Reviewerly/exHarmony huggingface dataset:
Content Based Network Representational Learning for Movie Recommendation (CNMovieRec)

Research in the domain of recommender systems mainly points out to two types of recommendation strategies, namely, Collaborative Filtering (CF) and Content based filtering (CBF). In CF, the idea is to find users similar to the active user (the user to whom recommendation needs to be done) and recommendation is done based on items liked by similar users. On the other hand, content based filtering make use of the explicit or implicit data provided by the users on different items so as to generate a user profile and recommendation is made based on this profile. Previous research mainly focusses on CF-based approaches and many interesting results have come out in the recent years. In this paper we focus on CBF wherein a novel framework is proposed called content-based network representational learning for movie recommendation (CNMovieRec). We also propose a group recommender system based on our new CNMovieRec. Our experimental findings demonstrate that the results related to the proposed content-based framework is comparable to those of CF techniques (in terms of MAE, MSE & RMSE) and the new model is able to overcome challenges of Data Sparsity and Cold-Start problems usually accompanying CF-models.
Black-Box Adversarial Attack for Deep Learning Classifiers in IoT Applications

The increasing adoption of Internet of Things (IoT) has resulted in the availability of big data, which can reveal valuable insights if processed efficiently. Classification tasks are very important in such applications, and Artificial Intelligence is widely used to solve these problems. This paper demonstrates that Deep Learning classifiers used in IoT environments are vulnerable to black-box adversarial attacks. Such attacks can render these models ineffective by causing severe performance issues. This paper develops a black-box adversarial attack mechanism to generate adversarial examples for data obtained from smart meters installed in residential houses. An analysis is presented to demonstrate that the statistical properties of these adversarial examples are indistinguishable from those of the true examples, and the performance of the targeted models degrades sharply when exposed to the proposed attack. Further, the inherent properties of the attack mechanism imply that it is able to evade the entire class of gradient masking based defence methods. The effectiveness of the proposed black-box adversarial attack is demonstrated on the publicly available United Kingdom-Domestic Appliance-Level Electricity smart meter dataset.
Skeleton-Based Human Activity Recognition of Low-voltage Distribution Room Based on MQTT Protocol and Wireless Sensor Network

Abstract For the activity recognition of operators in low-voltage distribution rooms, a human activity recognition system based on the bidirectional gated recurrent unit model (BiGRU) is proposed to realize the skeleton information acquisition by the WiFi Channel State Information (CSI) sensor. The CSI sensor is used to collect the activity data and the Kinect v2 camera is used to capture the skeleton of the human body as annotations of the CSI data. The BiGRU model is employed to effectively extract the characteristics of the data. Aiming at the problem of poor remote communication, the MQTT connection can be established by using Message Queuing Telemetry Transport (MQTT) protocol combined with ESP8266 WiFi serial port communication module is proposed to improve the poor communication situation and carry out better data communication. The system achieves remote intelligent monitoring of the activities of the operator in the low-voltage distribution room.
INTERACTIVE LEARNING METHODS IN THE DEVELOPMENT OF COMMUNICATIVE COMPETENCE

Communicative competence is of particular importance in human life, so its formation should be given special attention. This becomes possible when using interactive learning technologies. Interactive methods are based on the principles of interaction, activity of trainees, reliance on group experience, mandatory feedback
The Effect of Using Online Media on Learning Motivation and Its Impact on Improving the Results of Learning Bahasa Indonesia

This research uses the quantitative method to find out how much influence the use of Online Media on learning motivation and learning outcomes of students in class VIII MTs Al Muasyaroh Rancabungur Bogor.The problems in this study are formulated as follows: (1) The effect of using Online Media on learning motivation.(2) The influence of learning motivation on improving Indonesian Language Learning Outcomes.(3) The influence of the use of Online Media on the improvement of Indonesian Language Learning Outcomes.Data processing is used descriptive and verification statistical methods.The sample in this study consisted of 35 learners selected with sampling techniques.The analysis used for hypothesis testing is path analysis with SPSS.Research instruments use survey data in the form of questionnaires created in google form aimed at uncovering learning motivation using online media during the Covid-19 Pandemic.The survey type uses a Likert scale.Based on the results of the hypothesis test, the conclusion obtained is the coefficient of the path of influence of the use of Online Media on Learning Motivation is a positive value of 0.929 which indicates the use of Online Media has a significant influence on learner learning motivation.The Coefficient of Learning Motivation pathway to Indonesian Learning Outcomes is 0.919 which shows that Learning Motivation has a significant influence on The Results of Learning Indonesian.While the coefficient of online media usage path to the improvement of Indonesian Language Learning Outcomes is 0.930 which shows that the higher the use of Online Media provided by MTs Al Muasyaroh Rancabungur Bogor, it will have an impact on the increasing results of Learning the Indonesian language is also higher.Thus, the use of Online Media has a significant influence on the motivation of learning and The Results of Learning Indonesian in MTs Al Muasyaroh Rancabungur Bogor.
Social Sharing of Emotions in Social Media System on the Example of Creepypasta on Reddit

