nils-herrmann/clean_scirepeval huggingface dataset:
How deep are effects of language on thought? Time estimation in speakers of English, Indonesian, Greek, and Spanish

Do the languages that we speak affect how we experience the world? This question was taken up in a linguistic survey and two non-linguistic psychophysical experiments conducted in native speakers of English, Indonesian, Greek, and Spanish. All four of these languages use spatial metaphors to talk about time, but the particular metaphoric mappings between time and space vary across languages. A linguistic corpus study revealed that English and Indonesian tend to map duration onto linear distance (e.g., a long time), whereas Greek and Spanish preferentially map duration onto quantity (e.g., much time). Two psychophysical time estimation experiments were conducted to determine whether this cross-linguistic difference has implications for speakers’ temporal thinking. Performance on the psychophysical tasks reflected the relative frequencies of the ‘time as distance’ and ‘time as quantity’ metaphors in English, Indonesian, Greek, and Spanish. This was true despite the fact that the tasks used entirely nonlinguistic stimuli and responses. Results suggest that: (1.) The spatial metaphors in our native language may profoundly influence the way we mentally represent time.  Language can shape even primitive, low-level mental processes such as estimating brief durations – an ability we share with babies and non-human animals.
CHORUS DETECTION WITH COMBINED USE OF MFCC AND CHROMA FEATURES AND IMAGE PROCESSING FILTERS

A computationally efficient method for detecting a chorus section in popular and rock music is presented. The method utilizes a distance matrix representation that is obtained by summing two separate distance matrices calculated using the mel-frequency cepstral coefficient and pitch chroma features. The benefit of computing two separate distance matrices is that different enhancement operations can be applied on each. An enhancement operation is found beneficial only for the chroma distance matrix. This is followed by detection of the off-diagonal segments of small distance from the distance matrix. From the detected segments, an initial chorus section is selected using a scoring mechanism utilizing several heuristics, and subjected to further processing. This further processing involves using image processing filters in a neighborhood of the distance matrix surrounding the initial chorus section. The final position and length of the chorus is selected based on the filtering results. On a database of 206 popular & rock music pieces an average F-measure of 86% is obtained. It takes about ten seconds to process a song with an average duration of three to four minutes on a Windows XP computer with a 2.8 GHz Intel Xeon processor.
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs

We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). OLP uses its experience so far to estimate the MDP. It chooses actions by optimistically maximizing estimated future rewards over a set of next-state transition probabilities that are close to the estimates, a computation that corresponds to solving linear programs. We show that the total expected reward obtained by OLP up to time T is within C(P ) log T of the reward obtained by the optimal policy, where C(P ) is an explicit, MDP-dependent constant. OLP is closely related to an algorithm proposed by Burnetas and Katehakis with four key differences: OLP is simpler, it does not require knowledge of the supports of transition probabilities, the proof of the regret bound is simpler, but our regret bound is a constant factor larger than the regret of their algorithm. OLP is also similar in flavor to an algorithm recently proposed by Auer and Ortner. But OLP is simpler and its regret bound has a better dependence on the size of the MDP.
Multibeam Pillbox Antenna With Low Sidelobe Level and High-Beam Crossover in SIW Technology Using the Split Aperture Decoupling Method

A novel dual-layer multibeam pillbox antenna with a slotted waveguide radiating part in substrate-integrated waveguide (SIW) technology is proposed. In contrast to previous works, the design goal is to have a multibeam antenna with arbitrary low sidelobes and at the same time a high crossing level between adjacent radiated beams. These two constraints cannot be satisfied simultaneously for any passive and lossless multibeam antenna systems with a single radiating aperture due to beam orthogonality. Here, this limitation is overcome using the “split aperture decoupling” method which consists in using two radiating apertures. Each aperture is associated with a pillbox quasi-optical system with several integrated feed horns in its focal plane so as to steer the main beam in the azimuthal plane. The antenna operates at 24.15 GHz and presents very good scanning performance over an angular sector of ±40°, with a good agreement between full-wave simulations and measurements. The crossover level between adjacent beams is about -3 dB with a sidelobe level lower than -24 dB for the central beam and better than -11 dB for the extreme beam positions. The isolation between feed horns in the same pillbox system is better than 20 dB.
Non-Invasive Brain-to-Brain Interface (BBI): Establishing Functional Links between Two Brains

