nils-herrmann/clean_scirepeval huggingface dataset:
Freedom to roam: a study of mobile device adoption and accessibility for people with visual and motor disabilities

Mobile devices provide people with disabilities new opportunities to act independently in the world. However, these empowering devices have their own accessibility challenges. We present a formative study that examines how people with visual and motor disabilities select, adapt, and use mobile devices in their daily lives. We interviewed 20 participants with visual and motor disabilities and asked about their current use of mobile devices, including how they select them, how they use them while away from home, and how they adapt to accessibility challenges when on the go. Following the interviews, 19 participants completed a diary study in which they recorded their experiences using mobile devices for one week. Our results show that people with visual and motor disabilities use a variety of strategies to adapt inaccessible mobile devices and successfully use them to perform everyday tasks and navigate independently. We provide guidelines for more accessible and empowering mobile device design.
Accurate Deep Representation Quantization with Gradient Snapping Layer for Similarity Search

Recent advance of large scale similarity search involves using deeply learned representations to improve the search accuracy and use vector quantization methods to increase the search speed. However, how to learn deep representations that strongly preserve similarities between data pairs and can be accurately quantized via vector quantization remains a challenging task. Existing methods simply leverage quantization loss and similarity loss, which result in unexpectedly biased back-propagating gradients and affect the search performances. To this end, we propose a novel gradient snapping layer (GSL) to directly regularize the back-propagating gradient towards a neighboring codeword, the generated gradients are un-biased for reducing similarity loss and also propel the learned representations to be accurately quantized. Joint deep representation and vector quantization learning can be easily performed by alternatively optimize the quantization codebook and the deep neural network. The proposed framework is compatible with various existing vector quantization approaches. Experimental results demonstrate that the proposed framework is effective, flexible and outperforms the state-of-the-art large scale similarity search methods.
An OpenCL(TM) Deep Learning Accelerator on Arria 10

Convolutional neural nets (CNNs) have become a practical means to perform vision tasks, particularly in the area of image classification. FPGAs are well known to be able to perform convolutions efficiently, however, most recent efforts to run CNNs on FPGAs have shown limited advantages over other devices such as GPUs. Previous approaches on FPGAs have often been memory bound due to the limited external memory bandwidth on the FPGA device. We show a novel architecture written in OpenCL, which we refer to as a Deep Learning Accelerator (DLA), that maximizes data reuse and minimizes external memory bandwidth. Furthermore, we show how we can use the Winograd transform to significantly boost the performance of the FPGA. As a result, when running our DLA on Intel’s Arria 10 device we can achieve a performance of 1020img/s, or 23img/s/W when running the AlexNet CNN benchmark. This comes to 1382 GFLOPs and is 10x faster with 8.4x more GFLOPS and 5.8x better efficiency than the state-of-the-art on FPGAs. Additionally, 23 img/s/W is competitive against the best publicly known implementation of AlexNet on nVidia’s TitanX GPU.
Multimodal interaction: A review

People naturally interact with the world multimodally, through both parallel and sequential use of multiple perceptual modalities. Multimodal human–computer interaction has sought for decades to endow computers with similar capabilities, in order to provide more natural, powerful, and compelling interactive experiences. With the rapid advance in non-desktop computing generated by powerful mobile devices and affordable sensors in recent years, multimodal research that leverages speech, touch, vision, and gesture is on the rise. This paper provides a brief and personal review of some of the key aspects and issues in multimodal interaction, touching on the history, opportunities, and challenges of the area, especially in the area of multimodal integration. We review the question of early vs. late integration and find inspiration in recent evidence in biological sensory integration. Finally, we list challenges that lie ahead for research in multimodal human–computer interaction. Human interaction with the world is inherently multimodal (Bunt et al., 1998; Quek et al., 2002). We employ multiple senses, both sequentially and in parallel, to passively and actively explore our environment, to confirm expectations about the world and to perceive new information. We experience external stimuli through sight, hearing, touch, and smell, and we sense our internal kines-thetic state through proprioception. A given sensing modality may be used to simultaneously estimate several useful properties of one's environment – for example, audio cues may be used to determine a speaker's identity and location, to recognize the speaker's words and interpret the prosody of the utterance, to estimate the size and other characteristics of the surrounding physical space, and to identify other characteristics of the environment and simultaneous peripheral activities. Multiple sensing modalities give us a wealth of information to support interaction with the world and with one another. In stark contrast to human experience with the natural world, human–computer interaction has historically been focused on uni-modal communication – i.e., information or data communicated between human and computer primarily through a single mode or channel, such as text on a screen with a keyboard for input. While, technically, almost all interaction with computers has been multimodal to some degree – combining typed text with switches, buttons, mouse movement and clicks, and providing various visual and auditory output signals (including unintentional but useful audio cues such as the sound of a hard drive being accessed) – for much of interactive computing's history, the model of a single primary channel for data input, …
Flight Security and Safety of Drones in Airborne Fog Computing Systems

