Search results for: computer tasks

Authors: Sudip Subedi, Nipesh Pradhananga

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The musculoskeletal disorders, also known as MSDs, are common in construction workers. MSDs include lower back injuries, knee injuries, spinal injuries, and joint injuries, among others. Since most construction tasks are still manual, construction workers often need to perform repetitive, labor-intensive tasks. And they need to stay in the same or an awkward posture for an extended time while performing such tasks. It induces significant stress to the joints and spines, increasing the risk of getting into MSDs. Manual monitoring of such tasks is virtually impossible with the handful of safety managers in a construction site. This paper proposes a methodology for performing kinetic analysis of the working postures while performing such tasks in real-time. Skeletal of different workers will be tracked using a depth-sensing camera while performing the task to create training data for identifying the best posture. For this, the kinetic analysis will be performed using a human musculoskeletal model in an open-source software system (OpenSim) to visualize the stress induced by essential joints. The “safe posture” inducing lowest stress on essential joints will be computed for different actions involved in the task. The identified “safe posture” will serve as a basis for real-time monitoring and identification of awkward and unsafe postural behaviors of construction workers. Besides, the temporal simulation will be carried out to find the associated long-term effect of repetitive exposure to such observed postures. This will help to create awareness in workers about potential future health hazards and encourage them to work safely. Furthermore, the collected individual data can then be used to provide need-based personalized training to the construction workers.

Keywords: construction workers’ safety, depth sensing camera, human body kinetics, musculoskeletal disorders, real time monitoring, repetitive labor-intensive tasks

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Authors: Arth J. Shah, Madhu R. Kamble

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Voice biometric liveness detection is customized to certify an authentication process of the voice data presented is genuine and not a recording or synthetic voice. With the rise of deepfakes and other equivalently sophisticated spoofing generation techniques, it’s becoming challenging to ensure that the person on the other end is a live speaker or not. Voice Liveness Detection (VLD) system is a group of security measures which detect and prevent voice spoofing attacks. Motivated by the recent development of the Kolmogorov-Arnold Network (KAN) based on the Kolmogorov-Arnold theorem, we proposed KAN for the VLD task. To date, multilayer perceptron (MLP) based classifiers have been used for the classification tasks. We aim to capture not only the compositional structure of the model but also to optimize the values of univariate functions. This study explains the mathematical as well as experimental analysis of KAN for VLD tasks, thereby opening a new perspective for scientists to work on speech and signal processing-based tasks. This study emerges as a combination of traditional signal processing tasks and new deep learning models, which further proved to be a better combination for VLD tasks. The experiments are performed on the POCO and ASVSpoof 2017 V2 database. We used Constant Q-transform, Mel, and short-time Fourier transform (STFT) based front-end features and used CNN, BiLSTM, and KAN as back-end classifiers. The best accuracy is 91.26 % on the POCO database using STFT features with the KAN classifier. In the ASVSpoof 2017 V2 database, the lowest EER we obtained was 26.42 %, using CQT features and KAN as a classifier.

Keywords: Kolmogorov Arnold networks, multilayer perceptron, pop noise, voice liveness detection

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Authors: Luis Fernando Pinedo-Lomeli, Rosa Martha Lopez-Gutierrez, Jose Antonio Michel-Macarty, Cesar Cruz-Hernandez, Liliana Cardoza-Avendaño, Humberto Cruz-Hernandez

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Robots are working in industry and services performing repetitive or dangerous tasks, however, when flexible movement capabilities and complex tasks are required, the use of many robots is needed. Also, productivity can be improved by reducing times to perform tasks. In the last years, a lot of effort has been invested in research and development of collective control of mobile robots. This interest is justified as there are many advantages when two or more robots are collaborating in a particular task. Some examples are: cleaning toxic waste, transportation and manipulation of objects, exploration, and surveillance, search and rescue. In this work a study of collective movements of mobile robots is presented. A solution of collisions avoidance is developed. This solution is levered on a communication implementation that allows coordinate movements in different paths were avoiding obstacles.

