Search results for: traditional knowledge resources classification

Authors: Lilian Ijomah

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Restorative justice practices continue to gain recognition globally in the criminal and juvenile justice systems and schools. Despite considerable research, little is known about how juvenile detention center staff members’ knowledge, beliefs, and attitudes affect implementation. As with many interventions, effective implementation relies on the staff members who must do the daily work. This phenomenological study aimed to add to the existing literature by examining staff knowledge, beliefs, and attitudes on restorative justice practices, barriers to effective implementation, and potential differences in knowledge, beliefs, and attitudes between education staff and juvenile detention officers at the research site. The present study used semi-structured interviews and focus groups of both types of staff members who work with the youth in a juvenile justice facility to answer three research questions: (1) To what extent are staff members knowledgeable about the principles behind restorative approach to discipline and about how the approach should be carried out?; (2) What are staff member beliefs and attitudes toward the restorative justice program and its implementation in a juvenile justice setting?; and (3) What similarities and differences are there between (a) knowledge and (b) beliefs and attitudes of the educators and juvenile detention officers? A total of 28 staff members participated, nine educators, and 19 detention officers. The findings for the first research question indicated that both groups (educators and juvenile detention officers) were knowledgeable about two of the three principles of restorative justice: repairing the harm done by the offender and reducing risks for future occurrence; but did not show clear knowledge of one principle, active involvement from all stakeholders. For research question 2, staff beliefs and attitudes were categorized into two types, positive beliefs and attitudes (e.g., that restorative justice is more appropriate than the use of punitive measures) and negative beliefs and attitudes (e.g., that restorative justice is ‘just another program that creates extra work for staff’). When the two staff groups were compared to answer research question 3, both groups were found to have similar knowledge (showing knowledge of two of the three principles) and somewhat different beliefs and attitudes – both groups showed a mix of positive and negative, but the educators showed somewhat more on the positive side. Both groups also identified barriers to implementation such as the perception of restorative justice as ‘soft’, lack of knowledge and exposure to restorative justice, shortage of resources and staff, and difficulty sustaining the restorative justice approach. The findings of this study are largely consistent with current literature but also extend the literature by studying staff knowledge, attitudes, and beliefs in a juvenile detention center and comparing the two staff groups. Recommendations include assessing staff knowledge and attitudes toward restorative justice during the hiring process, ensuring adequate staff training, communicating clearly to build positive attitudes and beliefs, providing adequate staffing, and building a sense of community.

Keywords: juvenile justice, restorative justice, restorative practices, staff attitudes and beliefs

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Authors: Maria Ilyushkina

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The article addresses the problem of translating vocative texts with intertextual references and describes the influence of language on how knowledge and meaning are developed in the field of advertising. The starting point of the article takes advertisements from the sphere of tourism and the way we choose, translate, and interpret intertexts. The article focuses on the perception and understanding of the information in printed texts advertising recreational facilities and services for tourists as the target audience by representatives of other cultures and the knowledge intertexts convey. The authors argue that intertextuality complicates translation leading to knowledge asymmetries. Studying typical communicative failures is considered to be of great importance, allowing for improvement in the practice of translation in the sphere of advertising as well as preventing the fallacious transfer of knowledge when translating foreign intertexts.

Keywords: advertising, translation, intertext, Russian culture, knowledge asymmetries, tourism, vocative texts

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Authors: Racha Khairallah, Lamia Ben Hamida

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Our paper attempts to examine how Swiss manufacturing firms manage to learn from patent citations to improve their innovation performance. We argue that the assessment of these effects needs a detailed analysis of spillovers according to the source of knowledge with respect to formal and informal patent citations made in European and internal search, the horizontal and vertical mechanisms by which knowledge spillovers take place, and the technological characteristics of innovative firms that able them to absorb external knowledge and integrate it in their existing innovation process. We use OECD data and find evidence that knowledge spillovers occur only from horizontal and backward linkages. The importance of these effects depends on the type of citation, in which the references to non-patent literature (informal citations made in European and international searches) have a greater impact. In addition, only firms with high technological capacities benefit from knowledge spillovers from formal and informal citations. Low-technology firms fail to catch up and efficiently learn external knowledge from patent citations.

