Reviewerly/exHarmony huggingface dataset:
21.4: Light‐emitting One‐way Observable Aero Signage Display Which Enables to Make Transparent View from Back Side Using Light Transmission Guide

You end up observing mirrored images of graphic signs when you watch light‐emitting glass tubes from back. So our research group proposed the passive light‐emitting aero signage display which is floating in the air and enables to make transparent view from back side to avoid observing mirrored images in 2020. But this signage display has a defect that it emits no light itself and you cannot observe its signage in the dark night without another light source for illuminating surfaces of the signage display. This paper shows a one‐way observable display for aero signage which enables to make transparent from back side and emits light itself using a lighting guide tube. The authors utilize a cylindrical long transparent acrylic stick as the lighting guide. Our proposed light‐emitting and color generating mechanism realize the one‐way observable imaging which enables to make invisible from back side. You may use connectable or jointable lighting guide blocks instead of the long, unbroken and jointless lighting guide tube such as a cylindrical transparent acrylic stick. This paper also describes a one‐way observable display for aero signage which enables to joint pixel modules having optical lighting guide block and emits light itself using this lighting guide. The connectable lighting guide blocks reconstruct the unidirectional imaging which enables to emit lights and make invisible from back.
Smart-Views: Decentralized OLAP View Management Using Blockchains

In this work we explore the use of a blockchain as an immutable ledger for publishing fact records in a decentralized data warehouse. We also exploit the ledger for storing smart views, i.e. definitions of frequent data cube computations in the form of smart contracts. Our techniques model and take into consideration existing interdependencies between the smart views to expedite their computation and maintenance.
Basing Sequent Systems on Exclusive-Or

In the standard Gentzen-type systems for classical logic, the right hand side of a sequent is interpreted as the inclusive-or of its elements. In this paper we investigate what happens if the exclusive-or \(\oplus \) is used instead. We provide corresponding analytic systems, and some of the decision procedures that are based on them. The latter are particularly efficient for the negation-equivalence fragment of classical logic.
Improving Machine Understanding of Human Intent in Charts

Charts composed of images and text are a classical and compact method of displaying and comparing various data. Automated data extraction from charts is critical in the machine understanding and recognition of human intent and the meaning inherent in charts. Complex processes of automated chart data extraction have been divided into multiple basic tasks. However, these problems have not been solved well. In this paper, we principally focus on three key sub-tasks, including chart image classification (Task-1), text detection and recognition (Task-2), and text role classification (Task-3). For these tasks, we design and propose a set of effective methods. The experiments on the Adobe Synthetic and PubMedCentral datasets successfully demonstrate the effectiveness of our proposed systems. Notably, our proposed method outperforms competing systems from the ICDAR 2019 and ICPR 2020 CHART-Infographics competitions and achieved state-of-the-art performance. We hope this work will serve as a step toward enhancing the machine understanding of charts and inspiring new avenues for further research in the field of document analysis and recognition.
RealGraph <sup>web</sup>

In this demo, we present RealGraph Web , a web-based platform that provides various kinds of graph analysis services. RealGraph Web is based on RealGraph, a graph engine that addresses the problem of performance degradation in processing real-world big graphs, achieving great performance improvement up to 44 times over existing state-of-the-art graph engines. RealGraph Web runs on a single machine with a web-based interface, thereby allowing users to easily and conveniently enjoy graph analysis services and perform various graph algorithms anywhere on the web . In this demo, we present how a user can analyze a graph on RealGraph Web in three steps and get the analysis result quickly via a graphical user interface.
FIUD: A Framework to Identify Users of Devices

The device detected from the public network usually associates with an IP as the unique identity generally. The problem is that many registrants of devices are always different from their true users, which make it difficult for operators to discover whether the IPs are used normally. The research on users of IPs plays an important role to help us for network security and protection. In this paper, we are seeking the users of devices and investigating why they are exposed to the public from five aspects: SSL certificates, protocol banners, address, topology and location. We presented FIUD: A Framework to Identify Users of Devices to extract users automatically. FIUD is based on Seed Extension so as to ensure both accuracy and coverage of user identification. We evaluated our methodology in laboratory and in the real-world. Compared with the mature results in the industry, the experiment shows that our methodology has achieved higher performances to discover the true users of IPs. At the same time, we did the network measurement in Beijing based on our methodology.
Comment on “A Gibbs Sampler for a Class of Random Convex Polytopes,” by Pierre E. Jacob, Ruobin Gong, Paul T. Edlefsen, and Arthur P. Dempster

