Reviewerly/exHarmony huggingface dataset:
Image copy‐move forgery detection using sparse recovery and keypoint matching

Copy-move forgery (CMF) detection is one of the most practical problems in image forensics. The authenticity of the image becomes more crucial when the images are used in the criminal investigations, intelligence services, and medical documentations. In this study, the authors suggest a CMF detection algorithm. At first they they suggest to use a sparse recovery algorithm to identify the suspicious segments. To incorporate the colour information of the image segments, they propose to compare the histograms of the identified segments to detect the similar ones. The keypoints of those parts are obtained and the matched ones are located. In the last step, they suggest a morphology scheme to extract the forged region. They have evaluated the proposed method in the detection of various forged images. The simulation results reveal the forgery detection capabilities of the suggested algorithm compared to the other state-of-the-art schemes. The proposed method has superiority over its counterparts in detecting the scaled forgeries. Moreover, the sparse recovery step enables the proposed algorithm to remove the genuine repeated patterns of the image, while the other CMF detection techniques wrongly consider those parts as a forgery. Furthermore, the proposed scheme is on-average faster than the other schemes.
The Combination of Analytical Hierarchy Process and Simple Multi-Attribute Rating Technique for The Selection of The Best Lecturer

This study aims to develop a model of decision support systems (DSS) for the selection of the best lecturer at the Faculty of Information Technology of Universitas Budi Luhur using a combination of Analytical Hierarchy Process (AHP) and Simple Multi-Attribute Rating Technique (SMART) methods. In this study, 12 (twelve) criteria were used to select the best lecturers: Educational level, Academic functional position, Lecturer Certification, Lecturer Semester Performance Index, Number of Respondents, Average Number of Meetings, Number of Research, Number of community service, Number of Publications, Number of Grants, Discipline submit final exam scores and Faculty Discipline.The results of the calculation of the consistency ratio (CR) for the best lecturer selection criteria obtained CR values = 0.06. The calculation results are not more than 0.1 or 10 percent, so the comparison of the best lecturer selection criteria is consistent and does not require revision of the assessment. Quality Testing of DSS application software for the best lecturer selection was tested based on 4 (four) McCall method variables, namely Functionality, Reliability, Usability, and Efficiency. The overall test results show that the quality of the application of the decision support system for the selection of the best lecturers has a "Good" criterion of 78.20 percent.
On document representations for detection of biased news articles

Detecting bias in text is an increasingly relevant topic, given the information overload problem. Automating this task is crucial for our needs of quality news consumption. With this in mind, we explore modern deep learning approaches, including contextualized word embeddings and attention mechanisms, to compare the effects of different document representation choices. We design token-wise, sentence-wise and hierarchical document representations. Focusing on hyperpartisan news detection, we show that hierarchical attention mechanisms are able to better capture information at different levels of granularity (including intra and inter-sentence), which seems to be relevant for this task. With an accuracy of 82.5%, our best performing system is based on an ensemble of hierarchical attention networks with ELMo embeddings, achieving state-of-the-art performance on the SemEval-2019 Task4 dataset.
Analysis of Korea's nuclear R&amp;D priorities based on private Sector's domestic demand using AHP

Korea successfully achieved energy independence in the shortest period of time from being the poorest country in terms of energy 50 years ago through steady development of nuclear technology. In the past, the nuclear industry has been driven through government-centered policy development, public institution-based research, and industrial facility and infrastructure construction. Consequently, South Korea became a nuclear energy powerhouse exporting nuclear power plants to the UAE, surpassing the level of domestic technological independence. However, in recent years, the nuclear industry in Korea has experienced a decline in new plant construction since the Fukushima accident in Japan, which caused changes in public perspectives regarding nuclear power plant operation, more stringent safety standards on the operation of nuclear power plants, and a shift in governmental energy policy. These changes are expected to change the domestic nuclear industry ecosystem. Therefore, in this study, we investigate the priority of technology development investment from the perspective of experts in private nuclear power companies, shifting the focus from government-led nuclear R&D policies. To establish a direction in nuclear technology development, a survey was conducted by applying an analytic hierarchy analysis to experts who have worked in nuclear power plants for more than 15 years. The analysis items of focus were the 3 attributes of strategic importance, urgency, and business feasibility of four major fields related to nuclear energy: nuclear safety, decommissioning, radioactive waste management, and strengthening industrial competitiveness.
Topic Modelling: A Comparison of The Performance of Latent Dirichlet Allocation and LDA2vec Model on Bangla Newspaper