Keywords: innovation performance, patent citation, absorptive capacity, knowledge spillover mechanisms

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Authors: Yulong Wang, Yuan Yan Tang, Cuiming Zou, Lina Yang

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This paper presents a novel sparse representation method for robust pattern classification. Generalized orthogonal matching pursuit (GOMP) is a recently proposed efficient sparse representation technique. However, GOMP adopts the mean square error (MSE) criterion and assign the same weights to all measurements, including both severely and slightly corrupted ones. To reduce the limitation, we propose an information-theoretic GOMP (ITGOMP) method by exploiting the correntropy induced metric. The results show that ITGOMP can adaptively assign small weights on severely contaminated measurements and large weights on clean ones, respectively. An ITGOMP based classifier is further developed for robust pattern classification. The experiments on public real datasets demonstrate the efficacy of the proposed approach.

Keywords: correntropy induced metric, matching pursuit, pattern classification, sparse representation

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Authors: Charlotte Bose Iro-Idoro, Adebisi Folake Osore, Tajudeen Adisa Jimoh

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All changes in the office environment were and are still driven by advances in technology. The impact of computers on office work has resulted in numerous changes in office activities, procedures and the expectations from office managers and secretaries. This study investigated the level of knowledge and use of computer office application packages by secretaries and office managers in higher educational institutions in Ogun State and the implications of these on their performance enhancement. The study is an ex post facto research and adopted the survey design for the collection of data. Two hypotheses were formulated, and a questionnaire was developed and tested at 0.05 level of significance. All office managers and secretaries in the service of higher educational institutions in Ogun State, Nigeria formed the population of the study. The study was limited to federal institutions and a total of 120 office managers/secretaries were selected to form the sample such that 40 office managers/secretaries were randomly selected from each of the three Federal higher institutions in the State, that is Federal University of Agriculture, Abeokuta, Federal Polytechnic, Ilaro and Federal College of Education, Osiele, Abeokuta, Ogun State. Analysis of data and hypotheses tests were carried out with frequency counts, percentage and T-Test. The result indicated varying levels of awareness on office application tools with limited knowledge and use of computer application packages by office managers/secretaries. The results also showed that good knowledge and high use of office application tools enhance performance of office managers/secretaries. The study recommended that there should be maximum institutional resources and support and personal development on the part of the office managers to ensure update knowledge and maximal use of office application tools by office managers/secretaries.

Keywords: application packages, computer, office managers, performance enhancement

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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: Ichihan Tai, Bill Olson, Paul Blessner

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Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.

Keywords: early warning system, knowledge management, market prediction, topic modeling.

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Authors: Jularat Chumnaul

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In forensic science, corpses from various homicides are different; there are both complete and incomplete, depending on causes of death or forms of homicide. For example, some corpses are cut into pieces, some are camouflaged by dumping into the river, some are buried, some are burned to destroy the evidence, and others. If the corpses are incomplete, it can lead to the difficulty of personally identifying because some tissues and bones are destroyed. To specify gender of the corpses from skeletal remains, the most precise method is DNA identification. However, this method is costly and takes longer so that other identification techniques are used instead. The first technique that is widely used is considering the features of bones. In general, an evidence from the corpses such as some pieces of bones, especially the skull and pelvis can be used to identify their gender. To use this technique, forensic scientists are required observation skills in order to classify the difference between male and female bones. Although this technique is uncomplicated, saving time and cost, and the forensic scientists can fairly accurately determine gender by using this technique (apparently an accuracy rate of 90% or more), the crucial disadvantage is there are only some positions of skeleton that can be used to specify gender such as supraorbital ridge, nuchal crest, temporal lobe, mandible, and chin. Therefore, the skeletal remains that will be used have to be complete. The other technique that is widely used for gender specifying in forensic science and archeology is skeletal measurements. The advantage of this method is it can be used in several positions in one piece of bones, and it can be used even if the bones are not complete. In this study, the classification and cluster analysis are applied to this technique, including the Kth Nearest Neighbor Classification, Classification Tree, Ward Linkage Cluster, K-mean Cluster, and Two Step Cluster. The data contains 507 particular individuals and 9 skeletal measurements (diameter measurements), and the performance of five methods are investigated by considering the apparent error rate (APER). The results from this study indicate that the Two Step Cluster and Kth Nearest Neighbor method seem to be suitable to specify gender from human skeletal remains because both yield small apparent error rate of 0.20% and 4.14%, respectively. On the other hand, the Classification Tree, Ward Linkage Cluster, and K-mean Cluster method are not appropriate since they yield large apparent error rate of 10.65%, 10.65%, and 16.37%, respectively. However, there are other ways to evaluate the performance of classification such as an estimate of the error rate using the holdout procedure or misclassification costs, and the difference methods can make the different conclusions.