"Comment on “A Gibbs Sampler for a Class of Random Convex Polytopes,” by Pierre E. Jacob, Ruobin Gong, Paul T. Edlefsen, and Arthur P. Dempster." Journal of the American Statistical Association, 116(535), pp. 1196–1197
A Research on Application of Fuzzy Logic in Data Communication

In current scenario security is the most important problem in passing information from one point to another point. Missile  codes, number bank accounts and encryption keys are the major areas which are facing the security issues. One may find a solution using the mathematical concept of Fuzzy Logic with the help of encryption algorithm. Also, affine transformation is used to encrypt the context. Implementation of cipher a message and decipher it back to the original message, can be intricate and caring data from unconstitutional access is the foremost difficulty. Integrity is also the main perception in data communication. In this paper, Advanced Encryption Standard (AES) algorithm is used to enhance the performance of encryption in data communication and this algorithm plays very important role in multimedia data transfer applications. The proposed system describes the Advance Encryption Standard (AES) algorithm to ensure both security and integrity in data communication.
Tableaux and Restricted Quantification for Systems Related to Weak Kleene Logic

Logic-driven applications like knowledge representation typically operate with the tools of classical, first-order logic. In these applications’ standard, extensional domains—e.g., knowledge bases representing product features—these deductive tools are suitable. However, there remain many domains for which these tools seem overly strong. If, e.g., an artificial conversational agent maintains a knowledge base cataloging e.g. an interlocutor’s beliefs or goals, it is unlikely that the model’s contents are closed under Boolean logic. There exist propositional deductive systems whose notions of validity and equivalence more closely align with legitimate inferences over such intentional contexts. E.g., philosophers like Kit Fine and Stephen Yablo have made compelling cases that Richard Angell’s \(\mathsf {AC}\) characterizes synonymy, under which such intentional contexts should be closed. In this paper, we adapt several of these systems by introducing sufficient quantification theory to support e.g. subsumption reasoning. Given the close relationship between these systems and weak Kleene logic, we initially define a novel theory of restricted quantifiers for weak Kleene logic and describe a sound and complete tableau proof theory. We extend the account of quantification and tableau calculi to two related systems: Angell’s \(\mathsf {AC}\) and Charles Daniel’s \(\mathsf {S}^{\star }_{\mathtt {fde}}\), providing new tools for modeling and reasoning about agents’ mental states.
AI Soft Law and the Mitigation of Product Liability Risk

Artificial intelligence systems present various types of risks, but are generally not subject to comprehensive government regulation at this time. Soft law measures such as private standards have been developed to fill this governance gap, but a major limitation of such standards is that they are not directly enforceable, and so many companies may not comply with them. This article examines the thesis that compliance with soft law standards may provide some protection from product liability, thus providing additional incentive for companies to adopt and comply with such standards. Using U.S. case law as the focus of analysis, compliance with private standards does not provide an absolute shield against liability, but can provide persuasive evidence of due care that can diminish liability risks in most U.S. state jurisdictions. Conversely, while failure to abide by soft law standards does not automatically result in liability, it can provide probative evidence that a company has not exercised due care, and thus should be held liable. Moreover, compliance with soft law standards can provide an effective defense against punitive damages in many States if a companyâ€™s AI system does harm people or property and is held liable for compensating such damages. These findings suggest that protecting against such liability provides a powerful incentive for companies to comply with soft law standards.
Alleviating the unfairness of recommendation by eliminating the conformity bias

Abstract Recommendation systems play a critical role in the Internet era. In the past decade, many methods have been proposed to improve the accuracy of recommendation. However, simply increasing the accuracy is usually accompanied by the popularity bias in the recommendation results. In addition, from the perspective of item providers the popularity bias will bring unfairness in exposure of different items to users. In this paper, we propose a method to alleviate problems of unfairness and popularity bias. The main idea is based on the hypothesis that some interactions of users with popular items are not due to their preferences, but attribute to the conformity propensity, which aggravates the popularity bias problem. Thus we consider to eliminate these so called “unreliable interactions” to alleviate the unfairness. We propose a two-step preferential diffusion process between conformity users and popular items in the random walk recommendation algorithm to reduce the effect of unreliable interaction and alleviate the popularity bias and unfairness under the condition of ensuring accuracy. The experiment results on the two benchmark datasets, Movie-Lens and Netflix, show that our algorithm can improve the fairness of recommendation results and alleviate the popularity bias while achieving slightly increased recommendation accuracy.
Towards reliable and transparent vaccine phase III trials with smart contracts