Keywords: synchronization, communication, robots, legos

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Authors: L. Freeborn

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Recent years have seen an increasing number of neuroimaging studies related to language learning as imaging techniques such as fMRI and EEG have become more widely accessible to researchers. By using a variety of structural and functional neuroimaging techniques, these studies have already made considerable progress in terms of our understanding of neural networks and processing related to first and second language acquisition. However, the methodological designs employed in neuroimaging studies to test language learning have been questioned by applied linguists working within the field of second language acquisition (SLA). One of the major criticisms is that tasks designed to measure language learning gains rarely have a communicative function, and seldom assess learners’ ability to use the language in authentic situations. This brings the validity of many neuroimaging tasks into question. The fundamental reason why people learn a language is to communicate, and it is well-known that both first and second language proficiency are developed through meaningful social interaction. With this in mind, the SLA field is in agreement that second language acquisition and proficiency should be measured through learners’ ability to communicate in authentic real-life situations. Whilst authenticity is not always possible to achieve in a classroom environment, the importance of task authenticity should be reflected in the design of language assessments, teaching materials, and curricula. Tasks that bear little relation to how language is used in real-life situations can be considered to lack construct validity. This paper first describes the typical tasks used in neuroimaging studies to measure language gains and proficiency, then analyses to what extent these tasks can validly assess these constructs.

Keywords: neuroimaging studies, research design, second language acquisition, task validity

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Authors: Amir Zand-Moghadam, Mahnaz Alizadeh

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The aim of the present study was to investigate the effect of teacher-scaffolding versus peer-scaffolding on EFL learners’ metapragmatic awareness in the paradigm of task-based language teaching (TBLT). To this end, a number of dialogic information-gap tasks requiring two-way interactant relationship were designed for the five speech acts of request, refusal, apology, suggestion, and compliment following Ellis’s (2003) model. Then, 48 intermediate EFL learners were randomly selected, homogenized, and assigned to two groups: 26 participants in the teacher-scaffolding group (Group One) and 22 in the peer-scaffolding group (Group Two). While going through the three phases of pre-task, while-task, and post-task, the participants in the first group completed the designed tasks by the teacher’s interaction, scaffolding, and feedback. On the other hand, the participants in the second group were required to complete the tasks in expert-novice pairs through peer scaffolding in all the three phases of a task-based syllabus. The findings revealed that the participants in the teacher-scaffolding group developed their L2 metapragmatic awareness more than the peer-scaffolding group. Thus, it can be concluded that teacher-scaffolding is more effective than peer scaffolding in developing metapragmatic awareness among EFL learners. It can also be claimed that the use of tasks can be more influential when they are accompanied by teacher-scaffolding. The findings of the present study have implications for language teachers and researchers.

Keywords: ILP, metapragmatic awareness, scaffolding, task-based instruction

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Authors: Sinan Serkan Çağlı

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This study explores the effectiveness of incorporating storytelling tasks into language acquisition programs for young learners. The research investigates how storytelling, as a pedagogical tool, can contribute to the enhancement of language acquisition skills in children. Drawing upon relevant literature and empirical data, this article examines the impact of storytelling on vocabulary development, comprehension, and overall language proficiency in early childhood education in Turkey. The study adopts a qualitative approach, including classroom observations and interviews with teachers and students. Findings suggest that storytelling tasks not only foster linguistic competence but also stimulate cognitive and socio-emotional development in young learners. Additionally, the article explores various storytelling techniques and strategies suitable for different age groups. It is evident that integrating storytelling tasks into language learning environments can create engaging and effective opportunities for young learners to acquire language skills in a natural and enjoyable way. This research contributes valuable insights into the pedagogical practices that promote language acquisition in early childhood, emphasizing the significance of storytelling as a powerful educational tool, especially in Turkey for EFL students.

