Reviewerly/exHarmony huggingface dataset:
EmergEventMine: End-to-End Chinese Emergency Event Extraction Using a Deep Adversarial Network

With the rapid development of the internet and social media, extracting emergency events from online news reports has become an urgent need for public safety. However, current studies on the text mining of emergency information mainly focus on text classification and event recognition, only obtaining a general and conceptual cognition about an emergency event, which cannot effectively support emergency risk warning, etc. Existing event extraction methods of other professional fields often depend on a domain-specific, well-designed syntactic dependency or external knowledge base, which can offer high accuracy in their professional fields, but their generalization ability is not good, and they are difficult to directly apply to the field of emergency. To address these problems, an end-to-end Chinese emergency event extraction model, called EmergEventMine, is proposed using a deep adversarial network. Considering the characteristics of Chinese emergency texts, including small-scale labelled corpora, relatively clearer syntactic structures, and concentrated argument distribution, this paper simplifies the event extraction with four subtasks as a two-stage task based on the goals of subtasks, and then develops a lightweight heterogeneous joint model based on deep neural networks for realizing end-to-end and few-shot Chinese emergency event extraction. Moreover, adversarial training is introduced into the joint model to alleviate the overfitting of the model on the small-scale labelled corpora. Experiments on the Chinese emergency corpus fully prove the effectiveness of the proposed model. Moreover, this model significantly outperforms other existing state-of-the-art event extraction models.
Introducing a Real-Time Advanced Eye Movements Analysis Pipeline

Real-Time Advanced Eye Movements Analysis Pipeline (RAEMAP) is an advanced pipeline to analyze traditional positional gaze measurements as well as advanced eye gaze measurements. The proposed implementation of RAEMAP includes real-time analysis of fixations, saccades, gaze transition entropy, and low/high index of pupillary activity. RAEMAP will also provide visualizations of fixations, fixations on AOIs, heatmaps, and dynamic AOI generation in real-time. This paper outlines the proposed architecture of RAEMAP.
A framework of dynamic selection method for user classification in touch-based continuous mobile device authentication

Continuous authentication can provide a mechanism to continuously monitor mobile devices while a user is actively using it, after passing the initial-login authentication phase. Touch biometric is one of the promising modality to realise continuous authentication on mobile devices by distinguishing between the touch strokes performed by the legitimate and illegitimate users through classification algorithms. While the benefit of the scheme is promising, the effectiveness of different classification methods are not thoroughly understood. Little consideration has been given on the combination of multiple classifiers to perform continuous authentication. In this paper, we propose a novel classification framework for touch-based continuous mobile device authentication (CMDA), utilising dynamic selection of classifiers (DS). Instead of classifying all touch strokes using the same classifier, the proposed framework classifies each touch sample using the most promising classifier(s) from a pool of classifiers. Based on the proposed framework, we evaluated various DS methods in multiple scenarios across four touch datasets. The aim of this evaluation is to assess the feasibility of DS on touch-based CMDA. We then compared these DS methods with well-known single classifiers and static ensemble methods. The experimental results show the potential and feasibility of the DS methods to improve the authentication performance of touch-based CMDA against the benchmark methods. We found that DS methods are capable of producing promising results with relatively low equal error rate (EER) in many scenarios of the datasets, with relatively high consistencies. The obtained results would be valuable for further enhancement of existing user classification methods and the development of new DS methods in touch-based CMDA.
Pose Estimation-Assisted Dance Tracking System Based on Convolutional Neural Network

In the field of music-driven, computer-assisted dance movement generation, traditional music movement adaptations and statistical mapping models have the following problems: Firstly, the dance sequences generated by the model are not powerful enough to fit the music itself. Secondly, the integrity of the dance movements produced is not sufficient. Thirdly, it is necessary to improve the suppleness and rationality of long-term dance sequences. Fourthly, traditional models cannot produce new dance movements. How to create smooth and complete dance gesture sequences after music is a problem that needs to be investigated in this paper. To address these problems, we design a deep learning dance generation algorithm to extract the association between sound and movement characteristics. During the feature extraction phase, rhythmic features extracted from music and audio beat features are used as musical features, and coordinates of the main points of human bones extracted from dance videos are used for training as movement characteristics. During the model building phase, the model's generator module is used to achieve a basic mapping of music and dance movements and to generate gentle dance gestures. The identification module is used to achieve consistency between dance and music. The self-encoder module is used to make the audio function more representative. Experimental results on the DeepFashion dataset show that the generated model can synthesize the new view of the target person in any human posture of a given posture, complete the transformation of different postures of the same person, and retain the external features and clothing textures of the target person. Using a whole-to-detail generation strategy can improve the final video composition. For the problem of incoherent character movements in video synthesis, we propose to optimize the character movements by using a generative adversarial network, specifically by inserting generated motion compensation frames into the incoherent movement sequences to improve the smoothness of the synthesized video.
3D-Augmented Contrastive Knowledge Distillation for Image-based Object Pose Estimation

