Reviewerly/exHarmony huggingface dataset:
Affection of the part of speech elements in Vietnamese text readability

While English text readability has been studied for a long time, investigating text readability in Vietnamese, a low-resourced language with poorresearch technologies and data sets questionable of international importance, is at its beginnings. In readability research, it is generally the “word” that has been carefully investigated. Based on the comparison of elements affecting readability of the “word” unit in English, we determine the parts of speech (POS) in Vietnamese that were found to influence Vietnamese text readability. In this study, prose texts in Vietnamese textbooks at different difficulty level were taken as the data to find out the POS frequencies and their correlations. In terms of frequency, our findings can initially assist users when editing documents, reforming textbooks, and question banks for native Vietnamese in general and foreigners in particular. Even more important, with these findings we can identify those linguistic elements that are considered the “potential” POS affecting Vietnamese text readability, and make grounds for further studies.
Determining Optimal Architecture of CNN using Genetic Algorithm for Vehicle Classification System

Convolutional neural network is a machine learning that provides a good accura-cy for many problems in the field of computer vision, such as segmentation, de-tection, recognition, as well as classification systems. However, the results and performance of the system are affected by the CNN architecture. In this paper, we propose the utilization of evolutionary computation using genetic algorithm to de-termine the optimal architecture for CNN with transfer learning strategy from parent network. Furthermore, the optimal CNN produced is used as a model for the case of the vehicle type classification system. To evaluate the effectiveness of the utilization of evolutionary computing to CNN, the experiment will be conducted using vehicle classification datasets.
Quantized Frank-Wolfe: Communication-Efficient Distributed Optimization.

How can we efficiently mitigate the overhead of gradient communications indistributed optimization? This problem is at the heart of training scalablemachine learning models and has been mainly studied in the unconstrainedsetting. In this paper, we propose Quantized Frank-Wolfe (QFW), the firstprojection-free and communication-efficient algorithm for solving constrainedoptimization problems at scale. We consider both convex and non-convexobjective functions, expressed as a finite-sum or more generally a stochasticoptimization problem, and provide strong theoretical guarantees on theconvergence rate of QFW. This is done by proposing quantization schemes thatefficiently compress gradients while controlling the variance introduced duringthis process. Finally, we empirically validate the efficiency of QFW in termsof communication and the quality of returned solution against naturalbaselines.
Comment on “Optimal convex approximations of quantum states”

In a recent paper, M. F. Sacchi [Phys. Rev. A 96, 042325 (2017)] addressed the general problem of approximating an unavailable quantum state by the convex mixing of different available states. For the case of qubit mixed states, we show that the analytical solutions in some cases are invalid. In this Comment, we present complete analytical solutions for the optimal convex approximation. Our solutions can be viewed as correcting and supplementing the results in the aforementioned paper.
Comparing Digital Tools for Implementing a Generative System for the Design of Customized Tableware

The process of motorcycle seat styling is a grey system with partially known and partially unknown information and is influenced by various factors.In this study, Grey Modelling (GM)(1,1) is used to predict the style of a motorcycle seat, and the shape features of the seat are extracted via morphological analysis and are parameterized.The process of shape evolution is established, and the modelling characteristics are predicted by GM(1,1).The kansei study is performed using five adjectives describing the seat styles to establish the equation of kansei regression analysis.The regression analysis is employed to modify predictive modelling.A certain brand of motorcycle seats is modelled to analyse and verify the feasibility and scientific applicability of adopting GM(1,1) in predicting motorcycle seat styling, which provided a feasible and effective reference for the motorcycle seat design.
A Lattice-Based Incremental Signature Scheme

A digital signature is a fundamental cryptographic primitive that is used heavily in information society for guaranteeing the authenticity of digital information. Incremental signature, introduced by Bellare, Goldreich, and Goldwasser (CRYPTO'94), is a digital signature that enables a signer to sign messages quickly when these messages are similar. It is extremely useful for authenticating, e.g., big data since there are huge messages in big data and many of them only have small differences. In this paper, we propose an incremental signature scheme from lattices. Our scheme is proven secure against adaptive chosen-message attacks in the standard model, assuming the k -small integer solutions problem on lattices is intractable. We also conduct experiments to evaluate its efficiency. The experimental results demonstrate that our incremental signature scheme is effective for signing a series of messages with many overlaps.
Data-Driven Adapting for Fine-Tuning Chinese Teaching Materials: Using Corpora as Benchmarks

