nils-herrmann/clean_scirepeval huggingface dataset:
Optimal Design of Shell-and-Tube Heat Exchangers by Different Strategies of Differential Evolution

This paper presents the application of Differential Evolution (DE) for the optimal design of shell-and-tube heat exchangers. The main objective in any heat exchanger design is the estimation of the minimum heat transfer area required for a given heat duty, as it governs the overall cost of the heat exchanger. Lakhs of configurations are possible with various design variables such as outer diameter, pitch, and length of the tubes; tube passes; baffle spacing; baffle cut etc. Hence the design engineer needs an efficient strategy in searching for the global minimum. In the present study for the first time DE, an improved version of Genetic Algorithms (GAs), has been successfully applied with different strategies for 1,61,280 design configurations using Bells method to find the heat transfer area. In the application of DE 9680 combinations of the key parameters are considered. For comparison, GAs are also applied for the same case study with 1080 combinations of its parameters. For this optimal design problem, it is found that DE, an exceptionally simple evolution strategy, is significantly faster compared to GA and yields the global optimum for a wide range of the key parameters.
A 405-nW CMOS Temperature Sensor Based on Linear MOS Operation

This brief presents a CMOS temperature sensor suitable for ultralow-power applications. With a MOS transistor operating in the linear region, a linear relationship between delay and temperature can be obtained. A differential sensing architecture is utilized to reduce the signal offset and increase the effective signal-to-noise ratio. A design methodology concerning power optimization and improved sensor linearity is also presented. The sensor, which occupies 0.0324 mm2, is fabricated using the TSMC 0.18-¿m one-polysilicon six-metal (1P6M) process. Measurement results show that the sensor consumes 405 nW with a 1-V supply at 1 ksample/s at room temperature. An inaccuracy value of -0.8°C to +1°C from 0°C to 100°C after calibration is achieved.
Survey on Cloud Computing Security

Cloud computing may be defined as management and provision of resources, software, applications and information as services over the cloud (internet) on demand. Cloud computing is a model for enabling convenient, on demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction . With its ability to provide users dynamically scalable, shared resources over the Internet and avoid large upfront fixed costs, cloud computing has recently emerged as a promising hosting platform that performs an intelligent usage of a collection of services, applications, information and infrastructure comprised of pools of computer, network, information and storage resources. However along with these advantages, storing a large amount of data including critical information on the cloud motivates highly skilled hackers thus creating a need for the security to be considered as one of the top issues while considering Cloud Computing. In this paper we explain the cloud computing along with its open secure architecture advantages in brief and emphasize on various security threats in cloud computing also the existing methods to control them along with their pros and cons.
A feasible method for optimization with orthogonality constraints

Minimization with orthogonality constraints (e.g., X>X = I) and/or spherical constraints (e.g., ‖x‖2 = 1) has wide applications in polynomial optimization, combinatorial optimization, eigenvalue problems, sparse PCA, p-harmonic flows, 1-bit compressive sensing, matrix rank minimization, etc. These problems are difficult because the constraints are not only non-convex but numerically expensive to preserve during iterations. To deal with these difficulties, we apply the Cayley transform — a Crank-Nicolson-like update scheme — to preserve the constraints and based on it, develop curvilinear search algorithms with lower flops compared to those based on projections and geodesics. The efficiency of the proposed algorithms is demonstrated on a variety of test problems. In particular, for the maxcut problem, it exactly solves a decomposition formulation for the SDP relaxation. For polynomial optimization, nearest correlation matrix estimation and extreme eigenvalue problems, the proposed algorithms run very fast and return solutions no worse than those from their stateof-the-art algorithms. For the quadratic assignment problem, a gap 0.842% to the best known solution on the largest problem “tai256c” in QAPLIB can be reached in 5 minutes on a typical laptop.
Modelling of trends in Twitter using retweet graph dynamics

In this paper we model user behaviour in Twitter to capture the emergence of trending topics. For this purpose, we first extensively analyse tweet datasets of several different events. In particular, for these datasets, we construct and investigate the retweet graphs. We find that the retweet graph for a trending topic has a relatively dense largest connected component (LCC). Next, based on the insights obtained from the analyses of the datasets, we design a mathematical model that describes the evolution of a retweet graph by three main parameters. We then quantify, analytically and by simulation, the influence of the model parameters on the basic characteristics of the retweet graph, such as the density of edges and the size and density of the LCC. Finally, we put the model in practice, estimate its parameters and compare the resulting behavior of the model to our datasets.
On the Security of Machine Learning in Malware C&C Detection: A Survey

