Reviewerly/exHarmony huggingface dataset:
Hierarchical and Non-Hierarchical Multi-Agent Interactions Based on Unity Reinforcement Learning

The open-source Unity platform, where agents can be trained using hierarchical or non-hierarchical reinforcement learning, supports the use of games and simulations as environments for multiple-agent interactions. In this demonstration, we present hierarchical and non-hierarchical multi-agent interactions based on Unity reinforcement learning, specifically, hierarchical reinforcement learning that sets different levels of agent's observations to achieve the goal. We created four multi-agent scenarios in the Unity environment, namely, Crawler, Tennis, Banana Collector, and Soccer, to test the interaction performances of hierarchical and non-hierarchical reinforcement learning. The simulation-interaction performances show that hierarchical reinforcement learning can be applied to multi-agent environments and can compete with agents trained via non-hierarchical reinforcement learning. The demonstration video can be viewed at the following link: https://youtu.be/YQYQwLPXaL4.
A Qualitative Approach to Composing Value-Aligned Norm Systems

Research in normative multi-agent systems has explored several approaches to compute the norm system (set of norms) required to make coordination possible. More recently, norm selection supposes an already available collection of norms from which to select a norm system to enact. A key aspect in this selection process is the consideration of moral values together with preferences among them, thus the selection follows the principle: the more preferred the values promoted by a norm system, the more preferred the norm system. Unfortunately, norm selection follows a quantitative approach despite the qualitative nature of the information available to the decision maker. In this paper we provide a novel qualitative approach to norm selection by formalising the process to infer a norm system ranking from the value preferences. We provide an encoding of this qualitative problem into a linear program and show that their solutions are equivalent.
Civil Litigation Principles Transformation in Sight of Digitalization Impact on Legal Culture

The introduction of information technology in the legal activities of state bodies of the Russian Federation reflects most in administration of justice. Use of information and technological tools in the activities of justice bodies inevitably raises the question of how the so-called “electronic justice” relates to the model of legal proceedings defined in the law, and what is the relationship between the legal principles of the administration of justice and the above-mentioned set of tools. Of greatest interest is the consideration of this issue within the framework of the economic justice model, since it is crucial in ensuring the activity of arbitration courts that the most noticeable results of introducing modern technologies have been achieved there.
Geometry to the Rescue: 3D Instance Reconstruction from a Cluttered Scene

3D object instance reconstruction from a cluttered 2D scene image is an ill-posed problem. The main challenge is posed by the lack of geometric information in color images and heavy occlusions that lead to incomplete shape details. To deal with this problem, existing works on 3D instance reconstruction directly learn the mapping between the intensity image and the corresponding 3D volume model. Different from these works, we propose to explicitly incorporate 2.5D geometric cues, such as the surface normal, relative depth, and height, while generating full 3D shapes from 2D images. With an intermediate step focused on estimating these 2.5D geometric features, we propose a novel convolutional neural network design that progressively moves from 2D to full 3D estimation. Our model automatically generates instance-specific surface normal maps, relative depth, and height that are compactly encoded within our network design and consequently used to improve the 3D instance reconstruction. Our experimental results on the large-scale synthetic SUNCG dataset and the real-world NYU depth v2 dataset demonstrate the effectiveness of the proposed approach where it beats the state-of-the-art Factored3D network [15].
Insomnia During the COVID-19 Outbreak in Brazil

Introduction: The outbreak of COVID-19 among humans in Brazil and its spread around the world is strongly impacting mental health. Health problems such as anxiety, depression, insomnia and fear were the most related to. High levels of stress during the day can impair quality and time of sleep.&#x0D; Method: The purpose of this article was to update, through a bibliographic search, the effects of the pandemic on sleep disorders, with insomnia being the most related. A systematic search had been carried out in the main databases: Lilacs, Bireme and PubMed. The languages used in the search were Portuguese and English, from articles published this year. Using the following keywords: Sleep quality, COVID-19, Insomnia, Cognitive behavioral therapy.&#x0D; Discussion: The concern about financial and political uncertainties, feelings of impotence and fear of self-contamination and of family members are factors that corroborate with the worsening of the sleep quality of the population and health professionals’ working on the front lines of the fight against the pandemic, leading to increased cases of insomnia. Behavioral Cognitive Therapy, hypnotherapy and social interaction mediated by Web conferencing tools are the best ways to treat these symptoms at the moment. Even so, the brazilian literature on the subject is extremely scarce, making it an extremely relevant subject for future research.
DEVOPS PROCESS MODEL ADOPTION IN SAUDI ARABIA: AN EMPIRICAL STUDY

