Reviewerly/exHarmony huggingface dataset:
Impact of Super-Resolution and Human Identification in Drone Surveillance

In the scene of large crowd gatherings and challenging visiting places such as rough hills and high glass buildings, acquisition of the images through normal cameras is difficult and next to impossible. In all such scenarios, the drone becomes a useful acquisition sensor to capture the detailed information of the scene and the objects present there. With the rapid development of consumer unmanned aerial vehicles (UAV) or drones, the utilization of these devices became extremely easy. The popular use-case of the drones can be seen for the surveillance to identify any possible threats in the large crowd gathering and recognize the different individuals present in the crowd. However, the images captured using the drones are generally taken from a significant distance to avoid any collision; hence these images generally suffer in quality such as low resolution, motion blur, and other environmental factors. The impact of these artifacts has been seen in the face recognition performance using several machine learning algorithms on large-scale drone databases namely Drone SURF. In this research, we intend to tackle the above artifacts by looking at the problem from the perspective of super-resolution of low-quality images. We have studied state-of-the-art (SOTA) super-resolution algorithms and see whether current methods are capable of handling the challenges of drone images. Apart from that, we have also evaluated another SOTA deep network developed for object detection for human segmentation in drone images. The proposed research provides interesting findings highlighting the limitations of existing works from the perspective of handling drone images. We would like the readers to go through the paper to find out the current limitations and possible future directions in drone image surveillance.
Unity Based A* Algorithm Used in Shortest Path Finding Problem for Helicopters

Crew failure from visibility is common in military landing, take-off and runway-related accidents. Especially in the field of aviation, there are many studies aimed at reducing accidents caused by bad weather. In this study, an application has been developed that allows the helicopter to reach a target point using shortest path in a designated large terrain. It has been ensured that the pilot can land safely in adverse weather conditions. With Unity Hub's NavMesh and A* algorithm feature, the helicopter was given a safe 3D path recommendations in the 3D model using a height map of the terrain. A* algorithm and navigation mesh have been used to suggest a dynamic road in the field based on the position of the helicopter removed from a specific point. Experimental results show that the proposed 3D shortest path is successful in minimizing the route length and degree of risk.
Dirichlet process and Bayesian nonparametricmodels

Bayesian nonparametric models have been extensively developed and widely used in statistics, machine learning and other areas since the ground breaking work of Ferguson. The fundamental of Bayesian nonparametric models is a special class of random probability measures: Dirichlet processes. This paper introduces the constructions, properties and some recent developments of the Dirichlet processes as well as their applications to Bayesian nonparametric estimation problems. We are also concerned with two-parameter Poisson-Dirichlet processes, Beta processes and more general stick-breaking processes and their properties.
Nonparametric two-sample tests for equality of distributions using projections

We propose a nonparametric two-sample test, which generalizes the Cramér-von Mises test through projections, to test for equality of two distributions in high dimensions. The population version of our proposed generalized Cramér-von Mises statistic is nonnegative and equals zero if and only if the two distributions are identical, ensuring that our proposed test is consistent against all the fixed alternatives. In addition, our proposed test statistic has an explicit form and is completely free of tuning parameters. It requires no moment conditions and hence is robust to the presence of outliers and heavy-tail observations. We study the asymptotic behaviors of our proposed test under both the “large sample size, fixed dimension" and the “fixed sample size, large dimension" paradigms. In the former paradigm, we show that the asymptotic power of our proposed test does not depend on the size ratio of the two random samples. This ensures that our proposed test can be readily applied to imbalanced samples. In the latter paradigm, we observe that, surprisingly, the two distributions are equal if and only if their first two moments are equal. In this paradigm, we suggest tailoring our proposed test to detect location shifts and scale differences, which further enhances the power performance of our proposed test significantly. Numerical studies confirm that our proposals are superior to many existing tests in high-dimensional two-sample test problems.
Geology of Zagros metamorphosed volcaniclastic sandstones: a key for changing the Mawat Ophiolite Complex to a metamorphic core complex, Kurdistan Region, NE-Iraq

