Reviewerly/exHarmony huggingface dataset:
English-Manipuri Cross-Lingual Embedding: A Preliminary Study

This work presents a preliminary study of cross-lingual embedding between English and Ma-nipuri, a Tibeto-Burman language and a low-resource language. Initially, the work focuses on understanding the quality of cross-lingual embedding by evaluating the bilingual dictionary induction task on various su-pervised, weakly supervised, and unsupervised state-of-the-art methods. Experimental result shows poor performance in all the state-of-the-art techniques in English-Manipuri cross-lingual embedding. The work then moves on to understanding the impact of linguistic features, such as the morphologically rich nature of the Manipuri language, on the performance of the Bilingual Dictionary Induction(BDI) task.
Pipeline Chain-of-Thought: A Prompt Method for Large Language Model Relation Extraction

The development of language models has been influencing approaches to relation extraction (RE) problems. Although large language models (LLMs) have demonstrated breakthrough potential in certain aspects, they are still in the exploratory stage for RE tasks. Currently, the mainstream approach to improving the RE performance of LLMs is through prompt fine-tuning, but most methods require providing entity information in the prompt, which effectively only allows the LLMs to perform relationship classification tasks. We propose the Pipeline Chain-of-Thought (Pipeline-COT), which breaks down the RE task into steps and transforms it into reasoning tasks that have flat scaling curves, thereby enabling the use of Chain-of-Thought (COT) to enhance model inference. In addition, our method utilizes n-shot samples to provide signals for the Bayesian inference of the model by prompting the LLMs to focus on specific concepts to generate answers. We evaluated pipeline-COT on the Chinese dataset DuIE2.0, and compared with baseline methods that require including entity information in the prompt, our method still shows competitive performance.
Research on the Application of Transformer in Encrypted Traffic Recognition

Encrypted traffic recognition is a crucial research area in network security. However, traditional recognition methods cannot meet practical needs due to the concealment and diversity of encrypted traffic. The emergence of deep learning techniques provides new ideas for encrypted traffic recognition. As a neural network model based on the attention mechanism, transformer exhibits excellent sequence modeling ability and has achieved successful applications in various domains. In this paper, we introduce the current research status of applying transformer in the field of encrypted traffic recognition, analyze the technical development line, propose the framework of encrypted traffic recognition built upon hybrid learning, and look forward to the development direction of transformer in the domain of encrypted traffic recognition, so as to provide reference and reference for further improving the accuracy and practicality of encrypted traffic recognition.
A Two-Stage Distillation Method: Teach Model to Answer Questions After Comprehending the Document

Multi-choice Machine Reading Comprehension (MRC) is a challenging extension of Natural Language Processing (NLP) that requires the ability to comprehend the semantics and logical relationships between entities in a given text. The MRC task has traditionally been viewed as a process of answering questions based on the given text. This single-stage approach has often led the network to concentrate on generating the correct answer, potentially neglecting the comprehension of the text itself. As a result, many prevalent models have faced challenges in performing well on this task when dealing with longer texts. In this paper, we propose a two-stage knowledge distillation method that teaches the model to better comprehend the document by dividing the MRC task into two separate stages. Our experimental results show that the student model, when equipped with our method, achieves significant improvements, demonstrating the effectiveness of our method.
Sarcasm Detection in Arabic Text Using Contextualized Models

One of the most difficult issues in text classification is detecting sarcasm, especially in informal Arabic with a lot of syntactic and semantic ambiguity. Our goal is to identify whether the provided text is sarcastic or not. We have tested 19 models that are pre-trained on Arabic Language for two datasets which are ArSarcasm-v2 and Saudi-Dialect-Irony-Dataset. The highest accuracy we achieved is 78.73% accuracy using AraBERT v2 base [1] on ArSarcasm-v2 and 71.18% using mBERT [2] on Saudi-Dialect-Irony-Dataset.
MCSSpell:Optimal Path Selection of Candidate Characters by Integrating Multimodal Information and Copy Mechanism for Chinese Spelling Correction

Chinese spelling correction (CSC) is a traditional Natural Language Processing task that aims to detect and correct spelling errors in Chinese text. Many advanced studies have adopted BERT non-autoregressive language models, masking method performs well on the CSC task. However, the model still has certain limitations. Firstly, BERT predicts tokens based on the assumption of independence, meaning it does not learn the relationships between masked tokens. As a result, it fails to model character-level dependencies effectively. Additionally, BERT tends to correct characters to more common habitual expressions, leading to the problem of over correction. To address these issues, this paper proposes a novel Candidate Character Optimal Path Selector (CCOPS) that models the dependencies between adjacent Chinese characters using attention mechanisms to alleviate the issue of character coherence. We incorporate a particular copy mechanism into the selector, which guides the model to choose the original character when both the original input and the candidate output are plausible, mitigating the problem of over correction. Furthermore, we effectively integrate the multimodal information of characters to guide error correction in terms of semantics, phonetics, and visual similarities. The experiments show that our model, compared to the latest research, has improved Detection-Level F1 by 2.7%, 1.2%, and 1.2% on three datasets, and Correction-Level F1 by 2.6%" 0.8%" and 1.3% respectively,
Aspect Sentiment Triplet Extraction via Affective Knowledge and Enhanced Graph Attention Networks

