Reviewerly/exHarmony huggingface dataset:
On the extendability of quaternary linear codes with four weights modulo 16

We consider the extendability of linear codes over the field of order 4 whose codewords have at most four weights modulo 16. For instance, we prove that every [n,k,d]4 code with d≡−3(mod16), k≥3, is extendable if all possible weights of codewords are congruent to 0,−1,−2,−3(mod16). Such a code with d≡−1 or −2(mod16) is extendable as well except for some cases.
Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System (LMS) is an application software that is used in automation, delivery, administration, tracking, and reporting of courses and programs in educational sector.The LMS which exploits machine learning (ML) has the ability of accessing user data and exploit it for improving the learning experience.The recently developed artificial intelligence (AI) and ML models helps to accomplish effective performance monitoring for LMS.Among the different processes involved in ML based LMS, feature selection and classification processes find beneficial.In this motivation, this study introduces Glowwormbased Feature Selection with Machine Learning Enabled Performance Monitoring (GSO-MFWELM) technique for LMS.The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS.The proposed GSO-MFWELM technique involves GSO-based feature selection technique to select the optimal features.Besides, Weighted Extreme Learning Machine (WELM) model is applied for classification process whereas the parameters involved in WELM model are optimally fine-tuned with the help of Mayfly Optimization (MFO) algorithm.The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance.The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects.The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.
An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

In today's digital world, millions of individuals are linked to one another via the Internet and social media.This opens up new avenues for information exchange with others.Sentiment analysis (SA) has gotten a lot of attention during the last decade.We analyse the challenges of Sentiment Analysis (SA) in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which we first produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter.We also choose domain experts to manually annotate Marathi microblogging posts with positive, negative, and neutral polarity.In addition, to show the efficient use of the annotated dataset, an ensemble-based model for sentiment analysis was created.In contrast to others machine learning classifier, we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation (cv), outcomes as 97.77%, f-score is 97.89%.
Triplet Label Based Image Retrieval Using Deep Learning in Large Database

Recent days, Image retrieval has become a tedious process as the image database has grown very larger.The introduction of Machine Learning (ML) and Deep Learning (DL) made this process more comfortable.In these, the pair-wise label similarity is used to find the matching images from the database.But this method lacks of limited propose code and weak execution of misclassified images.In order to get-rid of the above problem, a novel triplet based label that incorporates context-spatial similarity measure is proposed.A Point Attention Based Triplet Network (PABTN) is introduced to study propose code that gives maximum discriminative ability.To improve the performance of ranking, a correlating resolutions for the classification, triplet labels based on findings, a spatialattention mechanism and Region Of Interest (ROI) and small trial information loss containing a new triplet cross-entropy loss are used.From the experimental results, it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank (mRR) and mean Average Precision (mAP) in the CIFAR-10 and NUS-WIPE datasets.
Touching data with PropellerHand

Abstract Immersive analytics often takes place in virtual environments which promise the users immersion. To fulfill this promise, sensory feedback, such as haptics, is an important component, which is however not well supported yet. Existing haptic devices are often expensive, stationary, or occupy the user’s hand, preventing them from grasping objects or using a controller. We propose PropellerHand, an ungrounded hand-mounted haptic device with two rotatable propellers, that allows exerting forces on the hand without obstructing hand use. PropellerHand is able to simulate feedback such as weight and torque by generating thrust up to 11 N in 2-DOF and a torque of 1.87 Nm in 2-DOF. Its design builds on our experience from quantitative and qualitative experiments with different form factors and parts. We evaluated our prototype through a qualitative user study in various VR scenarios that required participants to manipulate virtual objects in different ways, while changing between torques and directional forces. Results show that PropellerHand improves users’ immersion in virtual reality. Additionally, we conducted a second user study in the field of immersive visualization to investigate the potential benefits of PropellerHand there. Graphical abstract
Design of Personalized Push Algorithm of Hot Social News Based on User Interest Model

