nils-herrmann/clean_scirepeval huggingface dataset:
A Single-Chip Multiprocessor

I ntegrated circuit processing technology offers increasing integration density, which fuels microprocessor performance growth. Within 10 years it will be possible to integrate a billion transistors on a reasonably sized silicon chip. At this integration level, it is necessary to find parallelism to effectively utilize the transistors. Currently, processor designs dynamically extract parallelism with these transistors by executing many instructions within a single, sequential program in parallel. To find independent instructions within a sequential sequence of instructions, or thread of control, today's processors increasingly make use of sophisticated architectural features. Examples are out-of-order instruction execution and speculative execution of instructions after branches predicted with dynamic hardware branch prediction techniques. Future performance improvements will require processors to be enlarged to execute more instructions per clock cycle. 1 However, reliance on a single thread of control limits the parallelism available for many applications, and the cost of extracting parallelism from a single thread is becoming prohibitive. This cost manifests itself in numerous ways, including increased die area and longer design and verification times. In general, we see diminishing returns when trying to extract parallelism from a single thread. To continue this trend will trade only incremental performance increases for large increases in overall complexity. Although this parallelization might be achieved dynamically in hardware, we advocate using a software approach instead, allowing the hardware to be simple and fast. Emerging parallel compilation technologies , 2 an increase in the use of inherently parallel applications (such as multimedia), and more widespread use of multitasking operating systems should make this feasible. Researchers have proposed two alternative microar-chitectures that exploit multiple threads of control: simultaneous multithreading (SMT) 3 and chip multi-processors (CMP). 4 SMT processors augment wide (issuing many instructions at once) superscalar processors with hardware that allows the processor to execute instructions from multiple threads of control concurrently when possible, dynamically selecting and executing instructions from many active threads simultaneously. This promotes much higher utilization of the processor's execution resources and provides latency tolerance in case a thread stalls due to cache misses or data dependencies. When multiple threads are not available, however, the SMT simply looks like a conventional wide-issue superscalar. CMPs use relatively simple single-thread processor cores to exploit only moderate amounts of parallelism within any one thread, while executing multiple threads in parallel across multiple processor cores. If an application cannot be effectively decomposed into threads, CMPs will be underutilized. From a …
On-board real-time tracking of pedestrians on a UAV

Recent technical advances in Unmanned Aerial Vehicles (UAV) made a realm of applications possible. In this paper we focus on the application of following a walking pedestrian in real-time, using optimised pedestrian detection and object tracking. For this we use an on-board embedded system, offering an optimal ratio of computational power and weight. We extend the commonly used ground plane estimation technique, used to reduce the search space, based on the sensor data off the UAV. The integration of the ground plane constraint obtains a significant speed-up over the already optimised Aggregate Channel Feature (ACF) detector. To compensate for the frames without detections, we use a particle tracker based on color information. We successfully validated our system on a flying UAV.
Autonomously controlling flexible timelines: From domain-independent planning to robust execution

The introduction of more onboard autonomy in future single and multi-satellite missions is both a question of limited onboard resources and of how far can we actually thrust the autonomous functionalities deployed on board. In-flight experience with nasa's Deep Space 1 and Earth Observing 1 has shown how difficult it is to design, build and test reliable software for autonomy. The degree to which system-level onboard autonomy will be deployed in the single and multi satellite systems of tomorrow will depend, among other things, on the progress made in two key software technologies: autonomous onboard planning and robust execution. Parallel to the developments in these two areas, the actual integration of planning and execution engines is still nowadays a crucial issue in practical application. This paper presents an onboard autonomous model-based executive for execution of time-flexible plans. It describes its interface with an apsi-based timeline-based planner, its control approaches, architecture and its modelling language as an extension of apsl's ddl. In addition, it introduces a modified version of the classical blocks world toy planning problem which has been extended in scope and with a runtime environment for evaluation of integrated planning and executive engines.
TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction

Keyphrase extraction is the task of identifying single or multi-word expressions that represent the main topics of a document. In this paper we present TopicRank, a graph-based keyphrase extraction method that relies on a topical representation of the document. Candidate keyphrases are clustered into topics and used as vertices in a complete graph. A graph-based ranking model is applied to assign a significance score to each topic. Keyphrases are then generated by selecting a candidate from each of the topranked topics. We conducted experiments on four evaluation datasets of different languages and domains. Results show that TopicRank significantly outperforms state-of-the-art methods on three datasets.
Information-Seeking Behavior in Generation Y Students : Motivation , Critical Thinking , and Learning Theory

