nils-herrmann/clean_scirepeval huggingface dataset:
Statistical Voice Conversion Techniques for Body-Conducted Unvoiced Speech Enhancement

In this paper, we present statistical approaches to enhance body-conducted unvoiced speech for silent speech communication. A body-conductive microphone called nonaudible murmur (NAM) microphone is effectively used to detect very soft unvoiced speech such as NAM or a whispered voice while keeping speech sounds emitted outside almost inaudible. However, body-conducted unvoiced speech is difficult to use in human-to-human speech communication because it sounds unnatural and less intelligible owing to the acoustic change caused by body conduction. To address this issue, voice conversion (VC) methods from NAM to normal speech (NAM-to-Speech) and to a whispered voice (NAM-to-Whisper) are proposed, where the acoustic features of body-conducted unvoiced speech are converted into those of natural voices in a probabilistic manner using Gaussian mixture models (GMMs). Moreover, these methods are extended to convert not only NAM but also a body-conducted whispered voice (BCW) as another type of body-conducted unvoiced speech. Several experimental evaluations are conducted to demonstrate the effectiveness of the proposed methods. The experimental results show that 1) NAM-to-Speech effectively improves intelligibility but it causes degradation of naturalness owing to the difficulty of estimating natural fundamental frequency contours from unvoiced speech; 2) NAM-to-Whisper significantly outperforms NAM-to-Speech in terms of both intelligibility and naturalness; and 3) a single conversion model capable of converting both NAM and BCW is effectively developed in our proposed VC methods.
On the Use of Client Identity Information for Face Antispoofing

With biometrics playing the role of a password which cannot be replaced if stolen, the necessity of establishing counter-measures to biometric spoofing attacks has been recognized. Regardless of the biometric mode, the typical approach of antispoofing systems is to classify the biometric evidence based on features discriminating between real accesses and spoofing attacks. For the first time, to the best of our knowledge, this paper studies the amount of client-specific information within these features and how it affects the performance of antispoofing systems. We make use of this information to build two client-specific antispoofing solutions, one relying on a generative and another one on a discriminative paradigm. The proposed methods, tested on a set of state-of-the-art antispoofing features for the face mode, outperform the client-independent approaches with up to 50% relative improvement and exhibit better generalization capabilities on unseen types of spoofing attacks.
EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks

Sensorimotor-based brain-computer interfaces (BCIs) have achieved successful control of real and virtual devices in up to three dimensions; however, the traditional sensor-based paradigm limits the intuitive use of these systems. Many control signals for state-of-the-art BCIs involve imagining the movement of body parts that have little to do with the output command, revealing a cognitive disconnection between the user's intent and the action of the end effector. Therefore, there is a need to develop techniques that can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device.  We extend previous EEG source imaging (ESI) work to decoding natural hand/wrist manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation.  We report an increase of up to 18.6% for individual task classification and 12.7% for overall classification using the proposed ESI approach over the traditional sensor-based method.  ESI is able to enhance BCI performance of decoding complex right-hand motor imagery tasks.  This study may lead to the development of BCI systems with naturalistic and intuitive motor imaginations, thus facilitating broad use of noninvasive BCIs.
A Profile-Based Approach to Just-in-Time Scalability for Cloud Applications

Cloud platforms offer resource utilization as on demand service, which lays the foundation for applications to scale during runtime. However, just-in-time scalability is not achieved by simply deploying applications to cloud platforms. Existing approaches require developers to rewrite their applications to leverage the on-demand resource utilization, thus bind applications to specific cloud infrastructure. In this paper, profiles are used to capture experts’ knowledge of scaling different types of applications. The profile-based approach automates the deployment and scaling of applications in cloud. Just-in-time scalability is achieved without binding to specific cloud infrastructure. A real case is used to demonstrate the process and feasibility of this profile-based approach.
Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble

This short paper reports the method and the evaluation results of Casio and Shinshu University joint team for the ISBI Challenge 2017 – Skin Lesion Analysis Towards Melanoma Detection – Part 3: Lesion Classification hosted by ISIC. Our online validation score was 0.958 with melanoma classifier AUC 0.924 and seborrheic keratosis classifier AUC 0.993.
Affective state recognition from EEG with deep belief networks

