nils-herrmann/clean_scirepeval huggingface dataset:
Advances in Instance Selection for Instance-Based Learning Algorithms

The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time can be slow. When noisy instances are present classification accuracy can suffer. Drawing on the large body of relevant work carried out in the past 30 years, we review the principle approaches to solving these problems. By deleting instances, both problems can be alleviated, but the criterion used is typically assumed to be all encompassing and effective over many domains. We argue against this position and introduce an algorithm that rivals the most successful existing algorithm. When evaluated on 30 different problems, neither algorithm consistently outperforms the other: consistency is very hard. To achieve the best results, we need to develop mechanisms that provide insights into the structure of class definitions. We discuss the possibility of these mechanisms and propose some initial measures that could be useful for the data miner.
A System-on-Chip EPC Gen-2 Passive UHF RFID Tag With Embedded Temperature Sensor

This paper presents a system-on-chip passive RFID tag with an embedded temperature sensor for the EPC Gen-2 protocol in the 900-MHz UHF frequency band. A dual-path clock generator is proposed to support both applications with either very accurate link frequency or very low power consumption. On-chip temperature sensing is accomplished with a time-readout scheme to reduce the power consumption. Moreover, a gain-compensation technique is proposed to reduce the temperature sensing error due to process variations by using the same bandgap reference of the tag to generate bias currents for both the current-to-digital converter and the clock generator of the sensor. Also integrated is a 128-bit one-time-programmable (OTP) memory array based on gate-oxide antifuse without extra mask steps. Fabricated in a standard 0.18- μm CMOS process with analog options, the 1.1-mm2 tag chip is bonded onto an antenna using flip-chip technology to realize a complete tag inlay, which is successfully demonstrated and evaluated in real-time wireless communications with commercial RFID readers. The tag inlay achieves a sensitivity of -6 dBm and a sensing inaccuracy of ±0.8° C (3 σ inaccuracy) over operating temperature range from -20°C to 30°C with one-point calibration.
Human Activity Detection in Smart Home Environment with Self-Adaptive Neural Networks

One of key components in the development of smart home technology is the detection and recognition of activities of daily life. Based on a self-adaptive neural network called growing self-organizing maps (GSOM), this paper presents a new computational approach to cluster analysis of human activities of daily living within smart home environment. It was tested on a dataset collected from a set of simple state-change sensors installed on a one-bedroom apartment during a period of about two weeks. The results obtained indicate that, due to its advanced evolving, self- adaptive properties, the GSOM exhibits several appealing features in the analysis of useful patterns encoded in daily activity data. The approaches described in this paper contribute to the development of a user-friendly and interactive data-mining platform for the analysis of human activities within smart home environment through the improvement of pattern discovery, visualization and interpretation.
UltraGesture: Fine-Grained Gesture Sensing and Recognition

With the rising of AR/VR technology and miniaturization of mobile devices, gesture is becoming an increasingly popular means of interacting with smart devices. Some pioneer ultrasound based human gesture recognition systems have been proposed. They mostly rely on low resolution Doppler Effect, and hence focus on whole hand motion and cannot deal with minor finger motions. In this paper, we present UltraGesture, a Channel Impulse Response (CIR) based ultrasonic finger motion perception and recognition system. CIR measurements can provide with 7 mm resolution, rendering it sufficient for minor finger motion recognition. UltraGesture encapsulates CIR measurements into an image, and builds a Convolutional Neural Network model to classify these images into different categories, which corresponding to distinct gestures. Our system runs on commercial speakers and microphones that already exist on most mobile devices without hardware modification. Our results show that UltraGesture achieves an average accuracy of greater than 97% for 12 gestures including finger click and rotation.
Statistical learning by 8-month-old infants.

Learners rely on a combination of experience-independent and experience-dependent mechanisms to extract information from the environment. Language acquisition involves both types of mechanisms, but most theorists emphasize the relative importance of experience-independent mechanisms. The present study shows that a fundamental task of language acquisition, segmentation of words from fluent speech, can be accomplished by 8-month-old infants based solely on the statistical relationships between neighboring speech sounds. Moreover, this word segmentation was based on statistical learning from only 2 minutes of exposure, suggesting that infants have access to a powerful mechanism for the computation of statistical properties of the language input.
Image detail enhancement with spatially guided filters

