nils-herrmann/clean_scirepeval huggingface dataset:
Fundamentals of Database Systems, 2nd Edition

Make more knowledge even in less time every day. You may not always spend your time and money to go abroad and get the experience and knowledge by yourself. Reading is a good alternative to do in getting this desirable knowledge and experience. You may gain many things from experiencing directly, but of course it will spend much money. So here, by reading fundamentals of database systems 3rd edition, you can take more advantages with limited budget.
Interneurons of the neocortical inhibitory system

Mammals adapt to a rapidly changing world because of the sophisticated cognitive functions that are supported by the neocortex. The neocortex, which forms almost 80% of the human brain, seems to have arisen from repeated duplication of a stereotypical microcircuit template with subtle specializations for different brain regions and species. The quest to unravel the blueprint of this template started more than a century ago and has revealed an immensely intricate design. The largest obstacle is the daunting variety of inhibitory interneurons that are found in the circuit. This review focuses on the organizing principles that govern the diversity of inhibitory interneurons and their circuits.
Dynamic Control of Receiver Buffers in Mobile Video Streaming Systems

We propose a novel algorithm to efficiently transmit multiple Variable-Bit-Rate (VBR) video streams from a base station to mobile receivers in wide-area wireless networks. The algorithm multicasts video streams in bursts to save the energy of mobile devices. In addition, the algorithm adaptively controls the buffer levels of mobile devices receiving different video streams according to the bit rate of the video stream being received by each device. Compared to previous algorithms, the new algorithm enables dynamic control of the wireless channel and allows the base station to transmit more video data on time to mobile receivers. This is done by providing finer control over the bandwidth allocation of the wireless channel. The problem of optimizing energy saving has been shown to be NP-Complete. We prove that our algorithm finds a feasible schedule if one exists and always produces a correct schedule even when dropped frames are unavoidable. We analytically bound the gap between the energy saving resulting from our algorithm and the optimal energy saving and show that our results are close to optimal. We analyze the tradeoff between the fine control over bandwidth allocation and energy saving and demonstrate that in practical situations, flexible and finer control of bandwidth allocation will result in significantly lower frame loss rates while achieving higher energy saving. We have implemented the proposed algorithm as well as two other recent algorithms in a mobile video streaming testbed. Our extensive analysis and results demonstrate that the proposed algorithm outperforms the other two algorithms; it results in higher energy saving for mobile devices and fewer dropped video frames.
Circularly polarized array antenna with corporate-feed network and series-feed elements

In this paper, corporate-feed circularly polarized microstrip array antennas are studied. The antenna element is a series-feed slot-coupled structure. Series feeding causes sequential rotation effect at the element level. Antenna elements are then used to form the subarray by applying sequential rotation to their feeding. Arrays having 4, 16, and 64 elements were made. The maximum achieved gains are 15.3, 21, and 25.4 dBic, respectively. All arrays have less than 15 dB return loss and 3 dB axial ratio from 10 to 13 GHz. The patterns are all quite symmetrical.
Mobile robot kinematic reconfigurability for rough terrain

Future planetary exploration missions will use rovers to perform tasks in rough terrain. Working in such conditions, a rover could become trapped due to loss of wheel traction, or even tip over. The Jet Propulsion Laboratory has developed a new rover with the ability to reconfigure its structure to improve tipover stability and ground traction. This paper presents a method to control this reconfigurability to enhance system tipover stability. A stability metric is defined and optimized online using a quasi-static model. Simulation and experimental results for the Sample Return Rover (SRR) are presented. The method is shown to be practical and yields significantly improved stability in rough terrain.
Internet X.509 Public Key Infrastructure Certificate and Certificate Revocation List (CRL) Profile

This document specifies an Internet standards track protocol for the Internet community, and requests discussion and suggestions for improvements. Please refer to the current edition of the "Internet Official Protocol Standards" (STD 1) for the standardization state and status of this protocol. Distribution of this memo is unlimited.  This document updates the handling of DirectoryString in the Internet X.509 Public Key Infrastructure Certificate and Certificate Revocation List (CRL) Profile, which is published in RFC 3280. The use of UTF8String and PrintableString are the preferred encoding. The requirement for exclusive use of UTF8String after December 31, 2003 is removed.
The Blockchain as a Software Connector

