nils-herrmann/clean_scirepeval huggingface dataset:
CNP: An FPGA-based processor for Convolutional Networks

Convolutional Networks (ConvNets) are biologicallyinspired hierarchical architectures that can be trained to perform a variety of detection, recognition and segmentation tasks. ConvNets have a feed-forward architecture consisting of multiple linear convolution filters interspersed with pointwise non-linear squashing functions. This paper presents an efficient implementation of ConvNets on a low-end DSPoriented Field Programmable Gate Array (FPGA). The implementation exploits the inherent parallelism of ConvNets and takes full advantage of multiple hardware multiplyaccumulate units on the FPGA. The entire system uses a single FPGA with an external memory module, and no extra parts. A network compiler software was implemented, which takes a description of a trained ConvNet and compiles it into a sequence of instructions for the ConvNet Processor (CNP). A ConvNet face detection system was implemented and tested. Face detection on a 512 × 384 frame takes 100ms (10 frames per second), which corresponds to an average performance of 3.4×109 connections per second for this 340 million connection network. The design can be used for low-power, lightweight embedded vision systems for micro-UAVs and other small robots.
Paper 265-25 Sample Size Computations and Power Analysis with the SAS System

Statistical power analysis characterizes the ability of a study to detect a meaningful effect size—for example, the difference between two population means. It also determines the sample size required to provide a desired power for an effect of scientific interest. Proper planning reduces the risk of conducting a study that will not produce useful results and determines the most sensitive design for the resources available. Power analysis is now integral to the health and behavioral sciences, and its use is steadily increasing wherever empirical studies are performed. SAS Institute is working to implement power analysis for common situations such as t-tests, ANOVA, comparison of binomial proportions, equivalence testing, survival analysis, contingency tables and linear models, and eventually for a wide range of models and designs. An effective graphical user interface reveals the contribution to power of factors such as effect size, sample size, inherent variability, type I error rate, and choice of design and analysis. This presentation demonstrates issues involved in power analysis, summarizes the current state of methodology and software, and outlines future directions.
Software architecture: a roadmap

Over the past decade software architecture has received increasing attention as an important subfield of software engineering. During that time there has been considerable progress in developing the technological and methodological base for treating architectural design as an engineering discipline. However, much remains to be done to achieve that goal. Moreover, the changing face of technology raises a number of new challenges for software architecture. This paper examines some of the important trends of software architecture in research and practice, and speculates on the important emerging trends, challenges, and aspirations.
Communication Efficient Distributed Optimization using an Approximate Newton-type Method

We present a novel Newton-type method for distributed optimization, which is particularly well suited for stochastic optimization and learning problems. For quadratic objectives, the method enjoys a linear rate of convergence which provably improves with the data size, requiring an essentially constant number of iterations under reasonable assumptions. We provide theoretical and empirical evidence of the advantages of our method compared to other approaches, such as one-shot parameter averaging and ADMM.
Mathematical Models of Fads Explain the Temporal Dynamics of Internet Memes

Internet memes are a pervasive phenomenon on the social Web. They typically consist of viral catch phrases, images, or videos that spread through instant messaging, (micro) blogs, forums, and social networking sites. Due to their popularity and proliferation, Internet memes attract interest in areas as diverse as marketing, sociology, or computer science and have been dubbed a new form of communication or artistic expression. In this paper, we examine the merits of such claims and analyze how collective attention into Internet memes evolves over time. We introduce and discuss statistical models of the dynamics of fads and fit them to meme related time series obtained from Google Trends. Given data as to more than 200 memes, we find that our models provide more accurate descriptions of the dynamics of growth and decline of collective attention to individual Internet memes than previous approaches from the literature. In short, our results suggest that Internet memes are nothing but fads.
A new approach to developing and implementing eager database replication protocols

Database replication is traditionally seen as a way to increase the availability and performance of distributed databases. Although a large number of protocols providing data consistency and fault-tolerance have been proposed, few of these ideas have ever been used in commercial products due to their complexity and performance implications. Instead, current products allow inconsistencies and often resort to centralized approaches which eliminates some of the advantages of replication. As an alternative, we propose a suite of replication protocols that addresses the main problems related to database replication. On the one hand, our protocols maintain data consistency and the same transactional semantics found in centralized systems. On the other hand, they provide flexibility and reasonable performance. To do so, our protocols take advantage of the rich semantics of group communication primitives and the relaxed isolation guarantees provided by most databases. This allows us to eliminate the possibility of deadlocks, reduce the message overhead and increase performance. A detailed simulation study shows the feasibility of the approach and the flexibility with which different types of bottlenecks can be circumvented.
Value Directed Exploration in Multi-Armed Bandits with Structured Priors

