Reviewerly/exHarmony huggingface dataset:
Handling Cross-cutting Properties in Automatic Inference of Lexical Classes: A Case Study of Chintang

In the context of the ongoing AGGREGATION project concerned with inferring grammars from interlinear glossed text, we explore the integration of morphological patterns extracted from IGT data with inferred syntactic properties in the context of creating implemented linguistic grammars. We present a case study of Chintang, in which we put emphasis on evaluating the accuracy of these predictions by using them to generate a grammar and parse running text. Our coverage over the corpus is low because the lexicon produced by our system only includes intransitive and transitive verbs and nouns, but it outperforms an expert-built, oracle grammar of similar scope.
Sinkhorn–Knopp theorem for PPT states

Given a PPT state $$A=\sum _{i=1}^nA_i\otimes B_i \in M_k\otimes M_k$$ and a rank k tensor v within the image of A, we provide an algorithm that checks whether the positive map $$G_A:M_k\rightarrow M_k$$ , $$G_A(X)=\sum _{i=1}^n tr(A_iX)B_i$$ , is equivalent to a doubly stochastic map. This procedure is based on the search for Perron eigenvectors of completely positive maps and unique solutions of, at most, k unconstrained quadratic minimization problems. As a corollary, we can check whether this state can be put in the filter normal form. This normal form is an important tool for studying quantum entanglement. An extension of this procedure to PPT states in $$M_k\otimes M_m$$ is also presented.
Construing Microservice Architectures: State-of-the-Art Algorithms and Research Issues

Cloud Computing is one of the leading paradigms in the IT industry. Earlier, cloud applications used to be built as single monolithic applications, and are now built using the Microservices Architectural Style. Along with several advantages, the microservices architecture also introduce challenges at the infrastructural level. Five such concerns are identified and analysed in this paper. The paper presents the state-of-art in different infrastructural concerns of microservices, namely, load balancing, scheduling, energy efficiency, security and resource management of microservices. The paper also suggests some future trends and research domains in the field of microservices.
Application of grayscale expansion for accuracy improvement in phase-measuring profilometry

A method based on grayscale expansion for improving measurement accuracy in phase-measuring profilometry (PMP) is proposed. Digital light projector (DLP) has been widely used for projecting phase-shifting sinusoidal grating in PMP. However, due to its international standardization, DLP can only project at most 256 grayscales for all gray patterns. Moreover, the gray digitalization error may cause lower fidelity for the projected sinusoidal grating. In order to reduce the gray digitalization error, the sinusoidal grating can thus be designed as a 10-bit static pattern with 766 grayscales at most and then is split into three 8-bit static patterns to synthesize a repeated video. While this repeated video, rather than the static pattern, is projected onto the measured object by strictly controlling CCD camera integral time to be an integral multiple of three video refresh cycles, the captured deformed patterns are of 766 grayscales and can provide much more detailed information in phase calculation than those with 256 grayscales. Experimental results demonstrate the feasibility and validity of the proposed method. Both the measuring precision and the repeatability of PMP are improved efficiently.
Identifying Information Security Risks in a Social Network Using Self-organising Maps

Managing information security risks in an organisation is one of the most important tasks an organisation has. Unfortunately, due to the complexity of most organisational systems, identifying information security risks can be difficult. One way to identify possible risks in an organisation is to make use of Social Network Analysis (SNA). While they can be used to identify risks, the metrics calculated using SNA are often numerous and daunting to managers unfamiliar with SNA. Furthermore, as the data in this form tend to be uncomfortable to process, educating managers about risks in their organisation can be quite difficult. Also, as these metrics often require quantitative processing in order to be useful, SNA on its own is not always an attractive method to use to identify risks in an organisation. In this paper the use of self-organising maps to identify possible information security risks in an organisation is investigated. Risk data were obtained from an organisation that deals in risk management, which were used to build a social network. A number of metrics associated with risk were calculated from the network, and these metrics were used to cluster the various entities using a self-organising map. Certain entities that pose a possible information security risk were identified. The results suggest that it may be viable to use self-organising maps, in concord with SNA, to more easily identify risks in an organisation using visual methods.
Comparative Analysis on variants of Neural Networks: An Experimental Study

