Reviewerly/exHarmony huggingface dataset:
Similarity query processing for high-dimensional data

Similarity query processing has been an active research topic for several decades. It is an essential procedure in a wide range of applications. Recently, embedding and auto-encoding methods as well as pre-trained models have gained popularity. They basically deal with high-dimensional data, and this trend brings new opportunities and challenges to similarity query processing for high-dimensional data. Meanwhile, new techniques have emerged to tackle this long-standing problem theoretically and empirically. In this tutorial, we summarize existing solutions, especially recent advancements from both database (DB) and machine learning (ML) communities, and analyze their strengths and weaknesses. We review exact and approximate methods such as cover tree, locality sensitive hashing, product quantization, and proximity graphs. We also discuss the selectivity estimation problem and show how researchers are bringing in state-of-the-art ML techniques to address the problem. By highlighting the strong connections between DB and ML, we hope that this tutorial provides an impetus towards new ML for DB solutions and vice versa.
They Might NOT Be Giants Crafting Black-Box Adversarial Examples Using Particle Swarm Optimization

As machine learning is deployed in more settings, including in security-sensitive applications such as malware detection, the risks posed by adversarial examples that fool machine-learning classifiers have become magnified. Black-box attacks are especially dangerous, as they only require the attacker to have the ability to query the target model and observe the labels it returns, without knowing anything else about the model. Current black-box attacks either have low success rates, require a high number of queries, produce adversarial images that are easily distinguishable from their sources, or are not flexible in controlling the outcome of the attack. In this paper, we present AdversarialPSO, (Code available: https://github.com/rhm6501/AdversarialPSOImages ) a black-box attack that uses few queries to create adversarial examples with high success rates. AdversarialPSO is based on Particle Swarm Optimization, a gradient-free evolutionary search algorithm, with special adaptations to make it effective for the black-box setting. It is flexible in balancing the number of queries submitted to the target against the quality of the adversarial examples. We evaluated AdversarialPSO on CIFAR-10, MNIST, and Imagenet, achieving success rates of 94.9%, 98.5%, and 96.9%, respectively, while submitting numbers of queries comparable to prior work. Our results show that black-box attacks can be adapted to favor fewer queries or higher quality adversarial images, while still maintaining high success rates.
Secure Cloud Auditing with Efficient Ownership Transfer

Cloud auditing with ownership transfer is a provable data possession scheme meeting verifiability and transferability simultaneously. In particular, not only cloud data can be transferred to other cloud clients, but also tags for integrity verification can be transferred to new data owners. More concretely, it requires that tags belonging to the old owner can be transformed into that of the new owner by replacing the secret key for tag generation while verifiability still remains. We found that existing solutions are less efficient due to the huge communication overhead linear with the number of tags. In this paper, we propose a secure auditing protocol with efficient ownership transfer for cloud data. Specifically, we sharply reduce the communication overhead produced by ownership transfer to be independent of the number of tags, making it with a constant size. Meanwhile, the computational cost during this process on both transfer parties is constant as well.
A Dynamic Motion Planning Framework for Autonomous Driving in Urban Environments

We present a framework for robust autonomous driving motion planning system in urban environments which includes trajectory refinement, trajectory interpolation, avoidance of static and dynamic obstacles, and trajectory tracking. Given road centerline, our approach smoother the original line via cubic spline. Fifth order Bezier curve is then used to generate more humanlike trajectories that guarantee at least second order continuity and curvature continuity. Dynamic trajectory planning task is decoupled into lateral spatial and longitudinal velocity planning problems. A bunch of candidate trajectory sets are generated and evaluated by an object function which considers kinematic feasibility, trajectory smoothness, driving comfort and collisionchecking. Meanwhile, an LQG controller is used to generate longitudinal velocity profile to ensure safety and comfort. After that, spatial and velocity profiles are transformed into commands executed by lateral steering and longitudinal acceleration controllers. This framework is validated within a simulation study and has been deployed on our autonomous vehicle shown in Fig. 1 that has travelled thousands of miles in urban environments.
On the Diffusion and Impact of Code Smells in Web Applications

