Reviewerly/exHarmony huggingface dataset:
Two-factor authentication in industrial Internet-of-Things: Attacks, evaluation and new construction

Due to the sensitiveness of the physical environments and resource-constrained nature of edge devices, how to securely and efficiently access real-time data in Industrial Internet-of-Things (IIoT) is becoming an increasingly imperative concern. Though intensive efforts have been made to design password-based user authentication schemes for IIoT environments, the majority of them are subject to weaknesses, either suffering from various known attacks or lack of critical features. To ameliorate this situation, firstly, we put forward a criteria set on the basis of related state-of-art evaluation set to fairly assess authentication schemes for IIoT environments. The effectiveness and practicality of our criteria are tested by 42 representative schemes. Secondly, we revisit two representative schemes, namely Amin et al.'s scheme and Gope–Hwang's scheme, as case studies to demonstrate the common pitfalls in designing robust schemes. Finally, we propose a practical authentication scheme for Industrial Internet-of-Things with provable security, and shed light on how to tackle node capture attack by using the "honeywords" technique. The security and performance evaluation results show that our new scheme is superior to other related ones, and thus, more suitable for IIoT environments.
Homomorphic Encryption Technology for Cloud Computing

Four kinds of single homomorphic encryption algorithm were summarized for the advantages of homomorphic encryption technology in the cloud environment. It analyzed the security characteristics of four kinds of encryption algorithms. On the basis of experiments, the paper compared the efficiency of four single homomorphic encryption algorithms, gave the application scenarios of various algorithms, and introduced the research and application of single homomorphic encryption algorithm in the cloud environment. We described five kinds of fully homomorphic encryption algorithms, summarized the research situation and its application in the cloud environment and the performance of some fully homomorphic encryption algorithms were analyzed. Finally, we proposed the research direction for the subsequent studies.
An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems

Abstract Designing, evaluating, and improving visual analytics (VA) systems is a primary area of activities in our discipline. In this paper, we present an ontological framework for recording and categorizing technical shortcomings to be addressed in a VA workflow, reasoning about the causes of such problems, identifying technical solutions, and anticipating secondary effects of the solutions. The methodology is built on the theoretical premise that designing a VA workflow is an optimization of the cost‐benefit ratio of the processes in the workflow. It makes uses three fundamental measures to group and connect “symptoms”, “causes”, “remedies”, and “side‐effects”, and guide the search for potential solutions to the problems. In terms of requirement analysis and system design, the proposed methodology can enable system designers to explore the decision space in a structured manner. In terms of evaluation, the proposed methodology is time‐efficient and complementary to various forms of empirical studies, such as user surveys, controlled experiments, observational studies, focus group discussions, and so on. In general, it reduces the amount of trial‐and‐error in the lifecycle of VA system development.
A Multi-Criteria Decision Making approach based on the Choquet integral for assessing the credibility of User-Generated Content

The Social Web promotes social interactions among people through Web 2.0 technologies. In this context, User-Generated Content (UGC) spreads across social media platforms in the absence of traditional intermediaries that can verify both the believability of the content and the reliability of the sources that generated it. For this reason, the problem of how to assess the credibility of UGC is receiving nowadays increasing attention. In the literature, several approaches have tackled this issue mainly as a classification problem, by categorizing information into genuine and fake. The majority of the proposed solutions follows a data-driven approach, by employing supervised or semi-supervised machine learning techniques that act on multiple features related to credibility. Despite its effectiveness, however, machine learning may present some possible drawbacks, including data-dependency and the possible inscrutability of the contribution that single or interacting features have in the final classification process. In this paper, a Multi-Criteria Decision Making approach is proposed, aimed to assess the credibility of UGC. A given information item (alternative) is evaluated with respect to the considered credibility features (criteria) based on prior domain knowledge, where an overall credibility estimate is obtained by means of a suitable model-driven approach based on aggregation operators. The credibility estimate allows to classify credible UGC with respect to non-credible one, and can also be used to provide a ranking of the alternatives with respect to credibility. To consider interactions among features, the Choquet integral is employed.
<i>ClustMe</i>: A Visual Quality Measure for Ranking Monochrome Scatterplots based on Cluster Patterns

