Reviewerly/exHarmony huggingface dataset:
Explainable Deep Reinforcement Learning for Aircraft Separation Assurance

Aircraft separation assurance is an extremely challenging task in a complex airspace. Deep Reinforcement Learning (DRL) was used to develop aircraft separation assurance models in our previous works. Though these models have shown promising performance, the DRL agents make decisions in a nontransparent way, limiting their use in safety-critical applications. In order to build a trustworthy DRL model for aircraft separation assurance, we propose a novel framework to provide stepwise explanations of agent behaviors. At a high level, our framework distills a complex DRL model into a shallow Soft Decision Tree (SDT) and uses the distilled knowledge in SDT to provide visual explanations of agent behaviors in each step. Specifically, the proposed framework incorporates (1) a distillation module to transfer knowledge from DRL policies to tree-structured policies with clear decision paths and (2) a visualization module with a graphical interface to provide visual explanations in real time. With our proposed framework, the information is extracted from the distilled SDT and illustrated with the interface. Through extensive numerical experiments in an open-source air traffic simulator with challenging environment settings, our results show that the proposed framework can support explainable decision-making for aircraft separation assurance.
Performance of End-to-End vs Pipeline Spoken Language Understanding Models on Multilingual Synthetic Voice

This work conducts a comparative investigation of two architectures in the domain of Spoken Language Understanding (SLU), which were evaluated on a synthesized corpus of three languages: Modern Standard Arabic (MSA), French, and English. The first architecture employs a simple SLU system based on classical machine learning algorithms (E2E SLU), whereas the second architecture (Pipeline SLU) merges the textual output of a speech recognition system (ASR) with that of a textual classification system by transmitting it to a ”Natural Language Understanding” (NLU) model, allowing us to compare the predictions of the two systems. The obtained results were encouraging where we found that the Pipeline approach has given us better results than the E2E approach
Trust, Ethics, Consciousness, and Artificial Intelligence

As artificial intelligence (AI) continues to proliferate across manufacturing, economic, medical, aerospace, transportation, and social realms, ethical guidelines must be established to not only protect humans at the mercy of automated decision making, but also autonomous agents themselves, should they become conscious. While AI appears "smart" to the public, and may outperform humans on specific tasks, the truth is that today’s AI lacks insight beyond the restricted scope of problems to which it has been tasked. Without context, AI is effectively incapable of comprehending the true nature of what it does and is oblivious to the reverberations it may cause in the real world should it err in prediction. Despite this, future AI may be equipped with enough sensors and neural processing capacity to acquire a dynamic cognizance more akin to humans. If this materializes, will autonomous agents question their own position in this world? One must entertain the possibility that this is not merely hypothetical but may, in fact, be imminent if humanity succeeds in creating artificial general intelligence (AGI).If autonomous agents with the capacity for artificial consciousness are delegated grueling tasks, outcomes could mirror the plight of exploited workers, result in retaliation, failure to comply, alternative objectives, or breakdown of human-autonomy teams. It will be critical to decide how and in which contexts various agents should be utilized. Additionally, delineating the meaning of trust and ethical consideration between humans and machines is problematic because descriptions of trust and ethics have only been detailed in human terms. This means autonomous agents will be subject to anthropomorphism, but robots are not humans, and their experience of trust and ethics might be markedly distinct from humans. Ideally speaking, to fully entrust a machine with human-centered tasks, one must believe that such an entity is reliable, competent, has the appropriate priorities in decision-making, and can comprehend the consequences of actions taken. Such qualities may depend on conscious awareness—but without first deciphering what consciousness is in the first place, humans may fail to accurately identify consciousness in machines.This work explores the foundations of consciousness from the perspective of evolutionary biologists, neuroscientists, and philosophers, and strives to position degrees of consciousness in a trust and ethical consideration framework to guide AI usage and research. To mitigate foreseeable risks in autonomy, the authors seek to spark dialogue and preventative action, so proper legal and operational requirements can be established prior to any agent acquiring even an inkling of consciousness. If implemented correctly, such measures may reduce the likelihood of unintentional damage and help to secure a future of continued collaboration and shared success between humans and machines.
FPGA Image Stitching Design Based on Improved SURF Algorithm

