nils-herrmann/clean_scirepeval huggingface dataset:
An examination of organizational and team commitment in a self-directed team environment.

A model hypothesizing differential relationships among predictor variables and individual commitment to the organization and work team was tested. Data from 485 members of sewing teams supported the existence of differential relationships between predictors and organizational and team commitment. In particular, intersender conflict and satisfaction with coworkers were more strongly related to team commitment than to organizational commitment. Resource-related conflict and satisfaction with supervision were more strongly related to organizational commitment than to team commitment. Perceived task interdependence was strongly related to both commitment foci. Contrary to prediction, the relationships between perceived task interdependence and the 2 commitment foci were not significantly different. Relationships with antecedent variables help explain how differential levels of commitment to the 2 foci may be formed. Indirect effects of exogenous variables are reported.
Satellite Attitude Control and Power Tracking with Energy / Momentum Wheels

A control law for an integrated power/attitude control system (IPACS) for a satellite is presented. Four or more energy/momentum wheels in an arbitrary noncoplanar con guration and a set of three thrusters are used to implement the torque inputs. The energy/momentum wheels are used as attitude-control actuators, as well as an energy storage mechanism, providing power to the spacecraft. In that respect, they can replace the currently used heavy chemical batteries. The thrusters are used to implement the torques for large and fast (slew) maneuvers during the attitude-initialization and target-acquisition phases and to implement the momentum management strategies. The energy/momentum wheels are used to provide the reference-tracking torques and the torques for spinning up or down the wheels for storing or releasing kinetic energy. The controller published in a previous work by the authors is adopted here for the attitude-tracking function of the wheels. Power tracking for charging and discharging the wheels is added to complete the IPACS framework. The torques applied by the energy/momentum wheels are decomposed into two spaces that are orthogonal to each other, with the attitude-control torques and power-tracking torques in each space. This control law can be easily incorporated in an IPACS system onboard a satellite. The possibility of the occurrence of singularities, in which no arbitrary energy pro le can be tracked, is studied for a generic wheel cluster con guration. A standard momentum management scheme is considered to null the total angular momentum of the wheels so as to minimize the gyroscopic effects and prevent the singularity from occurring. A numerical example for a satellite in a low Earth near-polar orbit is provided to test the proposed IPACS algorithm. The satellite’s boresight axis is required to track a ground station, and the satellite is required to rotate about its boresight axis so that the solar panel axis is perpendicular to the satellite–sun vector.
Probabilistic Typology: Deep Generative Models of Vowel Inventories

Linguistic typology studies the range of structures present in human language. The main goal of the field is to discover which sets of possible phenomena are universal, and which are merely frequent. For example, all languages have vowels, while most—but not all—languages have an [u] sound. In this paper we present the first probabilistic treatment of a basic question in phonological typology: What makes a natural vowel inventory? We introduce a series of deep stochastic point processes, and contrast them with previous computational, simulation-based approaches. We provide a comprehensive suite of experiments on over 200 distinct languages.
The ContikiMAC Radio Duty Cycling Protocol

Low-power wireless devices must keep their radio transceivers off as much as possible to reach a low power consumption, but must wake up often enough to be able to receive communication from their neighbors. This report describes the ContikiMAC radio duty cycling mechanism, the default radio duty cycling mechanism in Contiki 2.5, which uses a power efficient wake-up mechanism with a set of timing constraints to allow device to keep their transceivers off. With ContikiMAC, nodes can participate in network communication yet keep their radios turned off for roughly 99% of the time. This report describes the ContikiMAC mechanism, measures the energy consumption of individual ContikiMAC operations, and evaluates the efficiency of the fast sleep and phase-lock optimizations.
Application of repulsive force and genetic algorithm to multi-manipulator collision avoidance

Manipulator collision avoidance using genetic algorithms is presented. Control gains in the collision avoidance control model are selected based on genetic algorithms. A repulsive force is artificially created using the distances between the robot links and obstacles, which are generated by a distance computation algorithm. Real-time manipulator collision avoidance control has achieved. A repulsive force gain is introduced through the approaches for definition of link coordinate frames and kinematics computations. The safety distance between objects is affected by the repulsive force gain. This makes the safety zone adjustable and provides greater intelligence for robotic tasks under the ever-changing environment.
DeepFont: Identify Your Font from An Image