Keywords: storytelling, language acquisition, young learners, early childhood education, pedagogy, language proficiency

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Authors: Genady Grabarnik, Serge Yaskolko

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Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders.

Keywords: introductory computer and data science education, generative AI, large language models, application of LLMS to computer and data science education

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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: Keyi Wang

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Similar to the touchscreen, hand gesture based human-computer interaction (HCI) is a technology that could allow people to perform a variety of tasks faster and more conveniently. This paper proposes a training method of an image-based hand gesture image and video clip recognition system using a CNN (Convolutional Neural Network) with a dataset. A dataset containing 6 hand gesture images is used to train a 2D CNN model. ~98% accuracy is achieved. Furthermore, a 3D CNN model is trained on a dataset containing 4 hand gesture video clips resulting in ~83% accuracy. It is demonstrated that a Cozmo robot loaded with pre-trained models is able to recognize static and dynamic hand gestures.

Keywords: deep learning, hand gesture recognition, computer vision, image processing

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Authors: Izabella Szollosi, Klara Marton

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Aphasia can be defined as a weakness in accessing linguistic information. Accessing linguistic information is strongly related to information processing, which in turn is associated with the cognitive control system. According to the literature, a deficit in the cognitive control system interferes with language processing and contributes to non-fluent speech performance. The aim of our study was to explore this hypothesis by investigating how cognitive control interacts with language performance in participants with non-fluent aphasia. Cognitive control is a complex construct that includes working memory (WM) and the ability to resist proactive interference (PI). Based on previous research, we hypothesized that impairments in domain-general (DG) cognitive control abilities have negative effects on language processing. In contrast, better DG cognitive control functioning supports goal-directed behavior in language-related processes as well. Since stroke itself might slow down information processing, it is important to examine its negative effects on both cognitive control and language processing. Participants (N=52) in our study were individuals with non-fluent Broca’s aphasia (N = 13), with transcortical motor aphasia (N=13), individuals with stroke damage without aphasia (N=13), and unimpaired speakers (N = 13). All participants performed various computer-based tasks targeting cognitive control functions such as WM and resistance to PI in both linguistic and non-linguistic domains. Non-linguistic tasks targeted primarily DG functions, while linguistic tasks targeted more domain specific (DS) processes. The results showed that participants with Broca’s aphasia differed from the other three groups in the non-linguistic tasks. They performed significantly worse even in the baseline conditions. In contrast, we found a different performance profile in the linguistic domain, where the control group differed from all three stroke-related groups. The three groups with impairment performed more poorly than the controls but similar to each other in the verbal baseline condition. In the more complex verbal PI condition, however, participants with Broca’s aphasia performed significantly worse than all the other groups. Participants with Broca’s aphasia demonstrated the most severe language impairment and the highest vulnerability in tasks measuring DG cognitive control functions. Results support the notion that the more severe the cognitive control impairment, the more severe the aphasia. Thus, our findings suggest a strong interaction between cognitive control and language. Individuals with the most severe and most general cognitive control deficit - participants with Broca’s aphasia - showed the most severe language impairment. Individuals with better DG cognitive control functions demonstrated better language performance. While all participants with stroke damage showed impaired cognitive control functions in the linguistic domain, participants with better language skills performed also better in tasks that measured non-linguistic cognitive control functions. The overall results indicate that the level of cognitive control deficit interacts with the language functions in individuals along with the language spectrum (from severe to no impairment). However, future research is needed to determine any directionality.

