Reviewerly/exHarmony huggingface dataset:
The NLP4NLP Corpus (II): 50 Years of Research in Speech and Language Processing

The NLP4NLP corpus contains articles published in 34 major conferences and journals in the field of speech and natural language processing over a period of 50 years (1965-2015), comprising 65,000 documents, gathering 50,000 authors, including 325,000 references and representing approximately 270 million words. This paper presents an analysis of this corpus regarding the evolution of the research topics, with the identification of the authors who introduced them and of the publication where they were first presented, and the detection of epistemological ruptures. Linking the metadata, the paper content and the references allowed us to propose a measure of innovation for the research topics, the authors and the publications. In addition, it allowed us to study the use of language resources, in the framework of the paradigm shift between knowledge-based approaches and content-based approaches, and the reuse of articles and plagiarism between sources over time. Numerous manual corrections were necessary, which demonstrated the importance of establishing standards for uniquely identifying authors, articles, resources or publications.
Multidimensional Approach Based on Deep Learning to Improve the Prediction Performance of DNN Models

The most of collected data samples from E-learning systems consist of correlated information caused by overlapping input instances, which decrease the classifier credibility and reliability. This paper presents an improved classification model based on Deep Learning and Principal Component Analysis (PCA) method as its use in reducing the dimensionality of data. By this task, we introduce the best learning process to extract just the useful parameters that describe students’ per-formances in an E-learning system. One of the primary goals of this technique is to help earlier in detecting the dropouts and discovering of students who need special attention, so that the teachers could provide the appropriate counseling at the right time. This study presents the proposal approach and its algorithms. In addition, it shows how deep neural network was modeled in the training phase, and how PCA helps in the elimination of correlated information in our dataset to increase the classifier performance. Finally, we introduce an example of an appli-cation of the method in a data mining scenario, find out more references for fur-ther information.
Anxiety in Classroom Presentation in Teaching - Learning Interaction in English for Students of Indonesian Study Program at Higher Education

Anxiety in the classroom presentation has a debilitating effect on students’ success in learning a foreign language (FL) or a second language (L2). This article aimed at investigating students’ Anxiety in classroom presentation in Teaching – Learning Interaction of English as a Foreign Language for Students of Indonesian Study Program in general at higher education levels in Indonesia. The participants of this study were the students of Indonesian Education and Literature Department, Faculty of Education and Literature, Universitas Negeri Makassar (N = 62). There were 55 or 88.71% females and 7 or 11.29% males from the seventh (42 or 67.75%) and ninth (20 or 32.25%) semesters. The findings reveal that the most frequent source of students’ anxiety was attending disorganized classroom presentation. The implication of the study is that anxiety is an interesting phenomenon in language study which potentially contributes to students’ failure. Therefore, teachers at a school need to manage the class well, while the lecturers at the university need to give a lot of time for students to learn the topics for discussion.
A Novel Hierarchical Coding Progressive Transmission Method for WMSN Wildlife Images

In the wild, wireless multimedia sensor network (WMSN) communication has limited bandwidth and the transmission of wildlife monitoring images always suffers signal interference, which is time-consuming, or sometimes even causes failure. Generally, only part of each wildlife image is valuable, therefore, if we could transmit the images according to the importance of the content, the above issues can be avoided. Inspired by the progressive transmission strategy, we propose a hierarchical coding progressive transmission method in this paper, which can transmit the saliency object region (i.e. the animal) and its background with different coding strategies and priorities. Specifically, we firstly construct a convolution neural network via the MobileNet model for the detection of the saliency object region and obtaining the mask on wildlife. Then, according to the importance of wavelet coefficients, set partitioned in hierarchical tree (SPIHT) lossless coding is utilized to transmit the saliency image which ensures the transmission accuracy of the wildlife region. After that, the background region left over is transmitted via the Embedded Zerotree Wavelets (EZW) lossy coding strategy, to improve the transmission efficiency. To verify the efficiency of our algorithm, a demonstration of the transmission of field-captured wildlife images is presented. Further, comparison of results with existing EZW and discrete cosine transform (DCT) algorithms shows that the proposed algorithm improves the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) by 21.11%, 14.72% and 9.47%, 6.25%, respectively.
Efficient and robust detection of multipartite Greenberger-Horne-Zeilinger-like states