As font is one of the core design concepts, automatic font identification and similar font suggestion from an image or photo has been on the wish list of many designers. We study the Visual Font Recognition (VFR) problem  LFE, and advance the state-of-the-art remarkably by developing the DeepFont system. First of all, we build up the first available large-scale VFR dataset, named AdobeVFR, consisting of both labeled synthetic data and partially labeled real-world data. Next, to combat the domain mismatch between available training and testing data, we introduce a Convolutional Neural Network (CNN) decomposition approach, using a domain adaptation technique based on a Stacked Convolutional Auto-Encoder (SCAE) that exploits a large corpus of unlabeled real-world text images combined with synthetic data preprocessed in a specific way. Moreover, we study a novel learning-based model compression approach, in order to reduce the DeepFont model size without sacrificing its performance. The DeepFont system achieves an accuracy of higher than 80% (top-5) on our collected dataset, and also produces a good font similarity measure for font selection and suggestion. We also achieve around 6 times compression of the model without any visible loss of recognition accuracy.
Association Toward a Theory of Culturally Relevant Pedagogy

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discovRE: Efficient Cross-Architecture Identification of Bugs in Binary Code

The identification of security-critical vulnerabilities is a key for protecting computer systems. Being able to perform this process at the binary level is very important given that many software projects are closed-source. Even if the source code is available, compilation may create a mismatch between the source code and the binary code that is executed by the processor, causing analyses that are performed on source code to fail at detecting certain bugs and thus potential vulnerabilities. Existing approaches to find bugs in binary code 1) use dynamic analysis, which is difficult for firmware; 2) handle only a single architecture; or 3) use semantic similarity, which is very slow when analyzing large code bases. In this paper, we present a new approach to efficiently search for similar functions in binary code. We use this method to identify known bugs in binaries as follows: starting with a vulnerable binary function, we identify similar functions in other binaries across different compilers, optimization levels, operating systems, and CPU architectures. The main idea is to compute similarity between functions based on the structure of the corresponding control flow graphs. To minimize this costly computation, we employ an efficient pre-filter based on numeric features to quickly identify a small set of candidate functions. This allows us to efficiently search for similar functions in large code bases. We have designed and implemented a prototype of our approach, called discovRE, that supports four instruction set architectures (x86, x64, ARM, MIPS). We show that discovRE is four orders of magnitude faster than the state-of-the-art academic approach for cross-architecture bug search in binaries. We also show that we can identify Heartbleed and POODLE vulnerabilities in an Android system image that contains over 130,000 native ARM functions in about 80 milliseconds.
Context-awareness and the smart grid: Requirements and challenges

New intelligent power grids (smart grids) will be an essential way of improving efficiency in power supply and power consumption, facilitating the use of distributed and renewable resources on the supply side and providing consumers with a range of tailored services on the consumption side. The delivery of efficiencies and advanced services in a smart grid will require both a comprehensive overlay communications network and flexible software platforms that can process data from a variety of sources, especially electronic sensor networks. Parallel developments in autonomic systems, pervasive computing and context-awareness (relating in particular to data fusion, context modelling, and semantic data) could provide key elements in the development of scalable smart grid data management systems and applications that utilise a multi-technology communications network.  a future direction towards devising a context-aware middleware platform for the smart grid, as well as associated requirements and challenges. Smart grids will transform the methods of generating electric power and the monitoring and billing of consumption. The drivers behind the development of smart grids include economic, political and technical elements. Major initiatives have been launched in Europe by the EU Commission  and the European Electricity Grid Initiative . In the US, overall policies are set out by the National Science and Technology Council  while grid modernisation is specifically described in a report by the GridWise Alliance . The main policy drivers for power grid development are as follows: Promote the integration of distributed renewable power sources (e.g. wind, solar, wave and tidal power, geothermal, biofuel); Provide significant reductions in carbon dioxide (CO 2) emissions through the phasing-out of fossil fuel power plants. This is to help meet agreed world targets in reducing greenhouse gases and combatting climate change; Promote the use of electric vehicles as an alternative to fossil fuelled transport systems; Renew and upgrade older grid transmission infrastructure to provide greater efficiency and security of supply; Introduce two-way ''smart'' metering to facilitate both power saving and power production by consumers; Apart from these policy drivers, power production and distribution will also have to operate in an increasingly deregulated and competitive market environment.
Issues of Knowledge Management in the Public Sector

The new economy not only poses challenges, but also offers opportunities for both private and public sectors alike. To meet the challenges and take the opportunities, government must take active initiatives to adopt new management tools, techniques and philosophies of the private sector and adapt to its circumstance. Knowledge management (KM) is such an area that needs to be further explored and exploited for its full benefits to be reaped. Key issues, challenges, and opportunities of KM in the public sector need to be addressed and better understood
"OMG, from here, I can see the flames!": a use case of mining location based social networks to acquire spatio-temporal data on forest fires

