Reviewerly/exHarmony huggingface dataset:
Human Intelligence Versus Artificial Intelligence: A Comparison of Traditional and AI-Based Methods for Prospect Generation

This contribution deals with a comparison of two traditional approaches and one AI-based data mining tool to collect and interpret data for prospect generation. Traditional prospect generation methods like manual web search or using purchased data from external providers may create high costs and efforts and are subject to failures and waste coverage through outdated and untargeted data. In contrast, AI-based methods claim to provide better results at lower costs. Based on a real case, the authors compare effects of these three prospect generation methods. AI-based data mining tools compensate for some weaknesses of other methods, especially because they do not need pre-defined selection criteria which might bias the results. In addition, they involve less effort from the researcher. However, the results in generating concrete prospects may be still weaker than with traditional methods if web crawling activities are influenced by underlying databases. For academic research in the field of prospect generation, this study provides a fact-based comparison of approaches. Implications for businesses include the advice to combine methods rather than to rely on a single approach. The time available for research and the complexity of the target market have an influence on the selection of the prospect generation approach.
A Framework for Context-Aware Query Processing

Using natural language processing (NLP) and artificial intelligence techniques to handle database queries can make query results more useful and accurate, especially in context-aware applications, where their query results depend on the user and the surrounding environment settings. The existing research work [1] can be extended by adding context-aware services to the Location Based Services (LBSS) server of each cell. A new framework has been proposed for handling context-aware queries based on analysis of a user's query text, and a General Query Format (GQF) has been suggested to exchange queries between different database systems (Relational, NoSQL). A prototype has been implemented by using Python 3.6.10 (Anaconda package) and NLTK 3.5 library to analyze the dependency between text words of a user's query and convert the query text into various formats. The main contributions of this paper are summarized as follows: Selecting a cell location-based service server to provide context-aware services to users in its cell. Suggesting to unify query formats among database systems. Using natural language techniques to analyze the user's query text written in the user's natural language to determine which database will be accessed, and convert it into a standard query format that can be executed by database systems, and a technique for handling the context-aware quires.
Study of Data Repetitive Record Cleaning Technology of MES System in High-Tech Process

High-tech industries such as semiconductors have become important pillars of Taiwan’s economy under the raging COVID-19 epidemic. The MES system actively collects and monitors the production materials generated in the manufacturing process and integrates the core business required by the company’s production such as orders, suppliers, property management, production, equipment maintenance, quality control, and other processes to improve the company’s manufacturing process and production efficiency. Big Data of the process is the foundation of information. Good data quality is the basic condition for effective data analysis applications such as OLAP and data mining. The recursive field matching algorithm uses the recursive structure of the text field. The Sorted-Neighborhood Method (SNM) algorithm cannot solve all the matching records detected by a single keyword. Data cleaning is a relatively mature field that expects more breakthroughs and innovations. There is still a lot of room for technological development, but there is still a lot of work to be done, the specific recommendations already provided.
Detection Mechanism of News-text matching based on Knowledge Graph

Online news is an important means to spread information. However, in order to attract readers, some online news writers often add pictures that have nothing to do with the news content, which seriously affects the credibility of the media. To solve these problems, we propose a news detection mechanism based on the knowledge graph, which can enrich the news image description and use the news image description to calculate the sentence similarity with the news text, so as to identify whether a news belongs to the news image mismatch. The realization of the image-text matching detection mechanism of news enhance the credibility of social media and purify the network environment.
Gujarati Task Oriented Dialogue Slot Tagging Using Deep Neural Network Models

In this paper, the primary focus is of Slot Tagging of Gujarat Dialogue, which enables the Gujarati language communication between human and machine, allowing machines to perform given task and provide desired output. The accuracy of tagging entirely depends on bifurcation of slots and word embedding. It is also very challenging for a researcher to do proper slot tagging as dialogue and speech differs from human to human, which makes the slot tagging methodology more complex. Various deep learning models are available for slot tagging for the researchers, however, in the instant paper it mainly focuses on Long Short-Term Memory (LSTM), Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) and Long Short-Term Memory – Conditional Random Field (LSTM-CRF), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network - Bidirectional Long Short-Term Memory (CNN-BiLSTM) and Bidirectional Long Short-Term Memory – Conditional Random Field (BiLSTM-CRF). While comparing the above models with each other, it is observed that BiLSTM models performs better than LSTM models by a variation ~2% of its F1-measure, as it contains an additional layer which formulates the word string to traverse from backward to forward. Within BiLSTM models, BiLSTM-CRF has outperformed other two Bi-LSTM models. Its F1-measure is better than CNN-BiLSTM by 1.2% and BiLSTM by 2.4%.
Development of a Virtual Environment for Extrasomatic Communication