Keywords: skeletal measurements, classification, cluster, apparent error rate

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Authors: Sadia Anwara, Weng Qingxionga, Jahan Zeb Aslamb

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Drawing on the Conservation of Resources Theory and Theory of Displaced Aggression, current research study aims to explore the impact of an emerging destructive leadership style i.e., Punitive Supervision on the Employees’ Innovative Work Behavior (IWB) and Employee’s Knowledge Hiding (EKH) within the hospitality sector of Pakistan. This paper further elaborates the underlying mechanism by introducing job security as the mediator and Perceived Organisational Support (POS) as the coping mechanism to manage the deteriorating effects of Punitive supervision on the IWS and EKH. Two wave data (N=267) was obtained from the frontline employees of the hospitality sector of Pakistan in order to test the hypothesized moderation mediation model. Study findings unveiled that, punitive supervision negatively affects employees' innovative work behavior (IWB) and increases employee’s knowledge hiding (EKH), with job insecurity serving as a significant mediator in these relationships. Specifically, punitive supervision increases employees' perceptions of job insecurity, decreasing their innovative work behaviors and increasing their tendencies to engage in knowledge hiding. From a managerial perspective, this research study suggests that managers must evaluate their behavior and leadership style to prevent the drastic effect of dark leadership on the employee’s IWB and EKH. In addition, organizations must strive to foster an organizational culture of trust and open communication to reduce job insecurity. Employees should receive sufficient training and development opportunities to reduce job insecurity, while clear performance expectations and constructive feedback should be encouraged to help them excel.

Keywords: punitive supervision, job insecurity, perceived organisational support, innovative work behavior, knowledge hiding

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Authors: Evan Lowhorn, Rocio Alba-Flores

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The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands.

Keywords: classification, computer vision, convolutional neural networks, drone control

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Authors: A. Abdullah, N. ul Haq, A. Nasim

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Objectives: This study was aimed to assess the knowledge, attitude, and practice of anemia among females attending Bolan Medical Complex Quetta, Balochistan. Methods: A quantitative cross-sectional study by adopting a questionnaire containing 3 dimensions knowledge (15 questions), Attitude (5 questions), and Practice (4 questions) for the assessment of knowledge, attitude and practice of anemia among females was conducted. All females attending Bolan Medical Complex Quetta, Balochistan were approached for the study. Descriptive statistics were used to describe demographic and KAP related characteristics of the females regarding anemia.All data were analyzed by using SPSS (Statistical Package of Social Sciences) software program version 20.0. Results: Data was collected from six hundred and thirteen (613) participants. Majority of the respondents (n=180, 29.4%) were categorized in the age group of 29-33 years. Participants had knowledge regarding anemia was (n= 564, 91.9%), and attitude was (n= 516, 84.0%) whereas practice was (n=437, 71.3%). Multitative analysis revealed the negative correlation between Attitude-practice (P= -0.040) and a significant figure (0.001) was present between knowledge-attitude. Occupation and reason of diagnosis were not predictive of better KAP. Conclusions: Knowledge, attitude, and practice of Anemia shows a satisfactory response in this study. Furthermore, study finding implicates the need for health promotion among females. Improving nutritional knowledge and information related Anemia can result in better control and management.

Keywords: anemia, knowledge attitude and practice, females, college

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Authors: GüLcan Keskin

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In a globalizing world, it is more difficult for developing counties to gain a competitive advantage compared to developed countries. Entrepreneurship is an important factor for economic growth in developing countries. Entrepreneurship is not only in the success of an entrepreneur’s own businesses, but also plays an important role in regional and national development. Entrepreneurship is the factor that triggers change for the country to accelerate the creation, dissemination, and implementation of new thoughts, leading to the emergence of industries that supports economic growth and development as it increases productivity by creating a competitive advantage. Therefore, it is an important factor for countries to develop economically and socially in a global world. As the know-how of the local products belongs to the region, it is a value that should not be lost. Having know-how provides a competitive advantage to the region. On the other hand, traditional products can be tailored to today’s trade understanding to appeal to more audiences. The primary aim of the study is to examine the interaction mechanism between traditional products and innovation in the context of related literature. The second aim of the study is to show the effect the traditional products to competitive advantage.