The emergence of innovative web applications, often labelled as Web 2.0, has permitted an unprecedented increase of content created by non-specialist users. In particular, Location-based Social Networks (LBSN) are designed as platforms allowing the creation, storage and retrieval of vast amounts of georeferenced and user-generated contents. LBSN can thus be seen by Geographic Information specialists as a timely and cost-effective source of spatio-temporal information for many fields of application, provided that they can set up workflows to retrieve, validate and organise such information. This paper aims to improve the understanding on how LBSN can be used as a reliable source of spatio-temporal information, by analysing the temporal, spatial and social dynamics of Twitter activity during a major forest fire event in the South of France in July 2009.
Modelling and Pid Control Design of Nonlinear Educational Model Ball & Plate

This paper is focused on modelling and control of nonlinear dynamical system Ball & Plate in language Matlab/Simulink. PID/PSD controller is used in closed loop feedback control structure for the purpose of control. The verification of designed PID control algorithms, the same as nonlinear model of dynamical system, is performed with functional blocks of Simulink environment. This paper includes testing of designed PID control algorithms on real model Ball & Plate using multifunction I/O card MF 614, which communicates with PC by the functions of Real Time Toolbox. Visualization of the simulation results is realized by internet applications, which use Matlab Web Server.
A 12-bit, 45-MS/s, 3-mW Redundant Successive-Approximation-Register Analog-to-Digital Converter With Digital Calibration

This paper presents a sub-radix-2 redundant architecture to improve the performance of switched-capacitor successive-approximation-register (SAR) analog-to-digital converters (ADCs). The redundancy not only guarantees digitally correctable static nonlinearities of the converter, it also offers means to combat dynamic errors in the conversion process, and thus, accelerating the speed of the SAR architecture. A perturbation-based digital calibration technique is also described that closely couples with the architecture choice to accomplish simultaneous identification of multiple capacitor mismatch errors of the ADC, enabling the downsizing of all sampling capacitors to save power and silicon area. A 12-bit prototype measured a Nyquist 70.1-dB signal-to-noise-plus-distortion ratio (SNDR) and a Nyquist 90.3-dB spurious free dynamic range (SFDR) at 22.5 MS/s, while dissipating 3.0-mW power from a 1.2-V supply and occupying 0.06-mm2 silicon area in a 0.13-μm CMOS process. The figure of merit (FoM) of this ADC is 51.3 fJ/step measured at 22.5 MS/s and 36.7 fJ/step at 45 MS/s.
Rigid point feature registration using mutual information

We have developed a new mutual information-based registration method for matching unlabeled point features. In contrast to earlier mutual information-based registration methods, which estimate the mutual information using image intensity information, our approach uses the point feature location information. A novel aspect of our approach is the emergence of correspondence (between the two sets of features) as a natural by-product of joint density estimation. We have applied this algorithm to the problem of geometric alignment of primate autoradiographs. We also present preliminary results on three-dimensional robust matching of sulci derived from anatomical magnetic resonance images. Finally, we present an experimental comparison between the mutual information approach and other recent approaches which explicitly parameterize feature correspondence.
Dimensionality Reduction and Topic Modeling: From Latent Semantic Indexing to Latent Dirichlet Allocation and Beyond

The bag-of-words representation commonly used in text analysis can be analyzed very efficiently and retains a great deal of useful information, but it is also troublesome because the same thought can be expressed using many different terms or one term can have very different meanings. Dimension reduction can collapse together terms that have the same semantics, to identify and disambiguate terms with multiple meanings and to provide a lower-dimensional representation of documents that reflects concepts instead of raw terms. In this chapter, we survey two influential forms of dimension reduction. Latent semantic indexing uses spectral decomposition to identify a lower-dimensional representation that maintains semantic properties of the documents. Topic modeling, including probabilistic latent semantic indexing and latent Dirichlet allocation, is a form of dimension reduction that uses a probabilistic model to find the co-occurrence patterns of terms that correspond to semantic topics in a collection of documents. We describe the basic technologies in detail and expose the underlying mechanism. We also discuss recent advances that have made it possible to apply these techniques to very large and evolving text collections and to incorporate network structure or other contextual information.
NeuFlow: Dataflow vision processing system-on-a-chip

This paper presents a bio-inspired vision system-on-a-chip - neuFlow SoC implemented in the IBM 45 nm SOI process. The neuFlow SoC was designed to accelerate neural networks and other complex vision algorithms based on large numbers of convolutions and matrix-to-matrix operations. Post-layout characterization shows that the system delivers up to 320 GOPS with an average power consumption of 0.6 W. The power-efficiency and portability of this system is ideal for embedded vision-based devices, such as driver assistance, and robotic vision.
Modeling a biped robot on Matlab/SimMechanics

