Reviewerly/exHarmony huggingface dataset:
RODFormer: High-Precision Design for Rotating Object Detection with Transformers

Aiming at the problem of Transformers lack of local spatial receptive field and discontinuous boundary loss in rotating object detection, in this paper, we propose a Transformer-based high-precision rotating object detection model (RODFormer). Firstly, RODFormer uses a structured transformer architecture to collect feature information of different resolutions to improve the collection range of feature information. Secondly, a new feed-forward network (spatial-FFN) is constructed. Spatial-FFN fuses the local spatial features of 3 × 3 depthwise separable convolutions with the global channel features of multilayer perceptron (MLP) to solve the deficiencies of FFN in local spatial modeling. Finally, based on the space-FFN architecture, a detection head is built using the CIOU-smooth L1 loss function and only returns to the horizontal frame when the rotating frame is close to the horizontal, so as to alleviate the loss discontinuity of the rotating frame. Ablation experiments of RODFormer on the DOTA dataset show that the Transformer-structured module, the spatial-FFN module and the CIOU-smooth L1 loss function module are all effective in improving the detection accuracy of RODFormer. Compared with 12 rotating object detection models on the DOTA dataset, RODFormer has the highest average detection accuracy (up to 75.60%), that is, RODFormer is more competitive in rotating object detection accuracy.
Human-Computer Interaction with Hand Gesture Recognition Using ResNet and MobileNet

Sign language is the native language of deaf people, which they use in their daily life, and it facilitates the communication process between deaf people. The problem faced by deaf people is targeted using sign language technique. Sign language refers to the use of the arms and hands to communicate, particularly among those who are deaf. This varies depending on the person and the location from which they come. As a result, there is no standardization about the sign language to be used; for example, American, British, Chinese, and Arab sign languages are all distinct. Here, in this study we trained a model, which will be able to classify the Arabic sign language, which consists of 32 Arabic alphabet sign classes. In images, sign language is detected through the pose of the hand. In this study, we proposed a framework, which consists of two CNN models, and each of them is individually trained on the training set. The final predictions of the two models were ensembled to achieve higher results. The dataset used in this study is released in 2019 and is called as ArSL2018. It is launched at the Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia. The main contribution in this study is resizing the images to 64 ∗ 64 pixels, converting from grayscale images to three-channel images, and then applying the median filter to the images, which acts as lowpass filtering in order to smooth the images and reduce noise and to make the model more robust to avoid overfitting. Then, the preprocessed image is fed into two different models, which are ResNet50 and MobileNetV2. ResNet50 and MobileNetV2 architectures were implemented together. The results we achieved on the test set for the whole data are with an accuracy of about 97% after applying many preprocessing techniques and different hyperparameters for each model, and also different data augmentation techniques.
User-Defined Privacy-Preserving Traffic Monitoring Against n-by-1 Jamming Attack

Traffic monitoring services collect traffic reports and respond to users’ traffic queries. However, the reports and queries may reveal the user’s identity and location. Although different anonymization techniques have been applied to protect user privacy, a new security threat arises, namely, n-by-1 jamming attack, in which an anonymous contributing driver impersonates <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> drivers and uploads <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> normal reports by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> reporting devices. Such an attack will mislead the traffic monitoring service provider and further degrade the service quality. Existing traffic monitoring services do not support customized queries, and private information retrieval techniques cannot be applied directly in traffic monitoring. We formally define the new attack and propose a traffic monitoring scheme TraJ to defend the attack and achieve user-defined location privacy. Specifically, we bridge anonymous contributing drivers without disclosing their speed set by using private set intersection. Each RSU collects time traffic reports and structures a weighted proximity graph to filter out malicious colluding drivers. We design a user-defined privacy-preserving query method by encoding complex road network. We leverage the uploading phase from private aggregation to collect traffic conditions and allow requesting drivers to dynamically and privately query traffic conditions. We provide a formal analysis of TraJ to prove its privacy and security properties. We also construct a prototype based on a real-world dataset and Android smartphones to demonstrate its feasibility and efficiency. A formal analysis demonstrates the privacy and security properties. Extensive experiments illustrate the performance and defense efficacy.
Performance evaluation of machine learning tools for detection of phishing attacks on web pages

This paper analyses and implements a rule-based approach for phishing detection using the three machine learning models trained on a dataset consisting of fourteen (14) features. The machine learning algorithms are; k-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM). Among the three algorithms used, it was discovered that Random Forest model proved to deliver the best performance. Rules were extracted from the Random Forest Model and embedded into a Google chrome browser extension called PhishNet. PhishNet is built during the course of this research using web technologies such as HTML, CSS, and Javascript. As a result, PhishNet facilitates highly efficient phishing detection for the web.
An Empirical Analysis of Machine Learning Techniques in Phishing E-mail detection