Abstract Mawat Ophiolite Complex is located about 36 km to the northeast of Sulaimani city and directly to the east-northeast of Mawat town near the border of Iran in the northeastern Iraq. The complex has about 600-km2 surface area and consists of high mountain terrains that subjected to intense geological investigations from the fiftieth of previous century till now. According to previous studies, the complex contains tens of igneous rocks such as basalt, metabasalt, tuff, diabase, metadiabase, diorite dykes, periodotite, serpentinite, serpentinite-matrix mélange, gabbro, metagabbro, harzbergite, pyroxenite, plagiogranite, pegmatite, granitiod rocks and dunite. They added occurrences of the volcanic and subvolcanic rocks in the form of dykes or basaltic flows. The present study tries to change the petrology and tectonics of whole complex from Ophiolite Complex to Metamorphic Core Complex. The revision includes refusal of all the above igneous rocks, instead they considered as medium grade regional metamorphism of different types of volcaniclastic sandstones (volcanic wackes), arenites and greywackes (impure sandstones) which sourced predominantly from remote volcanic source area inside Iran. The revision depended on several conjugate field and laboratory evidences inside the complex. These evidences such as absence of pillow basalt, volcanic flows, glass shards, volcanic cones, dykes, sills, contact metamorphism, dilatational structures and flow structures. Other evidences are presence of cross beddings, erosional surfaces, lensoidal channel fills, metamorphosed conglomerate, exposures of thousands of laminated planar beds and transition from fresh volcaniclastic sandstones to its medium grade metamorphosed counterparts, which previously considered as igneous rocks of ophiolite types. Another, evidence, in contrast to ophiolite section, the basalt location is at the base of the claimed ophiolite section while plutonic (dunite and peridotite) rocks located at its top. These locations of the two rocks contradict the definition of ophiolites. Accordingly, the present study changed the geological map of the whole Mawat area from igneous outcrops to metamorphosed volcaniclastic sandstones, arenites and greywackes that belong to Walash-Naoperdan Series. The parent rocks of the series transformed to different types of regionally metamorphosed rocks by deep burial during Eocene. During the burial, diageneses and metamorphisms enhanced by complex mixture of materials from different source areas and seawaters environments. Later, they uplifted, unroofed and exhumed during Pliocene as a core complex.
Methods and Software Complex Development for Remote Electronic Voting Based on Ethereum Blockchain Platform

The most promising way to increase voters’ confidence in the remote electronic voting (REV) procedure is a voting method based on Ethereum blockchain platform. However, the existing solutions using this method faced a range of problems: ensuring the secrecy of the vote and openness of the procedure for society, pressure on the voter and a guarantee of the reliability of the whole system. In this work, a method for constructing a REV is proposed that solves these problems. It is similar in structure to the traditional voting method, using the same principle and processes. The Ethereum blockchain based REV process is described in detail. It was shown that received votes are securely stored in the Ethereum blockchain network, and the correctness of the vote addressing to the selected candidate can always be checked in real time. The description of smart contract algorithm that implements the transfer of vote from voter to candidate using transactions and determines the winner who received the highest number of votes was provided. It was demonstrated that keccak256 hashing algorithm and secp256k1 elliptic curve signatures ensure transactions’ maximum protection, reliability, and non-rollability. The developed REV technique based on Ethereum blockchain platform increases the efficiency of data security and confidentiality, transparency and anonymity of the voting procedure, and solves the problem of coercion. The results of the work have been implemented programmatically and can be used not only in the electoral system, but also wherever there is need of remote voting.
A Channel-Wise Spatial-Temporal Aggregation Network for Action Recognition

A very challenging task for action recognition concerns how to effectively extract and utilize the temporal and spatial information of video (especially temporal information). To date, many researchers have proposed various spatial-temporal convolution structures. Despite their success, most models are limited in further performance especially on those datasets that are highly time-dependent due to their failure to identify the fusion relationship between the spatial and temporal features inside the convolution channel. In this paper, we proposed a lightweight and efficient spatial-temporal extractor, denoted as Channel-Wise Spatial-Temporal Aggregation block (CSTA block), which could be flexibly plugged in existing 2D CNNs (denoted by CSTANet). The CSTA Block utilizes two branches to model spatial-temporal information separately. In temporal branch, It is equipped with a Motion Attention Module (MA), which is used to enhance the motion regions in a given video. Then, we introduced a Spatial-Temporal Channel Attention (STCA) module, which could aggregate spatial-temporal features of each block channel-wisely in a self-adaptive and trainable way. The final experimental results demonstrate that the proposed CSTANet achieved the state-of-the-art results on EGTEA Gaze++ and Diving48 datasets, and obtained competitive results on Something-Something V1&amp;V2 at the less computational cost.
Knowledge Acquisition and Satisfaction of Virtual Microscopy Usage Among Medical Students of Universiti Sains Malaysia