Transforming a vaccine concept into a real vaccine product is a complicated process and includes finding suitable antigens and regulatory, technical, and manufacturing obstacles. A relevant issue within this scope is the clinical trial process. Monitoring and ensuring the integrity of trial data using the traditional system is not always feasible. The search for a vaccine against the coronavirus SARS-CoV-2 illustrates this situation. The scientific credibility of findings from several vaccines’ clinical trials contributed to distorted perceptions concerning the benefits and risks of the drug. This scenario is ideal for applying technologies such as Blockchain and Smart Contracts in healthcare issues. This paper proposes a protocol based on Smart Contracts, named VaccSC, to enable transparency, accounting, and confidentiality to Phase III of vaccine experiments. The protocol was implemented in Solidity language, and results show that the VaccSC enables double-blindness, randomization, and the auditability of clinical data, even in the presence of dishonest participants.
Research on English Teaching Model Strategy Based on Computer

The 21st century is an information age, with the development of China's economic progress, computer technology as an auxiliary tool has been introduced into college teaching. In this paper, the characteristics and difficulties of computer English teaching are analyzed in detail. Combined with the application characteristics of computer information technology, the application orientation is regarded as the main reference in the optimization process of computer English teaching mode. At the same time, the optimization suggestions of computer English teaching mode are discussed.
Comparing Different Techniques of Urdu Text Summarization

Automatic summarization is the process of generating condensed version of text. A wide variety of data is available on the web, and to make reading quick and efficient for people, summarization is required. The challenge that comes with summarization is to retain the actual meaning and yet reduce the size of the text. This can be done in two ways, abstractive and extractive. This research focuses on extractive summarization. News is one thing that should be available to be read on the go, and this research works on the summarization of Urdu news. Different extraction based summarization algorithms are used to compare the one that is best suited for Urdu data, given the same data and circumstances. As a part of this research, a sentence segmenter is also created that identifies the ending word of a sentence, since proper sentence boundaries need to be defined to proceed with summarization. The results of all these have been compared with and without preprocessing the input, and the evaluation methodology is equivalent to ROGUE-I. The results show that there are some algorithms that do work slightly better when given non preprocessed data, but the majority of the algorithms give better results on preprocessed input.
Cyber Crimes and Victimisation of Women in India with Special Reference to Cyber Obscenity

The traditional Indian society places women in a very high regards, the Vedas glorified women as the mother, the creator, one who gives life and worshipped her as a “Devi” or Goddess. The women occupied a vital role and as such her subjugation and mistreatment were looked upon as demeaning to not only the woman but towards the whole society. However, in modern times women are viewed and portrayed as sex objects, she is treated inferior to men in various societal spheres and functions, this has created a huge gender bias between the men and women where even the men think that their wrongdoings towards women cannot be penalized. Cybercrime and internet bullying works in similar manner where the wrong-doers are not afraid of any authority that can penalize. The cyber world in itself has a virtual reality where anyone can hide or even fake his identity, this gift of internet is used by the criminally minded to commit wrongful acts and then hide under the blanket provided by the internet.
Generalised Partial Association in Causal Rules Discovery

One of the most significant challenges for machine learning nowadays is the discovery of causal relationships from data. This causal discovery is commonly performed using Bayesian like algorithms. However, more recently, more and more causal discovery algorithms have appeared that do not fall into this category. In this paper, we present a new algorithm that explores global causal association rules with Uncertainty Coefficient. Our algorithm, CRPA-UC, is a global structure discovery approach that combines the advantages of association mining with causal discovery and can be applied to binary and non-binary discrete data. This approach was compared to the PC algorithm using several well-known data sets, using several metrics.
Strategy of English Assisted Learning Platform Based on Dynamic System Theory

In order to effectively overcome the problems existing in the process of using English assisted learning platform, such as complex system, uneven quality, lack of effective functions and low interaction, this paper proposes a strategy of English assisted learning platform based on dynamic system theory. The strategy explores and analyzes the operation mode of English assisted learning platform from three dimensions, namely, environment, learners and language factors. Based on this, the strategy also makes full use of a variety of research methods, such as questionnaire survey, case interview, mathematical statistics, etc., comprehensively analyzes the shortcomings and problems of the current English assisted learning platform, and then constructs a perfect English auxiliary learning platform by combining the dynamic system theory. The experimental results show that the strategy can effectively overcome the problems existing in the current English assisted learning platform, improve the efficiency of English assisted learning platform and user satisfaction, and then create a good learning environment.
Formal Analysis of Smart Contracts: Model Impact Factor on Criminality

