nils-herrmann/clean_scirepeval huggingface dataset:
PWM AC chopper control of single-phase induction motor for variable-speed fan application

This work presents a variable speed control method for fan applications. A pulse-width-modulated (PWM) AC chopper changes the effective value of the supply voltage applied to a single-phase induction motor. This variable supply voltage gives the ability to control the speed of the motor. Harmonics generated by the speed control unit are filtered by an input filter according to EN 61000-3-2 limits for harmonic current emissions standards. Experimental results on a 230 V, 210 W in-line centrifugal fan for exhaust ventilation application shows that PWM AC chopper is simple and cost effective control method compared to other methods in terms of simplicity and input harmonic content.
A Method for Enforcing Integrability in Shape from Shading Algorithms

Several recently developed techniques for reconstructing surface shape from shading information estimate surface slopes without ensuring that they a re integrable. This paper presents a n approach for enforcing integrability, a particular implementation of the approach, a n example of its application to extending a n existing shapefrom-shading algorithm, and experimental results showing the improvement that results from enforcing integrability. A possibly nonintegrable estimate of surface slopes is represented by a finite set of basis functions, and integrability is enforced by calculating the orthogonal projection onto a vector subspace spanning the set of integrable slopes. This projection maps closed convex sets into closed convex sets and, hence, is attractive as a constraint in iterative algorithms. The same technique is also useful for noniterative algorithms since it provides a least-squares fit of integrable slopes to nonintegrable slopes in one pass of the algorithm. The special case of Fourier basis functions is also formulated. This provides an intuitive frequency domain interpretation of shape from shading, a computationally efficient implementation using fast Fourier transforms, and a convenient method for introducing low-resolution information into the shape-from-shading solution. Reconstruction of surface height by integrating surface slope estimates is obtained as a byproduct of the integrability constraint. The integrability projection constraint was applied to extending a n iterative shape-from-shading algorithm of Brooks and Horn. Experimental results show that the extended algorithm converges faster and with less e r ror than the original version. Good surface reconstructions were obtained with and without known boundary conditions and for fairly complicated surfaces. Simulation examples show that the algorithm is robust with respect to large (but known) changes in illumination geometry, obtaining high-quality reconstructions even in the presence of significant shadowing. Other possible applications of this method to computer vision problems such as shape from texture and surface reconstruction from synthetic aperture radar imagery a re discussed.
Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data

The two main functions of bioinformatics are the organization and analysis of biological data using computational resources. Geneious Basic has been designed to be an easy-to-use and flexible desktop software application framework for the organization and analysis of biological data, with a focus on molecular sequences and related data types. It integrates numerous industry-standard discovery analysis tools, with interactive visualizations to generate publication-ready images. One key contribution to researchers in the life sciences is the Geneious public application programming interface (API) that affords the ability to leverage the existing framework of the Geneious Basic software platform for virtually unlimited extension and customization. The result is an increase in the speed and quality of development of computation tools for the life sciences, due to the functionality and graphical user interface available to the developer through the public API. Geneious Basic represents an ideal platform for the bioinformatics community to leverage existing components and to integrate their own specific requirements for the discovery, analysis and visualization of biological data.



Binaries and public API freely available for download at http://www.geneious.com/basic, implemented in Java and supported on Linux, Apple OSX and MS Windows. The software is also available from the Bio-Linux package repository at http://nebc.nerc.ac.uk/news/geneiousonbl.
Study of a bus-based disruption-tolerant network: mobility modeling and impact on routing

We study traces taken from UMass DieselNet, a Disruption-Tolerant Network consisting of WiFi nodes attached to buses. As buses travel their routes, they encounter other buses and in some cases are able to establish pair-wise connections and transfer data between them. We analyze the bus-to-bus contact traces to characterize the contact process between buses and its impact on DTN routing performance. We find that the all-bus-pairs aggregated inter-contact times show no discernible pattern. However, the inter-contact times aggregated at a route level exhibit periodic behavior.Based on analysis of the deterministic inter-meeting times for bus pairs running on route pairs, and consideration of the variability in bus movement and the random failures to establish connections, we construct generative route-level models that capture the above behavior. Through trace-driven simulations of epidemic routing, we find that the epidemic performance predicted by traces generated with this finer-grained route-level model is much closer to the actual performance that would be realized in the operational system than traces generated using the coarse-grained all-bus-pairs aggregated model. This suggests the importance in choosing the rightlevel of model granularity when modelingmobility-related measures such as inter-contact times in DTNs.
SELF-SERV: A Platform for Rapid Composition of Web Services in a Peer-to-Peer Environment

