Miracle-dz/UniFAR-FTUs huggingface dataset:
A SURVEY ON ANONYMOUS NETWORKS AND RELATED MENACES

Anonymous network helps to preserve user identity for secure communication in the network. The paper depicts a survey of anonymous networks and the various forms of threats faced by these networks. The survey begins by the e-mail anonymity which is treated to be the first conception in the field of network anonymity. A diversified set of attacks inimical to the anonymous networks are illustrated.
Incentive Effects of Extreme CEO Pay Cuts

We examine the causes and consequences of sharp CEO pay cuts, a phenomenon that has been mostly overlooked in the attention paid to overall rising executive pay. We find that a large CEO pay cut in response to poor stock performance is not uncommon. Good corporate governance structures strengthen the link between poor performance and CEO pay cut. After the pay cut, the CEO can restore his pay level by reversing the poor performance. Pay cuts are only a short-term substitute for dismissal—a pay-cut CEO with continued poor performance is more likely to be dismissed than a CEO with similar performance whose pay was not cut. For most firms, performance improves and the CEO’s pay rises. The likelihood and severity of pay cuts is especially strong for firms in the bottom half of the underperforming sample. This discontinuity means that the overall pay for performance relation is stronger than generally found in linear regressions. Together, our results show that the possibility of these large compensation cuts provides ex ante incentives for CEOs to exert effort to avoid poor performance and ex post incentives to improve poor performance once pay is cut.
Sparse Coding for Object Recognition

In recent years, the application of sparse coding techniques has led to frameworks that match or set the state-of-the-art in object recognition tasks. Despite such success, applying sparse coding to vision tasks presents unique challenges and many papers addressing these concerns appear in top conferences annually. This paper acts as an introduction to the subject of sparse coding, identifies the key research areas improving its applicability to recognition tasks, and surveys recent approaches to treating these challenges.
Brain integrative function driven by musical training during real-world music listening

Burunat Pérez, Iballa Brain integrative function driven by musical training during real-world music listening Jyväskylä: University of Jyväskylä, 2017, 74 p. (+ included articles) (Jyväskylä Studies in Humanities) ISSN 1459-4323; 302 (nid.) ISSN 1459-4331; 302 (PDF) ISBN 978-951-39-6908-0 (nid.) ISBN 978-951-39-6909-7 (PDF) The present research investigated differences in the brain dynamics of continuous, real-world music listening between listeners with and without professional musical training, using functional magnetic resonance imaging (fMRI). A replication study was aimed at validating the reliability of the naturalistic approach to studying brain responses to music, wherein the brain signal and the acoustic information extracted from the musical stimulus were correlated. After a successful replication, a series of three studies dealt with differences in integrative brain function during music listening between musicians and nonmusicians. Findings (a) emphasized the crucial role of the distinctive postural and kinematic symmetry in instrument playing on the symmetry of brain responses to music listening, evidencing a crossmodal transfer of symmetry from sensorimotor to perceptual processing systems; (b) provided novel evidence for increased cerebello-hippocampal functional coupling in musicians as a function of musical predictability compared to nonmusicians, likely mediated by action simulation mechanisms; (c) highlighted differences in pulse clarity processing between groups and uncovered an associated action-perception network overlapping with areas previously observed to tightly interact in rhythm processing. In conclusion, the present research findings, obtained using a naturalistic auditory stimulation paradigm, will advance the understanding of brain integrative function during real-world music listening, in listeners with and without musical expertise. Particularly, this thesis has implications for a better understanding of training-induced crossmodal reorganization. The new evidence brought by the present findings will hopefully guide the generation and development of future testable hypotheses.
L-type calcium channels as drug targets in CNS disorders

L-type calcium channels are present in most electrically excitable cells and are needed for proper brain, muscle, endocrine and sensory function. There is accumulating evidence for their involvement in brain diseases such as Parkinson disease, febrile seizures and neuropsychiatric disorders. Pharmacological inhibition of brain L-type channel isoforms, Cav1.2 and Cav1.3, may therefore be of therapeutic value. Organic calcium channels blockers are clinically used since decades for the treatment of hypertension, cardiac ischemia, and arrhythmias with a well-known and excellent safety profile. This pharmacological benefit is mainly mediated by the inhibition of Cav1.2 channels in the cardiovascular system. Despite their different biophysical properties and physiological functions, both brain channel isoforms are similarly inhibited by existing calcium channel blockers. In this review we will discuss evidence for altered L-type channel activity in human brain pathologies, new therapeutic implications of existing blockers and the rationale and current efforts to develop Cav1.3-selective compounds.
Non-Gaussianity and non-stationarity detection in underwater ambient noise using hypothetical tests