Keywords: cognitive control, information processing, language performance, non-fluent aphasia

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Authors: Shu-Min Tsao, Chung-Ming Lo, Shao-Chun Chen

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Most diabetes patients tend to suffer from its complication of retina diseases. Therefore, early detection and early treatment are important. In clinical examinations, using color fundus image was the most convenient and available examination method. According to the exudates appeared in the retinal image, the status of retina can be confirmed. However, the routine screening of diabetic retinopathy by color fundus images would bring time-consuming tasks to physicians. This study thus proposed a computer-aided exudate diagnosis for the screening of diabetic retinopathy. After removing vessels and optic disc in the retinal image, six quantitative features including region number, region area, and gray-scale values etc… were extracted from the remaining regions for classification. As results, all six features were evaluated to be statistically significant (p-value < 0.001). The accuracy of classifying the retinal images into normal and diabetic retinopathy achieved 82%. Based on this system, the clinical workload could be reduced. The examination procedure may also be improved to be more efficient.

Keywords: computer-aided diagnosis, diabetic retinopathy, exudate, image processing

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Authors: K. M. Yuen

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This research examined the literature review of wedding preparation’s challenges and its developmental tasks of family transition under family life cycle. Five interviewees were invited to share their experiences on the differences with their parents in regard to wedding preparations and coping strategies. Some coping strategies and processes were highlighted for facilitating the family to achieve the developmental tasks during the wedding preparation. However, those coping strategies and processes may only act as the step and the behavior, while “concern towards parents” was found to be the essential element behind these behaviors. In addition to pre-marital counseling, a developmental group was suggested to develop under the framework of family life cycle and its related coping strategies on working with the newlyweds who encountered intergenerational differences in regard to their wedding preparations.

Keywords: wedding preparation, difference, parents, family life cycle, developmental tasks, coping strategies, process

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Authors: Anuradha Banerjee

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Energy aware operation is one of the visionary goals in the area of robotics because operability of robots is greatly dependent upon their residual energy. Practically, the tasks allocated to robots carry different priority and often an upper limit of time stamp is imposed within which the task needs to be completed. If a robot is unable to complete one particular task given to it the task is reallocated to some other robot. The collection of robots is controlled by a Central Monitoring Unit (CMU). Selection of the new robot is performed by a fuzzy controller called Task Reallocator (TRAC). It accepts the parameters like residual energy of robots, possibility that the task will be successfully completed by the new robot within stipulated time, distance of the new robot (where the task is reallocated) from distance of the old one (where the task was going on) etc. The proposed methodology increases the probability of completing globally assigned tasks and saves huge amount of energy as far as the collection of robots is concerned.

Keywords: energy-efficiency, fuzzy-controller, priority, reallocation, task

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the model-agnostic meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to an exponential growth of computation, this paper also proposes a key data extraction method that only extract part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: mutex task generation, data augmentation, meta-learning, text classification.

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Authors: Bowen Ding, Yihao Kuang

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In recent years, large-scale pre-trained language models have achieved state-of-the-art performance on a variety of natural language processing tasks. The word vectors produced by these language models can be viewed as dense encoded presentations of natural language that in text form. However, it is unknown how much linguistic information is encoded and how. In this paper, we construct several corresponding probing tasks for multiple linguistic information to clarify the encoding capabilities of different language models and performed a visual display. We firstly obtain word presentations in vector form from different language models, including BERT, ELMo, RoBERTa and GPT. Classifiers with a small scale of parameters and unsupervised tasks are then applied on these word vectors to discriminate their capability to encode corresponding linguistic information. The constructed probe tasks contain both semantic and syntactic aspects. The semantic aspect includes the ability of the model to understand semantic entities such as numbers, time, and characters, and the grammatical aspect includes the ability of the language model to understand grammatical structures such as dependency relationships and reference relationships. We also compare encoding capabilities of different layers in the same language model to infer how linguistic information is encoded in the model.

