Reviewerly/exHarmony huggingface dataset:
ICT and Human Development in Selected Devolving Countries

Human development and planning is one of the most prominent activities in any country. There are several factors that can be effective in achieving this. One of the key elements in this way is ICT. In recent decades, this technology has evolved widely across all social and economic spheres and has had a profound impact on organizations, human resources and the economics of micro and macro societies. Considering the impact of using information and communication technology can be a good way to increase the level of productivity and development of each country. The purpose of this paper is to investigate the impact of information and communication technology on human development index. Therefore, the investigated model has been using panel data for 15 selected developing countries over the period of 2012 to 2017. The estimation results show that ICT has a positive and significant effect on human development index. While foreign direct investment and value added have a positive impact and inflation has a negative impact on the human development index.
Blockchain Technology as a Risk Mitigation Tool in Supply Chain

The main purpose of this paper is to investigate the research development in supply chain risk management (SCRM) and to connect the new emerging digital technology Blockchain that could help to mitigate risks in any supply chain.Emerging literature, case studies, citations are used to fulfil this the task.The study will explore Blockchain and supply chain risks in details and will compare, how Blockchain based supply chain can mitigate these risks.There are many risks associated to supply chains but we will limit our study to only supply risk and study different areas of Blockchain application in supply chain with supply risk.Blockchain technologies have a potential to upgrade current supply chains.This will not only increase its performance in terms of speedy flow of raw material, finished goods, information and money flow but also help to mitigate current risk in supply chain by increasing visibility and transparency of supply chain.Smart contracts, IoT and Blockchain can serve together to achieve maximum efficiency and effectiveness in SCM.This research contributes to explore further research fields of Blockchain technology in different areas of supply chain risk management.
PVOPM: Verifiable Privacy-preserving Pattern Matching with Efficient Outsourcing in the Malicious Setting

Outsourced pattern matching delegates the task of finding all positions pattern <inline-formula><tex-math notation="LaTeX">$P$</tex-math></inline-formula> appears in text <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> to the cloud from resource-constrained devices. Unfortunately, it has brought a series of security and privacy issues. Most of the state-of-the-art either disclosed text/pattern privacy or exploited the computationally-intensive techniques of public key fully homomorphic encryption (FHE), commitment schemes and zero knowledge proof to achieve both text/pattern privacy and verifiability. To address these issues, as a building block, an efficient privacy preserving verifiable outsourced discrete fourier transform protocol OVFT is first devised based on any one-way trapdoor permutation (OWTP). Based on OVFT, we propose an efficient secure verifiable outsourced polynomial multiplication protocol OPVML which is further exploited in designing our final protocol PVOPM for verifiable privacy-preserving outsourced pattern matching. Without exploiting public key FHE, the proposed PVOPM achieves both verifiability and text/pattern privacy against the collusion between the cloud and malicious receiver/sender, by generating authentication proofs of constant size and executing constant times of any one-way trapdoor permutation, independent to both the text size <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> and the pattern size <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> . Finally, formal security proof under universal composable (UC) model and extensive evaluations demonstrate the efficiency and practicability of our proposed PVOPM.
White Blood Cell Image Segmentation Based on Color Component Combination and Contour Fitting

Background: White Blood Cell (WBC) image segmentation plays a key role in cell morphology analysis. However, WBC segmentation is still a challenging task due to the diversity of WBCs under different staining conditions. Objective: In this paper, we propose a novel WBC segmentation method based on color component combination and contour fitting to segment WBC images accurately. Methods: Specifically, the proposed method first uses color component combination and image thresholding to achieve nucleus segmentation, then uses a color prior to remove image background, and extracts the initial WBC contour via Canny edge detection, and finally judges and closes the unclosed WBC contour by contour fitting. Accordingly, cytoplasm segmentation is achieved by subtracting the nucleus region from the WBC region. Results: Experimental results on 100 WBC images under rapid staining condition and 50 WBC images under standard staining condition showed that the proposed method improved segmentation accuracy of white blood cells under rapid and standard staining conditions. Conclusion: The proposed color component combination and contour fitting is effective in WBC segmentation task.
Deep Learning of Fuzzy Weighted Multi-Resolution Depth Motion Maps with Spatial Feature Fusion for Action Recognition

