Reviewerly/exHarmony huggingface dataset:
Spam Detection Using Machine Learning

Emails are essential in present century communication however spam emails have contributed negatively to the success of such communication. Studies have been conducted to classify messages in an effort to distinguish between ham and spam email by building an efficient and sensitive classification model with high accuracy and low false positive rate. Regular rule-based classifiers have been overwhelmed and less effective by the geometric growth in spam messages, hence the need to develop a more reliable and robust model. Classification methods employed includes SVM (support vector machine), Bayesian, Naive Bayes, Bayesian with Adaboost, Naive Bayes with Adaboost. However, for this project, the Bayesian was employed using Python programming language to develop a classification model. Keywords: machine learning (ML), machine learning classifier, Naive Bayes, SVM, Adaboost, spam classification, ham. DOI: 10.7176/CEIS/11-3-04 Publication date: May 31 st 2020
FORMATION OF CULTURAL VALUES OF YOUTH

The value system for the modern young man is a certain foundation in building his tactics of attitude to the world. The content of the article analyzes the category of values of youth from various levels, as their social need for self-development
Normal move out with linear variation of velocity with respect to depth

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Attention-based Deep Reinforcement Learning Model for Pair-Wise Interaction Recommendation

Traditional recommendation algorithms generate a recommendation list for the user by rating prediction or ranking based on their historical record. However, most of these methods still faced with two problems: (1) only considering users’ positive feedback; (2) seeking to maximize the immediate recommendation effect. To address the above issues, we propose a novel deep reinforcement learning based interactive recommendation model, named DDQN-AM. Specifically, we first use long short-term memory network to capture positive and negative interest signals from user’s historical behavior as Deep Double Q-Network(DDQN)’s input states. Secondly, we also propose an attentional mechanism(AM) to learn the effects of contextual information for weight calculation. Extensive experiments on real data show that the proposed DDQN-AM model outperforms the state-of-the-art algorithms, and verify the importance of attention mechanism.
LibGuides: LPS Daily Learning Challenges: Rocks and Minerals

Fun, optional activities to help parents keep their kids reading, writing and creating while out of school.
Trolls at the polls

This article considers how politically motivated Internet trolling, within the context of the Malaysia from May 2018 until February 2020, made use of affordances of algorithms and platforms to achieve their goals, from targeted attacks on individuals to collective interventions for advancing social and informational justice. Centering on the importance of digital platforms and algorithms in framing and shaping online communication, this article explores the decisions, actions, and policies which, framed and shaped by these algorithms, produced a particular space in Malaysian political discourse that enables Internet-based political trolls. Attention is given to the infrastructure of trolling, as well as the platforms supporting and cultivating the practice of trolling that are usually international in their ownership, development, and user base. By focusing on the trollish practices of a “minor” non-Western community in Asia, we attempt to theorize the effects of digital infrastructure at the periphery of multinational platforms based on participant-observation research and media-textual analysis.
A Study on Recent Laws in Contesting Cyber Crimes in India

In this modern era everyone is moving in direction the field ofdigitization and modernization which is helpful in many fields like ecommerce and many more. But it is also creating offenses which arereferred as cybercrimes. There are several ways to stop cybercrimesand to do that there should be consciousness on the various lawsrelating to cybercrimes. The some of the acts like IT ACT 2000,National Cyber Security Policy etc. are framed as a measure toprotect from these crimes. Though the tenure cybercrime didn’thave any origin in law but there are numerous laws which avertcyber pornography and various other cybercrimes. This paper mainlyfocuses on various laws which are helpful in preventing respectivecybercrimes in India and several aspects where enforcement withcyber laws lacks
Three-dimensional reconstruction filtering algorithm based on single ray picture of pipe welds

Abstract With constraints in single image, such as collinear equations and coplanarity, a real-scale object model can be obtained, and real scenes of regular objects can be reconstructed, by using three-dimensional reconstruction algorithms such as grid processing and interpolation fitting. A 3D reconstruction algorithm based on single pipe weld seam ray picture is proposed. Firstly, recognizing and segmenting the weld image, and using non-linear filtering to enhance the gray level of the image. Secondly, identifying the inner and outer edge positions of the weld, and performing threshold segmentation, morphological filtering, connected area detection, edge detection, line detection, image rotation, and inner wall area extraction. Thirdly, based on the image of the elliptical opening of the weld seam, the inverse perspective transformation is used to restore the formation of the weld seam root and the weld 3D scatter points. Finally, 3D point cloud is meshed, the function of point-to-surface is achieved, and a three-dimensional model of the welding tumor is generated. The experimental results demonstrate that the algorithm can accurately and effectively perform 3D reconstruction of the weld seam based on a single 2D pipe radiograph of the weld seam, and realize accurate identification of the weld nodules at the root of the pipe through-flow weld.
Inflection Points in Technology Law