While there have been a considerable number of corpus-based studies informing the content of teaching materials, direct explorations of corpora by teachers to adapt source texts (i.e., data-driven adapting) for classroom teaching remain a largely unexplored area. This chapter examines how teachers adapt new texts in a more comprehensible manner for L2 Chinese learners using an online system, Chi-Editor. Chi-Editor was developed to automatically assess text complexity and tag Chinese words and sentences for text simplification purposes. The evaluation of a text in terms of its level of difficulty and annotation of difficult words and long sentences in the text are produced based on the data mining of linguistic features from a corpus of roughly 350 widely-used textbooks, selected from an anthology of L2 Chinese teaching materials and packages produced by over 1000 publishers around the world. To investigate both the process and outcome of data-driven adapting using Chi-Editor, a case study was conducted, involving a team of teachers working on the adaptation of texts. Results are discussed in terms of the effectiveness of the data-driven adapting practices by teachers in a classroom setting. Overall, the results contribute strong evidence that teachers can learn and benefit from data-driven adapting and support the notion that corpus data, including linguistic features, can be employed to facilitate the text simplification process. Implications are also given for integrating the data-driven adapting process into regular teacher-prepared L2 Chinese materials for classroom teaching.
Agregación de datos bibliográficos por medio de servicios Sparql

This note describes the metadata aggregation process carried out by the Ignacio Larramendi Foundation through the Sparql Endpoint of the Miguel de Cervantes Virtual Library. The results obtained are summarized and it is showed how Sparql language and Sparql Endpoint are a useful tool to access data in large repositories. Some difficulties for its use are described.
Wrangle and Corral that License Agreement

It can be a daunting task to keep track of everything related to the management of electronic resources, including vendor licenses. What terms does the university/State/Province require in a license agreement? What cannot be in the license agreement? Where exactly are you in that process? Is it under review? Sent for signature? With the university’s legal office? Waiting on the vendor? Is it in the electronic resource management (ERM) system? Is it filed (either print or electronic or both)? Two librarians presented their use of open source tools for managing the licensing workflow. Alexis Linoski discussed how to use Trello to manage the license workflow at the Georgia Institute of Technology from receipt to entry in the ERM. Carolyn Carpan discussed how the Collection Strategies Unit at the University of Alberta Libraries is working on a project to display usage rights to users, using the Centralized Online Resources Acquisitions and Licensing module.
“STRIPPED WALLETS, RIPPED HEARTS” VICTIMS OF FINANCIAL FRAUD: AN ANALYSIS BEYOND FINANCIAL FRAUD

There is a strong sense of negativity attached to victims of financial fraud. Despite the media are filled with their stories, the comprehension of the real victimisation experiences are not that evident. Victims of financial fraud are often viewed as individuals who lost only their money. Along with that they are considered as greedy and a sense of blame is thrown at them. Therefore a study beyond the financial loss was warranted. The aim of this paper is to examine the impacts of fraud victimisation other than financial impact. As the first objective, the theoretical background, including victim blaming theory, just world theory, were analysed. Secondly the emotional and psychological impacts, reporting experiences and seeking formal help were examined in relation with the responses of the victims. Based on interviews with 30 victims of fraud which consisted of victims of swindling, embezzlement, credit card fraud and Ponzi scheme, this paper establishes the profound emotional and psychological impact post victimisation. The paper also examined victim blaming, relationship breakdown, vulnerability of older people and in the worst case suicide.&nbsp
Computer Forensics in Cloud Computing Systems

The challenges on traditional Computer Forensic mythology in facing with the growing adoption of Cloud Computing services and models by corporations and organizations. We analyse the results of recent research in analysis of computers’ physical memory, new methodologies and tools, and how these technologies can be applied to computer forensics in this specific context.
Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center

Typically, the production data centers function with various risk factors, such as for instance the network dynamicity, topological asymmetry, and switch failures. Hence, the load-balancing schemes should consider the sensing accurate path circumstances as well as the reduction of failures. However, under dynamic traffic, current load-balancing schemes use the fixed parameter setting, resulting in suboptimal performances. Therefore, we propose a multi-level dynamic traffic load-balancing (MDTLB) protocol, which uses an adaptive approach of parameter setting. The simulation results show that the MDTLB outperforms the state-of-the-art schemes in terms of both the flow completion time and throughput in typical data center applications.
Explosion Scene Forensic Image Interpretation

