nils-herrmann/clean_scirepeval huggingface dataset:
Applications of WASPAS Method in Manufacturing Decision Making

In order to survive in the present day global competitive environment, it now becomes essential for the manufacturing organizations to take prompt and correct decisions regarding effective use of their scarce resources. Various multi-criteria decision-making (MCDM) methods are now available to help those organizations in choosing the best decisive course of actions. In this paper, the applicability of weighted aggregated sum product assessment (WASPAS) method is explored as an effective MCDM tool while solving eight manufacturing decision making problems, such as selection of cutting fluid, electroplating system, forging condition, arc welding process, industrial robot, milling condition, machinability of materials, and electro-discharge micro-machining process parameters. It is observed that this method has the capability of accurately ranking the alternatives in all the considered selection problems. The effect of the parameter λ on the ranking performance of WASPAS method is also studied.
Design and Development of a Planar Linear Variable Differential Transformer for Displacement Sensing

In this paper, we present a new, efficient displacement sensor using core-less planar coils that are magnetically coupled. The sensor consists of two planar stationary coils and one moving coil. The mutual inductance between the stationary coils and the moving coils are measured, and the displacement is computed. The sensor design was validated using numerical computation. Two prototype sensors of different dimensions were fabricated and tested. The first prototype sensor developed has a measurement range of 70 mm and an R.M.S. error of 0.8% and the second sensor has a measurement range of 56 mm and an R.M.S. error in measurement of 0.9%. The signal output from the sensor is made tolerant to errors due to variations in the vertical position of the moving coil. The new sensor is low in cost, easy to manufacture, and can be used in a number of industrial displacement sensing applications.
On-demand transparency for improving hardware Trojan detectability

Malevolent Trojan circuits inserted by layout modifications in an IC at untrustworthy fabrication facilities are difficult to detect by traditional post-manufacturing testing. In this paper, we develop a novel low-overhead design methodology that facilitates the detection of inserted Trojan hardware in an IC through logic testing. As a byproduct, it also increases the security of the design by design obfuscation. Application of the proposed design methodology to an 8-bit RISC processor and a JPEG encoder resulted in improvement in Trojan detection probability significantly. It also obfuscated the design with verification mismatch for 90% of the verification points, while incurring moderate area, power and delay overheads.
Non-Negative Matrix Factorization Revisited: Uniqueness and Algorithm for Symmetric Decomposition

Non-negative matrix factorization (NMF) has found numerous applications, due to its ability to provide interpretable decompositions. Perhaps surprisingly, existing results regarding its uniqueness properties are rather limited, and there is much room for improvement in terms of algorithms as well. Uniqueness aspects of NMF are revisited here from a geometrical point of view. Both symmetric and asymmetric NMF are considered, the former being tantamount to element-wise non-negative square-root factorization of positive semidefinite matrices. New uniqueness results are derived, e.g., it is shown that a sufficient condition for uniqueness is that the conic hull of the latent factors is a superset of a particular second-order cone. Checking this condition is shown to be NP-complete; yet this and other results offer insights on the role of latent sparsity in this context. On the computational side, a new algorithm for symmetric NMF is proposed, which is very different from existing ones. It alternates between Procrustes rotation and projection onto the non-negative orthant to find a non-negative matrix close to the span of the dominant subspace. Simulation results show promising performance with respect to the state-of-art. Finally, the new algorithm is applied to a clustering problem for co-authorship data, yielding meaningful and interpretable results.
Determinants of Successful Website Design Relative Importance and Recommendations for Effectiveness

The success of e-commerce for any company, especially if it is not a well-known name, is greatly dependent on the appropriate design of its Website. This study investigates the determinants of an effective Website. A literature survey indicated that the major categories of determinants are: page loading speed, business content, navigation efficiency, security, and marketing/customer focus. The relative importance of each category was determined by counting the number of citations in the literature, and by soliciting the opinions of end users. The paper concentrates on experts’ recommendations of how to create an effective Website from an e-commerce point of view.
HUMAN DECISIONS AND MACHINE PREDICTIONS.

Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; and these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.
Global mutual information-based feature selection approach using single-objective and multi-objective optimization

Feature selection is an important preprocessing step in data mining. Mutual information-based feature selection is a kind of popular and effective approaches. In general, most existing mutual information-based techniques are greedy methods, which are proven to be efficient but suboptimal. In this paper, mutual information-based feature selection is transformed into a global optimization problem, which provides a new idea for solving feature selection problems. Firstly, a single-objective feature selection algorithm combining relevance and redundancy is presented, which has well global searching ability and high computational efficiency. Furthermore, to improve the performance of feature selection, we propose a multi-objective feature selection algorithm. The method can meet different requirements and achieve a tradeoff among multiple conflicting objectives. On this basis, a hybrid feature selection framework is adopted for obtaining a final solution. We compare the performance of our algorithm with related methods on both synthetic and real datasets. Simulation results show the effectiveness and practicality of the proposed method.
Organizational citizenship behavior and the quantity and quality of work group performance.

Despite the widespread interest in the topic of organizational citizenship behaviors (OCBs), little empirical research has tested the fundamental assumption that these forms of behavior improve the effectiveness of work groups or organizations in which they are exhibited. In the present study, the effects of OCBs on the quantity and quality of the performance of 218 people working in 40 machine crews in a paper mill located in the Northeastern United States were examined. The results indicate that helping behavior and sportsmanship had significant effects on performance quantity and that helping behavior had a significant impact on performance quality. However, civic virtue had no effect on either performance measure.
A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

Current deep learning approaches have been very successful using convolutional neural networks trained on large graphical-processing-unit-based computers. Three limitations of this approach are that (1) they are based on a simple layered network topology, i.e., highly connected layers, without intra-layer connections; (2) the networks are manually configured to achieve optimal results, and (3) the implementation of the network model is expensive in both cost and power. In this article, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. We use the MNIST dataset for our experiment, due to input size limitations of current quantum computers. Our results show the feasibility of using the three architectures in tandem to address the above deep learning limitations. We show that a quantum computer can find high quality values of intra-layer connection weights in a tractable time as the complexity of the network increases, a high performance computer can find optimal layer-based topologies, and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware.
T-PaD: Tactile Pattern Display through Variable Friction Reduction

In this paper we discuss the theory, design and construction of a haptic display for creating texture sensations through variations in surface friction. Ultrasonic frequency, low amplitude vibrations between two flat plates have been shown to create a squeeze film of air between the two plate surfaces thereby reducing the friction (Salbu, 1964; Weisendanger, 2001). We show that a reduction of friction will also occur between a human finger and a vibrating plate. Thus, a vibrating plate can serve as a haptic interface. The amplitude of vibration can also be correlated to the amount of friction reduction between the plate and the finger. Varying the surface friction between the finger and the haptic interface is a way of indirectly controlling shear forces on the finger during active exploration. Using finger position and velocity feedback on the display allows for the creation of spatial texture sensations
A deliberate practice account of typing proficiency in everyday typists.

The concept of deliberate practice was introduced to explain exceptional performance in domains such as music and chess. We apply deliberate practice theory to intermediate-level performance in typing, an activity that many people pursue on a regular basis. Sixty university students with several years typing experience participated in laboratory sessions that involved the assessment of abilities, a semistructured interview on typing experience as well as various typing tasks. In line with traditional theories of skill acquisition, experience (amount of typing since introduction to the keyboard) was related to typing performance. A perceptual speed test (digit-symbol substitution) and a measure of motor abilities (tapping) were not significantly related to performance. In line with deliberate practice theory, the highest level of performance was reported among participants who had attended a typing class in the past and who reported to adopt the goal of typing quickly during everyday typing. Findings suggest that even after several years of experience engagement in an everyday activity can serve as an opportunity for further skill improvement if individuals are willing to push themselves.
Interactive tabletops in education

