nils-herrmann/clean_scirepeval huggingface dataset:
Factorization Machines with libFM

Factorization approaches provide high accuracy in several important prediction problems, for example, recommender systems. However, applying factorization approaches to a new prediction problem is a nontrivial task and requires a lot of expert knowledge. Typically, a new model is developed, a learning algorithm is derived, and the approach has to be implemented.
 Factorization machines (FM) are a generic approach since they can mimic most factorization models just by feature engineering. This way, factorization machines combine the generality of feature engineering with the superiority of factorization models in estimating interactions between categorical variables of large domain. libFM is a software implementation for factorization machines that features stochastic gradient descent (SGD) and alternating least-squares (ALS) optimization, as well as Bayesian inference using Markov Chain Monto Carlo (MCMC). This article summarizes the recent research on factorization machines both in terms of modeling and learning, provides extensions for the ALS and MCMC algorithms, and describes the software tool libFM.
Adaptive assist-as-needed controller to improve gait symmetry in robot-assisted gait training

This paper introduces the overall design of ALEX III, the third generation of Active Leg Exoskeletons developed by our group. ALEX III is the first treadmill-based rehabilitation robot featuring 12 actively controlled degrees of freedom (DOF): 4 at the pelvis and 4 at each leg. As a first application of the device, we present an adaptive controller aimed to improve gait symmetry in hemiparetic subjects. The controller continuously modulates the assistive force applied to the impaired leg, based on the outputs of kernel-based non-linear filters, which learn the movements of the healthy leg. To test the effectiveness of the controller, we induced asymmetry in the gait of three young healthy subjects adding ankle weights (2.3kg). Results on kinematic data showed that gait symmetry was recovered when the controller was active.
Z - CRIME: A data mining tool for the detection of suspicious criminal activities based on decision tree

Data mining is the extraction of knowledge from large databases. One of the popular data mining techniques is Classification in which different objects are classified into different classes depending on the common properties among them. Decision Trees are widely used in Classification. This paper proposes a tool which applies an enhanced Decision Tree Algorithm to detect the suspicious e-mails about the criminal activities. An improved ID3 Algorithm with enhanced feature selection method and attribute- importance factor is applied to generate a better and faster Decision Tree. The objective is to detect the suspicious criminal activities and minimize them. That's why the tool is named as “Z-Crime” depicting the “Zero Crime” in the society. This paper aims at highlighting the importance of data mining technology to design proactive application to detect the suspicious criminal activities.
Female orgasm(s): one, two, several.

There is general agreement that it is possible to have an orgasm thru the direct simulation of the external clitoris. In contrast, the possibility of achieving climax during penetration has been controversial.



Six scientists with different experimental evidence debate the existence of the vaginally activated orgasm (VAO).



To give reader of The Journal of Sexual Medicine sufficient data to form her/his own opinion on an important topic of female sexuality.



Expert #1, the Controversy's section Editor, together with Expert #2, reviewed data from the literature demonstrating the anatomical possibility for the VAO. 3 presents validating women's reports of pleasurable sexual responses and adaptive significance of the VAO. Echographic dynamic evidence induced Expert # 4 to describe one single orgasm, obtained from stimulation of either the external or internal clitoris, during penetration. Expert #5 reviewed his elegant experiments showing the uniquely different sensory responses to clitoral, vaginal, and cervical stimulation. Finally, the last Expert presented findings on the psychological scenario behind VAO.



The assumption that women may experience only the clitoral, external orgasm is not based on the best available scientific evidence.
Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.
Novel Shifted Real Spectrum for Exact Signal Reconstruction

Retrieval of the phase of a signal is one of the major problems in signal processing. For an exact signal reconstruction, both magnitude, and phase spectrum of the signal is required. In many speech-based applications, only the magnitude spectrum is processed and the phase is ignored, which leads to degradation in the performance. Here, we propose a novel technique that enables the reconstruction of the speech signal from magnitude spectrum only. We consider the even-odd part decomposition of a causal sequence and process only on the real part of the DTFT of the signal. We propose the shifting of the real part of DTFT of the sequence to make it non-negative. By adding a constant of sufficient value to the real part of the DTFT, the exact signal reconstruction is possible from the magnitude or power spectrum alone. Moreover, we have compared our proposed approach with recently proposed phase retrieval method from magnitude spectrum of the Causal Delta Dominant (CDD) signal. We found that the method of phase retrieval from CDD signal and proposed method are identical under certain approximation. However, proposed method involves the less computational cost for the exact processing of the signal.
Estrus Detection in Dairy Cows from Acceleration Data using Self-learning Classification Models

