Reviewerly/exHarmony huggingface dataset:
SHyLoC 2.0: A Versatile Hardware Solution for On-Board Data and Hyperspectral Image Compression on Future Space Missions

In this paper, we present the design, implementation and results of a set of IP cores that perform on-board hyperspectral image compression according to the CCSDS 123.0-B-1 lossless standard, specifically designed to be suited for on-board systems and for any kind of hyperspectral sensor.As entropy coder, the sample-adaptive entropy coder defined in the 123.0-B-1 standard or the low-complexity blockadaptive encoder defined by the CCSDS 121.0-B-2 lossless standard could be used.Both IPs, 123.0-B-1 and 121.0-B-2, are part of SHyLoC 2.0, and can be used together for compression of hyperspectral images, being also possible the compression of any kind of data using only the 121-IP.SHyLoC 2.0 improves and extends the capabilities of SHyLoC 1.0, currently available at the ESA IP Cores library, increasing its compression efficiency and throughput, without compromising the resources footprint.Moreover, it incorporates new features, such as the unit-delay predictor option defined by the CCSDS 121.0-B-2 standard, and burst capabilities in the external memory interface of the CCSDS 123-IP, among others.Dedicated architectures have been designed for all the possible input image sample arrangements, in order to maximise throughput and reduce the hardware resources utilization.The design is technology-agnostic, enabling the mapping of the VHDL code in different FPGAs or ASICs.Results are presented for a representative group of well-known space-qualified FPGAs, including the new NanoXplore BRAVE family.A maximum throughput of 150 MSamples/s is obtained for Xilinx Virtex XQR5VFX130 when the SHyLoC 2.0 CCSDS-123 IP is configured in Band-Interleaved by Pixel (BIP) order, using only the 4% of LUTs and less than the 1% of internal memory.
Attacks on Machine Learning: Adversarial Examples in Connected and Autonomous Vehicles

Connected and autonomous vehicles (CAV a.k.a. driverless cars) offset human response for transportation infrastructure, enhancing traffic efficiency, travel leisure, and road safety. Behind the wheels of these mobile robots lies machine learning (ML) to automate mundane driving tasks and make decisions from situational awareness. Attacking ML, the brain of driverless cars, can cause catastrophes. This paper proposes a novel approach to attack CAV by fooling its ML model. Using adversarial examples in CAVs, the work demonstrates how adversarial machine learning can generate attacks hardly detectable by current ML classifiers for CAV misbehavior detection. First, adversarial datasets are generated by a traditional attack engine, which CAV misbehavior detection ML models can easily detect. Building attack ML model takes two phases: training and testing. Using supervised learning, Phase I trains the model on the time-series data, converted from the adversarial datasets. Phase II tests the model, which leads, for the next round of model improvement. The initial round deploys K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms, respectively. The next round, guided by deep learning (DL) models, uses Logistic Regression (LG) of neural network and Long Short-Term Memory (LSTM) of recurrent neural network. The results, in precision-recall (PR) and receiver operating characteristic (ROC) curves, validate the effectiveness of the proposed adversarial ML models. This work reveals the vulnerability in ML. At the same time, it shows the promise to protect critical infrastructure by studying the opponent strategies. Future work includes retraining the adversarial ML models with real-world datasets from pilot CAV sites.
Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification

Many real-world applications, such as city scale traffic monitoring and control, requires large scale re-identification. However, previous ReID methods often failed to address two limitations in existing ReID benchmarks, i.e., low spatiotem-poral coverage and sample imbalance. Notwithstanding their demonstrated success in every single benchmark, they have difficulties in generalizing to unseen environments. As a result, these methods are less applicable in a large scale setting due to poor generalization. In seek for a highly generalizable large-scale ReID method, we present an adversarial domain-invariant feature learning framework (ADIN) that explicitly learns to separate identity-related features from challenging variations, where for the first time "free" annotations in ReID data such as video timestamp and camera index are utilized. Furthermore, we find that the imbalance of nuisance classes jeopardizes the adversarial training, and for mitigation we propose a calibrated adversarial loss that is attentive to nuisance distribution. Experiments on existing large-scale person/vehicle ReID datasets demonstrate that ADIN learns more robust and generalizable representations, as evidenced by its outstanding direct transfer performance across datasets, which is a criterion that can better measure the generalizability of large scale Re-ID methods.
Toward Blockchain-Powered Trusted Collaborative Services for Edge-Centric Networks

