Reviewerly/exHarmony huggingface dataset:
MOSE

Multi-user oblivious storage allows users to access their shared data on the cloud while retaining access pattern obliviousness and data confidentiality simultaneously. Most secure and efficient oblivious storage systems focus on the utilization of the maximum network bandwidth in serving concurrent accesses via a trusted proxy. How- ever, since the proxy executes a standard ORAM protocol over the network, the performance is capped by the network bandwidth and latency. Moreover, some important features such as access control and security against active adversaries have not been thoroughly explored in such proxy settings. In this paper, we propose MOSE, a multi-user oblivious storage system that is efficient and enjoys from some desirable security properties. Our main idea is to harness a secure enclave, namely Intel SGX, residing on the untrusted storage server to execute proxy logic, thereby, minimizing the network bottleneck of proxy-based designs. In this regard, we address various technical design chal- lenges such as memory constraints, side-channel attacks and scala- bility issues when enabling proxy logic in the secure enclave. We present a formal security model and analysis for secure enclave multi-user ORAM with access control. We optimize MOSE to boost its throughput in serving concurrent requests. We implemented MOSE and evaluated its performance on commodity hardware. Our evaluation confirmed the efficiency of MOSE, where it achieves approximately two orders of magnitudes higher throughput than the state-of-the-art proxy-based design, and also, its performance is scalable proportional to the available system resources.
Imaging properties of 3D printed breast phantoms for lesion localization and Core needle biopsy training

Breast cancer is the most commonly diagnosed malignancy in females and frequently requires core needle biopsy (CNB) to guide management. Adequate training resources for CNB suffer tremendous limitations in reusability, accurate simulation of breast tissue, and cost. The relatively recent advent of 3D printing offers an alternative for the development of breast phantoms for training purposes. However, the feasibility of this technology for the purpose of ultrasound (US) guided breast intervention has not been thoroughly studied.We designed three breast phantom models that were printed in multiple resins available through Stratasys, including VeroClear, TangoPlus and Tissue Matrix. We also constructed several traditional breast phantoms using chicken breast and Knox gelatin for comparison. These phantoms were compared side-by-side for ultrasound penetrance, simulation of breast tissue integrity, anatomic accuracy, reusability, and cost.3D printed breast phantoms were more anatomically accurate models than traditional breast phantoms. The chicken breast phantom provided acceptable US beam penetration and material hardness for simulation of human breast tissue integrity. Sonographic image quality of the chicken breast phantom was the most accurate overall. The gelatin-based phantom also had acceptable US beam penetration and image quality; however, this material was too soft and poorly simulated breast tissue integrity. 3D printed phantoms were not visible under US.There is a large unmet need for a printable material that is truly compatible with multimodality imaging for breast and other soft tissue intervention. Further research is warranted to create a realistic, reusable and affordable material to 3D print phantoms for US-guided intervention training.
Using Ring Signatures For An Anonymous E-Voting System

This paper describes the mechanisms for using ring signatures to ensure anonymity in a decentralized e-voting system. Unlike standard signature algorithms that allow you to uniquely authenticate the authorship of the signature, a ring signature allows you to hide the true public key for verification among the public keys of other participants in the system. At the same time, the use of blockchain technology allows you to verify the integrity of the users votes, as well as verify their authenticity (binding with valid public keys) and to ensure transparency in the calculation of votes and verification of the correctness of the accounting of its votes by an individual user.
Characterizing Sources of Uncertainty to Proxy Calibration and Disambiguate Annotator and Data Bias

Supporting model interpretability for complex phenomena where annotators can legitimately disagree, such as emotion recognition, is a challenging machine learning task. In this work, we show that explicitly quantifying the uncertainty in such settings has interpretability benefits. We use a simple modification of a classical network inference using Monte Carlo dropout to give measures of epistemic and aleatoric uncertainty. We identify a significant correlation between aleatoric uncertainty and human annotator disagreement (r ≈ .3). Additionally, we demonstrate how difficult and subjective training samples can be identified using aleatoric uncertainty and how epistemic uncertainty can reveal data bias that could result in unfair predictions. We identify the total uncertainty as a suitable surrogate for model calibration, i.e. the degree we can trust model's predicted confidence. In addition to explainability benefits, we observe modest performance boosts from incorporating model uncertainty.
Hybrid deep learning and machine learning approach for passive image forensic

Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (DL) and machine learning-based approach is used in this study for passive image forgery detection. A DL algorithm classifies images into the forged and not forged categories, whereas colour illumination localises forgery. The simulated results are compared to other algorithms on public datasets. The simulated results achieved 99% accuracy for CASIA1.0, 98% accuracy for CASIA2.0, 98% accuracy for BSDS300, 97% accuracy for DVMM, and 99% accuracy for CMFD image manipulation dataset.
Low power CNN hardware FPGA implementation

A convolution Neural Networks (CNN) goes under the wide umbrella of Deep Neural Networks (DNN) whose applications are widely used. For example, the later are used in robotics and different applications of recognition like speech recognition and facial recognition, also nowadays in autonomous cars. Therefore the aim of implementing the CNN is to be used in real time applications. As a result of that, Graphics processing units (GPUs) are used but their worst disadvantage is it's high power consumption which can't be used in daily used equipments. The target of this paper is to solve the power consumption problem by using Field Programmable Array (FPGA) which has low power consumption, and flexible architecture. The implementation architecture of Alex Network, which consists of three fully connected layers and five convolution layers, on FPGA will depend on two main techniques parallelism of resources, and pipelining inside of some layers.
Cybersecurity, Information Assurance, and Big Data Based on Blockchain

Blockchain is a secure, immutable, and tamper proof technology that is gaining rapid momentum in the cybersecurity area. With the use of blockchain, participants are guaranteed information security for health data, insurance data, auction data, etc. with the blockchain hash function, which makes data tamper proof. Because users rely on a consensus function where all users must be informed of and approve any changes to data on the blockchain, tampering is almost impossible. The recent development of smart contracts, which are bits of code written into the blockchain, further tightens security on blockchain. This paper introduces the roles, applications, and challenges of blockchain. Cybersecurity and information assurance based on blockchain are presented. Blockchain-based big data security and blockchain-based healthcare cybersecurity are discussed.
Text feature extraction based on stacked variational autoencoder

This paper presents a text feature extraction model based on stacked variational autoencoder (SVAE). A noise reduction mechanism is designed for variational autoencoder in input layer of text feature extraction to reduce noise interference and improve robustness and feature discrimination of the model. Three kinds of deep SVAE network architectures are constructed to improve ability of representing learning to mine feature intension in depth. Experiments are carried out in several aspects, including comparative analysis of text feature extraction model, sparse performance, parameter selection and stacking. Results show that text feature extraction model of SVAE has good performance and effect. The highest accuracy of SVAE models of Fudan and Reuters datasets is 13.50% and 8.96% higher than that of PCA, respectively.
Natural Virtual Reality User Interface to Define Assembly Sequences for Digital Human Models

Digital human models (DHMs) are virtual representations of human beings. They are used to conduct, among other things, ergonomic assessments in factory layout planning. DHM software tools are challenging in their use and thus require a high amount of training for engineers. In this paper, we present a virtual reality (VR) application that enables engineers to work with DHMs easily. Since VR systems with head-mounted displays (HMDs) are less expensive than CAVE systems, HMDs can be integrated more extensively into the product development process. Our application provides a reality-based interface and allows users to conduct an assembly task in VR and thus to manipulate the virtual scene with their real hands. These manipulations are used as input for the DHM to simulate, on that basis, human ergonomics. Therefore, we introduce a software and hardware architecture, the VATS (virtual action tracking system). This paper furthermore presents the results of a user study in which the VATS was compared to the existing WIMP (Windows, Icons, Menus and Pointer) interface. The results show that the VATS system enables users to conduct tasks in a significantly faster way.
A Survey of Cloud Computing Security Challenges, Issues and their Countermeasures

Cloud computing is an emerging paradigm that provides on demand access to various resources such as servers, storage, applications, networking etc., over the internet. Cloud computing enables access to shared pool of resources, which are managed by third party cloud service providers. It comes with wide range of benefits such as low operating cost, efficiency, scalability, flexibility, etc. The cloud users can access different services and/or resources at anytime and from anywhere on pay per use basis. Hence many individual users, organizations, and businesses are moving towards cloud computing and sharing large amount of data on cloud. However, the security concern is the biggest barrier for wide adoption of cloud computing. The security issues exist in context of infrastructure, data and storage, access control in cloud environment. This paper explores various security issues in all these perspectives. The paper briefly reviews the security issues at infrastructure level, data level, and also discusses the concept of Identity and Access Control in cloud. Also the different solutions to avoid or alleviate the security issues in cloud environment are discussed.
Free-Lunch Saliency via Attention in Atari Agents

