Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF A DIGITAL SYSTEM FOR CREATING AN EDUCATIONAL SCHEDULE AT THE UNIVERSITY

In modern conditions of the development of higher education, the need to use digital methods of presenting the schedule of training sessions becomes obvious. The use of information systems in higher educational institutions is not uncommon. The paper presents one of the possible options for the software implementation of the approach to the development of a web application for the preparation of the curriculum at the university. Initially, existing solutions in this area were analyzed, but it was found that their capabilities were insufficient for the specifics of the specific task under consideration. The main goal of the algorithm and program development is to increase the efficiency of employees. Currently scheduling is done manually. The developed application allows to automate a time-consuming process, reduce the number of possible technical errors, increase the speed and accuracy of decision-making. The approach that was chosen to solve the initial problem is to create a unified system of several components that interact with each other and allow to present the schedule of training sessions in a convenient way. A unified digital schedule system was implemented, which includes an application for creating a training schedule, a chat bot for digital presentation of the schedule, an application for managing and configuring the chat bot of each of the faculties for faculty heads. The problem of unifying the schedule has also been solved, which made it possible to scale the “digital schedule” available at one of the faculties for the entire university as a whole, as well as to improve it.
THE METHOD OF COLLECTING DATA ON THE INTERACTION OF THE STUDENT WITH THE E-LEARNING SYSTEM

In the context of digitalization of education, the learning system is being improved and learning management systems (LMS) for mixed and distance learning are being introduced into the learning process. An integral part of any online education system is an electronic information and educational environment. Modern e-learning systems do not provide students with adaptive content, but work on the principle of “the same material for everyone”. The collection and analysis of data on user interaction in the electronic information and educational environment is attracting more and more attention as a promising approach to promoting a modern understanding of the learning process, and also contributes to understanding student’s behavior. The need to collect data on student’s interaction is motivated by the emergence of many educational analysts, who seek to use data analysis to support decisions made at each level of education. The collection and analysis of available data on the participation rate in the elements of the course of students, their activity contribute to the understanding and optimization of learning, as well as the assessment of the educational environment, where learning takes place. The paper considers one of the ways to collect data on the student's work in the system by developing and implementing a plugin in LMS Moodle. The developed plugin successfully collects statistics on the interaction of students in the e-education system with the EIOS system.This statistics can be used for further analysis. To test the developed plugin, different versions of LMS Moodle were installed. As a result of testing the plugin, it was revealed that the application functions with earlier versions of Moodle, but has a relatively lower speed compared to the versions above.
Multi-behavior Attention Mechanisms Graph Neural Networks based on Session Recommendation

Under current behavior, session-based recommendation attempts to predict the next item our users will purchase. Previous research approaches on session-based recommendation are based on users' target behaviors, however, they ignore the problem that accurate session vector identification cannot be obtained from the limited user target behaviors. For this purpose, this paper proposes a multi-behavioral attention mechanism model based on graph neural networks, which can perform a deeper representation of multi-behavioral interaction sequences for session-based recommendations, and at the same time can make more accurate predictions for long sessions. Specifically, using the target and auxiliary behavior sequences of the user, we construct a multi-relational item graph. Secondly our model uses GNN to extract item-to-item relationships. After that each behavior sequence learns the dependencies by applying a spatial attention mechanism to fuse the target and auxiliary behavior sequences, and finally to predict the next item interacting with the target behavior. Through experiments on two publicly available datasets, it is demonstrated that our model performs well on HR and MRR compared to other representative recommendation models.
Multi-Step Ahead Prediction of Main Steam Flow Using PCA and Pyraformer in MSWI Process

The effective multi-step forecasting of the main steam flow in municipal solid waste incineration (MSWI) can help the steam power generation module to make decision responses in advance and control the system effectively. As the main steam flow is affected by multiple process flows and the strong coupling between each process flow in the MSWI system, there are numerous variables affecting the main steam flow, and the variable redundancy is high, and the noise is high. This results in low accuracy of steam flow prediction. To solve the above problem, a multi-step prediction method based on principal component analysis (PCA) and Pyraformer model is proposed. Based on the multi-modular MSWI process and low coupling between modules, we divided the variables affecting the main steam flow into four parts and carried out PCA dimension reduction treatment. The processed data was input into the Pyraformer model to obtain the multi-step predicted value of the main steam flow. Pyraformer is a time series prediction model based on Transformer, which can capture different range of time dependencies and has better computational efficiency and prediction effect for prediction tasks with more variable input. Experiments show that the proposed method has high prediction accuracy and stability, which can provide a better basis for advanced decision making of steam power generation tasks and other processes of MSWI.
OVERVIEW OF CANDIDATES FOR QUANTUM-RESISTANT CIPHERS

