nils-herrmann/clean_scirepeval huggingface dataset:
Switching control strategy to extend the ZVS operating range of a Dual Active Bridge AC/DC converter

A switching control strategy to extend the zero-voltage-switching (ZVS) operating range of a Dual Active Bridge (DAB) AC/DC converter to the entire input-voltage interval and the full power range is proposed. The converter topology consists of a DAB DC/DC converter, receiving a rectified AC line voltage via a synchronous rectifier. The DAB comprises a primary side half bridge and secondary side full bridge, linked by a high-frequency isolation transformer and inductor. Using conventional control strategies, the soft-switching boundary conditions are exceeded at the higher voltage conversion ratios of the AC input interval. A novel pulse-width-modulation strategy to fully eliminate these boundaries and its analysis are presented in this paper, allowing increased performance (in terms of efficiency and stresses). Additionally, by using a half bridge / full bridge configuration, the number of active components is reduced. A prototype converter was constructed and experimental results are given to validate the theoretical analyses and practical feasibility of the proposed strategy.
Smart helmet with sensors for accident prevention

The impact when a motorcyclist involves in a high speed accident without wearing a helmet is very dangerous and can cause fatality. Wearing a helmet can reduce shock from the impact and may save a life. There are many countries enforcing a regulation that requires the motorcycle's rider to wear a helmet when riding on their motorcycle, Malaysia is an example. With this reason, this project is specially developed as to improve the safety of the motorcycle's rider. Motorcyclist will be alarmed when the speed limit is exceeded. A Force Sensing Resistor (FSR) and BLDC Fan are used for detection of the rider's head and detection of motorcycle's speed respectively. A 315 MHz Radio Frequency Module as wireless link which able to communicate between transmitter circuit and receiver circuit. PIC16F84a is a microcontroller to control the entire component in the system. Only when the rider buckled the helmet then only the motorcycle's engine will start. A LED will flash if the motor speed exceeds 100 km/hour.
Characterizing and profiling scientific workflows

Researchers working on the planning, scheduling, and execution of scientific workflows need access to a wide variety of scientific workflows to evaluate the performance of their implementations. This paper provides a characterization of workflows from six diverse scientific applications, including astronomy, bioinformatics, earthquake science, and gravitational-wave physics. The characterization is based on novel workflow profiling tools that provide detailed information about the various computational tasks that are present in the workflow. This information includes I/O, memory and computational characteristics. Although the workflows are diverse, there is evidence that each workflow has a job type that consumes the most amount of runtime. The study also uncovered inefficiency in a workflow component implementation, where the component was re-reading the same data multiple times.
Exploiting Deep Representations for Neural Machine Translation

Advanced neural machine translation (NMT) models generally implement encoder and decoder as multiple layers, which allows systems to model complex functions and capture complicated linguistic structures. However, only the top layers of encoder and decoder are leveraged in the subsequent process, which misses the opportunity to exploit the useful information embedded in other layers. In this work, we propose to simultaneously expose all of these signals with layer aggregation and multi-layer attention mechanisms. In addition, we introduce an auxiliary regularization term to encourage different layers to capture diverse information. Experimental results on widely-used WMT14 English⇒German and WMT17 Chinese⇒English translation data demonstrate the effectiveness and universality of the proposed approach.
The Analysis of Emotion Recognition from GSR Based on PSO

A method for recognizing the emotion states of subjects based on 30 features extracted from their Galvanic Skin Response (GSR) signals was proposed. GSR signals were acquired by means of experiments attended by those subjects. Next the data was normalized with the calm signal of the same subject after being de-noised. Then the normalized data were extracted features before the step of feature selection. Immune Hybrid Particle Swarm Optimization (IH-PSO) was proposed to select the feature subsets of different emotions. Classifier for feature selection was evaluated on the correct recognition as well as number of the selected features. At last, this paper verified the effectiveness of the feature subsets selected with another new data. All performed in this paper illustrate that IH-PSO can achieve much effective results, and further more, demonstrate that there is significant emotion information in GSR signal.
MAP Inference for Bayesian Inverse Reinforcement Learning

The difficulty in inverse reinforcement learning (IRL) aris es in choosing the best reward function since there are typically an infinite number of eward functions that yield the given behaviour data as optimal. Using a Bayes i n framework, we address this challenge by using the maximum a posteriori (MA P) estimation for the reward function, and show that most of the previous IRL al gorithms can be modeled into our framework. We also present a gradient metho d for the MAP estimation based on the (sub)differentiability of the poster ior distribution. We show the effectiveness of our approach by comparing the performa nce of the proposed method to those of the previous algorithms.
Recent progress and trends in predictive visual analytics

