Miracle-dz/UniFAR-FTUs huggingface dataset:
<inline-formula><tex-math notation="LaTeX">${{\sf Colosseum}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">Colosseum</mml:mi></mml:math><inline-graphic xlink:href="mondal-ieq1-3111169.gif"/></alternatives></inline-formula>: Regression Test Prioritization by Delta Displacement 

The problem of test-case prioritization has been pursued for over three decades now and continues to be one of the active topics in software testing research. In this paper, we focus on a code-coverage based regression test-prioritization solution (<inline-formula><tex-math notation="LaTeX">${{\sf Colosseum}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">Colosseum</mml:mi></mml:math><inline-graphic xlink:href="mondal-ieq2-3111169.gif"/></alternatives></inline-formula>) that takes into account the position of changed (delta) code elements (basic-blocks) along the loop-free straight-line execution path of the regression test-cases. We propose a heuristic that logically associates each of these paths with three parameters: (i) the offset (displacement <italic>a</italic>) of the first delta from the starting basic-block, (ii) the offset (displacement <italic>c</italic>) of the last delta from the terminating basic block, and (iii) the average scattering (displacement <italic>b</italic>) within all the intermediate basic-blocks. We hypothesize that a regression test-case path with a shorter overall displacement has a good chance of propagating the affects of the code-changes to the observable outputs in the program. <inline-formula><tex-math notation="LaTeX">${{\sf Colosseum}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">Colosseum</mml:mi></mml:math><inline-graphic xlink:href="mondal-ieq3-3111169.gif"/></alternatives></inline-formula> prioritizes test-cases with smaller overall displacements and executes them early in the regression test-execution cycle. The underlying intuition is that the probability of a test-case revealing a regression fault depends on the probability of the corresponding change propagation. The change in this context can potentially lead to an error. Extending this logic, delta displacement provides an approximation to failed error propagation. Evaluation on 20 open-source C projects from the Software-artifact Infrastructure Repository and GitHub (totaling: 694,512 SLOC, 280 versions, and 69,305 test-cases) against four state-of-the-art prioritizations reveals that: <inline-formula><tex-math notation="LaTeX">${{\sf Colosseum}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">Colosseum</mml:mi></mml:math><inline-graphic xlink:href="mondal-ieq4-3111169.gif"/></alternatives></inline-formula> outperforms the competitors with an overall 84.61% success in terms of 13 prioritization effectiveness metrics, majority of which prefer to execute top-<inline-formula><tex-math notation="LaTeX">$k\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>k</mml:mi><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="mondal-ieq5-3111169.gif"/></alternatives></inline-formula> prioritized test-cases.
Deep Learning for the selection of the best modular robots self-reconfiguration algorithm

Modular Robots Self Reconfiguration (MRSR) is one of the most challenging problems in nowadays robotics field. This problem consists in the determination of how a set of identical modular robots, with local knowledge of the system and limited energy and computational capacities, can reorganize themselves into a target topology or shape. MRSR has received great attention from the research community. Therefore, a lot of centralized and decentralized algorithms were designed to answer this problem. Unfortunately, the analysis of why and when an algorithm is better than another is less studied. In this paper, we proposed a hybrid centralized/distributed modular robots reconfiguration approach. In this approach, a convolution neural network system is used to estimate the most adapted distributed reconfiguration algorithm according to the initial shape formed by the modular robots and the target shape. Two distributed algorithms are studied: C2SR and TBSR. The designed CNN model allows determining which option is the best for a given reconfiguration problem: use of the C2SR algorithm, use of the TBSR algorithm, or both algorithms are equivalent. The obtained results show that the ML tool succeeds 97.25% of the time to determine the suitable algorithm based on the initial and the final shapes. In addition, the system can be extended to any number of algorithms. Our contribution is the production of a neural network built for the selection of the best modular robots self-reconfiguration algorithm.
Studying Laser-Induced Spin Currents Using Ultrafast Extreme Ultraviolet Light

Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. Next-generation magnetic-memory devices and heat-assisted magnetic-recording applications will require a better understanding of magnetic multilayers and their interactions with optical-laser pulses. In this thesis, by combining the advantages of ultrabroad-band extreme-ultraviolet light including ultrafast time resolution, element selectivity and tabletop easy access, I report three findings in the study of ultrafast magnetization dynamics in itinerant ferromagnets. First, I experimentally prove that the transverse magneto-optical Kerr response with extreme-ultraviolet light has a purely magnetic origin and that our experimental technique is an artifact-free ultrafast magnetic probe. Second, I demonstrate the first ultrafast magnetization enhancement driven by ultrafast spin currents in Ni/Ru/Fe multilayers. Third, I engineer the sample system by choosing either insulating or spin-scattering spacer layers between the Ni and Fe magnetic layers and by structural ordering. Then, I control the competition between ultrafast spin-flip scattering and superdiffusive spin-current mechanisms; either of these processes may to be the dominant mechanism in ultra-fast demagnetization. Finally, I report two continuing experiments that are promising for future ultrafast magnetization studies with extreme-ultraviolet sources. These experiments are resonant-magnetic small-angle-scattering and the generation of bright circularly polarized high harmonics accompanied by a demonstration of the first x-ray magnetic circular dichroism with a tabletop system. To my wife Esma and my daughter Zeynep Semra. v Acknowledgements First of all, I would like to thank my advisors, Margaret Murnane and Henry Kapteyn, for not only granting me to work in their research group but also supporting, trusting, and challenging me through out graduate school. Then, I thank to Tom Silva for being my third virtual advisor who is a great source of knowledge about magnetism and a great mind for difficult problems. Next, I thank Chan La-o-Vorakiat, the former graduate student, who built the setup and taught me how the experiment works. Moreover, it was a pleasure to work with the magnetism team in Kapteyn-Murnane group including Patrik Grychtol, Ronny Knut, and Dmitry Zusin who is taking over the project bravely. I am also very grateful to our collaborators Technology where we get all wonderful samples with great generosity. For very recent excitement about circularly polarized harmonics, I thank Oren Cohen and Ofer Kfir from Technion University. I am also grateful other members …
OCVC: An Overlapping-Enabled Cooperative Computing Protocol in Vehicular Fog Computing

Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload in vehicular network. Since individual vehicular fog node is incapable of providing ultra-reliable and low-latency services constrained by limited resources, cooperation among vehicles becomes an attractive attempt to promote quality of service (QoS). In this paper, we propose a novel Overlapping-enabled Cooperative Vehicular Computing architecture in VFC, termed OCVC, to fully utilize vehicular fog nodes' local potential resources. The proposed OCVC architecture enables vehicles to participate in different fog groups simultaneously different from traditional cooperative computing architecture. In addition, we propose a distributed OCVC scheme to solve the complicated computing group for-mation, overlapping resource allocation, and task assignment problem based on overlapping coalition formation (OCF) game framework. We conduct experiments in several metrics and numerical results show that the proposed OCVC scheme per-forms at least 5 % better than other benchmarks under different conditions.
An Approach for P2P Based Botnet Detection Using Machine Learning

The internet has developed and transformed the world dramatically in recent years, which has resulted in several cyberattacks. Cybersecurity is one of society’s most serious challenge, costing millions of dollars every year. The research presented here will look into this area, focusing on malware that can establish botnets, and in particular, detecting connections made by infected workstations connecting with the attacker’s machine. In recent years, the frequency of network security incidents has risen dramatically. Botnets have previously been widely used by attackers to carry out a variety of malicious activities, such as compromising machines to monitor their activities by installing a keylogger or sniffing traffic, launching Distributed Denial of Service (DDOS) attacks, stealing the identity of the machine or credentials, and even exfiltrating data from the user’s computer. Botnet detection is still a work in progress because no one approach exists that can detect a botnet’s whole ecosystem. A detailed analysis of a botnet, discuss numerous parameter’s result of detection methods related to botnet attacks, as well as existing work of botnet identification in field of machine learning are discuss here. This paper focuses on the comparative analysis of various classifier based on design of botnet detection technique which are able to detect P2P botnet using machine learning classifier.
Steady-State Analysis and Comparison of SSFB, SDFB and DSFB MMC-based STATCOM

This work focuses on the steady-state analysis of three types of MMC based STATCOM. For a given STATCOM rating, Double-Star Full Bridge, Single-Star Full Bridge and Single-Delta Full Bridge have been compared in terms of design and losses. In this approach, the number of submodules is chosen according to the voltage and current ratings of semiconductor devices while the submodule capacitor value is obtained by following an energy storage criterion to maintain the submodule voltages within an acceptable voltage range.
Applying Action Design Research to Digital Social Innovation: A Case of Automated Flood Detection System in Rural Region

Digital Social Innovation (DSI) is an action-oriented initiative aiming to tackle social challenges by introducing digital technologies. However, the extant literature tends to focus on the technological output of innovation and less on the methodological domain, particularly regarding the in-depth engagement with the social context, which hinders the construction of emergent social knowledge. This study presents a showcase of how Action Design Research (ADR) can be a promising methodological endeavor to a digital social innovation project. Based on a DSI project on the development of an automated flood detection system in a rural village in Indonesia, we show how innovation can be enacted as a collaborative and reflective process between the innovators and the community, which further enables us to formalize an understanding of the social context in innovation. This study elaborates the methodological guidelines to DSI while also reveals the contextual differences inherent to bottom-level development of a nation, in which formal and informal social actors at the village level both take the same role in assisting national endeavor toward development.
Automated Techniques on Indian Legal documents: A Review