Many software development organizations are starting adopting the Development-Operation (DevOps) to foster better collaboration between development and operations teams, in order to improve the software development process quality and efficiency; therefore, it’s very important to measure the adoption of the DevOps for these organizations. Maturity models are used as a tool to assess the effectiveness of organizational processes on adopting certain practices and identify what capabilities they need to acquire next in order to improve their performance and reach higher maturity level. However, there is few DevOps maturity models recently have been emerged as means to assess DevOps adopted practices. This research aims to conduct an empirical field study to assess the DevOps adoption level in seven Saudi organizations using one of the published DevOps maturity models namely Bucena model. The findings show that the Saudi organizations are ranging from the third level (Defined) to the fourth level (Managed) in their adoption. This shows promise in the Saudi organization adoption of DevOps as no organization of the surveyed ones recorded in lower levels.
Sentiment Analysis of Regional Languages Written in Roman Script on Social Media

Increasing popularity of smart phones, economic Internet packages and social media have changed the way of life the people are living. Social media plays an important role in today’s generation. They use it for their own promotion, entertainment and even to an extent they share their sorrows and joys on social media. At the same time, the way of expressing themselves on social media has also changed a lot. People prefer to express themselves in their own regional language. As a result, the way of writing on social media has been changed. This has also resulted into the emergence of new languages which is blend of any regional language and English language. The opinions are written in regional language but in Roman script. With the availability of such massive opinionated data on social media, it is of no use if such data is not organized and used properly. This huge data may be used for the machines to learn and make them capable of taking the decisions like human beings. This paper discusses about the importance of opinion mining in blend of two languages and hence proposes a neural network architecture that can be trained on such opinions as negative or positive.
Double String Tandem Repeats

A tandem repeat is an occurrence of two adjacent identical substrings. In this paper, we introduce the notion of a double string, which consists of two parallel strings, and we study the problem of locating all tandem repeats in a double string. The problem introduced here has applications beyond actual double strings, as we illustrate by solving two different problems with the algorithm of the double string tandem repeats problem. The first problem is that of finding all corner-sharing tandems in a 2-dimensional text, defined by Apostolico and Brimkov. The second problem is that of finding all scaled tandem repeats in a 1d text, where a scaled tandem repeat is defined as a string UU' such that U' is discrete scale of U. In addition to the algorithms for exact tandem repeats, we also present algorithms that solve the problem in the inexact sense, allowing up to k mismatches. We believe that this framework will open a new perspective for other problems in the future.
Application of Hidden Markov Models in Speech Command Recognition

In this study, vector quantization and hidden Markov models were used to achieve speech command recognition. Pre-emphasis, a hamming window, and Mel-frequency cepstral coefficients were first adopted to obtain feature values. Subsequently, vector quantization and HMMs (hidden Markov models) were employed to achieve speech command recognition. The recorded speech length was three Chinese characters, which were used to test the method. Five phrases pronounced mixing various human voices were recorded and used to test the models. The recorded phrases were then used for speech command recognition to demonstrate whether the experiment results were satisfactory.
Head Pose Estimation of Face: Angle of Roll, Yaw, and Pitch of the Face Image

Face recognition has achieved immense success in near-frontal images, but in case of pose variation, it remains an unsolved problem for better efficiency. Many researchers have proposed different approaches for pose-invariant face recognition (PIFR) but still, need to explore more. Pose estimation is useful in pose-invariant face recognition and computer vision. PIFR needs a proper pose estimation to achieve better performance. The head pose estimation can be beneficial for synthesized and reconstruction of the frontal face image. In this paper, we have proposed a geometric approach to estimate the angle of roll, yaw, and pitch of the face image. This estimation will be useful to improve the correlation with frontal images and also it will help to reconstruct a frontal face image. Also, an estimated angle can be used to improve the performance of face recognition. ...
Optimization of Local Ordering Technique for Nearest Neighbour Circuits

