nils-herrmann/clean_scirepeval huggingface dataset:
On the Impossibility of Informationally Efficient Markets

If competitive equilibrium is defined as a situation in which prices are such that all arbitrage profits are eliminated, is it possible that a competitive economy always be in equilibrium? Clearly not, for then those who arbitrage make no (private) return from their (privately) costly activity. Hence the assumptions that all markets, including that for information, are always in equilibrium and always perfectly arbitraged are inconsistent when arbitrage is costly. We propose here a model in which there is an equilibrium degree of disequilibrium: prices reflect the information of informed individuals (arbitrageurs) but only partially, so that those who expend resources to obtain information do receive compensation. How informative the price system is depends on the number of individuals who are informed; but the number of individuals who are informed is itself an endogenous variable in the model. The model is the simplest one in which prices perform a well-articulated role in conveying information from the informed to the uninformed. When informed individuals observe information that the return to a security is going to be high, they bid its price up, and conversely when they observe information that the return is going to be low. Thus the price system makes publicly available the information obtained by informed individuals to the uniformed. In general, however, it does this imperfectly; this is perhaps lucky, for were it to do it perfectly, an equilibrium would not exist. In the introduction, we shall discuss the general methodology and present some conjectures concerning certain properties of the equilibrium. The remaining analytic sections of the paper are devoted to analyzing in detail an important example of our general model, in which our conjectures concerning the nature of the equilibrium can be shown to be correct. We conclude with a discussion of the implications of our approach and results, with particular emphasis on the relationship of our results to the literature on "efficient capital markets."
Software clone detection: A systematic review

Reusing software by means of copy and paste is a frequent activity in software development. The duplicated code is known as a software clone and the activity is known as code cloning. Software clones may lead to bug propagation and serious maintenance problems.  This study reports an extensive systematic literature review of software clones in general and software clone detection in particular.  We used the standard systematic literature review method based on a comprehensive set of 213 articles from a total of 2039 articles published in 11 leading journals and 37 premier conferences and
New developments in capacitive encoders for servo drives

Speed and position encoders are essential components in electrical drives. High resolution encoders are necessary to obtain acceptable speed control behaviour. Therefore optical encoders are mainly used in servo drives as position (and speed) feedback. Optical encoders, however, do not provide a robustness comparable to electrical motors - hard environmental influences like mechanical shock and/or vibrations often damage the optical disc inside the encoder. Using resolvers provides better mechanical robustness - their resolution, however, is not sufficient for good speed control behaviour. Capacitive (electrcial) [electrical] encoders are an attempt to combine good robustness with high resolution. Technical results obtained with a first commercial product encourage to proceed with further research and development in this area.
Revising The Wordnet Domains Hierarchy: Semantics, Coverage And Balancing

The continuous expansion of the multilingual information society has led in recent years to a pressing demand for multilingual linguistic resources suitable to be used for different applications. In this paper we present the WordNet Domains Hierarchy (WDH), a language-independent resource composed of 164, hierarchically organized, domain labels (e.g. Architecture, Sport, Medicine). Although WDH has been successfully applied to various Natural Language Processing tasks, the first available version presented some problems, mostly related to the lack of a clear semantics of the domain labels. Other correlated issues were the coverage and the balancing of the domains. We illustrate a new version of WDH addressing these problems by an explicit and systematic reference to the Dewey Decimal Classification. The new version of WDH has a better defined semantics and is applicable to a wider range of tasks.
Panoramic video textures

This paper describes a mostly automatic method for taking the output of a single panning video camera and creating a panoramic video texture (PVT): a video that has been stitched into a single, wide field of view and that appears to play continuously and indefinitely. The key problem in creating a PVT is that although only a portion of the scene has been imaged at any given time, the output must simultaneously portray motion throughout the scene. Like previous work in video textures, our method employs min-cut optimization to select fragments of video that can be stitched together both spatially and temporally. However, it differs from earlier work in that the optimization must take place over a much larger set of data. Thus, to create PVTs, we introduce a dynamic programming step, followed by a novel hierarchical min-cut optimization algorithm. We also use gradient-domain compositing to further smooth boundaries between video fragments. We demonstrate our results with an interactive viewer in which users can interactively pan and zoom on high-resolution PVTs.
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning

Recently there has been significant interest in training machine-learning models at low precision: by reducing precision, one can reduce computation and communication by one order of magnitude. We examine training at reduced precision, both from a theoretical and practical perspective, and ask: is it possible to train models at end-to-end low precision with provable guarantees? Can this lead to consistent order-ofmagnitude speedups? We mainly focus on linear models, and the answer is yes for linear models. We develop a simple framework called ZipML based on one simple but novel strategy called double sampling. Our ZipML framework is able to execute training at low precision with no bias, guaranteeing convergence, whereas naive quantization would introduce significant bias. We validate our framework across a range of applications, and show that it enables an FPGA prototype that is up to 6.5× faster than an implementation using full 32-bit precision. We further develop a variance-optimal stochastic quantization strategy and show that it can make a significant difference in a variety of settings. When applied to linear models together with double sampling, we save up to another 1.7× in data movement compared with uniform quantization. When training deep networks with quantized models, we achieve higher accuracy than the state-of-theart XNOR-Net. ETH Zurich, Switzerland Massachusetts Institute of Technology, USA IST Austria, Austria University of Rochester, USA. Correspondence to: Hantian Zhang <hantian.zhang@inf.ethz.ch>, Ce Zhang <ce.zhang@inf.ethz.ch>. Proceedings of the 34 th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017.
Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a “patch” against known attacks are insufficient. Indeed, next-generation IoT challenges will require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. To this end, machine learning (ML) and software-defined networking (SDN) will be key to provide both reconfigurability and intelligence to the IoT devices. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges related to the application of ML and SDN to address existing and next-generation IoT security threats.
Emphasizing Essential Words for Sentiment Classification Based on Recurrent Neural Networks

With the explosion of online communication and publication, texts become obtainable via forums, chat messages, blogs, book reviews and movie reviews. Usually, these texts are much short and noisy without sufficient statistical signals and enough information for a good semantic analysis. Traditional natural language processing methods such as Bow-of-Word (BOW) based probabilistic latent semantic models fail to achieve high performance due to the short text environment. Recent researches have focused on the correlations between words, i.e., term dependencies, which could be helpful for mining latent semantics hidden in short texts and help people to understand them. Long short-term memory (LSTM) network can capture term dependencies and is able to remember the information for long periods of time. LSTM has been widely used and has obtained promising results in variants of problems of understanding latent semantics of texts. At the same time, by analyzing the texts, we find that a number of keywords contribute greatly to the semantics of the texts. In this paper, we establish a keyword vocabulary and propose an LSTM-based model that is sensitive to the words in the vocabulary; hence, the keywords leverage the semantics of the full document. The proposed model is evaluated in a short-text sentiment analysis task on two datasets: IMDB and SemEval-2016, respectively. Experimental results demonstrate that our model outperforms the baseline LSTM by 1%~2% in terms of accuracy and is effective with significant performance enhancement over several non-recurrent neural network latent semantic models (especially in dealing with short texts). We also incorporate the idea into a variant of LSTM named the gated recurrent unit (GRU) model and achieve good performance, which proves that our method is general enough to improve different deep learning models.
Comparison of controllable transmission ratio type variable stiffness actuator with antagonistic and pre-tension type actuators for the joints exoskeleton robots

Exoskeleton robots are used as assistive limbs for elderly persons, rehabilitation for paralyzed persons or power augmentation purposes for healthy persons. The similarity of the exoskeleton robots and human body neuro-muscular system maximizes the device performance. Human body neuro-muscular system provides a flexible and safe movement capability with minimum energy consumption by varying the stiffness of the human joints regularly. Similar to human body, variable stiffness actuators should be used to provide a flexible and safe movement capability in exoskeletons. In the present day, different types of variable stiffness actuator designs are used, and the studies on these actuators are still continuing rapidly. As exoskeleton robots are mobile devices working with the equipment such as batteries, the motors used in the design are expected to have minimal power requirements. In this study, antagonistic, pre-tension and controllable transmission ratio type variable stiffness actuators are compared in terms of energy efficiency and power requirement at an optimal (medium) walking speed for ankle joint. In the case of variable stiffness, the results show that the controllable transmission ratio type actuator compared with the antagonistic design is more efficient in terms of energy consumption and power requirement.
Motion-based motion deblurring

