nils-herrmann/clean_scirepeval huggingface dataset:
Macro process of knowledge management for continuous innovation

The purpose of this research is to understand the mechanisms of knowledge management (KM) for innovation and provide an approach for enterprises to leverage KM activities into continuous innovation.  By reviewing the literature from multidisciplinary fields, the concepts of knowledge, KM and innovation are investigated. The physical, human and technological perspectives of KM are distinguished with the identification of two core activities for innovation: knowledge creation and knowledge usage. Then an essential requirement for continuous innovation –an internalization phase is defined. The systems thinking and human-centred perspectives are adopted for providing a comprehensive understanding about the mechanisms of KM for innovation.  A networking process of continuous innovation based on KM is proposed by incorporating the phase of internalization. According to the three perspectives of KM, three sources of organizational knowledge assets in innovation are identified. Then based on the two core activities of innovation, a meta-model and a macro process of KM are proposed to model the mechanisms of KM for continuous innovation. Then, in order to operationalize the KM mechanisms, a hierarchical model is constructed by integrating three sources of knowledge assets, the meta-model and the macro process into the process of continuous innovation. This model decomposes the complex relationships between knowledge and innovation into four layers.  According to the lessons learned about KM practices in previous research, the three perspectives of KM should collaborate with each other for successful implementation of KM projects for innovation; and the hierarchical model provides a suitable architecture to implement systems of KM for innovation.  The meta-model and macro process of KM explain how the next generation of KM can help the value creation and support the continuous innovation from the systems thinking perspective. The hierarchical model illustrates the complicate knowledge dynamics in the process of continuous innovation.
A Literature Review and Classification of Recommender Systems on Academic Journals

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid‐1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors “Recommender system”, “Recommendation system”, “Personalization system”, “Collaborative filtering” and “
Vaginal Labiaplasty: Defense of the Simple “Clip and Snip” and a New Classification System

Vaginal labiaplasty has become a more frequently performed procedure as a result of the publicity and education possible with the internet. Some of our patients have suffered in silence for years with large, protruding labia minora and the tissue above the clitoris that is disfiguring and uncomfortable and makes intercourse very difficult and painful. We propose four classes of labia protrusion based on size and location: Class 1 is normal, where the labia majora and minora are about equal. Class 2 is the protrusion of the minora beyond the majora. Class 3 includes a clitoral hood. Class 4 is where the large labia minora extends to the perineum. There are two principal means of reconstructing this area. Simple amputation may be possible for Class 2 and Class 4. Class 2 and Class 3 may be treated with a wedge resection and flap advancement that preserves the delicate free edge of the labia minora (Alter, ). Class 4 may require a combination of both amputation of the clitoral hood and/or perineal extensions and rotation flap advancement over the labia minora. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Be Quiet? Evaluating Proactive and Reactive User Interface Assistants

This research examined the ability of an anthropomorphic interface assistant to help people learn and use an unfamiliar text-editing tool, with a specific focus on assessing proactive assistant behavior. Participants in the study were introduced to a text editing system that used keypress combinations for invoking the different editing operations. Participants then were directed to make a set of prescribed changes to a document with the aid either of a paper manual, an interface assistant that would hear and respond to questions orally, or an assistant that responded to questions and additionally made proactive suggestions. Anecdotal evidence suggested that proactive assistant behavior would not enhance performance and would be viewed as intrusive. Our results showed that all three conditions performed similarly on objective editing performance (completion time, commands issued, and command recall), while the participants in the latter two conditions strongly felt that the assistant’s help was valuable.
A Role for Chordless Cycles in the Representation and Retrieval of Information

This paper explains how very large network structures can be reduced, or consolidated, to an assemblage of chordless cycles (cyclic structures without cross connecting links), that is called a trace, for storage and later retreival. After developing a basic mathematical framework, it illustrates the reduction process using a most general (with directed and undirected links) network. A major theme of the paper is that this approach appears to model actual biological memory, as well as offering an attractive digital solution.
Robust Nighttime Vehicle Detection by Tracking and Grouping Headlights