The automation of Web services interoperation is gaining a considerable momentum as a paradigm for effective Business-to-Business collaboration . Established enterprises are continuously discovering new opportunities to form alliances with other enterprises, by offering value-added integrated services. However, the technology to compose Web services in appropriate time-frames has not kept pace with the rapid growth and volatility of available opportunities. Indeed, the development of integrated Web services is often ad-hoc and requires a considerable effort of lowlevel programming. This approach is inadequate given the size and the volatility of the Web. Furthermore, the number of services to be integrated may be large, so that approaches where the development of an integrated service requires the understanding of each of the underlying services are inappropriate. In addition, Web services may need to be composed as part of a short term partnership, and disbanded when the partnership is no longer profitable. Hence, the integration of a large number of Web services requires scalable and flexible techniques, such as those based on declarative languages. Also, the execution of an integrated service in existing approaches is usually centralised, whereas the underlying services are distributed and autonomous. This calls for the investigation of distributed execution paradigms (e.g., peer-to-peer models), that do not suffer of the scalability and availability problems of centralised coordination . Motivated by these concerns, we have developed the SELF-SERV platform for rapid composition of Web
Taint-based directed whitebox fuzzing

We present a new automated white box fuzzing technique and a tool, BuzzFuzz, that implements this technique. Unlike standard fuzzing techniques, which randomly change parts of the input file with little or no information about the underlying syntactic structure of the file, BuzzFuzz uses dynamic taint tracing to automatically locate regions of original seed input files that influence values used at key program attack points (points where the program may contain an error). BuzzFuzz then automatically generates new fuzzed test input files by fuzzing these identified regions of the original seed input files. Because these new test files typically preserve the underlying syntactic structure of the original seed input files, they tend to make it past the initial input parsing components to exercise code deep within the semantic core of the computation. We have used BuzzFuzz to automatically find errors in two open-source applications: Swfdec (an Adobe Flash player) and MuPDF (a PDF viewer). Our results indicate that our new directed fuzzing technique can effectively expose errors located deep within large programs. Because the directed fuzzing technique uses taint to automatically discover and exploit information about the input file format, it is especially appropriate for testing programs that have complex, highly structured input file formats.
Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning

A major challenge for video captioning is to combine audio and visual cues. Existing multi-modal fusion methods have shown encouraging results in video understanding. However, the temporal structures of multiple modalities at different granularities are rarely explored, and how to selectively fuse the multi-modal representations at different levels of details remains uncharted. In this paper, we propose a novel hierarchically aligned cross-modal attention (HACA) framework to learn and selectively fuse both global and local temporal dynamics of different modalities. Furthermore, for the first time, we validate the superior performance of the deep audio features on the video captioning task. Finally, our HACA model significantly outperforms the previous best systems and achieves new state-of-the-art results on the widely used MSR-VTT dataset.
Test Results and Torque Improvement of the 50-kW Switched Reluctance Motor Designed for Hybrid Electric Vehicles

A switched reluctance motor (SRM) has been developed as one of the possible candidates of rare-earth-free electric motors. A prototype machine has been built and tested. It has competitive dimensions, torque, power, and efficiency with respect to the 50-kW interior permanent magnet synchronous motor employed in the hybrid electric vehicles (Toyota Prius 2003). It is found that competitive power of 50-kW rating and efficiency of 95% are achieved. The prototype motor provided 85% of the target torque. Except the maximum torque, the most speed-torque region is found to be covered by the test SRM. The cause of discrepancy in the measured and calculated torque values is examined. An improved design is attempted, and a new experimental switched reluctance machine is designed and built for testing. The results are given in this paper.
Successful practices in customer relationship management

Many companies have initiated projects to improve on customer orientation and plan the implementation of customer relationship management (CRM) systems. Among the desired benefits are increased customer satisfaction and retention by providing personalized products and value added services. Although the potentials of CRM are obvious only a few successful CRM implementations are known in practice. This article describes the results of a cross-industry benchmarking project in which 120 companies participated. The results show that there is no 'unique' CRM project and that successful implementations are rarely technical projects. From the research six critical success factors for CRM projects emerged: step-wise evolution, straightforward implementation and long-term project, organizational redesign, integrated system architecture of standard components, change management, and top management support. The six successful practice companies show examples of how these critical success factors are applied.
Assessment of computer science learning in a scratch-based outreach program