Automatic estrus detection techniques in dairy cows have been present by different traits. Pedometers and accelerators are the most common sensor equipment. Most of the detection methods are associated with the supervised classification technique, which the training set becomes a crucial reference. The training set obtained by visual observation is subjective and time consuming. Another limitation of this approach is that it usually does not consider the factors affecting successful alerts, such as the discriminative figure, activity type of cows, the location and direction of the sensor node placed on the neck collar of a cow. This paper presents a novel estrus detection method that uses k-means clustering algorithm to create the training set online for each cow. And the training set is finally used to build an activity classification model by SVM. The activity index counted by the classification results in each sampling period can measure cow’s activity variation for assessing the onset of estrus. The experimental results indicate that the peak of estrus time are higher than that of non-estrus time at least twice in the activity index curve, and it can enhance the sensitivity and significantly reduce the error rate.
Automatic Detection for JavaScript Obfuscation Attacks in Web Pages through String Pattern Analysis

Recently, most of malicious web pages include obfuscated codes in order to circumvent the detection of signature-based detection systems .It is difficult to decide whether the sting is obfuscated because the shape of obfuscated strings are changed continuously. In this paper, we propose a novel methodology that can detect obfuscated strings in the malicious web pages. We extracted three metrics as rules for detecting obfuscated strings by analyzing patterns of normal and malicious JavaScript codes. They are N-gram, Entropy, and Word Size. N-gram checks how many each byte code is used in strings. Entropy checks distributed of used byte codes. Word size checks whether there is used very long string. Based on the metrics, we implemented a practical tool for our methodology and evaluated it using read malicious web pages. The experiment results showed that our methodology can detect obfuscated strings in web pages effectively.
Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals

An autoencoder-based framework that simultaneously reconstruct and classify biomedical signals is proposed. Previous work has treated reconstruction and classification as separate problems. This is the first study that proposes a combined framework to address the issue in a holistic fashion.  For telemonitoring purposes, reconstruction techniques of biomedical signals are largely based on compressed sensing (CS); these are “designed” techniques where the reconstruction formulation is based on some “assumption” regarding the signal. In this study, we propose a new paradigm for reconstruction—the reconstruction is “learned,” using an autoencoder; it does not require any assumption regarding the signal as long as there is sufficiently large training data. But since the final goal is to analyze/classify the signal, the system can also learn a linear classification map that is added inside the autoencoder. The ensuing optimization problem is solved using the Split Bregman technique.  Experiments were carried out on reconstructing and classifying electrocardiogram (ECG) (arrhythmia classification) and EEG (seizure classification) signals.  Our proposed tool is capable of operating in a semi-supervised fashion. We show that our proposed method is better in reconstruction and more than an order magnitude faster than CS based methods; it is capable of real-time operation. Our method also yields better results than recently proposed classification methods.  This is the first study offering an alternative to CS-based reconstruction. It also shows that the representation learning approach can yield better results than traditional methods that use hand-crafted features for signal analysis.
Effect of language resources on automatic speech recognition for Amharic

This paper presents our investigation of the effect of language resources on the performance of Amharic speech recognition. We have used language model training text of different sizes and seen the effect on word error rate (WER) reduction. Moreover, we have investigated the effect of handling language issues (germination, epenthetic vowel insertion and glottal stop consonant pronunciation) on the performance of speech recognition systems using data-driven phone-level transcriptions. The results of our experiments show that only slight reduction in WER can be obtained by increasing language model training text. However, proper transcription of gemination, the epenthetic vowel and the glottal stop consonant did not bring performance improvement for Amharic speech recognition. This can be attributed to the larger number of phone HMM acoustic models (62 compared to 37 phone set of the grapheme-based phone-level transcriptions) trained with a small (5 hrs) training speech.
Enhanced Features Ranking and Selection using Recursive Feature Elimination ( RFE ) and k-Nearest Neighbor Algorithms in Support Vector Machine for Intrusion Detection System

