nils-herrmann/clean_scirepeval huggingface dataset:
Simulation of computer architectures: simulators, benchmarks, methodologies, and recommendations

Simulators have become an integral part of the computer architecture research and design process. Since they have the advantages of cost, time, and flexibility, architects use them to guide design space exploration and to quantify the efficacy of an enhancement. However, long simulation times and poor accuracy limit their effectiveness. To reduce the simulation time, architects have proposed several techniques that increase the simulation speed or throughput. To increase the accuracy, architects try to minimize the amount of error in their simulators and have proposed adding statistical rigor to their simulation methodology. Since a wide range of approaches exist and since many of them overlap, this paper describes, classifies, and compares them to aid the computer architect in selecting the most appropriate one.
Automated Behavioral Regression Testing

When a program is modified during software evolution, developers typically run the new version of the program against its existing test suite to validate that the changes made on the program did not introduce unintended side effects (i.e., regression faults). This kind of regression testing can be effective in identifying some regression faults, but it is limited by the quality of the existing test suite. Due to the cost of testing, developers build test suites by finding acceptable tradeoffs between cost and thoroughness of the tests. As a result, these test suites tend to exercise only a small subset of the program's functionality and may be inadequate for testing the changes in a program. To address this issue, we propose a novel approach called Behavioral Regression Testing (BERT). Given two versions of a program, BERT identifies behavioral differences between the two versions through dynamical analysis, in three steps. First, it generates a large number of test inputs that focus on the changed parts of the code. Second, it runs the generated test inputs on the old and new versions of the code and identifies differences in the tests' behavior. Third, it analyzes the identified differences and presents them to the developers. By focusing on a subset of the code and leveraging differential behavior, BERT can provide developers with more (and more detailed) information than traditional regression testing techniques. To evaluate BERT, we implemented it as a plug-in for Eclipse, a popular Integrated Development Environment, and used the plug-in to perform a preliminary study on two programs. The results of our study are promising, in that BERT was able to identify true regression faults in the programs.
Band gap fluorescence from individual single-walled carbon nanotubes.

Fluorescence has been observed directly across the band gap of semiconducting carbon nanotubes. We obtained individual nanotubes, each encased in a cylindrical micelle, by ultrasonically agitating an aqueous dispersion of raw single-walled carbon nanotubes in sodium dodecyl sulfate and then centrifuging to remove tube bundles, ropes, and residual catalyst. Aggregation of nanotubes into bundles otherwise quenches the fluorescence through interactions with metallic tubes and substantially broadens the absorption spectra. At pH less than 5, the absorption and emission spectra of individual nanotubes show evidence of band gap-selective protonation of the side walls of the tube. This protonation is readily reversed by treatment with base or ultraviolet light.
Scalable Behaviors for Crowd Simulation

Crowd simulation for virtual environments offers many challenges centered on the trade-offs between rich behavior, control and computational cost. In this paper we present a new approach to controlling the behavior of agents in a crowd. Our method is scalable in the sense that increasingly complex crowd behaviors can be created without a corresponding increase in the complexity of the agents. Our approach is also more authorable; users can dynamically specify which crowd behaviors happen in various parts of an environment. Finally, the character motion produced by our system is visually convincing. We achieve our aims with a situation-based control structure. Basic agents have very limited behaviors. As they enter new situations, additional, situation-specific behaviors are composed on the fly to enable agents to respond appropriately. The composition is done using a probabilistic mechanism. We demonstrate our system with three environments including a city street and a theater.
Novel variable step size nlms algorithms for echo cancellation

In this paper we present two new variable step size (VSS) methods for adaptive filters. These VSS methods are so effective, they eliminate the need for a separate double-talk detection algorithm in echo cancellation applications. The key feature of both approaches is the introduction of a new near-end signal energy estimator (NESEE) that provides accurate and computationally efficient estimates even during double-talk and echo path change events. The first VSS algorithm applies the NESEE to the recently proposed Nonparametric VSS NLMS (NPVSS-NLMS) algorithm. The resulting algorithm has excellent convergence characteristics with an intrinsic immunity to double-talk. The second approach is somewhat more ad hoc. It is composed of a combination of an efficient echo path change detector and the NESEE. This VSS method also has excellent convergence, double talk immunity, and computational efficiency. Simulations demonstrate the efficacy of both proposed algorithms.
Improving Stateless Hash-Based Signatures

