Reviewerly/exHarmony huggingface dataset:
Towards secure asynchronous messaging with forward secrecy and mutual authentication

Instant messaging applications like WhatsApp and iMessage have been widely used to exchange public and private information for both individuals and organizations. At the same time, the asynchronicity nature of these applications also introduces various security issues. To this end, considerable efforts have been made to strengthen the security and privacy of these applications, where a major goal is to capture the forward secrecy of asynchronous messaging without sacrificing other reasonable security properties. In this paper, we mainly focus on forward-secure zero round-trip time (0-RTT) key exchange protocols. Such a protocol enables a user to send the encrypted application data along with the first protocol message, and thus is especially suitable for securing asynchronous messaging. Concretely, we introduce a new cryptographic primitive dubbed as puncturable identity-based matchmaking key encapsulation mechanism (PIB-MKEM). It captures the authenticity of ciphertexts, and also allows a receiver to repeatedly update his/her secret key with a puncture algorithm. Based on PIB-MKEM, we further put forth a generic construction of 0-RTT key exchange protocol that enjoys forward secrecy and mutual authentication as well as replay resistance in parallel. We also present a concrete construction of PIB-MKEM, which immediately instantiates the above generic 0-RTT key exchange protocol. Moreover, we implement the proposed PIB-MKEM construction, and provide extensive evaluations to demonstrate its practicability.
A novel model watermarking for protecting generative adversarial network

With the advance of deep learning, it definitely has achieved the unprecedented success in the community of artificial intelligence. However, the issue of the intellectual property (IP) protection towards deep learning model is usually ignored, which largely threats the interests of the model owner. Currently, although a few schemes of model watermarking have been continuously proposed, in order to protect the specific neural network designed for detection or classification task, most of them are hardly directly applicable to generative adversarial networks (GAN). To our knowledge, the GAN model has plays more and more important role in the computer vision, such as image-to-image translation, text-to-image translation, image inpainting and etc., which remarkably improves the capability of image generation. Similarly, the malicious attackers possibly steal a trained GAN model to infringe the IP of the true model owner. To address that challenging issue, it is proposed to establish the framework of model watermarking towards GAN model. In particular, we first establish the trigger set by combining the watermark label with the verification image. Next, the watermarked generator is efficiently trained on the premise of preserving the original model performance. Finally, only relying on the correct watermark label, the synthetic watermark can be successfully triggered by the model owner for IP protection. The extensive experiments have verified the effectiveness and generalization of our designed method, which can easily be applicable to the benchmark GAN models such as WGAN-GP, ProGAN and StyleGAN2. Moreover, our proposed model watermark is robust enough to resist against the mainstream attacks, such as parameter fine-tuning and model pruning.
EACP: An effective automatic channel pruning for neural networks

The large data scale and computational resources required by Convolutional Neural Networks (CNNs) hinder the practical application on mobile devices. However, channel pruning has become one of the most efficient methods for addressing this problem, with many existing researches proving its practicability in the field of model compression. The current channel pruning methods mainly start with the perspective of assessing the importance of channels or manual setting of the evaluation criteria, which requires unnecessary human intervention and shows the lack of certain automaticity. In this paper, an effective automatic channel pruning (EACP) method for neural networks is proposed. Specifically, we adopt the k-means++ method to cluster filters with similar features hierarchically in each convolutional layer, forming an initial compact compression structure. Subsequently, we use an improved social group optimization (SGO) algorithm to iteratively search and optimize the compression process of the post-clustered structure to find the optimal compressed structure. The effectiveness of the proposed approach is tested with respect to three leading CNN models on two image classification datasets. In CIFAR-10, our method reduces the FLOPs of GoogLeNet by 58.10 % and improves the accuracy by 0.20 % compared to the baseline.
Multiple UAVs Path Planning Based on Deep Reinforcement Learning in Communication Denial Environment

In this paper, we propose a C51-Duel-IP (C51 Dueling DQN with Independent Policy) dynamic destination path-planning algorithm to solve the problem of autonomous navigation and avoidance of multiple Unmanned Aerial Vehicles (UAVs) in the communication denial environment. Our proposed algorithm expresses the Q function output by the Dueling network as a Q distribution, which improves the fitting ability of the Q value. We also extend the single-step temporal differential (TD) to the N-step timing differential, which solves the problem of inflexible updates of the single-step temporal differential. More importantly, we use an independent policy to achieve autonomous avoidance and navigation of multiple UAVs without any communication with each other. In the case of communication rejection, the independent policy can achieve the consistency of multiple UAVs and avoid the greedy behavior of UAVs. In multiple-UAV dynamic destination scenarios, our work includes path planning, taking off from different initial positions, and dynamic path planning, taking off from the same initial position. The hardware-in-the-loop (HITL) experiment results show that our C51-Duel-IP algorithm is much more robust and effective than the original Dueling-IP and DQN-IP algorithms in an urban simulation environment. Our independent policy algorithm has similar effects as the shared policy but with the significant advantage of running in a communication denial environment.
GFRX: A New Lightweight Block Cipher for Resource-Constrained IoT Nodes

