nils-herrmann/clean_scirepeval huggingface dataset:
Build-to-order supply chain management : a literature review and framework for development

The build-to-order supply chain management (BOSC) strategy has recently attracted the attention of both researchers and practitioners, given its successful implementation in many companies including Dell computers, Compaq, and BMW. The growing number of articles on BOSC in the literature is an indication of the importance of the strategy and of its role in improving the competitiveness of an organization. The objective of a BOSC strategy is to meet the requirements of individual customers by leveraging the advantages of outsourcing and information technology. There are not many research articles that provide an overview of BOSC, despite the fact that this strategy is being promoted as the operations paradigm of the future. The main objective of this research is to (i) review the concepts of BOSC, (ii) develop definitions of BOSC, (iii) classify the literature based on a suitable classification scheme, leading to some useful insights into BOSC and some future research directions, (iv) review the selected articles on BOSC for their contribution to the development and operations of BOSC, (v) develop a framework for BOSC, and (vi) suggest some future research directions. The literature has been reviewed based on the following four major areas of decision-making: organizational competitiveness, the development and implementation of BOSC, the operations of BOSC, and information technology in BOSC. Some of the important observations are: (a) there is a lack of adequate research on the design and control of BOSC, (b) there is a need for further research on the implementation of BOSC, (c) human resource issues in BOSC have been ignored, (d) issues of product commonality and modularity from the perspective of partnership or supplier development require further attention and (e) the trade-off between responsiveness and the cost of logistics needs further study. The paper ends with concluding remarks.
LLLR Parsing: a Combination of LL and LR Parsing

A new parsing method called LLLR parsing is defined and a method for producing LLLR parsers is described. An LLLR parser uses an LL parser as its backbone and parses as much of its input string using LL parsing as possible. To resolve LL conflicts it triggers small embedded LR parsers. An embedded LR parser starts parsing the remaining input and once the LL conflict is resolved, the LR parser produces the left parse of the substring it has just parsed and passes the control back to the backbone LL parser. The LLLR(k) parser can be constructed for any LR(k) grammar. It produces the left parse of the input string without any backtracking and, if used for a syntax-directed translation, it evaluates semantic actions using the top-down strategy just like the canonical LL(k) parser. An LLLR(k) parser is appropriate for grammars where the LL(k) conflicting nonterminals either appear relatively close to the bottom of the derivation trees or produce short substrings. In such cases an LLLR parser can perform a significantly better error recovery than an LR parser since the most part of the input string is parsed with the backbone LL parser. LLLR parsing is similar to LL(∗) parsing except that it (a) uses LR(k) parsers instead of finite automata to resolve the LL(k) conflicts and (b) does not perform any backtracking.
Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics

In this paper, we propose two novel methods for face recognition under arbitrary unknown lighting by using spherical harmonics illumination representation, which require only one training image per subject and no 3D shape information. Our methods are based on the result which demonstrated that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace. We provide two methods to estimate the spherical harmonic basis images spanning this space from just one image. Our first method builds the statistical model based on a collection of 2D basis images. We demonstrate that, by using the learned statistics, we can estimate the spherical harmonic basis images from just one image taken under arbitrary illumination conditions if there is no pose variation. Compared to the first method, the second method builds the statistical models directly in 3D spaces by combining the spherical harmonic illumination representation and a 3D morphable model of human faces to recover basis images from images across both poses and illuminations. After estimating the basis images, we use the same recognition scheme for both methods: we recognize the face for which there exists a weighted combination of basis images that is the closest to the test face image. We provide a series of experiments that achieve high recognition rates, under a wide range of illumination conditions, including multiple sources of illumination. Our methods achieve comparable levels of accuracy with methods that have much more onerous training data requirements. Comparison of the two methods is also provided.
Easy Hyperparameter Search Using Optunity

Optunity is a free software package dedicated to hyperparameter optimization. It contains various types of solvers, ranging from undirected methods to direct search, particle swarm and evolutionary optimization. The design focuses on ease of use, flexibility, code clarity and interoperability with existing software in all machine learning environments. Optunity is written in Python and contains interfaces to environments such as R and MATLAB. Optunity uses a BSD license and is freely available online at http://www.optunity.net.
Determinants of Behavioral Intention to Mobile Banking Case From Yemen

