nils-herrmann/clean_scirepeval huggingface dataset:
Capsule Networks for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNNbased network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral–spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral–spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton’s capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral– spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral–spatial capsules, which are able to learn HSI spectral–spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted Manuscript received June 9, 2018; revised August 22, 2018; accepted September 19, 2018. This work was supported in part by the Ministerio de Educación (Resolución de 26 de diciembre de 2014 y de 19 de noviembre de 2015, de la Secretaría de Estado de Educación, Formación Profesional y Universidades, por la que se convocan ayudas para la formación de profesorado universitario, de los subprogramas de Formación y de Movilidad incluidos en el Programa Estatal de Promoción del Talento y su Empleabilidad, and en el marco del Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016), in part by the Junta de Extremadura (decreto 297/2014, ayudas para la realización de actividades de investigación y desarrollo tecnológico, and de divulgación y de transferencia de conocimiento por los Grupos de Investigación de Extremadura) under Grant GR15005, in part by the Generalitat Valenciana under Contract APOSTD/2017/007, in part by the Spanish Ministry of Economy under Project ESP2016-79503-C2-2P and Project TIN2015-63646-C5-5-R, in part by the National Natural Science Foundation of China under Grant 61771496, in part by the National Key Research and Development Program of China under Grant 2017YFB0502900, and in part by the Guangdong Provincial Natural Science Foundation under Grant 2016A030313254.
40-nm Embedded Split-Gate MONOS (SG-MONOS) Flash Macros for Automotive With 160-MHz Random Access for Code and Endurance Over 10 M Cycles for Data at the Junction Temperature of 170  $^{\circ}$C

First-ever 40-nm embedded split-gate MONOS (SG-MONOS) flash macros for automotive micro-controller unit (MCU) have been successfully developed. A SG-MONOS cell realizes high performance with low power consumption and intrinsically high data reliability thanks to the combination of split-gate and charge-trapping structure. In addition, newly developed circuit techniques greatly enhance the advantages of SG-MONOS cells and enable fast and reliable operations even at the junction temperature (Tj) of 170°C with small peripheral circuit area; 1) a sense amplifier with digital offset cancellation (SA-DOC) provides fast read operation over 160 MHz; 2) adaptable program current control scheme (APCCS) and intelligent erase scheme (IES) significantly decrease the program and erase time, which also results in improving the memory cells' reliability; and, 3) 3-D stacked unit capacitors achieve area-efficient charge pump. Two types of embedded flash (eFlash) macros with these technologies, code macro of 2 MB and data macro of 64 KB, were fabricated in a 40-nm eFlash process. The code macro demonstrates the capability of 160-MHz random read operation at Tj= 170°C, reaching 5.1 GB/s read throughput by simultaneous 256 bits read-out from two code macros. The data macro achieves the program/erase endurance over 10 million cycles at Tj= 170°C without any software-assisted techniques.
Personality and compatibility: a prospective analysis of marital stability and marital satisfaction.

The antecedents of marital stability (divorce or remaining married) and marital satisfaction (within the group that remains married) were investigated with a panel of 300 couples who were followed from their engagements in the 1930s until 1980. Twenty-two of the couples broke their engagements; of the 278 couples who married, 50 got divorced at some time between 1935 and 1980. Personality characteristics (measured by acquaintance ratings made in the 1930s) were important predictors of both marital stability and marital satisfaction. The three aspects of personality most strongly related to marital outcome were the neuroticism of the husband, the neuroticism of the wife, and the impulse control of the husband. In combination, the 17 major antecedent variables were moderately predictive of a criterion variable composed of both marital stability and marital satisfaction (R = .49). The three major aspects of personality accounted for more than half of the predictable variance. The remaining variance was accounted for by attitudinal, social-environment, and sexual history variables.
Cubical Type Theory: A Constructive Interpretation of the Univalence Axiom

This paper presents a type theory in which it is possible to directly manipulate n-dimensional cubes (points, lines, squares, cubes, etc.) based on an interpretation of dependent type theory in a cubical set model. This enables new ways to reason about identity types, for instance, function extensionality is directly provable in the system. Further, Voevodsky’s univalence axiom is provable in this system. We also explain an extension with some higher inductive types like the circle and propositional truncation. Finally we provide semantics for this cubical type theory in a constructive meta-theory.
Learning state representation for deep actor-critic control

Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). One advantage of DNNs is that they can cope with large input dimensions. Instead of relying on feature engineering to lower the input dimension, DNNs can extract the features from raw observations. The drawback of this end-to-end learning is that it usually requires a large amount of data, which for real-world control applications is not always available. In this paper, a new algorithm, Model Learning Deep Deterministic Policy Gradient (ML-DDPG), is proposed that combines RL with state representation learning, i.e., learning a mapping from an input vector to a state before solving the RL task. The ML-DDPG algorithm uses a concept we call predictive priors to learn a model network which is subsequently used to pre-train the first layer of the actor and critic networks. Simulation results show that the ML-DDPG can learn reasonable continuous control policies from high-dimensional observations that contain also task-irrelevant information. Furthermore, in some cases, this approach significantly improves the final performance in comparison to end-to-end learning.
Differentially-Fed Millimeter-Wave Yagi-Uda Antennas With Folded Dipole Feed

Differentially-driven Yagi-Uda antennas have been developed for millimeter-wave applications. The antennas are built on both sides of a Teflon substrate (¿r = 2.2). A key design aspect is the use of a folded dipole feed which increases the input impedance from 18 ¿ (standard dipole case) to 150 ¿ and is compatible with coplanar strip lines. A 7-element design results in a gain of 8-10 dB at 22-26 GHz and a cross-polarization level of < -22 dB. The differentially-fed Yagi-Uda antenna with folded dipole results in high radiation efficiency ( > 90% at 22-26 GHz) and is suitable for mm-wave point-to-point links communication systems.
MongoDB performance analysis: A comparative study between stand-alone and sharded cluster deployments with open data from Brazilian Bolsa familia program

In the "Big data Era", new trends like "Internet of Things", Social Networks and Open Data have contributed to a real explosion in data availability. To face these phenomena, the so-called NoSQL Databases arise, MongoDB being an important representative of this category of solutions. The present work aims to evaluate performance aspects of MongoDB when applied to the analysis of large volumes of open data related to Bolsa Família, and to evaluate the gains derived from the adoption of the cluster architecture versus the standalone mode. The results showed significant performance gains in the execution of aggregate queries with the cluster adoption and that the performance loss during the load can be minimized with workarounds.
Heliomote: enabling long-lived sensor networks through solar energy harvesting

The crucial need for long-lived and autonomous operation has elevated power and energy consumption to primary optimization metrics during wireless sensor network design. While most work in the field of power management and low-power design has focused on optimizing the energy consumer (i.e., the sensor node, including its hardware, software, applications, and network protocols), very little work has targeted the energy supply system itself. A practical approach to alleviating the problem of limited battery resources in sensor nodes is the use of environmental energy harvesting. Solar energy harvesting, in particular, holds significant promise since photovoltaic conversion techniques are now mature enough to permit the development of cheap and small, yet reasonably efficient, solar panels. Our demonstration showcases our recent research in designing solar energy harvesting systems, as well as harvesting aware performance scaling algorithms and network protocols .
Microwave Class-F and Inverse Class-F Power Amplifiers Designs using GaN Technology and GaAs pHEMT

This paper presents the designs and results of two high-efficiency harmonics-tuned microwave power amplifiers (PA): the first one is a 2 GHz class-F PA in monolithic integrated circuit (MMIC) by using GaN HEMT technology, and the other one is a 2.45-GHz inverse class-F PA using packaged GaAs pHEMT devices with PCB technology. In the class-F MMIC PA, field-plated GaN HEMT device is used for high-power performance. The 2.0-GHz class-F MMIC PA achieves a PAE of 50%, 38 dBm output power, and 6.2 W/mm power density. The inverse class-F PA at 2.45 GHz achieves 22.6 dBm output power and 73% PAE at 3 dB compression, and has very low cost
Small-footprint keyword spotting using deep neural networks

Our application requires a keyword spotting system with a small memory footprint, low computational cost, and high precision. To meet these requirements, we propose a simple approach based on deep neural networks. A deep neural network is trained to directly predict the keyword(s) or subword units of the keyword(s) followed by a posterior handling method producing a final confidence score. Keyword recognition results achieve 45% relative improvement with respect to a competitive Hidden Markov Model-based system, while performance in the presence of babble noise shows 39% relative improvement.
Analyze your Scratch projects with Dr . Scratch and assess your Computational Thinking skills

