Reviewerly/exHarmony huggingface dataset:
Blockchain‐based electric vehicle charging reservation scheme for optimum pricing

The adaptation of intelligent transportation system has evolved the quality of life of people with the huge demand for electric vehicles. Moreover, it becomes essential to schedule an electric vehicle for charging optimally. Therefore, this paper proposes a blockchain-based electric vehicle charging reservation scheme for optimum pricing. It primarily aims to secure data transactions between electric vehicles and charging stations. It uses the communication channel as 5G to ensure ultra-low latency and extremely high reliability. Furthermore, the proposed scheme uses a double-auction mechanism to optimize the payoff for both electric vehicles and charging stations. The performance of the proposed scheme is evaluated by distinguishing it from the conventional networks such as 4G and LTE-A. The performance parameters are considered profit and loss for electric vehicles, scalability, cost overhead for data storage, communication overhead for data transactions, and data storage cost. Results show that the proposed scheme is secure and achieves the optimized payoff for electric vehicles and charging stations compared with traditional approaches.
THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption

As more and more pre-trained language models adopt on-cloud deployment, the privacy issues grow quickly, mainly for the exposure of plain-text user data (e.g., search history, medical record, bank account).Privacy-preserving inference of transformer models is on the demand of cloud service users.To protect privacy, it is an attractive choice to compute only with ciphertext in homomorphic encryption (HE).However, enabling pre-trained models inference on ciphertext data is difficult due to the complex computations in transformer blocks, which are not supported by current HE tools yet.In this work, we introduce THE-X, an approximation approach for transformers, which enables privacy-preserving inference of pre-trained models developed by popular frameworks.THE-X proposes a workflow to deal with complex computation in transformer networks, including all the non-polynomial functions like GELU, softmax, and Layer-Norm.Experiments reveal our proposed THE-X can enable transformer inference on encrypted data for different downstream tasks, all with negligible performance drop but enjoying the theory-guaranteed privacy-preserving advantage.
Real-time denoising of ultrasound images based on deep learning

Abstract Ultrasound images are widespread in medical diagnosis for muscle-skeletal, cardiac, and obstetrical diseases, due to the efficiency and non-invasiveness of the acquisition methodology. However, ultrasound acquisition introduces noise in the signal, which corrupts the resulting image and affects further processing steps, e.g. segmentation and quantitative analysis. We define a novel deep learning framework for the real-time denoising of ultrasound images. Firstly, we compare state-of-the-art methods for denoising (e.g. spectral, low-rank methods) and select WNNM ( Weighted Nuclear Norm Minimisation ) as the best denoising in terms of accuracy, preservation of anatomical features, and edge enhancement. Then, we propose a tuned version of WNNM ( tuned-WNNM ) that improves the quality of the denoised images and extends its applicability to ultrasound images. Through a deep learning framework, the tuned-WNNM qualitatively and quantitatively replicates WNNM results in real-time. Finally, our approach is general in terms of its building blocks and parameters of the deep learning and high-performance computing framework; in fact, we can select different denoising algorithms and deep learning architectures.
Adapting vs. Pre-training Language Models for Historical Languages

As large language models such as BERT are becoming increasingly popular in Digital Humanities (DH), the question has arisen as to how such models can be made suitable for application to specific textual domains, including that of 'historical text'. Large language models like BERT can be pretrained from scratch on a specific textual domain and achieve strong performance on a series of downstream tasks. However, this is a costly endeavour, both in terms of the computational resources as well as the substantial amounts of training data it requires. An appealing alternative, then, is to employ existing 'general purpose' models (pre-trained on present-day language) and subsequently adapt them to a specific domain by further pre-training. Focusing on the domain of historical text in English, this paper demonstrates that pre-training on domain-specific (i.e. historical) data from scratch yields a generally stronger background model than adapting a present-day language model. We show this on the basis of a variety of downstream tasks, ranging from established tasks such as Part-of-Speech tagging, Named Entity Recognition and Word Sense Disambiguation, to ad-hoc tasks like Sentence Periodization, which are specifically designed to test historically relevant processing.
Evaluation of a Virtual Campus Adapted to Web-Based Virtual Reality Spaces: Assessments of Teachers and Students