We present several optimizations to SPHINCS, a stateless hash-based signature scheme proposed by Bernstein et al. in 2015: PORS, a more secure variant of the HORS few-time signature scheme used in SPHINCS; secret key caching, to speed-up signing and reduce signature size; batch signing, to amortize signature time and reduce signature size when signing multiple messages at once; mask-less constructions to reduce the key size and simplify the scheme; and Octopus, a technique to eliminate redundancies from authentication paths in Merkle trees. Based on a refined analysis of the subset resilience problem, we show that SPHINCS’ parameters can be modified to reduce the signature size while retaining a similar security level and computation time. We then propose Gravity-SPHINCS, our variant of SPHINCS embodying the aforementioned tricks. Gravity-SPHINCS has shorter keys (32 and 64 bytes instead of ≈ 1 KB), shorter signatures (≈ 30 KB instead of 41 KB), and faster signing and verification for the same security level as SPHINCS.
A controlled clothoid spline

A clothoid has the property that its curvature varies linearly with arclength. This is a useful feature for the path of a vehicle whose turning radius is controlled as a linear function of the distance travelled. Highways, railways and the paths of car-like robots may be composed of straight line segments, clothoid segments and circular arcs. Control polylines are used in computer aided design and computer aided geometric design applications to guide composite curves during the design phase. This article examines the use of a control polyline to guide a curve composed of segments of clothoids, straight lines, and circular arcs.
Brave new (interactive) worlds: A review of the design affordances and constraints of two 3D virtual worlds as interactive learning environments

Three-dimensional virtual worlds are an emerging medium currently being used in both traditional classrooms and for distance education. Three-dimensional (3D) virtual worlds are a combination of desk-top interactive Virtual Reality within a chat environment. This analysis provides an overview of Active Worlds Educational Universe and Adobe Atmosphere and the pedagogical affordances and constraints of the inscription tools, discourse tools, experiential tools, and resource tools of each application. The purpose of this review is to discuss the implications of using each application for educational initiatives by exploring how the various design features of each may support and enhance the design of interactive learning environments.
Web Personalization Using Web Mining: Concept and Research Issue

—Web mining is the application of the data mining which is useful to extract the knowledge. Web mining has been explored to different techniques have been proposed for the variety of the application. Most research on Web mining has been from a 'data-centric' or information based point of view. Web usage mining, Web structure mining and Web content mining are the types of Web mining. Web usage mining is used to mining the data from the web server log files. Web Personalization is one of the areas of the Web usage mining that can be defined as delivery of content tailored to a particular user or as personalization requires implicitly or explicitly collecting visitor information and leveraging that knowledge in your content delivery framework to manipulate what information you present to your users and how you present it. In this paper, we have focused on various Web personalization categories and their research issues.
A Novel high-speed transistorized 8x8 Multiplier using 4-2 Compressors

In this paper, a novel implementation of 8x8 Multiplier using 4-2 Compressors is presented; which produces quick results, especially for use in Digital Signal Processors and in Microprocessors. This multiplier uses a new partial-product reduction format which consecutively reduces the maximum output delay. The new design of multiplier requires less number of MOSFET’s compared to Wallace Tree Multipliers. The 4-2 Compressor used is made from high-speed and low-power XOR-XNOR module and transmission gate based Multiplexer. The delay and power-delay product (PDP) is compared with earlier Wallace and Dadda Multipliers, implemented with 4-2 Compressors and without compressors, and is proven to have minimum delay and PDP. The Simulation results were obtained using HSPICE at 0.18μm standard CMOS technology.  4-2 Compressors, partial-product reduction, Reduction format, Tree Multipliers, XOR-XNOR module, Multiplexer, 0.18μm CMOS.
Toward privacy-assured and searchable cloud data storage services