Web applications (web apps) have become one of the largest parts of the current software market over years. Modern web apps offer several business benefits over other traditional and standalone applications. Mainly, cross-platform compatibility and data integration are some of the critical features that encouraged businesses to shift towards the adoption of Web apps. Web apps are evolving rapidly to acquire new features, correct errors or adapt to new environment changes especially with the volatile context of the web development. These ongoing amends often affect software quality due to poor coding and bad design practices, known as code smells or anti-patterns. The presence of code smells in a software project is widely considered as form of technical debt and makes the software harder to understand, maintain and evolve, besides leading to failures and unforeseen costs. Therefore, it is critical for web apps to monitor the existence and spread of such anti-patterns. In this paper, we specifically target web apps built with PHP being the most used server-side programming language. We conduct the first empirical study to investigate the diffuseness of code smells in Web apps and their relationship with the change proneness of affected code. We detect 12 types of common code smells across a total of 223 releases of 5 popular and long-lived open-source web apps. The key findings of our study include: 1) complex and large classes and methods are frequently committed in PHP files, 2) smelly files are more prone to change than non-smelly files, and 3) Too Many Methods and High Coupling are the most associated smells with files change-proneness.
Dual Calibration Mechanism Based L<sub>2</sub>, p-Norm for Graph Matching

Unbalanced geometric structure caused by variations with deformations, rotations and outliers is a critical issue that hinders correspondence establishment between image pairs in existing graph matching methods. To deal with this problem, in this work, we propose a dual calibration mechanism (DCM) for establishing feature points correspondence in graph matching. In specific, we embed two types of calibration modules in the graph matching, which model the correspondence relationship in point and edge respectively. The point calibration module performs unary alignment over points and the edge calibration module performs local structure alignment over edges. By performing the dual calibration, the feature points correspondence between two images with deformations and rotations variations can be obtained. To enhance the robustness of correspondence establishment, the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,p</sub> -norm is employed as the similarity metric in the proposed model, which is a flexible metric due to setting the different p values. Finally, we incorporate the dual calibration and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,p</sub> -norm based similarity metric into the graph matching model which can be optimized by an effective algorithm, and theoretically prove the convergence of the presented algorithm. Experimental results in the variety of graph matching tasks such as deformations, rotations and outliers evidence the competitive performance of the presented DCM model over the state-of-the-art approaches.
Non-Locally up-Down Convolutional Attention Network for Remote Sensing Image Super-Resolution

Recently, single image super-resolution (SISR) has been widely applied in the field of remote sensing image processing and obtained remarkable performance.However, existing CNN-based remote sensing image super-resolution methods are unable to exploit shallow visual characteristics at global receptive fields, which results in the limited perceptual capability of these models.Furthermore, the low-resolution inputs and features contain abundant low-frequency information, which are weighed in channels and space equally, hence limiting the representational ability of CNNs.To solve these problems, we propose a nonlocally up-down convolutional attention network (NLASR) for remote sensing image super-resolution.First, a non-local features enhancement module (NLEB) is constructed to obtain the spatial context information of high-dimensional feature maps, which allows our network to utilize global information to enhance low-level similar structured texture information with effect, overcoming the defects of deficiency perceptual ability of shallow convolutional layers.Second, an enhanced up-sampling channel-wise attention (EUCA) module and enhanced down-sampling spatial-wise attention (EDSA) module are proposed to weight the features at multiple scales.By integrating the channel-wise and multi-scale spatial information, the attention modules are able to compute the response values from the multi-scale regions of each neuron and then establish the accurate mapping from low to high resolution space.Extensive experiments on NWPU-RESISC45 and UCMerced-LandUse datasets show that the proposed method can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.
Are Thermal Attacks Ubiquitous?

Recent work showed that using image processing techniques on thermal images taken by high-end equipment reveals passwords entered on touchscreens and keyboards. In this paper, we investigate the susceptibility of common touch inputs to thermal attacks when non-expert attackers visually inspect thermal images. Using an off-the-shelf thermal camera, we collected thermal images of a smartphone's touchscreen and a laptop's touchpad after 25 participants had entered passwords using touch gestures and touch taps. We show that visual inspection of thermal images by 18 participants reveals the majority of passwords. Touch gestures are more vulnerable to thermal attacks (60.65% successful attacks) than touch taps (23.61%), and attacks against touchscreens are more accurate than on touchpads (87.04% vs 56.02%). We discuss how the affordability of thermal attacks and the nature of touch interactions make the threat ubiquitous, and the implications this has on security.
FASE: A Fast and Accurate Privacy-Preserving Multi-Keyword Top-k Retrieval Scheme Over Encrypted Cloud Data

