nils-herrmann/clean_scirepeval huggingface dataset:
An Efficient Design Methodology for CNFET Based Ternary Logic Circuits

Ternary logic is a promising alternative to conventional binary logic. Implementation of ternary logic circuits however requires devices with multiple thresholds, which is a complex task with current CMOS technology. Carbon Nanotube based FETs (CNFETs) present an easier alternative for the implementation of ternary logic circuits since their threshold voltages can be varied by varying the diameter. In existing design methodology of ternary logic circuits, ternary signals are first converted to binary signals using ternary decoders which are then passed through binary gates and an encoder to get the final ternary output. Since every input signal requires a ternary decoder, the resulting circuits lead to increased area, power and delay. In this paper, authors suggest a new transistor-level methodology for designing CNFET-based ternary circuits without using ternary decoders. Elimination of decoders results in reduced area, power and delay. Simulation results indicate that up to 88% reduction in power consumption, 64% reduction in transistor count and 12 % reduction in delay can be achieved using the proposed approach when compared to the existing one.
Key performance indicators for PLM benefits evaluation: The Alcatel Alenia Space case study

From a theoretical point of view, people are used to think that the adoption of innovative business solutions, for optimizing the product lifecycle, should always guarantee a sufficient return on the investment (ROI), even if the concrete benefits that the investment has given to the company are sometimes difficult to evaluate. Looking at the technical literature it is possible to find a lot of ROI ‘s estimation metrics for the evaluation of business strategies, even if after the analysis of the theorical concepts no one faces with a real implementation of the methodology proposed. This paper proposes a solution, based on the Key Performance Indicator (KPI) method, for evaluating the benefits introduced by the adoption of a PLM tool in a one of a kind company. The methods have been validated with its implementation into an Aerospace and Defence company (Alcatel Alenia Space), highlighting the improvement created by the implementation of the PLM solution in the every day activities and showing the system’s contribution for some key-process as Configuration, Change and Documentation.
0.293-mm2 Fast Transient Response Hysteretic Quasi- $V^{2}$  DC–DC Converter With Area-Efficient Time-Domain-Based Controller in 0.35- $\mu$ m CMOS

A time-domain-control-based quasi-<inline-formula> <tex-math notation="LaTeX">$V^{2}$ </tex-math></inline-formula> buck converter with small area, fast transient response, wide load current range, and constant switching frequency is proposed in this paper. The proposed time-domain-based controller achieves compact core area and simplified constant switching frequency control by replacing the conventional voltage-domain comparator with a phase detector having an adaptive detection window. Moreover, a coupling-based inductor current emulator is also proposed to support the discontinuous conduction mode operation in the light load. The proposed buck converter is implemented using 0.35-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. The core area of the proposed buck converter is only 0.293 mm<sup>2</sup>. The measured peak efficiency in the continuous conduction mode is 92% and the end-to-end efficiency in the load current range of 25–700 mA is 73%. The undershoot/overshoot and recovery time with the up/down load current steps of 510 mA are 38/20 mV and 2.5/2.6 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, respectively.
Sustaining iterative game playing processes in DGBL: The relationship between motivational processing and outcome processing

Digital game-based learning (DGBL) has become a viable instructional option in recent years due to its support of learning motivation. Recent studies have mostly focused on identifying motivational factors in digital games (e.g., curiosity, rules, control) that support intrinsic motivation. These findings, however, are limited in two fronts. First, they did not depict the interactive nature of the motivational processing in DGBL. Second, they excluded the outcome processing (learners’ final effort versus performance evaluation) as a possible motivation component to sustain the iterative game playing cycle. To address these problems, situated in the integrative theory of Motivation, Volition, and Performance (MVP), this study examined the relationship between motivational processing and outcome processing in an online instructional game. The study surveyed 264 undergraduate students after playing the Trade Ruler online game. Based on the data collected by ARCS-based Instructional Materials Motivational Survey (IMMS), a regression analysis revealed a significant model between motivational processing (attention, relevance, and confidence) and the outcome processing (satisfaction). The finding preliminarily suggests that both motivational processing and outcome processing need to be considered when designing DGBL. Furthermore, the finding implies a potential relationship between intrinsic motives and extrinsic rewards in DGBL.
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces

