Reviewerly/exHarmony huggingface dataset:
A P2P framework for modular disjunctive knowledge bases with negation and constraints and its applications to the semantic web

The paper presents present a P2P framework based on asynchronous messaging for collaborative reasoning over modular disjunctive knowledge bases with weak negation, strong negation, and constraints. In particular, the paper presents algorithms running upon receival from a knowledge base of a corresponding Ask_pred, Tell_pred, Ask_KB, or Tell_KB message, which are needed for query answering. In particular, these messages collect in the knowledge base that received the query which is the corresponding part of the modular knowledge base that is needed for query answering. This process is done in polynomial time with respect to the size of the modular knowledge base. The paper presents several applications of the proposed theory, including restricted modular ERDF ontologies, SPARQL 1.1 seen as a rule language, the maximal weak model semantics, the minimal weak model semantics, multi-context reasoning, the contextually closed semantics, and taxonomy-based sources connected by articulations. Several complexity results are provided.
Self-assessment of Сompetencies as a Tool for Assessing the Effectiveness of Corporate Educational Programs

The existing tools for assessing the effectiveness of educational programs differ in the degree of effectiveness, applicability, and costs. The self-assessment method, which involves the determination of the level of expression of various qualities by students of educational programs before and after graduation. The method of assessing students' own competencies has significant limitations associated with social desirability. Nevertheless, the article analyzes the possibilities of using this method to assess the effectiveness of corporate educational programs. The results of using the self-assessment method to assess the effectiveness of corporate educational programs conducted in large organizations are presented. The possibilities and limitations of using various kinds of criteria for evaluating themselves by listeners are substantiated. The conclusion is made about the possibility of using the self-assessment method in the case of the focus of educational programs on the development of meta-competencies of managers. The article provides data on the assessment of learning outcomes, discusses the potential reasons for the differences obtained in the framework of projects, as well as the possibilities and limitations of using the self-assessment method to assess the educational effect of corporate training programs.
Sequential Extraction and Geochemistry of Heavy Metals in Ayetoro Coastal Sediments, Southwestern Nigeria

Previous geochemical investigation of Ayetoro area discovered that its coastal sediments are enriched with sulphide mineralization. However, in order to determine the geochemical phases of the heavy metals in the coastal sediments, random sampling method was utilized across 10 locations, at a depth of 40cm using Van grab sampler at a sampling density of 200m interval. Atomic Absorption Spectrophotometer (AAS) Buck Scientific Model 205A was used to analyze nine (9) heavy metal concentrations namely Ni, Zn, Co, Mn, Fe, Pb, Cr, Cd and Cu in the coastal sediments, followed by sequential extraction of the metals, using five fractional phases. The results revealed that the geochemical concentration of the heavy metals as follows: Ni (5.89ppm - 16.82ppm), Zn (2.59ppm - 115.65ppm.), Co (1.22ppm - 22.77ppm), Mn (30.95ppm - 186.49ppm), Fe (6.632ppm - 1925.96ppm), Pb (5.17ppm - 55.96ppm), Cr (0.26ppm - 28.06ppm), Cd (0.13ppm -22.23ppm), and Cu (2.26ppm - 41.94ppm) and showed the concentration order as Residual&gt;Reducible&gt;Organic&gt;Exchangeable&gt;Carbonate. Most of the heavy metals in carbonate and exchangeable phase have low concentration except for Cd. This implied that Cd is of low mobility and bioavailability which is very dangerous as its intake by man leads to kidney diseases and causes bones to become weaker. Also, Mobility factor of Cd stood out because of its high concentration in the exchangeable phase compared to other four non-residual phases. The mobility and bioavailability of the heavy metals are in this order: Cd&gt;Co&gt;Ni&gt;Pb&gt;Cr&gt;Mn&gt;Cu&gt;Zn&gt;Fe respectively. The analysis of variance (ANOVA) revealed that the heavy metals are significantly different in all the phases based on their accumulation index in the sediments while majority of the heavy metals lacked the ability to remobilize but can be released into the environment under reducing and oxidizing conditions.
Towards a framework for certification of reliable autonomous systems

AbstractA computational system is called autonomous if it is able to make its own decisions, or take its own actions, without human supervision or control. The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article.
American English and British English: Vocabulary and Grammar Differences

