nils-herrmann/clean_scirepeval huggingface dataset:
Automotive Software Architectures

Modern cars have evolved from mechanical devices into distributed cyber-physical systems which rely on software to function correctly. Starting from the 1970s the amount of electronics and software used has gradually increased from as little as one computer (Electronic Control Unit, ECU) to as much as 150 ECUs in 2015. The trend in the architecture, however, changes as companies look for ways to decrease the number of central computing nodes and connect them with the increased number of I/O nodes. In this chapter we provide an overview of the book and the conventions used in it and introduce the examples which we will use throughout. We describe the history of the automotive software anchoring the events in the evolution of the market of the electronics and software in modern cars. Towards the end of the chapter we also describe which directions can be pursued to deepen the knowledge of automotive software. 1.1 Software and Modern Cars The introduction of software to cars opened up plenty of opportunities—from the optimization of cars’ performance and to exciting infotainment features. Modern cars are full of electronics and the consumers are looking for car platforms which fully resemble software products. A good example of this kind of car is Tesla, which is known for innovations driven by software. The manufacturer is known for constantly pushing new versions of software to customers, providing them with new, exciting features almost every day. The software intensive systems in modern cars provide plenty of new opportunities, but they also require more careful design, implementation, verification and validation before they can be released to users. And although the practices of software engineering include methods and tools able to fulfill the needs for safety and reliability of the automotive software, they must be applied in an automotivespecific manner to address these needs. We could see the clear development of the automotive industry into a field less dominated by mechanical engineering but with a growing component of electronic and software engineering. We have seen the evolution of software from simple engine control algorithms of the 1970s to the advanced safety systems of the 2000s and the advanced connectivity of the 2010s. We can observe that the trends of using
A unified semantic knowledge base for IoT

In the Internet of Things (IoT), interoperability among heterogeneous entities is an important issue. Semantic modeling is a key catalyst to support interoperability. In this work, we present a unified semantic knowledge base for IoT that uses ontologies as the building blocks. Most of the current ontologies for IoT mainly focus on resources, services and location information. We build upon the current state-of-the-art ontologies to provide contextual information and set of policies to execute services. Our knowledge base consists of several ontologies viz, resource, location, context & domain, policy and service ontologies. This helps in building a unified knowledge representation for IoT entities. In our knowledge base, we specifically model dynamic environments in which IoT entities operate. Our knowledge base also facilitates service-composition, discovery and modeling for IoT in dynamic environments.
FOR PREDICTING STUDENT PERFORMANCE

Although data mining has been successfully implemented in the business world for some time now, its use in higher education is still relatively new, i.e. its use is intended for identification and extraction of new and potentially valuable knowledge from the data. Using data mining the aim was to develop a model which can derive the conclusion on students' academic success. Different methods and techniques of data mining were compared during the prediction of students' success, applying the data collected from the surveys conducted during the summer semester at the University of Tuzla, the Faculty of Economics, academic year 2010-2011, among first year students and the data taken during the enrollment. The success was evaluated with the passing grade at the exam. The impact of students' socio-demographic variables, achieved results from high school and from the entrance exam, and attitudes towards studying which can have an affect on success, were all investigated. In future investigations, with identifying and evaulating variables associated with process of studying, and with the sample increase, it would be possible to produce a model which would stand as a foundation for the development of decision support system in higher education.
Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements

Finding sparse solutions of under-determined systems of linear equations is a problem of significance importance in signal processing and statistics. In this paper we study an iterative reweighted least squares (IRLS) approach to find sparse solutions of underdetermined system of equations based on smooth approximation of the L0 norm and the method is extended to find sparse solutions from noisy measurements. Analysis of the proposed methods show that weaker conditions on the sensing matrices are required. Simulation results demonstrate that the proposed method requires fewer samples than existing methods, while maintaining a reconstruction error of the same order and demanding less computational complexity.
Ultra Low Power Wake-Up Radios: A Hardware and Networking Survey

In wireless environments, transmission and reception costs dominate system power consumption, motivating research effort on new technologies capable of reducing the footprint of the radio, paving the way for the Internet of Things. The most important challenge is to reduce power consumption when receivers are idle, the so called idle-listening cost. One approach proposes switching off the main receiver, then introduces new wake-up circuitry capable of detecting an incoming transmission, optionally discriminating the packet destination using addressing, then switching on the main radio only when required. This wake-up receiver technology represents the ultimate frontier in low power radio communication. In this paper, we present a comprehensive literature review of the research progress in wake-up radio (WuR) hardware and relevant networking software. First, we present an overview of the WuR system architecture, including challenges to hardware design and a comparison of solutions presented throughout the last decade. Next, we present various medium access control and routing protocols as well as diverse ways to exploit WuRs, both as an extension of pre-existing systems and as a new concept to manage low-power networking.
Demystifying Learning Analytics in Personalised Learning

This paper presents learning analytics as a mean to improve students’ learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper’s contribution to knowledge is in considering personalised learning within the context framework of learning analytics.
A program differencing algorithm for verilog HDL

