Reviewerly/exHarmony huggingface dataset:
Conflict of Irish Cultural Identity in Brian Friel’s Translation

This research purposes to analyze Irish cultural identity conflict in Brian Friel’s Translation, namely by analyzing why it happens and how it impacts to Irish. The method applied to analyze is descriptive qualitative method by doing content analysis through data collecting technique in the form of library research. From the data source of Brian Friel’s Translation, the researcher does the technique of interpretation by using Homi K. Bhabha’s postcolonialism theory of mimicry concept to identify the conflict of Irish cultural identity found in the literary work. The result of the research shows that the setting in Brian Friel’s Translation is Northern Ireland in the 19th century. Irish people are surrendered to be the British colony. British people colonize Irish in various ways; destroying Irish rights by forcing them to work very hard on potato plantation in the area where they live to fulfill British people food, forbidding Irish to use their own language or do their Catholic religious worship, executing Ordnance survey namely by replacing names of local places in Northern Ireland for the importance of imperialism forcibly. That colonialism causes suffering, starving and poverty. Through the dialogues, it reflects that British people want to abolish Irish language and culture to replace it to British language and culture which is considered more modern. AbstrakPenelitian ini bertujuan menganalisis konflik identitas budaya bangsa Irlandia dalam drama Translation karangan Brian Friel, yaitu dengan mengupas mengapa konflik itu terjadi, dan bagaimana akibatnya terhadap bangsa Irlandia. Metode yang diterapkan adalah metode deskriptif kualitatif, dengan menganalisa isi melalui teknik pengumpulan data berupa studi kepustakaan. Dari sumber data drama Translation karangan Brian Friel, peneliti memakai teknik interpretasi melalui teori postkolonialisme konsep mimikri dari Homi K Bhabha untuk mengidentifikasi konflik identitas budaya bangsa Irlandia yang ditemukan pada karya sastra tersebut. Hasil penelitian menunjukkan bahwa seting yang terdapat pada drama tersebut adalah pedesaan Irlandia Utara pada abad 19. Bangsa Irlandia takluk menjadi koloni bangsa Inggris. Bangsa Inggris menjajah dengan berbagai cara, menghancurkan hak-hak bangsa Irlandia dengan memaksa mereka kerja paksa di perkebunan kentang tempat tinggal mereka untuk memenuhi kebutuhan makanan rakyat Inggris, melarang mereka menggunakan bahasa asli atau melakukan ibadah agama Katolik, dan melakukan survei Ordnance yaitu mengganti paksa nama-nama lokal di Irlandia utara ke bahasa Inggris untuk kepentingan imperialisme. Penjajahan tersebut menyebabkan penderitaan, kelaparan dan kemiskinan. Melalui dialog-dialognya tercermin bahwa bangsa Inggris ingin melenyapkan budaya serta bahasa Irlandia, lalu menggantinya dengan budaya dan bahasa Inggris yang dianggap lebih modern.
A KP-ABE-Based ECC Approach for Internet of Medical Things

In Internet of Medical Things (IoMT), Healthcare providers carried out electronic Personal Health Record (PHR) to manage the health data of individuals in such a heterogeneous IoMT environment. PHR, on the other hand, comprises sensitive information about which privacy and security issues are important. Key Policy Attribute-Based Encryption (KP-ABE) is a modern encryption technique that provides security with an access control mechanism. Here, we propose a PHR Access Control (PAC) scheme by using KP-ABE for sharing personal health record in the IoMT environment. Elliptic Curve Cryptography (ECC) provides a strong notion of security with smaller key sizes. Therefore, we use the point scalar multiplication of ECC to reduce the computational cost of PHR owners and PHR users. The adoption of KP-ABE using ECC for exchanging PHR greatly enhanced overall efficiency while also ensuring personal health data security, according to the experimental investigation.
Performance Analysis of Mayfly Algorithm for Problem Solving in Optimization

Mayfly optimization is the recently developed optimization algorithm by Konstantinos Zervoudakis and Stelios Tsafarakis in the year 2020. It is encouraged by the mating process and flight behavior of mayflies and it is proposed with enhanced hybridization of the swarm algorithms and evolutionary algorithms for solving out the optimization complications and it has shown immense success in many fields in a very short time span. In this study, a comprehensive analysis of mayfly algorithm is highlighted which includes a literature survey, its inspiration, and methodology. Different modifications, comparative analysis, and several applications of mayfly algorithm are analyzed and future research directions for mayfly algorithm are discussed. It has been applied profitably to many domains such as engineering, medical, energy, computer science, and many more. This study can act as a foundation for researchers and scientists to enhance the performance of their research work by using the mayfly algorithm in the domain of optimization.
Modified Hybrid GWO-SCA Algorithm for Solving Optimization Problems

