nils-herrmann/clean_scirepeval huggingface dataset:
Converter-free multiple-voltage scaling techniques for low-powerCMOS digital design

Recent research has shown that voltage scaling is a very effective technique for low-power design. This paper describes a voltage scaling technique to minimize the power consumption of a combinational circuit. First, the converter-free multiple-voltage (CFMV) structures are proposed, including the p-type, the n-type, and the two-way CFMV structures. The CFMV structures make use of multiple supply voltages and do not require level converters. In contrast, previous works employing multiple supply voltages need level converters to prevent static currents, which may result in large power consumption. In addition, the CFMV structures group the gates with the same supply voltage in a cluster to reduce the complexity of placement and routing for the subsequent physical layout stage. Next, we formulated the problem and proposed an efficient heuristic algorithm to solve it. The heuristic algorithm has been implemented in C and experiments were performed on the ISCAS85 circuits to demonstrate the effectiveness of our approach.
Impact of Tourism on the Environment of a Destination

Tourism is an important part of national economy. On the other hand it can also be a source of some negative externalities. These are mainly environmental externalities, resulting in increased pollution, aesthetic or architectural damages. High concentration of visitors may also lead to increased crime, or aggressiveness. These may have negative effects on quality of life of residents and negative experience of visitors. The paper deals with the influence of tourism on destination environment. It highlights the necessity of sustainable forms of tourism and activities to prevent negative implication of tourism, such as education activities and tourism monitoring.  Tourism, Mass Tourism, Development, Sustainability, Tourism Impact, Monitoring.
Strategies and new developments in the generation of patient-specific pluripotent stem cells.

Generating pluripotent stem cells directly from cells obtained from patients is one of the ultimate goals in regenerative medicine. Two "reprogramming" strategies for the generation of pluripotent stem cells from somatic cells have been studied extensively: nuclear transfer to oocytes and fusion with ES cells. The recent demonstration that, in mouse, nuclear transfer into zygotes can also be effective if the recipient cells are arrested in mitosis provides an exciting new avenue for this type of approach. Patient-specific pluripotent cells could potentially also be generated by the spontaneous reprogramming of bone marrow cells, spermatogonial cells, and parthenogenetic embryos. A third overall type of strategy arose from the demonstration that pluripotent stem (iPS) cells can be generated from mouse fibroblasts by the introduction of four transcription factors (Oct-3/4, Sox2, c-Myc, and KLF4). Recent work has underlined the potential of this strategy by improving the efficiency of the process and demonstrating that iPS cells can contribute to many different tissues in vivo, including the germline. Taken together, these studies underscore the crucial roles of transcription factors and chromatin remodeling in nuclear reprogramming.
A Case Study in Robot Exploration

This paper reports on a case study in autonomous robot exploration. In particular, we describe a working mobile manipulator robot that explores our laboratory to identify and collect cups. This system is a first step toward our research goal of developing an architecture for robust robot planning, control, adaptation, error monitoring, error recovery, and interaction with users. We describe the current system, lessons learned from our earlier failures, organizing principles employed in the current system, and limits to the current approach. We also discuss the implications of this work for a more robust robot control architecture which is presently under development.
Aggression and Violent Behavior The neurobiology of antisocial personality disorder : The quest for rehabilitation and treatment ☆

a r t i c l e i n f o Keywords: Antisocial personality disorder Psychopathy Amygdala Orbitofrontal cortex Monoamine oxidase SNAP proteins Psychopathy is perhaps one of the most misused terms in the American public, which is in no small part due to our obsession with those who have no conscience, and our boldness to try and profile others with this disorder. Here, I present how psychopathy is seen today, before discussing the classification of psychopathy. I also explore the neurological differences in the brains of those with psychopathy, before finally taking a look at genetic risk factors. I conclude by raising some questions about potential treatment.
Real-Time Eye Blink Detection using Facial Landmarks

A real-time algorithm to detect eye blinks in a video sequence from a standard camera is proposed. Recent landmark detectors, trained on in-thewild datasets exhibit excellent robustness against a head orientation with respect to a camera, varying illumination and facial expressions. We show that the landmarks are detected precisely enough to reliably estimate the level of the eye opening. The proposed algorithm therefore estimates the landmark positions, extracts a single scalar quantity – eye aspect ratio (EAR) – characterizing the eye opening in each frame. Finally, an SVM classifier detects eye blinks as a pattern of EAR values in a short temporal window. The simple algorithm outperforms the state-of-the-art results on two standard datasets.
Improving learning of computational thinking using creative thinking exercises in CS-1 computer science courses

