Reviewerly/exHarmony huggingface dataset:
Double Deep Autoencoder for Heterogeneous Distributed Clustering

Given the issues relating to big data and privacy-preserving challenges, distributed data mining (DDM) has received much attention recently. Here, we focus on the clustering problem of distributed environments. Several distributed clustering algorithms have been proposed to solve this problem, however, previous studies have mainly considered homogeneous data. In this paper, we develop a double deep autoencoder structure for clustering in distributed and heterogeneous datasets. Three datasets are used to demonstrate the proposed algorithms, and show their usefulness according to the consistent accuracy index.
An improved two-point calibration method for stereo vision with rotating cameras in large FOV

At present, binocular stereo vision is gradually being applied for 3D coordinate measurements in large fields of view (FOVs). In this study, a binocular stereo vision system with fixed and non-zooming cameras in a large FOV is constructed, in which cameras can rotate horizontally and vertically. All intrinsic parameters except the focal length of the cameras are set to default values and these focal lengths are calibrated offline in advance. Only the pitch angle and yaw angle of each camera need to be obtained during rotation. Therefore, we present a novel calibration method by using two control points and transform the imaging model of the pitch and yaw angles into a quadratic equation of the tangent value of the pitch angle so that the closed-form solutions of the pitch and yaw angles can be obtained. Computer simulation and real experiments demonstrate the effectiveness of the proposed method.
Underwater Scene Segmentation by Deep Neural Network

A deep neural network architecture is proposed inthis paper for underwater scene semantic segmentation. Thearchitecture consists of encoder and decoder networks. Pretrained VGG-16 network is used as a feature extractor, while thedecoder learns to expand the lower resolution feature maps. Thenetwork applies max un-pooling operator to avoid large numberof learnable parameters, and, in order to make use of the featuremaps in encoder network, it concatenates the feature maps withdecoder and encoder for lower resolution feature maps. Ourarchitecture shows capabilities of faster convergence and betteraccuracy. To get a clear view of underwater scene, an underwaterenhancement neural network architecture is described in thispaper and applied for training. It speeds up the training processand convergence rate in training.
An improved anti-quantum MST<sub>3</sub> public key encryption scheme for remote sensing images

Series of public key encryption (PKE) schemes have been proposed for remote sensing image encryption; however, most of these schemes are not secure against quantum attacks in the new era of quantum computing. In this paper, we present an improved anti-quantum MST3 PKE scheme for remote sensing images. Using the MST3 cryptosystem, the proposed scheme can effectively resist the current quantum attacks. Moreover, with the help of a collision-resistant hash function, the proposed method achieves a nonmalleable property with respect to possible malleable attacks. Compared with the previous MST series encryption schemes, our scheme has a slightly higher efficiency.
The Mediating Effect of Organizational Commitment in the Impact of Transformational Leadership Style on Employee Performance: A Study of Divisional Secretariats in the Jaffna District

The purpose of this study is to examine the mediating effects of organizational commitment on the relationship between transformational leadership style and employees’ job performance in the Divisional Secretariats in Jaffna District. 287 employees were selected for this study. Leadership styles were measured using MLQ Rater Form 5-x, employee performance was measured using job performance Scale and organizational commitment was measured using Organizational Commitment Questionnaire. Exploratory factor analysis was performed to get factor structure and confirmatory factor analysis performed to confirm the validly and reliability of the study instruments. The results revealed that transformational leadership has a positive impact on job performance and on organizational commitment. It was also found that organizational commitment doesn’t have a mediating effect in the impact of transformational leadership on employee performance. The findings also revealed that organizational commitment doesn’t have impact on employee performance. This study recommends that the leaders should pay more attention in their leadership style as a way to improve employee performance. This study further suggests that future researchers should cover larger samples and the other public sector organizations to better understand the relationship between the variables.
Antenna array design by a contraction adaptive particle swarm optimization algorithm

Massive multiple input multiple output (MIMO) has drawn intensive attention of researchers for solving huge data transmissions in the fifth generation wireless communication. Antennas aligning in array bring in advantages for interference reduction of incoming wave signals and interference offset among antenna elements. The design of antenna array is studied in this paper. The design is turned into a maximization problem. A contraction adaptive particle swarm optimization (CAPSO) method is proposed to solve the problem. Different from previous methods, CAPSO is based on a contraction factor which limits the variation neighborhood during the solution searching process. The adaptive technique can tune the searching range of CAPSO. Simulations are reported comparing the CAPSO method with other methods. Convergence rate is analyzed through two toy functions. CAPSO shows a fast convergence property. Then it is used to solve antenna array design. The CAPSO method shows good performance for different numbers of array elements.
ASLM: Adaptive Single Layer Model for Learned Index

