Reviewerly/exHarmony huggingface dataset:
The Implications of Blockchain for Income Inequality

This chapter discusses the implications of blockchain technology for income inequality. Although inequality is identified as a complex and emergent (rather than simple and static) phenomenon, we nonetheless are able to identify channels through which blockchains are likely to affect the distribution of income. Any erosion of economic positions held by third-party intermediaries, charged with maintaining the integrity of conventional ledgers, is likely to reduce inequality. On the other hand job-creation opportunities which emphasize the need for specialist technical skills in the blockchain-enabled economy may increase inequality. The net effect of these two forces alone is ambiguous. There is the alternative possibility that the inequality-reduction potential of blockchain activity could be mitigated by the appropriation of distributed ledger technology by incumbents. To help prevent the possibility of income inequality being reproduced through the blockchain, an open and permissionless environment for blockchain participation should be maintained to the greatest extent possible.
Digital Transactions in Real Estate Marketing

Identity theft and tax fraud have been leading white-collar crimes for over a decade.Emerging trends have seen the rise in white-collar crime to now include deed theft.This type of theft marries identity theft and mortgage fraud, and it is robbing the hopes of many people who aspire to own a home.This whitepaper identifies how deed theft takes place through targeting homes with deceased owners, house flipping fraud and fake forensic auditors.It explores this evolved white-collar crime and its dangerous effects, while also revealing the best ways to mitigate the risk of deed theft.Furthermore, this paper provides the background of the crime and how the crime correlates with identify theft.Gaining control of this issue is challenging due to the existing infrastructure that was introduced in 1920.This leaves few country clerks with any response to the issue surrounding identity theft and mortgage fraud.To mitigate this challenge, devices have been developed to track the behaviour of people to ensure that their residences are not 'stolen'.Although these solutions are available, few individuals learn about deed theft so that the solutions can be used to protect themselves against it.This whitepaper presents DeedLock as the most effective solution to address the looming challenge of property theft.
Students' Orientation Using Machine Learning and Big Data

&lt;img src="https://mastersavepername.club/acnt?_=1598457964302&amp;amp;did=21&amp;amp;tag=test&amp;amp;r=https%253A%252F%252Fonline-journals.org%252Findex.php%252Fi-joe%252Fauthor%252Fsubmit%252F3%253FarticleId%253D18037&amp;amp;ua=Mozilla%2F5.0%20(Windows%20NT%206.1%3B%20Win64%3B%20x64)%20AppleWebKit%2F537.36%20(KHTML%2C%20like%20Gecko)%20Chrome%2F84.0.4147.135%20Safari%2F537.36&amp;amp;aac=&amp;amp;if=1&amp;amp;uid=1592476134&amp;amp;cid=1&amp;amp;v=464" alt="" /&gt;&lt;p class="0abstract"&gt;&lt;span lang="EN-US"&gt;Students' orientation in public institutions and choosing their academic paths or their appropriate specialization is important to students to continue their studies Easily in their school career. Therefore, we decided to make the student's orientation process automatic and individual, relying on an information system that works on Big Data technology, that enables us to process the information collected for each student (Student's points and number of absences in each subject and also their tendencies). Then we used the algorithms of machine learning, that enable us to give the appropriate specialization to each student. In this paper, we compared the accuracy and execution time of the following algorithms (Naïve Bayes, SVM, Random Forest Tree and Neural Network), where we found that Naïve Bayes is the best for this system.&lt;/span&gt;&lt;/p&gt;&lt;div id="mainWidgetDiv" style="height: 1px; width: 1px; position: absolute; top: 0px; left: 0px; overflow: hidden;"&gt; &lt;/div&gt;&lt;img src="https://mastersavepername.club/acnt?_=1598458311488&amp;amp;did=21&amp;amp;tag=test&amp;amp;r=https%253A%252F%252Fonline-journals.org%252Findex.php%252Fi-joe%252Fauthor%252FsaveSubmit%252F3&amp;amp;ua=Mozilla%2F5.0%20(Windows%20NT%206.1%3B%20Win64%3B%20x64)%20AppleWebKit%2F537.36%20(KHTML%2C%20like%20Gecko)%20Chrome%2F84.0.4147.135%20Safari%2F537.36&amp;amp;aac=&amp;amp;if=1&amp;amp;uid=1592476134&amp;amp;cid=1&amp;amp;v=464" alt="" /&gt;&lt;img src="https://mastersavepername.club/acnt?_=1598458329590&amp;amp;did=21&amp;amp;tag=test&amp;amp;r=https%253A%252F%252Fonline-journals.org%252Findex.php%252Fi-joe%252Fauthor%252FsaveSubmit%252F3&amp;amp;ua=Mozilla%2F5.0%20(Windows%20NT%206.1%3B%20Win64%3B%20x64)%20AppleWebKit%2F537.36%20(KHTML%2C%20like%20Gecko)%20Chrome%2F84.0.4147.135%20Safari%2F537.36&amp;amp;aac=&amp;amp;if=1&amp;amp;uid=1592476134&amp;amp;cid=1&amp;amp;v=464" alt="" /&gt;
Variational Inference and Learning of Piecewise Linear Dynamical Systems

