Reviewerly/exHarmony huggingface dataset:
Integrating External Event Knowledge for Script Learning

Script learning aims to predict the subsequent event according to the existing event chain.Recent studies focus on event co-occurrence to solve this problem.However, few studies integrate external event knowledge to solve this problem.With our observations, external event knowledge can provide additional knowledge like temporal or causal knowledge for understanding event chain better and predicting the right subsequent event.In this work, we integrate event knowledge from ASER (Activities, States, Events and their Relations) knowledge base to help predict the next event.We propose a new approach consisting of knowledge retrieval stage and knowledge integration stage.In the knowledge retrieval stage, we select relevant external event knowledge from ASER.In the knowledge integration stage, we propose three methods to integrate external knowledge into our model and infer final answers.Experiments on the widely-used Multi-Choice Narrative Cloze (MCNC) task show our approach achieves state-of-the-art performance compared to other methods.
Deep Neural Networks Using Capsule Networks and Skeleton-Based Attentions for Action Recognition

This work develops Deep Neural Networks (DNNs) by adopting Capsule Networks (CapsNets) and spatiotemporal skeleton-based attention to effectively recognize subject actions from abundant spatial and temporal contexts of videos. The proposed generic DNN includes four 3D Convolutional Neural Networks (3D_CNNs), Attention-Jointed Appearance (AJA) and Attention-Jointed Motion (AJM) generation layers, two Reduction Layers (RLs), two Attention-based Recurrent Neural Networks (A_RNNs), and an inference classifier, where RGB, transformed skeleton, and optical-flow channel streams are inputs. The AJA and AJM generation layers emphasize skeletons to the appearances and motions of a subject, respectively. A_RNNs generate attention weights over time steps to highlight rich temporal contexts. To integrate CapsNets in this generic DNN, three types of CapsNet-based DNNs are devised, where the CapsNets take over a classifier, A_RNN+classifier, and RL+A_RNN+classifier. The experimental results reveal that the proposed DNN using CapsNet as an inference classifier outperforms the other two CapsNet-based DNNs and the generic DNN adopting the feedforward neural network as an inference classifier. Additionally, our best CapsNet-based DNN achieves average accuracies of 98.5% for the state-of-the-art performance in UCF101, 82.1% for near-state-of-the-art performance in HMDB51, and 95.3% for panoramic videos, to the best of our knowledge. Particularly, it is determined that the generic CapsNet behaves as an outstanding inference classifier but is slightly worse than the A_RNN in interpreting temporal evidence for recognition. Therefore, the proposed DNN, which employs CapsNet to fulfill an inference classifier, can be superiorly applied to various context-aware visual applications.
Shared Standards versus Competitive Pressures in Journalism

Abstract Democratic societies need media that uphold journalistic standards of truthfulness and objectivity. But sensationalism has always been a temptation for journalists, and given the intense competition between news outlets, especially in the online world, there is pressure on them to ‘chase the clicks’. The article analyzes the incentive structures for journalists – focusing on the harmfulness of sensationalist framing as an example – and the challenges of establishing shared standards in a highly competitive online environment. Drawing on concepts and arguments from business ethics, it argues that the structure of this problem points to the need for an ‘ethics of sportsmanship’ that upholds journalistic standards despite competitive pressures. But the specific role and nature of the media imply that there can be no once‐and‐for‐all solution. Instead, there is a need for reflexivity, that is, for an ongoing dialogue about journalistic standards and the role of media in democratic societies.
Multiple-Clothing Detection and Fashion Landmark Estimation Using a Single-Stage Detector

Fashion image analysis has attracted significant research attention owing to the availability of large-scale fashion datasets with rich annotations.However, existing deep learning models for fashion datasets often have high computational requirements.In this study, we propose a new model suitable for low-power devices.The proposed network is a one-stage detector that rapidly detects multiple cloths and landmarks in fashion images.The network is designed as a modification of the EfficientDet originally proposed by Google Brain.The proposed network simultaneously trains the core input features with different resolutions and applies compound scaling to the backbone feature network.The bounding box/class/landmark prediction networks maintain the balance between the speed and accuracy.Moreover, a low number of parameters and low computational cost make it efficient.Without image preprocessing, we achieved 0.686 mean average precision (mAP) in the bounding box detection and 0.450 mAP in the landmark estimation on the DeepFashion2 validation dataset with an inference time of 42 ms.We obtained optimal results in extensive experiments with loss functions and optimizers.Furthermore, the proposed method has the advantage of operating in low-power devices.
DIGITAL STOCKS USING BLOCKCHAIN TECHNOLOGY THE POSSIBLE FUTURE OF STOCKS? 

