Reviewerly/exHarmony huggingface dataset:
Kosen Robot Contest Under COVID-19 Pandemic: Activities of Yonago KOSEN in 2020

The Robocon 2020 was the first to be held online due to the COVID-19 pandemic. Since it limited the production time and the production environment, it became essential to respond differently from the conventional competition. In addition, it was necessary to consider the prevention of transmission of the COVID-19 and the safety of robot development. To overcome these problems, we focused on hybrid home and school activities, and maximized the use of equipment such as 3D printers. This paper introduces the activities and results of the Robocon 2020 of Yonago KOSEN.
Robust Median-Ternary Pattern for Traffic Light Detection

Traffic light detection and classification still be challenging problems in driving assistance systems due to various unknown factors appearing along the driving roads, especially under night conditions. This research, therefore, introduces an efficient local feature that helps to make original feature input to be more robust under various driving conditions by investigating the benefits of ternary-encoding and global information. The proposed feature input is then transferred to the deep learning-based YOLO to detect and classify traffic light status. Experimental results show that the proposed method obtained stable performance with a detection rate of 73.18% and a classification rate of 92.22% under night conditions using the CCD dataset.
Reproducing Kernel Hilbert Space

Thus far, we have learned that a feature map $$\Psi : E\ni x\mapsto k(x,\cdot )$$ is obtained by the positive definite kernel $$k: E\times E\rightarrow {\mathbb R}$$ . In this chapter, we generate a linear space $$H_0$$ based on its image $$k(x,\cdot ) (x\in E$$ ) and construct a Hilbert space H by completing this linear space, where H is called the reproducing kernel Hilbert space (RKHS), which satisfies the reproducing property of the kernel k (k is the reproducing kernel of H).
Parabolic Threshold Discretization for Big Data

The quality of knowledge extracted during the process of knowledge extraction from raw data is intrinsically dependent on the data pre-processing phase. Discretization is the fundamental function in the data pre-processing phase. The main objective of discretization is to transform continuous infinite data into discrete finite data. Most of the discretization methods proposed in the literature are not adapted to Big Data context. In this paper, we propose a new discretization method that is adapted to Big Data. The method has been implemented and tested on several benchmark databases and also compared to well-known discretization methods. The results show that the proposed method is adapted to voluminous databases used in Big Data.
NeuraIC—Neural Image Caption Generator for Assistive Vision

High-accuracy Image Captioning models have been developed as a result of recent breakthroughs in object detection using Deep Learning. Although these models are quite precise, they tend to rely on expensive calculation, making them challenging to apply in real-time applications. In this paper authors follows certain heuristic strategies and core ideas of Image Captioning and its common methods. A specific sequence based implementation with a remarkable transformation and efficient beam search instead of greedy search carried out on low-end hardware. The proposed system compares the results calculated using a variety of metrics with high-quality models and analyses the reasons behind the model trained on the MS-COCO dataset that are because of the trade-off between rapidity and eminence of computation. In this proposed system, RESTful API endpoint will be created to be used on any device with an internet connection such as a mobile phone, IoT devices, clock, etc., this endpoint used to send an image to the model running on remote server which in response will generate and sent an caption describing the objects and their relationship with each other in image in a natural language.
A bio- medical Question Answering system for the Malayalam language using word embedding and Bidirectional Encoder Representation from Transformers.

Conversational search is the dominant intent of Question Answering, which is achieved through different NLP techniques, Deep Learning models. The advent of Transformer models has been a breakthrough in Natural Language Processing applications, which has attained a benchmark on state of NLP tasks such as question answering. Here we propose a semantic Malayalam Question Answering system that automatically answers the queries related to health issues. The Biomedical Question-Answering, especially in the Malayalam language, is a tedious and challenging task. The proposed model uses a neural network-based Bidirectional Encoder Representation from Transformers (BERT), to implement the question answering system. In this study, we investigate how to train and fine-tune a BERT model for Question-Answering. The system has been tested with our own annotated Malayalam SQUAD form health dataset. In comparing the result with our previous works - Word embedding and RNN based model, identified we find that our BERT model is more accurate than the previous models and achieves an F1 score of 86%.
The Implementation Method of Battlefield Situational Awareness System Based on Human Brain Perception Mechanism

In this paper, I propose a computational method for incorporating attentional mechanisms that mimic those of the human brain into a battlefield situational awareness system by dividing attention-related circuits in the human brain to make the attentional mechanisms of the human brain computable and applying them to battlefield multi-source situational analysis. This allows effective fusion of multi-source signals, and reduces the computational effort of battlefield situational awareness and effectively assists commanders in judgment and decision making.
Development and Validation of a Novel Technology for Postural Analysis and Human Kinematics

