Reviewerly/exHarmony huggingface dataset:
Neural Machine Translation with an Awareness of Semantic Similarity

Machine translation requires that source and target sentences have identical semantics. Previous neural machine translation (NMT) models have implicitly achieved this requirement using cross-entropy loss. In this paper, we propose a sentence Semantic-aware Machine Translation model (SaMT) which explicitly addresses the issue of semantic similarity between sentences in translation. SaMT integrates a Sentence-Transformer into a Transformer-based encoder-decoder to estimate semantic similarity between source and target sentences. Our model enables translated sentences to maintain the semantics of source sentences, either by using the Sentence-Transformer alone or by including an additional linear layer in the decoder. To achieve high-quality translation, we employ vertical and horizontal feature fusion methods, which capture rich features from sentences during translation. Experimental results showed a BLEU score of 36.41 on the IWSLT2014 $$German \rightarrow English$$ dataset, validating the efficacy of incorporating sentence-level semantic knowledge and using the two orthogonal fusion methods. Our code is available at https://github.com/aaa559/SaMT-master .
Parameter-Lite Adapter for Dynamic Entity Alignment

Entity alignment (EA) aims to link entities referring to the same real-world identity from different knowledge graphs (KGs). Most existing EA methods focus on static KGs, while practical graphs are growing and changing over time. Although some EA methods study dynamic settings to suit the changes, they perform suboptimal as they are unaware of knowledge oblivion and the prohibitive model size. To address the above issues, we propose a Parameter-Lite dynamic Entity Alignment model (PLEA), which leverages prior knowledge to embed entities and even represent unseen entities. We design a novel lightweight module that only trains a small number of parameters added by the adapter and keeps the original network fixed, so as to retain knowledge from previous snapshots with low computational cost. As for unseen entities, we design a regularized entity mapping mechanism to inject prior knowledge into unseen entity embeddings to improve representation ability. The experimental results on three real-world datasets demonstrate that our proposed PLEA archives up to 4% accuracy with only 50% of the number of parameters, compared with existing state-of-art methods.
Automatically Choosing Selection Operator Based on Semantic Information in Evolutionary Feature Construction

In recent years, genetic programming-based evolutionary feature construction has shown great potential in various applications. However, a critical challenge in applying this technique is the need to select an appropriate selection operator with great care. To tackle this issue, this paper introduces a novel approach that leverages the Thompson sampling technique to automatically choose the optimal selection operator based on semantic information of genetic programming models gathered during the evolutionary process. The experimental results on a standard symbolic regression benchmark containing 37 datasets show that the proposed adaptive operator selection algorithm outperforms expert-designed operators, demonstrating the effectiveness of the adaptive operator selection algorithm.
CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization

To efficiently extract textual information from color degraded document images is a significant research area. The prolonged imperfect preservation of ancient documents has led to various types of degradation, such as page staining, paper yellowing, and ink bleeding. These types of degradation badly impact the image processing for features extraction. This paper introduces a novelty method employing generative adversarial networks based on color channel using discrete wavelet transform (CCDWT-GAN). The proposed method involves three stages: image preprocessing, image enhancement, and image binarization. In the initial step, we apply discrete wavelet transform (DWT) to retain the low-low (LL) subband image, thereby enhancing image quality. Subsequently, we divide the original input image into four single-channel colors (red, green, blue, and gray) to separately train adversarial networks. For the extraction of global and local features, we utilize the output image from the image enhancement stage and the entire input image to train adversarial networks independently, and then combine these two results as the final output. To validate the positive impact of the image enhancement and binarization stages on model performance, we conduct an ablation study. This work compares the performance of the proposed method with other state-of-the-art (SOTA) methods on DIBCO and H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets. The experimental results demonstrate that CCDWT-GAN achieves a top two performance on multiple benchmark datasets. Notably, on DIBCO 2013 and 2016 dataset, our method achieves F-measure (FM) values of 95.24 and 91.46, respectively.
Word Segmentation of Hiragana Sentences Using Hiragana BERT

Abstract Unlike Western languages, word segmentation is necessary for Japanese sentences because they do not have word boundaries. The performances of existing morphological analyzers for Japanese sentences are very high. However, it is difficult to segment sentences mostly written in Hiragana, which is a Japanese writing system simpler than Kanji, because clues to segment the sentences decrease. In this study, we created a word segmentation model of Hiragana sentences using two types of BERT: unigram and bigram BERT models. We pre-trained the BERT models with Wikipedia and fine-tuned them with the core data of the Balanced Corpus of Contemporary Written Japanese for word segmentation. In addition to the two types of BERT-based word segmentation systems, we developed a word segmentation system for Hiragana sentences using KyTea, a toolkit developed for analyzing text, with a focus on languages requiring word segmentation. We compared them in word segmentation of Hiragana sentences. The experiments revealed that the unigram BERT-based word segmentation system outperformed the bigram BERT-based word segmentation system and the KyTea-based word segmentation system.
Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval

