Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational voting theory : of the agents, by the agents, for the agents (עם תקציר בעברית, תכן ושער נוסף: תורת הבחירות החישובית.)

The mathematical investigation of voting originated in the 17th century with such pioneers as the marquis de Condorcet and the chevalier de Borda, was taken up in the 18th century by Lewis Carroll, and exploded in the second half of the 20th century with the foundational work of Noble laureate Kenneth Arrow. Broadly speaking, the mathematical model of voting deals with a set of n agents that must reach a collective decision with respect to a set of m alternatives. Each agent submits a ranking of the alternatives; the outcome is then decided by a social choice function. The field of Multi-Agent Systems is much younger, barely two decades old, and is concerned with systems occupied by multiple heterogeneous, autonomous, and self-interested agents. Collective decision making in such systems is one of the prominent and most challenging issues. Fortunately, the centuries of work on Voting Theory can be leveraged to reach a consensus among agents, but applying voting in distributed computational settings requires a richer understanding of the computational aspects of voting. Achieving such an understanding is the goal of this thesis. We present our results on Computational Voting Theory in three parts, as follows. Part I: Elections and Approximation. The first part focuses on using the paradigm of approximation, so common in the theory of computer science, to obtain novel positive results with respect to Voting. Chapter 3 deals with the social choice function suggested by Lewis Carroll. It has been known for some time that it is computationally intractable to determine the score of an alternative under this rule, and consequently hard to determine the winner of the election. Our main result in this context is a randomized rounding algorithm that yields an O(logm) approximation ratio. In Chapter 4, we apply the concept of approximation to a different classic problem. Voting trees describe an iterative procedure for selecting a single vertex from a tournament. It has long been known that there is no voting tree that always singles out a vertex with maximum degree. We study the power of voting trees in approximating the maximum degree. We give upper and lower bounds on the worst-case ratio between the degree of the vertex chosen by a tree and the maximum degree, both for the deterministic model concerned with a single fixed tree, and for randomizations over arbitrary sets of trees. Part II: Elections and Computational Learning. The second part of the thesis studies the interplay between computational learning theory and voting theory. Chapter 5 investigates the learnability of two classes of social choice functions, as functions from the preferences of the agents to alternatives. We find that one of the two classes is efficiently learnable, whereas the other is harder to learn. We apply our results in an emerging theory: automated design of voting rules by learning.
A comparison of signalling alphabets

Two channels are considered; a discrete channel which can transmit sequences of binary digits, and a continuous channel which can transmit band limited signals. The performance of a large number of simple signalling alphabets is computed and it is concluded that one cannot signal at rates near the channel capacity without using very complicated, alphabets.
Regularity of Code: A New Structural Property and its Effect on Code Complexity and Comprehension (שער נוסף בעברית: רגולריות של קוד : מאפיין מבני חדש והשפעתו על מורכבות והבנת הקוד.)

Program comprehension is the process of building a mental model of a given source code. It lies at the basis of any software maintenance activity such as fixing bugs and adding new features. Maintenance is important because it consumes a large part of the resources allocated along a software lifecycle. Therefore, program comprehension is also important not only for the fact that it is a preliminary vital step in maintenance, but also for its criticality for the success of this activity. In particular, the better the code is understood the more likely the maintenance will be successful. However, comprehension is directly affected by complexity; the less complex is the code the easier a programmer can understand it. For example, nesting and non-linear flow are factors that affect complexity and probably make code harder to comprehend. Therefore, we wish to enhance comprehension by making programs less complex. This would be feasible if we could measure the complexity of programs. As summarized by H.James Harrington, “measurement is the first step that leads to control and eventually to improvement. If you cannot measure something, you cannot understand it. If you cannot understand it, you cannot control it. If you cannot control it, you cannot improve it. ” Over the years very many complexity metrics have been suggested. The large number of complexity metrics is probably an indication of a real difficulty in defining an ideal metric that is capable of reflecting complexity by providing
Scalable storage systems (עם תקציר בעברית ושער נוסף: מערכות איחסון להרחבה.)

