Reviewerly/exHarmony huggingface dataset:
Exploring Entities in Event Detection as Question Answering

In this paper, we approach a recent and under-researched paradigm for the task of event detection (ED) by casting it as a question-answering (QA) problem with the possibility of multiple answers and the support of entities. The extraction of event triggers is, thus, transformed into the task of identifying answer spans from a context, while also focusing on the surrounding entities. The architecture is based on a pre-trained and fine-tuned language model, where the input context is augmented with entities marked at different levels, their positions, their types, and, finally, their argument roles. Experiments on the ACE 2005 corpus demonstrate that the proposed model properly leverages entity information in detecting events and that it is a viable solution for the ED task. Moreover, we demonstrate that our method with different entity markers is particularly able to extract unseen event types in few-shot learning settings.
Analyzing the Effect of Badminton on Physical Health and Emotion Recognition on the account of Smart Sensors

Emotional ability is an important symbol of human intelligence. Human’s understanding of emotions, from subjective consciousness to continuous or discrete emotional dimensions, and then to physiological separability, has shown a trend of gradually diverging from psychological research to the field of intelligent human-computer interaction. This article is aimed at studying the effects of smart sensor-based emotion recognition technology and badminton on physical health. It proposes a method of using smart sensor technology to recognize badminton movements and emotions during the movement. And the impact of emotion recognition based on smart sensors and badminton sports on physical health is carried out in this article. Experimental results show that the emotion recognition technology based on smart sensors can well recognize the changes in people’s emotions during badminton sports, and the accuracy of emotion recognition is higher than 70%. At the same time, experiments show that badminton can greatly improve people’s physical fitness and strengthen people’s physique.
Equating Rasch Values and Expert Judgement Through Externally-Referenced Anchoring

This paper reports on the use of externally-referenced anchoring by LanguageCert as a methodology for calibrating language test materials and aligning test forms.The datasets used are taken from tests at each of the six levels of LanguageCert IESOL suite, all of which have been aligned to the CEFR through expert judgement.We illustrate in this paper the extent to which externally-referenced anchoring, using Item Response Theory (IRT) but based on expert judgement, can be used as an effective, reliable and valid methodology.The approach is based on the premise that successful anchoring may be achieved by reference to well-targeted, expertly-written test forms aligned to the underlying traits of a particular CEFR level by expert judgement and verified through the use of IRT.This study focuses on the analysis of 18 LanguageCert test forms, three at each CEFR level.The LanguageCert Item Difficulty (LID) scale, which underlies all LanguageCert test materials, is linked empirically to the CEFR, and each test was placed on the LID scale based at the midpoint of its distribution.This midpoint setting was then set as the externally-referenced anchor for a given CEFR level.The findings of this study indicate that, while the match between the distribution of items in the selected LanguageCert IESOL tests and the LID scale was not perfect, in general, a relatively close match between the items in the tests and the LID scale was found and, as a consequence, the corresponding CEFR level.For each test, most of the items fell between the 25th and 75th percentile of any given level: this range representing the lower and upper bounds of LID scale values for each CEFR level.These results demonstrate that LanguageCert IESOL test items are well set and appropriately positioned at respective CEFR levels on the basis of expert judgement.The study illustrates that externally-referenced anchoring based on expert judgement may be used as a methodology for aligning test forms to an external frame of reference, in this case the CEFR.
Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals

There have been many successful applications of sentence embedding methods.However, it has not been well understood what properties are captured in the resulting sentence embeddings depending on the supervision signals.In this paper, we focus on two types of sentence embedding methods with similar architectures and tasks: one fine-tunes pre-trained language models on the natural language inference task, and the other fine-tunes pre-trained language models on word prediction task from its definition sentence, and investigate their properties.Specifically, we compare their performances on semantic textual similarity (STS) tasks using STS datasets partitioned from two perspectives: 1) sentence source and 2) superficial similarity of the sentence pairs, and compare their performances on the downstream and probing tasks.Furthermore, we attempt to combine the two methods and demonstrate that combining the two methods yields substantially better performance than the respective methods on unsupervised STS tasks and downstream tasks.
A Heuristic Tool for Measuring Software Quality Using Program Language Standards

