Reviewerly/exHarmony huggingface dataset:
Multiscale quantum harmonic oscillator optimization algorithm with multiple quantum perturbations for numerical optimization

Multi-scale quantum harmonic oscillator algorithm (MQHOA) is a recent proposed intelligent algorithm, in which the optimization process can be regarded as the multi-scale quantum annealing process with respect to the constraint of the harmonic oscillator potential well. It has been proved effective and efficient to deal with unimodal and multimodal numerical optimization problems. However, it takes a long time for the particles system to reach the ground-state equilibrium at each annealing scale. Motivated by this situation, a diffusion Monte Carlo method based dynamic sampling regulation strategy is proposed to enhances the sampling efficiency by dynamically adjusting the sampling frequency. Moreover, the particles with different kinetic energies are introduced into the optimization system as quantum perturbations, which reduces the probability of the algorithm falling into a local optimum by keeping the different searching scales simultaneously. The theoretical derivations of this method are presented. The effectiveness of the algorithm is studied by comparing it with previous versions of the MQHOA and several famous intelligent algorithms on uni- and multimodal benchmark functions. The experimental results illustrate that the proposed method has a comparable performance for numerical optimization problems.
Intelligent Distribution for COVID-19 Vaccine Based on Economical Impacts

The Covid-19 pandemic is affecting the entire world, and its consequences have been human and economic losses. Jordan is one of the countries affected by the Covid-19 pandemic, including human and economic losses. In this paper, we will explain the vaccines used and the mechanism for distributing the vaccines in Jordan, and explain the economic impact of the Covid-19 pandemic in Jordan, especially tourism. An economic solutions is introduced in this paper by stimulating tourism through the proposed Algorithm and the idea of the program that we have proposed for protection.
Quality-Aware Distributed Computation and User Selection for Cost-Effective Federated Learning

Federated learning (FL) allows machine learning (ML) models to be trained on distributed edge computing devices without the need of collecting data from a large number of users. In distributed stochastic gradient descent which is a typical method of FL, the quality of a local parameter update is measured by the variance of the update, determined by the data sampling size (a.k.a. mini-batch size) used to compute the update. In this paper, we study quality-aware distributed computation for FL, which controls the quality of users' local updates via their mini-batch sizes. We first characterize the dependency of learning accuracy bounds on the quality of users' local updates over the learning process. It reveals that the impacts of local updates' quality on learning accuracy increase with the number of rounds in the learning process. Based on these insights, we develop cost-effective dynamic distributed learning algorithms that adaptively select participating users and their mini-batch sizes, based on users' communication and computation costs. Our result shows that for the case of IID data, it is optimal to select a set of users with the lowest communication costs in each round, and select more users with a larger total mini-batch size in a later round. We evaluate the proposed algorithms using simulation results, which demonstrate their efficiency.
Understanding peer review of software engineering papers

Peer review is a key activity intended to preserve the quality and integrity of scientific publications. However, in practice it is far from perfect. We aim at understanding how reviewers, including those who have won awards for reviewing, perform their reviews of software engineering papers to identify both what makes a good reviewing approach and what makes a good paper. We first conducted a series of interviews with recognised reviewers in the software engineering field. Then, we used the results of those interviews to develop a questionnaire used in an online survey and sent out to reviewers from well-respected venues covering a number of software engineering disciplines, some of whom had won awards for their reviewing efforts. We analyzed the responses from the interviews and from 175 reviewers who completed the online survey (including both reviewers who had won awards and those who had not). We report on several descriptive results, including: Nearly half of award-winners (45%) are reviewing 20+ conference papers a year, while 28% of non-award winners conduct that many. The majority of reviewers (88%) are taking more than two hours on journal reviews. We also report on qualitative results. Our findings suggest that the most important criteria of a good review is that it should be factual and helpful, which ranked above others such as being detailed or kind. The most important features of papers that result in positive reviews are a clear and supported validation, an interesting problem, and novelty. Conversely, negative reviews tend to result from papers that have a mismatch between the method and the claims and from papers with overly grandiose claims. Further insights include, if not limited to, that reviewers view data availability and its consistency as being important or that authors need to make their contribution of the work very clear in their paper. Based on the insights we gained through our study, we conclude our work by compiling a proto-guideline for reviewing. One hope we associate with our work is to contribute to the ongoing debate and contemporary effort to further improve our peer review models in the future.
An energy-efficient crypto-extension design for RISC-V

