Reviewerly/exHarmony huggingface dataset:
Remittances and domestic investment in Africa: do banking sector development and quality governance matter?

Migrant remittances to home countries have seen a significant increase over the years, especially in developing countries where due to a lack of jobs or unfavourable working conditions, citizens move to advanced countries to better their economic conditions and their dependents in home countries. This has been facilitated by globalisation in modern times. Whereas most previous studies have delved more into remittances and their impact on economic growth, less studies have examined the link between remittances and domestic investment. This study examined the impact of remittances on domestic investment in Africa using a system GMM econometric estimator. Our study departs from the few studies that examined this link by further investigating the moderating role of banking sector development and quality governance on the link between remittances and domestic investment. Using data from 41 African countries from 2004 to 2018, the study discovered that migrant remittances have a direct negative impact on domestic investment in home countries. The study, however, found that both banking sector development and quality governance significantly positively impact domestic investment in Africa. Thus, when we interacted banking sector development and good governance separately with remittances, each interactive term had a significant positive impact on domestic investment. This means that for remittances to influence domestic investment, banking sector and good governance will need to be improved.
Interpretation of Trace Element Chemistry of Zircons from Bor and Cukaru Peki: Conventional Approach and Random Forest Classification

The deposits of Bor and Cukaru Peki are important contributors to the Apuseni–Banat–Timok–Srednogorie (ABTS) belt’s metallogenic endowment. We use decision tree and random forest algorithms applied to zircon geochemistry data from Bor, Cukaru Peki and a selection of other localities within the ABTS. The resulting predictions, supported by high scores on the test set predictions for the random forest algorithm, suggest that it is possible to fingerprint the studied deposits and localities from the ABTS belt based on zircon geochemistry. These results take into account the multivariate geochemical patterns and can be used in combination with a widely accepted Eu anomaly indicator or assist in finding more subtle geochemical differences for systems where applying a single cut-off value does not result in a good separation between barren and mineralized rocks.
Evaluating Classification Research for Machine Translation Course Teaching

eaching evaluation can be divided into different types, additionally their functions and applicable conditions are different. According to different standards, teaching evaluation can be divided into different types: (1) according to different evaluation functions, it can be divided into pre-evaluation, intermediate evaluation, and post-evaluation; (2) according to different evaluation reference standards, it can be divided into relative evaluation, absolute evaluation, and individual difference evaluation; (3) according to different evaluation and analysis methods, it can be divided into qualitative and quantitative evaluation; (4) according to the different evaluation subjects, it can be divided into self-evaluation and others’ evaluation. This paper introduced research work using different types of teaching evaluation in the machine translation course according to different situations. The research results showed that the rational selection of different types of teaching evaluation methods and the combination of these methods can greatly promote teaching.
IMAGE CLASSIFIER RESILIENT TO ADVERSARIAL ATTACKS, FAULT INJECTIONS AND CONCEPT DRIFT – MODEL ARCHITECTURE AND TRAINING ALGORITHM

Context. The problem of image classification algorithms vulnerability to destructive perturbations has not yet been definitively resolved and is quite relevant for safety-critical applications. Therefore, object of research is the process of training and inference for image classifier that functioning under influences of destructive perturbations. The subjects of the research are model architecture and training algorithm of image classifier that provide resilience to adversarial attacks, fault injection attacks and concept drift.&#x0D; Objective. Stated research goal is to develop effective model architecture and training algorithm that provide resilience to adversarial attacks, fault injections and concept drift.&#x0D; Method. New training algorithm which combines self-knowledge distillation, information measure maximization, class distribution compactness and interclass gap maximization, data compression based on discretization of feature representation and semi-supervised learning based on consistency regularization is proposed.&#x0D; Results. The model architecture and training algorithm of image classifier were developed. The obtained classifier was tested on the Cifar10 dataset to evaluate its resilience over an interval of 200 mini-batches with a training and test size of mini-batch equals to 128 examples for such perturbations: adversarial black-box L∞-attacks with perturbation levels equal to 1, 3, 5 and 10; inversion of one randomly selected bit in a tensor for 10%, 30%, 50% and 60% randomly selected tensors; addition of one new class; real concept drift between a pair of classes. The effect of the feature space dimensionality on the value of the information criterion of the model performance without perturbations and the value of the integral metric of resilience during the exposure to perturbations is considered.&#x0D; Conclusions. The proposed model architecture and learning algorithm provide absorption of part of the disturbing influence, graceful degradation due to hierarchical classes and adaptive computation, and fast adaptation on a limited amount of labeled data. It is shown that adaptive computation saves up to 40% of resources due to early decision-making in the lower sections of the model, but perturbing influence leads to slowing down, which can be considered as graceful degradation. A multi-section structure trained using knowledge self-distillation principles has been shown to provide more than 5% improvement in the value of the integral mectric of resilience compared to an architecture where the decision is made on the last layer of the model. It is observed that the dimensionality of the feature space noticeably affects the resilience to adversarial attacks and can be chosen as a tradeoff between resilience to perturbations and efficiency without perturbations.
Feature extraction comparison for facial expression recognition using adaptive extreme learning machine