Motion blur due to camera motion can significantly degrade the quality of an image. Since the path of the camera motion can be arbitrary, deblurring of motion blurred images is a hard problem. Previous methods to deal with this problem have included blind restoration of motion blurred images, optical correction using stabilized lenses, and special CMOS sensors that limit the exposure time in the presence of motion. In this paper, we exploit the fundamental trade off between spatial resolution and temporal resolution to construct a hybrid camera that can measure its own motion during image integration. The acquired motion information is used to compute a point spread function (PSF) that represents the path of the camera during integration. This PSF is then used to deblur the image. To verify the feasibility of hybrid imaging for motion deblurring, we have implemented a prototype hybrid camera. This prototype system was evaluated in different indoor and outdoor scenes using long exposures and complex camera motion paths. The results show that, with minimal resources, hybrid imaging outperforms previous approaches to the motion blur problem. We conclude with a brief discussion on how our ideas can be extended beyond the case of global camera motion to the case where individual objects in the scene move with different velocities.
FPGA Implementation of QPSK modulator by using Hardware Co-simulation

The modulators are the basic requirement of communication systems they are designed to reduce the channel distortion & to use in RF communication hence many type of carrier modulation techniques has been already proposed according to channel properties & data rate of the system. QPSK (Quadrature Phase Shift Keying) is one of the modulation schemes used in wireless communication system due to its ability to transmit twice the data rate for a given bandwidth. The QPSK is the most often used scheme since it does not suffer from BER (Bit Error rate) degradation while the bandwidth efficiency is increased. It is very popular in Satellite communication. As the design of complex mathematical models such as QPSK modulator in „pure HDL‟ is very difficult and costly; it requires from designer many additional skills and is time-consuming. To overcome these types of difficulties, the proposed QPSK modulator can be implemented on FPGA by using the concept of hardware co-simulation at Low power. In this process, QPSK modulator is simulated with Xilinx System Generator Simulink software and later on it is converted in Very high speed integrated circuit Hardware Descriptive Language to implement it on FPGA. Along with the co-simulation, power of the proposed QPSK modulator can be minimized than conventional QPSK modulator. As a conclusion, the proposed architecture will not only able to operate on co-simulation platform but at the same time it will significantly consume less operational power.
Terrain sketching

Procedural methods for terrain synthesis are capable of creating realistic depictions of heightfield terrains with little user intervention. However, users often do wish to intervene in controlling the placement and shape of landforms, but without sacrificing realism. In this paper, we present a sketching interface to procedural terrain generation. This system enables users to draw the silhouette, spine and bounding curves of both extruding (hills and mountains) and embedding landforms (river courses and canyons).
 Terrain is interactively generated to match the sketched constraints using multiresolution surface deformation. In addition, the wavelet noise characteristics of silhouette strokes are propagated to the surrounding terrain. With terrain sketching users can interactively create or modify landscapes incorporating varied and complex land-forms.
Skipping-oriented Partitioning for Columnar Layouts

As data volumes continue to grow, modern database systems increasingly rely on data skipping mechanisms to improve performance by avoiding access to irrelevant data. Recent work  proposed a fine-grained partitioning scheme that was shown to improve the opportunities for data skipping in row-oriented systems. Modern analytics and big data systems increasingly adopt columnar storage schemes, and in such systems, a row-based approach misses important opportunities for further improving data skipping. The flexibility of column-oriented organizations, however, comes with the additional cost of tuple reconstruction. In this paper, we develop Generalized Skipping-Oriented Partitioning (GSOP), a novel hybrid data skipping framework that takes into account these rowbased and column-based tradeoffs. In contrast to previous columnoriented physical design work, GSOP considers the tradeoffs between horizontal data skipping and vertical partitioning jointly. Our experiments using two public benchmarks and a real-world workload show that GSOP can significantly reduce the amount of data scanned and improve end-to-end query response times over the state-of-theart techniques.
Project-Based Learning and the Role of Learning Boundaries

