Reviewerly/exHarmony huggingface dataset:
Document-level event causality identification via graph inference mechanism

Event causality identification is an important research task in natural language processing. Existing methods largely focus on identifying explicit causal relations, and give poor performance in implicit causalities, especially in the document level. In this paper, we formalize event causality identification as a graph-based edge prediction problem and propose a novel document-level context-based graph inference mechanism. Specifically, we use attention-based neural networks to automatically extract document-level contextual information, and a direction-sensitive graph inference mechanism to achieve information transfer and interaction among event causalities. Experimental results on the EventStoryLine v1.5 dataset show that our approach outperforms previous methods and baseline systems by a large margin in F1-score metrics (2.45% improvement on intra-sentence causalities and 3.08% improvement on cross-sentence causalities). Further analysis demonstrates that our model can effectively capture the document-level contextual information and latent causal information among events.
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning

High false-positive rate is a long-standing challenge for anomaly detection algorithms, especially in high-stake applications. To identify the true anomalies, in practice, analysts or domain experts will be employed to investigate the top instances one by one in a ranked list of anomalies identified by an anomaly detection system. This verification procedure generates informative labels that can be leveraged to re-rank the anomalies so as to help the analyst to discover more true anomalies given a time budget. Some re-ranking strategies have been proposed to approximate the above sequential decision process. Specifically, existing strategies have been focused on making the top instances more likely to be anomalous based on the feedback. Then they greedily select the top-1 instance for query. However, these greedy strategies could be sub-optimal since some low-ranked instances could be more helpful in the long-term. Motivated by this, in this work, we study whether modeling long-term performance can benefit active anomaly detection. This is a challenging task because it is unclear how long-term performance could be quantified. In addition, the query selection has a huge decision space, which is difficult to model. To address these challenges, we propose Active Anomaly Detection with Meta-Policy (Meta-AAD), a novel framework that learns a meta-policy for query selection. Specifically, Meta-AAD leverages deep reinforcement learning to train the meta-policy to select the most proper instance to explicitly optimize the number of discovered anomalies throughout the querying process. Meta-AAD is easy to deploy since a trained meta-policy can be directly applied to any new datasets without further tuning. Extensive experiments on 24 benchmark datasets demonstrate that Meta-AAD significantly outperforms the state-of-the-art re-ranking strategies and the unsupervised baseline. The empirical analysis shows that the trained meta-policy is transferable and inherently achieves a balance between long-term and short-term rewards.
Assessing the Moderating Effect of Security Technologies on Employees Compliance with Cybersecurity Control Procedures

The increase in cybersecurity threats and the challenges for organisations to protect their information technology assets has made adherence to organisational security control processes and procedures a critical issue that needs to be adequately addressed. Drawing insight from organisational theory literature, we develop a multi-theory model, combining the elements of the theory of planned behaviour, competing value framework, and technology—organisational and environmental theory to examine how the organisational mechanisms interact with espoused cultural values and employee cognitive belief to influence cybersecurity control procedures. Using a structured questionnaire, we deployed structural equation modelling (SEM) to analyse the survey data obtained from public sector information technology organisations in Nigeria to test the hypothesis on the relationship of socio-organisational mechanisms and techno-cultural factors with other key determinants of employee security behaviour. The results showed that knowledge of cybersecurity and employee cognitive belief significantly influence the employees’ intentions to comply with organisational cybersecurity control mechanisms. The research further noted that the influence of organisational elements such as leadership on employee security behaviour is mediated by espoused cultural values while the impact of employee cognitive belief is moderated by security technologies. For effective cybersecurity compliance, leaders and policymakers are therefore to promote organisational security initiatives that ensure incorporation of cybersecurity principles and practices into job descriptions, routines, and processes. This study contributes to behavioural security research by highlighting the critical role of leadership and cultural values in fostering organisational adherence to prescribed security control mechanisms.
HOReID: Deep High-Order Mapping Enhances Pose Alignment for Person Re-Identification

