Miracle-dz/UniFAR-FTUs huggingface dataset:
Tracking Versus Security: Investigating the Two Facets of Browser Fingerprinting. (Traçage versus Sécurité : Explorer les Deux Facettes des Empreintes de Navigateurs)

Nowadays, a wide range of devices can browse the web, ranging from smartphones, desktop computers, to connected TVs. To increase their browsing experience, users also customize settings in their browser, such as displaying the bookmark bar or their preferred languages. Customization and the diversity of devices are at the root of browser fingerprinting. Indeed, to manage this diversity, websites can access attributes about the device using JavaScript APIs, without asking for user consent. The combination of such attributes is called a browser fingerprint and has been shown to be highly unique, making of fingerprinting a suitable tracking technique. Its stateless nature makes it also suitable for enhancing authentication or detecting bots. In this thesis, I report three contributions to the browser fingerprinting field: 1. I collect 122K fingerprints from 2,346 browsers and study their stability over more than 2 years. I show that, despite frequent changes in the fingerprints, a significant fraction of browsers can be tracked over a long period; 2. I design a test suite to evaluate fingerprinting countermeasures. I apply our test suite to 7 countermeasures, some of them claiming to generate consistent fingerprints, and show that all of them can be identified, which can make their users more identifiable; 3. I explore the use of browser fingerprinting for crawler detection. I measure its use in the wild, as well as the main detection techniques. Since fingerprints are collected on the client-side, I also evaluate its resilience against an adversarial crawler developer that tries to modify its crawler fingerprints to bypass security checks.
Cheat Protection in Online Games

Competitive online video gaming (or eSports for short) is a booming industry. Current estimates predict global revenues of well over 1 billion dollars by the end of this year, Twitch.tv the largest eSports streaming network is now one of the biggest sites on the internet, and the International Olympic Committee is even considering to include eSports in the 2024 Summer Olympics. With this rising popularity comes a dark side in the form of cheating, however. Cheaters are willing to subvert the rules of the game by any means necessary. Many of them pay monthly subscription fees to get access to exclusive cheating software created by hackers. In this talk, I will discuss the most prevalent types of cheats used in online games and show how they give players a competitive advantage. I will then highlight the various steps that go into creating a cheat and give an overview of the various techniques and tools hackers use in each step, ranging from debuggers, differential data analysis tools, API hooking, bytehacking, etc. Next, I will present cheat mitigation strategies that can be incorporated into a game's design, and detection techniques that can be applied in both client-side and server-side cheat-protection software. Finally, I will zoom in on some of the most popular client-side anti-cheat solutions and highlight some of their capabilities.
Computational Linguistics: Introduction to the Thematic Issue

This special issue of Computacion y Sistemaspresents a selection of papers on natural language processing and computational linguistics, along with two regular papers. Natural language processing is an area of artificial intelligence and its applications devoted to analysis and generation of data streams involved in human communication using language, such as English or Spanish, typically in the form of text or speech, as well as, in multimodal setting, associated facial expressions and body language. It embraces natural language processing and computational linguistics in a wider context of their real-life applications in a wide range of disciplines.
Incremental Learning Introspective Movement Primitives From Multimodal Unstructured Demonstrations

Learning movement primitive from unstructured demonstrations has become a popular topic in recent years, which provides a natural way to endow human-inspired skills to robots. The main idea of movement primitives is that should suffice to reconstruct a large set of complex manipulation tasks. However, conventional learning methods mostly focus on the kinesthetic variables and ignore those critical introspective capacities in manipulation such as movement generalization and assessment of the sensory signals. In this paper, we investigate the association of generalization, fault detection, fault diagnoses, and task exploration during manipulation task, and call such movement primitives augmented with introspective capacities Introspective Movement Primitives (IMP). With our previous work, this paper mainly addresses how IMPs can be acquired by assessing the quality of multimodal sensory data of unstructured demonstrations and how they can incrementally create manipulation task by reverse execution and human interaction. Experimental evaluation on a human-robot collaborative packaging task with a Rethink Baxter robot, results indicate that our proposed method can effectively increase robustness towards external perturbations and adaptive exploration during robot manipulation task.
Analytical cache modeling and tilesize optimization for tensor contractions

