nils-herrmann/clean_scirepeval huggingface dataset:
A multi-objective auto-tuning framework for parallel codes

In this paper we introduce a multi-objective auto-tuning framework comprising compiler and runtime components. Focusing on individual code regions, our compiler uses a novel search technique to compute a set of optimal solutions, which are encoded into a multi-versioned executable. This enables the runtime system to choose specifically tuned code versions when dynamically adjusting to changing circumstances.
 We demonstrate our method by tuning loop tiling in cache-sensitive parallel programs, optimizing for both runtime and efficiency. Our static optimizer finds solutions matching or surpassing those determined by exhaustively sampling the search space on a regular grid, while using less than 4% of the computational effort on average. Additionally, we show that parallelism-aware multi-versioning approaches like our own gain a performance improvement of up to 70% over solutions tuned for only one specific number of threads.
ViP-CNN: Visual Phrase Guided Convolutional Neural Network

As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers attention because of its descriptive power and clear structure. It detects the objects and captures their pair-wise interactions with a subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each visual relationship is considered as a phrase with three components. We formulate the visual relationship detection as three inter-connected recognition problems and propose a Visual Phrase guided Convolutional Neural Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a Phrase-guided Message Passing Structure (PMPS) to establish the connection among relationship components and help the model consider the three problems jointly. Corresponding non-maximum suppression method and model training strategy are also proposed. Experimental results show that our ViP-CNN outperforms the state-of-art method both in speed and accuracy. We further pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is found to perform better than the pretraining on the ImageNet for this task.
High Current Ripple for Power Density and Efficiency Improvement in Wide Bandgap Transistor-Based Buck Converters

With the recent development and availability of wide bandgap devices in the market, more and more power converters are being designed with such devices. Given their fast commutation, when compared to their equivalent Si-based counterparts, these new devices allow increasing the converter's efficiency and/or power density. However, in order to fully avail these new devices, one should precisely know their switching characteristics and exploit it the best way possible. This paper recalls our own precise method to measure separately turn-on and turn-off energies of wide bandgap devices. This method is applied to commercially available SiC and GaN transistors and results show that they present much lower turn-off than turn-on energies. For that reason, we show that a SiC-based buck converter must have high current ripple in the output filter inductor in order to decrease transistor losses. Analysis of these losses as well as experimental results are presented. Finally, the precise design of a 2-kW SiC-based buck converter for aircraft applications is performed for different current ripples and switching frequencies. We show that current ripple higher than 250% of the dc load current significantly decreases the converter's losses, and consequently allows the increase of the switching frequency, which reduces the system volume and weight.
Deep learning with convolutional neural networks for EEG decoding and visualization

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping.
Mobile cloud computing: A survey

Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, frequent disconnections, and mobility. Mobile cloud computing can address these problems by executing mobile applications on resource providers external to the mobile device. In this paper, we provide an extensive survey of mobile cloud computing research, while highlighting the specific concerns in mobile cloud computing. We present a taxonomy based on the key issues in this area, and discuss the different approaches taken to tackle these issues. We conclude the paper with a critical analysis of challenges that have not yet been fully met, and highlight directions for
Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey

This paper surveys the current research directions of activity recognition using inertial sensors, with potential application in healthcare, wellbeing and sports. The analysis of related work is organized according to the five main steps involved in the activity recognition process: preprocessing, segmentation, feature extraction, dimensionality reduction and classification. For each step, we present the main techniques utilized, their advantages and drawbacks, performance metrics and usage examples. We also discuss the research challenges, such as user behavior and technical limitations, as well as the remaining open research questions.
Natural language processing: an introduction

To provide an overview and tutorial of natural language processing (NLP) and modern NLP-system design.



This tutorial targets the medical informatics generalist who has limited acquaintance with the principles behind NLP and/or limited knowledge of the current state of the art.



We describe the historical evolution of NLP, and summarize common NLP sub-problems in this extensive field. We then provide a synopsis of selected highlights of medical NLP efforts. After providing a brief description of common machine-learning approaches that are being used for diverse NLP sub-problems, we discuss how modern NLP architectures are designed, with a summary of the Apache Foundation's Unstructured Information Management Architecture. We finally consider possible future directions for NLP, and reflect on the possible impact of IBM Watson on the medical field.
Making It Simplext: Implementation and Evaluation of a Text Simplification System for Spanish

The way in which a text is written can be a barrier for many people. Automatic text simplification is a natural language processing technology that, when mature, could be used to produce texts that are adapted to the specific needs of particular users. Most research in the area of automatic text simplification has dealt with the English language. In this article, we present results from the Simplext project, which is dedicated to automatic text simplification for Spanish. We present a modular system with dedicated procedures for syntactic and lexical simplification that are grounded on the analysis of a corpus manually simplified for people with special needs. We carried out an automatic evaluation of the system’s output, taking into account the interaction between three different modules dedicated to different simplification aspects. One evaluation is based on readability metrics for Spanish and shows that the system is able to reduce the lexical and syntactic complexity of the texts. We also show, by means of a human evaluation, that sentence meaning is preserved in most cases. Our results, even if our work represents the first automatic text simplification system for Spanish that addresses different linguistic aspects, are comparable to the state of the art in English Automatic Text Simplification.
Using a Distributed Representation of Words in Localizing Relevant Files for Bug Reports

