Reviewerly/exHarmony huggingface dataset:
Extent of Utilization of Whatsapp for Enhancing Academic Learning by Business Education Students in Anambra State, Nigeria

Nigeria 1. IntroductionIf one invention has influenced the world the most over the past few decades, it is arguably WhatsApp.WhatsApp has improved modern interactive channels through which individuals and groups connect one another, share ideas, experiences, and information of interest.WhatsApp is a social network platform that operates on nearly all current types of device and operating systems.As a means of sending and receiving messages to and from individuals or groups, WhatsApp includes a variety of functions such as text messages, attached images, audio files, video files and link to web addresses (Cohavi, 2013).The utilization of WhatsAppin the field of education has greatly impacted on the quality of teaching and learning globally.WhatsApp platform provides online students opportunities to collaborate and corporate together to construct knowledge.It also encourages learners to learn by anticipating needs, make collaborative learning efficient and effective, build relationship that stimulates learner-to-learner for consistent and progressive learning.In support, Oghenetega, Oyenike and Ugeh (2014) posited that WhatsApp platform provides online students opportunities to collaborate and corporate together to learn.Similarly, WhatsApp provides new opportunities for innovating and evolving modern system of education and learning institutions, and for preparing learners for the 21 st century.Vervaart (2014) stressed that social networking (WhatsApp inclusive) has increased students' participation in academic group discussion, sharing of homework, projects, team work and ideas.The use of WhatsApp in business education classrooms facilitates students' delivery of homework and assignments.Business education lectures can utilize WhatsApp in improve their instructional processes and avail multiple benefits for collaborative professional development.Fischer (2013) stated that lecturers (business education lecturers inclusive) can create a group for their students to constitute a type of 'simple social network' for the class.WhatsApp enhance peer networking, reflection and sharing of classroom assignments, knowledge sharing, posting of conferences, workshops and training discussion.It can also help business education students to share links, answer questions from the
Development of Bilingual Pop Up Book Media on Javanese Puppet Stories for Elementary School Students

This research and development aim to develop bilingual pop-up book media on Javanese puppet stories that valid according to the material experts, media experts, and teachers, as well as practical students. The research uses an ADDIE model with 5 stages, that is analysis, design, development, implementation, and evaluation. The results of validation by the material expert, media expert, and by the user (teacher) sequentially get a percentage of 96.4; 100; and 95.4 percent. While the result of the practicality test on the small scale test and the large scale test got a percentage of 93.3 and 97.5 percent. Media is categorized as very valid and very practical and can be used for elementary school students.
A Deep Dive of Autoencoder Models on Low-Contrast Aquatic Images

Public aquariums and similar institutions often use video as a method to monitor the behavior, health, and status of aquatic organisms in their environments. These video footages take up a sizeable amount of space and require the use of autoencoders to reduce their file size for efficient storage. The autoencoder neural network is an emerging technique which uses the extracted latent space from an input source to reduce the image size for storage, and then reconstructs the source within an acceptable loss range for use. To meet an aquarium’s practical needs, the autoencoder must have easily maintainable codes, low power consumption, be easily adoptable, and not require a substantial amount of memory use or processing power. Conventional configurations of autoencoders often provide results that perform beyond an aquarium’s needs at the cost of being too complex for their architecture to handle, while few take low-contrast sources into consideration. Thus, in this instance, “keeping it simple” would be the ideal approach to the autoencoder’s model design. This paper proposes a practical approach catered to an aquarium’s specific needs through the configuration of autoencoder parameters. It first explores the differences between the two of the most widely applied autoencoder approaches, Multilayer Perceptron (MLP) and Convolution Neural Networks (CNN), to identify the most appropriate approach. The paper concludes that while both approaches (with proper configurations and image preprocessing) can reduce the dimensionality and reduce visual noise of the low-contrast images gathered from aquatic video footage, the CNN approach is more suitable for an aquarium’s architecture. As an unexpected finding of the experiments conducted, the paper also discovered that by manipulating the formula for the MLP approach, the autoencoder could generate a denoised differential image that contains sharper and more desirable visual information to an aquarium’s operation. Lastly, the paper has found that proper image preprocessing prior to the application of the autoencoder led to better model convergence and prediction results, as demonstrated both visually and numerically in the experiment. The paper concludes that by combining the denoising effect of MLP, CNN’s ability to manage memory consumption, and proper image preprocessing, the specific practical needs of an aquarium can be adeptly fulfilled.
An Overview of Uncertainty Calibration for Text Classification and the Role of Distillation