Neural Networks, with their remarkable capacity to get significance from convoluted information can be utilized to remove patterns that are too composite to be in any way seen by humans. A prepared neural network can be thought of as a specialist in the classification of data which is given to analyze. There are different kinds of Neural Networks like Artificial Neural Network (ANN), Feedforward Neural Network, Recurrent Neural Network(RNN), Recursive Recurrent Neural Network (RRNN), Convolutional Neural Network(CNN), Modular Neural Network (MNN), Restricted Boltzmann Machine (RBM) etc. In this paper, we have discussed the performance of ANN, CNN, RNN, and RBM where CNN has outplayed the remaining with accuracy of 97.81%.
Performances of OLAP Operations in Graph and Relational Databases

The increasing volume of data created and exchanged in distributed architectures has made databases a critical asset to ensure availability and reliability of business operations. For this reason, a new family of databases, called NoSQL, has been proposed. To better understand the impact this evolution can have on organizations it is useful to focus on the notion of Online Analytical Processing (OLAP). This approach identifies techniques to interactively analyze multidimensional data from multiple perspectives and is today essential for supporting Business Intelligence. The objective of this paper is to benchmark OLAP queries on relational and graph databases containing the same sample of data. In particular, the relational model has been implemented by using MySQL while the graph model has been realized thanks to the Neo4j graph database. Our results, confirm previous experiments that registered better performances for graph databases when re-aggregation of data is required.
Geometric measures of discordlike quantum correlations based on Tsallis relative entropy

A kind of new geometric measure of quantum correlations is formulated. The proposed formulation is in terms of the quantum Tsallis relative entropy and can naturally be viewed as a one-parameter extension quantum discordlike measure that satisfies all requirements of a good measure of quantum correlations. It is of an elegant analytic expression and contains several existing good quantum correlation measures as special cases. The partial coherence measure is also investigated.
Development of a Sleep Monitoring System by Using a Depth Sensor: A Pilot Study

Sleep is an essential part of health and longevity persons. As people grow older, the quality of their sleep becomes vital. Poor sleep quality can make negative physiological, psychological, and social impacts on the elderly population, causing a range of health problems including coronary heart disease, depression, anxiety, and loneliness. Early detection, proper diagnosis, and treatments for sleep disorders can be achieved by identifying sleep patterns through long-term sleep monitoring. Although many studies developed sleep monitoring systems by using non-invasive measures such as body temperature, pressure, or body movement signal, research is still limited to detect sleep position changes by using a depth camera. The present study is intended (1) to identify concerns on the existing sleep monitoring system based on the literature review and (2) propose to developing a non-invasive sleep monitoring system using an infrared depth camera. For the literature review, various journal/conference papers have been reviewed to understand the characteristics, tools, and algorithms of the existing sleep monitoring systems. For the system development and validation, we collected data for the sleep positions from two subjects (35 years old man and 84 years old women) during the four-hour sleep. Kinect II depth sensor was used for data collection. We found that the averaged depth data is useful measure to notify the participants’ positional changes during the sleep.
A tripartite-graph based recommendation framework for price-comparison services

The recommender systems help users who are going through numerous items (e.g., movies or music) presented in online shops by capturing each user?s preferences on items and suggesting a set of personalized items that s/he is likely to prefer [8]. They have been extensively studied in the academic society and widely utilized in many online shops [33]. However, to the best of our knowledge, recommending items to users in price-comparison services has not been studied extensively yet, which could attract a great deal of attention from shoppers these days due to its capability to save users? time who want to purchase items with the lowest price [31]. In this paper, we examine why existing recommendation methods cannot be directly applied to price-comparison services, and propose three recommendation strategies that are tailored to price-comparison services: (1) using click-log data to identify users? preferences, (2) grouping similar items together as a user?s area of interest, and (3) exploiting the category hierarchy and keyword information of items. We implement these strategies into a unified recommendation framework based on a tripartite graph. Through our extensive experiments using real-world data obtained from Naver shopping, one of the largest price-comparison services in Korea, the proposed framework improved recommendation accuracy up to 87% in terms of precision and 129% in terms of recall, compared to the most competitive baseline.
Efficient Estimation of Ontology Entities Distributed Representations

Ontologies have been used as a form of knowledge representation in different fields such as artificial intelligence, semantic web and natural language processing. The success caused by deep learning in recent years as a major upheaval in the field of artificial intelligence depends greatly on the data representation, since these representations can encode different types of hidden syntactic and semantic relationships in data, making their use very common in data science tasks. Ontologies do not escape this trend, applying deep learning techniques in the ontology-engineering field has heightened the need to learn and generate representations of the ontological data, which will allow ontologies to be exploited by such models and algorithms and thus automatizing different ontology-engineering tasks. This paper presents a novel approach for learning low dimensional continuous feature representations for ontology entities based on the semantic embedded in ontologies, using a multi-input feed-forward neural network trained using noise contrastive estimation technique. Semantically similar ontology entities will have relatively close corresponding representations in the projection space. Thus, the relationships between the ontology entities representations mirrors exactly the semantic relations between the corresponding entities in the source ontology.
Performance Evaluation of MadBoost on Face Detection

