Reviewerly/exHarmony huggingface dataset:
Detection of jamming and interference attacks in wireless communication network using deep learning technique

The Jamming and interference attacks aim to disable a wireless network, inducing a denial of service.Despite the resilience offered 5G is prone to these regarding the impact to the use of millimetre wave bands.In the last decade, several jamming detection techniques have been developed, including fuzzy logic, game theory, channel surfing, and some others statistical modeling.The plurality of these strategies are inadequate at detecting smart jammers.As a response, efficient and quick jamming and interference high-accuracy detection systems are all still in great demand.The usefulness of many deep learning models in detecting jamming and interference signals is analyzed in this paper.The types of signal features that could be used to diagnose jamming and interference signals are investigated, and a large dataset was created using these parameters.Deep learning algorithms are being kitted, tested, and sorely tested using this dataset.Logistic regression and naïve bayes are representations of these algorithms.The probability of detection, probability of false alarm and accuracy are being used to verify and validate the performance of these algorithms.The simulation results show that a logistic regression algorithm based on jamming detection and interference can detect jammers with perfect seating, a high possibility of detection, and a minimal probability of false alarm.
RESPON MAHASISWA PGMI TERHADAP PLATFORM WAKELET UNTUK PEMBELAJARAN ONLINE DI MASA PANDEMI COVID-19

The COVID-19 pandemic has caused many changes in various aspects of life, including in the education sector and the learning system. The application of learning and teaching from home policies for students and lecturers alike changes the face-to-face learning perspective online by utilizing various applications and platforms. This study aims to determine the effectiveness of Wakelet for higher education and the benefits of Wakelet in online learning. This research method is descriptive quantitative through a survey. The population of this study was students of the PGMI IAIN Padangsidimpuan study program semester 6. The sampling technique used in this study was probability sampling which included simple random sampling. The sample was determined based on the Slovin formula assisted by Microsoft Excel software, so the sample was 94 respondents. The data collection technique used a questionnaire by Google Form. Analysis of research data using descriptive statistics assisted by STATCAL and EAVIS software. The results showed that the Wakelet platform was categorized as effective and efficient for use in the university environment, especially in the PGMI IAIN Padangsidimpuan Study Program based on student responses at a percentage of 78.72%. The Wakelet platform is also easily accessible and innovative, varied, and creative based on student responses, which are 80.85%. Besides having an attractive appearance, the Wakelet platform also has benefits, including bringing together all types of student work collected, such as videos, article texts, images, PDF files of papers/e-books / e-journals / e-proceedings, sharing web links, media links,social media and others.
An intelligent system for transforming natural language queries into SQL and its execution

Abstract In today’s world information storing and retrieval plays an important role. Database systems play a key role in the new commercial system for information storage. For accessing the data from database, a person who is not having the knowledge of SQL may find themselves handicapped while dealing with the database. This presents a significant limitation in a developing country such as India, because even today, a very large majority of the population does not have the technical knowledge of how to deal with database systems. In such a case, Natural language processing (NLP) assumes a significant job. NLP is getting perhaps the most dynamic strategies utilized in Human-PC Interaction. It is a part of AI which is utilized for Information Retrieval, Machine interpretation, and Language Analysis. This paper, proposes a system which allows the user to query the database in a compatible mode language, through a convenient Graphical User Interface which results in the data required by the user.
Communicative approach with elements of bilingualism in teaching physics

In the article, taking into account the challenges of the global information revolution, modern socio-educational realities, the authors substantiate the need for a communicative approach in teaching physics as one of the methodological approaches of a new scientific direction – psycho didactics of physics. The purpose of the article is defined as the coverage of some results of experimental teaching of physics to high school students of a secondary school based on a communicative approach with elements of bilingualism, its essence is characterized. Using a specific example, the author’s methodology for implementing this approach was first demonstrated, the need for its integration with other psycho-didactic approaches with the aim of comprehensive development of the student’s personality in the process of teaching physics is emphasized. Some experimental data are presented that make it possible, as a first approximation, to optimistically evaluate the results of testing the methodology of the communicative approach with elements of bilingualism in physics education, the prospect of further studies to obtain data on a more voluminous statistical sample is outlined , as well as to identify the possibility of using the proposed methodology in studying other disciplines in natural sciences in both secondary and high school.
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels

In learning with noisy labels, the sample selection approach is very popular, which regards small-loss data as correctly labeled during training. However, losses are generated on-the-fly based on the model being trained with noisy labels, and thus large-loss data are likely but not certainly to be incorrect. There are actually two possibilities of a large-loss data point: (a) it is mislabeled, and then its loss decreases slower than other data, since deep neural networks "learn patterns first"; (b) it belongs to an underrepresented group of data and has not been selected yet. In this paper, we incorporate the uncertainty of losses by adopting interval estimation instead of point estimation of losses, where lower bounds of the confidence intervals of losses derived from distribution-free concentration inequalities, but not losses themselves, are used for sample selection. In this way, we also give large-loss but less selected data a try; then, we can better distinguish between the cases (a) and (b) by seeing if the losses effectively decrease with the uncertainty after the try. As a result, we can better explore underrepresented data that are correctly labeled but seem to be mislabeled at first glance. Experiments demonstrate that the proposed method is superior to baselines and robust to a broad range of label noise types.
Training Language Models under Resource Constraints for Adversarial Advertisement Detection

Eshwar Shamanna Girishekar, Shiv Surya, Nishant Nikhil, Dyut Kumar Sil, Sumit Negi, Aruna Rajan. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers. 2021.
MATHEMATICAL FOUNDATIONS OF ADVANCED INFORMATICS: SPECIFICS OF TEACHING THE COURSE WITH UTILIZATION OF THE SYSTEM GRAN AND UTILIZATION FOR PARTICIPANT-DIRECTED LEARNING

The article considers the issue of computer training of mathematics teachers in the context of using computer mathematics systems as a teaching tool. The analysis of propaedeutic tasks of the course “Mathematical foundations of Advanced Informatics” (MFAI) is carried out. The system of tasks is considered and situational tasks are analyzed with the use of rules-guidelines and methodical instructions for solving problems with the use of the GRAN system. An updated syllabus of the training course is offered taking into account modern requirements. The analysis of the logical-structural scheme of the training course “Mathematical foundations of Advanced Informatics” (MFAI) is considered. The main advantages of using the GRAN system are demonstrated. The results of the conducted pedagogical experiment using the computer mathematics system GRAN are offered. Criteria, indicators and methods of diagnosing the effectiveness of student learning are substantiated. The classification of problems is carried out and examples of didactic materials for teaching the course using computer mathematics systems GRAN are offered.
BORE: Bayesian Optimization by Density-Ratio Estimation

Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion encoded in an acquisition function, many of which are computed from the posterior predictive of a probabilistic surrogate model. Prevalent among these is the expected improvement (EI) function. The need to ensure analytical tractability of the predictive often poses limitations that can hinder the efficiency and applicability of BO. In this paper, we cast the computation of EI as a binary classification problem, building on the link between class-probability estimation and density-ratio estimation, and the lesser-known link between density-ratios and EI. By circumventing the tractability constraints, this reformulation provides numerous advantages, not least in terms of expressiveness, versatility, and scalability.
Multidimensional evaluation of Virtual Reality paradigms in clinical neuropsychology: the VR-Check framework

Virtual Reality (VR) represents a key technology of the 21st century, attracting substantial interest from a wide range of scientific disciplines. With regard to clinical neuropsychology, a multitude of new VR applications are being developed to overcome the limitations of classical paradigms. In consequence, researchers increasingly face the challenge of systematically evaluating the characteristics and quality of VR applications in order to design the optimal paradigm for their specific research question and study population. However, the manifold properties of contemporary VR are not adequately captured by the psychometric quality criteria of classical test theory (i.e., objectivity, reliability, validity), highlighting the need for an extended paradigm evaluation framework. To address this gap, we here propose a multidimensional evaluation framework for VR applications in clinical neuropsychology, summarized as an easy-to-use checklist (VR-Check). This framework rests on ten main evaluation dimensions encompassing cognitive domain specificity, ecological relevance, technical feasibility, user feasibility, user motivation, task adaptability, performance quantification, immersive capacities, training feasibility, and predictable pitfalls. We show how VR-Check enables systematic and comparative paradigm optimization by illustrating its application in an exemplary research project on the assessment of spatial cognition and executive functions with immersive VR. This application furthermore demonstrates how the framework allows researchers to identify across-domain tradeoffs, makes deliberate design decisions explicit, and optimizes the allocation of study resources. Complementing recent approaches to standardize clinical VR studies, the VR-Check framework enables systematic and project-specific paradigm optimization for behavioral and cognitive research in neuropsychology.
Visualization Techniques to Enhance Automated Event Extraction

