Reviewerly/exHarmony huggingface dataset:
INDIVIDUAL FREEDOM OF YOUTH IN CATEGORIES OF LEGAL CULTURE

The article reveals the categories of legal culture as a sphere of individual freedom, which allows a young person to realize own preferences, talents and interests. Each new generation faces to the legal achievements of the predecessors correspond to self-realization and prospects of realizing the collective meaning of social self-development. The analysis of works on the issue testifies to the relevance and multidimensionality of both the problem itself and the methodological approaches to its scientific solution. The formation of legal culture of modern youth is covered in multidisciplinary issues of legal culture, legal awareness of man and young people in particular, legal education, training in pedagogical, psychological, philosophical, sociological, political, economic sciences, culture, public administration and social communications. Legal consciousness is formed in the process of personal development. The humanistic nature of the formation of modern youth’s legal culture is represented by the embodiment of social self-movement, which requires general scientific structure and typology of methods of cognition and transformation of reality in their development and relationships. The main methodology of formation of legal culture as a phenomenon and process is defined by the idea of ​​development.
Multiresolution Neural Networks for Imaging

We present MR-Net, a general architecture for multiresolution neural networks, and a framework for imaging applications based on this architecture. Our coordinate-based networks are continuous both in space and in scale as they are composed of multiple stages that progressively add finer details. Besides that, they are a compact and efficient representation. We show examples of multiresolution image representation and applications to texturemagnification, minification, and antialiasing. This document is the extended version of the paper [PNS+22]. It includes additional material that would not fit the page limitations of the conference track for publication.
CDonVR: E-learning of a Cash Dispenser on Virtual Reality

Currently, virtual reality is booming and growing, being an attractive and unique technology in concept. This paper seeks to show the possible use of this technology for learning in the elderly. Specifically, it seeks to teach the operation of a bank teller, combining the possibility of learning in freedom or being guided through different steps while maintaining a controlled environment. The application will have audiovisual elements that provide a greater sense of realism and familiarity with the environment they find themselves in. During the process, the application will be able to collect information and statistics to know the most used actions of the users and the most complicated to perform. The development will be done using the Oculus Quest headset. The results obtained in the tests determine the satisfaction and importance of this work, giving critical points for future research and development.
DEERE: Document-Level Event Extraction as Relation Extraction

The descriptions of complex events usually span sentences, so we need to extract complete event information from the whole document. To address the challenges of document-level event extraction, we propose a novel framework named Document-level Event Extraction as Relation Extraction (DEERE), which is suitable for document-level event extraction tasks without trigger-word labelling. By well-designed task transformation, DEERE remodels event extraction as single-stage relation extraction, which can mitigate error propagation. A long text supported encoder is adopted in the relation extraction model to aware the global context effectively. A fault-tolerant event integration algorithm is designed to improve the prediction accuracy. Experimental results show that our approach advances the SOTA for the ChFinAnn dataset by an average F1-score of 3.7. The code and data are available at https://github.com/maomaotfntfn/DEERE.
Improved QMIXs for Multi-entity Asynchronous Cooperative Learning in Tactical Wargame

Wargame is a typical validation environment for human-machine confrontation, where multi-entity asynchronous cooperative confrontation is challenging to achieve good results with existing decision-making methods. To solve this problem, a MARL (Multi-agent reinforcement learning) based Multi-entity Asynchronous Cooperative Decisionmaking Framework (MACDF) for Tactical wargame is proposed. First, based on the characteristics of the wargame environment, the intelligent decision problem is modeled as decentralized partially observable Markov decision processes (Dec-POMDPs). Then we investigate asynchronous cooperative confrontation algorithms based on QMIX. Finally, the performance of the QMIX algorithm on the MACDF is improved by changing the error and reward calculation methods of the QMIX algorithm. The experiments show that the agent trained using the improved QMIXs learns quickly and has a high win rate.
MA-CharNet: Multi-angle fusion character recognition network

Irregular text recognition of natural scene is a challenging task due to large span of character angles and morphological diversity of a word. Recent work first rectifies curved word region, and then employ sequence algorithm to complete the recognition task. However, this strategy largely depends on rectification quality of the text region, and cannot be applied to large difference between tilt angles of character. In this work, a novel anchor-free network structure of rotating character detection is proposed, which includes multiple sub-angle domain branch networks, and the corresponding branch network can be selected adaptively according to character tilt angle. Meanwhile, a curvature Adaptive Text linking method is proposed to connect the discrete strings detected on the two-dimensional plane into words according to people's habits. We achieved state-of-the-art performance on two irregular texts (TotalText, CTW1500), outperforming state-of-the-art by 2.4% and 2.7%, respectively. The experimental results demonstrate the effectiveness of the proposed algorithm.
A Self-Learning Object Detection Method Based on Highly Reliable Sample Mining