As a result of rapid advances in information technology, the volume of information on the Internet is expanding at a breakneck rate. The World Wide Web has evolved into a vast and intricate information space. People have shifted from information deficiency to information overload. The characteristics of Internet information are dispersion, disorder, and mass. A challenging research topic is how to quickly, accurately, and efficiently extract vital information from vast information resources. Web search is becoming one of the Internet field’s study centers and focal points. Traditional web search algorithms focus on the link structure of the web and the hierarchical weight of web pages while ignoring the behavior of users, resulting in some search results that are insufficient and inaccurate. In addition, because each web page's hub value and authority value are calculated iteratively, web search is inefficient and susceptible to dispersion and generalization. This study fully integrates the user’s interest behavior and relevant, intelligent optimization algorithms to address the shortcomings of the traditional World Wide Web search algorithm, based on a synthesis and analysis of relevant domestic and international research. A method of user interest model construction and update for news recommendation is proposed to address the problem of user interest model construction and user interest drift in the news recommendation system. Initially, the original user interest model is constructed using a bisection K-means clustering algorithm and a vector space model. Subsequently, the forgetting function is constructed using the Ebbinghaus forgetting curve, and the user interest model is time-weighted to achieve the goal of updating the user interest model. User-based collaborative filtering recommendations and item-based collaborative filtering suggestions serve as the experiment’s baseline. The experimental results suggest that the recommendation performance of the original user interest model is enhanced, with the F value increasing by 4%. The modified model’s F value has increased by 1.3% compared to the previous version.
Research on Human Resource Management Performance Evaluation Method Based on Chaos Optimization Algorithm

All enterprise components and business activities are centered on the customer. Whether human resource management is reasonable and effective not only is a matter of human resource management, but also directly influences whether other resources can be used reasonably and effectively, and determines the efficiency of business operations. In human resource management, perfect performance evaluation criteria are developed based on the actual situation in order to evaluate employee productivity and work ethic. Nonetheless, in the process of actual enterprise HR management performance evaluation work, there are not only imperfect performance evaluation standards, but also relatively objective evaluation standards, and HR cannot conduct a comprehensive analysis of each position, which has a negative impact on the quality of enterprise HR management work. This paper improves the performance evaluation model for the original data mining and proposes a human resource management performance evaluation method based on chaotic optimization algorithm, which generates the initial values of evaluation data through chaotic logistic mapping, bringing the initial data closer to the optimal value, reducing the impact of random initialization on the algorithm’s performance, and when the model is trained, it can make the algorithm’s performance more stable. Capability thereby addresses the flaws in the original evaluation method. Experiments demonstrate that our method significantly improves the precision of model training and prediction.
General Board Geometry

Game boards are described in the Ludii general game system by their underlying graphs, based on tiling, shape and graph operators, with the automatic detection of important properties such as topological relationships between graph elements, directions and radial step sequences. This approach allows most conceivable game boards to be described simply and succinctly.
Multi-robot adversarial patrolling strategies via lattice paths

In full-knowledge multi-robot adversarial patrolling, a group of robots has to detect an adversary who knows the robots' strategy. The adversary can easily take advantage of any deterministic patrolling strategy, which necessitates the employment of a randomised strategy. While the Markov decision process has been the dominant methodology in computing the penetration detection probabilities on polylines, we apply enumerative combinatorics to characterise the penetration detection probabilities for four penetration configurations. It allows us to provide the closed formulae of these probabilities and facilitates characterising optimal random defence strategies. Comparing to iteratively updating the Markov transition matrices, we empirically show that our method reduces the runtime by up to several hours. This allows us extensive simulations on the two dominant robot movement types for patrolling a perimeter showing that a movement with direction is up to 0.4 more likely to detect an adversary. Therefore, our approach greatly benefits the theoretical and empirical analysis of optimal patrolling strategies with extendability to more complicated attacks and other structured environments.
Optimal secure fuzzy based multi keyword search over ciphered e-mail in cloud

