nils-herrmann/clean_scirepeval huggingface dataset:
Network Anomalies Detection Using Statistical Technique : A Chi-Square approach

Intrusion Detection System is used to detect suspicious activities is one form of defense. However, the sheer size of the network logs makes human log analysis intractable. Furthermore, traditional intrusion detection methods based on pattern matching techniques cannot cope with the need for faster speed to manually update those patterns. Anomaly detection is used as a part of the intrusion detection system, which in turn use certain data mining techniques. Data mining techniques can be applied to the network data to detect possible intrusions. The foremost step in application of data mining techniques is the selection of appropriate features from the data. This paper aims to build an Intrusion Detection System that can detect known and unknown intrusion automatically. Under a data mining framework, the IDS are trained with statistical algorithm, named Chi-Square statistics. This study shows the plan, implementation and the analyze of these threats by using a Chi-Square statistic technique, in order to prevent these attacks and to make a Network Intrusion detection system (NIDS). This proposed model is used to detect anomaly-based network to see how effective this statistical technique in detecting intrusions.
Adaptive optimal control for continuous-time linear systems based on policy iteration

In this paper we propose a new scheme based on adaptive critics for finding online the state feedback, infinite horizon, optimal control solution of linear continuous-time systems using only partial knowledge regarding the system dynamics. In other words, the algorithm solves online an algebraic Riccati equation without knowing the internal dynamics model of the system. Being based on a policy iteration technique, the algorithm alternates between the policy evaluation and policy update steps until an update of the control policy will no longer improve the system performance. The result is a direct adaptive control algorithm which converges to the optimal control solution without using an explicit, a priori obtained, model of the system internal dynamics. The effectiveness of the algorithm is shown while finding the optimal-load-frequency controller for a power system.
A low-offset latched comparator using zero-static power dynamic offset cancellation technique

A low-offset latched comparator using new dynamic offset cancellation technique is proposed. The new technique achieves low offset voltage without pre-amplifier and quiescent current. Furthermore the overdrive voltage of the input transistor can be optimized to reduce the offset voltage of the comparator independent of the input common mode voltage. A prototype comparator has been fabricated in 90 nm 9M1P CMOS technology with 152 µm2. Experimental results show that the comparator achieves 3.8 mV offset at 1 sigma at 500 MHz operating, while dissipating 39 μW from a 1.2 V supply.
Modal-Based Design of a Wideband Quadruple-Ridged Flared Horn Antenna

In this paper, a systematic design technique for quadruple-ridged flared horn antennas is presented, to enhance the radiation properties through the profiling of the ridge taper. The technique relies on control of the cutoff frequencies of specific modes inside the horn, instead of brute-force optimization. This is used to design a prototype antenna as a feed for an offset Gregorian reflector system, such as considered for the Square Kilometer Array (SKA) radio telescope, to achieve an optimized aperture efficiency from 2 to 12 GHz. The antenna is employed with a quadraxial feeding network that allows the excitation of the fundamental TE11 mode, while suppressing all other modes that causes phase errors in the aperture. Measured results confirm the validity of this approach, where good agreement is found with the simulated results.
Sleep and REM sleep disturbance in the pathophysiology of PTSD: the role of extinction memory

Post-traumatic stress disorder (PTSD) is accompanied by disturbed sleep and an impaired ability to learn and remember extinction of conditioned fear. Following a traumatic event, the full spectrum of PTSD symptoms typically requires several months to develop. During this time, sleep disturbances such as insomnia, nightmares, and fragmented rapid eye movement sleep predict later development of PTSD symptoms. Only a minority of individuals exposed to trauma go on to develop PTSD. We hypothesize that sleep disturbance resulting from an acute trauma, or predating the traumatic experience, may contribute to the etiology of PTSD. Because symptoms can worsen over time, we suggest that continued sleep disturbances can also maintain and exacerbate PTSD. Sleep disturbance may result in failure of extinction memory to persist and generalize, and we suggest that this constitutes one, non-exclusive mechanism by which poor sleep contributes to the development and perpetuation of PTSD. Also reviewed are neuroendocrine systems that show abnormalities in PTSD, and in which stress responses and sleep disturbance potentially produce synergistic effects that interfere with extinction learning and memory. Preliminary evidence that insomnia alone can disrupt sleep-dependent emotional processes including consolidation of extinction memory is also discussed. We suggest that optimizing sleep quality following trauma, and even strategically timing sleep to strengthen extinction memories therapeutically instantiated during exposure therapy, may allow sleep itself to be recruited in the treatment of PTSD and other trauma and stress-related disorders.
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images