Visual object detection is an artificial intelligence technique that locates specific objects from images, which is of great significance for practical applications. However, training general object detection models require many manually annotated images, bringing more labour and time cost. In order to improve the adaptability of the object detection model to the data environment changes, this paper proposes a self-learning object detection system based on high-reliability sample mining. We first train a SampleNet that can better mine reliable training samples from unlabeled data. We then use the combination of SampleNet and the basic object detection model to build a complementary residual training framework, continuously improving the sample mining ability and object detection tasks during the training process. The experimental results show that SampleNet can stably provide reliable pseudo samples for model training, and the complementary residual training framework improves the performance of basic object detection tasks.
Collaborative Resilience in Hybrid Quantum-Classical Networks

Resilience including protection and restoration is critical for keeping hybrid quantum-classical networks stable against failures to satisfy secure communication requirements. The game relationship between quantum and classical resources in hybrid quantum-classical networks is specified. A collaborative- resilience-based architecture is designed, where the concept of collaborative resilience is proposed, so as to improve the resource utilization and resilience efficiency. Different collaborative resilience strategies are illustrated and a collaborative dedicated protection (CDP) algorithm is proposed. The simulation results show that the CDP algorithm has higher protection success ratio and lower key cost compared with the non-collaborative algorithm.
Emojis in Deceptive Online Communication: The Frequency and Type of Emoji in Deceptive and NonDeceptive Online Messages

Background: Little research has been done on nonverbal deception cues in computer-mediated communication (CMC). However, deception is a daily occurrence and since much communication is shifting towards CMC, it is important to understand the difference between truthful and deceptive messages. Objective: This research obtained more insight in the use of emoji in deceptive messages by answering the question: Are the frequency and type of emoji different in deceptive compared to truthful online messages? Methods: Participants sent three screenshots of deceptive, and truthful messages to WhatsApp. The used emoji were counted and sorted into levels of valence (positive, negative, and neutral) and intensity (strong versus weak). Results: The results indicated that participants used more negative, weak emoji in deceptive compared with truthful messages and more positive, weak, and strong emoji in truth compared with deceit. No difference was found for the emoji frequency. Discussion: The results are discussed in the light of earlier research. However, this is the first study investigating the use of emoji in the context of computer-mediated deception. Conclusion: The type of emoji can be indicative of used as a nonverbal deception cue in online messages.
A novel fast image encryption scheme based on a new one-dimensional compound sine chaotic system

Abstract In the paper, a new one-dimensional (1-D) compound Sine chaotic system (CSCS) is first proposed. Then new chaotic maps are generated by the CSCS. And four novel generated maps are used for the illustration about the CSCS. Secondly, the results of performance analysis show that the four maps have large Lyapunov exponents and high complexity. Thirdly, a high-efficiency image encryption scheme is proposed by employing two of the four new produced chaotic maps. In the proposed encryption scheme, the simplest diffusion operation is used. And we use a variety of scrambling operations, such as Zigzag transform, Magic confusion and the row confusion. In addition, to increase key space and in order to improve the ability to resist two kinds of attacks, namely the known plaintext attack and the selected plaintext attack, the control parameters and the initial values of the two new chaotic systems are generated based on the SHA-256 function. Finally, compared to other schemes, simulation tests show that our scheme not only has higher security but also faster encryption speed.
DiVa: An Accelerator for Differentially Private Machine Learning

The widespread deployment of machine learning (ML) is raising serious concerns on protecting the privacy of users who contributed to the collection of training data. Differential privacy (DP) is rapidly gaining momentum in the industry as a practical standard for privacy protection. Despite DP's importance, however, little has been explored within the computer systems community regarding the implication of this emerging ML algorithm on system designs. In this work, we conduct a detailed workload characterization on a state-of-the-art differentially private ML training algorithm named DP-SGD. We uncover several unique properties of DP-SGD (e.g., its high memory capacity and computation requirements vs. non-private ML), root-causing its key bottlenecks. Based on our analysis, we propose an accelerator for differentially private ML named DiVa, which provides a significant improvement in compute utilization, leading to 2.6x higher energy-efficiency vs. conventional systolic arrays.
Analysis of the barriers of blockchain adoption in Land Record System