Cloud computing become increasingly more famous consistently, as numerous associations tend to outsource their information. With the outbreak of email message leakage, the protection and security of sensitive email data have become clients’ essential concerns. Encrypted email data is a superior method to ensure security, yet it will enormously restrict the searching. To take care of this difficulty keyword-based search over encrypted information is presented. The current search strategies permit the client to search utilizing just the specific keywords. There is no capacity to bear errors and format irregularities. In order to overcome those drawbacks, optimal secured fuzzy-based multi-keyword search over encrypted email data is proposed here. The email sender encrypts the email data before outsourcing the data to a cloud server. For encryption, the proposed method utilizes the optimal secure XOR (OSXOR) encryption algorithm. Here the key value is optimally selected by the mayfly optimization algorithm (MOA). After the encryption, the encrypted email is outsourced to the cloud server. The data owner creates an encrypted searchable index using an input file to enable querying across encrypted emails and then assigns either the index or the gathering of encrypted messages to a cloud server. The receiver receives them from the cloud server and is fed back information, but it is unable to comprehend the signal. The recipient of the encrypted email can decode it and create a search trapdoor in the encrypted email database. For searching, fuzzy-based multi-keyword search is proposed. The effectiveness of the proposed methodology is analyzed in terms of different metrics namely, Memory Usage, Execution time, Encryption and decryption time and search time. The experimental result shows that the proposed method takes a minimum search time is 0.51 s and it achieves maximum searching accuracy of 98%. The implementation is done in JAVA with a Cloud simulator.
Huberized one-class support vector machine with truncated loss function in the primal space

One-class support vector machine (OCSVM) is an important tool in machine learning and has been extensively used for one-class classification problems. The traditional OCSVM solves the primal problem by solving the dual problem, which is a quadratic programming problem. However, the computation of the quadratic programming is cubic and the storage complexity is quadratic with problem scale, so it is inefficient for training large-scale problems. In this paper, we propose to train OCSVM in primal space directly. Unfortunately, owing to the non-differentiability of hinge loss used in OCSVM, the OCSVM cannot be solved by the gradient-based optimization method which is first-order method that converges fast. On the other hand, the hinge loss is unbounded which makes the OCSVM less robust to outliers. The outliers will make the decision boundary severely deviate from the optimal hyperplane. To overcome the drawbacks, a huberized truncated loss function which is a nonconvex differentiable function is proposed to improve the robustness of the OCSVM. The huberized truncated loss function is insensitive to outliers as a substitute for hinge loss in traditional OCSVM. In contrast to traditional OCSVM, the primal objective function of robust OCSVM is differentiable. Considering the non-convexity of the optimization problem, we employ an accelerated proximal gradient algorithm to solve the robust OCSVM in the primal space. The numerical experiments on benchmark datasets and handwritten digit datasets show that the proposed method not only improves the robustness of the OCSVM , but also can reduce the computational complexity.
Disk Space Management Automation with CSI and Kubernetes

Kubernetes is a container orchestration system, which is used in production-ready platform as a service such as OpenShift. To manage and provision storage for application, the container storage interface (CSI) exists in container orchestration systems. The article contains comparison analyze of bare-metal CSI implementations that shows advantages and disadvantages of existing CSI implementations, based on which bare-metal implementation is thought to be a most powerful one, but not free from a gap for improvement. So, there is a need to extend CSI for bare-metal storage provisioning to avoid virtualization and cloud overhead and minimize manual storage management operations. The paper considers bare-metal CSI extension for automation of local disk management as well including ephemeral volume support.
Design and Development of a Mobile Outdoor AR Application for On-Site Visualization of Wind Turbines

This paper presents a mobile outdoor augmented reality application that enables a realistic three-dimensional on-site visualization of planned wind turbines in their surroundings. For this purpose, the requirements for such an application are presented, which were obtained from the discussion with representatives of the relevant target groups. In addition, the essential aspects of the graphical user interface and the technical concept (architecture) as well as the implementation and evaluation of the application are presented.
Effective Preprocessing and Normalization Techniques for COVID-19 Twitter Streams with POS Tagging via Lightweight Hidden Markov Model

The major focus of this research work is to refine the basic preprocessing steps for the unstructured text content and retrieve the potential conceptual features for further enhancement processes such as semantic enrichment and named entity recognition. Although some of the preprocessing techniques such as text tokenization, normalization, and Part-of-Speech (POS) tagging work exceedingly well on formal text, it has not performed well when it is applied into informal text such as tweets and short messages. Hence, we have given the enhanced text normalization techniques to reduce the complexity persist over the twitter streams and eliminate the overfitting issues such as text anomalies and irregular boundaries while fixing the grammar of the text. The hidden Markov model (HMM) has been pervasively used to extract the core lexical features from the Twitter dataset and suitably adapt the external documents to supplement the extraction techniques to complement the tweet context. Using this Markov process, the POS tags are identified as states of the Markov process, and words are the desired results of the model. As this process is very crucial for the next stage of entity extraction and classification, the effective handling of informal text is considered to be important and therefore proposed the most effective hybrid approach to deal with the issues appropriately.
Release conventions of open‐source software: An exploratory study