Fog computing is a new paradigm of distributed computing that extends the cloud's capability to the edge of the network. It can provide better quality of service, and is particularly suitable for IoT applications, which generally have stringent requirements on latency and reliability. In this article, we consider the flight security and safety of UAVs, which act as fog nodes in an airborne fog computing system. In particular, we propose a method to detect GPS spoofing based on the monocular camera and IMU sensor of a UAV. We also present an image localization approach to support UAV autonomous return using error reduction based on ORB feature detection and matching. The methods are demonstrated by experiments using a DJI Phantom 4 drone.
Multi-scale brain networks

The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales-of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure-irrespective of species, imaging modality, or spatial resolution.
Modeling Task Complexity in Crowdsourcing

Complexity is crucial to characterize tasks performed by humans through computer systems. Yet, the theory and practice of crowdsourcing currently lacks a clear understanding of task complexity, hindering the design of effective and efficient execution interfaces or fair monetary rewards. To understand how complexity is perceived and distributed over crowdsourcing tasks, we instrumented an experiment where we asked workers to evaluate the complexity of 61 real-world re-instantiated crowdsourcing tasks. We show that task complexity, while being subjective, is coherently perceived across workers; on the other hand, it is significantly influenced by task type. Next, we develop a high-dimensional regression model, to assess the influence of three classes of structural features (metadata, content, and visual) on task complexity, and ultimately use them to measure task complexity. Results show that both the appearance and the language used in task description can accurately predict task complexity. Finally, we apply the same feature set to predict task performance, based on a set of 5 years-worth tasks in Amazon MTurk. Results show that features related to task complexity can improve the quality of task performance prediction, thus demonstrating the utility of complexity as a task modeling property.
Robust Optimization for Deep Regression

Convolutional Neural Networks (ConvNets) have successfully contributed to improve the accuracy of regression-based methods for computer vision tasks such as human pose estimation, landmark localization, and object detection. The network optimization has been usually performed with L2 loss and without considering the impact of outliers on the training process, where an outlier in this context is defined by a sample estimation that lies at an abnormal distance from the other training sample estimations in the objective space. In this work, we propose a regression model with ConvNets that achieves robustness to such outliers by minimizing Tukey's biweight function, an M-estimator robust to outliers, as the loss function for the ConvNet. In addition to the robust loss, we introduce a coarse-to-fine model, which processes input images of progressively higher resolutions for improving the accuracy of the regressed values. In our experiments, we demonstrate faster convergence and better generalization of our robust loss function for the tasks of human pose estimation and age estimation from face images. We also show that the combination of the robust loss function with the coarse-to-fine model produces comparable or better results than current state-of-the-art approaches in four publicly available human pose estimation datasets.
Support vector machine classification and validation of cancer tissue samples using microarray expression data

DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyse this kind of data using support vector machines (SVMs). This analysis consists of both classification of the tissue samples, and an exploration of the data for mis-labeled or questionable tissue results.



We demonstrate the method in detail on samples consisting of ovarian cancer tissues, normal ovarian tissues, and other normal tissues. The dataset consists of expression experiment results for 97,802 cDNAs for each tissue. As a result of computational analysis, a tissue sample is discovered and confirmed to be wrongly labeled. Upon correction of this mistake and the removal of an outlier, perfect classification of tissues is achieved, but not with high confidence. We identify and analyse a subset of genes from the ovarian dataset whose expression is highly differentiated between the types of tissues. To show robustness of the SVM method, two previously published datasets from other types of tissues or cells are analysed. The results are comparable to those previously obtained. We show that other machine learning methods also perform comparably to the SVM on many of those datasets.


