nils-herrmann/clean_scirepeval huggingface dataset:
Melody Extraction From Polyphonic Music Signals Using Pitch Contour Characteristics

We present a novel system for the automatic extraction of the main melody from polyphonic music recordings. Our approach is based on the creation and characterization of pitch contours, time continuous sequences of pitch candidates grouped using auditory streaming cues. We define a set of contour characteristics and show that by studying their distributions we can devise rules to distinguish between melodic and non-melodic contours. This leads to the development of new voicing detection, octave error minimization and melody selection techniques. A comparative evaluation of the proposed approach shows that it outperforms current state-of-the-art melody extraction systems in terms of overall accuracy. Further evaluation of the algorithm is provided in the form of a qualitative error analysis and the study of the effect of key parameters and algorithmic components on system performance. Finally, we conduct a glass ceiling analysis to study the current limitations of the method, and possible directions for future work are proposed.
Multiple Access Gaussian Channels with Arbitrary Inputs: Optimal Precoding and Power Allocation

In this paper, we derive new closed-form expressions for the gradient of the mutual information with respect to arbitrary parameters of the two-user multiple access cha nnel (MAC). The derived relations generalize the fundamental relation between the derivative of the mutual i nformation and the minimum mean squared error (MMSE) to multiuser setups. We prove that the derivative of t he mutual information with respect to the signal to noise ratio (SNR) is equal to the MMSE plus a covariance induc e due to the interference, quantified by a term with respect to the cross correlation of the multiuser input esti mates, the channels and the precoding matrices. We also derive new relations for the gradient of the conditional and non-conditional mutual information with respect to the MMSE. Capitalizing on the new fundamental relations, we inv estigate the linear precoding and power allocation policies that maximize the mutual information for the two-u ser MAC Gaussian channels with arbitrary input distributions. We show that the optimal design of linear pre coders may satisfy a fixed-point equation as a function of the channel and the input constellation under specific set up . We show also that the non-mutual interference in a multiuser setup introduces a term to the gradient of the mut ual information which plays a fundamental role in the design of optimal transmission strategies, particular ly the optimal precoding and power allocation, and explains the losses in the data rates. Therefore, we provide a novel in terpretation of the interference with respect to the channel, power, and input estimates of the main user and the i n erferer.
Peer-to-Peer Lending – A Literature Review

The term online peer-to-peer lending (P2P) describes the loan origination process between private individuals on online platforms were financial institutions operate only as intermediates required by law. Initialized by groups in online social networks, first commercial online P2P lending platforms started in 2005. Thus online P2P lending is a relatively young research field. This paper gives a brief overview of the P2P lending market and reviews the research on the determinants of P2P lending. We distinguish between financial and demographic characteristics of the borrower, as well as social characteristics like friends and group affiliation. The reviewed literature gives insights on how the determinants affect the borrowers’ likelihood of successful funding, the final interest rate that has to be paid as well as the relationship of the borrowers’ characteristics and lending success.
Self-organizing map in Matlab : the SOM Toolbox

The Self-Organizing Map (SOM) is a vector quantization method which places the prototype vectors on a regular low-dimensional grid in an ordered fashion. This makes the SOM a powerful visualization tool. The SOM Toolbox is an implementation of the SOM and its visualization in the Matlab 5 computing environment. In this article, the SOM Toolbox and its usage are shortly presented. Also its performance in terms of computational load is evaluated and compared to a corresponding Cprogram.
Speech recognition by machine: A review

This paper provides a review of recent developments in speech recognition research. The concept of sources of knowledge is introduced and the use of knowledge to generate and verify hypotheses is discussed. The difficulties that arise in the construction of different types of speech recognition systems are discussed and the structure and performance of several such systems is presented. Aspects of component subsystems at the acoustic, phonetic, syntactic, and semantic levels are presented. System organizations that are required for effective interaction and use of various component subsystems in the presence of error and ambiguity are discussed.
Programmed population control by cell–cell communication and regulated killing

