Reviewerly/exHarmony huggingface dataset:
A Study on the Amendment to the Juvenile Act — Focusing on the Amendments for 18 and 19 Years Old Youths (Specific Juveniles) —

On March 2021, the bill for partly amending the Juvenile Act was submitted to the National Assembly. It was formed as it is on May 21, 2021 and will be implemented from April 1, 2022. The Juvenile Act and Criminal Act Subcommittee (for Juvenile Age and Offenders Treatment) of the Judiciary Committee has paid attention to the fact that 18 and 19 years old youths have the right to vote as a result of the amendment to the Public Office Election Act and they are treated as adults from 2022 as a result of the amendment to the Civil Act, and considered lowering the age at which the Juvenile Act is applicable. The amendments that have been completed through long discussions define 18 and 19 years old youths as “Specific Juveniles” and treat them different from persons under the age of 18 by making an exception. In other words, although 18 and 19 years youths still cannot be considered full-grown youths, they are beings expected to participate in the society with responsibilities having the right to vote and, therefore, they are defined as intermediate persons between juveniles and adults. The basic framework of the current Juvenile Act un which all cases are forwarded to family courts and probation is determined by conducting investigations and rendering a judgment, remains effective as to specific juveniles. As a result of these amendments, however, as for 18 and 19 years old youths, the scope of the so-called “referral to public prosecutor in principle” extends to the offenses punishable by imprisonment with or without forced labor for a short term of one (1) year. Moreover, the provision defining juveniles likely to commit a crime in the future is not applicable (as an exception for probation). The exception of report prohibition is applicable after indictment. If a suspect is forwarded to the prosecution and then, is subject to criminal judgment, an indefinite term of sentence does not apply and the exception of restrictions on qualifications does not apply as well (which means that they are subject to the same disposition as adults in principle). Those provisions are exceptions different from the provisions for persons under the age of 18. The discussion about specific juveniles are also a discussion about legal bases under juveniles should be defined under the Juvenile Act. Furthermore, the exceptions for specific juveniles open a couple of doors to rehabilitate 18 and 19 years old youths and, in certain cases, to hold them strictly responsible for the offenses they have committed. Taking into serious consideration whether relevant principles and exceptions under the Juvenile Act can possibly coexist, should of great help to establish directions and juvenile policies of the Juvenile Act in the future.
Method for measuring and evaluating the difficulty data of aerobics' complete sets of movements in university based on multiple regression

Aiming at the problem that the selection of the evaluation index of the complete set of aerobics difficulty data in colleges and universities is unreasonable, which leads to the low satisfaction of the aerobics coaches and referees, a method for the evaluation of the complete set of aerobics difficulty based on multiple REGR is proposed. SPSS13.2 software was used to evaluate the body shape index and body quality index that affect the specific technical level of college aerobics athletes. The final index was obtained through correlation analysis and cluster analysis, and the evaluation model was established by multiple regression method to evaluate the complete set of aerobics difficulty data in colleges and universities. The experimental results show that the evaluation indexes of college calisthenics difficulty movements set by the research method are reasonable, and the pass rate of athletes participating in the evaluation is high.
Augmented Face Representation Learning via Transitive Distillation

The wild face of large variations is hard to recognize in unconstrained scenarios. To tackle this issue, existing works synthesize and augment the variation-specific faces for recognition. However, directly feeding generated samples results in negative transfer, because the feature spaces are shifted compared with normal samples. Instead, we propose a transitive distillation network (TDNet) that introduces a transitive domain to transfer cross-variation representations, which alleviates the negative influence of synthesized data. Specifically, data of diverse variations are firstly synthesized. Then we construct distributions from different variations as teachers to distill student. The negative transfer is mitigated by adopting adaptor as a bridge to break large domain distance. To handle faces of different quality, we propose a novel strategy to define easy and hard samples, which are utilized to select specific transitive status. Meanwhile, bilateral classification with curriculum learning is proposed to improve confidence of synthesized data gradually, enhancing the robustness of representation learning. Experiments show that our method achieves superiority on unconstrained face benchmarks such as IJB-C and SCface, while maintaining competence on general test sets.
Depth Map Reconstruction Method in Control Problems for Robots and Mechatronic Systems