In this study we have implemented and validated the Azure Kinect system for the acquisition and analysis of the human kinematics to be applied to the practical clinic for physiotherapy and rehabilitation, as well as in research studies. The progressive increase in the ageing of the world population in the first world countries, increasingly demands the need to find new, more automated, and versatile technological systems for the acquisition and analysis of human movement data that help us to diagnose, track the evolution of the pathologies and determine how our movements influence the development of the musculoskeletal pathologies. In this work, we were able to develop a measurement technology and validate the ability of the system based on Deep Learning (DL) and Convolutional Neural Networks (CNN), to make precise and fast measurements in real-time compared to the gold standard goniometry used by clinicians. Its precision has allowed verifying its validity for the measurement of large body joints.
Registration of Point Clouds for Human Face Recognition

The human face is considered a popular biometric because of its non-intrusive acquisition procedure. There are two kinds of facial recognition, one using 2D face images and the other involving 3D face images. Various factors like pose, illumination, occlusion, and expression affecting the performance of a 2D face recognition system can be tackled robustly using 3D face images. However, the accuracy of 3D face recognition depends on the proper registration of the 3D face images. So, this thesis aims to establish the importance of registration in the 3D face recognition area. Various approaches for face registration have been implemented, and we have shown how recognition performance improves after correct registration.
VM Anti-forensics: Detecting File Wiping Using File System Journals

Malware, tools, or other files deleted or created on a cloud Virtual Machine (VM) by an adversary may leave traces of the activities performed. Adversaries may securely delete the files left behind by their actions to minimize their footprint. There are several forensic procedures to recover deleted data. File system journaling is one such procedure for deleted data recovery in forensics for gathering evidence. However, when these files are wiped (secure-delete), recovery becomes impossible. Thus, it becomes crucial to detect file wiping to ensure the evidence collected for investigation is complete. In this paper, we exploit journaling to detect file wiping. File wiping detection is achieved by analyzing ExT file system journals. The results obtained shows accurate identification of file wiping in ExT journaled file system.
Learning a foreign language online: advantages and disadvantages

The paper provides a brief overview of the advantages and disadvantages of learning via Skype and gives the example of exercises used. The major educational benefit is in meeting the needs of the students who make decision of learning autonomously.
Preserving Electronic Medical Records Using Blockchain Technology

In our increasingly digitalized lives, privacy and protection are crucial. As security measures were put in place to monitor how information could be accessed, privacy controls were put in place to control whether and when they could access personal data. For fulfilling these constraints block chain technology is one of the most suitable technology. One of the most important resource for patients is reliable and full medical record records. Creating a secure storage room and loading it out. Nonetheless, protection, secrecy, confidentiality, and decentralization are all issues in the healthcare field. The emergence of the block chain technology can bring an ingenuity to solve this problem. So block chain technology and Ethereum based smart contracts provides an interesting and innovative way to keep track of Electronic Medical Records. This technology can help the patients to have a better control of their own data. In this design, a modern storage scheme based on block chain and database storage would be used to handle individual medical records. This assists in the storing and sharing scheme, which is invariant for any third party, and no one party can have full leverage over refinement.
Underwater Image Enhancement Using Fusion Stretch Method

Underwater image enhancement is an effective method for improving the captured underwater images that have been damaged owing to medium dispersion and absorption. Based on the principles of fusion, this method derives the inputs and the weight maps from the image’s degraded version. Two inputs representing color-corrected and contrast-enhanced versions of the original underwater image and three weight maps that seek to increase the visibility of degraded objects due to the medium dispersion and absorption are specified here to overcome the limitations of the underwater medium. This method is a single image approach that does not need specialized hardware or underwater conditions or scene expertise. In order to facilitate the transfer of edges and color contrast to the output image, the two fusion images, as well as their associated weight maps, are identified. The detailed qualitative and quantitative assessment show that the enhanced images are distinguished by improved illumination of the dark areas, enhanced global contrast, and sharpness of the edges.
Globalization and Inequality in Africa: The Way out of the Current Challenges

Globalization is another economic trend in the world with many expectations from the developing and developed countries. It is synonymous with the increasing economic interactions and activities of most world powers and the upcoming ones. This has further reduced the world to a global village which allows free flow of information at a very fast pace and has also widened the gaps of inequality between the developed world and the under-developed countries in Africa, Asia and the Latin America. This paper therefore examines the challenges of globalization and its effects on these inequalities most particularly in Africa and also proffer some workable solutions to these problems. However, the study found out that; capital flights, financial crimes, unemployment, lack of sovereignty and others were part of the challenges of globalization in Africa. In view of this, the paper recommended that the African continent should diversify her economy from the monolithic dependence on crude oil and other mineral resources whose prices are determined outside the shores of the continent among others.
Multivariate Geo-Chemo Statistical Characterization of Potential Source Rocks in the Middle Benue Trough, Nigeria

