nils-herrmann/clean_scirepeval huggingface dataset:
On the value of static analysis for fault detection in software

No single software fault-detection technique is capable of addressing all fault-detection concerns. Similarly to software reviews and testing, static analysis tools (or automated static analysis) can be used to remove defects prior to release of a software product. To determine to what extent automated static analysis can help in the economic production of a high-quality product, we have analyzed static analysis faults and test and customer-reported failures for three large-scale industrial software systems developed at Nortel Networks. The data indicate that automated static analysis is an affordable means of software fault detection. Using the orthogonal defect classification scheme, we found that automated static analysis is effective at identifying assignment and checking faults, allowing the later software production phases to focus on more complex, functional, and algorithmic faults. A majority of the defects found by automated static analysis appear to be produced by a few key types of programmer errors and some of these types have the potential to cause security vulnerabilities. Statistical analysis results indicate the number of automated static analysis faults can be effective for identifying problem modules. Our results indicate static analysis tools are complementary to other fault-detection techniques for the economic production of a high-quality software product.
Lung Cancer detection and Classification by using Machine Learning & Multinomial Bayesian

Image Processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-day life for various applications. Various techniques have been developed in Image processing during the last four to five decades. Most of the techniques are developed for enhancing images obtained from unmanned spacecrafts, space probes and military reconnaissance flights. Image Processing systems are becoming popular due to easy availability of powerful personnel computers, large size memory devices, graphics software etc. Medical image segmentation & classification play an important role in medical research field. The patient CT lung images are classified into normal and abnormal category. Then, the abnormal images are subjected to segmentation to view the tumor portion. Classification depends on the features extracted from the images. We mainly are concentrating on feature extraction stage to yield better classification performance. Texture based features such as GLCM (Gray Level Co-occurrence Matrix) features play an important role in medical image analysis. Totally 12 different statistical features were extracted. To select the discriminative features among them we use sequential forward selection algorithm. Afterwards we prefer multinomial multivariate Bayesian for the classification stage. Classifier performance will be analyses further. The effectiveness of the modified weighted FCM algorithm in terms of computational rate is improved by modifying the cluster center and membership value updating criterion. Objective of this paper is that To achieve a perfect classification by multivariate multinomial Bayesian
Project communication management patterns

In present, dynamically developing organizations, that often realize business tasks using the project-based approach, effective project management is of paramount importance. Numerous reports and scientific papers present lists of critical success factors in project management, and communication management is usually at the very top of the list. But even though the communication practices are found to be associated with most of the success dimensions, they are not given enough attention and the communication processes and practices formalized in the company's project management methodology are neither followed nor prioritized by project managers. This paper aims at supporting project managers and teams in more effective implementation of best practices in communication management by proposing a set of communication management patterns, which promote a context-problem-solution approach to communication management in projects.
Multi-Target Tracking by Discrete-Continuous Energy Minimization

The task of tracking multiple targets is often addressed with the so-called tracking-by-detection paradigm, where the first step is to obtain a set of target hypotheses for each frame independently. Tracking can then be regarded as solving two separate, but tightly coupled problems. The first is to carry out data association, i.e., to determine the origin of each of the available observations. The second problem is to reconstruct the actual trajectories that describe the spatio-temporal motion pattern of each individual target. The former is inherently a discrete problem, while the latter should intuitively be modeled in continuous space. Having to deal with an unknown number of targets, complex dependencies, and physical constraints, both are challenging tasks on their own and thus most previous work focuses on one of these subproblems. Here, we present a multi-target tracking approach that explicitly models both tasks as minimization of a unified discrete-continuous energy function. Trajectory properties are captured through global label costs, a recent concept from multi-model fitting, which we introduce to tracking. Specifically, label costs describe physical properties of individual tracks, e.g., linear and angular dynamics, or entry and exit points. We further introduce pairwise label costs to describe mutual interactions between targets in order to avoid collisions. By choosing appropriate forms for the individual energy components, powerful discrete optimization techniques can be leveraged to address data association, while the shapes of individual trajectories are updated by gradient-based continuous energy minimization. The proposed method achieves state-of-the-art results on diverse benchmark sequences.
A detailed GPU cache model based on reuse distance theory

