Reviewerly/exHarmony huggingface dataset:
Obfuscating EVES Algorithm and Its Application in Fair Electronic Transactions in Public Clouds

In EUROCRYPT'10, Hada proposed a secure obfuscator of encrypted signature scheme which can be executed on an untrusted server to solve the security problem of untrusted proxy signature. In Hada's scheme, the server can generate a valid signature for the user without obtaining user's secret key; however, the scheme cannot resist the collusion attack. In this paper, we extend the study of encrypted signature schemes and propose an obfuscator for encrypted verifiable encrypted signature (EVES), and model the application in electronic transactions. The proposed scheme cannot only prevent the semihonest server from obtaining user's sensitive information, but also resist to the collusion activities between verifier/receiver and untrusted cloud. We show that the obfuscation scheme achieves the requirement of virtual black-box security under standard cryptographic assumptions. The experiments show that the time of constructing the EVES obfuscator is 63 ms, the signing time of the obfuscated algorithm is 78 ms, and the verifying time of obfuscated signature is 63 ms, which is more efficient and practical than related schemes, and the scheme can be deployed in scenarios requiring electronic transactions in outsourced clouds.
Mechamagnets

We present Mechamagnets, a technique for facilitating the design and fabrication of haptic and functional inputs for physical interfaces. This technique consists of a set of 3D printed spatial constraints which facilitate different physical movements, as well as unpowered haptic profiles created by embedding static magnets in 3D printed parts. We propose the Mechamagnets taxonomy to map the design space of this technique for designers and makers. Furthermore, we leverage the use of magnets by instrumenting these objects with linear Hall effect sensors to create functional digital inputs. We showcase Mechamagnets with a series of novel physical interfaces made with this technique.
On the Impact of Outdated and Vulnerable Javascript Packages in Docker Images

Containerized applications, and in particular Docker images, are becoming a common solution in cloud environments to meet ever-increasing demands in terms of portability, reliability and fast deployment. A Docker image includes all environmental dependencies required to run it, such as specific versions of system and third-party packages. Leveraging on its modularity, an image can be easily embedded in other images, thus simplifying the way of sharing dependencies and building new software. However, the dependencies included in an image may be out of date due to backward compatibility requirements, endangering the environments where the image has been deployed with known vulnerabilities. While previous research efforts have focused on studying the impact of bugs and vulnerabilities of system packages within Docker images, no attention has been given to third-party packages. This paper empirically studies the impact of npm JavaScript package vulnerabilities in Docker images. We based our analysis on 961 images from three official repositories that use Node.js, and 1,099 security reports of packages available on npm, the most popular JavaScript package manager. Our results reveal that the presence of outdated npm packages in Docker images increases the risk of potential security vulnerabilities, suggesting that Docker maintainers should keep their installed JavaScript packages up to date.
Geo-accumulation vector model for evaluating the heavy metal pollution in the sediments of Western Dongting Lake

The conventional geo-accumulation index (GI) cannot deal with the uncertainty in the geochemical background information of pollutants or conduct a synthetic evaluation of heavy metal pollution in sediments. To solve these problems, a geo-accumulation vector (GV) model is designed based on uncertainty analysis theory. GV uses the probability vector to quantify the possibility for the pollution condition of a heavy metal to belong to a certain grade and then uses the first-order moment principle to determine its category. The GV model is applied in this study to evaluate heavy metal pollution in the sediments of Western Dongting Lake. The results show that (i) compared with the conventional GI model, the GV model has better capacity in evaluating synthetic pollution and in dealing with the uncertainty in the geochemical background information of sediments; (ii) the sediments of Li River estuary, Yuan River estuary, Songzi River estuary, and the central part of Western Dongting Lake are “moderately to heavily contaminated,” “moderately to heavily contaminated,” “moderately contaminated,” and “uncontaminated to moderately contaminated” by heavy metals, respectively. These sediments also have 0.19, 0.36, 0.48, and 0.15 possibilities to deteriorate to the next category, respectively; and (iii) all deterioration risks are introduced by cadmium.
Membership Affinity Lasso for Fuzzy Clustering