Facial expression recognition is an important part in the field of affective computing. Automatic analysis of human facial expression is a challenging problem with many applications. Most of the existing automated systems for facial expression analysis attempt to recognize a few prototypes emotional expressions such as anger, contempt, disgust, fear, happiness, neutral, sadness, and surprise. This paper aims to compare feature extraction methods that are used to detect human facial expression. The study compares the gray level co-occurrence matrix, local binary pattern, and facial landmark (FL) with two types of facial expression datasets, namely Japanese female facial expression (JFFE), and extended Cohn-Kanade (CK+). In addition, we also propose an enhancement of extreme learning machine (ELM) method that can adaptively select best number of hidden neurons adaptive ELM (aELM) to reach its maximum performance. The result from this paper is our proposed method can slightly improve the performance of basic ELM method using some feature extractions mentioned before. Our proposed method can obtain maximum mean accuracy score of 88.07% on CK+ dataset, and 83.12% on JFFE dataset with FL feature extraction.
A hybrid modified lightweight algorithm for achieving data integrity and confidentiality

&lt;span lang="EN-US"&gt;Encryption algorithms aim to make data secure enough to be decrypted by an attacker. This paper combines the Speck and the Salsa20 to make it difficult for an attacker to exploit any weaknesses in these two algorithms and create a new lightweight hybrid algorithm called Speck-Salsa20 algorithm for data integrity and confidentiality (SSDIC). SSDIC uses less energy and has an efficient throughput. It works well in both hardware and software and can handle a variety of explicit plaintext and key sizes. SSDIC solves the difficulties of the Speck algorithm. The sequence generated by Speck is not random and fails to meet an acceptable success rate when tested in statistical tests. It is processed by generating a random key using the Salsa20 algorithm. Salsa20 is a high-speed secure algorithm that is faster than advanced encryption standard (AES) and can be used on devices with low resources. It uses a 256-bit key hash function. The recovery of the right half of the original key of the Speck algorithm is also handled by modifying the Speck round function and the key schedule. Simulation results show, according to a National Institute of Standards and Technology (NIST) test, the performance achieved by the SSDIC is increased by nearly 66% more than that achieved from the Speck in terms of data integrity and confidentiality.&lt;/span&gt;
Blockchain technology and internet of things: review, challenge and security concern

&lt;span&gt;Blockchain (BC) has received high attention from many researchers recently because it has decentralization, trusted auditability, and transparency as its main properties. BC has contributed fundamentally to the development of applications like cryptocurrencies, health care, the internet of things (IoT), and so on. The IoT is envisioned to include billions of pervasive and mission-critical sensors and actuators connected to the internet. This network of smart devices is expected to generate and have access to vast amounts of information, creating unique opportunities for new applications, but significant security and privacy issues emerge concurrently because it does not contain robust security systems. BC provides many services like privacy, security, and provenance to the systems that depends on. This research includes analyzing and a comprehensive review of BC technologies. Moreover, the proposed solutions in academia with the methodologies that used to integrate blockchain with IoT are presented. Also, the types of attacks on blockchain are collected and classified. Furthermore, the main contributions and challenges that are included in the literature are explored, then the relevant recommendations for solving the explored challenges are proposed. In conclusion, the integration of BC with IoT could produce promising results in enhancing the security and privacy of IoT environment.&lt;/span&gt;
Monitoring Large Scale Production Processes Using a Rule-Based Visualization Recommendation System