Interactive tabletops are gaining increased attention from CSCL researchers. This paper analyses the relation between this technology and teaching and learning processes. At a global level, one could argue that tabletops convey a socio-constructivist flavor: they support small teams that solve problems by exploring multiple solutions. The development of tabletop applications also witnesses the growing importance of face-to-face collaboration in CSCL and acknowledges the physicality of learning. However, this global analysis is insufficient. To analyze the educational potential of tabletops in education, we present 33 points that should be taken into consideration. These points are structured on four levels: individual user-system interaction, teamwork, classroom orchestration, and socio-cultural contexts. God lies in the details.
The Design and FPGA Implementation of GF(2^128) Multiplier for Ghash

In this paper, we propose a high-speed parallel 128 bit multiplier for Ghash Function in conjunction with its FPGA implementation. Through the use of Verilog the designs are evaluated by using Xilinx Vertax5 with 65nm technic and 30,000 logic cells. The highest throughput of 30.764Gpbs can be achieved on virtex5 with the consumption of 8864 slices LUT. The proposed design of the multiplier can be utilized as a design IP core for the implementation of the Ghash Function. The architecture of the multiplier can also apply in more general polynomial basis. Moreover it can be used as arithmetic module in other encryption field.
InchwormClimber: A light-weight biped climbing robot with a switchable magnet adhesion unit

This article presents an inchworm climbing robot that is designed with switchable magnets and a single DOF arm. The InchwormClimber works on ferromagnetic structures and consumes little energy when climbing and can descend safely with almost zero energy consumption. Furthermore, considering the critical role of the adhesion unit in the overall functionality of the robot (weight, climbing speed and the payload), we optimized the switchable magnet unit for a higher adhesion force per mass unit.
Reversing and Smoothing the Multinomial Naive Bayes Text Classifier

The naive Bayes text classifier has long been a core technique in information retrieval and, more recently, it has emerged as a focus of research itself in machine learning. This paper is concerned with the naive Bayes text classifier in its multinomial model instantiation. This model and an “equivalent” reversed version proposed here are interpreted under the statistical framework of log-linear modelling. The reversed version provides an alternative way for parameter estimation, which (in a broad sense) is actually the main issue considered. The paper is to a large extent devoted to the study of the effects of parameter smoothing and document length normalization. For the purpose of parameter smoothing, we consider a standard smoothing method for text classification and two alternative techniques that are often used in the context of statistical language modelling for speech recognition. Empirical results are provided comparing these techniques and the effect of length normalization for both the multinomial model and its reversed version.
USE: A UML-based specification environment for validating UML and OCL

The Unified Modeling Language (UML) is accepted today as an important standard for developing software. UML tools however provide little support for validating and checking models in early development phases. There is also no substantial support for the Object Constraint Language (OCL). We present an approach for the validation of UML models and OCL constraints based on animation and certification. The USE tool (UML-based Specification Environment) supports analysts, designers and developers in executing UML models and checking OCL constraints and thus enables them to employ model-driven techniques for software production.
Influence of Brand Loyalty on Consumer Sportswear

The purpose of this research is to investigate how the respondents are influenced by factors of brand loyalty towards sportswear brands. Previous research adopted seven factors to test in the Malaysian environment. The seven factors of brand loyalty are brand name, product quality, price, style, promotion, service quality and store environment. Brand name has shown strong correlation with brand loyalty. In order to increase customer satisfaction and drive them to be brand loyalists, marketers are encouraged to develop aggressive marketing programs. Questionnaires were distributed and self-administered to 100 respondents. Descriptive analysis, one-way ANOVA and Pearson Correlation were used in this study. The research results showed that there is positive and significant relationship between factors of brand loyalty (brand name, product quality, price, style, promotion, service quality and store environment) with sportswear brand loyalty. Study of more focused factors that are appropriate to the Malaysian environment is recommended in order to obtain accurate information.
Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle Make and Model Recognition

