Reviewerly/exHarmony huggingface dataset:
MEE: A Novel Multilingual Event Extraction Dataset

Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i.e., participants) from text.Due to its importance, extensive methods and resources have been developed for Event Extraction.However, one limitation of current research for EE involves the under-exploration for non-English languages in which the lack of high-quality multilingual EE datasets for model training and evaluation has been the main hindrance.To address this limitation, we propose a novel Multilingual Event Extraction dataset (MEE) that provides annotation for more than 50K event mentions in 8 typologically different languages.MEE comprehensively annotates data for entity mentions, event triggers and event arguments.We conduct extensive experiments on the proposed dataset to reveal challenges and opportunities for multilingual EE.
How to Represent Context Better? An Empirical Study on Context Modeling for Multi-turn Response Selection

Building retrieval-based dialogue models that can predict appropriate responses based on the understanding of multi-turn context messages is a challenging problem.Early models usually concatenate all utterances or independently encode each dialogue turn, which may lead to an inadequate understanding of dialogue status.Although a few researchers have noticed the importance of context modeling in multiturn response prediction, there is no systematic comparison to analyze how to model context effectively and no framework to unify those methods.In this paper, instead of configuring new architectures, we investigate how to improve existing models with a better context modeling method.Specifically, we heuristically summarize three categories of turn-aware context modeling strategies which model the context messages from the perspective of sequential relationship, local relationship, and query-aware manner respectively.A Turn-Aware Context Modeling (TACM) layer is explored to flexibly adapt and unify these context modeling strategies to several advanced response selection models.Evaluation results on three public data sets indicate that employing each individual context modeling strategy or multiple strategies can consistently improve the performance of existing models.
TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer

Sanskrit Word Segmentation (SWS) is essential in making digitized texts available and in deploying downstream tasks.It is, however, non-trivial because of the sandhi phenomenon that modifies the characters at the word boundaries, and needs special treatment.Existing lexicon driven approaches for SWS make use of Sanskrit Heritage Reader, a lexicon-driven shallow parser, to generate the complete candidate solution space, over which various methods are applied to produce the most valid solution.However, these approaches fail while encountering out-of-vocabulary tokens.On the other hand, purely engineering methods for SWS have made use of recent advances in deep learning, but cannot make use of the latent word information on availability.To mitigate the shortcomings of both families of approaches, we propose Transformer based Linguistically Informed Sanskrit Tokenizer (TransLIST) consisting of (1) a module that encodes the character input along with latentword information, which takes into account the sandhi phenomenon specific to SWS and is apt to work with partial or no candidate solutions, (2) a novel soft-masked attention to prioritize potential candidate words and (3) a novel path ranking algorithm to rectify the corrupted predictions.Experiments on the benchmark datasets for SWS show that TransLIST outperforms the current state-of-the-art system by an average 7.2 points absolute gain in terms of perfect match (PM) metric. 1
Leveraging time-dependent lexical features for offensive language detection

We present a study on the integration of timesensitive information in lexicon-based offensive language detection systems.Our focus is on Offenseval sub-task A, aimed at detecting offensive tweets.We apply a semantic change detection algorithm over a short time span of two years to detect words whose semantics has changed and we focus particularly on those words that acquired or lost an offensive meaning between 2019 and 2020.Using the output of this semantic change detection approach, we train an Support Vector Machine (SVM) classifier on the Offenseval 2019 training set.We build on the already competitive SINAI system submitted to Offenseval 2019 by adding new lexical features, including those that capture the change in usage of words and their association with emerging offensive usages.We discuss the challenges, opportunities and limitations of integrating semantic change detection in offensive language detection models.Our work draws attention to an often neglected aspect of offensive language, namely that the meanings of words are constantly evolving and that NLP systems that account for this change can achieve good performance even when not trained on the most recent training data.
Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation

We present REFEREE, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct control for compression ratio.Our work is the first to demonstrate that reference-free, controlled sentence summarization is feasible via the conceptual framework of Symbolic Knowledge Distillation (West et al., 2022), where latent knowledge in pre-trained language models is distilled via explicit examples sampled from the teacher models, further purified with three types of filters: length, fidelity, and Information Bottleneck.Moreover, we uniquely propose iterative distillation of knowledge, where student models from the previous iteration of distillation serve as teacher models in the next iteration.Starting off from a relatively modest set of GPT3-generated summaries, we demonstrate how iterative knowledge distillation can lead to considerably smaller, but better summarizers with sharper controllability.A useful by-product of this iterative distillation process is a high-quality dataset of sentence-summary pairs with varying degrees of compression ratios.Empirical results demonstrate that the final student models vastly outperform the much larger GPT3-Instruct model in terms of the controllability of compression ratios, without compromising the quality of resulting summarization. 1
The Aligned Multimodal Movie Treebank: An audio, video, dependency-parse treebank

