nils-herrmann/clean_scirepeval huggingface dataset:
Implementation of Running Average Background Subtraction Algorithm in FPGA for Image Processing Applications

In this paper a new background subtraction algorithm was developed to detect moving objects from a stable system in which visual surveillance plays a major role. Initially it was implemented in MATLAB. Among all existing algorithms running average algorithm was choosen because of low computational complexity which is the major parameter of time in VLSI. The concept of the background subtraction is to subtract the current image with respect to the reference image and compare it with to the certain threshold values. We propose a new real time background subtraction algorithm which was implemented with verilog hdl in order to detect moving objects accurately. Our method involves three important modules background modelling; adaptive threshold estimation and finally fore ground extraction. Compared to all existing algorithms our method having low power consumption and low resource utilization. Here we have written the
Developing Leadership Character in Business Programs

Our objective is to encourage and enable leadership character development in business education. Building on a model of character strengths and their link to virtues, values, and ethical decision making, we describe an approach to develop leadership character at the individual, group, and organizational levels. We contrast this approach to existing practices that have focused on teaching functional content over character and address how business educators can enable leadership character development through their own behaviors, relationships, and structures. Most important, we provide concrete suggestions on how to integrate a focus on character development into existing business programs, both in terms of individual courses as well as the overall curriculum. We highlight that the development of leadership character must extend beyond student engagement in a course since “it takes a village” to develop character.
Emotion recognition from physiological signals using wireless sensors for presence technologies

In this article we describe a new approach to enhance presence technologies. First, we discuss the strong relationship between cognitive processes and emotions and how human physiology is uniquely affected when experiencing each emotion. Secondly, we introduce our prototype multimodal affective user interface. In the remainder of the paper we describe the emotion elicitation experiment we designed and conducted and the algorithms we implemented to analyse the physiological signals associated with emotions. These algorithms can then be used to recognise the affective states of users from physiological data collected via non-invasive technologies. The affective intelligent user interfaces we plan to create will adapt to user affect dynamically in the current context, thus providing enhanced social presence.
Socioeconomic status and health. The challenge of the gradient.

Socioeconomic status (SES) is consistently associated with health outcomes, yet little is known about the psychosocial and behavioral mechanisms that might explain this association. Researchers usually control for SES rather than examine it. When it is studied, only effects of lower, poverty-level SES are generally examined. However, there is evidence of a graded association with health at all levels of SES, an observation that requires new thought about domains through which SES may exert its health effects. Variables are highlighted that show a graded relationship with both SES and health to provide examples of possible pathways between SES and health end points. Examples are also given of new analytic approaches that can better illuminate the complexities of the SES-health gradient.
The Ethical Implications of HCI's Turn to the Cultural

We explore the ethical implications of HCI's turn to the ‘cultural’. This is motivated by an awareness of how cultural applications, in our case interactive performances, raise ethical issues that may challenge established research ethics processes. We review research ethics, HCI's engagement with ethics and the ethics of theatrical performance. Following an approach grounded in Responsible Research Innovation, we present the findings from a workshop in which artists, curators, commissioners, and researchers explored ethical challenges revealed by four case studies. We identify six ethical challenges for HCI's engagement with cultural applications: transgression, boundaries, consent, withdrawal, data, and integrity. We discuss two broader implications of these: managing tensions between multiple overlapping ethical frames; and the importance of managing ethical challenges during and after an experience as well as beforehand. Finally, we discuss how our findings extend previous discussions of Value Sensitive Design in HCI.
Improved Text Extraction from PDF Documents for Large-Scale Natural Language Processing

The inability of reliable text extraction from arbitrary documents is often an obstacle for large scale NLP based on resources crawled from the Web. One of the largest problems in the conversion of PDF documents is the detection of the boundaries of common textual units such as paragraphs, sentences and words. PDF is a file format optimized for printing and encapsulates a complete description of the layout of a document including text, fonts, graphics and so on. This paper describes a tool for extracting texts from arbitrary PDF files for the support of largescale data-driven natural language processing. Our approach combines the benefits of several existing solutions for the conversion of PDF documents to plain text and adds a language-independent post-processing procedure that cleans the output for further linguistic processing. In particular, we use the PDF-rendering libraries pdfXtk, Apache Tika and Poppler in various configurations. From the output of these tools we recover proper boundaries using on-the-fly language models and languageindependent extraction heuristics. In our research, we looked especially at publications from the European Union, which constitute a valuable multilingual resource, for example, for training statistical machine translation models. We use our tool for the conversion of a large multilingual database crawled from the EU bookshop with the aim of building parallel corpora. Our experiments show that our conversion software is capable of fixing various common issues leading to cleaner data sets in the end.
Spaceship Launch: Designing a Collaborative Exergame for Families