Image-based object pose estimation sounds amazing because in real applications the shape of object is oftentimes not available or not easy to take like photos. Although it is an advantage to some extent, un-explored shape information in 3D vision learning problem looks like "flaws in jade''. In this paper, we deal with the problem in a reasonable new setting, namely 3D shape is exploited in the training process, and the testing is still purely image-based. We enhance the performance of image-based methods for category-agnostic object pose estimation by exploiting 3D knowledge learned by a multi-modal method. Specifically, we propose a novel contrastive knowledge distillation framework that effectively transfers 3D-augmented image representation from a multi-modal model to an image-based model. We integrate contrastive learning into the two-stage training procedure of knowledge distillation, which formulates an advanced solution to combine these two approaches for cross-modal tasks. We experimentally report state-of-the-art results compared with existing category-agnostic image-based methods by a large margin (up to +5% improvement on ObjectNet3D dataset), demonstrating the effectiveness of our method.
A Transformer-based Model Integrated with Feature Selection for Credit Card Fraud Detection

Credit card fraud has grabbed a lot of attention in data science domain in the past several years. Most previous works tackled this problem by using machine learning methods equipped with hand-crafted features. In this paper, we proposed a novel multi-stage method combing both feature selection by a machine learning method, as well as a transformer-based model as a classifier. Concretely, we firstly extracted the top-k important features from the original dataset, where they are further used to train the self-designed deep learning model. Experimental results shown that our model can achieve better results compared to the baseline models. Since we also incorporated transformer layer into our deep learning model, we also tested its performance and the results proves the effectiveness.
An unsupervised autonomous learning framework for goal-directed behaviours in dynamic contexts

Abstract Due to their dependence on a task-specific reward function, reinforcement learning agents are ineffective at responding to a dynamic goal or environment. This paper seeks to overcome this limitation of traditional reinforcement learning through a task-agnostic, self-organising autonomous agent framework. The proposed algorithm is a hybrid of TMGWR for self-adaptive learning of sensorimotor maps and value iteration for goal-directed planning. TMGWR has been previously demonstrated to overcome the problems associated with competing sensorimotor techniques such SOM, GNG, and GWR; these problems include: difficulty in setting a suitable number of neurons for a task, inflexibility, the inability to cope with non-markovian environments, challenges with noise, and inappropriate representation of sensory observations and actions together. However, the binary sensorimotor-link implementation in the original TMGWR enables catastrophic forgetting when the agent experiences changes in the task and it is therefore not suitable for self-adaptive learning. A new sensorimotor-link update rule is presented in this paper to enable the adaptation of the sensorimotor map to new experiences. This paper has demonstrated that the TMGWR-based algorithm has better sample efficiency than model-free reinforcement learning and better self-adaptivity than both the model-free and the traditional model-based reinforcement learning algorithms. Moreover, the algorithm has been demonstrated to give the lowest overall computational cost when compared to traditional reinforcement learning algorithms.
AR Exhibitions for Sensitive Narratives: Designing an Immersive Exhibition for the Museum of Memory in Colombia

Augmented Reality (AR) in human rights museums and memorialization efforts can empower these initiatives to create stronger connections between audiences and victims; however, there is little research on the risks of depicting sensitive narratives through immersive technologies. We examined the opportunities and challenges of applying AR to memorialization by designing and deploying an AR application with a human rights museum in Colombia. We report lessons from our collaboration about navigating the risk of re-victimizing testimonial authors while creating engaging AR interactions. Furthermore, we report on a user study where participants interacted with our museum exhibition. Based on observations of our co-design process and the user study results, we discuss implications for immersive application design with strategies for selecting immersive content, balancing audience engagement, and identifying technology gaps. Finally, we reflect on the implications for collaborations between HCI researchers, human rights professionals, and organizations to inform designs involving sensitive narratives.
Use of electronic resources in research and learning in a health sciences library in Ghana: An analysis of awareness and perception of users

