Reviewerly/exHarmony huggingface dataset:
Translation-Based Implicit Annotation Projection for Zero-Shot Cross-Lingual Event Argument Extraction

Zero-shot cross-lingual event argument extraction (EAE) is a challenging yet practical problem in Information Extraction. Most previous works heavily rely on external structured linguistic features, which are not easily accessible in real-world scenarios. This paper investigates a translation-based method to implicitly project annotations from the source language to the target language. With the use of translation-based parallel corpora, no additional linguistic features are required during training and inference. As a result, the proposed approach is more cost effective than previous works on zero-shot cross-lingual EAE. Moreover, our implicit annotation projection approach introduces less noises and hence is more effective and robust than explicit ones. Experimental results show that our model achieves the best performance, outperforming a number of competitive baselines. The thorough analysis further demonstrates the effectiveness of our model compared to explicit annotation projection approaches.
Applications and Future of Dense Retrieval in Industry

Large-scale search engines are often designed as tiered systems with at least two layers. The L1 candidate retrieval layer efficiently generates a subset of potentially relevant documents (typically ~1000 documents) from a corpus many orders of magnitude larger in size. L1 systems emphasize efficiency and are designed to maximize recall. The L2 re-ranking layer uses a more computationally expensive, but more accurate model (e.g. learning-to-rank or neural model) to re-rank the candidates generated by L1 in order to maximize precision of the final result list.
An Approach for Audio-Visual Content Understanding of Video using Multimodal Deep Learning Methodology

This study contains an approach for recognizing the sound environment class from a video to understand the spoken content with its sentimental context via some sort of analysis that is achieved by the processing of audio-visual content using multimodal deep learning methodology. This approach begins with cutting the parts of a given video which the most action happened by using deep learning and this cutted parts get concanarated as a new video clip. With the help of a deep learning network model which was trained before for sound recognition, a sound prediction process takes place. The model was trained by using different sound clips of ten different categories to predict sound classes. These categories have been selected by where the action could have happened the most. Then, to strengthen the result of sound recognition if there is a speech in the new video, this speech has been taken. By using Natural Language Processing (NLP) and Named Entity Recognition (NER) this speech has been categorized according to if the word of a speech has connotation of any of the ten categories. Sentiment analysis and Apriori Algorithm from Association Rule Mining (ARM) processes are preceded by identifying the frequent categories in the concanarated video and helps us to define the relationship between the categories owned. According to the highest performance evaluation values from our experiments, the accuracy for sound environment recognition for a given video's processed scene is 70%, average Bilingual Evaluation Understudy (BLEU) score for speech to text with VOSK speech recognition toolkit's English language model is 90% on average and for Turkish language model is 81% on average. Discussion and conclusion based on scientific findings are included in our study.
Recommending Relevant Papers to Conference Participants: a Deep Learning Driven Content-based Approach

We introduce a novel system for personalized recommendations to conference attendees, to highlight the papers in the program that best match the attendee’s interests. To this end, we extend traditional structure-agnostic recommender system techniques through the use of deep learning, to exploit the rich semantic and topological information given by the abstracts of the papers and the citation relationship. The ultimate goal is twofold: i) to help attendees single out from a rich program the papers they most likely would like to see presented, and ii) to perform a tailored advertisement of an upcoming event to past attendees by catching their attention with specific contributions in the program of the conference.
Evaluation of Herd Behavior Caused by Population-scale Concept Drift in Collaborative Filtering

Concept drift in stream data has been well studied in machine learning applications. In the field of recommender systems, this issue is also widely observed, as known as temporal dynamics in user behavior. Furthermore, in the context of COVID-19 pandemic related contingencies, people shift their behavior patterns extremely and tend to imitate others' opinions. The changes in user behavior may not be always rational. Thus, irrational behavior may impair the knowledge learned by the algorithm. It can cause herd effects and aggravate the popularity bias in recommender systems due to the irrational behavior of users. However, related research usually pays attention to the concept drift of individuals and overlooks the synergistic effect among users in the same social group. We conduct a study on user behavior to detect the collaborative concept drifts among users. Also, we empirically study the increase of experience of individuals can weaken herding effects. Our results suggest the CF models are highly impacted by the herd behavior and our findings could provide useful implications for the design of future recommender algorithms.
Dos and Don’ts in Blockchain Research and Development

