nils-herrmann/clean_scirepeval huggingface dataset:
Voice activity detection with noise reduction and long-term spectral divergence estimation

The paper mainly focusses on an improved voice activity detection algorithm employing long-term signal processing and maximum spectral component tracking. The benefits of this approach have been analyzed in a previous work (Ramirez, J. et al.,  2003) with clear improvements in speech/non-speech discriminability and speech recognition performance in noisy environments. Two clear aspects are now considered. The first one, which improves the performance of the VAD in low noise conditions, considers an adaptive length frame window to track the long-term spectral components. The second one reduces misclassification errors in highly noisy environments by using a noise reduction stage before the long-term spectral tracking. Experimental results show clear improvements over different VAD methods in speech/pause discrimination and speech recognition performance. Particularly, improvements in recognition rate were reported when the proposed VAD replaced the VADs of the ETSI advanced front-end (AFE) for distributed speech recognition (DSR).
Deep Learning with Energy-efficient Binary Gradient Cameras

Power consumption is a critical factor for the deployment of embedded computer vision systems. We explore the use of computational cameras that directly output binary gradient images to reduce the portion of the power consumption allocated to image sensing. We survey the accuracy of binary gradient cameras on a number of computer vision tasks using deep learning. These include object recognition, head pose regression, face detection, and gesture recognition. We show that, for certain applications, accuracy can be on par or even better than what can be achieved on traditional images. We are also the first to recover intensity information from binary spatial gradient images—useful for applications with a human observer in the loop, such as surveillance. Our results, which we validate with a prototype binary gradient camera, point to the potential of gradient-based computer vision systems.
Multiple objective optimization in recommender systems

We address the problem of optimizing recommender systems for multiple relevance objectives that are not necessarily aligned. Specifically, given a recommender system that optimizes for one aspect of relevance, semantic matching (as defined by any notion of similarity between source and target of recommendation; usually trained on CTR), we want to enhance the system with additional relevance signals that will increase the utility of the recommender system, but that may simultaneously sacrifice the quality of the semantic match. The issue is that semantic matching is only one relevance aspect of the utility function that drives the recommender system, albeit a significant aspect. In talent recommendation systems, job posters want candidates who are a good match to the job posted, but also prefer those candidates to be open to new opportunities. Recommender systems that recommend discussion groups must ensure that the groups are relevant to the users' interests, but also need to favor active groups over inactive ones. We refer to these additional relevance signals (job-seeking intent and group activity) as extraneous features, and they account for aspects of the utility function that are not captured by the semantic match (i.e. post-CTR down-stream utilities that reflect engagement: time spent reading, sharing, commenting, etc). We want to include these extraneous features into the recommendations, but we want to do so while satisfying the following requirements: 1) we do not want to drastically sacrifice the quality of the semantic match, and 2) we want to quantify exactly how the semantic match would be affected as we control the different aspects of the utility function. In this paper, we present an approach that satisfies these requirements.
 We frame our approach as a general constrained optimization problem and suggest ways in which it can be solved efficiently by drawing from recent research on optimizing non-smooth rank metrics for information retrieval. Our approach features the following characteristics: 1) it is model and feature agnostic, 2) it does not require additional labeled training data to be collected, and 3) it can be easily incorporated into an existing model as an additional stage in the computation pipeline. We validate our approach in a revenue-generating recommender system that ranks billions of candidate recommendations on a daily basis and show that a significant improvement in the utility of the recommender system can be achieved with an acceptable and predictable degradation in the semantic match quality of the recommendations.
A 40-Gb/s Optical Transceiver Front-End in 45 nm SOI CMOS

A low-power, 40-Gb/s optical transceiver front-end is demonstrated in a 45-nm silicon-on-insulator (SOI) CMOS process. Both single-ended and differential optical modulators are demonstrated with floating-body transistors to reach output swings of more than 2 VPP and 4 VPP, respectively. A single-ended gain of 7.6 dB is measured over 33 GHz. The optical receiver consists of a transimpedance amplifier (TIA) and post-amplifier with 55 dB ·Ω of transimpedance over 30 GHz. The group-delay variation is ±3.9 ps over the 3-dB bandwidth and the average input-referred noise density is 20.5 pA/(√Hz) . The TIA consumes 9 mW from a 1-V supply for a transimpedance figure of merit of 1875 Ω /pJ. This represents the lowest power consumption for a transmitter and receiver operating at 40 Gb/s in a CMOS process.
Compact dual band three way bagley polygon power divider using composite right/left handed (CRLH) transmission lines