Recent advances in NLP systems, notably the pretraining-and-finetuning paradigm, have achieved great success in predictive accuracy.However, these systems are usually not well calibrated for uncertainty out-of-the-box.Many recalibration methods have been proposed in the literature for quantifying predictive uncertainty and calibrating model outputs, with varying degrees of complexity.In this work, we present a systematic study of a few of these methods.Focusing on the text classification task and finetuned large pretrained language models, we first show that many of the finetuned models are not well calibrated out-of-the-box, especially when the data come from out-of-domain settings.Next, we compare the effectiveness of a few widely-used recalibration methods (such as ensembles, temperature scaling).Then, we empirically illustrate a connection between distillation and calibration.We view distillation as a regularization term encouraging the student model to output uncertainties that match those of a teacher model.With this insight, we develop simple recalibration methods based on distillation with no additional inference-time cost.We show on the GLUE benchmark that our simple methods can achieve competitive out-of-domain (OOD) calibration performance w.r.t.more expensive approaches.Finally, we include ablations to understand the usefulness of components of our proposed method and examine the transferability of calibration via distillation.
UNBNLP at SemEval-2021 Task 1: Predicting lexical complexity with masked language models and character-level encoders

In this paper, we present three supervised systems for English lexical complexity prediction of single and multiword expressions for SemEval-2021 Task 1.We explore the use of statistical baseline features, masked language models, and character-level encoders to predict the complexity of a target token in context.Our best system combines information from these three sources.The results indicate that information from masked language models and character-level encoders can be combined to improve lexical complexity prediction.
Geospatial Interpretation of Onshore Hydrocarbon Micro–Seepage Induced Alterations in Soils and Sediments by Spectral Enhancement Techniques

Hydrocarbon micro – seepages are light hydrocarbon that cause oxidation – reduction reaction on the earth’s surface, resulting in alterations and anomalies such as red bed bleaching, ferrous iron enrichment and increase in the concentration of clay minerals and carbonate in overlying soils and sediments. Remote sensing has become a valuable tool in hydrocarbon micro – seepage studies and have been successfully used to interpret surface alterations and anomalies of minerals. In this study, Landsat 7 ETM+ remotely sensed data was utilized for interpreting the onshore hydrocarbon micro – seepage induced alterations zone in Ugwueme. Spectral enhancements techniques such as the principal component analysis (PCA), band ratio (BR) and false color composite (FCC) were adopted for delineating alteration zones. With Landsat 7 ETM+ band selection, and for PCA, the 1457PC3, 1345PC2 and 3457PC4 are the most suitable PC image for spectral enhancement of ferric iron, ferrous iron and clay minerals. Band ratio index such as (3/1), (7/5) and (2+5)/(3+4) also yields better enhancement for anomalous micro – seepage. The study shows that PCA, BR, FCC are good spectral enhancement techniques for interpreting hydrocarbon micro – seepage alterations in overlying soils and sediments.
KAMI index as an evaluation of academic information system security at XYZ university

XYZ University is one of the universities that has used information technology to create quality service for students and the entire academic community. This Information technology service is managed by Information Technology and Communication Center (PUSTIK) which is responsible to carry out the development, management, service, and maintaining the security of information and communication technology. Good information technology governance should be able to maintain information security. Therefore, it is necessary to evaluate information system security especially the security of academic information systems. This information system security evaluation uses Keamanan Informasi (KAMI) Index which refers to the ISO/IEC 27001:2013 standard to be able to determine the maturity level of information security. An evaluation of five areas of the KAMI Index shows the Information Security Risk Management area gets the lowest score at 10 out of a total of 72. The result of the KAMI Index dashboard shows that the maturity level of each area of information security is at levels I and I+ with a total score of 166. This means that the level of completeness of implement ISO 27001:2013 standard is in the inadequate category.
A Novel Multi-Level Pyramid Co-Variance Operators for Estimation of Personality Traits and Job Screening Scores

Recently, automatic personality analysis is becoming an interesting topic for computer vision. Many attempts have been proposed to solve this problem using time-based sequence information. In this paper, we present a new framework for estimating the Big-Five personality traits and job candidate screening variable from video sequences. The framework consists of two parts: (1) the use of Pyramid Multi-level (PML) to extract raw facial textures at different scales and levels; (2) the extension of the Covariance Descriptor (COV) to fuse different local texture features of the face image such as Local Binary Patterns (LBP), Local Directional Pattern (LDP), Binarized Statistical Image Features (BSIF), and Local Phase Quantization (LPQ). Therefore, the COV descriptor uses the textures of PML face parts to generate rich low-level face features that are encoded using concatenation of all PML blocks in a feature vector. Finally, the entire video sequence is represented by aggregating these frame vectors and extracting the most relevant features. The exploratory results on the ChaLearn LAP APA2016 dataset compare well with state-of-the-art methods including deep learning-based methods.
Distance learning as the alternative to tradional education in times of COVID-19 pandemic