Promoting computational thinking is one of the top priorities in CS education as well as in other STEM and non-STEM disciplines. Our innovative NSF-funded IC2Think project blends computational thinking with creative thinking so that students leverage their creative thinking skills to “unlock” their understanding of computational thinking. In Fall 2012, we deployed creative exercises designed to engage Epstein's creative competencies (Surrounding, Capturing, Challenging and Broadening) in introductory level CS courses targeting four different groups (CS, engineering, combined CS/physical sciences, and humanities majors). Students combined hands-on problem solving with guided analysis and reflection to connect their creative activities to CS topics such as conditionals and arrays and to real-world CS applications. Evaluation results (approximately 150 students) found that creative thinking exercise completion had a linear “dosage” effect. As students completed more exercises [0/1 - 4], they increased their long-term retention [a computational thinking test], F(3, 98) = 4.76, p =.004, partial Eta2 = .127 and course grades, F(3, 109) = 4.32, p =.006, partial Eta2 = .106. These findings support our belief that the addition of creative thinking exercises to CSCE courses improves the learning of computational knowledge and skills.
GreenMiner: a hardware based mining software repositories software energy consumption framework

Green Mining is a field of MSR that studies software energy consumption and relies on software performance data. Unfortunately there is a severe lack of publicly available software power use performance data. This means that green mining researchers must generate this data themselves by writing tests, building multiple revisions of a product, and then running these tests multiple times (10+) for each software revision while measuring power use. Then, they must aggregate these measurements to estimate the energy consumed by the tests for each software revision. This is time consuming and is made more difficult by the constraints of mobile devices and their OSes. In this paper we propose, implement, and demonstrate Green Miner: the first dedicated hardware mining software repositories testbed. The Green Miner physically measures the energy consumption of mobile devices (Android phones) and automates the testing of applications, and the reporting of measurements back to developers and researchers. The Green Miner has already produced valuable results for commercial Android application developers, and has been shown to replicate other power studies' results.
Knowledge-aware Multimodal Fashion Chatbot

Multimodal fashion chatbot provides a natural and informative way to fulfill customers' fashion needs. However, making it 'smart' in generating substantive responses remains a challenging problem. In this paper, we present a multimodal domain knowledge enriched fashion chatbot. It forms a taxonomy-based learning module to capture the fine-grained semantics in images and leverages an end-to-end neural conversational model to generate responses based on the conversation history, visual semantics, and domain knowledge. To avoid inconsistent dialogues, deep reinforcement learning method is used to further optimize the model.
SAM: the Swiss Army Menu

This article introduces the Swiss Army Menu (SAM), a radial menu that enables a very large number of functions on a single small tactile screen. The design of SAM relies on four different kinds of items, support for navigating in hierarchies of items and a control based on small thumb movements. SAM can thus offer a set of functions so large that it would typically have required a number of widgets that could not have been displayed in a single viewport at the same time.
A Switchable Iris Bandpass Filter Using RF MEMS Switchable Planar Resonators

A concept of using planar circuit resonance to disable 3D cavity resonance inside a rectangular waveguide filter is demonstrated. Switchable RF MEMS planar resonators are introduced inside the resonant cavities of a high Q-factor iris bandpass filter to turn the filter ON and OFF. The measurement confirms that this high Q-factor filter with insertion loss better than 0.1 dB can be converted to a bandstop filter with an isolation better than 30 dB for the same frequency and bandwidth.
Designing Business Models in the Era of Internet of Things - Towards a Reference Framework

The increasing pervasiveness of digital technologies, also refered to as "Internet of Things" (IoT), offers a wealth of business model opportunities, which often involve an ecosystem of partners. In this context, companies are required to look at business models beyond a firm-centric lens and respond to changed dynamics. However, extant literature has not yet provided actionable approaches for business models for IoT-driven environments. Our research therefore addresses the need for a business model framework that captures the specifics of IoT-driven ecosystems. Applying an iterative design science research approach, the present paper describes (a) the methodology, (b) the requirements, (c) the design and (d) the evaluation of a business model framework that enables researchers and practitioners to visualize, analyze and design business models in the IoT context in a structured and actionable way. The identified dimensions in the framework include the value network of collaborating partners (who); sources of value creation (where); benefits from collaboration (why). Evidence from action research and multiple case studies indicates that the framework is able to depict business models in IoT.
Adversarial Message Passing For Graphical Models