The goal of this paper is to clarify common misconceptions regarding the blockchain technology, and pointing out some worrisome practices in the field of blockchain technology. We first provide a brief introduction of the blockchain technology, highlighting its design principle, nuts and bolts, and the most fundamental innovation of the technology. Next, we elaborate on a key concept regarding data immutability. In particular, it is a mistake to equate blockchain to data immutability. Data immutability can only be achieved in large-scale public blockchains where the cost becomes an insurmountable barrier for any attempt to change the data recorded in the blockchain. The third topic we discuss is the differences between public, private, and consortium blockchains. We caution the use of private and consortium blockchains. The last topic we argue is regarding blockchain consensus. Because the proof of work (PoW) consensus algorithm has obvious drawbacks on energy consumption, many alternative algorithms have been proposed. We single out two most egregious wrong practices: (1) selecting a small set of validators for reaching consensus, and (2) adopting traditional distributed consensus.
Ranking Interruptus

Most of information retrieval effectiveness evaluation metrics assume that systems appending irrelevant documents at the bottom of the ranking are as effective as (or not worse than) systems that have a stopping criteria to 'truncate' the ranking at the right position to avoid retrieving those irrelevant documents at the end. It can be argued, however, that such truncated rankings are more useful to the end user. It is thus important to understand how to measure retrieval effectiveness in this scenario. In this paper we provide both theoretical and experimental contributions. We first define formal properties to analyze how effectiveness metrics behave when evaluating truncated rankings. Our theoretical analysis shows that de-facto standard metrics do not satisfy desirable properties to evaluate truncated rankings: only Observational Information Effectiveness (OIE) -- a metric based on Shannon's information theory -- satisfies them all. We then perform experiments to compare several metrics on nine TREC datasets. According to our experimental results, the most appropriate metrics for truncated rankings are OIE and a novel extension of Rank-Biased Precision that adds a user effort factor penalizing the retrieval of irrelevant documents.
Theoretical formulation/development of signal sampling with an equal arc length using the frame theorem

Abstract Nonuniform sampling with equal arc length intervals can be found in shape measurements with contact sensor arrays. In this study, the conditions of nonuniform spatial sampling with an equal arc length interval are derived from two frame theorems. First, for general nonuniform sampling, the condition is that the equal arc length interval of the sensors should be less than $$\frac{1}{4\Omega }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mrow> <mml:mn>4</mml:mn> <mml:mi>Ω</mml:mi> </mml:mrow> </mml:mfrac> </mml:math> . Second, for strictly increasing sampling (the sampling point set is strictly increasing), the condition is that the equal arc length interval of the sensors should be less than $$\frac{1}{2\Omega }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>Ω</mml:mi> </mml:mrow> </mml:mfrac> </mml:math> . The $$\Omega$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Ω</mml:mi> </mml:math> is the maximum frequency of the detected object. For the latter, if the sampling frequency is more than twice the sampling frequency required, the reconstruction error (RRMSE and MRE) is less than 5%. If the sampling frequency is more than 2.5 times, the reconstruction error is less than 3%. The simulation and the application test are carried out, and the results show that a sensor array with equal arc length interval can reconstruct the detected object with high accuracy.
IAOTP

Recently, the aspect-opinion term pairs (AOTP) extraction task has gained substantial importance in the domain of aspect-based sentiment analysis. It intends to extract the potential pair of each aspect term with its corresponding opinion term present in a user review. Some existing studies heavily relied on the annotated aspect terms and/or opinion terms, or adopted external knowledge/resources to figure out the task. Therefore, in this study, we propose a novel end-to-end solution, called an Interactive AOTP (IAOTP) model, for exploring AOTP. The IAOTP model first tracks the boundary of each token in given aspect-specific and opinion-specific representations through a span-based operation. Next, it generates the candidate AOTP by formulating the dyadic relations between tokens through the Biaffine transformation. Then, it computes the positioning information to capture the significant distance relationship that each candidate pair holds. And finally, it jointly models collaborative interactions and prediction of AOTP through a 2D self-attention. Besides the IAOTP model, this study also proposes an independent aspect/opinion encoding model (a RS model) that formulates relational semantics to obtain aspect-specific and opinion-specific representations that can effectively perform the extraction of aspect and opinion terms. Detailed experiments conducted on the publicly available benchmark datasets for AOTP, aspect terms, and opinion terms extraction tasks, clearly demonstrate the significantly improved performance of our models relative to other competitive state-of-the-art baselines.
An Intelligent Advertisement Short Video Production System via Multi-Modal Retrieval