Human action recognition (HAR) is an important yet challenging task. This paper presents a novel method. First, fuzzy weight functions are used in computations of depth motion maps (DMMs). Multiple length motion information is also used. These features are referred to as fuzzy weighted multi-resolution DMMs (FWMDMMs). This formulation allows for various aspects of individual actions to be emphasized. It also helps to characterise the importance of the temporal dimension. This is important to help overcome, e.g., variations in time over which a single type of action might be performed. A deep convolutional neural network (CNN) motion model is created and trained to extract discriminative and compact features. Transfer learning is also used to extract spatial information from RGB and depth data using the AlexNet network. Different late fusion techniques are then investigated to fuse the deep motion model with the spatial network. The result is a spatial temporal HAR model. The developed approach is capable of recognising both human action and human-object interaction. Three public domain datasets are used to evaluate the proposed solution. The experimental results demonstrate the robustness of this approach compared with state-of-the art algorithms.
Path Planning for Omnidirectional Wheeled Mobile Robot by Improved Ant Colony Optimization

This paper focuses on the path planning algorithm design for the omnidirectional wheel mobile robot. In view of some advantages of ant colony optimization (ACO), such as distributed computing ability, heuristic search strategy and strong robustness, ACO is adopted in this paper to search for the optimal path for the considered mobile robot. To avoid trapping into local minimum or the slower convergence during the process of path planning, some novel methods are proposed to improve the traditional ACO. Specifically, the distance and obstacle information are included in the heuristic information function to enhance local search ability and obtain faster convergence speed. The pheromone updating rule is also improved by introducing an extra time-varying pheromone updating item to avoid trapping into local minimum easily and increase the search efficiency. Besides, the optimal path obtained from the improved algorithm in each iteration is optimized for a second time by reducing the number of redundant intermediate nodes to make the path smoother and shorter. The simulation experiment is operated with MATLAB to verify the effectiveness and feasibility of the proposed path planning algorithm.
CAP: Context-Aware Programming for Cyber Physical Systems

Context-awareness is a prominently desired feature in computing systems. Smartphones, smart cards or tags, wearables, sensor nodes, and many other devices enable a system to compute context for different users and environment. With ever increasing advances in hardware for such devices, the interactions with users are increasing every day. This enables the collection of a large amount of data about users, systems, and physical environment. With such data available to be leveraged, context-awareness will soon become a necessity. Such type of data collection happens most frequently in sensing applications enabled by wireless sensor network (WSN) devices. This paper discusses the concept of context for sensing applications, specifically related to Cyber Physical Systems (CPS). The paper highlights key aspects of context and its definition. This paper proposes, to the best of the author's knowledge, the first programming approach to build context-aware applications for WSN-based CPS. This paper provides a proof of concept for a framework to detect, manage and deploy context-aware applications.
Visual Cues to Restore Student Attention based on Eye Gaze Drift, and Application to an Offshore Training System

Drifting student attention is a common problem in educational environments. We demonstrate 8 attention-restoring visual cues for display when eye tracking detects that student attention shifts away from critical objects. These cues include novel aspects and variations of standard cues that performed well in prior work on visual guidance. Our cues are integrated into an offshore training system on an oil rig. While students participate in training on the oil rig, we can compare our various cues in terms of performance and student preference, while also observing the impact of eye tracking. We demonstrate experiment software with which users can compare various cues and tune selected parameters for visual quality and effectiveness.
Optimal Transaction Throughput in Proof-of-Work Based Blockchain Networks

As of today, Bitcoin suffers with restrictive transaction throughput of 3&ndash;7 transactions per sec and the transaction confirmation takes several min as bitcoin blockchain was designed with a block creation time of 10 min and each block is restricted with less blocksize for fast transmission. In this paper, we obtained the optimal transaction throughput for a Proof-of-Work (PoW) based longest chain rule blockchain network (called bitcoin protocol). This is done by modeling the delay diameter (D) and double spending attack in a Erd o &uml; s-R e &acute; nyi random network topology as constraints. Through numerical results, it is shown that the throughput can be significatly improved without compromising the fairness of the network.
NAO robot obstacle avoidance based on fuzzy Q-learning