In recent years, enhancing image details without introducing artifacts has been attracting much attention in image processing community. Various image filtering methods have been proposed to achieve this goal. However, existing methods usually treat all pixels equally during the filtering process without considering the relationship between filtering strengths and image contents. In this paper, we address this issue by spatially distributing the filtering strengths with simple low-level features. First, to determine the pixel-wise filtering strength, we construct a spatially guided map, which exploits the spatial influence of image details based on the edge response of an image. Then, we further integrate this guided map into two state-of-the-art image filters and apply the improved filters to the task of image detail enhancement. Experiments demonstrate that our results generate better content-specific visual effects and introduce much less artifacts.
ATHYNOS: Helping Children with Dyspraxia Through an Augmented Reality Serious Game

Emerging technologies and ICT have changed the lifestyle of society, all scientific areas are taking advantage of technology to get a real development. Therapists realize the benefits of using serious games as an assistive tool in psychotherapy. Thus, this research examines relevant issues regarding Dyspraxia disorders in children and presents a comparative study between two therapies methods, one using a manual puzzle and other using ATHYNOS, an Augmented Reality Serious Game developed to help children with the Dyspraxia to improve their motor skills and hand-eye coordination through technology. The analysis of data results showed that exist a significant difference between both methods, proving that children playing with ATHYNOS got less time in the activity execution and also better performance.
Investigation of parallel operation of IGBTs

Widespread application of IGBTs in the past decade has resulted in dramatic improvement in performance of power electronic converters, with simultaneous reduction in costs. In order to further reduce the cost of power electronics systems and improve their reliability it is imperative that techniques for standardized and integrated architectures be adopted, allowing them to be mass-customized for a larger class of applications. One of the key factors that allow broad application of a power switching devices rated at a lower power level across large power levels is ease of parallel operation. This paper is devoted to presenting the results from investigation of parallel operation of IGBT devices. Experimental and computer simulation results are presented.
Composite Scale of Morningness: psychometric properties, validity with Munich ChronoType Questionnaire and age/sex differences in Poland.

The present study aimed at testing psychometric properties of the Composite Scale of Morningness (CSM) and validating it with mid sleep on free days (MSF) derived from the Munich ChronoType Questionnaire (MCTQ) in Poland, along with analyzing age and sex differences in the CSM and MSF. A sample of 952 Polish residents (62.6% females) aged between 13 and 46 was tested. Additionally, a sample of 33 university students were given MCTQ and filled in a sleep diary for 8 days. MSF derived from MCTQ was related to the one from sleep diary (r=.44). The study revealed good reliability of the CSM (α=.84) and its validity: greater morningness preference was associated with earlier MSF from MCTQ (r=-.52). CSM scores were distributed over its full range, with a mean of 34, and did not differ between sexes, although females were earlier than males by 23minutes in MSF. Regarding age, eveningness estimated with both CSM and MSF was greatest in subjects aged 16-18years, and a shift toward eveningness during puberty and a shift back toward morningness in older age was observed. The Polish version of the CSM consisted of two components of morningness. Cutoff scores were: for evening types (lower 10%) 24 or less, for morning types (upper 10%) 43 or more. The Polish CSM presents good psychometric properties, which are similar to those reported in other language versions, and also presents sex/age patterns similar to those found previously.
Diet and nutrition : A literature review of factors influencing blue-collar apprentices

A nutrient-rich diet could assist apprentices to maintain a healthy body and mind. The nature of most construction industry apprenticeships is such that it requires concentration and moderate to high levels of physical activity, and consequentially apprentices need a diet which can support these required high energy demands. Anecdotally, apprentices display a range of dietary practices, which appear to be moderated by their taste preferences, nutritional beliefs and knowledge, environment (e.g., availability of food; healthful dietary messages) and degree of prescription to traditional masculine stereotypes.
Exploring the factors associated with Web site success in the context of electronic commerce

Web sites are being widely deployed commercially. As the widespread use and dependency on Web technology increases, so does the need to assess factors associated with Web site success. The objective is to explore these factors in the context of electronic commerce (EC). The research framework was derived from information systems and marketing literature. Webmasters from Fortune 1000 companies were used as the target group for a survey. Four factors that are critical to Web site success in EC were identi®ed: (1) information and service quality, (2) system use, (3) playfulness, and (4) system design quality. An analysis of the data provides valuable managerial implications for Web site success in the context of electronic commerce.
Dynamic Pricing in Spatial Crowdsourcing: A Matching-Based Approach