Tumor segmentation from magnetic resonance (MR) images may aid in tumor treatment by tracking the progress of tumor growth and/or shrinkage. In this paper we present the first automatic segmentation method which separates non-enhancing brain tumors from healthy tissues in MR images to aid in the task of tracking tumor size over time. The MR feature images used for the segmentation consist of three weighted images (T1, T2 and proton density (PD)) for each axial slice through the head. An initial segmentation is computed using an unsupervised fuzzy clustering algorithm. Then, integrated domain knowledge and image processing techniques contribute to the final tumor segmentation. They are applied under the control of a knowledge-based system. The system knowledge was acquired by training on two patient volumes (14 images). Testing has shown successful tumor segmentations on four patient volumes (31 images). Our results show that we detected all six non-enhancing brain tumors, located tumor tissue in 35 of the 36 ground truth (radiologist labeled) slices containing tumor and successfully separated tumor regions from physically connected CSF regions in all the nine slices. Quantitative measurements are promising as correspondence ratios between ground truth and segmented tumor regions ranged between 0.368 and 0.871 per volume, with percent match ranging between 0.530 and 0.909 per volume.
Ternary neural networks for resource-efficient AI applications

The computation and storage requirements for Deep Neural Networks (DNNs) are usually high. This issue limits their deployability on ubiquitous computing devices such as smart phones, wearables and autonomous drones. In this paper, we propose ternary neural networks (TNNs) in order to make deep learning more resource-efficient. We train these TNNs using a teacher-student approach based on a novel, layer-wise greedy methodology. Thanks to our two-stage training procedure, the teacher network is still able to use state-of-the-art methods such as dropout and batch normalization to increase accuracy and reduce training time. Using only ternary weights and activations, the student ternary network learns to mimic the behavior of its teacher network without using any multiplication. Unlike its {-1,1} binary counterparts, a ternary neural network inherently prunes the smaller weights by setting them to zero during training. This makes them sparser and thus more energy-efficient. We design a purpose-built hardware architecture for TNNs and implement it on FPGA and ASIC. We evaluate TNNs on several benchmark datasets and demonstrate up to 3.1 χ better energy efficiency with respect to the state of the art while also improving accuracy.
Self-driving and driver relaxing vehicle

In the modern era, the vehicles are focused to be automated to give human driver relaxed driving. In the field of automobile various aspects have been considered which makes a vehicle automated. Google, the biggest network has started working on the self-driving cars since 2010 and still developing new changes to give a whole new level to the automated vehicles. In this paper we have focused on two applications of an automated car, one in which two vehicles have same destination and one knows the route, where other don't. The following vehicle will follow the target (i.e. Front) vehicle automatically. The other application is automated driving during the heavy traffic jam, hence relaxing driver from continuously pushing brake, accelerator or clutch. The idea described in this paper has been taken from the Google car, defining the one aspect here under consideration is making the destination dynamic. This can be done by a vehicle automatically following the destination of another vehicle. Since taking intelligent decisions in the traffic is also an issue for the automated vehicle so this aspect has been also under consideration in this paper.
Model-Based Threat Assessment for Avoiding Arbitrary Vehicle Collisions

This paper presents a model-based algorithm that estimates how the driver of a vehicle can either steer, brake, or accelerate to avoid colliding with an arbitrary object. In this algorithm, the motion of the vehicle is described by a linear bicycle model, and the perimeter of the vehicle is represented by a rectangle. The estimated perimeter of the object is described by a polygon that is allowed to change size, shape, position, and orientation at sampled time instances. Potential evasive maneuvers are modeled, parameterized, and approximated such that an analytical expression can be derived to estimate the set of maneuvers that the driver can use to avoid a collision. This set of maneuvers is then assessed to determine if the driver needs immediate assistance to avoid or mitigate an accident. The proposed threat-assessment algorithm is evaluated using authentic data from both real traffic conditions and collision situations on a test track and by using simulations with a detailed vehicle model. The evaluations show that the algorithm outperforms conventional threat-assessment algorithms at rear-end collisions in terms of the timing of autonomous brake activation. This is crucial for increasing the performance of collision-avoidance systems and for decreasing the risk of unnecessary braking. Moreover, the algorithm is computationally efficient and can be used to assist the driver in avoiding or mitigating collisions with all types of road users in all kinds of traffic scenarios.
Facebook and its Effects on Users' Empathic Social Skills and Life Satisfaction: A Double-Edged Sword Effect

