Reviewerly/exHarmony huggingface dataset:
Sample Attackability in Natural Language Adversarial Attacks

Adversarial attack research in natural language processing (NLP) has made significant progress in designing powerful attack methods and defence approaches.However, few efforts have sought to identify which source samples are the most attackable or robust, i.e. can we determine for an unseen target model, which samples are the most vulnerable to an adversarial attack.This work formally extends the definition of sample attackability/robustness for NLP attacks.Experiments on two popular NLP datasets, four state of the art models and four different NLP adversarial attack methods, demonstrate that sample uncertainty is insufficient for describing characteristics of attackable/robust samples and hence a deep learning based detector can perform much better at identifying the most attackable and robust samples for an unseen target model.Nevertheless, further analysis finds that there is little agreement in which samples are considered the most attackable/robust across different NLP attack methods, explaining a lack of portability of attackability detection methods across attack methods. 1
Corpus-Based Task-Specific Relation Discovery

Relation extraction is a crucial language processing task for various downstream applications, including knowledge base completion, question answering, and summarization.Traditional relation-extraction techniques, however, rely on a predefined set of relations and model the extraction as a classification task.Consequently, such closed-world extraction methods are insufficient for inducing novel relations from a corpus.Unsupervised techniques like OpenIE, which extract <head, relation, tail> triples, generate relations that are too general for practical information extraction applications.In this work, we contribute the following: 1) We motivate and introduce a new task, corpus-based task-specific relation discovery.2) We adapt existing data sources to create Wiki-Art, a novel dataset for task-specific relation discovery.3) We develop a novel framework for relation discovery using zero-shot entity linking, prompting, and type-specific clustering.Our approach effectively connects unstructured text spans to their shared underlying relations, bridging the data-representation gap and significantly outperforming baselines on both quantitative and qualitative metrics.Our code and data are available in our GitHub repository.
Dialogue State Tracking with Sparse Local Slot Attention

Dialogue state tracking (DST) is designed to track the dialogue state during the conversations between users and systems, which is the core of task-oriented dialogue systems. Mainstream models predict the values for each slot with fully token-wise slot attention from dialogue history. However, such operations may result in overlooking the neighboring relationship. Moreover, it may lead the model to assign probability mass to irrelevant parts, while these parts contribute little. It becomes severe with the increase in dialogue length. Therefore, we investigate sparse local slot attention for DST in this work. Slot-specific local semantic information is obtained at a sub-sampled temporal resolution capturing local dependencies for each slot. Then these local representations are attended with sparse attention weights to guide the model to pay attention to relevant parts of local information for subsequent state value prediction. The experimental results on MultiWOZ 2.0 and 2.4 datasets show that the proposed approach effectively improves the performance of ontology-based dialogue state tracking, and performs better than token-wise attention for long dialogues.
Driving Context into Text-to-Text Privatization

Metric Differential Privacy enables text-to-text privatization by adding calibrated noise to the vector of a word derived from an embedding space and projecting this noisy vector back to a discrete vocabulary using a nearest neighbor search.Since words are substituted without context, this mechanism is expected to fall short at finding substitutes for words with ambiguous meanings, such as 'bank'.To account for these ambiguous words, we leverage a sense embedding and incorporate a sense disambiguation step prior to noise injection.We encompass our modification to the privatization mechanism with an estimation of privacy and utility.For word sense disambiguation on the Words in Context dataset, we demonstrate a substantial increase in classification accuracy by 6.05%.
On The Real-world Performance of Machine Translation: Exploring Social Media Post-authors’ Perspectives

Many social networking sites (SNS) offer machine translation of posts in an effort to increase understanding, engagement, and connectivity between users across language barriers.However, the translations of these posts are still not 100% accurate and can be a cause of misunderstandings that can harm post-authors' professional or personal relationships.An exacerbating factor is on most SNS, authors cannot view the translation of their own posts, nor make corrections to inaccurate translations.This paper reports findings from a survey (N = 189) and an interview (N = 15) to explore users' concerns regarding this automatic form of machine translation.Our findings show that users are concerned about potential inaccuracies in the meaning of the translations of their posts, and would thus appreciate being able to view and potentially correct such translations.Additionally, we found that when users write posts in their native language, they write them for specific audiences, so they do not always want them translated.This underscores the urgency of providing users with more control over the translation of their posts.
Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding

In addressing the task of converting natural language to SQL queries, there are several semantic and syntactic challenges. It becomes increasingly important to understand and remedy the points of failure as the performance of semantic parsing systems improve. We explore semantic parse correction with natural language feedback, proposing a new solution built on the success of autoregressive decoders in text-to-SQL tasks. By separating the semantic and syntactic difficulties of the task, we show that the accuracy of text-to-SQL parsers can be boosted by up to 26% with only one turn of correction with natural language. Additionally, we show that a T5-base model is capable of correcting the errors of a T5-large model in a zero-shot, cross-parser setting.
An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models

Large-scale Pre-Trained Language Models (PTLMs) capture knowledge from massive human-written data which contains latent societal biases and toxic contents.In this paper, we leverage the primary task of PTLMs, i.e., language modeling, and propose a new metric to quantify manifested implicit representational harms in PTLMs towards 13 marginalized demographics.Using this metric, we conducted an empirical analysis of 24 widely used PTLMs.Our analysis provides insights into the correlation between the proposed metric in this work and other related metrics for representational harm.We observe that our metric correlates with most of the gender-specific metrics in the literature.Through extensive experiments, we explore the connections between PTLMs architectures and representational harms across two dimensions: depth and width of the networks.We found that prioritizing depth over width, mitigates representational harms in some PTLMs.Our code and data can be found at [place holder].
Towards Industrial Metaverse: Opportunities and challenges

In recent years, the emergence of the Metaverse has created a significant impact worldwide. It represents the ultimate application of the latest information and communication technologies, introducing a brand new digital world to our society. With the industry sector being the core engine of our society, it holds the key to unlocking the potential of Metaverse application, particularly in the form of an Industrial Metaverse. The success of the Industrial Metaverse will greatly contribute to the overall development of the Metaverse. Therefore, this paper conducts a survey on the Metaverse to gather evidence of the growing trend towards an Industrial Metaverse. Furthermore, the key technologies required for implementing an Industrial Metaverse are analyzed. Lastly, the opportunities and challenges associated with the Industrial Metaverse are discussed.
Emotion Analysis of Tweets Banning Education in Afghanistan

This paper introduces the first emotionannotated dataset for the Dari variant of Persian spoken in Afghanistan.The LetHer-Learn dataset contains 7,600 tweets posted in reaction to the Taliban's ban of women's rights to education in 2022 and has been manually annotated according to Ekman's emotion categories.We here detail the data collection and annotation process, present relevant dataset statistics as well as initial experiments on the resulting dataset, benchmarking a number of different neural architectures for the task of Dari emotion classification.
Knowledge Base Completion for Long-Tail Entities

Despite their impressive scale, knowledge bases (KBs), such as Wikidata, still contain significant gaps.Language models (LMs) have been proposed as a source for filling these gaps.However, prior works have focused on prominent entities with rich coverage by LMs, neglecting the crucial case of long-tail entities.In this paper, we present a novel method for LM-based-KB completion that is specifically geared for facts about long-tail entities.The method leverages two different LMs in two stages: for candidate retrieval and for candidate verification and disambiguation.To evaluate our method and various baselines, we introduce a novel dataset, called MALT, rooted in Wikidata.Our method outperforms all baselines in F1, with major gains especially in recall.
Identifying Quantifiably Verifiable Statements from Text

Humans often describe complex quantitative data using trend-based patterns.Trend-based patterns can be interpreted as higher order functions and relations over numerical data such as extreme values, rates of change, or cyclical repetition.One application where trends abound are descriptions of numerical tabular data.Therefore, the alignment of numerical tables and textual description of trends enables easier interpretations of tables.Most existing approaches can align quantities in text with tabular data but are unable to detect and align trend-based patterns about data.In this paper, we introduce the initial steps for aligning trendbased patterns about the data, i.e. the detection of textual description of trends and the alignment of trends with a relevant table.We introduce the problem of identifying quantifiably verifiable statements (QVS) in the text and aligning them with tables and datasets.We define the structure of these statements and implement a structured based detection.In our experiments, we demonstrate our method can detect and align these statements from several domains and compare favorably with traditional sequence labeling methods.
Prediction Of Closing Price Combined Stock Index (Ihsg) Using The Fuzzy Mamdani Method

This study proposes a method for predicting the closing IHSG stock price using the Mamdani fuzzy approach. This model uses historical closing stock price data as input, and generates closing stock price predictions using the Mamdani fuzzy rule. However, experimental results show that this model may not be suitable for predicting stock prices accurately and reliably. Therefore, this study does not recommend the use of the Mamdani fuzzy method for the purpose of predicting closing stock prices.
PECSO: An Improved Chicken Swarm Optimization Algorithm with Performance-Enhanced Strategy and Its Application