Summary Some previous studies have been carried out on the Nigeria Middle Benue Trough litho-biostratigraphic characterization of the coals and shales, organic petrologic, and general geology. This work has further mapped other exposures of the different potential source rock facies in the Awgu Formation that were wrongly positioned, consequently repositioning/rearranging the lithofacies of the Awgu Formation in the study area. Recently begot geochemical and vitrinite reflectance data show that the coal facies of the Awgu Formation in intra-continental rifted Middle Benue Trough are good, to excellent source rocks with kerogen of type III/II and type II and have the capability of generating oil and gas while the shale facies are poor, to good source rocks with kerogen of type III and have the potential of generating gas. Cluster and Factor analyses divide the source rocks into cluster I (coal facies 1) comprising 32.14% and cluster II comprising 67.86% of the whole samples, all of which show source rocks that range from excellent, good to poor. The begot data will further drive future exploration strategies, reduce the risk associated with the exploration of petroleum, thereby providing a framework to aid decisions on future scientific or commercial drilling in the study basin.
Applications

Just as supervised classification is used in many fields and for a variety of purposes, PU learning is extremely versatile and can be used in almost every application area of ML that involves classification. PU learning is used for automatic label identification in poorly supervised learning problems. In some areas, such as the biomedical field, PU learning is a natural fit as obtaining negative samples can be difficult or even impossible in some cases. In this chapter we present a survey of some existing PU applications to both illustrate work that is being done in this field and to encourage new ideas and directions for further research. These applications have been broken into broad, occasionally overlapping, categories and will be presented in alphabetical order in the sections below.
MFES Framework for Efficient Feature Selection Among Subsystems in Intelligent Building

The increasing trend of problem representation and high-dimensional data collection calls for the utilization of feature selection in many machines learning tasks and big data representations. However, identifying meaningful features from thousands of related features in the smart home data which are dissimilar in nature remains a nontrivial task. This has prompted for the deployment of a feature selection algorithm (FSA) that provides two possible solutions. First, to provide an efficient scheme that best optimizes the features for subsystem decisions and second, tackles feature subset selection bias problem. In this paper, a MFES framework for feature selection is proposed that uses a hybrid mechanism to tackle the problem of feature subset selection bias in intelligent building data. The mechanism uses the effectiveness of filters and accuracy of wrappers to obtain significant features for prediction. The proposed MFES framework resulted in 92.17% of accuracy as compared to the baseline approach resulting in 87.21% of accuracy. The experimental results show that efficient and better prediction accuracy can be achieved with a smaller feature set.
Maximum Likelihood and Other Methods to Estimate Variables

This chapter addresses the problem of estimating the distribution function of a random variable based on measurements. The method of maximum likelihood is the tool of choice when the distribution of the variable is known. The maximum-likelihood criterion is also useful in more complex applications that involve the fit of two-dimensional data and the estimation of fit parameters. Alternative means to estimate variables include the method of moments and the method of maximum entropy. In particular, the method of maximum entropy can be used when the parent distribution is not specified a priori.
Efficient Two-Party Exponentiation from Quotient Transfer

Secure multi-party computation (MPC) allows participating parties to jointly compute a function over their inputs while keeping them private. In particular, MPC based on additive secret sharing has been widely studied as a tool to obtain efficient protocols secure against a dishonest majority, including the important two-party case. In this paper, we propose a two-party protocol for an exponentiation functionality based on an additive secret sharing scheme. Our proposed protocol aims to securely compute a public base exponentiation $$a^x \mathrm {mod} \,~p$$ for some prime p, where the exponent $$x \in \mathbb {Z}_p$$ is a (shared) secret and the base $$a \in \mathbb {Z}_p$$ is public. Our protocol is based on a new simple but efficient approach involving quotient transfer that allows the parties to perform the most expensive part of the computation locally, and requires 3 rounds and 4 invocations of multiplication. As an intermediate primitive for our efficient two-party exponentiation protocol, we propose an efficient modulus conversion protocol. This protocol might be of independent interest.
Potential Assessment of the Tvf-Emd Decomposition Method In Forecasting Hourly Global Solar Radiation: Review and Case Studies

