Reviewerly/exHarmony huggingface dataset:
Cross-language Keyword Analysis of Digital Transformation for Business

Digital transformation enjoys a growing interest from both practitioners and researchers. It is an encompassing term to describe the wide scope of the changes that are happening due to the increased use of digital technologies and comprises terms such as e-business and industry 4.0. Although the change is prominent worldwide, it is still not well defined and one might wonder if the concept is researched in the same way across the globe. For this reason, we conduct a thorough network analysis of the keyword co-occurrences in both the English and Chinese literature. We construct, compare, and analyze two conceptual networks to advance our understanding of digital transformation further and to investigate if there exists a difference between the English and Chinese literature. Furthermore, we utilize the obtained keyword networks to validate a recently proposed digital transformation framework. The results indicate that the digital transformation has a broad scope, is ill-defined, and is closely related to digitalization and industry 4.0. In English, digital transformation discusses many technologies, industries, and topics whereas the Chinese core journals mostly discuss the publishing and media industries. Finally, we present a research agenda that calls for more research in the role of society, more research in the interactions between society and business, and the usage of the right terminology.
Quantum laboratory setup for future wireless communication systems

Fifth generation of wireless communication envisages a diverse network that imposes stringent requirements regarding capacity, reliability, security, latency and seamless connectivity. These lofty goals have driven researchers to look for technologies capable of supporting conventional methods. Application of quantum mechanics to the domain of communications has been considered for a while now but the reliance of any quantum phenomena on experimental verification remains an obstacle in its widespread use. To serve this purpose, we propose establishing a quantum optics laboratory in Turkey which will allow implementation, analysis and investigation of the various promising quantum communication tools. This work is directed towards description and experimental verification of some well-known quantum experiments, from relatively simpler ones such as single or entangled photon generation to the more evolved experiments like quantum random number generation and quantum key distribution.
Lethal Volcanic Gases at an Italian Country Club

High levels of carbon dioxide and hydrogen sulfide emitted by volcanic outgassing caused a deadly accident near Rome, Italy, in 2011, geoscientists have shown.
An Efficient Quality Enhancement Solution for Stereo Images

Recently, with additional information in the disparity variant, quality enhancement for stereo images has become an active research field. Current methods generally adopt cost volumes for stereo matching methods to learn correspondence between stereo image pairs. However, with the large disparity in the different viewpoints of stereo images, how to learn the accurate corresponding information remains a challenge. In addition, as the network deepens, traditional convolutional neural networks (CNNs) adopt cascading methods, which results in the high computational cost and memory consumption. In this paper, we propose an end-to-end effective CNN model. Channel-wise attention-based information distillation and long short-term memory (LSTM) are the basic components, which contribute to reconstruct high quality image (DCL network). Within a stereo image pair, we use high quality (HQ) image to guide the image reconstruction of low quality (LQ). To incorporate the stereo correspondence, information fusion-based LSTM module can be used to learn the disparity variant in stereo images. Specially, in order to distill and enhance effective features map, we introduce channel-wise attention-based a long distillation information module with the consideration of interdependencies among feature channels. Experimental results demonstrate that the proposed network achieves the best performance with comparatively less parameters.
Adaptive Way of Particle Swarm Algorithm Employing the Fuzzy Logic

The Image Swarm Intelligence calculations, in numerous enhancement issues, have always filled a need of worldwide hunt strategy. One of the issues went up against amid advancement is bunching issue. Contribution for a bunching procedure is an arrangement of information, which are then composed into various sub-gatherings. Current investigations have suggested that divided or isolated bunching calculations are being more fitted for grouping of wide and colossal data objects or datasets. A standout among the most and best regular partitional bunching calculations is k-means. K-implies calculation demonstrates a more quick union than PSO, however, then against nearby ideal territory is for the most part caught relying upon the arbitrary estimations of introductory centroids. A proficient crossbreed technique is displayed in this paper, specifically molecule swarm improvement with fluffy rationale or versatile molecule swarm enhancement (APSO) to determine information grouping issue. The PSO calculation finds a decent or close ideal arrangement in sensible time, however, its introduction was upgraded by seeding the underlying swarm with fuzzifier work. The versatile fluffy molecule swarm enhancement calculation (APSO) is contrasted and k-implies utilizing all-out execution time and bunching bunch blunder. It is found that the aggregate execution time for APSO technique outflanks the k-implies and had higher arrangement quality as far as bunching bunch blunder.
Using Butterfly-patterned Partial Sums to Draw from Discrete Distributions