In its most basic form, advertising video production communicates a message about a product or service to the public. In the age of digital marketing, where the most popular way to connect with audiences is through advertising videos. However, advertising video production is a costly and complicated process from creation, material shooting, editing to the final commercial video. Therefore, producing qualified advertising videos is a capital and talent-intensive task, which poses a huge challenge for start-ups or inexperienced ad creators. paper proposes an intelligent advertising video production system driven by multi-modal retrieval, which only requires the input of descriptive copy. This system can automatically generate scripts, then extract key queries, retrieve related short video materials in the video library, and finally synthesize short advertising videos. The whole process minimizes human input, greatly reduces the threshold for advertising video production and greatly improves output and efficiency. It has a modular design to encourage the study of new multi-modal algorithms, which can be evaluated in batch mode. It can also integrate with a user interface, which allows user studies and data collection in an interactive mode, where the back end can be fully algorithmic or a wizard of oz setup. The proposed system has been fully verified and has broad prospects in the production of short videos for commodity advertisements within Alibaba.
Multimodal Disentanglement Variational AutoEncoders for Zero-Shot Cross-Modal Retrieval

Zero-Shot Cross-Modal Retrieval (ZS-CMR) has recently drawn increasing attention as it focuses on a practical retrieval scenario, i.e., the multimodal test set consists of unseen classes that are disjoint with seen classes in the training set. The recently proposed methods typically adopt the generative model as the main framework to learn a joint latent embedding space to alleviate the modality gap. Generally, these methods largely rely on auxiliary semantic embeddings for knowledge transfer across classes and unconsciously neglect the effect of the data reconstruction manner in the adopted generative model. To address this issue, we propose a novel ZS-CMR model termed Multimodal Disentanglement Variational AutoEncoders (MDVAE), which consists of two coupled disentanglement variational autoencoders (DVAEs) and a fusion-exchange VAE (FVAE). Specifically, DVAE is developed to disentangle the original representations of each modality into modality-invariant and modality-specific features. FVAE is designed to fuse and exchange information of multimodal data by the reconstruction and alignment process without pre-extracted semantic embeddings. Moreover, an advanced counter-intuitive cross-reconstruction scheme is further proposed to enhance the informativeness and generalizability of the modality-invariant features for more effective knowledge transfer. The comprehensive experiments on four image-text retrieval and two image-sketch retrieval datasets consistently demonstrate that our method establishes the new state-of-the-art performance.
Deep Learning-Based Detection and Identification Method for Sports Health Video Dissemination

Sports health is gradually attracting attention, and computer vision technology is integrated into sports health to improve the quality of sports and increase the motivation of athletes. A deep learning sports health video propagation detection and recognition system is built through the mode of video propagation to provide real-time training information for sports and scientific body index parameters and exercise data for sports health programs. An athletics action estimation network (AAEN) is promoted, which initially obtains the correlation features and depth features between human skeleton key points through partial perception units. Then, all the joint point features are classified and correlated based on the affinity field range through the confidence map of the human skeletal node region. All video frames are then fused with similar joint features at the temporal level to extract motion key points in the time scale, and human posture prediction is achieved by fitting between the motion features and the dynamic database. To show the high efficiency of our method, we select three main databases for validation, and the results prove that AAEN outperforms by 13.96%, 16.90%, and 15.10% in precision, F1 score, and recall compared to the SOTA in sports health video detection and recognition. Our method also performs better overall in the same type of algorithms.
Blockchain Technology in Logistics

The following paper discusses the impact of using blockchain technology and the possibility of its development in sustainable logistics and supply chain management. The important organizational challenges include increased transportation charges, improvement technology in information management, supply chain services, sustainability measures, government protocols, and enormous databases. Thus, this industry has embraced developing technology such as Cloud computing and the Internet of Things (IoT) which is resulted in transportation fields to enhance automation and decentralization. Security and transparency have far-reaching ramifications in the digital era. This necessitates the industrial revolution of logistics by the advancement of blockchain technology.
BugListener