Despite the remarkable progress in recent years, person Re-Identification (ReID) approaches frequently fail in cases where the semantic body parts are misaligned between the detected human boxes. To mitigate such cases, we propose a novel High-Order ReID (HOReID) framework that enables semantic pose alignment by aggregating the fine-grained part details of multilevel feature maps. The HOReID adopts a high-order mapping of multilevel feature similarities in order to emphasize the differences of the similarities between aligned and misaligned part pairs in two person images. Since the similarities of misaligned part pairs are reduced, the HOReID enhances pose-robustness within the learned features. We show that our method derives from an intuitive and interpretable motivation and elegantly reduces the misalignment problem without using any prior knowledge from human pose annotations or pose estimation networks. This paper theoretically and experimentally demonstrates the effectiveness of the proposed HOReID, achieving superior performance over the state-of-the-art methods on the four large-scale person ReID datasets.
Reformative Noise-Immune Neural Network for Equality-Constrained Optimization Applied to Image Target Detection

Equality-constrained optimization problem captures increasing attention in the fields of computer science, control engineering, and applied mathematics. Almost all of the relevant issues suffer from kinds of intense or weak noises during the solving process, so that how to realize the noise deduction even noise elimination has increasingly become a sticky and significant problem. A lot of corresponding solving models are established for the equality-constrained optimization problem. However, the majority of them can find the optimal solution to a certain extent in the absence of noise disturbance, but few can behave a brilliant noise-resistance proficiency. On account of this discovery, a reformative noise-immune neural network (RNINN) model is constructed. In addition, the conventional gradient-based recursive neural network model and the zeroing recursive neural network model are presented to compare with the proposed RNINN model on convergence properties and noise-resistance capabilities. Lastly, the relative numerical experiment simulation and image target detection application are implemented to further elaborate on the robustness and efficiency of the RNINN model.
Fast Adaptation for Cold-Start Collaborative Filtering with Meta-Learning

Collaborative Filtering (CF), as one of the most popular approaches, is widely employed in recommender systems but suffers from the cold-start problem, where interactions are very limited for new users in the system. To deal with this issue, previous work has largely focused on utilizing various auxiliary information such as user profiles and social relationships to infer user preferences. However, the auxiliary information is not always available due to reasons such as user privacy concerns, making the CF approaches have to count on the limited interactions. Moreover, real-world situations require both accurate and quick recommendations for newly arrived users dynamically. Therefore, it is of critical importance to enable fast learning for new users during the training time of CF models. In this paper, we present a novel learning paradigm, named MetaCF, to learn an accurate CF model that makes fast adaptation on new users with limited interactions. Inspired by meta-learning, MetaCF treats the fast adaptation on a new user as a task and aims to learn a suitable model for initializing the adaption. To pursue a well-generalized model, MetaCF is equipped with a Dynamic Subgraph Sampling that accounts for the dynamic arrival of new users by dynamically generating representative adaptation tasks for existing users. Moreover, to stabilize the adaption procedure that faces the shortage of training samples, MetaCF further optimizes the learning rates for adaption in a fine-grained manner. MetaCF is applicable to any differentiable CF-based models where we demonstrate it on two representative ones, FISM [1] and NGCF [2]. Extensive experiments on three datasets validate the effectiveness of the proposed framework, which significantly outperforms state-of-the-art baselines by a large margin in the cold-start scenario where user-item interactions are limited.
Contextual semantic embeddings based on fine-tuned AraBERT model for Arabic text multi-class categorization

Despite that pre-trained word embedding models have advanced a wide range of natural language processing applications, they ignore the contextual information and meaning within the text. In this paper, we investigate the potential of the pre-trained Arabic BERT (Bidirectional Encoder Representations from Transformers) model to learn universal contextualized sentence representations aiming to showcase its usefulness for Arabic text Multi-class categorization. We propose to exploit the pre-trained AraBERT for contextual text representation learning in two different ways, transfer learning model and feature extractor. On the one hand, we employ the Arabic BERT (AraBERT) model after fine-tuning its parameters on the OSAC datasets to transfer its knowledge for the Arabic text categorization. On the other hand, we inquire into AraBERT performance, as a feature extractor model, by combining it with several classifiers, including CNN, LSTM, Bi-LSTM, MLP, and SVM. Finally, we conduct an exhaustive set of experiments comparing two BERT models, namely AraBERT and multilingual BERT. The findings show that the fine-tuned AraBERT model accomplishes state-of-the-art performance results and attains up to 99% in terms of F1-score and accuracy.
Multi-constrained cooperative path planning of multiple drones for persistent surveillance in urban environments