The usage of legal documents has increased tremendously due to its easy availability in electronic format. This has given a wide range of exposure to the research areas. An automated mechanism that processes legal data can lead the law to a new dimension. Different deep learning and artificial intelligence techniques have been applied to the Indian legal document. In this paper, we review about the different techniques like Recurrent Network Networks (RNN), Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Pre-training Approach(RoBERTa), Best Matching (BM25) algorithm, Support Vector Machine (SVM), Long Short-Term Memory(LSTM) that have been applied on the Indian legal document for summarising, prediction, text classification and multi-class text classification. This review will give a conclusion that the deep learning techniques will give a better performance than the others.
Scaling Properties of a Complete X-ray Selected Galaxy Group Sample

Upcoming X-ray surveys like eROSITA require precise calibration between X-ray observables and mass down to the low mass regime to set tight constraints on the fundamental cosmological parameters. Since an individual mass measurement is only possible for a relatively small number of objects it is crucial to have robust and well understood scaling relations that relate the total mass to easily observable quantities. The main goal of this work is to constrain the galaxy group scaling relations corrected for selection effects, and to quantify the influence of non-gravitational physics at the low-mass regime. We analyzed XMM-Newton observations for a complete sample of galaxy groups selected from the ROSAT All-Sky Survey and we compared the derived scaling properties with a galaxy cluster sample. To investigate the role played by the different non-gravitational processes we then compared the observational data with the predictions of hydrodynamical simulations. After applying the correction for selection effects (e.g. Malmquist bias) the L-M relation is steeper than the observed one. Its slope is also steeper than the value obtained by using the more massive systems of the HIFLUGCS sample. This behaviour can be explained by a gradual change of the true L-M relation which should be taken into account when converting the observational parameters into masses. The other observed scaling relations (not corrected for selection biases) do not show any break although the comparison with the simulations suggests that feedback processes play an important role in the formation and evolution of galaxy groups. Thanks to our master sample of 82 objects spanning two order of magnitude in mass we tightly constrain the dependence of the gas mass fraction on the total mass, finding almost a factor of two difference between groups and clusters.
The Variation of Core Loss in High-Frequency Transformers Under Different Load Conditions

Typically, the core loss of the high-frequency (HF) transformers is considered constant regardless of the load condition on the secondary side. This paper indicates that the various load conditions can affect the core loss in HF transformers due to the variation of winding parasitics elements and subsequently the transformer's excitation voltage and current waveforms, and the field interactions between the primary and secondary windings and between the windings and core. The contribution of this work is demonstrating the core loss variation depending on different load conditions in HF transformers, which is not commonly assessed and considered in previous studies. The captured experimental results are compared with the three-dimensional (3D) finite element analysis (FEA) results conducted in Ansys Maxwell, to provide a better comprehension.
Numerical Methods for Two-Phase Jet Flow

Two numerical methods are developed and analyzed for studying two-phase jet flows. The first numerical method solves the eigenvalue problem for the matrix system that is constructed from the pseudo-spectral discretization of the 3D linearized, incompressible, perturbed Navier-Stokes (N-S) equations for two-phase flows. This first numerical method will be denoted as LSA for “linear stability analysis. ” The second numerical method solves the 3D (nonlinear) N-S equations for incompressible, two-phase flows. The second numerical method will be denoted as DNS for “direct numerical simulation. ” In this thesis, predictions of jet-stability using the LSA method are compared with the predictions using DNS. Researchers have not previously compared LSA with DNS for the co-flowing two-phase jet problem. Researchers have only recently validated LSA with DNS for the simpler RayleighCapillary stability problem [77] [20] [103] [26]. In this thesis, a DNS method has been developed for cylindrical coordinate systems. Researchers have not previously simulated 3D, two-phase, jet flow, in cylindrical coordinate systems. The numerical predictions for jet flow are compared: (1) LSA with DNS (2) DNS-CLSVOF with DNS-LS, and (3) 3D rectangular with 3D cylindrical. “DNS-CLSVOF” denotes the coupled level set and volumeof-fluid method for computing solutions to incompressible two-phase flows [99]. “DNS-LS” denotes a novel hybrid level set and volume constraint method for simulating incompressible two-phase flows [89]. The following discoveries have been made in this thesis: (1) the DNS-CLSVOF method and the DNS-LS method both converge under grid refinement to the same results for predicting the break-up of a liquid jet before and after break-up; (2) computing jet break-up in 3D cylindrical coordinate systems is more efficient than computing jet breakup in 3D rectangular coordinate systems; and (3) the LSA method agrees with the DNS method for the initial growth of instabilities (comparison method made for classical Rayleigh-Capillary problem and co-flowing jet problem). It is found that for the classical Rayleigh-Capillary stability problem, the LSA prediction differs from the DNS prediction at later times.
Partial differential equations in several complex variables