A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem spurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the prediction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual analytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summarization of the predictive analytics workflow.
Multi-View Stereo Reconstruction with High Dynamic Range Texture

In traditional 3D model reconstruction, the texture information is captured in a certain dynamic range, which is usually insufficient for rendering under new environmental light. This paper proposes a novel approach for multi-view stereo (MVS) reconstruction of models with high dynamic range (HDR) texture. In the proposed approach, multi-view images are firstly taken with different exposure times simultaneously. Corresponding pixels in adjacent viewpoints are then extracted using a multi-projection method, to robustly recover the response function of the camera. With the response function, pixel values in the differently exposed images can be converted to the desired relative radiance values. Subsequently, geometry reconstruction and HDR texture recovering can be achieved using these values. Experimental results demonstrate that our method can recover the HDR texture for the 3D model efficiently while keep high geometry precision. With our reconstructed HDR texture model, high-quality scene re-lighting is exemplarily exhibited.
Beyond Geometric Path Planning: Learning Context-Driven Trajectory Preferences via Sub-optimal Feedback

We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than those arising from simple geometric constraints on robot’s trajectory, such as distance of the robot from human etc. Our preferences are rather governed by the surrounding context of various objects and human interactions in the environment. Such preferences makes the problem challenging because the criterion of defining a good trajectory now varies with the task, with the environment and across the users. Furthermore, demonstrating optimal trajectories (e.g., learning from expert’s demonstrations) is often challenging and non-intuitive on high degrees of freedom manipulators. In this work, we propose an approach that requires a non-expert user to only incrementally improve the trajectory currently proposed by the robot. We implement our algorithm on two high degree-of-freedom robots, PR2 and Baxter, and present three intuitive mechanisms for providing such incremental feedback. In our experimental evaluation we consider two context rich settings – household chores and grocery store checkout – and show that users are able to train the robot with just a few feedbacks (taking only a few minutes). Despite receiving sub-optimal feedback from non-expert users, our algorithm enjoys theoretical bounds on regret that match the asymptotic rates of optimal trajectory algorithms.
CM-BOF: visual similarity-based 3D shape retrieval using Clock Matching and Bag-of-Features

Content-based 3D object retrieval has become an active topic in many research communities. In this paper, we propose a novel visual similarity-based 3D shape retrieval method (CM-BOF) using Clock Matching and Bag-of-Features. Specifically, pose normalization is first applied to each object to generate its canonical pose, and then the normalized object is represented by a set of depth-buffer images captured on the vertices of a given geodesic sphere. Afterwards, each image is described as a word histogram obtained by the vector quantization of the image’s salient local features. Finally, an efficient multi-view shape matching scheme (i.e., Clock Matching) is employed to measure the dissimilarity between two models. When applying the CM-BOF method in non-rigid 3D shape retrieval, multidimensional scaling (MDS) should be utilized before pose normalization to calculate the canonical form for each object. This paper also investigates several critical issues for the CM-BOF method, including the influence of the number of views, codebook, training data, and distance function. Experimental results on five commonly used benchmarks demonstrate that: (1) In contrast to the traditional Bag-of-Features, the time-consuming clustering is not necessary for the codebook construction of the CM-BOF approach; (2) Our methods are superior or comparable to the state of the art in applications of both rigid and non-rigid 3D shape retrieval.
MSMOTE: Improving Classification Performance When Training Data is Imbalanced

Learning from data sets that contain very few instances of the minority class usually produces biased classifiers that have a higher predictive accuracy over the majority class, but poorer predictive accuracy over the minority class. SMOTE (Synthetic Minority Over-sampling Technique) is specifically designed for learning from imbalanced data sets. This paper presents a modified approach (MSMOTE) for learning from imbalanced data sets, based on the SMOTE algorithm. MSMOTE not only considers the distribution of minority class samples, but also eliminates noise samples by adaptive mediation. The combination of MSMOTE and AdaBoost are applied to several highly and moderately imbalanced data sets. The experimental results show that the prediction performance of MSMOTE is better than SMOTEBoost in the minority class and F-values are also improved.
Real-Time Human Motion Capture with Multiple Depth Cameras

