Reviewerly/exHarmony huggingface dataset:
Chatbot Using Python

The days of solely engaging with service through a keyboard are over.Users interact with the system more through voice assistant and chatbot.A chatbot is a computer program that can converse with humans using artificial intelligence in messaging platforms.Every time the chatbot gets input from the user it saves input and response which helps the chatbot with little initial knowledge to evolve using gathered responses.With increased responses, chatbot precision also gets increased.The ultimate goal of this project is to add a chatbot feature and API for Smt.Indira Gandhi college of engineering.This project will investigate how advancements in artificial intelligence and machine learning technologies are being used to improve many services.Specifically, it will look at the development of chatbots as a channel for information and distribution.The program selects the closest matching response from the closest matching system that matches input utilizing WordNet, it then chooses responses from known selection of statements for that response.This project aims to implements online chatbot system to assist user to access college website, using tool that expose artificial intelligence method such as NLP, allowing user to communicate with college chatbot and using a chatbot that can respond to natural language input and has been created using appropriate machine learning techniques.
The emergence of data sharing along complex supply chains

Purpose To improve supply chain performance, companies are now exploring new pathways including industry-wide data sharing initiatives along complex supply chains. The purpose of this paper is to stimulate research in this field by describing the benefits, obstacles and the governance required for supply chain data sharing initiatives. Design/methodology/approach Based on publicly available information complemented by interviews with practitioners, the authors describe how companies are establishing ambitious data sharing infrastructure and initiatives along their supply chains. Findings The authors describe how data sharing along supply chains is becoming increasingly important for many companies and how the automotive sector is working towards establishing a digital infrastructure for data sharing that could support a wide range of use cases. The article emphasises the importance of studying the governance of data ecosystems using new theoretical approaches. Finally, the authors suggest three areas for future research on data ecosystems, including their governance, the learning dynamics that will drive their adoption and their relationship with broader system-level changes. Originality/value This paper is the first, to the authors’ knowledge, that depicts how industry-wide data-sharing initiatives are expected to have an impact on supply chain performance. The authors highlight factors that affect the development and implementation of these initiatives along supply chains.
Design Considerations in Developing an Augmented Reality Learning Environment for Engaging Students in Covariational Reasoning

This study reports on findings of two design cycles of augmented reality environment intended to engage high school students in covariational reasoning. The study used a designed-based research method to develop and improve the learning environment. In this report, we present the initial design and discuss how it promoted students' engagement at elementary levels of covariation. Following this first cycle, we introduced a redesigned learning environment. We provide evidence of how the new design in the second cycle promoted students' engagement at advanced levels of covariational reasoning. Six groups of three 15- to 17-year-old students participated in the research. Using AR headsets, each group carried out two activities well-suited, in principle, to covariational reasoning. The students' interactions were video-recorded, and the theory of semiotic representation was used to analyze the degree of their engagement in covariational reasoning. The design emphasized multiple representations generally, and the compatibility between the explored phenomenon and its mathematical representations, specifically. Findings show that the design considerations in the second design cycle significantly improved the students' engagement at different levels of covariation, including advanced levels.&#x0D; &#x0D; Keywords—covariational reasoning, representations, design principles
Optical scrambler using WGM micro-bottle cavity

An optical scrambler using a whispering-gallery-mode (WGM) micro-bottle cavity to scramble a complex optical signal to generate an uncorrelated output is proposed. We experimentally demonstrated this micro-cavity scrambler by using chaotic laser light as the incident signal and studied the influence of the coupling state. Experiments achieved full scrambling with a low cross correlation of 0.028 between the output and the input. Results indicate that the scrambling effect originates from the interference among numerous WGMs in the bottle cavity. It is believed that the micro-bottle cavity with an efficient scrambling function can become a promising candidate for encryption.
Detecting Sentiment Polarities with Comparative Analysis of Machine Learning and Deep Learning Algorithms

As technology develops, customer reviews are used to assess the quality of products due to the increasing number of selling products online. The extraction of useful minerals from reviews are extremely important for future buyers who are seeking thoughts and sentiments to help their decision-making. Sentiment polarity detection is the process of categorizing the emotions expressed with text, mainly to identify whether the subjectiveness of the writer’s attitude toward the product, or service is positive, neutral or negative. To decrease sentiment mistakes on increasingly complex training data, we deploy machine and hybrid learning models that integrate multiple types of deep neural networks in this study. Then, we apply TF-IDF vectorization for extraction of valuable information in reviews. The paper proposes the comparison of performance between machine learning models, deep learning models and the combination of deep learning models to discover the sentiment polarity on online product reviews. We use the dataset collected from an e-commerce website (Amazon), which includes various product reviews. The experimental results display that combination of deep learning models outperform more machine learning algorithms.
Research and Design of Precision-Landing Drone in an Underground GPS-Denied Environment

