nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised Filterbank Learning Using Convolutional Restricted Boltzmann Machine for Environmental Sound Classification

In this paper, we propose to use Convolutional Restricted Boltzmann Machine (ConvRBM) to learn filterbank from the raw audio signals. ConvRBM is a generative model trained in an unsupervised way to model the audio signals of arbitrary lengths. ConvRBM is trained using annealed dropout technique and parameters are optimized using Adam optimization. The subband filters of ConvRBM learned from the ESC-50 database resemble Fourier basis in the mid-frequency range while some of the low-frequency subband filters resemble Gammatone basis. The auditory-like filterbank scale is nonlinear w.r.t. the center frequencies of the subband filters and follows the standard auditory scales. We have used our proposed model as a front-end for the Environmental Sound Classification (ESC) task with supervised Convolutional Neural Network (CNN) as a back-end. Using CNN classifier, the ConvRBM filterbank (ConvRBMBANK) and its score-level fusion with the Mel filterbank energies (FBEs) gave an absolute improvement of 10.65 %, and 18.70 % in the classification accuracy, respectively, over FBEs alone on the ESC-50 database. This shows that the proposed ConvRBM filterbank also contains highly complementary information over the Mel filterbank, which is helpful in the ESC task.
Clustering in sensor networks: A literature survey

Wireless sensor networks (WSNs) have recently gained the attention of researchers in many challenging aspects. The most important challenge in these networks is energy conservation. One of the most popular solutions in making WSNs energy-efficient is to cluster the networks. In clustering, the nodes are divided into some clusters and then some nodes, called cluster-heads, are selected to be the head of each cluster. In a typical clustered WSN, the regular nodes sense the field and send their data to the cluster-head, then, after gathering and aggregating the data, the cluster-head transmits them to the base station. Clustering the nodes in WSNs has many benefits, including scalability, energy-efficiency, and reducing routing delay. In this paper we present a state-of-the-art and comprehensive survey on clustering approaches. We first begin with the objectives of clustering, clustering characteristics, and then present a classification on the clustering algorithms in WSNs. Some of the clustering objectives considered in this paper include scalability, faulttolerance, data aggregation/fusion, increased connectivity, load balancing, and collision avoidance. Then, we survey the proposed approaches in the past few years in a classified manner and compare them based on different metrics such as mobility, cluster count, cluster size, and algorithm complexity. & 2014 Elsevier Ltd. All rights reserved.
Quantum random access memory.

A random access memory (RAM) uses n bits to randomly address N=2(n) distinct memory cells. A quantum random access memory (QRAM) uses n qubits to address any quantum superposition of N memory cells. We present an architecture that exponentially reduces the requirements for a memory call: O(logN) switches need be thrown instead of the N used in conventional (classical or quantum) RAM designs. This yields a more robust QRAM algorithm, as it in general requires entanglement among exponentially less gates, and leads to an exponential decrease in the power needed for addressing. A quantum optical implementation is presented.
Three-level active NPC converter: PWM strategies and loss distribution

The 3L-NPC (Neutral-Point-Clamped) is the most popular multilevel converter used in high-power medium-voltage applications. An important disadvantage of this structure is the unequal distribution of losses among the switches. The performances of 3L-NPC structure were improved by developing the 3L-ANPC (Active-NPC) converter which has more degrees of freedom. In this paper the switching states and the loss distribution problem are studied for different PWM strategies in a STATCOM application. The PSIM simulation results are shown in order to validate the PWM strategies studied for 3L-ANPC converter.
Understanding consumer acceptance of mobile payment services: An empirical analysis

Mobile technology has become increasingly common in today’s everyday life. However, mobile payment is surprisingly not among the frequently used mobile services, although technologically advanced solutions exist. Apparently, there is still a lack of acceptance of mobile payment services among consumers. The conceptual model developed and tested in this research thus focuses on factors determining consumers’ acceptance of mobile payment services. The empirical results show particularly strong support for the effects of compatibility, individual mobility, and subjective norm. Our study offers several implications for managers in regards to marketing mobile payment solutions to increase consumers’ intention to use these services.
Motives for Facebook use and expressing "true self" on the Internet

A survey on 143 university students was conducted to examine what motives young adults have for Facebook use, which of those motives were endorsed more than the others, and how those motives were related to the tendency of expressing one’s ‘‘true self’’ through Facebook use. According to the results, primary motive for Facebook use was to maintain long-distance relationships. This motive was followed by game-playing/entertainment, active forms of photo-related activities, organizing social activities, passive observations, establishing new friendships, and initiating and/or terminating romantic relationships. Another interesting result was that individuals’ tendency for expressing one’s true self on the Net had an influence on their Facebook use motives: The ones with high tendency to express their true self on the Internet reported to use Facebook for establishing new friendships and for initiating/terminating romantic relationships more than the individuals’ with low and medium levels of the same tendency did.
A multilevel inverter system for an induction motor with open-end windings