The virtual microscope has been employed as an adjunct tool to teach optical microscopy for histology learning in medical schools. However, there is no reliable evidence in the literature that virtual and optical microscopy impacts students’ learning. This study focuses on comparing two different methods in learning histology in Universiti Sains Malaysia, namely virtual microscopy and optical microscopy, with regard to medical students’ knowledge acquisition and satisfaction levels. A total number of 120 medical students, consisting of 53 first-year and 67 second-year students, were recruited. The students were divided into virtual microscopy and optical microscopy groups. During the one-day intervention, all students attended a pre-requisite lecture on “Histology of the Eye”, a slide demonstration and a hands-on session using a designated microscope. Students’ knowledge acquisition was evaluated through a pre- and post-practical evaluation and their satisfaction level on learning histology using respective learning tools was measured. The study revealed that the optical and virtual microscopy groups showed significant improvement from the pre- to post-practical tests scores with p &lt; 0.001, respectively. However, the mean increment was higher in virtual microscopy (38.51%) than in optical microscopy (35.08%). Furthermore, the virtual microscopy group had a significantly higher satisfaction score towards the learning tool than the optical microscopy group, p = 0.008. The knowledge acquisition of the virtual microscopy group was equal to the optical microscopy group as they were shown to have a similar improvement in the test scores, comprehension level and learning ability. However, students were nonetheless satisfied with the usage of virtual microscopy as a learning tool.
Mobile Phone Educational Applications: Their Importance in Academic Learning During Covid 19 Pandemic

The deadly infectious disease Coronavirus, also known as Covid19, has had a profound impact on the global economy. This tragedy is also being staged in the education community, and this concern is likely to resonate in the global education community. The outbreak of the Covid19 pandemic forced several schools and universities to close quickly. Various elements on the earth are affected, and there are issues that may surface for a full semester, or other issues that will be lost in the future. Flexibility is another attractive aspect of online learning; students can arrange their own or arrange time to apply for courses online. Face-to-face meetings combine with technology to form a combined learning and recovery classroom; this learning environment will increase the learning potential of students. Students can learn anytime, anywhere, and develop new skills in the way they ultimately learn from the grave. Government also recognizes that online learning is increasingly essential in this dynamic world. The severe emergence of the new crown virus could create another argument for upgrading, as this altitude can be a problem in times of crisis.
Keeping Curriculum Relevant: Identifying Longitudinal Shifts in Computer Science Topics through Analysis of Q&amp;A Communities

Keeping up with new knowledge being produced in computing related domains is a difficult task given the pace of change in the field. Specifically, in domains that are undergoing a lot of innovation, such as Data Science or Artificial Intelligence, updating curricula is not easy. Yet, there is a need to be cognizant of new topics in order to create and update curricula and keep it relevant. In this paper we present an innovative approach to help educators keep a better track of changes in a domain and be able to map their curricula objectives to emerging topics and technologies. We leverage Q&A sites, Reddit and StackExchange, which provide a useful online platform for sharing of information and thereby generate a valuable corpus of knowledge. We use Data Science as a case study for our work and through a longitudinal analysis of these sites we identify popular topics and how they have changed over time. We believe innovations such as these are essential for improving computer science education and for bridging the workplace-school divide in teaching of newer topics. Our unique and innovative approach can be applied to other CS topics as well.
Apply sentiment analysis technology in social Media as a tool to enhance the effectiveness of e-government : Application on Arabic and Mauritanian dialect ‘HASSANIYA’

Sentiment analysis is a technique that allows you to know the feeling behind the writing and to have an overview of the public opinion on certain subjects. In this work, we will adapt a business intelligence approach to create an intelligent dashboard on behalf of government to oversee, track and analyze citizen interactions with government decisions and achievements. Measure the degree of general satisfaction with the performance of government policy as a whole and with the performance of different sectors in particular through real comments that reflect the opinion of citizens on Facebook, written and shared on social networks and more particularly on the pages of the Mauritanian government in English, French, Arabic and the HASSANIYA dialect. The approach proposed will give on the one hand, a clear vision by making a group of citizens’ comments on current topics published on government Facebook pages, then indicate the feelings of each of them by classifying them according to the polarity of comments. On the other hand, it allows to save in a data warehouse only the relevant data which will then be consulted by the government sectors with the possibility of accessing only the data that concerns them, which will result in the speed of the analysis process. Based on Business Intelligence. This article represents a contribution to the sentiment analysis approach applied in Business Intelligence. This important study can determine the level of general satisfaction with the sectoral policies of the government, and measure the conformity of the government policies with the aspirations of the citizens.
Feature Extraction Method for Cross-Architecture Binary Vulnerability Detection

