nils-herrmann/clean_scirepeval huggingface dataset:
Wireless Hand Gesture Recognition Based on Continuous-Wave Doppler Radar Sensors

Short-range continuous-wave Doppler radar sensors have been mainly used for noncontact detection of various motions. In this paper, we investigate the feasibility to implement the function of a remote mouse, an input device of a computer, by recognizing human gestures based on a dual-channel Doppler radar sensor. Direct conversion architecture, symmetric subcarrier modulation, and bandpass sampling techniques are used to obtain a cost-effective solution. An arcsine algorithm and a motion imaging algorithm are proposed to linearly reconstruct the hand and finger motions in a 2-D plane from the demodulated Doppler phase shifts. Experimental results verified the effectiveness of the proposed architecture and algorithms. Different from the frequency-domain “micro-Doppler” approach, the proposed remote gesture recognition based on linear motion reconstruction is able to recognize definitive signatures for the corresponding motions, exhibiting promising potential in practical applications of human-computer interaction.
Assessment of Motor Functioning in the Preschool Period

The assessment of motor functioning in young children has become increasingly important in recent years with the acknowledgement that motor impairment is linked with cognitive, language, social and emotional difficulties. However, there is no one gold standard assessment tool to investigate motor ability in children. The aim of the current paper was to discuss the issues related to the assessment of motor ability in young pre-school children and to provide guidelines on the best approach for motor assessment. The paper discusses the maturational changes in brain development at the preschool level in relation to motor ability. Other issues include sex differences in motor ability at this young age, and evidence for this in relation to sociological versus biological influences. From the previous literature it is unclear what needs to be assessed in relation to motor functioning. Should the focus be underlying motor processes or movement skill assessment? Several key assessment tools are discussed that produce a general measure of motor performance followed by a description of tools that assess specific skills, such as fine and gross motor, ball and graphomotor skills. The paper concludes with recommendations on the best approach in assessing motor function in pre-school children.
Multi-layer FSS for gain improvement of a wide-band stacked printed antenna

In this paper, an attempt has been made to improve the gain of a wide-band antenna using two layers of frequency-selective surfaces. It is seen that using two layers it is possible to achieve flat transmission characteristics over a reasonable bandwidth. However with an actual antenna, the gain improvement has at present been achieved over only a small band. It is expected that with further optimization and possibly increasing the number of layers, wide-band gain enhancement can be achieved.
Tibial periosteal ganglion cyst: The ganglion in disguise

Soft tissue ganglions are commonly encountered cystic lesions around the wrist presumed to arise from myxomatous degeneration of periarticular connective tissue. Lesions with similar pathology in subchondral location close to joints, and often simulating a geode, is the less common entity called intraosseous ganglion. Rarer still is a lesion produced by mucoid degeneration and cyst formation of the periostium of long bones, rightly called the periosteal ganglion. They are mostly found in the lower extremities at the region of pes anserinus, typically limited to the periosteum and outer cortex without any intramedullary component. We report the case of a 62 year-old male who presented with a tender swelling on the mid shaft of the left tibia, which radiologically suggested a juxtacortical lesion extending to the soft tissue or a soft tissue neoplasm eroding the bony cortex of tibia. It was later diagnosed definitively as a periosteal ganglion in an atypical location, on further radiologic work-up and histopathological correlation.
Real-Time Online Action Detection Forests Using Spatio-Temporal Contexts

Online action detection (OAD) is challenging since 1) robust yet computationally expensive features cannot be straightforwardly used due to the real-time processing requirements and 2) the localization and classification of actions have to be performed even before they are fully observed. We propose a new random forest (RF)-based online action detection framework that addresses these challenges. Our algorithm uses computationally efficient skeletal joint features. High accuracy is achieved by using robust convolutional neural network (CNN)-based features which are extracted from the raw RGBD images, plus the temporal relationships between the current frame of interest, and the past and futures frames. While these high-quality features are not available in real-time testing scenario, we demonstrate that they can be effectively exploited in training RF classifiers: We use these spatio-temporal contexts to craft RF's new split functions improving RFs' leaf node statistics. Experiments with challenging MSRAction3D, G3D, and OAD datasets demonstrate that our algorithm significantly improves the accuracy over the state-of-the-art on-line action detection algorithms while achieving the real-time efficiency of existing skeleton-based RF classifiers.
Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study.