The most recent study trend is to combine two or more variations to improve the quality of solutions to practical and contemporary real-world global optimization challenges. In this work, a novel Sine Cosine Algorithm (SCA) and hybrid Grey Wolf Optimization (GWO) technique is tested on 10 benchmark tests. A hybrid GWOSCA is a mixture of the Sine Cosine Algorithm (SCA) for the exploration phase and the Grey Wolf Optimizer (GWO) for the exploitation phase in an undefined environment. The simulation findings reveal that the suggested hybrid technique outperforms, better than other known algorithms in the research community.
A Discrete Firefly-Based Task Scheduling Algorithm for Cloud Infrastructure

Cloud computing is the new era of technology. In this, tasks executed on the Virtual Machines (VMs) have different properties such as length, size, start time, priority, and execution time. These tasks need an effective and efficient scheduling policy. Task scheduling plays an influential role in allocating the VMs to the task. A well-planned scheduler helps to reduce the time it takes to complete tasks and improve resource utilization, both of which are essential factors in lowering costs. Efficient scheduling is the one that utilizes its resources to their full potential. To lower the makespan and increase the data center’s resource use, a novel task scheduler technique based on a discrete variant of the firefly algorithm (TSDFF) is developed. The technique is simulated on the CloudSim and the simulation result when contrasted with the other prevailing techniques turned out to be performing better in relation to resource usage and makespan.
CNN Architecture-Based Image Retrieval of Colonoscopy Polyp Frames

Manual interpretation and retrieval of colorectal polyps is a time-consuming and laborious task even for specialized medical experts. An automated system can help in information retrieval and timely treatment of polyps. This work comprises of a colonoscopy polyp image retrieval and detection pipeline through the proposed Convolutional Neural Network (CNN) architecture. A binary classification of polyps versus non-polyps has been carried out to retrieve information about polyps in the colonoscopic frames. To check the efficacy of the architecture, test set evaluation, feature mapping, and per epoch analysis of achieved loss and accuracy values have been done. An improved Jaccard index of 83.18% and specificity up to 94.50% have been reported for 33,000 polyp and non-polyp frames generated using publicly available colonoscopic databases. Results infer a maximum of 206 correctly detected polyps out of 215 polyp image frames. The developed architecture has also been compared with state-of-the-art work in this field.
A Legal Multi-Choice Question Answering Model Based on BERT and Attention

Legal question answering is one of the critical topics in the field of legal intelligence, and the judicial examination is a multi-choice question-answering task. However, previous scoring-based methods for the multi-choice task suffer from the classification bias problem and can not fully exploit the relationship between reference books, questions, and multi-choice answers. To address the issues, in this paper, we propose a BERT and attention based model for the judicial examination task. Specifically, we first use the BM25 algorithm to retrieve articles in the reference law books to provide certain information to answer a given question. Second, we input a question, its multi-choice answers, and the retrieved relevant articles to BERT for encoding. Third, we input the encoded information to the module of information fuse and match through attention mechanism to find deep relationships in articles, questions, and multi-choice answers. Finally, we concatenate the output of the attention mechanism and input it to a binary classifier to determine whether an answer option to the question is correct. We have done extensive experiments to show that our model significantly outperforms the baselines in accuracy. We also experimentally demonstrate the effectiveness of some components of our model. Moreover, we applied our method in the Challenge of AI in Law 2022 (CAIL 2022) and won the third prize in the judicial examination task in this competition.
Role and Relationship-Aware Representation Learning for Complex Coupled Dynamic Heterogeneous Networks

Representation learning for dynamic heterogeneous networks with complex coupling relationships in the real world can play an important role in the research of downstream tasks such as node classification, link prediction and recommendation systems. However, the existing network representation learning methods are difficult to retain the dynamic and heterogeneous nature of the network at the same time, and it is difficult to capture information with complex coupling and dynamic changes. At the same time, some potential but equally valuable information in the real network has not been fully mined and utilized. This work proposes a complex coupling dynamic heterogeneous role and relationship awareness model, which is a method that can effectively preserve network structure information and relationship information. In the model, a historical memory map is constructed at a given time step to preserve historical and current network information. On the historical memory map, through the guidance of the meta-path, a random walk process based on the role-aware strategy is performed to obtain the node sequence and effective semantic information. Finally, the node sequence is input into the proposed improved skip-gram model based on relationship awareness for training, so as to learn node embedding more effectively. Experiments on two real-world datasets confirm that the proposed model consistently outperforms the state-of-the-art representation learning methods on downstream tasks including node classification and link prediction.
Enhanced Entity Interaction Modeling for Multi-Modal Entity Alignment