Fuzzy clustering generates a membership vector for each data point in the dataset to indicate its belongingness to different clusters. This procedure can be regarded as an encoding process and the obtained vectors of memberships are the new representations of original data. Naturally, the affinities between new representations or the vectors of memberships should be consistent with the ones between original data points. For example, the data points close to each other should also take similar membership vectors. Such constraints on the affinities of memberships are valuable prior knowledge that should be imposed to the objective function of fuzzy clustering for better performance. To this end, we introduce the membership affinity lasso for fuzzy clustering in this paper. Utilizing alternating direction method of multipliers, an efficient approach is derived to optimize the general membership affinity lasso regularized fuzzy clustering model in offline manner. As illustrative examples, three new fuzzy clustering algorithms with the membership affinity lasso are proposed. Experiments on the synthetic and real data demonstrate the superiority and flexibility of the proposed algorithms.
‘Oh we can’t actually do anything about that’: The problematic nature of jurisdiction for online fraud victims

One of the most pressing challenges with policing online fraud relates to jurisdiction. Policing is traditionally based on territoriality, but the internet has changed this. Offenders in one country can target a victim in a second country, who is requested to send money to a third or fourth country. This article examines online fraud victims’ reporting to police. Specifically, it demonstrates the misconceptions that exist regarding jurisdiction, namely the relationship between the Australian Federal Police and state/territory police. A clear disconnect emerges between understandings and expectations of who can investigate what relating to online fraud. The Australian Cybercrime Online Reporting Network’s establishment in 2014 is a positive step but this has not fixed the issue entirely. Overall, the article argues that the jurisdictional challenges experienced by police are not understood by victims, and improvement is needed regarding awareness of victims and police alike, to reduce unnecessary, additional trauma to victims.
ASoVS: Abstractive Summarization of Video Sequences

In today's age, a massive amount of videos are produced every day, which contains audio, visual, and textual data. This constant increase is due to the ease of recording service in portable devices, such as mobile phones, tablets, or cameras. The major challenge is to understand the visual semantics and convert it into a condensed format, such as caption or summary to save storage space, enables users to index, navigate, and help gain information in less time. We propose an innovative joint end-to-end solution, Abstractive Summarization of Video Sequences, which uses the deep neural network to generate the natural language description and abstractive text summarization of an input video. This provides a text-based video description and abstractive summary, enabling users to discriminate between relevant and irrelevant information according to their needs. Furthermore, our experiments show that the joint model can attain better results than the baseline methods in separate tasks with informative, concise, and readable multi-line video description and summary in a human evaluation.
Acute Lymphoblastic Leukemia Cells Image Analysis with Deep Bagging Ensemble Learning

Acute lymphoblastic leukemia (ALL) is a blood cancer that leads to 111,000 death globally in 2015. Recently, diagnosing ALL often involves the microscopic image analysis with the help of deep learning (DL) techniques. However, as most medical related problems, deficiency training samples and minor visual difference between ALL and normal cells make the image analysis task quite challenging. Herein, an augmented image enhanced bagging ensemble learning with elaborately designed training subsets were proposed to tackle the above challenges. The weighted $$F_1$$-scores of the preliminary test set and final test are 0.84 and 0.88, respectively employing our ensemble model predictions and ranked within the top 10% in ISBI-2019 Classification of Normal versus Malignant White Blood Cancer Cells contest. Our results preliminarily demonstrate the efficacy of employing DL based techniques in ALL cells image analysis.
Monte Carlo fingerprinting of the terrestrial sources of different particle size fractions of coastal sediment deposits using geochemical tracers: some lessons for the user community

A sediment source fingerprinting method, including a Monte Carlo simulation framework, was used to quantify the contributions of terrestrial sources of fine- (< 63 μm) and coarse-grained (63–500 μm) sediments sampled from three categories of coastal sediment deposits in the Jagin catchment, south-east of Jask, Hormozgan province, southern Iran: coastal dunes (CD), terrestrial sand dunes or onshore sediments (TSD), and marine or offshore sediments (MD). Forty-nine geochemical properties were measured in the two size fractions and a three-stage statistical process consisting of a conservation test, the Kruskal–Wallis H test, and stepwise discriminant function analysis (DFA) was applied to select final composite fingerprints for terrestrial source discrimination. Based on the statistical tests, four final fingerprints comprising Be, Ni, K and Cu and seven final fingerprints consisting Cu, Th, Be, Al, La, Mg and Fe were selected for discriminating terrestrial sources of the coastal fine- and coarse-grained sediments, respectively. Two geological spatial sources, including Quaternary (clay flat, high and low level fans and valley terraces) and Palaeocene age deposits, were identified as the main terrestrial sources of the fine-grained sediment sampled from the coastal deposits. A geological spatial source consisting of sandstone with siltstone, mudstone and minor conglomerate (Palaeocene age deposits) was identified as the main terrestrial source for coarse-grained sediment sampled from the coastal deposits.
eHealth Integrity Model Based on Permissioned Blockchain