In modern robotic and mechatronic systems, technologies are in demand that makes it possible to build an optimal trajectory of movement of their actuators. Such technologies are formed by combining navigation methods and building a 3-D map of the surrounding space based on vision systems and are successfully used in robotics and mechatronics. But there is a problem, consisting of a decrease in the accuracy of planning the trajectory of movement, caused by incorrect sections on the map (depth map) due to incorrect determination of the distance to objects. Such defects appear as a result of poor lighting, specular or fine-grained surfaces of objects. This leads to the impossibility of obtaining reliable information about the depth. As a result, the effect of increasing the boundaries of objects (obstacles) appears, and the overlapping of objects makes it impossible to distinguish one object from another. This problem can be solved using image reconstruction methods. The article presents an approach based on a modified algorithm for searching for similar blocks using the concept of quaternions and anisotropic gradient. The analysis of the research results shows that the proposed method allows you to correctly restore the boundaries of objects on the depth map image when reconstructing 3-D scenes, which contributes to an increase in the accuracy of planning the trajectory of motion of the actuators robotic and mechatronic systems.
An Integrated Framework for Sentiment Analysis of Giant Cold Drinks Companies

Twitter is an open, freely available and widely used social media platform for sharing emotions, political and social opinions without hesitation. With the passage of time data generated by Twitter is increasing tremendously and due to availability of the such a huge amount of data, there is an opportunity for industry giants to analyze their current product reviews and made a better business making for the future and to conformance needs of customers. Similarly, there is an opportunity for research aspirants to explore the data to provide a better solution for modern problems. Therefore, in this research paper, we perform a detailed sentiment analysis of the three beverage giants (Pepsi Vs Fanta Vs Coca-cola). Specifically, we perform the classification of extracted tweeter data into three classes (positive, negative and neutral) then the visualization of the data is performed and at the end, naïve Bayes, decision tree and LSTM algorithm is applied to perform the prediction of tweets. Particularly, we use machine learning libraries and natural language toolkits for data extraction, data visualization, sentiment analysis and prediction task. The maximum accuracy obtained on Pepsi, Fanta and Coca-Cola is 89%, 92% and 91% respectively by applying the LSTM algorithm. The experimental result reveals that LSTM is a better technique for continuous data
Precise Identification of Network Security Threats Based on User Behavior

Due to the improper processing of data sets, the existing methods of network security threat identification are not accurate enough to meet the needs of today's network security. Based on the data set of user behavior obtained by using Python script, the abnormity degree of user network behavior is judged, and the user network behavior model is established. Combined with the data set of user behavior collected by network security protection equipment, the precise recognition of network security threats is realized by perceiving network attack events and threat situation elements and according to perceived network security threats and their association rules. Experimental results show that the proposed method greatly improves the accuracy of threat target recognition and shortens the time consumption of threat target recognition. The feasibility of the proposed method is verified.
Quantum Mechanics Fundamentals

This chapter is devoted to quantum mechanics fundamentals. The following topics from quantum mechanics are covered: state vectors, operators, density operators, measurements, and dynamics of a quantum system. In addition, we describe harmonic oscillators, orbital angular momentum, spin-1/2 systems, and hydrogen-like systems. These systems are of high importance in various quantum information processing systems and in quantum communications. The chapter is organized as follows. In Section 2.1 we introduce the basic concepts in quantum mechanics. In Section 2.2 we describe how eigenkets can be used as basis kets. The matrix representation of operators, kets, and bras is described in Section 2.3. Section 2.4 is devoted to Pauli operators, while Section 2.5 is related to density operators. Quantum measurements are described in Section 2.6. The uncertainty principle is introduced in Section 2.7. Section 2.8 deals with change of basis and diagonalization. Section 2.9 is devoted to the dynamics of quantum systems and the Schrödinger equation. Harmonic oscillator theory is used in many quantum computing applications, therefore Section 2.10 is devoted to the harmonic oscillator. Orbital angular momentum is described in Section 2.11, while spin-1/2 systems are described in Section 2.12. In Section 2.13 we describe hydrogen-like atoms. Finally, Section 2.14 concludes the chapter. Some problems for self-study are provided at the end of the chapter (see Section 2.15).
Research on Tension Setting Out Construction of an Overhead Transmission Line Based on Intelligent Real Scene Modeling

The error of the installation position of the unwinding block is large when the guiding rope is unrolled by the conventional tension unwinding construction method. To solve this problem, this paper proposes a tension setting out construction method of overhead transmission line based on intelligent real scene modeling. After the target object of the overhead transmission line is scanned, the scanned image is preprocessed, and the feature point cloud is extracted by denoising, simplification and gray correction. Then, the spatial coordinate data of the transmission line skeleton are matched, and an intelligent real scene model of the transmission line is built. Divide the tension paying off section on the real scene model, select the installation position of the paying off pulley, unfold and recover the guide rope to complete the tension paying off construction. The experimental results show that this method reduces the fitting error of the setting out pulley installation position and makes the pulley position positioning result more accurate.
Market Basket Analysis on Sales Transactions for Micro, Small and Medium Enterprises Using Apriori Algorithm to Support Business Promotion Strategy in RDA Hijab

