Reviewerly/exHarmony huggingface dataset:
An Efficient Lightweight Privacy-Preserving Mechanism for Industry 4.0 Based on Elliptic Curve Cryptography

The present trend of automation anddata interchange in industrial technology is known as Industry 4.0. Industry 4.0 is altering its next generation of distribution networks by making them more responsive and efficient. There are various issues when implementing Internet of Thing (IoT) devices in an Industry 4.0 scenario, mainly due to reduced IoT nodes or devices with insufficient infrastructure to operate security solutions. As a result, securing the environment will require a lightweight and effective security solution. The authors of this study offer a lightweight and flexible authentication method as part of an access control strategy for secure data exchange in-vehicle networks. Lightweight operations, hash functions, XOR operations, and concatenation are all used in the proposed protocol. The security of this approach is also evaluated using AVISPA. The authentication process between IoT networks and resilience to various security threats will be demonstrated. The experimental analysis shows that the computational cost attained is 7.96 s, and the communication cost attained is 834 bits. Similarly, the resource utilization for this computation is 11%, and the average verification time is 7 ms. The experimental analysis demonstrates that the proposed method is efficient with lower computational cost, communication costs, verification cost, and resource utilization than the existing state-of-the-art approaches
Methods for Automatic Identification of Vehicles in the its System

This paper deals with vehicle identification based on RFID technology, as well as intelligent transport system (ITS) classification, ITS components, classification of electronic identification means, radio frequency identification and the corresponding ISO standards. In the article, RFID technology is selected based on the materials discussed below. The identification methods are very diverse for different fields of application, i.e. each method is applicable to some and not applicable to some. For transport identification, identification technology such as RFID is more suitable. Accordingly, identification methods will be discussed below and emphasis will be placed on RFID technology.
Pairing-free certificateless authenticated encryption with keyword search

Public key authenticated encryption with keyword search (PAEKS) is a cryptographic primitive which ensures privacy of user's sensitive data while preserving the search ability over them. Of particular interest are certificateless PAEKS (CLAEKS) schemes which can further circumvent challenges such as certificate management and key escrow problem. Our goal in this paper is advancing existing literature on certificateless PAEKS schemes in two aspects. First, we notice that published security model of CLAEKS schemes assumes the existence of a single key generation center (KGC) to manage all the keys. Considering multiple KGCs can lead to enhancements in trust-related issues and appears more in line with reality, so we formulate the security model to cover CLAEKS with multiple KGCs. Second, as far as we know, all existing secure CLAEKS schemes are based on the time-consuming bilinear pairing operation which dramatically increases the computational cost. We construct an efficient pairing-free CLAEKS scheme and prove its security in our multiple-KGC security model. Our construction achieves security against keyword guessing attacks performed by both inside and outside adversaries. A comparison between our proposed scheme and other related schemes is also provided to assert the overall superiority of the proposed scheme.
Deep learning and model personalization in sensor-based human activity recognition

Abstract Human activity recognition (HAR) is a line of research whose goal is to design and develop automatic techniques for recognizing activities of daily living (ADLs) using signals from sensors. HAR is an active research filed in response to the ever-increasing need to collect information remotely related to ADLs for diagnostic and therapeutic purposes. Traditionally, HAR used environmental or wearable sensors to acquire signals and relied on traditional machine-learning techniques to classify ADLs. In recent years, HAR is moving towards the use of both wearable devices (such as smartphones or fitness trackers, since they are daily used by people and they include reliable inertial sensors), and deep learning techniques (given the encouraging results obtained in the area of computer vision). One of the major challenges related to HAR is population diversity, which makes difficult traditional machine-learning algorithms to generalize. Recently, researchers successfully attempted to address the problem by proposing techniques based on personalization combined with traditional machine learning. To date, no effort has been directed at investigating the benefits that personalization can bring in deep learning techniques in the HAR domain. The goal of our research is to verify if personalization applied to both traditional and deep learning techniques can lead to better performance than classical approaches (i.e., without personalization). The experiments were conducted on three datasets that are extensively used in the literature and that contain metadata related to the subjects. AdaBoost is the technique chosen for traditional machine learning, while convolutional neural network is the one chosen for deep learning. These techniques have shown to offer good performance. Personalization considers both the physical characteristics of the subjects and the inertial signals generated by the subjects. Results suggest that personalization is most effective when applied to traditional machine-learning techniques rather than to deep learning ones. Moreover, results show that deep learning without personalization performs better than any other methods experimented in the paper in those cases where the number of training samples is high and samples are heterogeneous (i.e., they represent a wider spectrum of the population). This suggests that traditional deep learning can be more effective, provided you have a large and heterogeneous dataset, intrinsically modeling the population diversity in the training process.
A secure energy trading system for electric vehicles in smart communities using blockchain