Nighttime traffic surveillance is difficult due to insufficient and unstable appearance information and strong background interference. We present in this paper a robust nighttime vehicle detection system by detecting, tracking, and grouping headlights. First, we train AdaBoost classifiers for headlights detection to reduce false alarms caused by reflections. Second, to take full advantage of the complementary nature of grouping and tracking, we alternately optimize grouping and tracking. For grouping, motion features produced by tracking are used by headlights pairing. We use a maximal independent set framework for effective pairing, which is more robust than traditional pairing-by-rules methods. For tracking, context information provided by pairing is employed by multiple object tracking. The experiments on challenging datasets and quantitative evaluation show promising performance of our method.
MFCC and VQ voice recognition based ATM security for the visually disabled

A biometrie based automatic teller machine which works with a two-tier security using voice and fingerprint recognition can help users which are visually challenged, allowing them to use the machine using only their biometric characteristics. An automated teller machine (ATM) requires a user to pass an identity test using their PIN before doing any financial transactions. The current method available for access control in ATM is based on cards and pins which increases the issues of unauthorized access on accounts via card skimming. It is eminently difficult to avoid another person from attaining and using someone else's card also regular smartcards can be lost, duplicated, stolen or falsified with accuracy. Another concern is the accessibility of ATM to differently abled people. These concerns can be overcome by using fingerprint recognition for authentication, as discussed from the researchers' previous study, and by adding up an additional voice recognition system feature as discussed on this paper. The four fingerprint sample patterns of an individual are completely separate and uncorrelated. The action of fingerprint recognition involves pre-processing, feature extraction and minutiae matching. Matching is done by comparing the user's fingerprint with the existing fingerprint database, images which were acquired at the time of opening an account in the bank account. Once the fingerprint of the user passes the authentication procedures of the system the user is now able to carry out further transactions using voice-based commands by speaking through a microphone. This model not only provides security but also accessibility to certain sections of the population like people with visual impairment and eye disabilities. The system uses biometric based user recognition. The authenticity of the account will be checked by the input of the user's fingerprint, this will then allow further transaction via voice recognition that implements a MFCC, DWT and VQ to continue. ATM users are identified by the lowest VQ distortion of each voice input. Out of 50 different legitimate user trials, 42 tries were identified while 8 legitimate user tries were denied of access in the system producing 84% accuracy.
Improving the Accuracy and Speed of Support Vector Machines

Bernhard Scholkopf" Max-Planck-Institut fur biologische Kybernetik , Spemannstr. 38 72076 Tubingen, Germany bs@mpik-tueb.mpg.de Support Vector Learning Machines (SVM) are finding application in pattern recognition , regression estimation , and operator inversion for ill-posed problems. Against this very general backdrop , any methods for improving the generalization performance, or for improving the speed in test phase, of SVMs are of increasing interest. In this paper we combine two such techniques on a pattern recognition problem. The method for improving generalization performance (the "virtual support vector" method) does so by incorporating known invariances of the problem. This method achieves a drop in the error rate on 10,000 NIST test digit images of 1.4% to 1.0%. The method for improving the speed (the "reduced set" method) does so by approximating the support vector decision surface. We apply this method to achieve a factor of fifty speedup in test phase over the virtual support vector machine. The combined approach yields a machine which is both 22 times faster than the original machine, and which has better generalization performance, achieving 1.1 % error. The virtual support vector method is applicable to any SVM problem with known invariances. The reduced set method is applicable to any support vector machine.
Front-End Factor Analysis for Speaker Verification

This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability space because it models both speaker and channel variabilities. Two speaker verification systems are proposed which use this new representation. The first system is a support vector machine-based system that uses the cosine kernel to estimate the similarity between the input data. The second system directly uses the cosine similarity as the final decision score. We tested three channel compensation techniques in the total variability space, which are within-class covariance normalization (WCCN), linear discriminate analysis (LDA), and nuisance attribute projection (NAP). We found that the best results are obtained when LDA is followed by WCCN. We achieved an equal error rate (EER) of 1.12% and MinDCF of 0.0094 using the cosine distance scoring on the male English trials of the core condition of the NIST 2008 Speaker Recognition Evaluation dataset. We also obtained 4% absolute EER improvement for both-gender trials on the 10 s-10 s condition compared to the classical joint factor analysis scoring.
Label Embedding Trees for Large Multi-Class Tasks