Index structures such as B-trees are important tools that DBAs use to enhance the performance of data access. However, with the approaching of the big data era, the amount of data generated in different domains have exploded. A recent study has shown that indexes consume about 55% of total memory in a state-of-the-art in-memory DBMS. Building indexes in traditional ways have encountered a bottleneck. Recent work proposes to use neural network models to replace B-tree and many other indexes. However, the proposed model is heavy, inaccuracy, and has failed to consider model updating. In this paper, a novel, simple learned index called adaptive single layer model is proposed to replace the B-tree index. The proposed model, using two data partition methods, is well-organized and can be applied to different workloads. Updating is also taken into consideration. The proposed model incorporates two data partition methods is evaluated in two datasets. The results show that the prediction error is reduced by around 50% and demonstrate that the proposed model is more accurate, stable and effective than the currently existing model.
MRI Denoising Using Low Rank Prior and Sparse Gradient Prior

Image priors have been successfully introduced to solve ill-posed problems, such as image denoising.In this paper, we propose a new denoising model for magnetic resonance images (MRIs) which employs the image low-rank and sparse gradient priors.First, we use a Gaussian mixture model (GMM) to guide the clustering of non-local self-similar patches by learning the structure of external noise-free MRI patches to help retain the low rank of the noisy MRI patch matrix.Second, we fit the heavy-tailed gradient of MRI with a hyper-Laplacian distribution to reduce ringing artifacts.Third, we adopt an alternating iterative algorithm.The experimental results show that our proposed algorithm outperforms many classical MRI denoising methods, such as unbiased nonlocal means (UNLM) filtering and block-matching and 3D (BM3D) filtering in both visual and numerical results.
Quantum process tomography via optimal design of experiments

Quantum process tomography, a primitive in many quantum information processing tasks, can be cast within the framework of the theory of design of experiments (DOE), a branch of classical statistics that deals with the relationship between inputs and outputs of an experimental setup. Such a link potentially gives access to the many ideas of the rich subject of classical DOE for use in quantum problems. The classical techniques from DOE, however, cannot be directly applied to the quantum process tomography due to the basic structural differences between the classical and quantum estimation problems. Here we properly formulate quantum process tomography as a DOE problem and examine several examples to illustrate the link and the methods. In particular, we discuss the common issue of nuisance parameters and point out interesting features in the quantum problem absent in the usual classical setting.
A Joint Training Model for Face Sketch Synthesis

The exemplar-based method is most frequently used in face sketch synthesis because of its efficiency in representing the nonlinear mapping between face photos and sketches. However, the sketches synthesized by existing exemplar-based methods suffer from block artifacts and blur effects. In addition, most exemplar-based methods ignore the training sketches in the weight representation process. To improve synthesis performance, a novel joint training model is proposed in this paper, taking sketches into consideration. First, we construct the joint training photo and sketch by concatenating the original photo and its sketch with a high-pass filtered image of their corresponding sketch. Then, an offline random sampling strategy is adopted for each test photo patch to select the joint training photo and sketch patches in the neighboring region. Finally, a novel locality constraint is designed to calculate the reconstruction weight, allowing the synthesized sketches to have more detailed information. Extensive experimental results on public datasets show the superiority of the proposed joint training model, both from subjective perceptual and the FaceNet-based face recognition objective evaluation, compared to existing state-of-the-art sketch synthesis methods.
Studying the effect of characteristic vector alteration on Arabic sentiment classification

In this paper we propose a new approach to analyze sentiments for the Arabic language. To overcome the scarcity and size limitation of the required Arabic language resources for training and analysis tasks, we built new lexical resources using different approaches. We have also integrated the morphological notion by creating both stemmed and lemmatized versions of word lexicons. Thereafter, the generated resources were used in the construction of a supervised model from a set of features considering the word negation context. Similarly, we have semantically segmented the lexicon in order to reduce the model vectors size and consequently improve the execution time.
Planning workflow executions when using spot instances in the cloud