This article describes the main trends in the development of cryptography, in particular, the need for active research in the field of postquantum cryptography. The purpose of the article was to review the latest trends in the field of post-quantum cryptography, due to the emergence of quantum algorithms, in order to reflect the current problems arising in the field of modern cryptography. The main method of research is the analysis of scientific literature for the period from 2016 to 2022. The use of quantum computer and quantum algorithms will significantly reduce the time of cracking keys with encryption and solve some types of cryptographic hash functions, which will lead to a significant increase in the length of keys and a decrease in performance. Due to the great economic influence of the USA, it is necessary to pay attention to the post–quantum NIST contests: from the algorithms under consideration, lattice-based algorithms will be standardized, which are the most reliable and efficient in terms of performance according to NIST, however, in order not to rely only on this type of algorithms, NIST selected alternative algorithms–isogeny–based, code–based, hash–based. So, there is a need to conduct research on the search for post-quantum algorithms of various types, which is confirmed by various NIST competitions. Post-quantum algorithms based on the computational difficulty of solving systems of many quadratic equations with many variables are also effective, such algorithms have not been considered by NIST, but they are relevant for further research. Since updating protocols, schemes and infrastructure objects must be carried out together with the introduction of new cryptographic algorithms, the replacement of currently used cryptographic algorithms can be very destructive and take decades, re-encryption with such a new algorithm of stored information and keys can also be used.
Learning Processes of Touchscreen Gesture Interaction in Older Adults and Children

Touchscreen mobile computers, including smartphones, are widely used today by people of all ages across the globe, from children to senior citizens. In Japan, significant numbers of seniors, such as active ones, have begun to learn how to use computers, including smartphones and other touchscreen devices. The Global and Innovation Gateway for All School Program has allowed children to own and use such devices. Despite the availability of scientific knowledge on how children and seniors interact with smartphone applications, not all of them find it easy to manipulate mobile touchscreens. In this article, we establish brief best practices for designing touchscreen interfaces. First, we summarize the existing literature on touchscreen interaction with children and seniors and then measure their performance of gesture interaction. The tasks included basic gestures, such as taps, swipes, and pinching motions. The results showed that the older adults became significantly faster in tapping and swiping with repetition of the gesture interactions, while no such learning effect was observed in children. We also observed differences between the strategies of older adults and children in tapping and pinching in/out.
New Universal Design Heuristics for Mobile Augmented Reality Applications

This paper presents new, novel and useful Universal Design principles in the context of mobile augmented reality (AR) applications. The principles can be used in an evaluative and/or design setting. The design of the principles was undertaken through experience-based practice. The new universal design principles for Mobile AR were then tested in an evaluation with four well known educational-type AR applications found in the Google Play store. The results of the evaluation closely matched public user comments about each application. Further, the evaluative activity using the new principles fostered design solutions for the problems found in the evaluation. From our activity, the new principles are very useful. It is suggested that these principles, if used, will help to develop better and more user friendly mobile AR applications.
Can the Use of Avatars Contribute to Human Well-Being?

In this paper, we discuss the effect of using an avatar on interpersonal communication. Past research has confirmed that changes in communication and cognition such as the “Proteus effect” and “disappearance of stereotypes” occur. In this research, we are discussing whether we can improve our own wellbeing. Users with a tendency to stutter cannot speak well face-to-face, but wearing an avatar reduces the number of times they stutter, and it was confirmed that stress was reduced from the biological response. Based on this, we discuss the impact of avatars on communication-related wellbeing.
Dynamic Graph-Guided Transferable Regression for Cross-Domain Speech Emotion Recognition

To deal with the problem of cross-domain speech emotion recognition (SER), in this paper, we propose a novel dynamic graph-guided transferable regression (DGTR) method. Specifically, a retargeted discriminant linear regression in the source domain is utilized to make the projection matrix discriminative. Meanwhile, an adaptive maximum entropy graph is designed for similarity measurement for different domains. Experiments on four popular datasets show that our method can achieve better performance compared with several related state-of-the-art methods.
Telecommunications and Remote Sensing: A Public Values Perspective

