Reviewerly/exHarmony huggingface dataset:
Using Genetic Algorithm in Inner Product to Resist Modular Exponentiation From Higher Order DPA Attacks

Evolutionary computation techniques have always provided fascinating results in all the fields of science and engineering. However in the area of computer security, their contribution has been comparatively very less. More specifically if we consider the side-channel attacks, use of these nature based techniques have been very nominal. Therefore, we proposed a secure protocol in this paper to combat against the Higher Order Differential Power Analysis attacks on modular exponentiation based cryptosystems using one of the popular evolutionary computation techniques. The proposed work first uses Genetic Algorithm for splitting the huge exponent within the modular exponentiation into multiple non-uniform shares. Then, this shares are randomly chosen for computing individual modular exponentiation with the help of nearest neighbor algorithm. Using Genetic Algorithm, our proposed protocol can generate reasonable number of shares which exposes secret exponent at the least. As a result, it provides significant resistance to Higher Order Differential Power Analysis attacks. Moreover, randomization in computing individual modular exponentiation secures the cryptosystem from generic power analysis attacks like SPA and DPA.
Deep Learning Techniques for Legal Text Summarization

Summarizing Legal Text data is a significant problem due to the extensive length and complexity involved in analyzing it. The advent of deep neural networks and their demanding application in Natural Language Processing(NLP) paves the way to conquer the legal domain text data. This paper proposes a systematic comparison of various deep learning strategies applied in summarizing Legal Texts. We begin the study with the introduction and evolution of different deep learning models used in text summarization. Through the work, we have identified the importance of various preparation mechanisms within deep learning to enhance the legal text summarization process. The work also focuses on the review of existing deep learning models used recently in the legal domain for different types of summarization. Models employing the sequence to sequence neural network architecture has gained much importance. This architecture progressed with transfer learning to fine-tune the state of the art pre-trained language models, leading to better accuracy in summarization.
Factorisation-Based Image Labelling

Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based labell propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labelling. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts.
Hand Gesture Recognition with Augmented Reality and Leap Motion Controller

The use of hand gestures is one of the commonly used communication approaches in human daily life, especially for the deaf and dumb. Hand gesture recognition can be adopted in human-computer interaction for converting hand gestures into words or sentences. Unfortunately, the same gesture may have diverse meanings in different countries. With the aim of eliminating the communication barriers between hearing-impaired communities and the general people, an efficient interaction user interface created with the augmented reality technique and leap motion controller for hand gesture recognition and translation is proposed in this paper. Five hand gestures captured by a leap motion controller were used for learning and recognizing through machine learning methodologies, including Support Vector Machine, K-Nearest Neighbor, Convolutional Neural Network, Deep Neural Network and Decision Tree. The experimental results from different classifiers reveal the practicability of employing hand gesture recognition in text translation. The hand gesture recognition system should be capable of reducing the communication gap between hearing disabilities and the public so as to avoid deaf and mute people being isolated from society.
AUE-Net: Automated Generation of Ultrasound Elastography Using Generative Adversarial Network

Ultrasonography is recommended as the first choice for evaluation of thyroid nodules, however, conventional ultrasound features may not be able to adequately predict malignancy. Ultrasound elastography, adjunct to conventional B-mode ultrasound, can effectively improve the diagnostic accuracy of thyroid nodules. However, this technology requires professional elastography equipment and experienced physicians.in the field of computational medicine, Generative Adversarial Networks (GANs) were proven to be a powerful tool for generating high-quality images. This work therefore utilizes GANs to generate ultrasound elastography images.this paper proposes a new automated generation method of ultrasound elastography (AUE-net) to generate elastography images from conventional ultrasound images. The AUE-net was based on the U-Net architecture and optimized by attention modules and feature residual blocks, which could improve the adaptability of feature extraction for nodules of different sizes. The additional color loss function was used to balance color distribution. In this network, we first attempted to extract the tissue features of the ultrasound image in the latent space, then converted the attributes by modeling the strain, and finally reconstructed them into the corresponding elastography image.a total of 726 thyroid ultrasound elastography images with corresponding conventional images from 397 patients were obtained between 2019 and 2021 as the dataset (646 in training set and 80 in testing set). The mean rating accuracy of the AUE-net generated elastography images by ultrasound specialists was 84.38%. Compared with that of the existing models in the visual aspect, the presented model generated relatively higher quality elastography images.the AUE-net generated ultrasound elastography images showed natural appearance and retained tissue information. Accordingly, it seems that B-mode ultrasound harbors information that can link to tissue elasticity. This study may pave the way to generate ultrasound elastography images readily without the need for professional equipment.
ChaCha20-in-Memory for Side-Channel Resistance in IoT Edge-Node Devices