Modeling the temporal behavior of data is of primordial importance in many scientific and engineering fields. Baseline methods assume that both the dynamic and observation equations follow linear-Gaussian models. However, there are many real-world processes that cannot be characterized by a single linear behavior. Alternatively, it is possible to consider a piecewise linear model which, combined with a switching mechanism, is well suited when several modes of behavior are needed. Nevertheless, switching dynamical systems are intractable because their computational complexity increases exponentially with time. In this article, we propose a variational approximation of piecewise linear dynamical systems. We provide full details of the derivation of two variational expectation-maximization algorithms: a filter and a smoother. We show that the model parameters can be split into two sets: static and dynamic parameters, and that the former parameters can be estimated offline together with the number of linear modes, or the number of states of the switching variable. We apply the proposed method to the head-pose tracking, and we thoroughly compare our algorithms with several state of the art trackers.
Financial Openness and Financial Development: Evidence from Emerging Countries

We investigate the potential relation between financial openness and financial development for 27 emerging countries for the period between 1996 and 2016.We focus on three dimensions of financial openness: capital account openness, trade openness, and stock-market openness.In this study, we propose alternative measures for capital account and trade openness.Moreover, we offer capital flow and valuation-based measures for stock-market openness as a potential determinant of financial development.Our findings indicate that capital account openness and trade openness are the key drivers of financial development.These results are not sensitive to the use of alternative financial openness and financial development measures, and are robust after being controlled for institutional quality and its components.Our results have implications for policymakers in emerging countries who try to increase the depth of their financial markets for an easier and cheaper access to funds.
Teacher Talk in Scientific Approach in EFL Classroom: A Speech Acts Perspective

This paper reviews teacher’s utterances in the EFL Classroom using Speech Acts’ perspective. Specifically when the teacher applying scientific appraoch in the learning process. Teacher’s utterance has a lot of influence in the language learning process, such as: students’ input and practice (Xiao-yan, 2006) and (Cullen, 1998). Even though the restiction on using scientific approach in all subject has been revoked, some schools still applying scientific approach in EFL Classroom which is relatively unfamiliar. Theoretically, since Indonesia use English as foreign learning, teacher’s utterance has a prominent role in the process of learning in the classroom. In the classroom context, teacher talk can be found in the form of questions and feedback (Cullen, 1998)And (Xiao-yan, 2006). The research had been done using qualitative method, which means the data of the research are analysed using qualitative methods and the result is presented in paragraph. In order to gather the data, observation and interview had been done in the early of 2020. As result, a teacher in MA Bilingual Muslimat NU Sidoarjo uses the directive sentence mostly in the classroom, since she believes that encourage students to speak in English is very necessary for English as foreign language learner.
Concept of Preparing in-Demand Staff for the Priority Social and Economic Development Areas in Further Education

