Reviewerly/exHarmony huggingface dataset:
Application of two-dimensional truncated singular value decomposition in image restoration

Abstract Large solution size and ill-posed problem often exists in image restoration. In order to overcome these problems, the application of two-dimensional truncated singular value decomposition(2-D TSVD) in large-scale image restoration is studied in this paper. The 2-D TSVD overcomes the inherent ill-posed problem and also solves the problem caused by limited storage in image restoration, which plays a regularization role. Experimental results show that this method is very effective in image restoration.
A Contextual Bayesian User Experience Model for Scholarly Recommender Systems

Since the advent of scholarly recommender systems (SRSs), more than 200 papers in the related area have been published. Many of these papers focus on proposing new and more accurate algorithms, or to enhance existing ones. Recently we have seen growing interest in embedding recommending methods into User Experience (UX), to enhance the value of RSs for users. Researchers have proposed that UX can be affected by bottlenecks in human perception, the preconceptions of the individual, and related factors such as personal and situational characteristics, which can be considered as contextual information. Although there are a few studies on developing User Models (UMs) in the field of SRSs, it has been emphasized that incorporating contextual information into user modelling and creating recommendations based on the users' information needs can be an effective approach to personalization and better UX with SRSs. The aim of this paper is to operationalize relevant contexts and to design a Bayesian UM for assisting the diagnosis of scholars' information needs in terms of accurate, novel, diverse, and popular research papers. The proposed user model can be embedded in the process of recommending and identifying the users' information needs which help recommenders to retrieve more appropriate recommendations and consequently leads to the enhancement of the UX for SRSs. Finally, the robustness and performance of the proposed Bayesian UM are evaluated.
Interpolation for intermediate logics via injective nested sequents

Abstract We introduce a novel, semantically inspired method of constructing nested sequent calculi for propositional intermediate logics. Applying recently developed methods for proving Craig interpolation to these nested sequent calculi, we obtain constructive proofs of the interpolation property for most non-trivial interpolable intermediate logics, as well as Lyndon interpolation for Gödel logic. Finally, we provide a prototype implementation combining proof search and countermodel construction.
Utilitarian or Relational? Exploring Indicators of User Orientation Towards Intelligent Agents

When interacting with an agent, some users with utilitarian orientation tend to treat an agent as an instrumental tool, while others with relational orientation find the design of humanlike features more pleasing. Along with technological advances in user modeling and prediction algorithms, intelligent agents nowadays can personalize their interaction by identifying such orientation of users towards them. While prior work has revealed several behavioral signs resulting from such difference in orientation, little attention is directed to more fundamental cues that precede the occurrence of actual interaction. In light of this issue, this study explores intrinsic properties of users related to their utilitarian or relational orientation towards intelligent agents. Qualitative analysis of responses revealed three user propensities contributing to individual differences in orientation: tolerance to unpredictability, sensitivity to privacy, and sensitivity to an agent's autonomy. We discuss future directions for leveraging our findings to design personalized interaction in intelligent agents.
Low-Light Image Enhancement Algorithm Based on Lime with Pre-Processing and Post-Processing

Due to the low visibility of the low-light image, it is not conducive to human observation and computer vision algorithm. Inspired by the human vision system (HVS), we propose a simple and effective method of low-light image enhancement. In the proposed method, firstly a sampler is used to get the optimal exposure ratio for the camera response model. Then a generator is used to synthesize dual-exposure images that are well-exposed in the regions where the original image is under-exposed. Next, the enhanced image is processed by using a part of Low-light Image Enhancement via Illumination Map Estimation (LIME) algorithm and the weight matrix of two images will be determined when fusing. After that, the combiner is used to get the synthesized image with all pixel well-exposed and finally a post-processing part is added to make the output image perform better. In the post-processing part, the best gray range of the image is adjusted, and the image is denoised and recomposed by using Block Machine 3-Dimensional (BM3D) model. Experiment results show that the proposed method can enhance the low-light images with less visual information distortions when comparing with those of several recent effective methods.
Insightful and Practical Multi-objective Convolutional Neural Network Architecture Search with Evolutionary Algorithms