In this paper a secure energy system is proposed that consists of private and public blockchains for vehicles in sustainable cities and society. The former protects vehicle owners from spatial and temporal information based attacks while the latter provides efficient energy trading in sustainable cities and society. In the proposed system, the dynamic demand based pricing policy for the vehicle owners is proposed using types of vehicles, time of demand and geographical locations. The vehicles’ social welfare and utility are maximized using an optimal scheduling method along with the proposed pricing policy. Also, the vehicle owners’ privacy is protected by applying differential privacy in the proposed consensus energy management algorithm. The numerical analyses show that 89.23% reduction in energy price is achieved as compared to 83.46%, 73.86% and 53.07% for multi-parameter pricing scheme (MPPS), fixed pricing scheme and time-of-use pricing scheme (ToU), respectively. Applying the proposed scheme, the owners can achieve about 81.46% reduction in their operating cost as compared to 80.48%, 69.75% and 68.29% for MPPS, fixed pricing scheme and ToU, respectively. Moreover, the proposed system is 60.32% secure as compared to 39.67% for MPPS system. Furthermore, using less information loss against considerable background knowledge of an attacker, higher privacy protection of vehicles is attained.
SceneFormer: Indoor Scene Generation with Transformers

We address the task of indoor scene generation by generating a sequence of objects, along with their locations and orientations conditioned on a room layout. Large-scale indoor scene datasets allow us to extract patterns from user-designed indoor scenes, and generate new scenes based on these patterns. Existing methods rely on the 2D or 3D appearance of these scenes in addition to object positions, and make assumptions about the possible relations between objects. In contrast, we do not use any appearance information, and implicitly learn object relations using the self-attention mechanism of transformers. We show that our model design leads to faster scene generation with similar or improved levels of realism compared to previous methods. Our method is also flexible, as it can be conditioned not only on the room layout but also on text descriptions of the room, using only the cross-attention mechanism of transformers. Our user study shows that our generated scenes are preferred to the state-of-the-art FastSynth scenes 53.9% and 56.7% of the time for bedroom and living room scenes, respectively. At the same time, we generate a scene in 1.48 seconds on average, 20% faster than FastSynth.
AMCRN: Few-Shot Learning for Automatic Modulation Classification

Deep learning (DL) has been widely applied in automatic modulation classification (AMC), while the superb performance highly depends on high-quality datasets. Motivated by this, the AMC under few-shot conditions is considered in this letter, where a novel network architecture is proposed, namely automatic modulation classification relation network (AMCRN), and verified with the baseline methods. Experimental results state that the accuracy of proposed AMCRN exceeds 90&#x0025; and 10&#x0025; to 50&#x0025; improvements are obtained compared with classical schemes when the signal-to-noise ratio (SNR) is greater than &#x2212;2 dB.
What do pre-trained code models know about code?

Pre-trained models of code built on the transformer architecture have performed well on software engineering (SE) tasks such as predictive code generation, code summarization, among others. However, whether the vector representations from these pre-trained models comprehensively encode characteristics of source code well enough to be applicable to a broad spectrum of downstream tasks remains an open question.One way to investigate this is with diagnostic tasks called probes. In this paper, we construct four probing tasks (probing for surface-level, syntactic, structural, and semantic information) for pre-trained code models. We show how probes can be used to identify whether models are deficient in (understanding) certain code properties, characterize different model layers, and get insight into the model sample-efficiency.We probe four models that vary in their expected knowledge of code properties: BERT (pre-trained on English), CodeBERT and CodeBERTa (pre-trained on source code, and natural language documentation), and GraphCodeBERT (pre-trained on source code with dataflow). While GraphCodeBERT performs more consistently overall, we find that BERT performs surprisingly well on some code tasks, which calls for further investigation.
Elastic Resource Allocation Against Imbalanced Transaction Assignments in Sharding-Based Permissioned Blockchains