We describe a simd technique for drawing values from multiple discrete distributions, such as sampling from the random variables of a mixture model, that avoids computing a complete table of partial sums of the relative probabilities. A table of alternate (“butterfly-patterned”) form is faster to compute, making better use of coalesced memory accesses; from this table, complete partial sums are computed on the fly during a binary search. Measurements using cuda 7.5 on an nvidia Titan Black gpu show that this technique makes an entire machine-learning application that uses a Latent Dirichlet Allocation topic model with 1,024 topics about 13% faster (when using single-precision floating-point data) or about 35% faster (when using double-precision floating-point data) than doing a straightforward matrix transposition after using coalesced accesses.
Coordinating Expertise to Preserve and Increase Discoverability of Key University of Tennessee Agricultural Serials

This article describes a successful collaborative effort between a branch and the main campus library at the University of Tennessee (UT), Knoxville, to digitize agricultural serials. Experts in agricultural content, preservation, digitization, and metadata worked to preserve three agricultural print publications and make approximately 3800 issues discoverable for patrons on campus and beyond. This discussion provides a background to Extension and the Experiment Station in Tennessee, outlines previous attempts made to digitize agricultural serials at UT Libraries, and details decisions made during the digitization process and metadata creation. These experiences offer a model for other libraries pursuing similar digitization projects.
Use of computational realistic models for the cardiac ejection fraction calculation

Abstract Ejection fraction is one of the most useful clinical descriptors to determine the cardiac function of a subject. For this reason, obtaining the value of this descriptor is of vital importance and requires high precision. However, in the clinical routine, to generate the mentioned descriptor value, a geometric hypothesis is assumed, obtaining an approximate value for this fraction, usually by excess, and which is a dependent-operator. The aim of the present work is to propose the accurate calculation of the ejection fraction from realistic models, obtained computationally, of the cardiac chamber called right ventricle. Normally, the geometric hypothesis that makes this ventricle coincide with a pyramidal type geometric shape, is not usually, fulfilled in subjects affected by several cardiac pathologies, so as an alternative to this problem, the computational segmentation process is used to generate the morphology of the right ventricle and from it proceeds to obtain, accurately, the ejection fraction value. In this sense, an automatic strategy based on no-lineal filters, smart operator and region growing technique is propose in order to generate the right ventricle ejection fraction. The results are promising due we obtained an excellent correspondence between the manual segmentation and the automatic one generated by the realistic models.
Dual Attention Message Passing Model for Scene Graph Generation

Recently, scene graph has emerged as a powerful tool for enhancing the performance of scene understanding. Many algorithms have been proposed to generate scene graph and most of them include message passing process which is used to transfer information between nodes in scene graph. However, these algorithms did not fully consider the imbalance of information transmission in their message passing process. To address this defect, a novel Dual Attention Message Passing (DAMP) model consisted of internal and external attention mechanisms is proposed in this paper, which can regulate and control information transmission robustly. In our model, external attention mechanism is used to determine the importance of all nodes to the target node, and internal attention mechanism is used to reinforce correlated information between the source node and target node. On Visual Relationship Detection datasets, the proposed model can achieve comparable results with the state-of-the-arts.
An Efficient Rule Processing Architecture Based on Reconfigurable Hardware

In the recovery of security strings in identity authentication mechanism, the combination of dictionary and string transformation rules is an effective method. However, the processing of string transformation rules faces challenges such as performance and energy efficiency. The existing tools and researches are based on software. It’s difficult to meet the needs of actual recovery systems. In this paper, an efficient rule processing architecture based on reconfigurable hardware is proposed. The rules are processed using FPGA for the first time. A rule processor is designed and implemented on Xilinx Zynq XC7Z030 chip. The experimental results show that the performance of the rule processor is better than that of Intel i7-6700 CPU in typical cases. The performance power ratio of the rule processor is 1.4–2.1 times higher than that of NVIDIA GeForce GTX 1080 Ti GPU and 70 times higher than that of CPU, which effectively improves the speed and efficiency of rule processing.
The influence of the aperture diaphragm on the size of the subjective speckles and the application of this in speckle photography

