Reviewerly/exHarmony huggingface dataset:
Legal Culture as A Basic Component of Professional Training in Vocational Education

Introduction: Improving the legal literacy and legal awareness of citizens is an integral part of building a democratic state. One of the ways to this improvement is the development of legal culture as an integrated nationwide program, covering all segments of the population, especially youth and students of all education levels. Methods: In the new conditions of the Russian Federation’s state system, the foreground task is to form a general legal culture, as well as to develop practical skills of students in vocational education institutions. Legal culture is a universal, basic component of professional training in any field. Results: Legal education and training are intended to solve the problem of forming a legal culture of students. The content of legal culture should not only be reflected in the Law course and other educational subjects comprising elements of legislation studies, but it should also develop opinions and attitudes of students to the law implementation in general. In the process of legal education in vocational education institutions, consideration of age and individual characteristics requires not just passive adaptation of the material and methods to the characteristics of students, but their active development in accordance with the general goal of forming legal awareness. Discussion: To be involved in efficient legal education, students must know the requirements of civil society for every individual and their own capacities, see their negative and positive traits, objectively evaluate them, avoid underestimating or overestimating their abilities and capacities, level of aspirations. Conclusion: The development of vocational education in modern conditions provides opportunities for increasing the importance and relevance of legal culture. The high level of legal culture has a positive influence on the development of personality and professional activities of graduates and on the harmonization of interrelations in society.
User Experience of Academic Library Websites

Despite the indispensable technological shifts, the success of a website is anchored on effectiveness, perception, valuableness, satisfaction, reliability, and efficiency dimensions. In this research, heuristic evaluation enabled to assess the primary content elements on academic library websites homepages. The heuristic method's findings were compared to taskbased usability tests. Usability heuristics results correlate to task-based usability testing findings by supplementing measurement of users' expectations in multifaceted magnitudes. Cultural and one's national origin determines students' assessment and usability of universities' libraries' websites to a great deal. Universities can benefit from optimized operational performance by upholding underlying principles of heuristic model to achieve exceptional outcomes. This study derives ultimate satisfactory consciousness that heuristic evaluation's principles guide to design an academic library's website that complements efficiency and satisfaction.
Local patterns for offline Arabic handwritten recognition

Offline recognition of Arabic handwritten text is a challenging problem due to the cursive nature of the language and high inters and intra variability in the way of writing. Majority of the existing approaches are based on structural and statistical features and are constrained for a specific task with vast amount of pre-processing steps. In this paper, we explore the performance of local features for unconstrained offline Arabic text recognition with no prior assumptions or pre-processing steps. Our approach is based on local SIFT features. To capture important information and remove any redundancy, we apply a fisher encoding algorithm, and a dimensionality reduction approach, principle component analysis (PCA). The resulted features are combined with a contemporary support vector machine (SVM) classifier and tested on a dataset of 12 different classes. There has been great improvements in recall and precision values in comparison with that of SIFT features alone or with that of SIFT features and other encoding algorithms, with more that 35% improvements when tested with 5-fold cross-validation test.
New Results on the Chebyshev Polynomial Bounds for Classes of Univalent Functions

A. In the present work, by considering a general subclass of univalent functions and using the Chebyshev polynomials, the author determine coefficient bounds for functions in this class.In particular, results, which are presented in this paper, are higher coefficient estimates on the coefficient |a 4 | and |a 5 | for functions in the function class.Furthermore, we provide the non-linear functional |a 2 a 4 -a 2 3 | for the class.2010
Alpha Modeling of Nakayasu Synthetic Unit Hydrograph for Part of Watersheds in Indonesia

The lacks of hydrograph data in the field has become the drawback of the hydraulic structure planning. However, such a conditional deficiency in particular, has urgently placed the Synthetic Unit Hydrograph (SUH) models to be very great utility. The Synthetic Unit Hydrograph (SUH) is a popular model that is used in many water resources designs especially in design flood analysis in ungagged watershed. One of the SUH that is usually used in Indonesia is Nakayasu SUH. This model is depended on the α parameter that is influencing the unit hydrograph ordinate and time base. This research intends to build a model of α parameter that is as the characteristic factor of part of watersheds in Indonesia. The methodology consists of observed unit hydrograph analysis for obtaining the α parameter in each watershed, to collect the characteristic data in each watershed, and then to formulate the α parameter model that is as the function of watershed characteristic. The result is formulation model of α that can be used to analyze the design flood in the watershed in Indonesia
Ensem_SLDR: Classification of Cybercrime using Ensemble Learning Technique