When running workflows in the cloud it is appealing to use spot instances that can be acquired at a fraction of the cost of on-demand instances. Unfortunately, spot instances can be revoked at any time, creating uncertainty about task completion times, which is an impairment for workflows with timeliness requirements. While workflow scheduling has been subject to extensive research, the problem of optimally scheduling deadline-constraint workflows in the cloud while dealing with the uncertainty caused by spot instance revocations has not been fully addressed. In this paper, we plan the execution of workflows in cloud environments to minimize the monetary cost while being subject to timeliness constraints. Our approach constructs a Markov Decision Process (MDP) of the workflow execution and looks up for the optimal policy taking into account the user preferences in time and cost. The optimal solution is generated offline and actions selected on-the-fly, depending on the occurrence of failures due to instance revocations. Experimental results with a real-world scientific workflow application demonstrate that, in comparison to approaches that rely on simple heuristics to schedule tasks, our planning-based approach is able to generate reliable solutions that are cheaper and able to meet deadlines.
Three-path interference of a photon and reexamination of the nested Mach-Zehnder interferometer

We present a three-path photon interferometer, theoretically investigate the behavior of the photon passing through it, and revisit the surprising experimental outcomes introduced by Danan et al. [Phys. Rev. Lett. 111, 240402 (2013)]. Our study shows that the ``discontinuous trajectory'' behavior of the photon results from the interference of the three propagation paths of the photon, which originate from the light source and end in the detector. If two of the three paths are adjusted in a destructive interference condition, there will be a zero probability domain in the overlap region of these two paths. However, these two paths can still individually interfere with the third path and contribute to the final output. In this condition, the discontinuous trajectory phenomenon appears. Our theory has a clear physical image and agrees with the experimental outcomes very well.
Cloud computing in the quantum era

Cloud computing has become the prominent technology of this era. Its elasticity, dynamicity, availability, heterogeneity, and pay as you go pricing model has attracted several companies to migrate their businesses' services into the cloud. This gives them more time to focus solely on their businesses and reduces the management and backup overhead leveraging the flexibility of cloud computing. On the other hand, quantum technology is developing very rapidly. Experts are expecting to get an efficient quantum computer within the next decade. This has a significant impact on several sciences including cryptography, medical research, and other fields. This paper analyses the reciprocal impact of quantum technology on cloud computing and vice versa.
Students’ Ability at Changing Direct into Indirect Speech and Indirect into Direct Speech

Reported speech is one of difficult topics in learning English grammar, especially in changing the form from direct into indirect or reverse. Descriptive quantitative was used to find out the students’ ability in changing direct into indirect speech and reverse. There were five sentences that were changed by the students for two kinds of test. First, the test consisted of five direct sentences and the second, it consisted five indirect sentences. As the conclusion of the students’ answers, the students found difficulties in changing those two kinds of sentences.
INNOVATIVE COMPETENCE OF A TEACHER: BEST EUROPEAN PRACTICES

The essence of the innovative competence of the teacher in the way of integration into the world space of education. The main tendencies of teacher's training for professional innovation activity are described. Best European practices according to the using of innovative studying technologies in the work with students are examined. The author proves the need for partner collaboration, group forms of activities to solve problematic learning problems at school through Problem Based Learning. Especially, the issues of development of creative initiative of children in collective work are highlighted. The content and typical difficulties in the practical using of Blended Learning are described. It is noted that Blended Learning combines traditional and distant models of studying, it can take place not only in the lecture room but outside, in synchronous or asynchronous regimes, and it predicts an extensive using of ICT in work with students. The technology of Inquiry Based Learning in teaching STEAM school subjects is presented, especially in the process of working with remote educational laboratories and Inquiry Learning Space (ILS). Some innovative tools for their practical using according to the work with students are implemented. The importance of using Project Based Learning for integration of educational subjects in the New Ukrainian School is also characterized, which gives an opportunity to form a coherent picture of the world in them.
Review of research on blockchain application development method

The blockchain technology is complex and involves a wide range of fields, which leads to the lack of uniform specifications for the development of blockchain applications. Although blockchain used to be divided according to the decentralized degree by some organizations, which are difficult to give developers specific guidance and results. To this end, this paper proposed a feature-oriented classification method of blockchain applications based on the analysis and comparison of current typical blockchain applications and frameworks, including digital currency blockchain, development platform blockchain, decentralized application and extended blockchain, helping developer create blockchain applications of a targeted manner and clarifying the functional architecture of different types of above various blockchain applications. Finally, the above classification method has been verified by the analysis of an extended blockchain.
A time-series based aggregation scheme for topic detection in Weibo short texts

