Reviewerly/exHarmony huggingface dataset:
Data Analytics of Human Development Index(HDI) with Features Descriptive and Predictive Mining

The value of the Human Development Index (HDI) in Indonesia is increasing every year. Indonesia has many provinces and districts/cities, it makes the government need more time to analyze data. This research purpose a new method to analyze the data of HDI with a descriptive and predictive mining method. There are two main results of this research. First, a segmentation of HDI data into four segments, there are low, medium, high, and very high. Second, a prediction of HDI data. Before analyzing data, the system does data preprocessing to repair the missing data (cleaning) and normalization (transformation) to convert data into a smaller range(from 0 to 1). To get a segmentation result use the descriptive mining method, in this method, there are two steps, the first system does grouping and labeling data based on the value of HDI indicators(life expectancy, expected years of schooling, mean years of schooling and income per capita) use Hierarchical Clustering Centroid Linkage Method. Second, the system does the interpretation process based on the distance between centroid every cluster and ground(0,0). To get a prediction result use the predictive mining method, this process uses a Weighted Moving Average(WMA) with the last three years of HDI data. The result of this research, the variance accuracy value of the descriptive mining method is 0,203, and the Mean Absolute Percentage Error(MAPE) value of the predictive mining method is 0,27%.
A New Embedding Technique Based On Psychovisual Threshold for Robust and Secure Compressed Video Steganography

Videos are often compressed to reduce storage and transmission payload at the expense of lower quality due to bandwidth-related issues. Most video steganography techniques do not provide robustness against compression technique. Thus, it is vital to develop a steganography technique that can be resistant against compression. This research proposed a new embedding technique in video steganography based on object motion and modified entropy. The object motions in the video frame were determined by horizontal and vertical motion vectors. The video frames that had object motion were computed by modified entropy. The proposed scheme embedded data along with the object motion by modifying Discrete Cosine Transform (DCT) coefficients in the video frames. Six DCT coefficients were selected in the middle frequency using DCT-psychovisual effects of hiding messages. The experimental results showed that the scheme achieved good robustness of message recovery in terms of Bit Error Rate (BER) and Normalised CrossCorrelation (NC). The recovered message of the proposed steganography scheme can survive video compression,
Combining Active Learning and Data Augmentation for Image Classification

To solve the problem that the data annotation in image classification task requires a lot of time and economic costs, and a large number of unlabeled images cannot be effectively utilized in reality, an image classification method combining active learning algorithm and data augmentation is proposed. First, data augmentation is performed on a small number of labeled samples, the classification model is initially trained, and then, according to the sampling strategy of active learning, the samples are selected and labeled by experts, which are the most conducive to model training from the unlabeled set. The labeled set is updated by adding the new labeled samples. The same process is performed until the requirements are met. In this paper, experiments are carried out on the digits dataset and the Cifar10 set. The results show that the image classification method proposed in this paper can effectively enhance the accuracy of image classification.
An Efficient Parallel Implementation of Face Detection System Using CUDA

Face detection is a highly efficient component in diverse domains such as security surveillance. Especially, the Viola-Jones algorithm has achieved significant performances in the field of detection face. In the last years, graphics processors have fast become the mainstay to solve the problem of detection face applications and to accelerate data parallel computing. This is due to their flexibility, and in particular, to the single-instruction, multiple-data execution model exploited for streaming processors by a Graphics Processing Unit (GPU). Therefore, in this paper, the researchers develop a robust face detection implementation based on the GPU component. The implementation has been optimized by following up a strategy to use the different memory resources in GPU and the warp scheduler technique, so as to accelerate the access to the memory, with better exploitation of resources proved by GPU. The results display that the suggested method is very important and consumes less execution time compared with the standard implementation and sequential implementation.
Hardening Firefox against Injection Attacks

Web browsers display content in the form of HTML, CSS and JavaScript retrieved from the world wide web. The loaded content is subject to the web security model and considered untrusted and potentially malicious. To complicate security matters, Firefox uses the same technologies to render its user interface as it does to render untrusted web content which blurs the distinction between the two privilege levels.Getting interactions between the two correct turns out to be complicated and has led to numerous real-world security vulnerabilities. We study those vulnerabilities to discover common threats and explain how we address them systematically to harden Firefox.
Towards Communication Profile, Topology and Node Failure Aware Process Placement