Educational institutions have continuously adapted to new realities in school education, accelerated recently by the COVID-19 pandemic’s transformation of learning modalities. This article analyzes teachers’ and Students’ acceptance of a web-based virtual reality (WebVR) tool called Virtual Campus proposed to overcome the limitations of teaching strategies using video conferencing platforms. To measure the acceptance of the Virtual Campus, we designed an instrument based on the Technology Acceptance Model (TAM) that involves variables related to online contexts, the future perception of using the tool, skills development, and appreciation and recommendation. The results indicate that the participants favorably accepted WebVR technology as an alternative teaching methodology in emerging learning scenarios and intend to use it post-pandemic. The best-valued elements were interactions in the socialization spaces, the simulation of presence, and the environmental dynamics. The principal areas of opportunity for improvement were overcoming technical problems and improving the internet connection quality; however, these did not affect the participants’ recommendations. Future studies should incorporate variables related to analyzing learning mediated by WebVR-based strategies and using different methodological designs to compare the findings.
Computer-Aided Breast Cancer Diagnosis: Comparative Analysis of Breast Imaging Modalities and Mammogram Repositories

Abstract: The accurate assessment or diagnosis of breast cancer depends on image acquisition and image analysis and interpretation. The accurate assessment or diagnosis of breast cancer depends on image acquisition and image analysis and interpretation. The expert radiologist makes image interpretation, and this process has been greatly benefited by computer technology. For image acquisition, various imaging modalities have been developed and used over the years. This research examines several imaging modalities and their associated benefits and drawbacks. Commonly used parameters such as sensitivity and specificity are also offered to evaluate the usefulness of different imaging modalities. The main focus of the research is on mammograms. Despite the availability of breast cancer datasets of imaging modalities such as MRI, ultrasounds, and thermograms, mammogram datasets are used mainly by the domain researcher. They are considered an international gold standard for the early detection of breast cancer. We discussed and analyzed widely used and publicly available mammogram repositories. We further discussed some common key constraints related to mammogram datasets to develop the deep learning based computer-aided diagnosis (CADx) systems for breast cancer. The ideas for their improvements have also been presented.
A dual watermarking scheme for identity protection

Abstract A novel dual watermarking scheme with potential applications in identity protection, media integrity maintenance and copyright protection in both electronic and printed media is presented. The proposed watermarking scheme uses the owner’s signature and fingerprint as watermarks through which the ownership and validity of the media can be proven and kept intact. To begin with, the proposed watermarking scheme is implemented on continuous-tone/greyscale images, and later extended to images achieved via multitoning, an advanced version of halftoning-based printing. The proposed watermark embedding is robust and imperceptible. Experimental simulations and evaluations of the proposed method show excellent results from both objective and subjective view-points.
Markov Models Applications in Natural Language Processing: A Survey

Markov models are one of the widely used techniques in machine learning to process natural language. Markov Chains and Hidden Markov Models are stochastic techniques employed for modeling systems that are dynamic and where the future state relies on the current state. The Markov chain, which generates a sequence of words to create a complete sentence, is frequently used in generating natural language. The hidden Markov model is employed in named-entity recognition and the tagging of parts of speech, which tries to predict hidden tags based on observed words. This paper reviews Markov models' use in three applications of natural language processing (NLP): natural language generation, named-entity recognition, and parts of speech tagging. Nowadays, researchers try to reduce dependence on lexicon or annotation tasks in NLP. In this paper, we have focused on Markov Models as a stochastic approach to process NLP. A literature review was conducted to summarize research attempts with focusing on methods/techniques that used Markov Models to process NLP, their advantages, and disadvantages. Most NLP research studies apply supervised models with the improvement of using Markov models to decrease the dependency on annotation tasks. Some others employed unsupervised solutions for reducing dependence on a lexicon or labeled datasets.
Real-time path planning for autonomous vehicle based on teaching–learning-based optimization

Abstract This paper presents an online path planning approach for an autonomous tracked vehicle in a cluttered environment based on teaching–learning-based optimization (TLBO), considering the path smoothness, and the potential collision with the surrounding obstacles. In order to plan an efficient path that allows the vehicle to be autonomously navigated in cluttered environments, the path planning problem is solved as a multi-objective optimization problem. First, the vehicle perception is fully achieved by means of inertial measurement unit (IMU), wheels odometry, and light detection and ranging (LiDAR). In order to compensate the sensors drift to achieve more reliable data and improve the localization estimation and corrections, data fusion between the outputs of wheels odometry, LiDAR, and IMU is made through extended Kalman filter (EKF). Then, TLBO is proposed and applied to determine the optimum online path, where the objectives are to find the shortest path to reach the target destination, and to maximize the path smoothness, while avoiding the surrounding obstacles, and taking into account the vehicle dynamic and algebraic constraints. To check the performance of the proposed TLBO algorithm, it is compared in simulation to genetic algorithm (GA), particle swarm optimization (PSO), and a hybrid GA–PSO algorithm. Finally, real-time experiments based on robot operating system (ROS) implementation are conducted to validate the effectiveness of the proposed path planning algorithm.
Multi-View Document Representation Learning for Open-Domain Dense Retrieval