Cloud computing is envisioned as the next generation architecture of IT enterprises, providing convenient remote access to massively scalable data storage and application services. While this outsourced storage and computing paradigm can potentially bring great economical savings for data owners and users, its benefits may not be fully realized due to wide concerns of data owners that their private data may be involuntarily exposed or handled by cloud providers. Although end-to-end encryption techniques have been proposed as promising solutions for secure cloud data storage, a primary challenge toward building a full-fledged cloud data service remains: how to effectively support flexible data utilization services such as search over the data in a privacy-preserving manner. In this article, we identify the system requirements and challenges toward achieving privacy-assured searchable outsourced cloud data services, especially, how to design usable and practically efficient search schemes for encrypted cloud storage. We present a general methodology for this using searchable encryption techniques, which allows encrypted data to be searched by users without leaking information about the data itself and users¿ queries. In particular, we discuss three desirable functionalities of usable search operations: supporting result ranking, similarity search, and search over structured data. For each of them, we describe approaches to design efficient privacy-assured searchable encryption schemes, which are based on several recent symmetric-key encryption primitives. We analyze their advantages and limitations, and outline the future challenges that need to be solved to make such secure searchable cloud data service a reality.
Internet-based and other computerized psychological treatments for adult depression: a meta-analysis.

Computerized and, more recently, Internet-based treatments for depression have been developed and tested in controlled trials. The aim of this meta-analysis was to summarize the effects of these treatments and investigate characteristics of studies that may be related to the effects. In particular, the authors were interested in the role of personal support when completing a computerized treatment. Following a literature search and coding, the authors included 12 studies, with a total of 2446 participants. Ten of the 12 studies were delivered via the Internet. The mean effect size of the 15 comparisons between Internet-based and other computerized psychological treatments vs. control groups at posttest was d = 0.41 (95% confidence interval [CI]: 0.29-0.54). However, this estimate was moderated by a significant difference between supported (d = 0.61; 95% CI: 0.45-0.77) and unsupported (d = 0.25; 95% CI: 0.14-0.35) treatments. The authors conclude that although more studies are needed, Internet and other computerized treatments hold promise as potentially evidence-based treatments of depression.
Does Colorspace Transformation Make Any Difference on Skin Detection?

Skin detection is an important process in many of computer vision algorithms. It usually is a process that starts at a pixel-level, and that involves a pre-process of colorspace transformation followed by a classification process. A colorspace transformation is assumed to increase separability between skin and non-skin classes, to increase similarity among different skin tones, and to bring a robust performance under varying illumination conditions, without any sound reasonings. In this work, we examine if the colorspace transformation does bring those benefits by measuring four separability measurements on a large dataset of 805 images with different skin tones and illumination. Surprising results indicate that most of the colorspace transformations do not bring the benefits which have been assumed.
Strategic Models for Business Intelligence

Business Intelligence (BI) promises a range of technologies for using information to ensure compliance to strategic and tactical objectives, as well as government laws and regulations. These technologies can be used in conjunction with conceptual models of business objectives, processes and situations (aka business schemas) to drive strategic decision-making about opportunities and threats etc. This paper focuses on three key concepts for strategic business models -situation, influence and indicator -and how they are used for strategic analysis. The semantics of these concepts are defined using a state-ofthe-art upper ontology (DOLCE+). We also propose a method for building a business schema, and demonstrate alternative ways of formal analysis of the schema based on existing tools for goal and probabilistic reasoning.
When more is too much: Operationalizing technology overload and exploring its impact on knowledge worker productivity

Individuals within organizations are beginning to make an important realization: more information technology (IT) usage in the workplace can, at times, lead to productivity losses. We conceptualize this frequently observed, but largely ignored phenomenon as technology overload, when additional technology tools begin to crowd out one’s productivity instead of enhancing it. We found support for three main factors contributing technology-based productivity losses through information overload, communication overload, and system feature overload. Interestingly, these factors are a function of the individuals who use the technology, not the technology itself. In this paper, we present the results from three studies that (1) develop and pre-test a scale measurement for technology overload and its distinct dimensions, (2) validate the instrument, and (3) explore the relationship between technology overload and knowledge worker productivity. Our findings demonstrate the relationship between information technology usage and knowledge worker productivity, and they suggest how tradeoffs can be managed to ameliorate technology overload.
Incremental Learning From Stream Data

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method.
Crime Information Extraction from Police and Witness Narrative Reports

To solve crimes, investigators often rely on interviews with witnesses, victims, or criminals themselves. The interviews are transcribed and the pertinent data is contained in narrative form. To solve one crime, investigators may need to interview multiple people and then analyze the narrative reports. There are several difficulties with this process: interviewing people is time consuming, the interviews - sometimes conducted by multiple officers - need to be combined, and the resulting information may still be incomplete. For example, victims or witnesses are often too scared or embarrassed to report or prefer to remain anonymous. We are developing an online reporting system that combines natural language processing with insights from the cognitive interview approach to obtain more information from witnesses and victims. We report here on information extraction from police and witness narratives. We achieved high precision, 94% and 96% and recall, 85% and 90%, for both narrative types.
Architecture recovery of Apache 1 . 3 — A case study

