nils-herrmann/clean_scirepeval huggingface dataset:
A Heuristic Asymptotic Formula Concerning the Distribution of Prime Numbers

Suppose fx, h , ■ • ■ , fk are polynomials in one variable with all coefficients integral and leading coefficients positive, their degrees being h\ , h2, •• -, A* respectively. Suppose each of these polynomials is irreducible over the field of rational numbers and no two of them differ by a constant factor. Let Q(fx ,f2, • • • ,fk ; N) denote the number of positive integers n between 1 and N inclusive such that /i(n), f2(n), • ■ ■ , fk(n) are all primes. We ignore the finitely many values of n for which some /,(n) is negative.) Then heuristically we would expect to have for N large
Frank Miller: Inventor of the One-Time Pad

The invention of the one-time pad is generally credited to Gilbert S. Vernam and Joseph O. Mauborgne. We show that it was invented about 35 years earlier by a Sacramento banker named Frank Miller. We provide a tentative identification of which Frank Miller it was, and speculate on whether or not Mauborgne might have known of Miller’s work, especially via his colleague Parker Hitt.
Proactive Maintenance of Railway Switches

Railway switches operate in harsh environmental conditions; still, their reliability requirements are high due to safety and economic factors. Once deployed, their maintenance depends on the data collected on their status, and the decisions on due corrective actions. The more regular this data collection and decision cycle is, the better confidence their operator has on effective and proper service. Proactive maintenance, in general, targets exactly this: rather than scheduling maintenance actions based on operating hours or servicing volume, actions should be taken when it is really needed. This requires the effective combination of data collection, analysis, presentation and decision making processes. The MANTIS project proposes a reference architecture for proactive maintenance, supported by the concept of cyber-physical systems. Besides suggesting data collection, processing and presentation methods and tools for this, the results were applied in various domains - including production systems, the energy grid, utility vehicles, or railways. The current paper presents data collection, analysis and presentation concepts related to proactive maintenance, applied on railway switches.
Analysis and Prediction of Crimes by Clustering and Classification

Crimes will somehow influence organizations and institutions when occurred frequently in a society. Thus, it seems necessary to study reasons, factors and relations between occurrence of different crimes and finding the most appropriate ways to control and avoid more crimes. The main objective of this paper is to classify clustered crimes based on occurrence frequency during different years. Data mining is used extensively in terms of analysis, investigation and discovery of patterns for occurrence of different crimes. We applied a theoretical model based on data mining techniques such as clustering and classification to real crime dataset recorded by police in England and Wales within 1990 to 2011. We assigned weights to the features in order to improve the quality of the model and remove low value of them. The Genetic Algorithm (GA) is used for optimizing of Outlier Detection operator parameters using RapidMiner tool. crime; clustering; classification; genetic algorithm; weighting; rapidminer
Developments in fungal taxonomy.

Fungal infections, especially those caused by opportunistic species, have become substantially more common in recent decades. Numerous species cause human infections, and several new human pathogens are discovered yearly. This situation has created an increasing interest in fungal taxonomy and has led to the development of new methods and approaches to fungal biosystematics which have promoted important practical advances in identification procedures. However, the significance of some data provided by the new approaches is still unclear, and results drawn from such studies may even increase nomenclatural confusion. Analyses of rRNA and rDNA sequences constitute an important complement of the morphological criteria needed to allow clinical fungi to be more easily identified and placed on a single phylogenetic tree. Most of the pathogenic fungi so far described belong to the kingdom Fungi; two belong to the kingdom Chromista. Within the Fungi, they are distributed in three phyla and in 15 orders (Pneumocystidales, Saccharomycetales, Dothideales, Sordariales, Onygenales, Eurotiales, Hypocreales, Ophiostomatales, Microascales, Tremellales, Poriales, Stereales, Agaricales, Schizophyllales, and Ustilaginales).
A Hybrid Approach for Credit Card Fraud Detection using Rough Set and Decision Tree Technique

To make the business accessible to a large number of customers worldwide, many companies small and big have put up their presence on the internet. Online businesses gave birth to e-commerce platforms which in turn use digital modes of transaction such as credit-card, debit card etc. This kind of digital transaction attracted millions of users to transact on the internet. Along came the risk of online credit card frauds.
Beyond weight loss: a review of the therapeutic uses of very-low-carbohydrate (ketogenic) diets