Multi-modal Entity Alignment (MMEA) aims to find equivalent entities across different multi-modal knowledge graphs (MMKGs). Most existing methods focus on how to encode or fuse information from different modalities effectively, without considering the critical interactions between entities, especially those between an entity and its neighbors within each modality. To fill the gap, we propose a novel model namely Enhanced Entity Interaction Modeling for Multi-modal Entity Alignment (EIEA). Specifically, we first utilize multiple separate pre-trained models to acquire single-modal data based entities’ embeddings. Then, the module Enhanced Entity Representation (EER) is designed to mine interactions between entities and their neighborhoods, and facilitate effective multi-modal embedding fusion using a weighting mechanism. Finally, through contrastive learning, we ensure that the aligned entities have higher similarity than non-aligned ones within each modality. The extensive experiments demonstrate that EIEA outperforms the state-of-the-art baselines on three benchmark datasets.
View Distribution Alignment with Progressive Adversarial Learning for UAV Visual Geo-Localization

Unmanned Aerial Vehicle (UAV) visual geo-localization aims to match images of the same geographic target captured from different views, i.e., the UAV view and the satellite view. It is very challenging due to the large appearance differences in UAV-satellite image pairs. Previous works map images captured by UAVs and satellites to a shared feature space and employ a classification framework to learn location-dependent features while neglecting the overall distribution shift between the UAV view and the satellite view. In this paper, we address these limitations by introducing distribution alignment of the two views to shorten their distance in a common space. Specifically, we propose an end-to-end network, called PVDA (Progressive View Distribution Alignment). During training, feature encoder, location classifier, and view discriminator are jointly optimized by a novel progressive adversarial learning strategy. Competition between feature encoder and view discriminator prompts both of them to be stronger. It turns out that the adversarial learning is progressively emphasized until UAV-view images are indistinguishable from satellite-view images. As a result, the proposed PVDA becomes powerful in learning location-dependent yet view-invariant features with good scalability towards unseen images of new locations. Compared to the state-of-the-art methods, the proposed PVDA requires less inference time but has achieved superior performance on the University-1652 dataset.
Graph Neural Network with Neighborhood Reconnection

Graph Neural Network (GNN) models have become increasingly popular for network analysis, especially in node classification tasks. However, the effectiveness of GNNs is compromised by two limitations. First, they implicitly assume that networks are homophilous, leading to decreased performance on heterophilous or random networks commonly found in the real world. Second, they tend to ignore the known node labels, inferring node labels merely from the node features and network structure. This is mainly rooted in the non-uniformity of node degrees, which makes it hard to directly aggregate label information. Hence, we propose a novel framework NRGNN, short for Graph Neural Network with Neighborhood Reconnection. NRGNN adjusts the network structure to increase homophily and uniformize node degrees. Then it applies message-passing-based or PageRank-based GNNs to the reconstructed networks, addressing the two limitations of GNNs. We evaluate NRGNN against 14 state-of-the-art baselines for node classification tasks. The empirical results demonstrate that NRGNN outperforms almost all the baselines, regardless of whether the datasets are homophilous, heterophilous, or random. The techniques might be adapted to more network analysis tasks.
Multi-Sampling Item Response Ranking Neural Cognitive Diagnosis with Bilinear Feature Interaction

Cognitive diagnosis is a fundamental task in educational data mining that aims to discover students’ proficiency in knowledge concepts. Neural cognitive diagnosis combines deep learning with cognitive diagnosis, breaking away from artificially defined interaction functions. However, existing cognitive diagnosis models mostly start from the interaction of students’ answers, ignoring the feature interaction between test items and knowledge concepts. Meanwhile, few of the previous models consider the monotonicity of knowledge concept proficiency. To address these issues, we present a novel cognitive diagnosis method, called multi-sampling item response ranking neural cognitive diagnosis with bilinear feature interaction. We first allow the ratio in loss function to adjust the impact between pointwise sampling and pairwise sampling to strengthen the monotonicity. At the same time, we replace element product feature interaction with bilinear feature interaction in the multi-sampling item response ranking neural cognitive diagnosis to enhance interaction in the deep learning process. Specifically, our model is stable and can be easily applied to cognitive diagnosis. We observed improvements over the previous state-of-the-art baselines on real-world datasets.
Reversible Data Hiding in Encrypted Images Based on a Multi-Granularity Adaptive Classification Mechanism

In the era of cloud computing, many people pay attention to privacy protection issues while transferring data to the cloud, leading to the emergence of Reversible Data Hiding in Encrypted Images (RDHEI). The main difficulty in RDHEI is how to fully use the redundant room in the image to improve the Embedding Rate (ER). To solve this problem, an efficient RDHEI method based on Multi-Granularity Adaptive Classification (MGAC) mechanism is proposed in this paper. In image encryption, a block-based image encryption algorithm based on Cellular Neural Networks (CNN) hyper-chaotic system is utilized, including confusion and diffusion. The room reservation part uses the MGAC mechanism based on the block’s global correlation and local correlation. Specifically, each block is classified according to the quantitative characteristics of the same bit plane in this block. Afterward, different room reservation strategies are applied to different types of blocks adaptively. The multi-granularity is reflected in each block’s rough reservation and refined reservation operations. The image decryption part realizes the separability of data extraction and image recovery. Extensive experimental results show that the average ER of the proposed method on the datasets BOSSbase, BOWS-2, and UCID is about 0.16bpp higher than that of the baseline method on average. Meanwhile, the image security performance of the proposed method has also been improved.
Multi-level Contrastive Learning for Commonsense Question Answering