Abstract Different from the usual surveillance task in which the goal is to achieve complete coverage of the specified area, the cooperative path planning problem of drones for persistent surveillance task is studied in this paper considering multiple constraints of the covered area. The goal is to maximize the combinational coverage area of drones while giving preference to the area that hasn’t been visited beyond a certain time interval. The influence of shooting resolution and blocking of buildings are considered, and the state information of each grid is defined to record the visit information of the ground area. Considering the characteristic of the established model, the multi-constrained cooperative path planning (MCCPP) algorithm is developed. The grids which have not been visited for a long time are received special attentions, and the drone is led to reducing the flight height to cover the gird which has a special requirement on the shooting resolution. The cooperation mechanism among drones is also set to ensure that all the drones can determine the next path point synchronously. An emergency path planning algorithm with the continuous checking strategy is designed for a drone to fly to the specified area and finish a complete coverage of it.
Stability of salp swarm algorithm with random replacement and double adaptive weighting

The salp swarm algorithm is a newly population-based search algorithm. Because the original salp swarm algorithm has low search efficiency and is easy to fall into local optimum, in this paper, we propose an enhanced salp swarm algorithm, which combines two strategies with the original salp swarm algorithm. One is the random replacement strategy, which can replace the current position with the optimal solution position with a certain probability of speeding up the convergence rate. The other tactic is double adaptive weight, which can expand the search scope throughout the early stages and enhance exploitation capability in the later stages. With the cooperation and guidance of the two mechanisms, the algorithm's convergence speed is accelerated, and the exploitation capacity is meritoriously increased. The proposed method's performance is compared with three mainstream meta-heuristics and four advanced algorithms on four necessary test cases. The extensive analysis and recorded results indicate that the proposed method outperforms these algorithms in terms of the accuracy of the solution and convergence speed. Finally, we apply the developed method to four well-known engineering design problems (welded beam design problem; cantilever beam design; I-beam design; and multiple disk clutch brake) to validate the algorithm's effectiveness for some constrained challenge. The results show that our algorithm has significant advantages in solving practical problems with constraints and unknown search spaces.
MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views

Autonomous driving requires the inference of actionable information such as detecting and classifying objects, and determining the drivable space. To this end, we present Multi-View LidarNet (MVLidarNet), a two-stage deep neural network for multi-class object detection and drivable space segmentation using multiple views of a single LiDAR point cloud. The first stage processes the point cloud projected onto a perspective view in order to semantically segment the scene. The second stage then processes the point cloud (along with semantic labels from the first stage) projected onto a bird's eye view, to detect and classify objects. Both stages use an encoder-decoder architecture. We show that our multi-view, multi-stage, multi-class approach is able to detect and classify objects while simultaneously determining the drivable space using a single LiDAR scan as input, in challenging scenes with more than one hundred vehicles and pedestrians at a time. The system operates efficiently at 150 fps on an embedded GPU designed for a self-driving car, including a postprocessing step to maintain identities over time. We show results on both KITTI and a much larger internal dataset, thus demonstrating the method's ability to scale by an order of magnitude.
Unsupervised Learning of Dense Optical Flow, Depth and Egomotion with Event-Based Sensors

We present an unsupervised learning pipeline for dense depth, optical flow and egomotion estimation for autonomous driving applications, using the event-based output of the Dynamic Vision Sensor (DVS) as input. The backbone of our pipeline is a bioinspired encoder-decoder neural network architecture - ECN. To train the pipeline, we introduce a covariance normalization technique which resembles the lateral inhibition mechanism found in animal neural systems.Our work is the first monocular pipeline that generates dense depth and optical flow from sparse event data only, and is able to transfer from day to night scenes without any additional training. The network works in self-supervised mode and has just 150k parameters. We evaluate our pipeline on the MVSEC self driving dataset and present results for depth, optical flow and and egomotion estimation. Thanks to the efficient design, we are able to achieve inference rates of 300 FPS on a single Nvidia 1080Ti GPU. Our experiments demonstrate significant improvements upon works that used deep learning on event data, as well as the ability to perform well during both day and night.
Multidisciplinary digital competencies of pre-service vocational teachers