Communication through email is the most secure medium to transfer files, sensitive information, and messages in this new normal where most people work from home and classes are already online. Attackers take advantage of this by implementing social engineering attacks, and the most commonly known attack is Email Phishing. This attack aims to lure the individual into clicking malicious links that can automatically steal personal information like bank and credit card accounts, client details, passwords, and more. This research aims to develop multiple machine learning models to classify if the email is legitimate or a phishing email and recommend the most suitable model that performed the best and discuss how accurately it performed. The classifiers used achieved 99% of accuracy for detecting phishing and legitimate emails even though trained with an unbalanced data.
Low-Resolution Infrared Array Sensor for Counting and Localizing People Indoors: When Low End Technology Meets Cutting Edge Deep Learning Techniques

In this paper, we propose a method that uses low-resolution infrared (IR) array sensors to identify the presence and location of people indoors. In the first step, we introduce a method that uses 32 × 24 pixels IR array sensors and relies on deep learning to detect the presence and location of up to three people with an accuracy reaching 97.84%. The approach detects the presence of a single person with an accuracy equal to 100%. In the second step, we use lower end IR array sensors with even lower resolution (16 × 12 and 8 × 6) to perform the same tasks. We invoke super resolution and denoising techniques to faithfully upscale the low-resolution images into higher resolution ones. We then perform classification tasks and identify the number of people and their locations. Our experiments show that it is possible to detect up to three people and a single person with accuracy equal to 94.90 and 99.85%, respectively, when using frames of size 16 × 12. For frames of size 8 × 6, the accuracy reaches 86.79 and 97.59%, respectively. Compared to a much complex network (i.e., RetinaNet), our method presents an improvement of over 8% in detection.
A New Wave Energy Converter for Marine Data Buoy

Providing stable and reliable power supply for offshore equipment with special power requirements, such as marine data platforms and marine buoys far away from the coast is currently topical research issue. At present, most of these devices use photovoltaic power generation or install high-capacity batteries and carry out regular replacement of electricity supply schemes. Due to the high dependence of photovoltaic power generation on solar radiation and the high cost arising from frequent battery replacements, wave energy is considered as a solution. Wave energy converters (WECs) are devices that convert the energy stored in waves into electrical energy, and its main advantage is the sustainable supply of electricity. In this article, a new built-in wave energy converter with high reliability and low maintenance costs is proposed and investigated for marine data buoys. Its core is a direct-drive energy conversion unit based on a magnetic lead screw design. The device can passively follow the pitching state of the buoy under the impact of waves to maximize its power generation. The proposed WEC is introduced and investigated through model analysis and prototype experiments. The experimental results confirm the power generation performance of a 50 kg power generation prototype and show the advantages of the proposed device.
A hybrid model to identify fall occurrence from electronic health records

Falls are a leading cause of unintentional injury in the elderly. Electronic health records (EHRs) offer the unique opportunity to develop models that can identify fall events. However, identifying fall events in clinical notes requires advanced natural language processing (NLP) to simultaneously address multiple issues because the word "fall" is a typical homonym.We implemented a context-aware language model, Bidirectional Encoder Representations from Transformers (BERT) to identify falls from the EHR text and further fused the BERT model into a hybrid architecture coupled with post-hoc heuristic rules to enhance the performance. The models were evaluated on real world EHR data and were compared to conventional rule-based and deep learning models (CNN and Bi-LSTM). To better understand the ability of each approach to identify falls, we further categorize fall-related concepts (i.e., risk of fall, prevention of fall, homonym) and performed a detailed error analysis.The hybrid model achieved the highest f1-score on sentence (0.971), document (0.985), and patient (0.954) level. At the sentence level (basic data unit in the model), the hybrid model had 0.954, 1.000, 0.988, and 0.999 in sensitivity, specificity, positive predictive value, and negative predictive value, respectively. The error analysis showed that that machine learning-based approaches demonstrated higher performance than a rule-based approach in challenging cases that required contextual understanding. The context-aware language model (BERT) slightly outperformed the word embedding approach trained on Bi-LSTM. No single model yielded the best performance for all fall-related semantic categories.A context-aware language model (BERT) was able to identify challenging fall events that requires context understanding in EHR free text. The hybrid model combined with post-hoc rules allowed a custom fix on the BERT outcomes and further improved the performance of fall detection.
Towards high performance homomorphic encryption for inference tasks on CPU: An MPI approach

