Reviewerly/exHarmony huggingface dataset:
A Proposed Structure for Managing IT Diploma Qualifications in South Africa

IT diploma and advanced diploma qualifications are presently offered by public and private universities in South Africa. The public universities include research universities, comprehensive universities and Universities of Technology. Historically in South Africa, IT diploma programme content was collaboratively managed by the Technikon Computer Lecturers Association (TECLA). TECLA comprised of Technikon academics, who met annually to discuss the IT diploma programmes and curriculum content. TECLA ceased to exist when the South African Government introduced new university structures in 2005. Internationally and in South Africa, university IT programmes are accredited by professional accreditation bodies, such as ABET and the British Computer Society. Presently, there is no academic body that coordinates the curriculum and ensures standardised curriculum content and quality assurance standards for the different types of IT diploma qualifications offered by the various academic institutions in South Africa. In this paper, a South African IT Diploma Advisory Board (SAITDAB) is proposed that will be associated with the South African Computing Accreditation Board (SACAB). The SACAB is established under the control of the Institution of IT Professionals of South Africa (IITPSA). The purpose of this research study is to investigate how other international IT professional bodies manage diploma programme content and standards and to obtain the opinion of academics at institutions offering IT diploma programmes on the proposed establishment of the SAITDAB. The findings indicate strong support for establishing a rationale and process for the creation of a Body of Knowledge for IT diploma and IT advanced diploma programmes offered by South African Universities by the SAITDAB.
A Study of Applying Unsupervised Learning Methods for Document Clustering and Automatic Categorization of Software

Software categorization is the task to group software into categories that briefly represent the behavior of software. However, with the rapid growth of software, manual software categorization has become almost impossible and expensive. Therefore, automatic software categorization has become necessary. In this paper, we propose to utilize two different document clustering methods, nonnegative matrix factorization (NMF) and spectral clustering, to fulfill the automatic software categorization respectively. In our work, we not only compare our performance with an existing automatic software categorization method LACT, but we also make profound analysis on the difference between our both clustering methods. Our methods require only at most about 1/10 execution time of LACT while the fastest one is hundreds of times faster than LACT, achieving at most 26% and 100% better performances based on two criteria, BCubed F1-measure and Adjusted Rand Index.
Binocular Visual Mechanism Guided No-Reference Stereoscopic Image Quality Assessment Considering Spatial Saliency

In recent years, with the popularization of 3D technology, stereoscopic image quality assessment (SIQA) has attracted extensive attention. In this paper, we propose a two-stage binocular fusion network for SIQA, which takes binocular fusion, binocular rivalry and binocular suppression into account to imitate the complex binocular visual mechanism in the human brain. Besides, to extract spatial saliency features of the left view, the right view, and the fusion view, saliency generating layers (SGLs) are applied in the network. The SGL apply multi-scale dilated convolution to emphasize essential spatial information of the input features. Experimental results on four public stereoscopic image databases demonstrate that the proposed method outperforms the state-of-the-art SIQA methods on both symmetrical and asymmetrical distortion stereoscopic images.
Comparison of Different Classification Algorithms for Extraction Information from Invoice Images Using an N-Gram Approach

Artificial intelligence (AI) has started to be used in many areas today.One of these areas is the accounting sector.Accounting companies may sometimes be inadequate especially in the face of intense invoicing transactions of large companies.This problem raised the need to process invoices by an Artificial Intelligence powered system.The goal of this work is to determine the best machine learning model to extract information such as invoice number, invoice date, due date, delivery date, total gross, total net, vat amount and IBAN from the invoice image files.Information obtained by the Tesseract Optical Character Recognition (OCR) system has been converted into n-gram format.A number of attributes of the n-gram are calculated such as the coordinates, the length, the width, the line number, the template information of n-grams, the Levenshtein and the Jaro-Winkler distances between the candidate ngrams and the keywords in the control keywords list.The use of the Levenshtein distance between candidate n-grams and the control keywords has resulted in a sufficiently high predictive rate.The most appropriate model and features are determined for the training.Algorithms such as Random Forest, Gradient Boosting Machine, Extreme Gradient Boosting, K-Nearest Neighbors, AdaBoost and Decision Tree were compared as prediction models.A total of 9910 invoices were used by splitting 80% for training and 20% for testing.It was observed that the Random Forest model using the Levenshtein distance is the best model with an average F1 score of 0.9137.
The Corrosion of Academic Character