Today, as the increasing the amount of using internet, there are so most information interchanges are performed in that internet. So, the methods used as intrusion detective tools for protecting network systems against diverse attacks are became too important. The available of IDS are getting more powerful. Support Vector Machine was used as the classical pattern reorganization tools have been widely used for Intruder detections. There have some different characteristic of features in building an Intrusion Detection System. Conventional SVM do not concern about that. Our enhanced SVM Model proposed with an Recursive Feature Elimination (RFE) and kNearest Neighbor (KNN) method to perform a feature ranking and selection task of the new model. RFE can reduce redundant & recursive features and KNN can select more precisely than the conventional SVM. Experiments and comparisons are conducted through intrusion dataset: the KDD Cup 1999 dataset.
Assistive infrared sensor based smart stick for blind people

Blind people need some aid to feel safe while moving. Smart stick comes as a proposed solution to improve the mobility of both blind and visually impaired people. Stick solution use different technologies like ultrasonic, infrared and laser but they still have drawbacks. In this paper we propose, light weight, cheap, user friendly, fast response and low power consumption, smart stick based on infrared technology. A pair of infrared sensors can detect stair-cases and other obstacles presence in the user path, within a range of two meters. The experimental results achieve good accuracy and the stick is able to detect all of obstacles.
A roadmap for traffic engineering in SDN-OpenFlow networks

Software Defined Networking (SDN) is an emerging networking paradigm that separates the network control plane from the data forwarding plane with the promise to dramatically improve network resource utilization, simplify network management, reduce operating cost, and promote innovation and evolution. Although traffic engineering techniques have been widely exploited in the past and current data networks, such as ATM networks and IP/ MPLS networks, to optimize the performance of communication networks by dynamically analyzing, predicting, and regulating the behavior of the transmitted data, the unique features of SDN require new traffic engineering techniques that exploit the global network view, status, and flow patterns/characteristics available for better traffic control and management. This paper surveys the state-of-the-art in traffic engineering for SDNs, and mainly focuses on four thrusts including flow management, fault tolerance, topology update, and traffic analysis/characterization. In addition, some existing and representative traffic engineering tools from both industry and academia are explained. Moreover, open research issues for the realization of SDN traffic engineering solutions are discussed in detail.
Study and Development of Novel Feature Selection Framework for Heart Disease Prediction

Heart disease prediction is designed to support clinicians in their diagnosis. We proposed a method for classifying the heart disease data. The patient’s record is predicted to find if they have symptoms of heart disease through Data mining. It is essential to find the best fit classification algorithm that has greater accuracy on classification in the case of heart disease prediction. Since the data is huge attribute selection method used for reducing the dataset. Then the reduced data is given to the classification .In the Investigation, the hybrid attribute selection method combining CFS and Filter Subset Evaluation gives better accuracy for classification. We also propose a new feature selection method algorithm which is the hybrid method combining CFS and Bayes Theorem. The proposed algorithm provides better accuracy compared to the traditional algorithm and the hybrid Algorithm CFS+FilterSubsetEval.
The labor economics of paid crowdsourcing

We present a model of workers supplying labor to paid crowdsourcing projects. We also introduce a novel method for estimating a worker's reservation wage - the key parameter in our labor supply model. We tested our model by presenting experimental subjects with real-effort work scenarios that varied in the offered payment and difficulty. As predicted, subjects worked less when the pay was lower. However, they did not work less when the task was more time-consuming. Interestingly, at least some subjects appear to be "target earners," contrary to the assumptions of the rational model. The strongest evidence for target earning is an observed preference for earning total amounts evenly divisible by 5, presumably because these amounts make good targets. Despite its predictive failures, we calibrate our model with data pooled from both experiments. We find that the reservation wages of our sample are approximately log normally distributed, with a median wage of $1.38/hour. We discuss how to use our calibrated model in applications.
Knowledge Representation Issues in Musical Instrument Ontology Design

This paper presents preliminary work on musical instruments ontology design, and investigates heterogeneity and limitations in existing instrument classification schemes. Numerous research to date aims at representing information about musical instruments. The works we examined are based on the well known Hornbostel and Sach’s classification scheme. We developed representations using the Ontology Web Language (OWL), and compared terminological and conceptual heterogeneity using SPARQL queries. We found evidence to support that traditional designs based on taxonomy trees lead to ill-defined knowledge representation, especially in the context of an ontology for the Semantic Web. In order to overcome this issue, it is desirable to have an instrument ontology that exhibits a semantically rich structure.
A data-driven approach for pedestrian intention estimation

