Reviewerly/exHarmony huggingface dataset:
Gouvernance et investissements directs étrangers dans les pays en développement

The main objective of this work is to study the role of governance indicators as determinants of FDI in African countries. It is in this sense that we conducted an analysis of panel data from 44 African countries over a 31-year period (1985-2015) using the generalized least squares method (GLSM).Our estimates show the following: (i) market size (GDP, current US$) has a negative impact on FDI to Africa, (ii) openness to trade has a positive impact on FDI flows, (iii) infrastructure (telephone lines (per 100 persons) have a negative effect on FDI flows to African countries, (iv) official exchange rate (local currency units per US$), average for the period) attracts FDI flows to Africa, (v) the rule of law and the functioning of the state attract ideas to African countries, (vi) the fight against corruption, the quality of regulation and political stability negatively affect ideas in Africa, (viii) the current account balance (% of GDP) positively affects the entry of ideas in Africa.
A Comparison of Fuzzy-PID Controller, Fuzzy Logic Controller and PID Controller of an Autonomous Robot

Recently, autonomous mobile robots have been increasingly developed for many applications related with surveillance, transportation, and inspection. This paper introduces an autonomous robot motion control based on PID, Fuzzy, and Fuzzy-PID controllers. The experiment is implemented on a mobile robot to compare the performances among three controllers. The comparison results show that Fuzzy-PID controller is the most suitable controller to deal with the rise time, steady sate error and overshoot problems.
A content-based late fusion approach applied to pedestrian detection

The diversity of pedestrians detectors proposed in recent years has encouraged some works to fuse them to achieve a more accurate detection. The intuition behind it is to combine the detectors based on its spatial consensus. The hypothesis is that a location pointed by multiple detectors has a high probability of actually belonging to a pedestrian, while false positive regions have little consensus among detectors (small support) which allows discarding the false positives in these regions. We proposed a novel method called Content-Based Spatial Consensus (CSBC), which, in addition to relying on spatial consensus, considers the content of the detection windows to learn a weighted-fusion of pedestrian detectors. The result is a reduction in false alarms and an enhancement in the detection. In this work, we also demonstrated that there is small influence of the feature used to learn the contents of the windows of each detector, which enables our method to be efficient even employing simple features. The CSBC overcomes state-of-the-art fusion methods in the ETH dataset and the Caltech dataset. Particularly, our method is also more efficient, since fewer detectors are necessary to achieve expressive results.
Degraded Document Image Binarization Using Active Contour Model

Document image binarization separates the foreground from background which is a very crucial pre-processing step in OCR. Accuracy of binarization immensely influences the accuracy of OCR. Various degradations like inadequate illumination, complex background, ink bleed, smear, etc. make binarization a challenging exercise. In this paper, we propose a novel method for binarization based on Active Contour Model (ACM) which is different from current thresholding techniques. Instead of calculating a global or local threshold value, it uses the energy-minimization concept of ACM to accomplish binarization. Background of the document image is estimated adaptively and then removed to handle noises in degraded documents. Active contour model is then applied to the image to get the binary output. Comprehensive experimentation has been completed effectively with benchmark DIBCO series datasets. A comparison with other existing methods verifies the efficiency of the proposed method.
ECO: The Single Currency Agenda in West Africa the Single Currency Agenda in West Africa

The Economic Community of West African States (ECOWAS) agreed to embrace the ECO as its single currency for the 15 countries within the community and set the deadline for 2020. This, however, does not seem feasible as the Anglophone countries such as Nigeria and Ghana have pulled out on the ground that the Francophone countries have breached the spirit of the Economic Community’s Currency Scheme.One of the vital reasons for the decision to adopt a single currency was because the crucial monetary problems plaguing west African countries seemed to be supranational, especially the constraints caused by the International Monetary Systems. The community and various stakeholders believed that these monetary problems could be fixed by currency integration. In light of the COVID-19 pandemic, there is no doubt that there will be a distortion in the global economy. Consequently, this would affect the International Monetary Systems. A shift in the International Monetary Systems could cause either an appreciation or depreciation in the currencies of African countries, particularly in West Africa. Thus, this might be a great time for African leaders and key stakeholders to revisit the decision of a single currency in West Africa.This article analyses the reasons why a currency integration might be necessary for West Africa, the benefits and costs of a currency integration for these Nations. This article also points out the legal, structural and political constraints affecting the implementation of a single currency in this region such as differences in National Laws, Inadequate Infrastructure, political Instability e.t.c. and recommends solutions to these constraints.
Fuzzy logic based on image processing to control a dc motor

