Reviewerly/exHarmony huggingface dataset:
A multi-scale level set method based on local features for segmentation of images with intensity inhomogeneity

Images with intensity inhomogeneity pose significant challenges in image segmentation. Local region-based level set models have recently been recognized as promising methods to segment such images. In these models, local intensity information in a neighborhood of predetermined size is extracted and then embedded into the energy functional, guiding the evolution of deformable contour toward desired boundaries. The local neighborhood intensities are assumed to be rather constant; therefore, the selection of neighborhood size greatly influences effectiveness and robustness. Complex image characteristics, such as variation in degree of intensity inhomogeneity and noise levels among regions, can lead to severe challenges for accurate image segmentation when using only a fixed scale parameter for local regions. We propose a new multi-scale local feature-based level set method for image segmentation with an improved strategy based on previous studies of multi-scale image filtering methods, which allow for automatic selection of filtering scale parameters. Our novel method can adaptively determine the optimal scale parameter for each pixel during contour evolution, alleviating the challenges caused by severe intensity inhomogeneity. First, we define a Local Maximum Description Difference feature (LMDD), based on multi-scale local region descriptors. We incorporate the LMDD, associated with the maximum response of multi-scale high-pass filters for each pixel, into three local region based level set models with Chan-Vese (CV)-like structure to construct the energy functional. Finally, we complete the segmentation through minimization of this energy. Our experimental results illustrate the good performance of the proposed level set method for segmenting images with severe intensity inhomogeneity.
Virtual Immersive Learning Environments (VILEs) Based on Digital Storytelling to Enhance Deeper Learning for Undergraduate Students

This research aimed at proposing virtual immersive learning environments (VILEs) based on digital storytelling to enhance deeper learning for undergraduate students in all fields. The VILEs based on digital storytelling to enhance deeper learning for undergraduate students was developed based on the review of the literature, the expert&amp;rsquo;s interview and evaluated by five experts. The research results were found that the VILEs based on digital storytelling to enhance deeper learning for undergraduate students consist of three components were (1) Immersive technology, (2) Digital storytelling process, and (3) Stories Evaluation. The experts also evaluated which step of the VILEs based on digital storytelling to enhance deep learning for undergraduate students was suitable for the development of the respective aspects of deeper learning.
Double serial adaptation mechanism for keystroke dynamics authentication based on a single password

Cyber-attacks have spread all over the world to steal information such as trade secrets, intellectual property and banking data. Facing the danger of the insecurity of saved data (personal, professional, official, etc.), keystroke dynamics was proposed as an interesting, non-intrusive, inexpensive, permanent and weakly constrained solution for users. Based on the typing rhythm of users, it improves logical access security. Nevertheless, it was demonstrated that such an authentication mechanism would need a larger number of samples to enroll the typing characteristics of users. Moreover, these registered characteristics generally undergo aging effects after a time span. Different solutions have been suggested to remedy these variability problems, including template adaptation. In this paper, we propose a double serial adaptation strategy that considers a single-capture-based enrollment process. When using the authentication system, the template of users and the decision/adaptation thresholds are updated. Experimental results on three public keystroke dynamics datasets show the benefits of the proposed method.
A survey on Adversarial Attacks and Defenses in Text.

Deep neural networks (DNNs) have achieved remarkable success in various tasks (e.g., image classification, speech recognition, and natural language processing). However, researches have shown that DNN models are vulnerable to adversarial examples, which cause incorrect predictions by adding imperceptible perturbations into normal inputs. Studies on adversarial examples in image domain have been well investigated, but in texts the research is not enough, let alone a comprehensive survey in this field. In this paper, we aim at presenting a comprehensive understanding of adversarial attacks and corresponding mitigation strategies in texts. Specifically, we first give a taxonomy of adversarial attacks and defenses in texts from the perspective of different natural language processing (NLP) tasks, and then introduce how to build a robust DNN model via testing and verification. Finally, we discuss the existing challenges of adversarial attacks and defenses in texts and present the future research directions in this emerging field.
Sentiment Analysis of Arabic Jordanian Dialect Tweets

Sentiment Analysis (SA) of social media contents has become one of the growing areas of research in data mining.SA provides the ability of text mining the public opinions of a subjective manner in real time.This paper proposes a SA model of Arabic Jordanian dialect tweets.Tweets are annotated on three different classes; positive, negative, and neutral.Support Vector Machines (SVM) and Naïve Bayes (NB) are used as supervised machine learning classification tools.Preprocessing of such tweets for SA is done via; cleaning noisy tweets, normalization, tokenization, namely, Entity Recognition, removing stop words, and stemming.The results of the experiments conducted on this model showed encouraging outcomes when Arabic light stemmer/segment is applied on Arabic Jordanian dialect tweets.Also, the results showed that SVM has better performance than NB on such tweets' classifications.
Towards Low Latency Multi-viewpoint 360° Interactive Video: A Multimodal Deep Reinforcement Learning Approach