In this paper, a multilevel inverter system for an open-end winding induction motor drive is described. Multilevel inversion is achieved by feeding an open-end winding induction motor with two two-level inverters in cascade (equivalent to a three-level inverter) from one end and a single two-level inverter from the other end of the motor. The combined inverter system with open-end winding induction motor produces voltage space-vector locations identical to a six-level inverter. A total of 512 space-vector combinations are available in the proposed scheme, distributed over 91 space-vector locations. The proposed inverter drive scheme is capable of producing a multilevel pulsewidth-modulation (PWM) waveform for the phase voltage ranging from a two-level waveform to a six-level waveform depending on the modulation range. A space-vector PWM scheme for the proposed drive is implemented using a 1.5-kW induction motor with open-end winding structure.
Ontology Reasoning with Deep Neural Networks

The ability to conduct logical reasoning is a fundamental aspect of intelligent behavior, and thus an important problem along the way to human-level artificial intelligence. Traditionally, symbolic logic-based methods from the field of knowledge representation and reasoning have been used to equip agents with capabilities that resemble human logical reasoning qualities. More recently, however, there has been an increasing interest in using machine learning rather than symbolic logic-based formalisms to tackle these tasks. In this paper, we employ state-of-the-art methods for training deep neural networks to devise a novel model that is able to learn how to effectively perform logical reasoning in the form of basic ontology reasoning. This is an important and at the same time very natural logical reasoning task, which is why the presented approach is applicable to a plethora of important real-world problems. We present the outcomes of several experiments, which show that our model learned to perform precise ontology reasoning on diverse and challenging tasks. Furthermore, it turned out that the suggested approach suffers much less from different obstacles that prohibit logic-based symbolic reasoning, and, at the same time, is surprisingly plausible from a biological point of view.
Deception Detection in News Reports in the Russian Language: Lexics and Discourse

Different language markers can be used to reveal the differences between structures of truthful and deceptive (fake) news. Two experiments are held: the first one is based on lexics level markers, the second one on discourse level is based on rhetorical relations categories (frequencies). Corpus consists of 174 truthful and deceptive news stories in Russian. Support Vector Machines and Random Forest Classifier were used for text classification. The best results for lexical markers we got by using Support Vector Ma-chines with rbf kernel (f-measure 0.65). The model could be developed and be used as a preliminary filter for fake news detection.
Interactive virtual try-on clothing design systems

Clothing computer design systems include three integrated parts: garment pattern design in 2D/3D, virtual try-on and realistic clothing simulation. Some important results have been obtained in pattern design and clothing simulation since the 1980s. However, in the area of virtual try-on, only limited methods have been proposed which are applicable to some defined garment styles or under restrictive sewing assumptions. This paper presents a series of new techniques from virtually sewing up complex garment patterns on human models to visualizing design effects through physical-based real-time simulation. We first employ an hierarchy of ellipsoids to approximate human models in which the bounding ellipsoids are optimized recursively.Wealso present a newscheme for including contact friction and resolving collisions. Four types of user interactive operation are introduced to manipulate cloth patterns for pre-positioning, virtual sewing and later obtaining cloth simulation. In the cloth simulation, we propose a simplified cloth dynamic model and an integration scheme to realize a high quality realtime cloth simulation.We demonstrate the robustness of our proposed systems by complex garment style virtual try-on and cloth simulation.
Distributed-Representation Based Hybrid Recommender System with Short Item Descriptions

Collaborative filtering (CF) aims to build a model from users' past behaviors and/or similar decisions made by other users, and use the model to recommend items for users. Despite of the success of previous collaborative filtering approaches, they are all based on the assumption that there are sufficient rating scores available for building high-quality recommendation models. In real world applications, however, it is often difficult to collect sufficient rating scores, especially when new items are introduced into the system, which makes the recommendation task challenging. We find that there are often " short " texts describing features of items, based on which we can approximate the similarity of items and make recommendation together with rating scores. In this paper we " borrow " the idea of vector representation of words to capture the information of short texts and embed it into a matrix factorization framework. We empirically show that our approach is effective by comparing it with state-of-the-art approaches.
Joint computation and communication cooperation for mobile edge computing