Keywords: e-commerce, economic growth, entrepreneurship, traditional products

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Authors: Sawsan J. Al-husseini, Talib A. Dosa

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Educational institutions are today facing increasing pressures due to economic, political and social upheaval. This is only exacerbated by the nature of education as an intangible good which relies upon the intellectual assets of the organisation, its staff. Top management support has been acknowledged as having a positive general influence on knowledge management and creativity. However, there is a lack of models linking top management support, knowledge sharing, and innovation within higher education institutions, in general within developing countries, and particularly in Iraq. This research sought to investigate the impact of top management support on innovation through the mediating role of knowledge sharing in Iraqi private HEIs. A quantitative approach was taken and 262 valid responses were collected to test the causal relationships between top management support, knowledge sharing, and innovation. Employing structural equation modelling with AMOS v.25, the research demonstrated that knowledge sharing plays a pivotal role in the relationship between top management support and innovation. The research has produced some guidelines for researchers as well as leaders, and provided evidence to support the use of knowledge sharing to increase innovation within the higher education environment in developing countries, particularly Iraq.

Keywords: top management support, knowledge sharing, innovation, structural equation modelling

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Authors: Anna Ching-Yu Wong

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Introduction: Textbooks play a sizable portion of the overall cost of higher education students. It is a board consent that open education resources (OER) reduce the te4xtbook costs and provide students a way to receive high-quality learning materials at little or no cost to them. However, there is less agreement over exactly how much. This study presents an approach for calculating OER savings by using SUNY Canton NON-OER courses (N=233) to estimate the potentially textbook savings for one semester – Fall 2022. The purpose in collecting data is to understand how much potentially saved from using OER materials and to have a record for future further studies. Literature Reviews: In the past years, researchers identified the rising cost of textbooks disproportionately harm students in higher education institutions and how much an average cost of a textbook. For example, Nyamweya (2018) found that on average students save $116.94 per course when OER adopted in place of traditional commercial textbooks by using a simple formula. Student PIRGs (2015) used reports of per-course savings when transforming a course from using a commercial textbook to OER to reach an estimate of $100 average cost savings per course. Allen and Wiley (2016) presented at the 2016 Open Education Conference on multiple cost-savings studies and concluded $100 was reasonable per-course savings estimates. Ruth (2018) calculated an average cost of a textbook was $79.37 per-course. Hilton, et al (2014) conducted a study with seven community colleges across the nation and found the average textbook cost to be $90.61. There is less agreement over exactly how much would be saved by adopting an OER course. This study used SUNY Canton as a case study to create an approach for estimating OER savings. Methodology: Step one: Identify NON-OER courses from UcanWeb Class Schedule. Step two: View textbook lists for the classes (Campus bookstore prices). Step three: Calculate the average textbook prices by averaging the new book and used book prices. Step four: Multiply the average textbook prices with the number of students in the course. Findings: The result of this calculation was straightforward. The average of a traditional textbooks is $132.45. Students potentially saved $1,091,879.94. Conclusion: (1) The result confirms what we have known: Adopting OER in place of traditional textbooks and materials achieves significant savings for students, as well as the parents and taxpayers who support them through grants and loans. (2) The average textbook savings for adopting an OER course is variable depending on the size of the college and as well as the number of enrollment students.

Keywords: textbook savings, open textbooks, textbook costs assessment, open access

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Authors: Javier Alfonso Bonilla-Chávez, Angélica Lozano

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In North America, more than 2,000 people annually die in accidents related to railroad tracks. In 2020, collisions at grade crossings were the main cause of deaths related to railway accidents in Mexico. Railway networks have constant interaction with motor transport users, cyclists, and pedestrians, mainly in grade crossings, where is the greatest vulnerability and risk of accidents. Usually, accidents at grade crossings are directly related to risky behavior and non-compliance with regulations by motorists, cyclists, and pedestrians, especially in developing countries. Around the world, countries classify these crossings in different ways. In Mexico, according to their dangerousness (high, medium, or low), types A, B and C have been established, recommending for each one different type of auditive and visual signaling and gates, as well as horizontal and vertical signaling. This classification is based in a weighting, but regrettably, it is not explained how the weight values were obtained. A review of the variables and the current approach for the grade crossing classification is required, since it is inadequate for some crossings. In contrast, North America (USA and Canada) and European countries consider a broader classification so that attention to each crossing is addressed more precisely and equipment costs are adjusted. Lack of a proper classification, could lead to cost overruns in the equipment and a deficient operation. To exemplify the lack of a good classification, six crossings are studied, three located in the rural area of Mexico and three in Mexico City. These cases show the need of: improving the current regulations, improving the existing infrastructure, and implementing technological systems, including informative signals with nomenclature of the involved crossing and direct telephone line for reporting emergencies. This implementation is unaffordable for most municipal governments. Also, an inventory of the most dangerous grade crossings in urban and rural areas must be obtained. Then, an approach for improving the classification of grade crossings is suggested. This approach must be based on criteria design, characteristics of adjacent roads or intersections which can influence traffic flow through the crossing, accidents related to motorized and non-motorized vehicles, land use and land management, type of area, and services and economic activities in the zone where the grade crossings is located. An expanded classification of grade crossing in Mexico could reduce accidents and improve the efficiency of the railroad.