Keywords: language models, probing task, text presentation, linguistic information

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Authors: Franziska Schaefer, Marlene Kuhn, Heiner Otten

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In the very near future, digitization will be the main challenge a company has to master to survive in a highly competitive market. Developing the right transformation strategy by considering all relevant aspects determines the success or failure of a company. Especially the digital focus on the customer plays a key role in creating sustainable competitive advantages, also leading to new tasks within the quality management. Therefore, quality management needs to be particularly addressed to support the upcoming digital change. In this paper, we present an analysis of existing digital transformation approaches and derive a transformation strategy from a quality management perspective. We identify and classify different transformation dimensions and assess their relevance to quality management tasks, resulting in a quality-adapted digital transformation model. Furthermore, we introduce applicable and customized quality management methods to support the presented digital transformation tasks. With our developed model we provide a digital transformation guideline from a quality perspective to master future disruptive changes.

Keywords: digital transformation, digitization, quality management, strategy

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Authors: Babak Pourghahramani

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One of the modern crimes against property and ownership in the cyber-space is the computer fraud. Despite being modern, the aforementioned crime has its roots in the principles of religious jurisprudence. In some cases, this crime is compatible with the traditional regulations and that is when the computer is considered as a crime commitment device and also some computer frauds that take place in the context of electronic exchanges are considered as crime based on the E-commerce Law (approved in 2003) but the aforementioned regulations are flawed and until recent years there was no comprehensive law in this regard; yet after some years the Computer Crime Act was approved in 2009/26/5 and partly solved the problem of legal vacuum. The present study intends to investigate the computer fraud according to Iran's Computer Crime Act and by taking into consideration the international documents.

Keywords: fraud, cyber fraud, computer fraud, classic fraud, computer crime

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Authors: M. K. Benhaoua, A. K. Singh, A. E. H. Benyamina, A. Kumar, P. Boulet

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In this paper, we propose a new packing strategy to find free resources for run-time mapping of application tasks on NoC-based Heterogeneous MPSoCs. The proposed strategy minimizes the task mapping time in addition to placing the communicating tasks close to each other. To evaluate our approach, a comparative study is carried out. Experiments show that our strategy provides better results when compared to latest dynamic mapping strategies reported in the literature.

Keywords: heterogeneous MPSoCs, NoC, dynamic mapping, routing

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Authors: Letizia Maria Drammis, Maria Antonella Brandimonte

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The idea that people use space representations to think about time and number received support from several lines of research. However, how these representations develop in children and then shape space-time and space-number mappings is still a debated issue. In the present study, 40 children (20 pre-schoolers and 20 elementary-school children) performed 4 main tasks, which required the use of more concrete (non-symbolic) or more abstract (symbolic) space-time and space-number associations. In the non-symbolic conditions, children were required to order pictures of everyday-life events occurring in a specific temporal order (Temporal sequences) and of quantities varying in numerosity (Numerical sequences). In the symbolic conditions, they were asked to perform the typical time-to-position and number-to-position tasks by mapping time-related words and numbers onto lines. Results showed that children performed reliably better in the non-symbolic Time conditions than the symbolic Time conditions, independently of age, whereas only pre-schoolers performed worse in the Number-to-position task (symbolic) as compared to the Numerical sequence (non-symbolic) task. In addition, only older children mapped time-related words onto space following the typical left-right orientation, pre-schoolers’ performance being somewhat mixed. In contrast, mapping numbers onto space showed a clear left-right orientation, independently of age. Overall, these results indicate a cross-domain difference in the way younger and older children process time and number, with time-related tasks being more difficult than number-related tasks only when space-time tasks require symbolic representations.

Keywords: space-time associations, space-number associations, orientation, children

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Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on HSI dataset Indian Pines. The results confirm the capability of the proposed method.