As part of this study, it was suggested that, like other social media, Reddit allows to embody models of communicative interaction in a digital environment. The comments under three creepypastas published on the subreddit NoSleep, were selected as the object of the study. To prove the original thesis, the comments were analyzed from the perspective of social sharing of emotions (SSE) — a process characteristic of real interpersonal communication. The comments were studied manually using the method of intentional analysis. As a result of the study, the main intentional categories characteristic of the social sharing of emotions in the comments on NoSleep were established. It was concluded that SSE in the comments under creepypastas generally coincides with the existing models, and the differences found can be explained by the specifics of the digital environment and the concrete features of Reddit as social media.
Teaching reform and practice based on OBE concept: Taking the course of Python programming and practice as an example

Python Programming and Practice is a professional course for computer related majors. Through teaching practice, and based on OBE (Outcome-based Education) concept of course reform, we have revised the course syllabus, reformulated the teaching plan, standardized the classroom teaching, adopted online and offline mixed teaching model to teach the basic knowledge online, and to teach with project case method in offline classroom teaching. The course is completely transformed into outcome-based teaching, which improves the depth and breadth of the course teaching, and improves the students’ practical ability.
Uncertainty entropy-based exploratory evaluation method and its applications on missile effectiveness evaluation

Some attributes are uncertain for evaluation work because of incomplete or limited information and knowledge. It leads to uncertainty in evaluation results. To that end, an evaluation method, uncertainty entropy-based exploratory evaluation (UEEE), is proposed to guide the evaluation activities, which can iteratively and gradually reduce uncertainty in evaluation results. Uncertainty entropy (UE) is proposed to measure the extent of uncertainty. First, the belief degree distributions are assumed to characterize the uncertainty in attributes. Then the belief degree distribution of the evaluation result can be calculated by using uncertainty theory. The obtained result is then checked based on UE to see if it could meet the requirements of decision-making. If its uncertainty level is high, more information needs to be introduced to reduce uncertainty. An algorithm based on the UE is proposed to find which attribute can mostly affect the uncertainty in results. Thus, efforts can be invested in key attribute(s), and the evaluation results can be updated accordingly. This update should be repeated until the evaluation result meets the requirements. Finally, as a case study, the effectiveness of ballistic missiles with uncertain attributes is evaluated by UEEE. The evaluation results show that the target is believed to be destroyed.
Secure Communication Based On Key Generation With Quantum Reinforcement Learning

Data security and secure communication is one of the most important issues of today. In this study, a quantum-based&#x0D; method for secure communication is proposed. In the proposed method, the necessary secret key in communication is generated&#x0D; locally by each participant through quantum gates. The quantum gates are taught by using quantum reinforcement learning (QRL).&#x0D; Proposed study is simulated using the Qiskit library for Python. Proposed study performs the learning action with an accuracy of&#x0D; 87.95% for 195 gates, 85.47% for 128 gates, 83.59% for 64 gates, 76.25% for 32 gates. As the key size increases, the performance&#x0D; of the method increases. The participants don’t share the secret key in the presented method. Thus, the communication becomes&#x0D; more secure. In the study, the method is also examined in terms of security. Security analysis shows that the proposed method&#x0D; provide secure communication.
A flexible ensemble regression model of extreme learning machine for missing value imputation of DNA microarray data

Missing value imputation (MVI) is important for DNA microarray data analysis because microarray data with missing values would significantly degrade the performance of the downstream analysis. Although there have been lots of MVI algorithms for dealing with the missing DNA microarray data, we note that most of them have a lack of robustness owing to only adopting the single model. In this paper, a flexible and robust MVI algorithm named EELMimpute is proposed to address missing DNA microarray data imputation problem. First, the algorithm constructs a relevant feature space for the missing target gene, where the relevant feature space only includes those co-expression genes of the target gene based on calculating their Pearson's correlation coefficients. Then, some fix-sized feature subspaces are randomly extracted from the relevant feature space to construct extreme learning machine (ELM) regression models between the extracted genes and the target gene. Furthermore, selecting those models without missing input gene values to construct the ensemble framework, and then imputing the missing gene by calculating the average output of all models included in the ensemble framework. Experimental results show that the EELMimpute algorithm is able to reduce the estimated errors in comparison with several previous imputation algorithms.
Malicious uses of blockchains by malware: from the analysis to Smart-Zephyrus