Multi-armed bandits are a quintessential machine learning problem requiring balancing exploration with exploitation. While there has been progress in developing algorithms with strong theoretical guarantees, there has been less focus on practical near-optimal finite-time performance. In this paper, we propose an algorithm for Bayesian multi-armed bandits that utilizes approximate value functions. Building on previous work on UCB and Gittins index, we introduce linearly-separable value functions that capture the benefit of exploration when choosing the next arm to pull. Our algorithm enjoys a sub-linear performance guarantee and our simulation results confirm its strength in problems with structured priors. The simplicity and generality of our approach makes it a strong candidate for use in more complex multi-armed bandit problems.
Identifying Equivalent URLs Using URL Signatures

In the standard URL normalization mechanism, URLs are normalized syntactically by a set of predefined steps. In this paper, we propose to enhance the standard URL normalization by incorporating the semantically meaningful metadata of the Web pages. The metadata taken into account are the body texts of the Web pages, which can be extracted during HTML parsing. Given a URL which has undergone the standard normalization mechanism, we construct its URL signature by hashing or fingerprinting the body text of the associated Web page using Message-Digest algorithm 5. URLs which share identical signatures are considered to be equivalent in our scheme. The experimental results show that our proposed method helps to further reduce redundant Web information retrieval by 34.57% in comparison with the standard URL normalization mechanism.
Heterogeneous Cellular Network With Energy Harvesting-Based D2D Communication

The concept of mobile user equipment (UE) relay (UER) has been introduced to support device-to-device (D2D) communications for enhancing communication reliability. However, as the UER needs to use its own power for other UE's data transmission, relaying information in D2D communication may be undesirable for the UER. To overcome this issue, motivated by the recent advances in energy harvesting (EH) techniques, we propose a D2D communication provided EH heterogeneous cellular network (D2D-EHHN), where UERs harvest energy from an access point (AP) and use the harvested energy for D2D communication. We develop a framework for the design and analysis of D2D-EHHN by introducing the EH region (EHR) and modeling the status of harvested energy using Markov chain. The UER distribution is derived, and a transmission mode selection scheme including the efficient UER selection method is proposed. The network outage probability is derived in close form to measure the performance of D2D-EHHN. Based on our analysis results, we explore the effects of network parameters on the outage probability and the optimal offloading bias in terms of the outage probability. Particularly, we show that having a high EH efficiency enhances the performance of D2D-EHHN, but can also degrade, especially for dense network.
Action Reaction Learning : Analysis and Synthesis of Human Behaviour

We propose Action-Reaction Learning as an approach for analyzing and synthesizing human behaviour. This paradigm uncovers causal mappings between past and future events or between an action and its reaction by observing time sequences. We apply this method to analyze human interaction and to subsequently synthesize human behaviour. Using a time series of perceptual measurements, a system automatically uncovers a mapping between gestures from one human participant (an action) and a subsequent gesture (a reaction) from another participant. A probabilistic model is trained from data of the human interaction using a novel estimation technique, Conditional Expectation Maximization (CEM). The system drives a graphical interactive character which probabilistically predicts the most likely response to the user's behaviour and performs it interactively. Thus, after analyzing human interaction in a pair of participants, the system is able to replace one of them and interact with a single remaining user.
The DL-Lite Family and Relations

The recently introduced series of description logics under the common moniker ‘DLLite’ has attracted attention of the description logic and semantic web communities due to the low computational complexity of inference, on the one hand, and the ability to represent conceptual modeling formalisms, on the other. The main aim of this article is to carry out a thorough and systematic investigation of inference in extensions of the original DL-Lite logics along five axes: by (i) adding the Boolean connectives and (ii) number restrictions to concept constructs, (iii) allowing role hierarchies, (iv) allowing role disjointness, symmetry, asymmetry, reflexivity, irreflexivity and transitivity constraints, and (v) adopting or dropping the unique name assumption. We analyze the combined complexity of satisfiability for the resulting logics, as well as the data complexity of instance checking and answering positive existential queries. Our approach is based on embedding DL-Lite logics in suitable fragments of the one-variable first-order logic, which provides useful insights into their properties and, in particular, computational behavior.
Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space—classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm— using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.
Resource Containers: A New Facility for Resource Management in Server Systems

