nils-herrmann/clean_scirepeval huggingface dataset:
Machine learning-based run-time anomaly detection in software systems: An industrial evaluation

Anomalies are an inevitable occurrence while operating enterprise software systems. Traditionally, anomalies are detected by threshold-based alarms for critical metrics, or health probing requests. However, fully automated detection in complex systems is challenging, since it is very difficult to distinguish truly anomalous behavior from normal operation. To this end, the traditional approaches may not be sufficient. Thus, we propose machine learning classifiers to predict the system's health status. We evaluated our approach in an industrial case study, on a large, real-world dataset of 7.5 • 106 data points for 231 features. Our results show that recurrent neural networks with long short-term memory (LSTM) are more effective in detecting anomalies and health issues, as compared to other classifiers. We achieved an area under precision-recall curve of 0.44. At the default threshold, we can automatically detect 70% of the anomalies. Despite the low precision of 31 %, the rate in which false positives occur is only 4 %.
Minimalistic CNN-based ensemble model for gender prediction from face images

Despite being extensively studied in the literature, the problem of gender recognition from face images remains difficult when dealing with unconstrained images in a cross-dataset protocol. In this work, we propose a convolutional neural network ensemble model to improve the state-of-the-art accuracy of gender recognition from face images on one of the most challenging face image datasets today, LFW (Labeled Faces in the Wild). We find that convolutional neural networks need significantly less training data to obtain the state-of-the-art performance than previously proposed methods. Furthermore, our ensemble model is deliberately designed in a way that both its memory requirements and running time are minimized. This allows us to envision a potential usage of the constructed model in embedded devices or in a cloud platform for an intensive use on massive image databases.
Learning Disentangled Representations of Texts with Application to Biomedical Abstracts

We propose a method for learning disentangled representations of texts that code for distinct and complementary aspects, with the aim of affording efficient model transfer and interpretability. To induce disentangled embeddings, we propose an adversarial objective based on the (dis)similarity between triplets of documents with respect to specific aspects. Our motivating application is embedding biomedical abstracts describing clinical trials in a manner that disentangles the populations, interventions, and outcomes in a given trial. We show that our method learns representations that encode these clinically salient aspects, and that these can be effectively used to perform aspect-specific retrieval. We demonstrate that the approach generalizes beyond our motivating application in experiments on two multi-aspect review corpora.
Detection of Implicit Citations for Sentiment Detection

Sentiment analysis of citations in scientific papers is a new and interesting problem which can open up many exciting new applications in bibliometrics. Current research assumes that using just the citation sentence is enough for detecting sentiment. In this paper, we show that this approach misses much of the existing sentiment. We present a new corpus in which all mentions of a cited paper have been annotated. We explore methods to automatically identify these mentions and show that the inclusion of implicit citations in citation sentiment analysis improves the quality of the overall sentiment assignment.
High sigma measurement of random threshold voltage variation in 14nm Logic FinFET technology

Random variation of threshold voltage (Vt) in MOSFETs plays a central role in determining the minimum operating voltage of products in a given process technology. Properly characterizing Vt variation requires a large volume of measurements of minimum size devices to understand the high sigma behavior. At the same time, a rapid measurement approach is required to keep the total measurement time practical. Here we describe a new test structure and measurement approach that enables practical characterization of Vt distributions to high sigma and its application to 14nm Logic FinFET technology. We show that both NMOS and PMOS single fin devices have very low random Vt variation of 19mV and 24mV respectively, normally distributed out to +/-5σ.
Side scan sonar segmentation using deep convolutional neural network

Side scan sonar (SSS) is a vital sensor for autonomous underwater vehicles (AUVs) to do ocean survey. Many methods have been proposed to carry out SSS image segmentation, among which machine learning algorithms provide outstanding performance. Machine learning algorithms like support vector machine (SVM) and convolutional neural networks (CNN) are the most used. When SVM is used to do pixel-level classification or segmentation, image features should be chosen based on researchers' experience. So the result may be not the optimal one. On the other hand, deep CNN can extract features of images automatically through its special architecture and can capture highly nonlinear mappings between inputs and outputs. Therefore, in this paper, we propose to use deep CNN to segment images of SSS into three parts: highlight areas with objects, regions of shadow and sea-bottom reverberation areas. The CNN used in this paper is derived from fully convolutional networks (FCN) which is trained by manually segmented sonar data, takes patches of the same size as inputs while training and outputs the segmentation results of images directly. It is an end-to-end architecture which can output segmentation results of side scan sonar images with any size after training. Then we post-process the results of CNN by using Markov random fields (MRF). Experimental results demonstrate that segmentation method based on CNN and MRF is applicable in SSS image segmentation.
Artificial Neural Network Method for Solution of Boundary Value Problems With Exact Satisfaction of Arbitrary Boundary Conditions

