Reviewerly/exHarmony huggingface dataset:
Bystanders Unveiled: Introducing a Comprehensive Cyberbullying Corpus with Bystander Information

This paper introduces a new cyberbullying dataset, CYBY23, that includes Twitter threads containing both the main posts and the replies from bystanders. The dataset is organized based on conversation ID and consists of 112 threads, totaling around 639 tweets. The unique aspect of this dataset is the inclusion of labels for bystanders' roles, which provides a comprehensive understanding of the bullying incident and helps identify the level of aggressiveness in cyberbullying. This type of information is not available in existing datasets that only label isolated tweets. By incorporating bystanders' roles, annotators gain a deeper understanding of real-world scenarios, leading to improved machine learning performance and better classification of cyberbullying. The dataset is freely available, promoting collaboration among researchers, ensuring result reliability, and enabling the reuse of Twitter datasets. It also offers a cost-effective way for non-technical researchers to leverage Twitter data in their scientific investigations.
Towards Framing the Agents &amp; Artifacts Conceptual Model at the Knowledge Level: First Ideas and Experiments

In this contribution, we propose an extension of the Knowledge Level as introduced by Newell in the A.I. context and refined by Jennings in agent-based software engineering to include also the agent environment as a first-class analysis/design dimension. We revisit and refine the Agents & Artifacts (A &A) conceptual model to be at the Knowledge Level by explicitly introducing a semantic layer based on Knowledge Graphs, and we discuss the benefits with some practical examples.
GRILLBot: A multi-modal conversational agent for complex real-world tasks

We present GRILLBot, an open-source multi-modal task-oriented voice assistant to help users perform complex tasks, focusing on the domains of cooking and home improvement. GRILLBot curates and leverages web information extraction to build coverage over a broad range of tasks for which a user can receive guidance. To represent each task, we propose TaskGraphs as a dynamic graph unifying steps, requirements, and curated domain knowledge enabling contextual question answering, and detailed explanations. Multi-modal elements play a key role in GRILLBot both helping the user navigate through the task and enriching the experience with helpful videos and images that are automatically linked throughout the task. We leverage a contextual neural semantic parser to enable flexible navigation when interacting with the system by jointly encoding stateful information with the conversation history. GRILLBot enables dynamic and adaptable task planning and assistance for complex tasks by combining elements of task representations that incorporate text and structure, combined with neural models for search, question answering, and dialogue state management. GRILLBot competed in the Alexa prize TaskBot Challenge as one of the finalists.
iEval: Interactive Evaluation Framework for Open-Domain Empathetic Chatbots

Building an empathetic chatbot is an important objective in dialog generation research, with evaluation being one of the most challenging parts. By empathy, we mean the ability to understand and relate to the speakers’ emotions, and respond to them appropriately. Human evaluation has been considered as the current standard for measuring the performance of open-domain empathetic chatbots. However, existing evaluation procedures suffer from a number of limitations we try to address in our current work. In this paper, we describe iEval, a novel interactive evaluation framework where the person chatting with the bots also rates them on different conversational aspects, as well as ranking them, resulting in greater consistency of the scores. We use iEval to benchmark several state-of-the-art empathetic chatbots, allowing us to discover some intricate details in their performance in different emotional contexts. Based on these results, we present key implications for further improvement of such chatbots. To facilitate other researchers using the iEval framework, we will release our dataset consisting of collected chat logs and human scores.
PCOAST: A Pauli-Based Quantum Circuit Optimization Framework

This paper presents the Pauli-based Circuit Optimization, Analysis, and Synthesis Toolchain (PCOAST), a framework for quantum circuit optimizations based on the commutative properties of Pauli strings. Prior work has demonstrated that commuting Clifford gates past Pauli rotations can expose opportunities for optimization in unitary circuits. PCOAST extends that approach by adapting the technique to mixed unitary and non-unitary circuits via generalized preparation and measurement nodes parameterized by Pauli strings. The result is the PCOAST graph, which enables novel optimizations based on whether a user needs to preserve the quantum state after executing the circuit, or whether they only need to preserve the measurement outcomes. Finally, the framework adapts a highly tunable greedy synthesis algorithm to implement the PCOAST graph with a given gate set. PCOAST is implemented as a set of compiler passes in the Intel® Quantum SDK. In this paper, we evaluate its compilation performance against two leading quantum compilers, Qiskit and t|ket>, We find that PCOAST reduces total gate count by 32.53% and 43.33% on average, compared to the best performance achieved by Qiskit and t|ket> respectively, two-qubit gates by 29.22% and 20.58%, and circuit depth by 42.02% and 51.27%.
Analysis of the Capabilities of a Crowdsourcing Educational Platform as an Innovative Product in the Context of Networking of Educational Activities at a University