(1) Background: Large eHealth systems should have a mechanism to detect unauthorized changes in patients’ medical documentation, access permissions, and logs. This is due to the fact that modern eHealth systems are connected with many healthcare providers and sites. (2) Methods: Design-science methodology was used to create an integrity-protection service model based on blockchain technology. Based on the problem of transactional transparency, requirements were specified and a model was designed. After that, the model’s security and performance were evaluated. (3) Results: a blockchain-based eHealth integrity model for ensuring information integrity in eHealth systems that uses a permissioned blockchain with off-chain information storage was created. In contrast to existing solutions, the proposed model allows information removal, which in many countries’ eHealth systems is a legal requirement, and is based on a blockchain using the Practical Byzantine Fault Tolerant algorithm. (4) Conclusion: A blockchain can be used to store medical data or only security-related data. In the proposed model, a blockchain is mainly used to implement a data-integrity service. This service can be implemented using other mechanisms, but a blockchain provides a solution that does not require trusted third parties, works in a distributed eHealth environment, and supports document removal.
Sliding mode control based on a hybrid grey-wolf-optimized extreme learning machine for robot manipulators

In order to improve the control accuracy of a robot manipulator, the scheme of sliding mode control (SMC) based on extreme learning machine (ELM) is proposed. First, a hybrid grey wolf optimization algorithm based on fuzzy weights and a differential evolution algorithm is proposed to overcome the problems with ELM, which randomly selects hidden layer weights and biases. Then, this improved ELM was introduced into the SMC and used to address external interference and uncertainties. Finally, discontinuous control signals were compensated in this scheme. Specifically, the discontinuous control signal is serialized, which effectively reduces the chattering phenomenon of the SMC. In contrast with the proportional–integral–derivative (PID) approach and other SMC methods, the two-link manipulator simulation results demonstrate that the proposed SMC scheme can achieve high-precision, high-speed tracking and can suppress chatter under the existing model error and external disturbance conditions.
The Voice Makes the Car: Enhancing Autonomous Vehicle Perceptions and Adoption Intention through Voice Agent Gender and Style

The present research explores how autonomous vehicle voice agent (AVVA) design influences autonomous vehicle passenger (AVP) intentions to adopt autonomous vehicles. An online experiment (N = 158) examined the role of gender stereotypes in response to an AVVA with respect to the technology acceptance model. The findings indicate that characteristics of the AVVA that are more consistent with the stereotypical expectation of the social role (informative male AVVA and social female AVVA) foster greater perceived ease of use (PEU) and perceived usefulness (PU) than inconsistent conditions (social male AVVA and informative female AVVA). The study offers theoretical implications regarding the technology acceptance model in the context of autonomous technologies as well as practical implications for the design of autonomous vehicle voice agents.
Stealthy Porn: Understanding Real-World Adversarial Images for Illicit Online Promotion

Recent years have witnessed the rapid progress in deep learning (DP), which also brings their potential weaknesses to the spotlights of security and machine learning studies. With important discoveries made by adversarial learning research, surprisingly little attention, however, has been paid to the real-world adversarial techniques deployed by the cybercriminal to evade image-based detection. Unlike the adversarial examples that induce misclassification using nearly imperceivable perturbation, real-world adversarial images tend to be less optimal yet equally effective. As a first step to understand the threat, we report in the paper a study on adversarial promotional porn images (APPIs) that are extensively used in underground advertising. We show that the adversary today's strategically constructs the APPIs to evade explicit content detection while still preserving their sexual appeal, even though the distortions and noise introduced are clearly observable to humans. To understand such real-world adversarial images and the underground business behind them, we develop a novel DP-based methodology called Male`na, which focuses on the regions of an image where sexual content is least obfuscated and therefore visible to the target audience of a promotion. Using this technique, we have discovered over 4,000 APPIs from 4,042,690 images crawled from popular social media, and further brought to light the unique techniques they use to evade popular explicit content detectors (e.g., Google Cloud Vision API, Yahoo Open NSFW model), and the reason that these techniques work. Also studied are the ecosystem of such illicit promotions, including the obfuscated contacts advertised through those images, compromised accounts used to disseminate them, and large APPI campaigns involving thousands of images. Another interesting finding is the apparent attempt made by cybercriminals to steal others' images for their advertising. The study highlights the importance of the research on real-world adversarial learning and makes the first step towards mitigating the threats it poses.
A new data clustering algorithm based on critical distance methodology