A method for solving boundary value problems (BVPs) is introduced using artificial neural networks (ANNs) for irregular domain boundaries with mixed Dirichlet/Neumann boundary conditions (BCs). The approximate ANN solution automatically satisfies BCs at all stages of training, including before training commences. This method is simpler than other ANN methods for solving BVPs due to its unconstrained nature and because automatic satisfaction of Dirichlet BCs provides a good starting approximate solution for significant portions of the domain. Automatic satisfaction of BCs is accomplished by the introduction of an innovative length factor. Several examples of BVP solution are presented for both linear and nonlinear differential equations in two and three dimensions. Error norms in the approximate solution on the order of 10-4 to 10-5 are reported for all example problems.
Recent approaches to non-intrusive load monitoring techniques in residential settings

The concept of Smart Grids is closely related to energy conservation and load shedding concepts. However, it is difficult to quantify the effectiveness of energy conservation efforts in residential settings without any sort of end-use energy information as feedback. In order to achieve that, load monitoring methods are normally used. In recent years, non-intrusive load monitoring (NILM) approaches are gaining popularity due to their minimal installation requirements and cost effectiveness. For a NILM system to work, only one sensor at the entry point to a home is required. Fluctuations in the aggregate power consumption signals are used to mathematically estimate the composition of operation of appliances. This approach eliminates the requirement of installing plug-meters for every appliance in the house. In this paper, we provide a review of recent research efforts on state-of-the-art NILM algorithms before concluding with a baseline and overall vision for our future research direction.
Real-Time EMG Driven Lower Limb Actuated Orthosis for Assistance As Needed Movement Strategy

This paper presents a new approach to control a wearable knee joint exoskeleton driven through the wearer’ s intention. A realistic bio-inspired musculoskeletal knee joint model is used to control the exoskeleton. This model takes in to account changes in muscle length and joint moment arms as well as the dynamics of muscle activation and muscle contrac tion during lower limb movements. Identification of the model parameters is done through an unconstrained optimization problem formulation. A control law strategy based on the principle of assistance as needed is proposed. This approach guarantees asymptotic stability of the knee joint orthosis and adaptat ion to human-orthosis interaction. Moreover, the proposed contr ol law is robust with respect to external disturbances. Experimental validations are conducted online on a healthy subject during flexion and extenion of their knee joint. The proposed control strategy has shown satisfactory performances in terms of tr acking trajectory and adaptation to human tasks completion.
Advanced Control Scheme for an IPM Synchronous Generator-Based Gearless Variable Speed Wind Turbine

This paper proposes a direct control strategy for an interior permanent magnet synchronous generator-based variable speed wind turbine. In this scheme, the requirement of the continuous rotor position is eliminated as all the calculations are done in the stator reference frame. This scheme possesses advantages such as lesser parameter dependence and reduced number of controllers compared with the traditional indirect vector control scheme. The direct control scheme is simpler and can eliminate some of the drawbacks of traditional indirect vector control scheme. The proposed control scheme is implemented in MATLAB/SimPowerSystems and the results show that the controller can operate under constant and varying wind speeds. Finally, a sensorless speed estimator is implemented, which enables the wind turbine to operate without the mechanical speed sensor. The simulation and experimental results for the sensorless speed estimator are presented.
Software.zhishi.schema: A Software Programming Taxonomy Derived from Stackoverflow

In this paper, we are the first to construct a software programming taxonomy from Stackoverflow. More precisely, we propose a machine learning based method with novel features to capture the hierarchical semantic structure of tags in Stackoverflow. A graph pruning algorithm is applied to eliminate the conflicts by constructing a Directed Acyclic Graph (DAG). As a result, our dataset, named Software.zhishi.schema, contains 38,205 concepts together with 36,249 subsumption relations. In order to further test the usability of our published data, we adopt a similarity computing task of words from software programming which is one of the most fundamental tasks in the software repository mining area. The results show that our dataset can outperform other knowledge bases due to its high coverage with finergrained domain concepts.
Algorithms for Maximum Independent Sets

An algorithm is presented which finds (the size of) a maximum independent set of an n vertex graph in time O(2°.276”) ’ p lm roving on a previous bound of O(2”13). The improvement comes principally from three sources: first, a modified recursive algorithm based on a more detailed study of the possible subgraphs around a chosen vertex; second, an improvement, not in the algorithm but in the time bound proved, by an argument about connected regular graphs; third, a time-space trade-off which can speed up recursive algorithms from a fairly wide class.
Design of outer rotor IPM type PMSM for 3 wheel electric vehicle