Treebanks have traditionally included only text and were derived from written sources such as newspapers or the web.We introduce the Aligned Multimodal Movie Treebank (AMMT) †, an English language treebank derived from dialog in Hollywood movies which includes transcriptions of the audiovisual streams with word-level alignment, as well as part of speech tags and dependency parses in the Universal Dependencies (UD) formalism.AMMT consists of 31,264 sentences and 218,090 words, that will amount to the 3rd largest UD English treebank and the only multimodal treebank in UD.We find that parsers on this dataset often have difficulty with conversational speech and that they often rely on punctuation which is often not available from speech recognizers.To help with the web-based annotation effort, we also introduce the Efficient Audio Alignment Annotator (EAAA) ‡, a companion tool that enables annotators to significantly speed-up their annotation processes.
Influence Functions for Sequence Tagging Models

Many language tasks (e.g., Named Entity Recognition, Part-of-Speech tagging, and Semantic Role Labeling) are naturally framed as sequence tagging problems.However, there has been comparatively little work on interpretability methods for sequence tagging models.In this paper, we extend influence functions -which aim to trace predictions back to the training points that informed them -to sequence tagging tasks.We define the influence of a training instance segment as the effect that perturbing the labels within this segment has on a test segment level prediction.We provide an efficient approximation to compute this, and show that it tracks with the true segment influence, measured empirically.We show the practical utility of segment influence by using the method to identify systematic annotation errors in two named entity recognition corpora.Code to reproduce our results is available at https://github.com/successar/Segment_Influence_Functions.
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning

Detecting out-of-domain (OOD) intents from user queries is essential for avoiding wrong operations in task-oriented dialogue systems.The key challenge is how to distinguish indomain (IND) and OOD intents.Previous methods ignore the alignment between representation learning and scoring function, limiting the OOD detection performance.In this paper, we propose a unified neighborhood learning framework (UniNL) to detect OOD intents.Specifically, we design a K-nearest neighbor contrastive learning (KNCL) objective for representation learning and introduce a KNNbased scoring function for OOD detection.We aim to align representation learning with scoring function.Experiments and analysis on two benchmark datasets show the effectiveness of our method.
Building a Secure and High-Performance HMAC Processor Based on SHA-3 for the Cloud-Based Financial Sector

A cloud-based financial environment can provide cost savings, scalability, security, and accessibility benefits to financial organizations, making it an attractive option for financial operations. Several issues relate to the authenticity and integrity of messages in a cloud-based financial environment, like "man in the middle" attacks, data tampering, data loss, and data breaches. To address these issues, it is important to implement security measures such as HMAC-SHA3 to ensure the authenticity and integrity of messages in a cloud-based financial environment. It uses one of the SHA-3 hash functions, such as SHA3-256 or SHA3-512, to calculate the HMAC value for a message. The HMAC result is next added to a message and delivered to the receiver. The recipient then determines the HMAC score for the communication utilizing the same private key and compares it with the HMAC values assigned to the message in order to confirm the message's authenticity. If the values match, it means that the message has not been tampered with and is authentic. If the values do not match, it means that the message has been tampered with or is not authentic, and the recipient should discard the message. HMAC-SHA3 is a secure cryptographic method that is used to ensure the authenticity and integrity of messages in a cloud-based financial environment, helping to protect against attacks such as man-in-the-middle attacks where a malicious actor could intercept and tamper with financial messages being transmitted between two parties. The result shows that HMAC-SHA3 is better in terms of throughput and efficiency compared to the MAC algorithm, making it a more effective method of solving many real-time security issues in cloud-based applications.
An Architecture for Collective Dispute Resolution in Ubiquitous and Context-Aware Systems