Keywords: accidents, grade crossing, railroad, traffic safety

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Authors: P. S. Jagadeesh Kumar, Yang Yung, Wenli Hu

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Speech emotion classification is a dominant research field in finding a sturdy and profligate classifier appropriate for different real-life applications. This effort accentuates on classifying different emotions from speech signal quarried from the features related to pitch, formants, energy contours, jitter, shimmer, spectral, perceptual and temporal features. Tensor deep stacking neural networks were supported to examine the factors that influence the classification success rate. Facial electromyography signals were composed of several forms of focuses in a controlled atmosphere by means of audio-visual stimuli. Proficient facial electromyography signals were pre-processed using moving average filter, and a set of arithmetical features were excavated. Extracted features were mapped into consistent emotions using bilinear mapping. With facial electromyography signals, a database comprising diverse emotions will be exposed with a suitable fine-tuning of features and training data. A success rate of 92% can be attained deprived of increasing the system connivance and the computation time for sorting diverse emotional states.

Keywords: speech emotion classification, tensor deep stacking neural networks, facial electromyography, bilinear mapping, audio-visual stimuli

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Authors: Chico A. E. Hindarto

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Knowledge management practices are more contextual when they combine with the corporate culture. Each entity has a specific cultural climate that enables knowledge sharing in both functional and individual levels. The interactions between people within organization can be influenced by the culture and how the knowledge is transmitted. On the other hand, these interactions have impacts in culture modification as well. Storytelling is one of the methods in delivering the knowledge throughout the organization. This paper aims to explore the possibility in using a quasi-folklore in the family-based business. Folklore is defined as informal tradition culture that spreading through a word-of-mouth, without knowing the source of the story. In this paper, the quasi-folklore term is used to differentiate it with the original term of folklore. The story is created by somebody in the organization, not like the folklore with unknown source. However, the source is not disclosed, in order to avoid the predicted interest from the story origin. The setting of family-based business is deliberately chosen, since the kinship is considerably strong in this type of entity. Through a thorough literature review that relates to knowledge management, storytelling, and folklore, this paper determines how folklore can be an option for knowledge sharing within the organization.

Keywords: folklore, family business, organizational culture, knowledge management, storytelling

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Authors: Sandra Mitrovic, Gaurav Singh

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In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

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Authors: Sedat Simsek, Tugay Arat

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Today's communication concept, which has a great revolution with the printing press which has been found by Gutenberg, has no boundary thanks to advanced communication devices and the internet. It is possible to take advantage in many areas, such as from medicine to social sciences or from mathematics to education, from the computers that was first produced for the purpose of military services. The use of these developing technologies in the field of education has created a great vision changes in both training and having education. Materials, which can be considered as basic communication resources and used in traditional education has begun to lose its significance, and some technologies have begun to replace them such as internet, computers, smart boards, projection devices and mobile phone. On the other hand, the programs and applications used in these technologies have also been developed. University students use virtual books instead of the traditional printed book, use cell phones instead of note books, use the internet and virtual databases instead of the library to research. They even submit their homework with interactive methods rather than printed materials. The traditional education system, these technologies, which increase productivity, have brought a new dimension to education. The aim of this study is to determine the influence of technologies in the learning process of students and to find whether is there any similarities and differences that arise from the their faculty that they have been educated and and their learning process. In addition to this, it is aimed to determine the level of ICT usage of students studying at the university level. In this context, the advantages and conveniences of the technology used by students are also scrutinized. In this study, we used surveys to collect data. The data were analyzed by using SPSS 16 statistical program with the appropriate testing.