Commonly used human motion capture systems require intrusive attachment of markers that are visually tracked with multiple cameras. In this work we present an efficient and inexpensive solution to markerless motion capture using only a few Kinect sensors. Unlike the previous work on 3d pose estimation using a single depth camera, we relax constraints on the camera location and do not assume a co-operative user. We apply recent image segmentation techniques to depth images and use curriculum learning to train our system on purely synthetic data. Our method accurately localizes body parts without requiring an explicit shape model. The body joint locations are then recovered by combining evidence from multiple views in real-time. We also introduce a dataset of ~6 million synthetic depth frames for pose estimation from multiple cameras and exceed state-of-the-art results on the Berkeley MHAD dataset.
The effect of Big Data Analytics Capability on Firm Performance

Big data analytics (BDA) has emerged as an important area of study for both academics and practitioners. Despite of rising potential value of BDA, a few studies have been conducted to investigate the effect of BDA on firm performance. In this research in progress, according to the challenges of BDA dimensions (volume, variety, velocity, veracity and value) we propose the BDA capability dimensions in line with IT capability concept. BDA infrastructure capability, BDA management capability, BDA personnel capability and relational BDA capability provide the overall BDA Capability concept. The study, by employing dynamic capability, proposes that BDA capability impacts on firm financial and market performance by mediated effect of operational performance. The finding of this research by providing essential BDA capability and its effect on firm performance can apply as a roadmap and fill the gap between managers’ expectation of BDA and what is emerged of BDA implementation.
Crawling a country: better strategies than breadth-first for web page ordering

This article compares several page ordering strategies for Web crawling under several metrics. The objective of these strategies is to download the most "important" pages "early" during the crawl. As the coverage of modern search engines is small compared to the size of the Web, and it is impossible to index all of the Web for both theoretical and practical reasons, it is relevant to index at least the most important pages.We use data from actual Web pages to build Web graphs and execute a crawler simulator on those graphs. As the Web is very dynamic, crawling simulation is the only way to ensure that all the strategies considered are compared under the same conditions. We propose several page ordering strategies that are more efficient than breadth- first search and strategies based on partial Pagerank calculations.
Erect wheel-legged stair climbing robot for indoor service applications

In this paper, an automatic erect stair climbing mobile robot is developed for indoor service applications. The design of high center of mass, tilt axis near ground, and the triangular wheel-legged structure enable the robot to climb stairs in a dynamic and self-balancing way. The wheel-legged mechanism also keeps the advantages of differential-wheeled mobile platforms when moving on flat ground, such as easy to control, saving power, and zero turning radius. Moreover, the overall mechanical design fits the requirements for a service robot to have proper height and small footprint, which can make human-robot interaction more natural and comfortable in indoor environment. Since the perceptual and control system of the robot are both well integrated, we successfully demonstrate the stair-climbing function and prove that the design and implementation of our work are feasible and efficient. As a result, the robot is expected to be a prototype of universal platform for indoor mobile service robots in the future.
Feature-based recommendation system

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems--a personalized information filtering technology used to identify a set of N items that will be of interest to a certain user. User-based and model-based collaborative filtering are the most successful technology for building recommender systems to date and is extensively used in many commercial recommender systems. The basic assumption in these algorithms is that there are sufficient historical data for measuring similarity between products or users. However, this assumption does not hold in various application domains such as electronics retail, home shopping network, on-line retail where new products are introduced and existing products disappear from the catalog. Another such application domains is home improvement retail industry where a lot of products (such as window treatments, bathroom, kitchen or deck) are custom made. Each product is unique and there are very little duplicate products. In this domain, the probability of the same exact two products bought together is close to zero. In this paper, we discuss the challenges of providing recommendation in the domains where no sufficient historical data exist for measuring similarity between products or users. We present feature-based recommendation algorithms that overcome the limitations of the existing top-n recommendation algorithms. The experimental evaluation of the proposed algorithms in the real life data sets shows a great promise. The pilot project deploying the proposed feature-based recommendation algorithms in the on-line retail web site shows 75% increase in the recommendation revenue for the first 2 month period.
Experience Mining Google's Production Console Logs

We describe our early experience in applying our console log mining techniques [19, 20] to logs from production Google systems with thousands of nodes. This data set is five orders of magnitude in size and contains almost 20 times as many messages types as the Hadoop data set we used in . It also has many properties that are unique to large scale production deployments (e.g., the system stays on for several months and multiple versions of the software can run concurrently). Our early experience shows that our techniques, including source code based log parsing, state and sequence based feature creation and problem detection, work well on this production data set. We also discuss our experience in using our log parser to assist the log sanitization.
Leveraging Geospatially-Oriented Social Media Communications in Disaster Response