Topic modeling is a statistical data mining method for organizing the documents with variable contents under similar topics.It is utilized to reveal the concealed topics from an enormous collection of articles or documents.In this research, we have conducted an experiment on the effectiveness of two trending topic modeling methods on Bangla news corpus.Our target is to find an effective way of analyzing Bangla news documents and categorize them automatically for a recommendation system and searching. Different Models have been found effective for different languages because of their unique morphological structure.In this research LDA and its hybrid with word2vec known as lda2vec have been chosen as techniques to extract topics from Bangla news documents.LDA is a matrix factorization technique and as our observation in this research, this model is effective on Bangla despite the syntax difference with English.For the testing and implementation purpose, we developed a technique to find topics for not factorized documents from LDA and lda2vec.As our finding, lda2vec gave 85.66% accuracy over topics for test documents where accuracy for LDA was 62.45% Which makes lda2vec more reliable for the implementation purpose.
Dynamic adaptation of service-based applications: a design for adaptation approach

Abstract A key challenge posed by the Next Generation Internet landscape is that modern service-based applications need to cope with open and continuously evolving environments and to operate under dynamic circumstances (e.g., changes in the users requirements, changes in the availability of resources). Indeed, dynamically discover, select and compose the appropriate services in such environment is a challenging task. Self-adaptation approaches represent effective instruments to tackle this issue, because they allow applications to adapt their behaviours based on their execution environment. Unfortunately, although existing approaches support run-time adaptation, they tend to foresee the adaptation requirements and related solutions at design-time, while working under a "closed-world" assumption. In this article our objective is that of providing a new way of approaching the design , operation and run-time adaptation of service-based applications, by considering the adaptivity as an intrinsic characteristic of applications and from the earliest stages of their development. We propose a novel design for adaptation approach implementing a complete lifecycle for the continuous development and deployment of service-based applications, by facilitating (i) the continuous integration of new services that can easily join the application, and (ii) the operation of applications under dynamic circumstances, to face the openness and dynamicity of the environment. The proposed approach has been implemented and evaluated in a real-world case study in the mobility domain. Experimental results demonstrate the effectiveness of our approach and its practical applicability.
ACO Based Variable Least Significant Bits Data Hiding in Edges Using IDIBS Algorithm

This work presents a double asymmetric data hiding technique. The first asymmetry is created by hiding secret data in the complex region of the cover image and keep the smooth region unaffected. Then another asymmetry is developed by hiding a different number of secret bits in the various pixels of the complex region. The proposed technique uses the ant colony optimization (ACO) based technique for the classification of complex and smooth region pixels. Then, the variable least significant bits (VLSB) data hiding framework is used to hide secret bits in the complex region of the cover image. A distance-based substitution technique, namely increasing distance increasing bits substitution algorithm, is used to ensure the asymmetry in the number of hidden bits. The double asymmetric framework enhances the security of the hidden secret data and makes the retrieval of hidden information difficult for unauthorized users. The algorithm results in high-quality stego images, and the hidden information does not attract the human visual system (HVS).
A Theory of Value for Value-Based Feature Selection in Software Engineering

Value-Based Software Engineering stresses the role of value in software related decisions. In the context of feature selection, software features judged to provide higher value take priority in the development process. This paper focuses on what value means when selecting software features. Using grounded theory, we conducted and analyzed semi-structured interviews with 21 key stakeholders (decision-makers) from three software/software-intensive companies, within a context where value-based decision-making was already established. Our analysis led to the building of a theory of value for value-based feature selection that identifies the nature of value propositions considered by key stakeholders when selecting software features (i.e., decision-making criteria for deciding upon software features, as suggested by Boehm (2003)). We found that some value propositions were common to all three company cases (core value propositions), whereas others were dependent upon the context in which a company operates, and the characteristics of the product under development (specific value propositions). Moreover, value propositions vary according to the stakeholder group and the type of feature being assessed. Our study provides significant insight into value in the context of feature selection, and generates new concepts around value-based feature selection such as new value propositions.
Intelligent English teaching prediction system based on SVM and heterogeneous multimodal target recognition

With the development of speech recognition technology, human services in many fields have gradually been replaced by intelligent voice services. Natural language processing technology and speech recognition technology are gradually becoming the key technologies in human-computer interaction systems . However, there are still some problems in intelligent speech recognition. In view of this, this study uses English as the basic language to carry out research and combines support vector machine to construct intelligent English recognition and predic-tion system to obtain EEG signals related to Chinese speech. Moreover, this paper adopts wavelet packet decomposition and co-space mode to extract the features of the acquired EEG signals and uses the support vector machine model to classify and compare the signal features. The research results show that the recognition rate of the traditional algorithm is significantly lower than the speech recognition rate of the algorithm model, and the algorithm of this study basically meets the requirements of the actual use of the system.
Putting the “I” in Interaction: Interactive Interfaces Personalized to Individuals

