nils-herrmann/clean_scirepeval huggingface dataset:
Online Learning of a Memory for Learning Rates

The promise of learning to learn for robotics rests on the hope that by extracting some information about the learning process itself we can speed up subsequent similar learning tasks. Here, we introduce a computationally efficient online meta-learning algorithm that builds and optimizes a memory model of the optimal learning rate landscape from previously observed gradient behaviors. While performing task specific optimization, this memory of learning rates predicts how to scale currently observed gradients. After applying the gradient scaling our meta-learner updates its internal memory based on the observed effect its prediction had. Our meta-learner can be combined with any gradient-based optimizer, learns on the fly and can be transferred to new optimization tasks. In our evaluations we show that our meta-learning algorithm speeds up learning of MNIST classification and a variety of learning control tasks, either in batch or online learning settings.
Deep Joint Image Filtering

Joint image filters can leverage the guidance image as a prior and transfer the structural details from the guidance image to the target image for suppressing noise or enhancing spatial resolution. Existing methods rely on various kinds of explicit filter construction or handdesigned objective functions. It is thus difficult to understand, improve, and accelerate them in a coherent framework. In this paper, we propose a learning-based approach to construct a joint filter based on Convolutional Neural Networks. In contrast to existing methods that consider only the guidance image, our method can selectively transfer salient structures that are consistent in both guidance and target images. We show that the model trained on a certain type of data, e.g., RGB and depth images, generalizes well for other modalities, e.g., Flash/Non-Flash and RGB/NIR images. We validate the effectiveness of the proposed joint filter through extensive comparisons with state-of-the-art methods.
Aspect Extraction from Product Reviews Using Category Hierarchy Information

Aspect extraction is a task to abstract the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those from parent categories. Hence, we propose an LDA-based generative topic model inducing the two-layer categorical information (CAT-LDA), to balance the aspects of both a parent category and its child categories. Our hypothesis is that child categories inherit aspects from parent categories, controlled by the hierarchy between them. Experimental results on 5 categories of Amazon.com products show that both common aspects of parent category and the individual aspects of subcategories can be extracted to align well with the common sense. We further evaluate the manually extracted aspects of 16 products, resulting in an average hit rate of 79.10%.
The effects of online social media on tourism websites

Web 2.0 and online social networking websites heavily affect today most of the online activities and their effect on tourism is obviously rather important. This paper aims at verifying the impact that online social networks (OSN) have on the popularity of tourism websites. Two OSNs have been considered: Facebook and Twitter. The pattern of visits to a sample of Italian tourism websites was analysed and the relationship between the total visits and those having the OSNs as referrals were measured. The analysis shows a clear correlation and confirms the starting hypothesis. Consequences and implications of these outcomes are discussed.
Features of Similarity

The metric and dimensional assumptions that underlie the geometric representation of similarity are questioned on both theoretical and empirical grounds. A new set-theoretical approach to similarity is developed in which objects are represented as collections of features, and similarity is described as a featurematching process. Specifically, a set of qualitative assumptions is shown to imply the contrast model, which expresses the similarity between objects as a linear combination of the measures of their common and distinctive features. Several predictions of the contrast model are tested in studies of similarity with both semantic and perceptual stimuli. The model is used to uncover, analyze, and explain a variety of empirical phenomena such as the role of common and distinctive features, the relations between judgments of similarity and difference, the presence of asymmetric similarities, and the effects of context on judgments of similarity. The contrast model generalizes standard representations of similarity data in terms of clusters and trees. It is also used to analyze the relations of prototypicality and family resemblance.
Text Classification Using Support Vector Machine with Mixture of Kernel

Recent studies have revealed that emerging modern machine learning techniques are advantageous to statistical models for text classification, such as SVM. In this study, we discuss the applications of the support vector machine with mixture of kernel (SVM-MK) to design a text classification system. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. More important, it is a transparent model and the optimal feature subset can be obtained automatically. A real Chinese corpus from Fudan University is used to demonstrate the good performance of the SVMMK.
HDPsent : Incorporation of Latent Dirichlet Allocation for Aspect-Level Sentiment into Hierarchical Dirichlet Process-Based Topic Models