Many institutions have created and deployed outreach programs for middle school students with the goal of increasing the number and diversity of students who later pursue careers in computer science. While these programs have been shown to increase interest in computer science, there has been less work on showing whether participants learn computer science content.
 We address two questions, one specific, and the other more general: (1) "What computer science did our middle school students learn in our interdisciplinary two-week summer camp?" (2) "How can computer science concepts be assessed in the context of Scratch-based outreach programs"? We address both questions by presenting the design of our summer camp, an overview of our curriculum, our assessment methodology, and our assessment results.
 Though the sample size is not statistically significant, the results show that a two-week, interdisciplinary, non-academic summer camp can be effective not only for engaging students, but also for imparting CS content. In just two weeks, with a curriculum not entirely focused on computer science, students displayed competence with event-driven programming, initialization of state, message passing, and say/sound synchronization. We have employed assessment methodologies that avoid written exams, an approach both outreach and classroom-based programs may find useful.
The role of emotion in moral psychology

Recent work in the cognitive and neurobiological sciences indicates an important relationship between emotion and moral judgment. Based on this evidence, several researchers have argued that emotions are the source of our intuitive moral judgments. However, despite the richness of the correlational data between emotion and morality, we argue that the current neurological, behavioral, developmental and evolutionary evidence is insufficient to demonstrate that emotion is necessary for making moral judgments. We suggest instead, that the source of moral judgments lies in our causal-intentional psychology; emotion often follows from these judgments, serving a primary role in motivating morally relevant action.
Movement segmentation using a primitive library

Segmenting complex movements into a sequence of primitives remains a difficult problem with many applications in the robotics and vision communities. In this work, we show how the movement segmentation problem can be reduced to a sequential movement recognition problem. To this end, we reformulate the original Dynamic Movement Primitive (DMP) formulation as a linear dynamical system with control inputs. Based on this new formulation, we develop an Expectation-Maximization algorithm to estimate the duration and goal position of a partially observed trajectory. With the help of this algorithm and the assumption that a library of movement primitives is present, we present a movement segmentation framework. We illustrate the usefulness of the new DMP formulation on the two applications of online movement recognition and movement segmentation.
Imitation, mirror neurons and autism

Various deficits in the cognitive functioning of people with autism have been documented in recent years but these provide only partial explanations for the condition. We focus instead on an imitative disturbance involving difficulties both in copying actions and in inhibiting more stereotyped mimicking, such as echolalia. A candidate for the neural basis of this disturbance may be found in a recently discovered class of neurons in frontal cortex, 'mirror neurons' (MNs). These neurons show activity in relation both to specific actions performed by self and matching actions performed by others, providing a potential bridge between minds. MN systems exist in primates without imitative and 'theory of mind' abilities and we suggest that in order for them to have become utilized to perform social cognitive functions, sophisticated cortical neuronal systems have evolved in which MNs function as key elements. Early developmental failures of MN systems are likely to result in a consequent cascade of developmental impairments characterised by the clinical syndrome of autism.
VibViz: Organizing, visualizing and navigating vibration libraries

With haptics now common in consumer devices, diversity in tactile perception and aesthetic preferences confound haptic designers. End-user customization out of example sets is an obvious solution, but haptic collections are notoriously difficult to explore. This work addresses the provision of easy and highly navigable access to large, diverse sets of vibrotactile stimuli, on the premise that multiple access pathways facilitate discovery and engagement. We propose and examine five disparate organization schemes (taxonomies), describe how we created a 120-item library with diverse functional and affective characteristics, and present VibViz, an interactive tool for end-user library navigation and our own investigation of how different taxonomies can assist navigation. An exploratory user study with and of VibViz suggests that most users gravitate towards an organization based on sensory and emotional terms, but also exposes rich variations in their navigation patterns and insights into the basis of effective haptic library navigation.
Integrated Dual-Channel X-Band Offset-Transmitter for Phase Steering and DDMA Arrays

