Reviewerly/exHarmony huggingface dataset:
Peer Online Training (POT) as Learning Activity in Computer Security Audit and Risks Management Teaching Module

Teaching and Lecturing Computer Security Audit & Risks Management module is challenging. With the increase demand of high skilled talent in computer security by industries, all subjects at Bachelor Degree Program are required to be up to date and complete with practical skill training. Many technical universities in Malaysia promote Practical Application Oriented Approach (PAO) as mode of delivery in teaching and learning. As we know, an online learning received serious attention due to pandemic Covid 19. Inspired from Professional Training in Cybersecurity, we implemented method call Peer Online Training (POT) as learning activity in teaching Computer Security Audit and Risks Management module. There are ten (10) students were given a task to conduct mini project related to ISMS, risks analysis and Incident Management. The project required students to conduct information searching, practical exercises and industry communications. Students need to utilize their skill in Microsoft Power point, Infographics, and Multimedia applications for developing a Training Contents. Each training video uploaded to Youtube Channel and the link shared among themselves. We also share the Youtube link to five panels from industry for content validations. The panels are from Cybersecurity Malaysia, Finance Officer at Higher Institution, and Inland Revenue ICT Director. All students were asked to participate for physical training and technique called “Ireland Hopping” were applied to improve training contents. The training also required students to conduct self and peer assessment. We observed students’ performance by (1) distributing feedback survey form and (2) personal interview. The results show that 95% students enjoyed Peer Online Training. Their feedback shows that POT increased understanding and improve their skill to explore audit activity. They recommended for more on online practical exercise in future. We concluded that POT able to act as value added tool in learning activity. It creates new insight for students to have better understanding in learning Computer Security Audit & Risk Management.
A Motion Planning Method for Visual Servoing Using Deep Reinforcement Learning in Autonomous Robotic Assembly

Assembly positioning by visual servoing (VS) is a basis for autonomous robotic assembly. In practice, VS control suffers potential stability and convergence problems due to image and physical constraints, e.g., field of view constraints, image local minima, obstacle collisions, and occlusion. Therefore, this article proposes a novel deep reinforcement learning-based hybrid visual servoing (DRL-HVS) controller for motion planning of VS tasks. DRL-HVS controller takes current observed image features and camera pose as inputs, and the core parameters of hybrid VS are dynamically optimized using a deep deterministic policy gradient (DDPG) algorithm to obtain an optimal motion scheme, considering image/physical constraints and robot motion performance. In addition, an adaptive exploration strategy is proposed to further improve the training efficiency by adaptively tuning the exploration noise parameters. In this way, the offline pretrained DRL-HVS controller in the virtual environment, where the DDPG actor–critic network is continuously optimized, can be quickly deployed to a real robot system for real-time control. Experiments based on an eye-in-hand VS system are conducted with a calibrated HIKVISION RGB camera mounted on the end-effector of a GSK-RB03A1 six degree-of-freedom (6-DoF) robot. Basic VS task experiments show that the proposed controller achieves better performance than the existing methods: the servoing time is 24% smaller than that of the five-dimensional VS method, a 100% success rate with the perturbed ranges of the initial position within 25 mm for translation and 20° for rotation, and a 48% efficiency improvement. Moreover, a planetary gear component assembly process case study, where the robot aims to automatically put the gears on the gear shafts, is conducted to demonstrate the applicability of the proposed method in practice.
Threshold Attribute-Based Credentials With Redactable Signature

Threshold attribute-based credentials are suitable for decentralized systems such as blockchains as such systems generally assume that authenticity, confidentiality, and availability can still be guaranteed in the presence of a threshold number of dishonest or faulty nodes. Coconut (NDSS’19) was the first selective disclosure attribute-based credentials scheme supporting threshold issuance. However, it does not support threshold tracing of user identities and threshold revocation of user credentials, which is desired for internal governance such as identity management, data auditing, and accountability. The communication and computation complexities of Coconut for verifying credentials are linear in the number of each user's attributes and thus costly. Addressing these issues, we propose a novel efficient threshold attribute-based anonymous credential scheme. While retaining all the features of Coconut, our scheme supports threshold tracing of user identities and threshold revocation of user credentials, and it significantly reduces the computational and communication complexities of credential verification. In addition, we prove that our scheme enjoys strong security features, including anonymity, blindness, traceability, and non-frameability.
Multispectral Remote Sensing Image Deblurring Using Auxiliary Band Gradient Information