De novo engineering of gene circuits inside cells is extremely difficult, and efforts to realize predictable and robust performance must deal with noise in gene expression and variation in phenotypes between cells. Here we demonstrate that by coupling gene expression to cell survival and death using cell–cell communication, we can programme the dynamics of a population despite variability in the behaviour of individual cells. Specifically, we have built and characterized a ‘population control’ circuit that autonomously regulates the density of an Escherichia coli population. The cell density is broadcasted and detected by elements from a bacterial quorum-sensing system, which in turn regulate the death rate. As predicted by a simple mathematical model, the circuit can set a stable steady state in terms of cell density and gene expression that is easily tunable by varying the stability of the cell–cell communication signal. This circuit incorporates a mechanism for programmed death in response to changes in the environment, and allows us to probe the design principles of its more complex natural counterparts.
Forwarding strategies in named data wireless ad hoc networks: Design and evaluation

Named Data Networking (NDN) is a promising information-centric architecture for the future Internet that is also gaining momentum in wireless ad hoc networks as an alternative paradigm to traditional IP networking. NDN shares with other information-centric proposals the same innovative concepts, such as named content, name-based routing, and in-network content caching. These principles and the simple and robust communication model, based on Interest and Data packets exchange, make NDN especially appealing for deployment in wireless ad hoc environments, characterized by a broadcast error-prone channel and time-varying topologies. Nevertheless, making NDN-based solutions really effective in ad hoc networks requires rethinking some of the basic NDN forwarding principles to cope with wireless links and node mobility. In this paper, we analyze two classes of forwarding approaches: (i) a minimalist, provider-blind forwarding strategy, only aimed at keeping packet redundancy on the broadcast wireless medium under control, without any knowledge about the neighborhood and the identity of the content sources; and (ii) a provider-aware strategy, which leverages soft state information about the content sources, piggybacked in Interest and Data packets and locally kept by nodes, to facilitate content retrieval. Performance evaluation is carried by means of ndnSIM, the official NDN simulator, that is overhauled for use in realistic wireless ad hoc environments. Results collected under variable traffic loads and topologies provide insights into the behavior of both forwarding approaches and help to derive a set of recommendations that are crucial to the successful design of a forwarding strategy for named data ad hoc wireless networking. & 2014 Elsevier Ltd. All rights reserved.
An interactive augmented reality system for learning anatomy structure

the study of the structure of the human body, is fundamental to medical education. In recent years, the hours devoted to anatomy are declining from the medical curriculum. This decline includes the reduction of course hours and an emphasis on early clinical experience. To adapt to those changes in anatomy education, various complementary methods with technology of three-dimensional visualization have been tried, and the explosion of image technology during the last few decades and this has brought anatomical education into a new world. In this study, we aim to use augmented reality (AR) technology to create an interactive learning system, which help medical students to understand and memorize the 3D anatomy structure easily with tangible augmented reality support. We speculate that by working directly with 3D skull model with visual support and tangible manipulate, this AR system can help young medical students to learn the complex anatomy structure better and faster than only with traditional methods.
MetaSync: File Synchronization Across Multiple Untrusted Storage Services

Cloud-based file synchronization services, such as Dropbox and OneDrive, are a worldwide resource for many millions of users. However, individual services often have tight resource limits, suffer from temporary outages or even shutdowns, and sometimes silently corrupt or leak user data. We design, implement, and evaluate MetaSync, a secure and reliable file synchronization service that uses multiple cloud synchronization services as untrusted storage providers. To make MetaSync work correctly, we devise a novel variant of Paxos that provides efficient and consistent updates on top of the unmodified APIs exported by existing services. Our system automatically redistributes files upon adding, removing, or resizing a provider. Our evaluation shows that MetaSync provides low update latency and high update throughput, close to the performance of commercial services, but is more reliable and available. MetaSync outperforms its underlying cloud services by 1.2-10× on three realistic workloads.
Content Delivery Networks: State of the Art, Insights, and Imperatives

Over the last decades, users have witnessed the growth and maturity of the Internet which has caused enormous growth in network traffic, driven by the rapid acceptance of broadband access, the increases in systems complexity, and the content richness. The over-evolving nature of the Internet brings new challenges in managing and delivering content to users, since for example, popular Web services often suffer congestion and bottlenecks due to the large demands posed on their services. Such a sudden spike in Web content requests (e.g. the one occurred during the 9/11 incident in USA) is often termed as flash crowds [14] or SlashDot [11] effects. It may cause heavy workload on particular Web server(s), and as a result a “hotspot”  can be generated. Coping with such unexpected demand causes significant strain on a Web server and eventually the Web servers are totally overwhelmed with the sudden increase in traffic, and the Web site holding the content becomes temporarily unavailable. A Content Delivery Network (CDN) [47, 51, 54, 61, 63] is a collaborative collection of network elements spanning the Internet, where content is replicated over several mirrored Web servers in order to perform transparent and effective delivery of content to the end users. Collaboration among distributed CDN components can occur over nodes in both homogeneous and heterogeneous environments. CDNs have evolved to overcome the inherent limitations of the Internet in terms of user perceived Quality of Service (QoS) when accessing Web content. They provide services that improve network performance by maximizing bandwidth, improving
How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation

We propose to exploit reconstruction as a layer-local training signal for deep learning. Reconstructions can be propagated in a form of target propagation playing a role similar to back-propagation but helping to reduce the reliance on derivatives in order to perform credit assignment across many levels of possibly strong nonlinearities (which is difficult for back-propagation). A regularized auto-encoder tends produce a reconstruction that is a more likely version of its input, i.e., a small move in the direction of higher likelihood. By generalizing gradients, target propagation may also allow to train deep networks with discrete hidden units. If the auto-encoder takes both a representation of input and target (or of any side information) in input, then its reconstruction of input representation provides a target towards a representation that is more likely, conditioned on all the side information. A deep auto-encoder decoding path generalizes gradient propagation in a learned way that can could thus handle not just infinitesimal changes but larger, discrete changes, hopefully allowing credit assignment through a long chain of non-linear operations. In addition to each layer being a good auto-encoder, the encoder also learns to please the upper layers by transforming the data into a space where it is easier to model by them, flattening manifolds and disentangling factors. The motivations and theoretical justifications for this approach are laid down in this paper, along with conjectures that will have to be verified either mathematically or experimentally, including a hypothesis stating that such auto-encoder mediated target propagation could play in brains the role of credit assignment through many non-linear, noisy and discrete transformations.
Deep-Spying: Spying using Smartwatch and Deep Learning

Wearable technologies are today on the rise, becoming more common and broadly available to mainstream users. In fact, wristband and armband devices such as smartwatches and fitness trackers already took an important place in the consumer electronics market and are becoming ubiquitous. By their very nature of being wearable, these devices, however, provide a new pervasive attack surface threatening users privacy, among others. In the meantime, advances in machine learning are providing unprecedented possibilities to process complex data efficiently. Allowing patterns to emerge from high dimensional unavoidably noisy data. The goal of this work is to raise awareness about the potential risks related to motion sensors built-in wearable devices and to demonstrate abuse opportunities leveraged by advanced neural network architectures. The LSTM-based implementation presented in this research can perform touchlogging and keylogging on 12-keys keypads with above-average accuracy even when confronted with raw unprocessed data. Thus demonstrating that deep neural networks are capable of making keystroke inference attacks based on motion sensors easier to achieve by removing the need for non-trivial preprocessing pipelines and carefully engineered feature extraction strategies. Our results suggest that the complete technological ecosystem of a user can be compromised when a wearable wristband device is worn.
Credit card fraud detection system

The use of credit cards is prevalent in modern day society. But it is obvious that the number of credit card fraud cases is constantly increasing in spite of the chip cards worldwide integration and existing protection systems. This is why the problem of fraud detection is very important now. In this paper the general description of the developed fraud detection system and comparisons between models based on using of artificial intelligence are given. In the last section of this paper the results of evaluative testing and corresponding conclusions are considered.
Characterizing radio resource allocation for 3G networks

3G cellular data networks have recently witnessed explosive growth. In this work, we focus on UMTS, one of the most popular 3G mobile communication technologies. Our work is the first to accurately infer, for any UMTS network, the state machine (both transitions and timer values) that guides the radio resource allocation policy through a light-weight probing scheme. We systematically characterize the impact of operational state machine settings by analyzing traces collected from a commercial UMTS network, and pinpoint the inefficiencies caused by the interplay between smartphone applications and the state machine behavior. Besides basic characterizations, we explore the optimal state machine settings in terms of several critical timer values evaluated using real network traces. Our findings suggest that the fundamental limitation of the current state machine design is its static nature of treating all traffic according to the same inactivity timers, making it difficult to balance tradeoffs among radio resource usage efficiency, network management overhead, device radio energy consumption, and performance. To the best of our knowledge, our work is the first empirical study that employs real cellular traces to investigate the optimality of UMTS state machine configurations. Our analysis also demonstrates that traffic patterns impose significant impact on radio resource and energy consumption. In particular, We propose a simple improvement that reduces YouTube streaming energy by 80% by leveraging an existing feature called fast dormancy supported by the 3GPP specifications.
ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection

Preprocessing To prepare the images for the network, each of the training images was resized to 192 pixels by 192 pixels. To create additional training images, each of the training images was elastically distorted. For each of the original training images, four randomly generated elastic distorted images were generated and then resized down to 192 by 192 pixels. In addition, each training image was also rotated 90 degrees and additional elastic distortions were applied to the rotated images.
"He Knows When You Are Sleeping" : Privacy and the Personal Robot Companion

One of the most compelling visions of future robots is that of the robot butler. An entity dedicated to fulfilling your every need. This obviously has its benefits, but there could be a flipside to this vision. To fulfill the needs of its users, it must first be aware of them, and so it could potentially amass a huge amount of personal data regarding its user, data which may or may not be safe from accidental or intentional disclosure to a third party. How may prospective owners of a personal robot feel about the data that might be collected about them? In order to investigate this issue experimentally, we conducted an exploratory study where 12 participants were exposed to an HRI scenario in which disclosure of personal information became an issue. Despite the small sample size interesting results emerged from this study, indicating how future owners of personal robots feel regarding what the robot will know about them, and what safeguards they believe should be in place to protect owners from unwanted disclosure of private information.
Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition

Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless, the problem of rain removal from a single image was rarely studied in the literature, where no temporal information among successive images can be exploited, making the problem very challenging. In this paper, we propose a single-image-based rain removal framework via properly formulating rain removal as an image decomposition problem based on morphological component analysis. Instead of directly applying a conventional image decomposition technique, the proposed method first decomposes an image into the low- and high-frequency (HF) parts using a bilateral filter. The HF part is then decomposed into a “rain component” and a “nonrain component” by performing dictionary learning and sparse coding. As a result, the rain component can be successfully removed from the image while preserving most original image details. Experimental results demonstrate the efficacy of the proposed algorithm.
High-Voltage Power Supply for Ozone Generation Based on Piezoelectric Transformer

Even though, nowadays, piezoelectric transformers (PTs) are only available with low power rating, there exist several low-power applications of ozone generation in which the use of this novel technology could be advantageous. Hence, the aim of this paper is to evaluate the possibilities of using PTs in the implementation of high-voltage power supplies for ozone generation. First, the possible topologies that can be used to drive the PT are identified. Then, the half-bridge inverter operating under zero-voltage switching (ZVS) is investigated, and the effect of the silent discharge generator (SDG) on the converter operation is analyzed. A new control circuit that allows the ZVS operation is proposed. The control circuit operates in closed loop by measuring the phase between the PT's resonant current and the switching pattern and adjusting the switching frequency to the optimum value to assure ZVS. A laboratory prototype for a 6-W SDG was tested, and obtained experimental results are shown.
Handwriting recognition research: Twenty years of achievement... and beyond

This panel discussion was a follow-up to the first edition of the ICFHR series, which was promoted as an international conference after 10 intensive and productive editions of the International Workshop on Frontiers in Handwriting Recognition initiated in 1990 in Montreal. It is essential, however, to recall that research on handwritten character recognition began some 40 years ago, and was expanded about 20 years ago to the more general notion of handwriting recognition (no longer constrained to isolated characters) that we work with today. The organizers of ICFHR 2008 wanted to take the opportunity to organize the panel discussion, chaired by M. Cheriet, to highlight progress in the field over the long course of its development. Panelists M. El Yacoubi, H. Fujisawa, D. Lopresti, and G. Lorette talked about the achievements of two decades of handwriting recognition and about its future. Following these outstanding presentations, conference attendees had the opportunity to air their questions and concerns during open and fruitful exchanges. Each section of this summary is drawn from the original presentation of a panelist. In Section 1, Dr. Hiromichi Fujisawa of Hitachi (Japan) discusses the impact of handwriting recognition technologies on the industrial sector. In Section 2, Dr. Mounim A. El Yacoubi of Institut Telecom, T&M SudParis (France) discusses the need to restructure the efforts of the handwriting recognition community. In Section 3, Dr. Guy Lorette of IRISA—Université de Rennes (France) highlights the recent achievements in handwriting recognition and others on the horizon. In Section 4, Dr. Daniel Lopresti of Lehigh University (USA) discusses the future of handwriting recognition research as a “Grand Challenge” problem. Section 5 concludes this summary by highlighting the salient points that will drive research on handwriting recognition in the future.