We aimed to identify the indicators of healthcare fraud and abuse in general physicians' drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse.



We applied data mining approach to a major health insurance organization dataset of private sector general physicians' prescription claims. It involved 5 steps: clarifying the nature of the problem and objectives, data preparation, indicator identification and selection, cluster analysis to identify suspect physicians, and discriminant analysis to assess the validity of the clustering approach.



Thirteen indicators were developed in total. Over half of the general physicians (54%) were 'suspects' of conducting abusive behavior. The results also identified 2% of physicians as suspects of fraud. Discriminant analysis suggested that the indicators demonstrated adequate performance in the detection of physicians who were suspect of perpetrating fraud (98%) and abuse (85%) in a new sample of data.



Our data mining approach will help health insurance organizations in low-and middle-income countries (LMICs) in streamlining auditing approaches towards the suspect groups rather than routine auditing of all physicians.
A systematic literature review: Information security culture

Human behavior inside organizations is considered the main threat to organizations. Moreover, in information security the human element consider the most of weakest link in general. Therefore it is crucial to create an information security culture to protect the organization's assets from inside and to influence employees' security behavior. This paper focuses on identifying the definitions and frameworks for establishing and maintaining information security culture inside organizations. It presents work have been done to conduct a systematic literature review of papers published on information security culture from 2003 to 2016. The review identified 68 papers that focus on this area, 18 of which propose an information security culture framework. An analysis of these papers indicate there is a positive relationship between levels of knowledge and how employees behave. The level of knowledge significantly affects information security behavior and should be considered as a critical factor in the effectiveness of information security culture and in any further work that is carried out on information security culture. Therefore, there is a need for more studies to identity the security knowledge that needs to be incorporated into organizations and to find instances of best practice for building an information security culture within organizations.
INTERACTIVE SAND-COVERED TERRAIN SURFACE MODEL WITH HAPTIC FEEDBACK

We describe a method for an intuitive haptic rendering and modeling of GIS terrain models represented as triangular irregular networks (TIN). The TIN terrain model does not allow representation of concavities and overhangs but it is sufficient for most GIS applications. Model editing is achieved by a set of virtual tools with various shapes which are controlled by a haptic device with force feedback. The haptic feedback makes manipulation with the terrain model much more intuitive than by the mouse, because it operates in 3D and it also provides better control with the force. The haptic device is point-based, so the forces provided by different tools must be mapped to a point. This, in effect, helps eliminating certain vibrations that could be caused by numerical errors and insufficient scene sampling. Our approach allows an adaptive adjustment of the level of detail of the model according to the terrain properties and user requirements. Haptic visualization in GIS models has been already used but such solutions are not common. Unlike existing approaches, our solution uses a general shape of the tool, it allows interactive editing of the TIN model, and it utilizes parallelism and coherence between consecutive states of the model that make haptic visualization faster and smoother.
Precision open-ended coaxial probes for in vivo and ex vivo dielectric spectroscopy of biological tissues at microwave frequencies

Hermetic stainless-steel open-ended coaxial probes have been designed for precision dielectric spectroscopy of biological tissue, such as breast tissue, over the 0.5-20-GHz frequency range. Robust data-processing techniques have also been developed for extracting the unknown permittivity of the tissue under test from the reflection coefficient measured with the precision probe and a vector network analyzer. The first technique, referred to as a reflection-coefficient deembedding method, converts the reflection coefficient measured at the probe's calibration plane to the desired aperture-plane reflection coefficient. The second technique uses a rational function model to solve the inverse problem, i.e., to convert the aperture-plane reflection coefficient to the tissue permittivity. The results of the characterization and validation studies demonstrate that these precision probes, used with the prescribed measurement protocols and data-processing techniques, provide highly accurate and reliable in vivo and ex vivo biological tissue measurements, including breast tissue spectroscopy.
Automated and Cooperative Vehicle Merging at Highway On-Ramps