Abstract Developments of Industry 4.0 require a set of multidisciplinary digital competencies for future vocational teachers, consisting of specific knowledge, motivational aspects, cognitive abilities and skills to fulfill the demands of digitally interconnected work situations. The competence model that is adapted from future work scenarios of vocational apprentices in Industry 4.0 includes attitudes towards digitization and handling of digital devices, information literacy, application of digital security standards, virtual collaboration, digital problem solving as well as a demonstration of reflective judgment of one’s actions in an interconnected and digital environment. Structural equation modeling was used to assess N = 205 pre-service vocational teachers between 18 and 35 years of age. The findings indicate the relationship of the proposed dimensions, measured through external- and self-assessments validate the proposed structure of the multidisciplinary digital competencies. However, attitude towards digitization can predict the self-efficacy of the relevant Multidisciplinary Digital Competencies but not the actual achievement in an external assessed scenario. Nevertheless, this study confirms that self-assessed multidisciplinary digital competencies can predict achievement in an external and qualitative-assessed competence test. Fit indices show an acceptable model conception, the reliability and construct validity of the model were confirmed. Findings suggest that the attitude towards digitization and the application of digital security standards are important, whereas the ability to solve digital problems seems to have a weak relation to the general multidisciplinary digital competencies of pre-service vocational teachers.
2.7 Gb/s Secure Key Generation and Distribution Using Bidirectional Polarization Scrambler in Fiber

A physical-layer secure key generation and distribution (SKGD) scheme in classical fiber channel is proposed and demonstrated using a bidirectional wide-band polarization scrambler (WBPS), to alter rapidly and symmetrically the state of polarization (SOP) for the two counter-propagating optical signals in fiber. Due to the optical channel reciprocity, highly-consistent key bits can be extracted from the correlated SOP fluctuations, while the key secrecy is ensured by the fiber channel uniqueness. A final key generation rate (KGR) of 2.7 Gb/s is demonstrated over 10 km single mode fiber (SMF), where error-free and true randomness of the generated key are achieved after post-processing. The obtained KGR at Gb/s makes a further step towards the implementation of one-time-pad data encryption with perfect secrecy in fiber networks.
Punch-Sketching E-textiles

Tangible toolkits enable individuals to explore concepts through combining components together and taking them apart. The strength and limitation of many e-textile toolkits is that threads hold them in place, and once put together they need destructive methods to take them apart. In this paper, we propose Punch-Sketching e-textiles, a drawing technique that uses a punch needle to iteratively prototype soft circuits. The benefits of this approach is sustainability and reusability where users can easily pull out circuits without damaging the materials or creating waste, while also testing out concepts using the actual threads that will be used in the final prototype. To validate our technique, we ran three studies comparing sewing and punching e-textiles through: 1) Understanding the process with two fiber artists; 2) Exploring the potential with four beginner users; and 3) Utilizing our methods further with 10 occupational therapists. Insights from these three studies include when and how to use each method, toolkit recommendations, considerations for iterative physical prototyping, sustainability, and accessibility.
Tracing the Source of Fake News using a Scalable Blockchain Distributed Network

In the news industry, as well as in social media, fake news detection and identification of news sources has become a central topic of discussion. In the era of digitization, anyone can easily generate or manipulate digital content and publish them on social media websites. On the one hand, these social networking platforms provide ample ease in modern-day communication but on the other hand, using such platforms has posed new challenges to real-world implementation like viral spreading of false/fake information with malicious intentions. In this paper, a naive blockchain and watermarking based social media framework is proposed to control the fake news propagation. We postulate a new blockchain model to mitigate existing challenges in this field. Moreover, the novel solution can help in reducing the spread of fake news by tracing the root or origin of the fake news on social media. Through our experimental results, we show that our blockchain-based solution is able to immediately stream data through a bloXroute server that can propagate data up to 100 times faster than conventional solutions.
Twitter Sentiment Analysis Using Supervised Machine Learning

Sentiment analysis aims to extract opinions, attitudes, as well as emotions from social media sites such as twitter. It has become a popular research area. The primary focus of the conventional way of sentiment analysis is on textual data. Twitter is the most renowned microblogging online networking site in which user posts updates related to different topics in the form of tweets. In this paper, a labeled dataset publicly available on Kaggle is used, and a comprehensive arrangement of pre-processing steps that make the tweets increasingly manageable to normal language handling strategies is structured. Since each example in the dataset is a pair of tweets and sentiment. So, supervised machine learning is used. In addition, sentiment analysis models based on naive Bayes, logistic regression, and support vector machine are proposed. The main intention is to break down sentiments all the more adequately. In twitter sentiment analysis, tweets are classified into positive sentiment and negative sentiment. This can be done using machine learning classifiers. Such classifiers will support a business, political parties, as well as analysts, etc., and so evaluate sentiments about them. By using training, data machine learning techniques correctly classify the tweets. So, this method doesn't require a database of words, and in this manner, machine learning strategies are better and faster to perform sentiment analysis.