With the prevailing of Internet-of-Things (IoT) technology, information security for ever-growing connected devices is an inevitable issue and gaining more attention. However, implementation of cryptography algorithms on battery-powered IoT devices is challenging due to limited power-budget. In this paper, we present an energy-efficient crypto-coprocessor. This coprocessor is designed with a unified pipelined structure for cryptography primitives of 128-bit or 256-bit data path and supports cryptography algorithms including AES, ECC and SHA. Since the clock tree and the sequential circuits dissipate a large percentage of the chip power, a conditional-charged flip-flop is proposed to reduce the clock tree power. Our design is integrated with an open-sourced RISC-V core as a crypto-extension, and shows both good flexibility and high energy-efficiency. This work is implemented in 28 nm technology and the power consumption for different cryptography applications is evaluated with post-layout simulation. When simulated with NIST prime fields curve P-256 and binary fields curve K-233, the energy consumed for one base point scalar multiplication is 43.54 μJ and 20.40 μJ, respectively. The proposed design consumes 0.0568 nJ/bit and 0.0288 nJ/bit for the AES-GCM mode and the AES-CBC mode, respectively. As for SHA-256, each bit requires 0.0874 nJ. Compared with previous works, this work provides both flexibility and high energy performance.
Opposition-Inspired synergy in sub-colonies of ants: The case of Focused Ant Solver

Recently, Opposition-Inspired Learning strategies were proposed to improve the search process of ant-based algorithms to solve combinatorial problems. In this paper, we propose a collaborative framework of these strategies called Multiple Opposite Synergic Strategy for Ants (MOSSA). Because of each strategy has a different goal, we expect that the ants algorithm will benefit from their collaboration. The algorithm strongly uses the pheromone matrix for accomplishing stigmergy. To evaluate our framework, we use a recently proposed algorithm to solve Constraint Satisfaction Problems named Focused Ant Solver. Results and statistical analysis show that using MOSSA, Focused Ant Solver is able to solve more problems from the transition phase.
Collaborative Software Modeling in Virtual Reality

Modeling is a key activity in conceptual design and system design. Through collaborative modeling, end-users, stakeholders, experts, and entrepreneurs are able to create a shared understanding of a system representation. While the Unified Modeling Language (UML) is one of the major conceptual modeling languages in object-oriented software engineering, more and more concerns arise from the modeling quality of UML and its tool-support. Among them, the limitation of the two-dimensional presentation of its notations and lack of natural collaborative modeling tools are reported to be significant. In this paper, we explore the potential of using Virtual Reality (VR) technology for collaborative UML software design by comparing it with classical collaborative software design using conventional devices (Desktop PC / Laptop). For this purpose, we have developed a VR modeling environment that offers a natural collaborative modeling experience for UML Class Diagrams. Based on a user study with 24 participants, we have compared collaborative VR modeling with conventional modeling with regard to efficiency, effectiveness, and user satisfaction. Results show that the use of VR has some disadvantages concerning efficiency and effectiveness, but the user's fun, the feeling of being in the same room with a remote collaborator, and the naturalness of collaboration were increased.
A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution

Recommender systems are challenging research problems being exploited to suggest new items or services, such as books, music and movies, and even people, to users based on information about the user profile or the recommended items. To date, collaborative filtering (CF) has become one of the most widely used approaches for recommendations. However, traditional CF methods usually cannot track temporal dynamic user preferences and topic changes to make appropriate suggestions. Moreover, the performance of CF is limited in the case of sparse data. In this paper, we propose a novel temporal recommender system based on multiple transitions in user preference drift, called MTUPD, which employs a multitransition factor and a forgetting time function to investigate the evolution of user preferences. In addition, we consider addressing the rating sparsity issue by using text reviews. Understanding the reviews can facilitate the system grasping whether or not a user is attracted by the appearance of an item and whether the facet of an item's appearance contributes the most to its ratings. To achieve this, we apply a topic model that automatically classifies hidden topic factors in each time period and incorporate the transition method for both user preferences and relevant review topics. Experiments show that our proposed model outperforms the compared models on eight promising datasets for temporal recommender systems.
Local-HDP: Interactive open-ended 3D object category recognition in real-time robotic scenarios

