Reviewerly/exHarmony huggingface dataset:
The effect of customer satisfaction on service quality: The case of Iraqi banks

This study aims to investigate the relationship of customer satisfaction towards service quality in Iraqi banks. It is suggested that despite the efforts provided by Iraqi banks to improve its service, there is still evidence of lacking of service quality. Service quality is important in gaining customer satisfaction. The aim of this study is to examine the service quality on customer satisfaction in Iraqi banks. The components of service quality consist of assurance, reliability, tangibility, empathy, and responsiveness and are identified as the independent variable (IV). As for dependent variable (DV), the customer satisfaction was chosen. This study follows quantitative approach; questionnaire was adopted from previous studies and were distributed among the Iraqi customers. The sample for this study consisted of 323 customers of Iraqi banks in Bagdad. SPSS 21 software was used to analysis the data. The results indicated that service quality components namely, assurance, reliability, tangibility, and empathy effect positively and significantly on customer satisfaction.
MobiScan: An enhanced invisible screen‐camera communication system for IoT applications

Abstract In recent years, dynamic and invisible screen‐camera short‐range communication for IoT devices have become a popular research field due to its superior user experience and no‐extra hardware required. However, most existing screen‐camera communication systems suffer from poor data security, high computational overhead, and limited capture angle, which make them infeasible in practice. In this work, we propose MobiScan, a dynamic and invisible screen‐to‐camera communication system that is able to ensure data security, real‐time communication, and flexible capture angle. MobiScan is composed of two parts: a novel fast frame correction scheme and a multilevel data pattern scheme to address the above problems. The fast frame correction scheme proposes a novel frame correct approach that utilizes the sensor data on the smartphone to correct the captured frame by reproducing the relationship between positions of the screen and the smartphone. Through this method, the scheme can save frame correction time without calculating frame content to identify the degree of deformation. The multilevel data pattern scheme protects the data privacy by sending the classified information to targeted users, which can reduce the time overhead and extend the application scenario by redesigning the data structure. The experimental results show that for flexible capture angle problem, the maximum capture angle is 180° in the rotate view and 60° in the side view, bottom view, and overlook view. For real‐time communication problem, the time overhead is reduced by 90% in the procedure of the frame correction. In the data decoding process, the time overhead is reduced by 10%. MobiScan enables a flexible capture angle on the PC platform for secure and real‐time communication.
ANN-Based Correction Model of Radiation and Temperature for Solar Energy Application in South of Algeria

This study proposes a hybrid solar radiation prediction model combining a Numerical Weather Prediction (NWP) simulation and an Artificial Neural Networks model (ANN). Weather Research and Forecasting (WRF) model is used to simulate meteorological and solar radiation parameters for one calendar year from January to December 2016 using six-hourly interval 1° × 1° NCEP FNL analysis data. The one calendar year results from the WRF model will be used as input data for ANN model to estimate the temperature and solar radiation, in a case study of a solar farm located in Adrar Province, Algeria. The results reveal that the use of the ANN model effectively improves the accuracy of solar and temperature prediction. The RMSE, MAE and Correlation were investigated for the model evaluated for hourly irradiation predictions. It was determined that the proposed hybrid WRF-ANN model performs better than WRF simulation values.
MLIFeat: Multi-level Information Fusion Based Deep Local Features

Accurate image keypoints detection and description are of central importance in a wide range of applications. Although there are various studies proposed to address these challenging tasks, they are far from optimal. In this paper, we devise a model named MLIFeat with two novel light-weight modules for multi-level information fusion based deep local features learning, to cope with both the image keypoints detection and description. On the one hand, the image keypoints are robustly detected by our Feature Shuffle Module (FSM), which can efficiently utilize the multi-level convolutional feature maps with marginal computing cost. On the other hand, the corresponding feature descriptors are generated by our well-designed Feature Blend Module (FBM), which can collect and extract the most useful information from the multi-level convolutional feature vectors. To study in-depth about our MLIFeat and other state-of-the-art methods, we have conducted thorough experiments, including image matching on HPatches and FM-Bench, and visual localization on Aachen-Day-Night, which verifies the robustness and effectiveness of our proposed model.
Unawareness without AU Introspection