A variety of algorithms have recently emerged in the field of cluster analysis. Consequently, based on the distribution nature of the data, an appropriate algorithm can be chosen for the purpose of clustering. It is difficult for a user to decide a priori which algorithm would be the most appropriate for a given dataset. Algorithms based on graphs provide good results for this task. However, these algorithms are vulnerable to outliers with limited information about edges contained in the tree to split a dataset. Thus, in several fields, the need for better clustering algorithms increases and for this reason utilizing robust and dynamic algorithms to improve and simplify the whole process of data clustering has become an urgent need. In this paper, we propose a novel distance-based clustering algorithm called the critical distance clustering algorithm. This algorithm depends on the Euclidean distance between data points and some basic mathematical statistics operations. The algorithm is simple, robust, and flexible; it works with quantitative data that are real-valued, not qualitative, and categorical with different dimensions. In this work, 26 experiments are conducted using different types of real and synthetic datasets taken from different fields. The results prove that the new algorithm outperforms some popular clustering algorithms such as MST-based clustering, K-means, and Dbscan. Moreover, the algorithm can precisely produce more reasonable clusters even when the dataset contains outliers and without specifying any parameters in advance. It also provides a number of indicators to evaluate the established clusters and prove the validity of the clustering.
Integrating Community Context Information Into a Reliably Weighted Collaborative Filtering System Using Soft Ratings

In this paper, we aim at developing a new collaborative filtering recommender system using soft ratings, which is capable of dealing with both imperfect information about user preferences and the sparsity problem. On the one hand, Dempster-Shafer theory is employed for handling the imperfect information due to its advantage in providing not only a flexible framework for modeling uncertain, imprecise, and incomplete information, but also powerful operations for fusion of information from multiple sources. On the other hand, in dealing with the sparsity problem, community context information that is extracted from the social network containing all users is used for predicting unprovided ratings. As predicted ratings are not a hundred percent accurate, while the provided ratings are actually evaluated by users, we also develop a new method for calculating user-user similarities, in which provided ratings are considered to be more significant than predicted ones. In the experiments, the developed recommender system is tested on two different data sets; and the experiment results indicate that this system is more effective than CoFiDS, a typical recommender system offering soft ratings.
Time-discretization for speeding-up scheduling of deadline-constrained workflows in clouds

In this paper we deal with the problem of scheduling workflow applications in multiple infrastructure-as-a-service (IaaS) providers, where the scheduler must determine on which computational resource each component of a workflow should be scheduled in an attempt to minimize the monetary cost of the workflow execution. In our previous work, we describe the λ-granularity approach that increases the degree of discrete time intervals in an integer linear programming (ILP)to speed up the scheduling of workflow applications with deadline constraints. To assist the selection of a specific discrete time interval (λ value) given a workflow and its deadline, we propose in this paper an aggregate objective function which is derived from historical evaluations of scheduling costs and scheduler runtimes. Results of simulations evince that the proposed AOF-based procedure was able to calculate a specific λ value that allowed the reduction of the ILP-based scheduler running time, although yet achieving cost-effective solutions.
Noise-Resistant Statistical Traffic Classification

Network traffic classification plays a significant role in cyber security applications and management scenarios. Conventional statistical classification techniques rely on the assumption that clean labelled samples are available for building classification models. However, in the big data era, mislabelled training data commonly exist due to the introduction of new applications and lack of knowledge. Existing statistical traffic classification techniques do not address the problem of mislabelled training data, so their performance become poor in the presence of mislabelled training data. To meet this challenge, in this paper, we propose a new scheme, Noise-resistant Statistical Traffic Classification (NSTC), which incorporates the techniques of noise elimination and reliability estimation into traffic classification. NSTC estimates the reliability of the remaining training data before it builds a robust traffic classifier. Through a number of traffic classification experiments on two real-world traffic data sets, the results show that the new NSTC scheme can effectively address the problem of mislabelled training data. Compared with the state of the art methods, NSTC can significantly improve the classification performance in the context of big unclean data.
Motion-blur kernel size estimation via learning a convolutional neural network

Deblurring is to restore a latent clear image as well as to estimate an underlying blur kernel from a single blurry image. Motion blur kernel size is a significant input parameter of existing deblurring algorithms. Setting the size manually, which is a tedious trial-and-error process, can hardly obtain a satisfactory result directly and effectively. In this paper, we formulate the kernel size estimation as a regression problem and construct a convolutional neural network (CNN) to resolve it. After training the CNN by a large number of simulated blurry images with labels, which are produced by a relative way, we can estimate the kernel sizes of a given blurry image without size limitation. Both quantitative and qualitative results of experiments show that our method can not only estimate the motion blur kernel size accurately but also help to yield better pictures with estimated kernel sizes as input parameters.