Reviewerly/exHarmony huggingface dataset:
Self-Attention Graph Residual Convolutional Networks for Event Detection with dependency relations

Event detection (ED) task aims to classify events by identifying key event trigger words embedded in a piece of text.Previous research have proved the validity of fusing syntactic dependency relations into Graph Convolutional Networks(GCN).While existing GCN-based methods explore latent node-to-node dependency relations according to a stationary adjacency tensor, an attention-based dynamic tensor, which can pay much attention to the key node like event trigger or its neighboring nodes, has not been developed.Simultaneously, suffering from the phenomenon of graph information vanishing caused by the symmetric adjacency tensor, existing GCN models can not achieve higher overall performance.In this paper, we propose a novel model Self-Attention Graph Residual Convolution Networks (SA-GRCN) to mine node-to-node latent dependency relations via self-attention mechanism and introduce Graph Residual Network (GRes-Net) to solve graph information vanishing problem.Specifically, a self-attention module is constructed to generate an attention tensor, representing the dependency attention scores of all words in the sentence.Furthermore, a graph residual term is added to the baseline SA-GCN to construct a GResNet.Considering the syntactically connection of the network input, we initialize the raw adjacency tensor without processed by the self-attention module as the residual term.We conduct experiments on the ACE2005 dataset and the results show significant improvement over competitive baseline methods.
The Dimensions of Transformational Leadership and Its Organizational Effects in Public Universities in Saudi Arabia: A Systematic Review

Organizations are dynamic entities, such that they are constantly developing and changing. As such, these entities require leadership capable of managing transformations. Transformational leadership is an effective leadership model that focuses on adapting to existing environmental circumstances whether through internal information, human and monetary resources, or different external variables. This study aims to highlight the characteristics of transformational leadership and their effects on the public universities in Saudi particularly, this study enlightens the urgent need to test the behavioral intentions in the public universities in Saudi Arabia. To achieve this goal, many major databases were used. The period of the study was set from 2011 to 2020 as the topic of the study is the current transformational leadership within the public universities in Saudi Arabia. There was a total of 47,400,000 studies on transformational leadership on Google, however, only 22 studies were reviewed which were entirely conducted in Saudi Arabian Universities. The results demonstrated that in the public universities in Saudi, transformational leadership is practiced across four dimensions: ideal effect, inspirational motivation, intellectual stimulation, and individual consideration. The documentary research approach was used to review the most recent aspects of administrative literature on the theory of transformational leadership and its organizational outcomes. In addition, NVIVO 11 was used to make a thematic analysis of the linkages between transformational leadership and organizational outcomes: organizational commitment, knowledge management practices, morale, employee empowerment, level of job satisfaction, administrative creativity, organizational citizenship behavior, and the level of transformation toward quality and job enrichment. Therefore, behavioral tendencies such as organizational commitment, empowerment, job satisfaction, and knowledge management practices have been studied more interestingly and critically. Furthermore, these behavioral aspects need to be explored more in public universities in Saudi Arabia.
Novel phasianidae inspired peafowl (Pavo muticus/cristatus) optimization algorithm: Design, evaluation, and SOFC models parameter estimation

This paper develops a novel peafowl (Pavo muticus/cristatus) optimization algorithm (POA), which contains its design, evaluation, and application in solid oxide fuel cell (SOFC) parameter estimation. POA mainly replicates the courtship, foraging, and chasing behaviors of peafowls swarm, in which three types of peafowls, i.e., peacocks, peahens, and peafowl cubs are employed to mimic the dynamic swarm behaviors and hierarchy during food searching. Particularly, effective and efficient exploratory and exploitative searching operators are implemented, i.e., food searching and unique rotation dancing behaviors of peacocks, as well as adaptive searching and approaching mechanism of peahens and peafowl. Two widely applied SOFC models, i.e., electrochemical model and steady-state model are adopted for validation under different operation conditions. Simulation results demonstrate that POA outperforms its competitors, e.g., particle swarm optimization (PSO), grey wolf optimization (GWO), ant lion optimization (ALO), and dragonfly algorithm (DA). For instance, under the validation on 79 cells based stack of electrochemical model, RMSE obtained by POA is only 0.42%, 0.27%, 2.05%, and 3.99% to that of ALO, DA, GWO, and PSO, respectively. In addition, 23 standard benchmark functions are used for analysis, experimental results show that POA can effectively explore desirable searching areas and locate the global optimal solutions. The source code of POA is publicly availabe at https://www.mathworks.com/matlabcentral/fileexchange/102809-peafowl-pavo-muticus-cristatus-optimization-algorithm.
Efficient Homomorphic Encryption Accelerator With Integrated PRNG Using Low-Cost FPGA

