Reviewerly/exHarmony huggingface dataset:
Cross-Sensor Deep Domain Adaptation for LiDAR Detection and Segmentation

A considerable amount of annotated training data is necessary to achieve state-of-the-art performance in perception tasks using point clouds. Unlike RGB-images, LiDAR point clouds captured with different sensors or varied mounting positions exhibit a significant shift in their input data distribution. This can impede transfer of trained feature extractors between datasets as it degrades performance vastly. We analyze the transferability of point cloud features between two different LiDAR sensor set-ups (32 and 64 vertical scanning planes with different geometry). We propose a supervised training methodology to learn transferable features in a pre-training step on LiDAR datasets that are heterogeneous in their data and label domains. In extensive experiments on object detection and semantic segmentation in a multi-task setup we analyze the performance of our network architecture under the impact of a change in the input data domain. We show that our pre-training approach effectively increases performance for both target tasks at once without having an actual multi-task dataset available for pre-training.
NETS

This paper addresses the problem of efficiently detecting outliers from a data stream as old data points expire from and new data points enter the window incrementally. The proposed method is based on a newly discovered characteristic of a data stream that the change in the locations of data points in the data space is typically very insignificant. This observation has led to the finding that the existing distance-based outlier detection algorithms perform excessive unnecessary computations that are repetitive and/or canceling out the effects. Thus, in this paper, we propose a novel set-based approach to detecting outliers, whereby data points at similar locations are grouped and the detection of outliers or inliers is handled at the group level. Specifically, a new algorithm NETS is proposed to achieve a remarkable performance improvement by realizing set-based early identification of outliers or inliers and taking advantage of the "net effect" between expired and new data points. Additionally, NETS is capable of achieving the same efficiency even for a high-dimensional data stream through two-level dimensional filtering . Comprehensive experiments using six real-world data streams show 5 to 25 times faster processing time than state-of-the-art algorithms with comparable memory consumption. We assert that NETS opens a new possibility to real-time data stream outlier detection.
Attributed network embedding via subspace discovery

Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network embedding algorithms attempt to integrate rich node content information with network topological structure for enhancing the quality of network embedding. In reality, networks often have sparse content, incomplete node attributes, as well as the discrepancy between node attribute feature space and network structure space, which severely deteriorates the performance of existing methods. In this paper, we propose a unified framework for attributed network embedding–attri2vec—that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space. The resultant latent subspace can respect network structure in a more consistent way towards learning high-quality node representations. We formulate an optimization problem which is solved by an efficient stochastic gradient descent algorithm, with linear time complexity to the number of nodes. We investigate a series of linear and non-linear transformations performed on node attributes and empirically validate their effectiveness on various types of networks. Another advantage of attri2vec is its ability to solve out-of-sample problems, where embeddings of new coming nodes can be inferred from their node attributes through the learned mapping function. Experiments on various types of networks confirm that attri2vec is superior to state-of-the-art baselines for node classification, node clustering, as well as out-of-sample link prediction tasks. The source code of this paper is available at https://github.com/daokunzhang/attri2vec .
Dependency-Aware Attention Control for Image Set-Based Face Recognition

This paper considers the problem of image set-based face verification and identification. Unlike traditional single sample (an image or a video) setting, this situation assumes the availability of a set of heterogeneous collection of orderless images and videos. The samples can be taken at different check points, different identity documents $etc$ . The importance of each image is usually considered either equal or based on a quality assessment of that image independent of other images and/or videos in that image set. How to model the relationship of orderless images within a set remains a challenge. We address this problem by formulating it as a Markov Decision Process (MDP) in a latent space. Specifically, we first propose a dependency-aware attention control (DAC) network, which uses actor-critic reinforcement learning for attention decision of each image to exploit the correlations among the unordered images. An off-policy experience replay is introduced to speed up the learning process. Moreover, the DAC is combined with a temporal model for videos using divide and conquer strategies. We also introduce a pose-guided representation (PGR) scheme that can further boost the performance at extreme poses. We propose a parameter-free PGR without the need for training as well as a novel metric learning-based PGR for pose alignment without the need for pose detection in testing stage. Extensive evaluations on IJB-A/B/C, YTF, Celebrity-1000 datasets demonstrate that our method outperforms many state-of-art approaches on the set-based as well as video-based face recognition databases.
A Comparative Study of Data Anonymization Techniques