There is a structural shift in communication as technologies become ever more pervasive, and this transposition has become more pronounced with the adoption of virtual and augmented realities. People are communicating with one another through different modes, but also interacting with insentient technologies for intentional purposes. This is known as extrasomatic communication. This work-in-progress paper presents research implementing a creative antidisciplinary methodology aimed at designing a framework of extrasomatic communication by engaging users in a real-to-virtual immersive environment.
Weakly Supervised Image Classification Based on Attention Mechanism

At present, the research of image classification mainly focuses on the detection and classification of objects. However, most of the current classification methods still rely on a large number of pixel level manual annotation samples, which often has a greater impact on practical problems. This paper will focus on the task of image classification in the weakly supervised mode. By combining attention mechanism with convolution neural network, the model can extract salient regions in the image circularly, and then use the multi branch VGG deep neural network model to adjust the classification of the extracted features, so that the model can focus on the features of the salient region, so as to improve the ability of this model. The experimental results show that the improved weak supervised learning model can achieve 87.2% and 85.7% classification accuracy under the premise of reducing the use of artificial features, which means that it can achieve better image classification effect when compared with other advanced image classification methods.
Blockchain Technology and General Issue of Tax Evasion via Bitcoin

Cryptocurrencies often also serve money laundering activities, terrorist financing, tax evasion, and other illegitimate activities with a market value of more than 7 billion euros across the globe, though the total amount is hardly measurable. Indeed, the blockchain technology involves many virtual currencies, including bitcoin, to conduct various financial transactions related practices throughout the world economies. Besides, other blockchain applications are making positive contributions to a wide array of other industries including healthcare, supply chain, manufacturing, etc. This technology which constitutes the backbone of digital assets transactions currencies is characterized by anonymity, privacy, security, and speed. In this sense, for tax administration authorities, detection of financial fraud and regulations with respect to taxation of virtual transactions pose newer emerging challenges. This chapter aims to examine the blockchain technology, cryptocurrencies, especially bitcoin, and look into regulations by world governments to combat tax evasion and illegal transactions.
Abstract: Semi-supervised Segmentation Based on Error-correcting Supervision

Pixel-level classification is an essential part of computer vision. For learning from labeled data, many powerful deep learning models have been developed recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation network is judged by an additional network.
GPS Tracking, QR code Scanner Based Application to Help the Health Workers and Common Citizens during Covid-19

Abstract At the end of 2019, a novel Corona Virus surfaced as a pathogen and eventually resulted in a pandemic situation. The infection rate unleashed havoc worldwide firstly affecting European nations then the US. So, in this paper we proposed a software application which intakes user data to calculate the threat based on the merits of reconducted survey which prompts the user to check if they are undergoing any of the medically proven symptoms and sends it to the local municipal clinic. This in turn will minimize the need for medical workers to visit door to door constantly at risk of getting infected. This software is equipped with features like GPS tracking, threat level predictor, contamination graph it also determines medicinal urgency etc. Basically, our system is supposed to assist the day to day workers and visit places in scenarios of maximum urgency. This proposed system is used to prevent allowing likely symptomatic as well as asymptomatic users to enter in the hotspot regions’ public places such as malls and markets. The application will generate a unique QR code for respective users which will depict their merit based on mandatory survey that whether that person is affected or not. The QR code can be used in order enter the public area places where scanners are placed.
Novel Authentication Mechanisms for Hash Code, CAPTCHA and OTP in Cyber Security Domain

The cyber world can be secured only through the systematic development of security measures for both system to network security. It cannot be achieved through haphazard seat-of-the-pants methods. Every system developed by an organization demands a layer of security for its' safe functioning. This security layer is built by various techniques to ensure confidentiality, integrity, and availability of organizational systems. As an entry point of the security layer, authentication mechanisms play a crucial role. The organizations provide user access to the services and systems by using various authentication mechanisms namely, hash code, CAPTCHA, and OTP. If such entry points are made strong, the systems and networks can be protected well from unauthorized intrusions. This article focuses on developing novel authentication mechanisms for hash code, CAPTCHA, and OTP, which tightens the authentication mechanisms of cybersecurity.
Al Seer Group – <i>Can a Distributor Build Brands?</i>

