nils-herrmann/clean_scirepeval huggingface dataset:
Successful Use of eProcurement in Supply Chains

Optimizing the internal supply chain for direct or production goods was a major element during the implementation of enterprise resource planning systems (ERP) which has taken place since the late 1980s. However, supply chains to the suppliers of indirect materials were not usually included due to low transaction volumes, low product values and low strategic importance of these goods. With the advent of the Internet, systems for streamlining indirect goods supply chains emerged and were adopted by many companies. In view of the paperprone processes in many companies, the implementation of these electronic procurement systems led to substantial improvement potentials. This research reports the quantitative and qualitative results of a benchmarking study which explores the use of the Internet in procurement (eProcurement). Among the major goals are to obtain more insight on how European and North American companies used and introduced eProcurement solutions as well as how these systems enhanced the procurement function. The analysis presents a heterogeneous picture and shows that all analyzed solutions emphasize different parts of the procurement and coordination process. Based on interviews and case studies the research proposes an initial set of generalized success factors which may improve future implementations and stimulate further success factor research.
Design and Analysis of the Active Dual-Mode Twisting Actuation Mechanism

This paper presents an active dual-mode twisting actuation mechanism, which is an automatic transmission system based on twisted string actuation. This mechanism produces two different transmission ratios by adjusting the radius of a twisted string. Automatic mode switching between the two transmission ratios is implemented using an active control mechanism. Then, the effect of the proposed mechanism is derived via kinetic and kinematic analyses. Mathematical models of the twisted string are derived and a control algorithm for the mode transition is proposed. The performance of the proposed mechanism is experimentally verified using a prototype of the active dual-mode twisting actuator.
A Three-Fed Microstrip Antenna for Wideband Circular Polarization

New design of a circular microstrip antenna with three feeds for wideband circular polarization is presented. Detailed theoretical analysis proves that a circular microstrip antenna, which is excited by three central symmetrical feeds with equal amplitudes and relative 120° phase shifts, can obtain good circular polarization characteristic. A broadband 120° phase shifter is developed using the metamaterial transmission line (MM TL). It consists of a Wilkinson power divider and three phase-adjusting TLs, namely a MM TL and two microstrips. The antenna is composed of two circular patches. The primary radiating patch under the parasitic patch is excited by three feeds. The proposed antenna can provide an impedance bandwidth of 54.75% and a 3-dB axial ratio bandwidth of 47.88%. The 3-dB gain bandwidth is as large as 40%, with the peak gain 8.8 dBic. In addition, the measured and simulated symmetrical radiation patterns are in good agreement.
Comparative Study of Different CPU Scheduling Algorithms Miss .

In Multiprogramming operating system, CPU scheduling plays a very important role. CPU scheduling deals with the problem that to which process the CPU should be allocated. For scheduling the processes in different ways, there are many different scheduling algorithms. This article deals with various scheduling algorithms like First-Come-First-Serve (FCFS) scheduling algorithm, Shortest Job First (SJF) scheduling algorithm, Priority scheduling algorithm, Round Robin (R-R) scheduling algorithm, Multilevel Queue scheduling algorithm and Multilevel Feedback Queue scheduling algorithm. FCFS scheduling algorithm is based on First-in-first-out concept and is non-pre-emptive scheduling algorithm. It is generally suitable for batch systems. SJF scheduling algorithm can be either pre-emptive or non-pre-emptive and it is based on burst time of the processes. Priority scheduling algorithm is necessarily a form of pre-emptive scheduling algorithm and it is based on the priorities given to the processes. R-R scheduling algorithm is also a pre-emptive scheduling algorithm and is based on the given time-quantum. It is generally suitable for time sharing systems. In multilevel queue scheduling, when the process enters in the system, it is permanently assigned to a queue depending upon its nature. It is pre-emptive in nature. Multilevel feedback queue is also pre-emptive and it allows the processes to move between queues.  Scheduling algorithm; First-Come-First-Serve scheduling; Shortest Job First scheduling; Priority scheduling; Round Robin scheduling; Multilevel Queue scheduling; Multilevel Feedback Queue scheduling
Text Mining

Im Blickpunkt dieses Artikels stehen die Funktionsweise und die Einsatzpotenziale des Text Mining. Text Mining läuft in einem mehrstufigen Prozess ab, dessen einzelne Schritte knapp vorgestellt werden. Der Fokus liegt hierbei auf der Datenaufbereitung, bei der mittels Techniken des Natural Language Processing Terme aus den zugrunde liegenden Texten extrahiert werden.
Blurred target tracking by Blur-driven Tracker