Petabyte and Exabyte storage systems are a reality. These systems introduce unique challenges to the systems architect because of their size and unique requirements. In this thesis, I suggest that access patterns to very large storage systems are long-tailed distributions. I explore three live systems and show that each of them has a very strong long-tailed access distribution. Based on this finding, I prove there is no operational benefit to be gained by applying a directed placement policy allocating items to storage units. I conclude this thesis with a discussion of impacts and future research opportunities based on these findings, including large scale caches, backup techniques and distributed databases.
A discrepancy problem with applications to linear recurrences, II

This is continued rom [0]. The following result gives an estimation for the discrepancy of a special s-dimensional sequence (Xn), n=l, 2,. Let us recall the definition o the discrepancy D. (x). Generally speaking the discrepancy is a measure for the distribution behaviour of (x) modulo 1. More precisely put A(x,., I) card [n N" {x) e I} or the number indices n such that the (componentwise) fractional part of x is contained in a given s-dimensional interval I. Then
THE MACROMOLECULAR CRYSTALLOGRAPHY BEAMLINE AT ELETTRA

A new beamline for macromolecular crystallography has been constructed and is currently under commissioning. Its source is the high‐power wiggler at the 2 GeV storage ring ELETTRA. The optics consists of a double‐crystal monochromator in nondispersive configuration followed by a toroidal focusing mirror, and is designed to allow both diffraction experiments with monochromatized beam in the energy range 4–25 keV, and Laue experiments with white beam. The experimental station is based on an imaging plate area detector from MarResearch and a model 5020 Huber 4‐circle diffractometer. To control the second apparatus, a plug‐in board interface for a personal computer and a crystallographic package, CS, have been developed.
A preliminary cyber-physical security assessment of the Robot Operating System (ROS)

Over the course of the last few years, the Robot Operating System (ROS) has become a highly popular software framework for robotics research. ROS has a very active developer community and is widely used for robotics research in both academia and government labs. The prevalence and modularity of ROS cause many people to ask the question: “What prevents ROS from being used in commercial or government applications? ” One of the main problems that is preventing this increased use of ROS in these applications is the question of characterizing its security (or lack thereof). In the summer of 2012, a crowd sourced cyber-physical security contest was launched at the cyber security conference DEF CON 20 to begin the process of characterizing the security of ROS. A small-scale, car-like robot was configured as a cyber-physical security “honeypot” running ROS. DEFFCON-20 attendees were invited to find exploits and vulnerabilities in the robot while network traffic was collected. The results of this experiment provided some interesting insights and opened up many security questions pertaining to deployed robotic systems. The Federal Aviation Administration is tasked with opening up the civil airspace to commercial drones by September 2015 and driverless cars are already legal for research purposes in a number of states. Given the integration of these robotic devices into our daily lives, the authors pose the following question: “What security exploits can a motivated person with little-to-no experience in cyber security execute, given the wide availability of free cyber security penetration testing tools such as Metasploit? ” This research focuses on applying common, low-cost, low-overhead, cyber-attacks on a robot featuring ROS. This work documents the effectiveness of those attacks.
Feasibility Study of Machine Learning & AI Algorithms for Classifying Software Requirements

Software requirements[15] description and classification is the fundamental and most important activity in the software engineering process. Requirements are obtained through an elicitation process which generally involves interaction with stakeholders such as; exchange of information in person, on notes, by email, on phone, through meetings, etc., which involves a communication language such as English. The description of requirements (ex: functional, non-functional, related others) encompasses few properties such as; understandability, completeness, accuracy, clarity, unambiguousness, testability and related others. Classifying requirements into functional and non-functional category using Machine learning approaches have proved to be successful in the past. The goodness of software requirement properties impact’s the quality levels during the development of a software product and on the resulting product quality. The classification should address semantic details and implicit information during classification to completely satisfy a requirement. This paper presents results of applying different ML algorithms using a simple problem (and data set) for classifying software requirements. The requirements have been described in English following semantic language rules adopted to ease the writing process. The requirement may be obtained from a use case tool (for example rational unified software) or alternate sources. The purpose of this research work is for understanding the application and use of Machine Learning algorithms for the problem of requirements classification, while providing inputs for developing a “software requirements definition and description framework” using English language.
Quantitative Optical Coherence Elastography: A Novel Intensity-Based Inversion Method Versus Strain-Based Reconstructions

Many diseases like cancer or atherosclerosis bear micro-scale tissue stiffness changes, which can be visualised by optical coherence elastography (OCE). OCE is a promising research field, where a growing number of publications present qualitative displacement maps. Quantitative OCE results have also been presented, but still lack high precision and good reproducibility, which are important for clinical applications. In this work, we compare three reconstruction methods for the Young's modulus in intensity-based quasi-static compressional OCE: uniaxial analysis, strain map based reconstruction facilitating a particle tracking improved optical flow (EOFM), and a novel image-based inverse reconstruction method (IIM). The quality of the proposed reconstruction methods is investigated by comparing their performance on twelve silicone elastomer phantoms with inclusions of varying size and stiffness. While the uniaxial reconstruction is strongly affected by lateral motion, EOFM is capable of deriving precise strain maps from consecutive OCT images during compression. However, for a valid Young's modulus reconstruction additional stress map information is required. IIM performs best, precisely reconstructing Young's modulus of inclusion and background, closely corresponding to separately determined ground truth values. The method offers a significant improvement of the relative error by a factor of ${3.5}$ compared to uniaxial and strain-based analysis.
Modelling and Performance Analysis of Photovoltaic System for Low Cost Lawn cutting Application

