Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy Preservation through Uniformity

Inter-vehicle communications disclose rich information about vehicle whereabouts. Pseudonymous authentication secures communication while enhancing user privacy thanks to a set of anonymized certificates, termed pseudonyms. Vehicles switch the pseudonyms (and the corresponding private key) frequently; we term this pseudonym transition process. However, exactly because vehicles can in principle change their pseudonyms asynchronously, an adversary that eavesdrops (pseudonymously) signed messages, could link pseudonyms based on the times of pseudonym transition processes. In this poster, we show how one can link pseudonyms of a given vehicle by simply looking at the timing information of pseudonym transition processes. We also propose "mix-zone everywhere": time-aligned pseudonyms are issued for all vehicles to facilitate synchronous pseudonym update; as a result, all vehicles update their pseudonyms simultaneously, thus achieving higher user privacy protection.
Cooperative multi-objective evolutionary support vector machines for multiclass problems

In recent years, evolutionary algorithms have been found to be effective and efficient techniques to train support vector machines (SVMs) for binary classification problems while multiclass problems have been neglected. This paper proposes CMOE-SVM: Cooperative Multi-Objective Evolutionary SVMs for multiclass problems. CMOE-SVM enables SVMs to handle multiclass problems via co-evolutionary optimization, by breaking down the original M-class problem into M simpler ones, which are optimized simultaneously in a cooperative manner. Furthermore, CMOE-SVM can explicitly maximize the margin and reduce the training error (the two components of the SVM optimization), by means of multi-objective optimization. Through a comprehensive experimental evaluation using a suite of benchmark datasets, we validate the performance of CMOE-SVM. The experimental results, supported by statistical tests, give evidence of the effectiveness of the proposed approach for solving multiclass classification problems.
Passwords in the Air: Harvesting Wi-Fi Credentials from SmartCfg Provisioning

Smart devices without an interactive UI (e.g., a smart bulb) typically rely on specific provisioning schemes to connect to wireless networks. Among all the provisioning schemes, SmartCfg is a popular technology to configure the connection between smart devices and wireless routers. Although the SmartCfg technology facilitates the Wi-Fi configuration, existing solutions seldom take into serious consideration the protection of credentials and therefore introduce security threats against Wi-Fi credentials. This paper conducts a security analysis against eight SmartCfg based Wi-Fi provisioning solutions designed by different wireless module manufacturers. Our analysis demonstrates that six manufacturers provide flawed SmartCfg implementations that directly lead to the exposure of Wi-Fi credentials: attackers could exploit these flaws to obtain important credentials without any substantial efforts on brute-force password cracking. Furthermore, we keep track of the smart devices that adopt such Wi-Fi provisioning solutions to investigate the influence of the security flaws on real world products. Through reversely analyzing the corresponding apps of those smart devices we conclude that the flawed SmartCfg implementations constitute a wide potential impact on the security of smart home ecosystems.
Monocular Visual-Inertial State Initialization for Micro Aerial Vehicles

Monocular visual-inertial state estimator can be classified into filterbased and optimization-based method. However, both of these methods require an initial state to bootstrap the system. In this paper, we propose a robust initialization algorithm to provide high-quality initial guess for the monocular visual-inertial system (VINS). The proposed method takes the up-to-scale camera poses estimated by monocular vision-only SLAM (simultaneous localization and mapping) and the IMU sensor measuements as input. The gyroscope bias are firstly estimated by minimizing the error between pre-integrated gyroscope measurements and camera attitude measurements. After that, a rough gravity vector is calculated by aligning the preintegrated measurements and camera translation measurements. The observable of the accelerometer bias is also checked in this step. The gravity vector is refined in the third step, with the velocity is estimated in this step as well as the acceleration bias if it is observable. The practicability of the proposed approach is validated by simulation and real datasets experiments.
Cooperative co-evolution with online optimizer selection for large-scale optimization

Cooperative co-evolution (CC) is an effective framework that can be used to solve large-scale optimization problems. It typically divides a problem into components and uses one optimizer to solve the components in a round-robin fashion. However the relative contribution of each component to the overall fitness value may vary. Furthermore, using one optimizer may not be sufficient when solving a wide range of components with different characteristics. In this paper, we propose a novel CC framework which can select an appropriate optimizer to solve a component based on its contribution to the fitness improvement. In each evolutionary cycle, the candidate optimizer and component that make the greatest contribution to the fitness improvement are selected for evolving. We evaluated the efficacy of the proposed CC with Optimizer Selection (CCOS) algorithm using large-scale benchmark problems. The numerical experiments showed that CCOS outperformed the CC model without optimizer selection ability. When compared against several other state-of-the-art algorithms, CCOS generated competitive solution quality.
FastTrack: Minimizing Stalls for CDN-Based Over-the-Top Video Streaming Systems