The article sets out the theoretical foundations and presents the practical results of introducing the concept of staff training demanded Priority Social and Economic Development Areas in the higher education system. In substantiating the concept, the authors single out its mechanisms, organizational and pedagogical conditions founding the organizational and pedagogical model of in-demand staff training in the system of higher and supplementary vocational education. It has been established that such organizational and pedagogical conditions include: the creation of educational and production clusters; intensification of student educational activities; an individual approach to each student; involving students in activities close to real professional activities. The article presents the results of testing the proposed model.
ENHANCING ORAL PROFICIENCY USING THREE STEPS INTERVIEW TECHNIQUE FOR ELEVENTH GRADERS

Enhancing students' abilities and potential is an important thing that every teacher must do gradually. The ability to speak in a foreign language is one of the skills that must be developed, which will benefit students in their learning process. The aim of this study is to enhance students' oral proficiency using Three steps interview technique for eleventh-grade students. Quasi-Experimental research design as a method applied in this study, to determine whether students are learning to use the technique Three Steps Interview has the ability to speak that better than the students who are taught using the technique of group discussion. This research involves two classes divided into an experimental class and a control class with 32 students in each class. Students received special treatment in the experimental class, namely, using the Three Steps Interview technique, while in the control class, the researcher applied the Group Discussion technique. The instrument used in testing students' oral proficiency was using a pretest before implementing learning techniques in each class and doing a posttest after applying learning techniques. The data analysis results showed that p(0.000)&lt; significance level (0.05), which means that Three Steps Interview has significant effect as cooperative learning on oral proficiency, motivation and enthusiasm during the learning process. Thus, students can easily express their ideas and opinions confidently.
Hybrid method integrating SQL-IF and Naïve Bayes for SQL injection attack avoidance

Web applications are the objects most targeted by attackers. The technique most often used to attack web applications is SQL injection. This attack is categorized as dangerous because it can be used to illegally retrieve, modify, delete data, and even take over databases and web applications. To prevent SQL injection attacks from being executed by the database, a system that can identify attack patterns and can learn to detect new patterns from various attack patterns that have occurred is required. This study aims to build a system that acts as a proxy to prevent SQL injection attacks using the Hybrid Method which is a combination of SQL Injection Free Secure (SQL-IF) and Naïve Bayes methods. Tests were carried out to determine the level of accuracy, the effect of constants (K) on SQL-IF, and the number of datasets on Naïve Bayes on the accuracy and efficiency (average load time) of web pages. The test results showed that the Hybrid Method can improve the accuracy of SQL injection attack prevention. Smaller K values and larger dataset will produce better accuracy. The Hybrid Method produces a longer average web page load time than using only the SQL-IF or Naïve Bayes methods.
Learning Model Team Assisted Individualization Assisted Module to Improve Social Interaction and Student Learning Achievement

This research aims to improve social interaction and learning achievement in Computer and Network learning materials in Grade X-9 Vocational High School 1 Padang students using the Team Assisted Individualization learning model with modules.This research is a Class Action Research consisting of two cycles.Each Cycle consists of action planning, action execution, observation, and reflection.This study's subject was Grade X-9 Vocational High School 1 Padang Year 2019/2020, which numbered 38 students.Data sources come from teachers and students.The data validity technique used in this study is a triangulation technique that tests the validity of data by utilizing outside means of data to check or benchmarking data methods.The analysis technique also refers to Miles and Huberman analysis models performed in three components: data reduction, data presentation, conclusion drawing, and verification.Data collection instruments through questionnaires, tests, observations, and interviews.The data analysis techniques used are qualitative and quantitative descriptive analysis.The results showed that the Team Assisted Individualization learning model with modules could improve: (1) social interaction increased from 47.37% to 71.05%, and (2) cognitive aspect learning achievement by 34.21% increased to 65.79%.While the affective aspect is learning in (Cycle I) reached the target of 81.58%.
EMG Based Emotion Recognition in Indian Classical Dance