Aspect Sentiment Triplet Extraction (ASTE) refers to a new and emerging task for sentiment analysis and it is designed to extract triplets consisting of aspect terms, opinion terms, and sentiments from sentences that are user comments. While Graph Convolution Networks (GCN) and external emotional knowledge have shown promise, they have limitations in assigning node weights and capturing intricate relationships. Particularly, GCN treats neighboring nodes uniformly during convolution, lacking the capability to allocate distinct weights based on node significance. In this paper, we propose a SenticNet enhanced Graph Attention neTworks (SN-GAT) model. Specifically, we employ Graph Attention neTworks (GAT) by integrating node and edge information to thoroughly account for variations in the importance of different nodes, acknowledging that certain nodes hold a more significant influence on the overall structure or outcome compared to others. Additionally, our model employs external affective knowledge from SenticNet, a resource encompassing emotions, sentiments, and affective associations in language, to refine word pair representations. This strategy enhances implicit results in aspect and opinion extraction. By conducting extensive experiments on baseline datasets, our proposed model demonstrates superior performance compared to other methods.
Encoding Paraphrase Information Generated by ChatGPT and Introducing Interrogative Word Control Mechanism for Question Generation

Answer-aware question generation means generating a relevant question based on a passage of text and an answer, where the answer is a subsequence within the text. In fact, different expressions of the same thing can affect human understanding, and the same holds true for models. Therefore, in this paper, we use paraphrase information to mimic different perspectives on the same fact and propose a new question generation model. Specially, we utilize ChatGPT to obtain paraphrase of the context. What sets our approach apart from previous work is that we directly model both the context and its paraphrase information using a shared encoder, enabling the model to capture semantic information from different perspectives and fully understand the context. Furthermore, we propose a new self-guided conditional layer normalization module (SCLN) that introduces interrogative words as conditional information into the encoder for the first time. This module adaptively maps interrogative word information to context information, enhancing the model's understanding of the context and controlling the interrogative word that generate question, thereby enhancing the answerability of the question. We conduct experiments on the SQuAD dataset and the NewsQA dataset. The experimental results show that our proposed model achieves significant performance improvements compared to several baseline models. Further human evaluation demonstrates that the questions generated by our model can comprehensively capture contextual information to improve question answerability.
Multi-Task Self-Supervised Learning Based Tibetan-Chinese Speech-to-Speech Translation

Speech-to-speech translation tasks are commonly tackled by using a three-level cascade system which comprises of speech recognition, machine translation, and speech synthesis. However, this approach suffers from the drawback of error accumulation at each stage. In contrast, the direct speech-to-speech translation model directly converts speech from the source language to the target language without relying on intermediate text generation, thereby avoiding the issue of incorrect transmission in cascading systems. Currently, there exist two categories for direct speech-to-speech translation methods. The first involves mapping the Mel-spectrogram of the source language speech to the Mel-spectrogram of the target language speech. However, this method often encounters challenges in convergence and producing the audible speech for the target language. The second type of methods is to learn a self-supervised discrete representation of the target language using an unlabeled speech corpus. This method entails training a sequence-to-sequence model on a real-world dataset, which then maps the source language speech to the discrete representation of the target language. Finally, a separately trained vocoder is utilized to convert the discrete unit sequence into a speech waveform. Given the limited availability of large-scale Tibetan-Chinese parallel speech corpora, this work adopts the second method to model Tibetan-Chinese speech-to-speech translation tasks. Additionally, a multi-task learning framework is designed in this work to enhance the performance of the speech translation model. Experimental results demonstrate that the Tibetan-Chinese speech-to-speech translation model based on multi-task self-supervised learning outperforms both the model based on spectrogram mapping and the single-task self-supervised learning model in terms of achieving a higher BLUE value.
Deciphering Storytelling Events: A Study of Neural and Prompt-Driven Event Detection in Short Stories

Detecting events in literary narratives is relatively challenging due to the presence of both realis and non-realis events. Realis events are those that actually take place. Unlike other domains, such as biomedical and news articles, events in literary narratives are not always factual. This paper introduces “Gatha-200”, a manually annotated dataset of 200 short stories for realis events. We employed several neural models for realis event detection. The best-performing neural model reached a macro Fl score of 92.2 %. Furthermore, we also evaluated the capability of the prompt-based learning approach to detect the realis events in zero-shot and few-shot settings and got promising results.
Relevance of Language-Specific Training on Image Caption Synthesis for Low Resource Assamese Language