Background: Quality is a critical aspect of any software system. Indeed, it is a key factor for the competitiveness, longevity, and effectiveness of software products. Code review facilitates the discovery of programming errors and defects, and using programming language standards is such a technique. Aim: In this study, we developed a code review technique for achieving maximum software quality by using programming language standards. Method: A Java Code Quality Reviewer tool (JCQR) was proposed as a practical technique. It is an automated Java code reviewer that uses SUN and other customized Java standards. Results: The JCQR tool produces new quality-measurement information that indicates applied, satisfied, and violated rules in a piece of code. It also suggests whether code quality should be improved. Accordingly, it can aid junior developers and students in establishing a successful programming attitude. Limitation: JCQR uses customized SUN-based Java programming language standards. Therefore, it fails to cover certain features of Java.
Grouping IoT devices by Trust and Meritocracy

In competitive Multi-Agent Systems (MASs) for IoT, an important task is to form friendships and groups for mutual cooperation. The existing proposals try to improve the profit of individual agent or group but, on the other hand, the most aggressive agents may take advantages from these approaches by means of malicious behaviors. In this work we model a MAS framework of non cooperative games by taking into account i) the reliability of agents relationships through a trust model and ii) the community ability to furnish a good environment to its members through its social capital. Thus, we designed an algorithm that maximizes the social capital. We emphasize two main findings: i) the result is a Nash equilibrium and ii) the agents are awarded only if they behave correctly.
Vocabulary Enhancement through Presentation, Practice, Production(PPP) Method in the Rural EFL Classrooms

The purpose of this research was to look into how to improve students' vocabulary achievement by using the Presentation, Practice, and Production method. This study used both quantitative research and an experimental design. In this study, 68 seventh-grade students from SMP Negeri in Bandung Barat participated. They were split into two groups: the control and the experimental. Both groups received the same pre-test. The Presentation, Practice, and Production method was taught to the experimental group, while the traditional method was taught to the control group. Statistical analysis of the data revealed a significant difference in enhancement between the experimental and control groups. Both groups improved, but the control group did not outperform the experimental group. The researcher recommended that the teacher teach vocabulary using the Presentation, Practice, Production method because it is effective in increasing student vocabulary in rural EFL classrooms.
Design of bridge health monitoring system based on B/S mode and SOA architecture

In order to improve the integration of bridge health monitoring data management and information planning, this paper presents the overall framework design method of the bridge health monitoring system. The system mainly includes information collection modules, network modules, and information adaptive processing modules. The interface scheduling of bridge health monitoring is the bus transmission control of the bridge health monitoring system in B/S mode, and the LCD controller is used to perform bridge health monitoring information analysis. The software development of the bridge health monitoring system is realised under the SOA architecture, and the software is optimised and managed using big data. The system test shows that the system can accurately monitor the health of the bridge, can comprehensively process the bridge working condition data, and effectively improve the reliability of the system.
Transmission of Secret Information Based on Time Instances

This Paper presents improvement and extension of previous methodology about timing steganography based on network steganography. The previous article uses time interval between two successive time instances of transmissions mixed with cryptography prior to hiding. However, this improvement tends to extend and provide new methods based on single time instance unlike the previous methodology which depend on two-time instances such as hours, minutes, second, millisecond etc. It further examines how to handle effect of different time zone and high precision timing for ultrafast timing such as millisecond, and many more which human actions is too slow for perfect timing. In addition, the extension based on Transmission Control Protocol and Internet Protocol (TCP-IP) status codes where each element of set of status code are index and the index represents certain numeric of combination for hiding. Finally, the cryptography method is improved and extended to series-based cryptography with any defined number of different cryptography methods combined altogether with multiple keys generated dynamically. The methods for both cryptography and steganography were integrated and each module carefully tested for their feasibility and appropriate analysis, comparisons presented too. A brief discussion of possible extension or application of Time Interval and Instance Steganography from network based to Video and Audio time Steganography are presented which depends on time such as rate of change of features in Video, or in Audio as well. However, these video and audio Time steganography are considered as out of scope for this article which is mainly about network Steganography.
Bifurcation point detection with parallel nested layer particle swarm optimization

This paper describes the parallelization of Nested Layer Particle Swarm Optimization (NLPSO) to improve the computation time for bifurcation point detection. While NLPSO allows for the search for bifurcation points with less prior knowledge than the previously used Newton's approach, a reduction in computation time is necessary. Parallelization with CUDA made bifurcation point identification with NLPSO up to 24× faster in our experiments.
Pay More Attention to History: A Context Modeling Strategy for Conversational Text-to-SQL

