nils-herrmann/clean_scirepeval huggingface dataset:
Generic Framework to Predict Repeat Behavior of Customers Using Their Transaction History

There exists a class of problems in e-commerce and retail businesses where the shopping behavior of customers is analyzed in order to predict their repeat behavior for products or retail stores. This analysis plays a crucial role in advertisement budgeting, product placement and relevant customer targeting. Researchers have addressed this problem by using standard predictive models, which use ad hoc features. We propose a metamodel that abstracts the different dimensions of data present in transactional datasets. These dimensions can be customer, product, offer, target, marketplace and transactions. Our framework also has abstract functions for comprehensive feature set generation, and includes different machine learning algorithms to learn prediction model. Our framework works end-to-end from feature engineering to reporting repeat probabilities of customers for products (or marketplace, brand, website or storechain). Moreover, the predicted repeat behavior of customers for different products along with their transactional history is used by our offer optimization model i-Prescribe to suggest products to be offered to customers with the goal of maximizing the return on investment of given marketing budget. We prove that our abstract features work on two different data-challenge datasets, by sharing experimental results.
Current sharing in three-phase LLC interleaved resonant converter

In this paper, a novel approach for multi-phase interleaved LLC resonant converter is presented. The proposed solution, based on the use of three LLC modules with star connection of transformer primary windings, allows a drastic reduction of the output current ripple and consequently of the output filter capacitor size. Differently from other multi-phase solutions, that are greatly susceptible to resonant components' tolerance causing current imbalance, the proposed topology exhibits an inherent current sharing capability. Moreover, a closed-loop phase-shift control is introduced to additionally compensate for current mismatch and completely balance the current supplied by each module. The benefit of such solution on the reduction of output current ripple and the phase-shift control interaction and effect on load-step variations are also investigated. Measurements on a prototype are added to simulations as validation of the assertions and proposals.
Applied machine vision of plants: a review with implications for field deployment in automated farming operations

Automated visual assessment of plant condition, specifically foliage wilting, reflectance and growth parameters, using machine vision has potential use as input for real-time variable-rate irrigation and fertigation systems in precision agriculture. This paper reviews the research literature for both outdoor and indoor applications of machine vision of plants, which reveals that different environments necessitate varying levels of complexity in both apparatus and nature of plant measurement which can be achieved. Deployment of systems to the field environment in precision agriculture applications presents the challenge of overcoming image variation caused by the diurnal and seasonal variation of sunlight. From the literature reviewed, it is argued that augmenting a monocular RGB vision system with additional sensing techniques potentially reduces image analysis complexity while enhancing system robustness to environmental variables. Therefore, machine vision systems with a foundation in optical and lighting design may potentially expedite the transition from laboratory and research prototype to robust field tool.
Infants rapidly learn word-referent mappings via cross-situational statistics

First word learning should be difficult because any pairing of a word and scene presents the learner with an infinite number of possible referents. Accordingly, theorists of children's rapid word learning have sought constraints on word-referent mappings. These constraints are thought to work by enabling learners to resolve the ambiguity inherent in any labeled scene to determine the speaker's intended referent at that moment. The present study shows that 12- and 14-month-old infants can resolve the uncertainty problem in another way, not by unambiguously deciding the referent in a single word-scene pairing, but by rapidly evaluating the statistical evidence across many individually ambiguous words and scenes.
Analysis of Rotor Core Eddy-Current Losses in Interior Permanent-Magnet Synchronous Machines

This paper presents the results of an investigation focused on the rotor core eddy-current losses of interior permanent-magnet (IPM) synchronous machines. First, analytical insight into the rotor core eddy-current losses of IPM machines is developed. Next, major design parameters that have the most significant impact on the rotor core eddy-current losses of IPM machines are identified. Finite-element analysis results are then presented to compare the predicted eddy-current losses in the machine core of IPM machines with one- and two-layer rotors coupled with concentrated- and distributed-winding stators. It is shown that the lowest total eddy-current losses in the machine core are achieved using a combination of distributed stator windings and two magnet layers per rotor pole, whereas minimizing only the rotor core eddy-current losses favors replacement of the rotor with a single-layer configuration.
Efficient Object Category Recognition Using Classemes