Data movement between processor and memory hierarchy is a fundamental bottleneck that limits the performance of many applications on modern computer architectures. Tiling and loop permutation are key techniques for improving data locality. However, selecting effective tile-sizes and loop permutations is particularly challenging for tensor contractions due to the large number of loops. Even state-of-the-art compilers usually produce sub-optimal tile-sizes and loop permutations, as they rely on naïve cost models. In this paper we provide an analytical model based approach to multi-level tile size optimization and permutation selection for tensor contractions. Our experimental results show that this approach achieves comparable or better performance than state-of-the-art frameworks and libraries for tensor contractions.
Physical Layer Evaluation of V2X Communications Technologies: 5G NR-V2X, LTE-V2X, IEEE 802.11bd, and IEEE 802.11p

Vehicular communications and connected cars have an eminence potential to improve road safety and reduce the number of accidents by sharing information with their surrounding. The state of the art technologies to enable vehicular communications are IEEE 802.11p and LTE-V2X. A number of studies and field trials are carried out to evaluate their performance and suitability in various scenarios. On one hand 3GPP (3rd Generation Partnership Project) is working on the next generation V2X technology 5G NR-V2X to address new use cases and improve the performance. On the other hand, an IEEE 802.11 study group NGV (next generation V2X) is identifying new use cases and requirements to define a possible amendment IEEE 802.11bd. In this paper, we evaluate and compare the PHY layer performance of these upcoming technologies for vehicle-to-vehicle (V2V) communications. The purpose of this study is to identify which technology is more suitable for V2V communications. Our results show that NR-V2V is expected to outperform all other standards (even IEEE 802.11bd) in terms of reliability, range, latency and data rates. However, IEEE 802.11bd is expected to be more reliable with improved range and throughput compared to IEEE 802.11p.
Nickel to Lego: Using Foolgle to Create Adversarial Examples to Fool Google Cloud Speech-to-Text API

Many companies offer automatic speech recognition or Speech-to-Text APIs for use in diverse applications. However, audio classification algorithms trained with deep neural networks (DNNs) can sometimes misclassify adversarial examples, posing a significant threat to critical applications. In this paper, we present a novel way to create adversarial audio examples using a genetic algorithm. Our algorithm creates adversarial examples by iteratively adding perturbations to the original audio signal. Unlike most white-box adversarial example generations, our approach does not require knowledge about the target DNN's model and parameters (black-box) and heavy computational power of GPU resources. To show the feasibility of the proposed idea, we implement a tool called, Foolgle, using a genetic algorithm that performs untargeted attacks to create adversarial audio examples and evaluate those with the state-of-the-art Google Cloud Speech-to-Text API. Our preliminary experiment results show that Foolgle deceives the API with a success probability of 86%.
An Analysis of DDoS Attacks on the Instant Messengers

Latest technologies of voice over IP (VoIP) and mobile messaging for smartphones messengers such as WhatsApp, Viber, Skype, etc., offer free-of-charge facilities of worldwide SMS, MMS, and voice calls to their users, unlike the traditional and expensive cellular or telephone networks’ services. Customers of the formerly mentioned messengers are estimated in millions because of the attractive features offered by them. However, these messengers face many cyber security threats and the required security features are either not available at all or are insufficient for efficiently countering the threats. Professionals working in the domain of cyber security are challenged by the devastating effects of distributed denial of service (DDoS) attacks on all major platforms including Apple Macintosh, Windows, Unix, and Linux. In this paper, we demonstrate the effect of DDoS attack on the performance of an IRC server using a test bed. We use a game theoretic model to analyze the feasibility of DDoS attacks on the IRC platform, keeping in view the attacker’s objective. The analysis will help the security experts to propose appropriate countermeasures to reduce the attackers’ utility, thereby making it less attractive for those attackers to launch the attack.
IoT fault management in cloud/fog environments