Very-low-carbohydrate diets or ketogenic diets have been in use since the 1920s as a therapy for epilepsy and can, in some cases, completely remove the need for medication. From the 1960s onwards they have become widely known as one of the most common methods for obesity treatment. Recent work over the last decade or so has provided evidence of the therapeutic potential of ketogenic diets in many pathological conditions, such as diabetes, polycystic ovary syndrome, acne, neurological diseases, cancer and the amelioration of respiratory and cardiovascular disease risk factors. The possibility that modifying food intake can be useful for reducing or eliminating pharmaceutical methods of treatment, which are often lifelong with significant side effects, calls for serious investigation. This review revisits the meaning of physiological ketosis in the light of this evidence and considers possible mechanisms for the therapeutic actions of the ketogenic diet on different diseases. The present review also questions whether there are still some preconceived ideas about ketogenic diets, which may be presenting unnecessary barriers to their use as therapeutic tools in the physician’s hand.
Peak-Piloted Deep Network for Facial Expression Recognition

In this work, we present a novel peak-piloted deep network (PPDN) that uses a sample with peak expression (easy sample) to supervise the intermediate feature responses for a sample of non-peak expression (hard sample) of the same type and from the same subject. The expression evolving process from nonpeak expression to peak expression can thus be implicitly embedded in the network to achieve the invariance to expression intensities.
Cooperative Communications for Cognitive Radio Networks

Cognitive radio is an exciting emerging technology that has the potential of dealing with the stringent requirement and scarcity of the radio spectrum. Such revolutionary and transforming technology represents a paradigm shift in the design of wireless systems, as it will allow the agile and efficient utilization of the radio spectrum by offering distributed terminals or radio cells the ability of radio sensing, self-adaptation, and dynamic spectrum sharing. Cooperative communications and networking is another new communication technology paradigm that allows distributed terminals in a wireless network to collaborate through some distributed transmission or signal processing so as to realize a new form of space diversity to combat the detrimental effects of fading channels. In this paper, we consider the application of these technologies to spectrum sensing and spectrum sharing. One of the most important challenges for cognitive radio systems is to identify the presence of primary (licensed) users over a wide range of spectrum at a particular time and specific geographic location. We consider the use of cooperative spectrum sensing in cognitive radio systems to enhance the reliability of detecting primary users. We shall describe spectrum sensing for cognitive radios and propose robust cooperative spectrum sensing techniques for a practical framework employing cognitive radios. We also investigate cooperative communications for spectrum sharing in a cognitive wireless relay network. To exploit the maximum spectrum opportunities, we present a cognitive space-time-frequency coding technique that can opportunistically adjust its coding structure by adapting itself to the dynamic spectrum environment.
End-to-end learning for music audio

Content-based music information retrieval tasks have traditionally been solved using engineered features and shallow processing architectures. In recent years, there has been increasing interest in using feature learning and deep architectures instead, thus reducing the required engineering effort and the need for prior knowledge. However, this new approach typically still relies on mid-level representations of music audio, e.g. spectrograms, instead of raw audio signals. In this paper, we investigate whether it is possible to apply feature learning directly to raw audio signals. We train convolutional neural networks using both approaches and compare their performance on an automatic tagging task. Although they do not outperform a spectrogram-based approach, the networks are able to autonomously discover frequency decompositions from raw audio, as well as phase-and translation-invariant feature representations.
Clinical Quality Needs Complex Adaptive Systems and Machine Learning

The vast increase in clinical data has the potential to bring about large improvements in clinical quality and other aspects of healthcare delivery. However, such benefits do not come without cost. The analysis of such large datasets, particularly where the data may have to be merged from several sources and may be noisy and incomplete, is a challenging task. Furthermore, the introduction of clinical changes is a cyclical task, meaning that the processes under examination operate in an environment that is not static. We suggest that traditional methods of analysis are unsuitable for the task, and identify complexity theory and machine learning as areas that have the potential to facilitate the examination of clinical quality. By its nature the field of complex adaptive systems deals with environments that change because of the interactions that have occurred in the past. We draw parallels between health informatics and bioinformatics, which has already started to successfully use machine learning methods.
Cloud Computing: Pros and Cons for Computer Forensic Investigations

