nils-herrmann/clean_scirepeval huggingface dataset:
Selecting the best VM across multiple public clouds: a data-driven performance modeling approach

Users of cloud services are presented with a bewildering choice of VM types and the choice of VM can have significant implications on performance and cost. In this paper we address the fundamental problem of accurately and economically choosing the best VM for a given workload and user goals. To address the problem of optimal VM selection, we present PARIS, a data-driven system that uses a novel hybrid offline and online data collection and modeling framework to provide accurate performance estimates with minimal data collection. PARIS is able to predict workload performance for different user-specified metrics, and resulting costs for a wide range of VM types and workloads across multiple cloud providers. When compared to sophisticated baselines, including collaborative filtering and a linear interpolation model using measured workload performance on two VM types, PARIS produces significantly better estimates of performance. For instance, it reduces runtime prediction error by a factor of 4 for some workloads on both AWS and Azure. The increased accuracy translates into a 45% reduction in user cost while maintaining performance.
Going Digital: A Survey on Digitalization and Large-Scale Data Analytics in Healthcare

We provide an overview of the recent trends toward digitalization and large-scale data analytics in healthcare. It is expected that these trends are instrumental in the dramatic changes in the way healthcare will be organized in the future. We discuss the recent political initiatives designed to shift care delivery processes from paper to electronic, with the goals of more effective treatments with better outcomes; cost pressure is a major driver of innovation. We describe newly developed networks of healthcare providers, research organizations, and commercial vendors to jointly analyze data for the development of decision support systems. We address the trend toward continuous healthcare where health is monitored by wearable and stationary devices; a related development is that patients increasingly assume responsibility for their own health data. Finally, we discuss recent initiatives toward a personalized medicine, based on advances in molecular medicine, data management, and data analytics.
Node Similarity with q -Grams for Real-World Labeled Networks

We study node similarity in labeled networks, using the label sequences found in paths of bounded length q leading to the nodes. (This recalls the q-grams employed in document resemblance, based on the Jaccard distance.) When applied to networks, the challenge is two-fold: the number of q-grams generated from labeled paths grows exponentially with q, and their frequency should be taken into account: this leads to a variation of the Jaccard index known as Bray-Curtis index for multisets. We describe nSimGram, a suite of fast algorithms for node similarity with q-grams, based on a novel blend of color coding, probabilistic counting, sketches, and string algorithms, where the universe of elements to sample is exponential. We provide experimental evidence that our measure is effective and our running times scale to deal with large real-world networks.
Semi-Supervised Learning With Deep Embedded Clustering for Image Classification and Segmentation

Deep neural networks usually require large labeled datasets to construct accurate models; however, in many real-world scenarios, such as medical image segmentation, labeling data are a time-consuming and costly human (expert) intelligent task. Semi-supervised methods leverage this issue by making use of a small labeled dataset and a larger set of unlabeled data. In this paper, we present a flexible framework for semi-supervised learning that combines the power of supervised methods that learn feature representations using state-of-the-art deep convolutional neural networks with the deeply embedded clustering algorithm that assigns data points to clusters based on their probability distributions and feature representations learned by the networks. Our proposed semi-supervised learning algorithm based on deeply embedded clustering (SSLDEC) learns feature representations via iterations by alternatively using labeled and unlabeled data points and computing target distributions from predictions. During this iterative procedure, the algorithm uses labeled samples to keep the model consistent and tuned with labeling, as it simultaneously learns to improve feature representation and predictions. The SSLDEC requires a few hyper-parameters and thus does not need large labeled validation sets, which addresses one of the main limitations of many semi-supervised learning algorithms. It is also flexible and can be used with many state-of-the-art deep neural network configurations for image classification and segmentation tasks. To this end, we implemented and tested our approach on benchmark image classification tasks as well as in a challenging medical image segmentation scenario. In benchmark classification tasks, the SSLDEC outperformed several state-of-the-art semi-supervised learning methods, achieving 0.46% error on MNIST with 1000 labeled points and 4.43% error on SVHN with 500 labeled points. In the iso-intense infant brain MRI tissue segmentation task, we implemented SSLDEC on a 3D densely connected fully convolutional neural network where we achieved significant improvement over supervised-only training as well as a semi-supervised method based on pseudo-labeling. Our results show that the SSLDEC can be effectively used to reduce the need for costly expert annotations, enhancing applications, such as automatic medical image segmentation.
Small Organ Segmentation in Whole-Body MRI Using a Two-Stage FCN and Weighting Schemes

