Reviewerly/exHarmony huggingface dataset:
RFaNet: Receptive Field-Aware Network with Finger Attention for Fingerspelling Recognition Using a Depth Sensor

Automatic fingerspelling recognition tackles the communication barrier between deaf and hearing individuals. However, the accuracy of fingerspelling recognition is reduced by high intra-class variability and low inter-class variability. In the existing methods, regular convolutional kernels, which have limited receptive fields (RFs) and often cannot detect subtle discriminative details, are applied to learn features. In this study, we propose a receptive field-aware network with finger attention (RFaNet) that highlights the finger regions and builds inter-finger relations. To highlight the discriminative details of these fingers, RFaNet reweights the low-level features of the hand depth image with those of the non-forearm image and improves finger localization, even when the wrist is occluded. RFaNet captures neighboring and inter-region dependencies between fingers in high-level features. An atrous convolution procedure enlarges the RFs at multiple scales and a non-local operation computes the interactions between multi-scale feature maps, thereby facilitating the building of inter-finger relations. Thus, the representation of a sign is invariant to viewpoint changes, which are primarily responsible for intra-class variability. On an American Sign Language fingerspelling dataset, RFaNet achieved 1.77% higher classification accuracy than state-of-the-art methods. RFaNet achieved effective transfer learning when the number of labeled depth images was insufficient. The fingerspelling representation of a depth image can be effectively transferred from large- to small-scale datasets via highlighting the finger regions and building inter-finger relations, thereby reducing the requirement for expensive fingerspelling annotations.
Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event Extraction

Event extraction in commodity news is a less researched area as compared to generic event extraction.However, accurate event extraction from commodity news is useful in a broad range of applications such as understanding event chains and learning event-event relations, which can then be used for commodity price prediction.The events found in commodity news exhibit characteristics different from generic events, hence posing a unique challenge in event extraction using existing methods.This paper proposes an effective use of Graph Convolutional Networks (GCN) with a pruned dependency parse tree, termed contextual sub-tree, for better event extraction in commodity news.The event extraction model is trained using feature embeddings from ComBERT, a BERT-based masked language model that was produced through domain-adaptive pre-training on a commodity news corpus.Experimental results show the efficiency of the proposed solution, which outperforms existing methods with F1 scores as high as 0.90.Furthermore, our pre-trained language model outperforms GloVe by 23%, and BERT and RoBERTa by 7% in terms of argument roles classification.For the goal of reproducibility, the code and trained models are made publicly available 1 .
Personalized Search-based Query Rewrite System for Conversational AI

Query rewrite (QR) is an emerging component in conversational AI systems, reducing user defect.User defect is caused by various reasons, such as errors in the spoken dialogue system, users' slips of the tongue or their abridged language.Many of the user defects stem from personalized factors, such as user's speech pattern, dialect, or preferences.In this work, we propose a personalized search-based QR framework, which focuses on automatic reduction of user defect.We build a personalized index for each user, which encompasses diverse affinity layers to reflect personal preferences for each user in the conversational AI.Our personalized QR system contains retrieval and ranking layers.Supported by user feedback based learning, training our models does not require hand-annotated data.Experiments on personalized test set showed that our personalized QR system is able to correct systematic and user errors by utilizing phonetic and semantic inputs.
Emergence and Functionality of 3D Videos

Millions of videos are created and watched every day. Quality of desired video plays a significant role in modern day applications. Deep fake videos have alarmed the established institutions while amused others. A Three-dimensional(3D) stereoscopic film or a 3D Video for that instance is a motion picture that enhances the illusion of depth perception, hence adding a third dimension. 3D videos can effortlessly validate and secure the authenticity of communication and broadcast process due to their auxiliary effect. In this research paper we attempt to observe and analyze numerous measures taken towards initiation and rise of 3D videos along with their efficacy from an end user perspective.
The Pipeline Model for Resolution of Anaphoric Reference and Resolution of Entity Reference

