Reviewerly/exHarmony huggingface dataset:
“Akuma’su Adie3” (A Hearts Desire) A Metal Mural Depicting The Psychology of Fraud

Fraud has over the past decade become an epidemic, impacting not only Ghana as a country or Africa as a continent but the whole world.It has proven to be a most challenging criminal act to tackle and address and hence has left many nations stunned.In Ghana Fraud accounts for about 47.7% of victimization rate in Ghana as was recorded in a 2010 report issued by the United Nations Office of Drugs and Crime (UNODC,2010).It has since then towered in severity, manifolds.In response to this increasing menace, the need arose to establish the Economic and Organized Crime Office (EOCO).This was because the "responsibility and management of fraud as well as corruption could no longer be left to police and other quasi anti-crime organizations alone.However, with the increasing sophistication of society, complex frauds and other economic crimes, EOCO despite many efforts has even become more widespread.Education and sensitization of the masses has become ever more needed in the prevention and detection of fraud.The project hence is an attempt to contribute to the education of the public regarding the severity of fraud and the need to be aware and vigilant in regards fraudulent tricks and antics through metal art.It employs various metal working methods and techniques in creating an amazing metal art which serves as an effective communicative tool for creating awareness on the psychology behind fraudulent acts in general and in particular Ghana.The production of this art piece further, exposes and widens the practical application and creativity of metal artists.
RETRACTED: Teaching Development and Application of Japanese Corpus Based on Computer Big Data

Abstract In order to ensure the rationality, diversity and scientificity of Japanese corpus, the process of its collection is random and the samples collected are extensive and rich, which provides conditions for the application of computer big data technology. Based on this, this paper first analyses the role of Japanese corpus in teaching development, and then studies the difficulties in the construction of Japanese corpus based on computer big data. Finally, the key technology and application of Japanese corpus teaching development and application based on computer big data are given.
Ταξινόμηση εικόνων με βαθιά μάθηση

The enormous increase in the volume of information on the World Web in the last decade is an undeniable fact, which is directly related to the rapid development of technology. Huge amounts of images, texts and videos are stored in databases. For this reason, the need to discover and extract knowledge from this data has increased, a process that will be executed automatically by the computer, i.e. by Machine Learning methods. In this thesis we will refer to some of them, for example Decision Trees, Bayes rule, nearest neighbors, Support Vector Machines and of course Artificial Neural Networks, for which we will talk about in detail. Deep Learning has dominated in recent years in fields of research of Machine Learning, such as computer vision, natural language processing and speech recognition. Deep Learning has brought some great breakthroughs and improvements, however there is still difficulty in understanding the optimization and architecture of deep neural networks. In the present thesis, my aim was to cover as much as possible the task of Image Classification using Deep Learning. Therefore, reference is made to the concept of Deep Learning, the Convolutional Neural Networks are analyzed and some well-known Deep Learning Networks are described. After that, how to build an image classifier is explained and then follows the implementation with Python, i.e. the construction of models that will be able to classify image datasets. To be more specific, the datasets Cats and Dogs, Cifar 10 and MNIST will be examined. Finally, we present two Deep Learning architectures, RMDL and Simple Net.
An Overview of Tools and Technologies Used for Opinion Mining and Sentiment Analysis

The endeavor of social media has formed many chances for people to publicly voice their beliefs, simply when they are employed to deliver an opinion hit a vital problem. Sentiment analysis is the process to finding the satisfaction information of a consumer’s perception about product, service or brand. Sentiment analysis is also called as opinion mining because it dealt with the huge amount of customer opinion. The analyzing process of customer opinion is playing a vital role in product sale. Sentiment analysis is to extract the features by the notions from others perception about particular product and buying experience. The Sentiment Analysis tool is to function on a series of expressions for a given item based on the quality and features.. To find the opinion rate in the form of unstructured data is been a challenging problem today. Thus, this paper discusses about Sentiment analysis methods and tools which are used to make clear opinion mining.
Diversifying Web Development through System Design Tract: A Python Approach

Web development is one of the influencing branch in the industry of programming today. Its risegives the feedback to have different solutions in the creation of web development projects. With this,diversification becomes the way in order to strategize the process of analysis, identification, acceptance orthe reduction of many uncertainties. This study, aimed to diversify the development of web through theinclusion of system design tract to increase the visibility of searching the location of connected documentsand Python as another language in building the page. With the method of Waterfall Model and through theevaluation of the experts the usability of ISO-9126-1 was impacted with the highest mean of 4.70 with thedescriptive rating of “Excellent” as to compare with the rest of the software components like functionality,reliability, efficiency, maintainability, and portability. The study was conducted from November 2018 toNovember 2019. With this, future investigation in studying the pre-requisites of varied software’s is herebyrecommended to meet the instability towards the target platform.
Publishing a Chatbot