Parents play a critical role in facilitating children's physical activity, as they are an important source of modeling and support. While Human-Computer Interaction (HCI) researchers have explored exergame design for children or adults separately, an important open area of work is identifying design guidelines for family exergames. One question that researchers have increasingly posed is, how can exergames be designed to avoid potential negative consequences of competition? To address these questions we designed Spaceship Launch, an exergame for parents and kids in lower income neighborhoods, where obesity is most prevalent. We describe our iterative design process: the formative study to identify design opportunities, our resulting system, and our field evaluation of the tool. Our findings highlight the impact of SL on physical activity intentions, and how parental preferences for in-game competition were aligned with the psychological needs of relatedness and competence. We conclude with design recommendations for future family-focused exergames.
Precise Undersampling Theorems

Undersampling theorems state that we may gather far fewer samples than the usual sampling theorem while exactly reconstructing the object of interest-provided the object in question obeys a sparsity condition, the samples measure appropriate linear combinations of signal values, and we reconstruct with a particular nonlinear procedure. While there are many ways to crudely demonstrate such undersampling phenomena, we know of only one mathematically rigorous approach which precisely quantifies the true sparsity-undersampling tradeoff curve of standard algorithms and standard compressed sensing matrices. That approach, based on combinatorial geometry, predicts the exact location in sparsity-undersampling domain where standard algorithms exhibit phase transitions in performance. We review the phase transition approach here and describe the broad range of cases where it applies. We also mention exceptions and state challenge problems for future research. Sample result: one can efficiently reconstruct a k-sparse signal of length N from n measurements, provided n ¿ 2k · log(N/n), for (k,n,N) large.k ¿ N.AMS 2000 subject classifications .
The DStar Method for Effective Software Fault Localization

Effective debugging is crucial to producing reliable software. Manual debugging is becoming prohibitively expensive, especially due to the growing size and complexity of programs. Given that fault localization is one of the most expensive activities in program debugging, there has been a great demand for fault localization techniques that can help guide programmers to the locations of faults. In this paper, a technique named DStar (D*) is proposed which can suggest suspicious locations for fault localization automatically without requiring any prior information on program structure or semantics. D* is evaluated across 24 programs, and is compared to 38 different fault localization techniques. Both single-fault and multi-fault programs are used. Results indicate that D* is more effective at locating faults than all the other techniques it is compared to. An empirical evaluation is also conducted to illustrate how the effectiveness of D* increases as the exponent * grows, and then levels off when the exponent * exceeds a critical value. Discussions are presented to support such observations.
The Role of Advanced Sensing in Smart Cities

In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities.
The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function

The ALS Functional Rating Scale (ALSFRS) is a validated rating instrument for monitoring the progression of disability in patients with amyotrophic lateral sclerosis (ALS). One weakness of the ALSFRS as originally designed was that it granted disproportionate weighting to limb and bulbar, as compared to respiratory, dysfunction. We have now validated a revised version of the ALSFRS, which incorporates additional assessments of dyspnea, orthopnea, and the need for ventilatory support. The Revised ALSFRS (ALSFRS-R) retains the properties of the original scale and shows strong internal consistency and construct validity. ALSFRS-R scores correlate significantly with quality of life as measured by the Sickness Impact Profile, indicating that the quality of function is a strong determinant of quality of life in ALS.
New Fast and Accurate Jacobi SVD Algorithm. I

This paper is the result of contrived efforts to break the barrier between numerical accuracy and run time efficiency in computing the fundamental decomposition of numerical linear algebra – the singular value decomposition (SVD) of a general dense matrix. It is an unfortunate fact that the numerically most accurate one–sided Jacobi SVD algorithm is several times slower than generally less accurate bidiagonalization based methods such as the QR or the divide and conquer algorithm. Despite its sound numerical qualities, the Jacobi SVD is not included in the state of the art matrix computation libraries and it is even considered obsolete by some leading researches. Our quest for a highly accurate and efficient SVD algorithm has led us to a new, superior variant of the Jacobi algorithm. The new algorithm has inherited all good high accuracy properties, and it outperforms not only the best implementations of the one–sided Jacobi algorithm but also the QR algorithm. Moreover, it seems that the potential of the new approach is yet to be fully exploited.
End-To-End Integration Testing Design

Integration testing has always been a challenge especially if the system under test is large with many subsystems and interfaces. This paper proposes an approach to design End-toEnd (E2E) integration testing, including test scenario specification, test case generation and tool support. Test scenarios are specified as thin threads, each of which represents a single function from an end user’s point of view. Thin threads can be organized hierarchically into a tree with each branch consisting of a set of related thin threads representing a set of related functionality. A test engineer can use thin-thread trees to generate test cases systematically, as well as carry out other related tasks such as risk analysis and assignment, regression testing, ripple effect analysis. A prototype tool has been developed to support E2E testing in a distributed environment on the J2EE platform.
A New Modular Bipolar High-Voltage Pulse Generator