With recent development in internet speed and reliability, cloud computing has become a more reliable solution for the user. In many cases where data privacy is critical, fully homomorphic encryption (FHE) can be a security solution for securing cloud computing. FHE enables computation on encrypted data, hence it ensures data privacy in case of cloud computing. One popular scheme of FHE is the BFV homomorphic encryption scheme, which is based on ring learning with error (RLWE) computation. The BFV scheme uses ring polynomials as the main object, hence its encryption, decryption, and evaluation require high-degree polynomial multiplication. In this paper, we present comprehensive design and implementation of a hardware architecture to accelerate encryption and decryption in BFV scheme. Our accelerator uses convolution approach for calculating a polynomial multiplication. To implement the convolution, we use a systolic array to calculate polynomial convolution followed by a simple delayed subtraction to calculate polynomial modulo reduction inside our accelerator’s core. Moreover, we use a built-in Gaussian pseudo-random number generator (PRNG) to generate Gaussian noise in the encryption operations. Finally, we implement the 1024 degrees BFV accelerator on the Xilinx PYNQ Z1 board and compare the encryption and decryption performances to other methods as well as a software implementation on Intel Core i7 with 8GB memory. Experimental results show that our accelerator outperforms the clock cycles of other methods with the same polynomial degrees 1024 up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$22\times $ </tex-math></inline-formula> . Moreover, our proposed Gaussian PRNG has better <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> correlation compared to the rotation-only-based PRNG. Finally, our accelerator accelerates up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9\times $ </tex-math></inline-formula> for encryption and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.5\times $ </tex-math></inline-formula> for decryption as well as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.8\times $ </tex-math></inline-formula> for overall compared to Microsoft SEAL on Intel Core i7 processor with 8GB memory. The proposed design is scalable for higher degrees polynomial multiplication and useful for security technology such as high-speed secure cloud computing, blind computing, and secure communication.
Agile Approaches for Cybersecurity Systems, IoT and Intelligent Transportation

To adapt to the rapidly increasing vulnerabilities in software products and cyber threats that exploit them, security professionals are actively working with software developers to produce more secure systems. In software development, agile methods are increasingly adopted in critical software projects where security risks are prominent challenges. This adoption stems from the fact that agile methods are highly iterative and support delivering services and products in smaller batches which allows security professionals to seamlessly integrate software development security activities with agile methodologies. In addition, the iterative nature of agile software development encourages frequent inspections, tests, and patching of software systems to mitigate cybersecurity risks and vulnerabilities. Considering the massive growth of the Internet of Things (IoT) and Intelligent Transportation Systems (ITS) products, the challenge of software development while addressing the security and safety concerns of these devices will continue to increase. This paper presents a comprehensive and detailed review of agile software development in the context of IoT, ITS, and their cybersecurity and risk challenges. Furthermore, we provide a systematic comparison of the reviewed literature based on a set of defined criteria. Finally, we provide a broader outlook and an outline for designing future security-enhanced agile software development solutions for IoT and ITS systems.
Quantum communication with time-bin entanglement over a wavelength-multiplexed fiber network

In a quantum network involving multiple communicating parties, an important goal is to establish high-quality pairwise entanglement among the users without introducing multiple entangled-photon sources which would necessarily complicate the overall network setup. Moreover, it is preferable that the pairwise entanglement of photons is in the time-bin degree of freedom as the photonic time-bin qubit is ideally suited for fiber-optic distribution. Here, we report an experimental demonstration of a field-deployable quantum communication network involving multiple users, all of whom share pairwise entanglement in the time-bin degree of freedom of photons. In particular, by utilizing a single spontaneous-parametric down-conversion source which produces a broadband pair of photons and the wavelength-division demultiplexing/multiplexing technology, all the communicating parties within the network are always simultaneously ready for quantum communication. To further demonstrate the practical feasibility of a quantum network with time-bin entanglement over a wavelength-multiplexed fiber network, we demonstrate entangled-photon quantum key distribution with three users, each separated by 60 km of optical fibers.
Simultaneous vehicle and lane detection via MobileNetV3 in car following scene