In today's digital era, it is a very common practice for organizations to collect data from individual users. The collected data is then stored in multiple databases which contain personally identifiable information (PII). This may lead to a major source of privacy risk for the database. Various privacy preservation techniques have been proposed such as perturbation, anonymization and cryptographic. In this study, five anonymization techniques are compared using the same dataset. In addition to that, this study reviews the strengths and weaknesses of the different technique. In the evaluation of efficiency, suppression is found as the most efficient while swapping is in the last place. It is also revealed that swapping is the most resource-consuming technique while suppressing being less resource consuming.
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates

In this work, we improve upon the stepwise analysis of noisy iterative learning algorithms initiated by Pensia, Jog, and Loh (2018) and recently extended by Bu, Zou, and Veeravalli (2019). Our main contributions are significantly improved mutual information bounds for Stochastic Gradient Langevin Dynamics via data-dependent estimates. Our approach is based on the variational characterization of mutual information and the use of data-dependent priors that forecast the mini-batch gradient based on a subset of the training samples. Our approach is broadly applicable within the information-theoretic framework of Russo and Zou (2015) and Xu and Raginsky (2017). Our bound can be tied to a measure of flatness of the empirical risk surface. As compared with other bounds that depend on the squared norms of gradients, empirical investigations show that the terms in our bounds are orders of magnitude smaller.
Regularizing Matrix Factorization with Implicit User Preference Embeddings for Web API Recommendation

Mashups have emerged as a popular technique, which composes value-added web services/APIs, to realize some complicated business needs. The rapid increase in the number of similarly functional web APIs, makes it challenging to find relevant ones for mashup development. Recommender systems have become highly important, because they reduce the myriad of web APIs, during web API selection. Most existing web API recommender systems, however, neglect the implicit user preferences, to personalize and precisely recommend web APIs to mashup developers. It is for this reason, that this work proposes a method, which considers both explicit and implicit user personalized preferences to make personalized web API recommendations, while improving recommendation accuracy and diversity. Specifically, we propose a regularized user preference embedded matrix factorization method, to personalize recommendations. We take advantage of users' implicit personalized preferences, which are obtained from their interactions (i.e. invoking or following) with web APIs and other users in the system. We demonstrate the effectiveness of our method by conducting extensive experiments on a real-world dataset crawled from www.programmableweb.com. We also compare our method with some baseline recommendation methods for verification.
Analysis of the K-Means Algorithm on Clean Water Customers Based on the Province

Abstract One of the important needs of environmental health is clean water. Clean water is the most important necessity of living beings in supporting survival. The study aimed to cluster the number of cleaned water customers by province (1995-2015). The method used is data mining clustering using k-means. The sample data used 34 provinces with attribute assessment of the number of cleaned water customers by province. The clustering process is done with 3 clusters, namely (C1) Cluster High, (C2) Cluster Normal and (C3) Cluster Low, for the number of cleaned water customers who are low on the need of clean water. The results showed, C1: 6 provinces, C2: 4 provinces and C3: 24 provinces. The end centroid values used are: C1 (296587.22), C2 (995898.56) and C3 (70832.29). The results obtained on the Davies-Bouldin index for “the number of cleaned water consumers” are -0.470. based on performance results, it can be concluded that k-means algorithm is best because it has the smallest Davies-Bouldin index value. Based on research results, 70% of Indonesian people are still low awareness of the need for clean water.
Optimal Randomness in Swarm-Based Search

Lévy flights is a random walk where the step-lengths have a probability distribution that is heavy-tailed. It has been shown that Lévy flights can maximize the efficiency of resource searching in uncertain environments and also the movements of many foragers and wandering animals have been shown to follow a Lévy distribution. The reason mainly comes from the fact that the Lévy distribution has an infinite second moment and hence is more likely to generate an offspring that is farther away from its parent. However, the investigation into the efficiency of other different heavy-tailed probability distributions in swarm-based searches is still insufficient up to now. For swarm-based search algorithms, randomness plays a significant role in both exploration and exploitation or diversification and intensification. Therefore, it is necessary to discuss the optimal randomness in swarm-based search algorithms. In this study, cuckoo search (CS) is taken as a representative method of swarm-based optimization algorithms, and the results can be generalized to other swarm-based search algorithms. In this paper, four different types of commonly used heavy-tailed distributions, including Mittag-Leffler distribution, Pareto distribution, Cauchy distribution, and Weibull distribution, are considered to enhance the searching ability of CS. Then four novel CS algorithms are proposed and experiments are carried out on 20 benchmark functions to compare their searching performances. Finally, the proposed methods are used to system identification to demonstrate the effectiveness.
Cookie monsters. Anatomy of a digital market infrastructure

