Reviewerly/exHarmony huggingface dataset:
Chatbot Using Dialogflow and Web Services

The Chatbots are helpful in making human effort minimal for less-complex tasks like answering messages and also to provide great satisfaction to the clients round the clock. This is considered a better initiative as the business organizers don't have to worry about reaching to customers. There are many proposed models to get the desired output most of these are helpful if the user input is always from the given set of words the software is designed for, but unfortunately, this is not the scenario always as the user shall have a different way to message. This and the other models are being replaced by the Artificial Intelligence systems which have the capability to identify the structure and wordsfrom a given context and use it predict the best possible outcomes. In our proposed model, we are using the same and it is handled by Dialogflow, which helps in achieving an end-to-end bot without the user being worried about the algorithm that should be used to train this bot. Using a configured server with Dialog flow helps in handling various requests. Using technologies like Dialog flow, Node.js in this proposed model, an effort is made to make better bots with better functionality and continuation in the conversation. The system here is proposed to analyse the user query and respond back to the user with an appropriate answer. Our model is developed to help user in searching restaurants around him with different cuisines, categories else to see the list of top reviews of a hotel and it’s cuisines. This chat-bot since being location dependent, it shall be using the location of the user to fetch real-time results for the queries put forward by the user.
Sentiment Analysis using Optimized Feature Sets in Different Twitter Dataset Domains

in the last years, the relevance of sentiment analysis is broad and dominant. The capability to take out insights from social data is a tradition that is being extensively accepted by all over globe. Sentiment Analysis has turn out to be a hot-trend issue of technical and marketplace research in the area of Natural Language Processing (NLP) and Machine Learning. Sentiment analysis is enormously useful in social media supervising as it permits us to expand an impression of the wider open estimation behind definite topics. Investigation of social media streams is typically limited to just essential sentiment analysis and count based metrics. This is of the same kind to just scratching the outside and missing out on those elevated value insight that is ahead of you to be discovered. There’s a lot of effort to be done, but perfections are being prepared every day. It is a way to appraise on paper or verbal language to settle on if the expression is favorable, unfavorable, or unbiased, and to what level. Today’s algorithm-based sentiment analysis tools can touch vast amount of client response constantly and precisely. Balancing with text analytics, sentiment analysis exposes the customer’s estimation concerning topics ranging from your goods and services to your position, your advertisements, or even your challengers. These efforts scrutinize the crisis of studying texts, like posts and reviews, uploaded by user on Twitter. The Support Vector Machine (SVM), k-nearest neighbors algorithm (KNN) and proposed optimized feature sets model is offered to progression the tweet features and to recognize the out of sight sentiments from these tweets. These essential concepts when used in combinations become a very significant tool for analyzing millions of variety conversations with human echelon accurateness. The projected optimized feature sets model Sentiment Analysis exercise the assessment metrics of Precision, Recall, F-score, and Accuracy. Also, average measures weighted F1-scores are constructive for categorization of Positive, Negative and Neutral multi-class problems. The running time of the technique is evaluates by accomplishing diverse methods in the same investigational setup consisting a cluster of 8 nodes. Planned optimized feature sets model Sentiment Analysis reachs 82 % accuracy as compare with SVM 78.6 % and KNN 75 %. Further, while analyzing sentiments of tweets we have measured only tweets in English acknowledged by Twitter streaming API.
ARTIST - a reAl-time low-effoRt mulTi-entity Interaction System for creaTing reusable and optimized MR experiences

ARTIST is a research approach introducing novel methods for real-time multi-entity interaction between human and non-human entities, to create reusable and optimized Mixed Reality (MR) experiences with low-effort, towards a Shared MR Experiences Ecosystem (SMRE2). As a result, ARTIST delivers high quality MR experiences, facilitating the interaction between a variety of entities which interact in a virtual and symbiotic way within a mega, virtual and fully-experiential world. Specifically, ARTIST aims to develop novel methods for low-effort (code-free) implementation and deployment of open and reusable MR content, applications and tools, introducing the novel concept of an Experience as a Trajectory (EaaT). In addition, ARTIST will provide tools for the tracking, monitoring and analysis of user behaviour and their interaction with the environment and with other users, towards optimizing MR experiences by recommending their reconfiguration, dynamically (at run-time) or statically (at development time). Finally, it will provide tools for synthesizing experiences into new mega and still reconfigurable EaaTs, enhancing them at the same time using semantically integrated related data/information available in disparate and heterogeneous resources.
Escalated Methods for Software Defects Audit in Repercussion and Effects Construe to Nature Inspired and Behavior Driven Mechanisms

