Miracle-dz/UniFAR-FTUs huggingface dataset:
A new framework of self-organization of vehicular networks

Vehicular networks are drawing a great attention from the research community and the automotive industry, where they are beneficial in providing ITS (Intelligent Transportation System) services as well as assisting the drivers on the road. In this context, vehicular networks are based on V2I (Vehicle to Infrastructure) and V2V (Vehicle to Vehicle) communications. The special characteristics of these networks such as high mobility, potentially large scale, and network partitioning introduce several challenges, which greatly impact the deployment of these networks. An efficient solution to these problems is to define a robust self-organizing architecture. Thus, the function of these dynamic networks can be quite improved. In this paper, we introduce a new proactive self-organizing protocol called CSP (Cluster-based Self-organizing Protocol) that uses the geographic clustering and the virtual backbone to structure intelligently the vehicular network. We compare CSP to other self-organizing solution by analyzing its performances using Qualnet simulator. Simulation results show good performance of CSP in terms of architecture stability, overhead and delivery ratio.
MAGIC: Path-Guided Concolic Testing

Concolic testing has been proposed as an effective technique to automatically test software. The goal of concolic testing is to generate test inputs to find faults by executing as many paths of a program as possible. However, due to the large state space, it is unrealistic to consider all of th e program paths for test input generation. Rather than exploring the paths based on the structure of the program as current concolic testing does, in this paper we generate tes t inputs and execute the program along the paths that have identified potential faults. We present a path-guided testi ng technique that combines path-sensitive static analysis wi th concolic testing. The program under test is statically analyzed before testing to find potential faults (suspicious st atements) and corresponding suspicious path segments. Then the program is tested, guided by static information, to avoi d generating test inputs for safe paths. A tool, MAGIC, has been implemented based on our technique to test for buffer overflow. We have experimentally evaluated MAGIC on a set of C benchmarks, and the results show that compared to concolic testing, MAGIC found about 2.5 times more faults, and using the path information, MAGIC triggers the faults 25.3 times faster on average for a set of benchmarks.
Good Enough Dependability: a New Paradigm for Building Dependable Systems Direction 1: Software Approaches for Building Error-resilient Applications

As computer systems become more and more entrenched in our society, ensuring their dependability is paramount to our overall well being. However, ensuring the dependability of complex, real-world systems is challenging. Dependability is often treated as an " all-or-nothing " proposition, with current techniques seeking to provide complete coverage. This proposition leads to high protection overheads in performance or power consumption. In contrast, my research proposes the idea of " good enough dependability " , where dependability is achieved in an incremental and on-demand manner, subject to performance and power overhead bounds. The key innovation is to identify the most important errors or security vulnerabilities in a system, and to selectively target them to achieve dependability at low performance and power overheads. The foundations of the good-enough dependability approach were laid in my PhD dissertation at the University of Illinois (UIUC), where I explored application-level techniques for targeted protection from soft (hardware) errors. My work showed that by leveraging the properties of the software application, one can achieve nearly the same coverage as all-or-nothing techniques such as full duplication for a small fraction of the overhead [1][2][3]. This approach has led to much follow-up work in the dependability community, such as SWAT [4] (UIUC) and Shoestring [5] (U. Michigan). Based on my thesis work, I was awarded the William Carter award by the IEEE Technical Committee on Fault-tolerant Computing (TC-FTC), and the IFIP Working Group on Dependability (WG 10.4), which is the most prestigious award in the field of dependable computing for PhD students. My research was also integrated into the Trusted Illiac cluster prototype at Illinois, which uses reconfigurable logic for runtime error and attack detection [6]. After my PhD, I spent a year as a post-doctoral researcher at Microsoft Research. While there, I initiated and led the Flikker project, which aims to save energy in mobile computing systems by deliberately lowering DRAM refresh frequency, and partitioning application data into critical and non-critical to tolerate the resulting errors. Flikker [7] was one of the first papers in the field of what is now known as approximate computing, and has spawned off numerous follow-up work such as the EnerJ project (at UW) [8] and Rely (at MIT) [9]. Flikker showed that it was possible to achieve significant energy savings by exposing hardware errors to the application, and selectively tolerating the most important errors by using programmer supplied annotations. Flikker thus …
Discrete Quantum Walks and Search Algorithms

Recently a number of discrete quantum walk based algorithms have been produced [1–6]. These are closely related to, or are specific cases of, structured search applied using a quantum walk. The formalism of discrete quantum walks has clear classical parallel in the form of Markov chains. Similarly, search via quantum walk has similar parallels in classical structured search. This report will review the field of search via discrete quantum walk and give a technical overview of the mathematical methods and structure.
High-Throughput, Library-Based Selection of a Murine Leukemia Virus Variant To Infect Nondividing Cells