AVAILABILITY
The SVM software is available at http://www.cs. columbia.edu/ approximately bgrundy/svm.
A PSO based method for detection of brain tumors from MRI

Detection of brain tumors from MRI is a time consuming and error-prone task. This is due to the diversity in shape, size and appearance of the tumors. In this paper, we propose a clustering algorithm based on Particle Swarm Optimization (PSO). The algorithm finds the centroids of number of clusters, where each cluster groups together brain tumor patterns, obtained from MR Images. The results obtained for three performance measures are compared with those obtained from Support Vector Machine (SVM) and Ada Boost. The performance analysis shows that qualitative results obtained from the proposed model are comparable with those obtained by SVM. However, to obtain better results from the proposed algorithm we need to carefully select the different values of PSO control parameters.
Channel Estimation for OFDM

Orthogonal frequency division multiplexing (OFDM) has been widely adopted in modern wireless communication systems due to its robustness against the frequency selectivity of wireless channels. For coherent detection, channel estimation is essential for receiver design. Channel estimation is also necessary for diversity combining or interference suppression where there are multiple receive antennas. In this paper, we will present a survey on channel estimation for OFDM. This survey will first review traditional channel estimation approaches based on channel frequency response (CFR). Parametric model (PM)-based channel estimation, which is particularly suitable for sparse channels, will be also investigated in this survey. Following the success of turbo codes and low-density parity check (LDPC) codes, iterative processing has been widely adopted in the design of receivers, and iterative channel estimation has received a lot of attention since that time. Iterative channel estimation will be emphasized in this survey as the emerging iterative receiver improves system performance significantly. The combination of multiple-input multiple-output (MIMO) and OFDM has been widely accepted in modern communication systems, and channel estimation in MIMO-OFDM systems will also be addressed in this survey. Open issues and future work are discussed at the end of this paper.
An instance level analysis of data complexity

Most data complexity studies have focused on characterizing the complexity of the entire data set and do not provide information about individual instances. Knowing which instances are misclassified and understanding why they are misclassified and how they contribute to data set complexity can improve the learning process and could guide the future development of learning algorithms and data analysis methods. The goal of this paper is to better understand the data used in machine learning problems by identifying and analyzing the instances that are frequently misclassified by learning algorithms that have shown utility to date and are commonly used in practice. We identify instances that are hard to classify correctly (instance hardness) by classifying over 190,000 instances from 64 data sets with 9 learning algorithms. We then use a set of hardness measures to understand why some instances are harder to classify correctly than others. We find that class overlap is a principal contributor to instance hardness. We seek to integrate this information into the training process to alleviate the effects of class overlap and present ways that instance hardness can be used to improve learning.
An Empirical Study of Learning from Imbalanced Data Using Random Forest

This paper discusses a comprehensive suite of experiments that analyze the performance of the random forest (RF) learner implemented in Weka. RF is a relatively new learner, and to the best of our knowledge, only preliminary experimentation on the construction of random forest classifiers in the context of imbalanced data has been reported in previous work. Therefore, the contribution of this study is to provide an extensive empirical evaluation of RF learners built from imbalanced data. What should be the recommended default number of trees in the ensemble? What should the recommended value be for the number of attributes? How does the RF learner perform on imbalanced data when compared with other commonly-used learners? We address these and other related issues in this work.
Effect of Multilayer Windings With Different Stator Winding Connections on Interior PM Machines for EV Applications

The interior permanent magnet (IPM) machine equipped with a fractional-slot concentrated winding (FSCW) has met an increasing interest in electric vehicle applications due to its higher power density and efficiency. Torque production is due to both PM and reluctance torques. However, one of the main challenges of FSCWs is their inability to produce a high-quality magnetomotive force (MMF) distribution, yielding undesirable rotor core and magnet eddy-current losses. Literature shows that the reduction of low-order space harmonics significantly reduces these loss components. Moreover, it has been previously shown that by employing a higher number of layers, although causing some reduction in the winding factor of the torque-producing MMF component, both machine saliency and reluctance torque components are improved. Recently, a dual three-phase winding connected in a star/delta connection has also shown promise to concurrently enhance machine torque of a surface-mounted PM machine while significantly reducing both rotor core and magnet losses. In this paper, a multilayer winding configuration and a dual three-phase winding connection are combined and applied to the well-known 12-slot/10-pole IPM machine with v-shaped magnets. The proposed winding layout is compared with a conventional double-layer winding, a dual three-phase double-layer winding, and a four-layer winding. The comparison is carried out using 2-D finite-element analysis. The comparison shows that the proposed winding layout, while providing similar output torque to a conventional double-layer three-phase winding, offers a significant reduction in core and magnet losses, correspondingly a higher efficiency, improves the machine saliency ratio, and maximizes the reluctance toque component.
Hierarchical classification: combining Bayes with SVM