Bitcoin is the first and most successful digital currency in the world.It trends in the news almost daily, with glowing reviews of the many benefits of an alternative and international currency.This paper explains the innovative aspect of the technological platform used to transfer Bitcoin from one party to another.This technology is called the Blockchain.The Blockchain eschews a bank or other intermediary and allows parties to transfer funds directly to one another, using a peer--peer system.This to disruptive technology has done for money transfers what email did for sending mailby removing the need for a trusted third party just as email removed the need for using the post office to send mail.This technology mainly used for peer--peer money to transfers, can also be extended to accomplish other forms of transfers.Blockchain technology can be used to buy and sell stocks.Real world stocks can be tokenized into digital stocks which can be easily transferred using peer--peer.These digital stocks to act similar to digital currency whose price is real time and fluctuates.Stocks exchanged completely peer--peer could resolve many of the issues facing the stock market today, to including high frequency trading and short sales.
Peran Teknologi Pendidikan dalam Pembelajaran

The development of information technology which is increasingly rapid in the current era of globalization cannot be avoided anymore its impact on the world of education. Global demands require the world of education to always adjust technological developments to efforts to improve the quality of education, especially adjusting the use of information and communication technology for education, especially in the learning process. This research is a library research method. Educational technology is the application of scientific knowledge to learning, as a result of which learning objectives can be achieved effectively and efficiently, which are not only limited to tools and goods or hardware but also software, and brainware. In the development and utilization of learning technology there are still three basic principles, namely: 1) System approach, 2) Learner centered, 3) Utilization of learning resources as much as possible (utilizing learning resources). The results of the study illustrate that educational technology has a very crucial role in learning. So it can be said that the discovery of educational technology education is a unity that cannot be separated.
Real-Time Production of PGA, PGV, Intensity, and Sa Shakemaps Using Dense MEMS-Based Sensors in Taiwan

Using low-cost sensors to build a seismic network for earthquake early warning (EEW) and to generate shakemaps is a cost-effective way in the field of seismology. National Taiwan University (NTU) network employing 748 P-Alert sensors based on micro-electro-mechanical systems (MEMS) technology is operational for almost the last 10 years. This instrumentation is capable of recording the strong ground motions of up to ± 2g and is dense enough to record the near-field ground motion. It has proven effective in generating EEW warnings and delivering real-time shakemaps to the concerned disaster relief agencies to mitigate the earthquake-affected regions. Before 2020, this instrumentation was used to plot peak ground acceleration (PGA) shakemaps only; however, recently it has been upgraded to generate the peak ground velocity (PGV), Central Weather Bureau (CWB) Intensity scale, and spectral acceleration (Sa) shakemaps at different periods as value-added products. After upgradation, the performance of the network was observed using the latest recorded earthquakes in the country. The experimental results in the present work demonstrate that the new parameters shakemaps added in the current work provide promising outputs, and are comparable with the shakemaps given by the official agency CWB. These shakemaps are helpful to delineate the earthquake-hit regions which in turn is required to assist the needy well in time to mitigate the seismic risk.
Active Boundary Loss for Semantic Segmentation

This paper proposes a novel active boundary loss for semantic segmentation. It can progressively encourage the alignment between predicted boundaries and ground-truth boundaries during end-to-end training, which is not explicitly enforced in commonly used cross-entropy loss. Based on the predicted boundaries detected from the segmentation results using current network parameters, we formulate the boundary alignment problem as a differentiable direction vector prediction problem to guide the movement of predicted boundaries in each iteration. Our loss is model-agnostic and can be plugged in to the training of segmentation networks to improve the boundary details. Experimental results show that training with the active boundary loss can effectively improve the boundary F-score and mean Intersection-over-Union on challenging image and video object segmentation datasets.
NLP-based digital forensic investigation platform for online communications

Digital (forensic) investigations will be increasingly important in both criminal investigations and civil litigations (e.g., corporate espionage, and intellectual property theft) as more of our communications take place over cyberspace (e.g., e-mail and social media platforms). In this paper, we present our proposed Natural Language Processing (NLP)-based digital investigation platform. The platform comprises the data collection and representation phase, the vectorization phase, the feature selection phase, and the classifier generation and evaluation phase. We then demonstrate the potential of our proposed approach using a real-world dataset, whose findings indicate that it outperforms two other competing approaches, namely: LogAnalysis (published in Expert Systems with Applications, 2014) and SIIMCO (published in IEEE Transactions on Information Forensics and Security, 2016). Specifically, our proposed approach achieves 0.65 in F1-score and 0.83 in precision, whilst LogAnalysis and SIIMCO respectively achieve 0.51 and 0.59 in F1-score and 0.49 and 0.58 in precision.
Graph Domain Adversarial Transfer Network for Cross-Domain Sentiment Classification