Speeded-Up Robust Features (SURF) is a robust and useful feature detector for various vision-based applications but it is unable to detect symmetrical objects. This paper proposes a new symmetrical SURF descriptor to enrich the power of SURF to detect all possible symmetrical matching pairs through a mirroring transformation. A vehicle make and model recognition (MMR) application is then adopted to prove the practicability and feasibility of the method. To detect vehicles from the road, the proposed symmetrical descriptor is first applied to determine the region of interest of each vehicle from the road without using any motion features. This scheme provides two advantages: there is no need for background subtraction and it is extremely efficient for real-time applications. Two MMR challenges, namely multiplicity and ambiguity problems, are then addressed. The multiplicity problem stems from one vehicle model often having different model shapes on the road. The ambiguity problem results from vehicles from different companies often sharing similar shapes. To address these two problems, a grid division scheme is proposed to separate a vehicle into several grids; different weak classifiers that are trained on these grids are then integrated to build a strong ensemble classifier. The histogram of gradient and SURF descriptors are adopted to train the weak classifiers through a support vector machine learning algorithm. Because of the rich representation power of the grid-based method and the high accuracy of vehicle detection, the ensemble classifier can accurately recognize each vehicle.
Representational similarity encoding for fMRI: Pattern-based synthesis to predict brain activity using stimulus-model-similarities

Patterns of neural activity are systematically elicited as the brain experiences categorical stimuli and a major challenge is to understand what these patterns represent. Two influential approaches, hitherto treated as separate analyses, have targeted this problem by using model-representations of stimuli to interpret the corresponding neural activity patterns. Stimulus-model-based-encoding synthesizes neural activity patterns by first training weights to map between stimulus-model features and voxels. This allows novel model-stimuli to be mapped into voxel space, and hence the strength of the model to be assessed by comparing predicted against observed neural activity. Representational Similarity Analysis (RSA) assesses models by testing how well the grand structure of pattern-similarities measured between all pairs of model-stimuli aligns with the same structure computed from neural activity patterns. RSA does not require model fitting, but also does not allow synthesis of neural activity patterns, thereby limiting its applicability. We introduce a new approach, representational similarity-encoding, that builds on the strengths of RSA and robustly enables stimulus-model-based neural encoding without model fitting. The approach therefore sidesteps problems associated with overfitting that notoriously confront any approach requiring parameter estimation (and is consequently low cost computationally), and importantly enables encoding analyses to be incorporated within the wider Representational Similarity Analysis framework. We illustrate this new approach by using it to synthesize and decode fMRI patterns representing the meanings of words, and discuss its potential biological relevance to encoding in semantic memory. Our new similarity-based encoding approach unites the two previously disparate methods of encoding models and RSA, capturing the strengths of both, and enabling similarity-based synthesis of predicted fMRI patterns.
Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method

Liver lesion segmentation is an important step for liver cancer diagnosis, treatment planning and treatment evaluation. LiTS (Liver Tumor Segmentation Challenge) provides a common testbed for comparing different automatic liver lesion segmentation methods. We participate in this challenge by developing a deep convolutional neural network (DCNN) method. The particular DCNN model works in 2.5D in that it takes a stack of adjacent slices as input and produces the segmentation map corresponding to the center slice. The model has 32 layers in total and makes use of both long range concatenation connections of U-Net  and short-range residual connections from ResNet . The model was trained using the 130 LiTS training datasets and achieved an average Dice score of 0.67 when evaluated on the 70 test CT scans, which ranked first for the LiTS challenge at the time of the ISBI 2017 conference.
ISTI@TREC Microblog Track 2011: Exploring the Use of Hashtag Segmentation and Text Quality Ranking

In the first year of the TREC Micro Blog track, our participation has focused on building from scratch an IR system based on the Whoosh IR library. Though the design of our system (CipCipPy) is pretty standard it includes three ad-hoc solutions for the track: (i) a dedicated indexing function for hashtags that automatically recognizes the distinct words composing an hashtag, (ii) expansion of tweets based on the title of any referred Web page, and (iii) a tweet ranking function that ranks tweets in results by their content quality, which is compared against a reference corpus of Reuters news. In this preliminary paper we describe all the components of our system, and the efficacy scored by our runs. The CipCipPy system is available under a GPL license.