A compact dual band three way power divider based on the Bagley Polygon has been implemented using composite right/left handed (CRLH) transmission lines consisting of microstrip lines and lumped elements for the GSM frequencies of 860MHz and 1.92GHz. Also, a dual band power divider consisting of conventional quarter wavelength (λ/4) transmission lines with shunt connections of open and short stubs has been implemented for comparison. An advantage of using the Bagley Polygon for three way power divider is that it allows an arbitrary phase selection at the third port by using single or dual band, CRLH or conventional transmission lines. The CRLH based power divider shows an area of 7.95 cm2 and a bandwidth of approximately 8% at both bands while the comparison structure shows an area of 51.98 cm2, a bandwidth of 5.8% at 860MHz, and 2.6% at 1.92GHz. Less than 5.5dB insertion loss has been achieved in both cases. Also, full wave structure simulation has been performed and the simulation results agree well with the measurement results.
Time Series Forecasting Using Neural Networks

Artificial neural networks are suitable for many tasks in pattern recognition and machine learning. In this paper we present an APL system for forecasting univariate time series with artificial neural networks. Unlike conventional techniques for time series analysis, an artificial neural network needs little information about the time series data and can be applied to a broad range of problems. However, the problem of network “tuning” remains: parameters of the backpropagation algorithm as well as the network topology need to be adjusted for optimal performances. For our application, we conducted experiments to find the right parameters for a forecasting network. The artificial neural networks that were found delivered a better forecasting performance than results obtained by the well known ARIMA technique.
Fault diagnosis of hydraulic pump based on stacked autoencoders

The fault diagnosis of hydraulic pump is always a challenging issue in the field of machinery fault diagnosis. Not only is manual feature extraction time-consuming and laborious, but also the diagnostic result is easily affected by subjective experience. The stacked autoencoders (SAE) which has powerful learning and representation ability is applied in hydraulic pump fault diagnosis, and it is directly used for training and identification based on vibration signals, so that we don't need to extract features manually. To avoid gradient vanishing and to improve the performance for small training set, ReLU activation function and Dropout strategy are both introduced into SAE. Validated by experiments, proposed SAE is superior to the BP, SVM and traditional SAE. And it can recognize hydraulic pump condition accurately even if the training set is small, which satisfies the engineering application.
Knowledge Graph Representation with Jointly Structural and Textual Encoding

The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts well. In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities. Specifically, we introduce three neural models to encode the valuable information from text description of entity, among which an attentive model can select related information as needed. Then, a gating mechanism is applied to integrate representations of structure and text into a unified architecture. Experiments show that our models outperform baseline by margin on link prediction and triplet classification tasks. Source codes of this paper will be available on Github.
Human genetics and disease: Mechanisms and consequences of somatic mosaicism in humans

Somatic mosaicism — the presence of genetically distinct populations of somatic cells in a given organism — is frequently masked, but it can also result in major phenotypic changes and reveal the expression of otherwise lethal genetic mutations. Mosaicism can be caused by DNA mutations, epigenetic alterations of DNA, chromosomal abnormalities and the spontaneous reversion of inherited mutations. In this review, we discuss the human disorders that result from somatic mosaicism, as well as the molecular genetic mechanisms by which they arise. Specifically, we emphasize the role of selection in the phenotypic manifestations of mosaicism.
Monitoring vital signs using early warning scoring systems: a review of the literature.

To evaluate the need for, and the development and utility of, pen-and-paper (Modified) Early Warning Scoring (MEWS/EWS) systems for adult inpatients outside critical care and emergency departments, by reviewing published literature.



Serious adverse events can be prevented by recognizing and responding to early signs of clinical and physiological deterioration.



Of 534 papers reporting MEWS/EWS systems for adult inpatients identified, 14 contained useable data on development and utility of MEWS/EWS systems. Systems without aggregate weighted scores were excluded.



MEWS/EWS systems facilitate recognition of abnormal physiological parameters in deteriorating patients, but have limitations. There is no single validated scoring tool across diagnoses. Evidence of prospective validation of MEWS/EWS systems is limited; neither is implementation based on clinical trials. There is no evidence that implementation of Westernized MEWS/EWS systems is appropriate in resource-poor locations.



Better monitoring implies better care, but there is a paucity of data on the validation, implementation, evaluation and clinical testing of vital signs' monitoring systems in general wards.