During code review tasks, comparing two versions of a hardware design description using existing program differencing tools such as diff is inherently limited because existing program differencing tools implicitly assume sequential execution semantics, while hardware description languages are designed to model concurrent computation. We designed a position-independent differencing algorithm to robustly handle language constructs whose relative orderings do not matter. This paper presents Vdiff, an instantiation of this position-independent differencing algorithm for Verilog HDL. To help programmers reason about the differences at a high-level, Vdiff outputs syntactic differences in terms of Verilog-specific change types. We evaluated Vdiff on two open source hardware design projects. The evaluation result shows that Vdiff is very accurate, with overall 96.8% precision and 97.3% recall when using manually classified differences as a basis of comparison.
Algorithm 583: LSQR: Sparse Linear Equations and Least Squares Problems

Received 4 June 1980; revised 23 September 1981, accepted 28 February 1982 This work was supported by Natural Sciences and Engineering Research Council of Canada Grant A8652, by the New Zealand Department of Scientific and Industrial Research; and by U S. National Science Foundation Grants MCS-7926009 and ECS-8012974, the Department of Energy under Contract AM03-76SF00326, PA No. DE-AT03-76ER72018, the Office of Naval Research under Contract N00014-75-C-0267, and the Army Research Office under Contract DAA29-79-C-0U0, Authors' addresses: C. C. Paige, School of Computer Science, McGill University, Montreal, Quebec, Canada H3A 2K6; M. A Saundem, Department of Operations Research, Stanford University, Stanford, CA 94305. Permmsion to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notme is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission.
Vehicle Localization Based on the Detection of Line Segments from Multi-Camera Images

For realizing autonomous vehicle driving and advanced safety systems, it is necessary to achieve accurate vehicle localization in cities. This paper proposes a method of accurately estimating vehicle position by matching a map and line segment features detected from images captured by a camera. Features such as white road lines, yellow road lines, road signs, and curb stones, which could be used as clues for vehicle localization, were expressed as line segment features on a two-dimensional road plane in an integrated manner. The detected line segments were subjected to bird’s-eye view transformation to transform them to the vehicle coordinate system so that they could be used for vehicle localization regardless of the camera configuration. Moreover, an extended Kalman filter was applied after a detailed study of the line observation errors for realizing real-time estimation. Vehicle localization was tested under city driving conditions, and the vehicle position was identified with sub-meter accuracy.
Semi-automated map creation for fast deployment of AGV fleets in modern logistics

Today, Automated Guided Vehicles (AGVs) still have a lowmarket share in logistics, compared to manual forklifts. We identified one of the main bottlenecks in the rather long deployment time which involves precise 2D mapping of the plant, 3D geo-referencing of pick-up/ drop positions and the manual design of the roadmap. The long deployment time has various reasons: in state-of-the-art plant installations, designated infrastructure is still necessary for the localization; themappingprocess requires highly skilled personnel; inmany cases unavailable or inappropriate position information of drop points for goodsmust be corrected on site. Finally, the design of the roadmap, performed by expert technicians is manually optimised in a tedious process to achieve maximum flow of goods for the plant operator. In total the setup of a plant to be ready for AGV operation is taking several months, binding highly skilled technicians and involves very time-consuming and costly on-site procedures. Therefore, we present an approach to AGV deployment which aims to drastically reduce the time, cost and involved personnel. First, we propose the employment of a novel, industrial-ready SICK 3D laser scanning technology in order to build an accurate and consistent virtual representation of the whole warehouse environment. By utilizing suitable segmentation and processing a semantic map is generated that contains 3D geo-referenced positions as well as a 2D localization map eliminating the need for dedicated solution to 2D mapping. Second, the semantic map provides a free space map which is used as a basis for the automatic roadmap creation in order to achieve optimal flow. So, this paper proposes an innovative methodology for obtaining, in a semi-automated manner, the map of an industrial environment where a system of multiple AGVs will be installed with less time and cost.
Quad-rotor lifting-transporting cable-suspended payloads control

This paper presents the control of quadrotor UAV with cable-suspended stability. A linear quadratic regulator (LQR) control algorithm is proposed for lifting and transporting the load. The nonlinear dynamic model of the vehicle is represented with considering the cable-suspended load in eight degree of freedom, then the model is linearized at the hovering point. Two modes of taking-off are used, starting with taking-off without the load effect then switching to taking-off with the effect of load. The simulation presents the results to show the system stability and verify the LQR gains. The results are compared with the PD controller results.
Description of the YaCy Distributed Web Search Engine

Distributed web search engines have been proposed to mitigate the privacy issues that arise in centralized search systems. These issues include censorship, disclosure of sensitive queries to the search server and to any third parties with whom the search service operator might share data, as well as the lack of transparency of proprietary search algorithms. YaCy is a deployed distributed search engine that aims to provide censorship resistance and privacy to its users. Its user base has been steadily increasing and it is currently being used by several hundreds of people every day. Unfortunately, there exists no document that thoroughly describes how YaCy exactly works. We therefore investigated the source code of YaCy and summarize in this document our findings and explanation on YaCy. We confirmed with the YaCy community that our description on YaCy is accurate.
A 21nV/√Hz chopper-stabilized multipath current-feedback instrumentation amplifier with 2µV offset