Autonomous precision landing techniques allow unmanned aerial vehicles (UAVs) to land on their own in a specific location and orientation using more precise techniques than conventional GPS navigation. Using these techniques can cut the landing precision margin of error from a matter of meters to a matter of centimeters. The process can be implemented in a number of different ways, but it always involves at least one sensor on the drone, such as a visual, thermal or infrared capable camera, gathering information from the ground or landing target. Corresponding with the sensor type(s) that the drone is equipped with are several localization markers necessary to facilitate autonomous precision landing. Such markers include fiducial tags such as AruCo markers or April tags for visual cameras, custom heat-strip signatures for thermal cameras, and IR beacons for infrared cameras. All three of these precision landing techniques are suitable for GPS-denied environments. Fiducial tags and visual sensors have a high technology readiness level. They are readily available for line-of-sight landings autonomously, and the latter two techniques (IR-locking and Thermal Imaging) are especially useful in light-deprived or heavily dusty environments such as an underground mine.
Empowerment and self-efficacy in patients with chronic disease; a systematic review study

Introduction: It is crucial for patients with chronic disease to learn the skills they need to manage their illnesses, which is called empowerment. Objectives: This review study aimed to evaluate the need for empowerment and self-efficacy in patients with chronic disease. Methods: The keywords were looked up in articles published between 2014 and 2023 using electronic databases, including Web of Science, PubMed, Embase, Scopus, and the search engine Google scholar. All obtained pertinent papers and books were analyzed based on the search method and the established criteria, and all redundant and unrelated items were excluded. Results: Results showed that empowerment and self-efficacy were conducted as a technique or strategy to guide patients and caregivers. An empowerment and self-efficacy model can promote quality of life (QoL) in chronic disease patients. Despite empowerment being beneficial for patients with chronic illnesses in previous studies, it should also be implemented for patients with other diseases. Conclusion: Given this literature results, the empowerment and self-efficacy model can improve the QoL in patients with chronic illnesses. Registration: This study has been compiled based on the PRISMA checklist, and its protocol was registered on the PROSPERO website (ID: CRD42023417639).
Knowledge Management for Information Querying System in Education via the Combination of Rela-Ops Model and Knowledge Graph

E-learning is an online educational system that uses electronic and technology tools and the internet. To facilitate the acquisition of knowledge, students can consult materials and interact directly with lecturers via e-learning educational systems. The method for developing an intelligent querying system for e-learning is suggested in this research. The knowledge base of the system is structured using the knowledge model combining an ontology and the two-layer knowledge graph, called Rela-KG model. The searching problems for knowledge content are studied and solved. The technique is applied to create an intelligent querying system on the course of Database Foundation in Information Technology (IT) at the university. This technique attempts to assist students in reviewing lectures and better comprehending course material through independent study. The experimental results indicate that it would be anticipated to support student access to online learning resources.
The disinformation in virtual communities: a systematic literature review

This article discusses disinformation in virtual communities. Freedom of speech and freedom of expression are conditions for creating a democratic society. This form of freedom of speech and expression is the creation of a healthy public sphere in people's lives. The real form of a healthy public sphere is that people are free to voice their opinions without being dominated by other parties (structural or non-structural). In addition, people are free to convey ideas and exchange opinions regardless of the existence of power barriers or because of differences in socioeconomic conditions. The public sphere can be created in the realm of reality as well as in virtual space. The intentional, large-scale, and systematic spread of disinformation seriously harms democracy and is a major challenge to democratic societies. Information disorder is not only a problem in Indonesia but has become a problem in many democratic countries in the world. As a problem with humanity, information disorder has been widely studied by various researchers. But the problem is that currently there is no clear concept related to this information chaos. Experts and the public are still confused about defining fake news, hoaxes, or disinformation. As a social problem, a solution to this information disorder is needed. To make a solution to the problem of disinformation, it is necessary to understand the concept of information disturbance clearly. Therefore, this systematic literature review generally aims to ascertain the concept of disinformation, its types, and how to solve disinformation.&#x0D;
WebLicht-Batch -- A Web-Based Interface for Batch Processing Large Input with the WebLicht Workflow Engine