With the advance of cloud computing technology, increasingly more documents are encrypted before being outsourced to the cloud for great convenience and economic savings. Thus, how to design a fast and accurate multi-keyword ranked search scheme over encrypted cloud data is of paramount importance. In this article, we propose a fast and accurate searchable encryption (FASE) scheme that supports accurate top-k multi-keyword retrieval. We utilize a homomorphic order-preserving encryption algorithm to encrypt the index and query vectors. The encryption method supports homomorphic addition, homomorphic multiplication, and order comparison over encrypted data, and it implements the secure calculation of relevance score between encrypted index and query vectors. The encryption method can not only ensure that the calculation of relevance score ( <inline-formula><tex-math notation="LaTeX">$SI_i * T$</tex-math></inline-formula> ) is not exposed to the cloud server, but also protect the privacy of ranking operator. Compared to the traditional method, there are no dummy keywords added to the query vector and document vector, and the top-k search precision of the FASE scheme is 100 percent. To improve the search efficiency, a large number of irrelevant documents are effectively filtered by matching the document mark vector and query mark vector, and the time cost for calculating the relevance score and ranking is greatly reduced. Furthermore, according to the two-round ranking of the keyword matching degree and the relevance score, not only more accurate search result is returned, but the search efficiency is also further improved. The theoretical analysis and experimental results show that the FASE scheme can achieve fast and accurate multi-keyword ranking search. In addition to ensuring data privacy and security, it can also effectively improve the search efficiency and reduce the time cost of creating an index, and it can return ranking results which more satisfy the user needs.
An analysis of security access control on healthcare records in the cloud

Advancements in data innovation connect with different fields, such as Keen Homes, medicine, healthcare, and smart vehicles. The inescapable, on-request access to medical resources for "each utilization model" incorporates another method for creating and delivering services. Healthcare organizations are gradually venturing into using the cloud to move record-based data to either Personal Health Records (PHR), Electronic Medical Records (EMR), or Electronic Health Records (EHR). In any case, security is a significant concern for healthcare data, and access control is one of the serious issues. We discuss different access controls in an EHR security advertisement approval process. Access control provides fine-grained access to services for the approved users while shielding the profitable assets from unauthorized access. At the time of conversion, both the privacy and the security of patient healthcare information should be saved. For this, one needs solid access-control systems that can shield health data from unapproved access. This research article presents different access-control requirements and types, and it reviews in detail the security analysis and basic use of the systems in medical applications. The plausibility of setting up a security-administration model is shown by proposing a secure e-Healthcare system for an e-Healthcare condition for maintaining safe personal health records. To conclude, the study chose reasonable access-control instruments for healthcare applications with proper security analyses and execution assessment after comparison with different access-control mechanisms.
Re-sampled inheritance compact optimization

Compact optimization is an alternative paradigm in the field of metaheuristics requiring a modest use of memory to optimize a problem. As opposed to population-based algorithms, which conduct the search by employing a set of candidate solutions, compact algorithms use a probabilistic model to describe how solutions are distributed over the search space. Compared to other Estimation of Distribution Algorithms, peculiar features such as the use of simple probabilistic models, in which variables are treated independently, and the need for a minimal number of solutions to be sampled to perform the search, make these algorithms suitable for those applications plagued by memory limitations. Compact algorithms show good results on different kinds of optimization problems but often prematurely converge and perform poorly on non-separable. In this paper, we attempt to overcome these limitations by combining compact algorithms with a restart mechanism named Re-Sampled Inheritance (RI) whose purpose is to avoid premature convergence while also inheriting parts of the variables from the best solution found so far. To assess the effect of the RI mechanism, we extensively test various existing compact algorithms, with and without RI, and compare the best RI-based compact algorithm against several competing algorithms on several optimization problems at different dimensionalities. We also evaluate the effect of the RI parameters on the overall algorithmic performance. Our numerical results not only show that RI consistently enhances the performances of compact algorithms, but also shed some light on the effectiveness of different compact logics at handling problems at different dimensionalities.
Blockchain-Based Access Control for IoT in Smart Home Systems

Smart home systems are featured by a variety of connected smart household devices, where Internet of Things (IoT) is one of the critical enablers in the smart home environment. Since these smart home IoT devices are working collaboratively, the access control among the IoT devices becomes more and more important because non-authorised access can result in resource misuse, home breach threats or private information disclosure. Thus, an effective access control in smart home systems is essential to prevent from unauthorized use of the available resources. However, most of the access control schemes in smart home systems are still lack of decentralized peer trust and hard to control the security and credibility of the smart home IoT network. This paper therefore proposes a Blockchain-based Access Control (BAC) solution by integrating the Blockchain technique to IoT networks, where the agent-based policy is proposed to improve the efficiency of the Blockchain management. In order to validate the BAC solution, we demonstrate the implementation process of the proposed BAC in the parental control scenario and also evaluate performance and feasibility in a simulated smart home.
Extreme Learning Machines for Signature Verification