We address the problem of combining topic modeling with sentiment analysis within a generative model. While the Hierarchical Dirichlet Process (HDP) has seen recent widespread use for topic modeling alone, most current hybrid models for concurrent inference of sentiments and topics are not based on HDP. In this paper, we present HDPsent, a new model which incorporates Latent Dirichlet Allocation (LDA)-based sentiment learning into an HDP topic modeling framework. This model preserves the benefits of nonparametric Bayesian models for topic learning, while simultaneously learning latent sentiment aspects. It automatically generates different word distributions for each single sentiment polarity within each topic that has been learned. We present results using existing corpora consisting of multi-aspect hotel and restaurant reviews, and discuss ramifications and applications of such a model for product reviews that are intrinsically hierarchical.
Innovation management techniques and tools : a review from theory and practice

Knowledge is considered to be an economic driver in today's economy. It has become a commodity, a resource that can be packed and transferred. The objective of this paper is to provide a comprehensive review of the scope, trends and major actors (firms, organizations, government, consultants, academia, etc.) in the development and use of methods to manage innovation in a knowledge-driven economy. The paper identifies the main innovation management techniques (IMTs) aiming at the improvement of firm competitiveness by means of knowledge management. It will specifically focus on those IMTs for which knowledge is a relevant part of the innovation process. The research study, based on a survey at the European level, concludes that a knowledge-driven economy affects the innovation process and approach. The traditional idea that innovation is based on research (technology-push theory) and interaction between firms and other actors has been replaced by the current social network theory of innovation, where knowledge plays a crucial role in fostering innovation. Simultaneously, organizations in both public and private sectors have launched initiatives to develop methodologies and tools to support business innovation management. Higher education establishments, business schools and consulting companies are developing innovative and adequate methodologies and tools, while public authorities are designing and setting up education and training schemes aimed at disseminating best practices among all kinds of businesses.
A Wideband-to-Narrowband Tunable Antenna Using A Reconfigurable Filter

A novel microstrip circular disc monopole antenna with a reconfigurable 10-dB impedance bandwidth is proposed in this communication for cognitive radios (CRs). The antenna is fed by a microstrip line integrated with a bandpass filter based on a three-line coupled resonator (TLCR). The reconfiguration of the filter enables the monopole antenna to operate at either a wideband state or a narrowband state by using a PIN diode. For the narrowband state, two varactor diodes are employed to change the antenna operating frequency from 3.9 to 4.82 GHz continuously, which is different from previous work using PIN diodes to realize a discrete tuning. Similar radiation patterns with low cross-polarization levels are achieved for the two operating states. Measured results on tuning range, radiation patterns, and realized gains are provided, which show good agreement with numerical simulations.
Approaches to Text Mining Arguments from Legal Cases

This paper describes recent approaches using text-mining to automatically profile and extract arguments from legal cases. We outline some of the background context and motivations. We then turn to consider issues related to the construction and composition of a corpora of legal cases. We show how a Context-Free Grammar can be used to extract arguments, and how ontologies and Natural Language Processing can identify complex information such as case factors and participant roles. Together the results bring us closer to automatic identification of legal arguments.
The e-puck , a Robot Designed for Education in Engineering

Mobile robots have the potential to become the ideal tool to teach a broad range of engineering disciplines. Indeed, mobile robots are getting increasingly complex and accessible. They embed elements from diverse fields such as mechanics, digital electronics, automatic control, signal processing, embedded programming, and energy management. Moreover, they are attractive for students which increases their motivation to learn. However, the requirements of an effective education tool bring new constraints to robotics. This article presents the e-puck robot design, which specifically targets engineering education at university level. Thanks to its particular design, the e-puck can be used in a large spectrum of teaching activities, not strictly related to robotics. Through a systematic evaluation by the students, we show that the epuck fits this purpose and is appreciated by 90 percent of a large sample of students.
GPS Error Correction With Pseudorange Evaluation Using Three-Dimensional Maps

The accuracy of the positions of a pedestrian is very important and useful information for the statistics, advertisement, and safety of different applications. Although the GPS chip in a smartphone is currently the most convenient device to obtain the positions, it still suffers from the effect of multipath and nonline-of-sight propagation in urban canyons. These reflections could greatly degrade the performance of a GPS receiver. This paper describes an approach to estimate a pedestrian position by the aid of a 3-D map and a ray-tracing method. The proposed approach first distributes the numbers of position candidates around a reference position. The weighting of the position candidates is evaluated based on the similarity between the simulated pseudorange and the observed pseudorange. Simulated pseudoranges are calculated using a ray-tracing simulation and a 3-D map. Finally, the proposed method was verified through field experiments in an urban canyon in Tokyo. According to the results, the proposed approach successfully estimates the reflection and direct paths so that the estimate appears very close to the ground truth, whereas the result of a commercial GPS receiver is far from the ground truth. The results show that the proposed method has a smaller error distance than the conventional method.
Design of CMOS instrumentation amplifier using gm/ID methodology

