Reviewerly/exHarmony huggingface dataset:
Single image dehazing based on fusion strategy

In this paper, we propose a deep convolutional network for single image dehazing based on derived image fusion strategy. Instead of estimating the transmission map and atmospheric light as previously performed, we directly generate a haze-free image by the proposed end-to-end trainable neural network. We derive five maps from the original hazy image based on the characteristics of the hazy scene to improve the dehazing performance. First, the exposure map (EM) and saliency map (SM) complement each other to focus on details in far-away and near-region scenes. Second, the white balance map (BM) and gamma correction map (GM) are employed to recover the latent colour and intensity components of the scene. Finally, the haze veil map (VM) is introduced to enhance the global image contrast. To efficiently blend the five derived maps, we propose a U-shaped deep convolutional network consisting of encoder and decoder layers to generate a haze-free image. The convolutional layers transferred from the pretrained ResNet50 are used as encoder layers for hierarchical feature extraction. Two efficient blocks, named the cascaded residual block and the channel compression block, are proposed in the network for better dehazing performance. The final dehazed result is generated by combining the significant features of the different derived maps. Additionally, perceptual loss is introduced for better visual quality. The experimental results for both synthetic and natural hazy images demonstrate that our algorithm performs comparably or even better than state-of-the-art methods in terms of the peak signal-to-noise ratio (PSNR), structure similarity (SSIM) and visual quality.
Credit Card Fraud Detection in E-Commerce

Often the challenge associated with tasks like fraud detection is the lack of all likely patterns needed to train suitable supervised learning models. This problem accentuates when the fraudulent patterns are not only scarce, they also change over time. Change in fraudulent patterns is because fraudsters continue to innovate novel ways to circumvent measures put in place to prevent fraud. Limited data and continuously changing patterns makes learning significantly difficult. We hypothesize that good behavior does not change with time and data points representing good behavior have consistent spatial signature under different groupings. Based on this hypothesis we are proposing an approach that detects fraudulent patterns in large data sets by assigning a consistency score to each data point using an ensemble of clustering methods. Our main contribution is proposing a novel method that can detect outliers in large datasets and is robust to changing patterns. We also argue that area under the ROC curve, although a commonly used metric to evaluate outlier detection methods is not the right metric. Since outlier detection problems have a skewed distribution of classes, precision-recall curves are better suited because precision compares false positives to true positives (outliers) rather than true negatives (inliers) and therefore is not affected by the problem of class imbalance. The proposed approach is tested on a large real world credit card fraud detection dataset available through Kaggle. We report our performance on both AUPRC and AUROC and show meaningful improvements over the baseline methods.
B-IoT: Blockchain Driven Internet of Things with Credit-Based Consensus Mechanism

Internet of Things (IoT) plays an indispensable role in our daily life, in many cases, IoT systems are implemented following the client-server paradigm, which are vulnerable to single point of failures and malicious attacks. Due to the resilience and security promise of blockchain, the idea of combining blockchain and IoT has gained considerable attention in recent years. However, blockchains are power-intensive and low-throughput, which may not suitable for power-constrained IoT devices. To tackle these challenges, we present B-IoT, a blockchain based IoT system with credit-based consensus mechanism. We propose a credit-based proof-of-work (PoW) mechanism for IoT devices, which enhances security and improves transaction efficiency simultaneously. In order to protect the confidentiality of sensitive IoT data, we design a data authority management method to regulate the access to sensor data. In addition, our system is built based on a directed acyclic graph (DAG)-structured blockchain, which is more efficient than the satoshi-style blockchain. We implement a prototype of B-IoT on Raspberry Pi, and conduct case studies of a smart factory. Extensive evaluation and analysis results demonstrate that the proposed credit-based PoW mechanism and data access control are practical for IoT.
Synthesizing Privacy Preserving Traces: Enhancing Plausibility With Social Networks

Due to the popularity of mobile computing and mobile sensing, users' traces can now be readily collected to enhance applications' performance. However, users' location privacy may be disclosed to the untrusted data aggregator that collects users' traces. Cloaking users' traces with synthetic traces is a prevalent technique to protect location privacy. But the existing work that synthesizes traces suffers from the social relationship based de-anonymization attacks. To this end, we propose W <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -tess that synthesizes privacy-preserving traces via enhancing the plausibility of synthetic traces with social networks. The main idea of W <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -tess is to credibly imitate the temporal, spatial, and social behavior of users' mobility, sample the traces that exhibit similar three-dimension mobility behavior, and synthesize traces using the sampled locations. By doing so, W <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -tess can provide “differential privacy” on location privacy preservation. In addition, compared to the existing work, W <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -tess offers several salient features. First, both location privacy preservation and data utility guarantees are theoretically provable. Second, it is applicable to most geo-data analysis tasks performed by the data aggregator. Experiments on two real-world datasets, loc-Gwalla and loc-Brightkite, have demonstrated the effectiveness and efficiency of W <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -tess.
Privacy-Preserving Data Integrity Verification in Mobile Edge Computing