One of the main challenges in security today is defending against malware attacks. As trends and anecdotal evidence show, preventing these attacks, regardless of their indiscriminate or targeted nature, has proven difficult: intrusions happen and devices get compromised, even at security-conscious organizations. As a consequence, an alternative line of work has focused on detecting and disrupting the individual steps that follow an initial compromise and are essential for the successful progression of the attack. In particular, several approaches and techniques have been proposed to identify the command and control (C8C) channel that a compromised system establishes to communicate with its controller.
 A major oversight of many of these detection techniques is the design’s resilience to evasion attempts by the well-motivated attacker. C8C detection techniques make widespread use of a machine learning (ML) component. Therefore, to analyze the evasion resilience of these detection techniques, we first systematize works in the field of C8C detection and then, using existing models from the literature, go on to systematize attacks against the ML components used in these approaches.
Minimal MapReduce algorithms

MapReduce has become a dominant parallel computing paradigm for big data, i.e., colossal datasets at the scale of tera-bytes or higher. Ideally, a MapReduce system should achieve a high degree of load balancing among the participating machines, and minimize the space usage, CPU and I/O time, and network transfer at each machine. Although these principles have guided the development of MapReduce algorithms, limited emphasis has been placed on enforcing serious constraints on the aforementioned metrics simultaneously. This paper presents the notion of minimal algorithm, that is, an algorithm that guarantees the best parallelization in multiple aspects at the same time, up to a small constant factor. We show the existence of elegant minimal algorithms for a set of fundamental database problems, and demonstrate their excellent performance with extensive experiments.
Blood pressure is reduced and insulin sensitivity increased in glucose-intolerant, hypertensive subjects after 15 days of consuming high-polyphenol dark chocolate.

Flavanols from chocolate appear to increase nitric oxide bioavailability, protect vascular endothelium, and decrease cardiovascular disease (CVD) risk factors. We sought to test the effect of flavanol-rich dark chocolate (FRDC) on endothelial function, insulin sensitivity, beta-cell function, and blood pressure (BP) in hypertensive patients with impaired glucose tolerance (IGT). After a run-in phase, 19 hypertensives with IGT (11 males, 8 females; 44.8 +/- 8.0 y) were randomized to receive isocalorically either FRDC or flavanol-free white chocolate (FFWC) at 100 g/d for 15 d. After a wash-out period, patients were switched to the other treatment. Clinical and 24-h ambulatory BP was determined by sphygmometry and oscillometry, respectively, flow-mediated dilation (FMD), oral glucose tolerance test, serum cholesterol and C-reactive protein, and plasma homocysteine were evaluated after each treatment phase. FRDC but not FFWC ingestion decreased insulin resistance (homeostasis model assessment of insulin resistance; P < 0.0001) and increased insulin sensitivity (quantitative insulin sensitivity check index, insulin sensitivity index (ISI), ISI(0); P < 0.05) and beta-cell function (corrected insulin response CIR(120); P = 0.035). Systolic (S) and diastolic (D) BP decreased (P < 0.0001) after FRDC (SBP, -3.82 +/- 2.40 mm Hg; DBP, -3.92 +/- 1.98 mm Hg; 24-h SBP, -4.52 +/- 3.94 mm Hg; 24-h DBP, -4.17 +/- 3.29 mm Hg) but not after FFWC. Further, FRDC increased FMD (P < 0.0001) and decreased total cholesterol (-6.5%; P < 0.0001), and LDL cholesterol (-7.5%; P < 0.0001). Changes in insulin sensitivity (Delta ISI - Delta FMD: r = 0.510, P = 0.001; Delta QUICKI - Delta FMD: r = 0.502, P = 0.001) and beta-cell function (Delta CIR(120) - Delta FMD: r = 0.400, P = 0.012) were directly correlated with increases in FMD and inversely correlated with decreases in BP (Delta ISI - Delta 24-h SBP: r = -0.368, P = 0.022; Delta ISI - Delta 24-h DBP r = -0.384, P = 0.017). Thus, FRDC ameliorated insulin sensitivity and beta-cell function, decreased BP, and increased FMD in IGT hypertensive patients. These findings suggest flavanol-rich, low-energy cocoa food products may have a positive impact on CVD risk factors.
Efficient inverse kinematics for general 6R manipulators

| The inverse kinematics of serial manipulators is a central problem in the automatic control of robot manipula-tors. The main interest has been in inverse kinematics of a six revolute (6R) jointed manipulator with arbitrary geometry. It has been recently shown that the joints of a general 6R manipulator can orient themselves in 16 diierent con-gurations (at most), for a given pose of the end{eeector. However, there are no good practical solutions available, which give a level of performance expected of industrial ma-nipulators. In this paper, we present an algorithm and implementation for eecient inverse kinematics for a general 6R manipulator. When stated mathematically, the problem reduces to solving a system of multivariate equations. We make use of the algebraic properties of the system and the symbolic formulation used for reducing the problem to solving a univariate polynomial. However, the polynomial is expressed as a matrix determinant and its roots are computed by reducing to an eigenvalue problem. The other roots of the multivariate system are obtained by computing eigenvectors and substitution. The algorithm involves symbolic preprocessing, matrix computations and a variety of other numerical techniques. The average running time of the algorithm, for most cases, is 11 milliseconds on an IBM RS/6000 workstation. This approach is applicable to inverse kinematics of all serial manipulators.
Porcelain veneers: a review of the literature.