HPC systems need to keep growing in size to meet the ever-increasing demand for high levels of capability and capacity, often in tight time windows for urgent computation. However, increasing the size, complexity and heterogeneity of HPC systems also increases the risk and impact of system failures, that result in resource waste and aborted jobs. A major contributor to job completion time is the cost of interprocess communication. To address performance and energy efficiency, several prior studies have targeted improvements of communication locality. To meet this goal, they derive a mapping of MPI processes to system nodes in a way that reduces communication cost. However, such approaches disregard the effect of system failures. In this work, we propose a resource allocation approach for MPI jobs, considering both high performance and error resilience. Our approach, named Communication Profile, Topology and node Failure (CPTF), takes into account the application's communication profile, system topology and node failure probability for assigning job processes to nodes. We evaluate variants of CPTF through simulations of two MPI applications, one with a regular communication pattern (LAMMPS) and one with an irregular one (NPB-DT). In both cases, the variant of CPTF that strives to avoid failure-prone nodes and communication paths achieves lower time to complete job batches when compared to the default resource allocation policy of Slurm. It also exhibits the lowest ratio of aborted jobs. The average improvement in batch completion time is 67% for NPB-DT and 34% for LAMMPS.
A survey on deep learning techniques used for breast cancer detection

Breast cancer represents the highest percentage of cancers that affect women with 450000 deaths each year in the world. In Tunisia, it represents 30% of cancers diagnosed in women, thus occupying the first place in front of that of the cervix. In fact, it is important to identify breast cancer at an initial phase to decrease the death rate. In mammograms, the automatic mass recognition and classification remains a significant challenge and plays a critical role in helping radiologists to make a precise diagnosis. Recent improvements in the analysis of biomedical images using neural networks based on deep learning can be utilized to improve the CAD systems (computer-assisted diagnostic) performance. This paper presents the main deep learning approaches used for mammographic images, which can help us to identify research problems in current studies.
A ray tracing engine integrated with Blender and with uncertainty estimation: Description and initial results

This paper presents a ray tracing algorithm developed as a research and teaching tool. The motivations to pursue this task and some novel features of the algorithm are presented. Amongst them, it is possible to cite: (i) the receivers may grow in size, which saves some computational cost; (ii) sound intensity calculations are performed in a separate step than geometrical ray tracing; and (iii) those features allow Monte Carlo simulations for uncertainty prediction related to absorption data. The results obtained with the proposed algorithm are compared with the measured data (and other software packages) of Round Robins II (Elmia Hall) and III (PTB recording studio) and proved to be in good agreement with measured data. The ratio of the mean error by the JND of each parameter are compatible with the results presented by the other round robin participants. The product of this research is also scalable to an hybrid algorithm and alternatives to do so are provided through the discussion in the paper.
Optimization of Answer Set Programs for Consistent Query Answering by Means of First-Order Rewriting

Consistent Query Answering (CQA) with respect to primary keys is the following problem. Given a database instance that is possibly inconsistent with respect to its primary key constraints, define a repair as an inclusion-maximal consistent subinstance. Given a Boolean query q, the problem CERTAINTY(q) takes a database instance as input, and asks whether q is true in every repair. For every Boolean conjunctive query q, the complement of CERTAINTY(q) can be straightforwardly implemented in Answer Set Programming (ASP) by means of a generate-and-test approach: first generate a repair, and then test whether it falsifies the query. Theoretical research has recently revealed that for every self-join-free Boolean conjunctive query q, the complexity class of CERTAINTY(q) is one of FO, L-complete, or coNP-complete. Faced with this complexity trichotomy, one can hypothesize that in practice, the full power of generate-and-test is a computational overkill when CERTAINTY(q) is in the low complexity classes FO or L. We investigate part of this hypothesis within the context of ASP, by asking the following question: whenever CERTAINTY(q) is in FO, does a dedicated first-order algorithm exhibit significant performance gains compared to a generic generate-and-test implementation? We first elaborate on the construction of such dedicated first-order algorithms in ASP, and then empirically address this question.
How agile software development practitioners perceive the need for documenting quality requirements: a multiple case study

