nils-herrmann/clean_scirepeval huggingface dataset:
Deep Layered Learning in MIR

Deep learning has boosted the performance of many music information retrieval (MIR) systems in recent years. Yet, the complex hierarchical arrangement of music makes end-to-end learning hard for some MIR tasks – a very deep and flexible processing chain is necessary to model some aspect of music audio. Representations involving tones, chords, and rhythm are fundamental building blocks of music. This paper discusses how these can be used as intermediate targets and priors in MIR to deal with structurally complex learning problems, with learning modules connected in a directed acyclic graph. It is suggested that this strategy for inference, referred to as deep layered learning (DLL), can help generalization by (1) – enforcing the validity and invariance of intermediate representations during processing, and by (2) – letting the inferred representations establish the musical organization to support higher-level invariant processing. A background to modular music processing is provided together with an overview of previous publications. Relevant concepts from information processing, such as pruning, skip connections, and performance supervision are reviewed within the context of DLL. A test is finally performed, showing how layered learning affects pitch tracking. It is indicated that especially offsets are easier to detect if guided by extracted framewise fundamental frequencies.
G-tries: an Efficient Data Structure for Discovering Network Motifs

In this paper we propose a novel specialized data structure that we call g-trie, designed to deal with collections of subgraphs. The main conceptual idea is akin to a prefix tree in the sense that we take advantage of common topology by constructing a multiway tree where the descendants of a node share a common substructure. We give algorithms to construct a g-trie, to list all stored subgraphs, and to find occurrences on another graph of the subgraphs stored in the g-trie. We evaluate the implementation of this structure and its associated algorithms on a set of representative benchmark biological networks in order to find network motifs. To assess the efficiency of our algorithms we compare their performance with other known network motif algorithms also implemented in the same common platform. Our results show that indeed, g-tries are a feasible, adequate and very efficient data structure for network motifs discovery, clearly outperforming previous algorithms and data structures.
A novel approach to MRI Brain Tumor delineation with Independent Components & Finite Generalized Gaussian Mixture Models

Automated segmentation of tumors from a multispectral data set like that of the Magnetic Resonance Images (MRI) is challenging. Independent Component Analysis (ICA) and its variations for Blind Source Separation (BSS) have been employed in previous studies but have met with cumbersome obstacles due to its inherent limitations. Here we have approached the multispectral data set initially with feature extraction followed by a kernel shape based unsupervised classification method, Finite Generalized Gaussian Mixture Model (FGGM) ICA-FGGM model, for an improved classification of brain tissues in MRI. First, ICA is applied to MRI brain data from 3 source image sets T1, T2 and PD/ FLAIR images to get optimally feature extracted three independent components. FGGM model can then incorporate various distributions from peaked ones to flat ones; thereby overriding the disadvantages of conventional approaches trying to represent data using a single probability density function. ExpectationMaximization algorithm is used to estimate the model parameters. Experiments were carried out initially on synthetic image sets to validate the algorithm and then on normal and abnormal clinical multispectral MRI brain images. Comparative studies using quantitative and qualitative analysis against conventional approaches confirm the effectiveness and superiority of the proposed method.
Adding a Solar-Radiance Function to the Ho&#x161;ek-Wilkie Skylight Model

One prerequisite for realistic renderings of outdoor scenes is the proper capturing of the sky's appearance. Currently, an explicit simulation of light scattering in the atmosphere isn't computationally feasible, and won't be in the foreseeable future. Captured luminance patterns have proven their usefulness in practice but can't meet all user needs. To fill this capability gap, computer graphics technology has employed analytical models of sky-dome luminance patterns for more than two decades. For technical reasons, such models deal with only the sky dome's appearance, though, and exclude the solar disc. The widely used model proposed by Arcot Preetham and colleagues employed a separately derived analytical formula for adding a solar emitter of suitable radiant intensity. Although this yields reasonable results, the formula is derived in a manner that doesn't exactly match the conditions in their sky-dome model. But the more sophisticated a skylight model is and the more subtly it can represent different conditions, the more the solar radiance should exactly match the skylight's conditions. Toward that end, researchers propose a solar-radiance function that exactly matches a recently published high-quality analytical skylight model.
Modified reduced switch symmetrical multilevel inverter

