nils-herrmann/clean_scirepeval huggingface dataset:
Self-Organizing Roles on Agile Software Development Teams

Self-organizing teams have been recognized and studied in various forms-as autonomous groups in socio-technical systems, enablers of organizational theories, agents of knowledge management, and as examples of complex-adaptive systems. Over the last decade, self-organizing teams have taken center stage in software engineering when they were incorporated as a hallmark of Agile methods. Despite the long and rich history of self-organizing teams and their recent popularity with Agile methods, there has been little research on the topic within software wngineering. Particularly, there is a dearth of research on how Agile teams organize themselves in practice. Through a Grounded Theory research involving 58 Agile practitioners from 23 software organizations in New Zealand and India over a period of four years, we identified informal, implicit, transient, and spontaneous roles that make Agile teams self-organizing. These roles-Mentor, Coordinator, Translator, Champion, Promoter, and Terminator-are focused toward providing initial guidance and encouraging continued adherence to Agile methods, effectively managing customer expectations and coordinating customer collaboration, securing and sustaining senior management support, and identifying and removing team members threatening the self-organizing ability of the team. Understanding these roles will help software development teams and their managers better comprehend and execute their roles and responsibilities as a self-organizing team.
Sculpting by numbers

We propose a method that allows an unskilled user to create an accurate physical replica of a digital 3D model. We use a projector/camera pair to scan a work in progress, and project multiple forms of guidance onto the object itself that indicate which areas need more material, which need less, and where any ridges, valleys or depth discontinuities are. The user adjusts the model using the guidance and iterates, making the shape of the physical object approach that of the target 3D model over time. We show how this approach can be used to create a duplicate of an existing object, by scanning the object and using that scan as the target shape. The user is free to make the reproduction at a different scale and out of different materials: we turn a toy car into cake. We extend the technique to support replicating a sequence of models to create stop-motion video. We demonstrate an end-to-end system in which real-world performance capture data is retargeted to claymation. Our approach allows users to easily and accurately create complex shapes, and naturally supports a large range of materials and model sizes.
ImageNet Classification with Deep Convolutional Neural

The intended goal of the experiments was to create a deep, convolutional network that uses supervised learning to achieve better (lower) error rates than the rates previously observed, to identify images, on a highly challenging dataset. The parameters used for judging if the CNN is able to recognise the object is given by “Top-1” and “Top-5” predictions made – that is the top prediction made, and the the top 5 predictions made, and matching which ones are correct. The training set used was ILSVRC-2010 images, and ILSVRC-2012 images as training sets, and ILSVRC-2010 for the test set .which containd 15 million high resolution images in about 22,000 categories. Given a rectangular image, they first rescaled the image such that the shorter side was of length 256, and then cropped out the central 256x256 patch from the resulting image. The main feature of the architecture of the CNN is the different layers – there are 5 convolutional layers, and 3 fully connected layers, and this feature is the main feature which helps to reduce the error rates, as the removal of even a single layer degrades the performance by about 2%.
Output Range Analysis for Deep Neural Networks

Deep neural networks (NN) are extensively used for machine learning tasks such as image classification, perception and control of autonomous systems. Increasingly, these deep NNs are also been deployed in high-assurance applications. Thus, there is a pressing need for developing techniques to verify neural networks to check whether certain user-expected properties are satisfied. In this paper, we study a specific verification problem of computing a guaranteed range for the output of a deep neural network given a set of inputs represented as a convex polyhedron. Range estimation is a key primitive for verifying deep NNs. We present an efficient range estimation algorithm that uses a combination of local search and linear programming problems to efficiently find the maximum and minimum values taken by the outputs of the NN over the given input set. In contrast to recently proposed “monolithic” optimization approaches, we use local gradient descent to repeatedly find and eliminate local minima of the function. The final global optimum is certified using a mixed integer programming instance. We implement our approach and compare it with Reluplex, a recently proposed solver for deep neural networks. We demonstrate the effectiveness of the proposed approach for verification of NNs used in automated control as well as those used in classification.
Adoption of security as a service

Security as a Service systems enable new opportunities to compose security infrastructures for information systems. However, to date there are no holistic insights about their adoption and relevant predictors. Based on existing technology acceptance models we developed an extended application-specific research model including formative and reflective measures. The model was estimated applying the Partial Least Squares technique to address the prediction-oriented nature of the study. A subsequent online survey revealed that a large number of industries shows significant and steadily growing interest in Security as a Service. Adoption drivers were investigated systematically.
3D Slicer as an image computing platform for the Quantitative Imaging Network.