Traffic for internet video streaming has been rapidly increasing and is further expected to increase with the higher definition videos and IoT applications, such as 360 degree videos and augmented virtual reality applications. While efficient management of heterogeneous cloud resources to optimize the quality of experience is important, existing work in this problem space often left out important factors. In this paper, we present a model for describing a today’s representative system architecture for video streaming applications, typically composed of a centralized origin server and several CDN sites. Our model comprehensively considers the following factors: limited caching spaces at the CDN sites, allocation of CDN for a video request, choice of different ports from the CDN, and the central storage and bandwidth allocation. With the model, we focus on minimizing a performance metric, stall duration tail probability (SDTP), and present a novel, yet efficient, algorithm to solve the formulated optimization problem. The theoretical bounds with respect to the SDTP metric are also analyzed and presented. Our extensive simulation results demonstrate that the proposed algorithms can significantly improve the SDTP metric, compared to the baseline strategies. Small-scale video streaming system implementation in a real cloud environment further validates our results.
Genetic Relationships of Soft Rot Bacteria Isolated from Konjac in China by Amplified Fragment Length Polymorphism (AFLP) and 16S rDNA Gene Sequences

Twenty-three isolates of soft rot bacteria from konjac corms were examined for their diversity using 16S rDNAs and AFLP technology. Both methods clustered two groups, dependent on their biotype characterization of Pectobacterium carotovora subsp. carotovora (P.c.c) and Pectobacterium chrysanthemi (P.ch), respectively. Of all isolates, 17 (73.9%) belonged to P. ch, indicated as the main pathogenic bacteria of konjac producing areas in China. The genetic variation among isolates from the same biotype was also rich, not consistent with the distances of the geographic sources.
Early Diagnosis of Alzheimer's Disease Using Informative Features of Clinical Data

Diagnosing Alzheimer's disease (AD) is usually difficult, especially when the disease is in its early stage. However, treatment is most likely to be effective at this stage; bringing an advantage in improving the life of patients, diagnosis process. After years of research, still little is known about its detailed mechanism. The AD patients undergo different physical examinations, brain scans, and laboratory tests etc. that require them to physically visit the medical center multiple times. Such visits further result in each patient's massive data stored for clinical diagnosis. This elevates the possibility of using informative rich variables from this data for the early detection of AD with the help of Machine Learning (ML) techniques. However, the previously proposed models endure a number of limitations which place strong barriers towards the direct applicability of such models for accurate prediction. A number of classifiers have been utilized in the literature but none of the previous work utilized the two major categories of variables namely clinical diagnosis and clinical judgment. In this paper, we utilize these two categories of data and perform a comparative evaluation of the predominant machine learning algorithms in terms of prediction accuracy, precision, recall (AUC) and training time. Our experimental results revealed that Bayesian based classifiers improve AD detection accuracy and allows the meaningful interpretation of predictive model which assists in early prognosis of AD for each patient.
Calcined Corncob-Kaolinite Combo as New Sorbent for Sequestration of Toxic Metal Ions From Polluted Aqua Media and Desorption

This study investigated a new area of improving the adsorption capacity of clay using corn cob as an alternative means of clay modification to the more expensive and complicated chemical treatment techniques. Kaolinite Clay (KC), Calcined corncob-kaolinite Combo (CCKC), and Corncob (CC) adsorbents were utilized. The adsorbents were characterized by Fourier Transform-Infrared (FT-IR) Spectroscopy, Scanning Electron Microscopy (SEM), X-ray fluorescence spectroscopy, and Brunauer-Emmett-Teller (BET) surface area analyzer. Batch adsorption methodology was used to investigate the effect of pH, initial metal concentration, adsorbent dose, and contact time on adsorption of Pb (II) and Cd (II). A slight increase in BET surface area of 29.31 m2/g for CCKC from 14.12 m2/g for raw KC was achieved. The trend of metal adsorption on the adsorbents was in the order CC>CCKC>KC. The Langmuir isotherm was found to present the best fit for the unmodified adsorbents while the Freundlich was applicable for CCKC indicating multilayer heterogeneous surface. The pseudo second order kinetic model was found to be suitable in the kinetic analysis. Thermodynamic studies revealed a spontaneous physical adsorption process of metal ions on CCKC. The combo adsorbent showed highest percentage desorption (>70%) of Cd and Pb ions in both acid and basic media compared to the other adsorbents. The results of the study established the efficiency of calcined corn cob kaolinite combo as suitable adsorbent for metal ions.
A set-oriented MOEA/D