Abstract Software engineering (SE) methodologies are widely used in both academia and industry to manage the software development life cycle. A number of studies of SE methodologies involve interviewing stakeholders to explore the real‐world practice. Although these interview‐based studies provide us with a user's perspective of an organization's practice, they do not describe the concrete summary of releases in open‐source social coding platforms. In particular, no existing studies investigated how releases are evolved in open‐source coding platforms, which assist release planners to a large extent. This study explores software development patterns followed in open‐source projects to see the overall management's reflection on software release decisions rather than concentrating on a particular methodology. Our experiments on 51 software origins (with 1777k revisions and 12k releases) from the Software Heritage Graph Dataset (SWHGD) and their GitHub project boards (with 23k cards) reveal reasonably active project management with phase simplicity can release software versions more frequently and can follow the small release conventions of Extreme Programming. Additionally, the study also reveals that a combination of development and management activities can be applied to predict the possible number of software releases in a month ( ).
A Priori Determining the Performance of the Customized Naïve Associative Classifier for Business Data Classification Based on Data Complexity Measures

In the supervised classification area, the algorithm selection problem (ASP) refers to determining the a priori performance of a given classifier in some specific problem, as well as the finding of which is the most suitable classifier for some tasks. Recently, this topic has attracted the attention of international research groups because a very promising vein of research has emerged: the application of some measures of data complexity in the pattern classification algorithms. This paper aims to analyze the response of the Customized Naïve Associative Classifier (CNAC) in data taken from the business area when some measures of data complexity are introduced. To perform this analysis, we used classification datasets from real-world related to business, 22 in total; then, we computed the value of nine measures of data complexity to compare the performance of the CNAC against other algorithms of the state of the art. A very important aspect of performing this task is the creation of an artificial dataset for meta-learning purposes, in which we considered the performance of CNAC, and then we trained a decision tree as meta learner. As shown, the CNAC classifier obtained the best results for 10 out of 22 datasets of the experimental study.
Behaviour of True Artificial Peers

Typical current assistance systems often take the form of optimised user interfaces between the user interest and the capabilities of the system. In contrast, a peer-like system should be capable of independent decision-making capabilities, which in turn require an understanding and knowledge of the current situation for performing a sensible decision-making process. We present a method for a system capable of interacting with their user to optimise their information-gathering task, while at the same time ensuring the necessary satisfaction with the system, so that the user may not be discouraged from further interaction. Based on this collected information, the system may then create and employ a specifically adapted rule-set base which is much closer to an intelligent companion than a typical technical user interface. A further aspect is the perception of the system as a trustworthy and understandable partner, allowing an empathetic understanding between the user and the system, leading to a closer integrated smart environment.
Detectable, Traceable, and Manageable Blockchain Technologies BHE: An Attack Scheme against Bitcoin P2P Network

As the most successful cryptocurrency, bitcoin has become the primary target of attackers. The security risks existing in bitcoin network (P2P networks) may pose serious threats to itself. It has been proved that network attackers of the autonomous system level could isolate a specific set of bitcoin nodes using prefix hijacking attacks; since this attack achieves bitcoin partition by deleting all data packets of the victim node, it is easy to be discovered by the victim node, and cannot maintain a long-term connection (the partition will disappear after canceling the BGP hijacking) (Apostolaki M et al. (2017)). This paper proposes a new attack scheme—eclipse attack method based on BGP hijacking (BHE). The attack can occupy the network connection of the victim node, and only need to delete part of the TCP handshaking packets of the victim node during the attack, and it makes the attack more hidden and can occupy the network connection of the victim node for a long time. The innovation of the BHE attack is that it can control the peering decision of the victim node by controlling the victim node’s internal peer database (new table and tried table) and preventing the victim node from establishing a good connection. It enables the attacker to occupy all network connections of the victim node and become its natural network middleman. We verify the feasibility of the BHE attack through experimental evaluation and demonstrate that an attacker who can launch BGP hijacking may occupy all connections of the victim node within 20 minutes (ignoring the time of traffic diversion). To reduce the attack’s impact, the paper provides some countermeasures that can use in practice according to the basic characteristics of the attack.