Reviewerly/exHarmony huggingface dataset:
An Improved K-Means Clustering Algorithm Based on Density Selection

K-means clustering algorithm is an unsupervised learning method with simple principles, easy implementation, and strong adaptability. Aiming at the disadvantages of this algorithm that the clusters’ number is difficult to determine, sensitive to the initial cluster center, and the clustering result is easily impacted by the outliers, this paper proposes an improved clustering algorithm based on density selection, which compares the neighborhood density of each sample and the average density of all the samples, treats the samples with lower density as the outliers or isolated points, and then deletes them. After data pre-processing, the cluster validity index is modified to obtain the optimal clusters’ number by minimizing the cluster validity index, and then optimizes the initial cluster center by density selection strategy. Finally, it is verified by the experiment that the improved algorithm has better accuracy than the traditional K-means algorithm, and it can converge to the global minimum of SSE faster.
Research on Internet data security and privacy protection

Abstract The company's data leakage has always been a very serious problem. Many of the leaked data is the user's personal privacy data, which will bring a lot of trouble to users. In order to improve the security of the storage and use of personal private information in the database, two methods of lightweight private information encryption and social engineering anonymization are proposed to protect database information. We compare the proposed method with the application layer encryption and pre encryption methods. The experimental results show that the proposed method can effectively protect the user's personal privacy data, and can detect and recover the privacy data when managers need it. The storage efficiency of the three methods is close. Therefore, it has good application value in privacy protection.
Massive Small File Storage Scheme Based on Association Rule Mining

When HDFS stores a large number of small files, the NameNode will have insufficient memory space and high read latency, making NameNode a system bottleneck and seriously affecting the file processing capabilities and user experience of HDFS. This article proposes a solution to solve these two problems. HDFSA (Hadoop distributed file system based on association rule mining), a storage architecture based on association rule mining, uses the redis cluster to cache small files and merge and upload them to the DataNode and then uses association rule mining algorithms to preread the strongly associated files when downloading small files to improve the efficiency of reading massive small files. This experiment compares the system's memory occupancy rate with upload and download time indicators, and the results show that the proposed storage strategy reduces the memory occupancy of the NameNode, improves the efficiency of reading a large number of small files, and reduces the read latency of HDFS.
Documenting evidence of a reuse of ‘on the number of linear regions of deep neural networks’

We report here the reuse of theoretical insights from deep learning literature, used in a recent TSE '21 paper by Yedida & Menzies. The artifact replicated is the lower bound on the number of piecewise linear regions in the decision boundary of a feedforward neural network with ReLU activations, as studied by Montufar et al. We document the reuse of Theorem 4 from Montufar et al. by Yedida & Menzies.
Yea or nay to artificial intelligence? More questions than answers under Malaysian copyright law

Abstract The development of artificial intelligence draws heavily on training data which may be comprised of copyright works. Utilization of copyright works as such raises questions on copyright infringement as well as the possibilities of the usage being excused under the existing law. There are also concerns with respect to accessing copyright works in digital formats that are commonly protected by digital rights management tools. Restrictions on such usage may hamper the research and development of artificial intelligence applications. This paper examines the said issues with specific focus on first, the applicability of a seemingly Americanized fair dealing exception to copyright infringement under the Malaysian Copyright Act 1987; and second, the exceptions to legal protection of digital rights management. In doing so, the paper studies the existing models of the United States, United Kingdom, and the European Union. It ends with suggestions as to how copyright law may respond to the emerging needs and provide adequate latitude with the aim to promote research and development of artificial intelligence which would ultimately benefit the public at large.
Revulsion-Based Speech Categorization on Expressions in Social Media

Revulsion speech is a term that based on the congregation of a social community, derides and criticizes a group of people or an individual by characteristics such as mental disability, race, religion or gender inequality and others. Revulsion speech usually promotes malevolent stereotypes and encourages savagery toward individuals or a group. In the current situation, more people use social networking websites, resulting in a large amount of data being generated. As there are many target classes, handling such a vast amount of information is a critical and non-trivial task, and each group is exposed to unique revulsion-related words that complicate the automated classification task. The paper provides a detailed review of all the known techniques in the detection of revulsion speech and contrast between the various approaches.
Deep Reinforcement Image Matching with Self-Termination

Deep reinforcement learning-based image matching sequentially searches only the promising regions in the reference image that match the query, leading to a significantly small number of steps compared to traditional methods. Since existing methods do not have any function to judge whether the target region has been successfully identified or not, they continue to search until the preset maximum number of search steps is reached. In this paper, we propose a deep image matching network that can terminate the matching process by itself. Our network is designed to have a halting module that identifies whether the current reference region matches the query based on the image features and the search history. The entire network is effectively trained end-to-end in a framework of deep reinforcement learning that incorporates a new loss function to evaluate the accuracy of the termination decision. Experimental results demonstrate that our method can achieve highly competitive or better matching accuracy with fewer search steps than the existing methods.
The Security of Cryptosystems Based on Error-Correcting Codes