This study examines how Facebook usage affects individual's empathic social skills and life satisfaction. Following the self-presentational theory, the study explores a key component of the Internet paradox-whether Facebook suppresses or enhances users' interpersonal competence (specifically empathic social skills), given their respective personality makeup. Going further, the study assesses these events' subsequent impacts on users' psychological well-being. Analogous to a double-edged sword, Facebook activities are hypothesized to suppress the positive effect of a user's extraversion orientation on empathic social skills but lessen the negative effect of neuroticism on these skills. The study examines a sample of college-aged Facebook users (n=515), who responded to a large-scale online survey. The findings from a structural equation modeling analysis indicate that while empathic social skills are positively associated with life satisfaction, Facebook activities mainly exert suppression effects. Only upon low usage can Facebook activities lessen the negative effect of neuroticism on empathic social skills, suggesting that Facebook may appear as a less threatening platform for social interactions among neurotics. Yet, results in general suggest that undesirable effects may occur at high levels of Facebook usage whereby both extroverted and neurotic users displace real world social ties to online ones. The findings point to the complex ways in which social media usage may impact the livelihood of users.
Hierarchical Bayesian Time Series Models

Notions of Bayesian analysis are reviewed, with emphasis on Bayesian modeling and Bayesian calculation. A general hierarchical model for time series analysis is then presented and discussed. Both discrete time and continuous time formulations are discussed. An brief overview of generalizations of the fundamental hierarchical time series model concludes the article. Much of the Bayesian viewpoint can be argued (as by Jeereys and Jaynes, for examples) as direct application of the theory of probability. In this article the suggested approach for the construction of Bayesian time series models relies on probability theory to provide decompositions of complex joint probability distributions. Speciically, I refer to the familiar factorization of a joint density into an appropriate product of conditionals. Let x and y represent two random variables. I will not diierentiate between random variables and their realizations. Also, I will use an increasingly popular generic notation for probability densities: x] represents the density of x, xjy] is the conditional density of x given y, and x; y] denotes the joint density of x and y. In this notation we can write \Bayes's Theorem" as yjx] = xjy]]y]=x]: (1) y
Data, Schema, Ontology and Logic Integration

This paper gives a general definition of a “kind of schema” (often called a “meta-model” in the literature, but here called a “species”) along with general definitions for the schemas of a species, and for the databases, constraints, and queries over a given schema of a species. This leads naturally to a general theory of data translation and integration over arbitrary schemas of arbitrary species, based on schema morphisms, and to a similar general theory of ontology translation and integration over arbitrary logics. Institutions provide a general notion of logic, and Grothendieck flattening provides a general tool for integrating heterogeneous schemas, species and logics, as well as theories, such as ontologies, over different logics. Many examples of our novel concepts are included, some rather detailed. An initial section introduces data integration and ontologies for readers who are not specialists, with some emphasis on challenges. A brief review of universal algebra is also given, though some familiarity with category theory is assumed in later sections.
Building Expression into Virtual Characters

Virtual characters are an important part of many 3D graphical simulations. In entertainment or training applications, virtual characters might be one of the main mechanisms for creating and developing content and scenarios. In such applications the user may need to interact with a number of different characters that need to invoke specific responses in the user, so that the user interprets the scenario in the way that the designer intended. Whilst representations of virtual characters have come a long way in recent years, interactive virtual characters tend to be a bit “wooden” with respect to their perceived behaviour. In this STAR we give an overview of work on expressive virtual characters. In particular, we assume that a virtual character representation is already available, and we describe a variety of models and methods that are used to give the characters more “depth” so that they are less wooden and more plausible. We cover models of individual characters’ emotion and personality, models of interpersonal behaviour and methods for generating expression.
Scale Invariant Feature Transform on the Sphere: Theory and Applications

A SIFT algorithm in spherical coordinates for omnidirectional images is proposed. This algorithm can generate two types of local descriptors, Local Spherical Descriptors and Local Planar Descriptors. With the first ones, point matching between two omnidirectional images can be performed, and with the second ones, the same matching process can be done but between omnidirectional and planar images. Furthermore, a planar to spherical mapping is introduced and an algorithm for its estimation is given. This mapping allows to extract objects from an omnidirectional image given their SIFT descriptors in a planar image. Several experiments, confirming the promising and accurate performance of the system, are conducted.
Stereo vision images processing for real-time object distance and size measurements