Text-to-SQL is a task that converts a natural language question into a structured query language (SQL) to retrieve information from a database. Large language models (LLMs) work well in natural language generation tasks, but they are not specifically pre-trained to understand the syntax and semantics of SQL commands. In this paper, we propose an LLM-based framework for Text-to-SQL which retrieves helpful demonstration examples to prompt LLMs. However, questions with different database schemes can vary widely, even if the intentions behind them are similar and the corresponding SQL queries exhibit similarities. Consequently, it becomes crucial to identify the appropriate SQL demonstrations that align with our requirements. We design a de-semanticization mechanism that extracts question skeletons, allowing us to retrieve similar examples based on their structural similarity. We also model the relationships between question tokens and database schema items (i.e., tables and columns) to filter out scheme-related information. Our framework adapts the range of the database schema in prompts to balance length and valuable information. A fallback mechanism allows for a more detailed schema to be provided if the generated SQL query fails. Ours outperforms state-of-the-art models and demonstrates strong generalization ability on three cross-domain Text-to-SQL benchmarks.
Unsupervised Contrastive Learning of Sentence Embeddings Through Optimized Sample Construction and Knowledge Distillation

Unsupervised contrastive learning of sentence embedding has been a recent focus of researchers. However, issues such as unreasonable division of positive and negative samples and poor data enhancement leading to text semantic changes still exist. We propose an optimized data augmentation method that combines contrastive learning’s data augmentation with unsupervised sentence pair modelling’s distillation. Our data augmentation uses in-sentence tokens for positive examples and text similarity for negative examples, while the distillation is conducted without supervised pairs. Experimental results on the STS task show that our method achieves a Spearman correlation of 81.03%, outperforming existing STS benchmarks.
Prompting Generative Language Model with Guiding Augmentation for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in natural language processing concerning the automatic extraction of (aspect term, opinion term, sentiment polarity) triplets from a given text. Current end-to-end generative methods achieved high results by treating it as a sequence generation task with a generative pretrained language mode (e.g., T5). However, these architectures usually suffered from the objective gaps between the pre-training tasks and fine-tuning tasks, leading to suboptimal results. Further more, they can only provide information on what is a valid triplet, but no explicit guidance on what is not a triplet, which can not fully capture the correlation between aspects and opinions. To address above issues, we propose the generative prompt to bridge the gap between pre-training and fine-tuning of generative pretrained language model via text infilling task. And we propose guiding augmentation, which drops the aspect or opinion in the sentence by depicting a tree structure to generate diverse similar sentences and new target sequences. In this way, the main differences between these augmented samples are the dropped aspect or opinion term, and the model can understand the ASTE task knowledge better through the explicit variant constraints. Experimental results confirm that our method outperforms previous state-of-the-art (SOTA) methods on four public ASTE datasets.
Research on the Construction and Application of an Intelligent Learning System to Enhance College English Listening and Speaking

This study seeks to investigate the development and implementation of an intelligent learning system designed to enhance college students’ English listening and speaking skills. By integrating theories of language acquisition, cognitive psychology, and educational technology research, a smart college English audio-visual and oral learning system has been created. This system allows students to assess their listening and speaking capabilities, identify areas for improvement through automated evaluation, receive real-time feedback, and access personalized learning recommendations. Empirical research findings indicate that intelligent learning systems can significantly enhance students’ listening and speaking skills, leading to improved pronunciation accuracy, fluency, and grammatical correctness.
Research on the Enhancement of College Students’ Scientific Research and Innovation Ability from the Perspective of Curriculum Teaching：A Case Study of Research Methodology in English

As an introductory basic course for college students in Chinese-foreign cooperative programs to engage in scientific research, Research Methodology in English is of great significance for improving their scientific research and innovation ability. However, in the actual teaching process, it faces many problems such as uneven language level, difficult teaching content and difficult operation in practical training. Taking the teaching and research reform of this course as an example, this paper aims to improve the teaching quality and enhance students’ scientific research and innovation ability by optimizing teaching objectives, reconstructing teaching content, integrating teaching means, innovating teaching activities, and rebuilding teaching process, and hopes to provide reference for related teaching reform and optimization of the curriculum system of scientific research and innovation ability.
Improving Speaker Recognition by Time-Frequency Domain Feature Enhanced Method