Accurate and robust segmentation of small organs in wholebody MRI is difficult due to anatomical variation and class imbalance. Recent deep network based approaches have demonstrated promising performance on abdominal multi-organ segmentations. However, the performance on small organs is still suboptimal as these occupy only small regions of the whole-body volumes with unclear boundaries and variable shapes. A coarse-to-fine, hierarchical strategy is a common approach to alleviate this problem, however, this might miss useful contextual information. We propose a two-stage approach with weighting schemes based on auto-context and spatial atlas priors. Our experiments show that the proposed approach can boost the segmentation accuracy of multiple small organs in whole-body MRI scans.
Visualizing Association Rules in Hierarchical Groups

Association rule mining is one of the most popular data mining methods. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. Sifting manually through large sets of rules is time consuming and strenuous. Visualization has a long history of making large amounts of data better accessible using techniques like selecting and zooming. However, most association rule visualization techniques are still falling short when it comes to a large number of rules. In this paper we present a new interactive visualization technique which lets the user navigate through a hierarchy of groups of association rules. We demonstrate how this new visualization techniques can be used to analyze a large sets of association rules with examples from our implementation in the R-package arulesViz.
StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow

Stack Overflow (SO) has been a great source of natural language questions and their code solutions (i.e., question-code pairs), which are critical for many tasks including code retrieval and annotation. In most existing research, question-code pairs were collected heuristically and tend to have low quality. In this paper, we investigate a new problem of systematically mining question-code pairs from Stack Overflow (in contrast to heuristically collecting them). It is formulated as predicting whether or not a code snippet is a standalone solution to a question. We propose a novel Bi-View Hierarchical Neural Network which can capture both the programming content and the textual context of a code snippet (i.e., two views) to make a prediction. On two manually annotated datasets in Python and SQL domain, our framework substantially outperforms heuristic methods with at least 15% higher F1 and accuracy. Furthermore, we present StaQC (Stack Overflow Question-Code pairs), the largest dataset to date of ∼148K Python and ∼120K SQL question-code pairs, automatically mined from SO using our framework. Under various case studies, we demonstrate that StaQC can greatly help develop data-hungry models for associating natural language with programming language1.
Drugging Undruggable Molecular Cancer Targets.

Cancer, more than any other human disease, now has a surfeit of potential molecular targets poised for therapeutic exploitation. Currently, a number of attractive and validated cancer targets remain outside of the reach of pharmacological regulation. Some have been described as undruggable, at least by traditional strategies. In this article, we outline the basis for the undruggable moniker, propose a reclassification of these targets as undrugged, and highlight three general classes of this imposing group as exemplars with some attendant strategies currently being explored to reclassify them. Expanding the spectrum of disease-relevant targets to pharmacological manipulation is central to reducing cancer morbidity and mortality.
Exploring OpenStreetMap Capability for Road Perception

Drivable space detection or road perception is one of the most important tasks for autonomous driving. Sensorbased vision/laser systems may have limited performance in bad illumination/weather conditions, a prior knowledge of the road from the map data is expected to improve the effectiveness. This paper is to employ the map information extracted from the OpenStreetMap (OSM) data, and explore its capability for road perception. The OSM data can be used to render virtual street views, and further refined to provide the prior road mask. The OSM masks can be also combine with image processing and Lidar point clouding approaches to characterize the drivable space. Using a Fully Convolutional Neural Network (FCNN), the OSM availability for deep learning methods is also discussed.
ALICE Chatbot: Trials and Outputs

A chatbot is a conversational agent that interacts with users using natural language. Multi chatbots are available to serve in different domains. However, the knowledge base of chatbots is hand coded in its brain. This paper presents an overview of ALICE chatbot, its AIML format, and our experiments to generate different prototypes of ALICE automatically based on a corpus approach. A description of developed software which converts readable text (corpus) into AIML format is presented alongside with describing the different corpora we used. Our trials revealed the possibility of generating useful prototypes without the need for sophisticated natural language processing or complex machine learning techniques. These prototypes were used as tools to practice different languages, to visualize corpus, and to provide answers for questions.
Aluminum Alloying in Local Contact Areas on Dielectrically Passivated Rear Surfaces of Silicon Solar Cells

We present a detailed study on the rear contact formation of rear-surface-passivated silicon solar cells by full-area screen printing and alloying of aluminum pastes on the locally opened passivation layer. We demonstrate that applying conventional Al pastes exhibits two main problems: (1) high contact depths leading to an enlargement of the contact area and (2) low thicknesses of the Al-doped p+ Si regions in the contact points resulting in poor electron shielding. We show that this inadequate contact formation can be directly linked to the deficiently low percentage of silicon that dissolves into the Al-Si melt during alloying. Thus, by intentionally adding silicon to the Al paste, we could significantly improve the contact geometry by reducing the contact depth and enlarging the Al-p+ thickness in the contact points, enabling a simple industrially feasible way for the rear contact formation of silicon solar cells.
The Ventilation Effect on Stator Convective Heat Transfer of an Axial-Flux Permanent-Magnet Machine