Purpose This paper aims to propose a novel active SLAM framework to realize avoid obstacles and finish the autonomous navigation in indoor environment. Design/methodology/approach The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. The localization of the robot is based on FastSLAM algorithm. Findings Simulation results of avoiding obstacles using traditional Q-learning algorithm, optimized Q-learning algorithm and FOQL algorithm are compared. The simulation results show that the improved FOQL algorithm has a faster learning speed than other two algorithms. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective. Originality/value The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective.
Near-Synonyms Within the Same Qur’anic Verse: A Contrastive English-Arabic Lexical Analysis

The Holy Qur&amp;rsquo;an has features that are difficult for translators to transfer into another language, and they face problems in conveying the different shades of meaning of the verses. From a wide variety of these problems is synonymy. Synonymy refers to words that mean the same or show semantic resemblance to one another. This study examines the translation of two root-sharing synonymous Arabic words, namely استطاع and اسطاع in five well-known English translations. These include Pickthall (1930), Ali (1982), Arberry (1996), Abdel Haleem (2004), and Al-Hilali and Khan (2018). These translations are selected because they are popular in the Muslim World in addition to the fact that the translators belong to different linguistic, religious and cultural backgrounds. The analysis shows that the translators were inconsistent in their selections of the English equivalents for the words under study. Furthermore, they did not convey the slight differences between the words and translated them similarly and interchangeably. The study concludes that some Qur&amp;rsquo;anic words are untranslatable, and cannot be rendered into another language, and therefore, translators are recommended to include explanatory notes between brackets or as footnotes in order to acknowledge the non-Arab readers that repetition of the words was not haphazard but intended for specific purposes.
Verbal mismatch in Right-Node Raising

Right-Node Raising (RNR, Ross 1967; Chaves 2014) has been claimed to require phonological identity between the missing material and the shared element. Our corpus investigations provide examples of RNR with verb form mismatch with and without syncretism in English and French. Two acceptability experiments show that lack of phonological identity does not affect the acceptability of RNR. We argue further that RNR without phonological identity cannot be taken to be a case of cataphoric VP-ellipsis in French and that it should not be analyzed as such in English. As regards the status of RNR with verb form mismatch, two positions are available: either it is considered to be grammatical, in which case the phonological resolution principle of Pullum &amp;amp; Zwicky (1986) does not hold, or it is considered to be ungrammatical but repaired (or “recycled”, Arregui et al. 2006; Frazier 2013). The high acceptability of cases with mismatch compared with ungrammatical controls casts doubt on the applicability of the recycling hypothesis in such cases. In order to account for the broader range of data established by our corpus and experimental results, we propose a new analysis of RNR based on lexeme identity rather than form identity.
EFL Students' Challenges in Learning Speaking Skills: A Case Study in Mechanical Engineering Department

Nowadays, speaking mastery is important due to the position of English as a universal language (Nazara, 2011). This research used a case study as the research design, which aimed to answer the research questions such as challenges faced by the students in learning speaking skills and strategies implemented by the students to overcome the challenges. There were three participants in this research, namely two Mechanical Engineering Department, Olaf and Elsa, and one English teacher in the Mechanical Engineering Department, Anna. The data obtained were through interviews and students’ speaking grades through classroom presentations. The result showed four challenges, such as lack of vocabulary, nervousness, unsupportive environment, and lack of grammar knowledge. Besides, there were five strategies, such as codeswitching, seeing the audiences as the statues, using google translate machines, making a personal approach to the English teacher, and encouraging self.
Business Process Automation: A Workflow Incorporating Optical Character Recognition and Approximate String and Pattern Matching for Solving Practical Industry Problems

Companies are relying more on artificial intelligence and machine learning in order to enhance and automate existing business processes. While the power of OCR (Optical Character Recognition) technologies can be harnessed for the digitization of image data, the digitalized text still needs to be validated and enhanced to ensure that data quality standards are met for the data to be usable. This research paper focuses on finding and creating an automated workflow that can follow image digitization and produce a dictionary consisting of the desired information. The workflow introduced consists of a three-step process that is implemented after the OCR output has been generated. With the introduction of each step, the accuracy of key-value matches of field names and values is increased. The first step takes the raw OCR output and identifies field names using exact string matching and field-values using regular expressions from an externally maintained file. The second step introduces index pairing that matches field-values to field names based on the location of the field name and value on the document. Finally, approximate string matching is introduced to the workflow, which increases accuracy. By implementing these steps, the F-measure for key-value pair matches is measured at 60.18% in the first step, 80.61% once index pairing is introduced, and finally 90.06% after approximate string matching is introduced. The research proved that accurate usable data can be obtained automatically from images with the implementation of a workflow after OCR.
PDA: A private domains approach for improved MSB steganography image