Dense retrieval has achieved impressive advances in first-stage retrieval from a largescale document collection, which is built on bi-encoder architecture to produce single vector representation of query and document.However, a document can usually answer multiple potential queries from different views.So the single vector representation of a document is hard to match with multi-view queries, and faces a semantic mismatch problem.This paper proposes a multi-view document representation learning framework, aiming to produce multiview embeddings to represent documents and enforce them to align with different queries.First, we propose a simple yet effective method of generating multiple embeddings through viewers.Second, to prevent multi-view embeddings from collapsing to the same one, we further propose a global-local loss with annealed temperature to encourage the multiple viewers to better align with different potential queries.Experiments show our method outperforms recent works and achieves state-of-the-art results.
Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go

Aligning with ACL 2022 special Theme on "Language Diversity: from Low Resource to Endangered Languages", we discuss the major linguistic and sociopolitical challenges facing development of NLP technologies for African languages.Situating African languages in a typological framework, we discuss how the particulars of these languages can be harnessed.To facilitate future research, we also highlight current efforts, communities, venues, datasets, and tools.Our main objective is to motivate and advocate for an Afrocentric approach to technology development.With this in mind, we recommend what technologies to build and how to build, evaluate, and deploy them based on the needs of local African communities.2020.Massive vs. curated embeddings for lowresourced languages: The case of Yorùbá and Twi.
Adapting Coreference Resolution Models through Active Learning

Neural coreference resolution models trained on one dataset may not transfer to new, lowresource domains.Active learning mitigates this problem by sampling a small subset of data for annotators to label.While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood.This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs.We compare uncertainty sampling strategies and their advantages through thorough error analysis.In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents.The findings contribute to a more realistic development of coreference resolution models.
Multilingual Translation with Extensible Multilingual Pretraining and  Finetuning

Recent work demonstrates the potential of multilingual pretraining of creating one model that can be used for various tasks in different languages. Previous work in multilingual pretraining has demonstrated that machine translation systems can be created by finetuning on bitext. In this work, we show that multilingual translation models can be created through multilingual finetuning. Instead of finetuning on one direction, a pretrained model is finetuned on many directions at the same time. Compared to multilingual models trained from scratch, starting from pretrained models incorporates the benefits of large quantities of unlabeled monolingual data, which is particularly important for low resource languages where bitext is not available. We demonstrate that pretrained models can be extended to incorporate additional languages without loss of performance. We double the number of languages in mBART to support multilingual machine translation models of 50 languages. Finally, we create the ML50 benchmark, covering low, mid, and high resource languages, to facilitate reproducible research by standardizing training and evaluation data. On ML50, we demonstrate that multilingual finetuning improves on average 1 BLEU over the strongest baselines (being either multilingual from scratch or bilingual finetuning) while improving 9.3 BLEU on average over bilingual baselines from scratch.
Augury: Using Data Memory-Dependent Prefetchers to Leak Data at Rest

Microarchitectural side-channel attacks are enjoying a time of explosive growth, mostly fueled by novel transient execution vulnerabilities. These attacks are capable of leaking arbitrary data, as long as it is possible for the adversary to read that data into the processor core using transient instructions. In this paper, we present the first microarchitectural attack that leaks data at rest in the memory system, i.e., never directly read into the core speculatively or non-speculatively. This technique is enabled by a previously unreported class of prefetcher: a data memory-dependent prefetcher (DMP). These prefetchers are designed to allow prefetching of irregular address patterns such as pointer chases. As such, DMPs examine and use the contents of memory directly to determine which addresses to prefetch. Our experiments demonstrate the existence of a pointer-chasing DMP on recent Apple processors, including the A14 and M1. We then reverse engineer the details of this DMP to determine the opportunities for and restrictions it places on attackers using it. Finally, we demonstrate several basic attack primitives capable of leaking pointer values using the DMP.
Code Generation Using Machine Learning: A Systematic Review