In this paper, the optimization design of outer rotor IPM type PMSM was carried out for electric vehicle traction having 3 wheel system. The half round shape was proposed to reduce the cogging torque due to IPM machine characteristics. The object of optimization design is to find out most effective factor in reducing the torque ripple which may occurs the noise, vibration and uncomfortable riding of EV. In order to satisfy the low cogging torque, some design parameter such as air-gap, number of slot, poles, and slot open size was evaluated. From the simulation results of the outer rotor type IPM, we find out that the combination of slot open and depth of magnet is most important factor for reducing the cogging torque and high performance in low speed operational area. Moreover, the prototype of designed outer rotor IPM type PMSM was carried out and verified their properties.
A Machine Learning Approach to Software Requirements Prioritization

Deciding which, among a set of requirements, are to be considered first and in which order is a strategic process in software development. This task is commonly referred to as requirements prioritization. This paper describes a requirements prioritization method called Case-Based Ranking (CBRank), which combines project's stakeholders preferences with requirements ordering approximations computed through machine learning techniques, bringing promising advantages. First, the human effort to input preference information can be reduced, while preserving the accuracy of the final ranking estimates. Second, domain knowledge encoded as partial order relations defined over the requirement attributes can be exploited, thus supporting an adaptive elicitation process. The techniques CBRank rests on and the associated prioritization process are detailed. Empirical evaluations of properties of CBRank are performed on simulated data and compared with a state-of-the-art prioritization method, providing evidence of the method ability to support the management of the tradeoff between elicitation effort and ranking accuracy and to exploit domain knowledge. A case study on a real software project complements these experimental measurements. Finally, a positioning of CBRank with respect to state-of-the-art requirements prioritization methods is proposed, together with a discussion of benefits and limits of the method.
Towards Risk Scoring of Bitcoin Transactions

If Bitcoin becomes the prevalent payment system on the Internet, crime fighters will join forces with regulators and enforce blacklisting of transaction prefixes at the parties who offer real products and services in exchange for bitcoin. Blacklisted bitcoins will be hard to spend and therefore less liquid and less valuable. This requires every recipient of Bitcoin payments not only to check all incoming transactions for possible blacklistings, but also to assess the risk of a transaction being blacklisted in the future. We elaborate this scenario, specify a risk model, devise a prediction approach using public knowledge, and present preliminary results using data from selected known thefts. We discuss the implications on markets where bitcoins are traded and critically revisit Bitcoin’s ability to serve as a unit of account.
Research on autonomous stairs climbing for the shape-shifting robot

In this paper, we present an algorithm for a shape-shifting robot climbing stairs autonomously. The robot has been used several times for search and rescue missions. Because of the special environment of tasks, the robot must have the ability to climb stairs autonomously to help the operators. Due to the characters of robot in "T" configuration, the mathematical model of force is established and the whole process of climbing stairs is analyzed. Grouser-tread hooking, track-stair edge frictional force et al. are all considered during the process of climbing. A description of its dynamic model is also presented which is involved in the controller. The controller is comprised of center control and heading control, which can help the robot climb stairs safely and quickly. The effectiveness of the algorithms proposed are verified by experiment.
Text Dependent Speaker Recognition using Deep Neural Networks

Speaker Recognition is used for identification of a person depending on the characteristics contained in the speech signal. In this paper we propose the use of Deep Neural Network (DNN) for text dependent speaker Recognition system (SRS). Mel Frequency Cepstral Coefficients (MFCC) and Auto-encoder (Butterfly Structure Neural Network) are used to extract the features of speech signal at the initial stage. The previously obtained coefficients are then used to train the DNN to later classify the speakers. DNN can be directly used to extract features and classify speakers but the MFCC and Auto-encoder are used initially for data compression and maximum number of feature extraction thus aiming to get better efficiency and faster results. Deep Auto-encoder, DNN, MFCC, Speaker
Cuff-Less Estimation of Blood Pressure Using Pulse Transit Time and Pre-ejection Period

Non-invasive and cuff-less measurement of arterial blood pressure (BP) is desirable for continuous patient monitoring. Among the various possible techniques pulse transit time (PTT) based approach for estimation of BP is the most promising one. But change in BP is reflected both in PTT and pre-ejection period (PEP). We propose to measure PTT using multiple PPG and PPG-ECG combination recorded at two different sites along the same artery and argue that this approach will eliminate a number of assumptions like constant PEP, involved in the most common method of measuring PTT using QRS complex as timing reference. This approach can help in finding an appropriate BP calibration methodology since true PTT can be measured by this technique.
The application of graph theoretical analysis to complex networks in the brain