IoT has been promoting the view of sensors as smart objects capable of sensing data from the real world and interacting with other smart entities over the Internet. Software applications built over the IoT are usually designed to work at many different layers of the network, from the very "Edge" of it, i.e., close to sensors, up to the Cloud, or anywhere in between. In such a "Fog" context where the number of IoT devices continues to grow and the IoT business logic can be distributed in any computing element of the network, there is a strong need of automating the service provisioning activities in a way that a high QoS is ensured against potential faults, being the latter of hardware or software type. In this work we discuss the design of a fault management framework that strives to provide service continuity in IoT contexts by enforcing a strategy that hierarchically distributes the fault handling responsibilities to all the layers of the Fog where the IoT application resides.
Digital Video Source Acquisition Forgery Technique Based on Pattern Sensor Noise Extraction

Digital camera of a smartphone is a component frequently used by people to capture a large number of videos, which can illustrate situations that compromise their presumption of innocence in court cases. Usually, these videos circulate on the Internet, prone to intentional manipulation to prosecute one person or exempt another. In this sense, digital videos are a matter of great importance for forensic science, because they can be useful to verify the authenticity of such evidence in judicial processes, helping to make sound decisions. However, it is possible that criminals or attackers know weaknesses of forensic techniques and use anti-forensic techniques to manipulate videos without leaving any trace of the procedure performed. Forensic science confronts anti-forensic techniques, analyzes them rigorously and applies anti-measures in the development of techniques to detect anti-forensic operations. In this paper, anti-forensic techniques are proposed to perform the source anonymization and forgery in MP4 videos.
Wireless Channel Characteristics for UAV-Based Radio Access Networks in Urban Environments

The use of unmanned air vehicles (UAVs) as "flying base stations" is gaining significant traction in the scientific and industrial community. Such UAV-based wireless networks could be quickly deployed to increase the range of a network or to replace a damaged infrastructure in emergencies. One crucial requirement of UAV-based communication networks is understanding the distinctive features of UAV propagation channels, especially the angular spreads and delay spreads that dictate the performances of Orthogonal Frequency Division Multiplexing (OFDM) technology and Multiple-Input Multiple-Output (MIMO) system. In this paper, we analyze the angular spreads and delay spreads of the air-to-ground wireless channels based on extensive ray-tracing simulations. Our results indicate that, contrary to expectations, the angular spreads and the delay spreads at the ground-based Mobile Terminals (MTs) are more substantial in Line-of- Sight (LoS) conditions than in Non-Line-of-Sight (NLoS) conditions, which can be explained by the particular geometry of the air-to-ground channels. These results also make meaningful suggestions for the UAV-based communication system design.
Energy Efficient Power and Subcarrier Allocation for Downlink Non-Orthogonal Multiple Access Systems

Non-orthogonal multiple access (NOMA) has attracted both academic and industrial interest since it has been considered as one of the promising 5G technologies in order to increase connectivity and spectral efficiency. In this paper, we focus on a downlink NOMA network, where a single base station serves a set of users through multiple subchannels. The goal is to jointly optimize energy efficiency (EE) and fairness among users with respect to the subcarrier and power allocation parameters. To achieve this with acceptable complexity, we propose a novel greedy subcarrier assignment scheme. Due to the fractional form of the EE expression and the existence of interference, the power allocation problem is non-convex. To this end, we first transform this into an equivalent subtractive form, which is then solved by using fractional programming with sequential optimization of the power allocation vectors. Simulation results reveal the effectiveness of the proposed scheme in terms of EE and fairness among users compared to baseline schemes. Finally, the proposed algorithms are of fast convergence, low complexity, and insensitive to the initial values.
Clef Design