General-purpose operating systems provide inadequate support for resource management in large-scale servers. Applications lack sufficient control over scheduling and management of machine resources, which makes it difficult to enforce priority policies, and to provide robust and controlled service. There is a fundamental mismatch between the original design assumptions underlying the resource management mechanisms of current general-purpose operating systems, and the behavior of modern server applications. In particular, the operating system’s notions of protection domain and resource principal coincide in the process abstraction. This coincidence prevents a process that manages large numbers of network connections, for example, from properly allocating system resources among those connections. We propose and evaluate a new operating system abstraction called a resource container, which separates the notion of a protection domain from that of a resource principal. Resource containers enable fine-grained resource management in server systems and allow the development of robust servers, with simple and firm control over priority policies.
Design and PID control of two wheeled autonomous balance robot

In this study, a two-wheeled autonomous balance robot has been designed and implemented practically. A visual computer interface based on Qt-Creator has been created. Thanks to the computer interface, different control algorithms can be performed on the robot easily, control parameters can be set up online, filter algorithms in various structures can be tried and the reaction of these changeable values to the system can be observed. The effects of some controllers such as Proportional (P), Proportional-Integral (PI), Proportional-Integral-Derivative (PID) on developed robot have been viewed successfully. Kalman Filters have been used for a stable control of the system and it has seen that the system can balance itself for a long time with optimum PID control parameters obtained.
Efficient Evolutionary Algorithm for Single-Objective Bilevel Optimization

Bilevel optimization problems are a class of challenging optimization problems, which contain two levels of optimization tasks. In these problems, the optimal solutions to the lower level problem become possible feasible candidates to the upper level problem. Such a requirement makes the optimization problem difficult to solve, and has kept the researchers busy towards devising methodologies, which can efficiently handle the problem. Despite the efforts, there hardly exists any effective methodology, which is capable of handling a complex bilevel problem. In this paper, we introduce bilevel evolutionary algorithm based on quadratic approximations (BLEAQ) of optimal lower level variables with respect to the upper level variables. The approach is capable of handling bilevel problems with different kinds of complexities in relatively smaller number of function evaluations. Ideas from classical optimization have been hybridized with evolutionary methods to generate an efficient optimization algorithm for generic bilevel problems. The efficacy of the algorithm has been shown on two sets of test problems. The first set is a recently proposed SMD test set, which contains problems with controllable complexities, and the second set contains standard test problems collected from the literature. The proposed method has been evaluated against two benchmarks, and the performance gain is observed to be significant.
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization

The Rocchio relevance feedback algorithm is one of the most popular and widely applied learning methods from information retrieval. Here, a probabilistic analysis of this algorithm is presented in a text categorization framework. The analysis gives theoretical insight into the heuristics used in the Rocchio algorithm, particularly the word weighting scheme and the similarity metric. It also suggests improvements which lead to a probabilistic variant of the Rocchio classi er. The Rocchio classi er, its probabilistic variant, and a naive Bayes classi er are compared on six text categorization tasks. The results show that the probabilistic algorithms are preferable to the heuristic Rocchio classi er not only because they are more well-founded, but also because they achieve better performance.
Lattice-based access control models

Lattice-based access control models were developed in the early 1970s to deal with the confidentiality of military information. In the late 1970s and early 1980s, researchers applied these models to certain integrity concerns. Later, application of the models to the Chinese Wall policy, a confidentiality policy unique to the commercial sector, was demonstrated. A balanced perspective on lattice-based access control models is provided. Information flow policies, the military lattice, access control models, the Bell-LaPadula model, the Biba model and duality, and the Chinese Wall lattice are reviewed. The limitations of the models are identified.<<ETX>>
gStore: a graph-based SPARQL query engine