Initially emerged in the financial sector,blockchain technology has shown its potential to be adopted in many areas – one being the land record system in India. However,there are many barriers to its mass scale usage for the land record system. This study analyzes these barriers to develop an Interpretive Structure Model for better management of the opportunities in utilizing blockchain platforms for land record system and associated transactions in a trust-free,transparent, and threat-free manner. The barriers‘Disputability’,‘Centralization’, and‘Public acceptance’were identified as the most critical barriers that have been found to obstruct the usage of blockchain in land record system,whereas barriers‘Digital readiness’,‘Internet speed’, and‘Infrastructure’ were found to affect the adoption the least. The data centralization will help the land record system in maintaining the record efficiently and can avoid disputes among the public. In future, the land record system should focus on adopting blockchain-based land record system to increase the transparency,traceability, and smoothness of the land record system.
Readability Level Analysis and the Usage of Complex Words on Grade 8 Students’ Argumentative Essay

Background: Writing as medium of communications has become the pivotal accomplishments of language learning objectives. It still becomes the challenging part especially for learners who come from non-English speaking country. Their hindrances to elaborate the sentences, lack vocabularies and use the right words in their essay make the essay difficult to understand. Purpose: Therefore, the significance of the research is to identify the readability level of student’s essay written by grade eight learners under Cambridge curriculum. Design and methods: This readability is to define which level of readers is suitable to read the text and who will understand it. Then, one of the elements in analyzing the level of readability is the number of complex words the texts. Therefore, there are about 13 students’ essays were examined using Flesh-Kincaid Ease Formula and SMOG index. Results: The result found out the they have diverse readability level in between 7th grade to college level. By these results as well, it appears the number of complex words used in their essay which then being described of what kinds of complex sentences used in their essays.
Performance Evaluation of SIMON and SPECK Block Ciphers to Secure IoT-Enabled Smart Cities

The Internet of things (IoT) has presented a smart world by interconnecting everyday objects to enable a superior everyday smart life. The IoT-enabled smart cities and smart mobility brought a completely revolutionary idea but with a compromise in the security. The IoT devices that offer smart applications like smart cities and smart mobility are generally resource-constraint and battery-operated. Raspberry Pi is a device widely used for such applications. SIMON and SPECK are the families of lightweight block ciphers that can offer encryption facilities to smart city applications. The paper presents the performance evaluation of the SIMON and SPECK family of lightweight block ciphers on the Raspberry Pi device. A trade-off between security and performance is observed between SIMON and SPECK implementations. The simulation results show that SPECK offers a good security level with better performance in Raspberry Pi hardware.
Denoising Architecture for Unsupervised Anomaly Detection in Time-Series

Anomalies in time-series provide insights of critical scenarios across a range of industries, from banking and aerospace to information technology, security, and medicine. However, identifying anomalies in time-series data is particularly challenging due to the imprecise definition of anomalies, the frequent absence of labels, and the enormously complex temporal correlations present in such data. The LSTM Autoencoder is an Encoder-Decoder scheme for Anomaly Detection based on Long Short Term Memory Networks that learns to reconstruct time-series behavior and then uses reconstruction error to identify abnormalities. We introduce the Denoising Architecture as a complement to this LSTM Encoder-Decoder model and investigate its effect on real-world as well as artificially generated datasets. We demonstrate that the proposed architecture increases both the accuracy and the training speed, thereby, making the LSTM Autoencoder more efficient for unsupervised anomaly detection tasks.
Cognitive Distortion While Attending Online Class: Study on Banjar College Student

Online learning has begun to take place in Indonesia since March 2020 simultaneously in all educational institutions due to the outbreak of the Covid-19 outbreak. Sudden changes make it difficult for students to process information and lead to various patterns of thinking errors which are known as cognitive distortions. This study aims to reveal the patterns of cognitive distortions that students have during the online lectures. The research design used is a phenomenological qualitative design. Analysis of cognitive distortions was collected through in-depth interviews and document studies to 8 Banjar ethnic students. The results of the study found that there were 3 cognitive distortions, namely: (1) catastrophizing; (2) all-or-nothing thinking; and (3) should statements. The effects of these cognitive distortions include feeling lazy, bored, afraid, uncomfortable, drowsy, shaking, and heart palpitations. In the context of student behavior, students also tend to neglect lectures, access social media, sleep, and go to hang out with friends. Further research is needed to explore student’s cognitive distortions in post-Covid-19 pandemic situation or with diverse populations.
Neural Modelling of Dynamic Systems with Time Delays Based on an Adjusted NEAT Algorithm