Blockchain is an emerging technology for decentralized and transactional data sharing across a large network of untrusted participants. It enables new forms of distributed software architectures, where components can find agreements on their shared states without trusting a central integration point or any particular participating components. Considering the blockchain as a software connector helps make explicitly important architectural considerations on the resulting performance and quality attributes (for example, security, privacy, scalability and sustainability) of the system. Based on our experience in several projects using blockchain, in this paper we provide rationales to support the architectural decision on whether to employ a decentralized blockchain as opposed to other software solutions, like traditional shared data storage. Additionally, we explore specific implications of using the blockchain as a software connector including design trade-offs regarding quality attributes.
Real-time three-dimensional smooth path planning for unmanned aerial vehicles in completely unknown cluttered environments

This paper presents a real-time three-dimensional path planning algorithm for improving autonomous navigation of Unmanned Aerial Vehicles (UAVs) operating in completely unknown cluttered environments. The algorithm generates smooth paths consisting of continuous piecewise Bézier curves in real time. Specifically, a RRT-based waypoint generation algorithm is firstly proposed for the exploration of collision-free waypoints successively during flight. Besides, a novel real-time path smoothing technique is developed to generate continuous collision-free paths that satisfy the motion constraints of UAVs. This is achieved by fitting Bézier curves between consecutive waypoints based on the particle swarm optimization (PSO) algorithm. Lastly, a path selection strategy is also introduced to seek for an optimum path when multiple solutions are available. The simulation results demonstrate the superiority of the proposed real-time three-dimensional smooth path planning algorithm.
Real time monocular visual odometry using Optical Flow: Study on navigation of quadrotors UAV

Navigation is an important thing in so many intelligent systems. Nowadays, without any hesitation Global Positioning System (GPS) is the most common method for navigation. Nevertheless, in certain environments, GPS will become inaccurate due to the multi-path problem. In this paper, we propose a dead-reckoning system for quadrotor using feature-based monocular visual odometry to overcome that problem. The visual odometry design will be implemented into the fix bottom camera which is the monocular camera in quadrotor. The algorithm is validated by performing several tests. The results of the tests verify that the visual odometry algorithm can determine the position and orientation with good accuracy.
Automatic Control of Students' Attendance in Classrooms Using RFID

Radio frequency identification (RFID) is one of the automatic identification technologies more in vogue nowadays. There is a wide research and development in this area trying to take maximum advantage of this technology, and in coming years many new applications and research areas will continue to appear. This sudden interest in RFID also brings about some concerns, mainly the security and privacy of those who work with or use tags in their everyday life. RFID has, for some time, been used to access control in many different areas, from asset tracking to limiting access to restricted areas. In this paper we propose an architecture and a prototype of a system that uses distributed RFID over Ethernet and we demonstrate how to automate an entire students' attendance registration system by using RFID in an educational institution environment. Although the use of RFID systems in educational institutions is not new, it is intended to show how the use of it came to solve daily problems in our university.
Identifying the semantic orientation of terms using S-HAL for sentiment analysis

0950-7051/$ see front matter 2012 Elsevier B.V. A http://dx.doi.org/10.1016/j.knosys.2012.04.011 ⇑ Corresponding author at: MOE Key Laboratory Network Security, Xi’an Jiaotong University, Xi’an 71 82667964. E-mail addresses: txu@mail.xjtu.edu.cn (T. Xu (Q. Peng). Sentiment analysis continues to be a most important research problem due to its abundant applications. Identifying the semantic orientation of subjective terms (words or phrases) is a fundamental task for sentiment analysis. In this paper, we propose a new method for identifying the semantic orientation of subjective terms to perform sentiment analysis. The method takes a classification approach that is based on a novel semantic orientation representation model called S-HAL (Sentiment Hyperspace Analogue to Language). S-HAL basically produces a set of weighted features based on surrounding words, and characterizes the semantic orientation information of words via a specific feature space. Because the method incorporates the idea underlying HAL and the hypothesis verified by the method of semantic orientation inference from pointwise mutual information (SO-PMI), it can quickly and accurately identify the semantic orientation of terms without the use of an Internet search engine. The results of an empirical evaluation show that our method outperforms other known methods.
Toward an Efficient C-RAN Optical Fronthaul for the Future Networks: A Tutorial on Technologies, Requirements, Challenges, and Solutions