Much commentary has been circulating in academe regarding the research skills, or lack thereof, in members of ‘‘Generation Y,’’ the generation born between 1980 and 1994. The students currently on college campuses, as well as those due to arrive in the next few years, have grown up in front of electronic screens: television, movies, video games, computer monitors. It has been said that student critical thinking and other cognitive skills (as well as their physical well-being) are suffering because of the large proportion of time spent in sedentary pastimes, passively absorbing words and images, rather than in reading. It may be that students’ cognitive skills are not fully developing due to ubiquitous electronic information technologies. However, it may also be that academe, and indeed the entire world, is currently in the middle of a massive and wideranging shift in the way knowledge is disseminated and learned.
Notice of RetractionKinematics and dynamics simulation of the Slider-Crank mechanism based on Matlab/Simulink

This paper mainly addressed the kinematics and dynamics simulation of the Slider-Crank mechanism. After proposing a mathematical model for the forward displacement of the slider-crank mechanism, the mathematical models for the forward velocity and acceleration of the slider-crank mechanism are constructed, respectively. According to the theory of statical equilibrium, the mathematical model for the forward dynamics of the slider-crank mechanism is constituted as well based on the acceleration analysis of each component part of this mechanism under consideration. Taking into account of mathematical models for the forward kinematics and dynamics of the slider-crank mechanism, simulation models for the forward kinematics and dynamics of the slider-crank mechanism are constituted in the Matlab/Simulink simulation platform and the forward kinematics and dynamics simulation of the slider-crank mechanism was successfully accomplished based on Matlab/Simulink by which an arduous and complicated mathematical manipulation can be avoided and a lot of computation time can be saved. Examples of the simulation for the forward kinematics and dynamics of a slider-crank mechanism are given to demonstrate the above-mentioned theoretical results.
Deep learning model based breast cancer histopathological image classification

The automatic and precision classification for breast cancer histopathological image has a great significance in clinical application. However, the existing analysis approaches are difficult to addressing the breast cancer classification problem because the feature subtle differences of inter-class histopathological image and the classification accuracy still hard to meet the clinical application. Recent advancements in data-driven sharing processing and multi-level hierarchical feature learning have made available considerable chance to dope out a solution to this problem. To address the challenging problem, we propose a novel breast cancer histopathological image classification method based on deep convolutional neural networks, named as BiCNN model, to address the two-class breast cancer classification on the pathological image. This deep learning model considers class and sub-class labels of breast cancer as prior knowledge, which can restrain the distance of features of different breast cancer pathological images. In addition, an advanced data augmented method is proposed to fit tolerance whole slide image recognition, which can full reserve image edge feature of cancerization region. The transfer learning and fine-tuning method are adopted as an optimal training strategy to improve breast cancer histopathological image classification accuracy. The experiment results show that the proposed method leads to a higher classification accuracy (up to 97%) and displays good robustness and generalization, which provides efficient tools for breast cancer clinical diagnosis.
BioWar: scalable agent-based model of bioattacks

While structured by social and institutional networks, disease outbreaks are modulated by physical, economical, technological, communication, health, and governmental infrastructures. To systematically reason about the nature of outbreaks, the potential outcomes of media, prophylaxis, and vaccination campaigns, and the relative value of various early warning devices, social context, and infrastructure, must be considered. Numerical models provide a cost-effective ethical system for reasoning about such events. BioWar, a scalable citywide multiagent network numerical model, is described in this paper. BioWar simulates individuals as agents who are embedded in social, health, and professional networks and tracks the incidence of background and maliciously introduced diseases. In addition to epidemiology, BioWar simulates health-care-seeking behaviors, absenteeism patterns, and pharmaceutical purchases, information useful for syndromic and behavioral surveillance algorithms.
Learning Regular Expressions for the Extraction of Product Attributes from E-commerce Microdata

A large number of e-commerce websites have started to markup their products using standards such as Microdata, Microformats, and RDFa. However, the markup is mostly not as fine-grained as desirable for applications and mostly consists of free text properties. This paper discusses the challenges that arise in the task of matching descriptions of electronic products from several thousand e-shops that offer Microdata markup. Specifically, our goal is to extract product attributes from product offers, by means of regular expressions, in order to build well structured product specifications. For this purpose we present a technique for learning regular expressions. We evaluate our attribute extraction approach using 1.9 million product offers from 9,240 e-shops which we extracted from the Common Crawl 2012, a large public Web corpus. Our results show that with our approach we are able to reach a similar matching quality as with manually defined regular expressions.
Accurate positioning in ultra-wideband systems