The increasingly fierce business competition requires business owners to always innovate to create effective and efficient business promotion strategies to develop their business. One way to get recommendations for business promotion strategies is to process sales transaction data owned by the business. With data mining, the data can be processed so that new knowledge is obtained to support the creation of new business promotion strategies. The method used is the method of market basket analysis association rules with a priori algorithm. The results of the analysis in this study can be concluded for the rule with a combination of 2 items that have the highest confidence value of 41.61%, the lift ratio value of 12.10, and the frequency of the rule in the highest testing data is 66, that is, if consumers buy navy neck cuffs, then he will also buy white neck cuffs. And for the rule with a combination of 3 items that have the highest confidence value of 50.00%, the lift ratio value of 11.94, and the frequency of the rule in the highest testing data is 13, that is, if the consumer buys black neck cuffs and navy neck cuffs then he will also buy a light khaki neck cuff.
Characterizing client-side caches of audiovisual content sharing services: Findings and suggestions for forensics

In light of the advancements in computing technology, and an increase in the use of mobile devices, various forms of services have emerged in recent times. Particularly, audiovisual (AV) content-based services that stream broadcasts through personal channels or self-produced video content shared with friends has been gaining ground. It is evolving into a new platform category to share information or help people express themselves. Meanwhile, there have been instances across the globe where these services have been used to stream or share illegal content. So far, few systematic technical studies have been conducted to investigate crimes associated with online services. In the process of viewing, sharing, and distributing illegal videos, digital artifacts may be saved on digital devices such as a PC or smartphone. This paper explores the client-side caches generated as a consequence of ‘viewing’ visible content through AV content-based services, including Dailymotion, Instagram, LINE, Snapchat, Telegram, TikTok, and YouTube, in various platforms including Windows, Android and iOS. Through analyzing caching mechanisms, this study categorizes and characterizes various AV caches observed during repeated experiments. This work also proposes algorithms (in combination with a developed open-source tool) for obtaining playable (visible) contents, by identifying and reassembling fragmented AV pieces.
Joint Detection, Tracking and Recognition of Multi-targets Based on Narrowband Radar Measurement Information

On the basis of the jump Markov system model Gaussian mixture probability hypothesis density filtering (JMS-GMPHDF), ajoint detection, tracking and recognition method of the multi-targets for the narrowband radar is presented, using the attribute information such as the speed and acceleration in the radar measurement. In the process of the multi-model Gaussian mixture probability hypothesis density filtering process of the radar multi-target measurement information processing, this method numbers each Gaussian term and extracts the track, which can form a clear track with the track number and carry out the track management. Moreover, according to the target motion model, the target acceleration control input and speed estimation are combined for the multi-target recognition and classification. Simulation result verifies that the method can effectively identify the target track while detecting and tracking the targets.
Self-managed Organization: A Role of Business Process Management

Among with process automation and digitalization trends, which requires a strong structural and process-oriented approach, new paradigm of self-managed organization become a mainstream cultivating the value of decentralization of decision power. With different titles as Management 3.0, holacracy, teal organization or reinventing organization, the same idea is spread – a transition from typical hierarchical management pyramid to the flat organization of self-organized teams with given authorities for decision making. Ongoing discussions are both supporting and criticizing. Most of them are focused on organizational changes, mind-set, and methods to assist transition. Keeping attention to the social component of the new management paradigm in organizational ecosystem, an attention to its technical side is missed. It includes business process and information system governance. The aim of the paper is to start a discourse and consider if self-managed organization benefit from a systematic approach of process management, as well as consider risks what can bring to business process continuity.
Modelling Speaker Attribution in Narrative Texts With Biased and Bias-Adjustable Neural Networks

Literary narratives regularly contain passages that different readers attribute to different speakers: a character, the narrator, or the author. Since literary narratives are highly ambiguous constructs, it is often impossible to decide between diverging attributions of a specific passage by hermeneutic means. Instead, we hypothesise that attribution decisions are often influenced by annotator bias, in particular an annotator's literary preferences and beliefs. We present first results on the correlation between the literary attitudes of an annotator and their attribution choices. In a second set of experiments, we present a neural classifier that is capable of imitating individual annotators as well as a common-sense annotator, and reaches accuracies of up to 88% (which improves the majority baseline by 23%).
Scene Classification using Regional and Nearest Neighbors of Local features