Recognition of necessities of connected and automated vehicles (CAVs) is gaining momentum. CAVs can improve both transportation network efficiency and safety through control algorithms that can harmonically use all existing information to coordinate the vehicles. This paper addresses the problem of optimally coordinating CAVs at merging roadways to achieve smooth traffic flow without stop-and-go driving. We present an optimization framework and an analytical closed-form solution that allows online coordination of vehicles at merging zones. The effectiveness of the efficiency of the proposed solution is validated through a simulation, and it is shown that coordination of vehicles can significantly reduce both fuel consumption and travel time.
Digital Marketing Maturity Models : Overview and Comparison

The variety of available digital tools, strategies and activities might confuse and disorient even an experienced marketer. This applies in particular to B2B companies, which are usually less flexible in uptaking of digital technology than B2C companies. B2B companies are lacking a framework that corresponds to the specifics of the B2B business, and which helps to evaluate a company’s capabilities and to choose an appropriate path. A B2B digital marketing maturity model helps to fill this gap. However, modern marketing offers no widely approved digital marketing maturity model, and thus, some marketing institutions provide their own tools. The purpose of this paper is building an optimized B2B digital marketing maturity model based on a SWOT (strengths, weaknesses, opportunities, and threats) analysis of existing models. The current study provides an analytical review of the existing digital marketing maturity models with open access. The results of the research are twofold. First, the provided SWOT analysis outlines the main advantages and disadvantages of existing models. Secondly, the strengths of existing digital marketing maturity models, helps to identify the main characteristics and the structure of an optimized B2B digital marketing maturity model. The research findings indicate that only one out of three analyzed models could be used as a separate tool. This study is among the first examining the use of maturity models in digital marketing. It helps businesses to choose between the existing digital marketing models, the most effective one. Moreover, it creates a base for future research on digital marketing maturity models. This study contributes to the emerging B2B digital marketing literature by providing a SWOT analysis of the existing digital marketing maturity models and suggesting a structure and main characteristics of an optimized B2B digital marketing maturity model. B2B digital marketing strategy, digital marketing, digital marketing maturity model, SWOT analysis.
Innovating with Concept Mapping

Student-generated concept maps (Cmaps) have been the preferred choice to design assessment tasks, which are time-consuming in real classroom settings. We have proposed the use of Cmap with errors (elaborated by teachers) to develop assessment tasks, as a way to address the logistical practicality obstacles usually found in classrooms. This paper compared two different tasks, finding the errors and judging the selected propositions in Cmap with errors, exploring two topics with different levels of difficulty. Our results confirmed Cmap with errors as a straightforward approach to include concept mapping into the classroom routine to foster the pedagogic resonance (the bridge between teacher knowledge and student learning), which is critical for motivating students to learn meaningfully. Moreover, Cmaps with errors are also amenable for on-line learning platforms. Future works of our research group will develop an automated process for evaluation and feedback using the task formats presented in this paper.
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl

We present DEPCC, the largest-to-date linguistically analyzed corpus in English including 365 million documents, composed of 252 billion tokens and 7.5 billion of named entity occurrences in 14.3 billion sentences from a web-scale crawl of the COMMON CRAWL project. The sentences are processed with a dependency parser and with a named entity tagger and contain provenance information, enabling various applications ranging from training syntax-based word embeddings to open information extraction and question answering. We built an index of all sentences and their linguistic meta-data enabling quick search across the corpus. We demonstrate the utility of this corpus on the verb similarity task by showing that a distributional model trained on our corpus yields better results than models trained on smaller corpora, like Wikipedia. This distributional model outperforms the state of art models of verb similarity trained on smaller corpora on the SimVerb3500 dataset.
Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

Currently autonomous or self-driving vehicles are at the heart of academia and industry research becauseof itsmulti-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a givenorigin to agivendestination,motionplanningmethods incorporate searching for apath to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have beenproposed in the literature. Thepurpose of this paper is to reviewexisting approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1)findingapath, (2) searching for the safestmanoeuvre and (3)determining themost feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existingmethods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performanceofplanningalgorithmsat all three levels. Somepromising areasof future focushave been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical reviewon planning techniques presented in this paper, along with theassociateddiscussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research.
Reduction of Unbalanced Axial Magnetic Force in Postfault Operation of a Novel Six-Phase Double-Stator Axial-Flux PM Machine Using Model Predictive Control