This paper investigates a comprehensive convolutional neural network (CNN) representation that encodes both layer connections, and computational block attributes for neural architecture search (NAS). We formulate NAS as a bi-objective optimization problem, where two competing objectives, i.e., the validation accuracy and the model complexity, need to be considered simultaneously. We employ the well-known multi-objective evolutionary algorithm (MOEA) nondominated sorting genetic algorithm II (NSGA-II) to perform multi-objective NAS experiments on the CIFAR-10 dataset. Our NAS runs obtain trade-off fronts of architectures of much wider ranges and better quality compared to NAS runs with less comprehensive representations. We also transfer promising architectures to other datasets, i.e., CIFAR-100, Street View House Numbers, and Intel Image Classification, to verify their applicability. Experimental results indicate that the architectures on the trade-off front obtained at the end of our NAS runs can be straightforwardly employed out of the box without any further modification.
May I Know your IBAN? Cracking the Short Message Service (SMS) as a Second Factor Authentication for Online Payments

Short messages service (SMS) has been widely used for E-commerce authentications in the past years. It is utilized to authenticate users for Email password resetting process, authenticating online purchase process and even resetting the password of different E-government services. In this work, a new simple hacking process has been implemented to show that SMS authentication can be cracked simply if smartphones users' grant permissions to any App without even reading these permissions or thinking why this App requires these permissions. An android App has been written with an underlying service to listen to SMSs and forward them to another smartphone. The application has been distributed among 20 persons as an experiment. 90% of the persons have downloaded the App and 80% of them have granted the permissions without reading it. Therefor we can Emails passwords `phished it' simply.
Bug Assignment-Utilization of Metadata Features Along with Feature Selection and Classifiers

In open source software bug repository, lots of bugs are filed or reported in a single days and to handle these with manually is difficult and time consuming process. To build an automatic bug triager is a good way to resolve these bugs efficiently with the use of machine learning based classifiers. This proposed work is built the triager by utilizing the bug metadata features like product name, keywords, bug summary and component name which are extracted after applying the feature selection algorithms that play the important role in triaging process. To measure the prediction accuracy, ML based classifiers like NB, SVM and DT are applied on the dataset. Four open source projects based datasets (Eclipse, Netbeans, Firefox and Freedesktop) are castoff to experiment the En-TRAM triager and achieved the improved outcomes from the state-of-art that goes to approximately 19%. The proposed work has empirically proved that selective inclusion of highly ranked metadata fields improves the bug triaging accuracy.
UML Representation of Object-Oriented Design Antipatterns

Nowadays the ability to apply, implement and modify patterns of design and architecture has become a one of primary skills for software engineers. Competence of pattern design and implementation involves detecting and correcting inefficient solutions known as antipatterns. However, unlike patterns, very few antipatterns have a graphical representation so that an inefficient solution to a specific problem can be detected visually and refactored. Detecting antipatterns is not simple even with full set of technical documentation. This paper proposes a graphical UML representation of antipatterns to detect them at various stages of the software lifecycle. It proposes a method to refactor described antipatterns to improve software design quality and avoid software development process risks. UML diagrams modeling of 18 antipatterns is presented and refactoring method for all of them was described. Most of antipatterns were diagrammed using information from text descriptions and additional notes about arguable properties of antipatterns were included.
A Robust Near-Duplicate Images Detection Approach with Ordinal Measure

Digital image is easy to be acquired, modified and redistributed either lawfully or unlawfully. A large number of similar images waste storage and computing resources, and sometimes it can lead to legal disputes. A robust algorithm of detecting near-duplicate images is proposed based on the image feature coming from ordinal measure. It can be used to detecting images that are altered to original image in contrast, saturation, blur and other visual appearance of color. And, the hash code is introduced to speed up the comparison and retrieval of near-duplicate images. The experiment on the test image sets proves the effectiveness and efficiency of the proposed method.
SpecBox: A Label-Based Transparent Speculation Scheme Against Transient Execution Attacks

Speculative execution techniques have been a cornerstone of modern processors to improve instruction-level parallelism. However, recent studies showed that this kind of techniques could be exploited by attackers to leak secret data via transient execution attacks, such as Spectre. Many defenses are proposed to address this problem, but they all face various challenges: (1) Tracking data flow in the instruction pipeline could comprehensively address this problem, but it could cause pipeline stalls and incur high performance overhead; (2) Making side effect of speculative execution imperceptible to attackers, but it often needs additional storage components and complicated data movement operations. In this article, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">label-based transparent speculation</i> scheme called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SpecBox</small> . It dynamically partitions the cache system to isolate speculative data and non-speculative data, which can prevent transient execution from being observed by subsequent execution. Moreover, it uses thread ownership semaphores to prevent speculative data from being accessed across cores. In addition, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SpecBox</small> also enhances the auxiliary components in the cache system against transient execution attacks, such as hardware prefetcher. Our security analysis shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SpecBox</small> is secure and the performance evaluation shows that the performance overhead on SPEC CPU 2006 and PARSEC-3.0 benchmarks is small.
Zero-Watermarking Algorithm Based on Modified Singular Value and Chaos Scrambling