We introduce a new descriptor for images which allows the construction of efficient and compact classifiers with good accuracy on object category recognition. The descriptor is the output of a large number of weakly trained object category classifiers on the image. The trained categories are selected from an ontology of visual concepts, but the intention is not to encode an explicit decomposition of the scene. Rather, we accept that existing object category classifiers often encode not the category per se but ancillary image characteristics; and that these ancillary characteristics can combine to represent visual classes unrelated to the constituent categories’ semantic meanings. The advantage of this descriptor is that it allows object-category queries to be made against image databases using efficient classifiers (efficient at test time) such as linear support vector machines, and allows these queries to be for novel categories. Even when the representation is reduced to 200 bytes per image, classification accuracy on object category recognition is comparable with the state of the art (36% versus 42%), but at orders of magnitude lower computational cost.
Study on advantages and disadvantages of Cloud Computing – the advantages of Telemetry Applications in the Cloud

As companies of all shapes and sizes begin to adapt to cloud computing, this new technology is evolving like never before. Industry experts believe that this trend will only continue to grow and develop even further in the coming few years. While Cloud computing is undoubtedly beneficial for mid-size to large companies, it is not without its downsides, especially for smaller businesses. In this paper we are presenting a list of advantages and disadvantages of Cloud computing technology, with a view to helping enterprises fully understand and adopt the concept of Cloud computing. Also, in the last chapter we are presenting a cloud application for telemetry with a focus on monitoring hydro-energy, in order to demonstrate the advantages that cloud technology can have for this domain. We consider that the way to make cloud vastly benefit all types of businesses is to know very well it's ups and downs and adapt to them accordingly.  Cloud Computing, Grid Computing, telemetry, architecture, advantages, disadvantages.
ELECTROACTIVE FABRICS AND WEARABLE BIOMONITORING DEVICES

The implementation of truly wearable, instrumented garments which are capable of recording biomechanical variables is crucial in several fields of application, from multimedia to rehabilitation, from sport to artistic fields. In this paper we discuss wearable devices (a smart shirt, a leotard and a glove) which can read and record the vital signs and movements of a subject wearing the system. The sensing function of the garments is based on piezo-resistive fabric sensors, based on carbon-loaded rubbers (CLR) and different conductive materials.
Object-Based Features for House Detection from RGB High-Resolution Images

Automatic building extraction from satellite images, an open research topic in remote sensing, continues to represent a challenge and has received substantial attention for decades. This paper presents an object-based and machine learning-based approach for automatic house detection from RGB high-resolution images. The images are first segmented by an algorithm combing a thresholding watershed transformation and hierarchical merging, and then shadows and vegetation are eliminated from the initial segmented regions to generate building candidates. Subsequently, the candidate regions are subjected to feature extraction to generate training data. In order to capture the characteristics of house regions well, we propose two kinds of new features, namely edge regularity indices (ERI) and shadow line indices (SLI). Finally, three classifiers, namely AdaBoost, random forests, and Support Vector Machine (SVM), are employed to identify houses from test images and quality assessments are conducted. The experiments show that our method is effective and applicable for house identification. The proposed ERI and SLI features can improve the precision and recall by 5.6% and 11.2%, respectively.
Training and Evaluating Improved Dependency-Based Word Embeddings

Word embedding has been widely used in many natural language processing tasks. In this paper, we focus on learning word embeddings through selective higher-order relationships in sentences to improve the embeddings to be less sensitive to local context and more accurate in capturing semantic compositionality. We present a novel multi-order dependency-based strategy to composite and represent the context under several essential constraints. In order to realize selective learning from the word contexts, we automatically assign the strengths of different dependencies between co-occurred words in the stochastic gradient descent process. We evaluate and analyze our proposed approach using several direct and indirect tasks for word embeddings. Experimental results demonstrate that our embeddings are competitive to or better than state-of-the-art methods and significantly outperform other methods in terms of context stability. The output weights and representations of dependencies obtained in our embedding model conform to most of the linguistic characteristics and are valuable for many downstream tasks.
Convolutional Neural Network Information Fusion based on Dempster-Shafer Theory for Urban Scene Understanding