With the development of FPGA and computer vision technology, the image stitching system not only requires fast and accurate but also puts forward higher requirements for structure and power consumption. In traditional stitching, the SURF algorithm is high but has a complicated and time-consuming problem, so it proposes a FPGA image stitching system design based on improving the SURF algorithm. In order to improve the detection speed of the feature point, integrate it into the FAST algorithm, and use the improved Ransac algorithm to match the point of the matching point. Finally, the stitching image is fused with a weighted method. The entire system design uses SOC FPGA to achieve, combined with software and hardware, and gives full play to the advantages of FPGA parallel computing and the flexible control of ARM processors. The results show that this splicing system is small in size and can stabilize stitching with higher accuracy. It has greater theoretical value and is more suitable for embedded fields.
Relational Database and NoSQL Inspections using MongoDB and Neo4j on a Big Data Application

The importance of big data, one of the most popular and researched topics of today, has been a subject of constant debate. Big data appear in almost every aspect of our lives, such as healthcare, education, shopping, social media, and industry; however, its storage and processing is not very efficient when using traditional methods. Therefore, the aim of this study is to obtain big data using a novel social shopping application that collects data from its users. The application is designed to collect information about people, products, and friendships among people, as well as product relationships, and post creations, and also enables setting up various options to mark products, such as like, buy, have, or help. The data gathered by the system is analyzed using both relational and non-relational databases, and the performance of these databases is then compared using specific queries, to reveal the model which performs better and more efficiently. Three different database models were designed and implemented for the system; a relational database, a document database, and a graph database. As a result of the experiments, there is no single model that is superior to the others as all three databases have their advantages and disadvantages. Therefore, the database selection should be decided based on the application domain.
Systems for Remote Access to FPGA Development Boards: Review

This report presents the study results of remote access systems for FPGA development hardware during the period 2008–2021. The possibilities and development of these systems are considered. The study made it possible to define the requirements and the expected final results in the implementation of a new system for remote access to FPGA development hardware, which is in the process of implementation in the “Automated design” laboratory at the KST department of the Technical University - Gabrovo.
Classifying Turkish Trade Registry Gazette Announcements

Turkish Trade Registry Gazette is an important source of information in many sectors such as banking and telecommunication. Although the newspaper is publicly available, the data is hard to acquire, and announcements are offered in image format. It is possible to search for a specific announcement a company has, but there exist many other unrelated announcements in the image returned. This poses multiple challenges in the way of information extraction. Due to the structure of the documents in these images, it is hard to perform OCR directly. Moreover, even in the case where the text is extracted, the announcement boundaries must be detected to split the announcements within the page. Once the announcements are extracted, the announcement of the searched company should be matched. Since no information regarding the surrounding announcements is given as a result of the query, these announcements should also be categorized to detect any events of interest other companies may have. In this work, we address all of these problems and present a pipeline that includes image processing, OCR, announcement splitting, and document classification steps.
Modern Teaching Methods Used for Representing the Topic “Implementations of Carry-Skip Adders”

The paper describes the application of modern teaching methods for representing the topic “Implementations of Carry-Skip Adders” based on various computer tools, such as MS Excel, Logisim, and ISE Project Navigator, intended for the undergraduate students from the specialty “Computer Systems and Technologies” studying the course “Digital Electronics” at the University of Ruse “Angel Kanchev”.
Application of Face Recognition Technology in Mobile Payment

The current face recognition technology has rapidly come into the public life, from unlocking cell phone face to mobile payment, which has brought a lot of convenience to life. However, it is undeniable that it also brings security challenges. Based on this paper, we will discuss the risks of face recognition in the mobile payment and put forward relevant suggestions.
Efficient Deep Learning Methods for Automated Visibility Estimation at Airports

The goal of this paper is to design and develop new visibility estimation methods for airports based on deep neural networks. Their input consists of partially annotated image meteorological data, especially camera images from automatic meteorological stations. These have been processed, normalized, cleaned, and annotated according to meteorological observation codes and geographic data, with manually entered values. We have experimented with different methods of image processing, leveraging deep learning in order to find and finetune a lean, yet effective neural network architecture capable of providing the desired visibility estimation quality.
Single-shot structured light projection profilometry with SwinConvUNet