Multispectral remote sensing images (RSI), including hyperspectral and multispectral images, contain adequate information of ground objects and areas and play important roles in environmental monitoring, weather forecasting, urban planning, etc. However, due to many inevitable external effects on the remote sensing pathway, remote sensing images are often degraded by blur. The fields of multispectral remote sensing image deblurring have witnessed great improvements in recent years, including both optimization-based and deep-learning-based methods. However, issues are to be addressed in the remote sensing image deblurring field, such as the incompatibility of general regularizations, lack of spectral correlations for multispectral RSIs, demands of blind deblurring for real world applications, and high costs of computation. To address these problems, we incorporate a novel prior exploiting gradient information similarity between different spectral bands, and we name it auxiliary band gradient information (ABGI) prior. We show that the ABGI prior is applicable to all gradient sparsity regularizations by a simple subtract-then-add step. Specifically, we apply ABGI prior to the patch-wise minimal pixel (PMP) prior based deblurring method, and we also prove that the PMP prior exhibits sparsity for clear natural RSIs. We estimate our method on remote sensing image datasets of different spectral types and geographic resolutions. Compared to other state-of-the-art deblurring methods, our method shows superior performance on both simulated and real world blur.
Unsupervised Text Style Transfer Through Differentiable Back Translation and Rewards

In this paper, we propose an end-to-end system for unsupervised text style transfer (UTST). Prior studies on UTST work on the principle of disentanglement between style and content features, which successfully accomplishes the task of generating style-transferred text. The success of a style transfer system depends on three criteria, viz. Style transfer accuracy, Content preservation of source, and Fluency of the generated text. Generated text by disentanglement-based method achieves better style transfer performance but suffers from the lack of content preservation as the previous works suggest. To develop an all-around solution to all three aspects, we use a reinforcement learning-based training objective that gives rewards to the model for generating fluent style transferred text while preserving the source content. On the modeling aspect, we develop a shared encoder and style-specific decoder architecture which uses the Transformer architecture as a backbone. This modeling choice enables us to frame a differentiable back translation objective aiding better content preservation as shown through a careful ablation study. We conclude this paper with both automatic and human evaluation, showing the superiority of our proposed method on sentiment and formality style transfer tasks. Code is available at https://github.com/newcodevelop/Unsupervised-TST .
RFM-GAN: Robust Feature Matching With GAN-Based Neighborhood Representation for Agricultural Remote Sensing Image Registration

Remote sensing images often encounter various challenges arising from differences in shooting time, location, equipment, sensors, and other factors. These disparities lead to image distortion and insufficient overlap between pairs of images captured at the same location. Consequently, the accuracy of agricultural remote sensing image registration is significantly compromised. This paper proposes a robust feature matching technique called GAN-based neighborhood representation (RFM-GAN) for the intricate registration of satellite images and unmanned aerial vehicle (UAV) images. The RFM-GAN method leverages a neighborhood representation approach based on a generative adversarial network (GAN) with two discriminators. This representation enhances the distinction between true matches (inliers) and false matches (outliers). Additionally, a dissimilarity measure network employing a self-supervised training approach, eliminating the need for manual labeling, is designed to handle the multi-view transformation of satellite and UAV images. The experimental results confirm that RFM-GAN outperforms seven other state-of-the-art methods in terms of satellite image and UAV image processing.
Imperceptible Adversarial Attack With Multigranular Spatiotemporal Attention for Video Action Recognition

In recent years, the application of video Internet of Things (IoT) in various cities and public places has brought unprecedented opportunities to the security field and achieved great success. However, the latest research shows that video recognition models are also vulnerable to adversarial examples, but adversarial examples based on physical attacks are easily detected by humans, making it difficult to pass human review. To address this problem, in this article, we propose to introduce a novel multigranular spatiotemporal attention network (MSANet), which can attack the video action recognition models imperceptibly. Specifically, to exploit video motion information more effectively and to reduce the detectability of attack perturbations, we design a multiplexed spatiotemporal attention module to select and enhance spatial regions and temporal frames at coarse-grained and fine-grained levels, respectively, thus maintaining a certain degree of smoothness while reducing the perturbation size and avoiding attacking overfitting. In addition, our proposed MSANet achieves imperceptible perturbations to video sequences through alternate iterative optimization combined with the PGD attack mechanism. extended experimental results on two different models (e.g., TDN and TSM) and two widely used data sets [HMDB-51 (Kuehne et al., 2011) and UCF-101 (Soomro et al., 2012)], compared to the state-of-the-art model, demonstrate the effectiveness of our devised video action recognition attack approach.
Placement of High Availability Geo-Distributed Data Centers in Emerging Economies

