nils-herrmann/clean_scirepeval huggingface dataset:
A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems

Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.
Improving the Robustness of Neural Networks Using K-Support Norm Based Adversarial Training

It is of significant importance for any classification and recognition system, which claims near or better than human performance to be immune to small perturbations in the dataset. Researchers found out that neural networks are not very robust to small perturbations and can easily be fooled to persistently misclassify by adding a particular class of noise in the test data. This, so-called adversarial noise severely deteriorates the performance of neural networks, which otherwise perform really well on unperturbed dataset. It has been recently proposed that neural networks can be made robust against adversarial noise by training them using the data corrupted with adversarial noise itself. Following this approach, in this paper, we propose a new mechanism to generate a powerful adversarial noise model based on K-support norm to train neural networks. We tested our approach on two benchmark datasets, namely the MNIST and STL-10, using muti-layer perceptron and convolutional neural networks. Experimental results demonstrate that neural networks trained with the proposed technique show significant improvement in robustness as compared to state-of-the-art techniques.
A Neural Algorithm of Artistic Style

Werner Reichardt Centre for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Germany Bernstein Center for Computational Neuroscience, Tübingen, Germany Graduate School for Neural Information Processing, Tübingen, Germany Max Planck Institute for Biological Cybernetics, Tübingen, Germany Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA ∗To whom correspondence should be addressed; E-mail: leon.gatys@bethgelab.org
State-of-the-art, single-phase, active power-factor-correction techniques for high-power applications - an overview

A review of high-performance, state-of-the-art, active power-factor-correction (PFC) techniques for high-power, single-phase applications is presented. The merits and limitations of several PFC techniques that are used in today's network-server and telecom power supplies to maximize their conversion efficiencies are discussed. These techniques include various zero-voltage-switching and zero-current-switching, active-snubber approaches employed to reduce reverse-recovery-related switching losses, as well as techniques for the minimization of the conduction losses. Finally, the effect of recent advancements in semiconductor technology, primarily silicon-carbide technology, on the performance and design considerations of PFC converters is discussed.
Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres

We use complex-weighted, deep networks to invert the effects of multimode optical fibre distortion of a coherent input image. We generated experimental data based on collections of optical fibre responses to greyscale input images generated with coherent light, by measuring only image amplitude (not amplitude and phase as is typical) at the output of 1m and 10m long, 105 μm diameter multimode fibre. This data is made available as the Optical fibre inverse problem Benchmark collection. The experimental data is used to train complex-weighted models with a range of regularisation approaches. A unitary regularisation approach for complexweighted networks is proposed which performs well in robustly inverting the fibre transmission matrix, which is compatible with the physical theory. A benefit of the unitary constraint is that it allows us to learn a forward unitary model and analytically invert it to solve the inverse problem. We demonstrate this approach, and outline how it has the potential to improve performance by incorporating knowledge of the phase shift induced by the spatial light modulator.
Point-of-Interest Demand Modeling with Human Mobility Patterns

Point-of-Interest (POI) demand modeling in urban regions is critical for many applications such as business site selection and real estate investment. While some efforts have been made for the demand analysis of some specific POI categories, such as restaurants, it lacks systematic means to support POI demand modeling. To this end, in this paper, we develop a systematic POI demand modeling framework, named Region POI Demand Identification (RPDI), to model POI demands by exploiting the daily needs of people identified from their large-scale mobility data. Specifically, we first partition the urban space into spatially differentiated neighborhood regions formed by many small local communities. Then, the daily activity patterns of people traveling in the city will be extracted from human mobility data. Since the trip activities, even aggregated, are sparse and insufficient to directly identify the POI demands, especially for underdeveloped regions, we develop a latent factor model that integrates human mobility data, POI profiles, and demographic data to robustly model the POI demand of urban regions in a holistic way. In this model, POI preferences and supplies are used together with demographic features to estimate the POI demands simultaneously for all the urban regions interconnected in the city. Moreover, we also design efficient algorithms to optimize the latent model for large-scale data. Finally, experimental results on real-world data in New York City (NYC) show that our method is effective for identifying POI demands for different regions.
Harmony Search Algorithm for Solving Sudoku