Robust visualization of complex data is critical for the effective use of NLP for event classification, as the volume of data is large and the high-dimensional structure of text makes data challenging to summarize succinctly. In event extraction tasks in particular, visualization can aid in understanding and illustrating the textual relationships from which machine learning tools produce insights. Through our case study which seeks to identify potential triggers of state-led mass killings from news articles using NLP, we demonstrate how visualizations can aid in each stage, from exploratory analysis of raw data, to machine learning training analysis, and finally post-inference validation.
IMPROVING STUDENTSâ€™ SPEAKING SKILLS BY USING PICTURE-CUED MEDIA TO THE EIGHTH GRADE STUDENTS OF SMP SANTO YOSEPH MEDAN

Speaking is considered as an important skill to be accomplished by students. As a language is used as a tool of communication, the ability of speaking or communicating with others takes an important role. People should know how to express thoughts, opinions, feelings, and ideas through the language. Teachers can also use media to enable the students to present ideas to individual peers, peers group, and entire classes. For example, students learn to speak on a subject of their own choice or on the teacherâ€™s topic through picture-cued media.This study is a Classroom Action Research which focuses on improving studentsâ€™ speaking skills by using picture-cued media. The teaching process was carried out in two cycles. The data consist of observation sheet and test. The data from the observation that have been taken from every cycle are analyzed descriptively, while the data from test are analyzed quantitatively. The result of this study shows that using picture-cued media can improve studentsâ€™ speaking skill. This is proven by studentsâ€™ test score that improved in each test. In the pre-test, the studentsâ€™ average score is 53.8. While in the formative test cycle I, the studentsâ€™ average score is 61.58, and in the post-test cycle II, the studentsâ€™ average score is 78.8. That means the picture-cued media can improve the studentsâ€™ speaking skill significantly.In this research, it is recommended that if the students are not confident in their speaking skills, the students can be given chances to prepare. Teachers are recommended to provide the interesting picture-cued media, control the studentsâ€™ activities in the classroom, give clear instructions and give chances to practice their English as frequently as possible by using picture-cued media.
Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving

With the recent advancements in deep learning, neural solvers have gained promising results in solving math word problems. However, these SOTA solvers only generate binary expression trees that contain basic arithmetic operators and do not explicitly use the math formulas. As a result, the expression trees they produce are lengthy and uninterpretable because they need to use multiple operators and constants to represent one single formula. In this paper, we propose sequence-to-general tree (S2G) that learns to generate interpretable and executable operation trees where the nodes can be formulas with an arbitrary number of arguments. With nodes now allowed to be formulas, S2G can learn to incorporate mathematical domain knowledge into problem-solving, making the results more interpretable. Experiments show that S2G can achieve a better performance against strong baselines on problems that require domain knowledge.
Internet of Things Adoption Among Micropreneurs in Regional Coast of Sabah

Abstract This study aimed to determine the awareness and usage of the internet of things (IoT) technologies in aquafarming within the coastal community of Sabah. The UTAUT approach was applied in identifying the acceptance and usage level among these farmers. In conducting the research, both quantitative and qualitative data were collected during the fieldwork from 4 different coastal locations, which specialize in 4 different aqua-farming products. For this preliminary study, the expected outcome of the research project is a UTAUT framework for Aquafarming for the community. The main objective of the study is to determine the awareness (hearing and seeing) and usage (use, time and purpose) of IoT technologies (smartphones, computers and tablets) in aqua-farming communities in Sabah and the moderating effects of age and experience linked between predictors and behavioral intention based on the multi-group analysis. The result of this research is expected to benefit the community in devising new IoT-based policies, which may assist aqua-farmers in their business.
Prediction of Crime Occurrence in case of Scarcity of Labeled Data

In line with technological developments, machine learning/data mining studies have significantly scaled up in crime analysis.The prediction of crime occurrences, the detection of the spatial/temporal distribution of the criminal cases, forecasting the type of crime are some of these study areas.By taking crime data resulting from a substantial increase in crime rates into consideration, unlabeled data can be utilized to enhance exploring the patterns of crime for future events or to make crime-related predictions easily.Therefore, in this study, active learning, selflearning, and random sampling techniques are applied to predict the outcome of criminal searches in England using the police data of 2019.According to the experimental analysis, active learning outperforms its counterparts using its entropy-based smart selection strategy data in case there is little labeled data.
Beyond The Ordinary: Designing a Mobile-assisted Funds-of-Knowledge-Featured Instructional Framework for English Learners

