nils-herrmann/clean_scirepeval huggingface dataset:
Musical instrument identification : A pattern-recognition approach

A statistical pattern-recognition technique was applied to the classification of musical instrument tones within a taxonomic hierarchy. Perceptually salient acoustic features— related to the physical properties of source excitation and resonance structure—were measured from the output of an auditory model (the log-lag correlogram) for 1023 isolated tones over the full pitch ranges of 15 orchestral instruments. The data set included examples from the string (bowed and plucked), woodwind (single, double, and air reed), and brass families. Using 70%/30% splits between training and test data, maximum a posteriori classifiers were constructed based on Gaussian models arrived at through Fisher multiplediscriminant analysis. The classifiers distinguished transient from continuant tones with approximately 99% correct performance. Instrument families were identified with approximately 90% performance, and individual instruments were identified with an overall success rate of approximately 70%. These preliminary analyses compare favorably with human performance on the same task and demonstrate the utility of the hierarchical approach to classification.
When my robot smiles at me: Enabling human-robot rapport via real-time head gesture mimicry

People use imitation to encourage each other during conversation. We have conducted an experiment to investigate how imitation by a robot affect people’s perceptions of their conversation with it. The robot operated in one of three ways: full head gesture mimicking, partial head gesture mimicking (nodding), and non-mimicking (blinking). Participants rated how satisfied they were with the interaction. We hypothesized that participants in the full head gesture condition will rate their interaction the most positively, followed by the partial and non-mimicking conditions. We also performed gesture analysis to see if any differences existed between groups, and did find that men made significantly more gestures than women while interacting with the robot. Finally, we interviewed participants to try to ascertain additional insight into their feelings of rapport with the robot, which revealed a number of valuable insights.
Validity of altmetrics data for measuring societal impact: A study using data from Altmetric and F1000Prime

Can altmetric data be validly used for the measurement of societal impact? The current study seeks to answer this question with a comprehensive dataset (about 100,000 records) from very disparate sources (F1000, Altmetric, and an in-house database based on Web of Science). In the F1000 peer review system, experts attach particular tags to scientific papers which indicate whether a paper could be of interest for science or rather for other segments of society. The results show that papers with the tag"good for teaching"do achieve higher altmetric counts than papers without this tag - if the quality of the papers is controlled. At the same time, a higher citation count is shown especially by papers with a tag that is specifically scientifically oriented ("new finding"). The findings indicate that papers tailored for a readership outside the area of research should lead to societal impact. If altmetric data is to be used for the measurement of societal impact, the question arises of its normalization. In bibliometrics, citations are normalized for the papers' subject area and publication year. This study has taken a second analytic step involving a possible normalization of altmetric data. As the results show there are particular scientific topics which are of especial interest for a wide audience. Since these more or less interesting topics are not completely reflected in Thomson Reuters' journal sets, a normalization of altmetric data should not be based on the level of subject categories, but on the level of topics.
On-road vehicle detection using evolutionary Gabor filter optimization

Robust and reliable vehicle detection from images acquired by a moving vehicle is an important problem with numerous applications including driver assistance systems and self-guided vehicles. Our focus in this paper is on improving the performance of on-road vehicle detection by employing a set of Gabor filters specifically optimized for the task of vehicle detection. This is essentially a kind of feature selection, a critical issue when designing any pattern classification system. Specifically, we propose a systematic and general evolutionary Gabor filter optimization (EGFO) approach for optimizing the parameters of a set of Gabor filters in the context of vehicle detection. The objective is to build a set of filters that are capable of responding stronger to features present in vehicles than to nonvehicles, therefore improving class discrimination. The EGFO approach unifies filter design with filter selection by integrating genetic algorithms (GAs) with an incremental clustering approach. Filter design is performed using GAs, a global optimization approach that encodes the Gabor filter parameters in a chromosome and uses genetic operators to optimize them. Filter selection is performed by grouping filters having similar characteristics in the parameter space using an incremental clustering approach. This step eliminates redundant filters, yielding a more compact optimized set of filters. The resulting filters have been evaluated using an application-oriented fitness criterion based on support vector machines. We have tested the proposed framework on real data collected in Dearborn, MI, in summer and fall 2001, using Ford's proprietary low-light camera.
Fog in Comparison to Cloud: A Survey