In the current scenario, enormous software suites are available but there is need to integrate the higher degree of robustness. This is in order to develop the full proof software application with the higher degree of performance and accuracy with minimum scaling of the error factors. There is need to work on the software audit because of the enormous assaults and vulnerabilities from different sources. The approach of software audit and management strategies can be elevated using Nature Inspired Approaches and DevOps based integrations. This manuscript is having the cavernous evaluation of different state of the art approaches for software defects management using the patterns of nature inspired and behavior driven mechanisms and found that the effective results and outcomes can be achieved using such approaches. The presented work is presenting the usage of behavior driven mechanism because of the key requirements to have the multi-dimensional evaluation of the software products.
Architecture self-attention mechanism: nonlinear optimization for neural architecture search

Neural Architecture Search (NAS) is a very prevalent method of automatically designing neural network architectures.It has recently drawn considerable attention since it relieves the manual design labour of neural networks.However, existing NAS methods ignore the interrelationships among candidate architectures in the search space.As a consequence, the objective neural architecture extracted from the search space suffers from performance unstable due to the interrelationship collapse.In this paper, we propose architecture self-attention mechanism for neural architecture search (ASM-NAS) to address the above problem.Specifically, the proposed architecture self-attention mechanism constructs the interrelationships among architectures by interacting information between any two candidate architectures.Through learning the interrelationships, it selectively emphasizes some architectures important to the network while suppressing unimportant ones, which provides significant references for the architecture selection.Therefore, we improves the performance stability of the architecture search by the above startegy.Besides, our proposed method is high-efficiency and executes architecture search with low time and space costs.Compared to other advanced NAS approaches, our ASM-NAS is able to achieve better architecture search performance on the image classification datasets of CIFAR10, CIFAR100, fashionMNIST and ImageNet.
The Other Side of Autonomous Weapons

The role of autonomy and artificial intelligence (AI) in armed conflict has sparked heated debate. The resulting controversy has obscured the benefits of autonomy and AI for compliance with international humanitarian law (IHL). Compliance with IHL often hinges on situational awareness: information about a possible target's behavior, nearby protected persons and objects, and conditions that might compromise the planner's own perception or judgment. This chapter argues that AI can assist in developing situational awareness technology (SAT) that will make target selection and collateral damage estimation more accurate, thereby reducing harm to civilians. SAT complements familiar precautionary measures such as taking additional time and consulting with more senior officers. These familiar precautions are subject to three limiting factors: contingency, imperfect information, and confirmation bias. This chapter breaks down SAT into three roles. Gatekeeper SAT ensures that operators have the information they need. In each of the three contexts, SAT can help fulfill IHL's mandate of “constant care” in the avoidance of harm to civilian persons and objects.
Conceptual Map Creation from Natural Language Processing: a Systematic Mapping Study

Context: Conceptual Maps (CMs) have been used to organize knowledge and facilitate learning and teaching in multiple domains. CMs also are used in multiple settings in education, since they are able to clarify the relationships between the subcomponents of a particular topic. However, the construction of a CM requires time and effort in identifying and structuring knowledge. In order to mitigate this problem, Natural Language Processing (NLP) techniques have been employed and have contributed to automate the extraction of concepts and relationships from texts. Objective: This article summarizes the main initiatives of building CMs from NLP. Method: A systematic mapping study was used to identify primary studies that present approaches on the use of NLP to automatically create CMs. Results: The mapping provides a description of 23 available articles that have been reviewed in order to extract relevant information on a set of Research Questions (RQ). From the RQ results, a framework was designed in order to present how NLP could be employed to construct CMs. From this framework, a solution graph was elaborated to present different solutions paths to construct CMs using NLP. Conclusions: The construction of CMs using NLP is still a recent field, however, it has been proven to be effective in assisting the automatic construction of CMs.
Conceptual Model of the Education Management Information System for Higher Education Institutions

This paper describes the role and features of using automated information systems in the activities of higher educational institutions, as well as an overview of existing information systems in the field of education.The concept and approaches of introducing electronic education information services for students, teachers and administrative staff, as well as the information system of higher education management in the Republic of Uzbekistan, are described.The higher education management information system covers all tasks related to the administrative, academic, scientific, financial and economic activities of educational institutions.The paper considers the particular characteristics of the education management and processes in Uzbekistan.Accordingly, the re-engineering of the business processes and workflow of the administrative and managerial aspects are designed.Finally, current research wraps up that traditional education management processes can be transferred to the e-services using promising opportunities of the information systems.
Advanced User Interface of Wheelchair based on Android