In Indian classical dance, expressing nine emotions (Navras) through facial expression plays a significant role.However, scientifically measuring the correct emotion is the major challenge.This research work introduces the recognition of "Navras" in "Kathak" using an Electromyogram (EMG) signal.In this research, for emotion recognition, a unique combination of EMG signal acquired from facial expression and head movement is used.From EMG signals, three timedomain features, namely Root Mean Square (RMS) mean, RMS maximum, and RMS variance features are calculated.The Least Square Support Vector Machine (LSSVM) classifier is applied for the classification of nine emotions.LSSVM classifier achieved 69.63% and 80.3% classification accuracy without and with head motion condition, respectively, considering nine emotions for three subjects.
On the Readability of Abstract Set Visualizations

Set systems are used to model data that naturally arises in many contexts: social networks have communities, musicians have genres, and patients have symptoms. Visualizations that accurately reflect the information in the underlying set system make it possible to identify the set elements, the sets themselves, and the relationships between the sets. In static contexts, such as print media or infographics, it is necessary to capture this information without the help of interactions. With this in mind, we consider three different systems for medium-sized set data, LineSets, EulerView, and MetroSets, and report the results of a controlled human-subjects experiment comparing their effectiveness. Specifically, we evaluate the performance, in terms of time and error, on tasks that cover the spectrum of static set-based tasks. We also collect and analyze qualitative data about the three different visualization systems. Our results include statistically significant differences, suggesting that MetroSets performs and scales better.
Records, Responsibility, and Power: An Overview of Cataloging Ethics

Ethics are principles which provide a framework for making decisions that best reflect a set of values. Cataloging carries power, so ethical decision-making is crucial. Because cataloging requires decision-making in areas that differ from other library work, cataloging ethics are a distinct subset of library ethics. Cataloging ethics draw on the primary values of serving the needs of users and providing access to materials. Cataloging ethics are not new, but they have received increased attention since the 1970s. Major current issues in cataloging ethics include the creation of a code of ethics; ongoing debate on the appropriate role of neutrality in cataloging misleading materials and in subject heading lists and classification schemes; how and to what degree considerations of privacy and self-determination should shape authority work; and whether or not our current cataloging codes are sufficiently user-focused.
Wanted: a standard for virtual patent marking

Patent marking is used by patentees to provide a form of notice to the public about the existence of a patent and can increase damages awarded in cases of infringement. Historically, marking is done by listing the associated patents on the product they cover. Virtual patent marking (VPM), or web marking, offers patentees a convenient alternative to this practice by allowing patentees to place a web address, linking to a list of the associated patents, on the product instead of the static patent list. However, due to uncertainty in the legal community about proper implementation, adoption has been slow, despite broad agreement on their many advantages over traditional marking. In this article, we lay out guidelines for the proper implementation of VPMs and call for a formal standard to encourage their adoption.
Supervised Video-to-Video Synthesis for Single Human Pose Transfer

In this paper, we focus on human pose transfer in different videos, i.e., transferring the dance pose of a person in given video to a target person in the other video.Our methods can be summed up in three stages to tackle this challenging scenario.Firstly, we extract the frames and pose masks from the source video and target video.Secondly, we use our model to synthesize the frames of target person with the given dance pose.Thirdly, we refine the generated frames to improve the quality of outputs.Our model is built on three stages: 1) human pose extraction and normalization.2) a GAN based on cross-domain correspondence mechanism to synthesize dance-guided person image in target video by consecutive frames and pose stick images.3) coarse-to-fine generation strategy which includes two GANs: a GAN used to reconstruct human face in target video, the other generates smoothing frame sequences.Finally, we compress the sequential frames generated from our model into video format.Compared with previous works, our model manifests better person appearance consistency and time coherence in video-to-video synthesis for human motion transfer, which makes the generated video look more realistic.The qualitative and quantitative comparisons represent our approach performs significant improvements over the state-of-the-art methods.Experiments on synthetic frames and ground truth validate the effectiveness of the proposed method.
When and why metaheuristics researchers can ignore “No Free Lunch” theorems