Discovering hot topics within social network like Twitter and Weibo, has received much attention in recent years. While topic models such as Latent Dirichlet Allocation (LDA) have been successfully applied in topic discovery, they are often less coherent when applied to microblog content which is known as “posts”. In this paper, we propose a time-series based aggregation scheme for topic modeling in Weibo. As Weibo topics are coherent within a time slice, we divide Weibo dataset into groups by time slice. With this scheme, posts in every group are aggregated into several longer pseudo-documents using paragraph-vector based similarity algorithms. While applying this scheme to LDA model, we dramatically decrease the topic model perplexity and increase the clustering quality, which also allows for better discovery of underlying topics in Weibo. Furthermore, we can let other topic models extended on LDA be directly used on such short texts.
Strengths and Weakness of Traditional and Agile Processes - A Systematic Review

In the software industry, there are several processes and methodologies that exist.The traditional processes and Agile methodologies have their own strengths and weaknesses.Agile methodologies overcome some of the weaknesses of traditional processes.Although in the recent years Agile methodologies have been used by software development companies, there is still a high ratio of software failures when compared with core engineering processes.The adoption of these processes in software development could alleviate software failures.This systematic study reviews the strengths and weaknesses of both traditional processes and Agile processes.The search strategy resulted in 91 papers, of which 25 primary studies are investigated between 2012 and 2019.The detailed search strategy has been presented in this study along with future directions.
A Software Development Productivity Framework

Productivity is a challenging concept to define, describe and to measure for any kind of knowledge work that involves non-routine creative tasks. Software development is a prime example of knowledge work, as it too often involves poorly defined tasks relying on extensive collaborative and creative endeavours. As in other areas of knowledge work, defining productivity in software development has been a challenge facing both researchers and practitioners that may wish to understand and improve it by introducing new tools or processes. In this chapter, we present a framework for conceptualizing productivity in software development according to three main dimensions that we propose are essential for understanding productivity. In order to help clarify productivity goals, we also propose a set of *lenses* that provide different perspectives for considering productivity along these three dimensions. We contend that any picture of productivity would be incomplete if the three dimensions and various lenses are not considered.
Phylogenic Multi-Lingual Dependency Parsing

Mathieu Dehouck, Pascal Denis. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Coarse-Fine Convolutional Neural Network for Person Re-Identification in Camera Sensor Networks

In this paper, we present a novel deep model named coarse-fine convolutional neural network (CFCNN) for person re-identification in camera sensor networks, which jointly learns global and multi-scale local features simultaneously. To this end, we design the CFCNN as a multi-branch network, which is composed of one coarse and two fine branches. Specifically, the global feature is learned from the coarse branch, and the two fine branches are developed to extract two kinds of local features with different scales. Afterward, each branch is followed by a classification loss to make the identity prediction. Finally, we obtain completed pedestrian representations via concatenating the learned global and all local features. We conduct a number of experiments to evaluate the effectiveness of the CFCNN on three datasets. The CFCNN achieves high rank-1 and mAP accuracy with 94.0%/81.2%, 64.6%/58.4%, and 85.7%/72.4% on Market-1501, CUHK03, and DukeMTMC-reID, respectively. These results significantly outperform the prior state-of-the-art methods.
Quality dimensions features for identifying high-quality user replies in text forum threads using classification methods

The Text Forum Threads (TFThs) contain a large amount of Initial-Posts Replies pairs (IPR pairs) which are related to information exchange and discussion amongst the forum users with similar interests. Generally, some user replies in the discussion thread are off-topic and irrelevant. Hence, the content is of different qualities. It is important to identify the quality of the IPR pairs in a discussion thread in order to extract relevant information and helpful replies because a higher frequency of irrelevant replies in the thread could take the discussion in a different direction and the genuine users would lose interest in this discussion thread. In this study, the authors have presented an approach for identifying the high-quality user replies to the Initial-Post and use some quality dimensions features for their extraction. Moreover, crowdsourcing platforms were used for judging the quality of the replies and classified them into high-quality, low-quality or non-quality replies to the Initial-Posts. Then, the high-quality IPR pairs were extracted and identified based on their quality, and they were ranked using three classifiers i.e., Support Vector Machine, Naïve Bayes, and the Decision Trees according to their quality dimensions of relevancy, author activeness, timeliness, ease-of-understanding, politeness, and amount-of-data. In conclusion, the experimental results for the TFThs showed that the proposed approach could improve the extraction of the quality replies and identify the quality features that can be used for the Text Forum Thread Summarization.