Collaborative edge computing (CEC) is a novel extension for several edge paradigms (e.g., mobile edge computing, fog computing, and cloudlet) to further enhance processing capabilities to support computation-intensive and latency-sensitive applications in edge-centric networks. While existing schemes keep adding new functionalities (e.g., collaborative computing and caching), they overlook an important issue of establishing trustworthiness among all CEC participants. In general, due to the heterogeneity of all participants, simply assuming trustworthiness among them is undesirable. To address this issue, in this article, we present BlockEdge, a blockchain-powered framework that delivers trusted CEC services. Toward this end, BlockEdge first introduces incentive schemes to attract edge nodes to participate in CEC tasks. Then it publicly and persistently stores all task results on blockchain to enable smart-contract-based correctness verification and automated punishment in case of failures. Such a built-in accountability scheme allows BlockEdge to establish a trust reputation system for all CEC stakeholders, which can be further used for reliable selection of CEC participators. Our security analysis and experimental evaluation demonstrate that BlockEdge is both technically feasible and financially beneficial. We hope that the blockchain-based trustworthiness establishment designed in BlockEdge can provide a fundamental primitive for building a more secure collaborative ecosystem, especially for complex networks such as 5G and IoT.
The automatic segmentation of residential solar panels based on satellite images: A cross learning driven U-Net method

Segmenting small-scale residential solar panels (RSPs) based on satellite images is an emerging data science problem in the renewable energy field. In this paper, we develop a cross learning driven U-Net (CrossNets) method and its extension, adaptive CrossNets, to automatically segment RSPs in satellite images. Proposed methods employ a group of generic U-Nets as a community and target to enhance the RSP segmentation performance. First, parameters of each generic U-Net in the community of CrossNets are initialized individually via the initialization with transfer learning and the classical initialization methods. Next, a novel training mechanism, cross learning, is developed to serve as a constraint for better optimizing CrossNets. Based on cross learning, each generic U-Net in the community first individually updates parameters at every epoch and next learns parameters from the best individual at specific epochs. Cross learning relieves the reliance of generic U-Nets on a careful initialization and better optimizes U-Nets. In testing, the result of the best performed generic U-Net in the community is selected as the final segmentation result of CrossNets. Adaptive CrossNets, a variant of CrossNets, is developed by applying an additional threshold to reduce the possibility of over-learning caused by cross learning. Satellite images collected from one city in U.S. are utilized to validate the performance of proposed methods. These images cover a large area of 135 km2 with 2794 RSPs. Compared with two generic U-Nets based benchmarks, our method can enhance the overall segmentation IoU by around 34% and 1.5%. Moreover, the segmentation robustness is improved from 1.191e−2 and 1.286e−4 to 2.481e−5. In addition, two new image datasets collected from other two cities in U.S. are applied to further examine the applicability of proposed methods.
Feature-Guided SAR-to-Optical Image Translation

The powerful performance of Generative Adversarial Networks (GANs) in image-to-image translation has been well demonstrated in recent years.However, most methods are focused on completing an isolated image translation task.With the complex scenes in optical images and high-frequency speckle noise in SAR images, the quality of generated images is often unsatisfactory.In this paper, a feature-guided method for SAR-to-optical image translation is proposed to better take the unique attributes of images into account.Specifically, in view of the diversity of structure features and texture features, VGG-19 network is used as the feature extractor in the task of cross-modal image translation.To ensure the acquisition of multilayer features in the process of image generation, feature matching is carried out on different layers.Loss function based on Discrete Cosine Transform is designed to filter out the high-frequency noise.The generated images show better performance in feature preservation and noise reduction, and achieve higher Image Quality Assessment scores compared with images generated by some famous methods.The superiority of our algorithm is also demonstrated by being applied to different networks.
A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.
Breast Cancer Classification and Prediction using Machine Learning

Breast cancer is a dominant cancer in women worldwide and is increasing in developing countries where the majority of cases are diagnosed in late stages.The projects that have already been proposed show a comparison of machine learning algorithms with the help of different techniques like the ensemble methods, data mining algorithms or using blood analysis etc.This paper proposed now presents a comparison of six machine learning (ML) algorithms: Naive Bayes (NB), Random Forest (RT), Artificial Neural Networks (ANN), Nearest Neighbour (KNN), Support Vector Machine (SVM) and Decision Tree (DT) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset which is extracted from a digitised image of an MRI.For the implementation of the ML algorithms, the dataset was partitioned into the training phase and the testing phase.The algorithm with the best results will be used as the backend to the website and the model will then classify the cancer as benign or malignant.
Deep Learning Initialized and Gradient Enhanced Level-Set Based Segmentation for Liver Tumor From CT Images