Once a bug in software is reported, developers have to determine which source files are related to the bug. This process is referred to as bug localization, and an automatic way of bug localization is important to improve developers' productivity. This paper proposes an approach called DrewBL to efficiently localize faulty files for a given bug report using a natural language processing tool, word2vec. In DrewBL, we first build a vector space model named semantic-VSM which represents a distributed representation of words in the bug report and source code files and next compute the relevance between them by feeding the constructed model to word2vec. We also present an approach called CombBL to further improve the accuracy of bug localization which employs not only the proposed DrewBL but also existing bug localization techniques, such as BugLocator based on textual similarity and Bugspots based on bug-fixing history, in a combinational manner. This paper gives our early experimental results to show the effectiveness and efficiency of the proposed approaches using two open source projects.
Min(e)d your tags: Analysis of Question response time in StackOverflow

Given a newly posted question on a Question and Answer (Q&A) site, how long will it take until an answer is received? Does response time relate to factors about how the question asker composes their question? If so, what are those factors? With advances in social media and the Web, Q&A sites have become a major source of information for Internet users. Response time of a question is an important aspect in these sites as it is associated with the users' satisfaction and engagement, and thus the lifespan of these online communities. In this paper we study and estimate response time for questions in StackOverflow, a popular online Q&A forum where software developers post and answer questions related to programming. We analyze a long list of factors in the data and identify those that have clear relation with response time. Our key finding is that tag-related factors, such as their “popularity” (how often the tag is used) and the number of their “subscribers” (how many users can answer questions containing the tag), provide much stronger evidence than factors not related to tags. Finally, we learn models using the identified evidential features for predicting the response time of questions, which also demonstrate the significance of tags chosen by the question asker.
Active re-configurable multibeam reflector antenna for satellite application

Recent results on active multifeed Ka-band developments for satellite communication are presented. Basically, the active multifeed system is an array-fed, single-offset reflector antenna. The antenna array is fed by an active microstrip beamforming network based on a low temperature cofired ceramics substrate with monolithic microwave integrated circuit based phase and amplitude actuators. In this paper, the benefits of such a technology are shown as well as first project results.
A floating-gate analog memory with bidirectional sigmoid updates in a standard digital process

A floating-gate current-output analog memory is implemented in a 0.13-μm digital CMOS process. The proposed memory cell achieves random-accessible and bidirectional updates with a sigmoid update rule. A novel writing scheme is proposed to obtain tunneling selectivity without on-chip highvoltage switches or charge pumps, and reduces interconnections and pin count. Parameters of empirical models for floating gate charge modification are extracted from measurements. Measurement and simulation results show that the proposed memory consumes 45 nW of power, has a 7-bit programming resolution, 53.8 dB dynamic range and 86.5 dB writing isolation.
Conversational Recommendation System with Unsupervised Learning

We will demonstrate a conversational products recommendation agent. This system shows how we combine research in personalized recommendation systems with research in dialogue systems to build a virtual sales agent. Based on new deep learning technologies we developed, the virtual agent is capable of learning how to interact with users, how to answer user questions, what is the next question to ask, and what to recommend when chatting with a human user. Normally a descent conversational agent for a particular domain requires tens of thousands of hand labeled conversational data or hand written rules. This is a major barrier when launching a conversation agent for a new domain. We will explore and demonstrate the effectiveness of the learning solution even when there is no hand written rules or hand labeled training data.
Barrage Relay Networks

A receiver-oriented perspective on capacity scaling in mobile ad hoc networks (MANETs) suggests that broadcast and multicast may be more natural traffic models for these systems than the random unicast pairs typically considered. Furthermore, traffic loads for the most promising near-term application for MANET technology — namely, networking at the tactical edge — are largely broadcast. The development of novel MANET approaches targeting broadcast first and foremost, however, has not been reported. Instead, existing system designs largely rely on fundamentally link-based, layered architectures, which are best suited to unicast traffic. In response to the demands of tactical edge communications, TrellisWare Technologies, Inc. developed a MANET system based on Barrage Relay Networks (BRNs). BRNs utilize an autonomous cooperative communication scheme that eliminates the need for link-level collision avoidance. The fundamental physical layer resource in BRNs is not a link, but a portion in space and time of a cooperative, multihop transport fabric. While initial hardware prototypes of BRNs were being refined into products by TrellisWare, a number of concepts similar to those that underlie BRNs were reported independently in the literature. That TrellisWare's tactical edge MANET system design and academic research reconsidering the standard networking approach for MANETs arrived at similar design concepts lends credence to the value of these emerging wireless network approaches.
Structured Prediction of Unobserved Voxels from a Single Depth Image

