Reviewerly/exHarmony huggingface dataset:
Trusted Execution Environment Hardware by Isolated Heterogeneous Architecture for Key Scheduling

A Trusted Execution Environment (TEE) sets a platform to secure applications based on the Chain-of-Trust (CoT). The starting point of the CoT is called the Root-of-Trust (RoT). However, the RoT implementation often relies on obscurity and provides little flexibility when generating keys to the system. In this paper, a TEE System-on-a-Chip (SoC) architecture is proposed based on a heterogeneous design by combining 64-bit Linux-capable processors with a 32-bit Micro-Controller Unit (MCU). The TEE is built on the 64-bit cores, while the 32-bit MCU takes care of sensitive data and activities. The MCU is isolated from the TEE side by an Isolated Bus (IBus) that sits above the conventional System Bus (SBus). Besides the 32-bit processor, the isolated sub-system contains a Random Access Memory (RAM), a Read-Only Memory (ROM) for storing the boot program, and another ROM for storing root keys. For cryptography accelerators, we have 512-bit Secure Hashing Algorithm 3 (SHA3-512), 128/256-bit Advanced Encryption Standard (AES-128/256), Ed25519, and True Random Number Generator (TRNG) attached to the Peripheral Bus (PBus). Additionally, besides the public channel, the TRNG module also has a private channel that goes directly to the IBus. With RoT implemented inside the isolated sub-system, the RoT is inaccessible from the TEE side after boot. Furthermore, the hidden MCU’s secure boot program makes the key generation flexible and could be updated for many security schemes. To summarize, the proposed design features a flexible and secure boot procedure with complete isolation from the TEE domain. Moreover, exclusive secure storage for the root key and cryptographic accelerators are available for the boot process. The implementation was tested on a Virtex-7 XC7VX485T Field-Programmable-Gate-Array (FPGA). It was also synthesized in a Very Large-Scale Integrated (VLSI) circuit with the ROHM-180nm process library.
Automatic road sign detection and recognition based on neural network

Road sign detection and recognition is an integral part of intelligent transportation systems. It increases protection by reminding the driver of the current condition of the route, such as notices, bans, limitations, and other valuable driving information. This paper describes a novel system for automatic detection and recognition of road signs, which is achieved in two main steps. First, the initial image is pre-processed using DBSCAN clustering algorithm. The clustering is performed based on color information, and the generated clusters are segmented using artificial neural networks (ANN) classifier. The resulting ROIs are then carried out based on their aspect ratio and size to retain only significant ones. Then, a shape-based classification is performed using ANN as classifier and HDSO as feature to detect the circular, rectangular and triangular shapes. Second, a hybrid feature is defined to recognize the ROIs detected from the first step. It involves a combination of the so-called GLBP-Color which is an extension of the classical gradient local binary patterns feature to the RGB color space and the local self-similarity feature. ANN, AdaBoost, and support vector machine have been tested with the introduced hybrid feature and the first one is selected as it outperforms the other two. The proposed method has been tested in outdoor scenes, using a collection of common databases, well known in the traffic sign community (GTSRB, GTSDB, and STS). The results demonstrate the effectiveness of our method when compared to recent state-of-the-art methods.
Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference

Abstract The task of ultra-fine entity typing (UFET) seeks to predict diverse and free-form words or phrases that describe the appropriate types of entities mentioned in sentences. A key challenge for this task lies in the large number of types and the scarcity of annotated data per type. Existing systems formulate the task as a multi-way classification problem and train directly or distantly supervised classifiers. This causes two issues: (i) the classifiers do not capture the type semantics because types are often converted into indices; (ii) systems developed in this way are limited to predicting within a pre-defined type set, and often fall short of generalizing to types that are rarely seen or unseen in training. This work presents LITE🍻, a new approach that formulates entity typing as a natural language inference (NLI) problem, making use of (i) the indirect supervision from NLI to infer type information meaningfully represented as textual hypotheses and alleviate the data scarcity issue, as well as (ii) a learning-to-rank objective to avoid the pre-defining of a type set. Experiments show that, with limited training data, LITE obtains state-of-the-art performance on the UFET task. In addition, LITE demonstrates its strong generalizability by not only yielding best results on other fine-grained entity typing benchmarks, more importantly, a pre-trained LITE system works well on new data containing unseen types.1
Predicting Embryo Viability Based on Self-Supervised Alignment of Time-Lapse Videos

