Reviewerly/exHarmony huggingface dataset:
Blended Learning is an Educational Innovation and Solution During the COVID-19 Pandemic

The COVID-19 pandemic has indeed forced people to do more activities from home, including the learning process. The learning from home policy is not accompanied by rules and regulations such as what this learning process should be carried out. As a result, various learning processes emerged, from what they are to complex things. This study aims to find innovations and learning solutions during a pandemic, to support learning from home. Online learning is the only option. It's just that, based on the problems and constraints revealed, this online learning must be packaged like traditional face-to-face learning. The choice then turned to blended learning, with various adjustments. Face-to-face learning is carried out via video conference if needed only. The shortcomings are then overcome by compiling learning materials in the form of problem-based, text-based, video, or multimedia practical guides. The learning process is complemented by the opening of active interactive communication channels. Learning evaluation is carried out not only on results but also considering the process and self-evaluation. All of this learning process should be as optimal as possible using a mobile device or smartphone.
Restoring Arabic vowels through omission-tolerant dictionary lookup

Vowels in Arabic are optional orthographic symbols written as diacritics above or below letters. In Arabic texts, typically more than 97 percent of written words do not explicitly show any of the vowels they contain; that is to say, depending on the author, genre and field, less than 3 percent of words include any explicit vowel. Although numerous studies have been published on the issue of restoring the omitted vowels in speech technologies, little attention has been given to this problem in papers dedicated to written Arabic technologies. In this research, we present Arabic-Unitex, an Arabic Language Resource, with emphasis on vowel representation and encoding. Specifically, we present two dozens of rules formalizing a detailed description of vowel omission in written text. They are typographical rules integrated into large-coverage resources for morphological annotation. For restoring vowels, our resources are capable of identifying words in which the vowels are not shown, as well as words in which the vowels are partially or fully included. By taking into account these rules, our resources are able to compute and restore for each word form a list of compatible fully vowelized candidates through omission-tolerant dictionary lookup. In our previous studies, we have proposed a straightforward encoding of taxonomy for verbs (Neme in Proceedings of the international workshop on lexical resources (WoLeR) at ESSLLI, 2011) and broken plurals (Neme and Laporte in Lang Sci, 2013, http://dx.doi.org/10.1016/j.langsci.2013.06.002). While traditional morphology is based on derivational rules, our description is based on inflectional ones. The breakthrough lies in the reversal of the traditional root-and-pattern Semitic model into pattern-and-root, giving precedence to patterns over roots. The lexicon is built and updated manually and contains 76,000 fully vowelized lemmas. It is then inflected by means of finite-state transducers (FSTs), generating 6 million forms. The coverage of these inflected forms is extended by formalized grammars, which accurately describe agglutinations around a core verb, noun, adjective or preposition. A laptop needs one minute to generate the 6 million inflected forms in a 340-MB flat file, which is compressed in 2 min into 11 MB for fast retrieval. Our program performs the analysis of 5000 words/second for running text (20 pages/second). Based on these comprehensive linguistic resources, we created a spell checker that detects any invalid/misplaced vowel in a fully or partially vowelized form. Finally, our resources provide a lexical coverage of more than 99 percent of the words used in popular newspapers, and restore vowels in words (out of context) simply and efficiently.
The Impact of ICT and FDI as Drivers to Economic Growth In ASEAN-8 Countries: A Panel Regression Analysis

Over the last few years, information and communication technology (ICT) has become a key catalyst for economic growth. The durability of this technology is demonstrated by the rapid proliferation of the Internet, mobile phones and cellular networks across the globe. However, among economic scholars, the question of exactly how the spread of ICT affects economic development and FDI, especially in ASEAN countries with differences in levels of income, remains unanswered. The aim of this study was essentially to explore the relationship between ICT dissemination, FDI and economic growth in ASEAN-8 countries. By using data from 2003 to 2017, the panel regression analysis was used to evaluate these relationships. The results showed that the dissemination of ICT and FDI are important and they have a positive effect on the ASEAN-8 countries’ economic development.
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications

Structural roles define sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities define sets of nodes with more connections inside the set than outside. Roles based on structural similarity and communities based on proximity are fundamentally different but important complementary notions. Recently, the notion of structural roles has become increasingly important and has gained a lot of attention due to the proliferation of work on learning representations (node/edge embeddings) from graphs that preserve the notion of roles. Unfortunately, recent work has sometimes confused the notion of structural roles and communities (based on proximity) leading to misleading or incorrect claims about the capabilities of network embedding methods. As such, this article seeks to clarify the misconceptions and key differences between structural roles and communities, and formalize the general mechanisms (e.g., random walks and feature diffusion) that give rise to community- or role-based structural embeddings. We theoretically prove that embedding methods based on these mechanisms result in either community- or role-based structural embeddings. These mechanisms are typically easy to identify and can help researchers quickly determine whether a method preserves community- or role-based embeddings. Furthermore, they also serve as a basis for developing new and improved methods for community- or role-based structural embeddings. Finally, we analyze and discuss applications and data characteristics where community- or role-based embeddings are most appropriate.
Studentsâ€™ Perception of Learning Management System Supported Smartphone: Satisfaction Analysis in Online Physics Learning

Learning Management System Supported Smartphone (LMS3) is a new and innovative application in online physics learning synchronously. The research aims to investigate the level of studentsâ€™ satisfaction in using LMS3 and determine predictor variables that affect the level of studentsâ€™ satisfaction. A descriptive study with survey method was implemented to 71 physics education students who attended online physics learning. The instrument in this research was a Studentsâ€™ Satisfaction of LMS3 Questionnaire (S2LMS3Q). The average percentage determined the level of studentâ€™s satisfaction, and multiple correlations and multiple regressions determined the predictor variables. The level of satisfaction in using LMS3 reached a high level with an average of 76.03%. At the 0.01 significance level, the adjusted R2 value of four predictor variables was 0.393. The multiple correlation analysis showed Î² for each predictor variable were 0.34 for gender, 0.07 for age, 0.43 for experience in using LMS, and 0.13 for internet connection. In conclusion, the level of studentsâ€™ satisfaction was high, and the most influential predictor variables were the experience in using LMS and age. This study suggests an in-depth evaluation of online learning through studentsâ€™ perceptions in order to improve the quality of further learning.
Agile Project Management: A Systematic Literature Review of Adoption Drivers and Critical Success Factors

With an emphasis on adaptive processes that respond to uncertainties, the Agile Project Management (APM) approach has evolved the way projects are managed beyond the traditional processes.This study aims to investigate recent literature on APM to discover the adoption drivers and the critical success factors that influence APM success and provide recommendations for the development of APM best practices.The study conducted a literature search on academic databases ABI/Inform, ACM Digital Library, EBSCO Host, and IEEE Xplore with keywords 'agile' and 'project management' for peer-reviewed English language articles published between January 2015 and January 2020 to discover insights regarding adoption drivers and critical success factors.Eleven (11) drivers of adoption and thirteen (13) critical success factors related to the project dimensions of Project, Team, and Culture were discovered.The findings of this study outline the current state of APM adoption and use and is relevant to project management practitioners and researchers.
Unsupervised anomaly detection for internal auditing: Literature review and research agenda

Auditing has to adapt to the growing amounts of data caused by digital transformation. One approach to address this and to test the full audit data population is to apply rules to the data. A disadvantage of this is that rules most likely only find errors, mistakes or deviations which were already anticipated by the auditor. Unsupervised anomaly detection can go beyond those capabilities and detect novel process deviations or new fraud attempts. We conducted a systematic review of existing studies which apply unsupervised anomaly detection in an auditing context. The results reveal that most of the studies develop an approach for only one specific dataset and do not address the integration into the audit process or how the results should be best presented to the auditor. We therefore develop a research agenda addressing both the generalizability of unsupervised anomaly detection in auditing and the preparation of results for auditors.
An Integrated Localization, Motion Planning and Obstacle Avoidance Algorithm in Belief Space

As robots are being increasingly used in close proximity to humans and objects, it is imperative that robots operate safely and efficiently under real-world conditions. Yet, the environment is seldom known perfectly. Noisy sensors and actuation errors compound to the errors introduced while estimating features of the environment. We present a novel approach (1) to incorporate these uncertainties for robot state estimation and (2) to compute the probability of collision pertaining to the estimated robot configurations. The expression for collision probability is obtained as an infinite series and we prove its convergence. An upper bound for the truncation error is also derived and the number of terms required is demonstrated by analyzing the convergence for different robot and obstacle configurations. We evaluate our approach using two simulation domains which use a roadmap-based strategy to synthesize trajectories that satisfy collision probability bounds.
Attacks on Artificial Intelligence [Last Word]

There is, today, a lot of hype around the issue of attacks on artificial intelligence (AI). There are huge numbers of research papers, white papers and reports about those attacks and potential defenses—which is reasonable given that AI techniques are today pervasive in all applications we may think of. As we increasingly rely on AI techniques for critical decisions, forecasts and analyses, concerns about whether these AI techniques can be attacked are certainly legitimate.
FPGA Realizations of Chaotic Epidemic and Disease Models Including Covid-19