Accurate forecasting of short-term global solar radiation is critical for the development of photovoltaic systems. However, its non-stationary characteristics caused by climatic factors make its estimation extremely challenging. In this regard, a newly learning technique for global radiation forecasting is proposed based on a time-varying filter-empirical mode decomposition, feature selection, and extreme learning machine. The proposed hybridization strategy consists of three main steps for understanding the fundamental behavioral aspects of hourly global solar radiation data. The first phase employs the TVF-EMD algorithm to deal with the variability of global solar radiation. Then, the feature selection step is employed to identify distinctive features set from the whole decomposed subseries. The selected features set are used to train and optimize our forecasting extreme learning machine model, then ELM model is used to assess the forecasting accuracy. The proposed TVF-EMD-RF-ELM model is evaluated and validated in different regions in Algeria with various climate conditions. The forecasting findings of the TVF-EMD algorithm demonstrate high accuracy compared to the recent version of empirical mode decomposition CEEMDAN. Overall forecasting periods, the TVF-EMD-RF-ELM model produces an error less than 8.3% in terms of normalized root mean square error nRMSE in all studied regions.
Multi-region Based Radial GCN Algorithm for Human Action Recognition

Action recognition is to classify the spatio-temporal changes of the human body as a qualitative pattern, so an efficient representation method that can reflect the structural characteristics of the human body is required. There-fore, in deep learning-based action recognition, graph convolutional network (GCN) algorithms with skeleton data as input were mainly used. However, these methods are difficult to use in the real situation without first obtaining accurate skeleton data. In this paper, we propose a multi-region based radial graph convolutional network (MRGCN) capable of end-to-end action recognition using only optical flow and gradient of the image. This method uses the optical flow and gradient as an oriented histogram, compresses it into a 6-dimensional feature vector, and uses it as an input to the network. Since the network that learns this feature vector has a radial hierarchical structure, it can learn the structural deformation of the human body. As a result of applying a performance experiment on 30 actions, MRGCN obtained Top-1 accuracy of 84.78%, which is higher than that of the existing GCN-based action recognition method. These results show that MRGCN is a high-performance action recognition algorithm suitable for using in the field of surveillance systems where skeleton data can-not be used.
Vocal Parameters Analysis for Amazigh Phonemes Recognition System

Several studies in the development of Automatic Speech Recognition (ASR) systems have been yet presented and well studied for many languages. However, the particularities of the Amazigh Language due to its specific acoustic features such as the emphasis on Amazigh consonants pose many problems for developing its automatic speech recognition system. In this paper, we deal with the spoken recognition problem in Amazigh Language. We consider here a particular case of the problem, focusing on the phonemes formants frequencies which are a principal variable in specifying the phonetic substance of discourse sounds. We propose an approach allowing the vocal parameters analysis of phonetic units, as well as a recognition algorithm by analyzing the formants.
The Pitfalls of Evaluating Cyber Defense Techniques by an Anonymous Population

In this work, we report on the results of recruiting anonymous (yet qualified) participants from Prolific, a crowdsourcing platform, to engage in hacking experiments that involve defensive cyber deception. This work is part of a longitudinal study undertaken by the authors in [4] and is exploratory in nature due to the final sample size. While the sample size is small, our preliminary results revealed some consistency in the behavioral patterns (e.g., risk-seeking behavior) between our population and populations recruited through other means. We also analyzed and identified variables (e.g., prior CTF experiences, non-naivety) that could be used to qualify anonymous participants or predict participant retention in longitudinal studies. Moreover, this research provides insights into the intricacies (e.g., validity threats) of evaluating defensive techniques using an anonymous, crowdsourced population, highlighting opportunities for future research that could be a key to enriching autonomous cyber defense [32].
Scheduling Complexity of Interleaving Search

miniKanren is a lightweight embedded language for logic and relational programming. Many of its useful features come from a distinctive search strategy, called interleaving search. However, with interleaving search conventional ways of reasoning about the complexity and performance of logical programs become irrelevant. We identify an important key component—scheduling—which makes the reasoning for miniKanren so different, and present a semi-automatic technique to estimate the scheduling impact via symbolic execution for a reasonably wide class of programs.
Control of a Four Degrees of Freedom Robot Using a Sine Cosine Algorithm for Joint Position

This paper needs to use its geometric model to control a planar robot with 4 degrees of freedom (DOFs). When calculating these models, the singularity problem is often encountered. To overcome this problem, we present in this paper a new efficient method to overcome this problem and compute the position of the joints of the 4R robot. This method uses a metaheuristic algorithm and the coordinates of four selected points in the object image to find the desired trajectory. The simulation results are performed using the Sine Cosine Algorithm (SCA). These results show the proposed method’s effectiveness in optimizing the positions of the joints of the 4 DOFs robot to control and find the optimal trajectory.
Assessment of Agricultural Knowledge Level Increased Among the Mobile Phone Users in Khyber Pakhtunkhwa of Pakistan

There-