The relevance of the presented research is based on the fact, that crowdsourcing, a mechanism created for combining people's efforts to solve projects and tasks, is currently a subject of much interest in the context of innovative products development. This is also the case of educational activity of the universities, since it is necessary to solve a number of practice-oriented learning tasks in educational sphere. However, the players of the educational market do not entirely use crowdsourcing as a technology of networking in education. In this context, studying the real cases on building models of crowdsourcing platforms in the educational environment is really urgent and novel. The main purpose of the study is to describe a model for building the crowdsourcing educational platform «S24» as an innovative product used as a technology for networking of the university educational environment. The research objectives include considering crowdsourcing as an educational technology, describing the model of an innovative educational platform built on the principles of crowdsourcing as well as providing the overview of the capabilities the innovative educational platform provides. The main research methodology is based on the case study method with the use of a systematic approach to the described platform. The case study focused on the crowdsourcing educational platform implemented by the University «Synergy». The platform is currently under testing and it is functioning as a practice-oriented platform in the field of creative industries. The review of the platform conducted in the research leads to the conclusion that it is necessary to use crowdsourcing technology for developing innovative products in education as well as for networking the educational learning environment.
Privacy-Preserving Machine Learning Using Functional Encryption: Opportunities and Challenges

With the advent of functional encryption, new possibilities for computation on encrypted data have arisen. Functional encryption enables data owners to grant third-party access to perform specified computations without disclosing their inputs. It also provides computation results in plaintext, unlike fully homomorphic encryption. The ubiquitousness of machine learning has led to the collection of massive private data in the cloud computing environment. This raises potential privacy issues and underscores the need for more private and secure computing solutions. Numerous efforts have been made in privacy-preserving machine learning (PPML) to address security and privacy concerns. There are approaches based on fully homomorphic encryption (FHE), secure multiparty computation (SMC), and, more recently, functional encryption (FE). Compared to FHE-based PPML techniques, FE-based PPML is still in its infancy. In this paper, we provide a survey of PPML works based on FE, summarizing state-of-the-art literature. We focus on inner-product-FE, function-hiding inner-product encryption, and quadratic-FE-based machine learning models for PPML applications. We analyze the performance and usability of the available FE libraries and their applications to PPML. We also discuss future research directions for FE-based PPML approaches. To the best of our knowledge, this is the first work to survey FE-based PPML approaches.
Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN

Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection.
Chaotic Maps with Tunable Mean Value—Application to a UAV Surveillance Mission

Chaos-related applications are abundant in the literature, and span the fields of secure communications, encryption, optimization, and surveillance. Such applications take advantage of the unpredictability of chaotic systems as an alternative to using true random processes. The chaotic systems used, though, must showcase the statistical characteristics suitable for each application. This may often be hard to achieve, as the design of maps with tunable statistical properties is not a trivial task. Motivated by this, the present study explores the task of constructing maps, where the statistical measures like the mean value can be appropriately controlled by tuning the map’s parameters. For this, a family of piecewise maps is considered, with three control parameters that affect the endpoint interpolations. Numerous examples are given, and the maps are studied through a collection of numerical simulations. The maps can indeed achieve a range of values for their statistical mean. Such maps may find extensive use in relevant chaos-based applications. To showcase this, the problem of chaotic path surveillance is considered as a potential application of the designed maps. Here, an autonomous agent follows a predefined trajectory but maneuvers around it in order to imbue unpredictability to potential hostile observers. The trajectory inherits the randomness of the chaotic map used as a seed, which results in chaotic motion patterns. Simulations are performed for the designed strategy.
Correlations between Locus of Control and Professional Motivation of Employees of the Ministry of Internal Affairs

