Reviewerly/exHarmony huggingface dataset:
Semantic Explanation for Deep Neural Networks Using Feature Interactions

Given the promising results obtained by deep-learning techniques in multimedia analysis, the explainability of predictions made by networks has become important in practical applications. We present a method to generate semantic and quantitative explanations that are easily interpretable by humans. The previous work to obtain such explanations has focused on the contributions of each feature, taking their sum to be the prediction result for a target variable; the lack of discriminative power due to this simple additive formulation led to low explanatory performance. Our method considers not only individual features but also their interactions, for a more detailed interpretation of the decisions made by networks. The algorithm is based on the factorization machine, a prediction method that calculates factor vectors for each feature. We conducted experiments on multiple datasets with different models to validate our method, achieving higher performance than the previous work. We show that including interactions not only generates explanations but also makes them richer and is able to convey more information. We show examples of produced explanations in a simple visual format and verify that they are easily interpretable and plausible.
The Scientific Imperative of Advanced Vocational Education Within the Framework of the System of Scientific and Methodological Support of Teachers

In the chapter of the monograph an organizational and activity model of project-organized advanced additional professional pedagogical education (hereinafter referred to as APPE) is presented, which is the basis for the activities of the center for scientific and methodological support of teachers of the K. Minin State Pedagogical University based on the analysis of scientific approaches to the development of the category "advanced education". A universal way of solving urgent problems of evolutionary improvement of the system of vocational education and training and an organizational and managerial mechanism for building a space of opportunities for continuous professional development of teachers is proposed.
Educational Application for Information Protection with the use of Cryptographic Methods - part I

The main purpose of this paper is to protect the transmission of important information from hacker attacks. This is achieved by encrypting the messages before transmission and decrypting them after their receipt, respectively. A Windows desktop application has been developed for purely educational purpose. For this reason, only basic substitution and transposition methods are implemented in it. Namely: Caesar cipher (simple monoalphabetic substitution), direct substitution cipher, polyalphabetic substitution cipher and matrix substitution cipher; a transposition cipher with text blocks or with text formatting. Three roles are implemented in the suggested application: administrator, teacher and student. The interface of the application consists of 2 parts: presentation and logical. Additionally, it contains a data section, including text files that the application can work with. This type of applications can be used in different areas of human life. For example, to protect information in custom text files (.doc,. txt,. pdf etc.) that can be written to various storage devices (hard drives, flash drives, DVDs, CDs etc.) or sent via e-mail or stored in cloud space. The information can be related to management, finance or military purposes. The transmitted data may be to banks, insurance companies or other corporations making responsible economic and financial transactions.
Measurement of Detection Rate Accuracy in Forecasting Crude Palm Oil Production using Fuzzy Time Series

Time Series is a superior method of predicting the future based on past data. Time series are also used in various businesses to make forecasts for profit. Time series data provide data visualization with statistical explanations necessary for business decisions. One of the businesses that operates for the needs of all elements is the Crude Palm Oil (CPO) commodity industry. Where the CPO price can be forecast using time series because it uses a series at the time available in fact. In this paper, 599 data of CPO price data were crawled from September 10, 2019 to April 30, 2021, then divided into 560 training data and 39 testing data. In this case, testing was carried out in measuring accuracy using MAPE in forecasting CPO prices. with time series getting 0.01781302% while accuracy is also measured by MAPE combined with detection rate gaining a percentage of 0.501031843%. This indicates that when forecasting with time series on CPO price data, the best accuracy is calculated using MAPE without any combination with other techniques.
Implementation of Web Data Mining Technology Based on Python

Abstract With the arrival of the era of big data, people have gradually realized the importance of data. Data is not just a resource, it is an asset. This paper mainly studies the realization of Web data mining technology based on Python. This paper analyzes the overall architecture design of distributed web crawler system, and then analyzes in detail the principles of crawler’s URL function module, crawler’s web crawl function module, crawler’s web page parsing function module, crawler’s data storage function module and so on. Each function module of the crawler system was tested on the experimental computer, and the data information was summarized for comparative analysis. The main significance of this paper lies in the design and implementation of a distributed web crawler system, which, to a certain extent, solves the problems of slow speed, low efficiency and poor scalability of traditional single computer web crawler, and improves the speed and efficiency of web crawler in grasping information and web page data.
Stereoscopic Video Quality Assessment Using Modified Parallax Attention Module