Many existing speaker recognition algorithms have the problem that single-domain feature extraction cannot represent the speech characteristics well, and this problem will affect the accuracy of speaker recognition. To solve this problem, we propose a time-frequency domain feature enhanced deep speaker (TFDS). The proposed algorithm can combine time domain and frequency domain, enhance the traditional MFCC feature extraction, and make up for the shortcomings of other algorithms that only extract features in a single domain. The deep speaker network architecture includes ResCNN, GRU, time averaging layer, style transformation layer, length normalization layer, and the loss is triple loss. Representation of experimental results performed on the librisspeech dataset results show that TFDS has higher accuracy and lower Equal Error Rate than deep speaker, and the time-frequency domain feature enhanced method can also be combined with other networks to improve the accuracy of speaker recognition.
Evolving a Better Scheduler for Diffusion Models

AI Generated Content (AIGC) is becoming phenomenally prominent and impactful. One of the key generative algorithms used in AIGC is the diffusion model which is widely used in generative images and audio. In comparison with other generative methods such as GAN (Generative Adversarial Network) and VAE (Variational Auto Encoder), diffusion models can generate samples of higher quality. To further improve diffusion models, especially in terms of sampling speed, we propose an evolutionary algorithm in this paper. That is to enhance the noise scheduler of the diffusion framework, thereby improving both performance and sampling speed. This is the first diffusion model that incorporates evolutionary algorithms. Our experiments show that evolved schedulers can bring concrete improvement in the generative process.
Cross-scale Dynamic Relation Network for Object Detection

The majority of object detectors only consider the features in region proposals, without taking the global context or the relationships between objects into detection. Conceivably, it would inevitably limit the improvement of performance. To tackle the problem, we introduce a Cross-Scale Dynamic Relation Network (CSDRN) that can explore the relationships between specific objects in an image, and its core components include a Cross-Scale Semantic-Aware Module (CSSAM), Dynamic Relation Graph Reasoning (DRGR), and Semantic Attention Fusion Module(SAFM). Through the CSSAM, the crucial information in feature maps of different scales achieve semantic interaction to obtain a cross-scale semantic feature. We activate the category knowledge in the image and combine the cross-scale semantic feature to create a dynamic relationship graph. Therefore, we can get more precise relation between objects. Guided by the relation, a semantic attention is generated to enrich the visual features. Experimental results on the COCO dataset show that the proposed CSDRN can effectively improve the detection performance, reaching 54.8% box AP, which is 3.9% box AP over the baseline. Moreover, 47.6% mask AP is achieved in instance segmentation, exceeding the baseline 3.6% mask AP.
An Empirical Study on Context Length for Open-Domain Dialog Generation

Transformer-based open-domain dialog models have become increasingly popular in recent years. These models typically represent context as a concatenation of a dialog history. However, there is no criterion to decide how many utterances should be kept adequate in a context. We try to figure out how the choice of context length affects the model. We experiment on three questions from coarse to fine: (i) Does longer context help model training? (ii) Is it necessary to change the training context length when dealing with dialogs of different context lengths? (iii) Do different dialog samples have the same preference for context length? Our experimental results show that context length, an often overlooked setting, deserves attention when implementing Transformer-based dialog models. Code is available at https://github.com/PKUAI-LINGroup/context-study .
English Teaching Practice and Research Under the Joyful English Philosophy

This paper examines English teaching practice and research under the Joyful English philosophy. It emphasizes the essence of the Joyful English philosophy, analyzes the characteristics of Joyful English teaching, and identifies the challenges faced in Joyful English education, and anticipates future trends, including the integration of education and technology, the fusion of interdisciplinary education and personalized learning, and the development of a multicultural education evaluation system. The paper underscores the value and impact of Joyful English teaching while also presenting methods to address challenges and seize future opportunities, aiming to continuously enhance the quality and effectiveness of English education.
Network Structure Embedding Method Based on Role Domain Feature

Network structure is formed by intricate connections between nodes, exploring and learning the network topological structural features has a profound impact in the field of network representation learning. Role refers to a collection of nodes with similar structural features in the network. So network representation learning that preserves node structure in a low-dimensional vector representation space is also known as role discovery, which focuses on partitioning the network into different sets of roles based on structural features. Although existing methods for network structure embedding have made some progress in role discovery tasks, most of them focus on the local structural features to generate node representations, resulting in the inability to learn multiaspect structural features of roles. Therefore, we propose a network structure embedding model URold, which uses role domain feature to enhance node structure representation capabilities and learn the proximity between roles. We conduct role discovery experiments on six real-world networks, and compare with eight state-of-the-art network structure embedding algorithms. The results show that our method URold achieves the best performance and demonstrates excellent role discovery ability.
Sarcasm Relation to Time: Sarcasm Detection with Temporal Features and Deep Learning