Abstract Data visualization plays an important role in the analysis of data and the identification of insights and characteristics within the dataset. Current visualization tools suffer from limitations with respect to the number of dimensions that can be displayed simultaneously. This often results in the inability to highlight hidden patterns or trends, affecting the analysts’ decision-making capabilities. In this scenario, it becomes imperative to propose systems that recommend useful insights to guide users to understand and explore data more efficiently. Addressing this problem, we propose a rule-based visualization recommendation(VizRec) system which caters for the research goal of providing an intelligent assistant that can guide users directly to relevant insights in the data. Our proposed VizRec system automates aspects of visualization design and recommends visualizations incorporating both the data characteristics and the diverse tasks representing users’ goals and intents using a knowledge-based rule engine. To ensure the correctness of knowledge-based rules and the dynamic nature of the rule engine, a formalization theory is proposed. We implemented our proposed model into a working tool for the exploration of complex production data generated from the manufacturing processes for the German excellence cluster “Internet of Production”. Our tool was able to generate recommendations capable of visualizing data insights regardless of the domain it is used in. The existing systems face several shortcomings ranging from the number of dimensions they can handle to the limited number of supported visualizations. The recommended visualizations derived from our proposed system were able to mitigate such challenges by adopting a generic rule-based approach incorporating visualizations handling multidimensional data. We evaluated our system in multiple fields, including the engineering domain, where production systems datasets are used to achieve user-intended tasks and gain valuable insights.
The Robot and I: The Rise of Hybrid ServiceTeams

Sciendo provides publishing services and solutions to academic and professional organizations and individual authors. We publish journals, books, conference proceedings and a variety of other publications.
Development of automotive basics learning module Sub-Subject Special Service Tools (SST) based on augmented reality

Based on preliminary research conducted in the Automotive Engineering class of SMK Negeri 3 Singaraja, it was found that the obstacles faced by students in the learning process were 90% of the 30 students stating that they did not have modules or other handbooks to learn the Special Service Tools (SST) sub-material. 73.3% stated that it was difficult to understand the material through teaching materials and methods applied by the teacher, so that students had difficulty in understanding how to use the types of Special Service Tools (SST) according to their functions. 90% of students stated that they were very happy and interesting if the learning activities of the Special Service Tools (SST) sub-material were presented using learning media that varied with audio, visual, audio-visual media. This research produced learning media in the form of Augmented Reality-based modules that can be accessed individually with a smartphone device to display 3-dimensional objects of the Special Service Tools (SST) type and how to use them. The research method used is research and development using the ADDIE model which consists of analysis, design, development, implementation, evaluation. The results of this study show that the learning module automotive basics sub subject Special Service Tools (SST) based on augmented reality is stated to be very feasible and very practical to use. The percentage of material experts, media experts, small and large group trials are 93%, 87.89%, 88.55% and 90.83% which are categorized as very feasible and very practical.
DSM-IDM-YOLO: Depth-wise Separable Module and Inception Depth-wise Module Based YOLO for Pedestrian Detection

Pedestrian detection is one of the most challenging research areas in computer vision. Compared to traditional hand-crafted methods, convolutional neural networks (CNNs) have superior detection results. The single-stage detection networks, particularly the state-of-the-art You Only Look Once (YOLO) network, have attained a satisfactory performance without compromising the computation speed in object detection. YOLO framework can be leveraged in pedestrian detection as well. In this work, we propose an improved YOLOv2, called DSM-IDM-YOLO. The proposed model uses a modified DarkNet19 integrated with three new modules, two depth-wise separable convolution modules and one inception depth-wise convolution module, leading to a comprehensive feature of an object in the image. The modules are integrated on top of features from multiple levels of the network. The proposed framework is computationally less expensive owing to its convolution design and a moderate number of layers. It aims to improve performance with minimal computational overhead. The proposed method is compared with state-of-the-art detection methods, i.e., Faster R-CNN, YOLOv2, YOLOv3, YOLOv4-tiny and Single Shot Multibox Detector (SSD). The performance results attest that the proposed method has effectively improved the detection. Three benchmark pedestrian datasets are used for experimental analysis: INRIA, PASCAL VOC 2012 and Caltech.
Image enhancement for underwater range-gated image with multi-slice integration method

Active illumination underwater brings the backscatter problem. The traditional pulsed laser range-gated imaging (PLRGI) system uses a small gate width to suppress most of the backscattered light. As a result, the depth of field (DOF) of the PLRGI system is small. In addition, the DOF has to be extended when the system is used for searching the target. There are several methods to extend the DOF. However, every method has its shortcoming. To overcome these problems, a multi-slice integration (MSI) method is proposed. In turbid water, the image captured by the MSI method also has the backscatter problem. The rolling ball and intensity transformation are proposed as an image enhancement method to remove the backscattered light and improve the brightness of the image. Experiments carried out in a towing boat tank and Mulan Lake show that the proposed method is better than lower-upper-threshold correlation, dark channel priors, and unsharp filtering for backscattered light removal.
COVID-19, Personal Data Protection and Privacy in India