Visual tracking plays an important role in many computer vision tasks. A common assumption in previous methods is that the video frames are blur free. In reality, motion blurs are pervasive in the real videos. In this paper we present a novel BLUr-driven Tracker (BLUT) framework for tracking motion-blurred targets. BLUT actively uses the information from blurs without performing debluring. Specifically, we integrate the tracking problem with the motion-from-blur problem under a unified sparse approximation framework. We further use the motion information inferred by blurs to guide the sampling process in the particle filter based tracking. To evaluate our method, we have collected a large number of video sequences with significatcant motion blurs and compared BLUT with state-of-the-art trackers. Experimental results show that, while many previous methods are sensitive to motion blurs, BLUT can robustly and reliably track severely blurred targets.
Robust Methods for Partial Least Squares Regression

Partial Least Squares Regression (PLSR) is a linear regression technique developed to deal with high-dimensional regressors and one or several response variables. In this paper we introduce robustified versions of the SIMPLS algorithm being the leading PLSR algorithm because of its speed and efficiency. Because SIMPLS is based on the empirical cross-covariance matrix between the response variables and the regressors and on linear least squares regression, the results are affected by abnormal observations in the data set. Two robust methods, RSIMCD and RSIMPLS, are constructed from a robust covariance matrix for high-dimensional data and robust linear regression. We introduce robust RMSECV and RMSEP values for model calibration and model validation. Diagnostic plots are constructed to visualize and classify the outliers. Several simulation results and the analysis of real data sets show the effectiveness and the robustness of the new approaches. Because RSIMPLS is roughly twice as fast as RSIMCD, it stands out as the overall best method.
Amphotericin B lipid complex for invasive fungal infections: analysis of safety and efficacy in 556 cases.

The safety and antifungal efficacy of amphotericin B lipid complex (ABLC) were evaluated in 556 cases of invasive fungal infection treated through an open-label, single-patient, emergency-use study of patients who were refractory to or intolerant of conventional antifungal therapy. All 556 treatment episodes were evaluable for safety. During the course of ABLC therapy, serum creatinine levels significantly decreased from baseline (P < .02). Among 162 patients with serum creatinine values > or = 2.5 mg/dL at the start of ABLC therapy (baseline), the mean serum creatinine value decreased significantly from the first week through the sixth week (P < or = .0003). Among the 291 mycologically confirmed cases evaluable for therapeutic response, there was a complete or partial response to ABLC in 167 (57%), including 42% (55) of 130 cases of aspergillosis, 67% (28) of 42 cases of disseminated candidiasis, 71% (17) of 24 cases of zygomycosis, and 82% (9) of 11 cases of fusariosis. Response rates varied according to the pattern of invasive fungal infection, underlying condition, and reason for enrollment (intolerance versus progressive infection). These findings support the use of ABLC in the treatment of invasive fungal infections in patients who are intolerant of or refractory to conventional antifungal therapy.
gem5-gpu: A Heterogeneous CPU-GPU Simulator

gem5-gpu is a new simulator that models tightly integrated CPU-GPU systems. It builds on gem5, a modular full-system CPU simulator, and GPGPUSim, a detailed GPGPU simulator. gem5-gpu routes most memory accesses through Ruby, which is a highly configurable memory system in gem5. By doing this, it is able to simulate many system configurations, ranging from a system with coherent caches and a single virtual address space across the CPU and GPU to a system that maintains separate GPU and CPU physical address spaces. gem5gpu can run most unmodified CUDA 3.2 source code. Applications can launch non-blocking kernels, allowing the CPU and GPU to execute simultaneously. We present gem5-gpu's software architecture and a brief performance validation. We also discuss possible extensions to the simulator. gem5-gpu is open source and available at gem5-gpu.cs.wisc.edu.
Supermarket commodity identification using convolutional neural networks

In recent years, with the rapid development of deep learning, it has achieved great success in the field of image recognition. In this paper, we applied the convolution neural network (CNN) on supermarket commodity identification, contributing to the study of supermarket commodity identification. Different from the QR code identification of supermarket commodity, our work applied the CNN using the collected images of commodity as input. This method has the characteristics of fast and non-contact. In this paper, we mainly did the following works: 1. Collected a small dataset of supermarket goods. 2. Built Different convolutional neural network frameworks in caffe and trained the dataset using the built networks. 3. Improved train methods by finetuning the trained model.
Move-optimized source code tree differencing