Nowadays, new tools and technologies are emerging rapidly. They are often used cross-culturally before being tested for suitability and validity. However, they must be validated to ensure that they work with all users, not just part of them. Mobile banking (as a new technology tool) has been introduced assuming that it performs well concerning authentication, among all members of the society. Our research aimed to evaluate authentication mobile banking user acceptance, through Technology Acceptance Model (TAM), in Arabic countries, namely Yemen. The results confirm the previous studies that have shown the importance of perceived ease of use and perceived usefulness. Furthermore, perceived ease of use plays a determinant role. KeywordsTechnology acceptance models; Mobile Banking;
Social norms and human cooperation

The existence of social norms is one of the big unsolved problems in social cognitive science. Although no other concept is invoked more frequently in the social sciences, we still know little about how social norms are formed, the forces determining their content, and the cognitive and emotional requirements that enable a species to establish and enforce social norms. In recent years, there has been substantial progress, however, on how cooperation norms are enforced. Here we review evidence showing that sanctions are decisive for norm enforcement, and that they are largely driven by non-selfish motives. Moreover, the explicit study of sanctioning behavior provides instruments for measuring social norms and has also led to deeper insights into the proximate and ultimate forces behind human cooperation.
Topically Driven Neural Language Model

Language models are typically applied at the sentence level, without access to the broader document context. We present a neural language model that incorporates document context in the form of a topic model-like architecture, thus providing a succinct representation of the broader document context outside of the current sentence. Experiments over a range of datasets demonstrate that our model outperforms a pure sentence-based model in terms of language model perplexity, and leads to topics that are potentially more coherent than those produced by a standard LDA topic model. Our model also has the ability to generate related sentences for a topic, providing another way to interpret topics.
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception

Unsupervised learning for visual perception of 3D geometry is of great interest to autonomous systems. Recent works on unsupervised learning have made considerable progress on geometry perception; however, they perform poorly on dynamic objects and scenarios with dark and noisy environments. In contrast, supervised learning algorithms, which are robust, require large labeled geometric dataset. This paper introduces SIGNet, a novel framework that provides robust geometry perception without requiring geometrically informative labels. Specifically, SIGNet integrates semantic information to make unsupervised robust geometric predictions for dynamic objects in low lighting and noisy environments. SIGNet is shown to improve upon the state-of-the-art unsupervised learning for geometry perception by 30% (in squared relative error for depth prediction). In particular, SIGNet improves the dynamic object class performance by 39% in depth prediction and 29% in flow prediction.
Routing in Delay/Disruption Tolerant Networks: A Taxonomy, Survey and Challenges

The introduction of intelligent devices with short range wireless communication techniques has motivated the development of Mobile Ad hoc NETworks (MANETs) during the last few years. However, traditional end-to-end based routing algorithms designed for MANETs are not much robust in the challenged networks suffering from frequent disruption, sparse network density and limited device capability. Such challenged networks, also known as Intermittently Connected Networks (ICNs) adopt the Store-Carry-Forward (SCF) behavior arising from the mobility of mobile nodes for message relaying. In this article, we consider the term ICNs as Delay/Disruption Tolerant Networks (DTNs) for the purpose of generalization, since DTNs have been envisioned for different applications with a large number of proposed routing algorithms. Motivated by the great interest from the research community, we firstly review the existing unicasting issue of DTNs because of its extensive research stage. Then, we also address multicasting and anycasting issues in DTNs considering their perspectives. A detail survey based on our taxonomy over the period from 2006 to 2010 is not only provided but also a comparison is given. We further identify the remaining challenges and open issues followed by an evaluation framework proposed for routing in DTNs. Finally, we summarize our contribution with three future research topics highlighted.
Web Credibility: Features Exploration and Credibility Prediction

The open nature of the World Wide Web makes evaluating webpage credibility challenging for users. In this paper, we aim to automatically assess web credibility by investigating various characteristics of webpages. Specifically, we first identify features from textual content, link structure, webpages design, as well as their social popularity learned from popular social media sites (e.g., Facebook, Twitter). A set of statistical analyses methods are applied to select the most informative features, which are then used to infer webpages credibility by employing supervised learning algorithms. Real dataset-based experiments under two application settings show that we attain an accuracy of 75% for classification, and an improvement of 53% for the mean absolute error (MAE), with respect to the random baseline approach, for regression.
Beyond von Neumann: Brain-computer structural metaphor