Multi-class classification becomes challenging at test time when the number of classes is very large and testing against every possible class can become computationally infeasible. This problem can be alleviated by imposing (or learning) a structure over the set of classes. We propose an algorithm for learning a treestructure of classifiers which, by optimizing the overall tree loss, provides superior accuracy to existing tree labeling methods. We also propose a method that learns to embed labels in a low dimensional space that is faster than non-embedding approaches and has superior accuracy to existing embedding approaches. Finally we combine the two ideas resulting in the label embedding tree that outperforms alternative methods including One-vs-Rest while being orders of magnitude faster.
Trichoscopy in Alopecias: Diagnosis Simplified

Trichoscopy is the term coined for dermoscopic imaging of the scalp and hair. This novel diagnostic technique, both simple and non-invasive, can be used as a handy bed side tool for diagnosing common hair and scalp disorders. Trichoscopic observations can be broadly grouped as hair signs, vascular patterns, pigment patterns and interfollicular patterns. In this article, we have briefly described the trichoscopic findings in the common categories of cicatricial and non-cicatricial alopecias such as androgenetic alopecia, alopecia areata, telogen effluvium, tinea capitis, trichotillomania, lichen planopilaris, discoid lupus erythematosus and hair shaft disorders. Besides diagnosing alopecia, it has the potential for obviating unnecessary biopsies and when a biopsy is still needed it is helpful in choosing an ideal biopsy site. Moreover, trichoscopy is a valuable tool for evaluating the treatment response photographically at each follow-up. The last statement here is deleted as asked.
Deep neural networks for small footprint text-dependent speaker verification

In this paper we investigate the use of deep neural networks (DNNs) for a small footprint text-dependent speaker verification task. At development stage, a DNN is trained to classify speakers at the frame-level. During speaker enrollment, the trained DNN is used to extract speaker specific features from the last hidden layer. The average of these speaker features, or d-vector, is taken as the speaker model. At evaluation stage, a d-vector is extracted for each utterance and compared to the enrolled speaker model to make a verification decision. Experimental results show the DNN based speaker verification system achieves good performance compared to a popular i-vector system on a small footprint text-dependent speaker verification task. In addition, the DNN based system is more robust to additive noise and outperforms the i-vector system at low False Rejection operating points. Finally the combined system outperforms the i-vector system by 14% and 25% relative in equal error rate (EER) for clean and noisy conditions respectively.
A ultra-low-energy convolution engine for fast brain-inspired vision in multicore clusters

State-of-art brain-inspired computer vision algorithms such as Convolutional Neural Networks (CNNs) are reaching accuracy and performance rivaling that of humans; however, the gap in terms of energy consumption is still many degrees of magnitude wide. Many-core architectures using shared-memory clusters of power-optimized RISC processors have been proposed as a possible solution to help close this gap. In this work, we propose to augment these clusters with Hardware Convolution Engines (HWCEs): ultra-low energy coprocessors for accelerating convolutions, the main building block of many brain-inspired computer vision algorithms. Our synthesis results in ST 28nm FD-SOI technology show that the HWCE is capable of performing a convolution in the lowest-energy state spending as little as 35 pJ/pixel on average, with an optimum case of 6.5 pJ/pixel. Furthermore, we show that augmenting a cluster with a HWCE can lead to an average boost of 40x or more in energy efficiency in convolutional workloads.
Sistema de Conversión Texto a Voz de Código Abierto Para Lenguas Ibéricas

This paper presents a text-to-speech system based on statistical synthesis which, for the first time, allows generating speech in any of the four official languages of Spain as well as English. Using the AhoTTS system already developed for Spanish and Basque as a starting point, we have added support for Catalan, Galician and English using the code of available open-source modules. The resulting system, named multilingual AhoTTS, has also been released as open-source and it is already being used in real applications.
Review of Semantic-Free Utterances in Social Human-Robot Interaction

Review of Semantic-Free Utterances in Social Human–Robot Interaction Selma Yilmazyildiz, Robin Read, Tony Belpeame & Werner Verhelst To cite this article: Selma Yilmazyildiz, Robin Read, Tony Belpeame & Werner Verhelst (2016) Review of Semantic-Free Utterances in Social Human–Robot Interaction, International Journal of Human–Computer Interaction, 32:1, 63-85,  To link to this article: http://dx.doi.org/10.1080/10447318.2015.1093856
Developing Smart Car Parking System Using Wireless Sensor Networks