Abstract We propose ClustMe, a new visual quality measure to rank monochrome scatterplots based on cluster patterns. ClustMe is based on data collected from a human‐subjects study, in which 34 participants judged synthetically generated cluster patterns in 1000 scatterplots. We generated these patterns by carefully varying the free parameters of a simple Gaussian Mixture Model with two components, and asked the participants to count the number of clusters they could see (1 or more than 1). Based on the results, we form ClustMe by selecting the model that best predicts these human judgments among 7 different state‐of‐the‐art merging techniques ( D emp ). To quantitatively evaluate ClustMe, we conducted a second study, in which 31 human subjects ranked 435 pairs of scatterplots of real and synthetic data in terms of cluster patterns complexity. We use this data to compare ClustMe's performance to 4 other state‐of‐the‐art clustering measures, including the well‐known Clumpiness scagnostics. We found that of all measures, ClustMe is in strongest agreement with the human rankings.
Motion sickness in passenger vehicles during test track operations

As automation transforms drivers into passengers, the deployment of automated vehicles (AVs) has the potential to greatly increase the incidence of motion sickness. A study was conducted to quantify motion sickness response of front-seat passengers performing ecologically relevant passenger activities during conditions consistent with driving on public roadways. Fifty-two adults with a large range of self-reported levels of motion sickness susceptibility and age participated in data collection on a closed test track in a passenger sedan. Motion sickness ratings increased with task vs. no-task and moderate vs. low acceleration test conditions. Increased motion sickness susceptibility was associated with higher motion sickness ratings. In comparison to older participants (age > 60), younger participants (age < 60) experienced increased motion sickness. This is the first in-vehicle study that systematically compared normative passenger activities and acceleration magnitudes typical of normative driving conditions on motion sickness response for a large, diverse sample of passengers, enabling the exploration of the effects of covariates. Practitioner summary: The data demonstrate that a relatively large range of motion sickness response can be expected to result from passengers performing visual tasks in passenger vehicles. Measurement and modelling efforts should seek to elucidate relationships among the factors contributing to motion sickness for the purpose of informing and prioritising future countermeasures for automated vehicles (AVs). Abbreviations: AV(S): automated vehicles; BMI: body mass index; BVP: blood volume pulse; EDA: electrodermal activity; FMS: fast motion sickness scale; GPS: global positioning system; IMU: inertial measurement unit; ISO: International Organization for Standardization; MISC: misery scale; MSDV: motion sickness dose value; NDS: naturalistic driving study; SAE: Society of Automotive Engineers International; UMTRI: The University of Michigan Transportation Research Institute Key Aspect of Research: Motion sickness may be an important barrier to widespread adoption of automated vehicles @UMTRI.
Perfect state transfer on abelian Cayley graphs

Perfect state transfer (PST) has great significance due to its applications in quantum information processing and quantum computation. In this paper we present a characterization of the connected simple Cayley graph Γ=Cay(G,S) having PST, where G is an abelian group and S is a non-empty set of G. We show that many previous results on periodicity and existence of PST of circulant graphs (where the underlying group G is cyclic) and cubelike graphs (G=(F2n,+)) can be derived or generalized to arbitrary abelian case in unified and more simple ways from our characterization. We also get several new results including answers to some questions raised before.
Estimating the Entropy Rate of Finite Markov Chains With Application to Behavior Studies

Predictability of behavior is an important characteristic in many fields including biology, medicine, marketing, and education. When a sequence of actions performed by an individual can be modeled as a stationary time-homogeneous Markov chain the predictability of the individual’s behavior can be quantified by the entropy rate of the process. This article compares three estimators of the entropy rate of finite Markov processes. The first two methods directly estimate the entropy rate through estimates of the transition matrix and stationary distribution of the process. The third method is related to the sliding-window Lempel–Ziv compression algorithm. The methods are compared via a simulation study and in the context of a study of interactions between mothers and their children.
Long-Term Visual Object Tracking Benchmark

We propose a new long video dataset (called Track Long and Prosper - TLP) and benchmark for single object tracking. The dataset consists of 50 HD videos from real world scenarios, encompassing a duration of over 400 min (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking. The proposed dataset paves a way to suitably assess long term tracking performance and train better deep learning architectures (avoiding/reducing augmentation, which may not reflect real world behaviour). We benchmark the dataset on 17 state of the art trackers and rank them according to tracking accuracy and run time speeds. We further present thorough qualitative and quantitative evaluation highlighting the importance of long term aspect of tracking. Our most interesting observations are (a) existing short sequence benchmarks fail to bring out the inherent differences in tracking algorithms which widen up while tracking on long sequences and (b) the accuracy of trackers abruptly drops on challenging long sequences, suggesting the potential need of research efforts in the direction of long-term tracking. Dataset and tracking results are available at https://amoudgl.github.io/tlp/ .
Decision-Making Under Uncertainty in Research Synthesis