This paper studies unawareness in terms of the lack of knowledge in a model that generalizes both a non-partitional standard-state-space model and a stationary generalized-state-space model. The resulting model may not necessarily satisfy AU Introspection: an agent, who is unaware of an event, is unaware of being unaware of it. Yet, the paper shows that such agent does not know whether she is unaware of it, i.e., she is ignorant of being unaware of it. First, the paper asks when and how the generalized model (in particular, a standard-state-space model) has a non-trivial form of unawareness and sensible properties of unawareness. Second, the paper studies the implications of the violation of AU Introspection. An agent, when facing infinitely many objects of knowledge, may know that there is an event of which she is unaware. Treating new information only at face value can cause an agent to become unaware of some event.
Local Pooling of Connected Supernodes in Lightning Networks for Blockchains

The lightning network (LN) is a special network in Bitcoin that uses offchain micropayment channels to scale the blockchain's capability to perform instant transactions without a global block confirmation process. However, micropayment scalability in a large LN and liquidation for small nodes still remain major challenges for the LN. In this paper, we introduce the notion of supernodes and the corresponding supernodes-based pooling to address these challenges. In order to meet the high adaptivity and low maintenance cost in the dynamic LN where users join and leave, supernodes are constructed locally without any global information or label propagation. Each supernode, together with a subset of (non-supernodes) neighbors, forms a supernode-based pool. These pools constitute a partition of the LN. Additionally, supernodes are self-connected. Micro-payment scalability is supported through node set reduction as only supernodes are involved in searching and in payment with other supernodes. Liquidation is enhanced through pooling to redistribute funds within a pool to external channels of its supernode. Extensive simulations have been conducted to validate the improvement in routing scalability and liquidation of the proposed architecture under different settings.
Robust Watermarking Scheme for Compressed Image Through DCT Exploiting Superpixel and Arnold Transform

The adventSingh, Prabhash Kumar ofJana, Biswapati digitalDatta, Kakali watermarking was to authenticate and uphold copyright protection in digital media. Since then, there was no turning back by the researchers to attain techniques of various scales to support the cause. In this paper, a rarely used superpixel for watermarking has been exploited to develop a new algorithm of its kind to exhibit robust watermarking technique for compressed images in tune with DCT and Arnold Transform. The recommended technique converts an RGB color image to YCbCr color model to use superpixel on only Y color component. Thereafter, a (8 $$\times $$ 8) partition is carried on for the obtained superpixel region, Cb, and Cr component. Based on the Arnold Transform, specific blocks are tested for heterogeneity to choose the non-uniform regions of the image for the embedding of the secret data. Next, the corresponding pixel values of the successful blocks are remodeled to quantized DCT and embed the binary watermark image. The embedding, as well as extraction procedures, are illustrated with a clear explanation along with a comparison to the state-of-the-art schemes to demonstrate the robustness, imperceptibility, and efficiency of the proposed technique.
Intelligent Fault-Tolerant Control System Design and Semi-Physical Simulation Validation of Aero-Engine

In order to improve the reliability and real-time of the control system of aero-engine, an intelligent fault-tolerant control system based on the online sequential extreme learning machine (OS-ELM) is proposed against the sensor faults. This system can realize the online fault diagnosis and signal reconstruction without a system model. And while considering the traditional PID control robustness and poor anti-interference ability and other shortcomings, an improved global fast non-singular terminal sliding mode control method is used to obtain better control effects, effectively solve the uncertainty problem in aero-engine, and give full play to aero-engine performance. To verify the feasibility and effectiveness of this system based on the above method, a turbofan engine is taken as the research object and semi-physical simulation experiments on fault-tolerant control are conducted on a semi-physical simulation test platform. Results show that the controller of this system can safely and reliably control the aero-engine without losing its control performance under the circumstance that there are faults in engine sensors. The purpose of fault-tolerant control is reached.
Analytical Framework of the Convergent Approach to the Creation of Musical Systems Based on Artificial Intelligence

In the music industry, researchers have been developing an approach to music as an informational phenomenon for decades. Interest in music as information becomes especially noticeable with the development of artificial intelligence. Artificial intelligence algorithms are used to determine musical preferences, provide personal recommendations on streaming services, and compose music. Artificial intelligence is predicted to have a strong impact on the music content creation process. Modern music software tools are showing impressive results. However, musical systems based on artificial intelligence lack human intelligence, i.e., the ability to understand the meaning of musical works. Focus on software and insufficient attention to human intelligence indicate that the problem of creating musical artificial intelligence remains extremely underestimated and is one of the urgent problems of modern information technologies. Objective of the research: theoretical substantiation and development of a convergent approach to the creation of musical systems based on artificial intelligence. Methods: information and semantic method for the study of cultural exchanges in the musical environment by A.B. Kayak; theoretical analysis of the probabilistic approach to the problem of meaning by V.V. Nalimov; method of probabilistic evaluation of meanings by S.G. Fadushin. Results: the analytical framework is provided for a convergent approach to the creation of musical systems based on artificial intelligence. The obtained results will allow to create information and control systems based on artificial intelligence in the musical environment. The information provided in the article may be of theoretical and practical interest for researchers and developers of artificial intelligence in the field of the music industry. The results may also be used by teachers, students, graduate students, researchers studying the philosophy and sociology of musical culture.
Is There A "Golden" Rule for Code Reviewer Recommendation? : —An Experimental Evaluation

