Reviewerly/exHarmony huggingface dataset:
Parallelizable and robust image segmentation model based on the shape prior information

Image segmentation is an important task in many fields, and there are plentiful models based on region or edges. Nowadays, the speed of calculation and the universal applicability of the model attract much attention. To some extent, the traditional energy model can segment images suffering from intensity inhomogeneity while it relies on initialization seriously. In this paper, we present a new model that consists of an arbitrary active contour model and proposed shape priori information term, which can segment various images accurately and provide an opportunity to carry on parallelizable calculation. The shape priori information term plays a key role in our energy functional and the shape priori information can be chosen diversely. This term also improves the robustness of our model including initial conditions and parameter adjustment. Besides, the split Bregman method is then applied to minimize the energy functional. Multiple experimental results and comparisons are shown to demonstrate the superiority of the proposed model. Firstly, fuzzy clustering, threshold and manual operation are used to be the shape priori information. Secondly, it is illustrated that our model is not sensitive to parameters and initial contours. Computation time and accuracy are also obviously improved when using the parallel algorithm.
Re-association of Body Parts: Illusory Ownership of a Virtual Arm Associated With the Contralateral Real Finger by Visuo-Motor Synchrony

Illusory ownership can be induced in a virtual body by visuo-motor synchrony. Our aim was to test the possibility of a re-association of the right thumb with a virtual left arm and express the illusory body ownership of the re-associated arm through a synchronous or asynchronous movement of the body parts through action and vision. Participants felt that their right thumb was the virtual left arm more strongly in the synchronous condition than in the asynchronous one, and the feeling of ownership of the virtual arm was also stronger in the synchronous condition. We did not find a significant difference in the startle responses to a sudden knife appearance to the virtual arm between the two synchrony conditions, as there was no proprioceptive drift of the thumb. These results suggest that a re-association of the right thumb with the virtual left arm could be induced by visuo-motor synchronization; however, it may be weaker than the natural association.
High-Confidence Attack Detection via Wasserstein-Metric Computations

This paper considers a sensor attack and fault detection problem for linear cyber-physical systems, which are subject to system noise that can obey an unknown light-tailed distribution. We propose a new threshold-based detection mechanism that employs the Wasserstein metric, and which guarantees system performance with high confidence employing a finite number of measurements. The proposed detector may generate false alarms with a rate $\Delta$ in normal operation, where $\Delta$ can be tuned to be arbitrarily small by means of a benchmark distribution which is part of our mechanism. Thus, the proposed detector is sensitive to sensor attacks and faults which have a statistical behavior that is different from that of the system's noise. We quantify the impact of stealthy attacks---which aim to perturb the system operation while producing false alarms that are consistent with the natural system's noise---via a probabilistic reachable set. To enable tractable implementation of our methods, we propose a linear optimization problem that computes the proposed detection measure and a semidefinite program that produces the proposed reachable set.
Weakly and semi supervised detection in medical imaging via deep dual branch net

This study presents a novel deep learning architecture for multi-class classification and localization of abnormalities in medical imaging illustrated through experiments on mammograms. The proposed network combines two learning branches. One branch is for region classification with a newly added normal-region class. Second branch is region detection branch for ranking regions relative to one another. Our method enables detection of abnormalities at full mammogram resolution for both weakly and semi-supervised settings. A novel objective function allows for the incorporation of local annotations into the model. We present the impact of our schemes on several performance measures for classification and localization, to evaluate the cost effectiveness of the lesion annotation effort. Our evaluation was primarily conducted over a large multi-center mammography dataset of ~3,000 mammograms with various findings. The results for weakly supervised learning showed significant improvement compared to previous approaches. We show that the time consuming local annotations involved in supervised learning can be addressed by a weakly supervised method that can leverage a subset of locally annotated data. Weakly and semi-supervised methods coupled with detection can produce a cost effective and explainable model to be adopted by radiologists in the field.
Recommendation via Collaborative Autoregressive Flows