Accurate positioning systems can be realized via ultra-wideband signals due to their high time resolution. In this article, position estimation is studied for UWB systems. After a brief introduction to UWB signals and their positioning applications, two-step positioning systems are investigated from a UWB perspective. It is observed that time-based positioning is well suited for UWB systems. Then time-based UWB ranging is studied in detail, and the main challenges, theoretical limits, and range estimation algorithms are presented. Performance of some practical time-based ranging algorithms is investigated and compared against the maximum likelihood estimator and the theoretical limits. The trade-off between complexity and accuracy is observed.
A novel broadband dual-polarized dipole antenna element for 2G/3G/LTE base stations

A novel broadband dual-polarized crossed-dipole antenna element with parasitic branches is designed for 2G/3G/LTE base stations. The proposed antenna mainly comprises a curved reflector, two crossed-dipoles, a pair of feeding strips, and two couples of balanced-unbalanced (BALUN) transformers. Compared to the traditional square-loop radiator dipole, the impedance bandwidth of the proposed antenna can be greatly improved after employing two parasitic metal stubs and two pairs of parasitic metal branches, and a better radiation performance of the proposed antenna can be obtained by optimizing the angle of the reflector. Simulation results show that the proposed antenna element can operate from 1.7 to 2.7 GHz with has an impedance bandwidth of VSWR <; 1.5, the port isolation of more than 30 dB, a stable radiation pattern with half-power beamwidth 65.2°-5.6° at H-plane and V-plane, and a relatively stable dipole antenna gain of 8.5 ± 0.4 (dBi). Furthermore, measured results have a good agreement with simulated ones.
FPGA implementation of AES encryption and decryption

Advanced Encryption Standard (AES), a Federal Information Processing Standard (FIPS), is an approved cryptographic algorithm that can be used to protect electronic data. The AES can be programmed in software or built with pure hardware. However Field Programmable Gate Arrays (FPGAs) offer a quicker and more customizable solution. This paper presents the AES algorithm with regard to FPGA and the Very High Speed Integrated Circuit Hardware Description language (VHDL). ModelSim SE PLUS 5.7g software is used for simulation and optimization of the synthesizable VHDL code. Synthesizing and implementation (i.e. Translate, Map and Place and Route) of the code is carried out on Xilinx - Project Navigator, ISE 8.2i suite. All the transformations of both Encryption and Decryption are simulated using an iterative design approach in order to minimize the hardware consumption. Xilinx XC3S400 device of Spartan Family is used for hardware evaluation. This paper proposes a method to integrate the AES encrypter and the AES decrypter. This method can make it a very low-complexity architecture, especially in saving the hardware resource in implementing the AES (Inv) Sub Bytes module and (Inv) Mix columns module etc. Most designed modules can be used for both AES encryption and decryption. Besides, the architecture can still deliver a high data rate in both encryption/decryption operations. The proposed architecture is suited for hardware-critical applications, such as smart card, PDA, and mobile phone, etc.
Predicting the Outcome of ODI Cricket Matches: A Team Composition Based Approach

With the advent of statistical modeling in sports, predicting the outcome of a game has been established as a fundamental problem. Cricket is one of the most popular team games in the world. With this article, we embark on predicting the outcome of a One Day International (ODI) cricket match using a supervised learning approach from a team composition perspective. Our work suggests that the relative team strength between the competing teams forms a distinctive feature for predicting the winner. Modeling the team strength boils down to modeling individual player’s batting and bowling performances, forming the basis of our approach. We use career statistics as well as the recent performances of a player to model him. Player independent factors have also been considered in order to predict the outcome of a match. We show that the k-Nearest Neighbor (kNN) algorithm yields better results as compared to other classifiers.
An evaluation of cybersecurity assessment tools on a SCADA environment

Increased concerns for electric grid cybersecurity has lead to the development of compliance requirements that must be evaluated by utilities. The North American Electric Reliability Council (NERC) has created Critical Infrastructure Protection (CIP) requirements for all cyber assets supporting the bulk power system[17]. This research explores whether the methodologies and tools commonly used for traditional information technology (IT) systems are sufficient to meet the cybersecurity assessment needs in power systems. This paper reviews these assessment tools to determine their ability to assist in the evaluation of the CIP requirements. In addition to the evaluation the tool capabilities, they are also reviewed for their potential to negatively impact the network availability properties. Evaluation was performed on the PowerCyber testbed at Iowa State University which implements real-world environment as in employs industry standard hardware, software and field devices. The results of this analysis is provided along with a review known gaps where current IT cybersecurity tools do not appropriately support SCADA environments.
A soft pneumatic actuator that can sense grasp and touch