Harmony search (HS) algorithm was applied to solving Sudoku puzzle. The HS is an evolutionary algorithm which mimics musicians’ behaviors such as random play, memory-based play, and pitch-adjusted play when they perform improvisation. Sudoku puzzles in this study were formulated as an optimization problem with number-uniqueness penalties. HS could successfully solve the optimization problem after 285 function evaluations, taking 9 seconds. Also, sensitivity analysis of HS parameters was performed to obtain a better idea of algorithm parameter values.
Implementing a Sensor Fusion Algorithm for 3 D Orientation Detection with Inertial / Magnetic Sensors

In this paper a sensor fusion algorithm is developed and implemented for detecting orientation in three dimensions. Tri-axis MEMS inertial sensors and tri-axis magnetometer outputs are used as input to the fusion system. A Kalman filter is designed to compensate the inertial sensors errors by combining accelerometer and gyroscope data. A tilt compensation unit is designed to calculate the heading of the system.
Exploring the impact of communication effectiveness on service quality, trust and relationship commitment in IT services

Most firms today are served by specialized IT service providers for the development and maintenance of their business information systems. During the IT service encounter, service providers and clients interact throughout the project, exchanging information, sharing knowledge and making critical decisions. From the IT service provider’s viewpoint, it is important to raise the level of clients’ relationship commitment during this service encounter as their business continuity depends upon clients assessment. Intuitively, effective communication and effective service quality, mediated by trust, are critical factors in raising the level of relationship commitment. This study proposes an empirical model consisting of T services ommunication effectiveness ervice quality unctional service quality echnical service quality four critical antecedents of relationship commitment (communication effectiveness, technical service quality, functional service quality and trust) and then tests the model using data points solicited from two global firms. Study results revealed that trust is a strong mediator for relationship commitment while functional, rather than technical, service quality is a stronger mediator in forming the clients’ trust.
Machine learning for medical diagnosis: history, state of the art and perspective

The paper provides an overview of the development of intelligent data analysis in medicine from a machine learning perspective: a historical view, a state-of-the-art view, and a view on some future trends in this subfield of applied artificial intelligence. The paper is not intended to provide a comprehensive overview but rather describes some subareas and directions which from my personal point of view seem to be important for applying machine learning in medical diagnosis. In the historical overview, I emphasize the naive Bayesian classifier, neural networks and decision trees. I present a comparison of some state-of-the-art systems, representatives from each branch of machine learning, when applied to several medical diagnostic tasks. The future trends are illustrated by two case studies. The first describes a recently developed method for dealing with reliability of decisions of classifiers, which seems to be promising for intelligent data analysis in medicine. The second describes an approach to using machine learning in order to verify some unexplained phenomena from complementary medicine, which is not (yet) approved by the orthodox medical community but could in the future play an important role in overall medical diagnosis and treatment.
Free-riding in BitTorrent Networks with the Large View Exploit

This paper presents an experimental study on the behavior of BitTorrent networks when selfish peers attempt to maintain high download rates without uploading. We modified a BitTorrent client so that it acquires a larger than normal view of a BitTorrent swarm and connects to all peers in its view. At the same time, the modified client does not upload any data to its peers. Our experimental results show that: a) our modified freerider client can achieve better download rates than a compliant client in most common-case public torrents; b) when the percentage of our modified free-rider clients in PlanetLab-residing torrents with ∼300 leechers is less than 40%, free-riders on average outperform compliant clients; and c) as the number of free-riders increases, both free-riders and compliant clients incur substantial performance degradation. These results suggest that the large view exploit is effective, and it has the potential for wide adoption.
Knowledge Base Population: Successful Approaches and Challenges

In this paper we give an overview of the Knowledge Base Population (KBP) track at the 2010 Text Analysis Conference. The main goal of KBP is to promote research in discovering facts about entities and augmenting a knowledge base (KB) with these facts. This is done through two tasks, Entity Linking – linking names in context to entities in the KB – and Slot Filling – adding information about an entity to the KB. A large source collection of newswire and web documents is provided from which systems are to discover information. Attributes (“slots”) derived from Wikipedia infoboxes are used to create the reference KB. In this paper we provide an overview of the techniques which can serve as a basis for a good KBP system, lay out the remaining challenges by comparison with traditional Information Extraction (IE) and Question Answering (QA) tasks, and provide some suggestions to address these challenges.
MEGA2: molecular evolutionary genetics analysis software