Keywords: continual learning, data reconstruction, remote sensing, hyperspectral image segmentation

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Authors: Maximilian Cedzich, Ingo Dietz Von Bayer, Roland Jochem

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In order for industrial companies to continue to succeed in dynamic, globalized markets, they must be able to train their employees in an agile manner and at short notice in line with the exogenous conditions that arise. For this purpose, it is indispensable to operate a proactive competence management system for employees that recognizes qualification needs timely in order to be able to address them promptly through qualification measures. However, there are hardly any approaches to be found in the literature that includes systematic, proactive competence management. In order to help close this gap, this publication presents a process model that systematically develops bottom-up, future-oriented target competence profiles based on the tasks of the employees. Concretely, in the first step, the tasks of the individual employees are examined for assumed future conditions. In other words, qualitative scenarios are considered for the individual tasks to determine how they are likely to change. In a second step, these scenario-based future tasks are translated into individual future-related target competencies of the employee using a matrix of generic task properties. The final step pursues the goal of validating the target competence profiles formed in this way within the framework of a management workshop. This process model provides industrial companies with a tool that they can use to determine the competencies required by their own employees in the future and compare them with the actual prevailing competencies. If gaps are identified between the target and the actual, these qualification requirements can be closed in the short term by means of qualification measures.

Keywords: dynamic globalized markets, employee competence management, industrial companies, knowledge management

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Authors: Tejas Dhadphale

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The user-centered design (UCD) approach has led to an increased interest in empathy within the product development process. Designers have explored several empathetic methods and tools such as personas, empathy maps, journey maps, user needs statements and user scenarios to capture and visualize users’ needs. The goal of these tools is not only to generate a deeper and shared understanding of user needs but also to become a point of reference for subsequent decision making, brainstorming and concept evaluation tasks. The purpose of this study is to measure the effect of empathy priming on divergent brainstorming tasks. This study compares the effects of three empathy tools, personas, empathy maps and user needs statements, on ideation fluency and originality of ideas during brainstorming tasks. In a three-between-subjects experimental design study, sixty product design students were randomly assigned to one of three conditions: persona, empathy maps and user needs statements. A one-way, between-subjects analysis of variance (ANOVA) revealed a a statistically significant difference in empathy priming on fluency and originality of ideas. Participants in the persona group showed higher ideation fluency and generated a greater number of original ideas compared to the other groups. The results show that participants in the user need statement group to generate a greater number of feasible and relevant ideas. The study also aims to understand how formatting and visualization of empathy tools impact divergent brainstorming tasks. Participants were interviewed to understand how different visualizations of users’ needs (personas, empathy maps and user needs statements) facilitated idea generation during brainstorming tasks. Implications for design education are discussed.

Keywords: empathy, persona, priming, Design research

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Authors: Ismail Celik, Ismail Sahin, Fetah Eren

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The computer, among the most important inventions of the twentieth century, has become an increasingly important component in our everyday lives. Computer games also have become increasingly popular among people day-by-day, owing to their features based on realistic virtual environments, audio and visual features, and the roles they offer players. In the present study, the metaphors students have for computer games are investigated, as well as an effort to fill the gap in the literature. Students were asked to complete the sentence—‘Computer game is like/similar to….because….’— to determine the middle school students’ metaphorical images of the concept for ‘computer game’. The metaphors created by the students were grouped in six categories, based on the source of the metaphor. These categories were ordered as ‘computer game as a means of entertainment’, ‘computer game as a beneficial means’, ‘computer game as a basic need’, ‘computer game as a source of evil’, ‘computer game as a means of withdrawal’, and ‘computer game as a source of addiction’, according to the number of metaphors they included.

Keywords: computer game, metaphor, middle school students, virtual environments

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Authors: Paulina Dwuznik

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For students of legal English, the skill of mediation is of special importance as it constitutes part of their everyday work. Developing the skill of mediation requires developing linguistic, communicative, textual, pragmatic, interactive, social, and intercultural competencies. The study conducted at the Open University of the University of Warsaw compared the results of a questionnaire concerning the needs of legal professionals relating to mediation tasks, which they perform at work with the analysis of the content of different legal English handbooks with special stress on the development of intercultural competence necessary in interlinguistic mediation. The study found that legal English handbooks focus mainly on terminology study, but some of them extend students' intercultural competence in a way which may help them to perform tasks of mediating concepts, texts, and communication. The author of the paper will present the correlation between intercultural competence and mediation skill and give some examples of mediation tasks which may be based on comparative intercultural content of some chosen academic legal English handbooks.