Entanglement is a key resource for quantum information processing. A widely used tool for detecting entanglement is the entanglement witness, where the measurement of the witness operator is guaranteed to be positive for all separable states and can be negative for certain entangled states. In reality, due to exponentially increasing the Hilbert-space dimension with respect to the system size, it is very challenging to construct an efficient entanglement witness for general multipartite entangled states. For $N$-partite Greenberger-Horne-Zeilinger (GHZ)-like states, the most robust witness scheme requires $N+1$ local measurement settings and can tolerate up to $1/2$ white noise. As a comparison, the most efficient witness for GHZ-like states only needs two local measurement settings and can tolerate up to $1/3$ white noise. There is a tradeoff between the realization efficiency, the number of measurement settings, and the detection robustness, the maximally tolerable white noise. In this paper, we study this tradeoff by proposing a family of entanglement witnesses with $k$ ($2\ensuremath{\le}k\ensuremath{\le}N+1$) local measurement settings. Considering symmetric local measurements, we calculate the maximal tolerable noise for any given number of measurement settings. Consequently, we design the optimal witness with a minimal number of settings for any given level of white noise. Our theoretical analysis can be applied to other multipartite entangled states with a strong symmetry. Our witnesses can be easily implemented in experiment and applied in practical multipartite entanglement detection under different noise conditions.
Proposal of a Conceptual Architecture System for Informing the User in the IoT Environment

Design and development of systems for delivering real-time information to people with disabilities and elderly persons need to be based on defined user requirements. For this purpose, the user requirements have been defined in this paper according to the everyday needs of people who use traffic networks and move in closed spaces. The logical presentation of the functionality of the informing system operation and its subsystems includes all the information (data) important for designing a user information delivery system. The paper presents a conceptual architecture system for delivering user informing services related to the environment based on the Internet of Things concept. The aim of the user informing service is an increase in the level of mobility of persons with disabilities and the senior age groups of users. In order to check the operation of the proposed architecture, the informing system operation was monitored on Arduino Uno and Raspberry Pi platforms in laboratory conditions. A simulation confirmed the interdependence of individual data from different subsystems in order to provide real-time information to the system user. The proposed conceptual architecture can contribute to a more efficient approach to the modeling of assistive technologies (with the aim of informing the users) based on dew/fog/cloud technologies in the Internet of Things environment.
Protecting Consumers from Cybercrime in the Banking and Financial Sector: An Analysis of the Legal Response in Nigeria

This paper1 examines the consumer protection regime under the Nigerian Cybercrimes Act with a view to assessing the extent to which it protects consumers from cybercrimes in the banking and financial sector. It finds that the regime is not adequate as it does not place sufficient obligations on banks and financial institutions to safeguard the personal information of their customers from unauthorized access. Additionally, the findings suggest the absence of an explicit regime for determining liability for unauthorized payment transactions in situations where a consumer’s electronic banking or payment information is compromised. The article also highlights examples of legal regimes in Europe and the United States that could be adopted in order to strengthen the consumer protection regime under the Act. Finally, some challenges impeding the protection of consumers from cybercrimes in the Nigerian banking and financial sector are pointed out along with proposed responses to address them.
Estimating Visibility of Annotations for View Management in Spatial Augmented Reality Based on Machine-Learning Techniques

Augmented Reality (AR) is a class of “mediated reality” that artificially modifies the human perception by superimposing virtual objects on the real world, which is expected to supplement reality. In visual-based augmentation, text and graphics, i.e., label, are often associated with a physical object or a place to describe it. View management in AR is to maintain the visibility of the associated information and plays an important role on communicating the information. Various view management techniques have been investigated so far; however, most of them have been designed for two dimensional see-through displays, and few have been investigated for projector-based AR called spatial AR. In this article, we propose a view management method for spatial AR, VisLP, that places labels and linkage lines based on the estimation of the visibility. Since the information is directly projected on objects, the nature of optics such as reflection and refraction constrains the visibility in addition to the spatial relationship between the information, the objects, and the user. VisLP employs machine-learning techniques to estimate the visibility that reflects human’s subjective mental workload in reading information and objective measures of reading correctness in various projection conditions. Four classes are defined for a label, while the visibility of a linkage line has three classes. After 88 and 28 classification features for label and linkage line visibility estimators are designed, respectively, subsets of features with 15 and 14 features are chosen to improve the processing speed of feature calculation up to 170%, with slight degradation of classification performance. An online experiment with new users and objects showed that 76.0% of the system’s judgments were matched with the users’ evaluations, while 73% of the linkage line visibility estimations were matched.
DNNVM