Vulnerability detection identifies defects in various commercial software. Because most vulnerability detection methods are based on the source code, they are not useful if the source code is unavailable. In this paper, we propose a binary vulnerability detection method and use our tool named BVD that extracts binary features with the help of an intermediate language and then detects the vulnerabilities using an embedding model. Sufficiently robust features allow the binaries compiled in cross-architecture to be compared. Consequently, a similarity evaluation provides more accurate results.
Novel 2-D Histogram-Based Soft Thresholding for Brain Tumor Detection and Image Compression

The objective of image compression is to extract meaningful clusters in a given image. Significant groups are possible with absolute threshold values. 1-D histogram-based multilevel thresholding is computationally complex and reconstructed image visual quality comparatively low because of equal distribution of energy over the entire histogram plan. So, 2-D histogram-based multilevel thresholding is proposed in this paper by maximizing the Renyi entropy with a novel hybrid Genetic Algorithm, Particle Swarm Optimization and Symbiotic Organisms Search (hGAPSO-SOS), and the obtained results are compared with state of the art optimization techniques. Recent study reveals that PSNR fails in measuring the visual quality because of mismatch with the objective mean opinion scores (MOS). So, we incorporate a weighted PSNR (WPSNR) and visual PSNR (VPSNR). Experimental results examined on Magnetic Resonance images of brain, and results with 2-D histogram reveal that hGAPSO-SOS method can be efficiently and accurately used in multilevel thresholding problem.
Using Gamification to Improve Literacy Skills

Students in the 21st century are learning by doing and playing. Teachers need to incorporate technology into everyday tasks. Games assist students in the learning process. Once students have learned a task through the playing process, they will remember this much easier and longer than simply doing a worksheet. Research shows students enjoy interactive and engaging activities and will choose these types of activities over pencil and paper types of activities. Teachers must prepare students for the future which involves more critical thinking and technological types of skills. Traditional teaching methods and styles have underused technology tools and pedagogical methods. The 2020 Covid pandemic and remote learning delivery style assisted teachers in developing new tools and methods to reach and teach all students with various and diverse needs.
DoMoBOT: An AI-Empowered Bot for Automated and Interactive Domain Modelling

Domain modelling transforms informal requirements written in natural language in the form of problem descriptions into concise and analyzable domain models. As the manual construction of these domain models is often time-consuming, error-prone, and labor-intensive, several approaches already exist to automate domain modelling. However, the current approaches suffer from lower accuracy of extracted domain models and the lack of support for system-modeller interactions. To better assist modellers, we introduce DoMoBOT, a web-based Domain Modelling BOT. Our proposed bot combines artificial intelligence techniques such as natural language processing and machine learning to extract domain models with higher accuracy. More importantly, our bot incorporates a set of features to bring synergy between automated model extraction and bot-modeller interactions. During these interactions, the bot presents multiple possible solutions to a modeller for modelling scenarios present in a given problem description. The bot further enables modellers to switch to a particular solution and updates the other parts of the domain model proactively. In this tool demo paper, we demonstrate how the implementation and architecture of DoMoBOT support the paradigm of automated and interactive domain modelling for assisting modellers.
A deep reinforcement learning algorithm based on modified Twin delay DDPG method for robotic applications

This paper proposes a deep reinforcement learning algorithm for autonomous robotics, in which we modify twin delay deep deterministic policy gradient (TD3) to adapt for autonomous robots with higher degree freedom in movement. To provide a robot with free movement in the 2D space without collisions against some obstacles, such as wall, a robot is equipped with three cameras. The images captured by camera are used to train Convolutional Neural Networks (CNN) to understand environment with collisions or not-collisions. We added two additional parameters, observation&#x2019; O&#x2019;, which are images obtained from cameras, and degrees of turns&#x0027; deg&#x2019; into the original TD3&#x2019; s parameters composed of four values: [state&#x0027;s&#x0027;, reward &#x2018;r&#x2019;, action &#x2018;a&#x2019; and next-state&#x0027;s&#x0027; &#x2018;]. To determine a next action with higher reward from the observation, two additional Neural Networks are constructed, being the first one determines an action from observation and the second one determines degree of turn from the observation and the action. The simulation results under three environments constructed by CoppeliaSim show a good performance of the proposed algorithm, reaching the target with higher rewards, even though the environments are unknown by robots.
Combining Sub-symbolic and Symbolic Methods for Explainability

Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed to provide insights into the GNN decision making process. These are first important steps on the way to explainability, but the generated explanations are often hard to understand for users that are not AI experts. To overcome this problem, we introduce a conceptual approach combining sub-symbolic and symbolic methods for human-centric explanations, that incorporate domain knowledge and causality. We furthermore introduce the notion of fidelity as a metric for evaluating how close the explanation is to the GNN's internal decision making process. The evaluation with a chemical dataset and ontology shows the explanatory value and reliability of our method.