This paper investigates the effect of the inlet configuration on cooling for an air-cooled axial-flux permanent-magnet (AFPM) machine. Temperature rises in the stator were measured and compared with results predicted using computational fluid dynamic (CFD) methods linked to a detailed machine loss characterization. It is found that an improved inlet design can significantly reduce the stator temperature rises. Comparison between the validated CFD model results and the values obtained from heat transfer correlations addresses the suitability of those correlations proposed specifically for AFPM machines.
Performance Analysis of Modified SEPIC Converter with Low Input Voltage

The paper presents performance analysis of modified SEPIC dc-dc converter with low input voltage and wide output voltage range. The operational analysis and the design is done for the 380W power output of the modified converter. The simulation results of modified SEPIC converter are obtained with PI controller for the output voltage. The results obtained with the modified converter are compared with the basic SEPIC converter topology for the rise time, peak time, settling time and steady state error of the output response for open loop. Voltage tracking curve is also shown for wide output voltage range. I.  Dc-dc converters are widely used in regulated switched mode dc power supplies and in dc motor drive applications. The input to these converters is often an unregulated dc voltage, which is obtained by rectifying the line voltage and it will therefore fluctuate due to variations of the line voltages. Switched mode dc-dc converters are used to convert this unregulated dc input into a controlled dc output at a desired voltage level. The recent growth of battery powered applications and low voltage storage elements are increasing the demand of efficient step-up dc–dc converters. Typical applications are in adjustable speed drives, switch-mode power supplies, uninterrupted power supplies, and utility interface with nonconventional energy sources, battery energy storage systems, battery charging for electric vehicles, and power supplies for telecommunication systems etc.. These applications demand high step-up static gain, high efficiency and reduced weight, volume and cost. The step-up stage normally is the critical point for the design of high efficiency converters due to the operation with high input current and high output voltage . The boost converter topology is highly effective in these applications but at low line voltage in boost converter, the switching losses are high because the input current has the maximum value and the highest step-up conversion is required. The inductor has to be oversized for the large current at low line input. As a result, a boost converter designed for universal-input applications is heavily oversized compared to a converter designed for a narrow range of input ac line voltage . However, recently new non-isolated dc–dc converter topologies with basic boost are proposed, showing that it is possible to obtain high static gain, low voltage stress and low losses, improving the performance with respect to the classical topologies. Some single stage high power factor rectifiers are presented in . A new …
Real-time elderly activity monitoring system based on a tri-axial accelerometer.

The purpose of this study is to develop an automatic human movement classification system for the elderly using single waist-mounted tri-axial accelerometer.



Real-time movement classification algorithm was developed using a hierarchical binary tree, which can classify activities of daily living into four general states: (1) resting state such as sitting, lying, and standing; (2) locomotion state such as walking and running; (3) emergency state such as fall and (4) transition state such as sit to stand, stand to sit, stand to lie, lie to stand, sit to lie, and lie to sit. To evaluate the proposed algorithm, experiments were performed on five healthy young subjects with several activities, such as falls, walking, running, etc.



The results of experiment showed that successful detection rate of the system for all activities were about 96%. To evaluate long-term monitoring, 3 h experiment in home environment was performed on one healthy subject and 98% of the movement was successfully classified.



The results of experiment showed a possible use of this system which can monitor and classify the activities of daily living. For further improvement of the system, it is necessary to include more detailed classification algorithm to distinguish several daily activities.
Kinetostatic Analysis of Passively Adaptive Robotic Finger with Distributed Compliance

1 University of Niš, Faculty of Mechanical Engineering, Department of Mechatronics, Aleksandra Medvedeva 14, 18000 Niš, Serbia 2Department of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia 3 Department of Computer Science, Islamic Azad University, Chalous Branch, Chalous 46615-397, Iran 4Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
Graph Queries in a Next-Generation Datalog System

Recent theoretical advances have enabled the use of special monotonic aggregates in recursion. These special aggregates make possible the concise expression and efficient implementation of a rich new set of advanced applications. Among these applications, graph queries are particularly important because of their pervasiveness in data intensive application areas. In this demonstration, we present our Deductive Application Language (DeAL) System, the first of a new generation of Deductive Database Systems that support applications that could not be expressed using regular stratification, or could be expressed using XY-stratification (also supported in DeAL) but suffer from inefficient execution. Using example queries, we will (i) show how complex graph queries can be concisely expressed using DeAL and (ii) illustrate the formal semantics and efficient implementation of these powerful new monotonic constructs.
On joint design with intrinsic variable compliance: derivation of the DLR QA-Joint