This paper presents a deep learning-based framework to detect sarcasm in relation to time. Deep N-gram features generated using the FastText algorithm, combined with temporal handcrafted temporal features are used to train several machine learning classifiers. Experimental results show that Logistic Regression performs the best among all the classifiers. The introduction of the handcrafted temporal features has also whosn to improve overall detection performance when compared to existing works in the field.
Guiding Task Learning by Hierarchical RL with an Experience Replay Mechanism Through Reward Machines

Recently, reinforcement learning (RL) has made great progress in theory and application. Whereas, challenges remain in RL, such as low sample utilization and difficulty in designing suitable reward functions. Therefore, this paper focuses on optimizing the structure of the reward function and improving sample utilization. We propose a hierarchical reinforcement learning (HRL) algorithm based on the options framework, which incorporates a segmented reward mechanism and an experience replay mechanism. The reward mechanism can help the agent grasp the reward function’s internal structure. The experience replay mechanism includes a buffer for storing typical experiences and a particular buffer for storing the special state experiences of the agent accessing the subtasks, which are conducive to training. We conducted single-task and multitask tests in multiple environments. Experimental results demonstrate that our algorithm has a better performance than baseline algorithms.
Detecting AI Planning Modelling Mistakes – Potential Errors and Benchmark Domains

AI planning systems can solve complex problems, leaving domain creation as one of the largest obstacles to a large-scale application of this technology. Domain modeling is a tedious, error-prone and manual process. Unfortunately, domain modelling assistance software is sparse and mostly restricted to editors with only surface-level functionality such as syntax highlighting. We address this important gap by proposing a list of potential domain errors which can be detected by problem parsers and modeling tools. We test well-known planning systems and modeling editors on models with those errors and report their results.
Digital Forensic Investigation Tools for Cases Related to Social Media and Cybersecurity

The entire world has moved into the digital realm as a result of the quickening pace of technological development. However, this change has also led to an increase in cybercrimes and security breach occurrences, which endanger user security and privacy. As a result, this study sought to examine how digital forensics, a significant advancement in cybersecurity, is used to combat cybercrime. The most recent developments in digital forensics, such as cloud forensics, social media forensics, and IoT forensics, have been examined in this study. With the use of these technologies, cybersecurity experts can protect data while identifying fraudsters by using the digital footprints that data processing and storage leave behind. Technical, operational, and personnel-related issues are among the specific dangers to digital forensics that have been identified by the research. These systems' high level of complexity, volume of data, chain of custody, personnel integrity, and the validity and accuracy of digital forensics are all significant barriers to their widespread adoption. However, the research has also noted the use of artificial intelligence, intrusion detection, and USB forensics as significant prospects for digital forensics that can make the processes simpler, more effective, and safe.
Diverse Policies Converge in Reward-Free Markov Decision Processes

Reinforcement learning has achieved great success in many decision-making tasks, and traditional reinforcement learning algorithms are mainly designed for obtaining a single optimal solution. However, recent works show the importance of developing diverse policies, which makes it an emerging research topic. Despite the variety of diversity reinforcement learning algorithms that have emerged, none of them theoretically answer the question of how the algorithm converges and how efficient the algorithm is. In this paper, we provide a unified diversity reinforcement learning framework and investigate the convergence of training diverse policies. Under such a framework, we also propose a provably efficient diversity reinforcement learning algorithm. Finally, we verify the effectiveness of our method through numerical experiments.
“A STUDY ON EFFECT OF EMPLOYEE PARTICIPATION WITH SPECIAL REFERENCE TO MANAGEMENT DECISION MAKING AT COTTON WORLD, DODDABALLAPUR”

This study investigates the impact of employee participation on management decision- making processes within organizations. By emphasizing the relationship between employee involvement and decision outcomes, the research aims to shed light on the advantages and challenges associated with incorporating employee perspectives. Through a comprehensive review of literature, empirical data collection, and analysis, the study examines how increased employee participation can enhance decision quality, innovation, and organizational commitment. Potential drawbacks and limitations are also considered, including potential conflicts and implementation barriers. Overall, the study contributes to a deeper understanding of the intricate dynamics between employee participation and the decision- making landscape. KEYWORDS: Employee satisfaction, Management decision making, Special reference. Employee involvement, decision outcome.