Reviewerly/exHarmony huggingface dataset:
VDNet: Vehicle Detection Network Using Computer Vision and Deep Learning Mechanism for Intelligent Vehicle System

Computer vision using deep learning has revolutionized the detection system and vehicle detection is no exception to it. The importance of vehicle classification and detection is in Intelligent Vehicle and Transportation systems which require making critical decisions based on this information; therefore it is a prominent area of work. This paper presents a vehicle detection model predicated on convolution neural network using bounding box annotations for marking the region of interest. The model is tuned to give best performance by evaluating the different parameter configurations. The implementation is done using Python and OpenCV and is trained upon Google Colab's free GPU access. The paper presents an efficient stepwise explanation of the process flow in detecting cars in various real life scenes. The proposed model is trained on the combination of two benchmark dataset and attains 94.66% accuracy and 95.13% precision.
Integrating Iterative Machine Teaching and Active Learning into the Machine Learning Loop

Scholars and practitioners are defining new types of interactions between humans and machine learning algorithms that we can group under the umbrella term of Human-in-the-Loop Machine Learning (HITL-ML). This paper is focused on implementing two approaches to this topic—Iterative Machine Teaching (iMT) and Active Learning (AL)—and analyzing how to integrate them in the learning loop. iMT is a variation of Machine Teaching in which a machine acts as a teacher that tries to transfer knowledge to a machine learning model. The focus of the problem in iMT is how to obtain the optimal training set given a machine learning algorithm and a target model. The idea is to learn a target concept with a minimal number of iterations with the smallest dataset. Active Learning, in contrast, is a specialized type of supervised learning in which humans are incorporated in the loop to act as oracles that analyze unlabeled data. AL allows us to achieve greater accuracy with less data and less training. Our proposal to incorporate iMT and AL into the machine learning loop is to use iMT as a technique to obtain the "Minimum Viable Data (MVD)" for training a learning model, that is, a dataset that allows us to increase speed and reduce complexity in the learning process by allowing to build early prototypes. Next, we will use AL to refine this first prototype by adding new data in an iterative and incremental way. We carried out several experiments to test the feasibility of our proposed approach. They show that the algorithms trained with the teachers converge faster than those that have been trained in a conventional way. Also, AL helps the model to avoid getting stuck and to keep improving after the first few iterations. The two approaches investigated in this paper can be considered complementary, as they correspond to different stages in the learning process.
A High-Level Ontology Network for ICT Infrastructures

The ICT infrastructures of medium and large organisations that offer ICT services (infrastructure, platforms, software, applications, etc.) are becoming increasingly complex. Nowadays, these environments combine all sorts of hardware (e.g., CPUs, GPUs, storage elements, network equipment) and software (e.g., virtual machines, servers, microservices, services, products, AI models). Tracking, understanding and acting upon all the data produced in the context of such environments is hence challenging. Configuration management databases have been so far widely used to store and provide access to relevant information and views on these components and on their relationships. However, different databases are organised according to different schemas. Despite existing efforts in standardising the main entities relevant for configuration management, there is not yet a core set of ontologies that describes these environments homogeneously, and which can be easily extended when new types of items appear. This paper presents an ontology network created with the purpose of serving as an initial step towards an homogeneous representation of this domain, and which has been already used to produce a knowledge graph for a large ICT company.
Robust Stability of Full Information Estimation

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 16 April 2020Accepted: 11 May 2021Published online: 04 October 2021Keywordsstate estimation, moving horizon estimation, Lyapunov function, Kalman filteringAMS Subject Headings93Publication DataISSN (print): 0363-0129ISSN (online): 1095-7138Publisher: Society for Industrial and Applied MathematicsCODEN: sjcodc
DLP: Achieve Customizable Location Privacy With Deceptive Dummy Techniques in LBS Applications

As a straightforward consequence of advances in the Internet of Things (IoT), location-based service (LBS) applications have been pervasive in our daily lives. Nevertheless, since those LBS applications will continuously collect and disclose users’ location data, major concerns on privacy leakage are raised. Aiming at the challenge, in this article, we first build up a detect module (DM) and employ it to investigate more than 80% of LBS applications are keen on tracking users. Then, to thwart the threats from those LBS applications, we exploit the deceptive dummy techniques and design a dummy-based location privacy preserving scheme, named dummy location provider (DLP), which comprises three algorithms, namely, Spread, Shift, and Switch. Specifically, Spread and Shift are in charge of generating deceptive dummies and trajectories. And with Switch, users’ real locations are replaced with dummy trajectories before being submitted to LBS applications. As a result, users can not only prevent applications from accessing location data arbitrarily, but also avoid being questioned by applications in terms of honesty. Furthermore, to guarantee necessary functions of LBS, DLP offers customizable privacy-preserving strategies for users, which can achieve flexible location data usage control. Finally, our DLP can also attain achievable and effortless deployment over smart devices. Detailed security analysis indicates that DLP resists inference attacks even facing skeptical applications. In addition, for performance evaluation, a DLP application (DLPA) is developed on the Android platform and tested in the real environment, and the extensive experimental results demonstrate that the DLPA is indeed effective and high efficiency in practice.
LR-SVM+: Learning Using Privileged Information with Noisy Labels

