nils-herrmann/clean_scirepeval huggingface dataset:
VENUS: Vertex-centric streamlined graph computation on a single PC

Recent studies show that disk-based graph computation on just a single PC can be as highly competitive as cluster-based computing systems on large-scale problems. Inspired by this remarkable progress, we develop VENUS, a disk-based graph computation system which is able to handle billion-scale problems efficiently on a commodity PC. VENUS adopts a novel computing architecture that features vertex-centric “streamlined” processing - the graph is sequentially loaded and the update functions are executed in parallel on the fly. VENUS deliberately avoids loading batch edge data by separating read-only structure data from mutable vertex data on disk. Furthermore, it minimizes random IOs by caching vertex data in main memory. The streamlined processing is realized with efficient sequential scan over massive structure data and fast feeding a large number of update functions. Extensive evaluation on large real-world and synthetic graphs has demonstrated the efficiency of VENUS. For example, VENUS takes just 8 minutes with hard disk for PageRank on the Twitter graph with 1.5 billion edges. In contrast, Spark takes 8.1 minutes with 50 machines and 100 CPUs, and GraphChi takes 13 minutes using fast SSD drive.
Active Literature Discovery for Scoping Evidence Reviews How Many Needles are There

Scoping reviews of the biomedical literature are very commonly used in health technology assessments to inform the planning of more detailed and resource-intensive evaluations. A typical task is to ‘map’ the literature addressing a specific clinical question, i.e., (i) identify as many relevant articles of interest as feasible under a constrained budget, and (ii) estimate how many such articles likely exist. These are competing objectives. Using active retrieval strategies (e.g., active learning) to realize the former aim immediately hinders our ability to achieve the latter: ‘naive’ estimates of the amount of relevant articles taken over an enriched sampled acquired through selective sampling will be inflated. We propose a novel method for correcting such estimates. We demonstrate the efficacy of our approach on three systematic review datasets, showing that we can achieve both aims: rapid evidence discovery and acceptably accurate estimation of the number of relevant articles.
An Evaluation of Cassandra for Hadoop

In the last decade, the increased use and growth of social media, unconventional web technologies, and mobile applications, have all encouraged development of a new breed of database models. NoSQL data stores target the unstructured data, which by nature is dynamic and a key focus area for "Big Data" research. New generation data can prove costly and unpractical to administer with SQL databases due to lack of structure, high scalability, and elasticity needs. NoSQL data stores such as MongoDB and Cassandra provide a desirable platform for fast and efficient data queries. This leads to increased importance in areas such as cloud applications, e-commerce, social media, bioinformatics, and materials science. In an effort to combine the querying capabilities of conventional database systems and the processing power of the MapReduce model, this paper presents a thorough evaluation of the Cassandra NoSQL database when used in conjunction with the Hadoop MapReduce engine. We characterize the performance for a wide range of representative use cases, and then compare, contrast, and evaluate so that application developers can make informed decisions based upon data size, cluster size, replication factor, and partitioning strategy to meet their performance needs.
A Supervised Learning Approach to Automatic Synonym Identification Based on Distributional Features

Distributional similarity has been widely used to capture the semantic relatedness of words in many NLP tasks. However, various parameters such as similarity measures must be handtuned to make it work effectively. Instead, we propose a novel approach to synonym identification based on supervised learning and distributional features , which correspond to the commonality of individual context types shared by word pairs. Considering the integration withpattern-based features , we have built and compared five synonym classifiers. The evaluation experiment has shown a dramatic performance increase of over 120% on the F-1 measure basis, compared to the conventional similarity-based classification. On the other hand, the pattern-based features have appeared almost redundant.
Mobile Computing and Databases - A Survey

The emergence of powerful portable computers, along with advances in wireless communication technologies, has made mobile computing a reality. Among the applications that are finding their way to the market of mobile computingthose that involve data managementhold a prominent position. In the past few years, there has been a tremendous surge of research in the area of data management in mobile computing. This research has produced interesting results in areas such as data dissemination over limited bandwith channels, location-dependent querying of data, and advanced interfaces for mobile computers. This paper is an effort to survey these techniques and to classify this research in a few broad areas.
A Mobile Knowledge-based System for On-Board Diagnostics and Car Driving Assistance

In-vehicle electronic equipment aims to increase safety, by detecting risk factors and taking/suggesting corrective actions. This paper presents a knowledge-based framework for assisting a driver via her PDA. Car data extracted under On Board Diagnostics (OBD-II) protocol, data acquired from PDA embedded micro-devices and information retrieved from the Web are properly combined: a simple data fusion algorithm has been devised to collect and semantically annotate relevant safety events. Finally, a logic-based matchmaking allows to infer potential risk factors, enabling the system to issue accurate and timely warnings. The proposed approach has been implemented in a prototypical application for the Apple iPhone platform, in order to provide experimental evaluation in real-world test drives for corroborating the approach. Semantic Web; On Board Diagnostics; Ubiquitous Computing; Data Fusion; Intelligent Transportation Systems
Wiselib: A Generic Algorithm Library for Heterogeneous Sensor Networks