The spread of epidemics and diseases is known to exhibit chaotic dynamics; a fact confirmed by many developed mathematical models. However, to the best of our knowledge, no attempt to realize any of these chaotic models in analog or digital electronic form has been reported in the literature. In this work, we report on the efficient FPGA implementations of three different virus spreading models and one disease progress model. In particular, the Ebola, Influenza, and COVID-19 virus spreading models in addition to a Cancer disease progress model are first numerically analyzed for parameter sensitivity via bifurcation diagrams. Subsequently and despite the large number of parameters and large number of multiplication (or division) operations, these models are efficiently implemented on FPGA platforms using fixed-point architectures. Detailed FPGA design process, hardware architecture and timing analysis are provided for three of the studied models (Ebola, Influenza, and Cancer) on an Altera Cyclone IV EP4CE115F29C7 FPGA chip. All models are also implemented on a high performance Xilinx Artix-7 XC7A100TCSG324 FPGA for comparison of the needed hardware resources. Experimental results showing real-time control of the chaotic dynamics are presented.
Research on Blended Teaching Model in College English Based on Mobile Learning APP

Mobile learning is the general trend of the times and the inevitable choice for the reform and development of college English teaching. This article attempts to use blended learning theory, mobile learning theory, and instructional design theory as a guide. In many English mobile learning apps, Superstar Learning Link is used as an example to design an online and offline blended teaching mode. It proposes thinking expansion and interaction in pre-class stage. Consolidation of input and output-oriented process are during class and evaluation and sublimation are made after class. The essay is aimed to break the boundaries between traditional teaching and mobile learning, combining synchronous learning with personalized learning in order to improve comprehensive levels of English.
Human Motion Gesture Recognition Based on Computer Vision

Human motion gesture recognition is the most challenging research direction in the field of computer vision, and it is widely used in human-computer interaction, intelligent monitoring, virtual reality, human behaviour analysis, and other fields. This paper proposes a new type of deep convolutional generation confrontation network to recognize human motion pose. This method uses a deep convolutional stacked hourglass network to accurately extract the location of key joint points on the image. The generation and identification part of the network is designed to encode the first hierarchy (parent) and the second hierarchy (child) and show the spatial relationship of human body parts. The generator and the discriminator are designed as two parts in the network, and they are connected together in order to encode the possible relationship of appearance and, at the same time, the possibility of the existence of human body parts and the relationship between each part of the body and its parental part coding. In the image, the key nodes of the human body model and the general body posture can be identified more accurately. The method has been tested on different data sets. In most cases, the results obtained by the proposed method are better than those of other comparison methods.
Higher-Order Moment-Based Anomaly Detection

The identification of anomalies is a critical component of operating complex, and possibly large-scale and geo-graphically distributed cyber-physical systems. While designing anomaly detectors, it is common to assume Gaussian noise models to maintain tractability; however, this assumption can lead to the actual false alarm rate being significantly higher than expected. Here we design a distributionally robust threshold of detection using finite and fixed higher-order moments of the detection measure data such that it guarantees the actual false alarm rate to be upper bounded by the desired one. Further, we bound the states reachable through the action of a stealthy attack and identify the trade-off between this impact of attacks that cannot be detected and the worst-case false alarm rate. Through numerical experiments, we illustrate how knowledge of higher-order moments results in a tightened threshold, thereby restricting an attacker's potential impact.
Adaptive Filter using Improved Pigeon Inspired Optimization Algorithm for Satellite Image Denoising

Satellite imaging is a current development in image processing; however, it faces a lot of challenges because of the environmental factors.For denoising, state-of-the-art method has developed some filters like the hyperspectral satellite images, which is not effectual.Moreover, this paper proposed an adaptive filter using the assist of an optimization approach for the satellite image denoising.The developed adaptive filter performs the image denoising via noise correction, noise identification, and image enhancement.In the satellite image by transforming the image to a binary image, the type-2 fuzzy filter recognizes the noisy pixels which are passed via the adaptive non-local mean filter for the noise correction.Subsequently, the kernel-based interpolation scheme performs the image enhancement, which is performed through the developed improved Pigeon optimization algorithm (IPOA).The whole experimentation of the developed denoising system is performed taking into consideration by satellite images from standard databases.It is obvious that the developed adaptive filter with the developed improved Pigeon optimization algorithm has enhanced performance with the PSNR values from the outcomes.
On Security Notions for Encryption in a Quantum World