The study of lightweight block ciphers has been a “hot topic”. As one of the main structures of block ciphers, the Feistel structure has attracted much attention. However, the traditional Feistel structure cipher changes only half of the plaintext in an iterative round, resulting in slow diffusion. Therefore, more encryption rounds are required to ensure security. To address this issue, a new algorithm, GFRX, is proposed, which combines a generalized Feistel structure and ARX (Addition or AND, Rotation, XOR). The GFRX algorithm uses an ARX structure with different non-linear components to deal with all the branches of a generalized Feistel structure so that it can achieve a better diffusion effect in fewer rounds. The results of a security analysis of the GFRX algorithm show that the effective differential attacks do not exceed 19 rounds and that the effective linear attacks do not exceed 13 rounds. Therefore, the GFRX algorithm has an adequate security level for differential and linear analysis. Avalanche test results obtained for the GFRX algorithm show that the GFRX algorithm has strong diffusion and only takes six rounds to meet the avalanche effect. In addition, the GFRX algorithm can achieve different serialization levels depending on different hardware resource requirements and can achieve full serialization, which ensures operational flexibility in resource-constrained environments.
Reshaping healthcare supply chain using chain-of-things technology and key lessons experienced from COVID-19 pandemic

The COVID-19 (Corona virus disease 2019) pandemic continues to slash through the entire humanity on the earth causing an international health crisis and financial uncertainty. The pandemic has formed a colossal disruption in supply chain networks. It has caused piling higher mortality in patients with comorbidities and generated a surging demand for critical care equipment, vaccines, pharmaceuticals, and cutting-edge technologies. Personal protective equipment, masks, ventilators, testing kits, and even commodities required for daily care have been scarce as lockdown and social distancing guidelines have kicked in. Amidst COVID-19, implementing and executing key processes of the healthcare supply chain (HSC) in a secured, trusted, effective, universally manageable, and the traceable way is perplexing owing to the fragile nature of the HSC, which is susceptible to redundant efforts and systemic risks that can lead to adverse impacts on consumer health and safety. Though the crisis shone a harsh light on the cracks and weaknesses of the HSC, it brings some significant insights into how HSC can be made more resilient and how healthcare industries figure out solutions to mitigate disruptions. While there are innumerable experiences learned from the disruption of this crisis, in this paper, five important areas to analyze the most vital and immediate HSC enhancements including building a resilient supply chain, thinking localization, implementing reliable reverse logistics, breaking down extant silos to achieve end-to-end visibility, and redesigning HSC using digitalization are emphasized. This work identifies important features related to CoT and HSC. Also, this study links these lessons to a potential solution through Chain of Things (CoT) technology. CoT technology provides a better way to monitor HSC products by integrating the Internet of Things (IoT) with blockchain networks. However, such an integrated solution should not only focus on the required features and aspects but also on the correlation among different features. The major objective of this study is to reveal the influence path of CoT on smart HSC development. Hence, this study exploits (i) fuzzy set theory to eliminate redundant and unrelated features; (ii) the Decision-Making and Experimental Evaluation Laboratory (DEMATEL) method to handle the intricate correlation among different features. This fuzzy-DEMATEL (F-DEMATEL) model attempts to direct CoT technology towards smart HSC by identifying the most influencing factors and investors are recommended to contribute to the development of application systems. This work also demonstrates how CoT can act a vital role in handling the HSC issues triggered by the pandemic now and in the post-COVID-19 world. Also, this work proposes different CoT design patterns for increasing opportunities in the HSC network and applied them as imperative solutions for major challenges related to traditional HSC networks.
Scalable and interoperable edge-based federated learning in IoT contexts

The analysis of data coming from massively deployed Internet of Things (IoT) devices pave the way to a myriad of intelligent applications in several vertical domains. Federated Learning (FL) has been recently proposed as a prominent solution to train Machine Learning (ML) models directly on top of devices (FL clients) generating data, instead of moving them to centralized servers in charge of training procedures. FL provides inherent benefits, mainly in terms of privacy preservation and reduction of network congestion for datasets exchange. Despite the huge recent research efforts, it still faces challenges for a practical implementation effectively targeting low communication footprint, robustness and interoperability. To fill this gap, in this work we propose a novel comprehensive framework built upon the Message Queue Telemetry Transport (MQTT) publish/subscribe messaging protocol and the Open Mobile Alliance (OMA) Lightweight Machine-to-Machine (LwM2M) semantics to facilitate FL operations and make them more suited to handle IoT devices acting as FL clients. The viability of the proposal as well as its communication efficiency compared to a literature solution are evaluated through a realistic Proof-of-Concept (PoC) under different link settings and for different datasets.
Blockchain and trust-based clustering scheme for the IoV