Human has the ability to roughly estimate the distance and size of an object because of the stereo vision of human's eyes. In this project, we proposed to utilize stereo vision system to accurately measure the distance and size (height and width) of object in view. Object size identification is very useful in building systems or applications especially in autonomous system navigation. Many recent works have started to use multiple vision sensors or cameras for different type of application such as 3D image constructions, occlusion detection and etc. Multiple cameras system has becoming more popular since cameras are now very cheap and easy to deploy and utilize. The proposed measurement system consists of object detection on the stereo images and blob extraction and distance and size calculation and object identification. The system also employs a fast algorithm so that the measurement can be done in real-time. The object measurement using stereo camera is better than object detection using a single camera that was proposed in many previous research works. It is much easier to calibrate and can produce a more accurate results.
A universal-input high-power-factor PFC pre-regulator without electrolytic capacitor for PWM dimming LED lighting application

High brightness white LED has attracted a lot of attention for its high efficacy, simple to drive, environmentally friendly, long lifespan and small size. The power supply for LED lighting also requires long life while maintaining high efficiency, high power factor and low cost. However, a typical design employs electrolytic capacitor as storage capacitor, which is not only bulky, but also with short lifespan, thus hampering the entire LED lighting system. To prolong the lifespan of power supply, it has to use film capacitor with small capacitance to replace electrolytic capacitor. In this paper, a universal input high efficiency, high power factor LED driver is proposed based on the modified SEPIC converter. Along with a relatively large voltage ripple allowable in a PFC design, the proposal of LED lamp driver is able to eliminate the electrolytic capacitor while maintaining high power factor. To increase the efficiency of LED driver, the presented SEPIC-derived converter is modified further as the twin-bus output stage for matching ultra-high efficiency twin-bus LED current regulator. The operation principle and related analysis is described in detail. A 50-W prototype has been built and tested to verify the proposed LED Driver.
A Question Answering System on Holy Quran Translation Based on Question Expansion Technique and Neural Network Classification

Suhaib Kh. Hamed Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, University Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia Tel:0060-1139044355  Suhaib83.programmer@gmail.com  In spite of great efforts that have been made to present systems that support the user’s need of the answers from the Holy Quran, the current systems of English translation of Quran still need to do more investigation in order to develop the process of retrieving the accurate verse based on user’s question. The Islamic terms are different from one document to another and might be undefined for the user. Thus, the need emerged for a Question Answering System (QAS) that retrieves the exact verse based on a semantic search of the Holy Quran. The main objective of this research is to develop the efficiency of the information retrieval from the Holy Quran based on QAS and retrieving an accurate answer to the user’s question through classifying the verses using the Neural Network (NN) technique depending on the purpose of the verses’ contents, in order to match between questions and verses. This research has used the most popular English translation of the Quran of Abdullah Yusuf Ali as the data set. In that respect, the QAS will tackle these problems by expanding the question, using WordNet and benefitting from the collection of Islamic terms in order to avoid differences in the terms of translations and question. In addition, this QAS classifies the Al-Baqarah surah into two classes, which are Fasting and Pilgrimage based on the NN classifier, to reduce the retrieval of irrelevant verses since the user’s questions are asking for Fasting and Pilgrimage. Hence, this QAS retrieves the relevant verses to the question based on the N-gram technique, then ranking the retrieved verses based on the highest score of similarity to satisfy the desire of the user. According to F-measure, the evaluation of classification by using NN has shown an approximately 90% level and the evaluation of the proposed approach of this research based on the entire QAS has shown an approximately 87% level. This demonstrates that the QAS succeeded in providing a promising outcome in this critical field.
Global analysis of Expectation Maximization for mixtures of two Gaussians

Maximization (EM) is among the most popular algorithms for estimating parameters of statistical models. However, EM, which is an iterative algorithm based on the maximum likelihood principle, is generally only guaranteed to find stationary points of the likelihood objective, and these points may be far from any maximizer. This article addresses this disconnect between the statistical principles behind EM and its algorithmic properties. Specifically, it provides a global analysis of EM for specific models in which the observations comprise an i.i.d. sample from a mixture of two Gaussians. This is achieved by (i) studying the sequence of parameters from idealized execution of EM in the infinite sample limit, and fully characterizing the limit points of the sequence in terms of the initial parameters; and then (ii) based on this convergence analysis, establishing statistical consistency (or lack thereof) for the actual sequence of parameters produced by EM.