ABSTRACT Gammaretroviruses, such as murine leukemia virus (MLV), are functionally distinguished from lentiviruses, such as human immunodeficiency virus, by their inability to infect nondividing cells. Attempts to engineer this property into MLV have been hindered by an incomplete understanding of early events in the viral life cycle. We utilized a transposon-based method to generate saturated peptide insertion libraries of MLV gag-pol variants with nuclear localization signals randomly incorporated throughout these overlapping genes. High-throughput selection of the libraries via iterative retroviral infection of nondividing cells led to the identification of a novel variant that successfully transduced growth-arrested cells. Vector packaging by cotransfection of the gag-pol.NLS variant with wild-type gag-pol produced high-titer virions capable of infecting neurons in vitro and in vivo. The capacity of mutant virions to transduce nondividing cells could help to elucidate incompletely understood mechanisms of the viral life cycle and greatly broaden the gene therapy applications of retroviral vectors. Furthermore, the ability to engineer key intracellular viral infection steps has potential implications for the understanding, design, and control of other postentry events. Finally, this method of library generation and selection for a desired phenotype directly in a mammalian system can be readily expanded to address other challenges in protein engineering.
Bemod: A DSGE Model for the Spanish Economy and the Rest of the Euro Area

In this paper we present the theoretical foundations and the simulation results obtained with a new dynamic general equilibrium model developed at the Banco de Espana for the Spanish economy and the rest of Euro area. The model is designed to help in simulating the effect of alternative shocks on the main aggregate variables. The main contributions of this work from a theoretical perspective are the modelling of a monetary union composed of two regions, the inclusion of housing as a durable good with its own sector of production and the degree and detail of the disaggregation considered for each country in the model, which replicates the Quarterly National Accounts. On the empirical side, the main contribution is the detailed calibration of the most important ratios of the Spanish and rest of the Euro area economies.
GPU Accelerated Face Recognition System with Enhanced Local Ternary Patterns Using OpenCL

Enhanced Local Ternary Patterns (ELTP) significantly improves performance over other feature descriptor methods including Local Binary Patterns (LBP) and Local Ternary Patterns (LTP). Sequential implementation of ELTP results in poor performance in terms of execution time for real time systems.Speed and accuracy are important characteristics of a real time face recognition system. With the aim of fulfilling both these criteria, this paper presents an implementation of GPU Accelerated Face Recognition System with ELTP using OpenCL framework. As a result of our Optimization techniques, we have achieved highest kernel execution speedup of 374x for ELTP image and histogram generation with 4096x4096 (16MP) image resolution when it is implemented on GPU. Face recognition with ELTP showed higher recognition rates on ORL database. We also implemented LBP and LTP algorithms on GPU and compared their performances with ELTP. Similar Optimization techniques were applied for LBP kernel executions, which resulted in much higher speedups when compared to their previous implementations. Experimental results demonstrated that Parallel implementation with ELTP on GPU (AMD Radeon HD 7650M) outperforms CPU based face recognition system using LBP in terms of speed and accuracy.
Opening Up Black Box Networks with CloudTalk

Optimizing distributed applications in the cloud requires network topology information, yet this information is kept confidential by the cloud providers. Today, applications can infer network properties and optimize accordingly but this is costly to get right. The cloud can optimize the network via load balancing, but the scope is limited to moving traffic between the available paths. In this paper we challenge this status quo. We show that network topology information is not confidential in the first place by conducting a study of Amazon's EC2 topology, and we show how such information can have a big impact in optimizing a web search application. We propose that applications and the network should break the silence and communicate to allow better optimizations that will benefit both parties. To this end we design CloudTalk, a very simple language that allows apps to express traffic patterns that are then ranked by the network. The ranking helps application pick the right way to implement its tasks. We provide a proof-of-concept implementation of CloudTalk showing that it is expressive enough to capture many traffic patterns and that it is feasible to use in practice.
Social network practices : an investigation into the perceptions of businesswomen