Liver and liver tumor segmentation provides vital biomarkers for surgical planning and hepatic diagnosis. In this paper, we propose and validate a novel level-set method integrating an enhanced edge indicator and an automatically derived initial curve for CT based liver tumor segmentation. At the preprocessing step, the CT image intensity values were truncated to lie in a fixed range to enhance the image contrast surrounding liver and liver tumor. To remove non-liver tissues for subsequent tumor segmentation, liver was firstly segmented using two convolutional neural networks in a coarse-to-fine manner. A 2D slice-based U-net was used to roughly localize the liver and a 3D patch-based fully convolutional network was used to refine the liver segmentation as well as to roughly localize the liver tumor. A novel level-set method was then presented to further refine the tumor segmentation. Specifically, the probabilistic distribution of the liver tumor was estimated using unsupervised fuzzy c-means clustering, which was then utilized to enhance the edge-detector used in level-set. Effectiveness of the proposed pipeline was validated on two publicly-available datasets. Experimental results identified the superior segmentation performance of the proposed pipeline over state-of-the-art methods.
Dual Graph Convolutional Network for Semantic Segmentation.

Exploiting long-range contextual information is key for pixel-wise prediction tasks suchas semantic segmentation. In contrast to previous work that uses multi-scale featurefusion or dilated convolutions, we propose a novel graph-convolutional network (GCN)to address this problem. Our Dual Graph Convolutional Network (DGCNet) modelsthe global context of the input feature by modelling two orthogonal graphs in a singleframework. The first component models spatial relationships between pixels in theimage, whilst the second models interdependencies along the channel dimensions ofthe network’s feature map. This is done efficiently by projecting the feature into anew, lower-dimensional space where all pairwise interactions can be modelled, beforereprojecting into the original space. Our simple method provides substantial benefitsover a strong baseline and achieves state-of-the-art results on both Cityscapes (82.0%mean IoU) and Pascal Context (53.7% mean IoU) datasets. Our code is available at:https://github.com/lzrobots/DGCNet
Rate Control for Video-Based Point Cloud Compression

Rate control is a necessary tool for video-based point cloud compression (V-PCC). However, there is no solution specified on this topic yet. In this paper, we propose the first rate control algorithm for V-PCC. Generally, a rate control algorithm is divided into two processes: bit allocation and bitrate control. In V-PCC, the total bits are composed of three parts: the header information including the auxiliary information and occupancy map, the geometry video, and the attribute video. The bit allocation aims to assign the total bits to these three parts. Since the auxiliary information and occupancy map are encoded losslessly, the bit cost of the header information is fixed. Therefore, we only need to assign bits between the geometry and attribute videos. Our first key contribution is the proposed video-level bit allocation algorithm between the geometry and attribute videos to optimize the overall reconstructed point cloud quality. Then we assign geometry and attribute video bits to each group of pictures (GOP), each frame, and each basic unit (BU). Our second key contribution is that we assign zero bits to the BUs with only unoccupied pixels. The unoccupied pixels are useless for the reconstructed quality of the point cloud and therefore should be assigned zero bits. In the bitrate control process, the encoding parameters are determined, and the model parameters are updated for each frame and BU to achieve the target bits. Our third key contribution is that we propose a BU-level model updating scheme to handle the case where various patches may be placed in different positions in neighboring frames. We use the auxiliary information to find the corresponding BU in the previous frame and apply its model parameters to the current BU. The proposed algorithms are implemented in the V-PCC and High Efficiency Video Coding (HEVC) reference software. The experimental results show that the proposed rate control algorithm can achieve significant bitrate savings compared with the state-of-the-art method.
Text based Sentiment Analysis using LSTM

Analyzing the big textual information manually is tougher and time-consuming.Sentiment analysis is a automated process that uses computing (AI) to spot positive and negative opinions from the text.Sentiment analysis is widely used for getting insights from social media comments, survey responses, and merchandise reviews to create data-driven decisions.Sentiment analysis systems are accustomed to add up to the unstructured text by automating business processes and saving hours of manual processing.In recent years, Deep Learning (DL) has garnered increasing attention within the industry and academic world for its high performance in various domains.Today, Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are the foremost popular types of DL architectures used.We do sentiment analysis on text reviews by using Long Short-Term Memory (LSTM).Recently, thanks to their ability to handle large amounts of knowledge, neural networks have achieved a good success on sentiment classification.Especially long STM networks.
Deep Multi-Patch Matching Network for Visible Thermal Person Re-Identification