With the advancement of technology, cybercrimes are surging at an alarming rate as miscreants pour into the world's modern reliance on the virtual platform. Due to the accumulation of an enormous quantity of cybercrime data, there is huge potential to analyze and segregate the data with the help of Machine Learning. The focus of this research is to construct a model, Ensem_SLDR which can predict the relevant sections of IT Act 2000 from the compliant text/subjects with the aid of Natural Language Processing, Machine Learning, and Ensemble Learning methods. The objective of this paper is to implement a robust technique to categorize cybercrime into two sections, 66 and 67 of IT Act 2000 with high precision using ensemble learning technique. In the proposed methodology, Bag of Words approach is applied for performing feature engineering where these features are given as input to the hybrid model Ensem_SLDR. The proposed model is implemented with the help of model stacking, comprising Support Vector Machine (SVM), Logistic Regression, Decision Tree, and Random Forest and gave better performance by having 96.55 % accuracy, which is higher and reliable than the past models implemented using a single learning algorithm and some of the existing hybrid models. Ensemble learning techniques enhance model performance and robustness. This research is beneficial for cyber-crime cells in India, which have a repository of detailed information on cybercrime including complaints and investigations. Hence, there is a need for model and automation systems empowered by artificial intelligence technologies for the analysis of cybercrime and their classification of its sections.
Service Profile based Ontological System for Selection and Ranking of Business Process Web Services

Service Oriented Architecture is the backbone of today's internet-based services.Instead of using in-house developed software's, business is intended for using web-based software system.This leads many software developers in the market to develop many web services offering different kind of services meeting customer requirements.This makes web services popular among business providers.The difficulty involves in selecting business process web service is many web services are available on the internet for the same kind of business requirement.To select the best business process web service from those services tedious task.To resolve this issue, this paper proposes an ontological system, which populates the knowledge about web services in its knowledgebase.Along with the knowledge about web service, their IOPE parameters and Quality of Service values are also taken into account for ranking the appropriate web services recommended from the knowledgebase.The performance evaluation of the proposed system depicts that the system performs consistently for complex business process requirements.
An Empirical Analysis to Identify the Effect of Indexing on Influence Detection using Graph Databases

The data generated on social media platforms such as Twitter, Facebook, LinkedIn etc. are highly connected. Such data can be efficiently stored and analyzed using graph databases due to the inherent property of graphs to model connected data. To reduce the time complexity of data retrieval from huge graph databases, various indexing techniques are used. This paper presents an extensive empirical analysis on popular graph databases i.e. Neo4j, ArangoDB and OrientDB; with an aim to measure the competencies and effectiveness of primitive indexing techniques on query response time to identify the influencing entities from Twitter data. The analysis demonstrates that Neo4j performs efficient and stable for load, relation and property queries compare to other two databases whereas the performance of OrientDB can be improved using primitive indexing.
A Hybrid-CPU-FPGA-based Solution to the Recovery of Sha256crypt-hashed Passwords

This paper presents an accelerator design for the password recovery of sha256crypt based on hybrid CPU-FPGA devices. By applying the brute-force attack computation model proposed in this paper, we decompose the sha256crypt function into two types of operations, namely the data dispatching and the block transforming. The data dispatching operation generates message blocks and the block transforming operation transforms message blocks into digests. These two operations are efficiently accelerated by the customized data dispatch unit and the pipelined block transform unit, respectively. Difficulties of adopting the pipeline technique are addressed also with the following techniques. The group scheduling is used to solve the data dependency that stalls the pipeline. The look-ahead execution eliminates the uncertainty of the execution path. The data path pruning and spatial-temporal multiplexing reduce the resource overhead of non-computing units.The proposed accelerator design is implemented and evaluated on the Xilinx Zynq-7000 XC7Z030-3 SoC. Our experimental results show that the proposed accelerator can improve energy efficiency by 2.54x over the state-of-the-art password recovery tool Hashcat running on an NVIDIA GTX1080Ti GPU. Compared with the pure FPGA-based implementation in John-the-Ripper, the proposed accelerator improves energy efficiency by 1.64x and improves resource efficiency by 1.69x.
Budget-aware task scheduling technique for efficient management of cloud resources

