nils-herrmann/clean_scirepeval huggingface dataset:
Formal Semantics and Analysis of BPMN Process Models using Petri Nets ?

The Business Process Modelling Notation (BPMN) is a standard for capturing business processes in the early phases of systems development. The mix of constructs found in BPMN makes it possible to obtain models with a range of semantic errors. The ability to statically check the semantic correctness of models is thus a desirable feature for modelling tools based on BPMN. However, the static analysis of BPMN models is hindered by ambiguities in the standard specification and the complexity of the language. The fact that BPMN integrates constructs from graph-oriented process definition languages with features for concurrent execution of multiple instances of a subprocess and exception handling, makes it challenging to provide a formal semantics of BPMN. Even more challenging is to define a semantics that can be used to analyse BPMN models. This paper proposes a formal semantics of BPMN defined in terms of a mapping to Petri nets, for which efficient analysis techniques exist. The proposed mapping has been implemented as a tool that generates code in the Petri Net Markup Language. This formalisation exercise has also led to the identification of a number of deficiencies in the BPMN standard specification.
The volcanormal density for radar-based extended target tracking

This paper presents a novel shape model for radar-based vehicle tracking. This model is a special probability density function (pdf), designed to approximate the radar measurement spread of a vehicle more adequately than widely used models. While this pdf is used to determine the likelihood of the spatial measurements, additionally a recent Doppler measurement model is integrated in a maximum likelihood estimator. This formulation allows the estimation of a vehicle's pose, motion and axis lengths based on the radar's spatial and Doppler dimension. On this basis, an efficient computation method is derived. An evaluation of actual radar and ground truth data demonstrates the precise estimation of a vehicle's state.
Distributed Compressed Sensing off the Grid

This letter investigates the joint recovery of a frequency-sparse signal ensemble sharing a common frequency-sparse component from the collection of their compressed measurements. Unlike conventional arts in compressed sensing, the frequencies follow an off-the-grid formulation and are continuously valued in [0, 1]. As an extension of atomic norm, the concatenated atomic norm minimization approach is proposed to handle the exact recovery of signals, which is reformulated as a computationally tractable semidefinite program. The optimality of the proposed approach is characterized using a dual certificate. Numerical experiments are performed to illustrate the effectiveness of the proposed approach and its advantage over separate recovery.
Region-Based Segmentation versus Edge Detection

This paper, we will review the main approaches of partitioning an image into regions by using gray values in order to reach a correct interpretation of the image. We mainly compare the region-based segmentation with the boundary estimation using edge detection. Image segmentation is an important step for many image processing and computer vision algorithms while an edge can be described informally as the boundary between adjacent parts of an image. A formal definition is elusive, but edge detection is nonetheless a useful and ubiquitous image processing task. After comparing we have come to a conclusion that the edge detection has advantage of not necessarily needing closed boundaries and also its computation is based on difference. The region-segmentation in spite of improving multi-spectral images has the drawback of being applied only on closed boundaries. To reach the result of edge detection we have used the technique of performance metrics and Canny edge detection. We have applied Canny ground truth to acquire more features via displaying more details.
Dynamic terrain traversal skills using reinforcement learning

The locomotion skills developed for physics-based characters most often target flat terrain. However, much of their potential lies with the creation of dynamic, momentum-based motions across more complex terrains. In this paper, we learn controllers that allow simulated characters to traverse terrains with gaps, steps, and walls using highly dynamic gaits. This is achieved using reinforcement learning, with careful attention given to the action representation, non-parametric approximation of both the value function and the policy; epsilon-greedy exploration; and the learning of a good state distance metric. The methods enable a 21-link planar dog and a 7-link planar biped to navigate challenging sequences of terrain using bounding and running gaits. We evaluate the impact of the key features of our skill learning pipeline on the resulting performance.
Haptic Feedback in Room-Scale VR

