Reviewerly/exHarmony huggingface dataset:
Kernel-Free Quadratic Surface Minimax Probability Machine for a Binary Classification Problem

In this paper, we propose a novel binary classification method called the kernel-free quadratic surface minimax probability machine (QSMPM), that makes use of the kernel-free techniques of the quadratic surface support vector machine (QSSVM) and inherits the advantage of the minimax probability machine (MPM) without any parameters. Specifically, it attempts to find a quadratic hypersurface that separates two classes of samples with maximum probability. However, the optimization problem derived directly was too difficult to solve. Therefore, a nonlinear transformation was introduced to change the quadratic function involved into a linear function. Through such processing, our optimization problem finally became a second-order cone programming problem, which was solved efficiently by an alternate iteration method. It should be pointed out that our method is both kernel-free and parameter-free, making it easy to use. In addition, the quadratic hypersurface obtained by our method was allowed to be any general form of quadratic hypersurface. It has better interpretability than the methods with the kernel function. Finally, in order to demonstrate the geometric interpretation of our QSMPM, five artificial datasets were implemented, including showing the ability to obtain a linear separating hyperplane. Furthermore, numerical experiments on benchmark datasets confirmed that the proposed method had better accuracy and less CPU time than corresponding methods.
FPGA Implementation of Particle Filters for Robotic Source Localization

Particle filtering is very reliable in modelling non-Gaussian and non-linear elements of physical systems, which makes it ideal for tracking and localization applications. However, a major drawback of particle filters is their computational complexity, which inhibits their use in real-time applications with conventional CPU or DSP based implementation schemes. The re-sampling step in the particle filters creates a computational bottleneck since it is inherently sequential and cannot be parallelized. This paper proposes a modification to the existing particle filter algorithm, which enables parallel re-sampling and reduces the effect of the re-sampling bottleneck. We then present a high-speed and dedicated hardware architecture incorporating pipe-lining and parallelization design strategies to supplement the modified algorithm and lower the execution time considerably. From an application standpoint, we propose a novel source localization model to estimate the position of a source in a noisy environment using the particle filter algorithm implemented on hardware. The design has been prototyped using Artix-7 field-programmable gate array (FPGA), and resource utilization for the proposed system is presented. Further, we show the execution time and estimation accuracy of the high-speed architecture and observe a significant reduction in computational time. Our implementation of particle filters on FPGA is scalable and modular, with a low execution time of about 5.62 μs for processing 1024 particles (compared to 64 ms on Intel Core i7-7700 CPU with eight cores clocking at 3.60 GHz) and can be deployed for real-time applications.
Improving the Availability of University Offerings for the Business Sector Based on Blended Learning Tools

In the dynamic contemporary world, characterised by the ubiquitous presence of information and com-munication technologies, the role and tasks of higher education institutions and their links with the so-cial and economic environment keep changing. These institutions also undergo the process of digital transformation and actively respond to the needs of entrepreneurs. Blended learning – a solution which sees the value in building interpersonal relationships while taking advantage of e-learning tools – can significantly increase the availability of an institution’s educational offering for the business sector.
TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation

Identifying whether a word carries the same meaning or different meaning in two contexts is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction.Most of the previous work in this area rely on language-specific resources making it difficult to generalise across languages.Considering this limitation, our approach to SemEval-2021 Task 2 is based only on pretrained transformer models and does not use any language-specific processing and resources.Despite that, our best model achieves 0.90 accuracy for English-English subtask which is very compatible compared to the best result of the subtask; 0.93 accuracy.Our approach also achieves satisfactory results in other monolingual and crosslingual language pairs as well.
Tilted Cross-Entropy (TCE): Promoting Fairness in Semantic Segmentation

Traditional empirical risk minimization (ERM) for semantic segmentation can disproportionately advantage or disadvantage certain target classes in favor of an (unfair but) improved overall performance. Inspired by the recently introduced tilted ERM (TERM), we propose tilted cross-entropy (TCE) loss and adapt it to the semantic segmentation set-ting to minimize performance disparity among target classes and promote fairness. Through quantitative and qualitative performance analyses, we demonstrate that the proposed Stochastic TCE for semantic segmentation can offer improved overall fairness by efficiently minimizing the performance disparity among the target classes of Cityscapes.
Quality of perception prediction in 5G slices for e-Health services using user-perceived QoS