The Disabled peoples and Patients with physical injuries needs special attention and they should be able to navigate to the desired place through easy operable Wheelchair. The paper describes the setup in wheelchair for Automation which can be controlled by Smartphone operates on Android application. It uses a Microcontroller which drives the DC motor to various directions which resulting in Wheelchair movement. Android application controls the Wheelchair motions based on the commands from the User.
Quantum Representation of Basic Probability Assignments Based on Mixed Quantum States

Understanding the uncertainty is a very important issue in Dempster-Shafer theory (DST) for modelling and fusing uncertain information. In traditional views, the uncertainty involved in a basic probability assignment (BPA) of DST is interpreted in many ways, including intervals of probabilities, non-additive probabilities, degrees of belief, random sets, etc. In this paper, the idea of quantum is imported in DST to interpret the origin of uncertainty in a BPA. Within the study, a novel scheme is proposed to implement the quantum representation of BPA. And several important concepts, including degree of quantum effect, generation of belief and plausibility measures based on BPA’s quantum representation, quantum entropy and observed entropy of a BPA, as well as their properties, are also investigated. The study provides a new viewpoint to understand the uncertainty involved in a BPA and interpret the BPA. Numerical examples and analysis about the quantum representation of BPA are given to illustrate the study.
KartoGraphI: Drawing a Map of Linked Data

A large number of semantic Web knowledge bases have been developed and published on the Web. To help the user identify the knowledge bases relevant for a given problem, and estimate their usability, we propose a declarative indexing framework and an associated visualization Web application, KartoGraphI . It provides an overview of important characteristics for more than 400 knowledge bases including, for instance, dataset location, SPARQL compatibility level, shared vocabularies, etc.
A Comprehensive Framework for Saturation Theorem Proving

A crucial operation of saturation theorem provers is deletion of subsumed formulas. Designers of proof calculi, however, usually discuss this only informally, and the rare formal expositions tend to be clumsy. This is because the equivalence of dynamic and static refutational completeness holds only for derivations where all deleted formulas are redundant, but the standard notion of redundancy is too weak: A clause C does not make an instance Cσ redundant. We present a framework for formal refutational completeness proofs of abstract provers that implement saturation calculi, such as ordered resolution and superposition. The framework modularly extends redundancy criteria derived via a familiar ground-to-nonground lifting. It allows us to extend redundancy criteria so that they cover subsumption, and also to model entire prover architectures so that the static refutational completeness of a calculus immediately implies the dynamic refutational completeness of a prover implementing the calculus within, for instance, an Otter or DISCOUNT loop. Our framework is mechanized in Isabelle/HOL.
CODEN: combined optimization-based decomposition and learning-based enhancement network for Retinex-based brightness and contrast enhancement

In this paper, we present a novel low-light image enhancement method by combining optimization-based decomposition and enhancement network for simultaneously enhancing brightness and contrast. The proposed method works in two steps including Retinex decomposition and illumination enhancement, and can be trained in an end-to-end manner. The first step separates the low-light image into illumination and reflectance components based on the Retinex model. Specifically, it performs model-based optimization followed by learning for edge-preserved illumination smoothing and detail-preserved reflectance denoising. In the second step, the illumination output from the first step, together with its gamma corrected and histogram equalized versions, serves as input to illumination enhancement network (IEN) including residual squeeze and excitation blocks (RSEBs). Extensive experiments prove that our method shows better performance compared with state-of-the-art low-light enhancement methods in the sense of both objective and subjective measures.
Building Physician-Scientist Skills in R Programming: A Short Workshop Report

Introduction: Statistical analysis programs require coding experience and a basic understanding of programming, skills which are not taught as part of medical school or residency curricula.Methods: We conducted a five-day course for early-career Nigerian physician-scientists interested in learning common statistical tests and acquiring R programming skills. The workshop included didactic presentations, small group learning activities, and interactive discussions. A baseline questionnaire captured participant demographics and solicited participants’ level of confidence in understanding/performing common statistical tests. REDCap questionnaires were emailed to obtain feedback on educational format and content. A post-workshop assessment covered participants’ overall impression of the program.Results: A total of 23 participants attended the program. Most participants were male (n=14, 60.9%) and at an early stage in their career (assistant professor, n=20, 87.0%). Approximately 70% of respondents indicated having received some prior training in statistics. The proportion of participants without experience using R and SAS software (90% and 85%, respectively) was greater than the corresponding proportions for Stata (55%) and SPSS (20%). Prior to the workshop, most respondents expressed being “not at all confident” in performing one-way ANOVA (60%), logistic regression (68%), simple linear regression (60%), and McNemar’s test (80%). There was a statistically significant post-workshop improvement in the level of confidence in understanding and performing common statistical tests. The course was rated on a 0-100 scale as “moderately difficult” (mean ±SD: 51.7 ± 19.5). Most participants felt comfortable in putting the knowledge learned into practice (82.2 ± 17.1).Conclusion and Public Health Implications: Introductory R can be taught to junior physician-scientists in resource-limited settings and can inform the development and implementation of similar training initiatives in analogous settings. Copyright © 2022 Aliyu et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0.
Addressing Adjacent Actor Privacy: Designing for Bystanders, Co-Users, and Surveilled Subjects of Smart Home Cameras