Most quantum architectures restrict qubit interactions. They allow a qubit to interact with another qubit only if they are directly connected, and this constraint is the nearest neighbour (NN) constraint. If the interacting qubits are not adjacent, we need to insert swap gates appropriately to make them adjacent. Since the insertion of swap gates increases the circuit cost, a minimal number of swaps has to be performed. This paper illustrates the possibility of swap gate reduction for 2D NN circuits by better re-ordering of qubits using a multi-window look-ahead approach. Using this technique, near optimal solutions for NN circuit conversion are obtained. Experimental evaluation shows the effectiveness of our proposed local re-ordering algorithm for the reduction of swap requirements. We have compared our results with the most recent results, and a significant improvement is observed, with a maximum of 37.5% swap gate reduction.
Comparative Survey of Digital Image Steganography Spatial Domain Techniques

In the modern world, confidentiality is a major issue in communication, so to increase the security cryptography and steganography came into the picture. Steganography techniques provide security from attacker. In this paper, we study some different research papers on various image steganography techniques. Steganography is hiding information in some other information. Nowadays, steganography used for copyright ownership by watermarking on digital data and also used to share asymmetric algorithm key sharing and also contains a survey on theory of color based on human vision system (HVS).
Bounded Suboptimal Path Planning with Compressed Path Databases

Compressed Path Databases (CPDs) are a state-of-the-art method for path planning. They record, for each start position, an optimal first move to reach any target position. Computing an optimal path with CPDs is extremely fast and requires no state-space search. The main disadvantages are overhead related: building a CPD usually involves an all-pairs precomputation, and storing the result often incurs prohibitive space overheads. Previous research has focused on reducing the size of CPDs and/or improving their online performance. In this paper we consider a new type of CPD, which can also dramatically reduce preprocessing times. Our idea involves computing first-move data for only selected target nodes; chosen in such a way as to guarantee that the cost of any extracted path is within a fixed bound of the optimal solution. Empirical results demonstrate that our new bounded suboptimal CPDs improve preprocessing times by orders of magnitude. They further reduce storage costs, and compute paths more quickly – all in exchange for only a small amount of suboptimality.
Workshop 2 Blockchain in Education

The education sector is an important one as the finance and healthcare sectors. Verification and security are appointed as an essential concern on universities campuses and after students leave. Data infringements target the records of students to steal data that can be sold by hackers or employed to make unauthentic identities. The blockchain technology (BCT) can be utilised to protect students records and identities; moreover, to make these attacks ineffective. Since data digitisation is becoming widely used by the school from nursery to university, blockchain technology can be vital in ensuring the privacy of students. Furthermore, the selected course information and the learning interactions graph can be stored in a ledger to identify learning patterns, enhance teaching, and build up new learning models. Over and above, BCT can be used to plan the strategy of the funding and to reward students for their achievements. Moreover, BCT can be employed to identify how the university can be restructured according to employers and students' new requirements. To summarise, BCT can boost the system of education in different methods. This technology is ideal for secure information networking, sharing, and storage. Also, the utilizing of BCT can be a benefit in safer, easier, and faster processes.
Generative Adversarial Imitation Learning from Failed Experiences (Student Abstract)

Imitation learning provides a family of promising methods that learn policies from expert demonstrations directly. As a model-free and on-line imitation learning method, generative adversarial imitation learning (GAIL) generalizes well to unseen situations and can handle complex problems. In this paper, we propose a novel variant of GAIL called GAIL from failed experiences (GAILFE). GAILFE allows an agent to utilize failed experiences in the training process. Moreover, a constrained optimization objective is formalized in GAILFE to balance learning from given demonstrations and from self-generated failed experiences. Empirically, compared with GAIL, GAILFE can improve sample efficiency and learning speed over different tasks.
UAS Traffic Management in Low-Altitude Airspace Based on Three Dimensional Digital Aerial Corridor System

With the rapid development of the unmanned aerial system, the flight environment in low-altitude airspace is becoming more and more complex, which causes some potential security risks. A variety of air traffic management for low-altitude drones have emerged, among which three-dimensional aerial corridor system is a typical kind of low-altitude supervision mechanism with artificial intelligent theory. Firstly, the principle of three-dimensional aerial corridor system was illustrated. Then, the process of low-altitude traffic management based on aerial corridor system was described, the mainstream anti-drone technology and the visualization technology were discussed, and the relative regulation for air traffic management platform for low-altitude security industry was analyzed. Finally, the future work for air traffic management platforms based on aerial corridor systems was explored correspondingly, which is of positive significance for the further development of low-altitude security industry.
Inductive Learning-Based SPARQL Query Optimization