Brain-inspired computer architectures have gained a lot of attention from computer scientists in the past few years, in an era where more improvements in performance, power, and scalability are increasingly becoming limited. In this paper, we develop a brain-computer structural metaphor where we raise some research questions to inspire computer architects to design efficient computer architectures in terms of the traditional performance metrics, or maybe in terms of new metrics that reflect the true nature of the future, inspired computing.
Meta-Learning via Feature-Label Memory Network

Deep learning typically requires training a very capable architecture using a large dataset. However, many important learning problems demand an ability to draw valid inferences from a small size dataset, and such problems pose a particular challenge for deep learning. In this regard, various researches on “meta-learning” are being actively conducted. Recent work has suggested a Memory Augmented Neural Network (MANN) for meta-learning. MANN is an implementation of a Neural Turing Machine (NTM) with the ability to rapidly assimilate new data in its memory, and use this data to make accurate predictions. In models such as the MANN, the input data samples and their appropriate labels from previous step are bound together in the same memory locations. This often leads to memory interference when performing a task as these models have to retrieve a feature of an input from a certain memory location and read only the label information bound to that location. In this paper, we tried to address this issue by presenting a more robust MANN. We revisited the idea of meta-learning and proposed a new memory augmented neural network by explicitly splitting the external memory into feature and label memories. The feature memory is used to store the features of input data samples and the label memory stores their labels. Hence, when predicting the label of a given input, the memory augmented network with separate feature and label memory unit uses the feature memory unit as a reference to extract the stored feature of the input, and based on that feature, it retrieves the label information of the input from the label memory unit. In order for the network to function in this framework, a new memory-writing module to encode label information into the label memory in accordance with the meta-learning task structure is designed. Here, we demonstrate that the memory-augmented network outperforms MANN by a large margin in supervised one-shot classification tasks using Omniglot and MNIST datasets.
Depression and memory impairment: a meta-analysis of the association, its pattern, and specificity.

The existing evidence paints an unclear picture of whether an association exists between depression and memory impairment. The purpose of this investigation was to determine whether depression is associated with memory impairment, whether moderator variables determine the extent of this association, and whether any obtained association is unique to depression. Meta-analytic techniques were used to synthesize data from 99 studies on recall and 48 studies on recognition in clinically depressed and nondepressed samples. Associations between memory impairment and other psychiatric disorders (e.g., schizophrenia, dementia) were also examined. A significant, stable association between depression and memory impairment was revealed. Further analyses indicated, however, that it is likely that depression is linked to particular aspects of memory, the linkage is found in particular subsets of depressed individuals, and memory impairment is not unique to depression.
CMOS 1.5V bandgap voltage reference

A low power and high precision CMOS bandgap voltage reference circuit is presented. Prototype of the circuit is fabricated using the 0.18 mum CMOS process. The power supply is only 1.5 V. It fulfills the first order PTAT (proportion to absolute temperature) temperature curvature compensation with a good PSRR (power supply rejection ratio). The measured results of this circuit show that the PSRR is 47 dB. The output voltage varies from 1.114 V to 1.117 V which is constant within 0.269% over the temperature range of 0~80 degC. The power dissipation is 0.22 mW at 1.5 V and the active area is 0.057 mm2
Remote-Touch: A Laser Input User–Display Interaction Technology

In this paper, we present a new user-display interaction technology in which a laser-pointer is used to select objects on a display screen. In this interaction technology, the user points to the object on the display, using a laser-pointer to make a selection. This allows interaction from a distance, which is an advantage over state-of-the-art touchscreen technologies. This interaction technology will be referred to as ldquoremote-touchrdquo. It consists of a laser-pointer, a transparent micro-structured light-guiding substrate, and position-sensitive detectors (PSDs). The substrate, which is patterned with a micro-structure of pyramid-shaped indentations, is put in front of the display. When the laser-light hits the display the micro-structure in the polymethyl methacrylate (PMMA) substrate reflects part of the laser-light to the sides of the display screen. The PSDs at the edge of the PMMA substrate read-out the position of the laser-spot. This paper focuses on the optical performance of the micro-structured PMMA substrate.
Phase-change random access memory: A scalable technology