In the Corrosion of Character, Richard Sennet () describes the consequences of working life under the new capitalism for the formation of character. In particular, he shows how flexibility and uncertainty, as two sides of the same coin, create a mentality that has ever less room for sound judgment and moral consideration. This paper concerns academic character formation and its corrosion. The focus is on PhD students because they are in the process of formation to become scientists and scholars. The aim is to provide a useful perspective for understanding the frame of mind engendered by the rules of the game of higher education as currently applied and construed. Arguing that character is the result of conduct, and not the other way around, the paper describes how the mindset of human beings involved in the pursuit of knowledge is inevitably bound up with everyday practices of academic training and study seen as forms of work. Since universities are perhaps the singular most important force of ideology production and reproduction in society, given that they both train the professions that enact and enforce it as well as produce the science and scholarship on which those professions are based, the education of those who are to be entrusted with the inculcation of the relevant norms and values is of fundamental relevance.
User Involvement for Application Development: Methods, Opportunities and Experiences from Three Different Academic Projects

This paper introduces several tools and methods of user inquiry and usability testing by means of three academic software development projects. The projects have different objectives and user groups and address different issues. This makes it possible to show the diverse ways of involving users in the development of solutions and allows to evaluate which human-centered approach might be suitable for similar projects and problems.
An Effective and Computationally Efficient Approach for Anonymizing Large-Scale Physical Activity Data

Publishing physical activity data can facilitate reproducible health-care research in several areas such as population health management, behavioral health research, and management of chronic health problems. However, publishing such data also brings high privacy risks related to re-identification which makes anonymization necessary. One of the challenges in anonymizing physical activity data collected periodically is its sequential nature. The existing anonymization techniques work sufficiently for cross-sectional data but have high computational costs when applied directly to sequential data. This article presents an effective anonymization approach, multi-level clustering-based anonymization to anonymize physical activity data. Compared with the conventional methods, the proposed approach improves time complexity by reducing the clustering time drastically. While doing so, it preserves the utility as much as the conventional approaches.
Educational Lexical Resource Enrichment Using Machine Learning Classifiers

Opinion mining is one of the most popular topics today in the Natural Language Processing (NLP) and Artificial Intelligence (AI) domains. It intends to analyze people's emotions, feelings, humor, appreciation, etc. It covers a wide field of applications and education is one of them. The study of opinions in the educational field can be very useful; the use of lexical resources specific to the studied field can help in different tasks of NLP. Besides, the improvement of these lexical resources can play an important role in the opinion extraction task as it improves the opinion detection process. Our work consists of the enrichment of a French lexical resource called DICO, dedicated to educational opinion mining through a recalculation of its polarities. This enrichment is based on the use of several features including the word embedding to extract semantic information from a corpus of annotated comments, built from various educational sources. This semantic information is used to develop different classification models such as the K-Nearest Neighbors, Support Vector Machine, Decision Tree, Naive Bayes, MLP, Random Forest, AdaBoost, and SGD. The development of classification models is implemented using the high-level programming language Python. These models classify the synsets of the lexical resource DICO, and the results of this classification are used for the recalculation of DICO polarities to get a new lexical resource: DICO-2. We compared the classification performances of the corpus using DICO with those obtained using DICO-2, and the results show that DICO-2 allows a better classification of opinions, with a noticeable increase in performances.
Toward a Mixed Tangle-Blockchain Architecture

This paper presents the design and implementation of a new platform that takes into consideration the requirements and constraints resulting from the industrial context based on IoT. This platform combines the “Tangle” and “Blockchain” techniques. Tangle is primarily designed to address scale-up issues and the relatively high cost (time and resource) of transactions in a traditional blockchain-based platform. Unlike the “blockchain” structure, it consists of a solid mathematical foundation called DAG (Direct Acyclic Graph). It uses a validation process in which transactions are entered into the distributed registry after authenticating two other randomly selected transactions according to a Poisson distribution (thus, the locations of the new transactions are chosen using random runs in the graph). Therefore, it is an easily scalable system that does not require mining or transaction fees. We aim to study the integration of Tangle and Blockchain techniques to improve the performance and scalability of distributed registry-based platforms to be adapted in industrial enterprises whose processes incorporate or are based on IoT.
Aspect-Level Sentiment Joint Detection Based on Graph Attention Network

Aspect-level sentiment analysis is to perform sentiment analysis of a specific aspect by learning the context information of the text, most of the existing methods are independent detection of the sentiment of the aspect or target, ignoring the existence of a certain correspondence between them. This paper proposes an aspect-level multi-element sentiment detection model for aspect-based sentiment analysis tasks, use BERT to get the initial embedding vector from the input, adopt BiGRU to obtain contextual semantic information, according to the dependency between aspect and target, an aspect-level dependency relationship-graph attention network is proposed, which effectively joint detection of {target, aspect, emotion} triples, and more accurately analyzes the sentiment polarity of the sentence in the corresponding aspect. In order to verify the effectiveness of the model, our method was evaluated on two restaurant data sets of SemEval 2015 task 12 and SemEval 2016 task 5, the experimental results show that the performance of the model proposed in this paper reached 58.66% and 67.14%, it has also been significantly improved in its sub-tasks, the performance exceeds the baseline system, and good experimental results have been achieved.
Bosonic Random Walk Neural Networks for Graph Learning