Cloud computing is a relatively new concept that offers the potential to deliver scalable elastic services to many. The notion of pay-per use is attractive and in the current global recession hit economy it offers an economic solution to an organizations’ IT needs. Computer forensics is a relatively new discipline born out of the increasing use of computing and digital storage devices in criminal acts (both traditional and hi-tech). Computer forensic practices have been around for several decades and early applications of their use can be charted back to law enforcement and military investigations some 30 years ago. In the last decade computer forensics has developed in terms of procedures, practices and tool support to serve the law enforcement community. However, it now faces possibly its greatest challenges in dealing with cloud computing. Through this paper we explore these challenges and suggest some possible solutions.
Psychodynamic psychotherapy for posttraumatic stress disorder related to childhood abuse--Principles for a treatment manual.

In this article, the authors present a psychodynamically oriented psychotherapy approach for posttraumatic stress disorder (PTSD) related to childhood abuse. This neurobiologically informed, phase-oriented treatment approach, which has been developed in Germany during the past 20 years, takes into account the broad comorbidity and the large degree of ego-function impairment typically found in these patients. Based on a psychodynamic relationship orientation, this treatment integrates a variety of trauma-specific imaginative and resource-oriented techniques. The approach places major emphasis on the prevention of vicarious traumatization. The authors are presently planning to test the approach in a randomized controlled trial aimed at strengthening the evidence base for psychodynamic psychotherapy in PTSD.
Publications Received

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Data-driven Upsampling of Point Clouds

High quality upsampling of sparse 3D point clouds is critically useful for a wide range of geometric operations such as reconstruction, rendering, meshing, and analysis. In this paper, we propose a data-driven algorithm that enables an upsampling of 3D point clouds without the need for hard-coded rules. Our approach uses a deep network with Chamfer distance as the loss function, capable of learning the latent features in point clouds belonging to different object categories. We evaluate our algorithm across different amplification factors, with upsampling learned and performed on objects belonging to the same category as well as different categories. We also explore the desirable characteristics of input point clouds as a function of the distribution of the point samples. Finally, we demonstrate the performance of our algorithm in single-category training versus multi-category training scenarios. The final proposed model is compared against a baseline, optimization-based upsampling method. The results indicate that our algorithm is capable of generating more accurate upsamplings with less Chamfer loss.
Sentiment Lexicon Expansion Based on Neural PU Learning, Double Dictionary Lookup, and Polarity Association

Although many sentiment lexicons in different languages exist, most are not comprehensive. In a recent sentiment analysis application, we used a large Chinese sentiment lexicon and found that it missed a large number of sentiment words used in social media. This prompted us to make a new attempt to study sentiment lexicon expansion. This paper first formulates the problem as a PU learning problem. It then proposes a new PU learning method suitable for the problem based on a neural network. The results are further enhanced with a new dictionary lookup technique and a novel polarity classification algorithm. Experimental results show that the proposed approach greatly outperforms baseline methods.
Speech enhancement based on deep denoising autoencoder

We previously have applied deep autoencoder (DAE) for noise reduction and speech enhancement. However, the DAE was trained using only clean speech. In this study, by using noisyclean training pairs, we further introduce a denoising process in learning the DAE. In training the DAE, we still adopt greedy layer-wised pretraining plus fine tuning strategy. In pretraining, each layer is trained as a one-hidden-layer neural autoencoder (AE) using noisy-clean speech pairs as input and output (or transformed noisy-clean speech pairs by preceding AEs). Fine tuning was done by stacking all AEs with pretrained parameters for initialization. The trained DAE is used as a filter for speech estimation when noisy speech is given. Speech enhancement experiments were done to examine the performance of the trained denoising DAE. Noise reduction, speech distortion, and perceptual evaluation of speech quality (PESQ) criteria are used in the performance evaluations. Experimental results show that adding depth of the DAE consistently increase the performance when a large training data set is given. In addition, compared with a minimum mean square error based speech enhancement algorithm, our proposed denoising DAE provided superior performance on the three objective evaluations.
Artificial Intelligence and its Role in Near Future