The role of supply chain members is often relegated to an ancillary role in brand building. Do distributors serve only as a conduit for movement of products confined to the business-to-business (B2B) market or do they possess the capability to transcend boundaries and help build the brand of the products that they distribute? Using a case study methodology, an exploration has been carried out on the success journey of Al Seer Group, one of the biggest and oldest Fast Moving Consumer Goods distributors in the United Arab Emirates, driven by the vision to be a brand-building partner. The organization is propelled by a robust people strategy, a process-based operational framework, a data-driven culture and a strategic reorientation that helped them to introduce the brand-building perspective to their stakeholders. This study encourages further research interest on employee retention strategies focused on the Millennial and Gen Z workforce, challenges of data-driven organizations in implementation of emerging technologies, the role of C-suite executives in organizational strategic orientation, and the brand-building perspective of B2B distributors.
Learning Rate Optimisation of an Image Processing Deep Convolutional Neural Network

This paper proposes the use of a mathematical model to identify an optimal learning rate (OLR) for an image processing deep convolutional neural network (DCNN). An OLR may be realized by defining the relationship between the learning rate and model performance. This relationship is meant to resolve the problem of arbitrarily selecting the initial learning rate. The benefit of an OLR includes improved training stability and reduced computational resources. An algorithm is developed to analyze an inputted DCNN model and subsequently render output parameters that may be used to aid in the selection of an OLR. The results rendered by the OLR algorithm proposes that an optimal learning rate improves model performance. The mathematical model is also capable of approximating the model performance to a high degree of accuracy averaging at 91%. Furthermore, a model validation graph is also extrapolated, which will illustrate the mathematical model accuracy and the region of interest (ROI); the ROI defines a region in the learning rate spectrum with a positive effect on model performance.
DAS: Deep Autoencoder with Scoring Neural Network for Anomaly Detection

Many anomaly detection methods are unsupervised e.g. they only utilize the non-anomalous data for model training. Data points that deviate from the majority of the pattern are deemed as anomalies. However, in many cases, anomaly labels are available which can help to guide the model learning for anomaly detection. We introduce an end-to-end anomaly score learning model composed of an autoencoder and a scoring neural network, which assigns an anomaly score to a given data point according to its level of abnormality. We jointly optimize the reconstruction loss and anomaly score function in an end-to-end manner. Experimental results on multiple datasets show that the proposed method appears to be superior over many state-of-the-art methods.
Assessment Models for Evaluating the Combined use of Agile, User-Centered Design and Lean Startup in the Context of Software Development: A Grey Literature Review

In an attempt to reduce the risk accompanied by innovation, IT companies have invested in fostering agile development practices in combination with Lean Startup and User-Centered Design. Going through this transformation in large companies can be a challenging endeavour without an instrument to help the teams to assess how they are evolving towards the new approach. This paper reports on a grey literature review that aimed to identify which instruments, if any, are used by industry professionals and how they use them. Unfortunately, we could not find any grey literature; thus we moved to seek to identify whether assessment models were available for a combined use of the approached two-by-two (e.g., agile and UCD), or even individually. We report on the 11 identified materials, which report on the existance of 7 distinct models. We learned that the Spotify Squad Health Check is the most cited model; that all models consider quality indicators; that they adopt distinct strategies to appluy the models such as workshops or online tools; and that the results yield by the models application is often recorded in whiteboards or online tools in the form of a backlog of improvements or of quality indicators. Our grey literature review results suggest that the topic is still maturing even among industry professionals and there is room for research in the topic.
AR@UNIMAP: A Development of Interactive Map Using Augmented Reality