Context-aware computing refers to the study of computer systems that may modify their behaviour in response to information about various things (such as people, locations, and objects) of interest. As ubiquitous computing seeks to deliver customised, contextualised services, it is directly tied to the usage of contexts. on-demand, transparent services. Many users typically share ubiquitous systems, which might result in conflicts during adaptation efforts owing to differences in individual profiles and/or incompatible environmental resources. A study area called "context-aware computing" describes systems that may modify their behaviour in response to any pertinent information about things (such as people, locations, and objects) of interest. While ubiquitous computing seeks to offer customised, open, and on-demand services, it is directly tied to the utilisation of contexts. As ubiquitous systems are created to be integrated into commonplace contexts and things like homes, vehicles, and offices, they are routinely used by several people.
Q-CASA Invited Speakers Quantum-Centric Supercomputing Strategies for Neuroscience problems: Challenges and Progress

For decades high-performance computing systems have allowed researchers, scientists and engineers to solve complex data- and compute-intensive problems. These parallel and distributed computing systems provided researchers to develop state of the art solutions in areas such as machine learning, health, and life science. While successful for a large number of problems, these systems face limitations when handling intractable problems which require either an enormous number of resources (i.e. number of bits, or power) or time to solve a problem (i.e. factoring integers). In this work we describe how quantum computers can enhance these systems by incorporating quantum computational principles to create a quantum-centric supercomputer. Recent developments include quantum middleware, error mitigation, and error suppression techniques to curtail these issues in near-term quantum devices and enable researchers to create quantum useful applications without having to wait for full fault-tolerant quantum computers. We also give an overview of dynamic circuits, circuit knitting, and various error suppression techniques recently developed to help minimize noise and execute quantum circuits on multiple quantum systems. Our preliminary work shows recent challenges and advancements in quantum-centric supercomputing when trying to solve neuroscience problems. We focus on functional MRI (fMRI) and diffusion MRI problems related to mental health and the potential areas where quantum computers can alleviate some of these challenges. We also present preliminary solutions to connectomics, tractography, and brain network analysis that can be used to help researchers identify neurodegenerative diseases or injuries.
Credit Card Fraud Detection using Machine Learning and Data Mining Techniques - a Literature Survey

Purpose: To understand the algorithms used in Credit Card Fraud Detection (CCFD) using Machine Learning (ML) and Data Mining (DM) techniques, Review key findings in the area and come up with research gaps or unresolved problem. To become knowledgeable about the current discussions in the area of ML and DM. Design/Methodology/Approach: The survey on CCFD using ML and DM was conducted based on data from academic papers, web articles, conference proceedings, journals and other sources. Information is reviewed and analysed. Results/Findings: Identification of credit card fraud is essential for protecting a person's or an organization's assets. Even though we have various safeguards in place to prevent fraudulent activity, con artists may develop a method to get around the checkpoints. We must create straightforward and efficient algorithms employing ML and DM to anticipate fraudulent activities in advance. Originality/Value: Study of ML and DM algorithms in CCFD from diverse sources is done. This area needs study due to recent methods by fraudsters in digital crime have developed. The information acquired will be helpful for creating new methodologies or improving the outcomes of current algorithms. Type of Paper: Literature Review.
EXPERIENCES WITH ACTIVE METHODOLOGIES IN BASIC EDUCATION: SYNTHESIS OF REPORTS IN A CYCLE OF WEBINARS DURING THE COVID-19 PANDEMIC

The objective of this study was to identify and analyze the active methodologies that were presented in a cycle of webinars about experiences developed with these methodologies in basic education.As a data collection instrument, for the production of empirical material, the analysis and observation of researchers related to twelve webinars held in 2021 were used.As a methodological analysis tool, Discursive Textual Analysis (ATD) was used.The results showed a wide range of active methodologies used, the most recurrent being the flipped classroom, problem-based and project-based learning and hybrid teaching.
Position-aware Human Activity Recognition with Smartphone Sensors based on Deep Learning Approaches

Numerous research studies on human activity recognition (HAR) have utilized smartphone sensors, such as accelerometers, gyroscopes, and magnetometers. The accelerometer, in particular, has been considerably explored as the primary sensor in HAR research. Recently, researchers have integrated different smartphone sensors to enhance recognition interpretation. Nevertheless, it is still challenging to determine the optimal usage of these sensors, either individually or in combination, for better identification effectiveness in various motion situations. In this article, we explore how different motion sensors behave during the activity recognition process, using various deep learning (DL) approaches to classify physical actions based on smartphone sensor data. We evaluate the DL models using a publicly benchmarked dataset of data collected from ten participants performing eight movements while carrying smartphones in different situations. Our experimental results reveal that, except for the magnetometer, each sensor can play a leading role in HAR, depending on the recognized activity, body position, data features employed, and classification approach. Additionally, we find that the combination of sensors only improves overall recognition interpretation when their individual performances are low.
Arrival times by Recurrent Neural Network for induced seismic events from a permanent network

