Reviewerly/exHarmony huggingface dataset:
MMArt-ACM'20: International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia 2020

The International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia (MMArt-ACM) solicits contributions on methodology advancement and novel applications of multimedia artworks and attractiveness computing that emerge in the era of big data and deep learning. Despite the strike of the Covid-19 pandemic, this workshop attracts submissions of diverse topics in these two fields, and the workshop program finally consists of five presented papers. The topics cover image retrieval, image transformation and generation, recommendation system, and image/video summarization. The actual MMArt-ACM'20 Proceedings are available in the ACM DL at: https://dl.acm.org/citation.cfm?id=3379173
Object Tracking using Motion Estimation based on Block Matching Algorithm

Object tracking and motion estimation are key components in large number of applications, ranging from the navigation of autonomous vehicles to video data compression. This object tracking uses the motion estimation for continuous tracking of distinctive features in a successive manner. This helps to detect the motion of features used in computing to detect the exact or appropriate solution by optimizing the block size. To obtain the better prediction quality by using variable size motion estimation, proposed a block matching algorithm for motion estimation, object tracking, and video data compression.
Introduction of Semantic Model to Help Speech Recognition

Current Automatic Speech Recognition (ASR) systems mainly take into account acoustic, lexical and local syntactic information. Long term semantic relations are not used. ASR systems significantly decrease performance when the training conditions and the testing conditions differ due to the noise, etc.. In this case the acoustic information can be less reliable. To help noisy ASR system, we propose to supplement ASR system with a semantic module. This module re-evaluates the N-best speech recognition hypothesis list and can be seen as a form of adaptation in the context of noise. For the words in the processed sentence that could have been poorly recognized, this module chooses words that correspond better to the semantic context of the sentence. To achieve this, we introduced the notions of a context part and possibility zones that measure the similarity between the semantic context of the document and the corresponding possible hypothesis. The proposed methodology uses two continuous representations of words: word2vec and FastText. We conduct experiments on the publicly available TED conferences dataset (TED-LIUM) mixed with real noise. The proposed method achieves a significant improvement of the word error rate (WER) over the ASR system without semantic information.
Simultaneous Inference for Massive Data: Distributed Bootstrap

In this paper, we propose a bootstrap method applied to massive data processed distributedly in a large number of machines. This new method is computationally efficient in that we bootstrap on the master machine without over-resampling, typically required by existing methods \cite{kleiner2014scalable,sengupta2016subsampled}, while provably achieving optimal statistical efficiency with minimal communication. Our method does not require repeatedly re-fitting the model but only applies multiplier bootstrap in the master machine on the gradients received from the worker machines. Simulations validate our theory.
Assessment of learning difficulties in the category of children with dyslexia

This study is aimed to assess the diagnostic criteria for children with dyslexia. The diagnostic criteria are based on behavioral symptoms that appear in children with difficulty in reading and writing. The measurement of criteria of dyslexia is based on the criteria found in the specific learning difficulties category in DSM-5. Measurements were made of children who were considered to have learning difficulties as indicated by the inability and poor learning outcomes. Measurements were by testing the ability to read and write. Research used a qualitative approach with a case study method to obtain a description of the results. Research subjects were conducted on 65 elementary school students who had indications of learning difficulties. The results on the ability to read and write obtained as many as 28 students who were indicated to have criteria of dyslexia. Materials testing with aspects of phonological awareness and orthographic aspects. The next measurement is done by conducting an intelligence test using WISC III. Combining the results of measurements of reading and writing abilities and the results of intelligence tests are used to diagnose children with dyslexia, based on the criteria for indications of dyslexia taken from DSM-5. The results category on children with dyslexia can be diagnosed using one or more of the categories available on DSM-5
Performance evaluation of windows virtual machines on a Linux host

Virtualization has experienced a dramatic expansion recently and today is ubiquitous in modern IT industry since it provides numerous benefits to companies and individual users. It increases efficiency, flexibility and scalability of IT equipment by enabling different software-based environments on a single physical hardware. Each virtual machine is a separate instance that is completely independent and separated from the computer hardware and it runs on emulated hardware. Emulated hardware is managed by virtualization tool that provides lower resources when compared to physical hardware. This paper presents a performance evaluation of three different virtual machines run by three recent versions of Windows operating system, namely Windows 7TM Professional, Windows 8.1TM Professional and Windows 10TM Professional, on a host computer system run by Linux Ubuntu. Performance measurement results show that Window 7 is the most suitable virtual operating system since it obtains the best performance when run on a Linux host.
A model-driven approach for the verification of an adaptive service composition