This paper mainly redesigns a zero watermarking extraction algorithm. The security and the concealment of zero watermarking can be improved by using chaotic encryption for both image and water copyright images. Extracting feature information from the low frequency region of wavelet can make zero watermarking have good stability. The SVD decomposition is used for low frequency region to extract the maximum singular value of the sub-block and multiply it by the correction factor α to generate the feature information. The feature information matrix and the copyright information matrix generate zero watermark information through the remainder operation. The experiment not only verifies the effect of chaotic encryption and wavelet transform on zero watermark extraction, but also gives the range of correction factor. Experimental results show that the proposed algorithm is effective in extracting zero watermark information.
Development of a Mechanism for the Management of University Research Activities Based on the Principles of Sustainable Development

The article is devoted to the topical issues of performance evaluation and management of university research groups for its sustainable development. Scientific groups are one of the effective levers for increasing and activation of research activities in the university, as they cover all areas of its activities: performance of research and development work, building a portfolio of orders, preparation of applications for intellectual property objects, preparation of articles and reports, training of highly qualified personnel and implementation of expert activities. To develop a methodology for assessing the effectiveness of research teams, the existing methods for analyzing the management of research activities of universities in Russian and world practice were considered, where their advantages and disadvantages were identified. The university’s organizational and economic mechanism of research activities management was developed based on the above and the principles of sustainable development of higher education institutions.
Opportunities of Digital Education for Sustainable Development of Society

The article emphasizes the importance of digital education for further development in connection with the active use by society of the opportunities that technologies open up in general. The strengths and weaknesses of the transition to digital education and the formation of exclusively network, digital and global institutions in the future are summarized, the threats that hinder the mass transition to blended forms of education are considered.
Analytical Study on Load Balancing Algorithms in Cloud Computing

With advancements in the Internet field, cloud computing is critical to provide services on a pay-per-use basis. It has become effortless to use due to its attributes such as fault tolerance, high availability, and scalability. Due to these features and their increased popularity, load balancing becomes extremely important to ensure that the tasks are appropriately distributed across servers to prevent confusion. Many people have created and developed various algorithms to provide a good overview and architecture to the cloud service. This paper aims to create a review for different algorithms of load balancing.
Analysing Games for Health through Users' Opinion Mining

Serious games are a category of games which purpose extends beyond entertainment. Among these, we find a specific type of games, exergames, which aim to promote physical activity. Despite the positive influence of exergames on their users, players often stop playing them after a short period of time, losing the positive benefits of gameplay. It is in this context, that the need to better understand the experience and the opinions of players emerges. To grasp what users feel and (dis)like is key so that games can be redesigned and improved to fit users preferences. This work proposes to analyse users' comments from YouTube, using Natural Language Processing techniques, to extract knowledge on usability, user experience and perceived impacts on health, in particular on quality of life, that could inform the redesign of the game thereafter. This paper is a work in progress that reports on preliminary work that explores users opinions about the Just Dance game. In mining users' opinions, the process of annotation of usability, user experience and quality of life dimensions is based on a pre-established vocabulary. Each extracted opinion is annotated with the concepts present in the opinion, using an approach that is based on the English dictionary lexicon in conjunction with sentiment analysis. The results obtained are then displayed on a dashboard, where the data extracted from the previously collected user comments can be viewed, analysed, and explored.
A Case-Based Reasoning Approach for a Decision Support System in Manufacturing

We propose Case-based reasoning (CBR) as an approach to assist human operators who control special purpose production machines. Our support system automatically extracts knowledge from machine data and creates recommendations, which help the operators solve problems with a production machine. This support has to be comprehensive and maintainable by process experts, also the system has to be easy transferable to different machines. We present CBR as a suitable approach for a decision support system in an industrial production.
Comparison of DBSCAN and PCA-DBSCAN Algorithm for Grouping Earthquake Area