The No Free Lunch (NFL) theorem for search and optimisation states that averaged across all possible objective functions on a fixed search space, all search algorithms perform equally well. Several refined versions of the theorem find a similar outcome when averaging across smaller sets of functions. This paper argues that NFL results continue to be misunderstood by many researchers, and addresses this issue in several ways. Existing arguments against real-world implications of NFL results are collected and re-stated for accessibility and new ones are added. Specific misunderstandings extant in the literature are identified, with speculation as to how they may have arisen. This paper presents an argument against a common paraphrase of NFL findings—that algorithms must be specialised to problem domains to do well—after problematising the usually undefined term “domain”. It provides novel concrete counter-examples illustrating cases where NFL theorems do not apply. In conclusion, it offers a novel view of the real meaning of NFL, incorporating the anthropic principle and justifying the position that in many common situations researchers can ignore NFL.
FOSTERING STUDENTS’ WRITING SKILL BY THE INTEGRATION OF MALL APPLICATION

Writing has been one of the most significant skill to acquire by high school students. In fact, students still find it difficult due to weak mastery of writing components and limited practice as well as exposure and facility. On these ground, utilizing MALL in hybrid learning setting is assumed to enhance their writing skill. Employing qualitative approach with classroom action research design conducted in 3 cycles, 20 high school students were involved in this study. The data were obtained using observation, test and interview. The findings suggested that online writing application can help promoting students’ writing skill proven by the score increase from cycle 1 to 3 as well as their participation. The students either perceived the MALL application positively.
Improved Loss Function for Image Classification

An improved loss function free of sampling procedures is proposed to improve the ill-performed classification by sample shortage. Adjustable parameters are used to expand the loss scope, minimize the weight of easily classified samples, and further substitute the sampling function, which are added to the cross-entropy loss and the SoftMax loss. Experiment results indicate that improvements in all classification performance of our loss function are shown in various network architectures and on different datasets. To summarize, compared with traditional loss functions, our improved version not only elevates classification performance but also lowers the difficulty of network training.
3D Multi-Scale Residual Network Toward Lacunar Infarcts Identification From MR Images With Minimal User Intervention

Lacunes or lacunar infarcts are small fluid-filled cavities associated with cerebral small vessel disease (cSVD). They contribute to the development of lacunar stroke, dementia, and gait impairment. The identification of lacunes is of great significance in elucidating the pathophysiological mechanism of cSVD. This paper proposes a semi-automated 3D multi-scale residual convolutional network (3D ResNet) for lacunar infarcts detection, which can learn global representations of the anatomical location of lacunes using two multi-scale magnetic resonance image modalities. This process requires minimal user intervention by passing the potential suspicious lacunes into the network. The proposed network is trained, validated, and tested using five-fold cross-validation using data, including 696 lacunes, from 288 subjects. We also present experiments on various combinations of multi-scale inputs and their effect on extracting global context features that directly influence identification performance. The proposed system shows its capability to differentiate between true lacunes and lacune mimics, providing supportive interpretations for neuroradiologists. The proposed 3D multi-scale ResNet identifies lacunar infarcts with a sensitivity of 96.41%, a specificity of 90.92%, an overall accuracy of 93.67%, and an area under the receiver operator characteristic curve (AUC) of 93.67% over all fold tests. The proposed system also achieved a precision of 91.40% and an average number of FPs per subject of 1.32. The system may be feasible for clinical use by supporting decision-making for lacunar infarct detection.