In this introduction to volume 16 of the Ohio State Technology Law Journal, we consider the current state of cyberlaw at the quarter-century mark, and its interactions with other areas of the law.This issue opens with two distinguished lectures by Mary Anne Franks and James Grimmelmann, who offer their thoughts on the current and future challenges in the law of cyberspace. The latter part of this issue consists of nine essays authored by participants in the Journal’s annual symposium, titled “Artificial Intelligence and the Future of Tax Law and Policy.” This issue is the first published symposium in the U.S. to ask how automation and artificial intelligence might disrupt tax law.We believe that these essays demonstrate that cyberlaw has moved, and is likely to continue to move, from the outskirts to the center of the law. The gravitational weight of discourse has shifted from whether cyberlaw can shed peripheral insight on the law, to whether the law as a whole is properly focused on the most salient aspects of a society that now meets, works, and resides substantially online.
IT Security Challenges During COVID-19 | Blog | I.S. Partners

The risk of a cyberattack is increasing as a result of the coronavirus disease 2019 (COVID-19) pandemic. Many of these attacks are targeting the IT ...
Video search for ambiguous requests

The spread of broadband networks has resulted in the spread of countless videos on the internet. Advances in video analysis technology make it possible to extract more exact metadata and make it easier to find the video we want. However, search services are not suitable for ambiguous affective video searches in which a specific query cannot be given, such as whether you want to watch while relaxing. To solve this problem, this paper considers video search method that can handle such ambiguous requests by utilizing existing search services and propose a method of replacing ambiguous requests by queries that are a group of multiple metadata of something concrete such as names and situations of objects. This paper performs classification experiments with three ambiguous requests using multiple metadata automatically attached to the videos by Google Cloud Video Intelligence and so on, and confirms that automatic classification by machine learning performs close to manual classification. This suggests that the proposed method is feasible.
Requirements Engineering that Balances Agility of Teams and System-level Information Needs at Scale

Context: Motivated by their success in software development, large-scale systems development companies are increasingly adopting agile methods and their practices. Such companies need to accommodate different development cycles of hardware and software and are usually subject to regulation and safety concerns. Also, for such companies, requirements engineering is an essential activity that involves upfront and detailed analysis which can be at odds with agile development methods. Objective: The overall aim of this thesis is to investigate the challenges and solution candidates of performing effective requirements engineering in an agile environment, based on empirical evidence. Illustrated with studies on safety and system-level information needs, we explore RE challenges and solutions in large-scale agile development, both in general and from the teams’ perspectives. Method: To meet our aim, we performed a secondary study and a series of empirical studies based on case studies. We collected qualitative data using interviews, focus groups and workshops to derive challenges and potential solutions from industry. Findings: Our findings show that there are numerous challenges of conducting requirements engineering in agile development especially where systems development is concerned. The challenges discovered sprout from an integration problem of working with agile methods while relying on established plan-driven processes for the overall system. We highlight the communication challenge of crossing the boundary of agile methods and system-level (or plan-driven) development, which also proves the coexistence of both methods. Conclusions: Our results highlight the painful areas of requirements engineering in agile development and propose solutions that can be explored further. This thesis contributes to future research, by establishing a holistic map of challenges and candidate solutions that can be further developed to make RE more efficient within agile environments.
hubungan penggunaan game online dengan hasil belajar kognitif biologi kelas XI MIPA di SMA N 5 Tanjungpinang

This study aimed to determine the correlation between use of online games and students’ biology learning outcomes at class XI IPA of SMAN 5 Tanjungpinang. The method used in this research is quantitative method by using quantitative correlation approach. The population in this study is the eleventh grade students of SMAN 5 Tanjungpinang and the sample is students of class XI IPA consisting of 49 students.Data analysis techniques used in this study is product moment correlation test. The result of product moment correlation test is obtained significance value of 0.034<0.05, which means there is a significant relationship between the use of online games with student biology learning outcomes at class XI IPA of SMAN 5 Tanjungpinang. With very hight categories and positif correlation directions.
Detecting image frames which contain a moving object from a severely distorted video stream using dynamic mode decomposition