Abstract Image interpretation is an important aspect in the field of forensic science; however, it is seldom reported how to use these techniques in explosion scene forensic investigations. On 12 August 2015, a series of explosions killed 165 people and injured hundreds more at a container storage station at the Port of Tianjin. In this study, we applied image interpretation methods to determine the seat of the explosion by analyzing low‐quality video clips of the event. The interpretation fits well with recently published standard operating procedures, including the hypothesis, evaluation, inference, and confirmation. Image processing was adopted to enhance the images while the explosion scene was reconstructed with the same images. Some important features were extracted and utilized to distinguish whether the flashes were caused by reflection or a real blast. We reveal the real explosion location, which guides the overall investigation. The results indicate that image interpretation is a powerful tool for forensic investigators to analyze low‐quality images in complicated explosions or fire accidents.
Exploring the Adoptions by Students of Web 2.0 Tools for E-Learning in Higher Education

Optimal public policies, including education, have been applied for the sustainable economic growth of the European Union. In European countries, the use of Web 2.0 tools for increasing the education quality is constantly expanding, even if it is divided into two categories. One category consists of developed countries, Organization for Economic Cooperation and Development (OECD) members where there are the strongest of computing tools companies. Another category consists of OECD partner countries which hopes to fulfill the OECD requirements. The main study aim is the exploration of Web 2.0 tools adoptions for e-learning in one OECD candidate. A case study details how behavioral perceptions have been applied. Thus, a survey containing questions about socio-demographic characteristics alongside respondents' perceptions related to Web 2.0 tools for e-learning in higher education was applied. The research outcomes confirm the students' limited knowledge of Web 2.0. Authorities must indicate what measures are necessary for large-scale adoption of all Web 2.0 tools useful for education.
Exploiting the input sparsity to accelerate deep neural networks

Efficient inference of deep learning models are challenging and of great value in both academic and industrial community. In this paper, we focus on exploiting the sparsity in input data to improve the performance of deep learning models. We propose an end-to-end optimization pipeline to generate programs for the inference with sparse input. The optimization pipeline contains both domain-specific and general optimization techniques and is capable of generating efficient code without relying on the off-the-shelf libraries. Evaluations show that we achieve significant speedups over the state-of-the-art frameworks and libraries on a real-world application, e.g., 9.8× over TensorFlow and 3.6× over Intel MKL on the detection in autonomous driving.
Awareness and Use of E-Resources Among Public Extension Personnel in Anambra State, Nigeria

The study assessed the awareness and use of e-resources among public extension personnel in the area.Stratified sampling technique was used in selecting respondents from each cadre of the Anambra State Agricultural Development Programme (ASADEP).A total of sixty-nine (69) agricultural extension staff was used for the study.Findings show the major e-resource tool that was available to the majority (94.2%) of extension staff in the area was mobile phone, while 62.3% indicated that computer was available to them.The majority (69.6%) of the respondents were each aware of Facebook, and email, while 56.5% were aware of twitter.Among those who were aware of email, the majority (59.4%) indicated they used it while 43.5% of those that were aware of Facebook indicated they used it.Results show that 15.9% of the respondents indicated they used e-journal in exchanging information on pests and diseases, 10.1% exchanged weather and climate information on e-mail, while 11.6% each indicated they used email to exchange information on farm inputs and market prices.On the other hand, 10.1% each used e-journal and e-mail to exchange information on processing methods.The study concluded by drawing attention to the very low adoption of e-resources in extension service delivery in the area.Efforts should be made by both federal and state governments to provide enabling technological environment and training opportunities for extension personnel in order to improve e-extension which is a veritable alternative towards solving the issues of dearth in extension staff that has been a persistent problem plaguing extension service in the country.
Prediction of Breast Cancer Type Based on Artificial Intelligence Technique

The fundamental cause of death among women in developing nations of the world is breast cancer.It is very common and is considered the second most dangerous disease all over the world due to its death rate.Recently, there has been a dramatic increase in breast cancer cases among women.It is known to us that at the time of mutation, cells get an ability to go on dividing without control and results in forming a tumor.This tumor can be of benign or malignant in nature.The benign tumors are not dangerous but malignant tumors are dangerous to health.The unchecked malignant tumors have the ability to spread in other parts of the body.At the primary stage, breast screening is a method of detecting breast cancer.The first step involves taking an x-ray of each breast which is called a mammogram.The mammogram can detect small changes in breast tissue which may indicate cancers which are too small to be felt.Artificial Neural Network is an effective tool that has found application in the field of medical diagnosis for early detection and diagnosis of breast cancer.If breast cancer is detected in early stage, chances of survival are comparatively high.
Educational assessment in digital environments: insights from different assessment contexts

Educational assessment in digital environments : insights from different assessment contexts
How to Make n-D Plain Maps Defined on Discrete Surfaces Alexandrov-Well-Composed in a Self-Dual Way