The design and implementation of hybrid, technology-enhanced learning environments is a sophisticated process, especially when incorporating relevant sociocultural factors to support culturally and linguistically diverse students’ learning. In this paper, I review and provide a description of an iterative design process for a mobile-assisted funds-of knowledge (FoK)-featured instructional framework that aims to facilitate middle-school-aged Latinx English Learners’ (ELs) literacy development in writing. The sociocultural lens of FoK considers ELs’ ordinary experiences as assets in creating inclusivity and engaging flow in learning, which are further enhanced by affordances of mobile-based writing applications. More specifically, this design process was achieved through fieldwork situated in a Latinx community and school district in a rural town in the Midwestern United States. The design process was contextualized into four stages: the precedent stage focusing on the formation of the design idea, the framing stage focusing on the preliminary design outline, the co-evolution stage focusing on the development of the designed product, and the prototype stage focusing on experimenting with the designed product through the ELs’ classroom writing practice. This instructional framework emphasizes the transformation of diverse ELs’ ordinary experiences by guiding them through five sequential learning steps: discovering, connecting, writing, sharing, and preserving culture. Formative and summative evaluation techniques were embedded through different research phases, such as a preliminary instrumental case study, an ethnographic case study, and the ELs’ classroom writing practice. This design case provides an example of socioculturally contextualizing the application of emerging technology to mediate learning for diverse student populations.
Research on the Integration of Artificial Intelligence and Education

Human intelligence and artificial intelligence have their own advantages and have the basis of deep integration. The integration of the two not only optimizes the effect of classroom teaching, but also challenges the essence of education. Based on the realization of the integration of artificial intelligence and education, this paper further discusses the feasible path of the integration of artificial intelligence and education from three aspects: reshaping the education concept, building a diversified education ecosystem and optimizing the education + artificial intelligence guarantee system. It provides a basic way to realize the integration of artificial intelligence and education.
DEVELOPMENT OF COACHING TECHNOLOGIES IN THE EDUCATIONAL SPACE OF THE UNIVERSITY

This paper highlights modern innovative approaches to the organization of the educational process in higher educational institutions based on coaching technologies. The specificity of the integration of coaching technologies in the educational process of higher education is stated. The essence and features of coaching and related concepts of training, consulting and mentoring are considered.
The Relationship between Job Characteristics and Job Satisfaction: The Mediating Role of Work Engagement in the Private Sector Organizations in Riyadh

This research is aimed at figuring out the role of work engagement as a mediator in the relationship between different dimensions of job characteristics and job satisfaction in the private sector organizations in Riyadh, Saudi Arabia. The respondents of this research were 321 employees working in the private sector. The data were collected using electronic questionnaires and analyzed in three phases: Descriptive statistics, confirmatory factor analysis to test dimensionality, and path analysis. In the first phase, descriptive statistics and correlation matrix of all variables were examined. In the second phase, the confirmatory factor analysis and reliability consistencies were conducted to examine the uni-dimensionality of each variable. In the third phase, the path analysis was conducted using Amos. The research findings showed that there is a significant relationship between job characteristics and job satisfaction. There is a greater relationship between work engagement and job satisfaction. As well as a significant relationship between most of job characteristics and work engagement. On the other side, the mediating role of work engagement could not be proven since there is a direct path between job characteristics and job satisfaction. The results of this research can be used as a reference for private sector organizations in Saudi Arabia to improve employees' satisfaction, and ultimately, support the achievement of organizational goals.
Retrospective Class Incremental Learning

Existing works study the Class Incremental learning (CIL) problem with the assumption that the data for previous classes are absent, or only a small subset of samples (known as exemplars) are accessible. Differently, we propose a new and practical setting called retrospective CIL, where all the previous data are accessible, but with bounded training budgets for old data replay. Since only a small subset of old samples can be replayed, it brings a new research problem, i.e., dynamically sampling old data along the incremental training process. As incremental learning particularly suffers from catastrophic forgetting, we propose to use the forgettability of the old samples as the sampling priorities to favour the forgotten samples during the dynamic sampling process. To achieve this, we introduce a forgetting rate metric with graph- based propagation to estimate the sample forgettability. The proposed method brings improvements on two benchmark datasets.