The development of web service compositions is a complex task that needs coherent mechanisms in order to maintain the quality of the provided business process and to satisfy user needs. This paper proposes a development process of an adaptable composed web service and mainly focuses on the reliability and the performance properties. It explores the model driven architecture transformation technique in order to formally model the whole service orchestration using the timed coloured Petri net formalism. The software CPN Tools offers, among others, the ASK-computational tree logic, the model checking technique and several monitors that will be exploited to describe and verify different properties at design time. They will also be used via access/CPN library in order to reason about the reconfiguration technique at runtime. An example of an identity card management process is given to prove the feasibility of the proposed solution.
Temporal Phenotyping using Deep Predicting Clustering of Disease Progression

Due to the wider availability of modern electronic health records, patient care data is often being stored in the form of time-series. Clustering such time-series data is crucial for patient phenotyping, anticipating patients' prognoses by identifying patients, and designing treatment guidelines that are tailored to homogeneous patient subgroups. In this paper, we develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest (e.g., adverse events, the onset of comorbidities). To encourage each cluster to have homogeneous future outcomes, the clustering is carried out by learning discrete representations that best describe the future outcome distribution based on novel loss functions. Experiments on two real-world datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks and identifies meaningful clusters that can be translated into actionable information for clinical decision-making.
Artificial Intelligence for Speech Recognition

Artificial Intelligence (AI) for speech recognition talks about the study and design of intelligent agents & also used to describe a property of machines or programs. It makes machines smarter and more useful and is less expensive than natural intelligence. Speech recognition converts spoken words to machine-readable input. It is also called Voice Recognition. The paper deals with various aspects of Speech recognition. Speech recognition includes-Voice dialling, Content-based spoken audio search, Speech-to-text processing, Performance of speech recognition systems. The paper states that speech recognition is usually specified in terms of accuracy and speed. Accuracy may be measured in terms of performance accuracy which is usually rated with word error rate, whereas speed is measured with the real time factor. This paper presents the Speech Recognition in Artificial intelligence systems and it is important to consider the environment in which the speech recognition system has to work.I.
2K-Reality and the Compliant Sports Augmentation Framework for Grassroots Sports

In this paper, we introduce the Compliant Sports Augmentation Framework (CSAF), which aims to promote a sociocultural approach to the design of sports technology for grassroots sports. The CSAF design criteria advocate enhancing the experiential qualities of grassroots sports by respecting, protecting and cultivating existing practices, meanings and values. We developed the CSAF by synthesising the theory, practice and evaluation components of our 2K-Reality design research project that sought to enhance the enjoyment of playing and watching pickup basketball with digital technology. The disciplines that contributed to our development of the CSAF include sports philosophy, sports psychology, games studies, public health and sports management.
Similarity Scoring for Dialogue Behaviour Comparison

The differences in decision making between behavioural models of voice interfaces are hard to capture using existing measures for the absolute performance of such models. For instance, two models may have a similar task success rate, but very different ways of getting there. In this paper, we propose a general methodology to compute the similarity of two dialogue behaviour models and investigate different ways of computing scores on both the semantic and the textual level. Complementing absolute measures of performance, we test our scores on three different tasks and show the practical usability of the measures.
Does Technological Self-Efficacy Decrease New Salesperson Job Insecurity: An Abstract

A well-established influencer of salesperson job performance that has not received as much attention over the years is job insecurity. Given the technological revolution that has been occurring in the field of professional selling for several decades, requiring salespeople to become more technologically proficient than their predecessors ever had to be, job insecurity regarding this unique aspect of professional selling is exceedingly overdue for academic inquiry. Job insecurity as an influencer of job performance has not received as much academic research attention as its counterparts. Chaker et al. (2016: 344) unambiguously declare that “salesperson insecurity has been largely neglected in the literature.” Ahearne et al.’s (2004) study on the impact of customer relationship management (CRM) technology on sales performance showed technology has an observable “disabling effect” on sales performance. Recognizing that the relationship between technology and sales performance is dependent on the salesperson’s level of ability, their study calls for further research “into the differing relationships between different expertise classes of users,” and into technological proficiency’s moderating effect on sales performance, yet not on the its possible effect on salesperson job insecurity. Johnson and Bharadwaj (2005) reported that firms were developing, “web sites designed to provide information and conduct transactions with customers, replacing many routine sales force activities…heightening job insecurity concerns” (p. 3). More than a decade later, their portentous observation demands further investigation. This study will investigate if technological self-efficacy creates a less insecure salesperson among newer salespeople compared to more experienced and established salespeople who learned how to sell without digital supplementation. This current study strives to help remedy this overlooked enigmatic gap in the existing literature of professional sales. It will not only contribute to the body of theoretical scholarship in this field but will additionally provide sales managers and practitioners with new and relevant information, which can aid in improving the performances of both new and established sales force members.
The mechanism for intellectual property protection under Chinese e-commerce law: more powerful than necessary