Recording vital signs is not enough. Patient safety continues to depend on nurses' clinical judgment of deterioration. Resources are needed to validate and evaluate MEWS/EWS systems in context.
Corpus-independent Generic Keyphrase Extraction Using Word Embedding Vectors

Keyphrase extraction from a given document is a difficult task that requires not only local statistical information but also extensive background knowledge. In this paper, we propose a graph-based ranking approach that uses information supplied by word embedding vectors as the background knowledge. We first introduce a weighting scheme that computes informativeness and phraseness scores of words using the information supplied by both word embedding vectors and local statistics. Keyphrase extraction is performed by constructing a weighted undirected graph for a document, where nodes represent words and edges are co-occurrence relations of two words within a defined window size. The weights of edges are computed by the afore-mentioned weighting scheme, and a weighted PageRank algorithm is used to compute final scores of words. Keyphrases are formed in post-processing stage using heuristics. Our work is evaluated on various publicly available datasets with documents of varying length. We show that evaluation results are comparable to the state-of-the-art algorithms, which are often typically tuned to a specific corpus to achieve the claimed results.
Defining Consumer Satisfaction

Joan L. Giese, Assistant Professor, Department of Marketing, Washington State University, Pullman, WA 99164-4730, (509)3356354, (509)335-3865 (fax), giesej@wsu.edu. Joseph A. Cote, Professor, Department of Marketing, Washington State University, Vancouver, WA 98686-9600, (360)546-9753, cote@vancouver.wsu.edu. Direct correspondence to Joan Giese. The authors would like to extend a special thank you to Robert Peterson (who served as editor for this paper) for his helpful comments in revising this manuscript.
A personalized recommender system for the cosmetic business

In order to have an effective command of the relationship between customers and products, we have constructed a personalized recommender system which incorporates content-based, collaborative filtering, and data mining techniques. We have also introduced a new scoring approach to determine customers’ interest scores on products. To demonstrate how our system works, we used it to analyze real cosmetic data and generate a recommender score table for sellers to refer to. After tracking its performance for 1 year, we have obtained quite impressive results.
Parameter design of voltage balancing circuit for series connected HV-IGBTs

Applying power semiconductor devices in series connection is a direct and effective scheme to improve the voltage rating and power rating of power electronic converter. The key issue of device series connection is voltage balancing in static switching state and dynamic switching state. Active clamping circuit samples overvoltage on insulated gate bipolar transistor (IGBT), injects current into the gate and thus could increase the gate voltage and suppress the overvoltage effectively. In this paper, a voltage balancing circuit for high voltage IGBT (HV-IGBT) series connection is presented. The voltage balancing circuit is composed of static state voltage balancing sub-circuit across collector and emitter, dynamic state voltage balancing sub-circuit across collector and emitter and active clamping sub-circuit across collector and gate. The functions and principles of the three sub-circuits are then described. Besides the topology, the parameters of the voltage balancing circuit influence the voltage balance of series connected HV-IGBTs seriously. Based on quantitatively analyzing the relationships among the parameters of HV-IGBTs, the parameters of voltage balancing circuit and the voltage balancing indexes of the series connected devices, a parameter design method for the presented voltage balancing circuit is proposed. This parameter design method comprehensively considers the switching loss of HV-IGBT, the loss of voltage balancing circuit, the electrical stress on HV-IGBT and the switching frequency in order to guarantee the efficiency, reliability and performance of the system. The proposed parameter design method is applied to the development process of a series connection circuit with Infineon 6500V/600A HV-IGBTs. Experimental results verify the validity and feasibility of the proposed method.
Effectiveness of virtual reality-based pain control with multiple treatments.

The current study explored whether immersive virtual reality continues to reduce pain (via distraction) with repeated use.



The study was conducted in a burn care unit at a regional trauma center.



Seven patients aged 9-32 years (mean age of 21.9 years; average of 23.7% total body surface area burned [range, 3-60%]) performed range-of-motion exercises of their injured extremity under an occupational therapist's direction on at least 3 separate days each.



For each physical therapy session, each patient spent equal amounts of time in virtual reality and in the control condition (no distraction). The mean duration of physical therapy in virtual reality was 3.5, 4.9, and 6.4 minutes for the first, second, and third session, respectively. Condition order was randomized and counter-balanced.



For each of the three physical therapy sessions, five visual analog pain scores for each treatment condition served as the dependent variables.



Pain ratings were statistically lower when patients were in virtual reality, and the magnitude of pain reduction did not diminish with repeated use of virtual reality. The results of this study may be examined in more detail at www.vrpain.com.