This paper describes the design of an indirect current feedback Instrumentation Amplifier (IA). Transistor sizing plays a major role in achieving the desired gain, the Common Mode Rejection Ratio (CMRR) and the bandwidth of the Instrumentation Amplifier. A gm/ID based design methodology is employed to design the functional blocks of the IA. It links the design variables of each functional block to its target specifications and is used to develop design charts that are used to accurately size the transistors. The IA thus designed achieves a voltage gain of 31dB with a bandwidth 1.2MHz and a CMRR of 87dB at 1MHz. The circuit design is carried out using 0.18μm CMOS process.
Relative Camera Pose Estimation Using Convolutional Neural Networks

This paper presents a convolutional neural network based approach for estimating the relative pose between two cameras. The proposed network takes RGB images from both cameras as input and directly produces the relative rotation and translation as output. The system is trained in an end-to-end manner utilising transfer learning from a large scale classification dataset. The introduced approach is compared with widely used local feature based methods (SURF, ORB) and the results indicate a clear improvement over the baseline. In addition, a variant of the proposed architecture containing a spatial pyramid pooling (SPP) layer is evaluated and shown to further improve the performance.
Positive psychology in clinical practice.

Positive psychology is the scientific study of positive experiences and positive individual traits, and the institutions that facilitate their development. A field concerned with well-being and optimal functioning, positive psychology aims to broaden the focus of clinical psychology beyond suffering and its direct alleviation. Our proposed conceptual framework parses happiness into three domains: pleasure, engagement, and meaning. For each of these constructs, there are now valid and practical assessment tools appropriate for the clinical setting. Additionally, mounting evidence demonstrates the efficacy and effectiveness of positive interventions aimed at cultivating pleasure, engagement, and meaning. We contend that positive interventions are justifiable in their own right. Positive interventions may also usefully supplement direct attempts to prevent and treat psychopathology and, indeed, may covertly be a central component of good psychotherapy as it is done now.
Gait Analysis Using Wearable Sensors

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications.
The Development of the Turtle Carapace " ( 1989 ) , by Ann

Campbell Burke examines the development and evolution  of vertebrates, in particular, turtles . Her Harvard University [7] experiments, described in "Development of the Turtle Carapace  Implications for the Evolution of a Novel Bauplan," were published in 1989. Burke used molecular techniques to investigate the developmental mechanisms responsible for the formation of the turtle shell. Burke's work with turtle embryos has provided empirical evidence for the hypothesis that the evolutionary origins of turtle morphology  depend on changes in the embryonic and developmental mechanisms underpinning shell production.
The Equivariant Tamagawa Number Conjecture : A survey

We give a survey of the equivariant Tamagawa number (a.k.a. Bloch-Kato) conjecture with particular emphasis on proven cases. The only new result is a proof of the 2-primary part of this conjecture for Tate-motives over abelian fields. This article is an expanded version of a survey talk given at the conference on Stark’s conjecture, Johns Hopkins University, Baltimore, August 5-9, 2002. We have tried to retain the succinctness of the talk when covering generalities but have considerably expanded the section on examples. Most of the following recapitulates well known material due to many people. Section 3 is joint work with D. Burns (for which , , ,  are the main references). In section 5.1 we have given a detailed proof of the main result which also covers the prime l = 2 (unavailable in the literature so far).
Semantic annotation of soccer videos: automatic highlights identification

Automatic semantic annotation of video streams allows both to extract significant clips for production logging and to index video streams for posterity logging. Automatic annotation for production logging is particularly demanding, as it is applied to non-edited video streams and must rely only on visual information. Moreover, annotation must be computed in quasi real-time. In this paper, we present a system that performs automatic annotation of the principal highlights in soccer video, suited for both production and posterity logging. The knowledge of the soccer domain is encoded into a set of finite state machines, each of which models a specific highlight. Highlight detection exploits visual cues that are estimated from the video stream, and particularly, ball motion, the currently framed playfield zone, players’ positions and colors of players’ uniforms. The highlight models are checked against the current observations, using a model checking algorithm. The system has been developed within the EU ASSAVID project.
How mangrove forests adjust to rising sea level.