Dempster-Shafer theory provides a sensor fusion framework that autonomously accounts for obstacle occlusion in dynamic, urban environments. However, to discern static and moving obstacles, the Dempster-Shafer approach requires manual tuning of parameters dependent on the situation and sensor types. The proposed methodology utilizes a deep fully convolutional neural network to improve the robust performance of the information fusion algorithm in distinguishing static and moving obstacles from navigable space. The image-like spatial structure of probabilistic occupancy allows a semantic segmentation framework to discern classes for individual grid cells. A subset of the KITTI LIDAR tracking dataset in combination with semantic map data was used for the information fusion task. The probabilistic occupancy grid output of the Dempster-Shafer information fusion algorithm was provided as input to the neural network. The network then learned an offset from the original DST result to improve semantic labeling performance. The proposed framework outperformed the baseline approach in the mean intersection over union metric reaching 0.546 and 0.531 in the validation and test sets respectively. However, little improvement was achieved in discerning moving and static cells due to the limited dataset size. To improve model performance in future work, the dataset will be expanded to facilitate more effective learning, and temporal data will be fed through individual convolutional networks prior to being merged in channels as input to the main network.
Adversarial and Clean Data Are Not Twins

Adversarial attack has cast a shadow on the massive success of deep neural networks. Despite being almost visually identical to the clean data, the adversarial images can fool deep neural networks into wrong predictions with very high confidence. In this paper, however, we show that we can build a simple binary classifier separating the adversarial apart from the clean data with accuracy over 99%. We also empirically show that the binary classifier is robust to a secondround adversarial attack. In other words, it is difficult to disguise adversarial samples to bypass the binary classifier. Further more, we empirically investigate the generalization limitation which lingers on all current defensive methods, including the binary classifier approach. And we hypothesize that this is the result of intrinsic property of adversarial crafting algorithms.
Neural basis of sensorimotor learning: modifying internal models

The neural basis of the internal models used in sensorimotor transformations is beginning to be uncovered. Sensorimotor learning involves the modification of such models. Different stages of sensory-motor processing have been explored with a continuum of experimental tasks, from learning arbitrary associations of sensory cues to movements, to adapting to altered kinematic and dynamic environments. Several groups have been studying changes in neuronal activity in cortical and subcortical areas that may be related to the acquisition and consolidation processes. We discuss the progress and challenges in understanding how these learning-related neural changes are involved in the modification of internal models, and offer future directions.
Work Related Stress, Burnout, Job Satisfaction and General Health of Nurses

Gaps in research focusing on work related stress, burnout, job satisfaction and general health of nurses is evident within developing contexts like South Africa. This study identified the relationship between work related stress, burnout, job satisfaction and general health of nurses. A total of 1200 nurses from four hospitals were invited to participate in this cross-sectional study (75% response rate). Participants completed five questionnaires and multiple linear regression analysis was used to determine significant relationships between variables. Staff issues are best associated with burnout as well as job satisfaction. Burnout explained the highest amount of variance in mental health of nurses. These are known to compromise productivity and performance, as well as affect the quality of patient care. Issues, such as security risks in the workplace, affect job satisfaction and health of nurses. Although this is more salient to developing contexts it is important in developing strategies and intervention programs towards improving nurse and patient related outcomes.
FinDroidHR: Smartwatch Gesture Input with Optical Heartrate Monitor

We present FinDroidHR, a novel gesture input technique for off-the-shelf smartwatches. Our technique is designed to detect 10 hand gestures on the hand wearing a smartwatch. The technique is enabled by analysing features of the Photoplethysmography (PPG) signal that optical heart-rate sensors capture. In a study with 20 participants, we show that FinDroidHR achieves 90.55% accuracy and 90.73% recall. Our work is the first study to explore the feasibility of using optical sensors on the off-the-shelf wearable devices to recognise gestures. Without requiring bespoke hardware, FinDroidHR can be readily used on existing smartwatches.
Deep Fusion LSTMs for Text Semantic Matching

Recently, there is rising interest in modelling the interactions of text pair with deep neural networks. In this paper, we propose a model of deep fusion LSTMs (DF-LSTMs) to model the strong interaction of text pair in a recursive matching way. Specifically, DF-LSTMs consist of two interdependent LSTMs, each of which models a sequence under the influence of another. We also use external memory to increase the capacity of LSTMs, thereby possibly capturing more complicated matching patterns. Experiments on two very large datasets demonstrate the efficacy of our proposed architecture. Furthermore, we present an elaborate qualitative analysis of our models, giving an intuitive understanding how our model worked.
Horizon Detection Using Machine Learning Techniques