This paper aims to model the spatial neighbors of local image features in regular and irregular contextual regions. By establishing the spatial neighborhoods of local image features, we incorporate them into the classical Bag of Words model by making an extension to it. To obtain more efficient representations of images based on spatial neighborhood information of local image features, we propose three neighborhood models: X-regional neighbors model, K-nearest neighbors model, and Fixed radius nearest neighbors model. For each of the proposed neighborhood models, we establish the spatial relationships between the local features in terms of the Euclidean distance in the specified neighborhood and capture the neighborhood information of the local features in a pair-wise fashion. Evaluation is obtained by performing scene classification experiments on the 15 scene categories dataset. The results show that the proposed models produce better results than the existing bag of words model and context models from literature in terms of classification accuracy.
Probabilistic Settlement Finality in Proof-of-Work Blockchains: Legal Considerations

The concept of settlement finality sits at the heart of any type of commercial transaction; whether the transaction is in physical or electronic form or is mediated by fiat currencies or cryptocurrencies. Transaction finality refers to the exact moment in time when proprietary interests in the object or medium of transaction pass from one party to his counterparty and the obligations of the parties to a transaction are discharged in an unconditional and irrevocable manner, i.e., in a way that cannot be reversed even by the subsequent legal defenses or actions against the counterparty. Given the benefits of finality in terms of legal certainty and its potential systemic implications, legal systems throughout the globe have devised mechanisms to determine the exact moment of the finality of a transaction and settlement of obligations conducted using fiat currencies as a medium of exchange. However, as the transactions involving cryptocurrencies fall beyond the scope of such rules, they introduce new challenges to determining the exact moment of finality in on-chain cryptocurrency transactions. This complexity arises because the finality of the transactions in the cryptocurrencies that rely on proof-of- work (PoW) consensus algorithms is probabilistic. The probabilistic finality makes the determination of the exact moment of operational finality nearly impossible.After discussing the mechanisms of settlement of contractual obligations in the traditional sale of goods as well as payment and settlement systems - which rather than relying on the concept of operational finality, rely upon the concept of legal finality - the paper argues that even in the traditional payment and settlement systems the determination of operational settlement finality is nearly impossible. This is because no transaction, even a transaction involving a cash payment, cannot be operationally deemed irrevocable as it remains prone to hacks or unwinding by electronic means or mere brute force. The paper suggests that the concept of finality is inherently a legal concept and, as is the case in the conventional finance, the moment of finality in PoW blockchains should also rely on the conceptual separation of operational finality from legal finality. However, given the decentralized nature of cryptocurrencies, defining the moment of finality in PoW blockchains, which may require a minimum level of institutional infrastructures and centralization to support the credibility of the finality, may face insurmountable challenges.
MTCNet: Multi-Task Complex Network for Concurrent Channel Estimation and Equalization

Convolutional neural networks (CNNs) have been widely adopted in various fields and the wireless communication domain is no exception; researchers leveraged CNNs for communication tasks such as channel estimation and equalization. They showed the potential of CNNs for these applications but were insufficient to be considered for real world implementation due to the limited applicability and high execution latency. In this paper, we propose a novel complex number-based neural network architecture, a multi-task loss function, and a training scheme to learn channel estimation and equalization tasks simultaneously in an end-to-end manner. The resulting single-stage CNN architecture, Multi-Task Complex Network (MTCNet), achieves better performance than any other conventional methods. In addition, to the best of our knowledge, MTCNet is the first CNN-based channel estimation and equalization that can operate with various subcarrier settings with inferencing time below 1 ms.
Patch-Based Capsule Network for Complex Images

Capsule Networks are neural networks that have the advantage of learning spatial and hierarchical information from data. They can learn and extract knowledge from smaller datasets (unlike other neural network algorithms); however, they perform poorly on complex and low-resolution images due to a problem called “crowding.” Crowding is attributed to the CapsNets’ attempt to read every object in an image (including background objects), resulting in poor performance. Therefore, this paper proposes a patch-based capsule network and a new squash function (power-B) to decompose an input image into smaller parts enabling the model to focus more on the relevant regions of interest. Experimental results show that the proposed model has efficient feature extraction capabilities, reduced computational time, and a fewer trainable number of parameters. The model’s performance is comparable to the state-of-the-art capsule network models by achieving overall recognition accuracies of 94.62%, 75.68%, and 92.82% for fashion-MNIST, CIFAR 10, and polyp datasets, respectively.