Publishing a bot is most likely one of the last steps within your chatbot development life cycle as this operation is done when you built your chatbot and tested it locally until the point you are satisfied with the conversational experience. If this is the case, you can go ahead and publish your bot which enables you to connect it to the channels which are offered by the Azure Bot Service later on, which will be described in Chapter 8. The aim of this chapter, however, is to walk you through the process of publishing a Composer bot to Microsoft Azure.
On the Formation of Professional Competence of Future Social Pedagogues

The article explores the process of formation of professional competence of future social pedagogues in the prevention of drug addiction. The essence and content of the professional competence of future social pedagogues in the prevention of drug addiction of minors in the context of the modernization of teacher education are disclosed. The components and specifics of the prevention of drug addiction of minors as the subject of socio-pedagogical activity are determined. A model was developed and tested for the formation of professional competence of future social pedagogues in the prevention of drug addiction in minors, consisting of targeted, stimulating-motivational, substantiveactive, technological, and control-effective components and its pedagogical effectiveness was revealed. The methodic and criteria for assessing the professional competence of future social pedagogues in the prevention of drug addiction in minors are determined. Conclusions and recommendations on the formation of the competence of future social pedagogues in the prevention of drug addiction in minors are proposed.
LibGuides: CMDM 355: Social Media and Applications: Physical Library Resources

This guide highlights resources available to students in CMDM 355 : Social Media and Applications with Dr. Dillon. Learn about periodicals and microfilm available in-person in the library.
Research and Course Guides: Birds of a Feather: TCTC Learning Commons and Clemson Libraries for Bridge Students: Home

Outlines services, spaces, and collections that Bridge students can access at TCTC Learning Commons and Clemson Libraries.
New technological challenges for the regulation of consumersupply of digital content

The new Directive (EU) 2019/770 of 20 May 2019 on certainaspects concerning contracts for the supply of digital contentand digital services is one of the several secondary acts ofthe European law, which aim to regulate digital market for thebenefit of both the European consumers and digital dataproviders. However, the creation of the directive was long andcomplicated, and technological level and practices havesteadily moved forward during the legislative process. The aimof this article is to describe the issues that have arisen inpractice of supply of digital data to the consumer that the newdirective may not have foreseen, and henceforth may not be ableto regulate. In this regard, the attention will be given to theissue of updates, the so called “dark patterns” andenforceability of consumers’ rights.
RETRACTED: Intrusion Detection Using Machine Learning in Sensor Network

Abstract An exhaustive assessment of the usage of machine and (Artificial Intelligence)AI answers for IDS structures in Wireless Sensor Networks (WSNs). To this end, introducing Restricted Boltzmann Machine-based Clustered-Introduction Detection System (RBMC-IDS) of conceivable AI-based IDS structure for WSN perception of explicit establishments. The RBMC-IDS display examination contrasts it and the recently presented adaptable IDS based AI. It Adaptively Supervised Hybrid IDS (ASCH-IDS). It mathematical results show that RBMC-IDS and ASCH-IDS achieve a comparative distinguishing proof and precision rates, anyway the disclosure period of RBMC-IDS is generally twice of ASCH-IDS.
Tools Comparison for Social Knowledge Management Adoption to Support Collaboration Among Lecturers

Knowledge Management is one important component of an organization in determining the internal development of the organization. Knowledge Management enables organizations to collect, store, distribute, and reuse the knowledge that a company's assets have.Likewise in the area of education, knowledge management has now evolved to be easier to deploy. Many colleges have tried to figure out how to use social computing to collaborate with educators and students in knowledge management. The most frequent problem of knowledge management is the willingness of the user to use or distribute it. The collaboration and communication aspects of social computing or web 2.0 is the basic successful use of knowledge management. This paper conducted an analysis about the best tools and technology that can be adopt as social knowledge management system for lecturer in doing collaboration among units and other lecturers. As the result, one existing technology found suitable for lecturers to be used.
Spoken Multiple-Choice Question Answering Using Multi-turn Audio-extracter BERT