Bayesian inference on structured models typically relies on the ability to infer posterior distributions of underlying hidden variables. However, inference in implicit models or complex posterior distributions is hard. A popular tool for learning implicit models are generative adversarial networks (GANs) which learn parameters of generators by fooling discriminators. Typically, GANs are considered to be models themselves and are not understood in the context of inference. Current techniques rely on inefficient global discrimination of joint distributions to perform learning, or only consider discriminating a single output variable. We overcome these limitations by treating GANs as a basis for likelihood-free inference in generative models and generalize them to Bayesian posterior inference over factor graphs. We propose local learning rules based on message passing minimizing a global divergence criterion involving cooperating local adversaries used to sidestep explicit likelihood evaluations. This allows us to compose models and yields a unified inference and learning framework for adversarial learning. Our framework treats model specification and inference separately and facilitates richly structured models within the family of Directed Acyclic Graphs, including components such as intractable likelihoods, non-differentiable models, simulators and generally cumbersome models. A key result of our treatment is the insight that Bayesian inference on structured models can be performed only with sampling and discrimination when using nonparametric variational families, without access to explicit distributions. As a side-result, we discuss the link to likelihood maximization. These approaches hold promise to be useful in the toolbox of probabilistic modelers and enrich the gamut of current probabilistic programming applications.
AC losses in high frequency electrical machine windings formed from large section conductors

In the design of compact, power dense electrical machines found in automotive and aerospace applications it may be preferable to accept a degree of eddy current loss within the winding to realise a low cost high integrity winding. Commonly adopted techniques for analysing AC effects in electrical machine windings tend only to be applicable where the conductor dimensions are smaller than the skin depth and for ideally transposed windings. Further the temperature variation of AC losses is often overlooked. In this paper a more general approach is proposed based on an established analytical model and is equally applicable to windings where the conductor size greatly exceeds the skin depth. The model is validated against test measurements and FE analysis on representative single layer winding arrangements. A case study is used to illustrate the application of the proposed analytical method to the coupled electromagnetic and thermal modelling of a typical slot winding.
A Comparative Study on Shape Retrieval Using Fourier Descriptors with Different Shape Signatures

Shape is one of the most important features in Content Based Image Retrieval (CBIR). Many shape representations and retrieval methods exists. However, most of those methods either do not well represent shape or are difficult to do normalization (making matching hard). Among them, methods based Fourier descriptors (FD) achieve both well representation and well normalization. Different shape signatures have been exploited to derive FDs, however, FDs derived from different signatures can have significant different effect on the result of retrieval. In this paper, we build a Java retrieval framework to compare shape retrieval using FDs derived from different signatures. Common issues and techniques for shape representation and normalization are also analyzed in the paper. Data is given to show the retrieval result.
Mechanisms of visual attention in the human cortex.

A typical scene contains many different objects that, because of the limited processing capacity of the visual system, compete for neural representation. The competition among multiple objects in visual cortex can be biased by both bottom-up sensory-driven mechanisms and top-down influences, such as selective attention. Functional brain imaging studies reveal that, both in the absence and in the presence of visual stimulation, biasing signals due to selective attention can modulate neural activity in visual cortex in several ways. Although the competition among stimuli for representation is ultimately resolved within visual cortex, the source of top-down biasing signals derives from a network of areas in frontal and parietal cortex.
Lateral Inhibition-Inspired Convolutional Neural Network for Visual Attention and Saliency Detection

Lateral inhibition in top-down feedback is widely existing in visual neurobiology, but such an important mechanism has not be well explored yet in computer vision. In our recent research, we find that modeling lateral inhibition in convolutional neural network (LICNN) is very useful for visual attention and saliency detection. In this paper, we propose to formulate lateral inhibition inspired by the related studies from neurobiology, and embed it into the top-down gradient computation of a general CNN for classification, i.e. only category-level information is used. After this operation (only conducted once), the network has the ability to generate accurate category-specific attention maps. Further, we apply LICNN for weakly-supervised salient object detection. Extensive experimental studies on a set of databases, e.g., ECSSD, HKU-IS, PASCAL-S and DUT-OMRON, demonstrate the great advantage of LICNN which achieves the state-ofthe-art performance. It is especially impressive that LICNN with only category-level supervised information even outperforms some recent methods with segmentation-level super-
A Toolbox of Potato Genetic and Genomic Resources