This article studies the PBFT-based sharded permissioned blockchain, which executes in either a local datacenter or a rented cloud platform. In such permissioned blockchain, the transaction (TX) assignment strategy could be malicious such that the network shards may possibly receive imbalanced transactions or even bursty-TX injection attacks. An imbalanced transaction assignment brings serious threats to the stability of the sharded blockchain. A stable sharded blockchain can ensure that each shard processes the arrived transactions timely. Since the system stability is closely related to the blockchain throughput, how to maintain a stable sharded blockchain becomes a challenge. To depict the transaction processing in each network shard, we adopt the Lyapunov Optimization framework. Exploiting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">drift-plus-penalty</i> (DPP) technique, we then propose an adaptive resource-allocation algorithm, which can yield the near-optimal solution for each network shard while the shard queues can also be stably maintained. We also rigorously analyze the theoretical boundaries of both the system objective and the queue length of shards. The numerical results show that the proposed algorithm can achieve a better balance between resource consumption and queue stability than other baselines. We particularly evaluate two representative cases of bursty-TX injection attacks, i.e., the continued attacks against all network shards and the drastic attacks against a single network shard. The evaluation results show that the DPP-based algorithm can well alleviate the imbalanced TX assignment, and simultaneously maintain high throughput while consuming fewer resources than other baselines.
CyberChain: Cybertwin Empowered Blockchain for Lightweight and Privacy-Preserving Authentication in Internet of Vehicles

Internet of Vehicles (IoVs) presents promising opportunities for vehicle to everything (V2X) applications, wherein authentication acts as the cornerstone to realize trustworthy vehicular context and to support advanced applications. However, existing authentication schemes mainly depend on centralized servers with both security and privacy issues. In this paper, we propose a CyberTwin (CT) empowered blockchain framework for authentication, namely CyberChain, to reduce both the communication and storage cost while maintaining vehicular privacy. By designing a blockchain system in the cyberspace, we decouple the consensus process from the physical world, so that the operation cost of blockchain can be reduced. A Privacy-Preserving Parallel Pedersen Commitment (P4C) algorithm is designed to protect the privacy of vehicles and accelerate the authentication process. To further enhance the operation efficiency of CyberChain, we propose a Diffused Practical Byzantine Fault Tolerance (DPBFT) mechanism to reach consensus in the cyberspace that can reduce consensus latency. The proposed cyberchain framework and the associated mechanisms are evaluated by qualitative analysis and simulations. The evaluation results demonstrated that the proposed cyberchain based framework significantly improves the authentication performance in terms of authentication latency, privacy, communication overhead and storage cost.
Multi-level knowledge distillation for low-resolution object detection and facial expression recognition

Recently, remarkable object detection and facial expression recognition (FER) approaches have been made by researchers. However, all of these models are trained and tested on high-resolution images without considering that low-resolution images are more common in practical application. Therefore, to relieve this issue, in this paper we aim to propose a knowledge distillation approach to transfer the learned high-resolution features from a teacher network to a simpler structured student network trained on low-resolution inputs. In our approach, instead of transferring knowledge from the same layers of the teacher and student network, we chose multi-level knowledge of the teacher network to supervise single-level output of the student network. Furthermore, we do not directly use the knowledge from the teacher network, instead, before knowledge transfer we concatenate different level features of the teacher network and figure our what kind of information is important and what is redundant and set different weight value to them. Then we use these knowledge with different weights to guide the output of single layer student network to extract abundant features from the low-resolution images. To evaluate the effectiveness of our proposed approach, we apply this approach to two models for object detection and facial expression recognition tasks. Through our experiments, we find that, in object detection task, the CornerNet achieves an accuracy of 40.6% on the original MC COCO dataset, while this index drops dramatically to only 34.2% on the resolution degraded images. By comparison, our proposed model trained by our knowledge distillation approach achieves 35.4% and 33.4% on the original and resolution degraded datasets, respectively. At the same time, compared to CornerNet the number of layers of the proposed network has been reduced by about 60%. Furthermore, in the task of facial expression recognition and image classification, the similar experimental results can also be observed.
A Deep Learning-Based Framework for Phishing Website Detection

Phishing attackers spread phishing links through e-mail, text messages, and social media platforms. They use social engineering skills to trick users into visiting phishing websites and entering crucial personal information. In the end, the stolen personal information is used to defraud the trust of regular websites or financial institutions to obtain illegal benefits. With the development and applications of machine learning technology, many machine learning-based solutions for detecting phishing have been proposed. Some solutions are based on the features extracted by rules, and some of the features need to rely on third-party services, which will cause instability and time-consuming issues in the prediction service. In this paper, we propose a deep learning-based framework for detecting phishing websites. We have implemented the framework as a browser plug-in capable of determining whether there is a phishing risk in real-time when the user visits a web page and gives a warning message. The real-time prediction service combines multiple strategies to improve accuracy, reduce false alarm rates, and reduce calculation time, including whitelist filtering, blacklist interception, and machine learning (ML) prediction. In the ML prediction module, we compared multiple machine learning models using several datasets. From the experimental results, the RNN-GRU model obtained the highest accuracy of 99.18%, demonstrating the feasibility of the proposed solution.
On Multi-Modal Learning of Editing Source Code