When it is necessary to express changes between two source code files as a list of edit actions (an edit script), modern tree differencing algorithms are superior to most text-based approaches because they take code movements into account and express source code changes more accurately. We present 5 general optimizations that can be added to state-of-the-art tree differencing algorithms to shorten the resulting edit scripts. Applied to Gumtree, RTED, JSync, and ChangeDistiller, they lead to shorter scripts for 18-98% of the changes in the histories of 9 open-source software repositories. These optimizations also are parts of our novel Move-optimized Tree DIFFerencing algorithm (MTDIFF) that has a higher accuracy in detecting moved code parts. MTDIFF (which is based on the ideas of ChangeDistiller) further shortens the edit script for another 20% of the changes in the repositories. MTDIFF and all the benchmarks are available under an open-source license.
Improved multitask learning through synaptic intelligence

Deep learning has led to remarkable advances when applied to problems where the data distribution does not change over the course of learning. In stark contrast, biological neural networks continually adapt to changing domains, and solve a diversity of tasks simultaneously. Furthermore, synapses in biological neurons are not simply real-valued scalars, but possess complex molecular machinery enabling non-trivial learning dynamics. In this study, we take a first step toward bringing this biological complexity into artificial neural networks. We introduce a model of intelligent synapses that accumulate task relevant information over time, and exploit this information to efficiently consolidate memories of old tasks to protect them from being overwritten as new tasks are learned. We apply our framework to learning sequences of related classification problems, and show that it dramatically reduces catastrophic forgetting while maintaining computational efficiency.
Deep learning for word-level handwritten Indic script identification

We propose a novel method that uses convolutional neural networks (CNNs) for feature extraction. Not just limited to conventional spatial domain representation, we use multilevel 2D discrete Haar wavelet transform, where image representations are scaled to a variety of different sizes. These are then used to train different CNNs to select features. To be precise, we use 10 different CNNs that select a set of 10240 features, i.e. 1024/CNN. With this, 11 different handwritten scripts are identified, where 1K words per script are used. In our test, we have achieved the maximum script identification rate of 94.73% using multi-layer perceptron (MLP). Our results outperform the state-of-the-art techniques.
A Multicore Path to Connectomics-on-Demand

Connectomics is an emerging field of neurobiology that uses cutting edge machine learning and image processing to extract brain connectivity graphs from electron microscopy images. It has long been assumed that the processing of connectomics data will require mass storage and farms of CPUs and GPUs and will take months if not years. This talk shows the feasibility of designing a high-throughput connectomics-on-demand system that runs on a multicore machine with less than 100 cores and extracts connectomes at the terabyte per hour pace of modern electron microscopes. Building this system required solving algorithmic and performance engineering issues related to scaling machine learning on multicore architectures, and may have important lessons for other problem spaces in the natural sciences, where until now large distributed server or GPU farms seemed to be the only way to go.
Expert-augmented actor-critic for ViZDoom and Montezumas Revenge

We propose an expert-augmented actor-critic algorithm, which we evaluate on two environments with sparse rewards: Montezuma’s Revenge and a demanding maze from the ViZDoom suite. In the case of Montezuma’s Revenge, an agent trained with our method achieves very good results consistently scoring above 27,000 points (in many experiments beating the first world). With an appropriate choice of hyperparameters, our algorithm surpasses the performance of the expert data. In a number of experiments, we have observed an unreported bug in Montezuma’s Revenge which allowed the agent to score more than 800, 000 points.
SNAP: A General-Purpose Network Analysis and Graph-Mining Library

Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social-network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task.
 Here, we describe the Stanford Network Analysis Platform (SNAP), a general-purpose, high-performance system that provides easy-to-use, high-level operations for analysis and manipulation of large networks. We present SNAP functionality, describe its implementational details, and give performance benchmarks. SNAP has been developed for single big-memory machines, and it balances the trade-off between maximum performance, compact in-memory graph representation, and the ability to handle dynamic graphs in which nodes and edges are being added or removed over time. SNAP can process massive networks with hundreds of millions of nodes and billions of edges. SNAP offers over 140 different graph algorithms that can efficiently manipulate large graphs, calculate structural properties, generate regular and random graphs, and handle attributes and metadata on nodes and edges. Besides being able to handle large graphs, an additional strength of SNAP is that networks and their attributes are fully dynamic; they can be modified during the computation at low cost. SNAP is provided as an open-source library in C++ as well as a module in Python.
 We also describe the Stanford Large Network Dataset, a set of social and information real-world networks and datasets, which we make publicly available. The collection is a complementary resource to our SNAP software and is widely used for development and benchmarking of graph analytics algorithms.
Emotion, decision making and the orbitofrontal cortex.