In underwater signal processing, acoustic signals are used to transmit and receive information for communication. But the acoustic signals get affected due to spreading, reverberation, attenuation and absorption which produce ambient noise. Ambient noise is the background noise created due to natural sources as well as anthropogenic (man-made) sources. Ambient noise can be characterized as Gaussian, nonGaussian, stationary and non-stationary because of what the sonar system get disturbed. So to improve sonar system performance, characterization of ambient noise is important. In this paper, non-Gaussianity in tropical waters of the western coast of India is compared using various hypothetical tests like Lilliefors test, Anderson-Darling test, and Shapiro-Wilk test. Non-stationarity is identified using Kolmogorov-Smirnov two-sample test.
A framework for the automated generation of power-efficient classifiers for embedded sensor nodes

This paper presents a framework for power-efficient detection in embedded sensor systems. State detection is structured as a decision tree classifier that dynamically orders the activation and adjusts the sampling rate of the sensors (termed groggy wakeup), such that only the data necessary to determine the system state is collected at any given time. This classifier can be tuned to trade-off accuracy and power in a structured, parameterized fashion. An embedded instantiation of these classifiers, including real-time sensor control, is described. An application based on a wearable gait monitor provides quantitative support for this framework. The decision tree classifiers achieved roughly identical detection accuracies to those obtained using support vector machines while drawing three times less power. Both simulation and real-time operation of the classifiers demonstrate that our multi-tiered classifier determines states as accurately as a single-trigger (binary) wakeup system while drawing as little as half as much power and with only a negligible increase in latency.
Resource-Aware Ubiquitous Data Stream Querying

This paper proposes and develops a novel, iterative model for resource aware- ubiquitous data stream querying (RA-UDSQ). Our model provides timely results to mobile users at regular time intervals specified by the user, thereby executing continuous stream queries. This model is capable of adapting to high data rates of streams and limited memory resources available on a mobile device while executing continuous queries. We have experimentally compared our resource-aware ubiquitous data stream querying model with data stream querying without the adaptation. Our comparative analysis has shown that our RA-UDSQ can perform better in terms of memory and battery without compromising on accuracy of results when data rates are high and available resources are critically low.
Robust Ego-Motion Estimation with ToF Cameras

This paper presents an approach to estimate the ego-motion of a robot while moving. The employed sensor is a Time-of-Flight (ToF) camera, the SR3000 from Mesa Imaging. ToF cameras provide depth and reflectance data of the scene at high frame rates. The proposed method utilizes the coherence of depth and reflectance data of ToF cameras by detecting image features on reflectance data and estimating the motion on depth data. The motion estimate of the camera is fused with inertial measurements to gain higher accuracy and robustness. The result of the algorithm is benchmarked against reference poses determined by matching accurate 2D range scans. The evaluation shows that fusing the pose estimate with the data from the IMU improves the accuracy and robustness of the motion estimate against distorted measurements from the sensor.
Asynchronous Doubly Stochastic Proximal Optimization with Variance Reduction

In the big data era, both of the sample size and dimension could be huge at the same time. Asynchronous parallel technology was recently proposed to handle the big data. Specifically, asynchronous stochastic gradient descent algorithms were recently proposed to scale the sample size, and asynchronous stochastic coordinate descent algorithms were proposed to scale the dimension. However, a few existing asynchronous parallel algorithms can scale well in sample size and dimension simultaneously. In this paper, we focus on a composite objective function consists of a smooth convex function f and a separable convex function g. We propose an asynchronous doubly stochastic proximal optimization algorithm with variance reduction (AsyDSPOVR) to scale well with the sample size and dimension simultaneously. We prove that AsyDSPOVR achieves a linear convergence rate when the function f is with the optimal strong convexity property, and a sublinear rate when f is with the general convexity.
SAR Image Denoising Method Based on Improved Wiener Filtering and P-M&amp;LLT Partial Differential Equation

In order to remove the speckle noise produced by SAR system in the process of imaging, an improved SAR image denoising algorithm based on Wiener filtering and P-M&amp;LLT partial differential equation is proposed. In the algorithm, the image edge is preserved by P-M equation of the low order partial differential equations, and the higher order equation LLT method can recover the image smooth region and avoid the block effect. Experimental results show that the integrated model combining the three methods is the better solution to filter the speckle noise and keep the contradiction between the image details, and the noise effect of the SAR image will be better.
Error Performance Analysis for 4+12+16 APSK Signal over a Satellite Channel

