Reviewerly/exHarmony huggingface dataset:
Generative AI: Citations for Trust and Consensus

Generative AI uses a large set of sources to create content. The content generated by large language models is text. Often, that text contains statements that are inaccurate or false, sometimes called hallucinations. We explore how identifying citations for the generated text can enable people to determine whether to trust the statements in the text, by allowing different users to specify different trusted sets of sources as candidates for citations. Then we propose methods to eliminate or correct untrustworthy statements. We also consider how citations can help build consensus among people who have different trusted sources of information, by using a large language model to construct text, then editing the text so that it is supported by citations drawn from multiple sets of trusted sources. By using generative AI as a go-between, such a process may allow parties with mutual distrust to discover and confirm areas of agreement. This paper is a proposal for systems that enhance large language models’ usefulness and an outline of some challenges and methods for such systems; it is not a record of system development or testing.
Biomimetic and Psychophysical Investigations on Lifting Tasks for Developing Cooperative Reinforcement Learning Control of a Power Assist Robotic System

Different human-centric cooperative control strategies are used for enhancing human-friendliness in collaborative manipulation between a human user and a power assist robotic system (PARS). However, despite having tremendous prospects, investigations on developing machine learning-based cooperative controls for PARSs have not received much attention yet. It is hypothesized that a cooperative control strategy developed in the framework of reinforcement learning following biomimetic and psychophysical approaches may outperform the existing cooperative control methods for PARSs. As an initial effort towards developing a biomimetics and psychophysics-based cooperative reinforcement learning control strategy for PARSs, in this paper, we conducted a joint biomimetic and psychophysical study for object manipulation. To do so, we separately presented models of lifting objects manually and with a PARS considering weight perception, and kinematic and kinetic features (psychophysical approach), compared the model of manual lifting to that of power-assisted lifting (biomimetic approach), and conceptualized a cooperative reinforcement learning control framework for the PARS based on the biomimetic and psychophysical study results. The results showed that the perceived weights, kinetic features (peak load forces and load force rates), and kinematic features (peak velocities and peak accelerations) for manual lifting were higher than that for power-assisted lifting. A time delay between position and force trajectories was observed for power-assisted lifting, which was not observed for manual lifting. The findings were proposed to be used to develop a user-friendly cooperative reinforcement learning control framework for PARSs for handling large and heavy objects in various industries that could enhance human-robot interactions (HRI) and manipulation performance.
DBTS: Distributed Blockchain-Based Trust Scheme for Data Privacy and Security in Smart Cities

Smart cities are built on top of Internet of Things (IoT)-based networks, with the aim of improving the quality of life for their citizens and urban surroundings through the provision of smart solutions across numerous applications with streamlined communications. However, due to the complexity of devices involved in forming IoT networks, especially sensors, which play an important role in data acquisition and collecting sensitive information. However, there are various security threats and privacy issues with the smart city infrastructure and the processing of data. Moreover, due to the heterogeneous nature of devices and a lack of suitable security mechanisms and deployment scenarios, traditional security mechanisms are not feasible in the IoT spectrum. Also, the inclusion of malicious nodes and tampered data not only reduces the quality of service but also degrades the network's performance and lifetime. Blockchain, with its decentralized nature and distributed design, can play a significant role in securing IoT-based networks, specifically by providing trustworthy mechanisms for data acquisition. To this end, in this paper, we propose a distributed blockchain-based trustworthy scheme (DBTS) for a network of untrusted Internet of Things (IoT) devices. The suggested approach incorporates smart contracts into heterogeneous Internet of Things (IoT) networks to verify data's veracity.
Sim2Plan: Robot Motion Planning via Message Passing Between Simulation and Reality

Simulation-to-real is the task of training and developing machine learning models and deploying them in real settings with minimal additional training. This approach is becoming increasingly popular in fields such as robotics. However, there is often a gap between the simulated environment and the real world, and machine learning models trained in simulation may not perform as well in the real world. We propose a framework that utilizes a message-passing pipeline to minimize the information gap between simulation and reality. The message-passing pipeline is comprised of three modules: scene understanding, robot planning, and performance validation. First, the scene understanding module aims to match the scene layout between the real environment set-up and its digital twin. Then, the robot planning module solves a robotic task through trial and error in the simulation. Finally, the performance validation module varies the planning results by constantly checking the status difference of the robot and object status between the real set-up and the simulation. In the experiment, we perform a case study that requires a robot to make a cup of coffee. Results show that the robot is able to complete the task under our framework successfully. The robot follows the steps programmed into its system and utilizes its actuators to interact with the coffee machine and other tools required for the task. A noteworthy observation from the experiment is the speed and accuracy with which the robot completed the task. The robot can make a cup of coffee relatively quickly compared with traditional robot planning and control methods, and its movements were precise and efficient. Overall, the results of this case study demonstrate the potential benefits of our method that drive robots for tasks that require precision and efficiency. Further research in this area could lead to the development of even more versatile and adaptable robots, opening up new possibilities for automation in various industries.
MF-SET: A Multitask Learning Framework for Student Evaluation of Teaching