In Expressions of Change modifications to programs replace text files as the primary building blocks of software development. This novel approach yields structured historic information at arbitrary levels of program granularity across the programming toolchain. In this paper the associated questions of Programming Language Design are explored. We do so in the context of s-expressions, creating a modification-based infrastructure for languages in the Lisp family. We provide a framework for evaluation of the relative utility of different formalizations of program construction, which consists of the following: first, a requirement for completeness, meaning that a formalization of program construction should allow for the transformation of any valid program into any other. Second, a preference for succinctness over verbosity; succinctness of both of the formalization itself and typical expressions in the formalization. Third, a measure of the ability to clearly express intent. Fourth, a description of three ways in which the means of combination of the program itself and those of its means of construction may interact. Finally, we give a particular example of a formalization and use the provided framework to establish its utility.
Autonomous Inventory Monitoring through Multi-Modal Sensing (AIM3S) for Cashier-Less Stores

Auto-checkout technology could revolutionize physical retail by bringing down operating costs and enabling automated stores. The first step towards fully autonomous stores is automated live inventory monitoring. Existing automated approaches tend to focus on vision only and are cost prohibitive, slow, or inaccurate for practical real-world applications. To overcome these challenges we propose a novel sensor fusion framework through cameras, weight sensors and prior knowledge of item arrangement. Namely we focus on the tasks of event detection (i.e. when did customers pick up or return an item? ), and robust item classification (i.e. what product was it? ). In this demo we show the system can be implemented to accurately predict inventory in real-time.
Anatomy of Commercial IMSI Catchers and Detectors

IMSI catchers threaten the privacy of mobile phone users by identifying and tracking them. Commercial IMSI catcher products exploit vulnerabilities in cellular network security standards to lure nearby mobile devices. Commercial IMSI catcher's technical capabilities and operational details are still kept as a secret and unclearly presented due to the lack of access to these products from the research perspective. On the other hand, there are several solutions to detect such IMSI catchers to protect the privacy of mobile subscribers. However, detecting IMSI catchers effectively on commercial smartphones is still a challenge. In this paper, we present a systematic study of IMSI catchers, especially commercially available ones. Starting from publicly available product brochures, we analyze information from the international patent databases, attacking techniques used by them and vulnerabilities exploited in cellular networks (2G, 3G, and 4G). To this end, we survey IMSI catcher detection techniques and their limitations. Finally, we provide insights that we believe help guide the development of more effective and efficient IMSI catcher detection techniques.
Determining the Number of Effective Parameters in Kernel Density Estimation

The hat matrix maps the vector of response values in a regression to its predicted counterpart. The trace of this hat matrix is the workhorse for calculating the effective number of parameters in both parametric and nonparametric regression settings. Drawing on the regression literature, the standard kernel density estimate is transformed to mimic a regression estimate thus allowing extraction of a usable hat matrix for calculating the effective number of parameters of the kernel density estimate. Asymptotic expressions for the trace of this hat matrix are derived under standard regularity conditions for mixed, continuous, and discrete densities. Simulations validate the theoretical contributions. Several empirical examples demonstrate the usefulness of the method suggesting that calculating the effective number of parameters of a kernel density estimator maybe useful in interpreting differences across estimators.
Dictionary-based super resolution for positron emission tomography images

In this paper, a strategy to increase the resolution of positron emission tomography (PET) images, using a previously trained high resolution dictionary for the sinograms is proposed. The low resolution input sinogram is divided into patches of 5x5 samples. The sparse code of each patch is calculated and applied to the high resolution dictionary to obtain the best high resolution patch. The estimated high resolution sinogram is processed by the filtered backprojection (FBP) or by the ordered subsets expectation maximization (OSEM) reconstruction algorithm. Results show that, in both cases, the dictionary method outperforms the bicubic interpolation method by more 3% in PSNR. OSEM algorithm yields even better results than the FBP algorithm. However, the reconstruction time is exacerbated.
Joint Channel Selection and Spatial Reuse for Starvation Mitigation in IEEE 802.11ax WLANs