Agile software development (ASD) promotes minimal documentation and often prioritizes functional requirements over quality requirements (QRs). This may be beneficial in reducing the time to market of software. When considering QRs in ASD, the minimal documentation practice may be seen as a concern since QRs determine the success of software projects and are as well not easy to specify and document. Nevertheless, what do practitioners think of the necessity of documenting QRs in ASD? How do they perceive factors that may affect documentation of QRs in ASD? We conducted a multiple case study of three cases applying ASD, involving 12 participants. ASD practitioners identify that it is important to document QRs, and perceive that it contributes to ensuring quality, clarifying QRs, and helping in decision making. Time constraint, QR awareness and communication gaps on QRs influence the documentation of QRs in ASD. ASD teams may align their documentation practices to fit the sprint duration. The influence of QR awareness on documentation was dependent on project context and roles. Communication gaps can create confusion on QRs. Missing and outdated QR documentation may result in accruing technical debt, and lack of common understanding on QRs. The study synthesizes empirical evidence on the significance of documenting QRs in ASD and provides an insight into factors affecting documentation of QRs in ASD.
Efficient Incorporation of the RNS Datapath in Reverse Converter

The class of moduli sets in the form of {2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -1, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> +1, m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> } with m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ∈ {2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sup> +1, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sup> -1} has earned significant popularity in the implementation of the Residue Number System (RNS)-based computational systems, mainly thanks to the efficient arithmetic unit and a high degree of parallelism. However, its complicated inter-modulo computation leads to a high overhead associated with the complex reverse converter. This overhead is the main barrier for energy-efficient implementation of RNS-based devices, particularly for edge computing applications. This brief presents a new approach that embeds the reverse converter into the arithmetic unit of the RNS processor for the aforesaid well-known class of moduli sets. The effective hardware reuse in the proposed approach leads to an area and energy-efficient RNS realization for this class of moduli set. The experimental results based on 65 nm CMOS technology indicate the superiority of RNS realization by employing the proposed design methodology. The proposed architecture for a given RNS provides a substantial 17.4% area-saving and 13.32% less power-consumption on average compared to the traditional design approach, with the negligible penalty in delay.
A Genetic Algorithm Based on Auxiliary-Individual-Directed Crossover for Internet-of-Things Applications

In order to solve the large-scale, strong coupling, and nonlinear optimization problems in many Internet-of-Things (IoT) applications, such as intelligent infrastructure and smart city, this article proposes a real-coded genetic algorithm based on an auxiliary-individual-directed crossover operator (AIDX). AIDX is an alternative offspring framework of the directed crossover which uses auxiliary individual to reduce the search space of alternative offspring for the rapid optimization of multidimensional problems. In our solution AIDX, the parents-center distribution is adopted to reduce the risk of convergence to local optimization and enhance the stability of the algorithm. In addition, in order to increase the diversity of the population at the late stage of optimization, K-Bit-Swap (KBS) is used as a supplement for the exchange of genetic information among individuals in different dimensions. In the extensive experiments, 24 benchmarks with different dimensions are used to evaluate the performance of AIDX-GA. The results show that the proposed AIDX-GA has a significantly improved optimization effect on multidimensional optimization problems, and the stability of the algorithm is largely enhanced even when the global optimum is located near the boundary. AIDX-GA has also been evaluated in an industrial IoT case that identifies the resistance coefficients of a pipe network in the city infrastructure. The results show that the accuracy of AIDX-GA is high, and it has excellent universality and stability, which can be used to solve many IoT problems.
What Makes an Educational Augmented Reality Application Good?

In recent years, teachers have started to integrate augmented reality (AR) technology as a potential learning tool into classroom activities. The main concern is the quality of the existing applications rather than brand-newness. Therefore, this chapter focuses on the components of a good educational AR application based on the experiences of teachers and to examine the current situation on the integration of AR to learning environments. Qualitative methodology was used in this study. The sample consisted of 24 teachers working in public schools in Turkey. The data were collected via semi-structured interviews and analyzed through the thematic analysis. The results showed that the components of a good AR application in education were gathered under four main themes: “analysis process,” “design process,” “teaching-learning process,” and “evaluation components.” Implications for the design and use of the AR applications in educational settings were also included in the study.
A Case of Identity: Detection of Suspicious IDN Homograph Domains Using Active DNS Measurements