The development of Graph Neural Networks (GNNs) has led to great progress in machine learning on graph-structured data. These networks operate via diffusing information across the graph nodes while capturing the structure of the graph. Recently there has also seen tremendous progress in quantum computing techniques. In this work, we explore applications of multi-particle quantum walks on diffusing information across graphs. Our model is based on learning the operators that govern the dynamics of quantum random walkers on graphs. We demonstrate the effectiveness of our method on classification and regression tasks.
UX Design for Automated Driving: Industry Perspective

Automated driving is transforming the driving experience in the 21st-century vehicle. As a result, interacting with in-vehicle information systems, infotainment, in-car productivity or social interactions and real-life experiences with other passengers in the car, are slowly emerging as primary activities. UX researchers focus more and more on the users not only by developing products and services for them and enhancing their experiences but also actively involving them in co-designing for their own experience. Our research with designers inside the automotive industry suggests that the industry is exceptionally traditional regarding the methods and tools used to design and evaluate interactive experiences in comparison to other domains. In this chapter, we will report on the limitations of the industry in comparison to academia. Besides, we will report on the needs of the automotive UX practitioners and discuss the state of the art methods and tools that are most valued in the automotive industry.
Analysis of the Encryption and Compression Coding Performance in Contemporary Wavelet Methodologies on Medical Images

The purpose of this research paper is to investigate, explore, and analyze the coding performance of contemporary wavelet methodologies on medical images used for medical diagnostics, including Embedded Zerotree Wavelet (EZW), Set Partition in Hierarchical Trees (SPIHT), Spatial Orientation Tree Wavelet (STW), Wavelet Difference Reduction (WDR), and Adaptively Scanned Wavelet Difference Reduction (ASWDR). Rate-distortion methods of preparation are theoretically bound for compression static resources given an integrity requirement. However, there is no well-known limit for seemingly non-stationary pictures. The lack of a theoretical lower limit complicates the issue of photo compression. It is uncertain how much farther a picture can be compressed. There is no theoretically adequate metric of deformation that can be used to evaluate the performance of distinct images. Choosing the most suited wavelet filter for lossless image compression of clinical imaging data is one of the problems and challenging issues. We evaluated the trade between reconstructed picture quality and coder type in our research, and the simulation findings are reported in this paper. Transform coding is a well-known technique for compressing visual data. Two-dimensional (2-D) photographs are transferred from the spatial domain to the frequency domain via a transform-based compression approach. A successful transformation will compress useful information into a limited number of low-frequency transform coefficients. An HVS is more sensitive to low spatial frequency energy than it is to high spatial frequency energy. Thus, compression may be performed by quantizing the coefficients, such that only the most significant coefficients (low-frequency coefficients) are conveyed and the others are removed. By picking a wavelet function, quantizer, and statistical coder, a wavelet image compression system may be developed. We evaluated the influence of wavelet analysis and depiction, compression ratio, picture content, and resolution on image quality using a few various types of wavelets. We demonstrate in this paper that while looking for the optimum wavelet, it is necessary to take into consideration both objective and subjective metrics of image quality. We emphasize the DWT's performance advantage over the DCT.
Evaluation of Collision Resolution Methods Using Asymptotic Analysis

The basic idea of hashing is to use a hash function h(k) to reduce a giant universe to a reasonably small table such that U = {0, 1, .., |U | − 1} → T {0, 1, …, |T | − 1} with Pr <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</inf> {h(key) → T } = 1/|K| which is independent of h(y) for all x y ∈ U. Hashing has been an efficient implicit and explicit search technique for retrieving an element from a collection for many years. Collision is inevitable in a high load factor environment, thus several algorithms for resolving collision to find an alternative slot for a key x in a hash table have been presented. One of the important property of these algorithms is running time complexity. Here, we analysed the performance of these algorithms based on runtime analysis for retrieving and inserting a key using asymptotic analysis. We discovered that many of these algorithms have a non constant insertion and retrieval time. Furthermore, only one algorithm was discovered to have an exact constant time for both lookup and insertion of any arbitrary key into a hash table.
Text Detection of a Chinese Long and Dense Text Dataset

