Reviewerly/exHarmony huggingface dataset:
Resource-frugal classification and analysis of pathology slides using image entropy

Pathology slides of lung malignancies are classified using resource-frugal convolution neural networks (CNNs) that may be deployed on mobile devices. In particular, the challenging task of distinguishing adenocarcinoma (LUAD) and squamous-cell carcinoma (LUSC) lung cancer subtypes is approached in two stages. First, whole-slide histopathology images are downsampled to a size too large for CNN analysis but large enough to retain key anatomic detail. The downsampled images are decomposed into smaller square tiles, which are sifted based on their image entropies. A lightweight CNN produces tile-level classifications that are aggregated to classify the slide. The resulting accuracies are comparable to those obtained with much more complex CNNs and larger training sets. To allow clinicians to visually assess the basis for the classification -- that is, to see the image regions that underlie it -- color-coded probability maps are created by overlapping tiles and averaging the tile-level probabilities at a pixel level.
Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation

Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs. However, it can only translate between a single language pair and cannot produce translation results for multiple language pairs at the same time. That is, research on multilingual UNMT has been limited. In this paper, we empirically introduce a simple method to translate between thirteen languages using a single encoder and a single decoder, making use of multilingual data to improve UNMT for all language pairs. On the basis of the empirical findings, we propose two knowledge distillation methods to further enhance multilingual UNMT performance. Our experiments on a dataset with English translated to and from twelve other languages (including three language families and six language branches) show remarkable results, surpassing strong unsupervised individual baselines while achieving promising performance between non-English language pairs in zero-shot translation scenarios and alleviating poor performance in low-resource language pairs.
Unsupervised Adversarial Learning for Dynamic Background Modeling

Dynamic Background Modeling (DBM) is a crucial task in many computer vision based applications such as human activity analysis, traffic monitoring, surveillance, and security. DBM is extremely challenging in scenarios like illumination changes, camouflage, intermittent object motion or shadows. In this study, we proposed an end-to-end framework based on Generative Adversarial Network, which can generate dynamic background information for the task of DBM in an unsupervised manner. Our proposed model can handle the problem of DBM in the presence of the challenges mentioned above by generating data similar to the desired information. The primary aim of our proposed model during training is to learn all the dynamic changes in a scene-specific background information. While, during testing, inverse mapping of data to latent space representation in our model generates dynamic backgrounds similar to test data. The comparative analysis of our proposed model upon experimental evaluations on SBM.net and SBI benchmark datasets has outperformed eight existing methods for DBM in many challenging scenarios.
UAV Detection with a dataset augmented by domain randomization

Object detection for computer vision systems continues to be a complicated problem in real-world situations. For instance, autonomous vehicles need to operate with very small margins of error as they encounter safety-critical scenarios such as pedestrian and vehicle detection. The increased use of unmanned aerial vehicles (UAVs) by both government and private citizens has created a need for systems which can reliably detect UAVs in a large variety of conditions and environments. In order to achieve small margins of error, object detection systems, especially those reliant on deep learning methods, require large amounts of annotated data. The use of synthetic datasets provides a way to alleviate the need to collect annotated data. Unfortunately, the nature of synthetic dataset generation introduces a reality and simulation gap that hinders an object detector's ability to generalize on real world data. Domain randomization is a technique that generates a variety of different scenarios in a randomized fashion both to close the reality and simulation gap and to augment a hand-crafted dataset. In this paper, we combine the AirSim simulation environment with domain randomization to train a robust object detector. As a final step, we fine-tune our object detector on real-world data and compare it with object detectors trained solely on real-world data.
Intelligent technologies of human-computer gaming

The founder of AI Turing test Can listen, talk, look, and recognize.Typical tasks include image understanding, speech recognition, language translation, etc.Most are based deep learning models.Can understand, thinking, and cognize.Typical tasks include comprehend, inference, thinking, decision, etc., with an emphasis on cognitive reasoning and automatically learning ability.The development history of human-computer gaming 人工智能从诞生之始就以对抗人类智能为衡量准则 (著名的图灵测试) [7] .作为人工智能研究的 前沿方向, 人机对抗技术一直是国内外人工智能研究的热点, 以人机对抗为主要形式的人工智能研究 为探寻机器智能的内在生长机制和关键技术原理提供了一个极佳的试验环境和验证途径.基于人类 现有的知识和智能水平, 通过研究和实现新型的智能学习算法, 使得机器智能在可控的范围内不断接 近、达到甚至超过人类的智能水平.整个过程不仅能够让机器更加智能地为人类服务、将人类从一些 繁复复杂的任务中解脱出来, 而且人类也能够借鉴机器智能的发展过程, 提升自身的智能水平, 更深 刻地理解和掌握智能的内在本质和产生机理, 进而推动整个社会由信息化向智能化发展. 发展历史
Analysis of Blockchain Architecture in File Sharing Management for Tertiary Institution