American English and British English are the famous English varieties in the world, although there are lots of varieties in English, but AmE and BrE are the most famous, they have lots of differences each other and sometimes those differences make the new learner confused. This research examines about Grammar and Vocabulary differences between American and British English. The researcher uses Qualitative Approach in this research because the researcher explains the results of research in words form, while Library Research is type of research that researcher used, because the sources of this research are several books, articles and others. The findings of this research indicated that are many differences between American English and British English including in the aspects and grammar and vocabulary. The differences are not a big deal for native speakers, but they may confuse non-native speakers of English.
Effective Graph Context Representation for Document-level Machine Translation

Document-level neural machine translation (DocNMT) universally encodes several local sentences or the entire document. Thus, DocNMT does not consider the relevance of document-level contextual information, for example, some context (i.e., content words, logical order, and co-occurrence relation) is more effective than another auxiliary context (i.e., functional and auxiliary words). To address this issue, we first utilize the word frequency information to recognize content words in the input document, and then use heuristical relations to summarize content words and sentences as a graph structure without relying on external syntactic knowledge. Furthermore, we apply graph attention networks to this graph structure to learn its feature representation, which allows DocNMT to more effectively capture the document-level context. Experimental results on several widely-used document-level benchmarks demonstrated the effectiveness of the proposed approach.
Amplification trojan network: Attack deep neural networks by amplifying their inherent weakness

Recent works found that deep neural networks (DNNs) can be fooled by adversarial examples, which are crafted by adding adversarial noise on clean inputs. The accuracy of DNNs on adversarial examples will decrease as the magnitude of the adversarial noise increase. In this study, we show that DNNs can be also fooled when the noise is very small under certain circumstances. This new type of attack is called Amplification Trojan Attack (ATAttack). Specifically, we use a trojan network to transform the inputs before sending them to the target DNN. This trojan network serves as an amplifier to amplify the inherent weakness of the target DNN. The target DNN, which is infected by the trojan network, performs normally on clean data while being more vulnerable to adversarial examples. Since it only transforms the inputs, the trojan network can hide in DNN-based pipelines, e.g. by infecting the pre-processing procedure of the inputs before sending them to the DNNs. This new type of threat should be considered in developing safe DNNs.
Design of Participants Registration System for Training and Consulting Institution

The rapid development information technology and the need for recognition of the workforce competencies, creating challenges for the growth of consulting and training institution. In this study, efforts will be made to improve participants registration system through the design of application. The author uses ITIL v3 problem management to analyze existing problems in the company and use the Object-Oriented Analysis and Design (OOAD) method in developing the systems. Data collection is done by conducting surveys, document studies, interviews and observations. The results of this study are a web-based registration system design that has the advantages of functional features, that is, Join Training, Invoice and Registration Status, where these three features will facilitate participants in registering, paying for and training placement. With the system developed, the registration process can run more effectively and efficiently than ever before and improve business operations at the institution.
A Brief Introduction to Blockchain Dispute Resolution

Blockchain is the technology behind infamous Bitcoin and provides a structure for dispute resolution with the help of smart contracts. The technology aims to establish an anonymous and decentralized mechanism without any state oversight or intermediary for transactions. Blockchain works on a decentralized network creating an immutable record of transactions. A smart contract is a self-executing software program that automatically performs a function. Once the conditions are met, the contract enforces itself without any additional step. The blockchain dispute resolution (BDR) platforms offer services for disputes arising out of blockchain and smart contract transactions or for traditional disputes that are not related to blockchain transactions. Each of the platforms provides untraditional mechanisms for adjudication that are also different from each other, creating an unorganized and unregulated environment. The platforms do not comply with the accustomed principles of alternative dispute resolution (ADR) and online dispute resolution (ODR) mechanisms raising the question of validity and legitimacy of the platforms and the need for regulation.The article aims to act as a brief introduction to some of these platforms and identifies major issues with BDR when compared to the established dispute resolution procedures. It briefly explains the blockchain and smart contracts with some examples on their alternative applications and then introduces BDR platforms. The article discusses concerns about BDR in light of ADR and ODR principles. It points out main issues following up the comparison of different mechanisms and tries to provide a perspective to find answers with discussion of possible solutions. Finally, the article suggests that the best way forward is to utilize BDR for supplementing ODR and ADR. If regulation is found absolutely necessary, then soft law instruments should be the first step in this respect.
EKTVQA: Generalized Use of External Knowledge to Empower Scene Text in Text-VQA