Automatic Image Captioning (AIC) is the process of generating semantically and syntactically correct textual descriptions for images. Most existing research on AIC have focused on English language. In comparison, very few attempts have been under-taken to synthesise captions for low resource Indian languages like Assamese. This paper explores AIC for Assamese language through two distinct approaches. The first approach employs state-of-art AIC model pre-trained on English image-caption dataset to generate English captions for an input image. These captions are later translated to Assamese. The second approach exclusively trains the AIC model using an Assamese image-caption dataset to predict Assamese captions. Extensive experiments have been conducted and the performance of these two approaches is evaluated both qualitatively and using quantitative measures (BLEU-n and CIDEr). The performance comparison be-tween these two approaches reveals that the models exclusively trained on Assamese image-caption dataset yields better results quantitatively and qualitatively than the models pretrained on English and later trans-lated to Assamese.
DP-starJ: A Differential Private Scheme towards Analytical Star-Join Queries

Star-join query is the fundamental task in data warehouse and has wide applications in On-line Analytical Processing (olap) scenarios. Due to the large number of foreign key constraints and the asymmetric effect in the neighboring instance between the fact and dimension tables, even those latest dp efforts specifically designed for join, if directly applied to star-join query, will suffer from extremely large estimation errors and expensive computational cost. In this paper, we are thus motivated to propose DP-starJ, a novel Differentially Private framework for star-Join queries. DP-starJ consists of a series of strategies tailored to specific features of star-join, including 1) we unveil the different effects of fact and dimension tables on the neighboring database instances, and accordingly revisit the definitions tailored to different cases of star-join; 2) we propose Predicate Mechanism (PM), which utilizes predicate perturbation to inject noise into the join procedure instead of the results; 3) to further boost the robust performance, we propose a dp-compliant star-join algorithm for various types of star-join tasks based on PM. We provide both theoretical analysis and empirical study, which demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of accuracy, efficiency, and scalability.
F3KM: Federated, Fair, and Fast k-means

This paper proposes a federated, fair, and fast k-means algorithm (F3KM) to solve the fair clustering problem efficiently in scenarios where data cannot be shared among different parties. The proposed algorithm decomposes the fair k-means problem into multiple subproblems and assigns each subproblem to a client for local computation. Our algorithm allows each client to possess multiple sensitive attributes (or have no sensitive attributes). We propose an in-processing method that employs the alternating direction method of multipliers (ADMM) to solve each subproblem. During the procedure of solving subproblems, only the computation results are exchanged between the server and the clients, without exchanging the raw data. Our theoretical analysis shows that F3KM is efficient in terms of both communication and computation complexities. Specifically, it achieves a better trade-off between utility and communication complexity, and reduces the computation complexity to linear with respect to the dataset size. Our experiments show that F3KM achieves a better trade-off between utility and fairness than other methods. Moreover, F3KM is able to cluster five million points in one hour, highlighting its impressive efficiency.
Validating racial and ethnic non-bias of artificial intelligence decision support for diagnostic breast ultrasound evaluation

Breast ultrasound suffers from low positive predictive value and specificity. Artificial intelligence (AI) proposes to improve accuracy, reduce false negatives, reduce inter- and intra-observer variability and decrease the rate of benign biopsies. Perpetuating racial/ethnic disparities in healthcare and patient outcome is a potential risk when incorporating AI-based models into clinical practice; therefore, it is necessary to validate its non-bias before clinical use.Our retrospective review assesses whether our AI decision support (DS) system demonstrates racial/ethnic bias by evaluating its performance on 1810 biopsy proven cases from nine breast imaging facilities within our health system from January 1, 2018 to October 28, 2021. Patient age, gender, race/ethnicity, AI DS output, and pathology results were obtained.Significant differences in breast pathology incidence were seen across different racial and ethnic groups. Stratified analysis showed that the difference in output by our AI DS system was due to underlying differences in pathology incidence for our specific cohort and did not demonstrate statistically significant bias in output among race/ethnic groups, suggesting similar effectiveness of our AI DS system among different races (p>0.05 for all).Our study shows promise that an AI DS system may serve as a valuable second opinion in the detection of breast cancer on diagnostic ultrasound without significant racial or ethnic bias. AI tools are not meant to replace the radiologist, but rather to aid in screening and diagnosis without perpetuating racial/ethnic disparities.
Demystifying the QoS and QoE of Edge-hosted Video Streaming Applications in the Wild with SNESet