One of the major limitations of brain-computer interfaces (BCI) is their long calibration time, which limits their use in practice, both by patients and healthy users alike. Such long calibration times are due to the large between-user variability and thus to the need to collect numerous training electroencephalography (EEG) trials for the machine learning algorithms used in BCI design. In this paper, we first survey existing approaches to reduce or suppress calibration time, these approaches being notably based on regularization, user-to-user transfer, semi-supervised learning and a priori physiological information. We then propose new tools to reduce BCI calibration time. In particular, we propose to generate artificial EEG trials from the few EEG trials initially available, in order to augment the training set size. These artificial EEG trials are obtained by relevant combinations and distortions of the original trials available. We propose three different methods to do so. We also propose a new, fast and simple approach to perform user-to-user transfer for BCI. Finally, we study and compare offline different approaches, both old and new ones, on the data of 50 users from three different BCI data sets. This enables us to identify guidelines about how to reduce or suppress calibration time for BCI.
Optically transparent conductive polymer RFID meandering dipole antenna

In this paper, we present optically transparent flexible conductive polymer antennas for radio frequency identification systems. The designs for these antennas are presented along with simulated and measured results of antenna radiating properties. These conductive polymer antennas are compared to antennas with the same design fabricated out of copper. Finally, we include an analysis of the optical transparency of the conductive polymer antennas.
Modular and reconfigurable mobile robotics

With increasing demand on reliable robotic platforms that can alleviate the burden of daily painstaking tasks, researchers have focused their effort towards developing robotic platforms that possess a high level of autonomy and versatility in function. These robots, capable of operating either individually or in a group, also possess the structural modular morphology that enables them to adapt to the unstructured nature of a real environment. Over the past two decades, significant work has been published in this field, particularly in the aspects of autonomy, mobility and docking. This paper reviews the primary methods in the literature related to the fields of modular and reconfigurable mobile robotics. By bringing together aspects of modularity, including docking and autonomy, and synthesizing the most relevant findings, there is optimism that a more complete understanding of this field will serve as a starting ground for innovation and integration of such technology in the urban environment.
Blur-invariant copy-move forgery detection technique with improved detection accuracy utilising SWT-SVD

Majority of the existing copy-move forgery detection algorithms operate based on the principle of image block matching. However, such detection becomes complicated when an intelligent adversary blurs the edges of forged region(s). To solve this problem, the authors present a novel approach for detection of copy-move forgery using stationary wavelet transform (SWT) which, unlike most wavelet transforms (e.g. discrete wavelet transform), is shift invariant, and helps in finding the similarities, i.e. matches and dissimilarities, i.e. noise, between the blocks of an image, caused due to blurring. The blocks are represented by features extracted using singular value decomposition (SVD) of an image. Also, the concept of colour-based segmentation used in this work helps to achieve blur invariance. The authors’ experimental results prove the efficiency of the proposed method in detection of copy-move forgery involving intelligent edge blurring. Also, their experimental results prove that the performance of the proposed method in terms of detection accuracy is considerably higher compared with the state-of-theart.
Sentiment Identification in Code-Mixed Social Media Text

Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying presence of sentiment in text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral. Whenever there is presence of sentiment in text, it has a source (people, group of people or any entity) and the sentiment is directed towards some entity, object, event or person. Sentiment analysis tasks aim to determine the subject, the target and the polarity or valence of the sentiment. In our work, we try to automatically extract sentiment (positive or negative) from Facebook posts using a machine learning approach. While some works have been done in code-mixed social media data and in sentiment analysis separately, our work is the first attempt (as of now) which aims at performing sentiment analysis of code-mixed social media text. We have used extensive pre-processing to remove noise from raw text. Multilayer Perceptron model has been used to determine the polarity of the sentiment. We have also developed the corpus for this task by manually labelling Facebook posts with their associated sentiments.
Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images