In recent years, Neural Machine Translator (NMT) has shown promise in automatically editing source code. Typical NMT based code editor only considers the code that needs to be changed as input and suggests developers with a ranked list of patched code to choose from - where the correct one may not always be at the top of the list. While NMT based code editing systems generate a broad spectrum of plausible patches, the correct one depends on the developers’ requirement and often on the context where the patch is applied. Thus, if developers provide some hints, using natural language, or providing patch context, NMT models can benefit from them.As a proof of concept, in this research, we leverage three modalities of information: edit location, edit code context, commit messages (as a proxy of developers’ hint in natural language) to automatically generate edits with NMT models. To that end, we build Modit, a multi-modal NMT based code editing engine. With in-depth investigation and analysis, we show that developers’ hint as an input modality can narrow the search space for patches and outperform state-of-the-art models to generate correctly patched code in top-1 position.
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization

State-of-the-art abstractive summarization systems often generate hallucinations; i.e., content that is not directly inferable from the source text.Despite being assumed incorrect, we find that much hallucinated content is factual, namely consistent with world knowledge.These factual hallucinations can be beneficial in a summary by providing useful background information.In this work, we propose a novel detection approach that separates factual from non-factual hallucinations of entities.Our method utilizes an entity's prior and posterior probabilities according to pre-trained and finetuned masked language models, respectively.Empirical results suggest that our approach outperforms five baselines and strongly correlates with human judgments.Furthermore, we show that our detector, when used as a reward signal in an off-line reinforcement learning (RL) algorithm, significantly improves the factuality of summaries while maintaining the level of abstractiveness. 1
Critical Challenges to Adopt DevOps Culture in Software Organizations: A Systematic Review

DevOps is a set of practices and a cultural movement that aims to break down barriers between development and operation teams to improve collaboration and communication. Different organizations have embraced DevOps principles due to the massive potential, such as a much shorter time to production, increased reliability and stability. However, despite the widespread adoption of DevOps and its infrastructure, there is a lack of understanding and literature on the key concepts, practices, tools, and challenges associated with implementing DevOps strategies. The main goal of this research paper is to explore and discuss challenges related to DevOps culture and practices. Moreover, it describes how DevOps works in an organization, provides a detailed explanation of DevOps, and investigates the cultural challenges that organizations face when implementing DevOps. The proposed paper reveals ten critical challenges that need to be addressed in adopting the DevOps culture. The challenges are further analyzed on the basis of the various continents. According to the findings, the following critical challenges are considered during the implementation of a DevOps culture: lack of collaboration and communication, Lack of skill and knowledge, complicated infrastructure, Lack of management, Lack of DevOps approach, and trust confidence problems.
Deep learning based recommender system using cross convolutional filters

With the recent development of online transactions, recommender systems have increasingly attracted attention in various domains. The recommender system supports the users’ decision making by recommending items that are more likely to be preferred. Many studies in the field of deep learning-based recommender systems have attempted to capture the complex interactions between users’ and items’ features for accurate recommendation. In this paper, we propose a recommender system based on the convolutional neural network using the outer product matrix of features and cross convolutional filters. The proposed method can deal with the various types of features and capture the meaningful higher-order interactions between users and items, giving greater weight to important features. Moreover, it can alleviate the overfitting problem since the proposed method includes the global average or max pooling instead of the fully connected layers in the structure. Experiments showed that the proposed method performs better than the existing methods, by capturing important interactions and alleviating the overfitting issue.
Hospitality Industry 4.0 and Climate Change

This paper investigates under which conditions implementation of Industry 4.0 in the hospitality sector could help to combat climate change. The paper takes the form of a systematic literature review to examine the main pillars of Industry 4.0 in the hospitality industry and discuss how these technologies could help combat climate change. We propose five conditions under which Industry 4.0 could help to combat climate change. First, in the hospitality industry, increased use of Industry 4.0 technologies induces an increase in energy efficiency and a reduction of GHG. Second, increased use of Industry 4.0 technologies induces a reduction in water consumption and an increase in water use efficiency. Third, increased use of Industry 4.0 technologies induces a reduction in food waste. Fourth, increased use of Industry 4.0 technologies can promote Circular Hospitality 4.0. Fifth, increased use of Industry 4.0 technologies helps to reduce transport and travel. Hospitality Industry 4.0 technologies offer new opportunities for enhancing sustainable development and reducing GHG emissions through the use of environmentally friendly approaches to achieve the Paris Agreement objectives.