Quantum computers are distinguished by their enormous storage capacity and relatively high computing speed. Among the cryptosystems of the future, the best known and most studied which will resist when using this kind of computer are cryptosystems based on error-correcting codes. The use of problems inspired by the theory of error-correcting codes in the design of cryptographic systems adds an alternative to cryptosystems based on number theory, as well as solutions to their vulnerabilities. Their security is based on the problem of decoding a random code that is NP-complete. In this chapter, we will discuss the cryptographic properties of error-correcting codes, as well as the security of cryptosystems based on code theory.
REVIEW PAPER ON DATA MINING TECHNIQUES, ALGORITHMS AND TOOLS

Data mining has made considerable growth in recent time but the main drawback is missing of data has remained a confront for data mining algorithms. The main task of data mining is to traverse the large amount of different types and categorize and Summarize it. In current world data mining is moreover accepted and make huget growth within the field of all kinds of application in software feild. Data mining is a process of extracting business useful data from large databases by using data mining tools and techniques. Data mining techniques have used to improve performance in Healthcare, Educational, Business areas by extracting unknown and applying data mining tools and algorithms techniques. Data mining is process used to extract data and discover knowledge from it and presenting it to humans by more understandable format and data mining is used to analyzing the data that stores in large data warehouses and discover last unknown unknown data structures and relations out of large data. This is a review paper that introduces the study of classification technique on the basis of the algorithms which is used to make prediction on large data set to analyze the future prediction in healthcare sector. This paper focus on various ensemble learning techniques like Boosting and Bagging and also comparison of ensemble learning techniques with each other by using base classifiers.
GEIGER: Solution for small businesses to protect themselves against cyber-threats

In a world where cybersecurity has an increasing importance, any company, regardless of what sector, size or activity is related to, should rely on tools and solutions that can help it to be secure in the best possible way. The GEIGER platform described in this paper acts as a perfect fit for micro and small enterprises (MSEs). These companies need to be protected against threats but sometimes do not have the resources (money, personnel, time…) to deal with them. Often, private cybersecurity solutions are either expensive or hard to implement for micro and small companies. However, GEIGER is designed to bring cybersecurity principles, security countermeasures and awareness in a smooth and friendly way, with special focus on the MSEs. Its ability to adapt to new challenges comes in handy when dealing with sophisticated threats and the functionalities provided to help MSEs adopting a more prominent security posture. Having the support of an innovative solution can help MSEs to achieve a more effective approach regarding cybersecurity, which leads to a better overall business management and operation.
Detection of Fraudulent Credit Card Transactions by Computational Intelligence Models as a Tool in Digital Forensics

In this article, the problem of proper identification of the frauds in credit card transactions is investigated. The transactions are represented in the form of the data set where one of the variables describes the outcome – the fraud or the genuine action. Five state-of-the-art computational intelligence models are used to identify and predict the fraud. The accuracy, the receiver operating characteristic (ROC) and precision-recall (PR) analysis are utilized to measure the efficiency of the models. It is shown what performance measures must be selected in order to correctly detect fraudulent transactions.
Equivalence, group of automorphism and invariants of a family of rank metric codes arising from linearized polynomials

Let Fq denote the finite field with q=pλ elements. Maximum Rank metric codes (MRD for short) are subsets of Mm×n(Fq) whose number of elements attains the Singleton-like bound. The first MRD codes known were found by Delsarte (1978) and Gabidulin (1985). Sheekey (2015) presented a new class of MRD codes over Fq called twisted Gabidulin codes and also proposed a generalization of the twisted Gabidulin codes to the codes Hk,s(L1,L2). The equivalence and duality of twisted Gabidulin codes were discussed by Lunardon, Trombetti, and Zhou (2016). A new class of MRD codes in M2n×2n(Fq) was found by Trombetti-Zhou (2018). In this work, we characterize the equivalence of the class of codes proposed by Sheekey, generalizing the results known for twisted Gabidulin codes and Trombetti-Zhou codes. In the second part of the paper, we restrict ourselves to the case L1(x)=x, where we present its right nucleus, middle nucleus, Delsarte dual and adjoint codes. In the last section, we present the automorphism group of Hk,s(x,L(x)) and compute its cardinality. In particular, we obtain the number of elements in the automorphism group of some twisted Gabidulin codes.
Role of Quantum Technology in 6G