This paper presents MF-SET, a multi-task framework for sentiment analysis and aspect-opinion extraction in student evaluations of teaching (SET) in Spanish, which is an understudied area. We first prepared a novel aspect-opinion extraction dataset in Spanish that evaluates nine different aspects of teaching in both positive and negative light. We then developed a multi-task learning framework, including opinion segmentation, multi-class classification, and multi-label classification models for extracting information from Spanish text. The framework uses text generative abilities of GPT-3 combined with BERT to deal with the task. For multi-class classification, we used BERT with different feature sets and sentiments about teachers with respect to their recommendation, intellectual challenges, and learning guidance. Our results show that the model’s performance varies depending on the feature sets and classifiers. Opinion segmentation and multi-label classification were fine-tuned on GPT-3 for the best results of 0.68 Micro F1 for positive and 0.72 for negative aspects.
Which Semantics for Human-Machine Dialogue Systems?

In this paper, we present an intelligent memory system. The input of the system is a downward flow which, at the moment, consists of newspaper articles retrieved from the Web. The output consists of articles’ summaries intended to provide an intelligent dialogue system. The system also integrates an upward flow that comes from this intelligent dialogue system. It is used to regulate the downward flow. The goal is to ensure that the information provided by the intelligent memory system meets the expectations of the users of the intelligent dialogue system. Beyond the presentation of the intelligent memory system, the central question is the place of semantics in an information system that uses Artificial Intelligence and the place of the information understanding performed by the intelligent memory system during the information automatic analysis.
A Systematic Review for Privacy-Preserving Challenges of Blockchain-Based IoT Networks

Blockchain-based distributed ledger technology is a data management technology that stores a time-ordered set of transactions to verify all transactions without third-party organizations acting as authorities. Thus, it supplies privacy, integrity, and confidentiality due to its central attributes by implementing different strategies such as encryption, anonymization, and private contracts. This technology can develop decentralized and data-intensive applications that run on many devices, maintaining users’ privacy. Given these advantages, several studies have been conducted on improving privacy issues through the use of blockchain technology. As IoT networks still face significant privacy obstacles due to IoT big data volume, velocity, variety, and value With their exponential growth, some researchers have suggested overcoming them with decentralized blockchain solutions. This research aims to systematically review the current approaches to blockchain applications for preserving privacy in IoT, in order to highlight challenges, difficulties, and future research paths.
The Emotional Impact of Camera Techniques in Cinematic Virtual Reality: Examining Frame Shots and Angles

Virtual reality (VR) technology has been increasingly integrated into the film industry, leading to the emergence of Cinematic Virtual Reality (CVR), which offers a uniquely immersive viewing experience that allows audiences to experience “films inside films”. However, traditional film cinematic techniques do not readily translate into CVR, and important cinematographic techniques for this new medium need to be reconsidered. This study aims to investigate the embodiment effect in CVR, a phenomenon that significantly alters the audience’s emotional responses compared to traditional films. We focus on frame shots and camera angles in both mediums. A within-subjects experimental design is employed to compare emotional responses across traditional film and CVR experiences. A total of 40 participants were recruited for the experiment. Using a high-quality VR headset for the immersive cinema condition and a 2D screen for the traditional film condition, the participants were shown identical frame shots and camera angles of a hypothetical scene generated with Unreal Engine 5 (UE5). Their emotional responses were then measured with the Self-Assessment Manikin (SAM) methods. The results show marked differences in responses, suggesting that spatial cognition in VR produces different emotional experiences in CVR from those in traditional viewing condition. The findings contribute to an insight for effective CVR storytelling, informing practical cinematic techniques for filmmakers exploring this novel medium.
Phishing Faster: Implementing ChatGPT into Phishing Campaigns