With the ultimate intent of improving the quality of life, identification of human's affective states on the collected electroencephalogram (EEG) has attracted lots of attention recently. In this domain, the existing methods usually use only a few labeled samples to classify affective states consisting of over thousands of features. Therefore, important information may not be well utilized and performance is lowered due to the randomness caused by the small sample problem. However, this issue has rarely been discussed in the previous studies. Besides, many EEG channels are irrelevant to the specific learning tasks, which introduce lots of noise to the systems and further lower the performance in the recognition of affective states. To address these two challenges, in this paper, we propose a novel Deep Belief Networks (DBN) based model for affective state recognition from EEG signals. Specifically, signals from each EEG channel are firstly processed with a DBN for effectively extracting critical information from the over thousands of features. The extracted low dimensional characteristics are then utilized in the learning to avoid the small sample problem. For the noisy channel problem, a novel stimulus-response model is proposed. The optimal channel set is obtained according to the response rate of each channel. Finally, a supervised Restricted Boltzmann Machine (RBM) is applied on the combined low dimensional characteristics from the optimal EEG channels. To evaluate the performance of the proposed Supervised DBN based Affective State Recognition (SDA) model, we implement it on the Deap Dataset and compare it with five baselines. Extensive experimental results show that the proposed algorithm can successfully handle the aforementioned two challenges and significantly outperform the baselines by 11.5% to 24.4%, which validates the effectiveness of the proposed algorithm in the task of affective state recognition.
Performance of Biometric Quality Measures

We document methods for the quantitative evaluation of systems that produce a scalar summary of a biometric sample's quality. We are motivated by a need to test claims that quality measures are predictive of matching performance. We regard a quality measurement algorithm as a black box that converts an input sample to an output scalar. We evaluate it by quantifying the association between those values and observed matching results. We advance detection error trade-off and error versus reject characteristics as metrics for the comparative evaluation of sample quality measurement algorithms. We proceed this with a definition of sample quality, a description of the operational use of quality measures. We emphasize the performance goal by including a procedure for annotating the samples of a reference corpus with quality values derived from empirical recognition scores
30.10 A 1TOPS/W analog deep machine-learning engine with floating-gate storage in 0.13μm CMOS

Direct processing of raw high-dimensional data such as images and video by machine learning systems is impractical both due to prohibitive power consumption and the “curse of dimensionality,” which makes learning tasks exponentially more difficult as dimension increases. Deep machine learning (DML) mimics the hierarchical presentation of information in the human brain to achieve robust automated feature extraction, reducing the dimension of such data. However, the computational complexity of DML systems limits large-scale implementations in standard digital computers. Custom analog or mixed-mode signal processors have been reported to yield much higher energy efficiency than DSP [1-4], presenting the means of overcoming these limitations. However, the use of volatile digital memory in [1-3] precludes their use in intermittently-powered devices, and the required interfacing and internal A/D/A conversions add power and area overhead. Nonvolatile storage is employed in , but the lack of learning capability requires task-specific programming before operation, and precludes online adaptation.
The Capability Approach : a theoretical survey

This paper aims to present a theoretical survey of the capability approach in an interdisciplinary and accessible way. It focuses on the main conceptual and theoretical aspects of the capability approach, as developed by Amartya Sen, Martha Nussbaum, and others. The capability approach is a broad normative framework for the evaluation and assessment of individual well-being and social arrangements, the design of policies, and proposals about social change in society. Its main characteristics are its highly interdisciplinary character, and the focus on the plural or multidimensional aspects of well-being. The approach highlights the difference between means and ends, and between substantive freedoms (capabilities) and outcomes (achieved functionings).
Fast and Robust Archetypal Analysis for Representation Learning

We revisit a pioneer unsupervised learning technique called archetypal analysis,  which is related to successful data analysis methods such as sparse coding [18] and non-negative matrix factorization [19]. Since it was proposed, archetypal analysis did not gain a lot of popularity even though it produces more interpretable models than other alternatives. Because no efficient implementation has ever been made publicly available, its application to important scientific problems may have been severely limited. Our goal is to bring back into favour archetypal analysis. We propose a fast optimization scheme using an active-set strategy, and provide an efficient open-source implementation interfaced with Matlab, R, and Python. Then, we demonstrate the usefulness of archetypal analysis for computer vision tasks, such as codebook learning, signal classification, and large image collection visualization.
Improvement of power supply rejection ratio of LDO deteriorated by reducing power consumption

In this work, the bulk-gate controlled circuit to improve the power supply ripple ratio (PSRR) of a Low Dropout Regulator (LDO) which deteriorates due to lowering power consumption is proposed. Designing with 0.25 mum CMOS process, the simulation results by HSPICE shown that the proposed circuit provides a high performance of PSRR even though 1/10 of the power consumption is reduced compare to the conventional circuit. It is confirmed that about 40[dB] at 10[Hz] frequency and 20[dB] at 1[kHz] frequency of PSRR are together improved.
Current and projected rates of hip fracture in Canada.