Although it is one of the most widely used methods in recommender systems, Collaborative Filtering (CF) still has difficulties in modeling non-linear user-item interactions. Complementary to this, recently developed deep generative model variants (e.g., Variational Autoencoder (VAE)) allowing Bayesian inference and approximation of the variational posterior distributions in these models, have achieved promising performance improvement in many areas. However, the choices of variation distribution - e.g., the popular diagonal-covariance Gaussians - are insufficient to recover the true distributions, often resulting in biased maximum likelihood estimates of the model parameters. Aiming at more tractable and expressive variational families, in this work we extend the flow-based generative model to CF for modeling implicit feedbacks. We present the Collaborative Autoregressive Flows (CAF) for the recommender system, transforming a simple initial density into more complex ones via a sequence of invertible transformations, until a desired level of complexity is attained. CAF is a non-linear probabilistic approach allowing uncertainty representation and exact tractability of latent-variable inference in item recommendations. Compared to the agnostic-presumed prior approximation used in existing deep generative recommendation approaches, CAF is more effective in estimating the probabilistic posterior and achieves better recommendation accuracy. We conducted extensive experimental evaluations demonstrating that CAF can capture more effective representation of latent factors, resulting in a substantial gain on recommendation compared to the state-of-the-art approaches.
Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Vehicle detection is a crucial task for autonomous driving and demands high accuracy and real-time speed. Considering that the current deep learning object detection model size is too large to be deployed on the vehicle, this paper introduces the lightweight network to modify the feature extraction layer of YOLOv3 and improve the remaining convolution structure, and the improved Lightweight YOLO network reduces the number of network parameters to a quarter. Then, the license plate is detected to calculate the actual vehicle width and the distance between the vehicles is estimated by the width. This paper proposes a detection and ranging fusion method based on two different focal length cameras to solve the problem of difficult detection and low accuracy caused by a small license plate when the distance is far away. The experimental results show that the average precision and recall of the Lightweight YOLO trained on the self-built dataset is 4.43% and 3.54% lower than YOLOv3, respectively, but the computing speed of the network decreases 49 ms per frame. The road experiments in different scenes also show that the long and short focal length camera fusion ranging method dramatically improves the accuracy and stability of ranging. The mean error of ranging results is less than 4%, and the range of stable ranging can reach 100 m. The proposed method can realize real-time vehicle detection and ranging on the on-board embedded platform Jetson Xavier, which satisfies the requirements of automatic driving environment perception.
Prejudice in uncertain information merging: Pushing the fusion paradigm of evidence theory further

In his 1976 book, G. Shafer reinterprets Dempster lower probabilities as degrees of belief. He studies the fusion of independent elementary partially reliable pieces of evidence coming from different sources, showing that not all belief functions can be seen as the combination of simple support functions, representing such pieces of evidence, using Dempster rule. It only yields a special kind of belief functions called separable. In 1995, Ph. Smets has indicated that any non-dogmatic belief function can be seen as the combination of so-called generalized simple support functions, whose masses may lie outside the unit interval. It comes down to viewing a belief function as the result of combining two separable belief functions, one of which models reports from sources, and the other one expresses doubt, via a retraction operation. We propose a new interpretation of the latter belief function in terms of prejudice of the receiver, and consider retraction as a special kind of belief change. Its role is to weaken the support of some focal sets of a belief function, possibly stemming from the fusion of the incoming information. It provides an alternative extensive account of non-dogmatic belief functions as a theory of merging pieces of evidence and prejudices, which partially differs from Shafer approach's based on support functions and coarsenings. Retraction differs from discounting, revision, and from the symmetric combination of conflicting evidence. The approach relies on a so-called diffidence function on the positive reals ranging from full confidence to full diffidence. We also discuss information orderings and combination rules that rely on diffidence functions. Finally, we study the diffidence-based ordering and combination in the consonant case, and show that the diffidence view suggests a new branch of possibility theory, in agreement with likelihood functions.
Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs

Phishing is a malicious form of online theft and needs to be prevented in order to increase the overall trust of the public on the Internet. In this study, for that purpose, the authors present their findings on the methods of detecting phishing websites. Data mining algorithms along with classifier algorithms are used in order to achieve a satisfactory result. In terms of classifiers, the Naïve Bayes, SMO, and J48 algorithms are used. As for the feature selection algorithm; Gain Ratio Attribute and ReliefF Attribute are selected. The results are provided in a comparative way. Accordingly; SMO and J48 algorithms provided satisfactory results in the detection of phishing websites, however, Naïve Bayes performed poor and is the least recommended method among all.
Spatio-Temporal Handwriting Imitation

Most people think that their handwriting is unique and cannot be imitated by machines, especially not using completely new content. Current cursive handwriting synthesis is visually limited or needs user interaction. We show that subdividing the process into smaller subtasks makes it possible to imitate someone’s handwriting with a high chance to be visually indistinguishable for humans. Therefore, a given handwritten sample will be used as the target style. This sample is transferred to an online sequence. Then, a method for online handwriting synthesis is used to produce a new realistic-looking text primed with the online input sequence. This new text is rendered and style-adapted to the input pen. We show the effectiveness of the pipeline by generating in- and out-of-vocabulary handwritten samples that are validated in a comprehensive user study. Additionally, we show that also a typical writer identification system can partially be fooled by the created fake handwritings.
Improve Temporal Fourier Transform Profilometry for Complex Dynamic Three-Dimensional Shape Measurement