This book is intended as both an introductory text and a reference book for those interested in studying several complex variables in the context of partial differential equations. In the last few decades, significant progress has been made in the study of Cauchy-Riemann and tangential Cauchy-Riemann operators; this progress greatly influenced the development of PDEs and several complex variables. After the background material in complex analysis is developed in Chapters 1 to 3, the next three chapters are devoted to the solvability and regularity of the Cauchy-Riemann equations using Hilbert space techniques. The authors provide a systematic study of the Cauchy-Riemann equations and the \bar\partial-Neumann problem, including Hormander's L2 existence progress on the global regularity and irregularity of the \bar\partial-Neumann operators. The second part of the book gives a comprehensive study of the tangential Cauchy-Riemann equations, another important class of equations in several complex variables first studied by Lewy. An up-to-date account of the L2 theory for \bar\partial_b operator is given. Explicit integral solution representations are constructed both on the Heisenberg groups and on strictly convex boundaries with estimates in Holder and L2 spaces. Embeddability of abstract CR structures is discussed in detail here for the first time. Titles in this series are co-published with International Press, Cambridge, MA.
Finite Bit Quantization for Decentralized Learning Under Subspace Constraints

In this paper, we consider decentralized optimization problems where agents have individual cost functions to mini-mize subject to subspace constraints that require the min-imizers across the network to lie in low-dimensional sub-spaces. This constrained formulation includes consensus optimization as special case, and allows for more general task relatedness models such as multitask smoothness and coupled optimization. In order to cope with communication constraints, we propose and study a quantized differential based approach where the communicated estimates among agents are quantized. The analysis shows that, under some general conditions on the quantization noise, and for sufficiently small step-sizes µ, the strategy is stable in the mean-square error sense. The analysis also reveals the influence of the gradient and quantization noises on the performance.
Accurate Modeling of IGBT-Based Converters in PLECS

Control of modern electrical drive systems commonly requires a precise flux-linkage estimation. An accurate model of the commutation cell with non-ideal switches is a first step to improve the estimation of the flux. This paper analyzes und evaluates different modeling approaches for non-ideal IGBT-based converters and recommends their implementation with PLECS. In addition to modeling the dead time of the controller, compensation methods for further major contributors to volt-second errors of the switching cell are presented: turn-on and turn-off delay of the semiconductor devices, their on-stage voltage drops and the non-linear behavior during commutation time. Furthermore, this work presents how to parameterize the model using datasheet values of an IGBT-based power module.
DNN-Based Speech Quality Assessment for Binaural Signals

Quality prediction in the scope of speech communication is still a challenging tasks even though several proprietary as well as standardized models were developed for this purpose over the past decades. Scenarios including acoustic paths were not covered adequately so far, in particular for signals that are perceived binaurally. The present work introduces a novel quality prediction approach that is based on recent deep learning techniques, but also considers important traditional aspects of psychoacoustics, auditory databases and standardized terminal testing methods.
Geometry of the 20 November 2003 magnetic cloud

[1] This study is an attempt to find a coherent interpretation of the link between the 20 November 2003 magnetic cloud (MC) and its solar source. Most previous studies agree on the orientation of the MC, but the orientation is nearly perpendicular to the axis of the post-eruption arcade (PEA) or the orientation of the neutral line in the solar source region. We first determine the geometry of this MC by fitting methods with both torus and cylinder models. Three possible geometries are obtained, which can reproduce the observed magnetic field variations associated with the MC, one from the cylinder fit and two from the torus fit. The cylinder fit gives the MC orientation with a tilt of a large angle (∼60°) from the ecliptic plane and nearly perpendicular to the PEA axis, being similar to those from previous studies. In contrast, two torus fit results give the MC axis with tilt angles less than 20° from the ecliptic plane. The two torus results correspond to the spacecraft encounter with the eastern flank of the flux rope loop (model A) and the western flank of the loop (model B), respectively. In either case, the orientation of the loop around the apex is nearly parallel to the PEA as observed by the SOHO/extreme ultraviolet imaging telescope instrument in the most plausible solar source region of a halo coronal mass ejection (CME), which appeared in the field of view of Large Angle and Spectrometric Coronagraph (LASCO) C2 at 08:50 UT, 18 November 2003. The magnetic helicity of the PEA region is positive in agreement with the helicity of the MC. The 3-D reconstruction from the Solar Mass Ejection Imager data shows that the main part of the ejected plasma expands mainly to the west of the Sun-Earth line. Thus, we reach the most straightforward interpretation of the link between the MC and its solar source as follows. The MC was created in association with the launch of the CME that was first observed by the LASCO C2 at 08:50 UT, 18 November 2003, and propagated through interplanetary space with its orientation almost unchanged. The spacecraft encountered the eastern flank of the loop as described by model A.