In the business environment, businesswomen experience various challenges that impede their growth within organisations. Therefore, the social networking phenomenon has become much more than interacting and learning more about people within a social environment. Businesswomen are developing and utilising their social networks to gain much-needed social support in order to establish balance amongst their diverse responsibilities, including work and childcare responsibilities. The primary objective of this study was to investigate the social network practices of" Gauteng businesswomen. In order to gain insight into these social network practices, qualitative research in the form of in-depth interviews was used. The most important findings of this research are discussed below. • Networking is a learnable skill that can be adapted to any situation. It involves interacting with people, sharing information and is based on relationships that are developed and maintained. • An individual's networking efforts are driven by a specific motivation (business and/or personal) and are therefore focused. Furthermore, businesswomen utilise networking as a marketing tool on a personal level and within their business"es. • Social networking takes place in informal environments and individuals can utilise their social networks to make new friends. Social networking is also a gathering place for people with similar interests. • Social networks consist of supportive people and people with similar values. Business " contacts can also be found in social environments. • Businesswomen, especially businesswomen with children, experience various challenges in their social networking efforts. • Businesswomen perceive social networking as a valuable tool and utilise their social networks in order to gain support. • Successful social networking behaviour includes integrity, respect, love of other people, hard work, reciprocity, clear networking goals, confidence, leveraging available networking mechanisms and avoiding poor networking behaviour such as being impolite, not honouring meetings, disrespecting and taking advantage of other people and negativity. This research contributes to literature in that it provides further background to the concept of social networking. It further provides insight into the needs and expectations of
Compressive coded video compression using measurement domain motion estimation

In this paper, we propose a novel CS video compression scheme having a simple encoder and a decoder that performs measurement domain motion estimation in order to reconstruct the video frames. In the encoder, the sensing mechanism can be applied directly on the non-sparse video frame using a random sensing matrix and the frame can be reconstructed at the decoder using l1-minimization or any of the iterative greedy algorithms provided the sensing matrix satisfies D-RIP with a high probability. For reconstructing a video frame at the decoder, if the motion is estimated prior to reconstruction, CS recovery techniques can efficiently recover the sparse frame difference. In our previous work, we have shown that the motion estimation is possible in the measurements domain by using a circulant CS matrix instead of a random matrix. We propose to estimate the frame motion from the circulant CS measurements and reconstruct the video frame using the motion compensated frame difference sparsity. Experimental results show that our proposed method performs better than the traditional CS video compression schemes.
Quantitative Coronary Analysis Medical Image Processing Improved by Combining Wavelet Edge Detection and Segmentation

The Quantitative Coronary Analysis (QCA) is a useful method for physicians to diagnose heart artery stenosis. So the precision of coronary angiography image quality is very definite important. In this study we present a precision 2-D image processing method that combines gradient segmentation and region segmentation approaches with an entropy maximization procedure. This method allows us to utilize all available information to achieve the most robust segmentation results for coronary angiography analysis. The aim is to improve the methods of 2-D coronary angiography image processing those usually lack such as precision. The example of coronary angiography data of true medical images were used to test the validity of our method. We found that our method not only achieved the precision we sought but also has many interesting applications that shall be most useful in medical practices.
Incremental genetic programming via genetic transposition

Genetic transposition is a process of moving sequences of DNA to different positions within the genome of a single cell. Inspired by the role of genetic transposons in biology, we introduce a genetic transposition inspired mechanism in genetic programming (GP). This mechanism, a simple variation from seeding in incremental evolution, provides a more effective approach to the evolution of systems with multiple features.
Inhibiting and Detecting Offline Password Cracking Using ErsatzPasswords

In this work, we present a simple, yet effective and practical scheme to improve the security of stored password hashes, increasing the difficulty to crack passwords and exposing cracking attempts. We utilize a hardware-dependent function (HDF), such as a physically unclonable function (PUF) or a hardware security module (HSM), at the authentication server to inhibit offline password discovery. Additionally, a deception mechanism is incorporated to alert administrators of cracking attempts. Using an HDF to generate password hashes hinders attackers from recovering the true passwords without constant access to the HDF. Our scheme can integrate with legacy systems without needing additional servers, changing the structure of the hashed password file, nor modifying client machines. When using our scheme, the structure of the hashed passwords file, e.g., etc/shadow or etc/master.passwd, will appear no different than traditional hashed password files.1 However, when attackers exfiltrate the hashed password file and attempt to crack it, the passwords they will receive are ErsatzPasswords—“fake passwords. ” The ErsatzPasswords scheme is flexible by design, enabling it to be integrated into existing authentication systems without changes to user experience. The proposed scheme is integrated into the pam_unix module as well as two client/server authentication schemes: Lightweight Directory Access Protocol (LDAP) authentication and the Pythia pseudorandom function (PRF) Service [Everspaugh et al. 2015]. The core library to support ErsatzPasswords written in C and Python consists of 255 and 103 lines of code, respectively. The integration of ErsatzPasswords into each explored authentication system required less than 100 lines of additional code. Experimental evaluation of ErsatzPasswords shows an increase in authentication latency on the order of 100ms, which maybe acceptable for real world systems. We also describe a framework for implementing ErsatzPasswords using a Trusted Platform Module (TPM).
The link between fungi and severe asthma: a summary of the evidence