WebLicht is a workflow engine that gives researchers access to a well-inhabited space of natural language processing tools that can be combined into tool chains to perform complex natural language analyses. In this paper, we present WebLicht-Batch, a web-based interface to WebLicht's chainer back-end. WebLicht-Batch helps users to automatically feed large input data, or input data of multiple files into WebLicht. It disassembles large input into smaller, more digestible sizes, feeds the resulting parts into WebLicht's pipelining and execution engine, and then assembles the results of such processing into files that preserve the usual input-output dichotomy.
Enhancing awareness of industrial robots in collaborative manufacturing

The diffusion of Human-Robot Collaborative cells is prevented by several barriers. Classical control approaches seem not yet fully suitable for facing the variability conveyed by the presence of human operators beside robots. The capabilities of representing heterogeneous knowledge representation and performing abstract reasoning are crucial to enhance the flexibility of control solutions. To this aim, the ontology SOHO (Sharework Ontology for Human-Robot Collaboration) has been specifically designed for representing Human-Robot Collaboration scenarios, following a context-based approach. This work brings several contributions. This paper proposes an extension of SOHO to better characterize behavioral constraints of collaborative tasks. Furthermore, this work shows a knowledge extraction procedure designed to automatize the synthesis of Artificial Intelligence plan-based controllers for realizing flexible coordination of human and robot behaviors in collaborative tasks. The generality of the ontological model and the developed representation capabilities as well as the validity of the synthesized planning domains are evaluated on a number of realistic industrial scenarios where collaborative robots are actually deployed.
Exploring Cybercrime: An Emerging Phenomenon and Associated Challenges in Africa

This study traces the emergence, highlights the trends, strategies and associated challenges of policing cybercrime in Africa. The researcher also displayed the evolving complexities and escalations of related criminal activities. This qualitative study employed a non-empirical research design: Systematic review methodology to analyse grey literature and primary research studies peer-reviewed and published, restricted from 2002-2021, with non-sequential preference adoption considered. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Critical Appraisal Skill Programme (CASP) were employed to ensure the trustworthiness of the findings of this study based on reviewed conventional and seminal literature studies on this subject. This study revealed that for a long time in Africa, this crime existed and remains uncoded; however, relevant stakeholders paid limited attention in terms of offering strategies to effectively prevent, combat and investigate this crime, while the documented nature, effects and effects were at maximum scale. Moreover, the associated challenges for policing cybercrime in Africa are currently uncontrollable, as this crime is presently rising beyond unacceptable levels in the cited African countries. The opportunistic organised criminal networks are taking advantage of these existing loopholes. This study concludes and recommends that African countries should realise the importance of efficiently responding to this scourge in the affected countries, the envisaged strategies should be geared towards the African landscape, within a larger global context, while strongly introducing the use of technological advances and improvements, among others. This can aid in the correct usage of computers; staging of the database system; proper facilitation of cybercrime trends; and, surveillance, to eradicate increasing incidences of this crime in African countries. The relevant stakeholders should have access to, and share, information, irrespective of African geographical locations.
How Conversational are “Conversational Agents”?

The paper considers whether is it possible to view interactions with so-called conversational agents (chatbots, voice assistants, etc.) as a form of conversation. It is argued here that such conversational agents are conversational in a proper sense. To justify this conclusion, the analysis of the beginnings of 100 calls to a Russian municipal call center, processed by a chatbot, is conducted. The revealed features of the inquiry formulations, silences, and overlaps at the beginning of the calls show that users deal with the chatbot as a conversational partner and not as a voice user interface. It is proposed that to call an interaction a “conversation,” it is enough that at least one co-participant (the weak participation requirement) is able to understand all the turns in the interaction (the strong analyzability requirement) as part of the ongoing conversation.
Mining Geospatial Relationships from Text

A geospatial Knowledge Graph (KG) is a heterogeneous information network, capable of representing relationships between spatial entities in a machine-interpretable format, and has tremendous applications in logistics and social networks. Existing efforts to build a geospatial KG, have mainly used sparse spatial relationships, e.g., a district located inside a city, which provide only marginal benefits compared to a traditional database. In spite of the substantial advances in the tasks of link prediction and knowledge graph completion, identifying geospatial relationships remains challenging, particularly due to the fact that spatial entities are represented with single-point geometries, and textual attributes are frequently missing. In this study, we present GTMiner, a novel framework capable of jointly modeling Geospatial and Textual information to construct a knowledge graph, by mining three useful spatial relationships from a geospatial database, in an end-to-end fashion. The system is divided into three components: (1) a Candidate Selection module, to efficiently select a small number of candidate pairs; (2) a Relation Prediction component to predict spatial relationships between the entities; (3) a KG Refinement procedure, to improve both coverage and correctness of a geospatial knowledge graph. We carry out experiments on four cities' geospatial databases, from publicly-available sources and compare with existing algorithms for link prediction and geospatial data integration. Finally, we conduct an ablation study to motivate our design choices and an efficiency analysis to show that the time required by GTMiner for training and inference is comparable, or even shorter, than existing solutions.
A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy

Several "data-for-good" projects [1, 5, 12] initiated by major companies (e.g., Meta, Google) release to the public spatio-temporal datasets to benefit COVID-19 spread modeling [17, 47, 64] and understand human mobility [14, 24]. Most often, spatio-temporal data are provided in the form of snapshot high resolution population density information, where the released statistics capture population counts in small areas for short time periods. Since high resolution is required for utility (e.g., in modeling COVID hotspots) privacy risks are elevated. To prevent malicious actors from using the data to infer sensitive details about individuals, the released datasets must be first sanitized. Typically, [1, 5, 7, 12], differential privacy (DP) is employed as protection model, due to its formal protection guarantees that prevent an adversary to learn whether a particular individual's data has been included in the release or not.
A programmable true random number generator using commercial quantum computers

Random number generators (RNG) are essential elements in many cryptographic systems. True random number generators (TRNG) rely upon sources of randomness from natural processes such as those arising from quantum mechanics phenomena. We demonstrate that a quantum computer can serve as a high-quality, weakly random source for a generalized user-defined probability mass function (PMF). Specifically, QC measurement implements the process of variate sampling according to a user-specified PMF resulting in a word comprised of electronic bits that can then be processed by an extractor function to address inaccuracies due to non-ideal quantum gate operations and other system biases. We introduce an automated and flexible method for implementing a TRNG as a programmed quantum circuit that executes on commercially-available, gate-model quantum computers. The user specifies the desired word size as the number of qubits and a definition of the desired PMF. Based upon the user specification of the PMF, our compilation tool automatically synthesizes the desired TRNG as a structural OpenQASM file containing native gate operations that are optimized to reduce the circuit’s quantum depth. The resulting TRNG provides multiple bits of randomness for each execution/measurement cycle; thus, the number of random bits produced in each execution is limited only by the size of the QC. We provide experimental results to illustrate the viability of this approach.
Automated quantum oracle synthesis with a minimal number of qubits

Several prominent quantum computing algorithms—including Grover’s search algorithm and Shor’s algorithm for finding the prime factorization of an integer—employ subcircuits termed ‘oracles’ that embed a specific instance of a mathematical function into a corresponding bijective function that is then realized as a quantum circuit representation. Designing oracles, and particularly, designing them to be optimized for a particular use case, can be a non-trivial task. For example, the challenge of implementing quantum circuits in the current era of NISQ-based quantum computers generally dictates that they should be designed with a minimal number of qubits, as larger qubit counts increase the likelihood that computations will fail due to one or more of the qubits decohering. However, some quantum circuits require that function domain values be preserved, which can preclude using the minimal number of qubits in the oracle circuit. Thus, quantum oracles must be designed with a particular application in mind. In this work, we present two methods for automatic quantum oracle synthesis. One of these methods uses a minimal number of qubits, while the other preserves the function domain values while also minimizing the overall required number of qubits. For each method, we describe known quantum circuit use cases, and illustrate implementation using an automated quantum compilation and optimization tool to synthesize oracles for a set of benchmark functions; we can then compare the methods with metrics including required qubit count and quantum circuit complexity.
Limits of single-photon storage in a single <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">Λ</mml:mi></mml:math> -type atom

We theoretically investigate the limits of single-photon storage in a single $\Lambda$-type atom, specifically the trade-off between storage efficiency and storage speed. We show that a control field can accelerate the storage process without degrading efficiency too much. However, the storage speed is ultimately limited by the total decay rate of the involved excited state. For a single-photon pulse propagating in a regular one-dimensional waveguide, the storage efficiency has an upper limit of $50 \%$. Perfect single-photon storage can be achieved by using a chiral waveguide or the Sagnac interferometry. By comparing the storage efficiencies of Fock-state and coherent-state pulses, we reveal the influence of quantum statistics of light on photon storage at the single-photon level.