We have developed a new software package, Molecular Evolutionary Genetics Analysis version 2 (MEGA2), for exploring and analyzing aligned DNA or protein sequences from an evolutionary perspective. MEGA2 vastly extends the capabilities of MEGA version 1 by: (1) facilitating analyses of large datasets; (2) enabling creation and analyses of groups of sequences; (3) enabling specification of domains and genes; (4) expanding the repertoire of statistical methods for molecular evolutionary studies; and (5) adding new modules for visual representation of input data and output results on the Microsoft Windows platform.


AVAILABILITY
http://www.megasoftware.net.
Semi-Supervised Neural Machine Translation with Language Models

Training neural machine translation models is notoriously slow and requires abundant parallel corpora and computational power. In this work we propose an approach of transferring knowledge from separately trained language models to translation systems, also we investigate several techniques to improve translation quality when there is a lack of parallel data and computational resources. Our method is based on fusion between translation system and language model, and initialization of translation system with weights from pretrained language models. We show that this technique gives +2.2 BLEU score on En–Fr pair of WMT europarl-7 dataset and allows us to reach 20.7 BLEU on 20k parallel sentences in less than 8 hours of training on a single NVIDIA GeForce GTX 1080 Ti. We specifically note, that for this advance we use nothing but monolingual corpora for source and target languages. Significant part of presented results was obtained during DeepHack.Babel hackathon on low-resource machine translation organized by iPavlov Lab.
Design and Roadmap Methodology for Integrated Power Modules in High Switching Frequency Synchronous Buck Voltage Regulators

The proposed methodology encompasses four study phases that address the following key design aspects of integrated power modules (IPM) for voltage regulators: Fundamental switching understanding, determination of compact/accurate loss expressions and derivation of design rules, guidelines and figure of merits for converter optimisation. The applied methods are based on several modelling approaches suitable for the different study phases. The simplified loss model proves to be effective to breakdown the power losses of a state of the art IPM. Analysis of an optimised design leads to the identification of critical circuit and device parameters that may well condition the layout of future technological roadmap targets.
Analysing and improving the diagnosis of ischaemic heart disease with machine learning

Ischaemic heart disease is one of the world's most important causes of mortality, so improvements and rationalization of diagnostic procedures would be very useful. The four diagnostic levels consist of evaluation of signs and symptoms of the disease and ECG (electrocardiogram) at rest, sequential ECG testing during the controlled exercise, myocardial scintigraphy, and finally coronary angiography (which is considered to be the reference method). Machine learning methods may enable objective interpretation of all available results for the same patient and in this way may increase the diagnostic accuracy of each step. We conducted many experiments with various learning algorithms and achieved the performance level comparable to that of clinicians. We also extended the algorithms to deal with non-uniform misclassification costs in order to perform ROC analysis and control the trade-off between sensitivity and specificity. The ROC analysis shows significant improvements of sensitivity and specificity compared to the performance of the clinicians. We further compare the predictive power of standard tests with that of machine learning techniques and show that it can be significantly improved in this way.
A Multimodal Analysis of Floor Control in Meetings

The participant in a human-to-human communication who controls the floor bears the burden of moving the communication process along. Change in control of the floor can happen through a number of mechanisms, including interruptions, delegation of the floor, and so on. This paper investigates floor control in multiparty meetings that are both audio and video taped; hence, we analyze patterns of speech (e.g., the use of discourse markers) and visual cues (e.g, eye gaze exchanges) that are often involved in floor control changes. Identifying who has control of the floor provides an important focus for information retrieval and summarization of meetings. Additionally, without understanding who has control of the floor, it is impossible to identify important events such as challenges for the floor. In this paper, we analyze multimodal cues related to floor control in two different meetings involving five participants each.
Code block segmentation hardware architecture for LTE-Advanced