In order to compete for a prominent market share, network operators and service providers should retain and increase the verticals’ subscription, catering to their needs in order to differentiate themselves from competitors. In this scenario, verticals’ satisfaction arises of paramount importance. As such, user experience is becoming a reliable indicator for service providers and telecommunication operators to convey overall end-to-end system functioning. To properly estimate end user satisfaction, operators and service providers require efficient means for quality monitoring and estimation at all layers, in conjunction with mechanisms able to maintain said quality at optimum levels. Given these factors, this paper proposes a mechanism for Quality of Perception (QoP) estimation in e-Health services, enabling the QoP-aware management of network slices fulfilling the requirements of supported services. To this end, the paper proposes a cognitive-based architecture which allows for the collection and monitoring of verticals’ data to estimate QoP and provides mechanisms to re-configure the underlying network slices according to the monitored quality levels. A machine learning (ML) model is introduced that aims to forecast any future degradation in the quality perceived by vertical users. In case of a predicted degradation, the proposed architecture reacts and triggers the necessary remedial actions, referred as actuations. In order to evaluate the developed ML model and to showcase the interaction between the different components of the proposed architecture, an experimental study is presented with real data extracted from a roaming ambulance. In addition, a Proof of Concept of the actuation mechanism is demonstrated through an experimental testbed emulating e-Health services.
NIME Scores: a Systematic Review of How Scores Have Shaped Performance Ecologies in NIME

This paper investigates how the concept of score has been used in the NIME community.To this end, we performed a systematic literature review of the NIME proceedings, analyzing papers in which scores play a central role.We analyzed the score not as an object per se but in relation to the users and the interactive system(s).In other words, we primarily looked at the role that scores play in the performance ecology.For this reason, to analyze the papers, we relied on ARCAA, a recent framework created to investigate artifact ecologies in computer music performances.Using the framework, we created a scheme for each paper and clustered the papers according to similarities.Our analysis produced five main categories that we present and discuss in relation to literature about musical scores.
CWE Pattern Identification using Semantical Clustering of Programming Language Keywords

Applications are one of the most used attack surfaces, and they must be secured at source code level, early in the development phase. Developers' inherited culture of programming preserves the patterns of code writing within big organizations or developers' communities, opening an opportunity to use SAST (Static Application Security Testing) complementary solutions to identify insecure code early in the development phase. We propose an Intermediate Representation, strict enough to maintain the security vulnerabilities patterns as defined by MITRE with the Common Weaknesses Enumeration, at the same time agile enough to not strongly depend on the lexical and syntax structure of the programming language, but following programmers' behavior of writing code. The current research phase uses semantical clustering of instructions (keywords) found in C/C++ programs, based on Word Embeddings, which are transported via the resulting (numerical) Intermediate Representation to the various classifiers for security vulnerability patterns detection. We show that there is a good preservation of security patterns despite the generalization of keywords via semantical clustering. This opens an opportunity for innovation in security vulnerability patterns identification, which is more dependent on the programmers' code writing behavior than the programming language specific structure.
Data Warehouse Based on NoSQL: a literature mapping

The Data Warehouse context is in transformation in private and public organizations. New proposals for managing large volumes of data have been defined in the literature, such as the storage and analysis of data by non-relational DBMS, or NoSQL (Not-only SQL) Databases, under a Big Data scenario. Thus, Data Warehouses, which originally relied on relational databases, have been using NoSQL databases to analyze this massive data in recent years. Besides, there is the rise of hybrid transactional and analytical processing, in real-time, known as HTAP, which provides simplicity and quick support to decision making. In this context, this article has the objective to accomplish a literature mapping of DW based on NoSQL databases and advances in HTAP. A methodology for a systematic literature search protocol was used to answer the research questions. We found 72 papers in the last five years, which we analyzed in this paper.
Evaluation of the Clinical and Imaging Findings of Breast Examinations in a Tertiary Facility in Ghana