In the text sentiment classification task, some words are seemingly unrelated to the classification task, but they have a direct impact on the performance of classification model. For example, in the sentences “I have terminal cancer” and “Cancer is a very common disease”, it can be clearly found that the word “cancer” has two different sentiment tendencies in the daily life domain and the medical domain. In the daily life domain, the word “cancer” shows an extremely negative sentiment tendency. While in the medical domain, the word “cancer” is just a simple term with a relatively neutral sentiment tendency. Although current deep learning models have already achieved good performance through their powerful feature learning capabilities, there are serious deficiencies in dealing with the above problem. Therefore, from a new perspective, this paper proposes the Graph Domain Adversarial Transfer Network (GDATN) based on the idea of adversarial learning, which uses the labeled source domain data to predict the sentiment label of unlabeled target domain data. Firstly, GDATN extracts feature representations through the Bidirectional Long Short-Term Memory (BiLSTM) Network and Graph Attention Network (GAT) successively. Then, GDATN introduces the domain classifier to capture the domain-shared text feature representation with the Gradient Reversal Layer (GRL). In addition, an auxiliary task named the projection mechanism is constructed to further capture the domain-specific text feature representation in response to the text domain problem. Extensive experimental results on two benchmark datasets show that GDATN proposed in this paper outperforms the other six benchmark sentiment classification models, and GDATN has a better stability on different cross-domain pairs.
Ethics, public health and technology responses to COVID‐19

Abstract The COVID‐19 pandemic has infected millions around the world. Governments initially responded by requiring businesses to close and citizens to self‐isolate, as well as funding vaccine research and implementing a range of technologies to monitor and limit the spread of the disease. This article considers the use of smartphone metadata and Bluetooth applications for public health surveillance purposes in relation to COVID‐19. It undertakes ethical analysis of these measures, particularly in relation to collective moral responsibility, considering whether citizens ought, or should be compelled, to comply with government measures.
Data Fusion for Intelligent Crowd Monitoring and Management Systems: A Survey

Intelligent Crowd Monitoring and Management Systems (ICMMSs) have become effective resources for strengthening safety and security along with enhancing early-warning capabilities to manage emergencies in crowded situations of smart cities and massive gatherings events.The main advantage of such systems is their ability to detect multiple features associated with the crowd gathering, as they enable multi-source sensors, multi-modal data, and powerful intelligent and analytical methods.Unlike traditional crowd monitoring systems, which make use of simplex forms of different data types, data and information associated with crowded scenarios can be collected, fused, processed and analyzed in large quantities for accurate global assessment and enhanced decision making processes in an ICMMS.Therefore, data fusion is introduced as an enabler to decrease data quantity, reduce data dimensions, and improve data quality.In this paper, we first survey the literature on data fusion application in crowd monitoring systems as we are developing a state-of-the-art ICMMS with data fusion as a major platform enabler.Next, we discuss some popular data fusion architectures and classifications from different perspectives.Based on this, we propose a multi-sensor, multi-modal, and dimensional ICMMS architecture based on data fusion.Then, we identify the data fusion processes in the ICMMS and classify them into sensor fusion, feature-based data fusion, and decision fusion.Relevant algorithms, applications and examples of three classes are elaborated.Finally, future data fusion research directions are discussed.
Entanglement formation in continuous-variable random quantum networks

Abstract Entanglement is not only important for understanding the fundamental properties of many-body systems, but also the crucial resource enabling quantum advantages in practical information processing tasks. Although previous works on quantum networks focus on discrete-variable systems, light—as the only traveling carrier of quantum information in a network—is bosonic and thus requires a continuous-variable description. We extend the study to continuous-variable quantum networks. By mapping the ensemble-averaged entanglement dynamics on an arbitrary network to a random-walk process on a graph, we are able to exactly solve the entanglement dynamics. We identify squeezing as the source of entanglement generation, which triggers a diffusive spread of entanglement with a "parabolic light cone”. A surprising linear superposition law in the entanglement growth is predicted by the theory and numerically verified, despite the nonlinear nature of the entanglement dynamics. The equilibrium entanglement distribution (Page curves) is exactly solved and has various shapes depending on the average squeezing density and strength.
Geological Survey of New South Wales: All geophysical data now available online