Multiple-Input and Multiple-Output (MIMO) arrays provide more degrees of freedom than conventional phased arrays. They require that every transmitting element of the array can be identified when received. One way to achieve this is to give every element its own unique frequency. Offset-transmitters may be used to introduce MIMO or Doppler Division Multiple Access (DDMA) into phased-arrays without an excessive increase in waveform-generating hardware. Our dual-channel demonstrator IC can obtain a phase accuracy better than 1 degree and an spurious level of better than -65dBc for a single on-chip channel. This work investigates at X-band the effects of the limited on-chip isolation of 35dB, when multiple offset outputs are generated on a single chip for both beam steering and DDMA. In case of beam steering, the requirements on channel-to-channel isolation are less strict, making it well within reach. In the case of DDMA, we recommend increasing the channel-to-channel isolation by implementing multiple chips, in which case independent signals can be generated.
Measuring emotional intelligence with the MSCEIT V2.0.

Does a recently introduced ability scale adequately measure emotional intelligence (EI) skills? Using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; J. D. Mayer, P. Salovey, & D. R. Caruso, 2002b), the authors examined (a) whether members of a general standardization sample and emotions experts identified the same test answers as correct, (b) the test's reliability, and (c) the possible factor structures of EI. Twenty-one emotions experts endorsed many of the same answers, as did 2,112 members of the standardization sample, and exhibited superior agreement, particularly when research provides clearer answers to test questions (e.g., emotional perception in faces). The MSCEIT achieved reasonable reliability, and confirmatory factor analysis supported theoretical models of EI. These findings help clarify issues raised in earlier articles published in Emotion.
A Condensation Approach to Privacy Preserving Data Mining

In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many cases, users are unwilling to provide personal information unless the privacy of sensitive information is guaranteed. In this paper, we propose a new framework for privacy preserving data mining of multi-dimensional data. Previous work for privacy preserving data mining uses a perturbation approach which reconstructs data distributions in order to perform the mining. Such an approach treats each dimension independently and therefore ignores the correlations between the different dimensions. In addition, it requires the development of a new distribution based algorithm for each data mining problem, since it does not use the multi-dimensional records, but uses aggregate distributions of the data as input. This leads to a fundamental re-design of data mining algorithms. In this paper, we will develop a new and flexible approach for privacy preserving data mining which does not require new problem-specific algorithms, since it maps the original data set into a new anonymized data set. This anonymized data closely matches the characteristics of the original data including the correlations among the different dimensions. We present empirical results illustrating the effectiveness of the method.
Soft start-up for high frequency LLC resonant converter with optimal trajectory control

This paper investigates the soft start-up for high frequency LLC resonant converter with optimal trajectory control. Two methods are proposed to realize soft start-up for high frequency LLC converter by commercial low-cost microcontrollers (MCU). Both methods can achieve soft start-up with minimum stress and optimal energy delivery. One method is mixed-signal implementation by sensing resonant tank to minimize the digital delay. Another method is digital implementation with look-up table. Experimental results are demonstrated on 500kHz 1kW 400V/12V LLC converter.
Revisiting virtual memory for high performance computing on manycore architectures: a hybrid segmentation kernel approach

Page-based memory management (paging) is utilized by most of the current operating systems (OSs) due to its rich features such as prevention of memory fragmentation and fine-grained access control. Page-based virtual memory, however, stores virtual to physical mappings in page tables that also reside in main memory. Because translating virtual to physical addresses requires walking the page tables, which in turn implies additional memory accesses, modern CPUs employ translation lookaside buffers (TLBs) to cache the mappings. Nevertheless, TLBs are limited in size and applications that consume a large amount of memory and exhibit little or no locality in their memory access patterns, such as graph algorithms, suffer from the high overhead of TLB misses.
 This paper proposes a new hybrid kernel design targeting many-core CPUs, which manages the application's memory space by segmentation and offloads kernel services to dedicated CPU cores where paging is utilized. The method enables applications to run on top of the low-cost segmented memory management while allows the kernel to use the rich features of paging. We present the design and implementation of our kernel and demonstrate that segmentation can provide superior performance compared to both regular and large page based virtual memory. For example, running Graph500 on top of our segmentation design over Intel's Xeon Phi chip can yield up to 81% and 9% improvement compared to utilizing 4kB and 2MB pages in MPSS Linux, respectively.
Multimodal Deep Learning

Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train a deep network that learns features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. We validate our methods on the CUAVE and AVLetters datasets with an audio-visual speech classification task, demonstrating superior visual speech classification on AVLetters and effective multimodal fusion.