To determine the current values and estimate the projected values (to the year 2041) for annual number of proximal femoral fractures (PFFs), age-adjusted rates of fracture, rates of death in the acute care setting, associated length of stay (LOS) in hospital, and seasonal variation by sex and age in elderly Canadians.



Hospital discharge data for fiscal year 1993-94 from the Canadian Institute for Health Information were used to determine PFF incidence, and Statistics Canada population projections were used to estimate the rate and number of PFFs to 2041.



Canada.



Canadian patients 65 years of age or older who underwent hip arthroplasty.



PFF rates, death rates and LOS by age, sex and province.



In 1993-94 the incidence of PFF increased exponentially with increasing age. The age-adjusted rates were 479 per 100,000 for women and 187 per 100,000 for men. The number of PFFs was estimated at 23,375 (17,823 in women and 5552 in men), with a projected increase to 88,124 in 2041. The rate of death during the acute care stay increased exponentially with increasing age. The death rates for men were twice those for women. In 1993-94 an estimated 1570 deaths occurred in the acute care setting, and 7000 deaths were projected for 2041. LOS in the acute care setting increased with advancing age, as did variability in LOS, which suggests a more heterogeneous case mix with advancing age. The LOS for 1993-94 and 2041 was estimated at 465,000 and 1.8 million patient-days respectively. Seasonal variability in the incidence of PFFs by sex was not significant. Significant season-province interactions were seen (p < 0.05); however, the differences in incidence were small (on the order of 2% to 3%) and were not considered to have a large effect on resource use in the acute care setting.



On the assumption that current conditions contributing to hip fractures will remain constant, the number of PFFs will rise exponentially over the next 40 years. The results of this study highlight the serious implications for Canadians if incidence rates are not reduced by some form of intervention.
SubwayPS: towards smartphone positioning in underground public transportation systems

Thanks to rapid advances in technologies like GPS and Wi-Fi positioning, smartphone users are able to determine their location almost everywhere they go. This is not true, however, of people who are traveling in underground public transportation networks, one of the few types of high-traffic areas where smartphones do not have access to accurate position information. In this paper, we introduce the problem of underground transport positioning on smartphones and present SubwayPS, an accelerometer-based positioning technique that allows smartphones to determine their location substantially better than baseline approaches, even deep beneath city streets. We highlight several immediate applications of positioning in subway networks in domains ranging from mobile advertising to mobile maps and present MetroNavigator, a proof-of-concept smartphone and smartwatch app that notifies users of upcoming points-of-interest and alerts them when it is time to get ready to exit the train.
Effective leadership and decision-making in animal groups on the move

For animals that forage or travel in groups, making movement decisions often depends on social interactions among group members. However, in many cases, few individuals have pertinent information, such as knowledge about the location of a food source, or of a migration route. Using a simple model we show how information can be transferred within groups both without signalling and when group members do not know which individuals, if any, have information. We reveal that the larger the group the smaller the proportion of informed individuals needed to guide the group, and that only a very small proportion of informed individuals is required to achieve great accuracy. We also demonstrate how groups can make consensus decisions, even though informed individuals do not know whether they are in a majority or minority, how the quality of their information compares with that of others, or even whether there are any other informed individuals. Our model provides new insights into the mechanisms of effective leadership and decision-making in biological systems.
Design patterns for safety-critical embedded systems

Over the last few years, embedded systems have been increasingly used in safetycritical applications where failure can have serious consequences. The design of these systems is a complex process, which is requiring the integration of common design methods both in hardware and software to fulfill functional and non-functional requirements for these safety-critical applications. Design patterns, which give abstract solutions to commonly recurring design problems, have been widely used in the software and hardware domain. In this thesis, the concept of design patterns is adopted in the design of safetycritical embedded system. A catalog of design patterns was constructed to support the design of safety-critical embedded systems. This catalog includes a set of hardware and software design patterns which cover common design problems such as handling of random and systematic faults, safety monitoring, and sequence control. Furthermore, the catalog provides a decision support component that supports the decision process of choosing a suitable pattern for a particular problem based on the available resources and the requirements of the applicable patterns. As non-functional requirements are an important aspect in the design of safety-critical embedded systems, this work focuses on the integration of implications on non-functional properties in the existing design pattern concept. A pattern representation is proposed for safety-critical embedded application design methods by including fields for the implications and side effects of the represented design pattern on the non-functional requirements of the systems. The considered requirements include safety, reliability, modifiability, cost, and execution time. Safety and reliability represent the main non-functional requirements that should be provided in the design of safety-critical applications. Thus, reliability and safety assessment methods are proposed to show the relative safety and reliability improvement which can be achieved when using the design patterns under consideration. Moreover, a Monte Carlo based simulation method is used to illustrate the proposed assessment method which allows comparing different design patterns with respect to their impact on safety and reliability.
Tutelage and Collaboration for Humanoid Robots