One unfortunate consequence of the success story of wireless sensor networks (WSNs) in separate research communities is an evergrowing gap between theory and practice. Even though there is a increasing number of algorithmic methods for WSNs, the vast majority has never been tried in practice; conversely, many practical challenges are still awaiting efficient algorithmic solutions. The main cause for this discrepancy is the fact that programming sensor nodes still happens at a very technical level. We remedy the situation by introducing Wiselib, our algorithm library that allows for simple implementations of algorithms onto a large variety of hardware and software. This is achieved by employing advanced C++ techniques such as templates and inline functions, allowing to write generic code that is resolved and bound at compile time, resulting in virtually no memory or computation overhead at run time. The Wiselib runs on different host operating systems, such as Contiki, iSense OS, and ScatterWeb. Furthermore, it runs on virtual nodes simulated by Shawn. For any algorithm, the Wiselib provides data structures that suit the specific properties of the target platform. Algorithm code does not contain any platform-specific specializations, allowing a single implementation to run natively on heterogeneous networks. In this paper, we describe the building blocks of the Wiselib, and analyze the overhead. We demonstrate the effectiveness of our approach by showing how routing algorithms can be implemented. We also report on results from experiments with real sensor-node hardware.
Feature Selection, Perceptron Learning, and a Usability Case Study for Text Categorization

In this paper, we describe an automated learning approach to text categorization based on perception learning and a new feature selection metric, called correlation coefficient. Our approach has been teated on the standard Reuters text categorization collection. Empirical results indicate that our approach outperforms the best published results on this % uters collection. In particular, our new feature selection method yields comiderable improvement. We also investigate the usability of our automated hxu-n~ approach by actually developing a system that categorizes texts into a treeof categories. We compare tbe accuracy of our learning approach to a rrddmsed, expert system ap preach that uses a text categorization shell built by Cams gie Group. Although our automated learning approach still gives a lower accuracy, by appropriately inmrporating a set of manually chosen worda to use as f~ures, the combined, semi-automated approach yields accuracy close to the * baaed approach.
Mechanical design of a new pneumatically driven underactuated hand

This paper presents a new pneumatically driven underactuated hand with two fingers and 3 phalanxes per finger. Two evidences have led the design of this hand. Firstly, the use of pneumatic energy facilitates the underactuation of the hand, indeed a single T-connector suffices to share out one input among two outputs. Secondly, non-backdrivable mechanisms have to be used in the transmission of phalanx's motion so that the hand is capable of producing form-closed grasps. This latter design principle is justified using a newly developed method that permits to study the form-closure property of a grasp exerted by an underactuated hand. Moreover, the intriguing ejection phenomenon is avoided thanks to non-backdrivable mechanisms that prohibit any backward motion of phalanxes when correctly positioned. An original mechanism called the "pneumatic parallelogram" is described, it enables the hand to perform fine pinch grasps. Finally, the optimal design of both fingers is addressed with respect to the force-isotropy of the finger and the positiveness of phalanx forces
Inertia Weight strategies in Particle Swarm Optimization

Particle Swarm Optimization is a popular heuristic search algorithm which is inspired by the social learning of birds or fishes. It is a swarm intelligence technique for optimization developed by Eberhart and Kennedy  in 1995. Inertia weight is an important parameter in PSO, which significantly affects the convergence and exploration-exploitation trade-off in PSO process. Since inception of Inertia Weight in PSO, a large number of variations of Inertia Weight strategy have been proposed. In order to propose one or more than one Inertia Weight strategies which are efficient than others, this paper studies 15 relatively recent and popular Inertia Weight strategies and compares their performance on 05 optimization test problems.
OpenPR: An Open-Source Partial-Reconfiguration Toolkit for Xilinx FPGAs

The Xilinx Partial Reconfiguration Early Access Software Tools for ISE 9.2i has been an instrumental package for performing a wide variety of research on Xilinx FPGAs, and is now superseded with the corresponding non-free add-on for ISE 12.3. The original package was developed and offered by Xilinx as a downloadable add-on package to the Xilinx ISE 9.2 tools. The 9.2i toolkit provided a methodology for creating rectangular partial reconfiguration modules that could be swapped in and out of a static baseline design with one or more PR slots. This paper presents a new PR toolkit called Open PR that, for starters, provides similar functionality as the Xilinx PR Toolkit, yet is extendable to explore other modes of partial reconfiguration. The distinguishing feature of this toolkit is that it is being released as open source, and is intended to extend to the needs of individual researchers.
Acquisition of Position Error and Magnet Polarity for Sensorless Control of PM Synchronous Machines