Abstract The paper presents the results of a study showing the possibility of reducing the size of speckles to increase the sensitivity and accuracy of the measuring system based at the use of methods of speckle photography and speckle interferometry. The theoretical and experimental results the average size of speckles formed in the double-exposure specklegram depends on the aperture diaphragms of the optical system – circular aperture, annular aperture, square aperture and frame aperture – are presented. The experimental results forming of the diffraction halo and the Young’s fringes for a circular, annular, square and frame apertures are presented.
Online Multi-criteria Scheduling for Testing as a Service Cloud Platform

In this work a problem of scheduling execution of software tests in a Testing-as-a-Service (TaaS) cloud computing platform is considered. The problem is modeled as a specific case of the multi-objective online scheduling problem. The processing time of each software test is unknown during its first execution, but can be derived from its past executions. Three optimization criteria of average job flow time, maximal job flow time and average number of machines used are considered. A number of fast constructive algorithms and three metaheuristics are developed for the considered problem. Developed solving methods are compared during a computer experiment. Problem instances for the experiment are generated using data from real-life TaaS platform. Conclusions include comparison of effectiveness and speed of proposed solving methods. Possible enhancements of the solving methods are considered as well.
The use of information-educational environment in the training of future marine professionals: analysis of experience

The article actualizes the question of the suitability of the educational environment for training future marine professionals to the conditions of their future professional activity. Based on the analysis of scientific, methodological and pedagogical works, the experience of creating and implementing information and educational environments for the professional training of future marine professionals has been generalized. It is determined that such environment should include innovative educational activities, modern information and communication training and facilities for training students. The information and educational environment should be created onthe basis of a distributed infrastructure of technical and software complexes. It should be multivariate and multicomponent to accommodate all the needs of the learning process. The proliferation of cloud technologies encourages their use in the training of future marine professionals, taking into account all the risks posed by interaction with third parties in the implementation of the educational process using cloud services. Perspective in the train ing of future marine professionals is the use of intelligent training environment. It is important to ensure that future marine professionals are trained in an information and educational environment to provide end-to-end user identification, which is to ensure that users have automatic access to their resources. The author emphasizes that the effectiveness of training future marine professionals can be achieved through the appropriate organization of information and educational environment, if the creation and use of such tasks will be solved: achievement of the planned level of professional competence by all students; achievement of the planned quality of mastering the programs of training in the most rational way for the student; ensuring the desired effect of training in relation to the individual abilities of each student; most acceptable overcoming the contradictions between the increasing amount of information in a specific professional activity and a fixed time limit for its assimilation; the creation of conditions for teachers that, with the overall intensification of the training process, would not require them to increase efficiency of their physical force and moral influence.
FUTURE TEACHERS’ TRAINING FOR IMPLEMENTATION OF INDIVUDUAL APPROACH TO STUDENTS ON THE LESSONS OF LABOUR TRAINING AND TECHNOLOGIES

Relevancy of training future teachers of labor training and technologies for implementation of individual approach to students’ learning has been substantiated. Complex of actions to be taken into account for the successful execution of individual approach to the students' learning has been determined: identification of the basic individual peculiarities of students, which are manifested while studying particular subject; considering age features, defining students’ individual characteristics in order to take them into account in the process of learning, grouping students, developing differentiated tasks for students of different groups. Training of future teachers of labor training and technologies for individual approach implementation in accordance with stages and with gradual complication of tasks has been suggested. Notional stages of students’ training for the implementation of individual approach have been determined: initial understanding of the role of individual approach to students; learning of the essence of individualization and differentiation of training, creation of differentiated tasks; development of creative tasks taking into account individual and age features; formation of professional competencies in future teachers and consolidation of the existing knowledge and skills in implementing individual approach to students; improving the readiness of students to implementation of individual approach. A list of theoretical questions for students in order to understand the essence and peculiarities of implementation of individual approach to teaching students has been suggested. Requirements to the tasks on training future teachers for implementation of individual approach in teaching students in accordance with each stage have been developed.
Target-based Visual Navigation with Channel-aware Network