Deep learning techniques are utilized for most computer vision tasks. Especially, Convolutional Neural Networks (CNNs) have shown great performance in detection and classification tasks. Recently, in the field of Stereoscopic Video Quality Assessment (SVQA), 3D CNNs are used to extract spatial and temporal features from stereoscopic videos, but the importance of the disparity information which is very important did not consider well. Most of the recently proposed deep learning-based methods mostly used cost volume methods to produce the stereo correspondence for large disparities. Because the disparities can differ considerably for stereo cameras with different configurations, recently the Parallax Attention Mechanism (PAM) is proposed that captures the stereo correspondence disregarding the disparity changes. In this paper, we propose a new SVQA model using a base 3D CNN-based network, and a modified PAM-based left and right feature fusion model. Firstly, we use 3D CNNs and residual blocks to extract features from the left and right views of a stereo video patch. Then, we modify the PAM model to fuse the left and right features with considering the disparity information, and using some fully connected layers, we calculate the quality score of a stereoscopic video. We divided the input videos into cube patches for data augmentation and remove some cubes that confuse our model from the training dataset. Two standard stereoscopic video quality assessment benchmarks of LFOVIAS3DPh2 and NAMA3DS1-COSPAD1 are used to train and test our model. Experimental results indicate that our proposed model is very competitive with the state-of-the-art methods in the NAMA3DS1-COSPAD1 dataset, and it is the state-of-the-art method in the LFOVIAS3DPh2 dataset.
Blockchain Empowered Framework for Peer to Peer Lending

P2P(Peer to Peer) lending is the process of extending credit to unknown individuals through digital platforms. Trust is an imperative component for P2P lending. Blockchain and smart contracts have the potential to accelerate the growth of Peer-to-Peer lending as trust-inducement and decentralization are the two prominent features of blockchain. Lending on social platform is particularly unsecured because they are not backed up by collateral. In this paper, a token generation mechanism is proposed in lieu of the submission of digital assets and virtual currency as collateral. The paper also deploys the self executing smart contracts to allow investors to lend money to borrowers on pre-determined terms and conditions. In addition, a smart compensation management contract has been deployed to repay the outstanding amount to lender if the borrower has charged off.
Analyzing Public Opinion Based on Emotion Labeling Using Transformers

This research aimed to do sentiment analysis by conducting text classification targeting six basic human emotions (fear, anger, joy, sadness, disgust, and surprise) using state-of-the-art Natural Language Processing (NLP) technique called ‘Transformers'. More than 1000 tweet data are obtained from Twitter on the issue of the mudik prohibition policy issued by the government of Indonesia in May 2021. The result showed that most people are feeling sad (47%) and surprised (24%) about the mudik prohibition policy. The sad feeling is related to the publics' inability to come back to their hometown and missing their families there. Whereas the ‘surprised’ feelings are due to the contradiction of the mudik prohibition policy with other policies such as the opening of tourist attractions and malls. Our result also showed that the model can accurately predict and have high confidence in predicting the emotions even when the texts do not contain obvious words that are strongly associated with certain emotions. The average confidence score on the prediction is pretty high at 0.82 with most of the predictions having a confidence score higher than 0.95.
Radio Frequency Fingerprint Identification Based on Transfer Learning

Given the current radio frequency(RF) fingerprint identification methods based on deep learning, there is a problem of poor recognition performance in the scene of RF fingerprint offset. This paper designs an Internet of Things (IoT) devices identification system based on software defined radio(SDR) and transfer learning (TL) technology. The main advantage of this system is to use the universal software radio peripheral (USRP) to collect the RF signals of IoT devices and identify the RF signals preprocessed by SDR based on transfer learning. This system can reduce data redundancy and model complexity, has the characteristics of lightweight and high efficiency and meet different experimental scenarios' needs. The experimental results show that compared to reducing the recognition accuracy based on the deep learning method in RF fingerprint offset, transfer learning dramatically improves the recognition performance and reduces the model training time. Simultaneously, the experimental process is in a natural wireless communication environment, and the actual application has high reliability.
Communicating on Multivariate and Geospatial Data supported by ergonomics criteria: COVID-19 case

Since Fall 2019, the spread of SARS-CoV-2 virus has changed everyday life routines globally. Public health confinement measures have been taken to contain the propagation of the pandemic. An international effort has been made to model and predict the spatiotemporal evolution of the pandemic. Today, a main question arises on how to communicate complex multivariate, geospatial and time dependent information efficiently. A further challenge consists in communicating these information without any bias or place for misinterpretation, and for the largest targeted audience. In this regard the following paper will first identify ergonomics criteria for efficient data visualization, and then present several visualizations in a pre/post fashion, reflecting how visualizations initially proposed by data scientists can be improved after the application of ergonomics guidelines.
Dealing with Label Uncertainty in Web Service Anti-patterns Detection using a Possibilistic Evolutionary Approach