With self-supervised learning, both labeled and unlabeled data can be used for representation learning and model pretraining. This is particularly relevant when automating the selection of a patient's fertilized eggs (embryos) during a fertility treatment, in which only the embryos that were transferred to the female uterus may have labels of pregnancy. In this paper, we apply a self-supervised video alignment method known as temporal cycle-consistency (TCC) on 38176 time-lapse videos of developing embryos, of which 14550 were labeled. We show how TCC can be used to extract temporal similarities between embryo videos and use these for predicting pregnancy likelihood. Our temporal similarity method outperforms the time alignment measurement (TAM) with an area under the receiver operating characteristic (AUC) of 0.64 vs. 0.56. Compared to existing embryo evaluation models, it places in between a pure temporal and a spatio-temporal model that both require manual annotations. Furthermore, we use TCC for transfer learning in a semi-supervised fashion and show significant performance improvements compared to standard supervised learning, when only a small subset of the dataset is labeled. Specifically, two variants of transfer learning both achieve an AUC of 0.66 compared to 0.63 for supervised learning when 16% of the dataset is labeled.
FIDS: A Federated Intrusion Detection System for 5G Smart Metering Network

In a critical infrastructure such as Smart Grid (SG), providing security of the system and privacy of consumers are significant challenges to be considered. The SG developers adopt Machine Learning (ML) algorithms within the Intrusion Detection System (IDS) to monitor traffic data and network performance. This visibility safeguards the SG from possible intrusions or attacks that may trigger the system. However, it requires access to residents’ consumption information which is a severe threat to their privacy. In this paper, we present a novel method to detect abnormalities on a large scale SG while preserving the privacy of users. We design a Federated IDS (FIDS) architecture using Federated Learning (FL) in a 5G environment for the SG metering network. In this way, we design Federated Deep Neural Network (FDNN) model that protects customers’ information and provides supervisory management for the whole energy distribution network. Simulation results for a real-time dataset demonstrate the reasonable improvement of the proposed FDNN model compared with the state-of-the-art algorithms. The FDNN achieves approximately 99.5% accuracy, 99.5% precision/recall, and 99.5% f1-score when comparing with classification algorithms.
Pyramid Adversarial Training Improves ViT Performance

Aggressive data augmentation is a key component of the strong generalization capabilities of Vision Transformer (ViT). One such data augmentation technique is adversarial training (AT); however, many prior works [28,45] have shown that this often results in poor clean accuracy. In this work, we present pyramid adversarial training (PyramidAT), a simple and effective technique to improve ViT's overall performance. We pair it with a “matched” Dropout and stochastic depth regularization, which adopts the same Dropout and stochastic depth configuration for the clean and adversarial samples. Similar to the improvements on CNNs by AdvProp [61] (not directly applicable to ViT), our pyramid adversarial training breaks the trade-off between in-distribution accuracy and out-of-distribution robustness for ViT and related architectures. It leads to 1.82% absolute improvement on ImageNet clean accuracy for the ViT-B model when trained only on ImageNet-1K data, while simultaneously boosting performance on 7 ImageNet ro-bustness metrics, by absolute numbers ranging from 1.76% to 15.68%. We set a new state-of-the-art for ImageNet-C (41.42 mCE), ImageNet-R (53.92%), and ImageNet-Sketch (41.04%) without extra data, using only the ViT-B/16 backbone and our pyramid adversarial training. Our code is publicly available at pyramidat.github.io.
Deep Sparse Representation Based Image Restoration With Denoising Prior

As a powerful statistical signal modeling technique, sparse representation has been widely used in various image restoration (IR) applications. The sparsity-based methods have achieved leading performance in the past few decades. However, in recent years it has been surpassed by other methods, especially the recent deep learning based methods. In this paper, we address the question that whether sparse representation can be competitive again. The way we answer this question is to redesign it with a deep architecture. To be specific, we propose an end-to-end deep architecture that follows the process of the sparse representation based IR. In particular, we learn a sparse convolutional dictionary to replace the traditional dictionary, and a convolutional neural network (CNN) denoising prior to replace the image prior. Through end-to-end training, the parameters in convolutional dictionary and CNN denoiser can be jointly optimized. Experimental results on several representative IR tasks, including image denoising, deblurring and super-resolution, demonstrate that the proposed deep network can achieve superior performance against state-of-the-art model-based and learning-based methods.
An evolutionary multi-objective path planning of a fleet of ASVs for patrolling water resources