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.
Bias in error estimation when using cross-validation for model selection

Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate. We have evaluated the validity of using the CV error estimate of the optimized classifier as an estimate of the true error expected on independent data. We used CV to optimize the classification parameters for two kinds of classifiers; Shrunken Centroids and Support Vector Machines (SVM). Random training datasets were created, with no difference in the distribution of the features between the two classes. Using these "null" datasets, we selected classifier parameter values that minimized the CV error estimate. 10-fold CV was used for Shrunken Centroids while Leave-One-Out-CV (LOOCV) was used for the SVM. Independent test data was created to estimate the true error. With "null" and "non null" (with differential expression between the classes) data, we also tested a nested CV procedure, where an inner CV loop is used to perform the tuning of the parameters while an outer CV is used to compute an estimate of the error. The CV error estimate for the classifier with the optimal parameters was found to be a substantially biased estimate of the true error that the classifier would incur on independent data. Even though there is no real difference between the two classes for the "null" datasets, the CV error estimate for the Shrunken Centroid with the optimal parameters was less than 30% on 18.5% of simulated training data-sets. For SVM with optimal parameters the estimated error rate was less than 30% on 38% of "null" data-sets. Performance of the optimized classifiers on the independent test set was no better than chance. The nested CV procedure reduces the bias considerably and gives an estimate of the error that is very close to that obtained on the independent testing set for both Shrunken Centroids and SVM classifiers for "null" and "non-null" data distributions. We show that using CV to compute an error estimate for a classifier that has itself been tuned using CV gives a significantly biased estimate of the true error. Proper use of CV for estimating true error of a classifier developed using a well defined algorithm requires that all steps of the algorithm, including classifier parameter tuning, be repeated in each CV loop. A nested CV procedure provides an almost unbiased estimate of the true error.
Sensor Discovery on Linked Data

There has been a drive recently to make sensor data accessible on the Web. However, because of the vast number of sensors collecting data about our environment, finding relevant sensors on the Web is a non-trivial challenge. In this paper, we present an approach to discovering sensors through a standard service interface over Linked Data. This is accomplished with a semantic sensor network middleware that includes a sensor registry on Linked Data and a sensor discovery service that extends the OGC Sensor Web Enablement. With this approach, we are able to access and discover sensors that are positioned near named-locations of interest.
Sentiwordnet for Bangla Sentiwordnet for Bangla

Advances in NLP techniques have led to a great demand for tagging and analysis of the sentiments from unstructured natural language data over the last few years. A typical approach to sentiment analysis is to start with a lexicon of positive and negative words and phrases. In these lexicons, entries are tagged with their prior out of context polarity. Unfortunately all efforts found in literature deal mostly with English texts. In this squib, we propose a computational technique of generating an equivalent SentiWordNet (Bengali) from publicly available English Sentiment lexicons and English-Bengali bilingual dictionary. The target language for the present task is Bengali, though the methodology could be replicated for any new language. There are two main lexical resources widely used in English for Sentiment analysis: SentiWordNet (Esuli et. al., 2006) and Subjectivity Word List (Wilson et. al., 2005). SentiWordNet is an automatically constructed lexical resource for English which assigns a positivity score and a negativity score to each WordNet synset. The subjectivity lexicon was compiled from manually developed resources augmented with entries learned from corpora. The entries in the Subjectivity lexicon have been labelled for part of speech (POS) as well as either strong or weak subjective tag depending on reliability of the subjective nature of the entry.
A multi-loop voltage feedback filterless class-D switching audio amplifier using unipolar pulse-width-modulation

Voltage feedback is frequently used in class-D switching audio power amplifiers. This paper discusses the design and implementation of a low-cost filterless class-D, unipolar pulse-width modulation switching audio amplifier having a multi-loop voltage feedback scheme. Classical frequency-compensation techniques are used to design and stabilize the three voltage feedback loops implemented in this application. This design method proves to be a cost-effective solution for designing high-fidelity (hi-fi) audio amplifiers. The cost is reduced because no output filter is used, the required switching frequency is half of the one needed if bipolar PWM was used, and no current sensor is needed for feedback purposes. The output impedance is extremely low due to the reduction of the successive voltage loops, making the amplifier less load dependent. Simulation results show that a total harmonic distortion (THD) of 0.005% can be achieved using this topology, as well as a flat frequency response, free of phase distortion in the audio band. Experimental results show the feasibility of this control scheme, since a THD of 0.05% was achieved with a laboratory prototyped amplifier. A comparison of the performance of this audio amplifier with that of some commercial class-D audio amplifiers, reveals that our design can seriously compete with some of the ICs leading the market at a lower cost.
Deep correspondence restricted Boltzmann machine for cross-modal retrieval