Recently, machine learning (ML) methods have been used to create powerful language models for a broad range of natural language processing tasks. An important subset of this field is that of generating code of programming languages for automatic software development. This review provides a broad and detailed overview of studies for code generation using ML. We selected 37 publications indexed in arXiv and IEEE Xplore databases that train ML models on programming language data to generate code. The three paradigms of code generation we identified in these studies are description-to-code, code-to-description, and code-to-code. The most popular applications that work in these paradigms were found to be code generation from natural language descriptions, documentation generation, and automatic program repair, respectively. The most frequently used ML models in these studies include recurrent neural networks, transformers, and convolutional neural networks. Other neural network architectures, as well as non-neural techniques, were also observed. In this review, we have summarized the applications, models, datasets, results, limitations, and future work of 37 publications. Additionally, we include discussions on topics general to the literature reviewed. This includes comparing different model types, comparing tokenizers, the volume and quality of data used, and methods for evaluating synthesized code. Furthermore, we provide three suggestions for future work for code generation using ML.
A hybrid deep learning model with error correction for photovoltaic power forecasting

The penetration of photovoltaic (PV) power into modern power systems brings enormous economic and environmental benefits due to its cleanness and inexhaustibility. Therefore, accurate PV power forecasting is a pressing and rigid demand to reduce the negative impact of its randomness and intermittency on modern power systems. In this paper, we explore the application of deep learning based hybrid technologies for ultra-short-term PV power forecasting consisting of a feature engineering module, a deep learning-based point prediction module, and an error correction module. The isolated forest based feature preprocessing module is used to detect the outliers in the original data. The non-pooling convolutional neural network (NPCNN), as the deep learning based point prediction module, is developed and trained using the processed data to identify non-linear features. The historical forecasting errors between the forecasting and actual PV data are further constructed and trained to correct the forecasting errors, by using an error correction module based on a hybrid of wavelet transform (WT) and k-nearest neighbor (KNN). In the simulations, the proposed method is extensively evaluated on actual PV data in Limburg, Belgium. Experimental results show that the proposed hybrid model is beneficial for improving the performance of PV power forecasting compared with the benchmark methods.
Color image encryption scheme based on alternate quantum walk and controlled Rubik’s Cube

Abstract Aiming at solving the trouble that digital image information is easily intercepted and tampered during transmission, we proposed a color image encryption scheme based on alternate quantum random walk and controlled Rubik’s Cube transformation. At the first, the color image is separated into three channels: channel R, channel G and channel B. Besides, a random sequence is generated by alternate quantum walk. Then the six faces of the Rubik’s Cube are decomposed and arranged in a specific order on a two-dimensional plane, and each pixel of the image is randomly mapped to the Rubik’s Cube. The whirling of the Rubik’s Cube is controlled by a random sequence to realize image scrambling and encryption. The scrambled image acquired by Rubik’s Cube whirling and the random sequence received by alternate quantum walk are bitwise-XORed to obtain a single-channel encrypted image. Finally the three-channel image is merged to acquire the final encrypted image. The decryption procedure is the reverse procedure of the encryption procedure. The key space of this scheme is theoretically infinite. After simulation experiments, the information entropy after encryption reaches 7.999, the NPCR is 99.5978%, and the UACI is 33.4317%. The encryption scheme with high robustness and security has a excellent encryption effect which is effective to resist statistical attacks, force attacks, and other differential attacks.
Complete and Improved FPGA Implementation of Classic McEliece

We present the first specification-compliant constant-time FPGA implementation of the Classic McEliece cryptosystem from the third-round of NIST’s Post-Quantum Cryptography standardization process. In particular, we present the first complete implementation including encapsulation and decapsulation modules as well as key generation with seed expansion. All the hardware modules are parametrizable, at compile time, with security level and performance parameters. As the most time consuming operation of Classic McEliece is the systemization of the public key matrix during key generation, we present and evaluate three new algorithms that can be used for systemization while complying with the specification: hybrid early-abort systemizer (HEA), single-pass early-abort systemizer (SPEA), and dual-pass earlyabort systemizer (DPEA). All of the designs outperform the prior systemizer designs for Classic McEliece by 2.2x to 2.6x in average runtime and by 1.7x to 2.4x in time-area efficiency. We show that our complete Classic McEliece design for example can perform key generation in 5.2 ms to 20 ms, encapsulation in 0.1 ms to 0.5 ms, and decapsulation in 0.7 ms to 1.5 ms for all security levels on an Xlilinx Artix 7 FPGA. The performance can be increased even further at the cost of resources by increasing the level of parallelization using the performance parameters of our design.