In this paper we introduce a classification of intrinsically compliant joint mechanisms. Furthermore, we outline design considerations for realizing such devices in order to match the requirements for robust and performant actuation. Based on this elaboration, a new design concept is presented, the DLR QA-Joint. Its performance is investigated by various experiments, covering velocity increase using the elastic energy, joint protection capabilities, and control performance.
Histopathological Effect of Paraquat (Gramoxone) on the Digestive Gland of Freshwater Snail Lymnaea luteola (Lamarck: 1799) (Mollusca: Gastropoda)

Histopathological alterations induced by paraquat in the digestive gland of the freshwater snail Lymnaea luteola were investigated. Samples were collected from the Kondakarla lake (Visakhapatnam, Andhra Pradesh, India), where agricultural activities are widespread. Acute toxicity of series of concentration of paraquat to Lymnaea luteola was determined by recording snail mortality of 24, 48, 72 and 96 hrs exposures. The Lc50 value based on probit analysis was found to be 0.073 ml/L for 96 hrs of exposure to the herbicide. Results obtained shown that there were no mortality of snail either in control and those exposed to 0.0196 ml/L paraquat throughout the 96 hrs 100% mortality was recorded with 48hrs on exposed to 0.790 ppm concentration of stock solution of paraquat. At various concentrations paraquat causes significant dose dependent histopathological changes in the digestive gland of L.luteola. The histopathological examinations revealed the following changes: amebocytes infiltrations, the lumen of digestive gland tubule was shrunken; degeneration of cells, secretory cells became irregular, necrosis of cells and atrophy in the connective tissue of digestive gland.
General Embedded Quantization for Wavelet-Based Lossy Image Coding

Embedded quantization is a mechanism employed by many lossy image codecs to progressively refine the distortion of a (transformed) image. Currently, the most common approach to do so in the context of wavelet-based image coding is to couple uniform scalar deadzone quantization (USDQ) with bitplane coding (BPC). USDQ+BPC is convenient for its practicality and has proved to achieve competitive coding performance. But the quantizer established by this scheme does not allow major variations. This paper introduces a multistage quantization scheme named general embedded quantization (GEQ) that provides more flexibility to the quantizer. GEQ schemes can be devised for specific decoding rates achieving optimal coding performance. Practical approaches of GEQ schemes achieve coding performance similar to that of USDQ+BPC while requiring fewer quantization stages. The performance achieved by GEQ is evaluated in this paper through experimental results carried out in the framework of modern image coding systems.
Violence risk assessment and risk communication: the effects of using actual cases, providing instruction, and employing probability versus frequency formats.

This article describes studies designed to inform policy makers and practitioners about factors influencing the validity of violence risk assessment and risk communication. Forensic psychologists and psychiatrists were shown case summaries of patients hospitalized with mental disorder and were asked to judge the likelihood that the patient would harm someone within six months after discharge from the hospital. They also judged whether the patient posed a high risk, medium risk, or low risk of harming someone after discharge. Studies 1 and 2 replicated, with real case summaries as stimuli, the response-scale effects found by Slovic and Monahan (1995). Providing clinicians with response scales allowing more discriminability among smaller probabilities led patients to be judged as posing lower probabilities of committing harmful acts. This format effect was not eliminated by having clinicians judge relative frequencies rather than probabilities or by providing them with instruction in how to make these types of judgments. In addition, frequency scales led to lower mean likelihood judgments than did probability scales, but, at any given level of likelihood, a patient was judged as posing higher risk if that likelihood was derived from a frequency scale (e.g., 10 out of 100) than if it was derived from a probability scale (e.g., 10%). Similarly, communicating a patient's dangerousness as a relative frequency (e.g., 2 out of 10) led to much higher perceived risk than did communicating a comparable probability (e.g., 20%). The different reactions to probability and frequency formats appear to be attributable to the more frightening images evoked by frequencies. Implications for risk assessment and risk communication are discussed.
Combining the logical and the probabilistic in program analysis

Conventional program analyses have made great strides by leveraging logical reasoning. However, they cannot handle uncertain knowledge, and they lack the ability to learn and adapt. This in turn hinders the accuracy, scalability, and usability of program analysis tools in practice. We seek to address these limitations by proposing a methodology and framework for incorporating probabilistic reasoning directly into existing program analyses that are based on logical reasoning. We demonstrate that the combined approach can benefit a number of important applications of program analysis and thereby facilitate more widespread adoption of this technology.