The high-speed three-dimensional (3-D) shape measurement technique has become more and more popular recently, because of the strong demand for dynamic scene measurement. The single-shot nature of Fourier Transform Profilometry (FTP) makes it highly suitable for the 3-D shape measurement of dynamic scenes. However, due to the band-pass filter, FTP method has limitations for measuring objects with sharp edges, abrupt change or non-uniform reflectivity. In this paper, an improved Temporal Fourier Transform Profilometry (TFTP) algorithm combined with the 3-D phase unwrapping algorithm based on a reference plane is presented, and the measurement of one deformed fringe pattern producing a new 3-D shape of an isolated abrupt objects has been achieved. Improved TFTP method avoids band-pass filter in spatial domain and unwraps 3-D phase distribution along the temporal axis based on the reference plane. The high-frequency information of the measured object can be well preserved, and each pixel is processed separately. Experiments verify that our method can be well applied to a dynamic 3-D shape measurement with isolated, sharp edges or abrupt change. A high-speed and low-cost structured light pattern sequence projection has also been presented, it is capable of projection frequencies in the kHz level. Using the proposed 3-D shape measurement algorithm with the self-made mechanical projector, we demonstrated dynamic 3-D reconstruction with a rate of 297 Hz, which is mainly limited by the speed of the camera.
Hypergraph Simplification: Linking the Path-sum Approach to the ZH-calculus

The ZH-calculus is a complete graphical calculus for linear maps between qubits that admits a straightforward encoding of hypergraph states and circuits arising from the Toffoli+Hadamard gate set.In this paper, we establish a correspondence between the ZH-calculus and the path-sum formalism, a technique recently introduced by Amy to verify quantum circuits.In particular, we find a bijection between certain canonical forms of ZH-diagrams and path-sum expressions.We then introduce and prove several new simplification rules for the ZH-calculus, which are in direct correspondence to the simplification rules of the path-sum formalism.The relatively opaque path-sum rules are shown to arise naturally from two powerful families of rewrite rules in the ZH-calculus.The first is the extension of the familiar graph-theoretic simplifications based on local complementation and pivoting to their hypergraph-theoretic analogues: hyper-local complementation and hyper-pivoting.The second is the graphical Fourier transform introduced by Kuijpers et al., which enables effective simplification of ZH-diagrams encoding multi-linear phase polynomials with arbitrary real coefficients.
Digital Objects – FAIR Digital Objects: Which Services Are Required?

Some of the early Research Data Alliance working groups reused the notion of digital objects as digital entities described by metadata and referenced by a persistent identifier. In recent times the FAIR principles became a prominent role as framework for the sustainability of scientific data. Both approaches had always machine actionability, the capability of computational systems to use services on data without human intervention, in their focus. The more technical approach of digital objects turned out to provide a complementary view on several aspects of the policy framework of FAIR from a technical perspective. After a deeper analysis and integration of these concepts by a group of European data experts the discussion intensified on so called FAIR digital objects. But they need to be accompanied by services as building blocks for automated processes. We will describe the components of this framework and its potentials here, and also which services inside this framework are required.
A Comprehensive Primer to Library Learning Analytics Practices, Initiatives, and Privacy Issues

Universities are pursuing learning analytics practices to improve returns from their investments, develop behavioral and academic interventions to improve student success, and address political and financial pressures. Academic libraries are additionally undertaking learning analytics to demonstrate value to stakeholders, assess learning gains from instruction, and analyze student-library usage, et cetera. The adoption of these techniques leads to many professional ethics issues and practical concerns related to privacy. In this narrative literature review, we provide a foundational background in the field of learning analytics, library adoption of these practices, and identify ethical and practical privacy issues.
COVID-19 y la necesidad de actuar para mejorar las capacidades del Perú frente a las pandemias

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Breathing and Speech Planning in Spontaneous Speech Synthesis

Breathing and speech planning in spontaneous speech are coordinated processes, often exhibiting disfluent patterns. While synthetic speech is not subject to respiratory needs, integrating breath into synthesis has advantages for naturalness and recall. At the same time, a synthetic voice reproducing disfluent breathing patterns learned from the data can be problematic. To address this, we first propose training stochastic TTS on a corpus of overlapping breath-group bigrams, to take context into account. Next, we introduce an unsupervised automatic annotation of likely-disfluent breath events, through a product-of-experts model that combines the output of two breath- event predictors, each using complementary information and operating in opposite directions. This annotation enables creating an automatically-breathing spontaneous speech synthesiser with a more fluent breathing style. A subjective evaluation on two spoken genres (impromptu and rehearsed) found the proposed system to be preferred over the baseline approach treating all breath events the same.
Accurate infrared structured light sensing system for dynamic 3D acquisition