In any query optimization, the goal is to find the execution plan which is expected to return the result set without actually executing the query or subparts with optimal cost. The users are not expected to write their queries in such a way so that they can be processed efficiently; rather it is expected from system to construct a query evaluation plan that minimizes the cost of query evaluation. SPARQL is used for querying the ontologies, and thus, we need to implement SPARQL query optimization algorithms in semantic query engines, to ensure that query results are delivered within reasonable time. In this paper, we proposed an approach in which the learning is triggered by user queries. Then, the system uses an inductive learning algorithm to generate semantic rules. This inductive learning algorithm can automatically select useful join paths and properties to construct rules from an ontology with many concepts. The learned semantic rules are effective for optimization of SPARQL query because they match query patterns and reflect data regularities.
An Improved Non-Parametric Method for Multiple Moving Objects Detection in the Markov Random Field

Detecting moving objects in the stationary background is an important problem in visual surveillance systems. However, the traditional background subtraction method fails when the background is not completely stationary and involves certain dynamic changes. In this paper, according to the basic steps of the background subtraction method, a novel non-parametric moving object detection method is proposed based on an improved ant colony algorithm by using the Markov random field. Concretely, the contributions are as follows: 1) A new nonparametric strategy is utilized to model the background, based on an improved kernel density estimation; this approach uses an adaptive bandwidth, and the fused features combine the colours, gradients and positions. 2) A Markov random field method based on this adaptive background model via the constraint of the spatial context is proposed to extract objects. 3) The posterior function is maximized efficiently by using an improved ant colony system algorithm. Extensive experiments show that the proposed method demonstrates a better performance than many existing state-of-the-art methods.
Automatic Detection of Ship Based on Rotation Invariant RetinaNet

Ship detection in the aerial image is an active yet challenging in remote sensing image processing. Due to the difficulties of locating the arbitrarily rotated ships and the complexity of the background around the ship, the existing deep learning-based object detection algorithm is difficult to accurately identify the ship target. For multi-angle ships, the horizontal bounding box contains not only ship objects but also a lot of irrelevant backgrounds, they are learned by the deep convolutional network as a learning object, and this leads to the common misalignment between the final classification confidence and localization accuracy. In this paper, we propose a new module called rotation RetinaNet (RRNet) to handle this problem. Firstly, we start from the popular one-stage RetinaNet approach, with ResNet50 as a basic network. Then, we apply a rotation head to RetinaNet to guarantee the rotation invariance of the model. Finally, we add an angular loss to the original loss so that the model can learn the angular offset of the bounding box. As a consequence, the proposed RRNet achieves high performance on the open-source DOTA datasets.
Teaching Network Covert Channels using a Hands-on Approach

Nowadays, cybercriminals are using a diversity of techniques to keep their communication channels and exchanged messages secret and hidden. Information hiding techniques, such as network covert channels, are examples of techniques used to preserve secrecy and guarantee hidden communication channels that cannot be captured or discovered by security and surveillance state agencies. The inclusion of network covert channels in information security curricula is therefore considered a valuable extension. However, information hiding techniques are not covered sufficiently in most information security curricula. In addition, there are few educational publications that discuss teaching approaches of such techniques. This paper contributes to fill this gap by providing a method for teaching network covert channels using a hands-on lab based educational approach. The proposed hands-on lab activities can be used in didactic environments to build and detect network covert channels. The impact of offering the hands-on lab exercises on the students’ performance is discussed. Moreover, anonymous questionnaires were administered to students, who participated in the hands-on lab exercises, to evaluate student’s self-assessment of acquired skills and identify their overall satisfaction level about the offered hands-on lab exercises.
XBee-Based Sensor Nodes

Thus far into our journey of sensor networks, we have discovered how sensor networks can be formed, the type of nodes, and their roles that comprise a sensor network, and we’ve spent some time learning about XBee modules and how to program them with MicroPython.
Quaternion Polar Complex Exponential Transform and Local Binary Pattern-Based Fusion Features for Content-Based Image Retrieval

The presented work is a sincere effort to exhibit content-based image retrieval (CBIR) system utilized for extracting images from large databases. It makes use of image features like texture and color. Local binary pattern (LBP)-based operator brings about the information related to image texture by taking into consideration the surrounding pixel values. In spite of having its own advantages, this feature is not that superior at capturing the image’s information related to color. The present work rectifies this disadvantage by incorporating an extra color feature by the name quaternion polar complex exponential transform (QPCET) in addition to the LBP-based feature in the image retrieval system. The integrated QPCET- and LBP-based CBIR system exhibits better average retrieval efficiency compared to other accessible techniques tested on different benchmark databases.