This paper proposes a joint computation and communication cooperation approach in mobile edge computing (MEC) systems for improving the energy efficiency in mobile computing. In particular, we consider a basic three-node MEC system that consists of a user node, a helper node, and an access point (AP) node attached with an MEC server. We focus on the user's latency-constrained computation over a finite-length block and develop a four-slot protocol for implementing the joint computation and communication cooperation. Under this setup, we jointly optimize the task partition and time allocation, and the transmit power for offloading and central processing unit (CPU) frequencies of local computing at the user and the helper, so as to minimize their total energy consumption subject to the user's computation latency constraint. This problem is optimally solved via convex optimization techniques. Numerical results show that the proposed approach significantly improves the computation capacity and the energy efficiency for the user, as compared to other benchmark schemes without such a joint design.
Fog Removal from Color Images using Contrast Limited Adaptive Histogram Equalization

The images degraded by fog suffer from poor contrast. In order to remove fog effect, a Contrast Limited Adaptive Histogram Equalization (CLAHE)-based method is presented in this paper. This method establishes a maximum value to clip the histogram and redistributes the clipped pixels equally to each gray-level. It can limit the noise while enhancing the image contrast. In our method, firstly, the original image is converted from RGB to HSI. Secondly, the intensity component of the HSI image is processed by CLAHE. Finally, the HSI image is converted back to RGB image. To evaluate the effectiveness of the proposed method, we experiment with a color image degraded by fog and apply the edge detection to the image. The results show that our method is effective in comparison with traditional methods. KeywordsCLAHE, fog, degraded, remove, color image, HSI, edge detection.
Capture-Replay Testing for Android Applications

With the widespread popularity of Android devices, the number of Android applications has increased dramatically in recent years. In order to assure the quality of the applications, Android testing has drawn extensive attention. This paper proposes an approach to automate the testing of Android applications based on the Capture and Replay method. Particularly, the user events of Android applications are captured and converted into Robotium test scripts that can be executed to replay the recorded actions of users. The approach also allows inserting assertions when capturing user interactions for verifying the outputs of Android UI components. A supporting tool is implemented to illustrate the usefulness of the proposed approach.
A Novel "Credit Union" Model of Cloud Computing

Cloud Computing is drawing people’s attention from all walks of the IT world. It promises significant reduction of cost among many other advantages as it proclaims, including increased availability, fast provision, ondemand, and pay-per-use, etc. This paper presents a novel model of Cloud Computing, named “Credit Union” model (referred to as the CU model or CUM for short). This model is motivated by the cooperative business model of the many credit unions that have been widely practiced as a type of financial institutions world-wide. The CU model aims at utilizing the vast, underutilized computing resources in homes and offices, and transforming them into a selfprovisioned community cloud that mimics the business model of a credit union, i.e., membership and credits are obtained by contributing spare computing resources. Clouds built based on the CU model, referred to as CU clouds, bear the following advantageous characteristics comparing to the general clouds: complete vendor independence, improved availability (due to reduced internetdependence), better security, and superb sustainability (green computing). This paper expounds the principles and motivations of the CU model, addresses its implementation architecture and related issues, and outlooks prospect
POTENTIALITIES OF USRP-BASED SOFTWARE DEFINED RADAR SYSTEMS

Software Defined Radar is the latest trend in radar development. To handle enhanced radar signal processing techniques, advanced radars need to be able of generating various types of waveforms, such as frequency modulated or phase coded, and to perform multiple functions. The adoption of a Software Defined Radio system makes easier all these abilities. In this work, the implementation of a Software Defined Radar system for target tracking using the Universal Software Radio Peripheral platform is discussed. For the first time, an experimental characterization in terms of radar application is performed on the latest Universal Software Radio Peripheral NI2920, demonstrating a strongly improved target resolution with respect to the first generation platform.
The effects of handwriting experience on functional brain development in pre-literate children

In an age of increasing technology, the possibility that typing on a keyboard will replace handwriting raises questions about the future usefulness of handwriting skills. Here we present evidence that brain activation during letter perception is influenced in different, important ways by previous handwriting of letters versus previous typing or tracing of those same letters. Preliterate, five-year old children printed, typed, or traced letters and shapes, then were shown images of these stimuli while undergoing functional MRI scanning. A previously documented "reading circuit" was recruited during letter perception only after handwriting-not after typing or tracing experience. These findings demonstrate that handwriting is important for the early recruitment in letter processing of brain regions known to underlie successful reading. Handwriting therefore may facilitate reading acquisition in young children.
Pedestrian Detection: An Evaluation of the State of the Art