Like the case of any software, Web Services (WSs) developers could introduce anti-patterns due to the lack of experience and badly-planned changes. During the last decade, search-based approaches have shown their outperformance over other approaches mainly thanks to their global search ability. Unfortunately, these approaches do not consider the uncertainty of class labels. In fact, two experts could be uncertain about the smelliness of a particular WS interface but also about the smell type. Currently, existing works reject uncertain data that correspond to WSs interfaces with doubtful labels. Motivated by this observation and the good performance of the possibilistic K-NN classifier in handling uncertain data, we propose a new evolutionary detection approach, named Web Services Anti-patterns Detection and Identification using Possibilistic Optimized K-NNs (WS-ADIPOK), which can cope with the uncertainty based on the Possibility Theory. The obtained experimental results reveal the merits of our proposal regarding four relevant state-of-the-art approaches.
The Functions of Continuous Processes in Contemporary Electronic Dance Music

This article discusses the functions of continuous processes in contemporary electronic dance music (EDM), providing an analytical framework for discussing their structural and aesthetic roles in this repertoire. Continuous processes are musical gestures with continuous changes to musical parameters, rather than discrete, “step-by-step” ones. Examples include pitch slides (glissandos), crescendos, fade ins, accelerandos, and filter sweeps. Continuous processes in this repertoire are created with “continuous controllers” such as sliders and knobs, or are programmed into tracks with “automation curves.” Functionally, continuous processes often provide sonic instructions for dancers. They can be used by creators at strategic times to provide ornamentation, orientation (often accompanied by intensification or de-intensification), or disorientation (usually in breakdown sections). This article adds to existing analytical scholarship by drawing attention to the many roles of continuous processes in EDM, and showing how they contribute to the emotional waves experienced when listening to this music.
Research on Accurate Learning Evaluation for College Students Based on Big Data Technology

In this paper, we propose a novel strategy to accurately evaluate learning status and development potentiality for colleges students based on big data modeling and mining. Firstly, we propose an effective method to calculate students ability achievement based on the big data mining technology via three steps. In step 1, we construct the student ability achievement evaluation system, which reflects the “output-oriented concept”. In step 2, we model the educational big data, which embodies the “continuous improvement concept”. In step 3, we compute students ability achievement based on educational big data mining, which embodies the “student-centered concept”. Secondly, we discuss how to accurately evaluate college students learning status and development potentiality based on the ability achievements. Finally, we conduct a series of experiments to demonstrate the effectiveness of the proposed solution. Experimental results demonstrate that the proposed solution can effectively evaluate learning status and development potentiality, moreover, the proposed solution can recommend suitable courses and career plannings for college students.
Task-independent Recognition of Communication Skills in Group Interaction Using Time-series Modeling

Case studies of group discussions are considered an effective way to assess communication skills (CS). This method can help researchers evaluate participants’ engagement with each other in a specific realistic context. In this article, multimodal analysis was performed to estimate CS indices using a three-task-type group discussion dataset, the MATRICS corpus. The current research investigated the effectiveness of engaging both static and time-series modeling, especially in task-independent settings. This investigation aimed to understand three main points: first, the effectiveness of time-series modeling compared to nonsequential modeling; second, multimodal analysis in a task-independent setting; and third, important differences to consider when dealing with task-dependent and task-independent settings, specifically in terms of modalities and prediction models. Several modalities were extracted (e.g., acoustics, speaking turns, linguistic-related movement, dialog tags, head motions, and face feature sets) for inferring the CS indices as a regression task. Three predictive models, including support vector regression (SVR), long short-term memory (LSTM), and an enhanced time-series model (an LSTM model with a combination of static and time-series features), were taken into account in this study. Our evaluation was conducted by using the R 2 score in a cross-validation scheme. The experimental results suggested that time-series modeling can improve the performance of multimodal analysis significantly in the task-dependent setting (with the best R 2 = 0.797 for the total CS index), with word2vec being the most prominent feature. Unfortunately, highly context-related features did not fit well with the task-independent setting. Thus, we propose an enhanced LSTM model for dealing with task-independent settings, and we successfully obtained better performance with the enhanced model than with the conventional SVR and LSTM models (the best R 2 = 0.602 for the total CS index). In other words, our study shows that a particular time-series modeling can outperform traditional nonsequential modeling for automatically estimating the CS indices of a participant in a group discussion with regard to task dependency.
The impact of social media on the rights of the accused in Jordan: Does public opinion matter?