Fog computing is new buzz word in computing world after cloud computing. This new computing paradigm could be seen as an extension to cloud computing. Main aim of fog computing is to reduce the burden on cloud by gathering workloads, services, applications and huge data to near network edge. In this survey paper, we will discuss main characteristics of the Fog that are; 1. Mobility, 2. Location awareness, 3. Low latency, 4. Huge number of nodes, 5. Extensive geographical distribution, 6. Various real time applications and we explore the advantages and motivation of Fog computing, and analyze its applications for IOT.
Towards predictable datacenter networks

The shared nature of the network in today's multi-tenant datacenters implies that network performance for tenants can vary significantly. This applies to both production datacenters and cloud environments. Network performance variability hurts application performance which makes tenant costs unpredictable and causes provider revenue loss. Motivated by these factors, this paper makes the case for extending the tenant-provider interface to explicitly account for the network. We argue this can be achieved by providing tenants with a virtual network connecting their compute instances. To this effect, the key contribution of this paper is the design of virtual network abstractions that capture the trade-off between the performance guarantees offered to tenants, their costs and the provider revenue.
 To illustrate the feasibility of virtual networks, we develop Oktopus, a system that implements the proposed abstractions. Using realistic, large-scale simulations and an Oktopus deployment on a 25-node two-tier testbed, we demonstrate that the use of virtual networks yields significantly better and more predictable tenant performance. Further, using a simple pricing model, we find that the our abstractions can reduce tenant costs by up to 74% while maintaining provider revenue neutrality.
Validity and reliability of the brief COPE in carers of people with dementia: the LASER-AD Study.

The Brief COPE is a self-completed questionnaire measuring coping strategies. It comprises 14 subscales for which psychometric properties are described. Three composite subscales measuring emotion-focused, problem-focused, and dysfunctional coping have proved useful in clinical research and have content validity. We report psychometric properties of these subscales for the first time. One hundred twenty-five family carers of people with Alzheimer's disease completed the Brief COPE at time 1, 92 (82.9% of eligible carers) a year later, and 74 (77.1%) 2 years later. Internal consistencies were good for emotion-focused, problem-focused, and dysfunctional subscales (alpha = 0.72, 0.84, 0.75). Test-retest reliability over a year was demonstrated for emotion-focused, problem-focused, and dysfunctional subscales among carers in whom burden scores did not change significantly (r = 0.58, r = 0.72, r = 0.68; p < 0.001). Change in burden score over 2 years correlated with change in problem-focused and dysfunctional (r = 0.33, r = 0.32; p < 0.01) subscales, indicating sensitivity to change, but not with change on the emotion-focused scale. Change in emotion-focused coping correlated with change in problem-focused and dysfunctional coping (r = 0.40, r = 0.26; p < 0.05). Regression analyses indicated convergent and concurrent validity: emotion-focused coping was predicted by secure attachment (beta = 0.23) and by problem-focused coping (beta = 0.68); dysfunctional coping by burden (beta = 0.36) and less secure attachment (beta = -0.25) and problem-focused coping (beta = 0.31;all p < 0.05). The model predicting problem-focused coping included avoidant attachment (beta = 0.22; p = 0.014), social support (beta = 0.10; p = 0.25), care recipient activities of daily living impairment (beta = 0.12; p = 0.14) and less secure attachment (beta = -0.25; p = 0.011) and emotion-focused (beta = 0.53; p < 0.001) and dysfunctional coping (beta = 0.25, p = 0.006). These subscales are potentially useful in clinical research as they reflect possible components of interventions to change coping, although more information about sensitivity to change of the emotion-focused subscale is needed.
Improving Topic Coherence with Regularized Topic Models

Topic models have the potential to improve search and browsing by extracting useful semantic themes from web pages and other text documents. When learned topics are coherent and interpretable, they can be valuable for faceted browsing, results set diversity analysis, and document retrieval. However, when dealing with small collections or noisy text (e.g. web search result snippets or blog posts), learned topics can be less coherent, less interpretable, and less useful. To overcome this, we propose two methods to regularize the learning of topic models. Our regularizers work by creating a structured prior over words that reflect broad patterns in the external data. Using thirteen datasets we show that both regularizers improve topic coherence and interpretability while learning a faithful representation of the collection of interest. Overall, this work makes topic models more useful across a broader range of text data.
Geotagging with local lexicons to build indexes for textually-specified spatial data