Pedestrian detection is a key problem in computer vision, with several applications that have the potential to positively impact quality of life. In recent years, the number of approaches to detecting pedestrians in monocular images has grown steadily. However, multiple data sets and widely varying evaluation protocols are used, making direct comparisons difficult. To address these shortcomings, we perform an extensive evaluation of the state of the art in a unified framework. We make three primary contributions: 1) We put together a large, well-annotated, and realistic monocular pedestrian detection data set and study the statistics of the size, position, and occlusion patterns of pedestrians in urban scenes, 2) we propose a refined per-frame evaluation methodology that allows us to carry out probing and informative comparisons, including measuring performance in relation to scale and occlusion, and 3) we evaluate the performance of sixteen pretrained state-of-the-art detectors across six data sets. Our study allows us to assess the state of the art and provides a framework for gauging future efforts. Our experiments show that despite significant progress, performance still has much room for improvement. In particular, detection is disappointing at low resolutions and for partially occluded pedestrians.
Artificial Neural Networks Approach to the Forecast of Stock Market Price Movements

In this work we present an Artificial Neural Network (ANN) approach to predict stock market indices. In particular, we focus our attention on their trend movement up or down. We provide results of experiments exploiting different Neural Networks architectures, namely the Multi-layer Perceptron (MLP), the Convolutional Neural Networks (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks technique. We show importance of choosing correct input features and their preprocessing for learning algorithm. Finally we test our algorithm on the S&P500 and FOREX EUR/USD historical time series, predicting trend on the basis of data from the past n days, in the case of S&P500, or minutes, in the FOREX framework. We provide a novel approach based on combination of wavelets and CNN which outperforms basic neural networks approaches.  Artificial neural networks, Multi-layer neural network, Convolutional neural network, Long shortterm memory, Recurrent neural network, Deep Learning, Stock markets, Time series analysis, financial forecasting
Development of Control Circuit for Single Phase Inverter Using Atmel Microcontroller

This paper presents the development of control circuit for single phase inverter using Atmel microcontroller. The attractiveness of this configuration is the elimination of a microcontroller to generate sinusoidal pulse width modulation (SPWM) pulses. The Atmel microcontroller is able to store all the commands to generate the necessary waveforms to control the frequency of the inverter through proper design of switching pulse. In this paper concept of the single phase inverter and it relation with the microcontroller is reviewed first. Subsequently approach and methods and dead time control are discussed. Finally simulation results and experimental results are discussed.
Russian Named Entities Recognition and Classification Using Distributed Word and Phrase Representations

The paper presents results on Russian named entities classification and equivalent named entities retrieval using word and phrase representations. It is shown that a word or an expression’s context vector is an efficient feature to be used for predicting the type of a named entity. Distributed word representations are now claimed (and on a reasonable basis) to be one of the most promising distributional semantics models. In the described experiment on retrieving similar named entities the results go further than retrieving named entities of the same type or named entities-individuals of the same class: it is shown that equivalent variants of a named entity can be extracted. This result contributes to the task of unsupervised entities and semantic relations clustering and can be used for paraphrase search and automatic ontology population. The models were trained with word2vec on the Russian segment of parallel corpora used for statistical machine translation. Vector representations were constructed and evaluated for words, lexemes and noun phrases.
The GRIN Project: A Highly Optimising Back End for Lazy Functional Languages

Low level optimisations from conventional compiler technology often give very poor results when applied to code from lazy functional languages, mainly because of the completely diierent structure of the code, unknown control ow, etc. A novel approach to compiling laziness is needed. We describe a complete back end for lazy functional languages, which uses various interprocedural optimisations to produce highly optimised code. The main features of our new back end are the following. It uses a monadic intermediate code, called GRIN (Graph Reduction Intermediate Notation). This code has a very functional avourr, making it well suited for analysis and program transformations, but at the same time provides the low levell machinery needed to express many concrete implementation concerns. Using a heap points-to analysis, we are able to eliminate most unknown control ow due to evals (i.e., forcing of closures) and applications of higher order functions, in the program. A transformation machinery uses many, each very simple, GRIN program transformations to optimise the intermediate code. Eventually, the GRIN code is translated into RISC machine code, and we apply an interpro-cedural register allocation algorithm, followed by many other low level optimisations. The elimination of unknown control ow, made earlier, will help a lot in making the low level optimisations work well. Preliminary measurements look very promising: we are currently twice as fast as the Glasgow Haskell Compiler for some small programs. Our approach still gives us many opportunities for further optimisations (though yet unexplored).