To solve the problems of low convergence accuracy, slow speed, and common falls into local optima of the Chicken Swarm Optimization Algorithm (CSO), a performance enhancement strategy of the CSO algorithm (PECSO) is proposed with the aim of overcoming its deficiencies. Firstly, the hierarchy is established by the free grouping mechanism, which enhances the diversity of individuals in the hierarchy and expands the exploration range of the search space. Secondly, the number of niches is divided, with the hen as the center. By introducing synchronous updating and spiral learning strategies among the individuals in the niche, the balance between exploration and exploitation can be maintained more effectively. Finally, the performance of the PECSO algorithm is verified by the CEC2017 benchmark function. Experiments show that, compared with other algorithms, the proposed algorithm has the advantages of fast convergence, high precision and strong stability. Meanwhile, in order to investigate the potential of the PECSO algorithm in dealing with practical problems, three engineering optimization cases and the inverse kinematic solution of the robot are considered. The simulation results indicate that the PECSO algorithm can obtain a good solution to engineering optimization problems and has a better competitive effect on solving the inverse kinematics of robots.
Exploring the Impact of Virtual Reality Technology on International Tourism During the Period 2018 to 2023

This research study aims to explore the intersection of virtual reality with international tourism from 2018 to 2023, identifying the opportunities and challenges presented during this period of rapid technological progress. In this study the literature on the integration of virtual reality in international tourism is reviewed and current theories, concepts and empirical studies are analyzed by studying the developments, trends and research results in this area, identify opportunities to enhance tourist experiences using virtual reality technology. And conduct a comprehensive analysis of the challenges and limitations associated with the intersection of virtual reality with international tourism, including technological developments, infrastructure requirements, data security, privacy concerns and preserving the authenticity of tourist places and analyze the data collected to extract insights on the impacts, benefits and limitations of integrating virtual reality into international tourism. The results of this study also contribute to providing a comprehensive understanding of the opportunities and challenges arising from the intersection of virtual reality and international tourism from 2018 to 2023, and aims to inform industry stakeholders, policy makers and researchers about the possibility of leveraging virtual reality to enhance tourism experiences and guide the responsible adoption of virtual reality technologies in the tourism sector
Pengembangan Aplikasi Evaluasi Kegiatan Berbasis Android menggunakan Metode RAD (Rapid Application Development)

The development of technology is very fast, especially in the field of information systems. Companies, organizations, schools and governments definitely need the role of information technology in data security or management. The use of the internet is very much needed in government agencies because the internet is used as a means to provide fast and easy services to people who want to handle important documents. In this research, we developed an Android-based application to evaluate activities have been carried out. We named this application EL-AKIP which is an android-based application for Grobogan Regency designed using web 2.0 and restful api using the RAD (Rapid Application Development) method. The use of Rapid Application Development (RAD), which is a software development process model that is incremental, especially for short processing times. Application testing uses the black box testing method where the results show that the application passes the test and is feasible to use
Identifying Potentially Fraudulent Credit Card Transactions with the use of Soft Computing Methods

One of the most pervasive difficulties facing modern finance organisations, corporations, and governments is the alarming rise in fraudulent or dishonest practises around financial transactions, especially those using credit cards. As difficulties go, this is one of the more prevalent ones. To protect the interests of both consumers and businesses, it is crucial to implement a fraud detection system that heavily relies on the use of intelligent fraud detection tactics. Numerous fraud detection methods, strategies, and systems have been reported in the scholarly literature; many of them make use of intelligent strategies like algorithms and frameworks. This project begins out with an exploratory data analysis aimed at identifying potentially fraudulent credit card transactions. The authors next provide some categorization models that are implemented in soft computing by means of ingenious methodologies. For the aim of evaluation, several other classification algorithms such decision trees, random forests, and logistic regressions have been used in addition to the Knearest neighbour (K-NN) technique. The suggested model provides a more accurate, computationally efficient, and lightweight option for detecting credit card fraud.
Iconography of Musical Instruments of Early Middle Ages (according to archaeological data)

The article focuses on iconography of musical instruments of the early Middle Ages found during archeological excavations conducted in the territory of ancient Sogd in Uzbekistan and China in the late XX and early XXI centuvies.For example, some musical instruments were found in Kafirkala in 2018 by archeological expedition of Uzbek-Japanese group for the first time it investigated the view of painting musicology. Peculiarities of those musical instruments were learned, and there were conducted comparative analyses based on different scientific sources. Their organic connection with ancient musical instruments was determined. There are portrayal of musical instruments belonging to Sugd and China, and they are similar to musical instruments found in 2018 in Kafirkala. As a result of presenting some discoveries scientific news and some clarifications it was determined that composition of musical instruments belongs to the musical culture of Sugd in the first Middle Ages.
Survey Paper on Block-Chain in IoT