University libraries play a critical role by facilitating the use of their electronic resources and facilities available for students’ learning and research activities. Health and medical students and professionals are expected to use the electronic resources provided for their academic and research work. This study, therefore, seeks to investigate the acceptance and utilization of electronic resources in research and learning among users of the College of Health Sciences Library of the University of Ghana. The study employed a survey research design along with a quantitative approach to collect data from 128 respondents. A questionnaire was used for data collection, and it was analysed using the Statistical Package for the Social Sciences version 22 (SPSS) software. The study found that a majority of the respondents were aware of the electronic resources provided by the library, and they used various types of e-resources such as Google, Wikipedia, ScienceDirect and PubMed electronic sources. Moreover, the study found that the computer literacy skills of users have a positive relationship with the awareness of e-resources. Furthermore, there is no significant relationship between gender and the perceived usefulness of the use of e-resources. Last but not the least, the high cost of personal internet data and low internet bandwidth were challenges encountered by most of the respondents when accessing e-resources. The study made the following recommendations, among others, regular training on the use of e-resources, and libraries must collaborate effectively with lecturers to create awareness in the lecture hall and also some of the e-resources should be part of teaching instruction.
Holy Quran-Italian seq2seq Machine Translation with Attention Mechanism

Many machine translation studies have used large parallel groups to address sets of major European dialects. However, due to the lack of sufficient parallel information, few studies have considered Italian and Arabic. Moreover, dictionary-based translations of the Holy Quran from Arabic to Italian are usually incorrect. The meaning of the Quran has not been translated correctly. Because the dictionary-based translation considers the Quran to be a traditional document and translates it in order. This paper contributes in two ways. First, it presents a parallel corpus of 6237 Italian-Arabic sentences. Second, the paper introduces two deep learning models namely, long-shortterm memory (LSTM) sequence-to-sequence with an attention mechanism and Gated Recurrent Units (GRU) sequence-to-sequence with an attention mechanism for Arabic to Italian machine translation. Each of the proposed models is evaluated based on BLEU, ROUGE, and Cosine Similarity scores. The results indicate that the LSTM-based neural machine translation (NMT) outperforms the GRU-based NMT framework. The experimental results indicate that the LSTM model achieved mean scores of 0.96, 0.91, and 0.90 for Cosine Similarity, BLEU, and ROUGE, respectively. The GRU model achieved average scores of 0.94, 0.89, and 0.88 for Cosine Similarity, BLEU, and ROUGE scores, respectively.
On the form of parsed sentences for relation extraction

Parsed sentences convey rich syntactic information like part-of-speech (POS) tags and dependency trees, and have been widely adopted in relation extraction. However, existing methods either suffer from error propagation when using tree or graph form of the imperfect parse tree, or neglect the independent POS sequence because each POS embedding is combined with a word embedding to form the representation of the word. We propose to exploit the sequential form of POS tags beyond the popular tree or graph form of parse tree of a sentence. Our method naturally fills the gap between the original sentence and imperfect parse tree. It also enables the learnt POS embeddings to correspond and interact with word embeddings pre-trained by sequential models like GloVe or BERT. This property is further leveraged in a downstream entity masking task designed for relation extraction. We conduct extensive experiments on various type of relation extraction tasks. The results demonstrate that our model significantly outperforms the state-of-the-art approaches.
A novel privacy preserving blockchain based secure storage framework for electronic health records

Despite the continuous improvement of healthcare technology, implementing a patient health record system still has a significant challenge due to its sensitivity. Apart from storing and sharing patient data across various healthcare facilities, the system also has a tendency to distribute the data among various providers. Most patient health record systems (EHRs) use a centralized management structure to store and transmit patient information. This approach can create privacy concerns as it transfers sensitive information to a healthcare facility. Digitized Electronic Health Record EHRs that stores the patient's medical history gathers from various IoT devices or from the centralized or decentralized database. These database records are often shared among various healthcare organizations. Most patient health record systems typically adopt a centralized approach and store their patient information in the cloud. This approach can raise privacy concerns due to the amount of sensitive information that is stored in the cloud. This paper proposes blockchain framework for secured electronic health records sharing and provides full data access to the patient, who is the owner of his health records. With this novel approach, our framework applies the Ethereum blockchain smart contracts, the Inter-Planetary File System (IPFS) as an off-chain storage system and key management. Blockchain based encryption creates an effective approach while sharing health records in distributed framework. Proposed framework is more secure than previously available framework. EHR's will not be accessible from unauthorized users.
Characterizing and synthesizing the workflow structure of microservices in ByteDance Cloud