Although the small sample size limits generalizability. results provide converging preliminary evidence that virtual reality can function as a strong nonpharmacological pain reduction technique for burn patients during physical therapy. Results suggest that virtual reality does not diminish in analgesic effectiveness with three (and possibly more) uses. Virtual reality may also have analgesic potential for other painful procedures or pain populations. Practical implications are discussed.
RECOVERING INTRINSIC SCENE CHARACTERISTICS FROM IMAGES

We suggest that an appropriate role of early visual processing is to describe a scene in terms of intrinsic (vertical) characteristics-such as range, orientation, reflectance, and incident illumination-of the surface element visible at each point in the image. Support for this idea comes from three sources: the obvious utility of intrinsic characteristics for higher-level scene analysis; the apparent ability of humans to determine these characteristics, regardless of viewing conditions or familiarity with the scene; and a theoretical argument that such a description is obtainable, by a noncognitive and nonpurposive process, at least, for simple scene domains. The central problem in recovering intrinsic scene characteristics is that the information is confounded in the original light-intensity image: a single intensity value encodes all the characteristics of the corresponding scene point. Recovery depends on exploiting constraints, derived from assumptions about the nature of the scene and the physics of the imaging process.  Despite corsiderable progress in recent years, our understanding of the principles underlying visual perception remains primitive. Attempts to construct computer models for the interpretation of arbitrary scenes have resulted in such poor performance, limited range of abilities, and inflexibility that, were it not for the human existence proof, we might have been tempted long ago to conclude that high-performance, general-purpose vision is impossible. On the other hand, attempts to unravel the mystery of human vision, have resulted in a limited understanding of the elementary neurophysiology, and a wealth of phenomenological observations of the total system, but not, as yet, in a cohesive model of how the system functions. The time is right for those in both fields to take a broader view: those in computer vision might do well to look harder at the phenomenology of human vision for clues that might indicate fundamental inadequacies of current aproaches; these concerned with human vision might gain insights by thinking more about what information is sought, and how it might be obtained, from a computational point of view. This position has been strongly advocated for some time by Horn [18-20] and Marr [26-29] at MIT. Current scene analysis systems often use pictorial features, such as regions of uniform intensity, or step charges in intensity, as an initial level of description and then jump directly to descriptions at the level of complete objects. The limitations of this approach are well known : first, region-growing and edge-finding programs are unreliable in extracting the …
Ultra-Wideband Printed Slot Antenna With Graded Index Superstrate

An ultra-wideband (UWB) compact slot antenna with a directional radiation pattern is presented in this communication. The concept is based on dielectric loaded multi-element slot antennas. Wide bandwidth operation is achieved using a driven wide slot antenna fed via an off-centered microstrip line capable of creating a fictitious short along the slot and a number of parasitic antenna elements. The proposed slot antenna uses a graded index superstrate with tapered dielectric constants, from high index to low index , in order to further improve the bandwidth and achieve directional radiation pattern. The superstrate dimensions are carefully chosen so that a dielectric resonator mode is excited to provide radiation at the lowest frequency. A sensitivity study is carried out to optimize the geometric parameters of the slot antennas and the graded index superstrate in order to achieve the maximum bandwidth as well as an unidirectional and frequency invariant radiation pattern. Through this optimization, a compact antenna is designed, fabricated, and tested to show a VSWR value of lower than 2.5 across a 2.9:1 frequency range whose dimensions are 0.27 λ×0.2 λ×0.068 λ at the lowest frequency of operation.
How gamification can influence the web design and the customer to use the e-banking systems

The gamification is growing in e-business and the banks are looking for new ways to get more customers on their websites. Therefore, it is important to study what are the most appreciated features of the website that could influence the behaviour of the customer to use an electronic banking system with game features. The gamified e-banking suggests that rich elements/features associated with the games could influence other variables and therefore increasing the client loyalty, to spend more time and increasing the transactions on the website. The aim of this study is to look into the influence of gamification in the e-banking system. Based on the research of 180 publications and 210 variables that could influence the intention to use a certain technology this study develops a theoretical model representing the gamification influence on ease of use, information, web pages characteristics, web design and on the intention to use an e-banking with game features. The results from an online survey of 219 e-banking customers show that the gamification had a positive impact on all variables; special has a medium positive influence in web design and information and a large positive influence on customer intentions to use. Further analysis shows that the website ease of use plays has also a medium positive influence on the intention to use an e-banking gamified. Our findings also show that the clients give more importance to an attractive graphical and architecture website design, and less to web pages with so much information or having pleasure in using an e-banking system.