The rapid increase of human activities with direct influence on the environment has motivated the global awareness of the need to efficiently monitor the natural resources. Among the wide range of problems addressed, such as overuse of agrochemicals, uncontrolled waste, etc., the contamination of water resources plays a protagonist role, given its close links with biodiversity and the food chain. Water monitoring is considered one of the most efficient ways to deal with these problems, especially through the use of autonomous vehicles, which can boost the capabilities and efficiency of the monitoring routines with appropriate strategies. In this work, the monitoring problem is addressed by means of the Non-Homogeneous Patrolling Problem with closed circuits. This problem has a great computational complexity, especially when multiple targets are included in a monitoring mission. A formulation based on closed metric graphs and the application of a multi-objective genetic algorithm is proposed to provide Pareto-efficient monitoring solutions for a variable number of Autonomous Surface Vehicles. To address the multi-agent, multi-objective and constrained paradigm, efficient genetic operators have been designed for the generation of valid solutions in an affordable time. The method results in Pareto-efficient solutions for scenarios with disjoint and uncorrelated objectives, which outperform the fitness of other solutions by a factor of 2, on average. The results provide decision makers a method to find different non-dominated strategies depending on the monitoring needs, depending on fleet and vehicle size.
Pixel-Wise Energy-Biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes

State-of-the-art (SOTA) anomaly segmentation approaches on complex urban driving scenes explore pixel-wise classification uncertainty learned from outlier exposure, or external reconstruction models. However, previous uncertainty approaches that directly associate high uncertainty to anomaly may sometimes lead to incorrect anomaly predictions, and external reconstruction models tend to be too inefficient for real-time self-driving embedded systems. In this paper, we propose a new anomaly segmentation method, named pixel-wise energy-biased abstention learning (PEBAL), that explores pixel-wise abstention learning (AL) with a model that learns an adaptive pixel-level anomaly class, and an energy-based model (EBM) that learns inlier pixel distribution. More specifically, PEBAL is based on a non-trivial joint training of EBM and AL, where EBM is trained to output high-energy for anomaly pixels (from outlier exposure) and AL is trained such that these high-energy pixels receive adaptive low penalty for being included to the anomaly class. We extensively evaluate PEBAL against the SOTA and show that it achieves the best performance across four benchmarks. Code is available at https://github.com/tianyu0207/PEBAL .
Phishing Classification Techniques: A Systematic Literature Review

Phishing has become a serious and concerning problem within the past 10 years, with many reviews describing attack patterns and anticipating different method utilizations.This indicates that the results are still not comprehensive, subsequently leaving a critical gap in phishing reports.Therefore, this study aims to conduct a systematic review, to show a more crucial issue in phishing attacks, namely classification techniques.These issues were categorized into techniques, datasets, performance evaluation, and phishing types.The obtained results are expected to help developers prevent future phishing attacks more effectively, especially in selectively and carefully determining the techniques and evaluations to address specific types of phishing.
Variable seismic waveforms representation: Weak-supervised learning based seismic horizon picking

Seismic horizon picking via deep learning models have been advanced rapidly and proven popular. However, the prediction result is highly depended on the quality of the train set and the manually interpreted train set is very difficult to obtain. To guarantee a reasonable prediction result, we usually need manually interpret 20%–30% seismic data to generate the train set for the convolutional neural network (CNN) based seismic horizon picking methods, which is very time consuming. Besides, the performance of the deep learning based seismic horizon picking methods are also greatly influenced by the architecture of the selected CNN model. The core theory of using CNN for seismic horizon picking is recognizing different waveform patterns and classifying them with the corresponding labels. In this work, we first develop the variable waveform representation (VWR) algorithm to simulate the waveform features and then produce a large number of seismic traces as the train data. The VWR can deal with the task of the train set generation by only using very few seismic traces. Afterward, we propose a novel CNN model by integrating the holistically-nested module and the SegNet model for solving seismic horizon picking, which is termed as the HSegNet model. It should be noted that the holistically-nested module is able to recognize the holistic image and learn the multiple features, which is efficient for the waveform pattern recognition. To demonstrate the efficiency and robustness of our proposed HSegNet model, we apply it to field data and compare with the widely used U-Net model. The experiment results show that our proposed VWR-HSegNet can achieve the prediction result with 99% test accuracy by only manually interpreting 0.13% seismic traces. Whereas, the U-Net model can only get 91% test accuracy prediction result but using 30% manually interpreted seismic traces as the train set.
Geometry Sensitive Cross-Modal Reasoning for Composed Query Based Image Retrieval