Multilevel inverters (MLI) are widely used at the range of high voltage and high power for industrial applications. It is because of low harmonic distortion, less electromagnetic interference and upper range of dc link voltages. But the following type of inverter has few disadvantages like difficulty in voltage balancing, higher number of components and complexity in pulse width modulation control method. Objective of this paper is to compare the existing topologies by designing a 7-level modified reduced switch symmetrical MLI (MRSMLI) with five switches and same voltage sources in each level. Simulation and comparison of seven-level seven-switch, seven-level six-switch and seven-level five-switch MLI will be done using MATLAB/SIMULINK environment. The results obtained from a five-switch MLI will be verified with FFT Analysis design of seven-switch MLI. Output obtained will be then utilized for control of an induction motor. Simulation results will be verified by designing hardware setup of the proposed MRSMLI.
Memory-centric video processing

This work presents a domain-specific memory subsystem based on a two-level memory hierarchy. It targets the application domain of video post-processing applications including video enhancement and format conversion. These applications are based on motion compensation and/or broad class of content adaptive filtering to provide the highest quality of pictures. Our approach meets the required performance and has sufficient flexibility for the application domain. It especially aims at the implementation-wise most challenging applications: compute-intensive and bandwidth-demanding applications that provide the highest quality at high picture resolutions. The lowest level of the memory hierarchy, closest to the processing element, the L0 scratchpad, is organized specifically to enable fast retrieval of an arbitrarily positioned 2-D block of pixels to the processing element. To guarantee the performance, most of its addressing logic is hardwired, leaving a user a set of API for initialization and storing/loading the data to/from the L0 scratchpad. The next level of the memory hierarchy, the L1 scratchpad, minimizes the off-chip memory bandwidth requirements. The L1 scratchpad is organized specifically to enable efficient aligned block-based accesses. With lower data rates compared to the L0 scratchpad and aligned block access, software-based addressing is used to enable full flexibility. The two-level memory hierarchy exploits prefetching to further improve the performance.
Exploring Individual Differences in Online Addictions: the Role of Identity and Attachment

Research examining the development of online addictions has grown greatly over the last decade with many studies suggesting both risk factors and protective factors. In an attempt to integrate the theories of attachment and identity formation, the present study investigated the extent to which identity styles and attachment orientations account for three types of online addiction (i.e., internet addiction, online gaming addiction, and social media addiction). The sample comprised 712 Italian students (381 males and 331 females) recruited from schools and universities who completed an offline self-report questionnaire. The findings showed that addictions to the internet, online gaming, and social media were interrelated and were predicted by common underlying risk and protective factors. Among identity styles, ‘informational’ and ‘diffuse-avoidant’ styles were risk factors, whereas ‘normative’ style was a protective factor. Among attachment dimensions, the ‘secure’ attachment orientation negatively predicted the three online addictions, and a different pattern of causal relationships were observed between the styles underlying ‘anxious’ and ‘avoidant’ attachment orientations. Hierarchical multiple regressions demonstrated that identity styles explained between 21.2 and 30% of the variance in online addictions, whereas attachment styles incrementally explained between 9.2 and 14% of the variance in the scores on the three addiction scales. These findings highlight the important role played by identity formation in the development of online addictions.
IntelliArm: An exoskeleton for diagnosis and treatment of patients with neurological impairments

This paper presents a novel 7 (active) +2 (passive) degrees of freedom (DOF) exoskeleton to achieve effective rehabilitation of upper limbs with neurological impairment. The 7+2 DOF robot was designed for allowing anatomically correct motions in the upper extremity, and for measuring 18 axis forces/torques and 9 DOF positions. A novel four-step integrated rehabilitation approach was developed and tested including (1) accurate and quantitative diagnosis which is not practical to do during manual clinical examinations or with existing robotic devices; (2) strenuous stretching of spastic joints based on the diagnosis and with intelligent control to adjust the stretching velocity constantly according to the joint conditions; (3) voluntary movement exercise to perform realistic functional tasks based on the patient-specific diagnosis, and (4) outcome evaluations which assessed the treatment outcome quantitatively with multiple physiological measures.
Sindice.com: a document-oriented lookup index for open linked data