Keywords: education, communication technologies, role of technology, teaching

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Authors: Fredrick Olatunji Ajegbomogun, Bello Modinat Morenikeji, Okorie Nancy Chituru

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Electronic information resources are highly relevant to students' academic and research needs but are grossly underutilized, despite the institutional commitment to making them available. The under-utilisation of these resources could be attributed to a low level of study behaviour coupled with a low level of computer self-efficacy. This study assessed computer self-efficacy, study behaviour, and the use of electronic information resources by students in selected polytechnics in Ogun State. A simple random sampling technique using Krejcie and Morgan's (1970) Table was used to select 370 respondents for the study. A structured questionnaire was used to collect data on respondents. Data were analysed using frequency counts, percentages, mean, standard deviation, Pearson Product Moment Correlation (PPMC) and multiple regression analysis. Results reveal that the internet (= 1.94), YouTube (= 1.74), and search engines (= 1.72) were the common information resources available to the students, while the Internet (= 4.22) is the most utilized resource. Major reasons for using electronic information resources were to source materials and information (= 3.30), for research (= 3.25), and to augment class notes (= 2.90). The majority (91.0%) of the respondents have a high level of computer self-efficacy in the use of electronic information resources through selecting from screen menus (= 3.12), using data files ( = 3.10), and efficient use of computers (= 3.06). Good preparation for tests (= 3.27), examinations (= 3.26), and organization of tutorials (= 3.11) are the common study behaviours of the respondents. Overall, 93.8% have good study behaviour. Inadequate computer facilities to access information (= 3.23), and poor internet access (= 2.87) were the major challenges confronting students’ use of electronic information resources. According to the PPMC results, study behavior (r = 0.280) and computer self-efficacy (r = 0.304) have significant (p 0.05) relationships with the use of electronic information resources. Regression results reveal that self-efficacy (=0.214) and study behavior (=0.122) positively (p 0.05) influenced students' use of electronic information resources. The study concluded that students' use of electronic information resources depends on the purpose, their computer self-efficacy, and their study behaviour. Therefore, the study recommended that the management should encourage the students to improve their study habits and computer skills, as this will enhance their continuous and more effective utilization of electronic information resources.

Keywords: computer self-efficacy, study behaviour, electronic information resources, polytechnics, Nigeria

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Authors: Li Kewen, Su Zhaoxin, Wang Xingmou, Zhu Jian Bing

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Conventional reservoir prediction methods ar not sufficient to explore the implicit relation between seismic attributes, and thus data utilization is low. In order to improve the predictive classification accuracy of reservoir lithology, this paper proposes a deep learning lithology prediction method based on ResNet (Residual Neural Network) and SENet (Squeeze-and-Excitation Neural Network). The neural network model is built and trained by using seismic attribute data and lithology data of Shengli oilfield, and the nonlinear mapping relationship between seismic attribute and lithology marker is established. The experimental results show that this method can significantly improve the classification effect of reservoir lithology, and the classification accuracy is close to 70%. This study can effectively predict the lithology of undrilled area and provide support for exploration and development.

Keywords: convolutional neural network, lithology, prediction of reservoir, seismic attributes

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Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: Liqun Zou, Ling Wang, Xiaoli Chen

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Objective: Through the questionnaire about emergency nurses’ knowledge and attitudes regarding pain management to understand whether they are well mastered and practiced the related knowledge about pain management, providing a reference for continuous improvement of the quality of nursing care in acute pain and for improving the effect of management on emergency pain patients. Method: The Chinese version questionnaire about KASRP (knowledge and attitudes survey regarding pain) was handed out to 132 emergency nurses to do a study about the knowledge and attitude of pain management. Meanwhile, SPSS17.0 was used to do a descriptive analysis and variance analysis on collected data. Results: The emergency nurses’ correct answer rate about KASRP questionnaire is from 25% to 65% and the average correct rate is (44.65 + 7.85)%. In addition, there are 10 to 26 items being given the right answer. Therefore, the average correct items are (17.86 ± 3.14). Moreover, there is no statistical significant on the differences about the correct rate for different age, gender and work experience to answer; however, the difference of the correct rate in different education background and the professional title is significant. Conclusion: There is a remarkable lack of knowledge and attitude towards pain management in emergency nurses, whose basic knowledge of pain is sufficient. Besides, there is a deviation between the knowledge of pain management and clinical practice, which needs to be improved.