Recently, the fusion of 360° video and multi-viewpoint video, called multi-viewpoint (MVP) 360° interactive video, has emerged and created much more immersive and interactive user experience, but calls for a low latency solution to request the high-definition contents. Such viewing-related features as head movement have been recently studied, but several key issues still need to be addressed. On the viewer side, it is not clear how to effectively integrate different types of viewing-related features. At the session level, questions such as how to optimize the video quality under dynamic networking conditions and how to build an end-to-end mapping between these features and the quality selection remain to be answered. The solutions to these questions are further complicated given the many practical challenges, e.g., incomplete feature extraction and inaccurate prediction.This paper presents an architecture, called iView, to address the aforementioned issues in an MVP 360° interactive video scenario. To fully understand the viewing-related features and provide a one-step solution, we advocate multimodal learning and deep reinforcement learning in the design. iView intelligently determines video quality and reduces the latency without pre-programmed models or assumptions. We have evaluated iView with multiple real-world video and network datasets. The results showed that our solution effectively utilizes the features of video frames, networking throughput, head movements, and viewpoint selections, achieving at least 27.2%, 15.4%, and 2.8% improvements on the three video datasets, respectively, compared with several state-of-the-art methods.
Characterizing and identifying reverted commits

In practice, a popular and coarse-grained approach for recovering from a problematic commit is to revert it (i.e., undoing the change). However, reverted commits could induce some issues for software development, such as impeding the development progress and increasing the difficulty for maintenance. In order to mitigate these issues, we set out to explore the following central question: can we characterize and identify which commits will be reverted? In this paper, we characterize commits using 27 commit features and build an identification model to identify commits that will be reverted. We first identify reverted commits by analyzing commit messages and comparing the changed content, and extract 27 commit features that can be divided into three dimensions, namely change, developer and message, respectively. Then, we build an identification model (e.g., random forest) based on the extracted features. To evaluate the effectiveness of our proposed model, we perform an empirical study on ten open source projects including a total of 125,241 commits. Our experimental results show that our model outperforms two baselines in terms of AUC-ROC and cost-effectiveness (i.e., percentage of detected reverted commits when inspecting 20% of total changed LOC). In terms of the average performance across the ten studied projects, our model achieves an AUC-ROC of 0.756 and a cost-effectiveness of 0.746, significantly improving the baselines by substantial margins. In addition, we found that "developer" is the most discriminative dimension among the three dimensions of features for the identification of reverted commits. However, using all the three dimensions of commit features leads to better performance.
Operator Strategy Model Development in UAV Hacking Detection

An increasingly relevant security issue for unmanned aerial vehicles (UAVs, also known as drones) is the possibility of a global positioning system (GPS) spoofing attack. Given the existing problems in current GPS spoofing detection techniques and human visual advantages in searching and localizing targets, we propose a human-autonomy collaborative approach of human geolocation to assist UAV control systems in detecting GPS spoofing attacks. An interactive testbed and experiment were designed and used to evaluate this approach, which demonstrated that human-autonomy collaborative hacking detection is a viable concept. Using the hidden Markov model (HMM) approach, operator behavior patterns and strategies from the experiment were modeled via hidden states and transitions among them. These models revealed two dominant hacking detection strategies. Statistical results and expert performer evaluations show no significant difference between different hacking detection strategies in terms of correct detection. The detection strategy model suggests areas of future research in decision support tool design for UAV hacking detection. Also, the development of HMMs presents the feasibility of quantitatively investigating operator behavior patterns and strategies in human supervisory control scenarios.
MODEL PENGUATAN LITERASI DIGITAL MELALUI PEMANFAATAN E-LEARNING

This study aims to find a model for strengthening digital literacy at the University of Darussalam Gontor.This research uses descriptive qualitative method.Data are collected through observation and interviews with five lecturers in the Communication Study Program at the University of Darussalam Gontor.This research found a model of strengthening digital literacy through the use of e-learning.The model includes elements of communication and collaboration in the form of active participation in learning and research activities.It consists of individual competence components in the form of use skills, critical understanding, and communicative abilities.This research contributes to a model of strengthening digital literacy through the use of e-learning.
Evaluation of Post-Quantum Distributed Ledger Cryptography