There is current evidence to demonstrate a close association between fungal sensitisation and asthma severity. Whether such an association is causal remains to be confirmed, but this is explored by means of a detailed literature review. There is evidence from two randomised controlled trials that, in the example of allergic bronchopulmonary aspergillosis (ABPA), treatment with systemic antifungal therapy can offer a therapeutic benefit to ∼60% of patients. ABPA is only diagnosed if a combination of clinical and immunological criteria is achieved. It is not known whether such cases are a discrete clinical entity or part of a spectrum of the pulmonary allergic response to fungi or fungal products. This paper describes the epidemiological evidence that associates severity of asthma with fungi and discusses possible pathogenetic mechanisms. Many airborne fungi are involved, including species of Alternaria, Aspergillus, Cladosporium and Penicillium, and exposure may be indoors, outdoors or both. The potential for a therapeutic role of antifungal agents for patients with severe asthma and fungal sensitisation is also explored. Not only are many patients with severe asthma desperately disabled by their disease, but, in the UK alone, asthma accounts for 1,500 deaths per yr. The healthcare costs of these patients are enormous and any treatment option merits close scrutiny. Within this report, the case for the consideration of a new term related to this association is put forward. The current authors propose the term “severe asthma with fungal sensitisation”. However, it is recognised that enhanced and precise definition of fungal sensitisation will require improvements in diagnostic testing.
Automated BIST-based diagnostic solution for SOPC

This paper presents a diagnostic methodology to detect and locate faulty embedded cores IP cores in modern FPGAs. Parameterized Verilog models have been developed to apply the algorithm. Built-in sell-test (BIST) generation and synthesis performed in an automated flow for any given device. The approach is applicable to different cores including, block RAM, multiplier, DSP, etc. and is it scalable to different devices. The technique utilizes existing hardware redundancy and reconfigurability of an FPGA to achieve testability and diagnosis resolution without imposing any cost, area overhead or performance degradation. Experimental results show its efficiency in facilitating failure analysis process and expediting debugging procedure. It can also be applied to offline system testing and diagnosis for fault-tolerant applications
A Markov Decision Problem Based Approach to the Airline YMProblemVictoria

The task of allocating capacity to the diierent fare classes so that an airline's revenue is maximized is known as the Yield Management (YM) problem. In this research we develop a new approach to the YM problem that incorporates existing linear programming models and regression splines to bound and estimate the value function of a Markov decision problem (MDP) formulation of the YM problem. Our MDP-based method consists of an oo-line approximation to the value function and an on-line policy. No-shows and cancellations are not modeled; however, we do discuss how to incorporate these complicating features. Simulations, based on real data, reveal revenue gains of up to 8% over state-of-the-art YM approaches.
2016 Ieee International Conference on Big Data (big Data) the Self-avoiding Walk-jump (sawj) Algorithm for Finding Maximum Degree Nodes in Large Graphs

—In this paper we present a self-avoiding walk-jump (SAWJ) algorithm for finding a maximum degree node on a large assortative graph. We offer two contributions: i) we use the theory of absorbing Markov chains to effectively approximate the required search time as a function of the number of nodes, the edge density, and the assortativity, and ii) we measure the performance of our algorithm against competing algorithms (including star sampling) from the literature on the class of assortative Erd˝ os-Rényi (AER) random graphs, and on six real-world large graphs from the Stanford SNAP dataset. In most cases SAWJ significantly outperforms the competing algorithms.
Low-Level Cloud Motion Winds from Meteosat High-Resolution Visible Imagery

Abstract Low-level wind fields over the Atlantic have been derived from clouds in Meteosat high-resolution visible images experimentally with one production cycle per day over a period of more than 1 yr. The cloud motion winds from VIS imagery (VIS-CMW) use a template size of 32 × 32 VIS pixels, corresponding to about 80 km × 80 km at the subsatellite point, which is four times better than for the corresponding IR (infrared window) winds (160 km × 160 km). The yield is increased through the better spatial resolution of the VIS images and a better contrast between cloud and ocean surface, which effectively leads to an increase in wind vectors by a factor of 6. This implies a much better description of the low-level atmospheric flow by the VIS-CMW as compared to IR winds. The impact of the new VIS-CMW has been tested with a data assimilation experiment at the European Centre for Medium-Range Weather Forecasts, and small positive improvements have been found. The mean vector rms difference versus the verifyi.