The study was carried out at the Expert Forensic Center of the Ministry of Internal Affairs of Irkutsk in order to reveal correlations between locus of control and the employees’ professional motivation. The study involved 40 men aged between 23 and 50. The research revealed a high level of externality the respondents showed. There are high indicators on the internality scale in relation to health and illness. The employees recognize their responsibility in family relations however the figures are medium. The lowest rate of responsibility was revealed in the field of interpersonal and production relations. The highest rate of correlations was revealed between preferred choice of a motive for responsible work and high internality in the field of failures which amounted to 0,81. There is also a high correlation between the motive for achieving personal success and low internality in the field of family relations 0,77. There is a strong correlation of 0,71 between the financial motive, high internality in the field of achievements and general internality. There is a strong relationship between the financial motive and general internality, as well as between the financial motive and low internality in the area of failures r = 0,65 and r = 0,5. A high correlation r = 0,5 can be traced between the motive of a career and the length of service. An inverse correlation was revealed between the dominant motive of responsible work, the scales of general internality and internality in the field of achievements r = –0,43 and –0,48.
Efficient Estimation of Trainability for Variational Quantum Circuits

Parameterized quantum circuits used as variational ansatzes are emerging as promising tools to tackle complex problems ranging from quantum chemistry to combinatorial optimization. These variational quantum circuits can suffer from the well-known curse of barren plateaus, which is characterized by an exponential vanishing of the cost-function gradient with the system size, making training unfeasible for practical applications. Since a generic quantum circuit cannot be simulated efficiently, the determination of its trainability is an important problem. Here we find an efficient method to compute the gradient of the cost function and its variance for a wide class of variational quantum circuits. Our scheme relies on our proof of an exact mapping from randomly initialized circuits to a set of Clifford circuits that can be efficiently simulated on a classical computer by virtue of the celebrated Gottesman-Knill theorem. This method is scalable and can be used to certify trainability for variational quantum circuits and explore design strategies that can overcome the barren-plateau problem. As illustrative examples, we show results with up to 100 qubits.6 MoreReceived 20 February 2023Revised 7 September 2023Accepted 3 November 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040335Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningQuantum algorithmsQuantum circuitsQuantum computationQuantum information processingQuantum information theoryQuantum Information, Science & Technology
Mutation analysis for evaluating code translation

Abstract Source-to-source code translation automatically translates a program from one programming language to another. The existing research on code translation evaluates the effectiveness of their approaches by using either syntactic similarities (e.g., BLEU score), or test execution results. The former does not consider semantics, the latter considers semantics but falls short on the problem of insufficient data and tests. In this paper, we propose MBTA ( M utation- b ased Code T ranslation A nalysis), a novel application of mutation analysis for code translation assessment. We also introduce MTS ( M utation-based T ranslation S core), a measure to compute the level of trustworthiness of a translator. If a mutant of an input program shows different test execution results from its translated version, the mutant is killed and a translation bug is revealed. Fewer killed mutants indicate better code translation. MBTA is novel in the sense that mutants are compared to their translated counterparts, and not to their original program’s translation. We conduct a proof-of-concept case study with 612 Java-Python program pairs and 75,082 mutants on the code translators TransCoder and j2py to evaluate the feasibility of MBTA. The results reveal that TransCoder and j2py fail to translate 70.44% and 70.64% of the mutants, respectively, i.e., more than two-thirds of all mutants are incorrectly translated by these translators. By analysing the MTS results more closely, we were able to reveal translation bugs not captured by the conventional comparison between the original and translated programs.
Authentication of Holograms with Mixed Patterns by Direct LBP Comparison