In this paper we present the feature of Dr. Scratch that allows to automatically assessing the Computational Thinking skills of Scratch projects. The paper reviews similar initiatives, like Hairball, and investigates the literature with proposals for assessment of Scratch projects that we have studied and remixed in order to develop the Computational Thinking analysis. Then it introduces the various aspects that Dr. Scratch takes into consideration to compute a Computational Thinking score for a Scratch project and presents some preliminary findings of the analysis of over 100 investigated Scratch projects. Finally, future directions and limitations are presented and discussed.
DataPlay: interactive tweaking and example-driven correction of graphical database queries

Writing complex queries in SQL is a challenge for users. Prior work has developed several techniques to ease query specification but none of these techniques are applicable to a particularly difficult class of queries: quantified queries. Our hypothesis is that users prefer to specify quantified queries interactively by trial-and-error. We identify two impediments to this form of interactive trial-and-error query specification in SQL: (i) changing quantifiers often requires global syntactical query restructuring, and (ii) the absence of non-answers from SQL's results makes verifying query correctness difficult. We remedy these issues with DataPlay, a query tool with an underlying graphical query language, a unique data model and a graphical interface. DataPlay provides two interaction features that support trial-and-error query specification. First, DataPlay allows users to directly manipulate a graphical query by changing quantifiers and modifying dependencies between constraints. Users receive real-time feedback in the form of updated answers and non-answers. Second, DataPlay can auto-correct a user's query, based on user feedback about which tuples to keep or drop from the answers and non-answers. We evaluated the effectiveness of each interaction feature with a user study and we found that direct query manipulation is more effective than auto-correction for simple queries but auto-correction is more effective than direct query manipulation for more complex queries.
Answering Top-k Queries with Multi-Dimensional Selections: The Ranking Cube Approach

Observed in many real applications, a top-k query often consists of two components to reflect a user's preference: a selection condition and a ranking function. A user may not only propose ad hoc ranking functions, but also use different interesting subsets of the data. In many cases, a user may want to have a thorough study of the data by initiating a multi-dimensional analysis of the top-k query results. Previous work on top-k query processing mainly focuses on optimizing data access according to the ranking function only. The problem of efficient answering top-k queries with multi-dimensional selections has not been well addressed yet.This paper proposes a new computational model, called ranking cube, for efficient answering top-k queries with multi-dimensional selections. We define a rank-aware measure for the cube, capturing our goal of responding to multi-dimensional ranking analysis. Based on the ranking cube, an efficient query algorithm is developed which progressively retrieves data blocks until the top-k results are found. The curse of dimensionality is a well-known challenge for the data cube and we cope with this difficulty by introducing a new technique of ranking fragments. Our experiments on Microsoft's SQL Server 2005 show that our proposed approaches have significant improvement over the previous methods.
Customer Oriented Business Process Improvement Methodlogy for Public Sector Organizations

The globalization of businesses and the germination of customer satisfaction importance induced a profound transfer from transitional to relational marketing, together with the introduction of relationship marketing concept. To surmount this transition, business process improvement is acknowledged as one of the prevailing methods to conduct changes in organizations. Concerning the achievement of process superiority and the superlative services to customers, practitioners are advised to benefit the customeroriented BPI methodologies. While they have their roots in private sectors and manufacturing organizations, recently they are widely used in public sectors as well. Since the essential essence of BPI in public sectors is enhancing the satisfaction of customers, the existing approaches mainly focus on cost reduction and policy-based process introduction. Sifting through organizations’ processes, diagnosing befitting tools, and evaluating techniques can be a daunting task. The available literature on business process improvement in public sectors is full with recommendations on how to do improvements and the methodologies, but what is lacking is a customer-oriented BPI guideline together with necessary tools and technologies for each step. Following the mentioned gap, this paper proposes a customer-oriented methodology to conduct process improvement smoothly in public sectors besides the necessary tools and techniques for each phase.
Ordinal Regression Methods: Survey and Experimental Study

Ordinal regression problems are those machine learning problems where the objective is to classify patterns using a categorical scale which shows a natural order between the labels. Many real-world applications present this labelling structure and that has increased the number of methods and algorithms developed over the last years in this field. Although ordinal regression can be faced using standard nominal classification techniques, there are several algorithms which can specifically benefit from the ordering information. Therefore, this paper is aimed at reviewing the state of the art on these techniques and proposing a taxonomy based on how the models are constructed to take the order into account. Furthermore, a thorough experimental study is proposed to check if the use of the order information improves the performance of the models obtained, considering some of the approaches within the taxonomy. The results confirm that ordering information benefits ordinal models improving their accuracy and the closeness of the predictions to actual targets in the ordinal scale.
Trust and mobile commerce in North America