Smart contracts certainly provide a powerful functional surplus for maintaining the consistency of transactions in applications governed by blockchain technology. However, the intended level of automation might cause cascading effects that have to be checked by formal methods of algorithmic proof. Our smart contract formal model analysis framework uses the Finite State Machine (FSM) theory which is a model of behavior composed of states, transitions and actions. In this model, a state stores information about the past, a transition indicates a state change and is described by a condition that would need to be fulfilled to enable the transition. An action is a description of an activity that is to be performed at a given moment. These conditions are properties that are checked during program execution. This formal analysis framework allows us to define a set of invariants on Finite State Machine behavior model and to propose an anomaly detection system based on the invariants of the smart contract.
Dependent Bayesian nonparametric modeling of compositional data using random Bernstein polynomials

We discuss Bayesian nonparametric procedures for the regression analysis of compositional responses, that is, data supported on a multivariate simplex. The procedures are based on a modified class of multivariate Bernstein polynomials and on the use of dependent stick-breaking processes. A general model and two simplified versions of the general model are discussed. Appealing theoretical properties such as continuity, association structure, support, and consistency of the posterior distribution are established. Additionally, we exploit the use of spike-and-slab priors for choosing the version of the model that best adapts to the complexity of the underlying true data-generating distribution. The performance of the proposed model is illustrated in a simulation study and in an application to solid waste data from Colombia.
3D refined reconstruction for brushes based on multiple images

Chinese Calligraphy is one of the most esteemed visual art all over the world, and ink brushes are the traditional writing tool for Chinese calligraphy. We want to combine the traditional culture with the latest computer vision techniques, and an exact 3D model of brushes is a necessary prerequisite. However, traditional ink brushes have elongated and thin bodies, which is tough to create dense point clouds and an exact 3D reconstruction model. In this paper, we represent a new approach to achieve a complete 3D reconstruction model of brushes within an acceptable time. Our approach utilizes special brushes and extracts interesting points by using both SIFT and AKAZE. Through the combination of two feature extraction algorithms, interesting points could be doubled and be more focused on brushes rather than backgrounds. Moreover, we compare computational costs in different parameters in our 3D reconstruction pipeline and summary some experience to accelerate the reconstruction process. We decrease the number of neighbor cameras when creating a depth map, and reduce the number of output points to the extent that naked eyes cannot recognize the differences. Experimental results show that our approach is able to create a complete and consistent 3D reconstruction model within three minutes.
Early prediction and monitoring of sepsis using sequential <scp>long short term memory</scp> model

Abstract Sepsis is a severe life‐threatening disease, which is the body's extreme reverberation to any virus leading to failed organs, damaged tissue, or death. It requires accurate and efficient real‐time detection. Continuous and potent monitoring of patient health data can be useful in predicting the potential risks the patient might be exposed to, based on their recent history of medical records. Machine learning models have proven to be a significant approach in performing accurate and precise predictions, especially in the medical field. In this paper, a smart network with a long short term memory (LSTM) based model at its heart is proposed for early prediction of Sepsis for patients admitted in the ICU. The network operates on time series data and predicts the probability of a patient developing sepsis based on the historical medical data of the patient. It comprises Internet of Things based devices which have proven to be impactful in the areas of acquiring continuous and real‐time data in the healthcare field. In this paper, an architecture for early prediction and monitoring Sepsis with minimized latency through LSTM network is proposed by designing a decentralized prediction model in a fog‐based environment. The model had an accuracy of 95.1% after the last epoch with validation accuracy as 95%. The receiver operating characteristic curve area was reported as 0.864 for testing data with an accuracy of 95.1%. The model was not over‐fitted since the validation accuracy was significantly close to the training accuracy of the model.
Research on Personalized Recommendation Model and algorithm in deep learning mode

In order to improve the recommendation accuracy in collaborative filtering, this paper introduces deep learning into the recommendation algorithm, and proposes a personalized recommendation model and algorithm under the deep learning mode. Firstly, the advantages and disadvantages of common algorithms are analyzed; then, the deep learning mode is constructed, and the deep learning is integrated into the recommendation algorithm step by step; finally, the simulation data are verified by cosine similarity and probability based recommendation method, and the results show that the two methods get almost the same score, and get better recommendation results, which can effectively improve the recommendation progress and quality.