Email remains the top choice for both personal and business communication despite the increasing availability of social media options. However, its widespread use has also made it a prime target for cybercriminals looking to obtain credentials through phishing. To combat this, organizations can utilize phishing campaigns to train employees to identify and avoid such attacks, and Artificial Intelligence can aid in the quick deployment of such campaigns. This study aims to explore the potential of OpenAI’s ChatGPT, an Artificial Intelligence chatbot, in generating simulated phishing emails for employee training and phishing campaigns. Furthermore, it seeks to investigate how modifying the input given to ChatGPT can impact the content of the generated emails, making them more realistic and convincing to the end user. To evaluate ChatGPT’s effectiveness, the chatbot will be prompted to create various examples of phishing emails, and further testing will assess how social engineering techniques can be incorporated to improve their effectiveness in phishing campaigns. Ultimately, the study will demonstrate ChatGPT’s ability to generate phishing emails quickly and efficiently.
OCRs for Extracting Characters from Ballots in the Process of Automating the Integrity Test in the Brazilian Elections

In this paper, we present a methodology for extracting characters using Optical Character Recognition (OCR) in the context of automating integrity tests of Brazilian elections. To do this, we used a dataset consisting of 550 ballots identical to the ones used in the integrity tests of the Brazilian electronic ballot boxes, and performed a real-world test in an uncontrolled environment. The ballots were photographed by a robotic arm equipped with a camera attached to the arm. The 550 ballots were divided into five different patterns to simulate varying situations that might occur during integrity testing to evaluate the effectiveness of our OCR methodology. Finally, we evaluated the results by comparing the OCRs with metrics of precision and accuracy. The objectives of this paper were to test and discuss the feasibility of using OCRs in the process of automating integrity testing of electronic ballot boxes in Brazilian elections. Also, to create a methodology that can increase the precision, accuracy, and reliability of the data extracted by the OCRs using other technologies such as object detection as support. As a result of the paper, we could create a methodology robust enough to be included and tested along with the overall project of automating the integrity testing of electronic ballot boxes in Brazilian elections that received an innovation award from the Brazilian Judiciary.
HOLY: An Ontology Covering the Hydrogen Market

Abstract This paper presents the Hydrogen Ontology ( HOLY ), a domain ontology modeling the complex and dynamic structures of hydrogen-based markets. The hydrogen economy has become a politically and economically crucial sector for the transition to renewable energy, accelerating technological and socio-economic innovations. However, the attainment of market insights requires a large variety of informational concepts which are predominantly found in unstructured text data. HOLY provides the necessary structure for the representation of these concepts. Through a top-down approach, HOLY defines taxonomies based on a hierarchical structure of products and applications. In addition, to ensure reusability, the ontology incorporates components from established ontologies in its structure. As a result, HOLY consists of over 100 classes defining information about organizations, projects, components, products, applications, markets, and indicators. Hence, our work contributes to the systemic modeling of the hydrogen domain with a focus on its value chain. Formally, we represent and validate the ontology with Semantic Web Technologies. HOLY includes lexical-semantic information (e.g., synonyms, hyponyms, definitions, and examples) to simplify data integration into knowledge acquisition systems. Therefore, we provide a foundation for the retrieval, storage, and delivery of market insights. A first application based on HOLY at the Fraunhofer IIS offers an up-to-date market overview of developments in the fuel cell environment.
VOYAGE: A Large Collection of Vocabulary Usage in Open RDF Datasets

Shared vocabularies facilitate data integration and application interoperability on the Semantic Web. An investigation of how vocabularies are practically used in open RDF data, particularly with the increasing number of RDF datasets registered in open data portals, is expected to provide a measurement for the adoption of shared vocabularies and an indicator of the state of the Semantic Web. To support this investigation, we constructed and published VOYAGE, a large collection of vocabulary usage in open RDF datasets. We built it by collecting 68,312 RDF datasets from 517 pay-level domains via 577 open data portals, and we extracted 50,976 vocabularies used in the data. We analyzed the extracted usage data and revealed the distributions of frequency and diversity in vocabulary usage. We particularly characterized the patterns of term co-occurrence, and leveraged them to cluster vocabularies and RDF datasets as a potential application of VOYAGE. Our data is available from Zenodo at https://zenodo.org/record/7902675 . Our code is available from GitHub at https://github.com/nju-websoft/VOYAGE .
MMpedia: A Large-Scale Multi-modal Knowledge Graph