Virtual reality (VR) is now becoming a mainstream medium. Current systems like the HTC Vive offer accurate tracking of the HMD and controllers, which allows for highly immersive interactions with the virtual environment. The interactions can be further enhanced by adding feedback. As an example, a controller can vibrate when it is close to a grabbable ball. As such interactions are not exhaustingly researched, we conducted a user study. Specifically, we examine: 1. grabbing and throwing with controllers in a simple basketball game. 2. the influence of haptic and optical feedback on performance, presence, task load, and usability. 3. the advantages of VR over desktop for point-cloud editing. Several new techniques emerged from the point-cloud editor for VR. The bi-manual pinch gesture, which extends the handlebar metaphor, is a novel viewing method used to translate, rotate, and scale the point-cloud. Our new rendering technique uses the geometry shader to draw sparse point clouds quickly. The selection volumes at the controllers are our new technique to efficiently select points in point clouds. The resulting selection is visualized in real time. The results of the user study show that: 1. grabbing with a controller button is intuitive but throwing is not. Releasing a button is a bad metaphor for releasing a grabbed virtual object in order to throw it. 2. any feedback is better than none. Adding haptic, optical, or both feedback types to the grabbing improves the user performance and presence. However, only sub-scores like accuracy and predictability are significantly improved. Usability and task load are mostly unaffected by feedback. 3. the point-cloud editing is significantly better in VR with the bi-manual pinch gesture and selection volumes than on the desktop with the orbiting camera and lasso selections.
Gradient-based low rank method and its application in image inpainting

Conventional inpainting methods generally apply textures that are most similar to the areas around the missing region or use large image database. Recently, low rank property of data shows that the non-convex optimization decreases measurements. In this paper, we propose a new image prior, which implies the low rank prior knowledge of image gradients. The proposed detail-preserving image inpainting algorithm adopts the low rank regularization to gradient similarity minimization, termed gradient-based low rank approximation (Grad-LR), namely that we employ the low rank constraints in the horizontal and vertical gradients of the image and then reconstruct the desired image using the adaptive iterative singular-value thresholding of both derivatives. In the method, by incorporating the spatially adaptive iterative singular-value thresholding (SAIST) to optimize our gradient scheme, the deterministic annealing iterates the procedure efficiently. As a result, the strength of the algorithm is obvious when filling large missing region. Experimental results consistently demonstrate that the proposed algorithm works well for both structural and texture images and outperforms other techniques, in terms of both objective and subjective performance measures.
A deep learning approach to document image quality assessment

This paper proposes a deep learning approach for document image quality assessment. Given a noise corrupted document image, we estimate its quality score as a prediction of OCR accuracy. First the document image is divided into patches and non-informative patches are sifted out using Otsu's binarization technique. Second, quality scores are obtained for all selected patches using a Convolutional Neural Network (CNN), and the patch scores are averaged over the image to obtain the document score. The proposed CNN contains two layers of convolution, location blind max-min pooling, and Rectified Linear Units in the fully connected layers. Experiments on two document quality datasets show our method achieved the state of the art performance.
A Hybrid Document Feature Extraction Method Using Latent Dirichlet Allocation and Word2Vec

Latent Dirichlet Allocation (LDA) is a probabilistic topic model to discover latent topics from documents and describe each document with a probability distribution over the discovered topics. It defines a global hierarchical relationship from words to a topic and then from topics to a document. Word2Vec is a word-embedding model to predict a target word from its surrounding contextual words. In this paper, we propose a hybrid approach to extract features from documents with bag-of-distances in a semantic space. By using both Word2Vec and LDA, our hybrid method not only generates the relationships between documents and topics, but also integrates the contextual relationships among words. Experimental results indicate that document features generated by our hybrid method are useful to improve classification performance by consolidating both global and local relationships.
TrajAnalytics: A Web-Based Visual Analytics Software of Urban Trajectory

We present a web-based software, named TrajAnalytics, for the visual analytics of urban trajectory datasets. It allows users to interactively visualize and analyze the massive taxi trajectories over urban spaces. The software offers data management capability and enable various visual queries through a web interface. A set of visualization widgets and interaction functions are developed to promote easy user engagement. We implemented two prototypes with realworld datasets: (1) the origin/destination (OD) data of taxi trips collected in New York City; and (2) the taxi trajectory data collected in Porto City in Portugal. This open source software supports practitioners, researchers, and decision-makers in advancing transportation and urban studies in the new era of smart city.
Umbilical pilonidal sinus: a new treatment technique of sinus excision with umbilical preservation.