Keywords: emergency nurse, pain, KASRP questionnaire, pain management

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Authors: José A. Menezes, Giordano Cabral, Bruno T. Gomes

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Choosing good features is an essential part of machine learning. Recent techniques aim to automate this process. For instance, feature learning intends to learn the transformation of raw data into a useful representation to machine learning tasks. In automatic audio classification tasks, this is interesting since the audio, usually complex information, needs to be transformed into a computationally convenient input to process. Another technique tries to generate features by searching a feature space. Genetic algorithms, for instance, have being used to generate audio features by combining or modifying them. We find this approach particularly interesting and, despite the undeniable advances of feature learning approaches, we wanted to take a step forward in the use of genetic algorithms to find audio features, combining them with more conventional methods, like PCA, and inserting search control mechanisms, such as constraints over a confusion matrix. This work presents the results obtained on particular audio classification problems.

Keywords: feature generation, feature learning, genetic algorithm, music information retrieval

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Authors: Nicholas Milburn, Yu Liang, Dalei Wu

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Athlete performance scoring within the climbing do-main presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: classification, climbing, data imbalance, data scarcity, machine learning, time sequence

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Authors: Rameshwar Dahal, S. Ping Ho

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Although the demand for infrastructure development forms a promising market opportunity for international firms, the dominance of informal institutions over formal ones, investors are facing extraordinary institutional challenges when investing in emerging Least Developed Countries (LDCs). We believe that, in emerging LDCs, the project performance heavily depends on how well the entrants respond to the challenges exerted by the host institutional environments. Which primarily depends on how much they learn about the host institution and what strategy they apply in response. In Nepal, almost all international or global infrastructure projects are financed by international financers, so the procurement process of the infrastructure projects financed by foreign agencies is guided by the policies and regulations of the financer. Because of limited resources and the financers’ demand, contractors and consults are procured internationally. Moreover, the resources, including but not limited to construction material, manpower, and equipment, also need to be imported. Therefore, the involvement of international companies as an entrant in global infrastructure projects of LDCs is obvious. In a global project (GP), participants from different geographical and institutional environments hold different beliefs and have disparate interests. Therefore, the entrants face the challenges exerted by the host institutional environments. The entrants must either adapt to the institutions prevailing in the environment or resist the institutional pressures. It is hypothesized that, in emerging LDCs, the project performance heavily depends on how much the entrants learn about the host institutional knowledge and how well they respond to the institutional environments. While it is impossible to generalize the phenomenon and contextual conditions because of their vast diversity, this study has answered why and how participants’ level of institutional knowledge impacts the project's implementation performance. To draw that conclusion, firstly, we explored two typical GPs from Nepal. For this study, the data were collected by conducting interviews and examining the secondary data, such as the project reports published by the financers, project data provided by interviewees, and news reports. In an event analysis, firstly, we identify the sources, causes, or nature of the institutional challenges; secondly, we analyze the entrant’s responses to the exerted challenges and evaluate the impacts of the responses on the overall project performance. In this study, at first, the events occurred during the project implementation process have a causal link with the local institutions that demand the entrants’ response are extracted. Secondly, each event is scrutinized as the critical success factor of the case project. Finally, it is crucially examined whether and what institutional knowledge in these events played a critical role in project success or failure. The results also provide insights into the crucial institutional knowledge in LDCs and the subsequent strategy implications for undertaking projects in LDCs.

Keywords: emerging countries, LDC, project management, project performance, institutional knowledge, institutional theory

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Authors: Soo Hyung Kim

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Two different learning approaches were suggested: focusing on factual knowledge or focusing on the embedded meaning in the statements. Each way of learning has positive effects on different question categories, where factual knowledge helps more with simple fact questions, and searching for meaning in given information helps learn causal relationship and the embedded meaning. To test this belief, two groups of learners (12 male and 39 female adults aged 18-37) watched a ten-minute long Youtube video about various factual events of American history, their meaning, and the causal relations of the events. The fact group was asked to focus on factual knowledge in the video, and the meaning group was asked to focus on the embedded meaning in the video. After watching the video, both groups took multiple-choice questions, which consisted of 10 questions asking the factual knowledge addressed in the video and 10 questions asking embedded meaning in the video, such as the causal relationship between historical events and the significance of the event. From ANCOVA analysis, it was found that the factual knowledge showed higher performance on the factual questions than the meaning group, although there was no group difference on the questions about the meaning between the two groups. The finding suggests that teacher instruction plays an important role in learners constructing a different type of knowledge in online learning.