The advent of the Internet of Vehicles (IoV) leads to the proliferation of services proposed for the users. Its adoption aims mainly to make driving more secure and enhance the users' quality of experience. However, the deployment of multiple technologies that interact with each other brings out big security challenges. Indeed, it enlarges the attack vectors and surface. In the IoV ecosystem, cybersecurity issues can be driven from the security of communication links, security of devices, identity and liability, access control as well as privacy of drivers and vehicles. Trust management is one of the key mechanisms that can enforce the security of an IoV network. In a previous work (Hbaieb et al., 2021), we proposed a blockchain based trust management framework to enhance IoV security. Our framework showed good performance results. However, it was based on a centralized trust based approach. Thus, its performance is decreasing when dealing with effective scalability. Moreover, we did not focus on the energy consumption of the proposed model. In this paper, we propose an extension to enhance the highlighted limitations. For that, we are based on a decentralized trust process. We, also, introduce a clustering mechanism to construct our framework. Moreover, to take into account the quality of service (QoS) of our framework while enforcing security, we define our clustering process based on different parameters such as the trust value, the safety distance, and the energy factor. The evaluation of our proposal shows relevant results in terms of detection performance, communication overhead and runtime properties. The simulation results indicate that the formation of trustworthy clusters using blockchain reinforces the IoV reliability.
MXene‐Nanoflakes‐Enabled All‐Optical Nonlinear Activation Function for On‐Chip Photonic Deep Neural Networks

2D metal carbides and nitrides (MXene) are promising material platforms for on-chip neural networks owing to their nonlinear saturable absorption effect. The localized surface plasmon resonances in metallic MXene nanoflakes may play an important role in enhancing the electromagnetic absorption; however, their contribution is not determined due to the lack of a precise understanding of its localized surface plasmon behavior. Here, a saturable absorber made of MXene thin film and a silicon waveguide with MXene flakes overlayer are developed to perform neuromorphic tasks. The proposed configurations are reconfigurable and can therefore be adjusted for various applications without the need to modify the physical structure of the proposed MXene-based activator configurations via tuning the wavelength of operation. The capability and feasibility of the obtained results of machine-learning applications are confirmed via handwritten digit classification task, with near 99% accuracy. These findings can guide the design of advanced ultrathin saturable absorption materials on a chip for a broad range of applications.
BULB: Lightweight and Automated Load Balancing for Fast Datacenter Networks

Load balancing is essential for datacenter networks. However, prior solutions have significant limitations: they either are oblivious to congestion or involve a daunting and time-consuming parameter-tunning task over their heuristics for achieving good performance. Thus, we ask: is it possible to learn to balance datacenter traffic? While deep reinforcement learning (DRL) sounds like a good answer, we observe that it is too heavyweight due to the long decision-making latency. Therefore, we introduce BULB, a lightweight and automated datacenter load balancer. BULB learns link weights to guide the end-hosts to spread traffic, so as to free the central agent from quick flow-level decision-making. BULB offline trains a DRL agent for optimizing link weights but employs an imitation learning based approach to faithfully translate this agent’s DNN to a decision tree for online deployment. We implement a BULB prototype with a popular machine learning framework and evaluate it extensively in ns-3. The results show that BULB achieves up to 36.6%/56.4%, 19.9%/42.5%, 35.9%/54.8%, and 45.1%/67.7% better average/tail flow completion time than ECMP, CONGA, LetFlow, and Hermes, respectively. Moreover, BULB reduces the decision latency by 175 times while incurring only 2% performance loss after converting the DNN into a decision tree.
Evaluation of the Quality of Practical Teaching of Agricultural Higher Vocational Courses Based on BP Neural Network

Agriculture is the backbone of any developing or developed country that makes any living to survive. To make food available throughout the year, it is necessary to know about agriculture and the work and strategies involved. Hence, agricultural courses have to be introduced to higher education students. Additionally, agriculture-related methods are available in many higher education institutions for longer. However, students and teachers will face difficulties in real-time practical classes during certain challenging circumstances. These situations require the teacher to utilize trending technologies to improve the teaching and learning process and to make it more manageable. In this study, for this process, a novel neural network-based recognition algorithm (NN-RA) is implemented that works similarly to a backpropagation neural network (BP-NN) to provide a practical agriculture course. The proposed BP-NN is compared with the existing NN-RA, I-SC, and I-VDT algorithms based on the data transfer and signal-to-noise ratio. From the results, it can be observed that the proposed BP-NN attains a higher accuracy in data transfer of 99%.
Low-Resource Neural Machine Translation Improvement Using Source-Side Monolingual Data

