Reviewerly/exHarmony huggingface dataset:
Entrepreneurs’ Perception of Banks’ Social Responsibility : A Haitian Case Study

Banks are the pillars of entrepreneurship expansion and economic development. In developing countries, where there is little public financial support for entrepreneurs, it is clear that banks, among other financial institutions, should be part of the solution to the problem of financing economic activity. As financial intermediaries, commercial banks need to enjoy good perception among entrepreneurs to improve their profitability. To achieve such objective, banks sometimes adopt social responsibility strategies to influence public perception of banks’ behavior. How do Haitian entrepreneurs perceive Haitian banks’ social responsibility? To answer this question, we collect empirical data among entrepreneurs of all size (micro, small and large). The findings help interesting discussions of banks perception among entrepreneurs divided by demographic (gender, location) and economic (sector, size, assets) characteristics. Among the main conclusions, we find that banks enjoy very bad perception among entrepreneurs (all size). Also, the special concessions given by the Government and other international institutions to the banking industry in Haiti help very few to increase the financial services for Haitian entrepreneurs. From our conclusion arise questions for future research to study the relations between entrepreneurs’ own practices of corporate social responsibility and their perception of banks social responsibility.
OpenGAN: Open-Set Recognition via Open Data Generation

Real-world machine learning systems need to analyze novel testing data that differs from the training data. In K-way classification, this is crisply formulated as open-set recognition, core to which is the ability to discriminate open-set data outside the K closed-set classes. Two conceptually elegant ideas for open-set discrimination are: 1) discriminatively learning an open-vs-closed binary discriminator by exploiting some outlier data as the open-set, and 2) unsupervised learning the closed-set data distribution with a GAN and using its discriminator as the open-set likelihood function. However, the former generalizes poorly to diverse open test data due to overfitting to the training outliers, which unlikely exhaustively span the open-world. The latter does not work well, presumably due to the instable training of GANs. Motivated by the above, we propose OpenGAN, which addresses the limitation of each approach by combining them with several technical insights. First, we show that a carefully selected GAN-discriminator on some real outlier data already achieves the state-of-the-art. Second, we augment the available set of real open training examples with adversarially synthesized fake data. Third and most importantly, we build the discriminator over the features computed by the closed-world K-way networks. Extensive experiments show that OpenGAN significantly outperforms prior open-set methods.
On the Diffusion of Mobile Phone Innovations for Financial Inclusion

“Replications are an important part of the research process because they allow for greater confidence in the findings” (McEwan, Carpenter & Westerman, 2018, p. 235). This study extends Lashitew, van Tulder and Liasse (2019, RP) by addressing the concern of multicollinearity that affects the signs and significance of estimated coefficients. This article investigates nexuses between innovations in mobile money and financial inclusion in developing countries. Demand and supply factors that affect the diffusion of mobile services as well as macro-level institutional and economic factors are taken on board. The empirical evidence is based on Tobit regressions. The study finds that when the empirical analysis is robust to multicollinearity, two main tendencies are apparent: the significant findings of Lashitew et al. (2019) are confirmed and many new significant estimated coefficients emerge. While this study confirms the findings of the underlying research, it also goes further to improve the harmony in narratives between the predictors and the outcome variables. Accordingly, by accounting for multicollinearity, the earlier findings are now more consistent across the set of predictors (i.e. demand and supply factors) and the attendant financial inclusion outcomes (i.e. mobile money accounts, mobile used to send money and mobile used to receive money).
A New Journey From SDRTV to HDRTV

Nowadays modern displays are capable to render video content with high dynamic range (HDR) and wide color gamut (WCG). However, most available resources are still in standard dynamic range (SDR). Therefore, there is an urgent demand to transform existing SDR-TV contents into their HDR-TV versions. In this paper, we conduct an analysis of SDRTV-to-HDRTV task by modeling the formation of SDRTV/HDRTV content. Base on the analysis, we propose a three-step solution pipeline including adaptive global color mapping, local enhancement and highlight generation. Moreover, the above analysis inspires us to present a lightweight network that utilizes global statistics as guidance to conduct image-adaptive color mapping. In addition, we construct a dataset using HDR videos in HDR10 standard, named HDRTV1K, and select five metrics to evaluate the results of SDRTV-to-HDRTV algorithms. Furthermore, our final results achieve state-of-the-art performance in quantitative comparisons and visual quality. The code and dataset are available at this https URL.
The Impact of Transformational Leadership on the Performance of Tourist Police