Peer code review has been proven to be an effective practice for quality assurance, and widely adopted by commercial companies and open source communities as GitHub. However, identifying an appropriate code reviewer for a pull request is a non-trivial task considering the large number of candidate reviewers. Several approaches have been proposed for reviewer recommendation, yet none of them has conducted a complete comparison to explore which one is more effective. This paper aims at conducting an experimental evaluation of the commonly-used and state-of-the-art approaches for code reviewer recommendation. We begin with a systematic review of approaches for code reviewer recommendation, and choose six approaches for experimental evaluation. We then implement these approaches and conduct reviewer recommendation on 12 large-scale open source projects with 53,005 pull requests spanning two years. Results show that there is no golden rule when selecting code reviewer recommendation approaches, and the best approach varies in terms of different evaluation metrics (e.g., Top-5 Accuracy, MRR) and experimental projects. Nevertheless, TIE, which utilizes the textual similarity and file path similarity, is the most promising one. We also explore the sensitivity of these approaches to training data, and compare their time cost. This approach provides new insights and practical guidelines for choosing approaches for reviewer recommendation.
Surveillance Video Coding for Traffic Scene based on Vehicle Knowledge and Shared Library by Cloud-Edge Computing in Cyber-Physical-Social Systems

With rapid development of intelligent video surveillance systems based on cloud computing devices and edge computing devices in Cyber-Physical-Social Systems, massive surveillance video data has brings enormous challenge for video storage and transmission. However, existing surveillance video coding approaches hardly utilize intelligent video analysis results for improving video coding. This paper proposed a surveillance video coding scheme for traffic scene based on vehicle knowledge and shared library by cloud-edge computing in Cyber-Physical-Social Systems. Firstly, in order to provide the object library for synchronous application at the encode and decode side offline, a generation method of shared long-term foreground reference object library is proposed by using the existing large-scale monitoring vehicle object datasets. Then, to meet the requirement of low complexity and high-performance coding, a virtual foreground reference picture generation method with coding-oriented object retrieval is proposed. Experimental results show that the proposed scheme can obtain the satisfactory effect of the virtual foreground reference picture. Also, it can yield remarkable bit rate reductions, compared to HEVC.
The comparative study on agglomerative hierarchical clustering using numerical data

Abstract In theconventional way of convert data into a singleton or merging has many drawbacks mainly computational complexity. In this context hierarchical clustering method for quantitative measures of similarity among objects that could keep not only the structure of categorical attributes but also relative distance of numeric values. For numeric data the number of clusters can be validated through integral data, the hierarchical and partitioning methods the relationships among categorical items. In This Paper we hereinvestigate linkage criterions in hierarchical clustering algorithm performance calculations using with Euclidian distance measure and some clustering techniques and their applications have been discussed. It also describes the necessities to be calculated for constructing a well-organized to handle the huge data sets. As the study initially investigates distinct issues for creating clusters with numeric attributes. The efficiency is obtained by clustering of datasets that comprises of numeric attributes related to distinct applications. Significant issues such merging object naturally uses Euclidean distance is resolved by using Agglomerative methods.
AR Interfaces for Mid-Air 6-DoF Alignment: Ergonomics-Aware Design and Evaluation