Detecting a horizon in an image is an important part of many image related applications such as detecting ships on the horizon, flight control, and port security. Most of the existing solutions for the problem only use image processing methods to identify a horizon line in an image. This results in good accuracy for many cases and is fast in computation. However, for some images with difficult environmental conditions like a foggy or cloudy sky these image processing methods are inherently inaccurate in identifying the correct horizon. This paper investigates how to detect the horizon line in a set of images using a machine learning approach. The performance of the SVM, J48, and naive Bayes classifiers, used for the problem, has been compared. Accuracy of 90-99% in identifying horizon was achieved on image data set of 20 images
Facebook wall posts: a model of user behaviors

How do people interact with their Facebook wall? At a high level, this question captures the essence of our work. While most prior efforts focus on Twitter, the much fewer Facebook studies focus on the friendship graph or are limited by the amount of users or the duration of the study. In this work, we model Facebook user behavior: we analyze the wall activities of users focusing on identifying common patterns and surprising phenomena. We conduct an extensive study of roughly 7k users over 3 years during 4-month intervals each year. We propose PowerWall, a lesser known heavy-tailed distribution to fit our data. Our key results can be summarized in the following points. First, we find that many wall activities, including number of posts, number of likes, number of posts of type photo, can be described by the PowerWall distribution. What is more surprising is that most of these distributions have similar slope, with a value close to 1! Second, we show how our patterns and metrics can help us spot surprising behaviors and anomalies. For example, we find a user posting every two days, exactly the same count of posts; another user posting at midnight, with no other activity before or after. Our work provides a solid step toward a systematic and quantitative wall-centric profiling of Facebook user activity.
BYOD Security: A New Business Challenge

Bring Your Own Device (BYOD) is a rapidly growing trend in businesses concerned with information technology. BYOD presents a unique list of security concerns for businesses implementing BYOD policies. Recent publications indicate a definite awareness of risks involved in incorporating BYOD into business, however it is still an underrated issue compared to other IT security concerns. This paper focuses on two key BYOD security issues: security challenges and available frameworks. A taxonomy specifically classifying BYOD security challenges is introduced alongside comprehensive frameworks and solutions which are also analysed to gauge their limitations.
Absent mandibular gap in the retronasal triangle view: a clue to the diagnosis of micrognathia in the first trimester.

To describe a new ultrasound technique that may be useful for the diagnosis of micrognathia in the first trimester of pregnancy.



The retronasal triangle (RNT) view is a technique that captures the coronal plane of the face in which the primary palate and the frontal processes of the maxilla are visualized simultaneously. Normal first-trimester fetuses display a characteristic gap between the right and left body of the mandible in this view (the 'mandibular gap'). The presence or absence of this gap was evaluated and measured prospectively during real-time scanning (n = 154) and retrospectively by analyzing three-dimensional (3D) datasets (n = 50) in normal first-trimester fetuses undergoing screening for aneuploidy at 11-13 weeks' gestation. 3D datasets from 12 fetuses with suspected micrognathia were also collected and examined retrospectively for the same features.



The mandibular gap was identified in all 204 normal fetuses and increased linearly with increasing crown-rump length (y = 0.033x + 0.435; R(2) = 0.316), with no statistically significant differences between measurements obtained by two-dimensional ultrasound and 3D offline analysis. Among fetuses with suspected micrognathia, three 3D datasets were excluded from analysis because of poor image quality in one and the diagnosis of a normal chin in two. In the remaining nine fetuses, the mandibular gap was absent and was replaced by a bony structure representing the receding chin in seven (77.8%) cases and was not visualized due to severe retrognathia in the remaining two (22.2%) cases. All fetuses with micrognathia had associated anomalies, including seven with aneuploidy and two with skeletal dysplasia.



The RNT view may be a helpful technique for detecting micrognathia in the first trimester. The absence of the mandibular gap or failure to identify the mandible in this view is highly suggestive of micrognathia and should prompt a targeted ultrasound scan to assess for other anomalies. Further research is needed to determine the false-positive and false-negative rates of this technique.