The successful execution of location-based and feature-based queries on spatial databases requires the construction of spatial indexes on the spatial attributes. This is not simple when the data is unstructured as is the case when the data is a collection of documents such as news articles, which is the domain of discourse, where the spatial attribute consists of text that can be (but is not required to be) interpreted as the names of locations. In other words, spatial data is specified using text (known as a toponym) instead of geometry, which means that there is some ambiguity involved. The process of identifying and disambiguating references to geographic locations is known as geotagging and involves using a combination of internal document structure and external knowledge, including a document-independent model of the audience's vocabulary of geographic locations, termed its spatial lexicon. In contrast to previous work, a new spatial lexicon model is presented that distinguishes between a global lexicon of locations known to all audiences, and an audience-specific local lexicon. Generic methods for inferring audiences' local lexicons are described. Evaluations of this inference method and the overall geotagging procedure indicate that establishing local lexicons cannot be overlooked, especially given the increasing prevalence of highly local data sources on the Internet, and will enable the construction of more accurate spatial indexes.
Optimizing Area Under Roc Curve with SVMs

For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems misclassification costs are not known and thus, ROC curve and related metrics such as the Area Under ROC curve (AUC) can be a more meaningful performance measures. In this paper, we propose a quadratic programming based algorithm for AUC maximization and show that under certain conditions 2-norm soft margin Support Vector Machines can also maximize AUC. We present experiments that compare SVMs performances to those of other AUC maximization based algorithms and provide empirical analysis of SVMs behavior with regards to ROCbased metrics. Our main conclusion is that SVMs can maximize both AUC and accuracy compared to other algorithms like RankBoost that optimize only AUC .
A measurement study of insecure javascript practices on the web

JavaScript is an interpreted programming language most often used for enhancing webpage interactivity and functionality. It has powerful capabilities to interact with webpage documents and browser windows, however, it has also opened the door for many browser-based security attacks. Insecure engineering practices of using JavaScript may not directly lead to security breaches, but they can create new attack vectors and greatly increase the risks of browser-based attacks. In this article, we present the first measurement study on insecure practices of using JavaScript on the Web. Our focus is on the insecure practices of JavaScript inclusion and dynamic generation, and we examine their severity and nature on 6,805 unique websites. Our measurement results reveal that insecure JavaScript practices are common at various websites: (1) at least 66.4% of the measured websites manifest the insecure practices of including JavaScript files from external domains into the top-level documents of their webpages; (2) over 44.4% of the measured websites use the dangerous eval() function to dynamically generate and execute JavaScript code on their webpages; and (3) in JavaScript dynamic generation, using the document.write() method and the innerHTML property is much more popular than using the relatively secure technique of creating script elements via DOM methods. Our analysis indicates that safe alternatives to these insecure practices exist in common cases and ought to be adopted by website developers and administrators for reducing potential security risks.
Flexible Multimodal Tactile Sensing System for Object Identification

This work presents results towards realizing a flexible multimodal tactile sensing system for object identification. Using polymer substrates and simple fabrication, robust devices are made that can identify objects based on texture, temperature, as well as material properties such as hardness and thermal conductivity. These capabilities are possible using signal processing techniques and physical models along with individual sensing structures inspired by the specialization found in biological skin. These structures are used to sense various object parameters, and array-wide processing to identify texture using a Maximum Likelihood decision rule. 80% texture classification is achieved. In blind object identification tests, over 90% correct identification was achieved by measurement of material properties.
Controlling User Perceptions of Linguistic Style: Trainable Generation of Personality Traits

Recent work in natural language generation has begun to take linguistic variation into account, developing algorithms that are capable of modifying the system's linguistic style based either on the user's linguistic style or other factors, such as personality or politeness. While stylistic control has traditionally relied on handcrafted rules, statistical methods are likely to be needed for generation systems to scale to the production of the large range of variation observed in human dialogues. Previous work on statistical natural language generation (SNLG) has shown that the grammaticality and naturalness of generated utterances can be optimized from data; however these data-driven methods have not been shown to produce stylistic variation that is perceived by humans in the way that the system intended. This paper describes Personage, a highly parameterizable language generator whose parameters are based on psychological findings about the linguistic reflexes of personality. We present a novel SNLG method which uses parameter estimation models trained on personality-annotated data to predict the generation decisions required to convey any combination of scalar values along the five main dimensions of personality. A human evaluation shows that parameter estimation models produce recognizable stylistic variation along multiple dimensions, on a continuous scale, and without the computational cost incurred by overgeneration techniques.
Smooth Rotation Enhanced As-Rigid-As-Possible Mesh Animation