We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). This method allows an agent to learn independent topics for each category incrementally and to adapt to the environment in time. Each topic is a distribution of the visual words over a predefined dictionary. Using an inference algorithm, these latent variables are inferred from the dataset. Subsequently, the category of an object is determined based on the likelihood of generating a 3D object from the model. Hierarchical Bayesian approaches like Latent Dirichlet Allocation (LDA) can transform low-level features to high-level conceptual topics for 3D object categorization. However, the efficiency and accuracy of LDA-based approaches depend on the number of topics that is chosen manually. Moreover, fixing the number of topics for all categories can lead to overfitting or underfitting of the model. In contrast, the proposed Local-HDP can autonomously determine the number of topics for each category. Furthermore, the online variational inference method has been adapted for fast posterior approximation in the Local-HDP model. Experiments show that the proposed Local-HDP method outperforms other state-of-the-art approaches in terms of accuracy, scalability, and memory efficiency by a large margin. Moreover, two robotic experiments have been conducted to show the applicability of the proposed approach in real-time applications.
Education in Latin America: Toward the Digital Transformation in Universities

Education in Latin America turned to the use of distance communication strategies in the scenario's face of health, social, and economic consequences resulting from the COVID-19 pandemic. This abrupt change meant reviewing a set of conditions that drastically affected quality teaching and education in the region. This chapter addresses the conditions that universities in Latin America countries must face in order to ensure that their students can develop quality knowledge through the conditions present in distance and hybrid models, and the efforts that must be concentrated on in teacher's training, in order to professionalize in a more innovative manner in the face of digital and remote teaching. The factors that universities at the region should consider in their models to address the relevance and encourage the development of activities to meet their objectives and those of the community in which they take part. In the same way, the metrics, indicators and data for the areas of opportunity and the analysis of information used for decision making will be reviewed, and the use of artificial intelligence, Internet of Things, big data, data analytics, cloud computing and the complement information and communication technologies required in the digital transformation of the universities in the Latin America region.
The Facial Expression Recognition Method Based on Image Fusion and CNN

Facial expression recognition (FER) is an important task in the field of human-computer interaction. However, the traditional facial expression recognition task needs to be based on the hand-crafted features, and the feature extraction method is single; the facial expression recognition task based on deep learning cannot extract local texture features of the image and loss more information. Therefore, a facial expression recognition method based on image fusion and convolution neural network (FERFC) is proposed in this paper. Which fused the facial expression images after extracted by the local binary pattern (LBP) with the original images. It can effectively improved the utilization of images. Firstly, some image pre-processing approaches are used in this paper, such as data augmentation, face detection and data normalization. Secondly, the images of local texture features extracted by the LBP and the original images are fused in this step. Finally, the task of facial expression features learning and classification is completed by convolution neural network (CNN). The results show that the method proposed in this paper can accomplish the facial expression recognition task accurately. The recognition rate of reference database ‘Jaffe’, ‘CK+’ and ‘FER2013’ is 91.9%, 95.6% and 75.9%. The results show that the FERFC has significant advantages than traditional facial expression recognition. At the same time, the number of training samples is small, the FERFC still has obvious advantages and a higher robustness.
A Qualitative Review of 18 Years of Research on Workplace Deviance: New Vectors and Future Research Directions

Workplace deviance is a major cause of concern for organizations. It leads to an unpleasant work environment and results in decreased productivity, as well as financial and reputational losses for the organization. This paper synthesizes the vast amount of literature in the field of workplace deviance by qualitatively reviewing 245 papers published from 2003 till October 2020, highlighting the theoretical frameworks and set of antecedents of negative workplace deviant behavior. It further lays out future research directions concordant with the changing nature and scope of acts of deviance in the workplace that are rapidly transforming due to technology advancement, infusion and globalization. We discuss the changing socio-technological factors and their implications for perpetrator-victim dynamics and urge future researchers to study workplace deviance within this context.
Analysis and modeling conditional mutual dependency of metrics in software defect prediction using latent variables