In community-based software development, developers frequently rely on live-chatting to discuss emergent bugs/errors they encounter in daily development tasks. However, it remains a challenging task to accurately record such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the task of identifying and synthesizing bug reports from community live chats, and propose a novel approach, named BugListener, to address the challenges. Specifically, BugListener automates three sub-tasks: 1) Disentangle the dialogs from massive chat logs by using a Feed-Forward neural network; 2) Identify the bug-report dialogs from separated dialogs by leveraging the Graph neural network to learn the contextual information; 3) Synthesize the bug reports by utilizing Transfer Learning techniques to classify the sentences into: observed behaviors (OB), expected behaviors (EB), and steps to reproduce the bug (SR). BugListener is evaluated on six open source projects. The results show that: for bug report identification, BugListener achieves the average F1 of 77.74%, improving the best baseline by 12.96%; and for bug report synthesis task, BugListener could classify the OB, EB, and SR sentences with the F1 of 84.62%, 71.46%, and 73.13%, improving the best baselines by 9.32%, 12.21%, 10.91%, respectively. A human evaluation study also confirms the effectiveness of BugListener in generating relevant and accurate bug reports. These demonstrate the significant potential of applying BugListener in community-based software development, for promoting bug discovery and quality improvement.
Big Data Analysis Model for Vocational Education Employment Rate Prediction

Vocational education is an important means to promote the development of the national manufacturing industry. To correctly grasp the relationship between vocational education and the labor market, we start with the quality of vocational education and employment rate and study the interconnection between them. To this end, we propose a vocational education employment rate prediction method based on a big data model and build a vocational education quality assessment and employment rate prediction system. We draw on the big data cross-learning model to improve the model hyperparameters by using generalized intersection sets on the joint loss function to compensate for the shortcomings of dense vocational education datasets. We use the GSA algorithm to enhance the local features of different vocational education quality assessment index data series. To scientifically assess the recognition of vocational education, we evaluate the vocational education assessment indexes at the student level and the parent level to verify the reliability of the experiment. The experimental results prove that our method performs best in the prediction accuracy of vocational education quality assessment indicators, and the prediction accuracy rate stays above 91%. In the prediction of vocational education employment rate, the difference between the predicted and actual values of our method is the smallest, and the difference stays within 1%. Compared with other big data models, our method has higher prediction accuracy and better robustness.
Contrastive Active Learning Under Class Distribution Mismatch

Active learning(AL) has been successful based on the premise that labeled and unlabeled data come from the same class distribution. However, its performance undergoes a severe deterioration under class distribution mismatch, wherein the unlabeled data contain numerous instances out of the class distribution of labeled data. In this article, we solve this practical yet rarely studied problem by minimizing the AL error, which is formally defined and decomposed as the valid query error and invalid query error. Specifically, the invalid query error is associated with the queries from unknown categories, and the valid query error is attributed to less informative queries from target categories. In light of this discovery, we propose a contrastive AL framework, named ConAL, to simultaneously learn the semantics and distinctiveness of the instances by contrastive techniques, thereby reducing the invalid query error and valid query error, respectively. Theoretically, we prove that the AL error of ConAL has a tight upper bound. Experimentally, ConAL achieves superior performance on two benchmark datasets, CIFAR10 and CIFAR100, and a cross-dataset with class distribution across multi-datasets. Furthermore, we validate that the ConAL technique performs admirably even on the realistic dataset. To the best of our knowledge, ConAL is the first AL work for class distribution mismatch.
Design and implementation of the performance arts enhanced three-stage instructional model

There is a knowledge gap in best practices for incorporating performance arts into science instructions. The study focused on the design and implementation of the Performance Arts Enhanced 3-Stage Instructional Model (PAEIM), which incorporated drama, dance and music. The PAEIM was implemented in mixed factorial quasi-experimental research during the learning of the ecology unit in the secondary school biology curriculum. Empirical findings from the study exemplified PAEIM as an effective instructional model that significantly enhanced the conceptual understanding of ecology and impacted students’ self-determination and intrinsic motivation. The reports of the findings also indicated that PAEIM enhanced the students’ learning outcomes regardless of their gender. The result from the multivariate analysis revealed a significant main effect of treatment on combined motivation and the conceptual understanding of ecology. The study thereafter provided recommendations for scalability in the implementation of PAEIM in future research.
OnToRisk – a formal ontology approach to automate cyber security risk identification

The everchanging cyber risks landscape poses a significant threat to organisations and requires them to continuously manage their risks. Risk identification is the driving force of risk management, and it is typically performed manually, integrating expert knowledge and information from various systems. This hinders the ability to systematically identify new risks as they emerge. This paper introduces a new approach – OnToRisk – to automate aspects of the cyber security risk identification. The approach uses a formal ontology to integrate information from multiple constituent systems and organisational definitions, and then reason about the current organisational situation with respect to formally defined cyber risks. We describe an implementation of the approach to identify cyber vulnerability induced risks, as they become an emergent property of the organisation.