To make evidence-based recommendations to decision-makers, researchers conducting systematic reviews and meta-analyses must navigate a garden of forking paths: a series of analytical decision-points, each of which has the potential to influence findings. To identify challenges and opportunities related to designing systems to help researchers manage uncertainty around which of multiple analyses is best, we interviewed 11 professional researchers who conduct research synthesis to inform decision-making within three organizations. We conducted a qualitative analysis identifying 480 analytical decisions made by researchers throughout the scientific process. We present descriptions of current practices in applied research synthesis and corresponding design challenges: making it more feasible for researchers to try and compare analyses, shifting researchers' attention from rationales for decisions to impacts on results, and supporting communication techniques that acknowledge decision-makers' aversions to uncertainty. We identify opportunities to design systems which help researchers explore, reason about, and communicate uncertainty in decision-making about possible analyses in research synthesis.
From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval

In the large-scale image retrieval task, the two most important requirements are the discriminability of image representations and the efficiency in computation and storage of representations. Regarding the former requirement, Convolutional Neural Network is proven to be a very powerful tool to extract highly discriminative local descriptors for effective image search. Additionally, to further improve the discriminative power of the descriptors, recent works adopt fine-tuned strategies. In this article, taking a different approach, we propose a novel, computationally efficient, and competitive framework. Specifically, we first propose various strategies to compute masks, namely, SIFT-masks , SUM-mask , and MAX-mask , to select a representative subset of local convolutional features and eliminate redundant features. Our in-depth analyses demonstrate that proposed masking schemes are effective to address the burstiness drawback and improve retrieval accuracy. Second, we propose to employ recent embedding and aggregating methods that can significantly boost the feature discriminability. Regarding the computation and storage efficiency, we include a hashing module to produce very compact binary image representations. Extensive experiments on six image retrieval benchmarks demonstrate that our proposed framework achieves the state-of-the-art retrieval performances.
Constrained Discriminative Projection Learning for Image Classification

Projection learning is widely used in extracting discriminative features for classification. Although numerous methods have already been proposed for this goal, they barely explore the label information during projection learning and fail to obtain satisfactory performance. Besides, many existing methods can learn only a limited number of projections for feature extraction which may degrade the performance in recognition. To address these problems, we propose a novel constrained discriminative projection learning (CDPL) method for image classification. Specifically, CDPL can be formulated as a joint optimization problem over subspace learning and classification. The proposed method incorporates the low-rank constraint to learn a robust subspace which can be used as a bridge to seamlessly connect the original visual features and objective outputs. A regression function is adopted to explicitly exploit the class label information so as to enhance the discriminability of subspace. Unlike existing methods, we use two matrices to perform feature learning and regression, respectively, such that the proposed approach can obtain more projections and achieve superior performance in classification tasks. The experiments on several datasets show clearly the advantages of our method against other state-of-the-art methods.
MIASec: Enabling Data Indistinguishability Against Membership Inference Attacks in MLaaS

The emerging of machine learning has massively promoted the abilities of computational sustainability in natural resource management and allocation. Many Internet giants such as Google, Amazon, and Microsoft now provide Machine Learning as a Service (MLaaS) to meet the increasing demand for machine learning services. However, the prediction results of training data and testing data with the same machine learning model in MLaaS have remarkable differences, and thus the attackers can leverage machine learning techniques to launch the so-called membership inference attacks, i.e., to infer whether a record is in the training data or not. In this paper, we propose MIASec that can guarantee the data indistinguishability of the training data and thereby has the ability to defend against membership inference attacks in MLaaS. The key idea of MIASec is to narrow the dynamic ranges of vital features in the training data, such that the training data, the testing data, and even the synthetic data have almost semblable prediction results by the same machine learning model. With elaborated design on modifying the values of vital features in the training data, MIASec can thus reduce the differences between the model's outcomes of training data and testing data, thereby protecting the training data in effect while keeping the model's accuracy stable. We empirically evaluate MIASec on machine learning models trained by off-line neural networks and on-line MLaaS. Using realistic data and classification tasks, our experiment results show that MIASec can defend the membership inference attacks effectively. In particular, MIASec can reduce the precision and recall of attacks respectively by 11.7 and 15.4 percent in average, and by 18.6 and 21.8 percent at best.
When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time