Aligning hand-held objects into mid-air positions and orientations is important for many applications. Task performance depends on speed and accuracy, and also on minimizing the user's physical exertion. Augmented reality head-mounted displays (AR HMDs) can guide users during mid-air alignments by tracking an object's pose and delivering visual instruction directly into the user's field of view (FoV). However, it is unclear which AR HMD interfaces are most effective for mid-air alignment guidance, and how the form factor of current AR HMD hardware (such as heaviness and low FoV) affects how users put themselves into tiring body poses during mid-air alignment. We defined a set of design requirements for mid-air alignment interfaces that target reduction of high-exertion body poses during alignment. We then designed, implemented, and tested several interfaces in a user study in which novice participants performed a sequence of mid-air alignments using each interface.Results show that interfaces that rely on visual guidance located near the hand-held object reduce acquisition times and translation errors, while interfaces that involve aiming at a faraway virtual object reduce rotation errors. Users tend to avoid focus shifts and to position the head and arms to maximize how much AR visualization is contained within a single FoV without moving the head. We found that changing the size of visual elements affected how far out the user extends the arm, which affects torque forces. We also found that dynamically adjusting where visual guidance is placed relative to the mid-air pose can help keep the head level during alignment, which is important for distributing the weight of the AR HMD.
A Novel Discrete Particle Swarm Optimization For Travelling Salesman Problem Based on Dissipative Structure Theory

Aiming at the problems of slow convergence velocity and easy to fall into local optimum in discrete particle swarm optimization, a novel discrete particle swarm optimization based on dissipative structure theory (DDPSO) is proposed. First, based on the dissipative structure theory, set a reasonable inertia coefficient to improve the algorithm's ability to seek optimization and convergence speed; Second, the operation operator is introduced to improve the randomness and diversity of particles by adjusting the crossover probability between particles and enhance the global search ability of the algorithm; Finally, the algorithm is evaluated in the internationally commonly used library of TSP (TSPLIB). The results show that the novel algorithm is superior to other algorithms in terms of global optimization ability, convergence speed and stability.
Teacher and Context Factors Associated with the Educational Use of ICT: A Costa Rican Case Study

This paper studies the factors that influence the progress of teachers in Costa Rica with regards to their educational use of information and communication technologies (ICTs) in teaching and learning processes with their students. It addresses both the analysis of factors related to the figure of the teacher, their professional profile, and other factors that are external to them and are related to the context in which they practice. A qualitative method involving a case study of seven teachers was selected according to relevant and maximum variation sampling based on expert criteria. The information was collected utilizing in-depth interviews and systematic observation. The analysis of the information was based on the qualitative data analysis software program ATLAS.ti. The results highlight two categories of factors, those of the teacher, such as vocation, beliefs, teaching model, desire for personal fulfillment, or personal issues, and those of context, such as technical support and support of the management structure of the school, with more significant influence on the processes of appropriation and use of ICT in teaching practices of those factors linked to the teachers themselves.
An Intelligent Risk-Based Authentication Approach for Smartphone Applications

Authentication on smartphones is performed at the initial entry, mostly utilizing knowledge-based authentication methods which are fast and convenient. Device-based authentication does not guarantee that the user will utilize effective authentication credentials as many users choose less robust and easy to remember credentials. To reduce the explicit intervention from users and to increase user adoption, implicit authentication should be present. This approach authenticates users based on temporal access patterns to mobile devices, such as modeling the access behavior to applications. This paper presents an intelligent risk-based authentication method based on temporal access behavior to general applications on mobile devices. The risk score is calculated from the modeled pattern on the mobile device and the approach minimizes the required credentials based on the quality of this pattern. The evaluation of the presented method is achieved on real datasets and the results show the effectiveness of the approach. Importantly, the approach requires only a short period of application usage to build the model in addition to adapting to new app usage. Ultimately, the results show that the approach provides a low false acceptance rate and false rejection rate, which enhances its usability.
Combining Bandits and Lexical Analysis for Document Retrieval in a Juridical Corpora

Helping users to find pertinent documents within a big corpus through the use of simple queries on a search engine is a major concern in the information retrieval field. The work presented in this article combines the use of standard natural language processing methods to estimate the relevance of a document to a query with an online preference learning method to infer such kind of pertinence by analyzing the past behavior of other users making similar searches. The first contribution of this article is the proposition of a specific heuristic method, conceived for an open access online juridical corpus, to filter and interpret data collected from the user behavior while navigating on the search engine’s query interface, on the list of results, and on the documents themselves. The second contribution is an original way for combining multiarmed bandit algorithms for learning pertinence from the user implicit feedback with natural language processing techniques in order to define a unique ranking for the search results.
DeepFake Detection Algorithms: A Meta-Analysis

We analyzed the developed methods of computer vision in areas associated with recognition and detection of DeepFakes using various models and architectures of neural networks: mainly GAN and CNN. We also discussed the main types and models of these networks that are most effective in detecting and recognizing objects from different data sets, which were provided in the studied articles.