In this paper, we present a novel approach to the verification of users through their own handwritten static signatures using the extreme learning machine (ELM) methodology. Our work uses the features extracted from the last fully connected layer of a deep learning pre-trained model to train our classifier. The final model classifies independent users by ranking them in a top list. In the proposed implementation, the training set can be extended easily to new users without the need for training the model every time from scratch. We have tested the state of the art deep neural networks for signature recognition on the largest available dataset and we have obtained an accuracy on average in the top 10 of more than 90%.
High-Performance ELM for Memory Constrained Edge Computing Devices with Metal Performance Shaders

This paper proposes a block solution method for the Extreme Learning Machine. It combines the speed of a direct non-iterative solver with minimal memory requirements. The method is suitable for edge computing scenarios running on a mobile device with GPU acceleration. The implementation tested on the GPU of iPad Pro outperforms a laptop CPU, and trains a 19,000-neuron model using under one gigabyte of memory. It confirms the feasibility of Big Data analysis on modern mobile devices.
Imperceptible manipulation of lateral camera motion for improved virtual reality applications

Virtual Reality (VR) systems increase immersion by reproducing users' movements in the real world. However, several works have shown that this real-to-virtual mapping does not need to be precise in order to convey a realistic experience. Being able to alter this mapping has many potential applications, since achieving an accurate real-to-virtual mapping is not always possible due to limitations in the capture or display hardware, or in the physical space available. In this work, we measure detection thresholds for lateral translation gains of virtual camera motion in response to the corresponding head motion under natural viewing, and in the absence of locomotion, so that virtual camera movement can be either compressed or expanded while these manipulations remain undetected. Finally, we propose three applications for our method, addressing three key problems in VR: improving 6-DoF viewing for captured 360° footage, overcoming physical constraints, and reducing simulator sickness. We have further validated our thresholds and evaluated our applications by means of additional user studies confirming that our manipulations remain imperceptible, and showing that (i) compressing virtual camera motion reduces visible artifacts in 6-DoF, hence improving perceived quality, (ii) virtual expansion allows for completion of virtual tasks within a reduced physical space, and (iii) simulator sickness may be alleviated in simple scenarios when our compression method is applied.
Grand Reports: A Tool for Generalizing Association Rule Mining to Numeric Target Values

Since its introduction in the 1990s, association rule mining(ARM) has been proven as one of the essential concepts in data mining; both in practice as well as in research. Discretization is the only means to deal with numeric target column in today's association rule mining tools. However, domain experts and decision-makers are used to argue in terms of mean values when it comes to numeric target values. In this paper, we provide a tool that reports mean values of a chosen numeric target column concerning all possible combinations of influencing factors – so-called grand reports. We give an in-depth explanation of the functionalities of the proposed tool. Furthermore, we compare the capabilities of the tool with one of the leading association rule mining tools, i.e., RapidMiner. Moreover, the study delves into the motivation of grand reports and offers some useful insight into their theoretical foundation.
A novel detection fusion network for solid waste sorting

Vision-based object detection technology plays a very important role in the field of computer vision. It is widely used in many machine vision applications. However, in the specific application scenarios, like a solid waste sorting system, it is very difficult to obtain good accuracy due to the color information of objects that is badly damaged. In this work, we propose a novel multimodal convolutional neural network method for RGB-D solid waste object detection. The depth information is introduced as the new modal to improve the object detection performance. Our method fuses two individual features in multiple scales, which forms an end-to-end network. We evaluate our method on the self-constructed solid waste data set. In comparison with single modal detection and other popular cross modal fusion neural networks, our method achieves remarkable results with high validity, reliability, and real-time detection speed.
LMFAO: An engine for batches of group-by aggregates

LMFAO is an in-memory optimization and execution engine for large batches of group-by aggregates over joins. Such database workloads capture the data-intensive computation of a variety of data science applications. We demonstrate LMFAO for three popular models: ridge linear regression with batch gradient descent, decision trees with CART, and clustering with Rk-means.