The aim of this paper is to analyze the sharing and management of files in a tertiary institution using blockchain architecture.It is expected to enable an online system that could provide a decentralized architecture for multiple transfers and sharing of files amongst participants, to design a system that can provide data integrity and security of files using IBM Blockchain technology, to provide a system that can allow multiple user and multiple transactions at once.The research was motivated because of the security challenges associated with existing system which include: delay in transfer and sharing of files, much authority and task given to a single user in a centralized system, high risk of attack and loss of files, reduced speed of file transfer and file access/retrieval, limited number of users per time.Methodology adopted was Object Oriented Analysis Design Methodology (OOADM) in conjunction with Unified Modeling Language (UML) and IBM Blockchain Technology while the programming language used was HTML, CSS, Java and Node Js.The result after design was a decentralized cloud based file sharing and management system that enables multi-shared, replicated and permissioned transactions amongst participants in a network.
A new dynamic speech encryption algorithm based on lorenz chaotic map over internet protocol

This paper introduces a dynamic speech encryption algorithm based on Lorenz chaotic map over internet protocol to enhance the services of the real-time applications such as increases the security level and reduces latency. The proposed algorithm was divided into two processes: dynamic key generation process using 128-bit hash value to dynamically alter the initial secret keys, and encryption and decryption process using Lorenz system. In the proposed algorithm, the performance evaluation is carried out through efficient simulations and implementations and statistical analysis. In addition, the average time delay in the proposed algorithm and some of the existing algorithms such as AES is compared. The obtained results concluded that, the proposed dynamic speech encryption algorithm is effectually secured against various cryptanalysis attacks and has useful cryptographic properties such as confusion and diffusion for better voice communication in the voice applications field in the Internet.
A Review on Usability and Performance Evaluation in Virtual Reality Systems

Performance and usability are essential elements (non-functional requirements) of a virtual reality system. These are the main factors considered by a user for the extensive adoption of any emerging technology like virtual reality. Evaluation of such non-functional requirements is challenging, and gaining the interest of researchers. In this paper, Usability, its importance in virtual reality systems, issues relating to usability, and performance are discussed. Multiple evaluation techniques for usability like group testing, comparative, formative and user analysis have been reviewed. Performance evaluation in a virtual environment based on multiple factors like delay, latency, gender, sense of presence, and technological advancements are discussed. The key motive of this paper is to survey the existing and ongoing research studies in usability and performance evaluation of a virtual environment.
Self-Supervised and Controlled Multi-Document Opinion Summarization

We address the problem of unsupervised abstractive summarization of collections of user generated reviews through self-supervision and control.We propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents.This setting makes training simpler than previous approaches by relying only on standard log-likelihood loss and mainstream models.We address the problem of hallucinations through the use of control codes, to steer the generation towards more coherent and relevant summaries.Our benchmarks on two English datasets against graph-based and recent neural abstractive unsupervised models show that our proposed method generates summaries with a superior quality and relevance, as well as a high sentiment and topic alignment with the input reviews.This is confirmed in our human evaluation which focuses explicitly on the faithfulness of generated summaries.We also provide an ablation study showing the importance of the control setup in controlling hallucinations.
A First-Order Logic with Frames

Abstract We propose a novel logic, called Frame Logic (FL), that extends first-order logic (with recursive definitions) using a construct $$\textit{Sp}(\cdot )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Sp</mml:mi> <mml:mo>(</mml:mo> <mml:mo>·</mml:mo> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> that captures the implicit supports of formulas— the precise subset of the universe upon which their meaning depends. Using such supports, we formulate proof rules that facilitate frame reasoning elegantly when the underlying model undergoes change. We show that the logic is expressive by capturing several data-structures and also exhibit a translation from a precise fragment of separation logic to frame logic. Finally, we design a program logic based on frame logic for reasoning with programs that dynamically update heaps that facilitates local specifications and frame reasoning. This program logic consists of both localized proof rules as well as rules that derive the weakest tightest preconditions in FL.
Visualization techniques to support CCTV operators of smart city services