In spoken multiple-choice question answering (SMCQA) task, given a passage, a question, and multiple choices all in the form of speech, the machine needs to pick the correct choice to answer the question. A common strategy is to employ an automatic speech recognition (ASR) system to translate speech contents into auto-transcribed text. Therefore, a SMCQA task is reduced to a classic MCQA task. Under the strategy, bidirectional encoder representations from transformers (BERT) can achieve a certain level of performance despite ASR errors. However, previous studies have evidenced that acoustic-level statistics can compensate for text inaccuracies caused by ASR systems, thereby improving the performance of a SMCQA system. Accordingly, we concentrate on designing a BERT-based SMCQA framework, which not only inherits the advantages of contextualized language representations learned by BERT, but integrates acoustic-level information with text-level information in a systematic and theoretical way. Considering temporal characteristics of speech, we first formulate multi-turn audio-extracter hierarchical convolutional neural networks (MA-HCNNs), which encode acoustic-level features under various temporal scopes. Based on MA-HCNNs, we propose a multi-turn audio-extracter BERT-based (MA-BERT) framework for SMCQA task. A series of experiments demonstrates remarkable improvements in accuracy over selected baselines and SOTA systems on a published Chinese SMCQA dataset.
Exploring Transferability of Perturbations in Deep Reinforcement Learning

The use of Deep Neural Networks (DNNs) as function approximators has led to striking progress for reinforcement learning algorithms and applications. At the same time, deep reinforcement learning agents have inherited the vulnerability of DNNs to imperceptible adversarial perturbations to their inputs. Prior work on adversarial perturbations for deep reinforcement learning has generally relied on calculating an adversarial perturbation customized to each state visited by the agent. In this paper we propose a more realistic threat model in which the adversary computes the perturbation only once based on a single state. Furthermore, we show that to cause a deep reinforcement learning agent to fail it is enough to have only one adversarial offset vector in a black-box setting. We conduct experiments in various games from the Atari environment, and use our single-state adversaries to demonstrate the transferability of perturbations both between states of one MDP, and between entirely different MDPs. We believe our adversary framework reveals fundamental properties of the environments used in deep reinforcement learning training, and is a tangible step towards building robust and reliable deep reinforcement learning agents.
Smart Evaluation System

The field of artificial intelligence and machine learning has done tremendous development in recent times. Handwriting recognition has gained a lot of importance in the field of computer vision and pattern recognition. The aim of the paper is smart education, and to adopt a paperless procedure for evaluation, revaluation and preservation of examination answer sheets, digital technology must be used. The proposed system focuses on segmentation of answer sheets. Different techniques can be applied to improve the recognition performance such as pre-processing, feature extraction and classification. All the experiments are conducted on the MNIST dataset. In the proposed approach we used Convolutional Neural Network and KNN for training and classification of the digits. The solution will minimize the human error and save a lot of time.
Analysis of the legal and human rights requirements for genomics in and outside the EU : SIENNA D2.2

Analysis of the legal and human rights requirements for genomics in and outside the EU : SIENNA D2.2
Collaborative Normalization for Unsupervised Domain Adaptation

Batch Normalization (BN) as an important component assists Deep Neural Networks achieving promising performance for extensive learning tasks by scaling distribution of feature representations within mini-batches. However, the application of BN suffers from performance degradation under the scenario of Unsupervised Domain Adaptation (UDA), since the estimated statistics fail to concurrently describe two different domains. In this paper, we develop a novel normalization technique, named Collaborative Normalization (CoN), for eliminating domain discrepancy and accelerating the model training of neural networks for UDA. Unlike typical strategies only exploiting domain-specific statistics during normalization, our CoN excavates cross-domain knowledge and simultaneously scales features from various domains by mimicking the merits of collaborative representation. Our CoN can be easily plugged into popular neural network backbones for cross-domain learning. One the one hand, \textbf{theoretical analysis} guarantees that models with CoN promote discriminability of feature representations and accelerate convergence rate; on the other hand, \textbf{empirical study} verifies that replacing BN with CoN in popular network backbones effectively improves classification accuracy with \textbf{4\%} in most learning tasks across three cross-domain visual benchmarks.
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings

This paper frames a general prediction system as an observer traveling around a continuous space, measuring values at some locations, and predicting them at others. The observer is completely agnostic about any particular task being solved; it cares only about measurement locations and their values. This perspective leads to a machine learning framework in which seemingly unrelated tasks can be solved by a single model, by embedding their input and output variables into a shared space. An implementation of the framework is developed in which these variable embeddings are learned jointly with internal model parameters. In experiments, the approach is shown to (1) recover intuitive locations of variables in space and time, (2) exploit regularities across related datasets with completely disjoint input and output spaces, and (3) exploit regularities across seemingly unrelated tasks, outperforming task-specific single-task models and multi-task learning alternatives. The results suggest that even seemingly unrelated tasks may originate from similar underlying processes, a fact that the traveling observer model can use to make better predictions.
Sample efficient Quality Diversity for neural continuous control