Adaboost Viola-Jones method is indeed a profound discovery in detecting face images mainly because it is fast, light and one of the easiest methods of detecting face images among other techniques of face detection. Viola Jones uses Haar wavelet filter to detect face images and it produces almost 80%accuracy of face detection. This paper discusses proposed methodology and algorithms that involved larger library of filters used to create more discrimination features among the images by processing the proposed 15 Haar rectangular features (an extension from 4 Haar wavelet filters of Viola Jones) and used them in multiple adaptive ensemble process of detecting face image. After facial detection, the process continues with normalization processes by applying feature extraction such as PCA combined with LDA or LPP to extract our week learners’ wavelet for more classification features. Upon the process of feature extraction proposed feature selection to index these extracted data. These extracted vectors are used for training and creating MADBoost (Multiple Adaptive Diversified Boost)(an improvement of Adaboost, which uses multiple feature extraction methods combined with multiple classifiers) is able to capture, recognize and distinguish face image (s) faster. MADBoost applies the ensemble approach with better weights for classification to produce better face recognition results. Three experiments have been conducted to investigate the performance of the proposed MADBoost with three other classifiers, Neural Network (NN), Support Vector Machines (SVM) and Adaboost classifiers using Principal Component Analysis (PCA) as the feature extraction method. These experiments were tested against obstacles of POIES (Pose, Obstruction, Illumination, Expression, Sizes). Based on the results obtained, Madboost is found to be able to improve the recognition performance in matching failures, incorrect matching, matching success percentages and acceptable time taken to perform the classification task.
Adaptive Neural Control for Nonaffine Pure-Feedback System Based on Extreme Learning Machine

For nonaffine pure-feedback systems, an adaptive neural control method based on extreme learning machine (ELM) is proposed in this paper. Different from the existing methods, this scheme firstly converts the original system into a nonaffine system containing only one unknown term by equivalent transformation, thus avoiding the cumbersome and complex indirect design process of traditional backstepping methods. Secondly, a high-performance finite-time-convergence-differentiator (FD) is designed, through which the system state variables and their derivatives are accurately estimated to ensure the control effect. Thirdly, based on the implicit function theorem, the ELM neural network is introduced to approximate the uncertain items of the system, which simplifies the repeated adjustment process of the network training parameters. Meanwhile, the minimum learning parameter algorithm (MLP) is adopted to design the adaptive law for the norm of the network weight vector, which significantly reduces calculations. And it is theoretically proved that the closed-loop control system is stable and the tracking error is bounded. Finally, the effectiveness of the designed controller is verified by simulation.
The Impact of Retail Setting Antecedents on Organizational Citizenship Behavior through Job Satisfaction

This study aims to investigate the impact of retailing antecedents on Organizational Citizenship Behavior through job satisfaction of front line employees in Arabic Bank branches at Amman Capital. The study&amp;rsquo;s population included all front line employees in these branches at Amman Capital totaling (52) branches. The study sampling unit consists of individuals working at front line servicing clients in these branches totaling (235) individuals, where 235 questionnaires distributed at the surveyed employees, retrieved (232) and excluded (5) which comes to a total (227). To achieve the study objectives, the study used quantitative (descriptive analytical approach) through different statistical tools, most notably multiple regression analysis.&#x0D; &#x0D; The study showed number of results namely: The retail system antecedents (Leadership support, Empowerment and Professional Development) impacted the organizational citizenship behavior of frontline employees of Arabic Bank.&#x0D; &#x0D; The study is proposed a few recommendations for Arabic Bank branches; The managerial practices of Arabic bank management are better to link OCBs and job satisfaction, view it as a base to their strategies to manage the frontline employees.
Development of Basic Training for Teaching Measurement and Control to Junior High School Students

In 2017, the government announced the course of study guidelines. It showed the necessary qualification and ability for junior high school students and guidance on junior high school technology education. Because new content is included there, training of incoming teachers at junior high school is required. This paper proposes a training curriculum on measurement and control of junior high school technology. This training was practiced for junior high school teachers in Ehime. These members showed high evaluation. This training will enhance educational practice for junior high school students.
Color Correction for Stereoscopic Images Based on Gradient Preservation