The notification and taking down procedure of the newly enacted Chinese E-Commerce Law is uncannily applicable to all infringements of all categories of IP in selling all kinds of goods on e-platforms, even to those which cannot be determined with a sufficient degree of certainty on available evidence by the e-platform. This mechanism overreaches grounds for the DMCA notification and taking down procedure and Chinese preliminary injunction. It overstrains e-platforms’ capabilities to assess intellectual property infringement and is indifferent to e-platforms’ lack of legal and technological expertise. It is wrong to be obsessed with the DMCA procedure – which is ceteris paribus – without measuring up to the distinctive e-platforms, e-sellers and e-businesses. Both e-platforms and e-sellers fundamentally have a common interest in the good governance of the platforms. It is out of place for the Chinese E-Commerce Law to encourage e-platforms to be a conduit for passing notifications and to take measures upon notifications of IP infringement mechanically and unscrupulously.
Description of old maps in the Europeana Data Model

This paper presents research on archival map descriptions and aims to provide effective access to cartographic resources collected in digital libraries and archives. In the study, metadata profiling of archival maps is proposed, which is based on the ISO 19115-1 standard and contains map-specific information, which should be saved as metadata to provide the proper characteristics of the old maps. To develop metadata profiles for archival maps, some ISO metadata elements have been deleted, while others that are important for the maps have been added or changed. Controlled vocabularies with pre-defined values are also proposed. Metadata profiles for old maps are presented as Unified Modeling Language (UML) class diagrams. Moreover, the implementation of the proposed metadata profiles in the Europeana Data Model (EDM) is presented. EDM elements, such as features and properties, which enable the recording of map-specific data, are indicated. New metadata elements, proposed by the authors, have also been added.
Evolutionary Algorithm for Sinhala to English Translation

Machine Translation (MT) is an area in natural language processing, which focus on translating from one language to another. Many approaches ranging from statistical methods to deep learning approaches are used in order to achieve MT. However, these methods either require a large number of data or a clear understanding about the language. Sinhala language has less digital text which could be used to train a deep neural network. Furthermore, Sinhala has complex rules therefore, it is harder to create statistical rules in order to apply statistical methods in MT. This research focuses on Sinhala to English translation using an Evolutionary Algorithm (EA). EA is used to identifying the correct meaning of Sinhala text and to translate it to English. The Sinhala text is passed to identify the meaning in order to get the correct meaning of the sentence. With the use of the EA the translation is carried out. The translated text is passed on to grammatically correct the sentence. This has shown to achieve accurate results.
Explanatory Q&amp;A recommendation algorithm in community question answering

Purpose In community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all content. Thus, there is a need for further explanation of the concepts that appear in the answers. Moreover, the large number of question and answer (Q&amp;A) documents make manual retrieval difficult. This paper aims to alleviate these issues for CQA websites. Design/methodology/approach In the paper, an algorithm for recommending explanatory Q&amp;A documents is proposed. Q&amp;A documents are modeled with the biterm topic model (BTM) (Yan et al. , 2013). Then, the growing neural gas (GNG) algorithm (Fritzke, 1995) is used to cluster Q&amp;A documents. To train multiple classifiers, three features are extracted from the Q&amp;A categories. Thereafter, an ensemble classification model is constructed to identify the explanatory relationships. Finally, the explanatory Q&amp;A documents are recommended. Findings The GNG algorithm shows good clustering performance. The ensemble classification model performs better than other classifiers. The both effect and quality scores of explanatory Q&amp;A recommendations are high. These scores indicate the practicality and good performance of the proposed recommendation algorithm. Research limitations/implications The proposed algorithm alleviates information overload in CQA from the new perspective of recommending explanatory knowledge. It provides new insight into research on recommendations in CQA. Moreover, in practice, CQA websites can use it to help retrieve Q&amp;A documents and facilitate understanding of their contents. However, the algorithm is for the general recommendation of Q&amp;A documents which does not consider individual personalized characteristics. In future work, personalized recommendations will be evaluated. Originality/value A novel explanatory Q&amp;A recommendation algorithm is proposed for CQA to alleviate the burden of manual retrieval and Q&amp;A overload. The novel GNG clustering algorithm and ensemble classification model provide a more accurate way to identify explanatory Q&amp;A documents. The method of ranking the explanatory Q&amp;A documents improves the effectiveness and quality of the recommendation. The proposed algorithm improves the accuracy and efficiency of retrieving explanatory Q&amp;A documents. It assists users in grasping answers easily.
DenseNet-DC: Optimizing DenseNet Parameters Through Feature Map Generation Control