Mangroves are among the most well described and widely studied wetland communities in the world. The greatest threats to mangrove persistence are deforestation and other anthropogenic disturbances that can compromise habitat stability and resilience to sea-level rise. To persist, mangrove ecosystems must adjust to rising sea level by building vertically or become submerged. Mangroves may directly or indirectly influence soil accretion processes through the production and accumulation of organic matter, as well as the trapping and retention of mineral sediment. In this review, we provide a general overview of research on mangrove elevation dynamics, emphasizing the role of the vegetation in maintaining soil surface elevations (i.e. position of the soil surface in the vertical plane). We summarize the primary ways in which mangroves may influence sediment accretion and vertical land development, for example, through root contributions to soil volume and upward expansion of the soil surface. We also examine how hydrological, geomorphological and climatic processes may interact with plant processes to influence mangrove capacity to keep pace with rising sea level. We draw on a variety of studies to describe the important, and often under-appreciated, role that plants play in shaping the trajectory of an ecosystem undergoing change.
Privacy-Preserving Fraud Detection Across Multiple Phone Record Databases

Subscription fraud, i.e., customers signing up to a service with no intent to pay, causes significant losses in the telecommunication industry. Telecom operators have developed strategies to identify those fraudsters, but fraudsters tend to migrate from one carrier to another. Data sharing between telecoms would increase fraud detection rates, but phone records are protected by law and telecom operators might be reluctant to share information about fraudsters because they see it as giving a competitive advantage. We propose several protocols to enable fraud detection across multiple databases without revealing additional information. We also propose a model to generate phone records, with which we evaluate how the choice of parameters affects detection performance. We show feasibility, performance and costs with implementations of our protocols.
The multiple meanings of open government data: Understanding different stakeholders and their perspectives

The Multiple Meanings of Open Government  Understanding Different Stakeholders and Their Perspectives Felipe Gonzalez-Zapata & Richard Heeks Centre for Development Informatics, University of Manchester, Manchester, M13 9PL, UK Corresponding author: Prof. Richard Heeks, Centre for Development Informatics, IDPM, SEED, University of Manchester, Manchester, M13 9PL, UK, +44-161-275-2870 richard.heeks@manchester.ac.uk
GAS TURBINE PERFORMANCE DETERIORATION

With privatization and intense competition in the utility and petrochemical industry, there is a strong incentive for gas turbine operators to minimize and control performance deterioration, as this directly affects profitability. The area of gas turbine recoverable and nonrecoverable performance deterioration is comprehensively treated in this paper. Deterioration mechanisms including compressor and turbine fouling, erosion, increased clearances, and seal distress are covered along with their manifestations, rules of thumb, and mitigation approaches. Permanent deterioration is also covered. Because of the importance of compressor fouling, gas turbine inlet filtration, fouling mechanisms, and compressor washing are covered in detail. Approaches for the performance monitoring of steady-state and transient behavior are presented along with simulations of common deterioration modes of gas turbine combined cycles. As several gas turbines are used in cogeneration and combined cycles, a brief discussion of heat recovery steam generator performance monitoring is made.
Determinants of consumer engagement in electronic word-of-mouth ( eWOM ) in social networking sites

As more and more marketers incorporate social media as an integral part of the promotional mix, rigorous investigation of the determinants that impact consumers’ engagement in eWOM via social networks is becoming critical. Given the social and communal characteristics of social networking sites (SNSs) such as Facebook, MySpace and Friendster, this study examines how social relationship factors relate to eWOM transmitted via online social websites. Specifically, a conceptual model that identifies tie strength, homophily, trust, normative and informational interpersonal influence as an important antecedent to eWOM behaviour in SNSs was developed and tested. The results confirm that tie strength, trust, normative and informational influence are positively associated with users’ overall eWOM behaviour, whereas a negative relationship was found with regard to homophily. This study suggests that product-focused eWOM in SNSs is a unique phenomenon with important social implications. The implications for researchers, practitioners and policy makers of social media regulation are discussed.