We address efficient processing of SPARQL queries over RDF datasets. The proposed techniques, incorporated into the gStore system, handle, in a uniform and scalable manner, SPARQL queries with wildcards and aggregate operators over dynamic RDF datasets. Our approach is graph based. We store RDF data as a large graph and also represent a SPARQL query as a query graph. Thus, the query answering problem is converted into a subgraph matching problem. To achieve efficient and scalable query processing, we develop an index, together with effective pruning rules and efficient search algorithms. We propose techniques that use this infrastructure to answer aggregation queries. We also propose an effective maintenance algorithm to handle online updates over RDF repositories. Extensive experiments confirm the efficiency and effectiveness of our solutions.
A Model to Predict Employee Turnover Rate: Observing a Case Study of Chinese Enterprises

Employee turnover is a main focus in human resource management (HRM) because understanding it can be useful in providing decision support to managers. The goal of our study was to predict the amount of employee turnover based on certain variables. The properties of employees and job positions are represented by two variables. A state-transition mode is applied to describe an employee?s job state and the type of turnover. We propose a conditional semi-Markov (SMK) model for calculating the conditional amount of employee turnover. Then, we provide a data set of employee records to illustrate how these models work in reality and how the proposed method can be valuable to managers for developing recruitment plans, promotion rules, and retirement regulations.
The HAWKwood Database

We present a database consisting of wood pile images, which can be used as a benchmark to evaluate the performance of wood pile detection and surveying algorithms. We distinguish six database categories which can be used for different types of algorithms. Images of real and synthetic scenes are provided, which consist of 7655 images divided into 354 data sets. Depending on the category the data sets either include ground truth data or forestry specific measurements with which algorithms may be compared.
Composing Web services on the Semantic Web

Service composition is gaining momentum as the potential silver bullet for the envisioned Semantic Web. It purports to take the Web to unexplored efficiencies and provide a flexible approach for promoting all types of activities in tomorrow’s Web. Applications expected to heavily take advantage of Web service composition include B2B E-commerce and E-government. To date, enabling composite services has largely been an ad hoc, time-consuming, and error-prone process involving repetitive low-level programming. In this paper, we propose an ontology-based framework for the automatic composition of Web services. We present a technique to generate composite services from high-level declarative descriptions. We define formal safeguards for meaningful composition through the use of composability rules. These rules compare the syntactic and semantic features of Web services to determine whether two services are composable. We provide an implementation using an E-government application offering customized services to indigent citizens. Finally, we present an exhaustive performance experiment to assess the scalability of our approach.
Design of an LCL-filter for space vector PWM in grid-connected 3-level inverters system

This paper proposes a design scheme of the LCL-filter for space vector PWM (SVM) at the three-level inverter system. Although there are several studies in progress, the conventional methods are erroneous because they do not focus on the other switching methods. This paper shows the design methodology for an LCL-filter optimized in SVM switching. The design sequence for the LCL-filter is presented gradually. The inverter-side inductor was decided by an analysis of the ripple components mathematically. Based on the reactive power absorption ratio, the filter capacitor was determined. The grid-side inductor was determined by the ripple factor. Simulation results are shown to verify the validity of the proposed filter design method.
On the robustness of power systems: Optimal load-capacity distributions and hardness of attacking

We consider a power system with N transmission lines whose initial loads (i.e., power flows) L<inf>1</inf>,…, L<inf>N</inf> and capacities C1,…, C<inf>N</inf> are independent and identically distributed with the joint distribution P<inf>LC</inf>(x, y) = P [L ≤ x, C ≤ y]; the capacity C<inf>i</inf> defines the maximum flow allowed on line i. We survey some results on the robustness of this power system against random attacks (or, failures) that target a p-fraction of the lines, under a democratic fiber bundle-like model. Namely, when a line fails, the load it was carrying is redistributed equally among the remaining lines. We then consider the case where an adversary can launch a targeted attack, and present several results on the hardness of attacking optimally.
Analysis of Aloha Protocols for Underwater Acoustic Sensor Networks

In this paper, we present a study on Aloha and Slotted Aloha protocols for Underwater Sensor Networks (UWSN). Such simple random access protocols allow sensor nodes to communicate in UWSN. We present a simple mathematical analysis as well as simulations to show the performance of Aloha and Slotted Aloha in the acoustic environment. Our results show that long propagation delay of acoustic signals prohibits the coordination among nodes (e.g., slotted methods), and thus, it does not yield any performance gain.