A problem related to the development of an algorithm designed to find an architecture of artificial neural network used for black-box modelling of dynamic systems with time delays has been addressed in this paper. The proposed algorithm is based on a well-known NeuroEvolution of Augmenting Topologies (NEAT) algorithm. The NEAT algorithm has been adjusted by allowing additional connections within an artificial neural network and developing original specialised evolutionary operators. This resulted in a compromise between the size of neural network and its accuracy in capturing the response of the mathematical model under which it has been learnt. The research involved an extended validation study based on data generated from a mathematical model of an exemplary system as well as the fast processes occurring in a pressurised water nuclear reactor. The obtaining simulation results demonstrate the high effectiveness of the devised neural (black-box) models of dynamic systems with time delays.
Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies

Fine-tuning can be vulnerable to adversarial attacks. Existing works about black-box attacks on fine-tuned models (BAFT) are limited by strong assumptions. To fill the gap, we propose two novel BAFT settings, cross-domain and cross-domain cross-architecture BAFT, which only assume that (1) the target model for attacking is a fine-tuned model, and (2) the source domain data is known and accessible. To successfully attack fine-tuned models under both settings, we propose to first train an adversarial generator against the source model, which adopts an encoder-decoder architecture and maps a clean input to an adversarial example. Then we search in the low-dimensional latent space produced by the encoder of the adversarial generator. The search is conducted under the guidance of the surrogate gradient obtained from the source model. Experimental results on different domains and different network architectures demonstrate that the proposed attack method can effectively and efficiently attack the fine-tuned models.
Managing Spatial Big Data on the Data LakeHouse

The objective of this paper is to propose some of the best storage practices for using Spatial Big data on the Data Lakehouse. In fact, handling Big Spatial Data showed the limits of current approaches to store massive spatial data, either traditional such as geographic information systems or new ones such as extensions of augmented Big Data approaches. Our article is divided into four parts. In the first part, we will give a brief background of the data management system scene. In the second part, we will present the Data LakeHouse and how it responds to the problems of storage, processing and exploitation of big data while ensuring consistency and efficiency as in data warehouses. Then, we will recall the constraints posed by the management of Big Spatial Data. We end our paper with an experimental study showing the best storage practice for Spatial Big data on the Data LakeHouse. Our experiment shows that the partitioning of Spatial Big data over Geohash index is an optimal solution for the storage.
Remote Sensing Image Registration Via Cyclic Parameter Synthesis and Spatial Transformation Network

Aiming at the problems of insufficient feature extraction ability, many mismatching points and low registration accuracy of some remote sensing image registration algorithms, this study proposes a remote sensing image registration algorithm via cyclic parameter synthesis spatial transformation network. (1) We propose a feature extraction network framework combined with the improved spatial transformation network and improved Densely Connected Networks (DenseNet), which can focus on important areas of images for feature extraction.This framework can effectively improve the feature extraction ability of the model, so as to improve the model accuracy. (2) In the matching stage, we design the coarse filter and fine filter double filter architecture. Thus, the false matching points are effectively filtered out, which not only improves the robustness of the model but also improves the registration accuracy. Compared with the two traditional methods and two deep learning methods, the experimental results of this model are better in many indexes.
Vigenère Cipher Algorithm Optimization for Digital Image Security using SHA512

One of the popular cryptographic algorithms is the Vigenère Cipher. This algorithm is included in classical cryptographic algorithms, so its capabilities are limited to text-type data. Through this research, this research try to modify the Vigenère Cipher so that it can be used on digital image media. The improvement is performed using ASCII code as a Vigenère table and the key generated by the SHA512 hash technique with salt. The encryption and decryption process was carried out on ten jpg and ten png files and showed a 100% success rate. Speed and memory consumption tests on the encryption process by comparing it with the AES algorithm show that AES excels in speed with 409,467 Mb/s while Vigenère wins in memory consumption by utilizing only 5,0007 Kb for every Kilobytes of the processed digital image file.&#x0D;
Optical Fiber-Based Flexible and Wearable Motion Angle Sensor for Metacarpophalangeal Joint

In order to solve the problem that it is difficult to monitor the movement of metacarpophalangeal joint in hand rehabilitation training, a wearable metacarpophalangeal joint motion Angle sensor is designed and manufactured based on the principle of optical fiber macro bend loss. By analyzing the macro bending loss characteristics of the optical fiber and selecting the appropriate bending radius, the sensing silica gel module is made. Calibrate the sensor silicone module, make a hand wearable device by combining fabric gloves and wristbands to form a motion Angle sensor, and verify the calibration results. The experimental results show that the measurement range of the sensor is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0\sim 80^{\circ}$</tex> . The Pearson correlation coefficient of the validation curve is 0.9987, and the relative error is 0.5338%, with high measurement accuracy. It meets the needs of practical application, has important practical value for real-time gesture monitoring and action recognition.