Reviewerly/exHarmony huggingface dataset:
Spatio-Temporal Perturbations for Video Attribution

The attribution method provides a direction for interpreting opaque neural networks in a visual way by identifying and visualizing the input regions/pixels that dominate the output of a network. Regarding the attribution method for visually explaining video understanding networks, it is challenging because of the unique spatiotemporal dependencies existing in video inputs and the special 3D convolutional or recurrent structures of video understanding networks. However, most existing attribution methods focus on explaining networks taking a single image as input and a few works specifically devised for video attribution come short of dealing with diversified structures of video understanding networks. In this paper, we investigate a generic perturbation-based attribution method that is compatible with diversified video understanding networks. Besides, we propose a novel regularization term to enhance the method by constraining the smoothness of its attribution results in both spatial and temporal dimensions. In order to assess the effectiveness of different video attribution methods without relying on manual judgement, we introduce reliable objective metrics which are checked by a newly proposed reliability measurement. We verified the effectiveness of our method by both subjective and objective evaluation and comparison with multiple significant attribution methods.
Development and Validation of a Digital Learning Competence Scale: A Comprehensive Review

Digital learning competence (DLC) can help students learn effectively in digital learning environments. However, most of the studies in the literature focused on digital competencies in general without paying specific attention to learning. Therefore, this paper developed a DLC framework based on a comprehensive literature review, which consists of six dimensions, namely technology use, cognitive processing, digital reading skill, time-management, peer management and will management. This study then developed a scale to assess these competencies where 3473 middle school students participated in the scale validation process. Specifically, exploratory factor analysis, confirmatory factor analysis and item discrimination were used to validate this scale. The six dimensions accounted for 58.66% of the total variance of the scale. The overall internal consistency coefficient was 0.94 for the scale. The results showed that the developed DLC scale is a valid and reliable instrument for assessing middle school students’ digital learning competence. The findings of this study can help teachers and specialists to improve the competencies of their learners by providing a new validated scale that could be used to assess learners and identify their DLC weakness, hence provide the needed trainings accordingly.
A New Sentence-Based Interpretative Topic Modeling and Automatic Topic Labeling

This article presents a new conceptual approach for the interpretative topic modeling problem. It uses sentences as basic units of analysis, instead of words or n-grams, which are commonly used in the standard approaches.The proposed approach’s specifics are using sentence probability evaluations within the text corpus and clustering of sentence embeddings. The topic model estimates discrete distributions of sentence occurrences within topics and discrete distributions of topic occurrence within the text. Our approach provides the possibility of explicit interpretation of topics since sentences, unlike words, are more informative and have complete grammatical and semantic constructions inside. The method for automatic topic labeling is also provided. Contextual embeddings based on the BERT model are used to obtain corresponding sentence embeddings for their subsequent analysis. Moreover, our approach allows big data processing and shows the possibility of utilizing the combination of internal and external knowledge sources in the process of topic modeling. The internal knowledge source is represented by the text corpus itself and often it is a single knowledge source in the traditional topic modeling approaches. The external knowledge source is represented by the BERT, a machine learning model which was preliminarily trained on a huge amount of textual data and is used for generating the context-dependent sentence embeddings.
Part‐MOT: A multi‐object tracking method with instance part‐based embedding

Part-MOT, a one-stage anchor-free architecture which unifies the object identification representation and detection in one task for visual object tracking is presented. For object representation, a position relevant feature is obtained using the center-ness information, which takes advantage of the anchor-free ideal to encode the feature map as the instance-aware embedding. To adapt to the object's movement, the clustering-based method to get the global instance feature is introduced. This enables this approach more robust to make better tracking decisions. Part-MOT achieves the state-of-the-art performance on public datasets, with especially strong results for object deformation and movement changes.
Pedagogical Management in Inclusive Process of the Educational Institution

The aim of this article is to substantiate the requirements to the head of an inclusive education institution in the context of modern realities, to present the results of an experimental study of the development of education manager’s inclusive competence, the cognitive-activity and analytical-performance components of readiness to solve professional practical tasks. The professional profile of the head of an inclusive education institution (manager-leader-expert) is singled out, which combines pedagogical, psychological, inclusive competences, professional skills, as well as personal, business and professional qualities, value orientations, etc. The results of an empirical study of the level of inclusive readiness of modern leaders to form an inclusive environment in an educational institution are presented. Education managers from Ivano-Frankivsk and Cherkasy regions (Ukraine) (having 5-15 years of management experience) were involved in the experiment. Positive dynamics of increasing the level of readiness in the experimental group, for whom a series of thematic exercises (lectures, workshops, webinars) using innovative educational technologies was carried out.
IMPROVED ANTLION OPTIMIZATION ALGORITHM FOR QUADRATIC ASSIGNMENT PROBLEM