Under general anesthesia, careful inspection revealed a 3-mm ulcerated site in the depth of the umbilicus from which bits of hair protruded. A vertical skin incision through the umbilicus skirting one side of the sinuses was made. Diathermy cutting was used to minimize bleeding and ensure visibility of the lesion. There were several strands of hair projecting through three sinus openings . These communicated with a single subcutaneous tract that reached the sheath in the midline but did not branch laterally. All three openings and the tract were completely excised using a probe as a guide , approaching the tract superiorly, inferiorly, and laterally deep to the umbilicus, excluding the umbilical cicatrix and thus preserving it. The skin was closed primarily with subcuticular polypropylene, and the patient was given a course of antibiotics. He recovered uneventfully, had the sutures removed 10 days postoperatively, and has had no recurrence at 2 years.
Efficient batchwise dropout training using submatrices

Dropout is a popular technique for regularizing artificial neural networks. Dropout networks are generally trained by minibatch gradient descent with a dropout mask turning off some of the units—a different pattern of dropout is applied to every sample in the minibatch. We explore a very simple alternative to the dropout mask. Instead of masking dropped out units by setting them to zero, we perform matrix multiplication using a submatrix of the weight matrix—unneeded hidden units are never calculated. Performing dropout batchwise, so that one pattern of dropout is used for each sample in a minibatch, we can substantially reduce training times. Batchwise dropout can be used with fully-connected and convolutional neural networks. 1 Independent versus batchwise dropout Dropout is a technique to regularize artificial neural networks—it prevents overfitting [8]. A fully connected network with two hidden layers of 80 units each can learn to classify the MNIST training set perfectly in about 20 training epochs—unfortunately the test error is quite high, about 2%. Increasing the number of hidden units by a factor of 10 and using dropout results in a lower test error, about 1.1%. The dropout network takes longer to train in two senses: each training epoch takes several times longer, and the number of training epochs needed increases too. We consider a technique for speeding up training with dropout—it can substantially reduce the time needed per epoch. Consider a very simple `-layer fully connected neural network with dropout. To train it with a minibatch of b samples, the forward pass is described by the equations: xk+1 = [xk · dk]×Wk k = 0, . . . , `− 1.
Exploring the therapeutic potential of Ayahuasca: acute intake increases mindfulness-related capacities

Ayahuasca is a psychotropic plant tea used for ritual purposes by the indigenous populations of the Amazon. In the last two decades, its use has expanded worldwide. The tea contains the psychedelic 5-HT2A receptor agonist N,N-dimethyltryptamine (DMT), plus β-carboline alkaloids with monoamine-oxidase-inhibiting properties. Acute administration induces an introspective dream-like experience characterized by visions and autobiographic and emotional memories. Studies of long-term users have suggested its therapeutic potential, reporting that its use has helped individuals abandon the consumption of addictive drugs. Furthermore, recent open-label studies in patients with treatment-resistant depression found that a single ayahuasca dose induced a rapid antidepressant effect that was maintained weeks after administration. Here, we conducted an exploratory study of the psychological mechanisms that could underlie the beneficial effects of ayahuasca. We assessed a group of 25 individuals before and 24 h after an ayahuasca session using two instruments designed to measure mindfulness capacities: The Five Facets Mindfulness Questionnaire (FFMQ) and the Experiences Questionnaire (EQ). Ayahuasca intake led to significant increases in two facets of the FFMQ indicating a reduction in judgmental processing of experiences and in inner reactivity. It also led to a significant increase in decentering ability as measured by the EQ. These changes are classic goals of conventional mindfulness training, and the scores obtained are in the range of those observed after extensive mindfulness practice. The present findings support the claim that ayahuasca has therapeutic potential and suggest that this potential is due to an increase in mindfulness capacities.
Framework for a Digital Forensic Investigation

Forensics is essential for the successful prosecution of computer criminals. For a forensic investigation to be performed successfully there are a number of important steps that have to be considered and taken. The aim of this paper is to define a clear, step-by-step framework for the collection of evidence suitable for presentation in a court of law. Existing forensic models will be surveyed and then adapted to create a specific application framework for single computer, entry point forensics.
InsDal: A safe and extensible instrumentation tool on Dalvik byte-code for Android applications

Program instrumentation is a widely used technique in dynamic analysis and testing, which makes use of probe code inserted to the target program to monitor its behaviors, or log runtime information for off-line analysis. There are a number of automatic tools for instrumentation on the source or byte code of Java programs. However, few works address this issue on the register-based Dalvik byte-code of ever-increasing Android apps. This paper presents a lightweight tool, InsDal, for inserting instructions to specific points of the Dalvik byte-code according to the requirements of users. It carefully manages the registers to protect the behavior of original code from illegal manipulation, and optimizes the inserted code to avoid memory waste and unnecessary overhead. This tool is easy to use and has been applied to several scenarios (e.g. energy analysis, code coverage analysis). A demo video of our tool can be found at the website: https://www.youtube.com/watch?v=Fpw-aygZ3kE.
PCANet: A Simple Deep Learning Baseline for Image Classification?