In this paper visualization techniques for modern closed circuit television (CCTV) smart city services are discussed with application to prevention of threats. Unconventional approaches to the intelligent visual data processing are proposed in order to support video surveillance operators, thus to make their work less exhaustive and more effective. Although registration of a huge amount of video data requires development of intelligent and automatic signal processing information extraction techniques, improvement of visualization methods for operators is also a very important task, because of the crucial role the human factor plays and should always play in the decision making, e.g. in the operator reactions to various crisis situations, which can never be fully eliminated by artificial intelligence. Four software based mechanisms connected with a standard or with a slightly extended hardware are proposed as options for the CCTV operators. They utilize rather known ideas but are implemented with new extensions to original algorithms, as well as with additional, innovative modifications and solutions (not presented in the literature). With them they become reliable and efficient tools for the CCTV systems. First, generation of cylindrical panoramas is suggested in order to make long-time video content analysis of a defined area easier and faster. Using panoramas it is possible to reduce the time that is required to watch the video by a factor of hundreds or even thousands and perform an efficient compression of the video stream for the long-time storage. Second, the controlled stereovision option is discussed for quicker and more precise extraction of relevant information from the observed scene. Third, the thermo-vision is analyzed for faultless detection of pedestrians at night. Finally, a novel high dynamic range (HDR) technique is proposed, dedicated to the CCTV systems, in contrast to other typical entertainment oriented HDR approaches, for clear visualization of important and meaningful image details, otherwise invisible. We validated usefulness of the proposed techniques with many experiments presented in this paper.
Reflexões sobre as ações extensionistas e de pesquisa no combate à COVID-19 na universidade do estado do Rio Grande do Norte

The University, through research and extension activities, has a fundamental role to build new knowledge and enable the exchange of knowledge, discover and rediscover solutions together with the community and, in this process, contribute to the professional and citizen education of students.In this context, the general objective of this essay is to identify the actions developed by the State University of Rio Grande do Norte (UERN) in the fight against COVID-19.In its methodological form, the essay took a qualitative approach in promoting documental and bibliographic research from a phenomenological perspective.We highlighted a series of actions of an extension nature and research at UERN, in order to contribute to the fight against COVID-19 in the State of Rio Grande do Norte (RN), particularly in the municipality of Mossoró, where the Central Campus is located. of the institution.The actions range from workshops for the production of fabric masks to the production of soap and sanitizing products, assistance to the population by the faculties of Nursing and Medicine, programs on social networks with an informative character, among others.The activities developed show the importance of public universities with the professional and citizen training of students and the favoring of the knowledge produced in them to serve society.
Optimizing Deep-Neural-Network-Driven Autonomous Race Car Using Image Scaling

In this work we propose scaling down the image resolution of an autonomous vehicle and measuring the performance difference using pre-determined metrics. We formulated a testing strategy and provided suitable testing metrics for RC driven autonomous vehicles. Our goal is to measure and prove that scaling down an image will result in faster response time and higher speeds. Our model shows an increase in response rate of the neural models, improving safety and results in the car having higher speeds.
Design, Implementation and Performance Analysis of a Dual Axis Solar Tracking System

This study presents the design and construction of the Dual Axis Solar Tracking System to ensure maximum energy gain.The solar tracking system will automatically follow the sun's position to maximize the intensity of the light emitted from the sun.When the light intensity decreases, the system automatically changes its direction to get the maximum light intensity.Light Dependent Resistor (LDR) is used to track the coordinates of the sun.The two servo motors that receive signals from the central processing unit will turn the solar panel to the appropriate location for optimum performance.The energy results obtained by the dualaxis solar system are compared with single and fixed solar systems.This research provides optimal solar energy usage.
Augmented Reality in Brand Building and Marketing – Valves Industry

Digital media and the internet has provided us with infinite opportunities for brand building and marketing. Whenever a product is advertised, it must add value in terms of product and brand knowledge. Good product, design, content and advertisement tend to create a better image in mind of the client. Hence, there are more chances that the product will be chosen by the client. Augmented reality (AR) is an innovative technology which adds virtual objects to the reality that can create real like experiences. Interactive design helps to feel and experience the product without physically going anywhere or purchasing it. This creates a strong imprint in the customer's mind about the product, therefore it is easier to recall the product. This paper aims to investigate the effectiveness of Augmented reality in brand building and marketing along with the traditional mode of advertisement (digital and print). One of the world’s most complex and biggest industry is targeted for the study, the valves industry. An Augmented reality application is developed for the customers as well as the employees, then a survey is carried out to compare the results when only traditional methods of advertising are used and when Augmented reality is added to traditional ways of advertising.
Parallel machine arithmetic for recurrent number systems in non-quadratic fields