Breast diseases have been one of the major battles the world has been fighting. In winning this fight, the role of medical imaging cannot be overlooked. Breast imaging reveals hidden lesions which aid physicians to give the appropriate diagnosis and definitive treatment, hence this study, to determine the clinical and imaging findings of breast examinations to document the radiologic features in our setting. This cross-sectional retrospective study reviewed the sociodemographics, imaging reports (mammography and ultrasonography with BI-RADS scores and their features), and the clinical data of 425 patients from September 2017 to September 2020 in the Cape Coast Teaching Hospital. 72 solid lesions with their histology reports were also reviewed. Data obtained were organized, coded, and analyzed using Statistical Package for Social Sciences (SPSS Inc., Chicago, IL, USA) version 20.0. The results obtained were presented in appropriate tables and charts. A chi-squared test was employed for associations and statistical significance was specified at <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> <mo>≤</mo> <mn>0.05</mn> </math> . 63.29% of the patients were married, but only 18.59% had a positive family history of breast cancer. BI-RADS scores 1(57.46%) and 2(27.99%) were the most recurrent findings. The most common BI-RADS 2, 3, 4, and 5 imaging features were benign-looking axillary lymph nodes (66.33%), well-defined solid masses (61.54%), ill-defined solid masses (42.86%), and ill-defined solid masses with suspicious-looking axillary lymph nodes (100.00%), respectively. The most frequent indications were routine screening (49.18%), mastalgia (26.59%), and painless breast masses (19.77%). There was significant association between duration of symptoms and breast cancer ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>p</mi> <mtext> </mtext> <mtext>value</mtext> <mo>=</mo> <mn>0.007</mn> </math> ). In conclusion, routine breast screening and mastalgia were the topmost indications for breast imaging. BI-RADS 1 and 2 were the commonest BI-RADS scores, and benign-looking axillary lymph nodes and simple cysts were the most frequent imaging features for BI-RADS 2 and ill-defined solid masses and suspicious-looking axillary lymph nodes for BI-RADS 4 and 5.
Teachers’ Views on the Use of Mobile Phones in Schools

Mobile phones are the most popular and widely used mobile devices in the world. Besides providing users with different communication facilities (SMS messages, voice or video calls, social media etc.), they can also be used as a powerful tool for teaching and learning. Despite this popularity and widespread, the use of mobile phones in schools is the most controversial issue in Turkey, as well as all over the world. This study was aimed to determine the views of ICT teachers on students’ use of mobile phone in schools. The survey research was used in this study. Data were collected from 424 teachers working at secondary schools, high schools and vocational/technical high schools in Turkey. The results of the research revealed that mobile phones have become an indispensable tool for teachers to both access information and communicate in their professional and personal lives. On the other hand, it was determined that ICT teachers lean towards bans or restrictions on the use of mobile phones by students at school due to pedagogical concerns. We also found that teachers have different views about whether mobile phones are a need for students or not, although mobile phones are an important means for communication and gaining information in their lives. The results of the research showed that education and guidance activities for effective use of mobile phones are not carried out in most of the schools.
An Efficient Density-based clustering algorithm for face groping

This paper focuses on the following problem: Given a large number of unlabeled face images, group them into individual clusters, and the number of clusters cannot be known in advance. To this end, an Efficient Density-based clustering incorporated with the model of Graph partitioning (EDG) is proposed. 1. Inspired by the progress of graph partitioning clustering, a novel criterion that can be seen as a variant of the Normalized-cut model is employed to measure the similarity between two samples. 2. We only consider the similarities and connections on a subset of all possible pairs, i.e. the top-K nearest neighbors for each sample. Therefore, the computing and storage costs are linear w.r.t. the number of samples. In order to assess the performance of EDG on face images, extensive experiments based on a two-stage framework have been conducted on 19 benchmark datasets (14 middle-scale and 5 large-scale) from the literature. The experimental results have shown the effectiveness and robustness of our model, compared with the state-of-the-art methods. [code]
SR-Inpaint: A General Deep Learning Framework for High Resolution Image Inpainting

Recently, deep learning has enabled a huge leap forward in image inpainting. However, due to the memory and computational limitation, most existing methods are able to handle only low-resolution inputs, typically less than 1 K. With the improvement of Internet transmission capacity and mobile device cameras, the resolution of image and video sources available to users via the cloud or locally is increasing. For high-resolution images, the common inpainting methods simply upsample the inpainted result of the shrinked image to yield a blurry result. In recent years, there is an urgent need to reconstruct the missing high-frequency information in high-resolution images and generate sharp texture details. Hence, we propose a general deep learning framework for high-resolution image inpainting, which first hallucinates a semantically continuous blurred result using low-resolution inpainting and suppresses computational overhead. Then the sharp high-frequency details with original resolution are reconstructed using super-resolution refinement. Experimentally, our method achieves inspiring inpainting quality on 2K and 4K resolution images, ahead of the state-of-the-art high-resolution inpainting technique. This framework is expected to be popularized for high-resolution image editing tasks on personal computers and mobile devices in the future.
Translating Islamic identity into numbers