In recent years, Convolutional Neural Network(CNN) is becoming the state-of-the-art method in a wide range of Artificial Intelligence(AI) domains. The increasingly large and complex CNN models are both computation bound and I/O bound. FPGA-based accelerators driven by custom Instruction Set Architecture(ISA) achieve a balance between generality and efficiency, and leave much room for optimization. Operation fusion which fuses adjacent operations without saving intermediate results back to off-chip DDR can greatly alleviate bandwidth pressure, operations can be executed by different computation engines concurrently for latency hiding. To leverage optimizations, especially operation fusion on custom instruction-based accelerators, we propose a full-stack compiler DNNVM(Deep Neural Network Virtual Machine). DNNVM is an integration of optimizers for framework-independent computing graph, loops and data layouts, an assembler, a runtime supporter and a validation environment. DNNVM works in the context of deep learning frameworks and transforms CNN models into a directed acyclic graph, XGraph. After analyzing the interaction among fusion depth, tiling across multiple stages and on-chip memory capacity, DNNVM enumerates all potentially profitable fusion opportunities according to custom fusion templates upon XGraph, by a subgraph isomorphism algorithm. In addition, DNNVM searches for the optimal execution strategies by a heuristic shortest-path algorithm. On Xilinx [email protected], we achieve up to 1.26x speedup than naïve implementations without fusion on GoogLeNet. On Xilinx [email protected], we achieve the throughput of 2.82 TOPs/s for VGG, 1.38 TOPs/s for ResNet50 - he fastest ever reported on comparable FPGAs.
Gaussian probability bi‐histogram equalization for enhancement of the pathological features in medical images

Abstract In order to enhance the pathological features of medical images and aid the medical diagnosis, the image enhancement is a necessary process. This study presented the Gaussian probability model combining with bi‐histogram equalization to enhance the contrast of pathological features in medical images. There are five different bi‐histogram equalizations, namely, bi‐histogram equalization (BBHE), dualistic sub‐image histogram equalization (DSIHE), bi‐histogram equalization with a plateau limit (BHEPL), bi‐histogram equalization median plateau limit (BHEPL‐D), and bi‐histogram equalization with modified histogram bins (BHEMHB). The entropy, contrast, absolute mean brightness error (AMBE), and skewness difference are used to quantize the enhancement results. From the experimental result, it is observed that the entropy and contrast of the images can be effectively enhanced by using Gaussian probability bi‐histogram equalizations, and the Gaussian probability bi‐histogram equalization median plateau limit (GPBHEPL‐D) has the best enhanced result. The proposed GPBHEPL‐D method is effective in strengthening the pathological features in medical images, so as to increase the efficiency of doctors' diagnoses and computer‐aided detection.
Public Key Cryptosystem based on Matrices

In this article, a novel public key cryptosystem is introduced by using an abelian subgroup of GL(k, Z n ) where n and k are positive integers.Instead of exponentiation, the conjugation automorphisms are mainly used to define the public and private keys.This allows the calculations to be fast and effective.The security analysis of the cryptosystem is discussed and it is shown that the cryptosystem is highly secure.Moreover, proposed scheme also generalizes the main scheme given in [1].
The Influence of Compensation Management Strategy on Talent Retention: Exploring the Mediating Role of Structural Capital

Background: Retention of the employees is recognized as one of the significant problems of the present business world. The retention phenomena are observed to be impacted by the compensation practices employed by the firm.&#x0D; &#x0D; Objective: Based on this, the study aims to assess the compensation management strategies among Jordan commercial banks using structural capital approach.&#x0D; &#x0D; Methods: The study employed a quantitative design for assessing the employee retention. For this, data collection has been done using a survey based on a questionnaire.&#x0D; &#x0D; Results: The study has found a significant and positive impact of work life balance, satisfaction, and benefits on structural capital and talent retention. Moreover, the study failed to report a significant impact of salary on opportunity and job nature.&#x0D; &#x0D; Conclusion: The structure of the compensation management in the banking sector must encourage a new design for improving its impact intensity.
NATIVE LANGUAGE INTERFERENCE ON TARGET LANGUAGE WRITINGS OF INDONESIAN EFL STUDENTS: AN EXPLORATORY CASE STUDY

Possessing language students who are skilled in creating a structured, orderly and no error-found piece of composition constitutes a hope and a wish for many EFL teachers, as they are the individuals who undergo a big problem when their students commit numerous errors in EFL writing as a result of their native language interference. This study is aimed at exploring native language influence on students� English writings as well as investigating the salient and common grammatical errors in their writing with the purpose of checking whether or not Indonesian as the students� L1 influence them when writing in English. To this end, a corpus of 22 English essays written by students is examined and the errors are then categorized according to the following aspects: grammatical, lexico-semantic, mechanics, and word order sorts of errors. In this study, mixed methods research designs are used: quantitative and qualitative. The results revealed that UNIRA students commit different sorts of errors which are chiefly on account of their native language (Indonesian) interference. The students highly rely on their L1 in stating their thoughts, even though the ranking processes revealed that their essays hold different sorts of errors, those in the grammar and the lexico-semantic statistically constitute the most serious and recurrent ones.Keywords: grammatical sentence; L1 interference; lexico-semantic; writing.
Simultaneous full-field multi-experiment identification