Sensorless control of a permanent-magnet synchronous machine at low and zero speed is based on the injection of an oscillating high-frequency carrier signal. A particular demodulation technique serves to eliminate the estimation error introduced by pulsewidth modulation delay and the nonlinear characteristics of the inverter. Before the drive is started, the initial rotor position and the magnet polarity are detected. The initialization is performed by injecting an AC carrier and two short current pulses in a sequence.
Major Issues in Software Engineering Project Management

Software engineering project management (SEPM) has been the focus of much recent attention because of the enormous penalties incurred during software development and maintenance resulting from poor management. To date there has been no comprehensive study performed to determine the most significant problems of SEPM, their relative importance, or the research directions necessary to solve them. We conducted a major survey of individuals from all areas of the computer field to determine the general consensus on SEPM problems. Twenty hypothesized problems were submitted to several hundred individuals for their opinions. The 294 respondents validated most of these propositions. None of the propositions was rejected by the respondents as unimportant. A number of research directions were indicated by the respondents which, if followed, the respondents believed would lead to solutions for these problems.
General Knowledge Embedded Image Representation Learning

Image representation learning is a fundamental problem in understanding semantics of images. However, traditional classification-based representation learning methods face the noisy and incomplete problem of the supervisory labels. In this paper, we propose a general knowledge base embedded image representation learning approach, which uses general knowledge graph, which is a multitype relational knowledge graph consisting of human commonsense beyond image space, as external semantic resource to capture the relations of concepts in image representation learning. A relational regularized regression CNN (R$^3$CNN) model is designed to jointly optimize the image representation learning problem and knowledge graph embedding problem. In this manner, the learnt representation can capture not only labeled tags but also related concepts of images, which involves more precise and complete semantics. Comprehensive experiments are conducted to investigate the effectiveness and transferability of our approach in tag prediction task, zero-shot tag inference task, and content-based image retrieval task. The experimental results demonstrate that the proposed approach performs significantly better than the existing representation learning methods. Finally, observation of the learnt relations show that our approach can somehow refine the knowledge base to describe images and label the images with structured tags.
JSEL: Jamming Skin Enabled Locomotion

A soft, mobile, morphing robot is a desirable platform for traversing rough terrain and navigating into small holes. In this work, a new paradigm in soft robots is presented that utilizes jamming of a granular medium. The concept of activators (as opposed to actuators) is presented to jam and unjam cells that then modulate the direction and amount of work done by a single central actuator. A prototype jamming soft robot utilizing JSEL (Jamming Skin Enabled Locomotion) with external power and control is discussed and both morphing results and mobility (rolling) results are presented. Future directions for the design of a soft, hole traversing robot are discussed, as is the role and promises of jamming as an enabling technology for soft robotics.
ACRE: selecting methods for requirements acquisition

Synopsis A framework is presented which assists requirements engineers to choose methods for requirements acquisition. Practitioners are often unaware of the range of methods available. Even when practitioners are aware, most do not foresee the need to use several methods to acquire complete and accurate requirements. One reason for this is the lack of guidelines for method selection. The ACRE framework sets out to overcome these limitations. Method selection is achieved using questions driven from a set of facets which define the strengths and weaknesses of each method. The framework is presented as guidelines for requirements engineering practitioners. It has undergone some evaluation through its presentation to highly-experienced requirements engineers. Some results from this evaluation have been incorporated into the version of ACRE presented in the paper.
Automatic thumbnail cropping and its effectiveness

Thumbnail images provide users of image retrieval and browsing systems with a method for quickly scanning large numbers of images. Recognizing the objects in an image is important in many retrieval tasks, but thumbnails generated by shrinking the original image often render objects illegible. We study the ability of computer vision systems to detect key components of images so that automated cropping, prior to shrinking, can render objects more recognizable. We evaluate automatic cropping techniques 1) based on a general method that detects salient portions of images, and 2) based on automatic face detection. Our user study shows that these methods result in small thumbnails that are substantially more recognizable and easier to find in the context of visual search.
Tutorial on Agent-Based Modeling and Simulation PART 2: How to Model with Agents

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of interacting autonomous agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to do research. Some have gone so far as to contend that ABMS is a new way of doing science. Computational advances make possible a growing number of agent-based applications across many fields. Applications range from modeling agent behavior in the stock market and supply chains, to predicting the spread of epidemics and the threat of bio-warfare, from modeling the growth and decline of ancient civilizations to modeling the complexities of the human immune system, and many more. This tutorial describes the foundations of ABMS, identifies ABMS toolkits and development methods illustrated through a supply chain example, and provides thoughts on the appropriate contexts for ABMS versus conventional modeling techniques.
Block convolution: Towards memory-efficient inference of large-scale CNNs on FPGA