We present a fiber reinforced soft pneumatic actuator with integrated strain and pressure sensors. We demonstrate that combining these sensors into the same actuator enables proprioception of both actuator curvature and environmental contact forces. We describe the manufacture and integration of a simple liquid metal strain sensor, a pressure sensor, and electrical circuits used for the sensors. We derive a constant curvature model for the actuator which predicts actuator curvature from the air pressure, and other constants of manufacture. The utility of the sensor integration is demonstrated by using the actuator to distinguish successful grasps amongst a set of common cylindrical objects with varying diameter. The grasp radius is estimated from the relationship between the sensor pair. Contact forces with the environment (touch) may be inferred from sensor readings which deviate from unconstrained motion.
Ranking Nodes in Signed Social Networks

Social networks are inevitable part of modern life. A class of social networks is those with both positive (friendship or trust) and negative (enmity or distrust) links. Ranking nodes in signed networks remains a hot topic in computer science. In this manuscript, we review different ranking algorithms to rank the nodes in signed networks, and apply them to the sign prediction problem. Ranking scores are used to obtain reputation and optimism, which are used as features in the sign prediction problem. Reputation of a node shows patterns of voting towards the node and its optimism demonstrates how optimistic a node thinks about others. To assess the performance of different ranking algorithms, we apply them on three signed networks including Epinions, Slashdot and Wikipedia. In this paper, we introduce three novel ranking algorithms for signed networks and compare their ability in predicting signs of edges with already existing ones. We use logistic regression as the predictor and the reputation and optimism values for the trustee and trustor as features (that are obtained based on different ranking algorithms). We find that ranking algorithms resulting in correlated ranking scores, leads to almost the same prediction accuracy. Furthermore, our analysis identifies a number of ranking algorithms that result in higher prediction accuracy compared to others.
Maximum Resilience of Artificial Neural Networks

The deployment of Artificial Neural Networks (ANNs) in safety-critical applications poses a number of new verification and certification challenges. In particular, for ANN-enabled self-driving vehicles it is important to establish properties about the resilience of ANNs to noisy or even maliciously manipulated sensory input. We are addressing these challenges by defining resilience properties of ANN-based classifiers as the maximum amount of input or sensor perturbation which is still tolerated. This problem of computing maximum perturbation bounds for ANNs is then reduced to solving mixed integer optimization problems (MIP). A number of MIP encoding heuristics are developed for drastically reducing MIP-solver runtimes, and using parallelization of MIP-solvers results in an almost linear speed-up in the number (up to a certain limit) of computing cores in our experiments. We demonstrate the effectiveness and scalability of our approach by means of computing maximum resilience bounds for a number of ANN benchmark sets ranging from typical image recognition scenarios to the autonomous maneuvering of robots.
How valuable is medical social media data? Content analysis of the medical web

It is still an open question where to search for complying a specific information need due to the large amount and diversity of information available. In this paper, a content analysis of health-related information provided in the Web is performed to get an overview on the medical content available. In particular, the content of medical Question & Answer Portals, medical weblogs, medical reviews and Wikis is compared. For this purpose, medical concepts are extracted from the text material with existing extraction technology. Based on these concepts, the content of the different knowledge resources is compared. Since medical weblogs describe experiences as well as information, it is of large interest to be able to distinguish between informative and affective posts. For this reason, a method to classify blogs based on their information content is presented, which exploits high-level features describing the medical and affective content of blog posts. The results show that there are substantial differences in the content of various health-related Web resources. Weblogs and answer portals mainly deal with diseases and medications. The Wiki and the encyclopedia provide more information on anatomy and procedures. While patients and nurses describe personal aspects of their life, doctors aim to present health-related information in their blog posts. The knowledge on content differences and information content can be exploited by search engines to improve ranking, search and to direct users to appropriate knowledge sources.
Vivaldi antenna design in millimeter-wave-band with ultra wide bandwidth and high gain

A modified Vivaldi antenna is proposed in this paper especially for the millimeter-wave application. The metal support frame is used to fix the structured substrate and increased the front-to-back ratio as well as the radiation gain. Detailed design process are presented, following which one sample is designed with its working frequency band from 75GHz to 150 GHz. The sample is also fabricated and measured. Good agreements between simulated results and measured results are obtained.