3D real-time acquisition plays a vital role in computer graphics and computer vision. In this paper, we present a dynamic IR structured light sensing system with high resolution and accuracy for real-time 3D scanning. We adopt the Gray code combined with stripe shifting as our 3D acquisition’s coding strategy and parallelize the algorithm via the GPU in our IR 3D scanning system. Our built-up system can capture dense and high-precision 3D model sequences with a speed of 29 Hz. Furthermore, we propose a practical calibration method to obtain accurate calibration parameters for our system. Finally, various experiments are performed to verify the feasibility and accuracy of our proposed IR structured light sensing system.
Comparative analysis of texture feature extraction techniques for rice grain classification

Classifications of eight different varieties of rice grain are discussed in this study based on various texture models. Four local texture feature extraction techniques are proposed and three sets of texture features (SET-A, SET-B and SET-C) are formed, for the classification task. Performances of the proposed feature sets are compared with the existing techniques based on, run length matrix, co-occurrence matrix, size zone matrix, neighbourhood grey tone difference matrix and wavelet decomposition, towards classification of rice grain using a back propagation neural network (BPNN). The proposed techniques are also tested against publicly available data from Brodatz's texture data set and their results are compared with other techniques. The classification accuracy by the BPNN classifier is also compared with other statistical classifiers namely, K-nearest neighbour, linear discriminant classifier and Naive Bayes classifier. It is found that, the proposed feature sets yield better classification results on both rice data and Brodatz's data. Results show that, feature SET-B, is able to classify rice grain with an average classification accuracy of 99.63% with a minimum of six features.
Exploiting implicit social relationships via dimension reduction to improve recommendation system performance

The development of Web 2.0 and the rapid growth of available data have led to the development of systems, such as recommendation systems (RSs), that can handle the information overload. However, RS performance is severely limited by sparsity and cold-start problems. Thus, this paper aims to alleviate these problems. To realize this objective, a new model is proposed by integrating three sources of information: a user-item matrix, explicit and implicit relationships. The core strategy of this study is to use the multi-step resource allocation (MSRA) method to identify hidden relations in social information. First, explicit social information is used to compute the similarity between each pair of users. Second, for each non-friend pair of users, the MSRA method is applied to determine the probability of their relation. If the probability exceeds a threshold, a new relationship will be established. Then, all sources are incorporated into the Singular Value Decomposition (SVD) method to compute the missing prediction values. Furthermore, the stochastic gradient descent technique is applied to optimize the training process. Additionally, two real datasets, namely, Last.Fm and Ciao, are utilized to evaluate the proposed method. In terms of accuracy, the experiment results demonstrate that the proposed method outperforms eight state-of-the-art approaches: Heats, PMF, SVD, SR, EISR-JC, EISR-CN, EISR-PA and EISR-RAI.
The Boomerang Effects: An Analysis of the Pre and Post Dollarisation Era in Zimbabwe

Does dollarisation influence economic activity in Zimbabwe? The question has incited a lot of debates among researchers and analysts. In an attempt to answer this question, the study used an Auto Regressive Distributive Lag (ARDL) procedure, to investigate the effects of dollarisation on economic growth in Zimbabwe. The study employed quarterly data over a 14-year period between 2000 and 2014. The results of the study indicate that dollarisation, gross domestic investment and trade openness are positively related to economic growth. Based on the findings of the study, the paper recommends that Zimbabwean policy makers should establish additional complementary policies which foster economic integration with anchor countries to reduce credit risk. On the other hand, dollarisation should be maintained since it resulted in economic stability and improved financial sector credibility. It is therefore still premature to de-dollarise the economy until a sufficient level of credibility is gained by the central bank.
Soft Error Mitigation for Deep Convolution Neural Network on FPGA Accelerators

Convolution neural networks (CNNs) have been widely used in many applications. Field-Programmable Gate Array (FPGA) based accelerator is an ideal solution for CNNs in embedded systems. However, the single event upset (SEU) effect in FPGA device may have a significant influence on the performance of CNNs. In this paper, we analyze the sensibility of CNNs to SEU and present a fault-tolerant design for CNN accelerators. First, we find that SEU in processing elements (PEs) has the worst effects on CNNs since it produces proportional errors and will not get refreshed. Furthermore, it is indicated that the large positive perturbation contributes almost all of the performance loss. Based on such observations, we propose an error detecting scheme to locate incorrect PEs and give an error masking method to achieve fault-tolerance. Experiments demonstrate that the proposed method achieves similar fault-tolerant performance with the triple modular redundancy (TMR) scheme while the overhead is much lower than it.