We propose a novel Deep Neuroevolution algorithm, QD-RL, that combines the strengths of off-policy reinforcement learning (RL) algorithms and Quality Diversity (QD) approaches to solve continuous control problems with neural controllers. The QD part contributes structural biases by decoupling the search for diversity from the search for high return, resulting in efficient management of the exploration-exploitation trade-off. The RL part contributes sample efficiency by relying on off-policy gradient-based updates of the agents. More precisely, we train a population of off-policy deep RL agents to simultaneously maximize diversity within the population and the return of each individual agent. QD-RL selects agents interchangeably from a Pareto front or from a Map-Elites grid, resulting in stable and efficient population updates. Our experiments in the Ant-Maze and Ant-Trap environments show that QD-RL can solve challenging exploration and control problems with deceptive rewards while being two orders of magnitude more sample efficient than the evolutionary counterpart.
Disentangling style and content for low resource video domain adaptation: a case study on keystroke inference attacks

Keystroke inference attacks are a form of side-channels attacks in which an attacker leverages various techniques to recover a user’s keystrokes as she inputs information into some display (for example, while sending a text message or entering her pin). Typically, these attacks leverage machine learning approaches, but assessing the realism of the threat space has lagged behind the pace of machine learning advancements, due in-part, to the challenges in curating large real-life datasets. This paper aims to overcome the challenge of having limited number of real data by introducing a video domain adaptation technique that is able to leverage synthetic data through supervised disentangled learning. Specifically, for a given domain, we decompose the observed data into two factors of variation: Style and Content. Doing so provides four learned representations: real-life style, synthetic style, real-life content and synthetic content. Then, we combine them into feature representations from all combinations of style-content pairings across domains, and train a model on these combined representations to classify the content (i.e., labels) of a given datapoint in the style of another domain. We evaluate our method on real-life data using a variety of metrics to quantify the amount of information an attacker is able to recover. We show that our method prevents our model from overfitting to a small real-life training set, indicating that our method is an effective form of data augmentation. Code and data will be released after reviewal.
Explicit Factorization of Some Period Polynomials

Let p, t, q, n, m and r be positive integers, such that p is a prime number, \(q=p^t\), \(\gcd (q,n)=1\), \(m=\text{ ord}_n(q)\), and suppose that the prime factors of r divide n but not \((q^m-1)/n\), and that \(q^m \equiv 1 \pmod {4}\), if 4|r. Also let u such that \(u=\gcd (\frac{q^m-1}{q-1},\frac{q^m-1}{n})\). Assume that \(u=1\) or p is semiprimitive modulo u. Under these conditions, we are going to obtain the explicit factorization of the period polynomial of degree \(\gcd (\frac{q^{mr}-1}{q-1},\frac{q^{mr}-1}{nr})\) for the finite field \({\mathbb {F}}_{q^{mr}}\). In fact, we will see that such polynomial has always integer roots, meaning that the corresponding Gaussian periods are also integer numbers. As an application, we also determine the number of solutions of certain diagonal equations with constant exponent.
조선 초기 관료의 관청이동을 통해 본 주요 통치기구의 위상 - HAVNet 자료를 중심으로 -

This paper is an introductory study to comprehensively grasp the betweenness centrality of government offices in the early Joseon dynasty using HAVNet data. The process of extracting data around the “Annals of the Joseon Dynasty” and designing and building HAVNet as the basis for research was conducted by history and computer science researchers, which were not the usual method in Korean historical research until now. The approach and analysis results carried out in this study are fundamentally different from previous studies related to the government organizations in the early Joseon dynasty. It is unique study in that it was analyzed based on the contents recorded in the Annals of the Joseon dynasty, not on previous research framework of analysis of state administration and government organizations in the early Joseon dynasty. Based on this study, we will conduct an interdisciplinary research that comprehensively analyzes blood ties and relationships among all historical figures of the Joseon dynasty.
Clean Code: On the Use of Practices and Tools to Produce Maintainable Code for Long-Living

Maintaining a long-living software system is substantially related to the quality of the code the system is built from. In this experience report we describe how a set of practices and tools has been established and used on the early stages of a project. The approach is based on Clean Code and the use of well known static code analysis tools. The Tools and practices have been used with an immediate effect of having cleaner code that is easier to understand in the long term. Additional attention is given to the cultural aspect that is involved in reaching a mindset that will allow to set and uphold code quality standards. Reaching a common understanding is a team effort that requires leaving one's comfort zone. Finding common ground can significantly decide about failure or success in creating maintainable code.
TMaaS, a new cloud-based, vendor-neutral multimodal traffic management solution

Traffic Management as a Service (TMaaS) is an innovative collaboration between eight public and private partner organisations, including the City of Ghent (main urban authority). This consortium has spent the past three years researching and developing a web application that provides a cloud-based, vendor-neutral multimodal trafﬁc management solution for small and medium-sized cities. Urban mobility data from several stakeholders and public service providers are integrated and visualized in a clean, intuitive and customizable interface for trafﬁc operators and citizens. TMaaS is a European project co-funded by the Urban Innovative Actions initiative