Starvation problems in a dense IEEE 802.11 wireless local area network (WLAN) seriously degrade fairness among links in the network. Some transmitters enjoy the full opportunities to access their channels, while others have very few opportunities to transmit, especially when network traffic is saturated. This paper focuses on the joint channel selection and spatial reuse problems in IEEE 802.11ax WLANs. Our main objective is to improve throughput while mitigating starvation. We formulate a non-cooperative game and proved it to be an exact potential game (EPG). We design a joint channel selection and spatial reuse algorithm with only local information exchange, based on the proposed game model. Convergence of the proposed algorithm is guaranteed by the property of potential games when unilateral improvement dynamics is used as a learning algorithm.
All-in-One Framework for Detection, Unpacking, and Verification for Malware Analysis

Packing is the most common analysis avoidance technique for hiding malware. Also, packing can make it harder for the security researcher to identify the behaviour of malware and increase the analysis time. In order to analyze the packed malware, we need to perform unpacking first to release the packing. In this paper, we focus on unpacking and its related technologies to analyze the packed malware. Through extensive analysis on previous unpacking studies, we pay attention to four important drawbacks: no phase integration, no detection combination, no real-restoration, and no unpacking verification. To resolve these four drawbacks, in this paper, we present an all-in-one structure of the unpacking system that performs packing detection, unpacking (i.e., restoration), and verification phases in an integrated framework. For this, we first greatly increase the packing detection accuracy in the detection phase by combining four existing and new packing detection techniques. We then improve the unpacking phase by using the state-of-the-art static and dynamic unpacking techniques. We also present a verification algorithm evaluating the accuracy of unpacking results. Experimental results show that the proposed all-in-one unpacking system performs all of the three phases well in an integrated framework. In particular, the proposed hybrid detection method is superior to the existing methods, and the system performs unpacking very well up to 100% of restoration accuracy for most of the files except for a few packers.
Correlations of health behaviors and factors with high sensitivity C-reactive protein in diabetic population

Objective To explore the correlations of ideal cardiovascular health behaviors and factors with the level of high sensitivity C-reactive protein (hsCRP) in diabetic population. Methods A cross-sectional method was used in present study. Seven thousand eight hundred and sixty-nine workers with diabetes were recruited who participated in the 2006-2007 Kailuan health examination without history of cerebral and myocardial infarction and without data incompleted. All the information was obtained from the unified questionnaire and measurement of blood biochemistry. Multivariate logistic regression analysis was used to analyze the effects of ideal cardiovascular health behaviors and factors on the level of hsCRP. Results The hsCRP concentrations decreased with the increasing numbers of ideal cardiovascular health behaviors and factors, the medians of hsCRP concentrations were 1.16mg/L, 1.11mg/L, 0.90mg/L and 0.76mg/L in 0-1, 2, 3 and 4-6 ideal cardiovascular health groups, respectively (P<0.01). Multivariate logistic regression analysis showed that, after adjustment by age, gender, triglyceride, low density lipoprotein cholesterol (LDL-C) and high density lipoprotein cholesterol (HDL-C), the risk of hsCRP level exceeding 3mg/L in the subjects having 3 and 4-6 ideal cardiovascular health behaviors and factors was 0.60 times (95%CI 0.50-0.72) and 0.45 times (95%CI 0.34-0.61), respectively, than those having 0-1 ideal cardiovascular health behaviors and factors. Conclusion The hsCRP concentrations decreased with increasing numbers of ideal cardiovascular health behaviors and factors; the risk of hsCRP exceeding 3mg/L can be reduced by ideal cardiovascular health behaviors and factors. DOI: 10.11855/j.issn. 0577-7402.2013. 10.013