Electrical properties derived from the current– voltage (I–V) and power-voltage (P-V) characteristics of solar cells provide essential information about performance losses and device efficiency. Accurate evaluation of model parameters is desirable for predicting the performance of solar cell under varying climatic conditions. The conversion efficiency of a photo voltaic cell depends on various parameters of the cell such as light generated current, diode saturation current, series resistance, shunt resistance and ideality factor. This paper addresses two approaches to extract the parameters of single diode model. The error analysis proves the need for parameter estimation whose reliability depends on various assumption and energy conversion rations. The effectiveness of modelling is investigated using MATLAB/SIMULINK which clearly reveals that the numerical methods are best suited for parameter estimation under stochastic environmental condition. An experimental analysis was carried out on a 75 Watt Mono-crystalline photovoltaic. Finally, a low cost prototype of solar lawn cutter was proposed as a potential application to incorporate the promising sustainable technology
Binary Function Similarity Detection Based on Graph Neural Network with Self-Attention Mechanism

Binary function similarity searching is an important method for vulnerability search and software security assurance. In order to meet the requirements of software security detecting on mobile platforms, we improve the high-accuracy graph matching algorithm and propose a SAGEN (Self-Attention based Graph Embedding Network) model. SAGEN can reduce retrieval time and improve retrieval speed while ensuring high accuracy, which enables high-accuracy graph matching algorithms to be applied to practical scenarios. The method first obtains the control flow chart by the binary function file, and then generates the graph data by the control flow chart. After that, this paper proposes a self-attention based graph neural network to embed graphs into a vector space. Finally, the similarity score between graphs is obtained by calculating the distance between graph embedding vectors. The experiment results show that SAGEN can achieve better results than most of existing models in accuracy while ensuring meeting retrieval speed requirements.
Designing a Lightweight Convolutional Neural Network for Bird Audio Detection

State-of-the-art machine learning models have been used for a plethora of Sound Event Detection (SED) applications, including bird vocalization detection, which plays a significant role in monitoring the overall ecosystem health. However, many popular models are characterized by high complexity and, therefore, are rather unsuitable for operating on IoT edge devices. To address this limitation, this paper proposes a lightweight Convolutional Neural Network with emphasis on edge computing. The proposed architecture achieves an accuracy measure of 86.42% on a bird audio detection task with only 73,377 trainable parameters.
Learning-Based $H_\infty$ Path-Following Controller Design for Autonomous Ground Vehicles Subject to Stochastic Delays and Actuator Constraints

In order to decrease computation loads for path following control of autonomous ground vehicles (AGVs), in this article, we aim to design an output-feedback path following controller by using a learning-based least squares support vector machine (LS-SVM) model. The signal transmission delay, which would deteriorates the control performance, is considered as well. First, the LS-SVM model for vehicle path following system is trained. Then, the output-feedback controller subject to stochastic communication delay, which meets the stability condition, is designed for the LS-SVM model. The modeling error and bias terms of LS-SVM model are regarded as the disturbance term. In order to attenuate the effect of disturbance, $H_\infty$ controller is designed for the AGVs to track the target path. Grey wolf optimizer is employed to solve the controller design problem with $H_\infty$ performance and transform stability condition from bilinear matrix inequality to linear matrix inequality (LMI). Then, we employ efficient LMI toolbox to solve the LMI. To guarantee the effectiveness of proposed controller, characteristics of steering wheel in a hardware-in-loop platform are studied and rate of steering angle is regarded as the actuator constraints for designing controller. Experimental results based on the hardware-in-loop platform have verified the effectiveness of the proposed control strategy.
SSD Failure Prediction Based on Classification Models and Data Engineering

As the increasing demand of data storage in recent years, solid state drives (SSDs) have developed rapidly. However, the frequent occurrence of SSD failures is affecting the stability and reliability of the systems and the IT infrastructures which use SSDs. SSD failure prediction would help to improve the system reliability, but the current method is mainly based on the design rules of SSD, leading to weak performance and poor prediction accuracy. In this paper, we study methods for predicting SSD failures based on various classification models and investigate how to improve the accuracy of failure prediction and reduce the O&M costs. The publicly available S.M.A.R. T. (Self-Monitoring, Analysis and Reporting Technology) data-set is analysed, pre-processed, and then used as a basis for examining three classification models which could be employed to predict SSD failures. Moreover, we develop two failure prediction methods, both with the highest macro average F1 score amongst the methods and techniques considered. The experimental results show that both methods improve the performance and accuracy of SSD failure prediction subject to certain environmental and testing conditions.
Fractal Correlation of HRV for Postural Change in Young Males and Females