FPGA-based CNN accelerators are gaining popularity due to high energy efficiency and great flexibility in recent years. However, as the networks grow in depth and width, the great volume of intermediate data is too large to store on chip, data transfers between on-chip memory and off-chip memory should be frequently executed, which leads to unexpected offchip memory access latency and energy consumption. In this paper, we propose a block convolution approach, which is a memory-efficient, simple yet effective block-based convolution to completely avoid intermediate data from streaming out to off-chip memory during network inference. Experiments on the very large VGG-16 network show that the improved top-1/top-5 accuracy of 72.60%/91.10% can be achieved on the ImageNet classification task with the proposed approach. As a case study, we implement the VGG-16 network with block convolution on Xilinx Zynq ZC706 board, achieving a frame rate of 12.19fps under 150MHz working frequency, with all intermediate data staying on chip.
A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images

Detecting buildings from very high resolution (VHR) aerial and satellite images is extremely useful in map making, urban planning, and land use analysis. Although it is possible to manually locate buildings from these VHR images, this operation may not be robust and fast. Therefore, automated systems to detect buildings from VHR aerial and satellite images are needed. Unfortunately, such systems must cope with major problems. First, buildings have diverse characteristics, and their appearance (illumination, viewing angle, etc.) is uncontrolled in these images. Second, buildings in urban areas are generally dense and complex. It is hard to detect separate buildings from them. To overcome these difficulties, we propose a novel building detection method using local feature vectors and a probabilistic framework. We first introduce four different local feature vector extraction methods. Extracted local feature vectors serve as observations of the probability density function (pdf) to be estimated. Using a variable-kernel density estimation method, we estimate the corresponding pdf. In other words, we represent building locations (to be detected) in the image as joint random variables and estimate their pdf. Using the modes of the estimated density, as well as other probabilistic properties, we detect building locations in the image. We also introduce data and decision fusion methods based on our probabilistic framework to detect building locations. We pick certain crops of VHR panchromatic aerial and Ikonos satellite images to test our method. We assume that these crops are detected using our previous urban region detection method. Our test images are acquired by two different sensors, and they have different spatial resolutions. Also, buildings in these images have diverse characteristics. Therefore, we can test our methods on a diverse data set. Extensive tests indicate that our method can be used to automatically detect buildings in a robust and fast manner in Ikonos satellite and our aerial images.
Eigenvalues and eigenvectors of generalized DFT, generalized DHT, DCT-IV and DST-IV matrices

In this paper, the eigenvalues and eigenvectors of the generalized discrete Fourier transform (GDFT), the generalized discrete Hartley transform (GDHT), the type-IV discrete cosine transform (DCT-IV), and the type-IV discrete sine transform (DST-IV) matrices are investigated in a unified framework. First, the eigenvalues and their multiplicities of the GDFT matrix are determined, and the theory of commuting matrices is applied to find the real, symmetric, orthogonal eigenvectors set that constitutes the discrete counterpart of Hermite Gaussian function. Then, the results of the GDFT matrix and the relationships among these four unitary transforms are used to find the eigenproperties of the GDHT, DCT-IV, and DST-IV matrices. Finally, the fractional versions of these four transforms are defined, and an image watermarking scheme is proposed to demonstrate the effectiveness of fractional transforms.
Towards polynomial lower bounds for dynamic problems

We consider a number of dynamic problems with no known poly-logarithmic upper bounds, and show that they require nΩ(1) time per operation, unless 3SUM has strongly subquadratic algorithms. Our result is modular: (1) We describe a carefully-chosen dynamic version of set disjointness (the "multiphase problem"), and conjecture that it requires n^Omega(1) time per operation. All our lower bounds follow by easy reduction. (2) We reduce 3SUM to the multiphase problem. Ours is the first nonalgebraic reduction from 3SUM, and allows 3SUM-hardness results for combinatorial problems. For instance, it implies hardness of reporting all triangles in a graph. (3) It is plausible that an unconditional lower bound for the multiphase problem can be established via a number-on-forehead communication game.
A new way to enumerate cycles in graph

In many cases, the topology of communcation systems can be abstracted and represented as graph. Graph theories and algorithms are useful in these situations. In this paper, we introduced an algorithm to enumerate all cycles in a graph. It can be applied on digraph or undirected graph. Multigraph can also be used on for this purpose. It can be used to enumerate given length cycles without enumerating all cycles. This algorithm is simple and easy to be implemented.