Identification of constitutive parameters relies mainly on their sensitivity to the measurands. In particular, the specific static and kinematic responses controlled by each parameter of interest has to be captured by full-field measurements. The development of modern constitutive models has led to many new and interesting sample geometries and loading histories, aiming at maximizing the sensitivity to their delicate material parameters, especially through their kinematics of interest. However, it is often impossible to design an experiment that activates all material parameters of interest, and thus multiple experiments are needed. This paper discusses a methodology for combining the data from such multi-experiments into a single identification process to calibrate a complete set of parameters at once. Many different ways of merging experimental data exist, leading to unbiased identifications of the parameters of interest. However, only one optimal procedure leads to minimal uncertainty, taking into account the noise of each acquisition source. The proposed identification method is a natural extension of inverse methods such as Finite Element Method Updating (FEMU) with appropriate weights or Integrated Digital Image Correlation (I-DIC). This procedure is illustrated by the identification of the planar parameters of the so-called Hill48 anisotropic yield surface together with an exponential isotropic hardening law of AA2219.
Towards an Architecture for Handling Big Data in Oil and Gas Industries: Service-Oriented Approach

Existing architectures to handle big data in Oil & gas industry are based on industry-specific platforms and hence limited to specific tools and technologies.With these architectures, we are confined to big data single-provider solutions.The idea of multi-provider big data solutions is essential.When building up big data solutions, organizations should embrace the best-inclass technologies and tools that different providers offer.In this article, we hypothesize that the limitations of the proposed bigdata architectures for oil and gas industries can be addressed by a Service Oriented Architecture approach.In this article, we are proposing the idea of breaking complex systems to simple separate yet reliable distributed services.It should be noted that loose coupling exists between the interacting services.Thus, our proposed architecture enables petroleum industries to select the necessary services from the SOA-based ecosystem and create viable big data solutions.
Feature selection with graph mining technology

Many real world applications have problems with high dimensionality, which existing algorithms cannot overcome. A critical data preprocessing problem is feature selection, whereby its non-scalability negatively influences both the efficiency and performance of big data applications. In this research, we developed a new algorithm to reduce the dimensionality of a problem using graph-based analysis, which retains the physical meaning of the original high-dimensional feature space. Most existing feature-selection methods are based on a strong assumption that features are independent of each other. However, if the feature-selection algorithm does not take into consideration the interdependencies of the feature space, the selected data fail to correctly represent the original data. We developed a new feature-selection method to address this challenge. Our aim in this research was to examine the dependencies between features and select the optimal feature set with respect to the original data structure. Another important factor in our proposed method is that it can perform even in the absence of class labels. This is a more difficult problem that many feature-selection algorithms fail to address. In this case, they only use wrapper techniques that require a learning algorithm to select features. It is important to note that our experimental results indicates, this proposed simple ranking method performs better than other methods, independent of any particular learning algorithm used.
Diversity and Inclusivity in the Age of Wearables: A Buzzword, a Myth, an Uncertain Reality

‘Wearable Technology’ is a buzzword of our contemporary era. It could be argued there are few examples of aesthetically pleasing devices that are designed to meet our needs and/or our consumer desires. However, do we focus on design and aesthetics of technology as a holistic action with the capacity to simultaneously engage conceptual and practical shifts that make our society a place with no boundaries? To design inclusively is to engage the user deeply throughout the design process, sharing our practices and amalgamating people’s unique knowledge as technological interventions. Design diversity and inclusion seems to be used interchangeably with two other terminologies, a) Universal Design and b) Design for All. The terms have a parallel purpose but their origin and use is distinguished in various parts of the world. For example, Inclusive Design is used within Europe and goes beyond age, ethnicity, gender, sex, and disabilities to focus on other excluded groups to deliver mainstream solutions. Inspired by the limited understanding and choices around aesthetics and personalisation in wearables, this article discusses how we use technology to empower individuals in a variety of contexts; to improve our way of living in the world, through a number of contextual resources and practice-research, which were devised and conducted to address women’s concerns and preferences on wearable technologies.