Porcelain veneers are steadily increasing in popularity among today's dental practitioners for conservative restoration of unaesthetic anterior teeth. As with any new procedure, in vitro and in vivo investigations are required to assess the ultimate clinical efficacy of these restorations. The current literature was therefore reviewed in search for the most important parameters determining the long-term success of porcelain veneers.



Laboratory studies focusing on parameters in prediction of the clinical efficacy of porcelain veneers such as the tooth preparation for porcelain veneers, the selection and type of the adhesive system, the quality of marginal adaptation, the resistance against microleakage, the periodontal response, and the aesthetic characteristics of the restorations have been reviewed. The clinical relevance of these parameters was then determined by reviewing the results of short and medium to long-term in vivo studies involving porcelain veneers performed during the last 10 years.



The adhesive porcelain veneer complex has been proven to be a very strong complex in vitro and in vivo. An optimal bonded restoration was achieved especially if the preparation was located completely in enamel, if correct adhesive treatment procedures were carried out and if a suitable luting composite was selected. The maintenance of aesthetics of porcelain veneers in the medium to long term was excellent, patient satisfaction was high and porcelain veneers had no adverse effects on gingival health inpatients with an optimal oral hygiene. Major shortcomings of the porcelain veneer system were described as a relatively large marginal discrepancy, and an insufficient wear resistance of the luting composite. Although these shortcomings had no direct impact on the clinical success of porcelain veneers in the medium term, their influence on the overall clinical performance in the long term is still unknown and therefore needs further study.
Robust Fall Detection Using Human Shape and Multi-class Support Vector Machine

Falls and resulting physical-psychological consequences in the elderly are a major health hazard and a serious obstacle for independent living. So development of intelligent video surveillance systems is so important due to providing safe and secure environments. To this end, this paper proposes a novel approach for human fall detection based on human shape variation. Combination of best-fit approximated ellipse around the human body, projection histograms of the segmented silhouette and temporal changes of head pose, would provide a useful cue for detection different behaviors. Extracted feature vectors are finally fed to a multi-class support vector machine for precise classification of motions and determination of a fall event. Unlike existent fall detection systems that only deal with limited movement patterns, we considered wide range of motions consisting of normal daily life activities, abnormal behaviors and also unusual events. Reliable recognition rate of experimental results underlines satisfactory performance of our system.
Internet of Vehicles (IoV) for traffic management

Internet of Things (IoT) is a world-wide network connecting all the smart objects together. It is the way in which all things are enabled to talk with each other. Whenever those smart things being connected over internet are restricted to only vehicles, then it is called as Internet of Vehicles (IoV). With continuously increasing urban population and rapidly expanding cities, vehicle ownership has been increasing at an exponential rate. Hence, traffic management has become a great problem in our day today life. This paper provides IoV based traffic management solution to overcome the problem that is prevailing in our daily life.
An empirical study of the implementation of green supply chain management practices in the electrical and electronic industry and their relation to organizational performances *

*Corresponding Author Email: chien.mk@msa.hinet.net Tel.: +886 8 8647367; Fax: +886 8 8647123  This study aims to investigate the green supply chain management practices likely to be adopted by the electrical and electronic industry in Taiwan, which is dominated by Original Equipment Manufacturing and Original Designing and Manufacturing manufacturers, after the European Union implementation of the Restriction of Hazardous Substances and Waste Electrical and Electronic Equipment directives. The relationship between green supply chain management practices and environmental performance, as well as financial performance, is studied. The approach of the present research includes a literature review, in depth interviews and questionnaire surveys. The companies in the electrical and electronic industry approved by the International Organization for Standardization 14001 certification in Taiwan before December 2004 were sampled for empirical study. The data were then analyzed using statistical package for the social sciences, and structural equation modelling was used as a path analysis model to verify the hypothetical construction of the study. The results indicate that the original equipment manufacturing and original designing and manufacturing manufacturers in Taiwan’s electrical and electronic industry have adopted green procurement and green manufacturing practices in response to the current wave of international green issues and have generated favorable environmental and financial performances for the respective companies.
An application of automated reasoning in natural language question answering