The Antlion Optimization (ALO) algorithm is a meta-heuristic optimization algorithm based on the hunting of ants by antlions. The basic inadequacy of this algorithm is that it has long run time because of the random walk model used for the ant's movement. We improved some mechanisms in ALO algorithm, such as ants' random walking, reproduction, sliding ants towards antlion, elitism, and selection procedure. This proposed algorithm is called Improved Antlion Optimization (IALO) algorithm. To show the performance of the proposed IALO algorithm, we used different measurement metrics, such as mean best, standard deviation, optimality, accuracy, CPU time, and number of function evaluations (NFE). The proposed IALO algorithm was tested for different benchmark test functions taken from the literature. There are no studies regarding time analysis of ALO algorithm found in the literature. This study firstly aims to demonstrate the success of the proposed IALO algorithm especially in runtime analysis. Secondly, the IALO algorithm was also applied to the Quadratic Assignment Problem (QAP) known as a difficult combinatorial optimization problem. In QAP tests, the performance of the IALO algorithm was compared with the performances of the classic ALO algorithm and 14 well-known and recent meta-heuristic algorithms. The results of the benchmark test functions show that IALO algorithm is able to provide very competitive results in terms of mean best/standard deviation, optimality, accuracy, CPU time and NFE metrics. The CPU time results prove that IALO algorithm is faster than the classic ALO algorithm. As a result of the QAP tests, the proposed IALO algorithm has the best performance according to the mean cost, worst cost and standard deviation. The source codes of QAP with the proposed IALO algorithm are publicly available at https://github.com/uguryuzgec/QAP-with-IALO.
Evaluation of User Experience on Digital Learning Platform Website Using System Usability Scale (Case Study: Pijar Mahir)

User experience is one of the most important factors of an application. Pijar Mahir is a digital learning platform to increase job seekers skill. This research evaluates usability of pijarmahir’s website using system usability scale (SUS). SUS consists of ten statements as an evaluation instrument. Based on the results of SUS evaluation of the 101 respondents, Pijar Mahir’s website has an average SUS score of 72,67. Based on the result, 72,67 is in an “acceptable” category.
A Grid-based Representation for Human Action Recognition

Human action recognition (HAR) in videos is a fundamental research topic in computer vision. It consists mainly in understanding actions performed by humans based on a sequence of visual observations. In recent years, HAR have witnessed significant progress, especially with the emergence of deep learning models. However, most of existing approaches for action recognition rely on information that is not always relevant for this task, and are limited in the way they fuse the temporal information. In this paper, we propose a novel method for human action recognition that encodes efficiently the most discriminative appearance information of an action with explicit attention on representative pose features, into a new compact grid representation. Our GRAR (Grid-based Representation for Action Recognition) method is tested on several benchmark datasets demonstrating that our model can accurately recognize human actions, despite intra-class appearance variations and occlusion challenges.
Pedagogical Mechanisms Of The Formation Of The Social Outlook Of Future Teachers In The Context Of The Informatization Of Education

The formation of the right outlook for the future teachers is an important aspect of higher education system since the provision of theoretical knowledge is not enough for those students to become competent teachers who can manage the class successfully. The following article looks into the formation of mindset in future teachers.
Advanced Persistent Threats (APT)-Attribution-MICTIC Framework Extension

Analysis of one of the fundamental parts of the Advanced Persistent Threats (APT) Attacks.The phases of the APTs, their framing with the identification of criminals.Type of attack that normally requires resources only available to the State-State hacking.The importance of Attribution in the analysis of APTS.The unique and differentiating characteristics of this type of attacks compared to traditional cyber-attacks.Development of an extension for one of the few Frameworks applied to Attribution in APTs, the MICTIC.
CNN weight sharing based on a fast accuracy estimation metric

The computational workload involved in CNNs is typically out of reach for low-power embedded devices. The Approximate Computing paradigm can be exploited to reduce the CNN complexity since it improves performances and energy-efficiency by relaxing the need for fully accurate operations. In this work, we target weight-sharing as an approximate technique to reduce the memory footprint of a CNN. More in detail, we prove that optimizing the number of shared weights can enable significant network memory compression without noticeable accuracy loss without retraining or fine-tuning steps. However, we observe that the exploration time can easily explode in state-of-the-art CNNs. We thus propose the use of a fast accuracy estimation metric to guide the design space exploration and drastically reduce the exploration time up to 12×. Compared with state-of-the-art CNN approximation methods, we obtained more than 4× compression on GoogleNet on the ImageNet dataset with less than 1% accuracy loss in less than 5 h and without any retraining step.
Enhanced Energy Efficient Routing Protocol (EEE-RP) to forward the Data Packets and to improve QoS in Wireless Sensor Networks by Means of Machine Learning Methods