Abstract Image Processing was a form of processing of input data signals in the form of images. This input image was transformed into another image with certain techniques. The techniques used in image processing were intensity adjustment, histogram equalization, thresholding, motion blur, canny and median filtering. Image processing could be used to do tracking. This tracking was a job to follow the movement of the object caught on the camera. Tracking using image processing could be utilized in various fields. In this study, the DC motor control system was discussed by utilizing image processing to detect hexagon shaped objects. In addition to detecting the shape that was detected, this image processing was also to detect color. The color of the detected object was orange. This motor speed followed the object’s motion horizontally. So, if the object was shifted to the right, the system will rotate slowly, whereas if it was moved to the left, the system will rotate quickly. This motor control used a PG45 motor, webcam, and personal computer. The success of reading the location of the area and providing PWM output on the motor achieved almost match between simulation and experiment.
A Novel Approach for Video Captioning Based on Semantic Cross Embedding and Skip-Connection

Video Captioning is the task of describing the content of a video in simple natural language. Encoder-Decoder architecture is the most widely used architecture for this task. Recent works exploit the use of 3D Convolutional Neural Networks (CNNs), Transformers or by changing the structure of basic Long Short-Term Memory (LSTM) units used in Encoder-Decoder to improve the performance. In this paper, we propose the use of a sentence vector to improve the performance of the Encoder-Decoder model. This sentence vector acts as an intermediary between the video space and the text space. Thus, it is referred to as semantic cross embedding that bridges the two vector spaces, in this paper. The sentence vector is generated from the video and is used by the Decoder, along with previously generated words to generate a suitable description. We also employ the use of a skip-connection in the Encoder part of the model. Skip-connection is usually employed to tackle the vanishing gradients problem in deep neural networks. However, our experiments show that a two-layer LSTM with a skip-connection performs better than the Bidirectional LSTM, for our model. Also, the use of a sentence vector improves performance considerably. All our experiments are performed on the MSVD dataset.
TRAVERSE-Voice Commanded Self Navigating Indoor Robot

When a human is taken to a new place, a new location per se, the individual first tries to perceive it, then gets to the geography of it and finally maps it. By mapping the whole location, the human goes around the place without any difficulty. In this project, the implementation will be done in such a way that the robot also consumes the parameters of the area in the same way as the human, by first perceiving the location entirely, followed by mapping the whole place and then going around it, thereby marking the location to absolute precision and making it flawless for navigation. To accomplish this performance, a self-mapping algorithm using SLAM (Simultaneous Localization and Mapping), A* Algorithm for Path Planning is written and executed. The robot, by using ultrasonic sensors will measure the distances of the surrounding objects.
Deep Insights into Graph Adversarial Learning: An Empirical Study Perspective

Graph Neural Networks (GNNs) have shown to be vulnerable against adversarial examples in many works, which encourages researchers to drop substantial attention to its robustness and security. However, so far, the reasons for the success of adversarial attacks and the intrinsic vulnerability of GNNs still remain unclear. The work presented here outlines an empirical study to further investigate these observations and provide several insights. Experimental results, analyzed across a variety of benchmark GNNs on two datasets, indicate that GNNs are indeed sensitive to adversarial attacks due to its non-robust message functions. To exploit the adversarial patterns, we introduce two measurements to depict the randomness of node labels and features for a graph, noticing that the neighborhood entropy significantly increased under adversarial attacks. Furthermore, we find out that the adversarially manipulated graphs typically tend to be much denser and high-rank, where most of the dissimilar nodes are intentionally linked. And the stronger the attacks are, such as Metattack, the patterns are more apparent. To sum up, our findings shed light on understanding adversarial attacks on graph data and lead potential advancement in enhancing the robustness of GNNs.
Relation-Aware Alignment Attention Network for Multi-view Multi-label Learning

Multi-View Multi-Label (MVML) learning refers to complex objects represented by multi-view features and associated with multiple labels simultaneously. Modeling flexible view consistency is recently demanded, yet existing approaches cannot fully exploit the complementary information across multiple views and meanwhile preserve view-specific properties. Additionally, each label has heterogeneous features from multiple views and probably correlates with other labels via common views. Traditional strategy tends to select features that are distinguishable for all labels. However, globally shared features cannot handle the label heterogeneity. Furthermore, previous studies model view consistency and label correlations independently, where interactions between views and labels are not fully exploited. In this paper, we propose a novel MVML learning approach named Relation-aware Alignment attentIon Network (RAIN), where three types of relationships are considered. Specifically, 1) view interactions: capture diverse and complementary information for deep correlated subspace learning; 2) label correlations: adopt multi-head attention to learn semantic label embedding; 3) label-view dependence: dynamically extracts label-specific representation with the guidance of learned label embedding. Experiments on various MVML datasets demonstrate the effectiveness of RAIN compared with state-of-the-arts. We also experiment on one real-world Herbs dataset, which shows promising results for clinical decision support.
Development of an electronic book epub 3.0 as a learning resource for blended learning IPA Terpadu