The aim of this article is to examine the market arrangements built by the online marketing industry around small pieces of data now ubiquitous in digital markets–namely "http cookies." We show how cookies have become the backbone and the main vehicle of a vast market infrastructure, based on its ability to transform online behavioral information into data assets, and to attach these assets to advertising products. We examine the complex trading operations that are implemented from the elementary brick that constitutes the cookie. We also raise the question of the strength and durability of this infrastructure, at a time when it is disputed and seems weakened. Beyond the particular case of cookies, we identify three main operations that market infrastructures typically support: knowledge production, capitalization, and coordination. We also highlight the centrality of "datafication" (tracking, "data lake" building, matching, etc.) in the process of market digitalization. We thus contribute to the framing of the concept of (digital) market infrastructure.
A Blockchain-Based Privacy-Awareness Authentication Scheme With Efficient Revocation for Multi-Server Architectures

Multi-server authentication technology has become more and more popular with the extensive applications of networks.Although it has brought great convenience to people's life, security becomes a critical issue and attracts lots of attentions in both academia and industry.Over the past two decades, a series of multi-server authentication schemes without communication with the online registration center in each authentication phase using the self-certified public key cryptography have been proposed to enhance security.However, it may cause the single-point failure problem due to the centralized architecture.Besides, user revocation facility is not well resolved in these schemes.To the best of our knowledge, blockchain technology has lots of advantages, bringing a promising solution to the problems of single-point failure and user revocation compared with the traditional cryptography technologies.In this work, we apply the idea of blockchain technology to construct a privacy-awareness authentication scheme for the multi-server environment, which can achieve distributed registry and efficient revocation.Moreover, the proposed scheme not only provides multiple security requirements like mutual authentication, user anonymity and perfect forward secrecy, but also resists various kinds of malicious attacks.The security of the proposed scheme is proved by rigorous formal proof using the random oracle model.Compared with recently related schemes, the proposed scheme has better communication performance, which make it be very suitable for real-life applications.
Computationally-Secure and Composable Remote State Preparation

We introduce a protocol between a classical polynomial-time verifier and a quantum polynomial-time prover that allows the verifier to securely delegate to the prover the preparation of certain single-qubit quantum states The prover is unaware of which state he received and moreover, the verifier can check with high confidence whether the preparation was successful. The delegated preparation of single-qubit states is an elementary building block in many quantum cryptographic protocols. We expect our implementation of "random remote state preparation with verification", a functionality first defined in (Dunjko and Kashefi 2014), to be useful for removing the need for quantum communication in such protocols while keeping functionality. The main application that we detail is to a protocol for blind and verifiable delegated quantum computation (DQC) that builds on the work of (Fitzsimons and Kashefi 2018), who provided such a protocol with quantum communication. Recently, both blind an verifiable DQC were shown to be possible, under computational assumptions, with a classical polynomial-time client (Mahadev 2017, Mahadev 2018). Compared to the work of Mahadev, our protocol is more modular, applies to the measurement-based model of computation (instead of the Hamiltonian model) and is composable. Our proof of security builds on ideas introduced in (Brakerski et al. 2018).
An Approach of Virtual Reality Environment for Technicians Training in Upstream Sector

Upstream Oil & Gas production is conducted by companies who identify, extract, or produce raw materials. This industrial sector has grown year by year in the automation field, trying to improve their processes and productivity, and at the same time trying to ensure the safety of their workers, machinery and company. This is has become increasingly relevant to the Oil & Gas industry because low oil prices coupled with aging assets means profitability can only be achieved by making operations more productive. To achieve these goals, it is necessary to periodically train instrumentation technicians and field operators; however, it must be taken noted that most of the industrial equipment is really expensive. Due to this it has been necessary to use tools such as Virtual Reality (VR) and Augmented Reality (AR), which optimize and reduce both the time and the cost of training. This research proposes the development of a virtual training system for the commissioning, calibration and mounting HART transmitters in dynamic and dangerous environments for the human being such us Oil & Gas process facilities.
Biosensors and Internet of Things in smart healthcare applications: challenges and opportunities