Data discovery on the SemanticWeb requires crawling and indexing of statements, in addition to the ‘linked-data’ approach of de-referencing resourceURIs. Existing SemanticWeb search engines are focused on database-like functionality, compromising on index size, query performance and live updates.Wepresent Sindice, a lookup indexover SemanticWeb resources. Our index allows applications to automatically locate documents containing information about a given resource. In addition, we allow resource retrieval through inverse-functional properties, offer a full-text search and index SPARQL endpoints. Finally, we extend the sitemap protocol to efficiently index large datasets with minimal impact on data providers.
Reality mining based on Social Network Analysis

Mining is the extraction of hidden predictive information from large databases. The process of discovering interesting, useful, nontrivial patterns from large spatial datasets is called spatial data mining. When time gets associated it becomes spatio temporal data mining. The study of spatio temporal data mining is of great concern for the study of mobile phone sensed data. Reality Mining is defined as the study of human social behavior based on mobile phone sensed data. It is based on the data collected by sensors in mobile phones, security cameras, RFID readers, etc. All allow for the measurement of human physical and social activity. In this paper Netviz, Gephi and Weka tools have been used to convert and analyze the Facebook. Further, analysis of a reality mining dataset is also presented.
One Sentence One Model for Neural Machine Translation

Neural machine translation (NMT) becomes a new state-ofthe-art and achieves promising translation results using a simple encoder-decoder neural network. This neural network is trained once on the parallel corpus and the fixed network is used to translate all the test sentences. We argue that the general fixed network cannot best fit the specific test sentences. In this paper, we propose the dynamic NMT which learns a general network as usual, and then fine-tunes the network for each test sentence. The fine-tune work is done on a small set of the bilingual training data that is obtained through similarity search according to the test sentence. Extensive experiments demonstrate that this method can significantly improve the translation performance, especially when highly similar sentences are available.
Presence: concept, determinants, and measurement

The concept of presence, i.e. the sensation of ‘being there’ in a mediated environment, has received substantial attention from the virtual reality community, and is becoming increasingly relevant both to broadcasters and display developers. Although research into presence is still at an early stage of development, there is a consensus that presence has multiple determinants. To identify and test which parameters affect presence, a reliable, robust and valid means of measuring presence is required. In this paper, we describe the categories of factors thought to have an impact on presence. Furthermore, we present an overview of various approaches taken to measuring presence, which can be divided into two general categories: subjective measures and objective corrobor ative measures. Since presence is a subjective experience, the most direct way of assessment is through users’ subjective report. This approach has serious limitations however, and should be used judiciously. , such as postural, physiological or social responses to media, can be used to corroborate subjective measures, thereby overcoming some of their limitations. At present, the most promising direction for presence measurement is to develop and use an aggregate measure of presence that is comprised of both ubjective and objective components, tailored to the specific medium under study.
Information Systems Success: The Quest for the Dependent Variable

A large number of studies have been conducted during the last decade and a half attempting to identify those factors that contribute to information systems success. However, the dependent variable in these studies—I/S success —has been an elusive one to define. Different researchers have addressed different aspects of success, making comparisons difficult and the prospect of building a cumulative tradition for I/S research similarly elusive. To organize this diverse research, as well as to present a more integrated view of the concept of I/S success, a comprehensive taxonomy is introduced. This taxonomy posits six major dimensions or categories of I/S success—SYSTEM OUALITY, INFORMATION QUALITY, USE, USER SATISFACTION, INDIVIDUAL IMPACT, and ORGANIZATIONAL IMPACT. Using these dimensions, both conceptual and empirical studies are then reviewed (a total of 180 articles are cited) and organized according to the dimensions of the taxonomy. Finally, the many aspects of I/S success are drawn together into a descriptive model and its implications for future I/S research are discussed.
How to derive priorities in AHP: a comparative study

A heated discussion has arisen over the “best” priorities derivation method. Using a Monte Carlo simulation this article compares and evaluates the solutions of four AHP ratio scaling methods: the right eigenvalue method, the left eigenvalue method, the geometric mean and the mean of normalized values. Matrices with different dimensions and degree of impurities are randomly constructed. We observe a high level of agreement between the different scaling techniques. The number of ranking contradictions increases with the dimension of the matrix and the inconsistencies. However these contradictions affect only close priorities.
Correcting the Triplet Selection Bias for Triplet Loss