Amplifiers with low offset and low 1/f noise usually employ auto-zeroing (AZ) and/or chopping. However, AZ suffers from noise aliasing, and so requires more power dissipation for a given noise specification. Chopping, although free of noise aliasing, requires notch filters [1, 2] or AZ  to remove the ripple caused by up-modulated offset and 1/f noise. This paper describes a chopper-stabilized multi-path current-feedback instrumentation amplifier (CFIA), whose ripple is removed by a continuous-time (CT) ripple-reduction loop (RRL) . In contrast to , the notch created by the RRL is eliminated by the use of a multi-path architecture. This results in a CFIA with a smooth single-pole response, which also achieves low offset (2µV) and low noise (21nV/√Hz) in a power efficient manner (NEF=9.6). By appropriately connecting its inputs, the CFIA can also be used as an opamp. In this configuration, its offset is further reduced while its noise and NEF are halved.
Deep Learning for Detection of Object-Based Forgery in Advanced Video

Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN) to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNN model. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results.
Who Uses Bitcoin? An exploration of the Bitcoin community

Many cryptocurrencies have come into existence in recent years, with Bitcoin the most prominent among them. Although its short history has been volatile, the virtual currency maintains a core group of committed users. This paper presents an exploratory analysis of Bitcoin users. As a virtual currency and peer-to-peer payment system, Bitcoin may signal future challenges to state oversight and financial powers through its decentralized structure and offer of instantaneous transactions with relative anonymity. Very little is known about the users of Bitcoin, however. Utilizing publicly available survey data of Bitcoin users, this analysis explores the structure of the Bitcoin community in terms of wealth accumulation, optimism about the future of Bitcoin, and themes that attract users to the cryptocurrency. Results indicate that age, time of initial use, geographic location, mining status, engaging online discourse, and political orientation are all relevant factors that help explain various aspects of Bitcoin wealth, optimism, and attraction.
Control optimization for a power-split hybrid vehicle

Toyota hybrid system (THS) is used in the current best-selling hybrid vehicle on the market - the Toyota Prius. This hybrid system contains a power split planetary gear system which combines the benefits of series and parallel hybrid vehicles. This paper first develops a dynamic model to investigate the control strategy of the THS power train. An equivalent consumption minimization strategy (ECMS) is developed which is based on instantaneous optimization concept. The dynamic programming (DP) technique is then utilized to obtain a performance benchmark and insight toward fine-tuning of the ECMS algorithm for better performance
Hybrid approach to crime prediction using deep learning

Prevention is better that Cure. Preventing a crime from occurring is better than investigating what or how the crime had occurred. Just like vaccination is given to a child to prevent disease, in today's world with such higher crime rate and brutal crime happenings, it have become necessary to have a vaccination systems that prevents from crimes happening. By vaccinating society against crime it refers to various methods such as educating peoples, creating awareness, increasing efficiency and proactive policing methods and other deterrent techniques. Inspired by two different existing approach to crime prediction, the first one present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. Crime incident prediction has depends mainly on the historical crime record and various geospatial and demographic information . Even though it's promising, they do not take into account the rich and rapidly expanding social & web media context that surrounds incidents of interest. Next approach is based on the semantic analysis and natural language processing of Twitter posts via latent Dirichlet allocation, Topic detection Sentiment analysis[3[4]]. But both the techniques faces inherent limitations. Crime that happens these days are have following key characteristics such as crimes repeating in a periodic fashion, crimes occurring as a result of some other activity and occurrence of crimes pre indicated by some other information.
Characterizing and Detecting Skype-Relayed Traffic

Networked application developers have recently started to use end-users’ computers as relay nodes – application instances that also act as bridges between pairs of hosts running the same application. Relay nodes can bring costs to both users and network operators, at least in terms of increased bandwidth consumption. An interesting problem is to characterize the nature of relayed traffic and to detect its presence in the network. This paper focuses on characterizing and detecting relayed traffic generated by Skype, a popular voice over IP application that uses relays. Our technique relies solely on flow-level properties rather than on applicationor protocol-specific information. Using two different controlled experimental environments we generate and collect a large amount of Skype-relayed traffic. We propose several metrics to characterize the nature of relayed traffic. These metrics together with the results obtained from the experimental characterization of Skype-relayed traffic are used to detect Skyperelayed traffic traversing the access point of a large network. We show that the metrics proposed can be used to reliably detect Skype-relayed traffic. Finally, we believe the metrics proposed could be applied more broadly in the detection of relayed traffic generated by other multimedia applications.
A New Shape Signature for Fourier Descriptors

Shape-based image description is an important approach to content-based image retrieval (CBIR). A variety of techniques are reported in the literature that aim to represent objects based on their shapes; each of these techniques has its advantages and disadvantages. Fourier descriptor (FD), a simple yet powerful technique, has attractive properties such as rotational, scale, and translational invariance. In this paper we investigate this technique and present a novel shape registration method for extracting Fourier descriptors. When evaluated against curvature scale space (CSS) and Zernike moments (ZM) in shape-based image retrieval, the proposed technique exhibits superior performance.