Conversational text-to-SQL aims at converting multi-turn natural language queries into their corresponding SQL representations. One of the most intractable problem of conversational text-to-SQL is modeling the semantics of multi-turn queries and gathering proper information required for the current query. This paper shows that explicit modeling the semantic changes by adding each turn and the summarization of the whole context can bring better performance on converting conversational queries into SQLs. In particular, we propose two conversational modeling tasks in both turn grain and conversation grain. These two tasks simply work as auxiliary training tasks to help with multi-turn conversational semantic parsing. We conducted empirical studies and achieve new state-of-the-art results on large-scale open-domain conversational text-to-SQL dataset. The results demonstrate that the proposed mechanism significantly improves the performance of multi-turn semantic parsing.
Wacana Bencana: Android-Based Natural Disaster Simulation Game

Abstract—This research's purpose is to develop a game application with a natural disaster theme that will enable players to gain an understanding of emergency response knowledge towards natural disasters. This game’s title is Wacana Bencana and it has 3 modes of natural disaster: Flood, Earthquake, and Tsunami. The evaluation process of this game is done by distributing: a pre-test questionnaire, a post-test questionnaire, and followed by a user experience questionnaire. The result is, Wacana Bencana game able to increase the knowledge of its players about emergency responses towards natural disasters and players have a very positive reaction to the game. Keywords—Wacana Bencana, Natural Disaster, Simulation, Game
A reexamination of the impact of morphology on transposed character priming effects.

Using two-character Chinese word targets in a masked priming lexical-decision task, Gu and colleagues (2015) demonstrated a significant transposed character (TC) priming effect. More importantly, the priming effect was the same size for single-morpheme words and multiple-morpheme words, suggesting that TC priming effects are not influenced by morphemic structure. In Chinese, there are, however, two types of single-morpheme words, single-morpheme simple words (e.g., [similar to practice in English]) and single-morpheme complex words (e.g., [similar to carpet in English in that both components are words themselves and, hence, when presented in transposed order, may activate morphological information reflecting the individual components rather than the word itself]), a contrast that Gu et al. did not examine. In Experiment 1, we replicated Gu et al.'s finding of equal TC priming effects for their single- and multiple-morpheme words, although our priming effects were noticeably smaller than theirs. In Experiment 2, we split the single-morpheme condition in order to examine the TC priming effects for single-morpheme simple words, single-morpheme complex words and multiple-morpheme words. The results showed that the single-morpheme complex words produced the smallest priming effect, indicating that transposed morphemes can influence masked priming in Chinese; however, apparently only in an inhibitory fashion. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Spam detection by using machine learning based binary classifier

&lt;span lang="EN-US"&gt;Because &lt;span lang="EN-US"&gt;of its ease of use and speed compared to other communication applications, email is the most commonly used communication application worldwide. However, a major drawback is its inability to detect whether mail content is either spam or ham. There is currently an increasing number of cases of stealing personal information or phishing activities via email. This project will discuss how machine learning can help in spam detection. Machine learning is an artificial intelligence application that provides the ability to automatically learn and improve data without being explicitly programmed. A binary classifier will be used to classify the text into two different categories: spam and ham. This research shows the machine learning algorithm in the Azure-based platform predicts the score more accurately compared to the machine learning algorithm in visual studio, hybrid analysis and JoeSandbox cloud.&lt;/span&gt;&lt;/span&gt;
Blockchain-Based Trust Verification and Streaming Service Awareness for Big Data-Driven 5G and Beyond Vehicle-to-Everything (V2X) Communication

Vigorous security and transparency with minimum latency and a high data rate are some of the key requirements for 5G and beyond vehicular networks especially in case of big data-driven vehicle-to-everything (V2X) communication applications which plays an important role in enabling advance intelligent transportation systems (ITS) and Internet of Vehicle (IoV) based network. The trace-ability in blockchain makes it one of the vital options, back-boned by the decentralized network to ensure independent security and privacy by exploiting the hash of a previous transaction with the time stamp and announcing it publicly for rejection of possible adversary attack. Therefore, in this work, we have opted for blockchains for trusted authentication and service awareness in 5G and beyond vehicle-to-everything (V2X) communication-based vehicular networks to stream blocks by certifying privacy and security. Here, we have considered blockchain as intermediary trusted agent to witness communication between a vehicle node and edge node, where edge node also uploads microservices along with calling figure of microservices to the blockchain. Furthermore, to ensure vehicle privacy with joint verification of vehicular network and server, we have devised a mechanism in which every vehicle nodes anonymously ask for services from the edge server along with signing an individual’s identity. Moreover, based on generated vehicle requests and service awareness, we devised an improved edge cache scheme for the prior compilation of services at each edge. In comparison to the previous state-of-art work, the result shows that our proposed work handles the requests more efficiently to achieve a better service request cache hit rate generated by vehicles at the edge server.
TaskDrop: A competitive baseline for continual learning of sentiment classification