We propose a new approach to visualize saliency maps for deep neural network models and apply it to deep reinforcement learning agents trained on Atari environments. Our method adds an attention module that we call FLS (Free Lunch Saliency) to the feature extractor from an established baseline of Mnih et al, 2015. This addition results in a trainable model that can produce saliency maps, i.e., visualizations of the importance of different parts of the input for the agent's current decision making. We show experimentally that a network with an FLS module exhibits performance similar to the baseline (i.e., it is “free”, with no performance cost) and can be used as a drop-in replacement for reinforcement learning agents. We also design another feature extractor that scores slightly lower but provides higher-fidelity visualizations. In addition to attained scores, we report saliency metrics evaluated on the Atari-HEAD dataset of human gameplay.
Feature Selection Method Based on Chaotic Maps and Butterfly Optimization Algorithm

Feature selection (FS) is a challenging problem that attracted the attention of many researchers. FS can be considered as an NP hard problem, If dataset contains N features then $$2^{N}$$ solutions are generated with each additional feature, the complexity doubles. To solve this problem, we reduce the dimensionality of the feature by extracting the most important features. In this paper we integrate the chaotic maps in the standard butterfly optimization algorithm to increase the diversity and avoid trapping in local minima in this algorithm. The proposed algorithm is called Chaotic Butterfly Optimization Algorithm (CBOA).The performance of the proposed CBOA is investigated by applying it on 16 benchmark datasets and comparing it against six meta-heuristics algorithms. The results show that invoking the chaotic maps in the standard BOA can improve its performance with accuracy more than $$95\% $$.
A Survey on Cloud Computing Security Issues and Cryptographic Techniques

Cloud computing is an Internet-based computing model, having various resources used by distinct users in a concurrent manner. Apart from all of its advantages, it faces a major setback due to various data security issues. To overcome these issues, various security mechanisms have been proposed, such as cryptography and authentication. Cryptography can be used to provide data integrity, authorization for data manipulation, and also making the data unreadable to an interceptor through encryption. There are various classifications of models in cloud computing. The service models are classified as Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). There are several deployment models mainly distinguished by ownership which consists of public cloud, private cloud, and hybrid cloud. This survey mainly focuses on security issues in cloud service models and cloud deployment models along with various cryptographic mechanisms of data protection, such as symmetric key cryptography, asymmetric key cryptography, and their encryption algorithms.
Object Identification for Task-Oriented Communication with Industrial Robots

To make the human-robot collaboration effective, it may be necessary to provide robots with "senses" like vision and hearing. Task-oriented man-machine speech communication often relies on the use of abstract terms to describe objects. Therefore it is necessary to correctly map those terms into images of proper objects in a camera's field of view. This paper presents the results of our research in this field. A novel method for contour identification, based on flexible editable contour templates (FECT), has been developed. We demonstrate that existing methods are not appropriate for this purpose because it is difficult to formulate general rules that humans employ to rank shapes into proper classes. Therefore, the rules for shape classification should be individually formulated by the users for each application. Our aim was to create appropriate tool facilitating formulation of those rules as it could potentially be a very labor-intensive task. The core of our solution is FCD (flexible contour description) format for description of flexible templates. Users will be able to create and edit flexible contour templates, and thus, adjust image recognition systems to their needs, in order to provide task-oriented communication between humans and robots.
Metric Learning with Equidistant and Equidistributed Triplet-based Loss for Product Image Search

Product image search in E-commerce systems is a challenging task, because of a huge number of product classes, low intra-class similarity and high inter-class similarity. Deep metric learning, based on paired distances independent of the number of classes, aims to minimize intra-class variances and inter-class similarity in feature embedding space. Most existing approaches strictly restrict the distance between samples with fixed values to distinguish different classes of samples. However, the distance of paired samples has various magnitudes during different training stages. Therefore, it is difficult to directly restrict absolute distances with fixed values. In this paper, we propose a novel Equidistant and Equidistributed Triplet-based (EET) loss function to adjust the distance between samples with relative distance constraints. By optimizing the loss function, the algorithm progressively maximizes intra-class similarity and inter-class variances. Specifically, 1) the equidistant loss pulls the matched samples closer by adaptively constraining two samples of the same class to be equally distant from another one of a different class in each triplet, 2) the equidistributed loss pushes the mismatched samples farther away by guiding different classes to be uniformly distributed while keeping intra-class structure compact in embedding space. Extensive experimental results on product search benchmarks verify the improved performance of our method. We also achieve improvements on other retrieval datasets, which show superior generalization capacity of our method in image search.
Apophanies or Epiphanies? How Crawlers Impact Our Understanding of the Web