The objective of anaphora resolution in dialogue shared-task is to go above and beyond the simple cases of coreference resolution in written text on which NLP has mostly focused so far, which arguably overestimate the performance of current SOTA models.The anaphora resolution in dialogue shared-task consists of three subtasks; subtask1, resolution of anaphoric identity and non-referring expression identification, subtask2, resolution of bridging references, and subtask3, resolution of discourse deixis/abstract anaphora.In this paper, we propose the pipelined model (i.e., a resolution of anaphoric identity and a resolution of bridging references) for the subtask1 and the subtask2.In the subtask1, our model detects mention via the parentheses prediction.Then, we create mention representation using the token representation constituting the mention.Mention representation is fed to the coreference resolution model for clustering.In the subtask2, our model resolves bridging references via a MRC framework.We construct a query for each entity with "What is related of ENTITY?".The input of our model is query and documents(i.e., all utterances of dialogue).Then, our model predicts entity span that is answer for a query.
MLEC-QA: A Chinese Multi-Choice Biomedical Question Answering Dataset

Question Answering (QA) has been successfully applied in scenarios of human-computer interaction such as chatbots and search engines.However, for the specific biomedical domain, QA systems are still immature due to expert-annotated datasets being limited by category and scale.In this paper, we present MLEC-QA, the largest-scale Chinese multi-choice biomedical QA dataset, collected from the National Medical Licensing Examination in China.The dataset is composed of five subsets with 136,236 biomedical multi-choice questions with extra materials (images or tables) annotated by human experts, and first covers the following biomedical sub-fields: Clinic, Stomatology, Public Health, Traditional Chinese Medicine, and Traditional Chinese Medicine Combined with Western Medicine.We implement eight representative control methods and open-domain QA methods as baselines.Experimental results demonstrate that even the current best model can only achieve accuracies between 40% to 55% on five subsets, especially performing poorly on questions that require sophisticated reasoning ability.We hope the release of the MLEC-QA dataset can serve as a valuable resource for research and evaluation in open-domain QA, and also make advances for biomedical QA systems. 1
Applicability of Blockchain Technology in Securities Settlement

Technological advancements are often adopted to financial markets to improve their operations and safety. Blockchain technology has been recognized as one of the potential technologies to be utilized in capital markets. The goal of this article is to evaluate the applicability of using the blockchain technology in securities settlement process. First, the theoretical background of blockchain technology is reviewed and the current financial market infrastructure is examined. Then Central Securities Depositories Regulation and the current securities settlement processes are examined. Blockchain applicability framework designed by Gourisetti, Mylrea and Patangia is applied to assess the blockchain technology’s applicability to securities settlement. The results suggest that blockchain technology can be applied to securities settlement, and the used blockchain type should be a private blockchain with Proof-of-Authority consensus mechanism. A blockchain architecture model, based on a model provided by Zhuang, Chen, Shae and Shyu, and potential node structure for securities settlement are developed, taking into account the existing literature on blockchain technology, financial markets, and Central Securities Depositories Regulation. The proposed blockchain architecture model and node structure are then evaluated against scholar expected benefits and drawbacks of using blockchain for securities settlement and cross-border settlement efficiency. The evaluation reveals that the proposed blockchain technology model can potentially improve some of the current securities settlement issues, such as costly reconciliation and difficult cross-border securities settlement. At the same time, using blockchain technology in securities settlement would be challenging because the practical implementation time would be long and would require market-wide commitment. The main artefacts of this article are the proposed blockchain architecture model and node structure that would allow securities settlement processes to be executed using blockchain technology.
Machine Learning vs Human Learning

Machine Learning (ML) is a technology to make messages created by humans (text, images, speech etc.) more understandable for computers so that they could better answer humans' queries and needs when recalling this information. Here is considered the ML sub-area - Natural Language Processing (NLP) and presented examples of its methods using text corpuses created from MiproCE presentations.
Machine Learning–Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study