The security of tourists is one of the main requirements in countries familiar with tourism and pleasures. Many countries like UAE with large number of tourists have developed tourist police service to ensure the safety of tourists. Thus, the performance of law enforcement organizations such as tourist police department is essential to provide a safe destination for tourists and international guests. Accordingly, it is important that the leaders of tourist police departments to be effective and capable to deal with security challenge. Based on this argument, there is a need for a robust research to examine the impact of transformational leadership on police performance. This study is a theoretical attempt to examine the relationship between transformational leadership and organizational performance in tourism police department of Dubai. The information gathered through this study, would enable the researchers in policing to present recommendations for law enforcement organizations in UAE and other countries as well so that helping police officers to fight crimes and control threats on tourism industry.
Who's Waldo? Linking People Across Text and Images

We present a task and benchmark dataset for person-centric visual grounding, the problem of linking between people named in a caption and people pictured in an image. In contrast to prior work in visual grounding, which is predominantly object-based, our new task masks out the names of people in captions in order to encourage methods trained on such image-caption pairs to focus on contextual cues (such as rich interactions between multiple people), rather than learning associations between names and appearances. To facilitate this task, we introduce a new dataset, Who's Waldo, mined automatically from image-caption data on Wikimedia Commons. We propose a Transformer-based method that outperforms several strong baselines on this task, and are releasing our data to the research community to spur work on contextual models that consider both vision and language.
The Benefits of Android Applications for an Independent Learning in Learning English Language

Due to the pandemic condition, we rely more on the technology usage. Technology presents us plentiful advantages and features in the teaching and learning. Specifically in smart phones, android nowadays offer numerous applications of language learning in Play Store. This article shows the benefits of Android applications namely Duolingo, Busuu, Rosetta Store, and Memrise in learning English language. These apps enhance the user’s four language skills mastery using games, riddles and other various ways. By learning language independently, students may also their cognitive, meta-cognitive and affective skills. It aids learners to gain their goals in a fun and enjoyable ways. Further, it is also beneficial in terms of improving students’ motivation and confidence, easy and simple, practical and entertaining, and sustainability.
The Prediction of S-Wave Velocity using Gaussian Process Regression

Summary The S-wave velocity is a very important parameter for seismic exploration. However, the S-wave velocity from the logging data is difficult to be acquired compared with the P-wave velocity. Therefore, we propose a method for predicting S-wave velocity from the P-wave velocity based on Gaussian process regression (GPR). The GPR model is trained by logging data including the velocity of P-wave and S-wave from one of three wells in a certain area. The S-wave velocities of other two wells are predicted by the trained GPR model. The numerical results demonstrate that the predicted Swave velocities agree well with the ground-truth ones, which illustrates that the GPR scheme is an effective tool for predicting S-wave velocity.
DEVELOPMENT OF INTERACTIVE POWERPOINT ASSISTED LEARNING MEDIA TO IMPROVE STUDENT LEARNING OUTCOMES ON THE BEAUTIFUL THEME OF TOGETHERNESS IN CLASS IV SDN 157013 SIBABANGUN 5, CENTRAL TAPANULI REGENCY

This study aims to develop instructional media software to determine the feasibility and effectiveness of instructional media in improving student learning outcomes in the sub-theme of the Beauty of Togetherness. Researchers used media, namely interactive powerpoints in the design and development of instructional media. This type of research is research and development (R &amp; D). The effectiveness of Interactive multimedia based on the test of learning outcomes, namely the completeness of individual learning results data in class IV-A using interactive powerpoints on the beautiful theme of togetherness, obtaining an average score of 88.64% of 22 students stated that all students are complete and in class IV-B who use teaching materials for the theme of the beauty of togetherness obtained an average score of 75, 68% of 22 students declared 18 students completed and 4 students declared incomplete. Based on the results of the validator's assessment on the material expert, it is feasible to reach 4.45 with the very feasible category. Media experts get 4.50 in the very feasible category and design experts get 4.83 in the very feasible category. From the validation results, the feasibility of interactive powerpoint media is very feasible to use. The results of the difference test analysis showed that From the validation results, the feasibility of interactive powerpoint media is very feasible to use. The results of the difference test analysis showed that From the validation results, the feasibility of interactive powerpoint media is very feasible to use. The results of the difference test analysis showed thatt count&gt; t table (1.728&gt; 1.688)at the significant level (0.05) with the conclusion that Ha is accepted and Ho is rejected. This proves that interactive powerpoints are more effectively used and verified in improving student learning outcomes on the theme of the beauty of togetherness in Class IV SDN 157013 Sibabangun 5, Tapanuli Tengah Regency
Developing Japan Insights To The Students Of Sma Taruna Bakti Bandung