Staining and scanning of tissue samples for microscopic examination is fraught with undesirable color variations arising from differences in raw materials and manufacturing techniques of stain vendors, staining protocols of labs, and color responses of digital scanners. When comparing tissue samples, color normalization and stain separation of the tissue images can be helpful for both pathologists and software. Techniques that are used for natural images fail to utilize structural properties of stained tissue samples and produce undesirable color distortions. The stain concentration cannot be negative. Tissue samples are stained with only a few stains and most tissue regions are characterized by at most one effective stain. We model these physical phenomena that define the tissue structure by first decomposing images in an unsupervised manner into stain density maps that are sparse and non-negative. For a given image, we combine its stain density maps with stain color basis of a pathologist-preferred target image, thus altering only its color while preserving its structure described by the maps. Stain density correlation with ground truth and preference by pathologists were higher for images normalized using our method when compared to other alternatives. We also propose a computationally faster extension of this technique for large whole-slide images that selects an appropriate patch sample instead of using the entire image to compute the stain color basis.
Triple Pattern Fragments: A low-cost knowledge graph interface for the Web

Billions of Linked Data triples exist in thousands of RDF knowledge graphs on the Web, but few of those graphs can be queried live from Web applications. Only a limited number of knowledge graphs are available in a queryable interface, and existing interfaces can be expensive to host at high availability. To mitigate this shortage of live queryable Linked Data, we designed a low-cost Triple Pattern Fragments interface for servers, and a client-side algorithm that evaluates SPARQL queries against this interface. This article describes the Linked Data Fragments framework to analyze Web interfaces to Linked Data and uses this framework as a basis to define Triple Pattern Fragments. We describe client-side querying for single knowledge graphs and federations thereof. Our evaluation verifies that this technique reduces server load and increases caching effectiveness, which leads to lower costs to maintain high server availability. These benefits come at the expense of increased bandwidth and slower, but more stable query execution times. These results substantiate the claim that lightweight interfaces can lower the cost for knowledge publishers compared to more expressive endpoints, while enabling applications to query the publishers’ data with the necessary reliability.
Topology: A Source of Nonlinearity in the MEMS Thermal Accelerometer

This article reports the effect of package and constituent geometries on the response linearity of a micromachined single-axis thermal accelerometer. To investigate this phenomenon, a packaged device is filled with a high-density gas inside a sealed chamber on a rotation stage, and its response is measured as its sensitive axis of acceleration is tilted relative to the Earth's gravitational acceleration g 9.81ms2 . Linearity of the response is examined compared to an analytical model describing natural convection between horizontal heated concentric cylinders. In a 360 turn, full-scale nonlinearities of 3.1 and 0.9 are measured in each half cycle. This asymmetrical performance is attributed to a lower level of geometrical symmetry in the packaged device. To compensate for the modulated response, a geometrical correction factor is introduced to the model, improving its performance prediction with a symmetrical full-scale linearity error of 0.5.
Mostly Exploration-Free Algorithms for Contextual Bandits

The contextual bandit literature has traditionally focused on algorithms that address the explorationexploitation tradeoff. In particular, greedy algorithms that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy algorithms are desirable in practical settings where exploration may be costly or unethical (e.g., clinical trials). Surprisingly, we find that a simple greedy algorithm can be rate-optimal (achieves asymptotically optimal regret) if there is sufficient randomness in the observed contexts (covariates). We prove that this is always the case for a two-armed bandit under a general class of context distributions that satisfy a condition we term covariate diversity. Furthermore, even absent this condition, we show that a greedy algorithm can be rate-optimal with nonzero probability. Thus, standard bandit algorithms may unnecessarily explore. Motivated by these results, we introduce Greedy-First, a new algorithm that uses only observed contexts and rewards to determine whether to follow a greedy algorithm or to explore. We prove that this algorithm is rate-optimal without any additional assumptions on the context distribution or the number of arms. Extensive simulations demonstrate that Greedy-First successfully reduces experimentation and outperforms existing (exploration-based) contextual bandit algorithms such as Thompson sampling or UCB.
Measurement of return on marketing investment : A conceptual framework and the future of marketing metrics

There is growing recognition that firms in the contemporary business environment derive substantial and sustained competitive advantage from a bundle of intangible assets such as knowledge, networks and innovative capability. Measuring the return on such intangible assets has now become imperative for managers. The present manuscript focuses on the measurement of the return on marketing. We first discuss the conditions that make this task a high managerial priority. We then discuss measurement efforts to date, both in general management and marketing. We then offer a conceptual framework that places measurement efforts in a historical perspective. We conclude with a discussion on where the future of marketing metrics lies.
Individual differences in need for cognitive closure.