Objectives:To propose an energy-efficient routing protocol for forwarding Data Packets for effective transmission of captured data using Machine Learning Methods in order to enhance WSN service efficiency.Methods: This study proposes a new routing mechanism Enhanced Energy Efficient Routing Protocol (EEE-RP) to extend the network life by reducing end-to-end delays by forwarding data packets to their destinations in the most efficient and optimal way possible using the best path.To forward data packets in a dynamic and noise-free manner, machine learning approaches such as reinforcement learning, random walk data collection, and Markov decision process framework methods are used.Findings: To demonstrate the efficiency of the proposed EEE-RP protocol, the NS2 Version 35 simulator is used.The simulation results are compared to the baseline EH-WSN and ECO-LEACH protocols in terms of data arrival rate, packet drop ratio, packet delivery ratio, energy consumption, network traffic, delay and network lifetime to show the superior performance in forwarding the data packets in an efficient manner.Novelty: According to the results of the comprehensive study, EEE-RP performs better at detecting network failures, selecting the most appropriate route to forward data packets, minimizing energy consumption, minimizing delay time, and enhancing the network lifetime.
Towards a Smart Privacy-Preserving Incentive Mechanism for Vehicular Crowd Sensing

Vehicular crowd sensing is a promising approach to address the problem of traffic data collection by leveraging the power of vehicles. In various applications of vehicular crowd sensing, there exist two burning issues. First, privacy can be easily compromised when a vehicle is performing a crowd sensing task. Second, vehicles have no incentive to submit high-quality data due to the lack of fairness, which means that everyone gets the same paid, regardless of the quality of the submitted data. To address these issues, we propose a smart privacy-preserving incentive mechanism (SPPIM) for vehicular crowd sensing. Specifically, we first propose a new SPPIM model for the scenario of vehicular crowd sensing via smart contract on the blockchain. Then, we design a privacy-preserving incentive mechanism based on budget-limited reverse auction. Anonymous authentication based on zero-knowledge proof is utilized to ensure the privacy preservation of vehicles. To ensure fairness, the reward payments of winning vehicles are determined by not only the bids of vehicles but also their reputation and the data quality. Then, any rewarded vehicle can get the fair payment; on the contrary, malicious vehicles or task initiators will be punished. Finally, SPPIM is implemented by using smart contracts written via Solidity on a local Ethereum blockchain network. Both security analysis and experimental results show that the proposed SPPIM achieves privacy preservation and fair incentives at acceptable execution costs.
Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies

Researchers and researched populations are actively involved in participatory epidemiology. Such studies collect many details about an individual. Recent developments in statistical inferences can lead to sensitive information leaks from seemingly insensitive data about individuals. Typical safeguarding mechanisms are vetted by ethics committees; however, the attack models are constantly evolving. Newly discovered threats, change in applicable laws or an individual's perception can raise concerns that affect the study. Addressing these concerns is imperative to maintain trust with the researched population. We are implementing Lohpi: an infrastructure for building accountability in data processing for participatory epidemiology. We address the challenge of data-ownership by allowing institutions to host data on their managed servers while being part of Lohpi. We update data access policies using gossips . We present Lohpi as a novel architecture for research data processing and evaluate the dissemination, overhead, and fault-tolerance.
Pairing-Free for Public Key Encryption With Equality Test Scheme

The modular exponentiation has been proved better in terms of computational efficiency as compared to bilinear pairing. Therefore, discrete logarithm, a concept of modular exponentiation may be incorporated to present improved security schemes. With this aim, an efficient public key encryption scheme with equality test (PKEwET) is proposed in this paper. Unlike the most of existing bilinear pairing based PKEwET schemes, the presented scheme employs discrete logarithm to improve the computational efficiency. Moreover, we introduce the verification process during the test phase to validate the ciphertext and to improve the security comparative to the prevalent schemes. The performance analysis depicts that the proposed scheme is quite efficient and more practical than the existing PKEwET schemes. More precisely, compared to Lin et al.'s scheme, the time cost has been reduced by at least 39% for the decryption process. Furthermore, for the type-3 test phase, the time cost has been reduced by at least 68%.
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues

Anaphora and ellipses are two common phenomena in dialogues.Without resolving referring expressions and information omission, dialogue systems may fail to generate consistent and coherent responses.Traditionally, anaphora is resolved by coreference resolution and ellipses by query rewrite.In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding.Given an ongoing dialogue between a user and a dialogue assistant, for the user query, our joint learning model first predicts coreference links between the query and the dialogue context, and then generates a selfcontained rewritten user query.To evaluate our model, we annotate a dialogue based coreference resolution dataset, MuDoCo, with rewritten queries.Results show that the performance of query rewrite can be substantially boosted (+2.3% F1) with the aid of coreference modeling.Furthermore, our joint model outperforms the state-of-the-art coreference resolution model (+2% F1) on this dataset.
Real-time Triplet Loss Embedding Face Recognition for Authentication Student Attendance Records System Framework

The number of times students attend lectures has been identified as one of many success factors in the learning process in many studies. We proposed a framework of the student attendance system by using face recognition as authentication. Triplet loss embedding in FaceNet is suitable for face recognition systems because the architecture has high accuracy, quite lightweight, and easy to implement in the real-time face recognition system. In our research, triplet loss embedding shows good performance in terms of the ability to recognize faces. It can also be used for real-time face recognition for the authentication process in the attendance recording system that uses RFID. In our study, the performance for face recognition using k-NN and SVM classification methods achieved results of 96.2 +/- 0.1% and 95.2 +/- 0.1% accordingly. Attendance recording systems using face recognition as an authentication process will increase student attendance in lectures. The system should be difficult to be faked; the system will validate the user or student using RFID cards using facial biometric marks. Finally, students will always be present in lectures, which in turn will improve the quality of the existing education process. The outcome can be changed in the future by using a high-resolution camera. A face recognition system with facial expression recognition can be added to improve the authentication process. For better results, users are required to perform an expression instructed by face recognition using a database and the YOLO process.
Understanding Emojis for Sentiment Analysis

Many people use emojis to express themselves in a more clear and efficient way and the usage of such expressions has grown significantly. In recent years, there have been many research papers that analyze the meanings of individual emojis or show the accuracy of sentiment analysis when emojis are included. However, there is limited research done on understanding how emojis are used to show sentiment and how it affects sentiment analysis. In this paper, we analyze the usage of emojis in Tweets and their effects on the overall sentiment of the Tweet. We also introduce a pre-processing method for emojis that increases the effect of emojis and as a result, improves the sentiment analysis accuracy.
Interdisciplinary Skills Development Through Final Qualification Assessment: Survey Study for European and Oriental Languages Programs

The global pandemic and subsequent quarantine measures and restrictions have posed an array of challenges to the structure and procedure of the university summative assessment process. Qualification assessment for Foreign Languages major programs, in particular, is a strict regimen process that involves different stages (oral and written exams, final project viva, internal and external review). Cross-sectorial factors of societal change, that provide the backdrop for an interdisciplinary skillset critical transformation, crucial for the COVID-19 emergency educational framework, are considered. The study premise is based on the identification of various interdisciplinary competency principles, derivative of 21st-century skills for university staff members, and projected digital literacy requirements. It is determined how in the situation of the COVID-19 pandemic lockdown all elements of the Final Qualification Assessment at Borys Grinchenko Kyiv University for European and Oriental Languages programs have been relegated to the digital, remote, or blended format with the use of ICT tools and skills that comprise an interdisciplinary realm of Foreign Languages acquisition and assessment. Every step of the procedure adaptation to the digital format required accelerated development of interdisciplinary skills of all participants and officials and cross-sectorial activities, otherwise not carried out through assessment of Foreign Languages programs. © IMCIC 2021 - 12th International Multi-Conference on Complexity, Informatics, and Cybernetics, Proceedings. All rights reserved.
MagicInput

Text input systems based on device-free finger tracking technologies have attracted considerable attention in the use scenarios of mobile and the Internet-of-Things (IoT) devices. Issues pertaining to 2D tracking have prompted interest in using 1D finger trajectories for the recognition of handwritten letters. Nonetheless, 1D tracking imposes two major challenges: (i) Trajectory information loss from 2D to 1D; and (ii) Inter-user diversity in writing traits. These challenges could possibly be overcome by collecting a large training dataset for every user; however, this would impose an unacceptable burden on users. This paper presents a text input system with multi-language support without training using acoustic-based 1D finger tracking technology. We developed a novel data augmentation scheme, in which the handwritten image dataset MNISTs are used to create artificial datasets (called TrackMNISTs). We compensate for the trajectory information loss of 1D by creating personal dataset (from TrackMNIST) to match the writing habits of individual users. The proposed data augmentation mechanism is also applicable to multilingual letter recognition. In experiments, MagicInput achieved outstanding classification accuracy on unseen users: 10 digits (98.3%), 26 uppercase/lowercase English letters (97.8%/95.3%), 49 Japanese characters (91.4%), and the 30 commonly used Chinese characters (93.8%).