The article deals with the analysis of the world education situation caused by the COVID-19 pandemic. Distance learning has commonly been accepted as the sole and exclusive education solution at the time of emergent events resulting in closing of educational establishments in the world in 2020. The distance learning ideas, introduction and organizational issues have found their partial coverage in certain scientific works, but the problem of crisis replacement of traditional education by distance learning remains insufficiently studied. The distinct difference between the terms “distance learning” and “crisis distance education” is represented. It lies in sudden, unready and forceful implementation. To the differences we refer: suddenness, internationalism, popularity, expansion, imposition, etc. The advantages, disadvantages and problems of the distance learning in higher education during the pandemic are studied. Crisis can produce an environment where current changes in the system of education are more accessible than in regular times. The transition of the traditional education into the crisis distance education must be developmental. Presently the scientific researchers state that the role of the educator must be reinterpreted. So, we come to the problem which remains a sharply debated question leading to a conflict between two prevailing teaching orientations: the teacher-centered approach against the student-centered approach. Today, the teacher-centered approach is criticized for the reason of traditional standardized testing, failure to measure acquired knowledge and stimulate learning. Most online educators have found that a helpful way to think about teaching in a student-centered fashion will transform the distance learning into a producrive, effective and progressive process. Using distance learning technology, teachers should: inspire students, express creativity, provide individual support, encourage to learn, motivate students and engage them in the knowledge acquisition process. The three forms of interaction for students in the online environment (student-content, student-student, student-instructor) are analyzed. The situation of distance learning introduction in Ukraine is represented. Despite the fact that Ukrainian population is not characterized by a high level of digital literacy, there appear a number of other problems such as decreasing success rate of students, poor Internet quality and the lack or absence of devices for online learning. The study emphasizes that successful functioning of distance learning in crisis period should be based on a student‐centered approach to learning. The role of the teacher must be focused mainly on students and identifying of potential barriers of learning.
Robust Visual Relationship Detection towards Sparse Images in Internet-of-Things

Visual relationship can capture essential information for images, like the interactions between pairs of objects. Such relationships have become one prominent component of knowledge within sparse image data collected by multimedia sensing devices. Both the latent information and potential privacy can be included in the relationships. However, due to the high combinatorial complexity in modeling all potential relation triplets, previous studies on visual relationship detection have used the mixed visual and semantic features separately for each object, which is incapable for sparse data in IoT systems. Therefore, this paper proposes a new deep learning model for visual relationship detection, which is a novel attempt for cooperating computational intelligence (CI) methods with IoTs. The model imports the knowledge graph and adopts features for both entities and connections among them as extra information. It maps the visual features extracted from images into the knowledge-based embedding vector space, so as to benefit from information in the background knowledge domain and alleviate the impacts of data sparsity. This is the first time that visual features are projected and combined with prior knowledge for visual relationship detection. Moreover, the complexity of the network is reduced by avoiding the learning of redundant features from images. Finally, we show the superiority of our model by evaluating on two datasets.
Adaptive Learning User Implicit Trust Behavior Based on Graph Convolution Network

In the existing social recommendation algorithms, the performance of the algorithms is bothered by the problem of noise and sparsity in social information. In the past few years, researchers have usually focused on solving noise problem or sparse problem, but ignored a key problem, that is if only solving the noise problem, the sparse problem will become more serious, similarly, if only solving the sparse problem, the noise problem will become more serious. Recently, a few studies attempt to solve both noise and sparse problems, usually using explicit trust features to filter trust information and generate implicit trust friends, but this will depend heavily on the quality of explicit trust features. Therefore, in this paper, we propose an adaptive algorithm based on graph convolution network, The main idea is to learn users' implicit trust behavior adaptively. Briefly, we use Jaccard similarity coefficient to filter out unnecessary trust information, the reliable trust information will be the users' trust attributes, then put into the graph convolution network, because of the good feature extraction ability of graph convolution network, we can use adaptive network to learn implicit trust features based on users' features, the trust features can infer the trust attributes for all users. In order to achieve the best effect of learning, we use iterative repetition of the above process, to mining the implicit influence of user trust behavior, so as to alleviate noise and sparsity problems. Experimental result shows that, on three open datasets: LastFM, Douban and Gowalla, the recommendation performance is improved, which proves the effectiveness of the algorithm.
LEARNING EFFECTIVENESS ON TRAINING BASED ON TEAMS LEARNING TOURNAMENT MODEL AND GOOGLE CLASSROOM APPLICATION