Cloud computing technology offers many services using pay as per use concept where the user gets to specify constraints such as the budget. Task scheduling algorithms are therefore the most preferred option under a budget constraint which is used to improve some of the metrics like makespan and cost. In this paper, we propose a budget-aware scheduling (BAS) model to schedule the tasks based on the budget constraint. At first, the VMs which meet the budget are labelled and the task priority is determined. Next, the task attributes are checked and assigned to the resources which meet the budget constraint to keep makespan as low as possible with minimal cost for resource usage. The experiments demonstrate that the proposed model outperforms other algorithms by reducing average of makespan, mean of average response time, and the cost of resources with an increase in resource utilisation and profit of provider.
Classification of Malignant Melanoma and Benign Lung Cancer by using Deep Learning Based Neural Network

Humanoid Tumor is one of the utmost hazardous syndromes which is mostly affected by heritable uncertainty of manifold molecular modifications. Midst numerous methods of humanoid tumor, Lung cancer is the utmost communal one. To classify Lung cancer at an initial phase and examine them over several procedures entitled as segmentation and feature extraction. Here, in this scheme is suggested to emphasis extraordinary attentiveness of Melanoma Heir which bases the Lung Cancer. This development is based on samples replica skill is used for malignant melanoma Lung tumor recognition. In this scheme dissimilar stage for melanoma Lung cancer lesion classification i.e., first the Image Gaining Method, preprocessing, separation, define piece for Lung cancer Feature Collection regulates lesion description, classification methods. In the Feature abstraction by numerical image treating method includes, regularity detection, Border Detection, color, and width discovery and also we used GLCM for excerpt the surface based features. Here we planned the Neural Network to categorize the benign or malignant stage.
Spruce-A Intelligent Surveillance Device for Monitoring of Dustbins using Image Processing and Raspberry PI

In the current scenario, most recurrently we find that the Dust bins kept at various places mainly at public places in the cities are swarming because of elevation in garbage every day. Mainly it creates filthy and unhealthy conditions for humanity which leads to spreading of lethal diseases. In addition to that, anyone can hide eruptive material under this sort of mess and that can lead to loss of lives. To avoid these wretched situations a Waste Monitoring System using Internet of Things which is named as SPRUCE is proposed. This system is not designed for single dustbin rather multiple dustbins that are kept at different location in cities or campus; these garbage bins are designed with cheap device that helps in monitoring the level of trash inside the trash bins. In addition to that, SPRUCE can click the image and send it on server. This feature can help the monitoring authorities to identify as well locate them for further action. Moreover, these clicked images can help to identify the updated status of bins even by sitting at a dedicated place only and quickly a corrective action can be made to clean the dustbins.
A Framework for Implementing Machine Learning algorithms using Data sets

The rapid development of cloud computing, big data, machine learning and datamining made information technology and human society to enter new era of technology. Statistical and mathematical analysis on data given a new way of research on prediction and estimation using samples and data sets. Data mining is a mechanism that explores and analyzes many dis-organized or dis-ordered data to obtain potentially useful information and model it based on different algorithms. Machine learning is an iterative process rather than a linear process that requires each step to be revisited as more is learned about the problem. We discussed different machine learning algorithms that can manipulate data and analyses datasets based on best cases for accurate results. Design and Implementation of a framework that is associated with different machine learning algorithms. This paper expounds the definition, model, development stage, classification and commercial application of machine learning, and emphasizes the role of machine learning in data mining by deploying the framework. Therefore, this paper summarizes and analyzes machine learning technology, and discusses the use of machine learning algorithms in data mining. Finally, the mathematical analysis along with results and graphical analysis is given.
Indian Sign Language Classification (ISL) using Machine Learning

The significance of behavior-based robots and the indoor navigation system has increased significantly over the past few years in its monitoring and wayfinding, taking into account the existence of robot behavior in various fields of social interest.The basic strategy of the overall behavior tasks in a behavior-based system are divided into smaller independent behaviors that focus on the performance of specific tasks such as the behavior of a robot for indoor environments.One behavior will concentrate on the wall, while the other focuses on avoiding obstacles, etc. Indoor navigation is the main concern since almost a lot of individuals spend more time in the indoor climate.In different fields such as hospitals, transportation, marketing, and military purposes, it becomes an essential thought.The main goal of this paper to give a survey of distance estimation in IndoorGML, behaviors system, and the questions of their robotic implementation possibilities.In this essay, we define robot activities and two basic approaches to distance estimation, such as combinatorial planning and sampling-based planning.These two planning strategies are the principles of motion planning.Combinatorial planning is used for finding the path over the continuous configuration space without restoring it to approximations.The most used concept in planning is sampling-based planning, it provides a successful solution in wayfinding path planning, and because of this, it is performed in different robotics fields.Therefore, for distance estimation in IndoorGML maps, we apply combinatorial and sampling-based approaches.
Recommendation System: Techniques and Issues