There are few moving object detection techniques dealing with severely distorted video imagery, such as one taken from above the wavy water surface. In this paper, a method that identifies image frames containing a moving object from a video taken from above the wavy water surface is proposed. Considering the difficulty to apply common video processing techniques to such a video suffering from the severe distortion, the proposed method utilizes dynamic mode decomposition, a data-driven method for analysis of dynamical systems, to develop an algorithm that extracts information of a moving object from a video stream. The experimental evaluation shows that the proposed method is able to identify image frames containing a moving object from a severely distorted video stream.
Productivity and quality enrichment through multi criteria trajectory optimization of an industrial robot

To foster the fourth industrial revolution, a lot of efforts are being created to maximising the production by optimising the present manufacturing processes. One in every of such try is done to optimise activities of robot manipulators in industries. In this work, two new variants of intelligent algorithms, specifically, canonical particle swarm optimisation with mutation operator (CMPSO) and self accommodative differential evolution (SADE) are proposed to optimise the trajectory of a robot manipulator (MTAB ARISTO 6XT) to reduce production cost. Optimal trajectory is generated from the information of internal structure of robots by considering robot mechanics and dynamics. A multi criteria cost function represents the production price. The algorithms produce cost effective and collision-free trajectories by considering knot points on the trajectory. A productivity and economic study was carried out. The economic benefits yielded by CMPSO and SADE are good. The results proved the goodness of the proposed algorithms.
ProPoS: A Probabilistic Proof-of-Stake Protocol.

We present ProPoS, a Proof-of-Stake protocol dedicated, but not limited, to cryptocurrencies. ProPoS is a chain-based protocol that minimizes interactions between nodes through lightweight committee voting, resulting in a more simple, robust, and scalable proposal than competing systems. It also mitigates other drawbacks of previous systems, such as high reward variance and long confirmation times. ProPoS can support large node numbers by design, and provides probabilistic safety guarantees whereby a client makes commit decisions by calculating the probability that a transaction is reverted based on its blockchain view. We present a thorough analysis of ProPoS and report on its implementation and evaluation. Furthermore, our new technique of proving safety can be applied more broadly to other Proof-of-Stake protocols.
Digitally empowering students through authentic leadership

Universities have a responsibility to develop students who can engage in digital technologies and are capable of being successful in digital work environments. Innovative organisations integrate digital approaches within their organisational strategy, seeking work ready graduates who can enhance digital services. Yet, many graduates are not digitally fluent, which has contributed to nearly half (44 percent) of companies being unprepared for the digital delivery of services (Kane et al., 2016).The role of the educator is to address the learning needs of the student and create an environment where students can develop the capability to navigate their future personal and professional lives. Recent theoretical work has sought to understand the role of authentic leader behaviours in managers to develop digital empowerment in their employees (Crawford & Butler-Henderson, 2019). Authentic leader behaviours comprise the synthesis of awareness, sincerity, balanced processing, positive moral perspectives, and informal influence (Crawford et al., 2019). These can facilitate the development of four dynamic digital fluency attributes in employees: awareness, creativity, agility, and learning orientation.In this presentation, we discuss the transferability of the work on developing digital empowerment from manager to employee into the higher education sector by exploring how teacher behaviours can influence digital empowerment in their students. For this, we begin by explaining how educators are often considered leaders (Muijs & Harris, 2003), and how their behaviours can influence student development. We will conclude by presenting a model of digital fluency in students, informed by an educator’s authentic leader behaviour development.
Automatic Policy Synthesis to Improve the Safety of Nonlinear Dynamical Systems

Learning controllers merely based on a performance metric has been proven effective in many physical and non-physical tasks in both control theory and reinforcement learning. However, in practice, the controller must guarantee some notion of safety to ensure that it does not harm either the agent or the environment. Stability is a crucial notion of safety, whose violation can certainly cause unsafe behaviors. Lyapunov functions are effective tools to assess stability in nonlinear dynamical systems. In this paper, we combine an improving Lyapunov function with automatic controller synthesis to obtain control policies with large safe regions. We propose a two-player collaborative algorithm that alternates between estimating a Lyapunov function and deriving a controller that gradually enlarges the stability region of the closed-loop system. We provide theoretical results on the class of systems that can be treated with the proposed algorithm and empirically evaluate the effectiveness of our method using an exemplary dynamical system.
Extraction of distinctive keywords and articles from untranscribed historical newspaper images