A significant threat to the recent, wide deployment of machine learning-based systems, including deep neural networks (DNNs), is adversarial learning attacks. The main focus here is on evasion attacks against DNN-based classifiers at test time. While much work has focused on devising attacks that make small perturbations to a test pattern (e.g., an image) that induce a change in the classifier's decision, until recently there has been a relative paucity of work defending against such attacks. Some works robustify the classifier to make correct decisions on perturbed patterns. This is an important objective for some applications and for natural adversary scenarios. However, we analyze the possible digital evasion attack mechanisms and show that in some important cases, when the pattern (image) has been attacked, correctly classifying it has no utility---when the image to be attacked is (even arbitrarily) selected from the attacker's cache and when the sole recipient of the classifier's decision is the attacker. Moreover, in some application domains and scenarios, it is highly actionable to detect the attack irrespective of correctly classifying in the face of it (with classification still performed if no attack is detected). We hypothesize that adversarial perturbations are machine detectable even if they are small. We propose a purely unsupervised anomaly detector (AD) that, unlike previous works, (1) models the joint density of a deep layer using highly suitable null hypothesis density models (matched in particular to the nonnegative support for rectified linear unit (ReLU) layers); (2) exploits multiple DNN layers; and (3) leverages a source and destination class concept, source class uncertainty, the class confusion matrix, and DNN weight information in constructing a novel decision statistic grounded in the Kullback-Leibler divergence. Tested on MNIST and CIFAR image databases under three prominent attack strategies, our approach outperforms previous detection methods, achieving strong receiver operating characteristic area under the curve detection accuracy on two attacks and better accuracy than recently reported for a variety of methods on the strongest (CW) attack. We also evaluate a fully white box attack on our system and demonstrate that our method can be leveraged to strong effect in detecting reverse engineering attacks. Finally, we evaluate other important performance measures such as classification accuracy versus true detection rate and multiple measures versus attack strength.
Automated Data Slicing for Model Validation: A Big Data - AI Integration Approach

As machine learning systems become democratized, it becomes increasingly important to help users easily debug their models. However, current data tools are still primitive when it comes to helping users trace model performance problems all the way to the data. We focus on the particular problem of slicing data to identify subsets of the validation data where the model performs poorly. This is an important problem in model validation because the overall model performance can fail to reflect that of the smaller subsets, and slicing allows users to analyze the model performance on a more granular-level. Unlike general techniques (e.g., clustering) that can find arbitrary slices, our goal is to find interpretable slices (which are easier to take action compared to arbitrary subsets) that are problematic and large. We propose SliceFinder, which is an interactive framework for identifying such slices using statistical techniques. Applications include diagnosing model fairness and fraud detection, where identifying slices that are interpretable to humans is crucial. This research is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research.
Boolean degree 1 functions on some classical association schemes

We investigate Boolean degree 1 functions for several classical association schemes, including Johnson graphs, Grassmann graphs, graphs from polar spaces, and bilinear forms graphs, as well as some other domains such as multislices (Young subgroups of the symmetric group). In some settings, Boolean degree 1 functions are also known as completely regular strength 0 codes of covering radius 1, Cameron–Liebler line classes, and tight sets. We classify all Boolean degree 1 functions on the multislice. On the Grassmann scheme Jq(n,k) we show that all Boolean degree 1 functions are trivial for n≥5, k,n−k≥2 and q∈{2,3,4,5}, and that, for general q, the problem can be reduced to classifying all Boolean degree 1 functions on Jq(n,2). We also consider polar spaces and the bilinear forms graphs, giving evidence that all Boolean degree 1 functions are trivial for appropriate choices of the parameters.
Panoramic Robust PCA for Foreground–Background Separation on Noisy, Free-Motion Camera Video

This paper presents a new robust PCA method for foreground-background separation on freely moving camera video with possible dense and sparse corruptions. Our proposed method registers the frames of the corrupted video and then encodes the varying perspective arising from camera motion as missing data in a global model. This formulation allows our algorithm to produce a panoramic background component that automatically stitches together corrupted data from partially overlapping frames to reconstruct the full field of view. We model the registered video as the sum of a low-rank component that captures the background, a smooth component that captures the dynamic foreground of the scene, and a sparse component that isolates possible outliers and other sparse corruptions in the video. The low-rank portion of our model is based on a recent low-rank matrix estimator (OptShrink) that has been shown to yield superior low-rank subspace estimates in practice. To estimate the smooth foreground component of our model, we use a weighted total variation framework that enables our method to reliably decouple the true foreground of the video from sparse corruptions. We perform extensive numerical experiments on both static and moving camera video subject to a variety of dense and sparse corruptions. Our experiments demonstrate the state-of-the-art performance of our proposed method compared to existing methods both in terms of foreground and background estimation accuracy.