The corona pandemic altered many traditional and historical norms of society and law. COVID-19 created a humanitarian crisis in some parts of globe, while pandemic privacy and civil liberties were under threat all over world. To combat the deadly virus, individual liberty and equality were compromised. This paper focuses on how India's health problem has compromised people's right to privacy. It will highlight how strict executive policies led to the creation of a massive surveillance system in the name of combating the COVID-19 pandemic, as well as how the absence of any policy or legal framework led to the exclusion of individuals and their families who were suspected of having the virus or caring for those who were infected with the deadly virus. The paper uses case studies and data collected from primary as well as secondary sources. The authors will also point out how the absence of privacy regulation puts millions of citizens' private information at risk of being compromised or exploited against their will.
Gaussian guided frame sequence encoder network for action quality assessment

Abstract Can a computer evaluate an athlete’s performance automatically? Many action quality assessment (AQA) methods have been proposed in recent years. Limited by the randomness of video sampling and the simple strategy of model training, the performance of the existing AQA methods can still be further improved. To achieve this goal, a Gaussian guided frame sequence encoder network is proposed in this paper. In the proposed method, the image feature of each video frame is extracted by Resnet model. And then, a frame sequence encoder network is applied to model temporal information and generate action quality feature. Finally, a fully connected network is designed to predict action quality score. To train the proposed method effectively, inspired by the final score calculation rule in Olympic game, Gaussian loss function is employed to compute the error between the predicted score and the label score. The proposed method is implemented on the AQA-7 and MTL–AQA datasets. The experimental results confirm that compared with the state-of-the-art methods, our proposed method achieves the better performance. And detailed ablation experiments are conducted to verify the effectiveness of each component in the module.
Specification is Law: Safe Creation and Upgrade of Ethereum Smart Contracts

Smart contracts are the building blocks of the "code is law" paradigm: the smart contract's code indisputably describes how its assets are to be managed - once it is created, its code is typically immutable. Faulty smart contracts present the most significant evidence against the practicality of this paradigm; they are well-documented and resulted in assets worth vast sums of money being compromised. To address this issue, the Ethereum community proposed (i) tools and processes to audit/analyse smart contracts, and (ii) design patterns implementing a mechanism to make contract code mutable. Individually, (i) and (ii) only partially address the challenges raised by the "code is law" paradigm. In this paper, we combine elements from (i) and (ii) to create a systematic framework that moves away from "code is law" and gives rise to a new "specification is law" paradigm. It allows contracts to be created and upgraded but only if they meet a corresponding formal specification. The framework is centered around \emph{a trusted deployer}: an off-chain service that formally verifies and enforces this notion of conformance. We have prototyped this framework, and investigated its applicability to contracts implementing two widely used Ethereum standards: the ERC20 Token Standard and ERC1155 Multi Token Standard, with promising results.
Study for improvement of a non-face-to-face university piano accompaniment class

A survey was conducted on non-face-to-face piano accompaniment class students.The lecture video is divided into upper and lower screens so that the score and the hand position of the instructor can be viewed together.93% of the students played the lecture video in a place where piano practice was possible, and practice and learning were carried out together.100% of students used repeat play function and 33% of students used slow play function.86% of the students who used repeat play function answered that it was to learn repeatedly while looking at the hand position of the instructor, and 100% of the students who used slow play function answered that it was to better see the instructor's hand position.Satisfaction with the screen layout was relatively high at 4.93 points, and satisfaction with the non-face-to-face piano accompaniment method was very high at 5 points.33% of the students selected the excellent learning effect as an advantage.When the pandemic is over and options are available, 73% of students said they would choose the non-face-to-face method.Through this investigation, it is analyzed that, in the case of a non-face-to-face class on musical instrument practice, an appropriate screen layout
Cheng Fuzzy Time Series Model to Forecast the Price of Crude Oil in Malaysia

Crude oil is one of the important commodities to Malaysia. As a producer and exporter of oil and gas, Malaysia has gained high Gross Revenue from this sector. Crude oil is the global commodity and highly demanded. Therefore, major price changes on the commodity have a significant influence on world economy. Market sentiment, demand, and supply are some elements directly influencing the oil prices. Since crude oil is the backbone of businesses and is extremely important to the economy, it is essential to study the price of crude oil for future planning purposes. For that reason, this study proposes the use of the Fuzzy Time Series Cheng to predict crude oil price in Malaysia. In this study, Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are used to evaluate the forecast performance. The result shows that Fuzzy Time Series Cheng is able to produce a good result in forecasting since the analyses shows that the low value of RMSE and MAPE (less than 10 percent). Although this is the fundamental study but the finding may assist many sectors in Malaysia, such as governments, enterprises, investors, and businesses to produce a better economic planning in the future especially after the pandemic covid-19 phase.