Transcranial focused ultrasound (FUS) is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI). In conjunction with the use of brain-to-computer interface (BCI) techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat), thus creating a brain-to-brain interface (BBI). The implementation was aimed to non-invasively translate the human volunteer's intention to stimulate a rat's brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP) with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer's intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration) to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0±3.0% accuracy, with a time delay of 1.59±1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.
Symmetric Patterns and Coordinations: Fast and Enhanced Representations of Verbs and Adjectives

State-of-the-art word embeddings, which are often trained on bag-of-words (BOW) contexts, provide a high quality representation of aspects of the semantics of nouns. However, their quality decreases substantially for the task of verb similarity prediction. In this paper we show that using symmetric pattern contexts (SPs, e.g., “X and Y”) improves word2vec verb similarity performance by up to 15% and is also instrumental in adjective similarity prediction. The unsupervised SP contexts are even superior to a variety of dependency contexts extracted using a supervised dependency parser. Moreover, we observe that SPs and dependency coordination contexts (Coor) capture a similar type of information, and demonstrate that Coor contexts are superior to other dependency contexts including the set of all dependency contexts, although they are still inferior to SPs. Finally, there are substantially fewer SP contexts compared to alternative representations, leading to a massive reduction in training time. On an 8G words corpus and a 32 core machine, the SP model trains in 11 minutes, compared to 5 and 11 hours with BOW and all dependency contexts, respectively.
Measurement of e-service quality: An empirical study on online travel service

Information communication technologies (ICTs) have significantly revolutionized travel industry in the last decade. With an increasing number of travel companies participating in the Internet market, low price has become a minimum qualification to compete in the Internet market. As a result, e-service quality is becoming even more critical for companies to retain and attract customers in the digital age. This study focuses on e-service quality dimensions in the Internet market with an empirical study on online travel service. The purpose of this study is to develop a scale to evaluate e-service quality from the perspectives of both online companies and customers, which provides fresh insight into the dimensions of e-service quality. The results in this study indicate that trust from the perspective of customer and ease of use from the perspective of online company are the most critical and important facets in customers’ perception of online travel service quality, while reliability, system availability and responsiveness have influence on customer’s perception of online travel service quality as well, but the influence is not so strong as that of trust and ease of use. Online travel service companies should pay attention to the facets of reliability, system availability and responsiveness while focusing on the facets of ease of use and trust in order to improve their online travel service quality to customers.
Mobility robustness optimization in self-organizing LTE femtocell networks

Femtocell is a promising solution for enhancing the indoor coverage and capacity in wireless networks. However, for the small size of femtocell and potentially frequent power on/off, existing handover schemes may not be reliable enough for femtocell networks. Moreover, improper handover parameters settings may lead to handover failures and unnecessary handovers, which make it necessary to enhance the mobility robustness for femtocells. In this article, we propose a gradient method and cost function-based mobility robustness optimization scheme for long term evolution (LTE) femtocell self-organizing networks. Moreover, signalling overhead of the scheme is analyzed. Simulation results show that the proposed scheme has a better performance than the fixed parameters method in terms of reduced the number of handover failures and unnecessary handovers with limited signalling modifications.
Exact Evaluation of Catmull-Clark Subdivision Surfaces at Arbitrary Parameter Values

In this paper we disprove the belief widespread within the computer graphics community that Catmull-Clark subdivision surfaces cannot be evaluated directly without explicitly subdividing. We show that the surface and all its derivatives can be evaluated in terms of a set of eigenbasis functions which depend only on the subdivision scheme and we derive analytical expressions for these basis functions. In particular, on the regular part of the control mesh where Catmull-Clark surfaces are bi-cubic B-splines, the eigenbasis is equal to the power basis. Also, our technique is both efficient and easy to implement. We have used our implementation to compute high quality curvature plots of subdivision surfaces. The cost of our evaluation scheme is comparable to that of a bi-cubic spline. Therefore, our method allows many algorithms developed for parametric surfaces to be applied to Catmull-Clark subdivision surfaces. This makes subdivision surfaces an even more attractive tool for free-form surface modeling.
PrIter: A Distributed Framework for Prioritizing Iterative Computations