Considering the brain as a complex network of interacting dynamical systems offers new insights into higher level brain processes such as memory, planning, and abstract reasoning as well as various types of brain pathophysiology. This viewpoint provides the opportunity to apply new insights in network sciences, such as the discovery of small world and scale free networks, to data on anatomical and functional connectivity in the brain. In this review we start with some background knowledge on the history and recent advances in network theories in general. We emphasize the correlation between the structural properties of networks and the dynamics of these networks. We subsequently demonstrate through evidence from computational studies, in vivo experiments, and functional MRI, EEG and MEG studies in humans, that both the functional and anatomical connectivity of the healthy brain have many features of a small world network, but only to a limited extent of a scale free network. The small world structure of neural networks is hypothesized to reflect an optimal configuration associated with rapid synchronization and information transfer, minimal wiring costs, resilience to certain types of damage, as well as a balance between local processing and global integration. Eventually, we review the current knowledge on the effects of focal and diffuse brain disease on neural network characteristics, and demonstrate increasing evidence that both cognitive and psychiatric disturbances, as well as risk of epileptic seizures, are correlated with (changes in) functional network architectural features.
A Case Study on Quality Attribute Measurement using MARF and GIPSY

This literature focuses on doing a comparative analysis between Modular Audio Recognition Framework (MARF) and the General Intentional Programming System (GIPSY) with the help of different software metrics. At first, we understand the general principles, architecture and working of MARF and GIPSY by looking at their frameworks and running them in the Eclipse environment. Then, we study some of the important metrics including a few state of the art metrics and rank them in terms of their usefulness and their influence on the different quality attributes of a software. The quality attributes are viewed and computed with the help of the Logiscope and McCabe IQ tools. These tools perform a comprehensive analysis on the case studies and generate a quality report at the factor level, criteria level and metrics level. In next step, we identify the worst code at each of these levels, extract the worst code and provide recommendations to improve the quality. We implement and test some of the metrics which are ranked as the most useful metrics with a set of test cases in JDeodorant. Finally, we perform an analysis on both MARF and GIPSY by doing a fuzzy code scan using MARFCAT to find the list of weak and vulnerable classes.
Drugs interaction checker based on IoT

Providing effective and appropriate healthcare is one of the most important objectives of information and communication technologies (ICT). Internet of things (IoT) is one of the last advances in ICT, providing a global connectivity and management of sensors, devices, users and information. Our contribution is a solution to examine drug related problems based on IoT technologies, i.e. smart phones and Web to support ubiquitous Access, 6LoWPAN technology to support ubiquitous data collection of patients, sensors and hospitals, and RFID/NFC to support global identification. These technologies offer a wide range of applications in healthcare, which improves the quality of services, reduce mistakes, and even detect health anomalies from vital signs. This paper presents how IoT technology is applied in a pharmaceutical system to examine drugs in order to detect Adverse Drugs Reaction (ADR), harmful effects of pharmaceutical excipients, allergies, complications and contraindications related with liver and renal defects, and harmful side effects during pregnancy or lactation. Thereby, the system provides an enhanced approach assisting physicians in clinical decisions and drug prescribing. The solution presented is based on NFC (Near Field Communication) and barcode identification technologies, which have been integrated in common devices such as smart-phones, PDAs and PCs. The drug ID is matched with the Pharmaceutical Intelligent Information System to detect whether a specific drug is suitable with respect to the patient's health record or not.
Exemplary Automotive Attack Scenarios : Trojan Horses for Electronic Throttle Control System ( ETC ) and Replay Attacks on the Power Window System

The consideration of targeted security attacks is not yet common in the automotive domain where primarily safety requirements are considered. Hence this survey addresses the relatively new field of IT-security in automotive technology in order to motivate further research. With the emergence of automotive technologies like car-to-car (c2c) communication, the challenges increase. In order to show the necessity for such research we describe how in theory, a potential attack on the Electronic Throttle Control (ETC) could succeed, if no countermeasures are taken, with potentially drastic consequences on the safety of the vehicle, its occupants and its environment as a result. Also an attack on the power window system is shown using the implementation techniques already employed in the design of automotive components. Our goal is to show, that such exploited security vulnerabilities are likely to have serious consequences with respect to the vehicles safety. Therefore such attacks need to be considered and suitable countermeasures have to be identified. In order to achieve this, the connection of the two disciplines Safety and Security for automotive IT components needs to be discussed. We introduce two exemplary attacks, the Trojan Horse targeting at the Electronic Throttle Control System (ETC) and a replay attack on the electric window lift. The first attack is mainly discussed in theory, the second is elaborated in a practical simulation scenario. Finally we introduce a general investigation of potential ways of intrusion and present some first practical results from tests with current automotive hardware.