The paradigm of Learning Using Privileged Information (LUPI) always assumes that labels are annotated precisely. However, in practice, this assumption may be violated, as the labels may be heavily noisy, which inevitably degenerates the performance of learning algorithms in the LUPI paradigm. To handle the side effect of noisy labels, we propose a novel Label Noise Robust SVM+ (LR-SVM+) algorithm. Specifically, as the privileged information contains rich information of the latent labels, we first utilize it to infer underlying clean labels. Then we use the inference to modify the noisy labels. Comprehensive experiments demonstrate the necessity of studying label noise robust SVM+ and the effectiveness of the proposed method.
A Lightweight YOLOv4 Based SAR Image Ship Detection

In recent years, deep learning technology has made significant achievements in SAR image ship detection. However, many models often occupy too much memory and cannot achieve real-time detection in the micro platform. To solve this problem, this paper proposes a lightweight YOLOv4 ship detection method. First, the backbone uses the lightweight network MobileNetv2. Then, the standard convolution in PANet is replaced by depth-wise separable convolution, which can greatly reduce the number of parameters. In addition, this method embeds coordinate attention in MobileNetv2 to encodes for each channel. Compared with original YOLOv4 experimental results show that, the number of parameters is reduced by 40%, and the mAP reaches 95.5%.
Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment

Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their associated free-text reports accumulated in clinical routine without manual supervision. This paper proposes a Joint Image Text Representation Learning Network (JoImTeRNet) for pre-training on chest X-ray images and their radiology reports. The model was pre-trained on both the global image-sentence level and the local image region-word level for visual-textual matching. Both are bidirectionally constrained on Cross-Entropy based and ranking-based Triplet Matching Losses. The region-word matching is calculated using the attention mechanism without direct supervision about their mapping. The pre-trained multi-modal representation learning paves the way for downstream tasks concerning image and/or text encoding. We demonstrate the representation learning quality by cross-modality retrievals and multi-label classifications on two datasets: OpenI-IU and MIMIC-CXR. Our code is available at https://github.com/mshaikh2/JoImTeR_MLMI_2021.
Experimental realization of a quantum image classifier via tensor-network-based machine learning

Quantum machine learning aspires to overcome intractability that currently limits its applicability to practical problems. However, quantum machine learning itself is limited by low effective dimensions achievable in state-of-the-art experiments. Here we demonstrate highly successful classifications of real-life images using photonic qubits, combining a quantum tensor-network representation of hand-written digits and entanglement-based optimization. Specifically, we focus on binary classification for hand-written zeroes and ones, whose features are cast into the tensor-network representation, further reduced by optimization based on entanglement entropy and encoded into two-qubit photonic states. We then demonstrate image classification with a high success rate exceeding 98%, through successive gate operations and projective measurements. Although we work with photons, our approach is amenable to other physical realizations such as nitrogen-vacancy centers, nuclear spins and trapped ions, and our scheme can be scaled to efficient multi-qubit encodings of features in the tensor-product representation, thereby setting the stage for quantum-enhanced multi-class classification.
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency

Recent works have advanced the performance of self-supervised representation learning by a large margin. The core among these methods is intra-image invariance learning. Two different transformations of one image instance are considered as a positive sample pair, where various tasks are designed to learn invariant representations by comparing the pair. Analogically, for video data, representations of frames from the same video are trained to be closer than frames from other videos, i.e. intra-video invariance. However, cross-video relation has barely been explored for visual representation learning. Unlike intra-video invariance, ground-truth labels of cross-video relation is usually unavailable without human labors. In this paper, we propose a novel contrastive learning method which explores the cross-video relation by using cycle-consistency for general image representation learning. This allows to collect positive sample pairs across different video instances, which we hypothesize will lead to higher-level semantics. We validate our method by transferring our image representation to multiple downstream tasks including visual object tracking, image classification, and action recognition. We show significant improvement over state-of-the-art contrastive learning methods. Project page is available at https://happywu.github.io/cycle_contrast_video.
Information-Set Decoding with Hints