Effective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore, predicting a patient's hospitalization period may support the making of judicious decisions regarding bed management.First, this study aims to develop a machine learning (ML)-based predictive model for predicting the discharge probability of inpatients with cardiovascular diseases (CVDs). Second, we aim to assess the outcome of the predictive model and explain the primary risk factors of inpatients for patient-specific care. Finally, we aim to evaluate whether our ML-based predictive model helps manage bed scheduling efficiently and detects long-term inpatients in advance to improve the use of hospital processes and enhance the quality of medical services.We set up the cohort criteria and extracted the data from CardioNet, a manually curated database that specializes in CVDs. We processed the data to create a suitable data set by reindexing the date-index, integrating the present features with past features from the previous 3 years, and imputing missing values. Subsequently, we trained the ML-based predictive models and evaluated them to find an elaborate model. Finally, we predicted the discharge probability within 3 days and explained the outcomes of the model by identifying, quantifying, and visualizing its features.We experimented with 5 ML-based models using 5 cross-validations. Extreme gradient boosting, which was selected as the final model, accomplished an average area under the receiver operating characteristic curve score that was 0.865 higher than that of the other models (ie, logistic regression, random forest, support vector machine, and multilayer perceptron). Furthermore, we performed feature reduction, represented the feature importance, and assessed prediction outcomes. One of the outcomes, the individual explainer, provides a discharge score during hospitalization and a daily feature influence score to the medical team and patients. Finally, we visualized simulated bed management to use the outcomes.In this study, we propose an individual explainer based on an ML-based predictive model, which provides the discharge probability and relative contributions of individual features. Our model can assist medical teams and patients in identifying individual and common risk factors in CVDs and can support hospital administrators in improving the management of hospital beds and other resources.
A Practical Exploration of Constructive English Learning Platform Informatization Based on RBF Algorithm

The constructive English learning approach provides better technical support and learning concepts for English learners. This paper firstly introduces the theoretical basis of constructive English learning and the status of domestic and international research and analyzes the advantages over the traditional English learning approach. This paper also analyzes the RBF neural network model, especially the locally adjusted RBF algorithm, and considers the training model and construction method of the RBF neural network to simulate the learning pattern of good learners of English in grades 4 and 6 to better realize the effect of the English learning platform and allow the English learning platform to play a supportive role in the learning process of students, and through the evaluation of the design, we can better improve the software design, which can be improved by evaluating the design. In the subsequent research, we should add a more user-friendly interface design, use larger model samples to train the model to make it more universal in accuracy and ensure good stability in operation, and integrate multiple algorithms to improve the training algorithm of RBF neural network to obtain a more general algorithm model. The rich teaching methods can cultivate students’ independent inquiry ability, and the construction of this English learning platform can help improve students’ ability to use information technology. By making students become the main subject of teaching and learning, we can stimulate students’ enthusiasm and a good awareness of English learning. Using the radial basis function (RBF) algorithm and the constructive pod language teaching method, this English learning platform is a good and positive aid for students to learn English, and as a better English education tool, it has certain research value and positive significance for the study of English teaching.
Cloud Service Selection using DEA based on SMI Attributes

Cloud computing allows on-demand access and fast network connection to a shared resource pool. Most companies are switching to Cloud due to the popularity and benefits of using Cloud Services. So finding a suitable and best cloud provider is a challenge for all users. Several ranking methods, such as AHP, TOPSIS, had been suggested to solve this problem by multicriteria decision making techniques. But, many of the works focused on a subset of the main QoS attributes for ranking. Cloud Services Measurement Initiatives Consortium (CSMIC) has released Service Measurement Index attributes for effectively comparing the Cloud services. The comparison of services provided by cloud based on SMI attributes which are qualitative as well as quantitative in nature is studied in this paper by one of the non-parametric methods called Data Envelopment Analysis (DEA) and ranked the cloud services based on the efficiency scores obtained by DEA. The cloud users can select the best suitable Cloud service using the proposed approach that best suit their QoS requirements.
On the Link Between Subjective Score Prediction and Disagreement of Video Quality Metrics

Several video quality metrics (VQMs) have been proposed in many publications to predict how humans perceive video quality. It is common to observe significant disagreements amongst the quality predictions of these VQMs for the same video sequence. Following an extensive literature search, we found no publicised work that has investigated if such disagreements convey useful information on the accuracy of VQMs. Herein, a measure for quantifying the disagreements between VQMs is proposed. A small-scale subjective study is carried out to assess the effectiveness of our proposal. In particular, the proposed disagreement measure is shown to be extremely effective in determining whether the quality of any given processed video sequence (PVS) can be accurately predicted by the VQMs. This type of information is particularly useful for identifying video sequences that are likely to degrade the end-user’s quality of experience (QoE). Our proposal is also useful in selecting the most effective PVSs to be employed in a subjective test. We show that the proposed disagreement measure can be effectively predicted from bitstream features. This establishes a link between the capability to accurately assess the quality of a PVS and the way it is encoded. In addition, an analysis is conducted to compare the performances of some well-known and widely used open-source metrics and two proprietary metrics. The two proprietary metrics are used by a large media company for enhancing its delivery pipeline. The outcome of this comparison highlights the suitability of the open-source VQM, Video Multi-method Assessment Fusion (VMAF), as a good benchmark quality measure for both the industrial and academic environments.
Enriching Language Models with Visually-grounded Word Vectors and the Lancaster Sensorimotor Norms