Adapting power-electronic converters that are used in pulsed-power application attracted considerable attention during recent years. In this paper, a modular bipolar high-voltage pulse generator is proposed based on the voltage multipliers and half-bridge converters concept by using power electronics switches. This circuit is capable to generate repetitive high voltage bipolar pulses with a flexible output pattern, by using low voltage power supply and elements. The proposed topology was simulated in MATLAB/Simulink. To verify the circuit operation a four-stage laboratory prototype has been assembled and tested. The results confirm the theoretical analysis and show the validity of the converter scheme.
Knowledge actionability: satisfying technical and business interestingness

Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance and business expectation. In this paper, we propose a two-way significance framework for measuring knowledge actionability, which highlights both technical interestingness and domain-specific expectations. We further develop a fuzzy interestingness aggregation mechanism to generate a ranked final pattern set balancing technical and business interests. Real-life data mining applications show the proposed knowledge actionability framework can complement technical interestingness while satisfy real user needs.
Locate the Hate: Detecting Tweets against Blacks

Although the social medium Twitter grants users freedom of speech, its instantaneous nature and retweeting features also amplify hate speech. Because Twitter has a sizeable black constituency, racist tweets against blacks are especially detrimental in the Twitter community, though this effect may not be obvious against a backdrop of half a billion tweets a day. We apply a supervised machine learning approach, employing inexpensively acquired labeled data from diverse Twitter accounts to learn a binary classifier for the labels “racist” and “nonracist.” The classifier has a 76% average accuracy on individual tweets, suggesting that with further improvements, our work can contribute data on the sources of anti-black hate speech.
Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach

After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to "see" which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for indoor scene model training and communication with an Android client. To evaluate the performance, comparison experiments are conducted and the results demonstrate that a positioning accuracy of 1.32 m at 95% is achievable with the proposed solution. Both positioning accuracy and robustness are enhanced compared to approaches without scene constraint including commercial products such as IndoorAtlas.
A game-theoretic method of fair resource allocation for cloud computing services

As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions.
Universal approximation using incremental constructive feedforward networks with random hidden nodes

According to conventional neural network theories, single-hidden-layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes are universal approximators when all the parameters of the networks are allowed adjustable. However, as observed in most neural network implementations, tuning all the parameters of the networks may cause learning complicated and inefficient, and it may be difficult to train networks with nondifferential activation functions such as threshold networks. Unlike conventional neural network theories, this paper proves in an incremental constructive method that in order to let SLFNs work as universal approximators, one may simply randomly choose hidden nodes and then only need to adjust the output weights linking the hidden layer and the output layer. In such SLFNs implementations, the activation functions for additive nodes can be any bounded nonconstant piecewise continuous functions g : R --> R and the activation functions for RBF nodes can be any integrable piecewise continuous functions g : R --> R and integral of R g(x)dx not equal to 0. The proposed incremental method is efficient not only for SFLNs with continuous (including nondifferentiable) activation functions but also for SLFNs with piecewise continuous (such as threshold) activation functions. Compared to other popular methods such a new network is fully automatic and users need not intervene the learning process by manually tuning control parameters.
An Ensemble Model for Diabetes Diagnosis in Large-scale and Imbalanced Dataset

Diabetes is becoming a more and more serious health challenge worldwide with the yearly rising prevalence, especially in developing countries. The vast majority of diabetes are type 2 diabetes, which has been indicated that about 80% of type 2 diabetes complications can be prevented or delayed by timely detection. In this paper, we propose an ensemble model to precisely diagnose the diabetic on a large-scale and imbalance dataset. The dataset used in our work covers millions of people from one province in China from 2009 to 2015, which is highly skew. Results on the real-world dataset prove that our method is promising for diabetes diagnosis with a high sensitivity, F3 and G --- mean, i.e, 91.00%, 58.24%, 86.69%, respectively.
Visual Gesture Character String Recognition by Classification-Based Segmentation with Stroke Deletion

The recognition of character strings in visual gestures has many potential applications, yet the segmentation of characters is a great challenge since the pen lift information is not available. In this paper, we propose a visual gesture character string recognition method using the classification-based segmentation strategy. In addition to the character classifier and character geometry models used for evaluating candidate segmentation-recognition paths, we introduce deletion geometry models for deleting stroke segments that are likely to be ligatures. To perform experiments, we built a Kinect-based fingertip trajectory capturing system to collect gesture string data. Experiments of digit string recognition show that the deletion geometry models improve the string recognition accuracy significantly. The string-level correct rate is over 80%.