Video streaming applications (VSAs) are increasingly being deployed on large-scale edge platforms, which have the potential to significantly improve the quality of service (QoS) and end-user experience (QoE), ultimately maximizing business outcomes. However, there is currently very little understanding of how QoS, QoE, and the impact of QoS on QoE for VSAs on edge platforms in the wild and at scale. To close the knowledge gap, we collect SNESet, an active measurement dataset comprising QoS and QoE telemetry metrics of 8 VSAs over four months, covering end-users from 798 edge sites,30 cities, and 3 ISPs in one country.We characterize and compare the QoS and QoE metrics in SNESet with existing publicly available datasets, highlighting that SNESet includes a significantly greater number of metrics (horizontal diversity and vertical hierarchy) and provides more comprehensive coverage of specific metrics.Moreover, we qualitatively and quantitatively analyze the impact of QoS on QoE in both domain-general and domain-specific scenarios. Our findings can inform the system design decisions that different entities in the video ecosystem (content providers, video player designers, third-party optimizers, edge vendors) make to maximize end-users experience and ultimately maximize the business outcomes. We hope SNESet can attract more research efforts in the data management community, computer network community, and beyond.
Towards accurate knowledge transfer via target-awareness representation disentanglement

Abstract Fine-tuning deep neural networks pre-trained on large scale datasets is one of the most practical transfer learning paradigm given limited quantity of training samples. To obtain better generalization, using the starting point as the reference (SPAR), either through weights or features, has been successfully applied to transfer learning as a regularizer. However, due to the domain discrepancy between the source and target task, there exists obvious risk of negative transfer in a straightforward manner of knowledge preserving. In this paper, we propose a novel transfer learning algorithm, introducing the idea of T arget-awareness RE presentation D isentanglement ( $$\textrm{TRED}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>TRED</mml:mtext> </mml:math> ), where the relevant knowledge with respect to the target task is disentangled from the original source model and used as a regularizer during fine-tuning the target model. Two alternative approaches, maximizing Maximum Mean Discrepancy (Max-MMD) and minimizing mutual information (Min-MI) are introduced to achieve the desired disentanglement. Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average. $$\textrm{TRED}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>TRED</mml:mtext> </mml:math> also outperforms related state-of-the-art transfer learning regularizers such as $$\mathrm {L^2\text {-}SP}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>L</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mtext>-</mml:mtext> <mml:mi>SP</mml:mi> </mml:mrow> </mml:math> , $$\textrm{AT}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>AT</mml:mtext> </mml:math> , $$\textrm{DELTA}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>DELTA</mml:mtext> </mml:math> , and $$\textrm{BSS}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>BSS</mml:mtext> </mml:math> . Moreover, our solution is compatible with different choices of disentangling strategies. While the combination of Max-MMD and Min-MI typically achieves higher accuracy, only using Max-MMD can be a preferred choice in applications with low resource budgets.
Hate Speech, a Source of Linguistic, Religious and Ethnic Intolerance Among the Sub-Saharan African Peoples: The Case of Nigeria

Hate speech generates both immediate and long-lasting issues of interpersonal and intergroup communication and relations. This study rises to make a critical exposition of how hate speech serves as a source of linguistic, religious and ethnic intolerance among sub-Saharan African peoples, using the case of Nigeria. The primary data are drawn from selected annotated expressed hate speeches online and in print media sources. The secondary data were drawn from library and internet print materials. Descriptive survey design and analytical methods, qualitative approach and content analysis are employed. The analysis reveals that speeches expressing ethnic, religious and linguistic hatred against other groups cause intolerance among people(s). The intolerance leads to different phases of conflicts, as obtained in sub-Saharan Africa. The study concludes that hate speech is the bane of linguistic, religious and ethnic intolerance among sub-Saharan African peoples. Therefore, it is imperative to desist from the use of divisive language that does not promote harmony and co-existence of diverse ecological compositions defining post-colonial sub-Saharan Africa, which constitute linguistic, religious and ethnic groups.
Advancing in Reverse: A Comprehensive Characterization of IN-ADDR.ARPA Deployment

Reverse DNS (rDNS) is often used as a reliable data-source for critical services, such as mail, security appliances or geolocation services. Unlike forward DNS, rDNS remains understudied, especially from a deployment perspective. In this paper, we take steps towards closing the gap, starting at regional Internet registries, down to network operators in the lower hierarchy. To this end, we use public and complementary data sources and find that around 40% of allocated IPv4 address space has well-configured rDNS entries. We highlight regional differences as rDNS deployment is driven by mail and infrastructure providers in the developed world, while national Internet registries and national ISPs are drivers in the developing world. We study the use of classless delegation and the prevalence of configuration errors breaking DNS resolution. Finally, we observe that multi-regional organizations such as CDNs and mail providers actively invest effort towards improving rDNS deployment.