Fully homomorphic encryption (FHE) is a powerful tool enabling secure outsourcing to untrusted third-parties. As AI technologies mature and companies start offering AI-as-a-service (AIaaS), the privacy of their customers' data needs to be considered but the high overhead of existing FHE schemes is a major limitation. Prior work tried to address this but suffered accuracy degradation, lack of scalability, ciphertext expansion, and impractical performance issues. In this paper, we explore the utility of High-Performance Computing systems for accelerating the secure evaluation of deep convolutional neural networks (CNN) with FHE. We examine previous encrypted neural network architectures and propose a holistic re-design for the HPC environment, which introduces a new consideration for the latency of inter-node communication. The new architecture was implemented with MPI and OpenMP and its performance benchmarked against state-of-the-art FHE-based CNN evaluation methods. Our solution achieved improvements of between 6−34× for various CIFAR10 CNN architectures and more than 1500× for AlexNet, bringing FHE-based evaluation of AlexNet down from several hundred days to within 14 h. Such findings pave the way towards practical inference on encrypted medium resolution images through the use of HPC systems.
Cross-Domain Few-Shot Classification based on Lightweight Res2Net and Flexible GNN

Cross-Domain Few-Shot Classification aims to recognize new categories from unseen domains while each category has only a few support examples. But existing networks cannot be effectively applied to cross-domain scenario. To solve this problem, in this paper, we propose two new strategies, respectively for the encoder and the metric function of metric-based network: First, we propose a precise metric function named FGNN(Flexible GNN) to better measure the distance between images whether labeled or unlabeled; Second, based on the idea of multi-scale representation, we build a new hierarchical residual-like block which is applicable to lightweight ResNet structures such as ResNet-10. The constructed network—LR2Net(Lightweight Res2Net), performs much better than ResNet and provides a new scale-based strategy to constantly increase precision. Various feature encoders combined with metric function GNN or FGNN are verified through a lot of contrast experiments using leave-one-out setting on four datasets—CUB, Cars, Places and Plantae. As a result, the highest average precision of our combined networks achieves up to 2.22% and 2.26% improvement compared to the state-of-art under the 5-way 1-shot and 5-way 5-shot cross-domain classification.
Ad<sup>2</sup>Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking

Visual tracking is adopted to extensive unmanned aerial vehicle (UAV)-related applications, which leads to a highly demanding requirement on the robustness of UAV trackers. However, adding imperceptible perturbations can easily fool the tracker and cause tracking failures. This risk is often overlooked and rarely researched at present. Therefore, to help increase awareness of the potential risk and the robustness of UAV tracking, this work proposes a novel adaptive adversarial attack approach, i.e., Ad <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Attack, against UAV object tracking. Specifically, adversarial examples are generated online during the resampling of the search patch image, which leads trackers to lose the target in the following frames. Ad <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Attack is composed of a direct downsampling module and a super-resolution upsampling module with adaptive stages. A novel optimization function is proposed for balancing the imperceptibility and efficiency of the attack. Comprehensive experiments on several well-known benchmarks and real-world conditions show the effectiveness of our attack method, which dramatically reduces the performance of the most advanced Siamese trackers.
Noise-Robust Speech Recognition With 10 Minutes Unparalleled In-Domain Data

Noise-robust speech recognition systems require large amounts of training data including noisy speech data and corresponding transcripts to achieve state-of-the-art performances in face of various practical environments. However, such plenty of in-domain data is not always available in the real-life world. In this paper, we propose a generative adversarial network to simulate noisy spectrum from the clean spectrum (SimuGAN), where only 10 minutes of unparalleled in-domain noisy speech data is required as labels. Furthermore, we also propose a dual-path speech recognition system to improve the robustness of the system under noisy conditions. Experimental results show that the proposed speech recognition system achieves 7.3% absolute improvement with simulated noisy data by Simu-GAN over the best baseline in terms of word error rate (WER).
Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting

Class-agnostic counting (CAC) aims to count all instances in a query image given few exemplars. A standard pipeline is to extract visual features from exemplars and match them with query images to infer object counts. Two essential components in this pipeline are feature representation and similarity metric. Existing methods either adopt a pretrained network to represent features or learn a new one, while applying a naive similarity metric with fixed inner product. We find this paradigm leads to noisy similarity matching and hence harms counting performance. In this work, we propose a similarity-aware CAC framework that jointly learns representation and similarity metric. We first instantiate our framework with a naive baseline called Bilinear Matching Network (BMNet), whose key component is a learnable bilinear similarity metric. To further embody the core of our framework, we extend BMNet to BMNet+ that models similarity from three aspects: 1) representing the instances via their self-similarity to enhance feature robustness against intra-class variations; 2) comparing the similarity dynamically to focus on the key patterns of each exemplar; 3) learning from a supervision signal to impose explicit constraints on matching results. Extensive experiments on a recent CAC dataset FSC147 show that our models significantly outperform state-of-the-art CAC approaches. In addition, we also validate the cross-dataset generality of BMNet and BMNet+ on a car counting dataset CARPK. Code is at tiny.one/BMNet
Toward Trustworthy DeFi Oracles: Past, Present, and Future

With the rapid development of blockchain technology in recent years, all kinds of blockchain-based applications have emerged.Among them, the decentralized finance (DeFi) is one of the most successful applications, which is regarded as the future of finance.The great success of DeFi relies on the realworld data which is not directly available on the blockchain.However, due to the deterministic nature of blockchain, the blockchain cannot directly obtain indeterministic data from the outside world (off-chain).Thus, oracles have appeared as a viable solution to feed off-chain data to blockchain applications.In this paper, we carry out a comprehensive study on oracles, especially on DeFi oracles.We first briefly introduce the application scenarios of DeFi oracles, and then we talk about the past of DeFi oracles by categorizing them into several types based on their design features.After that, we introduce five popular DeFi oracles currently in use (such as Chainlink and Band Protocol), with the focus on their system architecture, data validation process, and their incentive mechanisms.Then, we compare these present DeFi oracles from their data trustworthiness, data source trustworthiness and their overall trust models.Finally, we propose a set of metrics for designing trustworthiness DeFi oracles, and propose a potential trust architecture and a few promising techniques for building future trustworthiness oracles.
Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning

We offer a practical unpaired learning based image dehazing network from an unpaired set of clear and hazy images. This paper provides a new perspective to treat image dehazing as a two-class separated factor disentanglement task, i.e., the task-relevant factor of clear image reconstruction and the task-irrelevant factor of haze-relevant distribution. To achieve the disentanglement of these two-class factors in deep feature space, contrastive learning is introduced into a CycleGAN framework to learn disentangled representations by guiding the generated images to be associated with latent factors. With such formulation, the proposed contrastive disentangled dehazing method (CDD-GAN) employs negative generators to cooperate with the encoder network to update alternately, so as to produce a queue of challenging negative adversaries. Then these negative adversaries are trained end-to-end together with the backbone representation network to enhance the discriminative information and promote factor disentanglement performance by maximizing the adversarial contrastive loss. During the training, we further show that hard negative examples can suppress the task-irrelevant factors and unpaired clear exemples can enhance the task-relevant factors, in order to better facilitate haze removal and help image restoration. Extensive experiments on both synthetic and real-world datasets demonstrate that our method performs favorably against existing unpaired dehazing baselines.
Text-to-image synthesis with self-supervised learning

• Integrating self-supervised learning into discriminator to improve learned representation. • The self-supervised signal enable generator to produce more diverse images. • Enhancing the training stability and image quality of generative adversarial nets. • A text-to-image synthesis framework with self-supervised learning. Text-to-image synthesis extracts the meaning from the text description and converts it into an image correspondingly. Text-to-image synthesis is widely leveraged in many applications, such as graphic design, image editing, etc. Text-to-image synthesis approaches are mainly built on the basis of generative adversarial networks . One of the main challenges in text-to-image synthesis is to generate images that are visually realistic. Not only that, the text-to-image synthesis model is inherently susceptible to overconfidence and training instability issues. To address these challenges, this paper proposes a self-supervised text-to-image synthesis with some enhancements, including self-supervised learning, feature matching, L1 distance loss, and one-sided label smoothing. The self-supervised learning offers more image variations thus improving the classification power of the discriminator. The feature matching and L1 distance functions motivate the generator to synthesize images that are visually more similar to the real images based on the given text description. The one-sided label smoothing adds a penalty value when the discriminator makes a correct classification to alleviate the overconfidence problem and to improve the training stability. The performance of the proposed self-supervised text-to-image synthesis is evaluated on the Oxford-102 and CUB datasets. The empirical results demonstrate that the proposed self-supervised text-to-image synthesis generates images with richer image content diversity, more visually realistic, and more semantically consistent with the given text description. The proposed self-supervised text-to-image synthesis also outshines the methods in comparison in terms of the inception score and Structural Similarity Index.