Geologically, the territory of Indonesia is where the three active tectonic plates meet which are always moving and colliding with each other, resulting in earthquakes, volcanic pathways, and faults. Earthquake is a natural disaster that cannot be avoided or prevented, but the consequences of earthquakes can be minimized. Based on data obtained from Meteorology, Climatology and Geophysics Agency (MCGA), earthquakes often occur in Indonesia. Data obtained from earthquakes can be grouped to map the area of earthquake occurrence and an analysis will be carried out to determine the characteristics of earthquake clustering areas. The clustering in this is study conducted with two experiments, first experiment is Density-Based Spatial Clustering of Applications with Noise (DBSCAN) without dimensional reduction and second experiment is DBSCAN clustering with dimensional reduction using Principal Component Analysis (PCA). The best cluster results can be found by calculating the value of Silhouette Index (SI) of each cluster. From the two experiments, the highest SI value was obtained in experiment using PCA, which was 0.4137. Then the second experiment was used as the best cluster results with the highest Dept and Magnitude features in clusters 19 and 17 which showed the 5 main regions where earthquakes often occur are Sumatra, Banda Sea, Moluccan Sea, Irian Jaya and Sulawesi
Voice Switching in Voice-Enabled Digital Assistants (VDAs)

Voice-enabled digital assistants (VDAs) provide users with options to switch the "out-of-the-box" or default voice interface. Numerous studies have investigated digital assistants. However, no studies have examined factors influencing user decisions to switch the default voice interface in VDAs. Informed by the similarity-attraction behavioral theory, we investigated whether perceived age, accent, gendered voice, and personality of the voice interface influence user decisions to switch the voice in VDAs. Guided by the status quo behavioral theory, we examined factors influencing user decisions to keep the default voice unchanged (status quo). We recruited thirty-one participants who took an online survey consisting of 42 questions, including 27 closed and 15 open-ended, collecting participants' demographic information, experience in and knowledge of how to switch the voice, voice switching behavior, and preferences, among others. We employed the Big Five Personality Traits to assess the participants' personality traits and the perceived personality of the voice in VDAs. We found that nearly 39% of the participants switched the voice interface in VDAs. Another finding is that the majority of male participants and all female participants (switchers and non-switchers) had a female-gendered voice in the VDAs. We detected a high correlation between the participants' own personality traits and the perceived personality traits of their VDAs. Factors such as perceived age, accent, and gender did not influence the decision of the majority of the participants to switch the voice interface. The findings have implications for designing VDAs with personalities that leverage the user experience (UX).
Hybrid recommender system based on fuzzy neural algorithm

Abstract The internet has provided much information to the users, and the huge amount of information has caused the overhead information problem so that recommender systems, as a solution by recommending related items, try to find the users favorite items. Most recommender systems recommend specific items based on user profile settings and past privileges. Various approaches, such as collaborative refinement and content‐based refinement, are used to build the recommender system. The proposed framework utilizes popular and unpopular user‐specific items and is built on the integration of the fuzzy neural network associative exploration approach (Anfis) with a collaborative refinement approach. This study proposes a hybrid recommender system that provides two‐dimensional space (user * Item) ancillary information to reflect users' preferences and items. Experimental results from experiments on the actual Movielens 1 m 6040 dataset and 3952 videos show that the mean error (MAE) of 2.79 compared to other low‐resolution methods also predicts a correct percentage of 21.75 to other windshields. Besides, experimental results show higher precision of the proposed system comparing to the former recommender systems.
Human Individual Difference Predictors in Cyber-Security: Exploring an Alternative Scale Method and Data Resolution to Modelling Cyber Secure Behavior

With the increase in reliance upon technology in our everyday lives, users are more vulnerable than ever to cybercrime and data security breaches. Whilst it is important, and valued, to develop technology-based interventions to mitigate this risk, it is also important to consider the impact of human error on cyber safety, and how this can be measured. Data collected from a diverse sample of 189 participants using an alternative measurement scale to more traditional Likert scales, the Visual Analogue Scales (VAS), was adopted for previously researched measures of individual differences (age, gender, education level, personality, decision-making style, risk-taking preferences, acceptance of the internet, and related Theory of Planned Behavior and Protection-Motivation Theory concepts) to expand understanding of the relationships between individual differences and user-end cybersecurity behaviors, and explore the significance of this alternative measure in the field of Cyber Psychology. Findings demonstrate the use of VAS can be a reliable and valid method capable of identifying a variety of potential human vulnerabilities and strengths on an individual level. These findings highlight the importance of considering a human-centered approach to cyber-security, and future research should consider then importance of these individual differences in tailoring practical interventions.