Mobile edge computing (MEC) is proposed as an extension of cloud computing in the scenarios where the end devices desire better services in terms of response time. Edge nodes are deployed at the proximity of the end devices, and it can pre-download parts of data stored in the cloud so that the end devices can access these data with low latency. However, because the edges are usually owned by individuals and small organizations, which have limited operation capacities for maintaining the machines, the data on the edges are easily corrupted (due to external attacks or internal hardware failures). Therefore, it is essential to verify data integrity in the MEC. We propose two Integrity Checking protocols for mobile Edge computing, called ICE-basic and ICE-batch, which are designed for the cases where the user wants to check data integrity on a single edge or multiple edges, respectively. Based on the concept of provable data possession and the technique of private information retrieval, our protocols allow a third-party verifier to check the data integrity on the edges without violating users' data privacy and query pattern privacy. We rigorously prove the security and privacy guarantees of the protocols. Furthermore, we have implemented a proof-of-concept system that runs ICE, and extensive experiments are conducted. The theoretical analysis and experimental results demonstrate the proposed protocols are efficient both in computation and communication.
An efficient image privacy scheme based on nonlinear chaotic system and linear canonical transformation

Making information invulnerable is one of the most critical issues of today's era where information is being sent from one source to another destination with expeditious rates. In this research article, linear canonical transform (LCT) along with Lorenz differential equation is subjected for the process of double image encryption for its inherent characteristics which implies the diffusion and confusion of current era. In the contemplated method standard color images; Lena and Pepper of size 256×256 are used. Image scrambling, LCT of first order and second order along with Lorenz system are adapted as the steps on encryption process. The reverse process of defined encryption scheme is endorsed for the reformation of the original plain images separately from the single encrypted image. For quality assessment of the encryption scheme; different security and statistical analysis are performed and histograms of all the images are analyzed. As a result, statistical assessments recommended that our contemplated scheme is legitimate for the security of digital images.
Engaging Feminist Solidarity for Comparative Research, Design, and Practice

Research in the fields of Computer Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI) is increasingly embracing and moving across borders. While universalism in such research is widely rejected, sole focus on the "particular'' is also commonly critiqued. Kentaro Toyama unpacks this tension, calling for balance via "deliberate efforts to understand the interplay of human universals and cultural differences, and how it pertains to design.'' In this paper, one such deliberate effort, we introduce the notion of feminist solidarity---as theorized by Chandra Mohanty---for drawing connections across borders in CSCW and HCI research, design, and practice. To enable contributions in these fields to cross cultures and geographies in productive ways, we draw attention to commonalities in the struggles and processes of resistance operating in different contexts of study. To do this, we present lessons learned from conducting three comparative studies in six contexts, which were located across various borders. The primary contribution of our analysis is to leverage a feminist solidarity-based approach towards extending conversations on comparative, transnational, and feminist CSCW and HCI research, design, and practice. Our focus remains on resource-constrained regions across the world, in both the Global North and South.
Towards image-based animal tracking in natural environments using a freely moving camera

Image-based tracking of individual animals can provide rich data to underpin breakthroughs in biological and medical research, but few if any existing methods extend to tracking unconstrained natural behaviour in the field. We have developed a visual tracking system for animals filmed with a freely moving hand-held or drone-operated camera in their natural environment. This exploits a global inference method for detecting motion of an animal against a cluttered background. Trajectories are then generated by a novel video key-frame selection scheme in combination with a geometrically constrained image stitching algorithm, resulting in a two-dimensional panorama image of the environment on which the dense animal path is displayed. By introducing a minimal and plausible set of constraints regarding the camera orientation and movement, we demonstrate that both per-frame animal positions and overall trajectories can be extracted with reasonable accuracy, for a range of different animals, environments and imaging modalities. Our method requires only a single uncalibrated camera, does not require marking or training data to detect the animal, and makes no prior assumptions about appearance of the target or background. In particular it can detect targets occupying fewer than 20 pixels in the image, and deal with poor contrast, highly dynamic lighting and frequent occlusion. Our algorithm produces highly informative qualitative trajectories embedded in a panorama of the environment. The results are still subject to rotational drift and additional scaling routines would be needed to obtain absolute real-world coordinates. It nevertheless provides a flexible and easy-to-use system to obtain rich data on natural animal behaviour in the field.
A comparative study of CNN-based super-resolution methods in MRI reconstruction and its beyond