This paper seeks to analyse the extent to which organizations can learn from projects by focusing on the relationship between projects and their organizational context. The paper highlights three dimensions of project-based learning: the practice-based nature of learning, project autonomy and knowledge integration. This analysis generates a number of propositions on the relationship between the learning generated within projects and its transfer to other parts of the organization. In particular, the paper highlights the ‘learning boundaries’ which emerge when learning within projects creates new divisions in practice. These propositions are explored through a comparative analysis of two case studies of construction projects. This analysis suggests that the learning boundaries which develop around projects reflect the nested nature of learning, whereby different levels of learning may substitute for each other. Learning outcomes in the cases can thus be analysed in terms of the interplay between organizational learning and project-level learning. The paper concludes that learning boundaries are an important constraint on attempts to exploit the benefits of projectbased learning for the wider organization.
An upper-body rehabilitation exoskeleton Harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation

We present an upper-body exoskeleton for rehabilitation, called Harmony, that provides natural coordinated motions on the shoulder with a wide range of motion, and force and impedance controllability. The exoskeleton consists of an anatomical shoulder mechanism with five active degrees of freedom, and one degree of freedom elbow and wrist mechanisms powered by series elastic actuators. The dynamic model of the exoskeleton is formulated using a recursive Newton–Euler algorithm with spatial dynamics representation. A baseline control algorithm is developed to achieve dynamic transparency and scapulohumeral rhythm assistance, and the coupled stability of the robot–human system at the baseline control is investigated. Experiments were conducted to evaluate the kinematic and dynamic characteristics of the exoskeleton. The results show that the exoskeleton exhibits good kinematic compatibility to the human body with a wide range of motion and performs task-space force and impedance control behaviors reliably.
Understanding Copyright Law in Online Creative Communities

Copyright law is increasingly relevant to everyday interactions online, from social media status updates to artists showcasing their work. This is especially true in creative spaces where rules about reuse and remix are notoriously gray. Based on a content analysis of public forum postings in eight different online communities featuring different media types (music, video, art, and writing), we found that copyright is a frequent topic of conversation and that much of this discourse stems from problems that copyright causes for creative activities. We identify the major types of problems encountered, including chilling effects that negatively impact technology use. We find that many challenges can be explained by lack of knowledge about legal or policy rules, including breakdowns in user expectations for the sites they use. We argue that lack of clarity is a pervasive usability problem that should be considered more carefully in the design of user-generated content platforms.
High-dynamic-range VGA with temperature compensation and linear-in-dB gain control

This paper presents the design and measured performance of a novel intermediate-frequency variable-gain amplifier for Wideband Code-Division Multiple Access (WCDMA) transmitters. A compensation technique for parasitic coupling is proposed which allows a high dynamic range of 77 dB to be attained at 400 MHz while using a single variable-gain stage. Temperature compensation and decibel-linear characteristic are achieved by means of a control circuit which provides a lower than /spl plusmn/1.5 dB gain error over full temperature and gain ranges. The device is fabricated in a 0.8-/spl mu/m 46 GHz f/sub T/ silicon bipolar technology and drains up to 6 mA from a 2.7-V power supply.
A Compact DC-30 GHz 0.13-um CMOS SP4T Switch

This paper presents a DC−30 GHz single-polefour-throw (SP4T) CMOS switch using 0.13 μm CMOS process. The CMOS transistor layout is done to minimize the substrate network resistance. The on-chip matching inductors and routing are designed for a very small die area (250 × 180 μm), and modeled using full-wave EM simulations. The SP4T CMOS switch result in an insertion loss of 1.8 dB and 2.7 dB at 5 GHz and 24 GHz, respectively. The isolation is > 25 dB up to 30 GHz and achieved using a series-shunt switch configuration. The measured input P1dB and IIP3 of the SP4T switch are 9 dBm and 21 dBm, respectively. To our knowledge, this is the first ultra wideband CMOS SP4T switch and with a very small chip area.
Objective Function Zero for the Routing Protocol for Low-Power and Lossy Networks (RPL)

The Routing Protocol for Low-Power and Lossy Networks (RPL) specification defines a generic Distance Vector protocol that is adapted to a variety of network types by the application of specific Objective Functions (OFs). An OF states the outcome of the process used by a RPL node to select and optimize routes within a RPL Instance based on the Information Objects available; an OF is not an algorithm. This document specifies a basic Objective Function that relies only on the objects that are defined in the RPL and does not use any protocol extensions. Information about the current status of this document, any errata, and how to provide feedback on it may be obtained at include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License.