Despite the many proposals to solve the neural machine translation (NMT) problem of low-resource languages, it continues to be difficult. The issue becomes even more complicated when few resources cover only a single domain. In this paper, we discuss the applicability of a source-side monolingual dataset of low-resource languages to improve the NMT system for such languages. In our experiments, we used Wolaytta–English translation as a low-resource language. We discuss the use of self-learning and fine-tuning approaches to improve the NMT system for Wolaytta–English translation using both authentic and synthetic datasets. The self-learning approach showed +2.7 and +2.4 BLEU score improvements for Wolaytta–English and English–Wolaytta translations, respectively, over the best-performing baseline model. Further fine-tuning the best-performing self-learning model showed +1.2 and +0.6 BLEU score improvements for Wolaytta–English and English–Wolaytta translations, respectively. We reflect on our contributions and plan for the future of this difficult field of study.
Biometric authentication security enhancement under quantum dot light-emitting diode display via fingerprint imaging and temperature sensing

We improved biometric authentication security using dual recognition based on fingerprint image detection and skin-temperature-change sensing under quantum dot light-emitting diode (QLED) displays. QLEDs are more advantageous than organic light-emitting diodes (OLEDs) in terms of the contrast classification of patterns such as those in fingerprint recognition, owing to their narrow full-width-half-maximum. In this work, scattered, transmitted, and reflected light was captured from the top of the QLED, improving the digital luminance by 25%, as compared with that of OLEDs, because the electroluminescence spectra of the QLED were sustained, whereas those of the OLED were distorted by the generated noise peaks. A QLED with eight apertures sized up to tens of micrometers, mimicking the actual wiring structure of commercialized smartphones, was implemented to detect human fingerprints. The QLED using reduced graphene oxide as the temperature sensor detected temperature changes instantaneously upon finger touch, showing a 2% temperature response based on the human body temperature; however, the temperature change was less than 0.1% for spoof fingerprints printed on paper. Thus, this study successfully enhanced biometric authentication security, through fingerprint recognition based on image sensing using an optical system with micrometer-sized apertures and skin-temperature detection under QLED displays.
Named Entity Recognition using CRF with Active Learning Algorithm in English Texts

Various Natural Language Processing (NLP) applications rely on Named Entity Recognition (NER) to help them sift through mountains of unstructured text data and find the information they need. Named Entity Recognition (NER) is the process of assigning labels to words in a text so that they can be sorted into categories. These state-of-the-art models achieve improved results despite limited resources, making language models increasingly valuable in a variety of NLP tasks. The Conditional Random Field and Active Learning Procedure form the basis of a novel Approach to named entity recognition discussed in this article. Following is an algorithmic description of how the AL-CRF model operates: Initially the samples are clustered with K-Means. Samples are used to train the fundamental CRF classifier, which is done by performing stratified sampling on the generated clusters. The following phase involves starting the selection process based on entropy. The training set is expanded to include examples with the greatest entropy values. The CRF classifier is then trained again using with a new training set, and the procedure is repeated. The AL's learning and selection procedure is repeatedly done until the harmonic mean stabilises and model for NER is obtained. The primary benefit of our method is that it is both more efficient and requires less manually marked training samples. Because of this, the procedure may become more reliable and cost-efficient.
Hyperparameter Learning for Deep Learning-Based Recommender Systems

Deep learning (DL)-based recommender system (RS), particularly for its advances in the recent five years, has been startling. It reshapes the architectures of traditional RSs by lifting their limitations in dealing with data sparsity and cold-start issues. Yet, the performance of DL-based RS, like many other DL-based intelligent systems, heavily relies on selecting hyperparameters. Unfortunately, the most common selection approach is still Grid Search that requires numerous computational resources and human efforts. Motivated by this, this paper proposes a general hyperparameter optimization framework, named DE-Opt, which can be applied to most existing DL-based RSs seamlessly. The main idea of DE-Opt is to incorporate differential evolution (DE) into the model training process of a DL-based RS at a layer-wise granularity to simultaneously auto-learn its two key hyperparameters, namely, the learning rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> and the regularization coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula> . As a result, the system performance in terms of recommendation accuracy and computational efficiency is uplifted. Experimental results on three benchmark datasets substantiate that: 1) DE-Opt is compatible with the most recent advancements of DL-based RS to automate their hyperparameter-tuning processes, and 2) DE-Opt excels among its state-of-the-art hyperparameter-optimization competitors in terms of both higher learning performance and lower runtime.