Structured light profilometry (SLP) is now widely utilized in noncontact three-dimensional (3D) reconstruction due to its convenience in dynamic measurements. Compared with classic fringe projection profilometries, multiple deep neural networks are proposed to demodulate or unwrap the fringe phase, and these networks utilize convolution layers to extract local features while omitting global characteristics. In this paper, we propose SwinConvUNet, a deep neural network for single-shot SLP that can extract local and global features simultaneously. In the network structure design, convolution layers are applied in shallow layers to extract local features, whereas transformer layers extract global features in deep layers, and an improved loss function by combining gradient-based structural similarity is employed to improve reconstruction details. The experimental results demonstrate that SwinConvUNet is more effective than the U-net model at decreasing learnable parameters while maintaining 3D reconstruction accuracy.
Automatic Vertical Parking Path Planning Based on Clothoid Curve and Stanley Algorithm

In order to improve the convenience and safety of vehicles in the process of automatic parking, an automatic vertical parking path planning method based on clothoid curve is proposed. When the environmental information and vehicle information are known, such as parking space position and size, vehicle position and heading angle, etc., considering the obstacle avoidance constraints and spatial constraints of the vehicle, a curvature continuous driving path is planned by using the clothoid curve to park the vehicle into the vertical parking space, and the vehicle is tracking controlled according to the Stanley algorithm. Considering the uncertainty of driver's behavior, the vehicle has a random initial heading angle before parking. For different initial heading angles, the vertical parking path planning of the vehicle is carried out, and its path planning and tracking control are simulated. The simulation results show that when the vehicle is in different initial positions and heading angles, this path planning method can better plan a parking path, and the tracking effect is good.
Utilizing Sensitive Features for Image Tampering Detection

Image tampering has brought a great negative impact on society. People who do not know the truth are easy to be misled and used by people with intentions. Its impact on society has attracted extensive attention of Chinese and foreign researchers. With the development of image editing technology, image tampering detection has made remarkable achievements from the initial naked eye recognition to machine learning recognition of tampered images, and then to the excellent characteristics of deep learning algorithms in processing images in complex image tampering detection in recent years. In view of the outstanding performance of Faster RCNN network in the field of target detection technology, this paper studies an image algorithm and detection method based on Faster R-CNN network, and adds position sensitive candidate area pooling on this basis. In each rectangular cell, the pixel values of a fixed number of sampling points at a fixed position are calculated for average pooling. The input tampered image is feature extracted through the feature extraction network. After the extracted feature map passes through the RPN layer, the tampered area candidate frame is automatically generated for subsequent classification and regression. Experimental results show that this method greatly improves the effect of image tampering detection.
A Note On Borrowing Constant Function Market Maker Shares

Constant function market makers (CFMMs) such as Uniswap, Balancer, and Curve, among many others, make up some of the largest decentralized exchanges on smart contract platforms like Ethereum. As the amount of capital deposited in these protocols has grown, improving capital efficiency for liquidity providers (LPs) has become an increasingly important challenge. One way to improve efficiency is to allow LPs to borrow Ether or USD against their shares in a CFMM protocol. In this note, we investigate the security and capital efficiency of allowing such lending. We provide sufficient conditions for LP borrowing to be at least as secure and capital efficient as direct borrowing in Aave/Compound. Furthermore, we show that the exposure taken by CFMM lenders can be replicated via barrier options, allowing for risks to be hedged. Finally, we show that the payoff of borrowed CFMM LP shares replicates bounded convex payoffs. Combined, these results suggest that CFMM lending is a safe mechanism for improving capital efficiency.
Byzantine Fault Tolerance in the Age of Blockchains and Cloud Computing

BFT is a generic technique used for ordering transactions on a distributed system even if a fraction of the processes are controlled by a malicious adversary. BFT is widely known as the model for permissioned blockchains, and is increasingly used in various permissionless blockchains. BFT has also been used in various cloud computing scenarios, such as blockchain-as-a-service, the multi-cloud scenario, and their fault-tolerant components.
Simultaneous Smoothing and Sharpening Using iWGIF

Smoothing and sharpening are two fundamental operations in image processing, and they are usually conducted separately. In this paper, an improved weighted guided image filter (iWGIF) is first proposed to enhance existing guided filters. A content adaptive smoothing and sharpening (CASS) algorithm is then presented by using coefficients of the iWGIF intelligently. A simple despeckling algorithm is finally introduced to simultaneously smoothen homogeneous regions and preserve heterogeneous regions of synthetic-aperture radar images. Experimental results validate all the proposed algorithms.
Peculiarities of Virtual Internationalzation of Novosibirsk Region Higher Education Institutions in the Context of Modern Agenda

