impact on training, career progression, and personal and professional wellbeing. In addition, documented and anecdotal evidence suggests that medical educators and training providers in all four nations (UK) are increasingly concerned about understanding neurodiversity and identifying and providing support for neurodivergent trainees. Summary of Work: A national Neurodiversity Task and Finish group were established to survey Health Education England local office Professional Support teams about insights into infrastructure, training for educators, triggers for assessment, resources, and intervention protocols. This group drew from educational leadership, professional and personal neurodiverse expertise, occupational medicine, employer human resource, and trainees. An online, exploratory survey was conducted to gather insights from supervisors and trainers across England using the Professional Support Units' platform. Summary of Results: This survey highlighted marked heterogeneity in the identification, assessment, and approaches to support and management of neurodivergent trainees and highlighted a 'deficit' approach to neurodiversity. It also demonstrated a paucity of educational and protocol resources for educators and supervisors in supporting neurodivergent trainees. Discussions and Conclusions: In phase one, we focused on faculty development. An educational repository for all supervising trainees using a thematic approach was formalised. This was guided by our survey findings specific for neurodiversity and took a triple 'A' approach: awareness, assessment, and action. This is further supported by video material incorporating stories in training as well as mobile workshops for trainers for more immersive learning. The subtle theme from both the survey and Task and finish group suggested a move away from deficit-focused methods toward a positive holistic, interdisciplinary approach within a biopsychosocial framework. Contributions: 1. Faculty Knowledge and basic understanding of neurodiversity are key to supporting trainees with known or underlying Neurodiverse conditions. This is further complicated by challenges around non-disclosure, varied presentations, stigma, and intersectionality. 2. There is national (and international) inconsistency in the approach to how trainees are managed once a neurodiverse condition is suspected or diagnosed. 3. A carefully constituted and focussed Task and Finish group can rapidly identify national inconsistencies in neurodiversity and implement rapid educational interventions. 4. Nuanced findings from surveys and discussion can reframe the approach to neurodiversity; from a medical model to a more comprehensive, asset-based, biopsychosocial model of support, fostering a cultural shift, accepting 'diversity' in all its manifestations, visible and hidden.

Keywords: neurodiversity, professional support, human considerations, workplace wellbeing

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Authors: A. Aman, S. M. Tang, F. H. Alharrassy

Abstract:

Omanisation was established to increase job opportunities for national employment in Sultanate of Oman. With half of the population below 25 years of age, the sultanate is striving to diversify the economy fast enough to meet the burgeoning number of jobseekers annually. On the other hand, training personnel to be competent oil and gas operators and technicians is a difficult task in a complex reservoir structures in Oman using highly advanced and sophisticated extracting processes. Coupled towards Omanisation which encourages nationals into the oil and gas sector so as to create sustainable employment for the local population, the challenge to churn out competent manpower became a daunting task. Immersive technologies provided the impetus to create a new digital media sector which provided job opportunities as well as the learning contents to enhance the competency-based training for the oil and gas sector in the Sultanate. This lead to a win-win-win collaboration amongst the government represented by the Information Technology Authority (ITA), private sector specialised company (represented by ASM Technologies), jobseekers and oil and gas organisations. This is also one of the first private-public partnership model in the Information Communication Technology (ICT) sector in Oman. A pilot phase was conducted for 8 months to develop four virtual applications for training in equipment and process engineering; oil rig familiarisation, Health Safety Environment (HSE) application, turbine application and the mechanical vapour compressor (MVC) water recycling plant in order to enhance the competency level of the trainees. The immersive applications were installed in operational settings which enabled new employees to practice and understand various processes and procedures regarding enhanced oil recovery. Existing employees used the application to review the working principles in order to carry out troubleshooting scenarios. Concurrently, these applications were also developed by local Omani resources within the country. This created job opportunities for job-seekers as well the establishment of a digital media sector. The purpose of this paper is to discuss how immersive technologies can enhance operational competencies, create job and establish a digital media sector in the Sultanate of Oman.

Keywords: immersive, virtual reality, operations training, Omanisation

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