random access memory: A scalable technology S. Raoux G. W. Burr M. J. Breitwisch C. T. Rettner Y.-C. Chen R. M. Shelby M. Salinga D. Krebs S.-H. Chen H.-L. Lung C. H. Lam Nonvolatile RAM using resistance contrast in phase-change materials [or phase-change RAM (PCRAM)] is a promising technology for future storage-class memory. However, such a technology can succeed only if it can scale smaller in size, given the increasingly tiny memory cells that are projected for future technology nodes (i.e., generations). We first discuss the critical aspects that may affect the scaling of PCRAM, including materials properties, power consumption during programming and read operations, thermal cross-talk between memory cells, and failure mechanisms. We then discuss experiments that directly address the scaling properties of the phase-change materials themselves, including studies of phase transitions in both nanoparticles and ultrathin films as a function of particle size and film thickness. This work in materials directly motivated the successful creation of a series of prototype PCRAM devices, which have been fabricated and tested at phase-change material cross-sections with extremely small dimensions as low as 3 nm · 20 nm. These device measurements provide a clear demonstration of the excellent scaling potential offered by this technology, and they are also consistent with the scaling behavior predicted by extensive device simulations. Finally, we discuss issues of device integration and cell design, manufacturability, and reliability.
The use of web 2.0 technologies in tourism industry: A conceptual model

Tourism industry has been transformed dramatically by the Internet and web technologies. In the web 2.0 era, travelers‟ behaviors are affected by widely available user generated contents in a number of online social media. Much research has been conducted on the use and impact of web 2.0 on travelers; however, little was known from tourism suppliers‟ perspective. Our research therefore aims to study the topic from a holistic perspective, including activities throughout the tourism supply chain. This paper presents an initial part of the research. It reviews prior literature on the use of web 2.0 technologies in the tourism industry and proposes a conceptual model, which will be used to guide an investigation to the topic.
Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery

We consider the use of transfer learning, via the use of deep Convolutional Neural Networks (CNN) for the image classification problem posed within the context of X-ray baggage security screening. The use of a deep multi-layer CNN approach, traditionally requires large amounts of training data, in order to facilitate construction of a complex complete end-to-end feature extraction, representation and classification process. Within the context of X-ray security screening, limited availability of training for particular items of interest can thus pose a problem. To overcome this issue, we employ a transfer learning paradigm such that a pre-trained CNN, primarily trained for generalized image classification tasks where sufficient training data exists, can be specifically optimized as a later secondary process that targets specific this application domain. For the classical handgun detection problem we achieve 98.92% detection accuracy outperforming prior work in the field and furthermore extend our evaluation to a multiple object classification task within this context.
Quantifying the Security Posture of Containerized Mission Critical Systems

Determining the security posture of containerized mission-critical systems is difficult given the vast number of parameters that determine a system's ability to withstand cyber-attacks. In many cases, technical audits can be performed to determine a system's security posture and to evaluate how well they are configured to protect against known cyber-threats. Properly configuring systems can lead to higher security, however, the configuration and auditing process can be time-consuming and error-prone. In addition, the results obtained from these audits can be difficult to summarize into one meaningful metric that accurately characterizes system's security posture as guided by customer needs. In this work, we propose an approach for computing a security-posture metric for containerized systems that supports operators during the sense-making process that follows traditional security audits. The results of this work can be used on a per-deployment case, taking into account what matters to operators of containerized mission-critical systems.
A Failure-Friendly Design Principle for Hash Functions

This paper reconsiders the established Merkle-Damg̊ard design principle for iterated hash functions. The internal state size w of an iterated n-bit hash function is treated as a security parameter of its own right. In a formal model, we show that increasing w quantifiably improves security against certain attacks, even if the compression function fails to be collision resistant. We propose the wide-pipe hash, internally using a w-bit compression function, and the double-pipe hash, with w = 2n and an n-bit compression function used twice in parallel.
A five level cascaded H-bridge inverter based on space vector pulse width modulation technique

Multilevel inverters brought a tremendous revolution in high power superior performance industrial drive applications. Minimizing harmonic distortion is a challenging issue for power electronic drive. This problem can be solved by developing suitable modulation technique. There are number of modulation techniques established in the last decade. Among them, the space vector pulse width modulation (SVPWM) is extensively used because of their easier digital realization, better DC bus utilization and lower THD. Conventional SVPWM requires different number of logical computations of on time calculations and different number of lookup table for each triangle due to having their different number of switching states. In this paper, a simple SVPWM is presented where proposed only four logical computations of on time calculation, four active switching states and four lookup table for each triangle. Five level cascaded h-bridge inverter (CHBI) based on SVPWM technique are modeled and analyzed by using MATLAB/Simulink and origin 6.1 with a passive R-L load that can be extended to any level. These results are compared with other lower levels of inverter and with previous established results. From these results, five level CHBI based on SVPWM shows better performance compared to others in terms of THD.