Visual navigation is major content of robot control, especially those based on target. We propose a channel-aware deep siamese actor-critic network for target-based visual navigation task. Compared with previous target-driven network, our model can obtain a joint representation of siamese network's output feature by using distance fusion method, and the approach significantly accelerates convergence of model's training. We improve the model performance to make the agent reach the goal in shorter path during the navigation by inserting a modified Squeeze-and-Excitation block in siamese layers, in which way the model can take dependencies between visual feature channels into consideration.
Implementation of Data Mining Sales of Milk Using Apriori Algorithm Method

Abstract The purpose of this research is to find out the items that consumers often buy. The method used in this research is descriptive. The results of this research was to identify the extent of the relationship between one item and another item, and to know the pattern of consumer buying interest in the items purchased. This research was conducted by discussing a combination of items that meet the minimum requirements of the value of support and the formation of association rules that meet the minimum requirements for trust. This research concludes that using the association rule method can help in making decisions about sales strategies.
A Behavior-Item Based Hybrid Intention-Aware Frame for Sequence Recommendation

Sequence recommendation is one of the hotspots of recommendation algorithm research. Most of the existing sequence recommendation methods focus on how to use the items’ attributes to characterize the user’s preferences, ignoring that the user behavior also can reflect the preference for items. However, user behavior often has problems of mis-interaction and random interaction, which leads to fully utilizing it difficultly. Therefore, this paper proposes a new Behavior-Item based Hybrid Intent-aware Framework (BIHIF). In this framework, the user’s main intent is extracted based on user behaviors and interactive items, respectively, the two intent vectors are combined and extracted by the full connection layer to obtain the user’s real intent. We use real intent and item vector to calculate the score of the candidate items and make Top-K recommendations. Based on the framework, we implement models respectively by MLP and GRU, which show good results in the experiments based on three real-world datasets.
To the question about trust in the modern school

The article gives a brief analysis of the monitoring, which was devoted to the study of the peculiarities of reflection of teachers about the presence of trust towards various entities of education in educational practice. Analysis of the obtained results has allowed to reveal features of trust in contemporary schools “through the eyes of teachers.” Was also identified factors hindering and contributing to the development of trust between teachers, and subjects of educational activity: students, parents, administration.
Elementary School Learning Media Application Based on Android with Customer Satisfaction Index Method

Abstract The objective of this research is to understand customer satisfaction, which is an assessment of a product or service that aims to improve the quality and service of a product or services. In this study the measurement method used is the Customer Satisfaction Index (CSI) method. This method is used to determine how much the level of user satisfaction with learning media applications. The result shows that the calculation of the level of customer satisfaction based on Tangibles, Reliability, Responsiveness, Assurance, and Empathy. The percentage value obtained is based on these five attributes using the Likert Scale, which is a scale of 1-5. Based on the results of the research conducted, the satisfaction of use in the application shows the criteria of satisfaction. This calculation is useful to find out the quality of this learning application. By knowing the value of satisfaction, it can be concluded that this application can help the learning process of children, however this application still not forgetting some suggestions from respondents to evaluate and improve the application to be even better.
Cloud Manufacturing Service Discovery and Matching Based on Keyword Synonymous Extension

Aiming at the problem that the discovery process of cloud manufacturing service is too small and the accuracy of matching process is not enough, the paper establishes a service discovery and matching model based on keyword synonym expansion. The model performs word segmentation normalization processing on user demand information to obtain the demand keyword set, and then synonymously expands the keyword based on the synonym dictionary based on cloud manufacturing platform resource information, and uses the word frequency-anti-document frequency method to filter the word set. Aiming at the risk of malicious or untrue evaluation of QoS evaluation information about the matching process, the evaluation credibility function is introduced to ensure the reliability of QoS information. Finally, the effectiveness and feasibility of the method are verified by a case.
Reservoir Computer using Speckle in a Multimode Waveguide

We demonstrate recovery of Mackey-Glass waveforms and multi-frequency sinusoids using speckle-based reservoir computer. Neurons and RF waveforms drive a spatial light modulator imaged onto a multimode fiber. Fiber output is imaged onto a camera, digitally processed and fed back into the fiber through the modulator.