Convolutional Neural Networks still suffer from the need for great computational power, oftenrestricting their use on various platforms. Therefore, we propose a new optimization method made for DenseNet, a convolutional neural network that has the characteristic of being completely connected. The objective of the method is to control the generation of the characteristic maps in relation to the moment the network is in, aiming to reduce the size of the network with the minimum of loss in accuracy. This control occurs reducing the number of feature maps through the addition of a new parameter called the Decrease Control or dc value, where the decrease occurs from half of the layers. In order to validate the behavior of the proposed model, experiments were performed using different image bases: MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, CALTECH-101, Cats vs Dogs and TinyImageNet. Some of the results achieved were: for the MNIST and Fashion-MNIST base, there was 43% parameter reduction. For the CIFAR-10 base achieved a 44% reduction in network parameters, while in base CIFAR-100 the parameter reduction are 43%. In the CALTECH-101 base the parameter optimization was 35%, while the Cats vs Dogs optimized 30% of model parameters. Finally, the TinyImageNet base was reduced 31% of the parameters.
South Africa's COVID-19 Tracing Database: Risks and rewards of which doctors should be aware.

In response to the COVID-19 pandemic, South Africa (SA) has established a Tracing Database, collecting both aggregated and individualised mobility and locational data on COVID-19 cases and their contacts. There are compelling public health reasons for this development, since the database has the potential to assist with policy formulation and with contact tracing. While potentially demonstrating the rapid facilitation through technology of an important public service, the Tracing Database does, however, infringe immediately upon constitutional rights to privacy and heightens the implications of ethical choices facing medical professionals. The medical community should be aware of this surveillance innovation and the risks and rewards it raises. To deal with some of these risks, including the potential for temporary rights- infringing measures to become permanent, there are significant safeguards designed into the Tracing Database, including a strict duration requirement and reporting to a designated judge. African states including SA should monitor this form of contact tracing closely, and also encourage knowledge-sharing among cross-sectoral interventions such as the Tracing Database in responding to the COVID-19 pandemic.
Grammar-compressed Self-index with Lyndon Words

We introduce a new class of straight-line programs (SLPs), named the Lyndon SLP, inspired by the Lyndon trees (Barcelo, 1990). Based on this SLP, we propose a self-index data structure of $O(g)$ words of space that can be built from a string $T$ in $O(n \lg n)$ expected time, retrieving the starting positions of all occurrences of a pattern $P$ of length $m$ in $O(m + \lg m \lg n + occ \lg g)$ time, where $n$ is the length of $T$, $g$ is the size of the Lyndon SLP for $T$, and $occ$ is the number of occurrences of $P$ in $T$.
AUTOMATIC CO-REGISTRATION OF MEG AND 3D DIGITIZATION USING 3D GENERALIZED HOUGH TRANSFORM

In this study, we propose a new automatic co-registration method for the coordinate systems of magnetoencephalography (MEG) data and third dimension digitizer (3D DIG) data of a head using the 3D generalized Hough transform (GHT) during image processing. The technique is important for the research of brain functionalities; it can be done automatically, and quickly combines data from functional brain mapping tools like MEG and DIG, etc. MEG is a measurement instrument used to noninvasively analyze the physiological activity of neurons with high temporal resolution, but it lacks the head-shape of subjects and head with respect to the MEG sensors. 3D DIG can record head- shape, facial features, and anatomical markers in a 3D coordinate system in real time. Thus, combining the two modalities is beneficial in correlating the obtained brain data with physiological activity. According to much of the research, the GHT is useful for recognizing or locating two 2D images. However, the GHT algorithm can be extended to the 3D GHT to automatically co-register the 3D data. In this study, we use the 3D GHT to co-register three subject datasets with MEG and 3D DIG data, and evaluate the average distance errors between the proposed method and the MEG160 system. Some of the experimental results demonstrate the applicability of the proposed 3D GHT accurately and efficiently.