HRV time series analysis is appropriate for the effective measure of the autonomic function. The major goal of this study is to determine the fractal relationship between young people's heart rate variability and changes in their body postures. Short- and long-term fractal exponent and their ratio are incorporated in three different postural electrocardiogram signals each for twenty participants. In addition, gender-wise fractal correlation is also manifested. Participants considered for this work were adults in the age group 19-26 years and consisted of 50% female. One-way analysis of variance and the Friedman test, which succeeds the Shapiro-Wilk test, was used for statistical analysis of fractals at a 5% significance level. Short-term fractal exponent, alpha1 as well as long-term fractal exponent, alpha2 values measured are of lower value for supine and increased for sitting and standing for both grouped data and gender-wise data. The alpha1 value rises as a result of this reciprocal alteration in autonomic control of the heart rate. The increase in alpha1 and alpha2 found while sitting and standing is indicative of the gain or yield of the fractal property of HRV toward less random fluctuations. The statistical test gave a significant difference in postural HRV for alpha1 and aplha2 with p<0.0001 and p=0.0020. Similarly, the gender-wise statistical analysis also bestowed significant differences with p=0.0185 and p=0.0016. However, there wasn't a significant difference seen for alpha1/alpha2. The present work concluded that both short-term and long-term exponent increased for postural change and the difference is statistically significant.
Edge Computing Solutions for Distributed Machine Learning

The rapid spread of the Internet of Things (IoT), with billions of connected devices, has generated huge amounts of data and asks for decentralized solutions for machine learning. However, performing complex learning tasks at the edge of the network is posing great challenges in terms of efficient management of data storing, transfer, and analysis. For these reasons, a lot of research and development effort is devoted to adapt different machine learning algorithms so that cooperative training and inference on local data occur directly at the edge of the network. This scenario represents a major challenge today due to the limited capacities of edge devices, the different technologies with which these devices work and communicate, and the lack of common software stacks to easily manage them. In this paper, we analyze distributed machine learning algorithms and how they should be adapted to run at the network edge and, if needed, cooperate with the cloud to ensure low latency, energy savings, privacy preserving and scalability. In particular, we briefly discuss how the main machine learning algorithms have been adapted to work in traditional distributed platforms (such as clusters, clouds, and HPC systems) and the main research work that has led these algorithms to run on resource-constrained edge devices. Then, a layered approach is introduced and discussed for adapting machine learning algorithms on edge-cloud architectures. Finally, we conclude the paper by describing some application scenarios that can benefit from this approach.
Chemically-inspired Memristor-based Neuron-like Oscillating Circuit

There is a constant push for ever increasing performance in traditional computing systems, leading to high power consumption and, in the end, to the incapacity of conventional electronics to handle heavy computing tasks, which usually require learning features. Thus, the development of novel nano-electronic devices with inherent neuromorphic characteristics and a low energy footprint has become a viable alternative. In order to simulate neuromorphic features utilizing memristive devices, the threshold switching effect is critical, which can be seen in the rich dynamics of metallic conductive filament (CF). In this paper, a realistic model of the unipolar nature of CBRAM devices is exploited to create a memristor-based oscillator that can integrate neuromorphic features. Bipolar memristive devices have been used to match the weight of the neurons in a crossbar configuration. The used physical model for these memristors was fitted to fabricated devices in order to achieve the expected accuracy in the circuit simulation. The oscillator’s output signal and behavior matched the theoretical background of biological neurons. Thus, this approach can be considered as the first step towards the development of low-power oscillation-based neuromorphic hardware with biological-like behavior.
Bias-Free Operation of Type-II GaInAsSb/InP High Speed Uni-Traveling Carrier Photodiodes

We report the first bias-free operation of top-illuminated uniform Type-II GaInAsSb/InP Uni-Traveling Carrier Photodiodes (UTC-PDs) for $\lambda =1.55\mu$m. A record-high zero-bias bandwidth of 78 GHz was achieved. The device includes a 100 nm uniform Ga<inf>0.81</inf>In<inf>0.19</inf>As<inf>0.65</inf>Sb<inf>0.35</inf> absorber and 225 nm uniform InP collector layer.