This article introduces an individual-difference measure of the need for cognitive closure. As a dispositional construct, the need for cognitive closure is presently treated as a latent variable manifested through several different aspects, namely, desire for predictability, preference for order and structure, discomfort with ambiguity, decisiveness, and close-mindedness. This article presents psychometric work on the measure as well as several validation studies including (a) a "known-groups" discrimination between populations assumed to differ in their need for closure, (b) discriminant and convergent validation with respect to related personality measures, and (c) replication of effects obtained with situational inductions of the need for closure. The present findings suggest that the Need for Closure Scale is a reliable and valid instrument of considerable potential utility in future "motivated social cognition" research.
Electrodermal responses: what happens in the brain.

Electrodermal activity (EDA) is now the preferred term for changes in electrical conductance of the skin, including phasic changes that have been referred to as galvanic skin responses (GSR), that result from sympathetic neuronal activity. EDA is a sensitive psychophysiological index of changes in autonomic sympathetic arousal that are integrated with emotional and cognitive states. Until recently there was little direct knowledge of brain mechanisms governing generation and control of EDA in humans. However, studies of patients with discrete brain lesions and, more recently, functional imaging techniques have clarified the contribution of brain regions implicated in emotion, attention, and cognition to peripheral EDA responses. Moreover, such studies enable an understanding of mechanisms by which states of bodily arousal, indexed by EDA, influence cognition and bias motivational behavior.
A Novel Approach of an Absolute Encoder Coding Pattern

This paper presents a novel approach of an absolute rotary and linear optical encoder codding patterns. The concept is based on the analysis of 2-D images to find a unique sequence that allows to unambiguously determine an angular shaft position. The adopted coding method allows to achieve a very high data density for specified number of tracks on the code pattern disc. Encoders based on the proposed solution enable the production from readily available and inexpensive components. Nevertheless, encoders retain high measuring accuracy and high dependability obtained in the classical approach. The optical device and a design of processing system is proposed. Finally, the feasibility of the pattern coding method is further proved with encoder prototype.
Adaptive VM Handoff Across Cloudlets

Cloudlet offload is a valuable technique for ensuring low end-to-end latency of resource-intensive cloud processing for many emerging mobile applications. This paper examines the impact of user mobility on cloudlet offload, and shows that even modest user mobility can result in significant network degradation. We propose VM handoff as a technique for seamlessly transferring VMencapsulated execution to a more optimal offload site as users move. Our approach can perform handoff in roughly a minute even over limited WANs by adaptively reducing data transferred. We present experimental results to validate our implementation and to demonstrate effectiveness of adaptation to changing network conditions and processing capacity. This research was supported by the National Science Foundation (NSF) under grant number IIS-1065336. Additional support was provided by the Intel Corporation, Google, Vodafone, and the Conklin Kistler family fund. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources.
Evolution of autonomic Database Management Systems

The evolution of computer is started from human dependent to self-management. With the development of technology, people want to manage the complex systems in an efficient and reliable manner. Database Management Systems (DBMSs) started from the single user with limited operations; and with the passage of time more functionality is added to meet the need of the users. Maximum number of operations, huge volume and multi-user environment also increased the complexity. Due to complexity and heterogeneity, job of DBAs become difficult and laborious. The solution to these DBMS's problems is self-managing or autonomic DBMS (ADBMS). An ADBMS have the ability to adopt changes according to the environment and available resources without or with less human intervention. This paper examines the three commercial leading DBMSs, IBM's DB2, Oracle by Oracle Corporation and Microsoft SQL Server with respect to autonomic characteristics. On the basis of analysis and other parameters autonomic computing (AC) maturity value is assigned to each autonomic component of three DBMSs. At the end, the evolving status of AC in DBMSs is presented.
Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions

Vehicular networking has significant potential to enable diverse applications associated with traffic safety, traffic efficiency and infotainment. In this survey and tutorial paper we introduce the basic characteristics of vehicular networks, provide an overview of applications and associated requirements, along with challenges and their proposed solutions. In addition, we provide an overview of the current and past major ITS programs and projects in the USA, Japan and Europe. Moreover, vehicular networking architectures and protocol suites employed in such programs and projects in USA, Japan and Europe are discussed.