Steganography is one of the secure techniques of protecting data inside a cover object.Images are the most popular cover objects for Steganography.It provides secret message between users.The current paper presents an enhanced Most Significant Bit (MSB) technique.In this paper, a Private Domains Approach (PDA) is proposed; each domain consists of RGB of a pixel of cover image.Bit No.5 is applied to store the secret information in light of the bit that achieved highest steganography rate and the less probability of error rate.Consequently, this technique is allowing an improved version of MSB technique based on Mean-Squared Error (MSE), Peak Signalto-Noise Ratio (PSNR).The experimental results show that our schemes perform well in terms of image quality.Generally; MSB technique produced the best stego-image quality in this paper.
A New Deep Neural Network Based Dynamic Fuzzy Cognitive Map Weight Updating Approach

Fuzzy cognitive maps are a soft computational technique with artificial neural network nature expressed by graphs used to model complex systems. Fuzzy cognitive maps are used in areas like health, business, energy, computer science, etc. in the literature. It is used to solve problems in many areas. Fuzzy cognitive maps have a non-dynamic method of weight determination and updating in their classical applications. This is usually done by subjecting the expert opinions to fuzzy membership functions. In some studies, the weights obtained by using expert data are improved by utilizing historical data of the system modeled. In some studies, weight determination and updating process is realized by using intuitive optimization methods without benefiting from expert opinions. Here, fuzzy cognitive maps using a different weight matrix for different input values are considered dynamic. In this study, we propose a dynamic fuzzy cognitive map structure that performs the weight update process with deep learning. Here, the cognitive map weights determined in the data set used for deep neural network training are determined by a genetic optimization-based method using past system data. The proposed new DFCM structure has been tested on two different scenarios. In addition, the performance comparison with deep artificial neural network models dealing with the same scenarios was performed. When the experimental and comparative results are examined, the performance of the proposed method is quite satisfactory.
Effect of Workplace Safety and Health Practices on Employee Commitment and Performance in Steel Manufacturing Companies in Rwanda

&#x0D; &#x0D; &#x0D; The purpose of this study was to establish the effects of workplace safety and health practices on the employees’ commitment and performance in Steel manufacturing companies in Rwanda. The target population of this study was 533 people, which comprised of Managers, Supervisors and employees. A simple random sampling technique was used to select a sample size of 229 respondents from the target population. The data was collected using questionnaires, interview guide and personal observation. The Statistical Package for Social Science (SPSS) software, version 21.0, was used to process data while descriptive statistics such as the means, modes, standard deviation, variances and inferential statistics were used to analyze the data. The findings of the study indicate that most workers are aware of the dangers of occupational health and safety in the workplace. Also the study found that although employees are aware of the occupational health and safety concerns they neglect to put on the Personal Protective Equipments saying that it is too hot. The study concludes that occupational health and safety significantly affects employee commitment and performance. This study recommends that management should insure the workers and provide them with personal protective equipments to minimize workplace injuries and accident. The study also recommends that management provide regular education and training on occupational health and safety concerns to prevent workplace injuries, hence, promote productivity.&#x0D; &#x0D; &#x0D;
Uncovering Students’ Attitude toward Vlogging Activities in Improving Students’ Speaking Ability

The objective of this study is to uncover the students’ attitude toward vlogging activities in improving students’ speaking ability. Participants of this study are 40 students majoring English Education who enrolled in transactional conversation subject at Universitas Teknokrat Indonesia. Research design used in this study is descriptive qualitative. Data were gained through questionnaire exposing students’ attitude toward the subject and students’ attitude toward using vlog to support learning process. There were 11 indicators with 32 questionnaire items. The results of this study reveal that Vlog helps students to understand the materials, improve students’ learning achievement, motivation, creativity, and engagement, provide interesting learning activities, efficient learning, effective learning, and lead students to be confident, independent and critical.