This paper evaluates the current cybersecurity vulnerability of the prolific use of Elliptical Curve Digital Signature Algorithm (ECDSA) cryptography in use by the Bitcoin Core, Ethereum, Bitcoin Cash, and enterprise blockchains such as Multi-Chain and Hyperledger projects Fabric, and Sawtooth Lake.These blockchains are being used in media, health, finance, transportation and government with little understanding, acknowledgment of the risk and no known plans for mitigation and migration to safer public-key cryptography.The second aim is to evaluate ECDSA against the threat of Quantum Computing and propose the most practical National Institute of Standards and Technology (NIST) Post-Quantum Cryptography candidate algorithm lattice-based cryptography countermeasure that can be implemented near-term and provide a basis for a coordinated industrywide lattice-based public-key implementation.Commercial quantum computing research and development is rapid and unpredictable, and it is difficult to predict the arrival of fault-tolerant quantum computing.The current state of covert and classified quantum computing research and advancement is unknown and therefore, it would be a significant risk to blockchain and Internet technologies to delay or wait for the publication of draft standards.Since there are many hurdles Post-Quantum Cryptography (PQC) must overcome for standardisation, coordinated large-scale testing and evaluation should commence promptly.
Team Autonomy in Large-Scale Agile

Large-scale software development is increasingly making use of agile practices.In large-scale projects, a team needs to align with other teams and the rest of the organization.This has been shown to threaten team autonomy, which, in turn, reduces responsiveness and flexibility.Hence, agile teams face challenges in adapting to larger-scale development.We conduct a multiple case study of three large-scale projects to investigate barriers to team autonomy in large-scale agile.Two barriers are identified: overall direction and external dependencies.We found that goals are often set by management without involving the teams, that they are often equal to deliverables and deadlines, and that team members often do not know what the goals are.Consequently, teams struggle with setting and communicating goals as well as establishing a shared direction.Organizational dependencies lead to teams having to deal with additional tasks, resulting in specific members shielding the teams from external noise.
The Influence of Variance in Learner Answers on Automatic Content Scoring

Automatic content scoring is an important application in the area of automatic educational assessment. Short texts written by learners are scored based on their content while spelling and grammar mistakes are usually ignored. The difficulty of automatically scoring such texts varies with the variance within the learner answers. In this paper, we first discuss factors that influence variance in learner answers, so that practitioners can better estimate if automatic scoring might be applicable to their usage scenario. We then compare the two main paradigms in content scoring: (i) similarity-based and (ii) instance-based methods, and discuss how well they can deal with each of the variance-inducing factors described before.
Structure tensor adaptive total variation for image restoration

Image denoising and restoration is one of the basic requirements in many digital image processing systems. Variational regularization methods are widely used for removing noise without destroying edges that are important visual cues. This paper provides an adaptive version of the total variation regularization model that incorporates structure tensor eigenvalues for better edge preservation without creating blocky artifacts associated with gradient-based approaches. Experimental results on a variety of noisy images indicate that the proposed structure tensor adaptive total variation obtains promising results and compared with other methods, gets better structure preservation and robust noise removal.
Unified Visual-Semantic Embeddings: Bridging Vision and Language with Structured Meaning Representations

We propose Unified Visual-Semantic Embeddings (UniVSE) for learning a joint space of visual and textual concepts. The space unifies the concepts at different levels, including objects, attributes, relations, and full scenes. A contrastive learning approach is proposed for the fine-grained alignment from only image-caption pairs. Moreover, we present an effective approach for enforcing the coverage of semantic components that appear in the sentence. We demonstrate the robustness of Unified VSE in defending text-domain adversarial attacks on cross-modal retrieval tasks. Such robustness also empowers the use of visual cues to resolve word dependencies in novel sentences.
BioTouchPass: Handwritten Passwords for Touchscreen Biometrics

This work enhances traditional authentication systems based on Personal Identification Numbers (PIN) and One-Time Passwords (OTP) through the incorporation of biometric information as a second level of user authentication. In our proposed approach, users draw each digit of the password on the touchscreen of the device instead of typing them as usual. A complete analysis of our proposed biometric system is carried out regarding the discriminative power of each handwritten digit and the robustness when increasing the length of the password and the number of enrolment samples. The new e-BioDigit database, which comprises on-line handwritten digits from 0 to 9, has been acquired using the finger as input on a mobile device. This database is used in the experiments reported in this work and it is available together with benchmark results in GitHub. Finally, we discuss specific details for the deployment of our proposed approach on current PIN and OTP systems, achieving results with Equal Error Rates (EERs) ca. 4.0% when the attacker knows the password. These results encourage the deployment of our proposed approach in comparison to traditional PIN and OTP systems where the attack would have 100% success rate under the same impostor scenario.
Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms

Ipsilateral views of digital mammograms help radiologists to localize and confirm abnormal lesions during diagnosis of breast cancers. This study aims at developing algorithms which improve accuracy of computer-aided diagnosis (CADx) for analyzing breast abnormalities on ipsilateral views. The proposed system is a fusion of single and two view systems. Single view approach detects and characterizes suspicious lesions on craniocaudal (CC) and medio-lateral oblique (MLO) view separately using geometric and textural features. Lesions detected on each view are paired with potential lesions on another view. The proposed algorithm computes the correspondence score of each lesion pair. Single view information is fused with two views correspondence score to discriminate malignant tumours from benign masses using the SVM classifier. Performance of SVM classifier is assessed using five-fold cross validation (CV), Kappa metric and ROC analysis. Algorithms are applied to 110 pairs of mammograms from local dataset and 74 pairs from open dataset. Single view scheme yielded image-based sensitivity of 91.63% and 88.17% at 1.35 and 1.51 false positives per image (FPs/I) on local and open dataset respectively. Single view classification yielded FPs/I of 1.03 and 1.20 with sensitivity of 70%. Fusion based two views scheme using SVM classifier produced average case-based sensitivity of 75.91% at 0.69 FPs/I and 73.65% at 0.72 FPs/I on local and open dataset respectively. Fusion of single view features with two view correspondence score leads to improved case-based detection sensitivity. Proposed fusion based approach results into accurate and reliable diagnosis of breast abnormalities than single view approach.
Smartphone Applications as a Teaching Technique for Enhancing Tertiary Learners' Speaking Skills: Perceptions and Practices

Utilizing technology and the Internet to keep up with the 21st century language teaching and learning has become trendy in this digital era, highlighting promising features that are expected to tackle one of the most demanding crucial skills in English as a foreign language (EFL) context; yet a difficult one to master; that is, speaking English accurately, confidently and fluently. This study aims at investigating teachers’ perceptions and the current actual practices of utilizing a package of three categories of smartphone applications as a prescribed teaching technique with the purpose of fostering Saudi tertiary students' oral skills, correlating these perceptions with actual classroom practices. Adopting a mixed-mode approach, the data for this study were gleaned from two data collection instruments– an electronic questionnaire designed using SurveyMonkey as a platform, and encoded data from focus group discussion. On receiving 45 complete responses, the IBM SPSS (V24.0) was used to do the statistics, whereas the data from the discussion were thematically analysed. The findings revealed that the majority of participants have high, inspiring positive perceptions of using smartphone applications in teaching speaking. Based on the data obtained, it is concluded that despite EFL teachers' high positive perceptions of smartphone applications used for promoting adult learners' speaking skills, classroom practices reveal weaknesses and insufficient actual experiences. Pedagogically, the study recommends innovating, implementing, and integrating smartphone applications in teaching oral skills, along with drilling teachers and learners on how to effectively utilize them.
Financial performance and corporate social responsibility in the banking sector of Bahrain: Can engagement moderate?

The present study attempted to understand how financial performance can be enhanced in the financial sector.Therein, the study worked to find out how Corporate Social Responsibility (CSR) and organizational engagement can be used to predict financial performance.In addition, the study also tested the moderating role of organizational engagement on the relationship between CSR and financial performance.Managerial level employees from seven retail banks in Bahrain were sampled for the present study.The results of the structural equation modelling reported significant impact of CSR on financial performance.Accordingly, the study also reported significant relationship between organizational engagement and financial performance.Notably, the study also reported significant moderation of organizational engagement on the CSR and financial performance relationship.The study forwards notable implications for theory and practice followed by scope for future studies.
Efficient single image dehazing by modifying the dark channel prior

Outdoor images can be degraded due to the particles in the air that absorb and scatter light. The produced degradation generates contrast attenuation, blurring, and distortion in pixels, resulting in low visibility. These limit the efficiency of computer vision systems such as target tracking, surveillance, and pattern recognition. In this paper, we propose a fast and effective method, through modification in the computation of the dark channel which significantly reduces the artifacts generated in the restored images presented when using the ordinary dark channel. According to our experimental results, our method produces better results than some state-of-the-art methods in both efficiency and restoration quality. The processing time in tests shows that the method is adequate for images with high-resolution and real-time video processing.