This paper studies how to incorporate small information leakages (called "hints") into information-set decoding (ISD) algorithms. In particular, the influence of these hints on solving the (n, k, t)-syndrome-decoding problem (SDP), i.e., generic syndrome decoding of a code of length n, dimension k, and an error of weight t, is analyzed. We motivate all hints by leakages obtainable through realistic side-channel attacks on code-based post-quantum cryptosystems. One class of studied hints consists of partial knowledge of the error or message, which allow to reduce the length, dimension, or error weight using a suitable transformation of the problem. As a second class of hints, we assume that the Hamming weights of sub-blocks of the error are known, which can be motivated by a template attack. We present adapted ISD algorithms for this type of leakage. For each third-round code-based NIST submission (Classic McEliece, BIKE, HQC), we show how many hints of each type are needed to reduce the work factor below the claimed security level. E.g., for Classic McEliece mceliece348864, the work factor is reduced below $$2^{128}$$ for 9 known error locations, 650 known error-free positions or known Hamming weights of 29 sub-blocks of roughly equal size.
Network Compression via Mixed Precision Quantization Using a Multi-Layer Perceptron for the Bit-Width Allocation

Deep Neural Networks (DNNs) are a powerful tool for solving complex tasks in many application domains.The high performance of DNNs demands significant computational resources, which might not always be available.Network quantization with mixed-precision across the layers can alleviate this high demand.However, determining layer-wise optimal bit-widths is non-trivial, as the search space is exponential.This article proposes a novel technique for allocating layer-wise bit-widths for a DNN using a multi-layer perceptron (MLP).The Kullback-Leibler (KL) divergence of the softmax outputs between the quantized and full precision network is used as the metric to quantify the quantization quality.We explore the relationship between the KL-divergence and the network size, and from our experiments observe that more aggressive quantization leads to higher divergence, and vice versa.The MLP is trained with layerwise bit-widths as labels and their corresponding KL-divergence as the input.The MLP training set, i.e. the pairs of the layer-wise bit-widths and their corresponding KL-divergence, is collected using a Monte Carlo sampling of the exponential search space.We introduce a penalty term in the loss to ensure that the MLP learns to predict bit-widths resulting in the smallest network size.We show that the layer-wise bitwidth predictions from the trained MLP result in reduced network size without degrading accuracy while achieving better or comparable results with SOTA work but with less computational overhead.Our method achieves up to 6x, 4x, 4x compression on VGG16, ResNet50, and GoogLeNet respectively, with no accuracy drop compared to the original full precision pretrained model, on the ImageNet dataset.
Diagnosing writing ability using China’s Standards of English Language Ability: Application of cognitive diagnosis models

Despite the growing interest in diagnostic assessment, few studies have applied cognitive diagnosis models (CDMs) to diagnosing writing ability. In this study, a diagnostic checklist was developed using the descriptors of China’s Standards of English Language Ability (CSE) and diagnostic results generated by the linear logistic model (LLM) were examined. Based on the descriptive parameters of CSE scales, four attributes were targeted, including grammatical knowledge, textual knowledge, functional knowledge, and sociolinguistic knowledge. The relationship between the descriptors and attributes was specified in a Q-matrix by four domain experts. Writing responses from 460 undergraduates were coded by 15 raters using the diagnostic checklist. Item parameters, attribute prevalence, and common attribute mastery profiles were analyzed. Results showed that examinees’ strengths and weaknesses were identified, and the diagnostic results differed across low and high proficiency levels. The practical implications and challenges of using the CDM-based checklist approach were further discussed.
Continuous Software Bug Prediction

Background: Many software bug prediction models have been proposed and evaluated on a set of well-known benchmark datasets. We conducted pilot studies on the widely used benchmark datasets and observed common issues among them. Specifically, most of existing benchmark datasets consist of randomly selected historical versions of software projects, which poses non-trivial threats to the validity of existing bug prediction studies since the real-world software projects often evolve continuously. Yet how to conduct software bug prediction in the real-world continuous software development scenarios is not well studied.
Self-supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images

With wide applications of image editing tools, forged images (splicing, copy-move, removal and etc.) have been becoming great public concerns. Although existing image forgery localization methods could achieve fairly good results on several public datasets, most of them perform poorly when the forged images are JPEG compressed as they are usually done in social networks. To tackle this issue, in this paper, a self-supervised domain adaptation network, which is composed of a backbone network with Siamese architecture and a compression approximation network (ComNet), is proposed for JPEG-resistant image forgery localization. To improve the performance against JPEG compression, ComNet is customized to approximate the JPEG compression operation through self-supervised learning, generating JPEG-agent images with general JPEG compression characteristics. The backbone network is then trained with domain adaptation strategy to localize the tampering boundary and region, and alleviate the domain shift between uncompressed and JPEG-agent images. Extensive experimental results on several public datasets show that the proposed method outperforms or rivals to other state-of-the-art methods in image forgery localization, especially for JPEG compression with unknown QFs.
Empirical Validation of cross-version and 10-fold cross-validation for Defect Prediction