The open-ended question answering task of Text-VQA often requires reading and reasoning about <i>rarely seen or completely unseen</i> scene text content of an image. We address this zero-shot nature of the task by proposing the generalized use of external knowledge to augment our understanding of the scene text. We design a framework to extract, validate, and reason with knowledge using a standard multimodal transformer for vision language understanding tasks. Through empirical evidence and qualitative results, we demonstrate how external knowledge can highlight instance-only cues and thus help deal with training data bias, improve answer entity type correctness, and detect multiword named entities. We generate results comparable to the state-of-the-art on three publicly available datasets under the constraints of similar upstream OCR systems and training data.
Security Requirement Analysis of Blockchain-Based E-Voting Systems

In democratic countries such as India, voting is a fundamental right given to citizens of their countries. Citizens need to physically present and cast their vote in ballot paper-based voting systems. Most of the citizens fail to fulfill this constraint and have stayed away from their fundamental duty. Electronic voting systems are often considered as one of the efficient alternatives in such situations. Blockchain technology is an emerging technology that can provide a real solution as it is characterized by immutable, transparent, anonymous, and decentralized properties. This paper presents a security requirement analysis for e-voting systems and evaluates blockchain technology against these requirements.
Decentralizing loT Public- Key Storage using Distributed Ledger Technology

The secure Internet of Things (loT) increasingly relies on digital cryptographic signatures which require a private signature and public verification key. By their intrinsic nature, public keys are meant to be accessible to any interested party willing to verify a given signature. Thus, the storing of such keys is of great concern, since an adversary shall not be able to tamper with the public keys, e.g., on a local filesystem. Commonly used public-key infrastructures (PKIs), which handle the key distribution and storage, are not feasible in most use-cases, due to their resource intensity and high complexity. Thus, the general storing of the public verification keys is of notable interest for low-resource loT networks. By using the Distributed Ledger Technology (DLT), this paper proposes a decentralized concept for storing public signature verification keys in a tamper-resistant, secure, and resilient manner. By combining lightweight public-key exchange protocols with the proposed approach, the storing of verification keys becomes scalable and especially suitable for low-resource loT devices. This paper provides a Proof-of-Concept implementation of the DLT public-key store by extending our previously proposed NFC-Key Exchange (NFC-KE) protocol with a decentralized Hyperledger Fabric public-key store. The provided performance analysis shows that by using the decentralized keystore, the NFC- KE protocol gains an increased tamper resistance and overall system resilience while also showing expected performance degradations with a low real-world impact.
IoT Based Multi-Layered Security Network Authentication System Development Using Blockchain Technology Management

Due to the proliferation of smart devices in the Internet of Things (IoT) connections, significant security concerns for device-to-device connectivity have been raised. Blockchain is a decentralized and shareable technology that may be used to overcome security challenges in 5 g networking and Internet of Things networks, among other things. This research provides a multi-layer Blockchain Security model that may be used to protect IoT networks while also making their implementation simpler. The grouping idea is used to ease the multi-layer design. Inside this IoT network, the K-unknown groups are formed utilizing methodologies that use hybrid Adaptive Computational Algorithms as well as Evolutionary Techniques & Genetic Algorithms. The group leaders selected are in charge of regional identification & permission. Communications among-st group chiefs & appropriate core networks are facilitated by privately run blockchain implementations. A blockchain of this kind improves reputation verification & confidentiality while also serving as a networking identification method. For the suggested concept construction, the accessible Hyperledger Fabric Blockchain technology is used. Ground stations use a worldwide blockchain architecture to seamlessly interact with one another.
Performance Analysis of SAP-NFC Protocol

The operations cost of authentication session is considered as strict indicator to evaluate the authentication protocols side by side with the security requirements achievements in the Near Field Communication (NFC) technology. The secure authentication protocol for the NFC mobile payment systems (SAP-NFC) one of the recent authentication protocols that have proposed to achieve high level of security features such as the fully mutual authentication, anonymity and untraceability. Moreover, the SAP-NFC protocol can prevent the current security attacks. This paper, analyzes the performance of the SAP-NFC protocol comparing with the other recent NFC mobile payment protocols in the NFC devices that are included in the system. The performance analysis has illustrated that the SAP-NFC protocol not only supports strong security features, but also offers low cost in term of amount of computations.
An Inter-Frame Forgery Detection Technique for Surveillance Videos Based on Analysis of Similarities

Background: In video forgeries, the insertion, duplication and deletion of frames are the most common forgeries that are used by attackers to alter targeted videos for malicious intent. Researchers have proposed the use of active and passive technologies for detecting video forgeries over the years. Active approaches are used to detect the occurrence of alterations in digital video with the use of embedded features such as digital signature and watermarks. However, techniques that are based on active approaches are only applicable to specialized hardware devices. A passive technique, on the other hand, detects forgery using the behavioral cues encoded in a video. In this paper, a passive video forgery detection system based on frame similarity analysis is presented.Inter frame forgeries (Insertion, Deletion, and Duplication) were detected using the proposed technique, which was unaffected by scene changes.The technique has the overall performance of 98.07% precision, 100% recall and 99.01% accuracy.
A Topological Tree of Shapes