A very efficient algorithm and hardware architecture for code block segmentation used on LTE-Advanced channel coding physical layer (PHY) is presented in this paper. Code block segmentation is a generic procedure which is commonly applied before turbo encoding. Its main function is to fragment a large transport block into smaller code blocks. This approach reduces memory requirements of the turbo code interleaver, without compromising its coding gain, since turbo encoder improves its performance as the size of the code block increases. The current work presents not only an optimized procedure with reduced computational complexity, but also an architecture with very low resource count, regarding ASIC or FPGA implementations, performing at a maximum frequency of 351 MHz on a FPGA architecture.
Experiences of integration and performance testing of multilingual OCR for printed Indian scripts

This paper presents integration and testing scheme for managing a large Multilingual OCR Project. The project is an attempt to implement an integrated platform for OCR of different Indian languages. Software engineering, workflow management and testing processes have been discussed in this paper. The OCR has now been experimentally deployed for some specific applications and currently is being enhanced for handling the space and time constraints, achieving higher recognition accuracies and adding new functionalities.
Copper-Graphene-Based Photonic Crystal Fiber Plasmonic Biosensor

We propose a photonic crystal fiber surface plasmon resonance biosensor where the plasmonic metal layer and the sensing layer are placed outside the fiber structure, which makes the sensor configuration practically simpler and the sensing process more straightforward. Considering the long-term stability of the plasmonic performance, copper (Cu) is used as the plasmonic material, and graphene is used to prevent Cu oxidation and enhance sensing performance. Numerical investigation of guiding properties and sensing performance is performed by using a finite-element method. The proposed sensor shows average wavelength interrogation sensitivity of 2000 nm/refractive index unit (RIU) over the analyte refractive indices ranging from 1.33 to 1.37, which leads to a sensor resolution of 5 × 10-5 RIU. Due to the simple structure and promising results, the proposed sensor could be a potential candidate for detecting biomolecules, organic chemicals, and other analytes.
Simple Bayesian Algorithms for Best Arm Identification

This paper considers the optimal adaptive allocation of measurement effort for identifying the best among a finite set of options or designs. An experimenter sequentially chooses designs to measure and observes noisy signals of their quality with the goal of confidently identifying the best design after a small number of measurements. I propose three simple Bayesian algorithms for adaptively allocating measurement effort. One is Top-Two Probability sampling, which computes the two designs with the highest posterior probability of being optimal, and then randomizes to select among these two. One is a variant a top-two sampling which considers not only the probability a design is optimal, but the expected amount by which its quality exceeds that of other designs. The final algorithm is a modified version of Thompson sampling that is tailored for identifying the best design. I prove that these simple algorithms satisfy a strong optimality property. In a frequestist setting where the true quality of the designs is fixed, one hopes the posterior definitively identifies the optimal design, in the sense that that the posterior probability assigned to the event that some other design is optimal converges to zero as measurements are collected. I show that under the proposed algorithms this convergence occurs at an exponential rate, and the corresponding exponent is the best possible among all allocation rules.
Remaining Useful Life Estimation of Critical Components With Application to Bearings

Prognostics activity deals with the estimation of the Remaining Useful Life (RUL) of physical systems based on their current health state and their future operating conditions. RUL estimation can be done by using two main approaches, namely model-based and data-driven approaches. The first approach is based on the utilization of physics of failure models of the degradation, while the second approach is based on the transformation of the data provided by the sensors into models that represent the behavior of the degradation. This paper deals with a data-driven prognostics method, where the RUL of the physical system is assessed depending on its critical component. Once the critical component is identified, and the appropriate sensors installed, the data provided by these sensors are exploited to model the degradation's behavior. For this purpose, Mixture of Gaussians Hidden Markov Models (MoG-HMMs), represented by Dynamic Bayesian Networks (DBNs), are used as a modeling tool. MoG-HMMs allow us to represent the evolution of the component's health condition by hidden states by using temporal or frequency features extracted from the raw signals provided by the sensors. The prognostics process is then done in two phases: a learning phase to generate the behavior model, and an exploitation phase to estimate the current health state and calculate the RUL. Furthermore, the performance of the proposed method is verified by implementing prognostics performance metrics, such as accuracy, precision, and prediction horizon. Finally, the proposed method is applied to real data corresponding to the accelerated life of bearings, and experimental results are discussed.