Keywords: factual knowledge, instruction, meaning-based knowledge, online learning

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Authors: Pradip Kumar Das

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As the entire world continues its rapid move towards industrialization, it has seriously threatened mankind’s ability to maintain an ecological balance. Geographical and natural forces have a significant influence on the location of industries. Industrialization is the foundation stone of the development of any country, while the unplanned industrialization and discharge of waste by industries is the cause of environmental pollution. There is growing degree of awareness and concern globally among nations about environmental degradation or pollution. Environmental resources endowed by the gift of nature and not manmade are invaluable natural resources of a country like India. Any developmental activity is directly related to natural and environmental resources. Economic development without environmental considerations brings about environmental crises and damages the quality of life of present, as well as future generation. As corporate sectors in the global market, especially in India, are becoming anxious about environmental degradation, naturally more and more emphasis will be ascribed to how environment-friendly the outcomes are. Maintaining accounts of such environmental and natural resources in the country has become more urgent. Moreover, international awareness and acceptance of the importance of environmental issues has motivated the development of a branch of accounting called “Environmental Accounting”. Environmental accounting attempts to detect and focus the resources consumed and the costs rendered by an industrial unit to the environment. For the sustainable development of mankind, a healthy environment is indispensable. Gradually, therefore, in many countries including India, environment matters are being given top most priority. Accounting and disclosure of environmental matters have been increasingly manifesting as an important dimension of corporate accounting and reporting practices. But, as conventional accounting deals with mainly non-living things, the formulation of valuation, and measurement and accounting techniques for incorporating environment-related matters in the corporate financial statement sometimes creates problems for the accountant. In the light of this situation, the conceptual analysis of the study is concerned with the rationale of environmental accounting on the economy and society as a whole, and focuses the failures of the traditional accounting system. A modest attempt has been made to throw light on the environmental awareness in developing nations like India and discuss the problems associated with the implementation of environmental accounting. The conceptual study also reflects that despite different anomalies, environmental accounting is becoming an increasing important aspect of the accounting agenda within the corporate sector in India. Lastly, a conclusion, along with recommendations, has been given to overcome the situation.

Keywords: environmental accounting, environmental degradation, environmental management, environmental resources

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Authors: Torben Hansen, Lars Gronholdt, Alexander Josiassen, Anne Martensen

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Consumers often exhibit a bias in their knowledge; they often think that they know more or less than they do. The concept of 'knowledge over/underconfidence' (O/U) has in previous studies been used to investigate such knowledge bias. O/U appears as a combination of subjective and objective knowledge. Subjective knowledge relates to consumers’ perception of their knowledge, while objective knowledge relates to consumers’ absolute knowledge measured by objective standards. This separation leads to three scenarios: The consumer can either be knowledge calibrated (subjective and objective knowledge are similar), overconfident (subjective knowledge exceeds objective knowledge) or underconfident (objective knowledge exceeds subjective knowledge). Knowledge O/U is a highly useful concept in understanding consumer choice behavior. For example, knowledge overconfident individuals are likely to exaggerate their ability to make right choices, are more likely to opt out of necessary information search, spend less time to carry out a specific task than less knowledge confident consumers, and are more likely to show high financial trading volumes. Through the use of financial services as a case study, this study contributes to previous research by examining how consumer knowledge O/U affects two types of trust (broad-scope trust and narrow-scope trust) and consumer relationship satisfaction. Trust does not only concern consumer trust in individual companies (i.e., narrow.-scope confidence NST), but also concerns consumer confidence in the broader business context in which consumers plan and implement their behavior (i.e., broad scope trust, BST). NST is defined as "the expectation that the service provider can be relied on to deliver on its promises’, while BST is defined as ‘the expectation that companies within a particular business type can generally be relied on to deliver on their promises.’ This study expands our understanding of the interplay between consumer knowledge bias, consumer trust, and relationship marketing in two main ways: First, it is demonstrated that the more knowledge O/U a consumer becomes, the higher/lower NST and levels of relationship satisfaction will be. Second, it is demonstrated that BST has a negative moderating effect on the relationship between knowledge O/U and satisfaction, such that knowledge O/U has a higher positive/negative effect on relationship satisfaction when BST is low vs. high. The data for this study comprises 756 mutual fund investors. Trust is particularly important in consumers’ mutual fund behavior because mutual funds have important responsibilities in providing financial advice and in managing consumers’ funds.

Keywords: knowledge, cognitive bias, trust, customer-seller relationships, financial services

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Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: classification, singing, spectral analysis, vocal emission, vocal register

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