Aiming at vehicle and lane detections on road scene, this paper proposes a vehicle and lane line joint detection method suitable for car following scenes. This method uses the codec structure and multi-task ideas, shares the feature extraction network and feature enhancement and fusion module. Both ASPP (Atrous Spatial Pyramid Pooling) and FPN (Feature Pyramid Networks) are employed to improve the feature extraction ability and real-time of MobileNetV3, the attention mechanism CBAM (Convolutional Block Attention Module) is introduced into YOLOv4, an asymmetric network architecture of "more encoding-less decoding" is designed for semantic pixel-wise segmentation network. The proposed model employed improved MobileNetV3 as feature ex-traction block, and the YOLOv4-CBAM and Asymmetric SegNet as branches to detect vehicles and lane lines, respectively. The model is trained and tested on the BDD100K data set, and is also tested on the KITTI data set and Chongqing road images, and focuses on the detection effect in the car following scene. The experimental results show that the proposed model surpasses the YOLOv4 by a large margin of +1.1 AP50, +0.9 Recall, +0.7 F1 and +0.3 Precision, and surpasses the SegNet by a large margin of +1.2 IoU on BDD100k. At the same time, the detection speed is 1.7 times and 3.2 times of YOLOv4 and SegNet, respectively. It fully proves the feasibility and effectiveness of the improved method.
Stochastic Triad Topology Based Particle Swarm Optimization for Global Numerical Optimization

Particle swarm optimization (PSO) has exhibited well-known feasibility in problem optimization. However, its optimization performance still encounters challenges when confronted with complicated optimization problems with many local areas. In PSO, the interaction among particles and utilization of the communication information play crucial roles in improving the learning effectiveness and learning diversity of particles. To promote the communication effectiveness among particles, this paper proposes a stochastic triad topology to allow each particle to communicate with two random ones in the swarm via their personal best positions. Then, unlike existing studies that employ the personal best positions of the updated particle and the neighboring best position of the topology to direct its update, this paper adopts the best one and the mean position of the three personal best positions in the associated triad topology as the two guiding exemplars to direct the update of each particle. To further promote the interaction diversity among particles, an archive is maintained to store the obsolete personal best positions of particles and is then used to interact with particles in the triad topology. To enhance the chance of escaping from local regions, a random restart strategy is probabilistically triggered to introduce initialized solutions to the archive. To alleviate sensitivity to parameters, dynamic adjustment strategies are designed to dynamically adjust the associated parameter settings during the evolution. Integrating the above mechanism, a stochastic triad topology-based PSO (STTPSO) is developed to effectively search complex solution space. With the above techniques, the learning diversity and learning effectiveness of particles are largely promoted and thus the developed STTPSO is expected to explore and exploit the solution space appropriately to find high-quality solutions. Extensive experiments conducted on the commonly used CEC 2017 benchmark problem set with different dimension sizes substantiate that the proposed STTPSO achieves highly competitive or even much better performance than state-of-the-art and representative PSO variants.
Generation of Schrödinger cat states with Wigner negativity using a continuous-wave low-loss waveguide optical parametric amplifier

Continuous-wave (CW) squeezed light is used in the generation of various optical quantum states, and thus is a fundamental resource of fault-tolerant universal quantum computation using optical continuous variables. To realize a practical quantum computer, a waveguide optical parametric amplifier (OPA) is an attractive CW squeezed light source in terms of its THz-order bandwidth and suitability for modularization. The usages of a waveguide OPA in quantum applications thus far, however, are limited due to the difficulty of the generation of the squeezed light with a high purity. In this paper, we report the first observation of Wigner negativity of the states generated by a heralding method using a waveguide OPA. We generate Schrödinger cat states at the wavelength of 1545 nm with Wigner negativity using a quasi-single-mode ZnO-doped periodically poled LiNbO3 waveguide module we developed. Wigner negativity is regarded as an important indicator of the usefulness of the quantum states as it is essential in the fault-tolerant universal quantum computation. Our result shows that our waveguide OPA can be used in wide range of quantum applications leading to a THz-clock optical quantum computer.
The Effects of Virtual Reality-Assisted Language Learning: A Meta-Analysis

Existing literature reflects that VR technology is widely used in language learning settings. Although many studies have identified the multiple benefits and affordance of Virtual Reality (VR) technologies in language learning, most studies are qualitative studies that do not provide substantial evidence to investigate the impact of this technology on language learning. To this end, this study conducted a meta-analysis of 21 quantitative studies with 1144 participants published between 2010 and 2021. The study’s main purpose was to examine the effects of VR on students’ language learning academic performance, including linguistic gains and affective gains. The results indicated that VR-assisted language learning had a medium effect on the linguistic gains (Hedges’ g = 0.662, 95% CI [0.398–0.925], p &lt; 0.001) and affective gains (Hedges’ g = 0.570, 95% CI [0.309–0.831], p &lt; 0.001) of students compared to non-VR conditions, respectively. Furthermore, the study further analyzed the impact of several moderator variables such as education levels, hardware types, language skills, target language, and L1/L2 on language learning gains. The research indicates that VR technology has a great potential to improve language learning as an educational resource and provides suggestions for further research and practice on the use of VR-assisted language learning.
Privacy-preserving COVID-19 contact tracing solution based on blockchain