Access to genetic and genomic resources can greatly facilitate biological understanding of plant species leading to improved crop varieties. While model plant species such as Arabidopsis have had nearly two decades of genetic and genomic resource development, many major crop species have seen limited development of these resources due to the large, complex nature of their genomes. Cultivated potato is among the ranks of crop species that, despite substantial worldwide acreage, have seen limited genetic and genomic tool development. As technologies advance, this paradigm is shifting and a number of tools are being developed for important crop species such as potato. This review article highlights numerous tools that have been developed for the potato community with a specific focus on the reference de novo genome assembly and annotation, genetic markers, transcriptomics resources, and newly emerging resources that extend beyond a single reference individual. El acceso a los recursos genéticos y genómicos puede facilitar en gran medida el entendimiento biológico de las especies de plantas, lo que conduce a variedades mejoradas de cultivos. Mientras que el modelo de las especies de plantas como Arabidopsis ha tenido cerca de dos décadas de desarrollo de recursos genéticos y genómicos, muchas especies de cultivos principales han visto desarrollo limitado de estos recursos debido a la naturaleza grande, compleja, de sus genomios. La papa cultivada está ubicada entre las especies de plantas que a pesar de su superficie substancial mundial, ha visto limitado el desarrollo de las herramientas genéticas y genómicas. A medida que avanzan las tecnologías, este paradigma está girando y se han estado desarrollando un número de herramientas para especies importantes de cultivo tales como la papa. Este artículo de revisión resalta las numerosas herramientas que se han desarrollado para la comunidad de la papa con un enfoque específico en la referencia de ensamblaje y registro de genomio de novo, marcadores genéticos, recursos transcriptómicos, y nuevas fuentes emergentes que se extienden más allá de la referencia de un único individuo.
Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension

Machine reading comprehension (MRC) has recently attracted attention in the fields of natural language processing and machine learning. One of the problematic presumptions with current MRC technologies is that each question is assumed to be answerable by looking at a given text passage. However, to realize human-like language comprehension ability, a machine should also be able to distinguish not-answerable questions (NAQs) from answerable questions. To develop this functionality, a dataset incorporating hard-to-detect NAQs is vital; however, its manual construction would be expensive. This paper proposes a dataset creation method that alters an existing MRC dataset, the Stanford Question Answering Dataset, and describes the resulting dataset. The value of this dataset is likely to increase if each NAQ in the dataset is properly classified with the difficulty of identifying it as an NAQ. This difficulty level would allow researchers to evaluate a machine’s NAQ detection performance more precisely. Therefore, we propose a method for automatically assigning difficulty level labels, which basically measures the similarity between a question and the target text passage. Our NAQ detection experiments demonstrate that the resulting dataset, having difficulty level annotations, is valid and potentially useful in the development of advanced MRC models.
Active sample selection in scalar fields exhibiting non-stationary noise with parametric heteroscedastic Gaussian process regression

This paper considers the modelling of scalar fields exhibiting non-stationary noise in the context of Gaussian Process (GP) regression. We show how a Heteroscedastic GP produces more accurate predictions of the variance of a process of this type compared to the standard Homoscedastic model. We present a parametric model for the noise process and derive analytical solutions to the Log Marginal Likelihood of the data and its gradients with respect to Hyper Parameters of the kernel and the noise process. We compare our parametric model to one which estimates a full GP for the noise and show analogous predictive performance with a model which has greater computational efficiency and is less complex to implement. We also discuss active sample selection in this framework and show through the numerical simulation of an arrested bathymetric front in an estuary, the superiority of using Mutual Information to Fisher Information, Entropy or Random sampling in terms of errors in the first two moments of the predicted distributions.
Database security threats: A survey study

Databases are the repositories of the most important and expensive information in the enterprise. With the increase in access to data stored in databases, the frequency of attacks against those databases has also increased. A database threat refers to an object, person or other entity that represents a risk of loss or corruption of sensitive data to an asset. Today, in many business organizations, the databases and data assets are poorly protected. Databases should be secured more than any systems in the organization. To secure a database environment, many database security models need to be developed. The purpose of the paper is to highlight and threat types and their impacts on sensitive data, and presents different security models. The assumption underlying this study is that by understanding the weaknesses and the threats facing databases, database administrators can then begin to create a security plan to better protect their databases.