The data center markets in emerging economies are being built at a furious pace. When high availability is required, as it always is in the modern digital economy, the placement of geo-distributed data centers may be influenced by factors such as technician shortage and under-developed infrastructure, both of which are typical in emerging economies. Although the data center availability subject in general has been well studied, it remains unclear how rapid and unbalanced economic development in emerging economies may affect the availability of geo-distributed data centers and their cost of ownership. In this paper, we incorporate the unbalanced availability of infrastructure and technician into the data center placement. The problem is first formulated as a mixed integer nonlinear program (MINLP).To solve this potentially large scale problem, we transform it into a QCQP, capable of handling heterogeneous workloads. The resulting problem can then be efficiently solved by off-the-shelf optimization toolboxes. With real-life data in China, we show how unbalanced development of infrastructure and technician shortage may affect the placement of data centers, and analyze the tradeoff between cost and availability. Our results indicate that technician shortage and unbalanced network infrastructure will lead to increased cost and distinct data center placement strategies.
Generative Sentiment Transfer via Adaptive Masking

Sentiment transfer aims at revising the input text to satisfy a given sentiment polarity while retaining the original semantic content. The nucleus of sentiment transfer lies in precisely separating the sentiment information from the content information. Existing explicit approaches generally identify and mask sentiment tokens simply based on prior linguistic knowledge and manually-defined rules, leading to low generality and undesirable transfer performance. In this paper, we view the positions to be masked as the learnable parameters, and further propose a novel AM-ST model to learn adaptive task-relevant masks based on the attention mechanism. Moreover, a sentiment-aware masked language model is further proposed to fill in the blanks in the masked positions by incorporating both context and sentiment polarity to capture the multi-grained semantics comprehensively. AM-ST is thoroughly evaluated on two popular datasets, and the experimental results demonstrate the superiority of our proposal.
Generalized LRS Estimator for Min-Entropy Estimation

The min-entropy is a widely used metric to quantify the randomness of generated random numbers, which measures the difficulty of guessing the most likely output. It is difficult to accurately estimate the min-entropy of a non-independent and identically distributed (non-IID) source. Hence, NIST Special Publication (SP) 800-90B adopts ten different min-entropy estimators and then conservatively selects the minimum value among ten min-entropy estimates. Among these estimators, the longest repeated substring (LRS) estimator estimates the collision entropy instead of the min-entropy by counting the number of repeated substrings. Since the collision entropy is an upper bound on the min-entropy, the LRS estimator inherently provides <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">overestimated</i> outputs. In this paper, we propose two techniques to estimate the min-entropy of a non-IID source accurately. The first technique resolves the overestimation problem by translating the collision entropy into the min-entropy. Next, we generalize the LRS estimator by adopting the general Rényi entropy instead of the collision entropy (i.e., Rényi entropy of order two). We show that adopting a higher order can reduce the variance of min-entropy estimates. By integrating these techniques, we propose a generalized LRS estimator that effectively resolves the overestimation problem and provides stable min-entropy estimates. Theoretical analysis and empirical results support that the proposed generalized LRS estimator improves the estimation accuracy significantly, which makes it an appealing alternative to the LRS estimator.
Improving Next Location Recommendation Services With Spatial-Temporal Multi-Group Contrastive Learning

Next location recommendation services play a pivotal role in Location-Based Social Networks (LBSNs) due to their ability to provide personalized recommendations of attractive destinations, resulting in substantial benefits for both users and service providers. Recent research indicates that these services are influenced by both sequential and geographical factors. However, we argue that most of these services fail to fully exploit the latent multi-group knowledge of location semantics and user preferences, resulting in suboptimal performance. Therefore, we propose STMGCL, a novel spatial-temporal multi-group contrastive learning-based method to discover intrinsic multi-group information for improving next location recommendation services. Specifically, STMGCL designs Spatial Group Contrastive Learning (SGCL) to extract multiple group knowledge regarding location semantics. Additionally, it develops Temporal Group Contrastive Learning (TGCL) to explore multiple user preference group information through a self-attention based encoder. Finally, we leverage a multi-task learning strategy and a generalized Expectation Maximization (EM) algorithm to ensure that STMGCL is optimized end-to-end with guaranteed convergence. Extensive experiments conducted on four real-world datasets demonstrate the superior performance of STMGCL over baselines.
An Information-Theoretic Secure Group Authentication Scheme for Quantum Key Distribution Networks