The exponential traffic growth, demand for high speed wireless data communications, as well as incessant deployment of innovative wireless technologies, services, and applications, have put considerable pressure on the mobile network operators (MNOs). Consequently, cellular access network performance in terms of capacity, quality of service, and network coverage needs further considerations. In order to address the challenges, MNOs, as well as equipment vendors, have given significant attention to the small-cell schemes based on cloud radio access network (C-RAN). This is due to its beneficial features in terms of performance optimization, cost-effectiveness, easier infrastructure deployment, and network management. Nevertheless, the C-RAN architecture imposes stringent requirements on the fronthaul link for seamless connectivity. Digital radio over fiber-based common public radio interface (CPRI) is the fundamental means of distributing baseband samples in the C-RAN fronthaul. However, optical links which are based on CPRI have bandwidth and flexibility limitations. Therefore, these limitations might constrain or make them impractical for the next generation mobile systems which are envisaged not only to support carrier aggregation and multi-band but also envisioned to integrate technologies like millimeter-wave (mm-wave) and massive multiple-input multiple-output antennas into the base stations. In this paper, we present comprehensive tutorial on technologies, requirements, architectures, challenges, and proffer potential solutions on means of achieving an efficient C-RAN optical fronthaul for the next-generation network such as the fifth generation network and beyond. A number of viable fronthauling technologies such as mm-wave and wireless fidelity are considered and this paper mainly focuses on optical technologies such as optical fiber and free-space optical. We also present feasible means of reducing the system complexity, cost, bandwidth requirement, and latency in the fronthaul. Furthermore, means of achieving the goal of green communication networks through reduction in the power consumption by the system are considered.
Efficient model-based 3D tracking of hand articulations using Kinect

The 3D tracking of the articulation of human hands is a theoretically interesting and challenging problem with numerous and diverse applications . Several attempts have been made to address the problem by considering markerless visual data. Existing approaches can be categorized into modeland appearance-based . In this work, we propose a model-based approach to the problem . We formulate it as an optimization problem that minimizes the discrepancy between the 3D structure and appearance of hypothesized 3D hand model instances, and actual visual observations. Optimization is performed with a variant of an existing stochastic optimization method (Particle Swarm Optimization PSO) . The most computationally demanding parts of the process have been implemented to run efficiently on a GPU . The resulting system performs hand articulations tracking at a rate of 15Hz.
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates

Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on off-policy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.
Content Based Lecture Video Retrieval Using Speech and Video Text Information

In the last decade e-lecturing has become more and more popular. The amount of lecture video data on the World Wide Web (WWW) is growing rapidly. Therefore, a more efficient method for video retrieval in WWW or within large lecture video archives is urgently needed. This paper presents an approach for automated video indexing and video search in large lecture video archives. First of all, we apply automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. Subsequently, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames and Automatic Speech Recognition (ASR) on lecture audio tracks. The OCR and ASR transcript as well as detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance and the effectiveness of proposed indexing functionalities is proven by evaluation.
Mammogram classification using deep learning features

This paper presents a method for classification of normal and abnormal tissues in mammograms using a deep learning approach. VGG-16 CNN deep learning architecture with convolutional filter of (3×3) is implemented on mammograms ROIs from the IRMA dataset. The deep feature matrix is computed from first fully connected layer. The results are evaluated using 10 fold cross validation on SVM, binary trees, simple logistics and KNN (with k=1, 3, 5) classifiers. The method produced 100% classification accuracies with AUC 1.0.
Targeted and depth-first exploration for systematic testing of android apps