A 4+12+16 Amplitude Phase Shift Keying (APSK) modulation outperforms other 32-APSK modulations such as rectangular or cross 32-quadrature amplitude modulations (QAMs) which have a high peak to average power ratio that causes non-negligible amplitude modulation to amplitude modulation (AM/AM) and amplitude modulation to phase modulation (AM/PM) distortions when the signal is amplified by a high-power amplifier (HPA). Thus, this modulation scheme has been recommended as modulation scheme in the Digital Video Broadcasting-Satellite2 (DVB-S2) standard. In this paper, we present a new bits-to-symbol mapping to improve a bit error rate (BER) for a 4+12+16 APSK signal in a nonlinear satellite channel and analyze the BER.
Metal filling impact on standard cells: definition of the metal fill corner concept

The objective of this paper is to evaluate the delay impact of staggered metal filling (Metal2) on the standard cells and their associated local interconnect (Metal1). A Design Of Experiment (DOE) is used to define a large range of filling pattern shapes and positions. This set of filling patterns is then inserted in a Ring Oscillator (RO). From the filled RO simulations, the RO delay is expressed as a function of the filling pattern features. The maximal timing error between the model and the simulation is 1.3%, validating the model. The filling impact on RO delay magnifies the one introduced by the front-end process variations (PV). Consequently, the filling influence is introduced for the minimal, typical and maximal corners, defined now with Process (P), Voltage (V), Temperature (T) and Filling density (F) characteristics.
Research Statement Safety, Security, and Correctness via Logical Relations

The goal of my research is to improve the security and reliability of software systems through the use of programming language technology. To that end, I am interested both in developing languages with more expressive type and proof systems and in enhancing and formally certifying the trustworthiness of languages and their implementations. Large software systems consist of hundreds or thousands of components, and many of these may be of uncertain origin. To ensure reliable and secure operation, it is important to defend against faulty or malicious code. Statically-typed programming languages provide facilities for information hiding—type abstraction mechanisms like existential types (the basis of abstract data types or ADTs) and parametric polymorphism— that allow programmers to write secure code. If access to some private implementation detail might enable an attack, then this detail is made inaccessible by hiding it behind an abstract interface (for instance, using an existential type). The theoretical justification for this comes from relational parametricity, a strong semantic property that guarantees representation independence—i.e., that the behavior of a client (or attacker) of an ADT cannot depend on the representation and implementation details hidden behind the abstract type. Unfortunately, type abstraction does not always guarantee information hiding in practice. One issue is that in languages with references (mutable memory cells), it is possible to establish covert channels through which attackers can discover information about the “hidden” representation of an abstract type. This is possible because current type systems do not provide effective mechanisms for keeping references used internally by an ADT (called local state) separate from references that the rest of the program has access to. Thus, a pointer to a “hidden” reference can escape, leaking information about internal representation details. A related but orthogonal problem is that we lack methods for reasoning about parametricity and representation independence in the presence of both type abstraction and references. My research has addressed both of these problems with the goal of enhancing language-based security in the presence of mutable references. Another issue is that the use of type abstraction to guarantee information hiding merely ensures that private details are hidden from source-level attackers. Once the code is compiled, it may nonetheless be vulnerable to target-level attackers. To preclude such vulnerability, we need security-preserving compilers that ensure that any private details hidden from source-level attackers are also hidden from target-level attackers. Below I describe my research on this and other topics as well as directions for future work.
Development of bending soft actuator with different braided angles

In recent years, many researchers have been focusing on the different novel techniques in designing pneumatic soft actuator that can produce bending motion. This paper presents a novel soft actuator design; a combination of different braided angles of artificial muscle applied on a single chamber soft actuator to produce bending motion. The actuator construction is based on the theory of contraction and extension of artificial muscle. It comprises of fiber-reinforced inside silicone rubber and is capable to create one-sided bending motion. Analysis of nonlinear finite element method is conducted to predict the direction and bending angles of the actuator before a prototype of actuator is fabricated. Based on the results, the developed soft actuator can realize bending motion after standard pressure driving experiment is executed.
CREDIT BASED LOAD BALANCING IN CLOUD ENVIRONMENT: A REVIEW