The impact of current telecommunications on life in developed countries is amazing, allowing for fast transmission of data over the Internet. This can also be usefully supported by sensor technologies, to allow for powerful remote sensing capabilities. As a result, huge amounts of data is showering us every second, inspiring in turn the developments in the area of data analytics where OLAP technologies allow for data extraction and fusion, for the sake of deriving really useful knowledge. Hence, ICT (Information and Communication Technology) is changing many aspects of our lives, making it possible to derive, transmit, and immediately use data on a global scale. This leads to the question: Has ICT improved our lives? Many answers to this question would be positive because the wide availability of useful data can facilitate many Human activities, no doubt about this. Nevertheless, this same data showering can also put at risk essential public values, such as safety, privacy, accountability, and trust. This paper analyzes the potential tensions between capabilities related to telecommunications and remote sensing, on one hand and such values, on the other hand, resulting in the definition of a corresponding conceptual model. Using it, we outline solution directions assuming a value-sensitive design over the developments of telecommunications/remote sensing systems. We provide illustrations accordingly, using several toy examples. Further research focuses on reflecting those conceptualizations in concrete design patterns (building blocks) that could usefully support the development of value sensitive ICT systems.
Cross-Modal Attention Mechanism for Weakly Supervised Video Anomaly Detection

Weakly supervised video anomaly detection aims to detect anomalous events with only video-level labels. Nevertheless, most existing methods ignore motion anomalies and the features extracted from pre-trained I3D or C3D contain unavoidable redundancy, which leads to inadequate detection performance. To address these challenges, we propose a cross-modal attention mechanism by introducing optical flow sequence. Firstly, RGB and optical flow sequences are input into pre-trained I3D to extract appearance and motion features. Then, we introduce a cross-modal attention module to reduce the task-irrelevant redundancy in these appearance and motion features. After that, optimized appearance and motion features are fused to calculate the clip-level anomaly scores. Finally, we employ the MIL ranking loss to enable better separation between the anomaly scores of anomalous and normal clips to achieve accurate detection of anomalous events. We conduct extensive experiments on the ShanghaiTech and UCF-Crime datasets to verify the efficacy of our method. The experimental results demonstrate that our method performs comparably to or even better than existing unsupervised and weakly supervised methods in terms of AUC, obtaining AUC of 91.49% on the ShanghaiTech dataset and 85.49% on the UCF-Crime dataset, respectively.
Enhanced Memory Adversarial Network for Anomaly Detection

Video anomaly detection (VAD) aims to detect abnormal behaviors or events during video monitoring. Recent VAD methods use a proxy task that reconstructs the input video frames, quantifying the degree of anomaly by computing the reconstruction error. However, these methods do not consider the diversity of normal patterns and neglect the scale differences of the abnormal foreground image between different video frames. To address these issues, we propose an unsupervised video anomaly detection method termed enhanced memory adversarial network, which integrates a dilated convolution feature extraction encoder and a feature matching memory module. The dilated convolution feature extraction encoder extracts features at different scales by increasing the receptive field. The feature matching memory module stores multiple prototype features of normal video frames, ensuring that the query features are closer to the prototypes while maintaining a distinct separation between different prototypes. Our approach not only improves the prediction performance but also considers the diversity of normal patterns. At the same time, it reduces the representational capacity of the predictive networks while enhancing the model’s sensitivity to anomalies. Experiments on the UCSD Ped2 and CUHK Avenue dataset, comparing our method with existing unsupervised video anomaly detection methods, show that our proposed method is superior in the AUC metric, achieving an AUC of 96.3% on the UCSD Ped2 dataset, and an AUC of 86.5% on the CUHK Avenue dataset.
CrowdFusion: Refined Cross-Modal Fusion Network for RGB-T Crowd Counting