As modern GPUs rely partly on their on-chip memories to counter the imminent off-chip memory wall, the efficient use of their caches has become important for performance and energy. However, optimising cache locality system-atically requires insight into and prediction of cache behaviour. On sequential processors, stack distance or reuse distance theory is a well-known means to model cache behaviour. However, it is not straightforward to apply this theory to GPUs, mainly because of the parallel execution model and fine-grained multi-threading. This work extends reuse distance to GPUs by modelling: (1) the GPU's hierarchy of threads, warps, threadblocks, and sets of active threads, (2) conditional and non-uniform latencies, (3) cache associativity, (4) miss-status holding-registers, and (5) warp divergence. We implement the model in C++ and extend the Ocelot GPU emulator to extract lists of memory addresses. We compare our model with measured cache miss rates for the Parboil and PolyBench/GPU benchmark suites, showing a mean absolute error of 6% and 8% for two cache configurations. We show that our model is faster and even more accurate compared to the GPGPU-Sim simulator.
Survey of Model-Based Reinforcement Learning: Applications on Robotics

Reinforcement learning is an appealing approach for allowing robots to learn new tasks. Relevant literature reveals a plethora of methods, but at the same time makes clear the lack of implementations for dealing with real life challenges. Current expectations raise the demand for adaptable robots. We argue that, by employing model-based reinforcement learning, the—now limited—adaptability characteristics of robotic systems can be expanded. Also, model-based reinforcement learning exhibits advantages that makes it more applicable to real life use-cases compared to model-free methods. Thus, in this survey, modelbased methods that have been applied in robotics are covered. We categorize them based on the derivation of an optimal policy, the definition of the returns function, the type of the transition model and the learned task. Finally, we discuss the applicability of model-based reinforcement learning approaches in new applications, taking into consideration the state of the art in both algorithms and hardware. A. S. Polydoros ( ) · L. Nalpantidis Department of Mechanical and Manufacturing Engineering, Aalborg University, AC Meyers Vaenge 15, 2450 Copenhagen SV, Denmark e-mail: athapoly@m-tech.aau.dk L. Nalpantidis e-mail: lanalpa@m-tech.aau.dk
Monitoring Public Health Concerns Using Twitter Sentiment Classifications

An important task of public health officials is to keep track of spreading epidemics, and the locations and speed with which they appear. Furthermore, there is interest in understanding how concerned the population is about a disease outbreak. Twitter can serve as an important data source to provide this information in real time. In this paper, we focus on sentiment classification of Twitter messages to measure the Degree of Concern (DOC) of the Twitter users. In order to achieve this goal, we develop a novel two-step sentiment classification workflow to automatically identify personal tweets and negative tweets. Based on this workflow, we present an Epidemic Sentiment Monitoring System (ESMOS) that provides tools for visualizing Twitter users' concern towards different diseases. The visual concern map and chart in ESMOS can help public health officials to identify the progression and peaks of concern for a disease in space and time, so that appropriate preventive actions can be taken. The DOC measure is based on the sentiment-based classifications. We compare clue-based and different Machine Learning methods to classify sentiments of Twitter users regarding diseases, first into personal and neutral tweets and then into negative from neutral personal tweets. In our experiments, Multinomial Naïve Bayes achieved overall the best results and took significantly less time to build the classifier than other methods.
Demonstrating map++: A crowd-sensing system for automatic map semantics identification

Digital maps have become a part of our everyday lives as they are integrated into a wide range of map-based services like traffic estimation, navigation systems, and many more. These services still have a huge opportunity for enhancements with semantically richer maps to support a large class of new services. In this demo, we demonstrate the MAP++ crowd-sensing system for map semantics identification. Map++ leverages standard smart-phone sensors to automatically enrich digital maps with various road semantics such as bridges, crossroads, roundabouts, underpasses, among others. The goal of this demo is to showcase Map++ in action where it takes crowd-sensed motion traces and process them automatically in real-time to identify the map semantics. The demo also allows attendees to analyze the effect of the Map++ different parameters on system performance and road semantics.
Cataloging Public Objects Using Aerial and Street-Level Images — Urban Trees

Each corner of the inhabited world is imaged from multiple viewpoints with increasing frequency. Online map services like Google Maps or Here Maps provide direct access to huge amounts of densely sampled, georeferenced images from street view and aerial perspective. There is an opportunity to design computer vision systems that will help us search, catalog and monitor public infrastructure, buildings and artifacts. We explore the architecture and feasibility of such a system. The main technical challenge is combining test time information from multiple views of each geographic location (e.g., aerial and street views). We implement two modules: det2geo, which detects the set of locations of objects belonging to a given category, and geo2cat, which computes the fine-grained category of the object at a given location. We introduce a solution that adapts state-of the-art CNN-based object detectors and classifiers. We test our method on "Pasadena Urban Trees", a new dataset of 80,000 trees with geographic and species annotations, and show that combining multiple views significantly improves both tree detection and tree species classification, rivaling human performance.
AVATAR: A Variable-Retention-Time (VRT) Aware Refresh for DRAM Systems