In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets for different tasks, including Labeled Faces in the Wild (LFW) for face verification; the MultiPIE, Extended Yale B, AR, Facial Recognition Technology (FERET) data sets for face recognition; and MNIST for hand-written digit recognition. Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)]. Even more surprisingly, the model sets new records for many classification tasks on the Extended Yale B, AR, and FERET data sets and on MNIST variations. Additional experiments on other public data sets also demonstrate the potential of PCANet to serve as a simple but highly competitive baseline for texture classification and object recognition.
The effects of specific learning difficulties on processes of multilingual language development

This paper reviews current research findings on how specific learning difficulties (SLDs) impact on the processes of multilingual language development. The review includes studies of young language learners in instructed classroom settings as well as of multilingual children in second language contexts. The paper starts with a definition of the concepts of disability and SLDs. Next, it discusses the interaction of cognitive factors with L1 and L2 literacy development and L2 learning. The paper also outlines possible ways in which SLDs can be identified in different L2 learning contexts. A detailed analysis of how cognitive factors influence the development of second language skills of young learners with learning difficulties is given in the last part of the paper.
Occupational therapy interventions for employment and education for adults with serious mental illness: a systematic review.

In this systematic review, we investigated research literature evaluating the effectiveness of occupational therapy interventions focusing on participation and performance in occupations related to paid and unpaid employment and education for people with serious mental illness. The review included occupation- and activity-based interventions and interventions addressing performance skills, aspects of the environment, activity demands, and client factors. The results indicate that strong evidence exists for the effectiveness of supported employment using individual placement and support to result in competitive employment. These outcomes are stronger when combined with cognitive or social skills training. Supported education programs emphasizing goal setting, skill development, and cognitive training result in increased participation in educational pursuits. The evidence for instrumental activities of daily living interventions that targeted specific homemaking occupations and supported parenting was limited but positive. Environmental cognitive supports, such as signs, and other compensatory strategies are useful in managing maladaptive behavior.
A Probabilistic Framework for Real-time 3D Segmentation using Spatial, Temporal, and Semantic Cues

The best-matching segment is then assigned to this groundtruth bounding box for the evaluation metric described in our paper, as well as for the metric described below. Some previous works have evaluated 3D segmentation using the intersection-over-union metric on 3D points . Note that our method segments the entire scene, as opposed to the method of Wang et al. , so the evaluation metric from Wang et al.  does not directly apply. However, we could modify the intersection-over-union metric  as follows: we can compute the fraction of ground-truth bounding boxes which have an intersection-over-union score less than a threshold τIOU , as
A comparative analysis of data center network architectures

Advances in data intensive computing and high performance computing facilitate rapid scaling of data center networks, resulting in a growing body of research exploring new network architectures that enhance scalability, cost effectiveness and performance. Understanding the tradeoffs between these different network architectures could not only help data center operators improve deployments, but also assist system designers to optimize applications running on top of them. In this paper, we present a comparative analysis of several well known data center network architectures using important metrics, and present our results on different network topologies. We show the tradeoffs between these topologies and present implications on practical data center implementations.
Using ontologies to improve semantic interoperability in health data.

The present-day health data ecosystem comprises a wide array of complex heterogeneous data sources. A wide range of clinical, health care, social and other clinically relevant information are stored in these data sources. These data exist either as structured data or as free-text. These data are generally individual person-based records, but social care data are generally case based and less formal data sources may be shared by groups. The structured data may be organised in a proprietary way or be coded using one-of-many coding, classification or terminologies that have often evolved in isolation and designed to meet the needs of the context that they have been developed. This has resulted in a wide range of semantic interoperability issues that make the integration of data held on these different systems changing. We present semantic interoperability challenges and describe a classification of these. We propose a four-step process and a toolkit for those wishing to work more ontologically, progressing from the identification and specification of concepts to validating a final ontology. The four steps are: (1) the identification and specification of data sources; (2) the conceptualisation of semantic meaning; (3) defining to what extent routine data can be used as a measure of the process or outcome of care required in a particular study or audit and (4) the formalisation and validation of the final ontology. The toolkit is an extension of a previous schema created to formalise the development of ontologies related to chronic disease management. The extensions are focused on facilitating rapid building of ontologies for time-critical research studies.