We study hierarchical classification in the general case when an instance could belong to more than one class node in the underlying taxonomy. Experiments done in previous work showed that a simple hierarchy of Support Vectors Machines (SVM) with a top-down evaluation scheme has a surprisingly good performance on this kind of task. In this paper, we introduce a refined evaluation scheme which turns the hierarchical SVM classifier into an approximator of the Bayes optimal classifier with respect to a simple stochastic model for the labels. Experiments on synthetic datasets, generated according to this stochastic model, show that our refined algorithm outperforms the simple hierarchical SVM. On real-world data, however, the advantage brought by our approach is a bit less clear. We conjecture this is due to a higher noise rate for the training labels in the low levels of the taxonomy.
THE NEXT PARADIGM SHIFT : FROM VEHICULAR NETWORKS TO VEHICULAR CLOUDS

The past decade has witnessed a growing interest in vehicular networking and its vast array of potential applications. Increased wireless accessibility of the Internet from vehicles has triggered the emergence of vehicular safety applications, locationspecific applications, and multimedia applications. Recently, Professor Olariu and his coworkers have promoted the vision of Vehicular Clouds (VCs), a non-trivial extension, along several dimensions, of conventional Cloud Computing. In a VC, the under-utilized vehicular resources including computing power, storage and Internet connectivity can be shared between drivers or rented out over the Internet to various customers, very much as conventional cloud resources are. The goal of this chapter is to introduce and review the challenges and opportunities offered by what promises to be the Next Paradigm Shift:From Vehicular Networks to Vehicular Clouds. Specifically, the chapter introduces VCs and discusses some of their distinguishing characteristics and a number of fundamental research challenges. To illustrate the huge array of possible applications of the powerful VC concept, a number of possible application scenarios are presented and discussed. As the adoption and success of the vehicular cloud concept is inextricably related to security and privacy issues, a number of security and privacy issues specific to vehicular clouds are discussed as well. Additionally, data aggregation and empirical results are presented. Mobile Ad Hoc Networking: Cutting Edge Directions, Second Edition. Edited by Stefano Basagni, Marco Conti, Silvia Giordano, and Ivan Stojmenovic.
Benchmarking Denoising Algorithms with Real Photographs

Lacking realistic ground truth data, image denoising techniques are traditionally evaluated on images corrupted by synthesized i.i.d. Gaussian noise. We aim to obviate this unrealistic setting by developing a methodology for benchmarking denoising techniques on real photographs. We capture pairs of images with different ISO values and appropriately adjusted exposure times, where the nearly noise-free low-ISO image serves as reference. To derive the ground truth, careful post-processing is needed. We correct spatial misalignment, cope with inaccuracies in the exposure parameters through a linear intensity transform based on a novel heteroscedastic Tobit regression model, and remove residual low-frequency bias that stems, e.g., from minor illumination changes. We then capture a novel benchmark dataset, the Darmstadt Noise Dataset (DND), with consumer cameras of differing sensor sizes. One interesting finding is that various recent techniques that perform well on synthetic noise are clearly outperformed by BM3D on photographs with real noise. Our benchmark delineates realistic evaluation scenarios that deviate strongly from those commonly used in the scientific literature.
Benefits of multisensory learning

Studies of learning, and in particular perceptual learning, have focused on learning of stimuli consisting of a single sensory modality. However, our experience in the world involves constant multisensory stimulation. For instance, visual and auditory information are integrated in performing many tasks that involve localizing and tracking moving objects. Therefore, it is likely that the human brain has evolved to develop, learn and operate optimally in multisensory environments. We suggest that training protocols that employ unisensory stimulus regimes do not engage multisensory learning mechanisms and, therefore, might not be optimal for learning. However, multisensory-training protocols can better approximate natural settings and are more effective for learning.