In spatial crowdsourcing, requesters submit their task-related locations and increase the demand of a local area. The platform prices these tasks and assigns spatial workers to serve if the prices are accepted by requesters. There exist mature pricing strategies which specialize in tackling the imbalance between supply and demand in a local market. However, in global optimization, the platform should consider the mobility of workers; that is, any single worker can be the potential supply for several areas, while it can only be the true supply of one area when assigned by the platform. The hardness lies in the uncertainty of the true supply of each area, hence the existing pricing strategies do not work. In the paper, we formally define this <u>G</u>lobal <u>D</u>ynamic <u>P</u>ricing(GDP) problem in spatial crowdsourcing. And since the objective is concerned with how the platform matches the supply to areas, we let the matching algorithm guide us how to price. We propose a <u>MA</u>tching-based <u>P</u>ricing <u>S</u>trategy (MAPS) with guaranteed bound. Extensive experiments conducted on the synthetic and real datasets demonstrate the effectiveness of MAPS.
Brain Connectivity Analysis Methods for Better Understanding of Coupling

Action, cognition, emotion and perception can be mapped in the brain by using set of techniques. Translating unimodal concepts from one modality to another is an important step towards understanding the neural mechanisms. This paper provides a comprehensive survey of multimodal analysis of brain signals such as fMRI, EEG, MEG, NIRS and motivations, assumptions and pitfalls associated with it. All these non-invasive brain modalities complement and restrain each other and hence improve our understating of functional and neuronal organization. By combining the various modalities together, we can exploit the strengths and flaws of individual brain imaging methods. Integrated anatomical analysis and functional measurements of human brain offer a powerful paradigm for the brain mapping. Here we provide the brief review on non invasive brain modalities, describe the future of co-analysis of these
Eating when bored: revision of the emotional eating scale with a focus on boredom.

The current study explored whether eating when bored is a distinct construct from other negative emotions by revising the emotional eating scale (EES) to include a separate boredom factor. Additionally, the relative endorsement of eating when bored compared to eating in response to other negative emotions was examined.



A convenience sample of 139 undergraduates completed open-ended questions regarding their behaviors when experiencing different levels of emotions. Participants were then given the 25-item EES with 6 additional items designed to measure boredom.



On the open-ended items, participants more often reported eating in response to boredom than the other emotions. Exploratory factor analysis showed that boredom is a separate construct from other negative emotions. Additionally, the most frequently endorsed item on the EES was "eating when bored".



These results suggest that boredom is an important construct, and that it should be considered a separate dimension of emotional eating.
Chinese Named Entity Identification Using Class-based Language Model

We consider here the problem of Chinese named entity (NE) identification using statistical language model(LM). In this research, word segmentation and NE identification have been integrated into a unified framework that consists of several class-based language models. We also adopt a hierarchical structure for one of the LMs so that the nested entities in organization names can be identified. The evaluation on a large test set shows consistent improvements. Our experiments further demonstrate the improvement after seamlessly integrating with linguistic heuristic information, cache-based model and NE abbreviation identification. ,QWURGXFWLRQ 1( LGHQWLILFDWLRQ is the key technique in many applications such as information extraction, question answering, machine translation and so on. English NE identification has achieved a great success. However, for Chinese, NE identification is very different. There is no space to mark the word boundary and no standard definition of words in Chinese. The Chinese NE identification and word segmentation are interactional in nature. This paper presents a unified approach that integrates these two steps together using a class-based LM, and apply Viterbi search to select the global optimal solution. The class-based LM consists of two sub-models, namely the context model and the entity model. The context model estimates the probability of generating a NE given a certain context, and the entity model estimates the probability of a sequence of Chinese characters given a certain kind of NE. In this study, we are interested in three kinds of Chinese NE that are most commonly used, namely person name (PER), location name (LOC) and organization name (ORG). We have also adopted a variety of approaches to improving the LM. In addition, a hierarchical structure for organization LM is employed so that the nested PER, LOC in ORG can be identified. The evaluation is conducted on a large test set in which NEs have been manually tagged. The experiment result shows consistent improvements over existing methods. Our experiments further demonstrate the improvement after integrating with linguistic heuristic information, cache-based model and NE abbreviation identification. The precision of PER, LOC, ORG on the test set is 79.86%, 80.88%, 76.63%, respectively; and the recall is 87.29%, 82.46%, 56.54%, respectively.  5HODWHG :RUN
Employing Aesthetic Principles for Automatic Photo Book Layout