Nowadays, the development of a prediction model is one of the most important research area. Prediction models are helpful in providing accurate results for unseen data or future data. The most important phase of the software development life cycle (SDLC) is testing. During testing, we face some uncertain behavior of the program due to the presence of defects. To remove these defects in the early stage of SDLC, we design Software Defect Prediction Model (SDPM). Although many SDPM have been developed using various Machine Learning (ML) and statistical technique, but the generalizability of results have prevailed because the developed models use the same dataset for training and testing. This study aims to develop SDPM using cross-version, which using two different versions of a project for training and testing. The historical labeled defect data of the previous version is used for the updated or upcoming version for defect prediction which is termed as Cross-Version Defect Prediction (CVDP). To complete experimentation, we have used 26 datasets from an open-source repository. The performance of SDPM is analyzed using performance metrics. The SDPM is also developed using 10-cross validation. In the end, the comparison of CVDP and 10-cross validation has been done using a statistical test. The aim of conducting this study is to analyze the applicability of cross-version defect prediction when a sufficient amount of data is not available for training and testing. According to statistical analysis, it has been observed that cross-version can be used if we have to test our prediction model for unseen projects or upcoming projects.
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning

Existing 3D human pose estimation algorithms trained on distortion-free datasets suffer performance drop when applied to new scenarios with a specific camera distortion. In this paper, we propose a simple yet effective model for 3D human pose estimation in video that can quickly adapt to any distortion environment by utilizing MAML, a representative optimization-based meta-learning algorithm. We consider a sequence of 2D keypoints in a particular distortion as a single task of MAML. However, due to the absence of a large-scale dataset in a distorted environment, we propose an efficient method to generate synthetic distorted data from undistorted 2D keypoints. For the evaluation, we assume two practical testing situations depending on whether a motion capture sensor is available or not. In particular, we propose Inference Stage Optimization using bone-length symmetry and consistency. Extensive evaluation shows that our proposed method successfully adapts to various degrees of distortion in the testing phase and outperforms the existing state-of-the-art approaches. The proposed method is useful in practice because it does not require camera calibration and additional computations in a testing set-up. Code is available at https://github.com/hanbyel0105/CamDistHumanPose3D.
Training Deep Networks for Prostate Cancer Diagnosis Using Coarse Histopathological Labels

Motivation: Accurate detection of prostate cancer using ultrasound data is a challenging yet highly relevant clinical question. A significant roadblock for training accurate models for cancer detection is the lack of histopathology labels with high resolution that correspond to the presence of cancer in the entire imaging or biopsy planes. Histopathology reports only provide a coarse, representation of cancer distribution in an image region; the distribution of cancer in itself is only approximately reported, making labels generated from these reports very noisy. Method: We propose a multi-constraint optimization method in a co-teaching framework with two deep neural networks. These networks are simultaneously and jointly trained, where each network uses data identified by its peer network as less noisy, to update itself. We propose two additional constraints based on the statistics of cancer distribution and noisy nature of labels to the conventional co-teaching framework. Results: We demonstrate the effectiveness of the proposed learning methodology using a challenging ultrasound dataset with 380 biopsy cores obtained from 89 patients during systematic prostate biopsy. Our results show that our proposed multi-constraint optimization method leads to a significant improvements in terms of area under the curve and balanced accuracy over baseline co-teaching method for detection of prostate cancer.
Attacking Recommender Systems With Plausible Profile

Recommender systems (RS) have become an essential component of web services due to their excellent performance. Despite their great success, RS have proved to be vulnerable to data poisoning attacks, which inject well-crafted fake profiles into RS, so that the target items can be maliciously recommended. In this paper, we first reveal that existing poisoning attacks in RS can be detected effortlessly, as the features of the generated fake profiles cannot be inconsistent with those of normal profiles all the time. We further propose RecUP, a poisoning attack in RS that can generate plausible profiles whose features stay almost the same as the normal ones, based on Generative Adversarial Networks (GAN). To tailor GAN for poisoning in RS, we develop HRGAN and devise a loss function to guide the training of the generator, along with a masking operation with selected potentially powerful profiles, so that the final generated profiles can perform malicious recommendations as expected. Evaluations against various defense methods using three real-world datasets show that, RecUP can generate the most plausible profiles while maintaining comparable attacking performance compared with state-of-the-art attacks.