In daily life user searched the many things over the internet on the basis of requirement with the help of search engines. Recommendation systems are widely used on the internet to help the user in discover the products or services that are best with their individual interest. RS effectively reduce the information overload by providing personalized suggestions to user when searching for items like movies, songs, or books etc. The main aim of RS is to help the users by providing the surface of information that relevant to them, fulfill their needs and their task. The paper provides an overview of RS and analyze the different approaches used for develop RS that include collaborative filtering, content-based filtering and hybrid approach of recommender system.
Secure Medical Image Processing Using Chaos And Dna Encryption Enhanced Using Reversible Data Hiding

Paper In the field of medication, information security is central. The photographs that are dealt with in the information base and those that are transmitted over the web ought to be shielded from threatening exercises. This paper proposes a novel picture encryption plan helped with reversible information covering framework to improve the nature of the shielded picture transmission. Confusion and DNA encoding norms are utilized for the encryption philosophy. Piecewise Linear Chaotic Map (PWLCM) and Logistic Map are related with produce all parameters the demonstrated calculation needs and DNA encoding headway fills in as an accomplice instrument. The information disguising and extraction process in the blended area submits mistake during the unraveling framework. This is in light of the way that the pixel estimations of the blended spread picture change during the information concealing technique. Thusly the usage of reversible information covering structure rather than the standard information masking methods helps in recuperation of the spread picture, acknowledging immaculate unscrambling. The test results display that the proposed procedure beats the current secure picture transmission approachs.
A study of correlation for reverse logistics barriers to solutions using structural equation modelling

Reverse logistics (RL) plays an important role in many industries especially in the electronics industry and helps sustainability businesses while also reducing environmental impacts. The electronics industry is one of the most rapidly growing industries in Thailand. Many companies adopted RL for their business process but still enforce some barriers that make it unsuccessful. Therefore, this study aims to investigate the relationship of RL barriers and solutions then examines the relationship between these factors. A conceptual model was developed by proposing four hypotheses in which structural equation modelling (SEM) was used to examine the proposed model. The main results of this study show that there are statistics significant of the proposed hypothesis. In conclusion, the proposed model could fit with the empirical data. The proposed results could reconfirm that there are statistics significant of the relationship between RL barriers and solutions of RL practices implementation in Thailand electronics industry.
Browser simulation-based crawler for online social network profile extraction

The rapid proliferation and extensive use of online social networks (OSNs) like Facebook, Twitter, Instagram, etc., has attracted the attention of academia and industry, since these networks store massive information in them. But, acquiring data from these OSNs, which is a prerequisite for conducting any research on them, is a daunting task, which can be because of privacy concerns on one hand and complexity of underlying technologies of these complex networks, on the other. This paper presents the design and implementation of a crawler based on browser simulation for extraction of Facebook users profile data while preserving the privacy. The breadth-first-search (BFS) algorithm approach was also adopted for sampling of around 0.235 million Facebook users. Though the main purpose of this work is the design of a crawler still, the results have been briefly presented in terms of various social network metrics and analysed from different aspects of privacy.
Gaze Touch Cross PIN: Secure Multimodal Authentication using Gaze and Touch PIN

The basic goal of information security is, to protect the privacy, reliability, and availability of information on devices that manipulate and store the information. To protect this information, the fundamental step is user authentication. The most common method for authentication on devices is the personal identification number (PIN) method, which is vulnerable to shoulder surfing attack. Shoulder surfing attack used by attacker especially in the crowded public places. For shoulder surfing attack prevention several methods had been proposed. This paper proposed a GazeTouchCrossPIN authentication method that overcome the limitations found in the earlier work. we propose a multimodal authentication system that combines between the gaze gesture and touch PIN authentication systems. The results illustrate that the proposed GazeTouchCrossPIN method is more secure hence it decreases the shoulder surfing rate in both side attacks and iterative attacks.
FAIR enough?

The Tony Davies Column offers a challenge to us all with another contribution on FAIR data, which should be Findable, Available, Interoperable and Readable. It is clearly the way we should all be going, everybody from manufacturers and software developers, through researchers to publishers needs to work together.