In recent years, the As-Rigid-As-Possible (ARAP) shape deformation and shape interpolation techniques gained popularity, and the ARAP energy was successfully used in other applications as well. We improve the ARAP animation technique in two aspects. First, we introduce a new ARAP-type energy, named SR-ARAP, which has a consistent discretization for surfaces (triangle meshes). The quality of our new surface deformation scheme competes with the quality of the volumetric ARAP deformation (for tetrahedral meshes). Second, we propose a new ARAP shape interpolation method that is superior to prior art also based on the ARAP energy. This method is compatible with our new SR-ARAP energy, as well as with the ARAP volume energy.
Object Recognition in 3D Scenes with Occlusions and Clutter by Hough Voting

n this work we propose a novel Hough voting approach for the detection of free-form shapes in a 3D space, to be used for object recognition tasks in 3D scenes with a significant degree of occlusion and clutter. The proposed method relies on matching 3D features to accumulate evidence of the presence of the objects being sought in a 3D Hough space. We validate our proposal by presenting a quantitative experimental comparison with state-of-the-art methods as well as by showing how our method enables 3D object recognition from real-time stereo data.
Inside 6th-Generation Intel Core: New Microarchitecture Code-Named Skylake

Skylake's core, processor graphics, and system on chip were designed to meet a demanding set of requirements for a wide range of power-performance points. Its coherent fabric was designed to provide high-memory bandwidth from multiple memory sources. Skylake's power management, which includes Intel Speed Shift technology, was designed to provide the largest dynamic power range among prior Intel processors. The Intel Architecture core delivers higher power efficiency, higher frequency, and a wider dynamic power range, supporting smaller form factors. Skylake's Gen9 graphics provides new features designed to maximize energy efficiency and bring the best visual experience for gaming and media. Skylake offers a rich performance monitoring unit that enhances software developers' ability to optimize their applications.
Quick Quiz: A Gamified Approach for Enhancing Learning

Gamification has the potential to improve the quality of learning by better engaging students with learning activities. Our objective in this study is to evaluate a gamified learning activity along the dimensions of learning, engagement, and enjoyment. The activity made use of a gamified multiple choice quiz implemented as a software tool and was trialled in three undergraduate IT-related courses. A questionnaire survey was used to collect data to gauge levels of learning, engagement, and enjoyment. Results show that there was some degree of engagement and enjoyment. The majority of participants (77.63 per cent) reported that they were engaged enough to want to complete the quiz and 46.05 per cent stated they were happy while playing the quiz. In terms of learning, the overall results were positive since 60.53 per cent of students stated that it enhanced their learning effectiveness. A limitation of the work is that the results are self-reported and the activity was used over a short period of time. Thus, future work should include longer trial periods and evaluating improvements to learning using alternative approaches to self-reported data.
Rectenna architecture based energy harvester for low power RFID application

Large scale implementation of active RFID tag technology has been restricted by the need for battery replacement. Prolonging battery lifespan may potentially promote active RFID tags which offer obvious advantages over passive RFID systems. This paper explores some opportunities to simulate and develop a prototype RF energy harvester for 2.4 GHz band specifically designed for low power active RFID tag application. This system employs a rectenna architecture which is a receiving antenna attached to a rectifying circuit that efficiently converts RF energy to DC current. Initial ADS simulation results show that 2 V output voltage can be achieved using a 7 stage Cockroft-Walton rectifying circuitry with -4.881 dBm (0.325 mW) output power under -4 dBm (0.398 mW) input RF signal. These results lend support to the idea that RF energy harvesting is indeed promising.
Enabling FPGAs in the cloud

Cloud computing is becoming a major trend for delivering and accessing infrastructure on demand via the network. Meanwhile, the usage of FPGAs (Field Programmable Gate Arrays) for computation acceleration has made significant inroads into multiple application domains due to their ability to achieve high throughput and predictable latency, while providing programmability, low power consumption and time-to-value. Many types of workloads, e.g. databases, big data analytics, and high performance computing, can be and have been accelerated by FPGAs. As more and more workloads are being deployed in the cloud, it is appropriate to consider how to make FPGAs and their capabilities available in the cloud. However, such integration is non-trivial due to issues related to FPGA resource abstraction and sharing, compatibility with applications and accelerator logics, and security, among others. In this paper, a general framework for integrating FPGAs into the cloud is proposed and a prototype of the framework is implemented based on OpenStack, Linux-KVM and Xilinx FPGAs. The prototype enables isolation between multiple processes in multiple VMs, precise quantitative acceleration resource allocation, and priority-based workload scheduling. Experimental results demonstrate the effectiveness of this prototype, an acceptable overhead, and good scalability when hosting multiple VMs and processes.