Abstract The permanent availability and relative obscurity of blockchains is the perfect ground for using them for malicious purposes. However, the use of blockchains by malwares has not been characterized yet. This paper analyses the current state of the art in this area. One of the lessons learned is that covert communications for malware have received little attention. To foster further defence-oriented research, a novel mechanism (dubbed Smart-Zephyrus) is built leveraging smart contracts written in Solidity. Our results show that it is possible to hide 4 Kb of secret in 41 s. While being expensive (around USD 1.82 per bit), the provided stealthiness might be worth the price for attackers.
Metamodelling of Noise to Image Classification Performance

Machine Learning (ML) has made its way into a wide variety of advanced applications, where high accuracies can be achieved when these ML models are evaluated in the same context as they were trained and validated on. However, when these high-accuracy models are exposed to out-of-distribution points such as noisy inputs, their performance could potentially degrade significantly. Recommending the most suitable ML model that retains a higher accuracy when exposed to these noisy inputs can overcome this performance degradation. For this, a mapping between the noise distribution at the input and the resulting accuracy needs to be obtained. Though, this relationship is costly to evaluate as this is a computationally intensive task. To minimize this computational cost, we employ metalearning to predict this mapping; that is, the performance of different ML models is predicted given the distribution parameters of the input noise. Although metalearning is an established research field, performance predictions based on noise distribution parameters have not been accomplished before. Hence, this research focuses on predicting the per-class classification performance based on the distribution parameters of the input noise. For this, our approach is twofold. First, in order to gain insights in this noise-to-performance relationship, we analyse the per-class performance of well-established convolutional neural networks through our multi-level Monte Carlo simulation. Second, we employ metalearning to learn this relationship between the input noise distribution and the resulting per-class performance in a sample-efficient way by incorporating Latin Hypercube Sampling. The noise performance analyses present novel insights about the per-class performance degradation when gradually increasing noise is augmented on the input. Additionally, we show that metalearning is capable of accurately predicting the per-class performance based on the noise distribution parameters. We also show the relationship between the number of metasamples and the metaprediction accuracy. Consequently, this research enables future work to make accurate classifier recommendations in noisy environments.
Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption

As the fertility rate declines, it becomes increasingly necessary for governments to guide power companies in introducing preferential tariffs to encourage nuclear families to have children. However, traditional household statistics for residential households are time-consuming and insufficient for enterprises seeking to adopt intelligent marketing schemes for different types of households. To address these issues, this paper proposes a nuclear family type identification method for residential electricity consumption based on a deep forest algorithm. The method first classifies nuclear households according to the number of children in them. Then, features are selected by combining the daily 48-point load and prior knowledge of nuclear families. The Pearson correlation coefficient and random forest importance ranking are used to remove features with low correlation and low importance. Additionally, features are classified based on their importance, and the number of features is balanced by stratified sampling to optimize the multi-granularity scan results and improve the model’s generalization. Finally, the improved cascade forest with feature input replacement base learner is trained, and the model is evaluated using accuracy evaluation metrics.The experimental results demonstrate that the proposed model accurately recognizes the number of children in different nuclear families and can be used in power companies to improve lean management. The results show that the improved method is effective in improving recognition com-pared to the original deep forest method, with recognition accuracy 5.1% higher than the random forest method and 0.7% higher than the deep forest method, reaching 94%.
DHSDJArch: An Efficient Design of Distributed Heterogeneous Stream-Disk Join Architecture

Heterogeneity is the key aspect of complex networks and smart devices for using it as nature of live streams. The heterogeneous stream-disk join is a significant research topic in real-time processing applications because it can directly affect the data analytics. Multiple issues, including stream loss, scalability, disk access cost, and data accuracy, should be considered during heterogeneous stream-disk join transformation. In this work we overcome these issues by introducing a distributed heterogeneous stream-disk join architecture DHSDJArch which can prevent stream data loss as well as maintaining balance between heterogeneous distributed data sources and accuracy of stream-disk join. A four phased distributed architecture is proposed for the multi-objective optimization to transform heterogeneous incomplete stream. To prevent stream loss, configuration of log retention is proposed based on the characteristics of distributed event streaming platform ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DESP</i> ). Specifically, two transformations are proposed to pre-process heterogeneous streams and to join pre-processed stream with distributed disk data by performing real-time disk access while compensating the differences between data sources and streaming application, respectively. We conduct comprehensive experimental study on real datasets to verify the performance of proposed architecture in terms of accuracy, log retention policy, scaling, stability and cloud data storage.
Multi-user conflict resolution mechanisms for smart home environments