This document presents experiences from a course in which the authors taught students a way to understand and model software systems and share their knowledge about them. The real–life system examined in the course was the World Wide Web (WWW) and the Apache HTTP Server. The conceptual architecture of the system was modeled using the Fundamental Modeling Concepts (FMC) which turned out to be well suited for sharing knowledge about both concepts and details of the system. Excerpts of the model are presented in this document.
Improved Depth Map Estimation from Stereo Images based on Hybrid Method

In this paper, a stereo matching algorithm based on image segments is presented. We propose the hybrid segmentation algorithm that is based on a combination of the Belief Propagation and Mean Shift algorithms with aim to refine the disparity and depth map by using a stereo pair of images. This algorithm utilizes image filtering and modified SAD (Sum of Absolute Differences) stereo matching method. Firstly, a color based segmentation method is applied for segmenting the left image of the input stereo pair (reference image) into regions. The aim of the segmentation is to simplify representation of the image into the form that is easier to analyze and is able to locate objects in images. Secondly, results of the segmentation are used as an input of the local window-based matching method to determine the disparity estimate of each image pixel. The obtained experimental results demonstrate that the final depth map can be obtained by application of segment disparities to the original images. Experimental results with the stereo testing images show that our proposed Hybrid algorithm HSAD gives a good performance.
Large scale distributed neural network training through online distillation

Techniques such as ensembling and distillation promise model quality improvements when paired with almost any base model. However, due to increased testtime cost (for ensembles) and increased complexity of the training pipeline (for distillation), these techniques are challenging to use in industrial settings. In this paper we explore a variant of distillation which is relatively straightforward to use as it does not require a complicated multi-stage setup or many new hyperparameters. Our first claim is that online distillation enables us to use extra parallelism to fit very large datasets about twice as fast. Crucially, we can still speed up training even after we have already reached the point at which additional parallelism provides no benefit for synchronous or asynchronous stochastic gradient descent. Two neural networks trained on disjoint subsets of the data can share knowledge by encouraging each model to agree with the predictions the other model would have made. These predictions can come from a stale version of the other model so they can be safely computed using weights that only rarely get transmitted. Our second claim is that online distillation is a cost-effective way to make the exact predictions of a model dramatically more reproducible. We support our claims using experiments on the Criteo Display Ad Challenge dataset, ImageNet, and the largest to-date dataset used for neural language modeling, containing 6 × 10 tokens and based on the Common Crawl repository of web data.
Adversarial Distillation of Bayesian Neural Network Posteriors

Bayesian neural networks (BNNs) allow us to reason about uncertainty in a principled way. Stochastic Gradient Langevin Dynamics (SGLD) enables efficient BNN learning by drawing samples from the BNN posterior using mini-batches. However, SGLD and its extensions require storage of many copies of the model parameters, a potentially prohibitive cost, especially for large neural networks. We propose a framework, Adversarial Posterior Distillation, to distill the SGLD samples using a Generative Adversarial Network (GAN). At test-time, samples are generated by the GAN. We show that this distillation framework incurs no loss in performance on recent BNN applications including anomaly detection, active learning, and defense against adversarial attacks. By construction, our framework distills not only the Bayesian predictive distribution, but the posterior itself. This allows one to compute quantities such as the approximate model variance, which is useful in downstream tasks. To our knowledge, these are the first results applying MCMC-based BNNs to the aforementioned applications.
Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize

Convolutional neural networks (CNNs) are a popular and highly performant choice for pixel-wise dense  prediction or generation. One of the commonly required components in such CNNs is a way to increase  the resolution of the network’s input. The lower resolution inputs can be, for example, low-dimensional  noise vectors in image generation [7] or low resolution (LR) feature maps for network visualization .  Originally described in Zeiler et al. , a network layer performing this upscaling task is commonly  referred to as a “Deconvolution layer”, and has been used in a wide range of applications including  super-resolution , semantic segmentation , flow estimation [6] and generative modeling [7]. The  deconvolution layer can be described and implemented in various ways. This led to many names that are  often used synonymously, including sub-pixel or fractional convolutional layer [7], transposed  convolutional layer , inverse, up, backward convolutional layer [5,6] and more.