In this paper, we study the multi-task sentiment classification problem in the continual learning setting, i.e., a model is sequentially trained to classify the sentiment of reviews of products in a particular category. The use of common sentiment words in reviews of different product categories leads to large cross-task similarity, which differentiates it from continual learning in other domains. This knowledge sharing nature renders forgetting reduction focused approaches less effective for the problem under consideration. Unlike existing approaches, where task-specific masks are learned with specifically presumed training objectives, we propose an approach called Task-aware Dropout (TaskDrop) to randomly sample a binary mask for each task. While the standard dropout generates and applies random masks for each training instance per epoch for regularization, random masks in TaskDrop are used for model capacity allocation and reuse to each coming task. We conducted experimental studies on Amazon review data and made comparison to various baselines and state-of-the-art approaches. Our empirical results show that regardless of simplicity, TaskDrop overall achieved competitive performance, especially after relatively long term learning. This demonstrates that the proposed random capacity allocation mechanism works well for continual sentiment classification.
A sight on defect detection methods for imbalanced industrial data

Product defect detection is a challenging task, especially in situations where is difficult and costly to collect defect samples. Which make it quite difficult to apply supervised algorithms as their performances decrease by training the model on imbalanced data. To tackle this problem, researchers used data augmentation and one-class classification to detect defects in industrial areas. In this paper, we list defect detection applications for imbalanced industrial data and we report the benefits and limitation of those methods.
Resource Baseline MAPPO for Multi-UAV Dog Fighting

Dog Fighting is the summit of multi-UAV control problem. In the present work we extend the PPO algorithm to multi-UAV environment and investigate the decentralized learning of UAVs by MAPPO algorithm. By adding the resource baseline of UAVs in the classical rewarding scheme, the convergence of calculating the reward matrix is theoretically proven. We also demonstrate how the training of multi-agent PPO algorithm accelerates. Competitive UAVs learn to dog fight efficiently. Two groups of UAVs trained under resources baseline find an optimal strategy to keep the fighting as long as possible. The present work demonstrates that resource baseline MAPPO can become a practical tool for studying the decentralized learning of multi-UAV systems in highly complex environments.
A proposal of ethical competence model for cyber security organization

A proactive cyber security plan to safeguard confidential information and privacy still lacks initiatives to avoid frequent harmful attacks. Cybersecurity professionals must possess ethical competence and prove worthy of overseeing valuable information for efficient decision‐making since ethical competence is fundamental for daily practice. There is a need to define what it means to be ethically competent in the era of IR4.0. The previous competence models still lack consideration of both artificial intelligence (AI) and emotional intelligence (EI) skills. AI brings new opportunities to cyber security organizations that focus on AI skills related to cognitive Intelligence or intelligent quotient (IQ). EI, which refers to emotional quotient (EQ), is a good predictor of ethical competence as it can perceive and express emotions precisely to facilitate thought to understand and manage emotions. However, practically, most cyber security organizations focused on AI skills and disregarded EI skills' roles. This research proposes a cyber artemotional model that blends AI skills and EI skills for cyber security employees. This research would benefit cyber security organizations with cyber artemotional model as employees ethical competence assessment, and it is in line with the demand of IR4.0.
C3KG: A Chinese Commonsense Conversation Knowledge Graph

Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps.To fill the gap, we curate a large-scale multi-turn human-written conversation corpus, and create the first Chinese commonsense conversation knowledge graph which incorporates both social commonsense knowledge and dialog flow information.To show the potential of our graph, we develop a graph-conversation matching approach, and benchmark two graph-grounded conversational tasks.
FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition

Current state-of-the-art deep learning based face recognition (FR) models require a large number of face identities for central training. However, due to the growing privacy awareness, it is prohibited to access the face images on user devices to continually improve face recognition models. Federated Learning (FL) is a technique to address the privacy issue, which can collaboratively optimize the model without sharing the data between clients. In this work, we propose a FL based framework called FedFR to improve the generic face representation in a privacy-aware manner. Besides, the framework jointly optimizes personalized models for the corresponding clients via the proposed Decoupled Feature Customization module. The client-specific personalized model can serve the need of optimized face recognition experience for registered identities at the local device. To the best of our knowledge, we are the first to explore the personalized face recognition in FL setup. The proposed framework is validated to be superior to previous approaches on several generic and personalized face recognition benchmarks with diverse FL scenarios. The source codes and our proposed personalized FR benchmark under FL setup are available at https://github.com/jackie840129/FedFR.