The past few years have seen major advances towards practical quantum systems. These include demonstration of quantum entanglement over a gap of over 1200 km, to significantly speeding up the solution of certain combinatorial problems. It stands to reason that eventually, quantum communication techniques may be practically viable in modern communication systems. Certain aspects of this technology are being considered to augment existing technologies, such as secure key distribution. However, this technology hasn’t matured to the point where it can take a forefront. In this chapter, we will explore various tenets of quantum communication, describe the challenges and role it will hopefully play in 6G and beyond communications.
Analysis of Cohesion and Coherence Paragraph and its Effect on Student Writing Ability

The importance of conducting research about analysis cohesion and coherence of paragraphs in a paper is a particular concern in efforts to improve students' writing ability. The problem that arises is there are still many student papers that are not good in terms of the cohesion and coherence of the paragraph. This case is caused by the lack of student understanding in terms of fusing sentences with other sentences and there is no logic in preparing paragraphs. Of course, this has an impact on the poor quality of his writings. The purpose of this study is to analyze the cohesion and coherence of paragraphs and their effect on students' writing ability. The subjects of this study were high school (SMA) students in Kediri, East Java, Indonesia. The research sample was conducted by a purposing sampling of 120 students. This study was analyzed by a qualitative descriptive analysis method, where the data obtained were interpreted by examining the data, checking the validity of the data, and describing the data. Data collection methods used in this study are documentary and non-test studies. The data analysis technique used in this descriptive qualitative research is an interactive data analysis technique. The results showed that the level of cohesion and coherence of paragraphs made by students through a paper was less than optimal. A review of students' papers shows that paragraphs are not cohesive and incoherent at 4.5%, cohesive but incoherent at 27%, and cohesive and coherent paragraphs at 68.5%. Referring to the data, it can be conclude that there is an influence between cohesion analysis and paragraph coherence by students on their writing ability.
Breaking the spell on the iconic work of Reza Negarestani “Cyclonopedia”

This article is dedicated to rational interpretation of the novel &amp;ldquo;Cyclonopedia&amp;rdquo; by the Iranian philosopher and writer Reza Negarestani, which particularly draws attention of the young audience. The author believes that it is impossible to grasp such works without interpretation and reconstruction of their themes and contents. Implementing the claimed approach, the author discusses the crucial topic of openness in the novel and peculiarities of the poetics of &amp;ldquo;Cyclonopedia&amp;rdquo;. There are two different understandings of openness described by R. Negarestani: one that is attributed to common forms of sociality, proclaiming freedom and open relationships in one or another way; and &amp;ldquo;epidemic openness&amp;rdquo;, which overtakes a person, even against his will, and destroys him. The author analyzes the concept of openness, suggesting that on the one hand, the external can be interpreted as listening to the trends of the &amp;ldquo;futureculture&amp;rdquo;, while on the other &amp;#8210; as criticism and objectification of concepts that claim to know and predict the future, but in fact cannot go beyond the traditional representations of modernism. Explaining why Negarestani compares openness to food, the author offers to consider the Tibetan archaic ritual &amp;ldquo;Chod&amp;rdquo; (severance), going through which the monks offer themselves to be devoured by demons. Based on this, the author formulates an assumption that in the course of creating &amp;ldquo;Cyclonopedia&amp;rdquo;, Negarestani was affected by the ancient and mystical concepts, and may even have read Davin-Neel&amp;rsquo;s research on the Tibetan ritual. The author also suggests that certain other themes of &amp;ldquo;Cyclonopedia&amp;rdquo; also need to be reconstructed. The article analyzes the reality of the novel: in addition to discussing the theory-fiction genre, the author believes that the reality of &amp;ldquo;Cyclonopedia&amp;rdquo; is attributed to phenomenological, which represents the peculiarities the consciousness of R. Negarestani, rather than the external events.
Omnidirectional movement tracking system combining depth information

In order to solve the problem that the traditional mobile tracking robot is easy to lose and has poor flexibility when following the target, a target tracking scheme based on depth camera is studied, and an omnidirectional mobile tracking system combining depth information is designed. Aiming at the problem that the image tracking target is not easy to separate from the background, a KCF algorithm was proposed to extract HOG feature from the depth image representing the distance information, and the scale filter was added to realize the self-adaptation to the dynamic target scale. To solve the problem of poor maneuverability of the robot, a tracking platform that can move omnidirectionally is built by using McNum wheel. By modeling the platform chassis and calculating the target image offset to the motor, the robot can realize omnidirectionally moving tracking with zero turning radius. The experimental results show that the image tracking algorithm can track the position and scale of the target in real time on the embedded system, and the robot can automatically follow the target at the set distance.
3D Multi-Object Online Tracking with Multi-View Clustering