Systematic exploration of Android apps is an enabler for a variety of app analysis and testing tasks. Performing the exploration while apps run on actual phones is essential for exploring the full range of app capabilities. However, exploring real-world apps on real phones is challenging due to non-determinism, non-standard control flow, scalability and overhead constraints. Relying on end-users to conduct the exploration might not be very effective: we performed a 7-use study on popular Android apps, and found that the combined 7-use coverage was 30.08% of the app screens and 6.46% of the app methods. Prior approaches for automated exploration of Android apps have run apps in an emulator or focused on small apps whose source code was available. To address these problems, we present A3E, an approach and tool that allows substantial Android apps to be explored systematically while running on actual phones, yet without requiring access to the app's source code. The key insight of our approach is to use a static, taint-style, dataflow analysis on the app bytecode in a novel way, to construct a high-level control flow graph that captures legal transitions among activities (app screens). We then use this graph to develop an exploration strategy named Targeted Exploration that permits fast, direct exploration of activities, including activities that would be difficult to reach during normal use. We also developed a strategy named Depth-first Exploration that mimics user actions for exploring activities and their constituents in a slower, but more systematic way. To measure the effectiveness of our techniques, we use two metrics: activity coverage (number of screens explored) and method coverage. Experiments with using our approach on 25 popular Android apps including BBC News, Gas Buddy, Amazon Mobile, YouTube, Shazam Encore, and CNN, show that our exploration techniques achieve 59.39--64.11% activity coverage and 29.53--36.46% method coverage.
Randomized Block Coordinate Descent for Online and Stochastic Optimization

Two types of low cost-per-iteration gradient descent metho ds have been extensively studied in parallel. One is online or stochastic gradient descent ( OGD/SG ), and the other is randomzied coordinate descent (RBCD). In this paper, we combine the two types o f methods together and propose online randomized block coordinate descent (ORBCD). At each itera t on, ORBCD only computes the partial gradient of one block coordinate of one mini-batch samples. ORBCD is well suited for the composite minimization problem where one function is the average of th e losses of a large number of samples and the other is a simple regularizer defined on high dimensional variables. We show that the iteration complexity of ORBCD has the same order as OGD or SGD. For strongly convex functions, by reducing the variance of stochastic gradients, we show that ORBCD can con verge at a geometric rate in expectation, matching the convergence rate of SGD with variance reductio n and RBCD.
Evaluation of automotive MIMO antennas for V2V communication in urban intersection scenarios

The usage of driving assistance systems that are based on V2X communication technologies as ITS-G5 are a promising technology to increase road safety as well as traffic efficiency. To allow for communication even in challenging propagation scenarios (such as busy urban intersection), we investigated a new concept for rooftop mounted beam-steerable vehicular antennas which is a 2-element monopole array (parallel and orthogonal to direction of travel) and a switchless reconfigurable antenna. It shows that the monopole array configurations give the best propagation results in any scenario and time step. However, the beams have to be steered dynamically to ensure the best possible communication.
Trends in camera based Automotive Driver Assistance Systems (ADAS)

Advance Driver Assistance Systems (ADAS), once limited to high end luxury automobiles are fast becoming popular with Mid and entry level segments driven in part by legislation coming in to effect in the latter part of this decade. These systems require support for a wide variety of applications, from surround-view visual systems to safety critical vision applications (eg Pedestrian Detect, automatic braking etc). In this white paper we describe some of the existing and emerging trends and applications in each of these segments along with the requirements and motivations for each of these features. We also highlight TI's automotive class TDA2x device, a state of the art automotive grade device capable of handling complex ADAS applications within a low power and cost budget.
Bayesian autocalibration for surveillance

In the context of visual surveillance of human activity knowledge about a camera's internal and external parameters is useful, as it allows for the establishment of a connection between image and world measurements. Unfortunately, calibration information is rarely available and difficult to obtain after a surveillance system has been installed. In this paper, a method for camera autocalibration based on information gathered by tracking people is developed. It brings two main contributions: first, we show how a foot-to-head plane homology can be used to obtain the calibration parameters and then we show an approach how to efficiently estimate initial parameter estimates from measurements; second, we present a Bayesian solution to the calibration problem that can elegantly handle measurement uncertainties, outliers, as well as prior information. It is shown how the full posterior distribution of calibration parameters given the measurements can be estimated, which allows making statements about the accuracy of both the calibration parameters and the measurements involving them
A Multiple Expert Approach to the Class Imbalance Problem Using Inverse Random under Sampling

In this paper, a novel inverse random under sampling (IRUS) method is proposed for class imbalance problem. The main idea is to severely under sample the negative class (majority class), thus creating a large number of distinct negative training sets. For each training set we then find a linear discriminant which separates the positive class from the negative class. By combining the multiple designs through voting, we construct a composite between the positive class and the negative class. The proposed methodology is applied on 11 UCI data sets and experimental results indicate a significant increase in Area Under Curve (AUC) when compared with many existing class-imbalance learning methods.