This paper presents an overview of our work towards building socially intelligent, cooperative humanoid robots that can work and learn in partnership with people. People understand each other in social terms, allowing them to engage others in a variety of complex social interactions including communication, social learning, and cooperation. We present our theoretical framework that is a novel combination of Joint Intention Theory and Situated Learning Theory and demonstrate how this framework can be applied to develop our sociable humanoid robot, Leonardo. We demonstrate the robot’s ability to learn quickly and effectively from natural human instruction using gesture and dialog, and then cooperate to perform a learned task jointly with a person. Such issues must be addressed to enable many new and exciting applications for robots that require them to play a long-term role in people’s daily lives.
Object Skeleton Extraction in Natural Images by Fusing Scale-Associated Deep Side Outputs

Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts. While object skeleton extraction in natural images is a very challenging problem, as it requires the extractor to be able to capture both local and global image context to determine the intrinsic scale of each skeleton pixel. Existing methods rely on per-pixel based multi-scale feature computation, which results in difficult modeling and high time consumption. In this paper, we present a fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the sequential stages in the network and the skeleton scales they can capture, we introduce a scale-associated side output to each stage. We impose supervision to different stages by guiding the scale-associated side outputs toward groundtruth skeletons of different scales. The responses of the multiple scaleassociated side outputs are then fused in a scale-specific way to localize skeleton pixels with multiple scales effectively. Our method achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors.
Image partitioning into convex polygons

The over-segmentation of images into atomic regions has become a standard and powerful tool in Vision. Traditional superpixel methods, that operate at the pixel level, cannot directly capture the geometric information disseminated into the images. We propose an alternative to these methods by operating at the level of geometric shapes. Our algorithm partitions images into convex polygons. It presents several interesting properties in terms of geometric guarantees, region compactness and scalability. The overall strategy consists in building a Voronoi diagram that conforms to preliminarily detected line-segments, before homogenizing the partition by spatial point process distributed over the image gradient. Our method is particularly adapted to images with strong geometric signatures, typically man-made objects and environments. We show the potential of our approach with experiments on large-scale images and comparisons with state-of-the-art superpixel methods.
Sensitivity Analysis for Direction of Arrival Estimation using a Root-MUSIC Algorithm

advantage of array antenna structures to better process the incoming signals. They also have the ability to identify multiple targets. This paper explores the eigen-analysis category of super resolution algorithm. A class of Multiple Signal Classification (MUSIC) algorithms known as a root-MUSIC algorithm is presented in this paper. The root-MUSIC method is based on the eigenvectors of the sensor array correlation matrix. It obtains the signal estimation by examining the roots of the spectrum polynomial. The peaks in the spectrum space correspond to the roots of the polynomial lying close to the unit circle. Statistical analysis of the performance of the processing algorithm and processing resource requirements are discussed in this paper. Extensive computer simulations are used to show the performance of the algorithms.
ProvDB: Lifecycle Management of Collaborative Analysis Workflows

As data-driven methods are becoming pervasive in a wide variety of disciplines, there is an urgent need to develop scalable and sustainable tools to simplify the process of data science, to make it easier for the users to keep track of the analyses being performed and datasets being generated, and to enable the users to understand and analyze the workflows. In this paper, we describe our vision of a unified provenance and metadata management system to support lifecycle management of complex collaborative data science workflows. We argue that the information about the analysis processes and data artifacts can, and should be, captured in a semi-passive manner; and we show that querying and analyzing this information can not only simplify bookkeeping and debugging tasks but also enable a rich new set of capabilities like identifying flaws in the data science process itself. It can also significantly reduce the user time spent in fixing post-deployment problems through automated analysis and monitoring. We have implemented a prototype system, PROVDB, on top of git and Neo4j, and we describe its key features and capabilities.