The working principles of the well-established multi-objective evolutionary algorithm Moea/d relies on the iterative and cooperative improvement of a number of single-objective sub-problems obtained by decomposition. Besides the definition of sub-problems, selection and replacement are, like in any evolutionary algorithm, the two core elements of Moea/d. We argue that these two components are however loosely coupled with the maintained population. Thereby, we propose to re-design the working principles of Moea/d by adopting a set-oriented perspective, where a many-to-one mapping between sub-problems and solutions is considered. Selection is then performed by defining a neighborhood relation among solutions in the population set, depending on the corresponding sub-problem mapping. Replacement is performed following an elitist mechanism allowing the population to have a variable, but bounded, cardinality during the search process. By conducting a comprehensive empirical analysis on a range of combinatorial multi- and many-objective NK-landscapes, we show that the proposed approach leads to significant improvements, especially when dealing with an increasing number of objectives. Our findings indicate that a set-oriented design can constitute a sound alternative for strengthening the practice of multi- and many-objective evolutionary optimization based on decomposition.
A Color Segmentation and Feature Matching Algorithm for Car Brake Pad Image Classification

During the manufacturing of car brake pads, classification of brake pads with different Appearance is an important process, which at present is done mostly through the human eye detection. In this paper, an intelligent car brake pad image classification algorithm based on machine vision technology is proposed. Firstly by using the high-resolution industrial camera with coaxial light source, the image of brake pad on the conveyor is acquired. Then a HSV color space conversion is conducted on the original image. By thresholding the H or S channel of the image and morphological processing, the brake pad foreground is accurately segmented. Following the segmentation, the Hu moments of the brake pad region is extracted as a shape descriptor of the pad. At last the 7-dimentional Hu feature is compared to the templates of different kinds of brake pads to find the best match. Experiments show that the proposed algorithm can successfully segment the brake pads from the dark indistinct background and the classification accuracy reaches 81.7%.
Building Python-Based Topologies for Massive Processing of Social Media Data in Real Time

In this paper we propose a streaming approach for real-time processing of huge amounts of data. CATENAE is a library for easy building and execution of Python topologies (e.g., web crawler, classifier). Topologies are designed for their deployment inside Docker containers and, thus, horizontal scaling, granular resource assignment and isolation can be achieved easily. Furthermore, micromodules can have its own dependencies (including the Python version), allowing the user to limit resources such as CPU or memory by instance. We describe an implementation of a use case composed of two topologies: (1) a crawler for tracking users in social media and (2) an early risk detector of depression. We also explain how CATENAE topologies can be connected to non-Python systems.
Helping john to make informed decisions on using social login

Users make two privacy-related decisions when signing up for a new Service Provider (SP): (1) whether to use an existing Single Sign-On (SSO) account of an Identity Provider (IdP), or not, and (2) the information the IdP is allowed to share with the SP under specific conditions. From a privacy point of view, the use of existing social network-based SSO solutions (i.e. social login) is not recommended. This advice, however, comes at the expense of security, usability, and functionality. Thus, in principle, it should be up to the user to consider all advantages and disadvantages of using SSO and to consent to requested permissions, provided that she is well informed. Another issue is that existing social login sign-up interfaces are often not compliant with legal privacy requirements for informed consent and Privacy by Default. Accordingly, our research focuses on enabling informed decisions and consent in this context. To this end, we identified users' problems and usability issues from the literature and an expert cognitive walkthrough. We also elicited end user and legal privacy requirements for user interfaces (UIs) providing informed consent. This input was used to develop a tutorial to inform users on the pros and cons of sign-up methods and to design SSO sign-up Uls for privacy. A between-subject laboratory study with 80 participants was used to test both the tutorial and the UIs. We demonstrate an increase in the level to which users are informed when deciding and providing consent in the context of social login.
The Language of Ethics and Double Standards in the Affective and Sexual Socialization of Youth. Communicative Acts in the Family Environment as Protective or Risk Factors of Intimate Partner Violence

One of the explanatory factors for the perpetuation of intimate partner violence (IPV) is the socialization process. There is broad literature on the role of family in socializing gender roles and the influence on reproducing IPV. However less research has been developed on the effects of communicative acts in the family environment as a protective or risk factor in front of IPV. This article presents sound evidence confirming the presence of language of ethics that is reproducing stereotyped models of attraction in heterosexual relationships, which empties good people of attractiveness and indirectly contributes to maintain the link between desire and aggressiveness. The language of ethics is characterized by integrating speech acts that exclusively include ethics and exclude desire when talking about egalitarian boys or men. To analyze this reality, a qualitative study has been conducted framed in the communicative methodology. This methodology has been recommended by the European Commission to conduct research on vulnerable groups and social inequalities, which has the aim to advance knowledge on social transformation. Drawing on this approach, three different data-collection techniques have been implemented: in-depth interviews, daily-life stories and focus groups. The fieldwork includes a sample of 52 young men and women between the ages of 18 and 23 from a vocational training high school, and 4 fathers and 4 mothers of some of these young people. The findings confirm the existence of a model of socialization that replicates family relations based on the maintenance of the double standards. Thus, mothers used to employ the language of ethics with their daughters fostering a controversial effect, that is, the latter prefer to start affective and sexual relationships with boys who are aggressive and not egalitarian. On the other hand, fathers used to employ language of desire with their sons stimulating the performance of chauvinist behaviors that denigrate women and girls.
Trigger correlation for dynamic system reconfiguration