Abstract Modern Cloud applications have evolved from monolithic systems to numerous distributed microservices whose workflows interact via Remote Procedure Calls (RPCs). The benchmarks focusing on individual microservice components are insufficient because the reference relationships between components, which form the microservice workflow structure, are another critical aspect of microservice applications. Unfortunately, understanding the characteristics of microservice workflow in the production Cloud remains a missing piece in the literature, which prevents the representative of microservice benchmarks. In this paper, we fill this gap by characterizing and synthesizing the microservice workflows based on the trace data of the Toutiao application that is running on ByteDance Cloud. We examine the microservice workflows starting from DAG graphs, introduce observed properties that are easy to ignore but important, show the artificiality of the workflow by statistic description, and explore the high cost of network overhead. We further synthesize the workflow following the characteristics observed. The extensive evaluations show that the synthesized microservice workflows have consistent statistical characteristics as the production ones. A case study applying the synthesized workflows proves its usability.
Integrating I/O Time to Virtual Time System for High Fidelity Container-based Network Emulation

Container-based network emulation provides an accurate, flexible, and cost-effective application design and evaluation testing environment. Enabling virtual time to processes running inside containers essentially improves the temporal fidelity of emulation experiments. However, the lack of precise time management during operations, such as disk I/O, network I/O, and GPU computation, often leads to virtual time advancement errors observed in existing virtual time systems. This paper proposes VT-IO, a lightweight virtual time system that integrates precise I/O time for container-based network emulation. We model and analyze the temporal error during I/O operations and develop a barrier-based time compensation mechanism in the Linux kernel. VT-IO enables accurate virtual time advancement with precise I/O time measurement and compensation. The experimental results show that the temporal error can be reduced from 87.31% to 3.6% and VT-IO only introduces around 2% overhead of the total execution time. Finally, we demonstrate VT-IO’s usability and temporal fidelity improvement with a case study of a Bitcoin mining application.
Semi-supervised sparse neighbor constrained co-clustering with dissimilarity and similarity regularization

Nonnegative matrix factorization (NMF) is a very effective method for high dimensional data analysis, which has been widely used in computer vision. However, the conventional NMF is unsupervised, and thus, it cannot utilize the label information. To this end, semi-supervised NMF is proposed, which performs the NMF with the guidance of the supervisory information. However, semi-supervised NMF fails to make full use of label information, which limits the performance of clustering using low dimensional representation, and samples and features cannot be clustered in a mutually reinforcing manner. To solve the above shortcomings, we propose a semi-supervised sparse neighbor constrained co-clustering with dissimilarity and similarity regularization model (SSCCDS). First, co-clustering is introduced to cluster samples and features simultaneously. Secondly, the model imposes similarity and dissimilarity regularization constraints on samples by low-dimensional representations. Specifically, similarity and dissimilar regularization constraints are imposed on labeled samples, and similarity regularization constraints are imposed on unlabeled data. Thirdly, the model proposes sparse neighbor constraints for feature consistent learning. Then, a multiplicative alternating scheme is proposed for objective optimization. A large number of experiments on different data sets show that SSCCDS has good clustering ability.
Classification of House Categories Using Convolutional Neural Networks (CNN)

Image classification of house categories, such as condominium, detached house, shophouse and townhouse, is still a major pain point and crucial burden for websites’ backend works. Because, this process is still using manual classification, which are low productivities and often found errors. The objective of this research is to develop an appropriate model of the “Convolutional Neural Networks (CNN)” for house categories classification. The results from 4 models processing revealed that the Based model return the highest overall accuracy value of 75.00 percent. The second, third and fourth overall accuracy values were MobileNet, of 73.24 ResNet50 of 72.78, and VGG-19 of 25.00 percent respectively. The MobileNet model had the highest value of Precision. While the Based model also had the highest values of Recall and F1-score, comparing to other three pre-trained models. In conclusion, the Based model was the most appropriate model to this research. However, the three models, including Based model, ResNet50 and MobileNet models, had small different accuracy values, which can be used for house categories classification.
Sketch to Building: Architecture Image Translation Based on GAN

Abstract Image-to-image translation with Generative Adversarial Networks has been extensively studied recently. However, it remains still very challenging for sketch-to-image translation owing to large domain gap and information asymmetry problems. In this paper, we introduce a novel two-step method as well as designing an interactive system which can generate different styles of architecture images from a hand-drawn sketch. In our framework, either an architecture sketch can be drawn and then translated into a color image, or an architecture photo can be loaded, followed by sketch generation, modification, and translation. Style images can also be manually selected for different output styles. Experimental results show that our method has better performance on both image quality and diversity than the other competitive methods.