In order to combat fraud, identity documents and currencies often include security elements such as guilloches, micro prints or holograms. This paper aims to authenticate such documents from videos acquired with a smartphone by analyzing the holograms. The proposed method consists of recognizing all the patterns of the hologram to determine if the document is genuine or not. The Local Binary Patterns (LBP) descriptor is used in this paper to represent the features of a hologram. For a given document, Multi LBP Models are built as a reference model. This model is then compared to the LBP models of the tested hologram to decide if the hologram exist or not in the document and then to determine if the document is genuine or not. Experiments are carried out on holograms of French Passports and Euro banknotes. The results show that the proposed strategy allows to determine if the document is an authentic document or falsified in a good accuracy. The code is available at https://github.com/mnchapel/authentication_f_holograms_with_mixed_patterns_by_direct_lbp_comparison.
Detecting Argumentative Fallacies in the Wild: Problems and Limitations of Large Language Models

Previous work on the automatic identification of fallacies in natural language text has typically approached the problem in constrained experimental setups that make it difficult to understand the applicability and usefulness of the proposals in the real world. In this paper, we present the first analysis of the limitations that these data-driven approaches could show in real situations. For that purpose, we first create a validation corpus consisting of natural language argumentation schemes. Second, we provide new empirical results to the emerging task of identifying fallacies in natural language text. Third, we analyse the errors observed outside of the testing data domains considering the new validation corpus. Finally, we point out some important limitations observed in our analysis that should be taken into account in future research in this topic. Specifically, if we want to deploy these systems in the Wild.
Offensive Language Detection in Arabizi

Detecting offensive language in under-resourced languages presents a significant real-world challenge for social media platforms. This paper is the first work focused on the issue of offensive language detection in Arabizi, an under-explored topic in an under-resourced form of Arabic. For the first time, a comprehensive and critical overview of the existing work on the topic is presented. In addition, we carry out experiments using different BERT-like models and show the feasibility of detecting offensive language in Arabizi with high accuracy. Throughout a thorough analysis of results, we emphasize the complexities introduced by dialect variations and out-of-domain generalization. We use in our experiments a dataset that we have constructed by leveraging existing, albeit limited, resources. To facilitate further research, we make this dataset publicly accessible to the research community.
A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?

Understanding the fundamental concepts and trends in a scientific field is crucial for keeping abreast of its continuous advancement. In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. We define three variables to encompass diverse facets of the evolution of research topics within NLP and utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data. Subsequently, we leverage this structure to measure the intensity of these relationships. By conducting extensive experiments on the ACL Anthology corpus, we demonstrate that our framework effectively uncovers evolutionary trends and the underlying causes for a wide range of NLP research topics. Specifically, we show that tasks and methods are primary drivers of research in NLP, with datasets following, while metrics have minimal impact.
Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks

The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the inability of approaching more complex natural language processing tasks. Such is the case of the automatic evaluation of complete professional argumentative debates. In this paper, we propose an original hybrid method to automatically predict the winning stance in this kind of debates. For that purpose, we combine concepts from argumentation theory such as argumentation frameworks and semantics, with Transformer-based architectures and neural graph networks. Furthermore, we obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.
Contrastive Hierarchical Discourse Graph for Vietnamese Extractive Multi-Document Summarization

Extractive Multi-Document Summarization (EMDS) plays a pivotal role in distilling information from multiple sources, enabling efficient knowledge synthesis and document retrieval. However, achieving high-quality EMDS, particularly in languages with unique linguistic characteristics such as Vietnamese, remains a challenge. In this paper, we adapt the Contrastive Hierarchical Discourse Graph (CHDG), a novel approach designed to address these challenges. CHDG operates at multiple levels, including sentence, section, document, and cluster of documents, capturing intricate discourse relationships and global thematic coherence. We employ a contrastive learning framework to enhance sentence representations, enabling CHDG to select coherent and contextually relevant sentences for the final summary. We evaluate CHDG on a benchmark Vietnamese news dataset, showcasing its superior performance in terms of ROUGE scores and human evaluation. Our results demonstrate the potential of CHDG to advance the state-of-the-art in Vietnamese EMDS, contributing to more effective information condensation and knowledge synthesis in this critical domain.
The stable in stablecoins

Stablecoins have garnered much attention as a key part of the emerging decentralized finance ("DeFi") ecosystem, and as a potential way to pay for goods and services. Stablecoins facilitate trades on crypto exchanges, serve as the underlying asset for many crypto loans, and allow market participants to avoid inefficiencies stemming from converting back to fiat currency for crypto trades.