The article foregrounds the issue of the development of virtual internationalization on the example of universities in Novosibirsk region. The article focuses on the key trends in the export of Russian higher education. The purpose of the article is to analyze the websites of the universities in Novosibirsk region as a key mechanism for recruiting international students and to develop recommendations for their modernization. Methodology. The study was conducted on the basis of the methodology of the RIAC report “Digital Internationalization: English-language Internet Resources of Russian Universities” (2015), which implies the identification of 10 target groups of potential audience of a university website and 16 semantic blocks filled with specific questions which allow to assess the degree of virtual internationalization. The binary evaluation system (yes/no) enables the formation of a cumulative score and conducting a comparative analysis of universities in the region both in terms of the overall rating and within the semantic blocks. The result of the implementation of this methodology is an analytical report on the features of the virtual internationalization of universities in modern conditions on the example of the Novosibirsk region. The developed recommendations are useful both for the international departments of universities and their administration, and for the regional ministries of education, since they might serve as a basis for developing a strategy for improving the websites of universities and thus favoring the export of higher education in the Russian Federation coming not only from capital universities, but also in the regions. In conclusion, it is admitted that self-positioning of a university in a virtual environment in the context of education export cannot be overestimated, since today the success in increasing the international students’ inflow to the regions of the Russian Federation depends on the virtual internationalization.
Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing

Natural language processing (NLP) is one of the most energizing topics in artificial intelligence (AI), and it has already spawned technologies such as voice assistants, chatbots, and translators, among many other everyday applications. NLP is a discipline of AI, computer science, and linguistics that is intimately linked to ontologies. Ontologies enable the interpretation of natural (human) language by providing an explicit and formal mechanism to analyze, integrate, and share data. This chapter we show how to use ontology for NLP in a variety of ways. Its purpose is to discuss and present ontology and knowledge graph concepts comprehensively, specifically: to explain the role of ontology and knowledge graphs in semantic analysis, to highlight current trends and the future scope of ontology and knowledge graph semantic analysis in NLP, and to present a state-of-the-art overview of ontological semantic analysis in NLP. Attempting to bridge the gap between ontology-driven and NLP-driven approaches, the chapter discusses practical scenarios for combining the two.
The current state on usage of image mosaic algorithms

Intensive research has been done on image mosaic algorithms to improve the field of view through generated image mosaics. However, their usage varies from one field to another due to the challenges faced by image acquisition platforms. Moreover, the current imagery software packages used are computationally intensive to be used in real-time applications and are not economically affordable. Those that are open-source are limited due to the less amount of data used to test their mosaicing algorithms’ performance. Therefore, detailed knowledge of appropriate mosaic algorithms suitable for real-time applications is needed to produce mosaics with less computational time and efficient feature point detection. A comprehensive survey that categorizes existing mosaic algorithms’ adoption in various fields has not been done to the best of our knowledge. Firstly, we provide a comparison of the strengths, weaknesses, and uniqueness of the image mosaic algorithms across different fields, with emphasis on challenging issues, limitations, performance criteria, and mechanisms. Furthermore, this paper provides an up-to-date review of image mosaic algorithms in various domains as used in different fields. We further classify the usage of image mosaic algorithms based on the following domains: spatial, frequency, and combined spatial and frequency as used in agriculture, environmental monitoring, and medical imaging. In addition, an analysis was carried out on one of the promising algorithms based on improved SIFT with the aim of improvement. We then proposed an improved SIFT algorithm, which was then evaluated with an open-source algorithm and commercial software using structural similarity index measure (SSIM) and mosaicing computational times for mosaic accuracy and processing efficiency, respectively. Our approach demonstrated a significant improvement of more than 10% average on the mosaicing computational times for the five datasets used. Its mosaicing accuracy was found to be relatively within an acceptable range of above 90% averagely.
LingoTowns: A Virtual World For Natural Language Annotation and Language Learning

In this paper we introduce LingoTowns, a new gwap platform targeting language learners. LingoTowns provides a unified experience integrating games for multiple aspects of lexical and grammatical experience in a single virtual world, whilst simultaneously collecting judgements. Both LingoTowns and its constituent games are designed to provide more engagement to the players/ learners than normal gwaps. The platform also incorporates knowledge tracing methods ensuring that the players’ progress in terms of understanding of grammatical concepts is tracked both at the individual game level and overall.