IoT edge devices process the data collected, which can contain sensitive information related to the user. It is crucial to incorporate robust encryption algorithms considering the resource and power budget of these devices. In this paper, we present a power-based SCA-resistant implementation of the ChaCha20 encryption algorithm for low-end devices by utilizing memory arrays. The 10T SRAM-based implementation performs simple operations (like NAND, NOR, XOR) on the bitlines and other operations like addition/subtraction, shifting, rotation on custom-designed in-memory elements tightly coupled to sense amplifiers (SA). The design is verified for multiple test vectors to generate power consumption signatures. Welch’s t-test is performed on these signatures to demonstrate that the design is highly resistant to power-based SCA. The proposed implementation of ChaCha20 runs at 250MHz at a 1.2V supply, in 65nm Low Standby Power (LSTP) technology, achieving a speedup of around 7 times in terms of execution time compared to the ARM Cortex A9 processor.
Optical encryption technology based on spiral phase coherent superposition and vector beam generation system

A new optical encryption technology based on spiral structured phase coherent superposition and vector beam generation system is proposed. Firstly, the original image is scrambled by Arnold transform, and the scrambled image is encoded into two phase plates by using the spiral structured phase plates and the principle of coherent superposition. Then these two phases are brought into the vector beam generation system, and Stokes parameters of vector beam are recorded on the output plane to complete the image encryption. The original image can be recovered with the correct keys when decrypted. The system has good key sensitivity and resistance to occlusion attacks. The simulation results verified the correctness and effectiveness of the method.
Bridging Community, History, and Culture in Personal Informatics Tools

A healthy diet and increased physical activity are essential for reducing the prevalence of cardiovascular disease and related deaths, a worldwide public health concern that disproportionately affects Black American communities. Still, Black Americans can face unique challenges meeting dietary and physical activity requirements due to inequities in access and quality of care, environmental and local factors, and difficulties in changing individual health behaviors. Personal informatics and self-tracking tools are one way of increasing awareness of health behaviors to motivate behavior change. However, there are still gaps in knowledge about what encourages different users to engage with personal informatics tools over time, particularly when used in collaborative, community-health settings. This paper contributes a nuanced understanding of fifteen participants' reasons for engaging in an existing community-based health education and behavior change program that combines collaborative self-tracking with culturally relevant content and social engagement to motivate heart-healthy behaviors. We illustrate participants' positive and negative experiences engaging in self-tracking and collaborative tasks during the program. We also discuss how participants envision that integrating technology might support or hinder participant engagement and the work of deploying community-based public health interventions. Finally, we discuss design implications for culturally informed, community-based personal informatics tools that engage Black American's in heart-healthy activities.
Does Social Presence Increase Perceived Competence?

Conversational agents (CA) have drawn increasing interest from HCI research. They have become popular in different aspects of our lives, for example, in the form of chatbots as the primary point of contact when interacting with an insurance company online. Additionally, CA find their way into collaborative settings in education, at work, or financial advisory. Researchers and practitioners are searching for ways to enhance the customer's experience in service encounters by deploying CA. Since competence is an important treat of a financial advisor, they only accept CA in their interaction with clients if it does not harm their impression on the client. However, we do not know how the social presence of the CA affects this perceived competence. We explore this by evaluating three prototypes with different social presences. For this, we conducted a video-based online survey. In contrast to prior studies focusing on single human-computer interaction, our study explores CA in a dyadic setting of two humans and one CA. First, our results support the Computers-Are-Social-Actors paradigm as the CA with a strong social presence was perceived as more competent than the other two designs. Second, our data show a positive correlation between CA's and advisor's competence. This implies a positive impact of the CA on the service encounter as the CA and advisor can be seen as a competent team.
Efficient and Privacy-Preserving Search Over Edge–Cloud Collaborative Entity in IoT

Limited by the storage and computing capacity of Internet of Things (IoT) devices, outsourcing encrypted entity data has become a prevalent trend. The existing IoT entity search methods lack the integration and utilization of both edge and cloud resources and the protection of user privacy. Besides, the traditional searchable encryption mode is inapplicable to state-time-varying entities in IoT. Therefore, in this article, an edge–cloud collaborative entity search method with privacy protection in IoT is proposed, fusing the advantages of edge and cloud resources to fulfill users’ needs for real-time search and privacy protection. Specifically, a secure search architecture and search method for edge–cloud collaboration is designed to support various needs of users, such as real-time search and global search. Then, an adaptive discrimination method for similar interested entities through attribute analysis and feature extraction is proposed to construct attribute-distinguished encrypted index and query vector groups, enabling efficient entity search meanwhile ensuring fast index update. Simulation results demonstrate that the proposed method with privacy protection can effectively improve the efficiency of entity search in IoT while safeguarding user privacy.
Spinning words as disguise: Shady services for ethical research?