Software defect prediction constitutes an important discipline in software development life-cycle. Among the techniques employed in this domain, Naive Bayes (NB) classifier is cited by a large number of researchers for its simple structure and remarkable classification performance notwithstanding the concern of whether it is theoretically justified or not. More concisely, NB is fundamentally built on the strong assumption of conditional independence of attributes, and the major question here is the compliance of software metrics with this assumption. To address this question, we propose a novel framework “MLMNB-SDP” equipped with a statistical hypothesis testing method to detect those software metrics with a significant conditional dependency. MLMNB-SDP is designed to handle conditional dependencies via a single latent variable in a predefined structure which is responsible for preserving the connection between pairs of software metrics when the class variables are instantiated. We evaluate the effectiveness of our approach based on its capability to measure conditional dependency of software metrics and defect prediction performance. For the former one, we employ Conditional Mutual Information (CMI), and for the later one we use three settings for defect prediction; (1) Within-Project Defect-Prediction (WPDP), (2) Cross-Project Defect-Prediction (CPDP), and (3) stratified k-fold cross-validation. Our metrics dependency analysis results indicate that traditional file-level software metrics demonstrate a significant conditional mutual dependency and the application of naive Bayes classifier in this domain is not theoretically acceptable. Our results based on the three settings indicate that MLMNB-SDP improves naive Bayes classifier 5.45% to 75.86% and outperforms well-known benchmark classifiers, i.e., Random Forest and Logistic Regression, regarding a significant increase in Precision, Recall, and F1 Score, Mathew’s Correlation Coefficient (MCC), and area under the ROC curve (AUC) values.
Determinants of Chinese Foreign Direct Investment in Africa

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Algorithmic Acceleration of B/FV-Like Somewhat Homomorphic Encryption for Compute-Enabled RAM

Somewhat Homomorphic Encryption (SHE) allows arbitrary computation with finite multiplicative depths to be performed on encrypted data, but its overhead is high due to memory transfer incurred by large ciphertexts. Recent research has recognized the shortcomings of general-purpose computing for high-performance SHE, and has begun to pioneer the use of hardware-based SHE acceleration with hardware including FPGAs, GPUs, and Compute-Enabled RAM (CE-RAM). CE-RAM is well-suited for SHE, as it is not limited by the separation between memory and processing that bottlenecks other hardware. Further, CE-RAM does not move data between different processing elements. Recent research has shown the high effectiveness of CE-RAM for SHE as compared to highly-optimized CPU and FPGA implementations. However, algorithmic optimization for the implementation on CE-RAM is underexplored. In this work, we examine the effect of existing algorithmic optimizations upon a CE-RAM implementation of the B/FV scheme [19], and further introduce novel optimization techniques for the Full RNS Variant of B/FV [6]. Our experiments show speedups of up to 784x for homomorphic multiplication, 143x for decryption, and 330x for encryption against a CPU implementation. We also compare our approach to similar work in CE-RAM, FPGA, and GPU acceleration, and note general improvement over existing work. In particular, for homomorphic multiplication we see speedups of 506.5x against CE-RAM [34], 66.85x against FPGA [36], and 30.8x against GPU [3] as compared to existing work in hardware acceleration of B/FV.
QoS-aware service composition based on context-free grammar and skyline in service function chaining using genetic algorithm

Service function chaining (SFC) is a mechanism that allows service providers to combine various service functions and exploit the available virtual infrastructure. The best selection of virtual services in the network is essential for meeting user requirements and constraints. This paper proposes a novel approach to generate the optimal composition of the service functions. To this end, a genetic algorithm based on context-free grammar (CFG) that adheres to the Internet Engineering Task Force (IETF) standard and Skyline was developed to use in SFC. The IETF uses cases of the data center, security, and mobile network filtered out the invalid service chains, which resulted in reduced search space. The proposed genetic algorithm found the Skyline service chain instance with the highest quality. The genetic operations were defined to ensure that the service function chains generated in the algorithm process were standard. The experimental results showed that the proposed service composition method outperformed the other methods regarding the quality of service (QoS), running time, and time complexity metrics. Ultimately, the proposed CFG could be generalized to other SFC use cases.
Prediction of diffuse radiation in solar energy applications: Turkey case study and compare with satellite data

In solar engineering applications, the components of the solar radiation a location must be known to design the systems. Due to the difficulties in the diffuse solar radiation (DSR) measurements, as this component has not been measured in everyplace, the DSR models have been established for various locations over the world. In this study, the models developed to constitute a relation between the monthly mean daily diffuse fraction and diffuse coefficient with clearness index and sunshine fraction in the open literature for Turkey are reviewed. In addition, the comparisons of the DSR values taken from the NASA – Surface meteorology and Solar Energy (NASA-SSE) and computed from the best models for example two cities are carried out. The performances of the models are investigated by various statistical test techniques. These statistical test techniques are root mean square error (RMSE) and mean absolute error (MAE). The statistical comparisons show that the best models give well agreement with the DSR values taken from the NASA-SSE. The difference between the yearly total DSR calculated from the best models and NASA-SSE is approximately 1.94% for Erzurum and 0.57% for Gebze.