Keywords: intercultural competence, legal English, mediation skill, teaching

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Authors: Pooja Mishra, Chris Pollett

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This paper explores efficient ways to implement various media-updating features like news aggregation, video conversion, and bulk email handling. All of these jobs share the property that they are periodic in nature, and they all benefit from being handled in a distributed fashion. The data for these jobs also often comes from a social or collaborative source. We isolate the class of periodic, one round map reduce jobs as a useful setting to describe and handle media updating tasks. As such tasks are simpler than general map reduce jobs, programming them in a general map reduce platform could easily become tedious. This paper presents a MediaUpdater module of the Yioop Open Source Search Engine Web Portal designed to handle such jobs via an extension of a PHP class. We describe how to implement various media-updating tasks in our system as well as experiments carried out using these implementations on an Amazon Web Services cluster.

Keywords: distributed jobs framework, news aggregation, video conversion, email

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Authors: Armineh Nourbakhsh, Sameena Shah, Carolyn Rose

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Layout-aware language models have been used to create multimodal representations for documents that are in image form, achieving relatively high accuracy in document understanding tasks. However, the large number of parameters in the resulting models makes building and using them prohibitive without access to high-performing processing units with large memory capacity. We propose an alternative approach that can create efficient representations without the need for a neural visual backbone. This leads to an 80% reduction in the number of parameters compared to the smallest SOTA model, widely expanding applicability. In addition, our layout embeddings are pre-trained on spatial and visual cues alone and only fused with text embeddings in downstream tasks, which can facilitate applicability to low-resource of multi-lingual domains. Despite using 2.5% of training data, we show competitive performance on two form understanding tasks: semantic labeling and link prediction.

Keywords: layout understanding, form understanding, multimodal document understanding, bias-augmented attention

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Authors: Khaled Barkaoui

Abstract:

Research on second-language (L2) learning tends to focus on comparing students with different levels of proficiency at one point in time. However, to understand L2 development, we need more longitudinal research. In this study, we adopt a longitudinal approach to examine changes in three indicators of L2 ability, complexity, accuracy, and fluency (CAF), as reflected in the writing of L2 learners when writing on different tasks before and after a period L2 instruction. Each of 85 Chinese learners of English at three levels of English language proficiency responded to two writing tasks (independent and integrated) before and after nine months of English-language study in China. Each essay (N= 276) was analyzed in terms of numerous CAF indices using both computer coding and human rating: number of words written, number of errors per 100 words, ratings of error severity, global syntactic complexity (MLS), complexity by coordination (T/S), complexity by subordination (C/T), clausal complexity (MLC), phrasal complexity (NP density), syntactic variety, lexical density, lexical variation, lexical sophistication, and lexical bundles. Results were then compared statistically across tasks, L2 proficiency levels, and time. Overall, task type had significant effects on fluency and some syntactic complexity indices (complexity by coordination, structural variety, clausal complexity, phrase complexity) and lexical density, sophistication, and bundles, but not accuracy. L2 proficiency had significant effects on fluency, accuracy, and lexical variation, but not syntactic complexity. Finally, fluency, frequency of errors, but not accuracy ratings, syntactic complexity indices (clausal complexity, global complexity, complexity by subordination, phrase complexity, structural variety) and lexical complexity (lexical density, variation, and sophistication) exhibited significant changes after instruction, particularly for the independent task. We discuss the findings and their implications for assessment, instruction, and research on CAF in the context of L2 writing.