Crowd counting is a crucial task in computer vision, offering numerous applications in smart security, remote sensing, agriculture and forestry. While pure image-based models have made significant advancements, they tend to perform poorly under low-light and dark conditions. Recent work has partially addressed these challenges by exploring the interactions between cross-modal features, such as RGB and thermal, but they often overlook redundant information present within these features. To address this limitation, we introduce a refined cross-modal fusion network for RGB-T crowd counting. The key design of our method lies in the refined cross-modal feature fusion module. This module initially processes the dual-modal information using a cross attention module, enabling effective interaction between the two modalities. Subsequently, it leverages adaptively calibrated weights to extract essential features while mitigating the impact of redundant ones. By employing this strategy, our method effectively combines the strengths of dual-path features. Building upon this fusion module, our network incorporates hierarchical layers of fused features, which are perceived as targets of interest at various scales. This hierarchical perception allows us to capture crowd information from both global and local perspectives, enabling more accurate crowd counting. Extensive experiments are conducted to demonstrate the superiority of our proposed method.
Adversarial Face Example Generation in AMBTC Compressed Domain

In recent years, adversarial examples have played a significant role in protecting image privacy and improving the robustness of deep learning models. Most adversarial example generations are conducted in uncompressed domain; however, most images are compressed in storage and network transmission, and the compression definitely degrades the adversarial effectiveness. Absolute moment block truncation coding (AMBTC) is popular for image compression. This paper aims to study the adversarial face example generation for AMBTC format. The method is proposed and optimized in compressed trio-data domain (CTD), the adversarial face example generation is optimized directly on the trio data of each block rather than on each pix. Since CTD method optimizes the trio data, it reduces the computational overhead. The experiments on LFW face database confirm that CTD method has satisfactory anti-compression ability for AMBTC format, and simultaneously has satisfactory image quality.
Dynamic Face Expression Generation with Efficient Neural Radiation Field

Lacking of sufficient generalization ability on novel perspectives and expressions, drivable face NeRF, is still a challenging problem. In this paper, we concentrate on two aspects of the drivable face NeRF, the representation power of the driving signal and the efficiency of NeRF rendering. Firstly, we look into the utilization of world-space keypoints as the driving signal of the dynamic face. We realize this by a keypoint lifting strategy based on front keypoints to obtain stable and robust world-space keypoints, which are used to drive the deformation field and the Neural Radiance Field in the canonical space simultaneously. Second, the world-space keypoints are utilized to guide the NeRF to efficiently sample points near the face surface, and the coarse level in the original NeRF can be skipped, which significantly accelerates the rendering speed. We have verified the effectiveness and superiority of our method through good experiments.
A Public-Key System Based on Primes and Addition

In this paper, we describe a public-key algorithm that uses random primes to construct a matrix that is made public. To encrypt an integer, say P, representing a given plaintext message, the integer P is used to select a unique set of integers S from this constructed matrix. To encrypt the message, the integers in the set S are added, and the resulting sum represents the encrypted message. To decrypt the message, three secret keys are used in a mathematical algorithm to determine the original message P from the sum.
Prompt-Based Transformer for Generalizable Person Re-identification with Image Masking

Domain generalizable (DG) person re-identification (ReID) aims to perform well on the unseen target domains by training on multiple source domains with different distribution, which is a realistic but challenging problem. Existing DG person ReID methods have not well explored the domain-specific knowledge based on the Transformer. In this paper, we propose a Prompt-based transformer framework which embeds domain-specific knowledge into different domain prompts, which are optionally optimized by different source domains. Furthermore, we exploit a pretext task of masking and predicting for DG ReID to broaden the understanding of model about data by learning from the signals of the corresponding matching image, which enables interaction between image pairs and improves the ability of generalization. Extensive experiments demonstrate that our method achieves state-of-the-art performances on the popular benchmarks.
The Role of Text in Visualizations: How Annotations Shape Perceptions of Bias and Influence Predictions

This paper investigates the role of text in visualizations, specifically the impact of text position, semantic content, and biased wording. Two empirical studies were conducted based on two tasks (predicting data trends and appraising bias) using two visualization types (bar and line charts). While the addition of text had a minimal effect on how people perceive data trends, there was a significant impact on how biased they perceive the authors to be. This finding revealed a relationship between the degree of bias in textual information and the perception of the authors' bias. Exploratory analyses support an interaction between a person's prediction and the degree of bias they perceived. This paper also develops a crowdsourced method for creating chart annotations that range from neutral to highly biased. This research highlights the need for designers to mitigate potential polarization of readers' opinions based on how authors' ideas are expressed.