The COVID-19 pandemic has severely affected daily life and caused a great loss to the global economy. Due to the very urgent need for identifying close contacts of confirmed patients in the current situation, the development of automated contact tracing app for smart devices has attracted more attention all over the world. Compared with expensive manual tracing approach, automated contact tracing apps can offer fast and precise tracing service, however, over-pursing high efficiency would lead to the privacy-leaking issue for app users. By combing with the benign properties (e.g., anonymity, decentralization, and traceability) of blockchain, we propose an efficient privacy-preserving solution in automated tracing scenario. Our main technique is a combination of non-interactive zero-knowledge proof and multi-signature with public key aggregation. By means of aggregating multiple signatures from different contacts at the mutual commitment phase, we only need fewer zero-knowledge proofs to complete the task of identifying contacts. It inherently leads to the benefits of saving storage and consuming less time for running verification algorithm on blockchain. Furthermore, we perform an experimental comparison by timing the execution of signature verification with and without aggregate signature, respectively. It shows that our solution can actually preserve the full-fledged privacy protection property with a lower computational cost.
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning

Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data. Recent works demonstrate that information exchanged during FL is subject to gradient-based privacy attacks and, consequently, a variety of privacy-preserving methods have been adopted to thwart such attacks. However, these defensive methods either introduce orders of magnitudes more computational and communication overheads (e.g., with homomorphic encryption) or incur substantial model performance losses in terms of prediction accuracy (e.g., with differential privacy). In this work, we propose $\textsc{FedCG}$, a novel federated learning method that leverages conditional generative adversarial networks to achieve high-level privacy protection while still maintaining competitive model performance. $\textsc{FedCG}$ decomposes each client's local network into a private extractor and a public classifier and keeps the extractor local to protect privacy. Instead of exposing extractors, $\textsc{FedCG}$ shares clients' generators with the server for aggregating clients' shared knowledge aiming to enhance the performance of each client's local networks. Extensive experiments demonstrate that $\textsc{FedCG}$ can achieve competitive model performance compared with FL baselines, and privacy analysis shows that $\textsc{FedCG}$ has a high-level privacy-preserving capability.
Contrast and Generation Make BART a Good Dialogue Emotion Recognizer

In dialogue systems, utterances with similar semantics may have distinctive emotions under different contexts. Therefore, modeling long-range contextual emotional relationships with speaker dependency plays a crucial part in dialogue emotion recognition. Meanwhile, distinguishing the different emotion categories is non-trivial since they usually have semantically similar sentiments. To this end, we adopt supervised contrastive learning to make different emotions mutually exclusive to identify similar emotions better. Meanwhile, we utilize an auxiliary response generation task to enhance the model's ability of handling context information, thereby forcing the model to recognize emotions with similar semantics in diverse contexts. To achieve these objectives, we use the pre-trained encoder-decoder model BART as our backbone model since it is very suitable for both understanding and generation tasks. The experiments on four datasets demonstrate that our proposed model obtains significantly more favorable results than the state-of-the-art model in dialogue emotion recognition. The ablation study further demonstrates the effectiveness of supervised contrastive loss and generative loss.
Multi-Chain Collaboration-Based Information Management and Control for the Rice Supply Chain

The issue of food quality and safety is a major concern. Rice is considered one of the three staple foods. Rice quality and safety problems have occurred frequently, which seriously affect human health. The rice supply chain is characterized by complex links, discrete data, and numerous types of hazardous substances. Strengthening the information management and control capabilities of the rice supply chain is an important means to ensure the quality and safety of rice. Based on multi-chain collaboration, we have conducted research on information management and control of the rice supply chain. First, a multi-chain collaborative model of “blockchain + sub-chain” is designed. Based on this model, the following four mechanisms are designed: a trusted chain mechanism, a multi-level sub-chain encryption mechanism, a trusted supervision mechanism, and a hierarchical consensus mechanism. These mechanisms jointly serve the multi-chain collaborative management and control of the rice supply chain information. Secondly, smart contracts and operating procedures are designed, and a comparative analysis of them is executed. Finally, the design and implementation of the prototype system is carried out, and an example is verified and analyzed in a grain enterprise. Results show that this model serves the information supervision of the rice supply chain by studying the multi-chain collaboration. The study solves the real-time data interaction problem between each link of the rice supply chain. The credible management of information and control of the rice supply chain is accomplished. This study applies new information technology to the coordination and resource sharing of the food supply chain and provides ideas for the digital transformation of the food industry.