Color correction can eliminate the color difference between similar images in image stitching and 3D video reconstruction. The result images generated by the local color correction algorithms usually show structure inconsistency problem with the input images. In order to solve this problem, we propose a structure consistent color correction algorithm for stereoscopic images based on gradient preservation. This method can not only eliminate color difference between reference and target images, but also optimize structure between the input target image and the result image. Firstly, the algorithm extracts the structure information of the target image and style information of the reference image using the SIFT algorithm and generates the structure image and the pixel matching image. Then an initial result image is generated by local pixel mapping. Finally the initial result image is iteratively optimized by the gradient preserving algorithm. The experimental results show that our algorithm can not only optimize the structure inconsistency, but also effectively process image pairs with large color difference.
Evolving Fuzzy Membership Functions for Soft Skills Assessment Optimization

This work proposes the design of a decision support tool able to guide the choices of any company HR manager in the evaluation of the profiles of PhD candidates. This paper is part of an ongoing research in the field of PhD profiling. The novelty here is an evolutionary fuzzy model, based on the Membership Functions (MFs) optimization, used to obtain the soft skills candidate profiles. The general aim of the project is the definition of a set of fuzzy rules that are very similar to those that a HR expert would otherwise have to calculate each time for each selected profile and for each individual skill.
Knowledge management adoption to financial institutions using fuzzy AHP and fuzzy TOPSIS techniques

The aim of this study is to identify and prioritise the knowledge management (KM) adoption in financial institutions. Results show that Melal Financial and credit institute, Kosar financial and credit institute and Noor financial and credit institute are on the first, second and last priority, respectively. The results obtained from fuzzy AHP methods indicate that human resources are on the first priority and organisational management, technological factors and cultural factors are on the next priority. Then regarding to the organisational management scales, it is realised that, proper organisational structure to create and share knowledge is most important. Regarding to the technological factors index, it is realised that, technological infrastructure to adopt KM is most important. Regarding to cultural factors index it is realised that, willingness and sharing spirit among members is most important. Regarding to the human resources index, it is realised that, time to share knowledge is most important.
Visual Analysis of Research Paper Collections Using Normalized Relative Compression

The analysis of research paper collections is an interesting topic that can give insights on whether a research area is stalled in the same problems, or there is a great amount of novelty every year. Previous research has addressed similar tasks by the analysis of keywords or reference lists, with different degrees of human intervention. In this paper, we demonstrate how, with the use of Normalized Relative Compression, together with a set of automated data-processing tasks, we can successfully visually compare research articles and document collections. We also achieve very similar results with Normalized Conditional Compression that can be applied with a regular compressor. With our approach, we can group papers of different disciplines, analyze how a conference evolves throughout the different editions, or how the profile of a researcher changes through the time. We provide a set of tests that validate our technique, and show that it behaves better for these tasks than other techniques previously proposed.
Fast Face Recognition Algorithm Based on Collaborative Representation Classification and Manhattan Norm

We propose two simple and efficient algorithms to improve the efficiency of collaborative representation based classification with regularized least square (CRC_RLS) in Face Recognition. Based on the observation that the reconstruction coefficient vector infinite-norm is correlated to the corresponding test sample label, we propose CRC with infinite-norm (CRC_IN) algorithm, which greatly improve the speed. Furthermore, we improve the classification accuracy by proposing another algorithm. The CRC with Manhattan-norm (CRC_MN) algorithm is proposed by utilizing the strong correlation between the maximal Manhattan-norm of all groups in reconstruction coefficient vector and test sample label. The extensive experiments clearly show that CRC_MI run faster than CRC_RLS by about 47~131 times, the accuracy is the same or similar to CRC_RLS.
Deep learning slow earthquakes in Cascadia and universal frictional characteristics

Slow earthquakes may trigger failure on neighboring locked faults that are stressed enough to break, and slow slip patterns may evolve before a nearby great earthquake. However, even in the clearest cases such as Cascadia, slow earthquakes and associated tremor have only been observed in intermittent and discrete bursts. By training a convolutional neural network to detect known tremor on a single seismic station in Cascadia, we isolate and identify tremor and slip preceding and following known larger slow events. The deep neural network can be used for the detection of quasi-continuous tremor, providing a proxy that quantifies the slow slip rate. Furthermore, the model trained in Cascadia recognizes tremor in other subduction zones and also along the San Andreas Fault at Parkfield, suggesting a universality of waveform characteristics and source processes, as posited from experiments and theory.