The paper proposes a new method of synthesis of computer arithmetic systems for "error-free" parallel calculations. The difference between the proposed approach and calculations in traditional systems of Residue Number Systems for the direct sum of modular rings is the parallelization of calculations in non-quadratic extensions of simple finite fields whose elements are represented in number systems generated by sequences of powers of roots of the characteristic polynomial of the recurrent sequence.
The ILASP system for Inductive Learning of Answer Set Programs

The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of examples in the context of some pre-existing background knowledge. Until recently, most research on ILP targeted learning Prolog programs. Our own ILASP system instead learns Answer Set Programs, including normal rules, choice rules and hard and weak constraints. Learning such expressive programs widens the applicability of ILP considerably; for example, enabling preference learning, learning common-sense knowledge, including defaults and exceptions, and learning non-deterministic theories. In this paper, we first give a general overview of ILASP's learning framework and its capabilities. This is followed by a comprehensive summary of the evolution of the ILASP system, presenting the strengths and weaknesses of each version, with a particular emphasis on scalability.
Efficient Quantum Circuits for Accurate State Preparation of Smooth, Differentiable Functions

Effective quantum computation relies upon making good use of the exponential information capacity of a quantum machine. A large barrier to designing quantum algorithms for execution on real quantum machines is that, in general, it is intractably difficult to construct an arbitrary quantum state to high precision. Many quantum algorithms rely instead upon initializing the machine in a simple state, and evolving the state through an efficient (i.e. at most polynomial-depth) quantum algorithm. In this work, we show that there exist families of quantum states that can be prepared to high precision with circuits of linear size and depth. We focus on real-valued, smooth, differentiable functions with bounded derivatives on a domain of interest, exemplified by commonly used probability distributions. We further develop an algorithm that requires only linear classical computation time to generate accurate linear-depth circuits to prepare these states, and apply this to well-known and heavily-utilized functions including Gaussian and lognormal distributions. Our procedure rests upon the quantum state representation tool known as the matrix product state (MPS). By efficiently and scalably encoding an explicit amplitude function into an MPS, a high fidelity, linear-depth circuit can directly be generated. These results enable the execution of many quantum algorithms that, aside from initialization, are otherwise depth-efficient.
Methods &amp;amp; Proposal for Metadata Guiding Principles for Scholarly Communications

This article describes an international community-based effort to create metadata guiding principles for adopting and using richer metadata and advancing its application in scholarly communications. These principles can facilitate the dissemination, discoverability and use/reuse of many types of research and scholarly outputs. While much work remains to be done, these principles serve as a starting point for the evolution of processes that span communities including publishers, researchers, scholars, authors and other creators, librarians, curators, custodians, and consumers of scholarly works. These aspirational Metadata 2020 Principles are designed to encompass the needs of our entire community while ensuring thoughtful, purposeful, and reusable metadata resources. They provide a framework for all of us to be good metadata citizens. They also provide a foundation for considering related work from Metadata 2020 and must be interpreted within the legal and practical context in which we operate. They are intended to guide the broadest possible cross-section of our community in improving research communications, publishing, and discoverability.
Optimization of a stair lift design to realize a safe transportation system for the elderly and the disabled when using stairs

Stair lift is transportation technology with electrical mechanical equipment to assist vertical movements in buildings, and it is used specifically and individually by the elderly and people with disabilities [1]. This paper has two objectives. The first is to optimize the ability of the existing stair lift structure by developing lighter stair lift designs and materials without reducing the capability of the stair lift. The second is to design a braking system on the stair lift. The optimization of a stair lift structural design is needed to ensure that the strength of the stair lift structure complies with the requirements of ASME standard 18.1. The methodologies of this study are redesigning and conducting numerical simulations with the finite element to get the optimum result. Numerical simulations on the guide rails were carried out on A36 steel and 6063 T5 aluminum materials by comparing 30° and 45° stair lift inclinations. The maximum load is 270.4 kg. The distance of support is 1000 mm. From the simulation results, the 6063 T5 aluminum profile can be used as a material substitution for guide rails. The chains for driving links need to be strengthened by adding the dimensions of the chain or increasing the number of chains. The maximum temperature distribution on the disc brakes is 130.56 °C.