Context-awareness is a pervasive computing enabling technology that allows context-aware applications to respond to multiple contexts such as activity, location, temperature, and so on. When many users attempt to access the same context-aware application, user conflicts may emerge. This issue is emphasized, and a conflict resolution approach is presented to address it. Although there are other conflict resolution approaches in the literature, the one presented here is unique in that it considers the users' special cases such as their sickness, examinations, and so on when resolving conflicts. The proposed approach is helpful when several users with different special cases try to access the same context-aware application. To demonstrate the usefulness of the proposed approach, a conflict manager is integrated with the UbiREAL simulated context-aware home environment. The integrated conflict manager resolves conflicts by taking users special cases into account and employing either automated, mediated, or hybrid conflict resolution approaches. The evaluation of the proposed approach demonstrates that users are satisfied with it and that it is critical and essential to employ users' special cases in detecting and resolving users conflicts.
ORB Feature Matching Algorithm Based on Multi-Scale Feature Description Fusion and Feature Point Mapping Error Correction

To improve the accuracy of feature extraction and description of various scales in traditional Oriented FAST and Rotated BRIEF (ORB) feature matching algorithm, this paper proposes an ORB feature matching algorithm based on multi-scale feature description fusion and feature point mapping error correction. Firstly, when establishing the feature scale pyramid, the method of using the same patch-size for description on each feature layer is adopted instead of using different patch-sizes on a unified feature layer in the original algorithm, which enhances the robustness of the descriptor and improves the matching accuracy. Secondly, FAST-SCORE maps are established on different scales, and the coordinates of high-level feature points mapped to the bottom layer are corrected to further improve the positioning accuracy of feature points. The algorithm is verified in remote sensing images, autonomous driving, and industrial automation fields. Experimental results show that when resisting theoretical interference, the average matching accuracy of the proposed algorithm is 67.9%, which is about 2.0 times that of the ORB algorithm, and the average stability is 14.0, which is about 1.5 times that of the ORB algorithm. After correcting the feature point mapping, the matching accuracy can be further improved by 19.2%, indicating that the improved algorithm has excellent robustness when resisting interference. In the experiments on the KITTI and custom datasets, the matching accuracy of the proposed algorithm reached 88.70% and 96.88%, respectively, which is an improvement of 10.15% and 1.2% compared to the ORB algorithm. At the same time, the matching time was reduced by 17.34% and 24.30%, ensuring the accuracy and real-time performance of the algorithm in practical scenarios.
Relevance of implementation, application and multimedia technologies’ development in tertiary education

The article deals with the advantages and prospects of implementing multimedia technologies necessary for the development of the traditional, already familiar model of education and bringing it to a new level of innovation. The introduction of multimedia technology in educational processes is one of the key moments of education informatization. Nowadays multimedia technologies are one of the most dynamically developing and promising areas of information services. The effectiveness of multimedia methods use directly depends on the professional competence of teachers and the ability to teach graduates the necessary profession skills, which meet modern requirements. The use of e-learning methods opens up opportunities for further implementation of new ideas and trends in the development of tertiary education in Russia. The technological process does not stand still and advanced IT technologies have become an integral part of modern education.
Effectiveness development and evaluation of the endowment funds’ investment activities of Russian universities

The article reveals the endowment funds’ formation issues of universities both in Russia and abroad, and compares their quantitative indicators. The review of publications by various scientists on the formation and use of endowment funds given in the article allows us to assess the importance of this type of endowment capital and to clarify their role in the universities’ activities. The article also pays attention to the sources of replenishment and spending directions of endowment funds of Russian universities in comparison with foreign universities. The paper presents a modern practices description of using endowment capital, the authors indicate the changes that have occurred in this way in the practice of Russian universities. As a problem, the authors of the article consider approaches to assessing the use effectiveness of endowment funds. In particular, the article calculates the endowment funds’ effectiveness of Russian universities using the Hansmann model. The result of the calculations showed the endowment funds’ effectiveness of the universities selected for the study, but, at the same time pointed out at difficulties in selecting the information necessary for calculations and serious differences in approaches to the endowment capital use of the universities themselves.