Many consumer Internet Things (IoT) devices involve spatial sensors such as cameras and microphones. These affect the privacy of nearby people. A prime example is smart home security cameras. We present our work developing scenarios, use cases, and design proposals for addressing smart camera privacy. Preliminary findings from a concept evaluation with 11 participants is presented. The outcomes of this research through design project foreground the importance and challenges of designing to support the privacy of nearby users. We outline actionable design responses while also raising limitations of technology approaches alone to address these issues.
Advantages of virtual reality for the participative design of work processes: An integrative perspective

BACKGROUND: The participative design of work processes is hampered by as-yet unresolved challenges. A root cause is seen in high information-pass-on-barriers. Virtual Reality (VR) may have a significant potential to overcome these challenges. Yet, there is no systematic understanding of which advantages provided by VR can support the participative design of work processes. OBJECTIVE: The present study aims to assess the potential of VR to support the participative design of work processes by conducting an integrative literature review identifying the advantages of VR in general work contexts and mapping them to known challenges in participative design of work processes. METHODS: The integrative literature review was conducted based on 268 sources of which 52 were considered for an in-depth analysis of the advantages offered by VR. RESULTS: The resulting conceptual framework consisted of 13 characteristic-related advantages (e.g., immersion, interactivity, flexibility) and 10 effect-related advantages (e.g., attractivity, involvement, cost efficiency) which readily address known challenges in the participative design of work processes. CONCLUSION: Mapping the advantages of VR to the challenges in participative design of work processes revealed a substantial potential of VR to overcome high information-pass-on-barriers. As such, employing VR in work process design initiatives represents a fruitful avenue for the promotion of prevention and employee health.
PANDA: Human-in-the-Loop Anomaly Detection and Explanation

The paper addresses the tasks of anomaly detection and explanation simultaneously, in the human-in-the-loop paradigm integrating the end-user expertise: it first proposes to exploit two complementary data representations to identify anomalies, namely the description induced by the raw features and the description induced by a user-defined vocabulary. These representations respectively lead to identify so-called data-driven and knowledge-driven anomalies. The paper then proposes to confront these two sets of instances so as to improve the detection step and to dispose of tools towards anomaly explanations. It distinguishes and discusses three cases, underlining how the two description spaces can benefit from one another, in terms of accuracy and interpretability.
Multi-Robot Adversarial Resilience using Control Barrier Functions

In this paper we present a control barrier functionbased (CBF) resilience controller that provides resilience in a multi-robot network to adversaries.Previous approaches provide resilience by virtue of specific linear combinations of multiple control constraints.These combinations can be difficult to find and are sensitive to the addition of new constraints.Unlike previous approaches, the proposed CBF provides network resilience and is easily amenable to multiple other control constraints, such as collision and obstacle avoidance.The inclusion of such constraints is essential in order to implement a resilience controller on realistic robot platforms.We demonstrate the viability of the CBF-based resilience controller on real robotic systems through case studies on a multi-robot flocking problem in cluttered environments with the presence of adversarial robots.
InfoLM: A New Metric to Evaluate Summarization &amp; Data2Text Generation

Assessing the quality of natural language generation (NLG) systems through human annotation is very expensive. Additionally, human annotation campaigns are time-consuming and include non-reusable human labour. In practice, researchers rely on automatic metrics as a proxy of quality. In the last decade, many string-based metrics (e.g., BLEU or ROUGE) have been introduced. However, such metrics usually rely on exact matches and thus, do not robustly handle synonyms. In this paper, we introduce InfoLM a family of untrained metrics that can be viewed as a string-based metric that addresses the aforementioned flaws thanks to a pre-trained masked language model. This family of metrics also makes use of information measures allowing the possibility to adapt InfoLM to different evaluation criteria. Using direct assessment, we demonstrate that InfoLM achieves statistically significant improvement and two figure correlation gains in many configurations compared to existing metrics on both summarization and data2text generation tasks.