Abstract The purpose of this study was to produce an electronic book on IPA Terpadu competency and find out the quality of the product as a learning resource for blended learning. An electronic book consisting of text, images, and video, readable on computers or other electronic devices. The development of this ebook consists of 3 stages: planning, design, and develop. Testing electronic book on various devices with different operating systems on mobile operating systems (android) and personal computers (Windows). The results of the study show that: (1) the developed product is an electronic book format epub 3.0 on IPA Terpadu, (2) the results of field test show that the developed product has a good quality, results with an average of 85% (very satisfied)
Formation of Universal Competencies of Undergraduate Students of Pedagogical Directions

The aim of the research was to develop and implement a teachers training master degree program with active integration of soft skills at all the stages to form students’ system and critical thinking, project management skills, teamwork and leadership, communication and cross-cultural interaction skills. To solve these problems, the program included specialized practice-orientated courses based on the principles of pedagogical ergonomics and the latest technologies (ball-rating system, workshop, case study, “agile” principle, pedagogical situations’ modeling and others). The research resulted is a unique, effective and validated program and a set of training materials to develop universal competencies of master degree students.
Stealing (Identity) From the Poor

The law of data breaches is new, dynamic, and evolving. The number and complexity of breaches increases each year and legal scholars, courts, and policymakers scramble to respond. In 2019, 14.4 million consumers became victims of identity theft, the most problematic consequence of data breaches for consumers. Indeed, one-third of all Americans have experienced identity theft at some point in their lives. Yet despite low-income groups comprising at least thirty percent of all identity theft victims, existing discourse and debate on the regulatory regime governing data breaches and identity theft primarily reflects the experiences and concerns of middle- and high-income groups. Debates remain uninformed by detailed analysis of how the use of illegally obtained data may uniquely harm low-income individuals and how these harms may be exacerbated for low-income victims who are Black. We lack careful theoretical assessment of the complex relationship between identity theft victimization and wider structures of inequality. This Article uses original data to fill these significant descriptive, theoretical, and normative gaps in the literature. It then turns toward exploring the common and understudied problem of aligning regulatory regimes with the needs of prototypical higher-income people, leaving those who are low-income to operate within a system that was not designed to help themâ€”what I call plutocentric regulation. Finally, the Article proposes a new federal agency, the Data Privacy and Identity Recovery Agency (DPIRA), to streamline the process for identity theft victims and make the recovery process equitable for all victims, regardless of their income.
Study on Generalized Prefer-Opposite (GPO) Algorithm for Constructing the De Bruijn Binary Sequence

Abstract De Bruijn binary sequence S n of order n is a string of bits s i ∈ {0,1} = {s 1 , …, s 2 n }, so that each string with length n , { a 1 , …, a n } ∈ {0,1} n appears exactly once. De Bruijn binary sequence can be built using the Generalized Prefer-Opposite (GPO) algorithm. The GPO algorithm is based on the Feedback Shift Register (FSR), which is a clock-regulated circuit with n sequential storage units. In running the algorithm, we need input consisting of the feedback function f ( x 0 , x 1 , …, x n −1 ) and initial state b = b 0 , b 1 , …, b n−1 . There are many combinations of feedback functions and initial states that can be used, then grouped into three families namely family one, family two and family three. The purpose of this research is studying the GPO algorithm which use the feedback function from family two <?CDATA $f({x}_{0},\mathrm{..},{x}_{n-1})=1+{\prod }_{i=t}^{n-1}{x}_{i},0\lt t\lt n,t=2,n=4$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>f</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mi>x</mml:mi> <mml:mn>0</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:mn>..</mml:mn> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>x</mml:mi> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> <mml:mo stretchy="false">)</mml:mo> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo> <mml:msubsup> <mml:mo>∏</mml:mo> <mml:mrow> <mml:mi>i</mml:mi> <mml:mo>=</mml:mo> <mml:mi>t</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msubsup> <mml:msub> <mml:mi>x</mml:mi> <mml:mi>i</mml:mi> </mml:msub> <mml:mo>,</mml:mo> <mml:mn>0</mml:mn> <mml:mo>&lt;</mml:mo> <mml:mi>t</mml:mi> <mml:mo>&lt;</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>t</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> and initial state b =1100 in constructing the de Bruijn binary sequence. The result of this study indicate that the GPO algorithm with the feedback function and the initial state from the family two construct a de Bruijn binary sequence.
A reexamination of the sports coaching methodology of Hirobumi Daimatsu, focusing on his war experiences