The LogAnswer system is an application of automated reasoning to the field of open domain question answering. In order to find answers to natural language questions regarding arbitrary topics, the system integrates an automated theorem prover in a framework of natural language processing tools. The latter serve to construct an extensive knowledge base automatically from given textual sources, while the automated theorem prover makes it possible to derive answers by deductive reasoning. In the paper, we discuss the requirements to the prover that arise in this application, especially concerning efficiency and robustness. The proposed solution rests on incremental reasoning, relaxation of the query (if no proof of the full query is found), and other techniques. In order to improve the robustness of the approach to gaps of the background knowledge, the results of deductive processing are combined with shallow linguistic features by machine learning.
Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction

A new practical application of neural network (NN) techniques to environmental numerical modeling has been developed. Namely, a new type of numerical model, a complex hybrid environmental model based on a synergetic combination of deterministic and machine learning model components, has been introduced. Conceptual and practical possibilities of developing hybrid models are discussed in this paper for applications to climate modeling and weather prediction. The approach presented here uses NN as a statistical or machine learning technique to develop highly accurate and fast emulations for time consuming model physics components (model physics parameterizations). The NN emulations of the most time consuming model physics components, short and long wave radiation parameterizations or full model radiation, presented in this paper are combined with the remaining deterministic components (like model dynamics) of the original complex environmental model--a general circulation model or global climate model (GCM)--to constitute a hybrid GCM (HGCM). The parallel GCM and HGCM simulations produce very similar results but HGCM is significantly faster. The speed-up of model calculations opens the opportunity for model improvement. Examples of developed HGCMs illustrate the feasibility and efficiency of the new approach for modeling complex multidimensional interdisciplinary systems.
Ensemble learning via negative correlation

This paper presents a learning approach, i.e. negative correlation learning, for neural network ensembles. Unlike previous learning approaches for neural network ensembles, negative correlation learning attempts to train individual networks in an ensemble and combines them in the same learning process. In negative correlation learning, all the individual networks in the ensemble are trained simultaneously and interactively through the correlation penalty terms in their error functions. Rather than producing unbiased individual networks whose errors are uncorrelated, negative correlation learning can create negatively correlated networks to encourage specialisation and cooperation among the individual networks. Empirical studies have been carried out to show why and how negative correlation learning works. The experimental results show that negative correlation learning can produce neural network ensembles with good generalisation ability.
Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components

Word embeddings have attracted much attention recently. Different from alphabetic writing systems, Chinese characters are often composed of subcharacter components which are also semantically informative. In this work, we propose an approach to jointly embed Chinese words as well as their characters and fine-grained subcharacter components. We use three likelihoods to evaluate whether the context words, characters, and components can predict the current target word, and collected 13,253 subcharacter components to demonstrate the existing approaches of decomposing Chinese characters are not enough. Evaluation on both word similarity and word analogy tasks demonstrates the superior performance of our model.
Web Development: Estimating Quick-to-Market Software

This presentation summarizes the results of experiments conducted over the past year to empirically validate extensions made in an attempt to use the COCOMO I1 Early Design model to accurately estimate web development effort and duration. The presentation starts by summarizing the challenges associated with estimating resources for web-based developments. Next, it describes a new sizing metric, web objects, and an adaptation of the Early Design model. WEBMO (Web Model) developed to meet these challenges. Both the size metric and model adaptation have been developed to address unique estimating issues identified as data from more than 40 projects was collected, normalized and analyzed in order to get a handle on the resources needed for quick-to-market software developments. The presentation concludes by discussing lessons learned from the effort and the next steps.
A BPMN-Based Design and Maintenance Framework for ETL Processes

Business Intelligence (BI) applications require the design, implementation, and maintenance of processes that extract, transform, and load suitable data for analysis. The development of these processes (known as ETL) is an inherently complex problem that is typically costly and time consuming. In a previous work, we have proposed a vendor-independent language for reducing the design complexity due to disparate ETL languages tailored to specific design tools with steep learning curves. Nevertheless, the designer still faces two major issues during the development of ETL processes: (i) how to implement the designed processes in an executable language, and (ii) how to maintain the implementation when the organization data infrastructure evolves. In this paper, we propose a model-driven framework that provides automatic code generation capability and ameliorate maintenance support of our ETL language. We present a set of model-to-text transformations able to produce code for different ETL commercial tools as well as model-to-model transformations that automatically update the ETL models with the aim of supporting the maintenance of the generated code according to data source evolution. A demonstration using an example is conducted as an initial validation to show that the framework covering modeling, code generation and maintenance could be used in practice.