Composed Query Based Image Retrieval (CQBIR) aims at retrieving images relevant to a composed query containing a reference image with a requested modification expressed via a textual sentence. Compared with the conventional image retrieval which takes one modality as query to retrieve relevant data of another modality, CQBIR poses great challenge over the semantic gap between the reference image and modification text in the composed query. To solve the challenge, previous methods either resort to feature composition that cannot model interactions in the query or explore inter-modal attention while ignoring the spatial structure and visual-semantic relationship. In this paper, we propose a geometry sensitive cross-modal reasoning network for CQBIR by jointly modeling the geometric information of the image and the visual-semantic relationship between the reference image and modification text in the query. Specifically, it contains two key components: a geometry sensitive inter-modal attention module (GS-IMA) and a text-guided visual reasoning module (TG-VR). The GS-IMA introduces the spatial structure into the inter-modal attention in both implicit and explicit manners. The TG-VR models the unequal semantics not included in the reference image to guide further visual reasoning. As a result, our method can learn effective feature for the composed query which does not exhibit literal alignment. Comprehensive experimental results on three standard benchmarks demonstrate that the proposed model performs favorably against state-of-the-art methods.
An effective approach for Arabic document classification using machine learning

Arabic text classification is one application of Natural Language Processing (NLP). It has been used to analyze and categorize Arabic text. Analyzing text has become an essential part of our lives because of the increasing number of text data which makes text classification a big data problem. Arabic text classification systems become significant to maintain vital information in many domains such as education, and health sector, and public services. In the presented research work, the Arabic text classification model is developed using various algorithms namely Multinomial Naïve Bayesian (MNB), Bernoulli Naïve Bayesian (BNB), Stochastic Gradient Descent (SGD), Logistic Regression (LR), Support vector classifier (SVC), Linear SVC, and convolutional neural networks (CNN). These algorithms have been implemented utilizing the Al-Khaleej dataset. The experiments are carried out with various representation models and it is observed that CNN with character level model outperforms others. The result of CNN exceeds the state-of-the-art machine learning method with an accuracy equal to 98. The presented methods will be useful in different domains, particularly on social media.
Three-Channel Infrared Imaging for Object Detection in Haze

Object detection is of wide application for its capability in recognizing and locating targets in scenes. Under heavy hazy conditions, however, the detection performance on RGB images will greatly degrade since the image contents are polluted. In this work, we propose an imaging system to acquire three-channel images in the shortwave infrared (SWIR) spectrum to facilitate object detection under hazy conditions. The system captures SWIR images in the form of pseudo color that are most suitable for detecting objects, such as pedestrians and vehicles. Two different types of filters, i.e., liquid crystal tunable filter (LCTF) and optical filters, are employed in our imaging system design. We use the LCTF to acquire narrowband hyperspectral images, which are fed into a band simulation model to generate wideband images for optimal band selection. We present a specific measure called recognition and localization (RL) score to evaluate the detection performance of three-band combinations. Based on the measure, optimal bands are determined using an efficient searching algorithm. Then, we customize three optical filters and install them on a filter wheel, with which we can acquire three-channel images in the SWIR spectrum. The effectiveness of our imaging system is evaluated on a self-collected RGB-SWIR image dataset. Experimental results indicate that, compared with the RGB images after haze removal, the three-channel SWIR images acquired by our imaging system are of higher quality and can achieve better object detection performance under hazy conditions.
Topic model-based recommender systems and their applications to cold-start problems

Recommender systems provide information and items that match a user’s preference. This study proposes hybrid recommender models that use content-based filtering and latent Dirichlet allocation (LDA)-based models. The proposed models are extensions of the LDA where the words correspond to user characteristics and item features and are found to be suitable for handling cold-start problems, as it provides predicted ratings for new users and items via its latent dimension. These models have the advantage of analyzing item topics, item feature topics, and user characteristic topics simultaneously. Experiments conducted with the MovieLens 1M dataset illustrate that the proposed models provide similar prediction performances as baseline recommender models and are superior to the baseline models regarding the interpretability of the user and item topics. • Topic models are implemented in recommender systems to solve cold start problems. • Proposed models are based on content-based filtering and latent Dirichlet allocation. • The proposed models outperform existing baselines in prediction accuracy.
Prediction and Prevention of Malicious URL Using ML and LR Techniques for Network Security

Understandable URLs are utilized to recognize billions of websites hosted over the present-day internet. Opposition who tries to get illegal admittance to the classified data may use malicious URLs and present them as URLs to users. Such URLs that act as an entry for the unrequested actions are known as malicious URLs. These wicked URLs can cause unethical behavior like theft of confidential and classified data. By using machine learning algorithm SVM, we can detect the malicious URLs. One of the essential features is to permit the benevolent URLs that are demanded by the customer and avoid the malicious URLs. Blacklisting is one of the basic and trivial mechanisms in detecting malicious URLs.