Service Providers1 aim at optimizing resource utilisation while respecting the Service Level Agreements (SLAs) entered with customers. Dynamic reconfiguration is a mechanism for rearranging, allocating and deallocating resources as workload varies. Rearranging, adding or deallocating resources are performed by actions according to elasticity rules triggered by certain conditional events, like threshold violations, called triggers. At runtime, more than one trigger may be generated at a time. Handling them independently may jeopardize certain properties such as availability; moreover, it may harm the stability of the system. In this paper we propose a model based approach for runtime correlation of triggers and the execution of their related elasticity rule actions. This approach is part of an overall framework for SLA compliance management and dynamic reconfiguration.
Towards Battery-free Radio Tomographic Imaging

Radio Tomographic Imaging (RTI) enables novel radio frequency (RF) sensing applications such as intrusion detection systems by observing variations in radio links caused by human actions. RTI applications are, however, severely limited by the requirement to retrofit existing infrastructure with energy-expensive sensors. In this demonstration, we present our ongoing efforts to develop the first battery-free RTI system that operates on minuscule amounts of energy harvested from the ambient environment. Our system eliminates the energy-expensive components employed on state-of-the-art RTI systems achieving two orders of magnitude lower power consumption. Battery-free operation enables a sustainable deployment, as RTI sensors could be deployed for long periods of time with little maintenance effort. Our demonstration showcases an intrusion detection scenario enabled by our system.
MOVELETS: exploring relevant subtrajectories for robust trajectory classification

Several methods for trajectory classification build models exploring trajectory global features, such as the average and the standard deviation of speed and acceleration, but for some applications these features may not be the best to determine the class. Other works explore local features, applying trajectory partition and discretization, that lose important movement information that could discriminate the class. In this work we propose a new method, called Movelets, to discover relevant subtrajectories without the need of a predefined criteria for either trajectory partition or discretization. We extend the concept of time series shapelets for trajectories, and to the best of our knowledge, this work is the first to use shapelets in the trajectory domain. We evaluated the proposed approach with several categories of datasets, including hurricanes, vehicles, animals, and transportation means, and show with extensive experiments that our method largely outperformed state of the art works, indicating that Movelets is very promising for trajectory classification.
Less detectable environmental changes in dynamic multiobjective optimisation

Multiobjective optimisation in dynamic environments is challenging due to the presence of dynamics in the problems in question. Whilst much progress has been made in benchmarks and algorithm design for dynamic multiobjective optimisation, there is a lack of work on the detectability of environmental changes and how this affects the performance of evolutionary algorithms. This is not intentionally left blank but due to the unavailability of suitable test cases to study. To bridge the gap, this work presents several scenarios where environmental changes are less likely to be detected. Our experimental studies suggest that the less detectable environments pose a big challenge to evolutionary algorithms.
Design and Implementation of Fault-Tolerant LISP Mapping System

The network applications become more complex such that they need to integrate big data. An emerging problem in current networks is scalability, such as the size of BGP routing table or the multihoming problem. The Locator/Identifier Separation Protocol (LISP) is a protocol to solve such problems. The main concept underlying LISP is a separation between the identifier and the locator. However, few studies have focussed on fault management in LISP mapping system. In this study, we design a fault management system for LISP mapping system on the Software-Defined Network (SDN) controller and modifying the functionality of the tunnel router. We introduce new control messages and processes to check for the survival of LISP mapping system and create a backup of records among controllers. Furthermore, we design and implement functionalities to exchange controller information among the tunnel routers. We implement a scenario to test our fault management system.
Cost-effective construction of Information Retrieval test collections

In this paper we describe our recent research on effective construction of Information Retrieval collections. Relevance assessments are a core component of test collections, but they are expensive to produce. For each test query, only a sample of documents in the corpus can be assessed for relevance. We discuss here a class of document adjudication methods that iteratively choose documents based on reinforcement learning. Given a pool of candidate documents supplied by multiple retrieval systems, the production of relevance assessments is modeled as a multi-armed bandit problem. These bandit-based algorithms identify relevant documents with minimal effort. One instance of these models has been adopted by NIST to build the test collection of the TREC 2017 common core track.