The progress of convolution neural network (CNN) based super-resolution has shown its potential in image processing community. Meanwhile, Compressed Sensing MRI (CS-MRI) provides the possibility to accelerate the traditional acquisition process of MRI. In this work, on the basis of decomposing the cascade network to be a series of alternating CNN-based sub-network and data-consistency sub-network, we investigate the performance of the cascade networks in CS-MRI by employing various CNN-based super-resolution methods in the CNN-based sub-network. Furthermore, recognizing that existing methods of exploring dense connection in the CNN-based sub-network are insufficient to utilize the feature information, we propose a dense connected cascade network for more accurate MRI reconstruction. Specifically, the proposed network densely connects both CNN-based sub-network and data-consistency sub-network, thus takes advantage of the data-consistency of k-space data in a densely connected fashion. Experimental results on various MR data demonstrated that the proposed network is superior to current cascade networks in reconstruction quality.
CVE-assisted large-scale security bug report dataset construction method

Identifying SBRs (security bug reports) is crucial for eliminating security issues during software development. Machine learning are promising ways for SBR prediction. However, the effectiveness of the state-of-the-art machine learning models depend on high-quality datasets, while gathering large-scale datasets are expensive and tedious. To solve this issue, we propose an automated data labeling approach based on iterative voting classification. It starts with a small group of ground-truth traing samples, which can be labeled with the help of authoritative vulnerability records hosted in CVE (Common Vulnerabilities and Exposures). The accuracy of the prediction model is improved with an iterative voting strategy. By using this approach, we label over 80k bug reports from OpenStack and 40k bug reports from Chromium. The correctness of these labels are then manually reviewed by three experienced security testing members. Finally, we construct a large-scale SBR dataset with 191 SBRs and 88,472 NSBRs (non-security bug reports) from OpenStack; and improve the quality of existing SBR dataset Chromium by identifying 64 new SBRs from previously labeled NSBRs and filtering out 173 noise bug reports from this dataset. These share datasets as well as the proposed dataset construction method help to promote research progress in SBR prediction research domain.
FPGA-Based Simulated Bifurcation Machine

Since many combinatorial optimization problems can be mapped onto ground-state search problems of Ising models, special-purpose machines for Ising problems have attracted intense attention. Simulated bifurcation (SB) is a recently proposed algorithm to solve these Ising problems. One of the remarkable features of SB is the high-degree parallelism underlying in the algorithm, providing an opportunity to solve the Ising problems very fast by massively parallel processing. In this work, we implement the SB algorithm on FPGAs by designing massively parallel custom circuits. We then compare the FPGA-based SB machines with a state-of-the-art machine called a coherent Ising machine (CIM), a highly optimized implementation of simulated annealing (SA), and GPU-based SB machines. SB machines with spin size of 2,048/4,096 (2K/4K) on an Arria10 GX1150 FPGA have 8,192 processing elements for the matrix-vector multiplication (MM) modules (the most computationally intensive part) and achieve computation throughput of 1,873/2,027 GMAC/s for the MM modules, outperforming 2K/4K SB machines on an Nvidia Tesla V100 GPU (113/183 GMAC/s). The 2K FPGA-SB solves all-to-all connected 2000-node MAX-CUT problem 14X (/124X) faster than the CIM (/the highly-optimized SA), with much better energy efficiency (288X better than the CIM).
Controllability of Bandlimited Graph Processes Over Random Time Varying Graphs

Controllability of complex networks arises in many technological problems involving social, financial, road, communication, and smart grid networks. In many practical situations, the underlying topology might change randomly with time, due to link failures such as changing friendships, road blocks or sensor malfunctions. Thus, it leads to poorly controlled dynamics if randomness is not properly accounted for. We consider the problem of controlling the network state when the topology varies randomly with time. Our problem concerns target states that are bandlimited over the graph; these are states that have nonzero frequency content only on a specific graph frequency band. We thus leverage graph signal processing and exploit the bandlimited model to drive the network state from a fixed set of control nodes. When controlling the state from a few nodes, we observe that spurious, out-of-band frequency content is created. Therefore, we focus on controlling the network state over the desired frequency band, and then use a graph filter to get rid of the unwanted frequency content. To account for the topological randomness, we develop the concept of controllability in the mean, which consists of driving the expected network state towards the target state. A detailed mean squared error analysis is performed to quantify the statistical deviation between the final controlled state on a particular graph realization and the actual target state. Finally, we propose different control strategies and evaluate their effectiveness on synthetic network models and social networks.
Gollum