Photos are a common way of preserving our personal memories. The visual souvenir of a personal event is often composed into a photo collage or the pages of a photo album. Today we find many tools to help users creating such compositions by different tools for authoring photo compositions. Some template-based approaches generate nice presentations, however, come mostly with limited design variations. Creating complex and fancy designs for, e.g., a personal photo book, still demands design and composition skills to achieve results that are really pleasing to the eye – skills which many users simply lack. Professional designers instead would follow general design principles such as spatial layout rules, symmetry, balance among the element as well color schemes and harmony. In this paper, we propose an approach to deliver principles of design and composition to the end user by embedding it into an automatic composition application. We identify and analyze common design and composition principles and transfer these to the automatic creation of pleasant photo compositions by employing genetic algorithms. In contrast to other approaches, we strictly base our design system on common design principles, consider additional media types besides text in the photo book and specifically take the content of photos into account. Our approach is both implemented in a web-based rich media application and a tool for the automatic transformation from blogs into photo books.
Pixel-wise Deep Learning for Contour Detection

We address the problem of contour detection via per-pixel classifications of edge point. To facilitate the process, the proposed approach leverages with DenseNet, an efficient implementation of multiscale convolutional neural networks (CNNs), to extract an informative feature vector for each pixel and uses an SVM classifier to accomplish contour detection. In the experiment of contour detection, we look into the effectiveness of combining per-pixel features from different CNN layers and verify their performance on BSDS500.
CortexNet: a Generic Network Family for Robust Visual Temporal Representations

In the past five years we have observed the rise of incredibly well performing feed-forward neural networks trained supervisedly for vision related tasks. These models have achieved super-human performance on object recognition, localisation, and detection in still images. However, there is a need to identify the best strategy to employ these networks with temporal visual inputs and obtain a robust and stable representation of video data. Inspired by the human visual system, we propose a deep neural network family, CortexNet, which features not only bottom-up feedforward connections, but also it models the abundant top-down feedback and lateral connections, which are present in our visual cortex. We introduce two training schemes — the unsupervised MatchNet and weakly supervised TempoNet modes — where a network learns how to correctly anticipate a subsequent frame in a video clip or the identity of its predominant subject, by learning egomotion clues and how to automatically track several objects in the current scene. Find the project website at tinyurl.com/CortexNet.
Image-based reconstruction of wire art

Objects created by connecting and bending wires are common in furniture design, metal sculpting, wire jewelry, etc. Reconstructing such objects with traditional depth and image based methods is extremely difficult due to their unique characteristics such as lack of features, thin elements, and severe self-occlusions. We present a novel image-based method that reconstructs a set of continuous 3D wires used to create such an object, where each wire is composed of an ordered set of 3D curve segments. Our method exploits two main observations: simplicity - wire objects are often created using only a small number of wires, and smoothness - each wire is primarily smoothly bent with sharp features appearing only at joints or isolated points. In light of these observations, we tackle the challenging image correspondence problem across featureless wires by first generating multiple candidate 3D curve segments and then solving a global selection problem that balances between image and smoothness cues to identify the correct 3D curves. Next, we recover a decomposition of such curves into a set of distinct and continuous wires by formulating a multiple traveling salesman problem, which finds smooth paths, i.e., wires, connecting the curves. We demonstrate our method on a wide set of real examples with varying complexity and present high-fidelity results using only 3 images for each object. We provide the source code and data for our work in the project website.
NoSQL Databases and Data Modeling Techniques for a Document-oriented NoSQL Database

NoSQL databases are an important component of Big Data for storing and retrieving large volumes of data. Traditional Relational Database Management Systems (RDBMS) use the ACID theorem for data consistency, whereas NoSQL Databases use a non-transactional approach called BASE. RDBMS scale vertically and NoSQL Databases can scale both horizontally (sharding) and vertically. Four types of NoSQL databases are Document-oriented,  Data modeling for Document-oriented databases is similar to data modeling for traditional RDBMS during the conceptual and logical modeling phases. However, for a physical data model, entities can be combined (denormalized) by using embedding. What was once called a foreign key in a traditional RDBMS is now called a reference in a Documentoriented NoSQL database.
Lessons from a Sensor Network Expedition

Habitat monitoring is an important driving application for wireless sensor networks (WSNs). Although researchers anticipate some challenges arising in the real-world deployments of sensor networks, a number of problems can be discovered only through experience. This paper evaluates a sensor network system described in an earlier work and presents a set of experiences from a four month long deployment on a remote island off the coast of Maine. We present an in-depth analysis of the environmental and node health data. The close integration of WSNs with their environment provides biological data at densities previous impossible; however, we show that the sensor data is also useful for predicting system operation and network failures. Based on over one million data and health readings, we analyze the node and network design and develop network reliability profiles and failure models.