Indistinguishability against adaptive chosen-ciphertext attacks (IND-CCA2) is usually considered the most desirable security notion for classical encryption. In this work, we investigate its adaptation in the quantum world, when an adversary can perform superposition queries. The security of quantum-secure classical encryption has first been studied by Boneh and Zhandry (CRYPTO’13), but they restricted the adversary to classical challenge queries, which makes the indistinguishability only hold for classical messages (IND-qCCA2). We extend their work by giving the first security notions for fully quantum indistinguishability under quantum adaptive chosen-ciphertext attacks, where the indistinguishability holds for superposition of plaintexts (qIND-qCCA2).
An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people

&lt;span&gt;The World Health Organization (WHO) reported in 2019 that at least 2.2 billion people were visual-impairment or blindness. The main problem of living for visually impaired people have been facing difficulties in moving even indoor or outdoor situations. Therefore, their lives are not safe and harmful. In this paper, we propose&lt;/span&gt;&lt;span&gt;d&lt;/span&gt;&lt;span&gt; an assistive application model based on deep learning: YOLOv3 with a Darknet-53 base network for visually impaired people on a smartphone. The Pascal VOC2007 and Pascal VOC2012 were used for the training set and used Pascal VOC2007 test set for validation. The assistive model was installed on a smartphone with an eSpeak synthesizer which generates the audio output to the user. The experimental result showed a high speed and also high detection accuracy. The proposed application with the help of technology will be an effective way to assist visually impaired people to interact with the surrounding environment in their daily life.&lt;/span&gt;
Human activity prediction using saliency-aware motion enhancement and weighted LSTM network

Abstract In recent years, great progress has been made in recognizing human activities in complete image sequences. However, predicting human activity earlier in a video is still a challenging task. In this paper, a novel framework named weighted long short-term memory network (WLSTM) with saliency-aware motion enhancement (SME) is proposed for video activity prediction. First, a boundary-prior based motion segmentation method is introduced to use shortest geodesic distance in an undirected weighted graph. Next, a dynamic contrast segmentation strategy is proposed to segment the moving object in a complex environment. Then, the SME is constructed to enhance the moving object by suppressing irrelevant background in each frame. Moreover, an effective long-range attention mechanism is designed to further deal with the long-term dependency of complex non-periodic activities by automatically focusing more on the semantic critical frames instead of processing all sampled frames equally. Thus, the learned weights can highlight the discriminative frames and reduce the temporal redundancy. Finally, we evaluate our framework on the UT-Interaction and sub-JHMDB datasets. The experimental results show that WLSTM with SME statistically outperforms a number of state-of-the-art methods on both datasets.
Radar Emitter Individual Identification Based on Convolutional Neural Network Learning

Radar Emitter Individual Identification is a key technology in modern electronic radar systems. This paper will focus on Radar Emitter Individual Identification (REII). Based on the advantages of Empirical Mode Decomposition (EMD) and bispectrum in signal processing, we propose an REII method based on the CNN. Firstly, the radar emitter signal is preprocessed. Secondly, the Hilbert–Huang Transform (HHT) spectrum and bispectrum are combined to form an image of the signal. Finally, in order to avoid loss of information and achieve the potential identification performance improvement, the signal image obtained is identified by the optimized CNN. Experimental results based on the measured signals show that the proposed method has high identification accuracy and is capable of meeting real-time identification requirements. The deep-learning-based identification method proposed in this paper has strong generalization ability and adaptability, which provides a new way for REII.
Performance Evaluation of Email Spam Text Classification Using Deep Neural Networks

Spam in email box is received because of advertising, collecting personal information, or to indulge malware through websites or scripts. Most often, spammers send junk mail with an intention of committing email fraud. Today spam mail accounts for 45% of all email and hence there is an ever-increasing need to build efficient spam filters to identify and block spam mail. However, notably today’s spam filters in use are built using traditional approaches such as statistical and content-based techniques. These techniques don’t improve their performance while handling huge data and they need a lot of domain expertise, human intervention and they neglect the relation between the words in context and consider the occurrence of the word. To address these limitations, we developed a spam filter using deep neural networks. In this work, various deep neural networks such as RNN, LSTM, GRU, Bidirectional RNN, Bidirectional LSTM, and Bidirectional GRU are used to a built spam filter. The experimentation was carried out on two datasets, one is a 20 newsgroup dataset, which contains multi-classes with 20,000 documents and the other is ENRON, a dataset contains 5,000 emails. The custom-designed models have performed well on both benchmark datasets and attained greater accuracy.