AI technology has long history which is actively and constantly changing and growing. It focuses on intelligent agents, which contains devices that perceives environment and based on which takes actions in order to maximize goal success chances. In this paper, we will explain the modern AI basics and various representative applications of AI. In context of modern digitalized world, Artificial Intelligence (AI) is the property of machines, computer programs and systems to perform the intellectual and creative functions of a person, independently find ways to solve problems, be able to draw conclusions and make decisions. Most artificial intelligence systems have the ability to learn, which allows people to improve their performance over time. The recent research on AI tools, including machine learning, deep learning and predictive analysis intended toward increasing the planning, learning, reasoning, thinking and action taking ability . Based on which, the proposed research intended towards exploring on how the human intelligence differs from the artificial intelligence . In addition, on how and in what way, the current artificial intelligence is clever than the human beings. Moreover, we critically analyze what the state-of-the art AI of today is capable of doing, why it still cannot reach human level intelligence and what are the open challenges existing in front of AI to reach and outperform human level of intelligence. Furthermore, it will explore the future predictions for artificial intelligence and based on which potential solution will be recommended to solve it within
Marchand Balun Embedded Probe

Planar-type Marchand baluns are embedded into on-wafer probes in order to provide accurate differential measurement capabilities for conventional single-ended measurement equipments such as the two-port vector network analyzer. In designing the planar-type Marchand balun, the values for differential- and common-mode characteristic impedances of the coupled transmission lines are analytically derived by using general transmission line theories. The derived characteristic impedances are realized using a double-sided single-layer printed circuit board surrounded by conductive fixtures. The proposed coupled transmission line structure provides convenient port interfaces to the coaxial cables and it is free from bond wires and vias, enabling one to achieve high cost efficiency and reproducibility.
A novel credit scoring model based on optimized random forest

With the rapid development of the credit industry, credit scoring models has become a very important issue in the credit industry. Many credit scoring models based on machine learning have been widely used. Such as artificial neural network (ANN), rough set, support vector machine (SVM), and other innovative credit scoring models. However, in practical applications, a large amount of irrelevant and redundant features in the credit data, which leads to higher computational complexity and lower prediction accuracy. So, the face of a large number of credit data, effective feature selection method is necessary. In this paper, we propose a novel credit scoring model, called NCSM, based on feature selection and grid search to optimize random forest algorithm. The model reduces the influence of the irrelevant and redundant features and to get the higher prediction accuracy. In NCSM, the information entropy is regarded as the heuristic to select the optimal feature. Two credit data sets in UCI database are used as experimental data to demonstrate the accuracy of the NCSM. Compared with linear SVM, CART, MLP, H2O RF models, the experimental result shows that NCSM has a superior performance in improving the prediction accuracy.
Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM) consists in the concurrent construction of a representation of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. The paper serves as a tutorial for the non-expert reader. It is also a position paper: by looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors’ take on two questions that often animate discussions during robotics conferences: do robots need SLAM? Is SLAM solved?
3 D Printing of Carbon Fiber Composites With Preferentially Aligned Fibers

This research focuses on 3D printed carbon-epoxy composite components in which the reinforcing carbon fibers have been preferentially aligned during the micro-extrusion process. Most polymer 3D printing techniques use unreinforced polymers. By adding carbon fiber as a reinforcing material, properties such as mechanical strength, electrical conductivity, and thermal conductivity can be greatly enhanced. However, these properties are significantly influenced by the degree of fiber alignment (or lack thereof). A Design of Experiments (DOE) approach was used to identify significant process parameters affecting preferential fiber alignment in the micro-extrusion process. A 2D Fast Fourier Transform (FFT) was used with ImageJ software to quantify the degree of fiber alignment in micro-extruded carbonepoxy pastes. Based on analysis of experimental results, tensile test samples were printed with fibers aligned parallel and perpendicular to the tensile axis. A standard test method for tensile properties of plastic revealed that the 3D printed test coupons with fibers aligned parallel to the tensile axis were significantly better in tensile strength and modulus. Results of this research can be used to 3D print components with locally controlled fiber alignment that is difficult to achieve via conventional composite manufacturing techniques.