Car parking is a serious problem and one of the major contributors to traffic congestion in urban areas. This challenge is as a result of sharp increase in numbers of automobiles on the roads. This paper presents the development of a smart parking system using wireless sensor networks. The system can monitor the state of every parking lot by deploying a magnetic sensor node on each lot and also identify improper parking using infrared sensor to check if vehicles are properly parked. The system uses Xbee radio for transmitting information to the base station which performs necessary information processing, analysis and interpretation on the data received to usable and meaningful format for the end users. The results obtained after qualitative testing of the developed prototype shows that server concurrency utilization is in the average of 12 users per minute. It was also observed that the system acquire speedup in terms of average response time of 1.414 seconds. This implies that the system is robust to handle large number of users and is also fast enough in terms of response which gives accurate information about the parking lot. With the system in place, traffic related hazards, fuel wastage and other relted hazards could be reduced.
Effects of a Dynamic Warm-Up, Static Stretching or Static Stretching with Tendon Vibration on Vertical Jump Performance and EMG Responses

The purpose of this study was to investigate the short-term effects of static stretching, with vibration given directly over Achilles tendon, on electro-myographic (EMG) responses and vertical jump (VJ) performances. Fifteen male, college athletes voluntarily participated in this study (n=15; age: 22±4 years old; body height: 181±10 cm; body mass: 74±11 kg). All stages were completed within 90 minutes for each participant. Tendon vibration bouts lasted 30 seconds at 50 Hz for each volunteer. EMG analysis for peripheral silent period, H-reflex, H-reflex threshold, T-reflex and H/M ratio were completed for each experimental phases. EMG data were obtained from the soleus muscle in response to electro stimulation on the popliteal post tibial nerve. As expected, the dynamic warm-up (DW) increased VJ performances (p=0.004). Increased VJ performances after the DW were not statistically substantiated by the EMG findings. In addition, EMG results did not indicate that either static stretching (SS) or tendon vibration combined with static stretching (TVSS) had any detrimental or facilitation effect on vertical jump performances. In conclusion, using TVSS does not seem to facilitate warm-up effects before explosive performance.
Wealth and happiness across the world: material prosperity predicts life evaluation, whereas psychosocial prosperity predicts positive feeling.

The Gallup World Poll, the first representative sample of planet Earth, was used to explore the reasons why happiness is associated with higher income, including the meeting of basic needs, fulfillment of psychological needs, increasing satisfaction with one's standard of living, and public goods. Across the globe, the association of log income with subjective well-being was linear but convex with raw income, indicating the declining marginal effects of income on subjective well-being. Income was a moderately strong predictor of life evaluation but a much weaker predictor of positive and negative feelings. Possessing luxury conveniences and satisfaction with standard of living were also strong predictors of life evaluation. Although the meeting of basic and psychological needs mediated the effects of income on life evaluation to some degree, the strongest mediation was provided by standard of living and ownership of conveniences. In contrast, feelings were most associated with the fulfillment of psychological needs: learning, autonomy, using one's skills, respect, and the ability to count on others in an emergency. Thus, two separate types of prosperity-economic and social psychological-best predict different types of well-being.
Utility-Based Cache Partitioning

This paper investigates the problem of partitioning a shared cache between multiple concurrently executing applications. The commonly used LRU policy implicitly partitions a shared cache on a demand basis, giving more cache resources to the application that has a high demand and fewer cache resources to the application that has a low demand. However, a higher demand for cache resources does not always correlate with a higher performance from additional cache resources. It is beneficial for performance to invest cache resources in the application that benefits more from the cache resources rather than in the application that has more demand for the cache resources. This paper proposes utility-based cache partitioning (UCP), a low-overhead, runtime mechanism that partitions a shared cache between multiple applications depending on the reduction in cache misses that each application is likely to obtain for a given amount of cache resources. The proposed mechanism monitors each application at runtime using a novel, cost-effective, hardware circuit that requires less than 2kB of storage. The information collected by the monitoring circuits is used by a partitioning algorithm to decide the amount of cache resources allocated to each application. Our evaluation, with 20 multiprogrammed workloads, shows that UCP improves performance of a dualcore system by up to 23% and on average 11% over LRU-based cache partitioning.