The study aims to determine learning effectiveness on training based on the Teams Learning Tournament (TLT) model combined with Google Classroom application. The research method used is descriptive qualitative collected through observation and qualitative analysis. The research was conducted in 2020 held in Teachers’ Training of Basic Islamic School (MI) Pangandaran. The results showed that TLT learning model combined with Google Classroom application can manage learning effectively. The results showed that TLT learning models supported by the Google Classroom app can effectively manage learning, which can be seen from an increase in attitudes in liveliness of up to 100%, an average knowledge increase of 80.75% and the skills of participants can skillfully compose a predetermined learning product with an average skill score of 85.86. TraineesTLT learning model and Google Classroom application can also be separated to manage learning, but it will be more effective if those two are combined in learning.
Benefits e-Procurement System

Procurement is one of the particular activities in business that demands high attention. In the service industry, the procurement department plays a major role in ensuring that the supply of raw materials and goods is planned, purchased, distributed, and consumed efficiently. An influx of activities such as requisition, acquisition, and quality checks are done in this department. In estimating the total cost of acquiring goods and services, it is important for organizations to identify unit costs and other costs associated with operations and maintenance that occur during purchasing and conserving of goods. Recent studies have reported the usage of an Electronic Procurement System (EPS) that replaces the conventional procurement system typically used in the service industry. This paper aims to explore the current practice, effectiveness and efficiency of the procurement systems used in the Malaysian service industry through a systematic review of literature. Findings of this study will assist service businesses to analyze their procurement activities and develop proper strategies that can reduce costs, increase efficiency, and most importantly, improve their bottom line.
Joint Slot Filling and Intent Detection with BLSTM-CNN-CRF

口语语言理解(SLU)中的槽填充和意图识别任务通常是分别进行建模,忽略了任务之间的关联性.基于深度学习优势提出一种BLSTM-CNN-CRF学习框架,为槽填充和意图识别任务构建联合模型.双向长短期记忆网络(BLSTM)对全句的单词标签进行标注,卷积神经网络(CNN)用以提取全句的语义特征,条件随机场(CRF)通过解码单词标签与语义特征,获得全句的最佳序列标签.在航空旅行信息系统(ATIS)数据集上的实验表明,联合模型在不依赖于任何人工特征的情况下获得较高性能.
ANALYSIS OF ULTRASOUND IMAGE BIOLOGICAL IMAGE ALGORITHM IN THE RESTORATION OF MUSCLE GROUP MOVEMENT FUNCTION

ABSTRACT Objective: By studying the recognition effect of ultrasonic biological image data analysis on muscle group motion function, the evaluation value and significance of ultrasonic biomedical image combination algorithm on muscle group motion function are discussed. Methods: A Gabor filtering algorithm is proposed to smooth the original image. The MVEF algorithm is used to enhance the ultrasonic image and binary further the image again. Using the principle of the Hove transform, the thickness of the muscle is automatically estimated. Results: The square of correlation coefficients of the manual measurement method, Gabor filtering algorithm and MVEF algorithm are 91.3%, 91.3% and 87.8%, respectively. The difference between the manual measurement and the estimation based on the Gabor filtering algorithm is 1.45 ± 0.48mm. The difference between the results of manual measurement and the MVEF algorithm is 1.38 ± 0.56mm. The computation time of the MVEF algorithm and Gabor algorithm are 5 seconds and 0.3 seconds, respectively. Conclusions: The algorithm proposed in this study can effectively measure the muscle thickness, fast, convenient and accurate, and can reflect the contractility of skeletal muscle well, which is of great value for the recognition and evaluation of muscle group movement function. Level of evidence II; Therapeutic studies - investigation of treatment results.
SOCIAL AND CULTURAL DETERMINANTS OF UNIVERSITY SOCIAL RESPONSIBILITY

The development of human civilization convincingly proves to us that a person, being born in a certain historical epoch in a certain country, prepares not for life in general, but for life in specific social and cultural conditions. Analysis of the available philosophical, pedagogical, sociological literature and areas of scientific and educational research indicates that researchers focus their attention on the problem of social responsibility of modern Universities. In particular, interest in this issue has grown significantly in recent years. The activities of Universities are never limited to the training of future specialists. Universities perform the social function of reproduction and development of society and its systems through the processes of translation of cultural norms in changing historical circumstances, each time on the basis of new social relations. Although among researchers there is no consensus on the definition of social responsibility of the University and the areas of its implementation, there is a general understanding of this phenomenon as a certain obligation of a higher education institution to society. That is why it is important to realize that the social responsibility of Universities should be considered in the context of the general social and cultural dynamics of a particular society. And if there were times when the social responsibility of the University could be represented by the concept of «literacy», then modern world makes completely different demands on education in general and on understanding of the University social responsibility in particular. The social responsibility of the University to society as a social institution, its individual groups and strata, is its commitment to contribute to the solution of important social problems.