Interactive data exploration and analysis is an inherently personal process. One's background, experience, interests, cognitive style, personality, and other sociotechnical factors often shape such a process, as well as the provenance of exploring, analyzing, and interpreting data. This Viewpoint posits both what personal information and how such personal information could be taken into account to design more effective visual analytic systems, a valuable and under-explored direction.
Correlation Between Number of Hidden Layers and Accuracy of Artificial Neural Network

Artificial neural network (ANN) can simply be defined as a computing system made up of several simple highly interconnected processing elements, which process information by their dynamic state response to external inputs. In this paper, neural network models have been trained and tested with different datasets and varying number of hidden layers for individual dataset, and its accuracy has been recorded and plotted. This paper aims on generalizing the effect of number of hidden layers on simple datasets in ANN.
Interactive Exploration of Longitudinal Cancer Patient Histories Extracted From Clinical Text

PURPOSE Retrospective cancer research requires identification of patients matching both categorical and temporal inclusion criteria, often on the basis of factors exclusively available in clinical notes. Although natural language processing approaches for inferring higher-level concepts have shown promise for bringing structure to clinical texts, interpreting results is often challenging, involving the need to move between abstracted representations and constituent text elements. Our goal was to build interactive visual tools to support the process of interpreting rich representations of histories of patients with cancer. METHODS Qualitative inquiry into user tasks and goals, a structured data model, and an innovative natural language processing pipeline were used to guide design. RESULTS The resulting information visualization tool provides cohort- and patient-level views with linked interactions between components. CONCLUSION Interactive tools hold promise for facilitating the interpretation of patient summaries and identification of cohorts for retrospective research.
A generalization of functional clustering for discrete multivariate longitudinal data

This paper presents a new model-based generalized functional clustering method for discrete longitudinal data, such as multivariate binomial and Poisson distributed data. For this purpose, we propose a multivariate functional principal component analysis (MFPCA)-based clustering procedure for a latent multivariate Gaussian process instead of the original functional data directly. The main contribution of this study is two-fold: modeling of discrete longitudinal data with the latent multivariate Gaussian process and developing of a clustering algorithm based on MFPCA coupled with the latent multivariate Gaussian process. Numerical experiments, including real data analysis and a simulation study, demonstrate the promising empirical properties of the proposed approach.
Invariant Extended Kalman Filter Applied to Tracking for Air Traffic Control

In this paper, a recent target model expressed in intrinsic coordinates and based on the Frenet-Serret frame is reviewed. The associated variant of the EKF suited to track the corresponding state, and called the Invariant Extended Kalman Filter (IEKF) is also reviewed. Our main contribution is to apply those recent tools to Air Traffic Control (ATC) situations. For ATC application, a good precision on the velocity vector is critical, especially its direction (i.e., the heading of the plane), to display a coherent view of the situation in the sky to the operator. This filter is implemented in a realistic ATC tracking chain to test the algorithm under realistic conditions, when inserted in the complete tracking pipeline. The new filter proposed is then compared to an Extended Kalman Filter (EKF) with a constant velocity target model, which is fairly simple but widely used in practice for ATC. We show the results are improved, especially during turns .
Does terrorism trigger online hate speech? On the association of events and time series

Hate speech is ubiquitous on the Web. Recently, the offline causes that contribute to online hate speech have received increasing attention. A recurring question is whether the occurrence of extreme events offline systematically triggers bursts of hate speech online, indicated by peaks in the volume of hateful social media posts. Formally, this question translates into measuring the association between a sparse event series and a time series. We propose a novel statistical methodology to measure, test and visualize the systematic association between rare events and peaks in a time series. In contrast to previous methods for causal inference or independence tests on time series, our approach focuses only on the timing of events and peaks, and no other distributional characteristics. We follow the framework of event coincidence analysis (ECA) that was originally developed to correlate point processes. We formulate a discrete-time variant of ECA and derive all required distributions to enable analyses of peaks in time series, with a special focus on serial dependencies and peaks over multiple thresholds. The analysis gives rise to a novel visualization of the association via quantile-trigger rate plots. We demonstrate the utility of our approach by analyzing whether Islamist terrorist attacks in Western Europe and North America systematically trigger bursts of hate speech and counter-hate speech on Twitter.
A Brief Survey on Architecture of Feedback Systems for Interactive E-Government ICT Platforms