Since late 2018 the Geological Survey of New South Wales (GSNSW) has undertaken a major project to catalogue, quality-assure and release company geophysical survey data across the state. A key deli...
Machine learning based distributed big data analysis framework for next generation web in IoT

For the advancement of the Internet of Things (IoT) and Next Generation Web, various applications have emerged to process structured or unstructured data. Latency, accuracy, load balancing, centralization, and others are issues on the cloud layer of transferring the IoT data. Machine learning is an emerging technology for big data analytics in IoT applications. Traditional data analyzing and processing techniques have several limitations, such as centralization and load managing in a massive amount of data. This paper introduces a Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT. We are utilizing feature extraction and data scaling at the edge layer paradigm for processing the data. Extreme Learning Machine (ELM) is adopting in the cloud layer for classification and big data analysis in IoT. The experimental evaluation demonstrates that the proposed distributed framework has a more reliable performance than the traditional framework.
Overlapping Communication With Computation in Parameter Server for Scalable DL Training

Scalability of distributed deep learning (DL) training with parameter server (PS) architecture is often communication constrained in large clusters. There are recent efforts that use a layer by layer strategy to overlap gradient communication with backward computation so as to reduce the impact of communication constraint on the scalability. However, the approaches could bring significant overhead in gradient communication. Meanwhile, they cannot be effectively applied to the overlap between parameter communication and forward computation. In this article, we propose and develop iPart, a novel approach that partitions communication and computation in various partition sizes to overlap gradient communication with backward computation and parameter communication with forward computation. iPart formulates the partitioning decision as an optimization problem and solves it based on a greedy algorithm to derive communication and computation partitions. We implement iPart in the open-source DL framework BigDL and perform evaluations with various DL workloads. Experimental results show that iPart improves the scalability of a cluster of 72 nodes by up to 94 percent over the default PS and 52 percent over the layer by layer strategy.
Revisiting the functional bootstrap in TFHE

The FHEW cryptosystem introduced the idea that an arbitrary function can be evaluated within the bootstrap procedure as a table lookup. The faster bootstraps of TFHE strengthened this approach, which was later named Functional Bootstrap (Boura et al., CSCML’19). From then on, little effort has been made towards defining efficient ways of using it to implement functions with high precision. In this paper, we introduce two methods to combine multiple functional bootstraps to accelerate the evaluation of reasonably large look-up tables and highly precise functions. We thoroughly analyze and experimentally validate the error propagation in both methods, as well as in the functional bootstrap itself. We leverage the multi-value bootstrap of Carpov et al. (CT-RSA’19) to accelerate (single) lookup table evaluation, and we improve it by lowering the complexity of its error variance growth from quadratic to linear in the value of the output base. Compared to previous literature using TFHE’s functional bootstrap, our methods are up to 2.49 times faster than the lookup table evaluation of Carpov et al. (CT-RSA’19) and up to 3.19 times faster than the 32-bit integer comparison of Bourse et al. (CT-RSA’20). Compared to works using logic gates, we achieved speedups of up to 6.98, 8.74, and 3.55 times over 8-bit implementations of the functions ReLU, Addition, and Maximum, respectively.
A distributed Content-Based Video Retrieval system for large datasets

Abstract With the rapid growth in the amount of video data, efficient video indexing and retrieval methods have become one of the most critical challenges in multimedia management. For this purpose, Content-Based Video Retrieval (CBVR) is nowadays an active area of research. In this article, a CBVR system providing similar videos from a large multimedia dataset based on query video has been proposed. This approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key frames for rapid browsing and efficient video indexing. The proposed method has been implemented on both single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments were performed using various benchmark action and activity recognition datasets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to previous studies.
Attention based GRU-LSTM for software defect prediction

Software defect prediction (SDP) can be used to produce reliable, high-quality software. The current SDP is practiced on program granular components (such as file level, class level, or function level), which cannot accurately predict failures. To solve this problem, we propose a new framework called DP-AGL, which uses attention-based GRU-LSTM for statement-level defect prediction. By using clang to build an abstract syntax tree (AST), we define a set of 32 statement-level metrics. We label each statement, then make a three-dimensional vector and apply it as an automatic learning model, and then use a gated recurrent unit (GRU) with a long short-term memory (LSTM). In addition, the Attention mechanism is used to generate important features and improve accuracy. To verify our experiments, we selected 119,989 C/C++ programs in Code4Bench. The benchmark tests cover various programs and variant sets written by thousands of programmers. As an evaluation standard, compared with the state evaluation method, the recall, precision, accuracy and F1 measurement of our well-trained DP-AGL under normal conditions have increased by 1%, 4%, 5%, and 2% respectively.