The task of cross-modal retrieval, i.e., using a text query to search for images or vice versa, has received considerable attentionwith the rapid growth of multi-modal web data. Modeling the correlations between different modalities is the key to tackle this problem. In this paper, we propose a correspondence restricted Boltzmann machine (Corr-RBM) to map the original features of bimodal data, such as image and text in our setting, into a low-dimensional common space, in which the heterogeneous data are comparable. In our Corr-RBM, two RBMs built for image and text, respectively are connected at their individual hidden representation layers by a correlation loss function. A single objective function is constructed to trade off the correlation loss and likelihoods of both modalities. Through the optimization of this objective function, our Corr-RBM is able to capture the correlations between two modalities and learn the representation of each modality simultaneously. Furthermore, we construct two deep neural structures using Corr-RBM as the main building block for the task of cross-modal retrieval. A number of comparison experiments are performed on three public real-world data sets. All of our models show significantly better results than state-of-the-art models in both searching images via text query and vice versa. & 2014 Elsevier B.V. All rights reserved.
Contactless EMG sensors embroidered onto textile

To obtain maximum unobtrusiveness with sensors for monitoring health parameters on the human body, two technical solutions are combined. First we propose contactless sensors for capacitive electromyography measurements. Secondly, the sensors are integrated into textile, so complete fusion with a wearable garment is enabled. We are presenting the first successful measurements with such sensors.  surface electromyography, capacitive transducer, embroidery, textile electronics, interconnect
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns.
Kinematics Modeling of a Wheel-Based Pole Climbing Robot (UT-PCR)

This paper is concerned with the derivation of the kinematics model of the University of Tehran-Pole Climbing Robot (UT-PCR). As the first step, an appropriate set of coordinates is selected and used to describe the state of the robot. Nonholonomic constraints imposed by the wheels are then expressed as a set of differential equations. By describing these equations in terms of the state of the robot an underactuated driftless nonlinear control system with affine inputs that governs the motion of the robot is derived. A set of experimental results are also given to show the capability of the UT-PCR in climbing a stepped pole.
Design of thermostat system based on Proteus simulation software

In order to solve the problem of precise temperature control, the thermoelectric cooler (TEC) principle widely used is analyzed for the design of the whole control process and selection of control parameters, and then accurate simulation model of the TEC is established in Proteus simulation software. Moreover, combined with the traditional circuit simulation model, the temperature control loop is designed, and the response characteristics of the system are tested using an input signal similar to the unit-step function to achieve the precise temperature control. Simulation results show that the proposed control circuits can precisely convert error signal to output voltage sent to TEC model, and TEC model behaves approximately like a two-pole system. The first pole starts at 20mHz and a second pole at 1Hz.
A study of application performance with non-volatile main memory

Attaching next-generation non-volatile memories (NVMs) to the main memory bus provides low-latency, byte-addressable access to persistent data that should significantly improve performance for a wide range of storage-intensive workloads. We present an analysis of storage application performance with non-volatile main memory (NVMM) using a hardware NVMM emulator that allows fine-grain tuning of NVMM performance parameters. Our evaluation results show that NVMM improves storage application performance significantly over flash-based SSDs and HDDs. We also compare the performance of applications running on realistic NVMM with the performance of the same applications running on idealized NVMM with the same performance as DRAM. We find that although NVMM is projected to have higher latency and lower bandwidth than DRAM, these difference have only a modest impact on application performance. A much larger drag on NVMM performance is the cost of ensuring data resides safely in the NVMM (rather than the volatile caches) so that applications can make strong guarantees about persistence and consistency. In response, we propose an optimized approach to flushing data from CPU caches that minimizes this cost. Our evaluation shows that this technique significantly improves performance for applications that require strict durability and consistency guarantees over large regions of memory.
Linking perceived service quality and service loyalty : a multi-dimensional perspective

In recent research on service quality it has been argued that the relationship between perceived service quality and service loyalty is an issue which requires conceptual and empirical elaboration through replication and extension of current knowledge. Focuses on the refinement of a scale for measuring service loyalty dimensions and the relationships between dimensions of service quality and these service loyalty dimensions. The results of an empirical study of a large sample of customers from four different service industries suggest that four dimensions of service loyalty can be identified: purchase intentions, word-of-mouth communication; price sensitivity; and complaining behaviour. Further analysis yields an intricate pattern of service quality-service loyalty relationships at the level of the individual dimensions with notable differences across
Task Scheduling and Server Provisioning for Energy-Efficient Cloud-Computing Data Centers

In this paper, we present an optimization model for task scheduling for minimizing energy consumption in cloud-computing data centers. The proposed approach was formulated as an integer programming problem to minimize the cloud-computing data center energy consumption by scheduling tasks to a minimum numbers of servers while keeping the task response time constraints. We prove that the average task response time and the number of active servers needed to meet such time constraints are bounded through the use of a greedy task-scheduling scheme. In addition, we propose the most-efficient server- first task-scheduling scheme to minimize energy expenditure as a practical scheduling scheme. We model and simulate the proposed scheduling scheme for a data center with heterogeneous tasks. The simulation results show that the proposed task-scheduling scheme reduces server energy consumption on average over 70 times when compared to the energy consumed under a (not-optimized) random-based task-scheduling scheme. We show that energy savings are achieved by minimizing the allocated number of servers.