Abstract Moving towards a modern age, Augmented Reality (AR) can be considered as one of the fastest developing technologies every year wherein it is enhanced by computer-generated perceptual information and sometimes across multiple sensory modalities, including visual and text. However, students have not been exposed to this technology since they are stuck with a narrow-minded conception of using papers even though it can be too overwhelming for them since they have to read the study books, brochures, and pamphlets to get information about their universities especially for the new students who recently registered and for the visitors. To overcome this problem, we are developing an application “AR UniMAP: Pauh Putra Residential College Interactive Map”. This project aims is to develop an informative ARMap along with some additional information about the campus for the students and visitors through interactive communication through the proposed system. Smartphone with the support of ARCore is used as the main platform for the application to be implemented in such a way to display the visuals of the buildings and each room in the campus through 2D and 3D along with some additional information through text pop up on the left and right side of the smartphone screen.. The key to this architecture is to expose university students with the AR technologies and to make it easier for them to access information about their university.
Learnability and Positive Equivalence Relations

Prior work of Gavryushkin, Khoussainov, Jain and Stephan investigated what algebraic structures can be realised in worlds given by a positive (= recursively enumerable) equivalence relation which partitions the natural numbers into infinitely many equivalence classes. The present work investigates the infinite one-one numbered recursively enumerable (r.e.) families realised by such relations and asks how the choice of the equivalence relation impacts the learnability properties of these classes when studying learnability in the limit from positive examples, also known as learning from text. For all choices of such positive equivalence relations, for each of the following entries, there are one-one numbered r.e. families which satisfy it: (a) they are behaviourally correctly learnable but not vacillatorily learnable; (b) they are explanatorily learnable but not confidently learnable; (c) they are not behaviourally correctly learnable. Furthermore, there is a positive equivalence relation which enforces that (d) every vacillatorily learnable one-one numbered family of languages closed under this equivalence relation is already explanatorily learnable and cannot be confidently learnable.
Learning Between the Lines: Interactive Learning Modules Within Corpus Design

The present paper reports on the advantages of learning inferences and understanding strategies from the interactive structure of a corpus. First of all, we introduce the SUGAR corpus for the cooking domain, describing its peculiar collection and annotation procedures. After this first overview, we show how information included within the corpus can be used to enhance the action interpretation in dialogue systems. This can be the case of linguistic elements or related lexical units which can be acquired from a linked database or from rephrasing strategies within the corpus itself. In all the AI-based approaches depending on a training process using large and representative corpora, the probability to correctly predict the creativity a speaker can perform in using language is lower than expected. Trying to capture most of the possible words and expressions a speaker could use is extremely necessary, but even an empirical, finite collection of cases could not be enough. For this reason, the use of our corpus, possibly in combination with online training, appears as an appealing solution.
Rare Computing: Removing Redundant Multiplications From Sparse and Repetitive Data in Deep Neural Networks

Recent research shows that 4-bit data precision is sufficient for Deep Neural Network (DNN) inference without accuracy degradation. Due to the low bit-width, a large amount of data is repeated. In this article, we propose a hardware architecture, named Rare Computing Architecture (RCA), that skips redundant computations due to repetitive data in the networks. By exploiting redundancy, RCA is not significantly affected by data-sparsity and maintains great improvements in performance and energy efficiency, while the improvements of existing DNN accelerators are vulnerable to variations in sparsity. In the RCA, repeated data in a window for censoring repetition are detected by a Redundancy Censoring Unit (RCU) and processed at a time, achieving high effective throughput. Additionally, we present a dataflow that exploits abundant data-reusability in DNNs, which enables the high-throughput computations to be ceaselessly performed without an increase of bandwidth for data-read. The proposed architecture is evaluated in two ways of exploiting weight- and activation-repetition. In the evaluation, RCA is compared to a value-agnostic computation and UCNN that is the state-of-the-art accelerator exploiting weight-repetition. Additionally, RCA is compared to Bit-pragmatic that exploits bit-level sparsity. Both evaluations demonstrate that the RCA shows steadily high improvements in performance and energy-efficiency.
Election model classifications of problem-based learning using a machine learning technique

Abstract Strategy learning based problem involve all college students to active in working class and find the solution of problems being discussed. The active involvement of all students in learning and the capability of think critically students hoped would improve learning outcomes especially cognitive students study results. This study aims to choose model classification and evaluate how much of accuracy and precision from the learning problem learning based using a technique machine learning. Classification problems based learning treatment based on student learning outcomes and creativity. Optimize parameters applied at an election algorithms applied with both operators select sub proses. By applying some algorithms so operator select sub proses will vote in the best automatic algorithms. The algorithms Decision Tree, Naïve Bayes, and Random Forest are acquired automatic algorithms Naive Bayes have the accuracy of 75 % of the kappa 2,3%.