The possibility to include Unicode characters in domain names allows users to deal with domains in their regional languages. This is done by introducing Internationalized Domain Names (IDN). However, the visual similarity between different Unicode characters - called homoglyphs - is a potential security threat, as visually similar domain names are often used in phishing attacks. Timely detection of suspicious homograph domain names is an important step towards preventing sophisticated attacks, since this can prevent unaware users to access those homograph domains that actually carry malicious content. We therefore propose a structured approach to identify suspicious homograph domain names based not on use, but on characteristics of the domain name itself and its associated DNS records. To achieve this, we leverage the OpenINTEL active DNS measurement platform, which performs a daily snapshot of more than 65% of the DNS namespace. In this paper, we first extend the existing Unicode homoglyph tables (confusion tables). This allows us to detect on average 2.97 times homograph domains compared to existing tables. Our proactive detection of suspicious IDN homograph domains provides an early alert that would help both domain owners as well as security researchers in preventing IDN homograph abuse.
Missing area reconstruction in 3D scene from multi-view satellite images for surveillance applications

Automatic 3-D recovery from multiview satellite images can be extremely useful for information extraction for the surveillance application. For 3-D scene reconstruction, one approach is to employ multiple cameras for creating a multiview image with the aim to make interactive free-viewpoint selection possible in 3-D data. In most cases, such a 3-D scene contains missing holes on depth maps that appear during the synthesis from multi-views. This paper presents an automated pipeline for processing multi-view satellite images to 3-D digital surface models. The proposed approach uses the modified exemplar-based technique. We propose an algorithm using the concepts of a sparse representation of quaternions, which use a new gradient to calculate the priority function by integrating the structure of quaternions with LPA-ICI (local polynomial approximation - the intersection of confidence intervals) and the saliency map. Moreover, the color information incorporates into the optimization criteria to obtain sharp inpainting results. For this purpose, we use the Hamiltonian quaternion framework. Compared with state-of-the-art techniques, the proposed algorithm provides plausible restoration of the depth map from multi-view satellite images, which makes them a promising tool for surveillance applications.
Adversarial Machine Learning for Self Harm Disclosure Analysis (Workshop Paper)

Adversarial Machine Learning has been gaining attention from the NLP community due to low interpretability and low robustness of the current state-of-the-art systems. In this work, we study the effect of various adversarial attacks for detection of suicidal intent in social media setting. Suicide Ideation is a sensitive issue and is a leading cause of death. We show how various models are rendered useless after attacks and perform adversarial training using the most ideal attacks to improve their robustness. We also conduct several experiments with the attacks to study their effect and propose an approach for adversarial training using Generative Adversarial Networks.
Effect of Modality on Human and Machine Scoring of Presentation Videos

We investigate the effect of observed data modality on human and machine scoring of informative presentations in the context of oral English communication training and assessment. Three sets of raters scored the content of three minute presentations by college students on the basis of either the video, the audio or the text transcript using a custom scoring rubric. We find significant differences between the scores assigned when raters view a transcript or listen to audio recordings in comparison to watching a video of the same presentation, and present an analysis of those differences. Using the human scores, we train machine learning models to score a given presentation using text, audio, and video features separately. We analyze the distribution of machine scores against the modality and label bias we observe in human scores, discuss its implications for machine scoring and recommend best practices for future work in this direction. Our results demonstrate the importance of checking and correcting for bias across different modalities in evaluations of multi-modal performances.
The Validity of Mobile Learning Management System (M-LMS) at University

This study aims to find out the results of expert validation on Mobile Learning Management System (M-LMS) in the subjects of Human and Computer Interaction at university. Mobile Learning Management System (M-LMS) is developed in order to create more dynamic communication between lecturers and students. Learning management system is a model and system that runs an administration which serves as an e-learning platform, which delivers and manages content, identifies, assesses, tracks progress, collects and presents data to monitor the learning process as a whole. The development procedure of this research uses Borg and Gall which is simplified into five stages as follow: (1) Conducting product analysis that will be developed, (2) Developing the initial product/ prototype, (3) Expert validation and revision, (4) Small scale field trial and product revision, (5) Large-scale field trials and final products. The stage of expert validation on Borg and Gall method is done at the third stage, namely the stage of expert validation and revision. This study produces a valid mobile learning management system (M-LMS) software for the course of Human and Computer Interaction.