Multirate refresh techniques exploit the non-uniformity in retention times of DRAM cells to reduce the DRAM refresh overheads. Such techniques rely on accurate profiling of retention times of cells, and perform faster refresh only for a few rows which have cells with low retention times. Unfortunately, retention times of some cells can change at runtime due to Variable Retention Time (VRT), which makes it impractical to reliably deploy multirate refresh. Based on experimental data from 24 DRAM chips, we develop architecture-level models for analyzing the impact of VRT. We show that simply relying on ECC DIMMs to correct VRT failures is unusable as it causes a data error once every few months. We propose AVATAR, a VRT-aware multirate refresh scheme that adaptively changes the refresh rate for different rows at runtime based on current VRT failures. AVATAR provides a time to failure in the regime of several tens of years while reducing refresh operations by 62%-72%.
The Nature-Nurture Debates: 25 Years of Challenges in Understanding the Psychology of Gender.

Nature-nurture debates continue to be highly contentious in the psychology of gender despite the common recognition that both types of causal explanations are important. In this article, we provide a historical analysis of the vicissitudes of nature and nurture explanations of sex differences and similarities during the quarter century since the founding of the Association for Psychological Science. We consider how the increasing use of meta-analysis helped to clarify sex difference findings if not the causal explanations for these effects. To illustrate these developments, this article describes socialization and preferences for mates as two important areas of gender research. We also highlight developing research trends that address the interactive processes by which nature and nurture work together in producing sex differences and similarities. Such theorizing holds the promise of better science as well as a more coherent account of the psychology of women and men that should prove to be more influential with the broader public.
Digital auto-tuning system for inductor current sensing in VRM applications

Inductor current sensing is becoming widely used in current programmed controllers for microprocessor applications. This method exploits a low-pass filter in parallel with the inductor to provide lossless current sense. A major drawback of inductor current sensing is that accurate sense the DC and AC components of the current signal requires precise matching between the low-pass filter time constant and the inductor time constant (L/RL). However, matching accuracy depends on the tolerance of the components and on the operating conditions; therefore it can hardly be guaranteed. To overcome this problem, a novel digital auto-tuning system is proposed that automatically compensates any time constant mismatch. This auto-tuning system has been developed for VRM current programmed controllers. It makes it possible to meet the adaptive voltage positioning requirements using conventional and low cost components, and to solve problems such as aging effects, temperature variations and process tolerances as well. A prototype of the auto-tuning system based on an FPGA and a commercial DC/DC controller has been designed and tested. The experimental results fully confirmed the effectiveness of the proposed method, showing an improvement of the current sense precision from about 30% up to 4%. This innovative solution is suitable to fulfill the challenging accuracy specifications required by the future VRM applications
Interactive Markerless Articulated Hand Motion Tracking Using RGB and Depth Data

Tracking the articulated 3D motion of the hand has important applications, for example, in human-computer interaction and teleoperation. We present a novel method that can capture a broad range of articulated hand motions at interactive rates. Our hybrid approach combines, in a voting scheme, a discriminative, part-based pose retrieval method with a generative pose estimation method based on local optimization. Color information from a multi-view RGB camera setup along with a person-specific hand model are used by the generative method to find the pose that best explains the observed images. In parallel, our discriminative pose estimation method uses fingertips detected on depth data to estimate a complete or partial pose of the hand by adopting a part-based pose retrieval strategy. This part-based strategy helps reduce the search space drastically in comparison to a global pose retrieval strategy. Quantitative results show that our method achieves state-of-the-art accuracy on challenging sequences and a near-real time performance of 10 fps on a desktop computer.
Virtual reality: past, present and future.