This paper investigates the postfault operation of a novel six-phase double-stator axial-flux permanent-magnet machine with detached winding connection. In previous research, this configuration was found to be superior to existing winding connection except that its unbalanced magnetic force in the postfault operation cannot be ignored. In this paper, an axial magnetic force balancing method (AMFBM) is proposed to reduce unbalanced magnetic force by deducing a set of special winding current. By comparing electromagnetic torque and axial magnetic force of the traditional winding current and the proposed method in postfault operations through a finite-element analysis, it is verified that AMFBM can reduce most of the unbalanced axial magnetic force as well as keeping torque ripple at a low level. In order to realize the AMFBM, finite control set model predictive control is adopted. A postfault model of dual-three-phase permanent-magnet machines with modified vector space decomposition method is first brought forward to predict the future behavior of the controlled variables with various voltage inputs. After that, a cost function is designed to track the desired winding current by evaluating all the predictions to decide the next step of the inverter. Experimental results show that the control scheme performs well in both dynamic and steady-state situations.
Adaptive Role Switching in Socially Interactive Agents for Children ’ s Language Learning

Learning language and literacy at a young age is important, as children’s early language ability can impact their later educational success [1][2]. However, one of the major barriers to early language and literacy learning for many children around the globe is a lack of resources in homes and schools. A variety of technological interventions, such as TV series and educational apps, were designed to help overcome such barriers and support children’s learning. However, not all of them necessarily provide children with conversational experiences, which have been found to significantly impact the children’s language-related neural development . Among a variety of educational media, embodied interactive agents (e.g., social robots) seem to be an effective yet resource-efficient tool that can enable children to learn through conversational turn taking. Specifically, embodied interactive agents can serve as learning companions for young children and provide more interactive and immersive learning experience. I explored how social robots could help promote children’s language and literacy learning. More specifically, I designed and computationally created a collaborative, engaging learning interaction between a robot and a child who play as peers. First, I designed a tablet-based literacy learning game called WordQuest using the design principles for educational games. Second, I developed a reinforcement learning model that enabled the robot to adaptively switch its collaborative roles (e.g., expert and novice roles) in a way that promoted children’s best learning. Third, I conducted an experiment with three conditions, which were fixed expert robot, fixed novice robot, and adaptive role switching robot, and tested on 60 children recruited from a local primary school in Boston. Last, I evaluated how the robot’s collaborative roles differentially affected children’s learning performance, engagement, and perception of the learning experiences. I found out that children across the three conditions all learned new words and had a very positive experience of playing WordQuest with the robot. In addition, children interacting with the adaptive robot consistently outperformed children from the other two conditions in terms of vocabulary acquisition and retention.  Associate Professor of Media Arts and Sciences
A3P: adaptive policy prediction for shared images over popular content sharing sites

More and more people go online today and share their personal images using popular web services like Picasa. While enjoying the convenience brought by advanced technology, people also become aware of the privacy issues of data being shared. Recent studies have highlighted that people expect more tools to allow them to regain control over their privacy. In this work, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. In particular, we examine the role of image content and metadata as possible indicators of users' privacy preferences. We propose a two-level image classification framework to obtain image categories which may be associated with similar policies. Then, we develop a policy prediction algorithm to automatically generate a policy for each newly uploaded image. Most importantly, the generated policy will follow the trend of the user's privacy concerns evolved with time. We have conducted an extensive user study and the results demonstrate effectiveness of our system with the prediction accuracy around 90%.
Case studies of fault diagnosis and energy saving in buildings using data mining techniques

Building Automation Systems (BASs) have been developed to provide a safe, comfortable, and energy-efficient indoor environment for households. A tremendous volume and enormous variety of building data are collected and stored in BASs. However, these data could not be completely utilized by traditional data analytics due to the huge volume and heterogeneous nature. In this paper, case studies are carried out using data mining techniques to find the potential value and discover the hidden knowledge in building area. We proposed a new fault diagnosis approach for chillers/AHU during operation. A promising guide to knowledge selection and interpretation in associate rule mining is presented to capture more energy saving potential opportunities. We also demonstrate the importance of occupancy information in improving building operation performance. The approaches and findings presented are not only for these cases but also feasible for more applications.