Japanese language, both in high school and high school (SMA), is a cross-interest subject that is given from the beginning to the end of the education period. This is in accordance with the national curriculum issued by the Ministry of National Education. Thus the ability to communicate in Japanese is seen as important to improve the quality of graduates. High school graduates will not only receive a graduation certificate but also other competency certificates including Japanese. The benefits of Japanese for students can be as a provision to continue their studies as well as additional abilities to enter the world of work. In response to this, the Japanese Language Study Program at Widyatama University Bandung as an institution that has the aim of developing and introducing Japanese language and culture is moved to provide knowledge of Japanese language and culture to high school students through Community Service (PKM) activities. This Community Service Activity (PKM) will be held online in the form of a webinar with the theme Developing Japanese Insights for High School Taruna Bakti Bandung Students. The participants of the webinar are students of the Senior High School Taruna Bakti Bandung, Class 3 Social.
Using deep learning to detecting abnormal behavior in IoT

The development of the Internet of Things (IoT) has increased exponentially, creating a rapid pace of changes and enabling it to become more and more embedded in daily life. This is often achieved through integration: IoT is being integrated into billions of intelligent objects, commonly labeled things, from which the service collects various forms of data regarding both these things themselves as well as their environment. While IoT and IoT-powered decices can provide invaluable services in various fields, unauthorized access and inadvertent modification are potential issues of tremendous concern. In this paper, we present a process for resolving such IoT issues using adapted Long Short-Term Memory (LSTM) recurrent neural networks (RNN). With this method, we utilize specialized deep learning (DL) methods to detect abnormal and/or suspect behavior in IoT systems. LSTM RNNs are adopted in order to construct a high-accuracy model capable of detecting suspicious behavior based on a dataset of IoT sensors readings. The model is evaluated using the Intel Labs dataset as a test domain, performing four different tests, and using three criteria: F1, Accuracy, and Time. The results obtained here demonstrate that the LSTM RNN model we create is capable of detecting abnormal behavior in IoT systems with high accuracy.
CRITICAL EVALUATION ON WSNS POSITIONING METHODS

WSN gained a lot of attention because they are small and economical devices with low power utilization, and finite computing resources are progressively being enfolded in various application situations, including environmental monitoring, reconnaissance tracking, and health monitoring.In many applications of this type, node localization is an intrinsic parameter of the system.To report events origin, routing, and network coverage (A&Q), assist group querying of the sensors Localization process is mandatory.Localization is categorized into two types, which are a range-based or range-free scheme which is further divided into two sub-schemes as a fully and hybrid.For the detailed analysis and study, we are going to investigate localization schemes based on static and mobile WSN in this work.This research opens the new paradigm and future planning for the localization algorithms mainly how sensors are deployed?What kind of measures are taken into account to boost the algorithms so that the system can calibrate itself in changing environments?
Cloud workflow scheduling algorithm based on multi-objective hybrid particle swarm optimisation

Particle swarm optimisation has been widely used in solving scheduling problems. This paper proposes a hybrid algorithm namely Hill Climbing with Multi-objective Particle Swarm Optimisation called ...
Massive Generation of Data with Random Variates

The Data Generation Language (DGL) has been widely used to generate random data for simulation and for software testing. Although DGL is highly versatile, its ability to handle different probability distributions was severely limited. The work described here corrects this problem by adding features that can be used to generate variates from a number of different probability distributions. This data can be used directly by a simulator or stored in a file or database table for future use. Variate generation makes use of a basic stream of uniformly distributed random numbers that can be generated by one of 47 different random number generators.
MmWave Radar and Vision Fusion based Object Detection for Autonomous Driving: A Survey

With autonomous driving developing in a booming stage, accurate objectdetection in complex scenarios attract wide attention to ensure the safety ofautonomous driving. Millimeter wave (mmWave) radar and vision fusion is amainstream solution for accurate obstacle detection. This article presents adetailed survey on mmWave radar and vision fusion based obstacle detectionmethods. Firstly, we introduce the tasks, evaluation criteria and datasets ofobject detection for autonomous driving. Then, the process of mmWave radar andvision fusion is divided into three parts: sensor deployment, sensorcalibration and sensor fusion, which are reviewed comprehensively. Especially,we classify the fusion methods into data level, decision level and featurelevel fusion methods. Besides, we introduce the fusion of lidar and vision inautonomous driving in the aspects of obstacle detection, object classificationand road segmentation, which is promising in the future. Finally, we summarizethis article.
TECHNOLOGICAL SOLUTIONS TO EMPOWER STUDENTS WHO ARE BLIND OR LOW VISION AS INDEPENDENT LEARNERS IN THE CHEMISTRY LABORATORY