We have developed a Recurrent Neural Network (RNN)-based phase picker for data obtained from a local seismic monitoring array specifically designated for induced seismicity analysis. The proposed algorithm was rigorously tested using real-world data from a network encompassing nine three-component stations. The algorithm is designed for multiple monitoring of repeated injection within the permanent array. For such an array, the RNN is initially trained on a foundational dataset, enabling the trained algorithm to accurately identify other induced events even if they occur in different regions of the array. Our RNN-based phase picker achieved an accuracy exceeding 80% for arrival time picking when compared to precise manual picking techniques. However, the event locations (based on the arrival picking) had to be further constrained to avoid false arrival picks. By utilizing these refined arrival times, we were able to locate seismic events and assess their magnitudes. The magnitudes of events processed automatically exhibited a discrepancy of up to 0.3 when juxtaposed with those derived from manual processing. Importantly, the efficacy of our results remains consistent irrespective of the specific training dataset employed, provided that the dataset originates from within the network.
Thinking about the Wave-Particle Duality with Plasma Theory and Explaining it based on the Oscillator and Qseudo-Oscillator Models

It is the first time for us to observe the quantum world with the plasma theory. Mean-while, many new concepts, such as the discrete quantum, precise quantum and the non-linear quantum, etc., and new horizons, i.e., cutting off the connection of interaction, or equivalently introducing destructive disturbance, are supposed into the quantum mechanics at the first. The normal and pseudo-oscillator models are introduced and used to explain the wave and particle duality of quantum field. By adding such new content, it is hoped that people can understand the physics behind the quantum mechanics, rather than recognizing it with the pure mathematic knowledge. It is suggested that the quantum world is better to consider the influence of outside environment, and then the quantum mechanics is turned into the quantum dynamics. The establishment of quantum dynamics is helpful for people better understand and hence utilize the quantum mechanics, such as the quantum optics, quantum communication and quantum computer, etc.
Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-constrained Cycles

Cycles are one of the fundamental subgraph patterns and being able to enumerate them in graphs enables important applications in a wide variety of fields, including finance, biology, chemistry, and network science. However, to enable cycle enumeration in real-world applications, efficient parallel algorithms are required. In this work, we propose scalable parallelisation of state-of-the-art sequential algorithms for enumerating simple, temporal, and hop-constrained cycles. First, we focus on the simple cycle enumeration problem and parallelise the algorithms by Johnson and by Read and Tarjan in a fine-grained manner. We theoretically show that our resulting fine-grained parallel algorithms are scalable, with the fine-grained parallel Read-Tarjan algorithm being strongly scalable. In contrast, we show that straightforward coarse-grained parallel versions of these simple cycle enumeration algorithms that exploit edge- or vertex-level parallelism are not scalable. Next, we adapt our fine-grained approach to enable the enumeration of cycles under time-window, temporal, and hop constraints. Our evaluation on a cluster with 256 CPU cores that can execute up to 1,024 simultaneous threads demonstrates a near-linear scalability of our fine-grained parallel algorithms when enumerating cycles under the aforementioned constraints. On the same cluster, our fine-grained parallel algorithms achieve, on average, one order of magnitude speedup compared to the respective coarse-grained parallel versions of the state-of-the-art algorithms for cycle enumeration. The performance gap between the fine-grained and the coarse-grained parallel algorithms increases as we use more CPU cores.
Lived Experience on Mentimeter as Students Response Systems (SRS) in ELT Classroom: A Swot Analysis

This study was aimed to get detail experience of educators on using Mentimeter in ELT Classroom. Three educators were selected based on their 3-year experience in using Mentimeter. By means of SWOT anaylisis, the findings were categorised into 4 points (Strength, weakness, opportunity and threat. The study found that Mentimeter has strength on the term of accessibility, students’ engagement, brainstorming, participation and simplicity. Afterwards, the study also reported that Mentimeter brings weakness on its implementation such as internet connection, visibility on user name and time consuming in open-ended questions point. Moreover, the Mentimeter also indicates promising features on its application such as for engaging students, providing quick responses, and facilitating data collection through features like polling and multiple-choice questions. Lastly, Mentimeter is reported to have some potential threats of reliance on device and internet connectivity in the classroom.