Recent studies have shown that the integration of external knowledge greatly improves the performance of commonsense question answering. However, the problems of semantic representation discrepancy between questions and external knowledge as well as weak discrimination between choices have not been well ameliorated. To address the above problems, we propose Multi-Level Contrastive Learning named MLCL for commonsense question answering, which includes instance-level and class-level contrastive learning modules. The instance-level contrastive module aims to align questions with knowledge of correct choice in semantic space, and class-level contrastive module focuses on how to make it easier to distinguish between correct and wrong choices. The model achieves state-of-the-art result in CommonsenseQA dataset and outperforms competitive approaches in OpenBookQA. In addition, adequate experiments verify the effectiveness of contrastive learning in multi-choice commonsense question answering.
Multi-level and Multi-interest User Interest Modeling for News Recommendation

User interest modeling is crucial for personalized news recommendation. Existing personalized news recommendation methods usually take the news data as the minimum interest modeling unit when modeling user’s interests. They ignored the low-level and high-level signals from user’s behaviors. In this paper, we propose a news recommendation method combined with multi-level and multi-interest user interest modeling, named MMRN. In contrast to existing methods, our MMRN model captures user interest at multiple levels, including word-level, news-level and higher-levels, then learns multiple user interests vectors in each level. Extensive experiments on a real-world dataset show our method can effectively improve the recommendation effect.
Semi-supervised Entity Alignment via Noisy Student-Based Self Training

Knowledge graph is a structured data model that captures the relationships between entities in the real world. Entity alignment (EA) has drawn significant attention in recent years as a potential means of identifying corresponding entities across different knowledge graphs. Although knowledge graph embedding-based entity alignment methods have recently obtained significant progress, the shortage of training data remains a severe challenge. Conventional approaches have attempted to solve this issue through semi-supervised learning but still suffer from the negative impacts of entity alignment. To resolute the above issues, we propose a semi-supervised framework with Noisy Student-based self training Entity Alignment named NSEA. Our framework proposes a new noisy student self-training strategy for obtaining diverse entity alignment pairs, and we also design an adaptive alignment selector to infer reliable entity pairs. Through extensive experiments on benchmark datasets, we demonstrate that our method outperforms most existing models in terms of accuracy and efficiency, highlighting its usefulness for large-scale and diverse knowledge graphs with insufficient annotated data.
Federated Prompting and Chain-of-Thought Reasoning for Improving LLMs Answering

We investigate how to enhance answer precision in frequently asked questions posed by distributed users using cloud-based Large Language Models (LLMs). Our study focuses on a typical situation where users ask similar queries that involve identical mathematical reasoning steps and problem-solving procedures. Due to the unsatisfactory accuracy of LLMs’ zero-shot prompting with standalone questions, we propose to improve the distributed synonymous questions using Self-Consistency (SC) and Chain-of-Thought (CoT) techniques with a crowd-sourced federated question pool. Our methods can generate significantly more accurate answers for all user queries without requiring sophisticated model-tuning. Through extensive experiments, we demonstrate that our proposed methods can significantly enhance question accuracy by fully exploring the synonymous nature of the questions and the consistency of the answers.
An Efficient One-Shot Network and Robust Data Associations in Multi-pedestrian Tracking

Recently, one-shot trackers, which integrate multi-tasks into a unified network, achieve good performances in multi-object video tracking and successfully handle the core challenge of multi-object tracking, that is, how to realize the trade-off between the high accuracy and real-time performance. In this paper, we abandon the traditional approach of redundant backbones and feature fusion networks commonly used by one-shot trackers, and propose a new one-shot model that is faster and lighter. We propose a new channel-spatial attention module to improve the detection and re-identification performance of the one-shot model for more robust tracking. Furthermore, in order to deal with complex video tracking scenarios more robust, we have made innovations in data association and proposed a new robust association method, which combines the advantages of the motion, appearance and the detection information to associate. On the MOT20 testing set, our proposed one-shot model with robust associations termed as BFMOT reduces the number of ID switches by 52.1% and improves the tracking accuracy (i.e. MOTA) by 6.7% compared with the state-of-the-art tracker. BFMOT runs close to 30 FPS on MOT16,17 testing sets, which is more oriented to real-time tracking.