Triplet loss, popular for metric learning, has made a great success in many computer vision tasks, such as fine-grained image classification, image retrieval, and face recognition. Considering that the number of triplets grows cubically with the size of training data, triplet selection is thus indispensable for efficiently training with triplet loss. However, in practice, the training is usually very sensitive to the selection of triplets, e.g., it almost does not converge with randomly selected triplets and selecting the hardest triplets also leads to bad local minima. We argue that the bias in the selection of triplets degrades the performance of learning with triplet loss. In this paper, we propose a new variant of triplet loss, which tries to reduce the bias in triplet selection by adaptively correcting the distribution shift on the selected triplets. We refer to this new triplet loss as adapted triplet loss. We conduct a number of experiments on MNIST and Fashion-MNIST for image classification, and on CARS196, CUB200-2011, and Stanford Online Products for image retrieval. The experimental results demonstrate the effectiveness of the proposed method.
Mathematical Models for Natural Gas Forecasting

It is vital for natural gas Local Distribution Companies (LDCs) to forecast their customers’ natural gas demand accurately. A significant error on a single very cold day can cost the customers of the LDC millions of dollars. This paper looks at the financial implication of forecasting natural gas, the nature of natural gas forecasting, the factors that impact natural gas consumption, and describes a survey of mathematical techniques and practices used to model natural gas demand. Many of the techniques used in this paper currently are implemented in a software GasDay , which is currently used by 24 LDCs throughout the United States, forecasting about 20% of the total U.S. residential, commercial, and industrial consumption. Results of GasDay’s forecasting performance also is presented.
Why People Continue to Play Online Games: In Search of Critical Design Factors to Increase Customer Loyalty to Online Contents

As people increasingly play online games, numerous new features have been proposed to increase players' log-on time at online gaming sites. However, few studies have investigated why people continue to play certain online games or which design features are most closely related to the amount of time spent by players at particular online gaming sites. This study proposes a theoretical model using the concepts of customer loyalty, flow, personal interaction, and social interaction to explain why people continue to play online network games. The study then conducts a large-scale survey to validate the model. Finally, it analyzes current online games to identify design features that are closely related to the theoretical concepts. The results indicate that people continue to play online games if they have optimal experiences while playing the games. This optimal experience can be attained if the player has effective personal interaction with the system or pleasant social interactions with other people connected to the Internet. Personal interaction can be facilitated by providing appropriate goals, operators and feedback; social interaction can be facilitated through appropriate communication places and tools. This paper ends with the implications of applying the study results to other domains such as e-commerce and cyber communities.
Multiple Classifier Systems

Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a weighted vote of their predictions The original ensemble method is Bayesian aver aging but more recent algorithms include error correcting output coding Bagging and boosting This paper reviews these methods and explains why ensembles can often perform better than any single classi er Some previous studies comparing ensemble methods are reviewed and some new experiments are presented to uncover the reasons that Adaboost does not over t rapidly
Systematic Variation of Prosthetic Foot Spring Affects Center-of-Mass Mechanics and Metabolic Cost During Walking

Lower-limb amputees expend more energy to walk than non-amputees and have an elevated risk of secondary disabilities. Insufficient push-off by the prosthetic foot may be a contributing factor. We aimed to systematically study the effect of prosthetic foot mechanics on gait, to gain insight into fundamental prosthetic design principles. We varied a single parameter in isolation, the energy-storing spring in a prototype prosthetic foot, the controlled energy storage and return (CESR) foot, and observed the effect on gait. Subjects walked on the CESR foot with three different springs. We performed parallel studies on amputees and on non-amputees wearing prosthetic simulators. In both groups, spring characteristics similarly affected ankle and body center-of-mass (COM) mechanics and metabolic cost. Softer springs led to greater energy storage, energy return, and prosthetic limb COM push-off work. But metabolic energy expenditure was lowest with a spring of intermediate stiffness, suggesting biomechanical disadvantages to the softest spring despite its greater push-off. Disadvantages of the softest spring may include excessive heel displacements and COM collision losses. We also observed some differences in joint kinetics between amputees and non-amputees walking on the prototype foot. During prosthetic push-off, amputees exhibited reduced energy transfer from the prosthesis to the COM along with increased hip work, perhaps due to greater energy dissipation at the knee. Nevertheless, the results indicate that spring compliance can contribute to push-off, but with biomechanical trade-offs that limit the degree to which greater push-off might improve walking economy.
EEG and MEG Data Analysis in SPM8

SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.