In 2013, Najman and Géraud proved that by working on a well-composed discrete representation of a gray-level image, we can compute what is called its tree of shapes, a hierarchical representation of the shapes in this image. This way, we can proceed to morphological filtering and to image segmentation. However, the authors did not provide such a representation for the non-cubical case. We propose in this paper a way to compute a well-composed representation of any gray-level image defined on a discrete surface, which is a more general framework than the usual cubical grid. Furthermore, the proposed representation is self-dual in the sense that it treats bright and dark components in the image the same way. This paper can be seen as an extension to gray-level images of the works of Daragon et al. on discrete surfaces.
Toward efficient architecture-independent algorithms for dynamic programs

Recursive divide-&-conquer algorithms are known for solving dynamic programming (DP) problems efficiently on shared-memory multicore machines. In this work, we extend them to run efficiently also on manycore GPUs and distributed-memory machines without changing their basic structure.
Developing audio data hiding scheme using random sample bits with logical operators

Sharp development progress of information technology has affected many aspects including data security. This is because classified data are often transferred between systems. In this case, data hiding exists to protect such data. Some methods which have been proposed, however, are not yet optimal concerning the amount of the secret and the quality of the resulted stego data. In this paper, we explore an audio file as the medium to carry the secret data which has been extracted into binary. Before the process begins, the cover is converted to binary and each sample’s bits are divided into two groups, one is used as the location of the embedded 4 bits whereas the second part locates the two bits that are randomly selected as the key. The experimental results have validated that the capacity is high and there is no much impact on the quality. Moreover, compared to the current LSB methods the security is exceedingly enhanced.
Multi-channel framelet denoising of diffusion-weighted images

Diffusion MRI derives its contrast from MR signal attenuation induced by the movement of water molecules in microstructural environments. Associated with the signal attenuation is the reduction of signal-to-noise ratio (SNR). Methods based on total variation (TV) have shown superior performance in image noise reduction. However, TV denoising can result in stair-casing effects due to the inherent piecewise-constant assumption. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of diffusion-weighted (DW) images. Specifically, we employ the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders, which will help avoid stair-casing effects. Instead of denoising each DW image separately, we collaboratively denoise groups of DW images acquired with adjacent gradient directions. In addition, we introduce a very efficient method for solving an ℓ0 denoising problem that involves only thresholding and solving a trivial inverse problem. We demonstrate the effectiveness of our method qualitatively and quantitatively using synthetic and real data.
Impact of inertia weight strategies in particle swarm optimization for solving economic dispatch problem

&lt;p&gt;Particle Swarm Optimization (PSO) is a population based stochastic optimization technique inspired by the social learning of birds or fish. Some of the appealing facts of PSO are its convenience, simplicity and easiness of implementation requiring but few parameters adjustments. Inertia Weight (ω) is one of the essential parameters in PSO, which often significantly the affects convergence and the balance between the exploration and exploitation characteristics of PSO. Since the adoption of this parameter, there have been large proposals for determining the value of Inertia Weight Strategy. In order to show the efficiency of this parameter in the Economic Dispatch problem(ED), this paper presents a comprehensive review of one or more than one recent and popular inertia weight strategies reported in the related literature. Among this five recent inertia weight four were randomly chosen for application and subject to empirical studies in this research, namely, Constant (ω), Random (ω), Global-Local Best (ω), Linearly Decreasing (ω), which are then compared in term of performance within the confines of the discussed optimization problem. Morever, the results are compared to those reported in the recent literature and data from SONELGAZ. The study results are quite encouraging showing the good applicability of PSO with adaptive inertia weight for solving economic dispatch problem.&lt;/p&gt;
Finding the Nearest Blood Donors using Dijkstra Algorithm

Now-a-days frequent mishaps like the road accident or other life-risking accident leading to operational emergencies are increasing day by day and the blood donor for such victims or patients are very hard to find in a short period. Sometimes it is really tough to collect the blood of the same group from the relatives of a patient in dying moments. The main objective of this work is to make a connection with these two groups who are in need of blood and who are willing to donate. We have determined the shortest distance between blood seeker and blood donors using the Dijkstra Algorithm. Anybody can contact the nearest blood donor of any group in a particular area quickly. By implementing this idea, the harassment of the victim can be lessened and so many lives can be saved.
Formation of psychological and pedagogical competence of university students by means of training work

Л. В. Оринина, И. В. Кашуба, И
The development of volunteer activities in the environment of students of educational organizations of the Russian Federation: The innovative approach

Н. Г. Супрун, И