This study shows how publishing, commenting, and analyzing crime details via social media can play a role in shaping public opinion and influences the proper conduct of criminal justice. It also discusses the impact of social media on the presumption of innocence, the confidentiality of investigation, and the judge’s emotional conviction by examining the most important cases that Jordan has witnessed recently and have had a wide resonance on social media. In addition, the paper clarifies the legal opinion on the permissibility of publishing and commenting on such issues in Jordanian legislation with reference to some of the relevant international agreements. The author employed the analytical methodology by using the document analysis of official legal documents in relation to social media in Jordan legislations and drew upon relevant international agreements and court rulings for our research. The research concluded that the presumption of innocence and the confidentiality of investigations conducted by the Public Prosecution must be respected, and that everything that might affect the proper conduct of criminal justice should be avoided.
Blockchain Technology Perspectives in Maritime Industry

Blockchain technology offers potential solutions to tackle issues related to paper documents flow and outdated manual business administrative procedures in maritime industry. While the Blockchain is still a relatively novel technology, mainly used in cryptocurrency applications, it is slowly gaining traction and attention within the maritime industry. This paper presents the possibilities of blockchain implementation in order to solve some of the challenges that the companies operating within the maritime industry are facing: administrative costs, traceability, quality control, transparency, payments management, and dispute resolutions, while highlighting key areas for blockchain application, and main challenges related to blockchain technology and its adoption in the maritime industry.
Towards a Graphical Grammar for Task Analysis Visualization

Task Analysis (TA) represents not one standard method, but rather a toolkit of methods that come with a large and complex range of outputs. Due to this fluidity in methods and resultant data, a standard method of analysis and visual data representation does not currently exist. Depending on the research methods used and desired outcomes, some visualization methods may be more appropriate than others. This body of work seeks to demonstrate the need for the establishment of a graphical grammar in this domain. Initial recommendations are provided for visualizing different task analysis methods. This research lays the groundwork towards the development of a full set of visualization best practices to allow practitioners to gain the most insight from their TA methods of choice.
Adapting Image Analysis Measures of Visual Clutter to Multiple Plane Augmented Reality User Interfaces

This study focuses on understanding and quantifying the effects of clutter in multi-plane Augmented Reality User Interfaces (AR UI’s). It determines if image analysis measures of visual clutter can be expanded from single plane AR UI’s to multi-plane AR UI’s as well as examining the effects of multiple UI planes, UI clutter, and target clutter have on user performance. Image analysis measures of clutter were specifically chosen as they can be applied to complex and naturalistic images that may be found in AR UI applications and have shown use in predicting user performance in single plane AR UI’s. In this experiment, twelve participants performed a visual search task of locating a target object in an array of objects where objects were located in planes of different distances. Participants completed this task under two different clutter levels (low and high) against seven different AR UI plane arrays (2m, 4m, 6m, 2m4m, 2m6m, 4m6m, 2m4m6m) where the target was found in every available AR UI plane for each combination, with repetition. Task performance was measured through response time. Results show significant differences in response time between clutter levels, and number of planes. The end goal of this research is to build upon previous studies to develop a clutter score algorithm for AR UI’s that can predict user performance based on image analysis measures.
Designing Virtual Pedagogical Agents and Mentors for Extended Reality

The use of virtual and augmented reality for educational purposes has seen a rapid increase in interest in recent years. Extended reality offers unique affordances to learners, and can enhance learning. Specifically, we are interested in the use of pedagogical agents in extended reality due to their potential to increase student motivation and learning. However, the design of pedagogical agents in extended reality is still a nascent area of study, which can be important in an immersive environment where social cues can be more salient. Pedagogical agent design aspects such as speech, appearance, and modality can prime social cues and affect learning outcomes and instructor perception. In this paper, we propose a project to investigate auditory and visual social cues of pedagogical agents in XR such as speech, ethnicity, and modality.
ML Model Change Detection and Versioning Service

Many machine learning applications call for methods to recommend when to update a deployed model if the underlying distribution of incoming data drifts over time. While necessary to generate accurate predictions towards intended business outcomes, updating a model in the form of re-training and maintaining model versions is a cost intensive operation. Current approaches in change detection and versioning do not cover mechanisms for automatically detecting significant change based on feature importance, correlation and semantic revisions occurring over time.We explore a modular approach towards model versioning focused on use-cases in the IT services industry. Our method attempts to detect and quantify changes across new and existing data sets based on statistical as well as semantic feature comparison. We demonstrate utility of our approach by implementing a change detection and model versioning service, and leveraging it for a risk analytic model built for a global IT service provider.