Iterative computations are pervasive among data analysis applications in the cloud, including Web search, online social network analysis, recommendation systems, and so on. These cloud applications typically involve data sets of massive scale. Fast convergence of the iterative computation on the massive data set is essential for these applications. In this paper, we explore the opportunity for accelerating iterative computations and propose a distributed computing framework, PrIter, which enables fast iterative computation by providing the support of prioritized iteration. Instead of performing computations on all data records without discrimination, PrIter prioritizes the computations that help convergence the most, so that the convergence speed of iterative process is significantly improved. We evaluate PrIter on a local cluster of machines as well as on Amazon EC2 Cloud. The results show that PrIter achieves up to 50x speedup over Hadoop for a series of iterative algorithms.
Parallel Power Iteration Clustering for Big Data using MapReduce in Hadoop

In today’s life Distributed Data Mining is most popular topic in research area because as data are increasing in day to day life there are so many problems occurs to handle them and there are also a solutions for that but still they are not as per expectation, still there are some issue already there in the Distributed Data Mining, among them mainly we are focus in this papers that about reducing computational cost. PIC (Power Iteration Clustering) is simple, fast and relatively scalable and uses matrix vector Multiplication. PIC is the combination of two things one is PI that is Power Iteration and remaining C for Clustering. In Power Iteration it finds the largest Eigenvector and then after it apply K-mean algorithm for clustering. Here this algorithm is fast and capable of finding good clusters in a less time but it is not capable for handling large data, so here another is parallel approach for PIC called Parallel Power Iteration Clustering and implementations for minimizing communication cost. Due to its parallel approach different nodes are also their so for make our system more robust and avoid node failure we use Hadoop platform using Map Reduce. Hadoop provide the environment to implement application in distributed environment and it can capable of handling node failure. In Hadoop each DataNode contains the replica of other Datablocks of other DataNode and location of that Datablocks detail store into Namenode. Due to that when any node will goes down or fails at this moment we can use Replica of that Crash node from the other node. Distributed Data Mining, Parallel Clustering, K-Mean Clustering, Threading, Hadoop, NameNode, DataNode,
User interactions in social networks and their implications

Social networks are popular platforms for interaction, communication and collaboration between friends. Researchers have recently proposed an emerging class of applications that leverage relationships from social networks to improve security and performance in applications such as email, web browsing and overlay routing. While these applications often cite social network connectivity statistics to support their designs, researchers in psychology and sociology have repeatedly cast doubt on the practice of inferring meaningful relationships from social network connections alone.
 This leads to the question: Are social links valid indicators of real user interaction? If not, then how can we quantify these factors to form a more accurate model for evaluating socially-enhanced applications? In this paper, we address this question through a detailed study of user interactions in the Facebook social network. We propose the use of interaction graphs to impart meaning to online social links by quantifying user interactions. We analyze interaction graphs derived from Facebook user traces and show that they exhibit significantly lower levels of the "small-world" properties shown in their social graph counterparts. This means that these graphs have fewer "supernodes" with extremely high degree, and overall network diameter increases significantly as a result. To quantify the impact of our observations, we use both types of graphs to validate two well-known social-based applications (RE and SybilGuard). The results reveal new insights into both systems, and confirm our hypothesis that studies of social applications should use real indicators of user interactions in lieu of social graphs.
CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks

Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size. We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network. For network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/ .
Edge-based compression of cartoon-like images with homogeneous diffusion