This paper presents a methodology for modeling a biped robot on Matlab/SimMechanics, which supports mathematical model development with time and effort savings. The model used for the biped robot simulation consists of 5-links which are connected through revolute joins. The identical legs have knee joints between the shank and thigh parts, and a rigid body forms the torso. Furthermore, modeling of ground contact forces is described. A PD controller is used on a linear model in state variable form in order to simulate the dynamic of the system. Results obtained from the dynamic simulation are presented.
Write-Optimized B-Trees

Large writes are beneficial both on individual disks and on disk arrays, e.g., RAID-5. The presented design enables large writes of internal B-tree nodes and leaves. It supports both in-place updates and large append-only (“log-structured”) write operations within the same storage volume, within the same B-tree, and even at the same time. The essence of the proposal is to make page migration inexpensive, to migrate pages while writing them, and to make such migration optional rather than mandatory as in log-structured file systems. The inexpensive page migration also aids traditional defragmentation as well as consolidation of free space needed for future large writes. These advantages are achieved with a very limited modification to conventional B-trees that also simplifies other B-tree operations, e.g., key range locking and compression. Prior proposals and prototypes implemented transacted B-tree on top of log-structured file systems and added transaction support to log-structured file systems. Instead, the presented design adds techniques and performance characteristics of log-structured file systems to traditional B-trees and their standard transaction support, notably without adding a layer of indirection for locating B-tree nodes on disk. The result retains fine-granularity locking, full transactional ACID guarantees, fast search performance, etc. expected of a modern B-tree implementation, yet adds efficient transacted page relocation and large, high-bandwidth writes.
Context-Attentive Embeddings for Improved Sentence Representations

While one of the first steps in many NLP systems is selecting what embeddings to use, we argue that such a step is better left for neural networks to figure out by themselves. To that end, we introduce a novel, straightforward yet highly effective method for combining multiple types of word embeddings in a single model, leading to state-of-the-art performance within the same model class on a variety of tasks. We subsequently show how the technique can be used to shed new insight into the usage of word embeddings in NLP systems.
Induction motor rotor bar damage evaluation with magnetic field analysis

Detection of defects in induction machine rotor bars for unassembled motors is required to evaluate machines considered for repair as well as fulfilling incremental quality assurance checks in the manufacture of new machines. Detection of rotor bar defects prior to motor assembly are critical in increasing repair efficiency and assuring the quality of newly manufactured machines. Many methods of detecting rotor bar defects in unassembled motors lack the sensitivity to find both major and minor defects in both cast and fabricated rotors along with additional deficiencies in quantifiable test results and arc-flash safety hazards. A process of direct magnetic field analysis can examine measurements from induced currents in a rotor separated from its stator yielding a high-resolution fingerprint of a rotor's magnetic field. This process identifies both major and minor rotor bar defects in a repeatable and quantifiable manner appropriate for numerical evaluation without arc-flash safety hazards.
Toward Geometric Deep SLAM

We present a point tracking system powered by two deep convolutional neural networks. The first network, MagicPoint, operates on single images and extracts salient 2D points. The extracted points are “SLAM-ready” because they are by design isolated and well-distributed throughout the image. We compare this network against classical point detectors and discover a significant performance gap in the presence of image noise. As transformation estimation is more simple when the detected points are geometrically stable, we designed a second network, MagicWarp, which operates on pairs of point images (outputs of MagicPoint), and estimates the homography that relates the inputs. This transformation engine differs from traditional approaches because it does not use local point descriptors, only point locations. Both networks are trained with simple synthetic data, alleviating the requirement of expensive external camera ground truthing and advanced graphics rendering pipelines. The system is fast and lean, easily running 30+ FPS on a single CPU.
NoSQL Database Performance Tuning for IoT Data - Cassandra Case Study

Data provided by Internet of Things (IoT) are time series and have some specific characteristics that must be considered with regard to storage and management. IoT data is very likely to be stored in NoSQL system databases where there are some particular engine and compaction strategies to manage time series data. In this article, two of these strategies found in the open source Cassandra database system are described, analyzed and compared. The configuration of these strategies is not trivial and may be very time consuming. To provide indicators, the strategy with the best time performance had its main parameter tested along 14 different values and results are shown, related to both response time and storage space needed. The results may help users to configure their IoT NoSQL databases in an efficient setup, may help designers to improve database compaction strategies or encourage the community to set new default values for the compaction strategies.