Hirobumi Daimatsu was a legendary sports coach in Japan, especially after coaching the Japanese women’s national volleyball team (“the Oriental Witches”) that won the World Championship in 1962 and the gold medal in the 1964 Tokyo Olympic Games. He was famous for his extreme training methods and had a great influence on coaching methodologies for Japanese sports as a whole. Although many studies have examined his way of thinking from various perspectives, the relationship between his war experiences and his approach to coaching has not been analyzed sufficiently. The present study aimed to examine how Daimatsu’s first-hand war experiences (“keiken”) developed into his coaching beliefs (“taiken”), focusing specifically on a theory created by Yoshida that war veterans’ understanding of their experiences had been changing over time from when they re-entered society and grew older. Yoshida made this transformation clear by referring to 5 periods since the end of the Second World War in 1945. Firstly, many demobilized soldiers including Daimatsu had to face civilians who hated the Japanese military just after defeat. They lost their morale, from 1945 to around 1950 could not talk about the military or the War. Secondly, even after former professional officers and wartime politicians had been rehabilitated, the veterans themselves still found it difficult to positively address some topics related to the War in the 1950s. Thirdly, the generation that had experienced the War who shouldered the responsibility of reconstruction from the destruction and devastation gradually gained confidence and became able to talk about their wartime experiences. Some of them discovered a positive meaning in their own experiences on the battlefield from the late 1950s to the late 1960s. Daimatsu was a typical example of the third period because he spoke clearly about the positive meaning of his war experiences. Penultimately, in the 1970s and 1980s, that generation of Japanese became able to accept the responsibility for the War, especially in Asia, and to gradually acknowledge the negative aspects of their experiences. Finally, in the 1990s, a small number of survivors chose to disclose tragic stories that had not come to light previously. Thus, Daimatsu was only one of a generation that had experienced the War and who became recognized as a spokesman for many of that generation who held common feelings.
Perancangan Sistem Informasi Voice Of Costumer Berbasis Android pada Dealer PT. Dunia Barusa Meulaboh

PT. Dunia Barusa is one of the largest companies in Aceh which is engaged in motorized vehicles or better known as car sales, service and spare parts. As a large company, PT. Dunia Barusa strives to continue to provide better service to its customers and maintain its relationship with its customers and potential customers. However, there are problems in terms of service and vehicle work processes when customer service. The problem is how customers/prospective customers can be served properly and correctly according to predetermined standards. So that is necessary for an application to get the voice of the customer in order to easily know the customer is satisfied or not satisfied after the vehicle is serviced. In this study, researchers used an Android application as a data processing medium to obtain information on customer satisfaction with existing services in Barusa World so that the information system was used as a benchmark in improving quality and meeting customer satisfaction.
Influencing Followership: Understanding the Perspective of Those Leading Active Discussions on Quora

As social media influence become increasingly popular, understanding why some posts are highly followed than others, especially from the perspective of those leading the discussion allows us to gain insight on how followership is being influenced. A qualitative study of eight participants leading active discussions on Quora was conducted using semi-structured in-depth interviews, followed by thematic analysis. The open coding method was used to iteratively code related answers to develop themes. Results suggest that copyright tactics, controversial answers and sharing new information are some of the mechanisms for influencing followership. These mechanisms are built overtime through conscious strong engagement and by writing a consistently well-thought-out answer. The motivation for leading and writing answers on Quora were more intrinsic than extrinsic, and most participants believed influencing followership should not be a concern if one has the right message.
Deep Semantic Hashing for Large-Scale Image Retrieval

With the rapid growth of volumes of unstructured data in the Internet, it is increasingly important to develop image-based search methods in the field of information retrieval. Hashing methods have been widely studied in favor of the low storage cost and fast retrieval speed. However, traditional hashing methods generate binary codes from handcrafted features, which limited its accuracy since the handcrafted features is hard to represent the semantic feature of the image. Recent studies have indicated that deep leaning models are superior in terms of feature extraction. Hence, in this paper, we employ the transfer learning strategy with attention mechanism on the extraction of semantic features and using difference hash algorithm to generate binary codes for image retrieval. In addition, the pre-trained CNN models also show good performance in semantic feature extraction. Instead of using the last layer of the pre-trained CNN model directly, we can provide a guidance on how to select semantic features in the task of image retrieval, by observing some changes between mean average precision and semantic features extracted from the pre-trained CNN model. Experimental results demonstrate that the mean average precision of proposed model achieves 0.944, 0.568 and 0.310 on the MNIST, Cifar-10 and Cifar-100 dataset respectively, which indicates that the transfer learning strategy with attention mechanism is effective in image retrieval.