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Visible Thermal Person Re-Identification</i> (VTReID) is a cross-modality retrieval problem in computer vision. Accurate VTReID is very challenging due to large modality discrepancies. In this work, we design a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-Patch Matching Network</i> (MPMN) framework to simultaneously mitigate the heterogeneity of coarse-grained and fine-grained visual semantics. In view of cross-modality matching, we verify that aligning modality distributions of the original features is likely to suffer from the selective alignment behavior, i.e., only focuses on easiest dimensions or subspaces. Inspired by adversarial learning, we propose a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-Patch Modality Alignment</i> (MPMA) loss to jointly balance and reduce the modality discrepancies of multi-patch features by mining hard subspaces and abandoning easy subspaces. Since multi-patch features are potentially complementary to each other, the semantic correlations between different patches should be exploited during training. Motivated by knowledge distillation, we put forward a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cross-Patch Correlation Distillation</i> (CPCD) loss to transfer the semantic knowledges across different patches. To balance multi-patch tasks, an effective <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Patch-Aware Priority Attention</i> (PAPA) method is further introduced to dynamically prioritize hard patch tasks during training. This paper experimentally demonstrates the effectiveness of the proposed methods, achieving superior performance over the state-of-the-art methods on RegDB and SYSU-MM01 datasets.
A Blockchain-Based Proxy Re-Encryption With Equality Test for Vehicular Communication Systems

Vehicular communication systems (VCS) are likely to play an increasingly important role in future smart city design, for example by improving road safety and traffic efficiency. However, there are underpinning security and privacy challenges, which may also result in under-utilization of vehicular data. In this paper, we introduce a new cryptographic primitive, namely: blockchain-based proxy re-encryption with equality test. Specifically, the proposed approach is designed to achieve reliable matching results by leveraging smart contracts, as well as efficient data sharing and privacy-preserving by combining the function of public-key encryption with equality test and proxy re-encryption. We also implement a prototype of the proposed approach and evaluate its performance with those of three other competing approaches. A comparative summary with three other competing approaches demonstrate that the proposed approach achieves all four features (i.e., decentralization, flexibility, non-interaction, and re-encryption). Findings from the prototype evaluation (using hyperledger fabric v1.4.2 as the test blockchain platform, and the fabric-sample repository) also demonstrate the utility of the proposed approach in practice.
Pivot-based Maximal Biclique Enumeration

Enumerating maximal bicliques in a bipartite graph is an important problem in data mining, with innumerable real-world applications across different domains such as web community, bioinformatics, etc. Although substantial research has been conducted on this problem, surprisingly, we find that pivot-based search space pruning, which is quite effective in clique enumeration, has not been exploited in biclique scenario. Therefore, in this paper, we explore the pivot-based pruning for biclique enumeration. We propose an algorithm for implementing the pivot-based pruning, powered by an effective index structure Containment Directed Acyclic Graph (CDAG). Meanwhile, existing literature indicates contradictory findings on the order of vertex selection in biclique enumeration. As such, we re-examine the problem and suggest an offline ordering of vertices which expedites the pivot pruning. We conduct an extensive performance study using real-world datasets from a wide range of domains. The experimental results demonstrate that our algorithm is more scalable and outperforms all the existing algorithms across all datasets and can achieve a significant speedup against the previous algorithms.
Eclipse: Preserving Differential Location Privacy Against Long-Term Observation Attacks

Mechanisms built upon geo-indistinguishability render location privacy, where a user can submit obfuscated locations to Location-Based Service providers but still be able to correctly utilize services. However, these mechanisms are vulnerable under inference attacks. Particularly, with background knowledge of a user’s obfuscated locations, an attacker can infer actual locations by carrying out long-term observation attacks. Unfortunately, how to defend long-term observation attacks in the field of differential location privacy remains open. In this paper, we first demonstrate the vulnerabilities of existing mechanisms under long-term observation attacks. In light of these vulnerabilities, we devise a novel mechanism, referred to as Eclipse, which bridges the gap between location protection and usability of services. Specifically, we harness geo-indistinguishability and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity to obfuscate locations and hide each location based on an anonymity set. As a result, our mechanism effectively perturbs the distribution of locations and suppresses leakage under long-term observation attacks. Moreover, the set of possible outputs is utilized to minimize the impacts to usability and correctness. We formally define and rigorously prove the security of the proposed mechanism by leveraging differential privacy. Moreover, we implement the proposed mechanism and conduct a series of experiments on real-world datasets to demonstrate its efficacy and efficiency.