Mobile Commerce (mCommerce) activities include the act of shopping and buying on mobile devices, along with the more recent emergence of mobile payment systems. Within North America, mCommerce activities have become increasingly popular and will likely continue on this upwards trend as mobile devices further proliferate markets. Historically, one common issue with the adoption and use of eCommerce systems (e.g., commerce activities on personal computers) is trust. Yet we know little of how trust and other social factors may affect mCommerce usageda new genre of commerce activities that explicitly occur on mobile devices. To help address this problem, we have conducted two studies that explore users' mCommerce activities. The first is a diary and interview study of mCommerce shoppers who have already adopted the technology and shop on their mobile devices regularly. Our study explores typical mCommerce routines and behaviors along with issues of trust, given its long-term concern for eCommerce. The second is a diary and interview study of new and existing users of mobile payment services in North America. Participants used a variety of services, including Google Wallet, Amazon Payments, LevelUp, Square and company apps geared towards payments (e.g., Starbucks). Our results show that when it comes to shopping on mobile devices, people have few trust concerns. Yet when mobile devices are used for payments within physical stores, trust issues emerge along with prepurchase anxiety and mental model challenges. We discuss these results and show the value in adapting and developing new trust mechanisms for mCommerce.
Visual Semiotics & Uncertainty Visualization: An Empirical Study

This paper presents two linked empirical studies focused on uncertainty visualization. The experiments are framed from two conceptual perspectives. First, a typology of uncertainty is used to delineate kinds of uncertainty matched with space, time, and attribute components of data. Second, concepts from visual semiotics are applied to characterize the kind of visual signification that is appropriate for representing those different categories of uncertainty. This framework guided the two experiments reported here. The first addresses representation intuitiveness, considering both visual variables and iconicity of representation. The second addresses relative performance of the most intuitive abstract and iconic representations of uncertainty on a map reading task. Combined results suggest initial guidelines for representing uncertainty and discussion focuses on practical applicability of results. Index Terms — uncertainty visualization, uncertainty categories, visual variables, semiotics.
AskHERMES: An online question answering system for complex clinical questions

Clinical questions are often long and complex and take many forms. We have built a clinical question answering system named AskHERMES to perform robust semantic analysis on complex clinical questions and output question-focused extractive summaries as answers.



This paper describes the system architecture and a preliminary evaluation of AskHERMES, which implements innovative approaches in question analysis, summarization, and answer presentation. Five types of resources were indexed in this system: MEDLINE abstracts, PubMed Central full-text articles, eMedicine documents, clinical guidelines and Wikipedia articles.



We compared the AskHERMES system with Google (Google and Google Scholar) and UpToDate and asked physicians to score the three systems by ease of use, quality of answer, time spent, and overall performance.



AskHERMES allows physicians to enter a question in a natural way with minimal query formulation and allows physicians to efficiently navigate among all the answer sentences to quickly meet their information needs. In contrast, physicians need to formulate queries to search for information in Google and UpToDate. The development of the AskHERMES system is still at an early stage, and the knowledge resource is limited compared with Google or UpToDate. Nevertheless, the evaluation results show that AskHERMES' performance is comparable to the other systems. In particular, when answering complex clinical questions, it demonstrates the potential to outperform both Google and UpToDate systems.



AskHERMES, available at http://www.AskHERMES.org, has the potential to help physicians practice evidence-based medicine and improve the quality of patient care.
Classification of text documents supervised by domain ontologies

The research objective is to establish an approach for supporting the classification of text documents referring to a specified domain. The focus is on the preliminary topic assignment to the documents used for training the model. The method implements domain ontology as background knowledge. The idea consists in extracting the preliminary topics for training the classifier by means of unsupervised machine learning on a text corpus and further alignment of the document vectors to concepts of the ontology. The results obtained by classification of new documents supervised by e-governance ontology with several machine learning algorithms showed sufficient match of their content to the ontology concepts. A conclusion is drawn that the approach can support the automatic extraction of documents relevant to any domain described by ontology.