The use of health and well-being monitoring technologies has been steadily increasing and such systems can now be found in smart homes, age-friendly workplaces, public spaces, and elsewhere. These monitoring technologies employ a wide variety of off-the-shelf smart sensors and medical devices to support functional, physiological, and behavioral monitoring and to address social interaction aspects of daily life. These systems focus either on specific health-related conditions or on supporting the more general aims of comfort, well-being, and quality of life. However, there remain several technological (interoperability, expandability, etc.) and societal (cost, privacy, etc.) challenges to be addressed before smart biosensor systems are widely adopted. Motivated by the above, this chapter highlights the challenges and opportunities surrounding the application of smart biosensors in healthcare and presents three state of the art solutions for leveraging smart sensors in this context. The first concerns a smart living solution platform that integrates heterogeneous sensors and assistive medical and mobile devices, enabling continuous data collection from the everyday lives of the elderly and data analytics that support personalized interventions. The second presents an Internet of Things (IoT) ecosystem comprising sensors and smart wearables to improve occupational safety and workforce productivity through personalized recommendations. The last case is an intelligent noninvasive biosignal recording system that detects potentially hazardous pathological conditions of infants during sleep.
VLA

Adversarial example attacks have become a growing menace to neural network-based face recognition systems. Generated by composing facial images with pixel-level perturbations, adversarial examples change key features of inputs and thereby lead to misclassification of neural networks. However, the perturbation loss caused by complex physical environments sometimes prevents existing attack methods from taking effect. In this paper, we focus on designing new attacks that are effective and inconspicuous in the physical world. Motivated by the differences in image-forming principles between cameras and human eyes, we propose VLA, a novel attack against black-box face recognition systems using visible light. In VLA, visible light-based adversarial perturbations are crafted and projected on human faces, which allows an adversary to conduct targeted or un-targeted attacks. VLA decomposes adversarial perturbations into a perturbation frame and a concealing frame, where the former adds modifications on human facial images while the latter makes these modifications inconspicuous to human eyes. We conduct extensive experiments to demonstrate the effectiveness, inconspicuousness, and robustness of the adversarial examples crafted by VLA in physical scenarios.
An Augmented Reality System to Support Fault Visualization in Industrial Robotic Tasks

The digitalization is transforming the very nature of factories, from automated systems to intelligent ones. In this process, industrial robots play a key role. Even if repeatability, precision and velocity of the industrial manipulators enable reaching considerable production levels, factories are required to face an increasingly competitive market, which requires being able to dynamically adapt to different situations and conditions. Hence, facilities are moving toward systems that rely on the collaboration between humans and machines. Human workers should understand the behavior of the robots, placing trust in them to properly collaborate. If a fault occurs on a manipulator, its movements are suddenly stopped for security reasons, thus workers may not be able to understand what happened to the robot. Therefore, the operators' stress and anxiety may increase, compromising the human-robot collaborative scenario. This work fits in this context and it proposes an adaptive Augmented Reality system to display industrial robot faults by means of the Microsoft HoloLens device. Starting from the methodology employed to identify which virtual metaphors best evoke robot faults, an adaptive modality is presented to dynamically display the metaphors in positions close to the fault location, always visible from the user and not occluded by the manipulator. A comparison with a non adaptive modality is proposed to assess the effectiveness of the adaptive solution. Results show that the adaptive modality allows users to recognize faults faster and with fewer movements than the non adaptive one, thus overcoming the limitation of the narrow field-of-view of the HoloLens device.
BHMUS: Blockchain Based Secure Outdoor Health Monitoring Scheme Using UAV in Smart City

Unmanned aerial vehicle (UAV) is an emerging technology that becomes popular not only in military operation but also in civil applications. Internet of things (IoT) is another popular technology which brings automation in our daily life. Like other areas, IoT also exposes its potential in healthcare. Using IoT sensors, it becomes easy to monitor the health of a user remotely. UAV consolidated with mobile edge computing (MEC) can provide real-time services in outdoor health monitoring. However, communication among them surrounds with cyber threats and data integrity issue. Blockchain is a data structure in which data are shared among peers. In this paper, a blockchain based secure outdoor health monitoring scheme using UAV is proposed for a smart city. In the proposed scheme, health data (HD) are accumulated from users wearable sensors and these HD are transmitted to the nearest MEC server via UAV. Prior to transmitting to MEC, HD experience encryption in order to provide protection against cyber threats. Moreover, after arriving at MEC, HD are diagnosed and if any abnormalities are found in the user's health, MEC server notifies the user and the nearest hospitals. When the processing is completed, HD are stored in blockchain with the consent of validators. Finally, simulation results and experimental set up are discussed in order to manifest the feasibility of the proposed scheme.