Abstract: IoT (Internet of Things) is one of the trendingtechnologies nowadays, due to its wide variety of applications butit has many limitations such as data security, centralization, etc. Block-chain can help us to resolve these issues through its decentralized and distributed ledger. There are many ways to integrate block-chain in IoT such as IoT-IoT, IoT- Block-chain, and Hybrid approach but challenges still exist.
Analysis of Parameters Influencing the Performance of Digital Image Encryption using ECC

Abstract: Digital images are widely used in various domains, including social media, military, and healthcare. However, the security of digital images is threatened by cyber-attacks and privacy concerns. Cryptography plays a crucial role in ensuring the privacy and integrity of data, including digital images. Encryption methods are employed to protect digital images from unauthorized access. Among these methods, elliptic curve cryptography (ECC) is particularly effective, offering robust security with shorter key lengths. This research paper analysis ECC-based encryption methods for digital image encryption, considering parameters that impact their performance.
Unusual Crowd Activity Detection in Video Using CNN, LSTM and OpenCV

Abstract: Suspicious behaviour is dangerous in public areas that may cause heavy causalities. There are various systems developed on the basis of video frame acquisition where motion or pedestrian detection occur but those systems are not intelligent enough to identify the unusual activities even at real time. It is required to recognized scamper situation at real time from video surveillance for quick and immediate management before any casualties. Proposed system focuses on recognizing suspicious activities and target to achieve a technique which is able to detect suspicious activity automatically using computer vision. Here system uses OpenCV library for classifying different kind of actions at real time. The motion influence map has been used to represent the motion analysis that frequently changes the position from one place to another. System uses pixel level presentation for making it easy to understand or identify the actual situation.
Voting Classification Model for Network Traffic Classification

Abstract: Network traffic classification has produced incredible concentration in the academic world alongside the industrial domain. A few procedures have been recommended and created in the course of the most recent twenty years. This segment makes a discussion on various classification strategies and partitions them into four classes dependent on their ordered development. The network traffic classification has various phases which include pre-processing, feature extraction and classification. In the previous year’s various machine techniques is designed for network traffic classification. The techniques which are already designed give low accuracy. In this research work, voting classification method is designed for network traffic classification which give high accuracy. The proposed model is implemented in python and results is analyzed in terms of accuracy, precision and recall.
Manajemen Kebencanaan pada Buku Bahasa Indonesia Kelas VII SMP

ndonesia is a country that has the potential to be heavily affected by disasters. These disasters are caused by natural and non-natural factors. The number of victims and losses due to disasters requires a response for all communities. This study aims to describe the form of disaster management in Indonesian books at the junior high school level. The data source for this research is Indonesian class VII books published by the Center for Books and Curriculum of the Ministry of Education and Culture in 2021. The data in this study are words, sentences, paragraphs, and discourses in Class VII Indonesian Books related to forms of disaster management. Data collection techniques used listening, analysis, and note-taking techniques. The data analysis technique uses interactive analysis which consists of data reduction, data presentation, and drawing conclusions elaborated with relevant theories. The results of the analysis show that out of the six chapters, there are four chapters of disaster management materials in Indonesian class VII books. The material includes elements of disaster consisting of prevention (mitigation), preparation (preparedness), response (response), and repair (recovery). These materials are displayed in the form of readings and practice questions scattered in descriptive texts, explanations, news, stories, and letters.
Robust and accurate depth estimation by fusing LiDAR and stereo

Abstract Depth estimation is one of the key technologies in some fields such as autonomous driving and robot navigation. However, the traditional method of using a single sensor is inevitably limited by the sensor’s performance. Therefore, a precise and robust method for fusing LiDAR and stereo cameras is proposed. This method fully combines the advantages of the LiDAR and stereo cameras, which can retain the advantages of the high precision of the LiDAR and the high resolution of images respectively. Compared with the traditional stereo matching method, the texture of the object and lighting conditions have less influence on the algorithm. Firstly, the depth of the LiDAR data is converted to the disparity of the stereo camera. Because the density of the LiDAR data is relatively sparse on the y -axis, the converted disparity map is up-sampled using the interpolation method. Secondly, in order to make full use of the precise disparity map, the disparity map and stereo-matching are fused to propagate the accurate disparity. Finally, the disparity map is converted to the depth map. Moreover, the converted disparity map can also increase the speed of the algorithm. We evaluate the proposed pipeline on the KITTI benchmark. The experiment demonstrates that our algorithm has higher accuracy than several classic methods.