Knowledge graphs serve as crucial resources for various applications. However, most existing knowledge graphs present symbolic knowledge in the form of natural language, lacking other modal information, e.g., images. Previous multi-modal knowledge graphs have encountered challenges with scaling and image quality. Therefore, this paper proposes a highly-scalable and high-quality multi-modal knowledge graph using a novel pipeline method. Summarily, we first retrieve images from a search engine and build a new Recurrent Gate Multi-modal model to filter out the non-visual entities. Then, we utilize entities’ textual and type information to remove noisy images of the remaining entities. Through this method, we construct a large-scale multi-modal knowledge graph named MMpedia, containing 2,661,941 entity nodes and 19,489,074 images. As we know, MMpedia has the largest collection of images among existing multi-modal knowledge graphs. Furthermore, we employ human evaluation and downstream tasks to verify the usefulness of images in MMpedia. The experimental result shows that both the state-of-the-art method and multi-modal large language model (e.g., VisualChatGPT) achieve about a 4% improvement on Hit@1 in the entity prediction task by incorporating our collected images. We also find that the multi-modal large language model is hard to ground entities to images. The dataset ( https://zenodo.org/record/7816711 ) and source code of this paper are available at https://github.com/Delicate2000/MMpedia .
The Scourge of Ransomware: The Cybercrime Growth Industry of the Early 2020s

This chapter traces the growth of that form of cybercrime termed “ransomware”—a particularly harmful digital crime whereby networks are penetrated, and databases hacked with the “captured” data “locked” by means of malicious software, resulting in users being unable to access information/data files until a ransom is paid to the cybercriminals. The development, sophistication and advanced professionalisation of ransomers modus operandi is described, ranging from the use of “phishing” and business email compromise (BEC) emails, to the encryption of penetrated network databases and files, and in the process becoming evermore destructive and complex, thus increasing the difficulties of victims detecting, preventing and combatting its spread worldwide.
Comprehensive Analysis of Freebase and Dataset Creation for Robust Evaluation of Knowledge Graph Link Prediction Models

Freebase is amongst the largest public cross-domain knowledge graphs. It possesses three main data modeling idiosyncrasies. It has a strong type system; its properties are purposefully represented in reverse pairs; and it uses mediator objects to represent multiary relationships. These design choices are important in modeling the real-world. But they also pose nontrivial challenges in research of embedding models for knowledge graph completion, especially when models are developed and evaluated agnostically of these idiosyncrasies. This paper lays out a comprehensive analysis of the challenges associated with the idiosyncrasies of Freebase and measures their impact on knowledge graph link prediction. The results fill an important gap in our understanding of embedding models for link prediction as such models were never evaluated using a proper full-scale Freebase dataset. The paper also makes available several variants of the Freebase dataset by inclusion and exclusion of the data modeling idiosyncrasies. It fills an important gap in dataset availability too as this is the first-ever publicly available full-scale Freebase dataset that has gone through proper preparation.
The SAREF Pipeline and Portal—An Ontology Verification Framework

The Smart Applications REFerence Ontology (SAREF) defines a modular set of versioned ontologies that enable semantic interoperability between different Internet of Things (IoT) vendor solutions across various IoT industries. The European Telecommunications Standards Institute Specialist Task Force (ETSI STF) 578 recently completed the “Specification of the SAREF Development Framework and Workflow and Development of the SAREF Community Portal for User Engagement”. This project specifies the development pipeline and workflow needed to accelerate the development of SAREF and its extensions along with the development of software that automates the generation of ontology portal content from SAREF sources on the public ETSI Forge. This paper describes the SAREF Pipeline that provides an efficient and robust support infrastructure for the Continuous Integration and Delivery of semantic ontology development.
The Wikibase Approach to the Enslaved.Org Hub Knowledge Graph

Many methodologies and platforms for creating, deploying, and defining the manner of knowledge graphs are available. For this paper, we single out the platform, Wikibase. Using Wikibase comes with many advantages: out-of-the-box software for de-referencing, a convenient user interface, a consistent way to track and record provenance and lineage, and the ability to execute SPARQL queries against an RDF representation of the knowledge graph. However, the provenance mechanism and the exact nature of the structure of the Wikibase representation can complicate developing a principled schema for knowledge graphs, as well as the approach to the materialization of the data for upload to the platform. In this paper, we detail the methodology used to design, implement, and deploy the Enslaved.Org Hub, a nationally recognized knowledge graph for documenting the peoples of the historical slave trade.
Benchmarking Geospatial Question Answering Engines Using the Dataset GeoQuestions1089

We present the dataset GeoQuestions1089 for benchmarking geospatial question answering engines. GeoQuestions1089 is the largest such dataset available presently and it contains 1089 questions, their corresponding GeoSPARQL or SPARQL queries and their answers over the geospatial knowledge graph YAGO2geo. We use GeoQuestions1089 to evaluate the effectiveness and efficiency of geospatial question answering engines GeoQA2 (an extension of GeoQA developed by our group) and the system of Hamzei et al. (2021).