In present days cloud computing is one of the greatest platform which provides storage of data in very lower cost and available for all time over the internet.But it has more critical issue like security, load management and fault tolerance. In this paper we are discussing Load Balancing approach. Many types of load concern with cloud like memory load, CPU load and network load. Load balancing is the process of distributing load over the different nodes which provides good resource utilization when nodes are overloaded with job. Load balancing has to handle the load when one node is overloaded. When node is overloaded at that time load is distributed over the other ideal nodes. Many algorithms are available for load balancing like Static load balancing and Dynamic load balancing. In present days cloud computing is one of the greatest platform which provides storage of data in very lower cost and available for all time over the internet.But it has more critical issue like security, load management and fault tolerance. In this paper we are discussing Load Balancing approach. Many types of load concern with cloud like memory load, CPU load and network load. Load balancing is the process of distributing load over the different nodes which provides good resource utilization when nodes are overloaded with job. Load balancing has to handle the load when one node is overloaded. When node is overloaded at that time load is distributed over the other ideal nodes. Many algorithms are available for load balancing like Static load balancing and Dynamic load balancing.
Research on Three-dimensional Cartilage Thickness Measurement of Femoral Head in Magnetic Resonance Images

Measuring the thickness of articular cartilage in three-dimensional (3D) magnetic resonance (MR) images is an important diagnostic procedure. A 3D measuring method was proposed to determine the femoral cartilage thickness in this paper. The cartilage thickness was defined as the distance between the two sides of the edges, which are the zero-crossing points of the second directional derivatives combined with Gaussian blurring along the normal direction of the cartilage surface. The spherical constraint of the femoral shape was utilized to confirm the normal direction. The zero-crossing method was demonstrated by mathematical simulation. Experiments were conducted using a resected femoral head to validate the method. The anatomical thickness data were proved to be well correlated with the zero-crossing thickness data.
Co-design of Full-duplex RFIC and Resource Allocation Algorithms

Full-duplex (FD) wireless -simultaneous transmission and reception at the same frequency -is an emerging wireless duplexing scheme that has the potential to significantly improve wireless network performance [1-2]. However, the biggest challenge associated with FD wireless is the tremendous amount of self-interference (SI) on top of the desired signal. The SI has to be suppressed below the receiver noise floor through isolation and cancellation as filtering the SI is not an option. An integrated CMOS implementation imposes constraints that render SI cancellation (SIC) techniques proposed in prior discrete-componentbased implementations (e.g. [3]) nonviable. Moreover, to fully utilize the benefits of FD communication, wireless systems will require a careful redesign of both the physical layer and the medium access control (MAC) layer.
Verbal Memory and IQ Predict Adaptive Behavior in Children and Adolescents with High-Functioning Autism Spectrum Disorders

: Adaptive deficits are commonly found in high functioning autism spectrum disorders (HF-ASD) despite of cognitive potential. Most studies have focused on the relationships between adaptive behavior and intellectual quotient (IQ) and have used correlations to study relationships between them. Few studies have analyzed cognitive variables other than IQ as potential predictors of adaptive behavior in HF-ASD using regression methods. This study aimed to analyze the impact of several cognitive variables on adaptive behavior in a sample of children and adolescents with HF- ASD. METHODS: Sample included 16 child and adolescent boys with HF-ASD (age between 7-17 years). Cognitive assessment included measures of general intelligence, visual memory, verbal memory, working memory and problem solving/flexibility tests. Vineland Adaptive Behavior Scales (VABS) was used to evaluate adaptive behavior. To establish the predictive capacity of the cognitive variables for adaptive functioning, linear regression models were fitted for each adaptive domain using a stepwise method. RESULTS: Verbal memory and IQ emerged as the main independent predictors for VABS adaptive scores. The 41% of the variance in Communication was predicted by IQ. The 35% of the variance in Daily Living Skills was predicted by verbal memory. Almost half of the variance in Socialization was predicted by both, verbal memory and IQ (49%). No other cognitive functions were associated with adaptive scores. CONCLUSIONS: The results highlight the strong impact of IQ and verbal memory on adaptive behavior in HF-ASD patients. These findings could contribute to identify potential targets of intervention.
Moving object detection under moving camera by using normal flows

Moving object detection under a moving camera has long been a very challenging task as the movements of the moving objects and motion caused by the camera are merged in the image sequence. Traditional methods tend to tackle this problem by establishing the matching features across the image frames, or by inferring optical flows. However, both matching features and calculating optical flows are ill-posed problems in the computer vision. Some artificial assumptions, for example smoothness assumption and continuity assumption, have to be made to get the solutions. Such assumptions describing an extremely ideal capturing scene usually do not hold. In this paper, we present a novel algorithm on detecting the moving object under a moving camera by using normal flows which can be calculated directly from the image sequence data without any artificial constraints. Therefore our novel method is completely independent of the structure of the captured scene or object. We tested our algorithm on both synthetic image data and real image sequences, and the experimental results showed the good performance of our algorithm.