This research aims to define various Islamic based identity profile to different individuals by identifying the various degrees of Islamic based identity profile. A scale of measurement in ordinal scale has been used to determine an Islamic based identity profile. The scale is subdivided into three main subsections, namely very rarely, average level and very frequently. By using the scale of measurement on an ordinal scale, it assists in developing a numerical hypothesis that is then used to determine an individual's Islamic based identity profile using the Dempster-Shafer theory of evidence. Using twenty-four set of questions, the research used the evidence presented to support a given Islamic based identity profile of a specific individual and filtered it using various degrees of probabilities of the evidence theory model, which have aided in proving or validating a particular hypothesis. The questions are divided into three types based on Islamic identity profile which include Fitrah, Khalifah and Din. The conclusion made is that we may be able to easily diagnose an individual’s Islamic based identity profile using Dempster-Shafer theory of evidence.
Scalable Non-observational Predicate Learning in ASP

Recently, novel ILP systems under the answer set semantics have been proposed, some of which are robust to noise and scalable over large hypothesis spaces. One such system is FastLAS, which is significantly faster than other state-of-the-art ASP-based ILP systems. FastLAS is, however, only capable of Observational Predicate Learning (OPL), where the learned hypothesis defines predicates that are directly observed in the examples. It cannot learn knowledge that is indirectly observable, such as learning causes of observed events. This class of problems, known as non-OPL, is known to be difficult to handle in the context of non-monotonic semantics. Solving non-OPL learning tasks whilst preserving scalability is a challenging open problem. We address this problem with a new abductive method for translating examples of a non-OPL task to a set of examples, called possibilities, such that the original example is covered iff at least one of the possibilities is covered. This new method allows an ILP system capable of performing OPL tasks to be "upgraded" to solve non-OPL tasks. In particular, we present our new FastNonOPL system, which upgrades FastLAS with the new possibility generation. We compare it to other state-of-the-art ASP-based ILP systems capable of solving non-OPL tasks, showing that FastNonOPL is significantly faster, and in many cases more accurate, than these other systems.
Cloud Computing

The global competition have extended the scope of trade and extended the supply chains causing the information management to be an essential part of the businesses. Many new technologies have recently been adapted by organizations in the era of digitalization. Cloud computing had an important place in these technologies and has been integrated widely to organizations. In recent days unexpected and risky periods such as the global pandemic increased the interest towards cloud computing topic in both academically and practically. The purpose of this study is to analyze and classify the contributions of the studies published in cloud computing field. The cloud literature is analyzed systematically from the management and business point of view. The review is limited with journal articles and papers published between 2014 and 2019. This research summarizes the current research attempts, discovers the research gaps and provides a research agenda for the future research on cloud computing within the context of information management in business and management by a global perspective.
LITERATURE CIRCLES IN READING CLASS: STUDENTS’ PARTICIPATION AND PERCEPTION

Literature circles are methods to teach reading that aim to bring an enjoyable atmosphere to the class. It refers to a group of students who read the same book and discuss what they have read, with different roles being assigned to each member. It is believed that literature circles can promote the students’ interest and help them get better reading comprehension. Therefore, literature circles are suitable to be implemented in reading classes in higher education. This study aims to investigate the students’ participation and perception towards literature circles in reading class. The research was conducted in reading classes of D3 and D4 English Study Programs of Business Administration Department, State Polytechnic of Malang. The research participants were two classes consisting of 50 students. Questionnaires were used to collect data from the respondents. The data was then analysed using the descriptive quantitative method. The finding shows that the students actively participated in the literature circles and positively perceived it.
Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector

Zero-shot learning (ZSL) aims to recognize objects from unseen classes, where the key is to transfer knowledge from seen classes to unseen classes by establishing appropriate mappings between visual and semantic features. Currently, the knowledge transfer in many existing works is rather limited due to various factors: (i) the widely used visual features are global ones, and they are not completely consistent with semantic attributes; (ii) only one mapping is learned, which is not able to effectively model diverse visual–semantic relations; (iii) the bias problem in the generalized ZSL (GZSL) could not be effectively handled. In this paper, we propose two techniques to alleviate these limitations. Firstly, we propose a Semantic-diversity transfer Network (SetNet) addressing the first two limitations, where (1) a multiple-attention architecture and a diversity regularizer are proposed to learn multiple local visual features being more consistent with semantic attributes and (2) a projector ensemble which geometrically takes diverse local features as inputs is proposed to diversify visual–semantic relations. Secondly, we propose an inner disagreement based domain detection module (ID3M) for GZSL to alleviate the bias problem, which picks out unseen-class data before class-level classification. Due to the lack of unseen-class data in the training stage, ID3M employs a novel self-contained training scheme and detects out unseen-class data based on a proposed inner disagreement criterion. Experimental results on three public datasets show that the proposed SetNet with the explored ID3M achieves a significant improvement against many state-of-the-art methods.