Multi-target tracking is an increasingly hot research topic. Since target tracking under a single view is difficult to solve the problem of target tracking, we propose an online tracking method that simultaneously locates and correlates the objects under multiple views. This idea consists mainly of a tracking-by-detection method and an internal association mechanism between cameras. First, we trained the LSTM classfication model for the tracking under a single view, in which we also retained the best features of unoccluded objects for re-identification. Then the hierarchical clustering is used for connecting the objects under different views with the accurate track fragments after camera calibration, and the output of clustering will be made for improving the effect of track fragments in the online tracking. In experiments, our method proved to be more robust to solve the occlusion problem, and can make superior performance in comparison with the state of the art methods on the Terrace videos and Passageway videos from EPFL CVLAB.
Soft Ranking Threshold Losses For Image Retrieval

This paper proposes a novel loss, soft ranking threshold loss, for driving deep networks to learn better representations for image retrieval. Instead of working in the metric space, our loss works in the rank space which has a more uniform distribution and explicit scale and bounds. Our loss reduces the ranks of the distances between anchor-positive pairs below the threshold while increasing the ones between anchor-negative pairs above the threshold. In addition to the basic form, two extensions are proposed for improving the effectiveness: hard thresholds and ranking margin. Experiments show that the proposed loss outperforms the state-of-the-art losses on image retrieval applications.
Enhanced Convolutional Neural Network for Age Estimation

The human face constitute various biometric features that could be used to estimate an important detail such as age. Variations in facial landmarks and appearances have presented challenges to automated age estimation. This account for limitations attributed to conventional approaches such as the traditional hand-crafted method, which cannot efficiently and adequately estimate age. In this study, a six layered Convolutional Neural Network (CNN) were proposed, which extract features from facial images taken in an uncontrolled environment, and classifies them into appropriate classes. Since a huge datasets is needed to obtain good accuracy from the trained model and minimize overfitting, data augmentation was performed on the datasets to balance the number of images in each class. The UTKFace dataset was used to train the model while validation was carried out on FGNET dataset. With the proposed novel method, an accuracy of 89.75% was recorded on the UTKFace dataset, which is a significant improvement over existing state-of-the-art methods previously implemented on the UTKFace dataset.
Novel cryptographic approach to enhance cloud data security

Abstract In today’s era, cloud computing has become the more promising business concept which impacted almost every section of our lives and business. Cloud computing for sure has grown rapidly to become one of the major areas of research. As it provides an on demand access to a shared pool of resources and makes easier for the organizations to use their data at any place and at anytime without considering hardware devices along them but cloud security is still the most crucial and considerable issue in each organization. So there is need for some secure authentication so that data on clouds remain safe and secure. In this context passwords are the basic form of authentication. so there is need of some mechanism which provides password security. Weak and poor passwords management leads to breach in cloud data. Passwords to gain access to secret data should be so as strong to prevent dictionary attacks and brute force attacks. In our proposed system a mechanism is presented to secure the data on cloud using combination of some algorithms viz: PBKDF2, Argon 2, AEs-256 and IDA. In this paper we proposed a method to generate the most secured cryptographic keys using the blend of two key derivation functions PBKDF2 and Argon 2. In our proposed model there would be no need to store and send key for encryption and decryption. Advanced Encryption Standard (AES) is used for encryption for encryption. Information Dispersal algorithm is used to prevent data breaching situations on clouds in financial institutions and provide better confidentiality, availability and integrity of data.
The validity of macromedia flash in the subject classification of living things junior high school in Tanjungpinang Kepulauan Riau (KEPRI)

The objective of this research was investigating students’ voices toward the use ofPadlet at the stage of undergraduate class in Indonesia. Thirty-three undergraduateclass students from a private university in Indonesia were the subject of thisresearch. This research was conducted in the form of a survey research design.The data were collected by employing observation and a close-endedquestionnaire with an adapted Likert scale. The data were then analyzed by usinginterpretive descriptive analysis. The results obtained show that these studentshave very diverse perceptions in the use of Padlet media in learning a subject (thesubject applied in this research was Theory of Learning). Padlet enabled studentsto understand the Theory of Learning (ToL) material; With Padlet, students couldexpress ideas or opinions or comments about Theory of Learning material easily;The learning was more varied and fun; Students could take part in discussionsabout TOL material outside the classroom (outdoor); Students could add picturesor videos to the Padlet wall easily; Students could find out the various opinions ofother students directly; Students could also collect many references related to ToLmaterial. Above all, in operating Padlet, the internet connection and network usedmust be good. If not so, the learning process would not run well.