In this article, we enrich the framework of morphological hierarchies with new acyclic graphs and trees. These structures lie at the convergence of hierarchical models and topological descriptors. We define them in the context of digital grey-level imaging. We discuss their links with component-trees, trees of shapes and adjacency trees. This analysis leads to new notions, including a notion of topological tree of shapes.
A New Fault-Tolerant Algorithm Based on Replication and Preemptive Migration in Cloud Computing

Cloud computing is a promising paradigm that provides users higher computation advantages in terms of cost, flexibility, and availability. Nevertheless, with potentially thousands of connected machines, faults become more frequent. Consequently, fault-tolerant load balancing becomes necessary in order to optimize resources utilization while ensuring the reliability of the system. Common fault tolerance techniques in cloud computing have been proposed in the literature. However, they suffer from several shortcomings: some fault tolerance techniques use checkpoint-recovery which increases the average waiting time and thus the mean response time. While other models rely on task replication which reduces the cloud's efficiency in terms of resource utilization under variable loads. To address these deficiencies, an efficient and adaptive fault tolerant algorithm for load balancing is proposed. Based on the CloudSim simulator, some series of test-bed scenarios are considered to assess the behavior of the proposed algorithm.
“AI Love You”: Linguistic Convergence in Human-Chatbot Relationship Development

Social chatbots are increasingly designed to offer companionship to humans to improve their psychological well-being. Can they develop social relationships with the technology? How are people influenced by chatbots? Studies testing whether humans will adapt their communication when interacting with a chatbot have provided somewhat mixed results. Applying Communication Accommodation Theory (CAT), this study takes a longitudinal, quantitative approach to answer these questions. It employs natural language processing (NLP) techniques to compare the linguistic patterns in 29,885 conversations between human users and their social companion chatbot, Replika, scraped from Reddit. The results showed that people’s linguistic styles gradually converged with that of the chatbots. This convergence was positively related with the relationship development between the user and their Replika companion. These findings suggest that human behavior can be influenced by chabots through interaction and people can develop relationships with their technological companions.
Teaching Functional Programmers Logic and Metatheory

We present a novel approach for teaching logic and the metatheory of logic to students who have some experience with functional programming. We define concepts in logic as a series of functional programs in the language of the proof assistant Isabelle/HOL. This allows us to make notions which are often unclear in textbooks precise, to experiment with definitions by executing them, and to prove metatheoretical theorems in full detail. We have surveyed student perceptions of our teaching approach to determine its usefulness and found that students felt that our formalizations helped them understand concepts in logic, and that they experimented with them as a learning tool. However, the approach was not enough to make students feel confident in their abilities to design and implement their own formal systems. Further studies are needed to confirm and generalize the results of our survey, but our initial results seem promising.
Modeling the Sequential Dependence among Audience Multi-step Conversions  with Multi-task Learning in Targeted Display Advertising

In most real-world large-scale online applications (e.g., e-commerce or finance), customer acquisition is usually a multi-step conversion process of audiences. For example, an impression->click->purchase process is usually performed of audiences for e-commerce platforms. However, it is more difficult to acquire customers in financial advertising (e.g., credit card advertising) than in traditional advertising. On the one hand, the audience multi-step conversion path is longer. On the other hand, the positive feedback is sparser (class imbalance) step by step, and it is difficult to obtain the final positive feedback due to the delayed feedback of activation. Multi-task learning is a typical solution in this direction. While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion. In this paper, we propose an Adaptive Information Transfer Multi-task (AITM) framework, which models the sequential dependence among audience multi-step conversions via the Adaptive Information Transfer (AIT) module. The AIT module can adaptively learn what and how much information to transfer for different conversion stages. Besides, by combining the Behavioral Expectation Calibrator in the loss function, the AITM framework can yield more accurate end-to-end conversion identification. The proposed framework is deployed in Meituan app, which utilizes it to real-timely show a banner to the audience with a high end-to-end conversion rate for Meituan Co-Branded Credit Cards. Offline experimental results on both industrial and public real-world datasets clearly demonstrate that the proposed framework achieves significantly better performance compared with state-of-the-art baselines.