Data generated by web crawlers has formed the basis for much of our current understanding of the Internet. However, not all crawlers are created equal and crawlers generally find themselves trading off between computational overhead, developer effort, data accuracy, and completeness. Therefore, the choice of crawler has a critical impact on the data generated and knowledge inferred from it. In this paper, we conduct a systematic study of the trade-offs presented by different crawlers and the impact that these can have on various types of measurement studies. We make the following contributions: First, we conduct a survey of all research published since 2015 in the premier security and Internet measurement venues to identify and verify the repeatability of crawling methodologies deployed for different problem domains and publication venues. Next, we conduct a qualitative evaluation of a subset of all crawling tools identified in our survey. This evaluation allows us to draw conclusions about the suitability of each tool for specific types of data gathering. Finally, we present a methodology and a measurement framework to empirically highlight the differences between crawlers and how the choice of crawler can impact our understanding of the web.
Multi-view image clustering based on sparse coding and manifold consensus

Multi-view clustering has received an increasing attention in many applications, where different views of objects can provide complementary information to each other. Existing approaches on multi-view clustering mainly focus on extending Non-negative Matrix Factorization (NMF) by enforcing the constraint over the coefficient matrices from different views in order to preserve their consensus. In this paper, we argue that it is more reasonable to utilize the high-level manifold consensus rather than the low-level coefficient matrix consensus (as conducted in state-of-the-art approaches) to better capture the underlying clustering structure of the data. For this purpose, we propose MMRSC (Multiple Manifold Regularized Sparse Coding), which aims to preserve the consensus over multiple manifold structures from different views. Experimental results on two publicly available real-world image datasets demonstrate that our proposed approach can significantly outperform the state-of-the-art approaches for the multi-view image clustering task. Moreover, we also conduct computational complexity analysis and the result shows that MMRSC can effective handle the multi-view clustering problem without increasing the computational cost as compared to GraphSC.
Inside Advertising: The Role of Presence in the Processing of Branded VR Content

Virtual reality (VR) has become a new playground for brands and advertisers. However, empirical evidence for the effectiveness of VR branded content is still scarce. The aim of this study is therefore to examine the effectiveness of branded content in virtual space and the role of presence in the processing of brand information when playing a branded VR game. An experiment (N = 81) was conducted (using HTC Vive hardware) and showed that playing branded VR games can improve (implicit) brand memory. Moreover, the study showed that the increase in immersion experienced from playing a branded VR game strengthens players’ brand memory.
LioNets: Local Interpretation of Neural Networks Through Penultimate Layer Decoding

Technological breakthroughs on smart homes, self-driving cars, health care and robotic assistants, in addition to reinforced law regulations, have critically influenced academic research on explainable machine learning. A sufficient number of researchers have implemented ways to explain indifferently any black box model for classification tasks. A drawback of building agnostic explanators is that the neighbourhood generation process is universal and consequently does not guarantee true adjacency between the generated neighbours and the instance. This paper explores a methodology on providing explanations for a neural network's decisions, in a local scope, through a process that actively takes into consideration the neural network's architecture on creating an instance's neighbourhood, that assures the adjacency among the generated neighbours and the instance.
Bernoulli Ballot Polling: A Manifest Improvement for Risk-Limiting Audits

We present a method and software for ballot-polling risk-limiting audits (RLAs) based on Bernoulli sampling: ballots are included in the sample with probability p, independently. Bernoulli sampling has several advantages: (1) it does not require a ballot manifest; (2) it can be conducted independently at different locations, rather than requiring a central authority to select the sample from the whole population of cast ballots or requiring stratified sampling; (3) it can start in polling places on election night, before margins are known. If the reported margins for the 2016 U.S. Presidential election are correct, a Bernoulli ballot-polling audit with a risk limit of 5% and a sampling rate of $$p_0=1\%$$ would have had at least a 99% probability of confirming the outcome in 42 states. (The other states were more likely to have needed to examine additional ballots). Logistical and security advantages that auditing in the polling place affords may outweigh the cost of examining more ballots than some other methods might require.