Language models are trained only on text despite the fact that humans learn their first language in a highly interactive and multimodal environment where the first set of learned words are largely concrete, denoting physical entities and embodied states.To enrich language models with some of this missing experience, we leverage two sources of information: (1) the Lancaster Sensorimotor norms, which provide ratings (means and standard deviations) for over 40,000 English words along several dimensions of embodiment, and which capture the extent to which something is experienced across 11 different sensory modalities, and (2) vectors from coefficients of binary classifiers trained on images for the BERT vocabulary.We pre-trained the ELECTRA model and fine-tuned the RoBERTa model with these two sources of information then evaluate using the established GLUE benchmark and the Visual Dialog benchmark.We find that enriching language models with the Lancaster norms and image vectors improves results in both tasks, with some implications for robust language models that capture holistic linguistic meaning in a language learning context.
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing

Auxiliary information from multiple sources has been demonstrated to be effective in zeroshot fine-grained entity typing (ZFET).However, there lacks a comprehensive understanding about how to make better use of the existing information sources and how they affect the performance of ZFET.In this paper, we empirically study three kinds of auxiliary information: context consistency, type hierarchy and background knowledge (e.g., prototypes and descriptions) of types, and propose a multi-source fusion model (MSF) targeting these sources.The performance obtains up to 11.42% and 22.84% absolute gains over stateof-the-art baselines on BBN and Wiki respectively with regard to macro F1 scores.More importantly, we further discuss the characteristics, merits and demerits of each information source and provide an intuitive understanding of the complementarity among them.
Developing a Framework for Detecting Phishing URLs using Machine Learning

The attack technique targeting end-users through phishing URLs is very dangerous nowadays. With this technique, attackers could steal user data or take control of the system, etc. Therefore, early detecting phishing URLs is essential. In this paper, we propose a method to detect phishing URLs based on supervised learning algorithms and abnormal behaviors from URLs. Finally, based on the research results, we build a framework for detecting phishing URLs through endusers. The novelty and advantage of our proposed method are that abnormal behaviors are extracted based on URLs which are monitored and collected directly from attack campaigns instead of using inefficient old datasets. Keywords— phishing URLs; detecting phishing URLs; abnormal behaviors of phishing URLs; Machine learning
Improving Pre-trained Vision-and-Language Embeddings for Phrase Grounding

Phrase grounding aims to map textual phrases to their associated image regions, which can be a prerequisite for multimodal reasoning and can benefit tasks requiring identifying objects based on language.With pre-trained vision-and-language models achieving impressive performance across tasks, it remains unclear if we can directly utilize their learned embeddings for phrase grounding without finetuning.To this end, we propose a method to extract matched phrase-region pairs from pre-trained vision-and-language embeddings and propose four fine-tuning objectives to improve the model phrase grounding ability using image-caption data without any supervised grounding signals.Experiments on two representative datasets demonstrate the effectiveness of our objectives, outperforming baseline models in both weakly-supervised and supervised phrase grounding settings.In addition, we evaluate the aligned embeddings on several other downstream tasks and show that we can achieve better phrase grounding without sacrificing representation generality. 1
Unsupervised Neural Machine Translation with Universal Grammar

Machine translation usually relies on parallel corpora to provide parallel signals for training.The advent of unsupervised machine translation has brought machine translation away from this reliance, though performance still lags behind traditional supervised machine translation.In unsupervised machine translation, the model seeks symmetric language similarities as a source of weak parallel signal to achieve translation.Chomsky's Universal Grammar theory postulates that grammar is an innate form of knowledge to humans and is governed by universal principles and constraints.Therefore, in this paper, we seek to leverage such shared grammar clues to provide more explicit language parallel signals to enhance the training of unsupervised machine translation models.Through experiments on multiple typical language pairs, we demonstrate the effectiveness of our proposed approaches.