Building a complete 3D model of a scene, given only a single depth image, is underconstrained. To gain a full volumetric model, one needs either multiple views, or a single view together with a library of unambiguous 3D models that will fit the shape of each individual object in the scene. We hypothesize that objects of dissimilar semantic classes often share similar 3D shape components, enabling a limited dataset to model the shape of a wide range of objects, and hence estimate their hidden geometry. Exploring this hypothesis, we propose an algorithm that can complete the unobserved geometry of tabletop-sized objects, based on a supervised model trained on already available volumetric elements. Our model maps from a local observation in a single depth image to an estimate of the surface shape in the surrounding neighborhood. We validate our approach both qualitatively and quantitatively on a range of indoor object collections and challenging real scenes.
Adaptive UWB-OFDM Synthetic Aperture Radar

This paper presents an empirical study of high resolution potentially jamming-resistant Synthetic Aperture Radar (SAR) system based on UWB-OFDM architecture. The Range-Doppler Algorithm (RDA) was used to construct a high resolution SAR image for simulation purpose. Suitable waveforms to achieve high resolution SAR imaging are proposed for both friendly and hostile environments. The proposed waveforms were tested for a point scatterer and for full 2-D SAR image construction. Simulation results demonstrate the benefits of using UWB-OFDM waveforms for SAR system, such as dynamic spectrum allocation, antijamming potential through pulse diversity-while having a potential to produce high resolution target images. The use of UWB-OFDM waveform for SAR system proves useful for high resolution image data collection for civilian purposes and provides significant anti-jamming capabilities for military purposes.
The haptic display of complex graphical environments

Force feedback coupled with visual display allows people to interact intuitively with complex virtual environments. For this synergy of haptics and graphics to flourish, however, haptic systems must be capable of modeling environments with the same richness, complexity and interactivity that can be found in existing graphic systems. To help meet this challenge, we have developed a haptic rendering system that allows f r the efficient tactile display of graphical information. The system uses a common high-level framework to model contact constraints, surface shading, friction and tex ture. The multilevel control system also helps ensure that the haptic device will remain stable even as the limits of the renderer’s capabilities are reached.
A Comparison of Face-based and Interaction-based Affect Detectors in Physics Playground

Increased attention to the relationships between affect and learning has led to the development of machine-learned models that are able to identify students’ affective states in computerized learning environments. Data for these affect detectors have been collected from multiple modalities including physical sensors, dialogue logs, and logs of students’ interactions with the learning environment. While researchers have successfully developed detectors based on each of these sources, little work has been done to compare the performance of these detectors. In this paper, we address this issue by comparing interaction-based and video-based affect detectors for a physics game called Physics Playground. Specifically, we report on the development and detection accuracy of two suites of affect and behavioral detectors. The first suite of detectors applies facial expression recognition to video data collected with webcams, while the second focuses on students’ interactions with the game as recorded in log-files. Ground–truth affect and behavior annotations for both faceand interactionbased detectors were obtained via live field observations during game-play. We first compare the performance of these detectors in predicting students’ affective states and off-task behaviors, and then proceed to outline the strengths and weakness of each approach.
Visual odometry

We present a system that estimates the motion of a stereo head or a single moving camera based on video input. The system operates in real-time with low delay and the motion estimates are used for navigational purposes. The front end of the system is a feature tracker. Point features are matched between pairs of frames and linked into image trajectories at video rate. Robust estimates of the camera motion are then produced from the feature tracks using a geometric hypothesize-and-test architecture. This generates what we call visual odometry, i.e. motion estimates from visual input alone. No prior knowledge of the scene nor the motion is necessary. The visual odometry can also be used in conjunction with information from other sources such as GPS, inertia sensors, wheel encoders, etc. The pose estimation method has been applied successfully to video from aerial, automotive and handheld platforms. We focus on results with an autonomous ground vehicle. We give examples of camera trajectories estimated purely from images over previously unseen distances and periods of time.
A spotlight on security and privacy risks with future household robots: attacks and lessons

Future homes will be populated with large numbers of robots with diverse functionalities, ranging from chore robots to elder care robots to entertainment robots. While household robots will offer numerous benefits, they also have the potential to introduce new security and privacy vulnerabilities into the home. Our research consists of three parts. First, to serve as a foundation for our study, we experimentally analyze three of today's household robots for security and privacy vulnerabilities: the WowWee Rovio, the Erector Spykee, and the WowWee RoboSapien V2. Second, we synthesize the results of our experimental analyses and identify key lessons and challenges for securing future household robots. Finally, we use our experiments and lessons learned to construct a set of design questions aimed at facilitating the future development of household robots that are secure and preserve their users' privacy.
Design Tradeoffs Between Stator Core Loss and Torque Ripple in IPM Machines

High stator core losses can pose a significant problem in interior permanent-magnet (IPM) machines operating over wide constant-power speed ranges. At lower speeds, the torque ripple can be undesirably large in some IPM machine designs, contributing to acoustic noise and vibration. While previous work has addressed these two problems independently, this paper shows that the conditions for reducing stator core losses during flux-weakening operation, dominated by harmonic eddy-current losses in the stator teeth, can conflict with the conditions for reducing the torque ripple of IPM machines. It is also shown that the resulting design tradeoffs depend on the details of the IPM machine topologies that are under consideration. The appropriate IPM machine topologies that offer more favorable tradeoffs are identified to achieve the best possible compromise of high-speed stator core losses and torque ripple characteristics.