Ethical researchers who want to quote public user-generated content without further exposing these sources have little guidance as to how to disguise quotes. Reagle (2021b) showed that researchers’ attempts to disguise phrases on Reddit are often haphazard and ineffective. Are there tools that can help? Automated word spinners, used to generate reams of ad-laden content, seem suited to the task. We select 10 quotations from fictional posts on r/AmItheButtface and “spin” them using Spin Rewriter and WordAi. We review the usability of the services and then (1) search for their spins on Google; and, (2) ask human subjects (N=19) to judge them for fidelity. Participants also disguise three of those phrases and these are assessed for efficacy and the tactics employed. We recommend that researchers disguise their prose by substituting novel words (i.e., swapping infrequently occurring words, such as “toxic” with “radioactive”) and rearranging elements of sentence structure. The practice of testing spins, however, remains essential even when using good tactics; a Python script is provided to facilitate such testing.
Adjacent Feature Propagation Network (AFPNet) for Real-Time Semantic Segmentation

With the development of deep learning, semantic segmentation has received considerable attention within the robotics community. For semantic segmentation to be applied to mobile robots or autonomous vehicles, real-time processing is essential. In this article, a new real-time semantic segmentation network, called the adjacent feature propagation network (AFPNet), is proposed to achieve high performance and fast inference. AFPNet executes in real time on a commercial embedded GPU. The network includes two new modules. The local memory module (LMM) is the first; it improves the upsampling accuracy by propagating the high-level features to the adjacent grids. The cascaded pyramid pooling module (CPPM) is the second; it reduces computational time by changing the structure of the pyramid pooling module. Using these two modules, the proposed AFPNet achieved 76.4% mean intersection-over-union on the Cityscapes test dataset, outperforming other real-time semantic segmentation networks. Furthermore, AFPNet was successfully deployed on an embedded board Jetson AGX Xavier and applied to the real-world navigation of a mobile robot, proving that AFPNet can be effectively used in a variety of real-time applications.
A Blockchain-Based IoT Framework for Oil Field Remote Monitoring and Control

The oil and gas industry involves a high level of operational expenditure and often faces high risks of asset safety and operational failures. It has never been so important to monitor and control the oil field operations remotely in real-time to ensure safety and efficiency. The traditional monitoring and control systems for oil field operations are typically centralized, prone to failure, and lack efficiency. Blockchain technology mitigates the centralization problem by creating a decentralized, immutable and transparent control environment for automatic monitoring and control of industrial operations. In this study, we propose a blockchain-based IoT framework for real-time monitoring and control to increase oil field operation and asset efficiency and safety. We present the key components of the framework, including the system architecture, operation flows, algorithms, and smart contracts. As a proof-of-concept modeling, a smart contract is developed and validated on a blockchain test platform. A comparative analysis shows the advantages of using blockchain technology and smart contract to provide trustworthy and automatic monitoring and control for oil field operations.
Robust detection of dehazed images via dual-stream CNNs with adaptive feature fusion

Image dehazing is common post-processing in automatic driving and video surveillance, which can improve image visual quality. However, it might also be used as an image forgery that is difficult to be perceived by naked eyes. Though image dehazing has attracted wide attention, there are still no works specially designed for this kind of forgery detection. By making extensive experiments and preliminary analysis, we observe that a dehazed image easily loses its illumination consistency that can be captured by inverse-intensity chromaticity (IIC) transformation. IIC is a transformed color space that well represents image illuminance map. In this work, a dehazing detection network (DDNet) is proposed to distinguish dehazed images from natural haze-free images. The proposed DDNet accepts RGB images and IIC images as inputs, which are fed into the backbone network, namely EfficientNet-B0, to learn features, respectively. To effectively fuse the learned RGB and IIC features, we also propose an adaptive feature fusion method, thereby improving detection capability. In addition, we build a dehazed image dataset, which includes 11432 pairs artificial, 367 pairs natural hazy images and their corresponding dehazed images, as the benchmark. Experimental results prove that the proposed DDNet achieves desirable detection accuracies and satisfactory robustness.
Boros: Secure and Efficient Off-Blockchain Transactions via Payment Channel Hub