This paper examines the architectural features of development an e-content target distribution system for building interactive e-government platforms on feedback systems part based on the most specific and effective web crawler architectures. The main features of implementation of web crawlers are analyzed, as systems of "deep" web searching and basis for cross-monitoring in the state Internet space in the context of software architecture, as well as the difficulties of their integration in the global information and communication space have been given. A prototype web crawler architecture is provided for use as a feedback system in interactive electronic content distribution platforms.
COVID-19 Contact-Tracing Apps: How to Prevent Privacy from Becoming the Next Victim

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Research on Nearest Neighbor Data Association Algorithm Based on Target “Dynamic” Monitoring Model

In order to solve the problem that the Nearest Neighbor Data Association (NNDA) algorithm cannot detect the “dynamic” change of the target, this paper proposes the nearest neighbor data association algorithm based on the Targets “Dynamic” Monitoring Model (TDMM). Firstly, the gate searching and updating of targets are completed based on TDMM, then the NNDA algorithm is utilized to achieve the data association of targets to realize track updating. Finally, the NNDA algorithm based on TDMM is realized by simulation. The experimental results show that the algorithm proposed can achieve “dynamic” monitoring in multi-target data association, and have more obvious advantages than Multiple Hypothesis Tracking (MHT) in timeliness and association performance.
Relational Metric Learning with Dual Graph Attention Networks for Social Recommendation

Existing social recommenders typically incorporate all social relations into user preference modeling, while social connections are not always built on common interests. In addition, they often learn a single vector for each user involved in two domains, which is insufficient to reveal user's complex interests to both items and friends. To tackle the above issues, in this paper, we consider modeling the user-item interactions and social relations simultaneously and propose a novel metric learning-based model called RML-DGATs. Specifically, relations in two domains are modeled as two types of relation vectors, with which each user can be regarded as being translated to both multiple item-aware and social-aware representations. Then we model the relation vectors by neighborhood interactions with two carefully designed dual GATs to fully encode the neighborhood information. Finally, the two parts are jointly trained under a dual metric learning framework. Extensive experiments on two real-world datasets demonstrate that our model outperforms the best baseline by 1.91% to 4.74% on three metrics for top-N recommendation and the performance gains are more significant under the cold-start scenarios.
Understanding the determinants of GIT post-adoption: perspectives from Mozambican institutions

This study aims to understand the determinants of geographical information technologies at the scale of post-adoption use and intention to increase the level of use in Mozambican institutions. Three known theories (diffusion of innovation theory, technology - organisation - environment framework, and institutional theory) have been used in order to accomplish the study. The data analysis showed that the variables compatibility, geographical scope, expansion opportunities, and normative pressure contribute to explaining GIT use. Relative advantage, complexity, coercive pressure, and mimetic pressure contribute to explaining the intention to increase GIT levels of use. The model revealed substantial power of explanation for GIT post-adoption.
A Video-Based Framework for Automatic 3D Localization of Multiple Basketball Players: A Combinatorial Optimization Approach

Sports complexity must be investigated at competitions; therefore, non-invasive methods are essential. In this context, computer vision, image processing, and machine learning techniques can be useful in designing a non-invasive system for data acquisition that identifies players' positions in official basketball matches. Here, we propose and evaluate a novel video-based framework to perform automatic 3D localization of multiple basketball players. The introduced framework comprises two parts. The first stage is player detection, which aims to identify players' heads at the camera image level. This stage is based on background segmentation and on classification performed by an artificial neural network. The second stage is related to 3D reconstruction of the player positions from the images provided by the different cameras used in the acquisition. This task is tackled by formulating a constrained combinatorial optimization problem that minimizes the re-projection error while maximizing the number of detections in the formulated 3D localization problem.
Outlier Detection Using Autoencoder Ensembles: A Robust Unsupervised Approach

Outlier analysis finds its applicability in multiple domains like finance, health, and manufacturing. As data keeps on increasing, it gets more challenging to have labels on them. Thus, unsupervised outlier analysis has started gaining center stage. The current paper makes use of an ensemble of autoencoders for outlier detection in a robust way. Autoencoders are suitable for dimensionality reduction, and they allow mapping of higher-order features to lower-order where they can be segregated. Experiments achieve state of the art results that are comparable with other neural network-based approaches on varied datasets. Ensemble framework provides robustness as it is able to deal with different domains and takes care of the inherent problem of over-fitting that comes with any neural network.