We present the first approach to automatic exploit generation for heap overflows in interpreters. It is also the first approach to exploit generation in any class of program that integrates a solution for automatic heap layout manipulation. At the core of the approach is a novel method for discovering exploit primitives---inputs to the target program that result in a sensitive operation, such as a function call or a memory write, utilizing attacker-injected data. To produce an exploit primitive from a heap overflow vulnerability, one has to discover a target data structure to corrupt, ensure an instance of that data structure is adjacent to the source of the overflow on the heap, and ensure that the post-overflow corrupted data is used in a manner desired by the attacker. Our system addresses all three tasks in an automatic, greybox, and modular manner. Our implementation is called GOLLUM, and we demonstrate its capabilities by producing exploits from 10 unique vulnerabilities in the PHP and Python interpreters, 5 of which do not have existing public exploits.
Caesar

Detecting activities from video taken with a single camera is an active research area for ML-based machine vision. In this paper, we examine the next research frontier: near real-time detection of complex activities spanning multiple (possibly wireless) cameras, a capability applicable to surveillance tasks. We argue that a system for such complex activity detection must employ a hybrid design: one in which rule-based activity detection must complement neural network based detection. Moreover, to be practical, such a system must scale well to multiple cameras and have low end-to-end latency. Caesar, our edge computing based system for complex activity detection, provides an extensible vocabulary of activities to allow users to specify complex actions in terms of spatial and temporal relationships between actors, objects, and activities. Caesar converts these specifications to graphs, efficiently monitors camera feeds, partitions processing between cameras and the edge cluster, retrieves minimal information from cameras, carefully schedules neural network invocation, and efficiently matches specification graphs to the underlying data in order to detect complex activities. Our evaluations show that Caesar can reduce wireless bandwidth, on-board camera memory, and detection latency by an order of magnitude while achieving good precision and recall for all complex activities on a public multi-camera dataset.
Fine-Grained Recognition of Activities of Daily Living through Structural Vibration and Electrical Sensing

Fine-grained non-intrusive monitoring of activities of daily living (ADL) enables various smart building applications, including ADL pattern assessments for older adults at risk for loss of safety or independence. Prior work in this area has focused on coarsegrained ADL recognition at the activity level (e.g., cooking, cleaning, sleeping), and/or course-grained (hourly or minutely) activity duration segmentation. It also typically relies on a high-density deployment of a variety of sensors. Finer-grained (sub-second-level and event-based) ADL recognition, could provide more detailed ADL information, which is crucial for enabling the assessment of patients' activity patterns and potential changes in behavior. To achieve this fine-grained ADL monitoring, we present a system that combines two emerging non-intrusive sparse sensing mechanisms: 1) vibration sensors to capture the action-induced structural vibration and 2) electrical sensor to capture appliance usage. To evaluate our system, we conducted real-world experiments with multiple human subjects to demonstrate the complementary information from these two sensing modalities. Results show that our system achieved an average 90% accuracy in recognizing activities, which is up to 2.6X higher than baseline systems considering each state-of-the-art sensing modality separately.
Handling bound constraints in CMA-ES: An experimental study

Bound constraints are the lower and upper limits defined for each coordinate of the solution. There are many methods to deal with them, but there is no clear guideline for which of them should be preferred. This paper is devoted to handling bound constraints in the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. It surveys 22 Bound Constraint Handling Methods (BCHMs). The experiments cover both unimodal and multimodal functions taken from the CEC 2017 and the BBOB benchmarks. The performance of CMA-ES was found to change when different BCHMs were used. The worst and the best BCHMs were identified. The results of CMA-ES with the best BCHM and restarts were compared on CEC 2017 with the results of recently published derivatives of Differential Evolution (DE).
Multi-Scale Cropping Mechanism for Remote Sensing Image Captioning

With the rapid development of artificial satellite, a large number of high resolution remote sensing images can be easily obtained now. Recently, remote sensing image captioning, which aims to generate accurate and concise descriptive sentences for remote sensing images, has been promoted by template-based model and encoder-decoder model with several related datasets released. Based on an encoder-decoder model, we propose a training mechanism of multi-scale cropping for remote sensing image captioning in this paper, which can extract more fine-grained information from remote sensing images and enhance the generalization performance of the base model. The experimental results on two datasets UCM-captions and Sydney-captions demonstrate that the proposed approach availably improves the performances in describing high resolution remote sensing images.