Keywords: second language writing, Fluency, accuracy, complexity, longitudinal

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Authors: Parker Townes, Aisouda Savadlou, Shoshana Weiss, Marina Jarenova, Suzzane Ferris, Dan Devoe, Russel Schachar, Scott Patten, Tomas Lange, Marlena Colasanto, Holly McGinn, Paul Arnold

Abstract:

Some research suggests obsessive-compulsive disorder (OCD) is associated with impaired executive functioning: higher-level mental processes involved in carrying out tasks and solving problems. Relevant literature was identified systematically through online databases. Meta-analyses were conducted for task performance metrics reported by at least two articles. Results were synthesized by the executive functioning domain measured through each performance metric. Heterogeneous literature was identified, typically involving few studies using consistent measures. From 29 included studies, analyses were conducted on 33 performance metrics from 12 tasks. Results suggest moderate associations of working memory (two out of five tasks presented significant findings), planning (one out of two tasks presented significant findings), and visuospatial abilities (one out of two tasks presented significant findings) with OCD in youth. There was inadequate literature or contradictory findings for other executive functioning domains. These findings suggest working memory, planning, and visuospatial abilities are impaired in pediatric OCD, with mixed results. More work is needed to identify the effect of age and sex on these results. Acknowledgment: This work was supported by the Alberta Innovates Translational Health Chair in Child and Youth Mental Health. The funders had no role in the design, conducting, writing, or decision to submit this article for publication.

Keywords: obsessive-compulsive disorder, neurocognition, executive functioning, adolescents, children

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Authors: Madina Tynybayeva, Sanya Zhumazhanova, Saltanat Kozhakhmetova, Merey Mussabayeva

Abstract:

One of the leading indicators of the education quality is the level of students’ educational achievements. The processes of modernization of Kazakhstani education system have predetermined the need to improve the national system by assessing the quality of education. The results of assessment greatly contribute to addressing questions about the current state of the educational system in the country. The monitoring of students’ educational achievements (MEAS) is the systematic measurement of the quality of education for compliance with the state obligatory standard of Kazakhstan. This systematic measurement is independent of educational organizations and approved by the order of the Minister of Education and Scienceof Kazakhstan. The MEAS was conducted in the regions of Kazakhstanfor the first time in 2022 by the National Testing Centre. The measurement does not have legal consequences either for students or for educational organizations. Students’ achievements were measured in three subject areas: reading, mathematics and science literacy. MEAS was held for the first time in April this year, 105 thousand students from 1436 schools of Kazakhstan took part in the testing. The monitoring was accompanied by a survey of students, teachers, and school leaders. The goal is to identify which contextual factors affect learning outcomes. The testing was carried out in a computer format. The test tasks of MEAS are ranked according to the three levels of difficulty: basic, medium, and high. Fourth graders are asked to complete 30 closed-type tasks. The average score of the results is 21 points out of 30, which means 70% of tasks were successfully completed. The total number of test tasks for 9th grade students – 75 questions. The results of ninth graders are comparatively lower, the success rate of completing tasks is 63%. MEAS participants did not reveal a statistically significant gap in results in terms of the language of instruction, territorial status, and type of school. The trend of reducing the gap in these indicators is also noted in the framework of recent international studies conducted across the country, in particular PISA for schools in Kazakhstan. However, there is a regional gap in MOES performance. The difference in the values of the indicators of the highest and lowest scores of the regions was 11% of the success of completing tasks in the 4th grade, 14% in the 9thgrade. The results of the 4th grade students in reading, mathematics, and science literacy are: 71.5%, 70%, and 66.9%, respectively. The results of ninth-graders in reading, mathematics, and science literacy are 69.6%, 54%, and 60.8%, respectively. From the surveys, it was revealed that the educational achievements of students are considerably influenced by such factors as the subject competences of teachers, as well as the school climate and motivation of students. Thus, the results of MEAS indicate the need for an integrated approach to improving the quality of education. In particular, the combination of improving the content of curricula and textbooks, internal and external assessment of the educational achievements of students, educational programs of pedagogical specialties, and advanced training courses is required.

Keywords: assessment, secondary school, monitoring, functional literacy, kazakhstan

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