Information-theoretic secure (ITS) authentication is an essential part of a quantum key distribution (QKD) system. In the QKD network, the authentication scheme based on the ITS QKD keys pre-shared by the QKD system. However, such a scheme is subject to the limitation that consumes a significant amount of authentication keys when the scale and communication requirements of QKD networks grow. To address the issue, we propose an ITS group authentication scheme for QKD networks that consumes considerably fewer keys. In our scheme, multiple QKD network nodes collaborate and complete the authentication as a group. Compared to the pre-shared keys authentication scheme, our scheme reduces the complexity of authentication key consumption from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O\left ({N^{2}d }\right) $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O\left ({N }\right) $ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N $ </tex-math></inline-formula> is the number of nodes and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d $ </tex-math></inline-formula> is the average path length of the network topology. The security of the proposed scheme is analyzed in detail and simulation experiments are conducted to demonstrate our scheme’s effectiveness for varying networks scales and communication requirements.
Exploiting Phrase Interrelations in Span-level Neural Approaches for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in modern natural language processing concerning the automatic extraction of (aspect phrase, opinion phrase, sentiment polarity) triplets from a given text. Current state-of-the-art methods achieve relatively high results by analyzing all possible spans extracted from a text. Due to a high number of analyzed spans, span-level methods usually apply some kind of pruning operators that interrupt the gradient flow. They also do not analyze interrelations between spans while constructing model output, relying on independent, sequential predictions for candidate triplets. This paper presents a new span-level approach that applies a learnable extractor of spans and a differentiable span selector that enables end2end training. The approach relies on a fully connected pairwise CRF model to capture interrelations between spans while constructing the output. Conducted experiments demonstrated that the proposed approach achieves superior results in terms of F1-score in comparison to other, state-of-the-art ASTE methods.
Knowledge-Enhanced Hierarchical Transformers for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE) aims to extract all potential pairs of emotions and corresponding cause(s) from a given document. Current methods have focused on extracting possible emotion-cause pairs by directly analyzing the given documents on the basis of a large training set. However, there are many hard-matching emotion-cause pairs that require commonsense knowledge to understand. Exploiting only the given documents is insufficient to capture the latent semantics behind these hard-matching emotion-cause pairs, which may downgrade the performance of existing ECPE methods. To fill this gap, we propose a Knowledge-Enhanced Hierarchical Transformers framework for the ECPE task. Specifically, we first inject commonsense knowledge into the given documents to construct the knowledge-enhanced clauses. To incorporate the injected knowledge into the clause representations, we then develop a hierarchical Transformers module that leverages two different types of transformer blocks to encode knowledge-enriched clause representations at both global and local stages. Experimental results show that our method achieves state-of-the-art performance.
Dual-Detector: An Unsupervised Learning Framework for Chinese Spelling Check

The task of Chinese Spelling Check (CSC) is to detect and correct spelling errors in Chinese sentences. Since the scale of labeled CSC training set is quite small, we propose an unsupervised Chinese spelling correction framework based on detectors. Two kinds of detectors: Dec-Err and Dec-Eva, are proposed to leverage the contextual information to detect misspelled characters and evaluate the corrections respectively. Both detectors are fine-tuned with our proposed hybrid mask strategy. Dec-Eva is a transformer encoder based detector, of which we modify the attention connections to reuse the contextual information and parallel evaluate possible corrections. Compared with supervised and unsupervised state-of-the-art methods, experimental studies show that our method achieves competitive results. Further empirical studies reveal the efficiency and flexibility of our method.
Inline Citation Classification Using Peripheral Context and Time-Evolving Augmentation

Citation plays a pivotal role in determining the associations among research articles. It portrays essential information in indicative, supportive, or contrastive studies. The task of inline citation classification aids in extrapolating these relationships; However, existing studies are still immature and demand further scrutiny. Current datasets and methods used for inline citation classification only use citation-marked sentences constraining the model to turn a blind eye to domain knowledge and neighboring contextual sentences. In this paper, we propose a new dataset, named 3Cext, which along with the cited sentences, provides discourse information using the vicinal sentences to analyze the contrasting and entailing relationships as well as domain information. We propose PeriCite, a Transformer-based deep neural network that fuses peripheral sentences and domain knowledge. Our model achieves the state-of-the-art on the 3Cext dataset by $$+0.09$$ F1 against the best baseline. We conduct extensive ablations to analyze the efficacy of the proposed dataset and model fusion methods.