Edges provide semantically important image features. In this paper a lossy compression method for cartoon-like images is presented, which is based on edge information. Edges together with some adjacent grey/colour values are extracted and encoded using a classical edge detector, binary compression standards such as JBIG and stateof-the-art encoders such as PAQ. When decoding, information outside these encoded data is recovered by solving the Laplace equation, i.e. we inpaint with the steady state of a homogeneous diffusion process. For the discrete reconstruction problem, we prove existence and uniqueness and establish a maximum-minimum principle. Furthermore, we describe an efficient multigrid algorithm. The result is a simple codec that is able to encode and decode in real time. We show that for cartoon-like images this codec can outperform the JPEG standard and even its more advanced successor JPEG2000.
Self-oscillating flyback driver for power LEDs

The self-oscillating flyback converter is a popular circuit for cost-sensitive applications due to its simplicity and low component count. It is widely employed in mobile phone chargers and as the stand-by power source in off-line power supplies for data-processing equipment. However, this circuit was almost not explored for supplying power LEDs. This paper presents a self-oscillating flyback driver for supplying Power LEDs directly, with no additional circuit. A simplified mathematical model of the LED was used to characterize the self-oscillating converter for driving the power LEDs. With the proposed converter the LEDs manufacturing tolerances and drifts over temperature presents little to no influence over the LED average current. This is proved by using the LED electrical model on the analysis.
NILC_USP: Aspect Extraction using Semantic Labels

This paper details the system NILC USP that participated in the Semeval 2014: Aspect Based Sentiment Analysis task. This system uses a Conditional Random Field (CRF) algorithm for extracting the aspects mentioned in the text. Our work added semantic labels into a basic feature set for measuring the efficiency of those for aspect extraction. We used the semantic roles and the highest verb frame as features for the machine learning. Overall, our results demonstrated that the system could not improve with the use of this semantic information, but its precision was increased.
Cyber-Threats Information Sharing in Cloud Computing: A Game Theoretic Approach

Cybersecurity is among the highest priorities in industries, academia and governments. Cyber-threats information sharing among different organizations has the potential to maximize vulnerabilities discovery at a minimum cost. Cyber-threats information sharing has several advantages. First, it diminishes the chance that an attacker exploits the same vulnerability to launch multiple attacks in different organizations. Second, it reduces the likelihood an attacker can compromise an organization and collect data that will help him launch an attack on other organizations. Cyberspace has numerous interconnections and critical infrastructure owners are dependent on each other's service. This well-known problem of cyber interdependency is aggravated in a public cloud computing platform. The collaborative effort of organizations in developing a countermeasure for a cyber-breach reduces each firm's cost of investment in cyber defense. Despite its multiple advantages, there are costs and risks associated with cyber-threats information sharing. When a firm shares its vulnerabilities with others there is a risk that these vulnerabilities are leaked to the public (or to attackers) resulting in loss of reputation, market share and revenue. Therefore, in this strategic environment the firms committed to share cyber-threats information might not truthfully share information due to their own self-interests. Moreover, some firms acting selfishly may rationally limit their cybersecurity investment and rely on information shared by others to protect themselves. This can result in under investment in cybersecurity if all participants adopt the same strategy. This paper will use game theory to investigate when multiple self-interested firms can invest in vulnerability discovery and share their cyber-threat information. We will apply our algorithm to a public cloud computing platform as one of the fastest growing segments of the cyberspace.
Unforgettable film music: The role of emotion in episodic long-term memory for music

Specific pieces of music can elicit strong emotions in listeners and, possibly in connection with these emotions, can be remembered even years later. However, episodic memory for emotional music compared with less emotional music has not yet been examined. We investigated whether emotional music is remembered better than less emotional music. Also, we examined the influence of musical structure on memory performance. Recognition of 40 musical excerpts was investigated as a function of arousal, valence, and emotional intensity ratings of the music. In the first session the participants judged valence and arousal of the musical pieces. One week later, participants listened to the 40 old and 40 new musical excerpts randomly interspersed and were asked to make an old/new decision as well as to indicate arousal and valence of the pieces. Musical pieces that were rated as very positive were recognized significantly better. Musical excerpts rated as very positive are remembered better. Valence seems to be an important modulator of episodic long-term memory for music. Evidently, strong emotions related to the musical experience facilitate memory formation and retrieval.