The somatic marker hypothesis provides a systems-level neuroanatomical and cognitive framework for decision making and the influence on it by emotion. The key idea of this hypothesis is that decision making is a process that is influenced by marker signals that arise in bioregulatory processes, including those that express themselves in emotions and feelings. This influence can occur at multiple levels of operation, some of which occur consciously and some of which occur non-consciously. Here we review studies that confirm various predictions from the hypothesis. The orbitofrontal cortex represents one critical structure in a neural system subserving decision making. Decision making is not mediated by the orbitofrontal cortex alone, but arises from large-scale systems that include other cortical and subcortical components. Such structures include the amygdala, the somatosensory/insular cortices and the peripheral nervous system. Here we focus only on the role of the orbitofrontal cortex in decision making and emotional processing, and the relationship between emotion, decision making and other cognitive functions of the frontal lobe, namely working memory.
methylDMV: Simultaneous Detection of Differential DNA Methylation and Variability with Confounder Adjustment

DNA methylation has emerged as promising epigenetic markers for disease diagnosis. Both the differential mean (DM) and differential variability (DV) in methylation have been shown to contribute to transcriptional aberration and disease pathogenesis. The presence of confounding factors in large scale EWAS may affect the methylation values and hamper accurate marker discovery. In this paper, we propose a exible framework called methylDMV which allows for confounding factors adjustment and enables simultaneous characterization and identification of CpGs exhibiting DM only, DV only and both DM and DV. The proposed framework also allows for prioritization and selection of candidate features to be included in the prediction algorithm. We illustrate the utility of methylDMV in several TCGA datasets. An R package methylDMV implementing our proposed method is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#methylDMV.
On active learning of record matching packages

We consider the problem of learning a record matching package (classifier) in an active learning setting. In active learning, the learning algorithm picks the set of examples to be labeled, unlike more traditional passive learning setting where a user selects the labeled examples. Active learning is important for record matching since manually identifying a suitable set of labeled examples is difficult. Previous algorithms that use active learning for record matching have serious limitations: The packages that they learn lack quality guarantees and the algorithms do not scale to large input sizes. We present new algorithms for this problem that overcome these limitations. Our algorithms are fundamentally different from traditional active learning approaches, and are designed ground up to exploit problem characteristics specific to record matching. We include a detailed experimental evaluation on realworld data demonstrating the effectiveness of our algorithms.
TOWARD MEMORY-BASED REASONING The intensive use of memory to recall specific episodes from the past rather than rules-should be the foundation of machine reasoning .

The traditional assumption in artificial intelligence (AI) is that most expert knowledge ·is encoded in the form of rules. We consider the phenomenon of rea­ soning from memories of specific episodes, however, to be the foundation of an intelligent system, rather than an adj unct to some other reasoning method. This theory contrasts with much of the current work in similarity-based learning, which tacitly assumes that learning is equivalent to the automatic generation of rules, and differs from work on "explanation-based" and "case-based" reasoning in that it does not depend on having a strong domain model. With the development of new parallel architec­ tures, specifically the Connection Machine@ system, the operations necessary to implement this approach . to reasoning have become sufficiently fast to allow experimentation. This article describes MBRtalk, an experimental memory-based reasoning system that has been implemented on the Connection Machine, as well as the application of memory-based reason­ ing to other domains.
Beat the Cheater: Computing Game-Theoretic Strategies for When to Kick a Gambler out of a Casino

Gambles in casinos are usually set up so that the casino makes a profit in expectation—as long as gamblers play honestly. However, some gamblers are able to cheat, reducing the casino’s profit. How should the casino address this? A common strategy is to selectively kick gamblers out, possibly even without being sure that they were cheating. In this paper, we address the following question: Based solely on a gambler’s track record, when is it optimal for the casino to kick the gambler out? Because cheaters will adapt to the casino’s policy, this is a game-theoretic question. Specifically, we model the problem as a Bayesian game in which the casino is a Stackelberg leader that can commit to a (possibly randomized) policy for when to kick gamblers out, and we provide efficient algorithms for computing the optimal policy. Besides being potentially useful to casinos, we imagine that similar techniques could be useful for addressing related problems—for example, illegal trades in financial markets.
The Essence of Functional Programming

This paper explores the use monads to structure functionalprograms. No prior knowledge of monads or category theory isrequired.
Monads increase the ease with which programs may be modified.They can mimic the effect of impure features such as exceptions,state, and continuations; and also provide effects not easilyachieved with such features. The types of a program reflect whicheffects occur.
The first section is an extended example of the use of monads. Asimple interpreter is modified to support various extra features:error messages, state, output, and non-deterministic choice. Thesecond section describes the relation between monads and thecontinuation-passing style. The third section sketches how monadsare used in a compiler for Haskell that is written in Haskell.