This paper proposes a novel approach to extract distinctive keywords from historical newspaper images without using character recognition. We converted an image of the text block on an entire newspaper page into a sequence of codes based on discretization of the feature vectors, an approach that eliminated the errors introduced by optical character recognition (OCR). This conversion makes it possible to analyze untranscribed newspaper images by using text-processing methods. We examined the daily occurrence of every tri-gram string, and extracted strings with a dense appearance as distinctive keywords. In addition, we highlighted articles that contain distinctive keywords as distinctive articles. The proposed method was evaluated on an archive of Japanese newspaper images published in the 19th century, and the results were promising.
The Present Condition in University Writing Education and Method of Writing by Utilizing Big Data

This discussion focuses on the use of big data to humanities writing. As the ICT industry developed, leading learner-centered universities showed a rapid change in writing, but they were still aware of the fact that they were not transcending the transition period. College writing is an important aspect of continuing support from the school authorities, maintaining teachers` curriculum, applying textbooks and pedagogy development practices, learner`s will and enthusiasm, goals and learning attitudes, sustainable interest in society and businesses. They are currently being in use because we have been staying and this is because we have not been able to find a clear sustainable point and have been failing in terms of research and discussion. Writing in the humanities using big data starts from the possibility of being able to play a role of anticipating the school-teacher- learner-society(enterprise). In order to write with big data in such a dimension, it should be preceded, firstly by a clear understanding of the instructor’s big data and accumulation of the knowledge base. Secondly, it deals with mastering big data analysis technique of the learner, and thirdly it handles the reorganization and organization of the textbook. If this is possible, the steps by which big data intervenes in humanistic writing would be planning, data collection and analysis. In the planning stage, first, it should be organized in a premise that meets dexterity level, rhetorical level, discourse level, learner`s data collecting and analyzing ability, vocabulary and expressive ability, implication as preparation requirement for composition ability and writing ability. Second, it deals with the reflection of accurate conclusions that can be predicted, and thirdly it handles the consideration of human identity issues. In the data collection and analysis stage, the value of big data depends on `interpretation`. Therefore, it is possible to accurately predict the arguments that have been difficult to infer or search by using unstructured data analysis such as text mining. However, since the use of big data is directly linked to the survival of human beings, it must be focused on reflection. And this should be clearly stated in the writing education of humanities learners. This is because the leading role of the humanities is important in the future convergence and integration problems related to the big data.
Assessing the Experience of Development of Personality’s Creative Potential in the Industry of Educational Services

It should be noted that the labor market has special requirements for specialty qualification of graduates of educational institutions with regard to the dynamic changes in various industries. Today, employees should not only have good knowledge, but be able to quickly and competently put them in practice. Accordingly, the current education system should train employees under the competency-based approach. In modern dynamic society, there is an arising for specialists who have creative, global thinking and broad knowledge and who can adapt to changing conditions. But in Russian society claiming to be among developed countries and seeking to achieve the international level, the future staff is trained with disregard to these factors. In our country, it is common to always look up to the West and adopt their current management and quality assessment system without taking into account the peculiarities of the Russian mentality and previous experience. In modern socio-cultural conditions of headlong development, domestic education falls under tough requirements to the level of teaching and educational process to be arranged and the outcome of student’s graduation from the educational institution. The article points out that the new post-industrial era needs qualitatively new people who are creative and responsibly working for the result.
Study on anti-scanning performance of WFRFT modulation signal

Abstract Based on the signal characteristics of the existing weighted fractional fourier transform (WFRFT) communication, this paper studies the equivalent signal to noise ratio (SNR) and bit error rate (BER) of the non-target receiver under the condition of the transformation parameter errors, with the purpose of measuring the anti-parameter scanning characteristics of the single-parameter WFRFT signal. The constellation splitting characteristics of multi-parameter WFRFT signal are further studied.Multi-parameter WFRFT modulation can not only improve the resistance of single-parameter system to parameter scanning detection, but also effectively hide the original modulation mode due to its characteristic of splitting modulation constellation.