This report provides a short survey of the field of virtual reality, highlighting application domains, technological requirements, and currently available solutions. The report is organized as follows: section 1 presents the background and motivation of virtual environment research and identifies typical application domain, section 2 discusses the characteristics a virtual reality system must have in order to exploit the perceptual and spatial skills of users, section 3 surveys current input/output devices for virtual reality, section 4 surveys current software approaches to support the creation of virtual reality systems, and section 5 summarizes the report.
An Unmanned Aerial Vehicle-Based Imaging System for 3D Measurement of Unpaved Road Surface Distresses

Road condition data are important in transportation management systems. Over the last decades, significant progress has been made and new approaches have been proposed for efficient collection of pavement condition data. However, the assessment of unpaved road conditions has been rarely addressed in transportation research. Unpaved roads constitute approximately 40% of the U.S. road network, and are the lifeline in rural areas. Thus, it is important for timely identification and rectification of deformation on such roads. This article introduces an innovative Unmanned Aerial Vehicle (UAV)-based digital imaging system focusing on efficient collection of surface condition data over rural roads. In contrast to other approaches, aerial assessment is proposed by exploring aerial imagery acquired from an unpiloted platform to derive a threedimensional (3D) surface model over a road distress area for distress measurement. The system consists of a lowcost model helicopter equipped with a digital camera, a Global Positioning System (GPS) receiver and an Inertial Navigation System (INS), and a geomagnetic sensor. A set of image processing algorithms has been developed for precise orientation of the acquired images, and generation of 3D road surface models and orthoimages, which allows for accurate measurement of the size and the dimension of the road surface distresses. The developed system has been tested over several test sites ∗To whom correspondence should be addressed. . with roads of various surface distresses. The experiments show that the system is capable for providing 3D information of surface distresses for road condition assessment. Experiment results demonstrate that the system is very promising and provides high accuracy and reliable results. Evaluation of the system using 2D and 3D models with known dimensions shows that subcentimeter measurement accuracy is readily achieved. The comparison of the derived 3D information with the onsite manual measurements of the road distresses reveals differences of 0.50 cm, demonstrating the potential of the presented system for future practice.
CLOSED-FORM DESIGN METHOD OF AN N-WAY DUAL-BAND WILKINSON HYBRID POWER DIVIDER

In this paper, the closed-form design method of an Nway dual-band Wilkinson hybrid power divider is proposed. This symmetric structure including N groups of two sections of transmission lines and two isolated resistors is described which can split a signal into N equiphase equiamplitude parts at two arbitrary frequencies (dual-band) simultaneously, where N can be odd or even. Based on the rigorous evenand odd-mode analysis, the closed-form design equations are derived. For verification, various numerical examples are designed, calculated and compared while two practical examples including two ways and three ways dual-band microstrip power dividers are fabricated and measured. It is very interesting that this generalized power divider with analytical design equations can be designed for wideband applications when the frequency-ratio is relatively small. In addition, it is found that the conventional N-way hybrid Wilkinson power divider for single-band applications is a special case (the frequency-ratio equals to 3) of this generalized power divider.
Co-evolving recurrent neurons learn deep memory POMDPs

Recurrent neural networks are theoretically capable of learning complex temporal sequences, but training them through gradient-descent is too slow and unstable for practical use in reinforcement learning environments. Neuroevolution, the evolution of artificial neural networks using genetic algorithms, can potentially solve real-world reinforcement learning tasks that require deep use of memory, i.e. memory spanning hundreds or thousands of inputs, by searching the space of recurrent neural networks directly. In this paper, we introduce a new neuroevolution algorithm called Hierarchical Enforced SubPopulations that simultaneously evolves networks at two levels of granularity: full networks and network components or neurons. We demonstrate the method in two POMDP tasks that involve temporal dependencies of up to thousands of time-steps, and show that it is faster and simpler than the current best conventional reinforcement learning system on these tasks.
CRF-Filters: Discriminative Particle Filters for Sequential State Estimation

Particle filters have been applied with great success to various state estimation problems in robotics. However, particle filters often require extensive parameter tweaking in order to work well in practice. This is based on two observations. First, particle filters typically rely on independence assumptions such as "the beams in a laser scan are independent given the robot's location in a map". Second, even when the noise parameters of the dynamical system are perfectly known, the sample-based approximation can result in poor filter performance. In this paper we introduce CRF-filters, a novel variant of particle filtering for sequential state estimation. CRF-filters are based on conditional random fields, which are discriminative models that can handle arbitrary dependencies between observations. We show how to learn the parameters of CRF-filters based on labeled training data. Experiments using a robot equipped with a laser range-finder demonstrate that our technique is able to learn parameters of the robot's motion and sensor models that result in good localization performance, without the need of additional parameter tweaking.