Geospatially-oriented social media communications have emerged as a common information resource to support crisis management. Our research compares the capabilities of two popular systems used to collect and visualize such information Project Epic’s Tweak the Tweet (TtT) and Ushahidi. Our research uses geospatially-oriented social media gathered by both projects during recent disasters to compare and contrast the frequency, content, and location components of contributed information to both systems. We compare how data was gathered and filtered, how spatial information was extracted and mapped, and the mechanisms by which the resulting synthesized information was shared with response and recovery organizations. In addition, we categorize the degree to which each platform in each disaster led to actions by first responders and emergency managers. Based on the results of our comparisons we identify key design considerations for future social media mapping tools to support crisis management.
Effects of upper limb robot-assisted therapy on motor recovery in subacute stroke patients

There is little evidence available on the use of robot-assisted therapy in subacute stroke patients. A randomized controlled trial was carried out to evaluate the short-time efficacy of intensive robot-assisted therapy compared to usual physical therapy performed in the early phase after stroke onset. Fifty-three subacute stroke patients at their first-ever stroke were enrolled 30 ± 7 days after the acute event and randomized into two groups, both exposed to standard therapy. Additional 30 sessions of robot-assisted therapy were provided to the Experimental Group. Additional 30 sessions of usual therapy were provided to the Control Group. The following impairment evaluations were performed at the beginning (T0), after 15 sessions (T1), and at the end of the treatment (T2): Fugl-Meyer Assessment Scale (FM), Modified Ashworth Scale-Shoulder (MAS-S), Modified Ashworth Scale-Elbow (MAS-E), Total Passive Range of Motion-Shoulder/Elbow (pROM), and Motricity Index (MI). Evidence of significant improvements in MAS-S (p = 0.004), MAS-E (p = 0.018) and pROM (p < 0.0001) was found in the Experimental Group. Significant improvement was demonstrated in both Experimental and Control Group in FM (EG: p < 0.0001, CG: p < 0.0001) and MI (EG: p < 0.0001, CG: p < 0.0001), with an higher improvement in the Experimental Group. Robot-assisted upper limb rehabilitation treatment can contribute to increasing motor recovery in subacute stroke patients. Focusing on the early phase of stroke recovery has a high potential impact in clinical practice.
A new hybrid ant colony optimization algorithm for feature selection

In this paper, we propose a new hybrid ant colony optimization (ACO) algorithm for feature selection (FS), called ACOFS, using a neural network. A key aspect of this algorithm is the selection of a subset of salient features of reduced size. ACOFS uses a hybrid search technique that combines the advantages of wrapper and filter approaches. In order to facilitate such a hybrid search, we designed new sets of rules for pheromone update and heuristic information measurement. On the other hand, the ants are guided in correct directions while constructing graph (subset) paths using a bounded scheme in each and every step in the algorithm. The above combinations ultimately not only provide an effective balance between exploration and exploitation of ants in the search, but also intensify the global search capability of ACO for a highquality solution in FS. We evaluate the performance of ACOFS on eight benchmark classification datasets and one gene expression dataset, which have dimensions varying from 9 to 2000. Extensive experiments were conducted to ascertain how AOCFS works in FS tasks. We also compared the performance of ACOFS with the results obtained from seven existing well-known FS algorithms. The comparison details show that ACOFS has a remarkable ability to generate reduced-size subsets of salient features while yielding significant classification accuracy.
A functional approach to the assessment of self-mutilative behavior.

This study applied a functional approach to the assessment of self-mutilative behavior (SMB) among adolescent psychiatric inpatients. On the basis of past conceptualizations of different forms of self-injurious behavior, the authors hypothesized that SMB is performed because of the automatically reinforcing (i.e., reinforced by oneself; e.g., emotion regulation) and/or socially reinforcing (i.e., reinforced by others; e.g., attention, avoidance-escape) properties associated with such behaviors. Data were collected from 108 adolescent psychiatric inpatients referred for self-injurious thoughts or behaviors. Adolescents reported engaging in SMB frequently, using multiple methods, and having an early age of onset. Moreover, the results supported the structural validity and reliability of the hypothesized functional model of SMB. Most adolescents engaged in SMB for automatic reinforcement, although a sizable portion endorsed social reinforcement functions as well. These findings have direct implications for the understanding, assessment, and treatment of SMB.