In the context of future urban automated driving many important problems remain unsolved. A critical one is the analysis and prediction of pedestrian movements around urban roads. Especially the analysis of non-critical situations has not received much attention in the past. This paper focuses on analyzing and predicting movements of pedestrians approaching crosswalks, a very crucial pedestrian-vehicle interaction in urban scenarios. In our previous work, we analyzed the performance of a data-driven Support Vector Machine-based architecture, and the relevance of specific features to infer pedestrian crossing intentions. In this paper, we will use our previous results as baseline to compare against an architecture based on neural networks for time-series classification. In particular we analyze the effectiveness of dense and Long-Short-Term-Memory networks. Furthermore, we will be looking into enhancing our feature vectors by adding LiDAR based images to the classification process. Additionally the evaluation provides an estimate for the temporal prediction horizon. The approaches presented are validated with real world trajectories recorded in Germany. Our results show an average accuracy improvement of 10-20% with respect to our previous Support Vector Machine-based approach.
Internet of Nano-Things, Things and Everything: Future Growth Trends

The current statuses and future promises of the Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano-Things (IoNT) are extensively reviewed and a summarized survey is presented. The analysis clearly distinguishes between IoT and IoE, which are wrongly considered to be the same by many commentators. After evaluating the current trends of advancement in the fields of IoT, IoE and IoNT, this paper identifies the 21 most significant current and future challenges as well as scenarios for the possible future expansion of their applications. Despite possible negative aspects of these developments, there are grounds for general optimism about the coming technologies. Certainly, many tedious tasks can be taken over by IoT devices. However, the dangers of criminal and other nefarious activities, plus those of hardware and software errors, pose major challenges that are a priority for further research. Major specific priority issues for research are identified.
Technique to Develop Auto Load Shedding and Islanding Scheme to Prevent Power System Blackout

Abnormal condition in a power system generally leads to a fall in system frequency, and it leads to system blackout in an extreme condition. This paper presents a technique to develop an auto load shedding and islanding scheme for a power system to prevent blackout and to stabilize the system under any abnormal condition. The technique proposes the sequence and conditions of the applications of different load shedding schemes and islanding strategies. It is developed based on the international current practices. It is applied to the Bangladesh Power System (BPS), and an auto load-shedding and islanding scheme is developed. The effectiveness of the developed scheme is investigated simulating different abnormal conditions in BPS.
Stationary and moving targets detection on FMCW radar using GNU radio-based software defined radio

This paper discusses the implementation of GNU radio-based software defined radio (SDR) for designing a frequency modulated continuous wave (FMCW) radar to detect stationary and moving targets. The use of SDR system in which its components are implemented by means of software is to reduce cost and complexity in the design and implementation. Whilst the signal processing of FMCW radar is carried out using Matlab R® with triangular linear frequency modulation (LFM) waveform to obtain the target distance and the target relative speed for stationary and moving target, respectively. From the result, it is shown that the radar is successfully implemented using GNU radio-based SDR with the capability in distance target detection of 14.79km for a moving target away from the radar with the relative speed of 50m/s.
Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci

We undertook a meta-analysis of six Crohn's disease genome-wide association studies (GWAS) comprising 6,333 affected individuals (cases) and 15,056 controls and followed up the top association signals in 15,694 cases, 14,026 controls and 414 parent-offspring trios. We identified 30 new susceptibility loci meeting genome-wide significance (P < 5 × 10−8). A series of in silico analyses highlighted particular genes within these loci and, together with manual curation, implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP. Combined with previously confirmed loci, these results identify 71 distinct loci with genome-wide significant evidence for association with Crohn's disease.
Deep Keyphrase Generation

Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ranked and selected the most meaningful ones. These approaches could neither identify keyphrases that do not appear in the text, nor capture the real semantic meaning behind the text. We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks. We name it as deep keyphrase generation since it attempts to capture the deep semantic meaning of the content with a deep learning method. Empirical analysis on six datasets demonstrates that our proposed model not only achieves a significant performance boost on extracting keyphrases that appear in the source text, but also can generate absent keyphrases based on the semantic meaning of the text. Code and dataset are available at https://github.com/memray/seq2seq-