In daily life, it is important to interpret information correctly from different types of texts. After applying the neural network to object detection, a breakthrough has been made in natural scene text detection. However, only a few research has focused on dense, long segments of Chinese text. Moreover, there are hardly any datasets that include Chinese text boxes with large gap in aspect ratio and short interval. Considering these, we propose a Network to Detect Chinese Long text (DCLnet) and a Chinese Long and Dense text Dataset (CLD). It can not only accurately detect long and dense Chinese text, but also predict text in arbitrary directions using rotated quadrilateral shapes. In this method, we have improved AdvancedEAST model. The feature extraction part selects Resnet50, one of the latest networks. Additionally, reduce-redundancy module is added before the prediction stage to reduce redundant computations. We conduct several experiments on datasets including ICDAR2015 and CLD. Through comparative analysis, the detection accuracy of the algorithm for long and dense Chinese text is obviously better than the previous methods. It achieves a competitive F-score of 0.761 on CLD dataset.
Performance Analysis of Sense Embeddings in Multilingual WSD Framework

Execution of Word Sense Disambiguation (WSD) is one of the difficult undertakings in the space of Natural language processing (NLP). Age of sense clarified corpus for multilingual WSD is far off for most languages regardless of whether assets are accessible. In this paper we propose a solo technique utilizing word and sense embeddings for working on the presentation of WSD frameworks utilizing untagged corpora and make two bags to be specific context bag and wiki sense bag to create the faculties with most noteworthy closeness. Wiki sense bag gives outer information to the framework needed to help the disambiguation exactness. We investigate Word2Vec model to produce the sense back and notice huge execution acquire for our dataset.
The Siren Call of Content Moderation Formalism

Systems of online content moderation governance are becoming some of the most elaborate and extensive bureaucracies in history, and they are deeply imperfect and need reform. Would-be reformers of content moderation systems are drawn to a highly rule-bound and formalistic vision of how these bureaucracies should operate, but the sprawling chaos of online speech is too vast, ever-changing, and varied to be brought into consistent compliance with rigid rules. This essay argues that the quest to make content moderation systems ever more formalistic will not fix public and regulatory concerns about the legitimacy and accountability of how platforms moderate content on their services. The largest social media platforms operate massive unelected, unaccountable, and increasingly complex bureaucracies that decide to act or not act on millions of pieces of content uploaded to their platforms every day. A formalistic model, invoking judicial-style norms of reasoning and precedent, is doomed to fail at this scale and complexity. As these governance systems mature, it is time to be content moderation realists about the task ahead.
Vision-Based End-to-End Deep Learning for Autonomous Driving in Next-Generation IoT Systems

The Internet of Things (IoT) systems have grown and become an essential part of everyday lives. Since most computations are performed in the cloud, traditional IoT systems are not designed to meet the requirements of high reliability and low latency in autonomous driving systems. With the help of deep learning, IoT network is evolving into the Internet of Autonomous Things (IoAT). IoAT as the next-generation IoT network is expected to process more information locally and transform computers into autonomous things, not only freely roaming in and interacting with the physical environment without human guidance but also making proactive decisions based on the IoAT network information. This chapter presents an overview of research on using end-to-end deep learning technologies for computer vision-based autonomous driving systems. It briefly discusses the ethics of autonomous driving; it also describes autonomous driving paradigms and the associated deep learning methodologies. Furthermore, it proposes an IoAT-compatible low-cost, low-latency, high-accuracy, and high-reliability CNN-LSTM-based autonomous driving model that incorporates temporal information, transfer learning, and navigational command. It also provides a detailed analysis against existing models. Finally, the chapter draws its conclusions and discusses future research directions to further improve system performance.
Rapid prototyping approach for audio augmented reality user experiences

Audio consumer devices, such as headphones, are increasingly supporting spatial audio. This opens opportunities for UX designers to explore the design domain of audio-only Augmented Reality (AAR) experiences. The paper documents a hands-on approach to prototyping audio AR with a combination of game engine software, headphones, and a smartphone. The approach was delivered at SIGGRAPH2021 in the Labs session format.
Status Quo Intelligent Evaluation of the Network Security Legal Awareness: Modelling and Simulations

Status quo intelligent evaluation of the network security legal awareness with modeling and simulations is studied in this paper. Through the security risk assessment of the network system, various network security issues that appear in the application process of the network system can be effectively dealt with in real time. Proxy firewall technology is aimed at personal computer users who need to use a proxy network. When personal computer users access remotely through a proxy server, they often need to then lower the personal computer firewall protection level due to the need for data transmission. With this requirement, this paper proposes the novel modelling method to construct the efficient system. The experiment has proven the satisfactory performance.