In June 1994, representatives of governments and international organisations around the globe ratified the ‘Salamanca Statement on Principles Policy and Practice in Special Needs Education’ (UNESCO, 1994). This rights-based focus on inclusive learning furthered the goals of Education for All (World Conference on Education for All: Meeting Basic Learning Needs, 1990) of providing quality basic education for all children, youths and adults. Today, the importance of inclusion of people with disabilities within education continues to be recognised in the international community and is explicitly mentioned in the targets of the United Nations’ Sustainable Development Goals (United Nations).While emerging evidence indicates an increase in the number of students with disabilities enrolling into science, technology, engineering and mathematics, this population is still underrepresented as a result of technological and attitudinal barriers.At the School of Chemistry, at The University of Sydney, we are aiming to build an inclusive learning environment for all. This paper will discuss the advanced technological developments over the last ten years which have helped students who are blind or low vision (BLV) to work independently in the Chemistry laboratory (Devi et al., 2021). This paper will also highlight our future endeavours to further enhance the laboratory learning experience of BLV students.REFERENCESDevi, P., Motion, A., Bhattacharya, J., Supalo, A. C & Schmid, S. (2021) Unpublished results, The University of Sydney.United Nations. Sustainable Development Goals and Disability. Retrieved June 6, 2021 from https://www.un.org/development/desa/disabilities/about-us/sustainable-development-goals-sdgs-and-disability.html.UNESCO (1994). The Salamanca Statement and Framework for Action on Special Needs Education, World Conference on Special Needs Education: Access and Quality, Salamanca, Spain, 7- 10 June. https://unesdoc.unesco.org/ark:/48223/pf0000098427.World Conference on Education for All: Meeting Basic Learning Needs, (1990). World declaration on education for all and framework for action to meet basic learning needs adopted by the World Conference on Education for All: Meeting Basic Learning Needs, Jomtien, Thailand, 5-9 March 1990. https://bangkok.unesco.org/sites/default/files/assets/ECCE/JomtienDeclaration.pdf
XRDI: A Database of X-Ray Dangerous Items

In order to provide a data set and basic detection indicators for the identification and detection of X-ray dangerous items, the study proposed a method to generate X-ray dangerous items data, which is to collect more than ten kinds of dangerous items and over 100 kinds of non-dangerous items. The original data under different photographing directions is processed by segmenting and denoising, random rotating and zooming, and gray-scale image overlay synthesis based on the principle of X-ray imaging. Finally, a data set containing 10,000 positive samples and 100,000 negative samples is generated, which contains three difficulty levels divided by photographing directions. The research uses several existing mainstream target detection algorithms to conduct algorithm experiments on different data sets, gives the mAP and Recall results of different kinds of dangerous items under different difficulty test sets, organizes and compares the results under the conditions of different training sets and test sets, explores the influence of the number of training samples on the results. These results verified the research significance of the data set in the field of dangerous items identification and detection, and laid the foundation for subsequent research.
Feasibility Study of Hybridization Schemes in Recommender Systems

Nowadays, Internet is overwhelmed with vast amount of data which makes the process of selection of related information more and more difficult. One of the significant difficulties observed by a user is to discover relevant information as per their requirement or interest. Recommender system is an intelligent information filtering system which filters the overloaded information from past and present and gives the result based on the user’s personal interest. Principal techniques of RS are Collaborative Filtering (CF) and Content Based Filtering (CBF). CF is considered to be the best in all domains and always outperforms. Recent Research studies have focused on mixing recommendation methodologies including Collaborative, Content-based and Knowledge based methods in hybrid recommender system research in order to improve recommendation results. This shift in research focus calls for having a sound idea about basic recommendation algorithms and hybridization concepts, schemes and mixing sequence. The paper attempts to explore, analyze and interpret all possible combinations of hybridization methods that are viable both in design and implementation. Finally, precise remarks were able to be delivered upon redundancy and feasibility options of various hybrid recommender system combinations in burke’s recommender system space.
Monocular Depth Estimation via Convolutional Neural Network with Attention Module

Abstract The depth accurately estimated from the RGB image can be used in applications such as 3D reconstruction and scene display. In depth estimation, Convolutional Neural Network (CNN) plays an important role. However, most of the existing CNN-based depth estimation methods often do not take full advantage of the space and channel information in the local receptive field, resulting in lower-resolution depth maps. Based on the extant Encoder-Decoder network architecture, a simple and effective attention module is introduced to learn the discerning feature and the semantic feature in this paper. First, the intermediate feature map is generated by the encoder, and then the attention module is used to generate the attention maps along the two dimensions of the space and channel of the feature map. Finally, the refined output generated by multiplying the attention maps to intermediate feature maps is input to the subsequent neural network for further work. Our works show that the suggest approach has obtained good achievements on NYU Depth v2.
An improved convolutional neural network based on centre loss for facial expression recognition

In recent years, due to the development of deep learning and convolutional neural network (CNN), the field of computer vision has been booming. As a subdivision of face recognition technology, faci...