Payment channel, which allows two parties to perform micropayments without involving the blockchain, has become a promising proposal to improve the scalability of decentralized ledgers. Payment channels have been extended to payment networks to utilize existing channels as intermediary links to route coins to others. However, payments through multiple channels bear nontrivial overheads, including time cost, charges at intermediate nodes, etc. In this paper, we propose channel hub, a novel off-chain system to shorten the routing path for the payment network. Channel hub allows transferring coins directly from one payment channel to another within the same hub, so that it can be viewed as a shortcut device for the underlying payment network. Compared with payment hub in literature, which connects participant nodes, our channel hub is more efficient and lightweight, because it connects channels already constructed by the payment network. Based on channel hub, we design a new protocol named Boros to perform secure off-chain cross-channel transfers. We present the security definition of Boros and formally prove its security using the UC-framework. To demonstrate the feasibility of Boros, we develop a proof-of-concept prototype running on Ethereum. Performance evaluation shows that Boros can effectively shorten the off-chain payment path.
Proof of Pseudonym: Blockchain-Based Privacy Preserving Protocol for Intelligent Transport System

Intelligent Transportation Systems is the future for safe and secure transportation. Vehicles in the ITS share basic safety information which can prompt the disclosure of the real identity of the vehicles. Thus, adversaries can misuse these safety messages. Pseudonyms are alias granted to vehicles by trusted authorities to conceal their original identities. To avoid linkability, various pseudonym generation and distribution protocols have been proposed. Such protocols pose overheads in the system as they are performed by Central Authorities. Therefore, re-utilizing the existing pseudonyms through shuffling is the most optimal mechanism for ITS. The Blockchain is a digital ledger and tamper-resistant record of transactions. It eliminates the need of central authority as well as provides anonymity of transactions resulting in more secure and privacy protected solution. To handle distribution optimization issue in the pseudonym shuffling process without a central authority, the blockchain is used with its distributed consensus. The shuffling results are logged in blocks as transactions. Pseudonym shuffle randomness is achieved via blockchain and it provides robustness in the structure. When one system fails, the rest would continue to work. The method also provide fully traceable record in case of certification revocation. The existing blockchain-based pseudonym shuffling mechanism uses traditional consensus algorithms to support the cryptography operation. This leads to overhead in terms of execution time and memory usage. This research proposes Proof of Pseudonym consensus protocol for the shuffling scheme to improve the efficiency of consensus as compared to Proof of Work, Proof of Kernel Work and, Proof of Elapsed Time in terms of time and memory. The execution time of Proof of Pseudonym is shorter than other algorithms. The security and privacy analysis revealed that our scheme achieves identity privacy, unlinkability, and non-repudiation properties. Threat analysis evaluates the proposed protocol in terms of both internal and external attacks.
Multipurpose Bio-Monitored Integrated Circuit in a Contact Lens Eye-Tracker

We present the design, fabrication, and test of a multipurpose integrated circuit (Application Specific Integrated Circuit) in AMS 0.35 µm Complementary Metal Oxide Semiconductor technology. This circuit is embedded in a scleral contact lens, combined with photodiodes enabling the gaze direction detection when illuminated and wirelessly powered by an eyewear. The gaze direction is determined by means of a centroid computation from the measured photocurrents. The ASIC is used simultaneously to detect specific eye blinking sequences to validate target designations, for instance. Experimental measurements and validation are performed on a scleral contact lens prototype integrating four infrared photodiodes, mounted on a mock-up eyeball, and combined with an artificial eyelid. The eye-tracker has an accuracy of 0.2°, i.e., 2.5 times better than current mobile video-based eye-trackers, and is robust with respect to process variations, operating time, and supply voltage. Variations of the computed gaze direction transmitted to the eyewear, when the eyelid moves, are detected and can be interpreted as commands based on blink duration or using blinks alternation on both eyes.
Sentiment Analysis in Arabic Social Media Using Deep Learning Models

There are limited research contributions targeting sentiment analysis in feedback in Arabic gulf dialect, in particular, and the Arabic language in general. Furthermore, the inadequate and limited adoption of classification techniques and natural language processing is noticeable in the sentiment analysis projects addressing the Arabic language. Hence, this paper focuses on analyzing the sentiments in automobile and real estate domains through the application of the state-of-the-art word-embedding model “BERT” and a collection of deep learning models (GRU, LSTM, CNN, CNN-GRU and BiLSTM). The results of classification revealed that combining the BERT with deep learning models have shown efficiency in analyzing sentiments and yielded outstanding results.