Reviewerly/exHarmony huggingface dataset:
CERG: Chinese Emotional Response Generator with Retrieval Method

The dialogue system has always been one of the important topics in the domain of artificial intelligence. So far, most of the mature dialogue systems are task-oriented based, while non-task-oriented dialogue systems still have a lot of room for improvement. We propose a data-driven non-task-oriented dialogue generator “CERG” based on neural networks. This model has the emotion recognition capability and can generate corresponding responses. The data set we adopt comes from the NTCIR-14 STC-3 CECG subtask, which contains more than 1.7 million Chinese Weibo post-response pairs and 6 emotion categories. We try to concatenate the post and the response with the emotion, then mask the response part of the input text character by character to emulate the encoder-decoder framework. We use the improved transformer blocks as the core to build the model and add regularization methods to alleviate the problems of overcorrection and exposure bias. We introduce the retrieval method to the inference process to improve the semantic relevance of generated responses. The results of the manual evaluation show that our proposed model can make different responses to different emotions to improve the human-computer interaction experience. This model can be applied to lots of domains, such as automatic reply robots of social application.
Problems in the Estimation of the Key Parameters using MLE in Lung Cancer Screening

We have been working in the area of cancer screening modeling for many years. A well-known and frequently used model in cancer screening is the progressive three-state model [1], where all cancer patients are assumed to go through three states: the disease-free state when one is cancer-free or the cancer is in an early stage that no technology can find; the preclinical state when one without symptom but cancer could be detected by screening, and the clinical state when cancer related symptoms show up. There are three key parameters in the model: a) the screening sensitivity, the probability of a positive screening result given that one is in the preclinical state; b) the distribution of sojourn time, which measures the time duration in the preclinical state; and c) the transition density, which measures the time duration in the disease-free state, or the onset age of the preclinical state. These three parameters are called the key parameters since they determines the screening processes and all other terms, for example, lead time (diagnosis time advanced by screening), probability of over-diagnosis, etc., are functions of these three. Therefore accurate estimation of these three key parameters is critical and lays a foundation for all other estimations.
Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods

Graph-based semi-supervised learning is the problem of propagating labels from a small number of labelled data points to a larger set of unlabelled data. This paper is concerned with the consistency of optimization-based techniques for such problems, in the limit where the labels have small noise and the underlying unlabelled data is well clustered. We study graph-based probit for binary classification, and a natural generalization of this method to multi-class classification using one-hot encoding. The resulting objective function to be optimized comprises the sum of a quadratic form defined through a rational function of the graph Laplacian, involving only the unlabelled data, and a fidelity term involving only the labelled data. The consistency analysis sheds light on the choice of the rational function defining the optimization.
Meningkatkan Keamanan Webserver Aplikasi Pelaporan Pajak Daerah Menggunakan Metode Penetration Testing Execution Standar

Regional Tax Reporting Application Webserver is one of the public services for taxpayers to report their sales transactions. This application can be accessed on the domain http://sptpd.payakumbuhkota.go.id. This application is public, so the principles of information security must be applied to prevent cyber attacks. The principles of information security include confidentiality, integrity, and availability. To apply this information security principle, it is necessary to conduct vulnerability assesment of the application webserver. This study aims to improve the security of the application webserver so that the data and information in it is secure. The method used in this study is the Penetration Testing Execution Standard which is one of the methods developed by the Pentest Organization to become a standard in analyzing or auditing security systems. The results of vulnerability testing using software Acunetix, Nikto, BurpSuite and Owasp, there are seven types of vulnerabilities, namely: X-Frame Header Options is Missing, CSRF Attack, Cookie Without Only Flash, DNS Vulnerability, Ddos Attack, Bruteforce Page Login and Open Port. The vulnerability can be exploited, where the level of application vulnerability is in the medium category. The recommendations for fixing vulnerabilities can be applied by the developer, so that after repairs are made, the vulnerability level of the application webserver is in the low category and there is only one type of vulnerability, namely Brute Force Page Login.
Diverging cyclic radial shearing interferometry for single-shot wavefront sensing

In this investigation, we describe a simple cyclic radial shearing interferometer for single-shot wavefront sensing. Instead of using the telescope lens system used in typical radial shearing interferometry, a single lens is used to generate two diverging radial shearing beams. This simple modification leads to the advantages of conveniently adjusting the radial shearing ratio, compactness of the system, and practical ease of alignment. With the aid of a polarization pixelated CMOS camera, the spatial phase-shifting technique is used to extract the phase with a single image. The most important feature is the fringe contrast enhancement by reducing the aberrations caused by the complicated optical system even though an incoherent light is used. The experimental results show the fringe contrast enhancement is at least 0.1 better than that of the conventional method, and the wavefronts are properly reconstructed with less than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>0.071</mml:mn> </mml:mrow> <mml:mi>λ<!-- λ --></mml:mi> </mml:math> root-mean-squared wavefront error regardless of the coherence of the light.
Speeding-up verification of digital signatures

In 2003, Fischlin introduced the concept of progressive verification in cryptography to relate the error probability of a cryptographic verification procedure to its running time. It ensures that the verifier confidence in the validity of a verification procedure grows with the work it invests in the computation. Le, Kelkar and Kate recently revisited this approach for digital signatures and proposed a similar framework under the name of flexible signatures. We propose efficient probabilistic verification procedures for popular signature schemes in which the error probability of a verifier decreases exponentially with the verifier running time. We propose theoretical schemes for the RSA and ECDSA signatures based on some elegant idea proposed by Bernstein in 2000 and some additional tricks. We also present a general practical method, that makes use of efficient error-correcting codes, for signature schemes for which verification involves a matrix/vector multiplication.
An Elastica-Driven Digital Curve Evolution Model for Image Segmentation

Geometric priors have been shown to be useful in image segmentation to regularize the results. For example, the classical Mumford–Shah functional uses region perimeter as prior. This has inspired much research in the last few decades, with classical approaches like the Rudin–Osher–Fatemi and most graph-cut formulations, which all use a weighted or binary perimeter prior. It has been observed that this prior is not suitable in many applications, for example for segmenting thin objects or some textures, which may have high perimeter/surface ratio. Mumford observed that an interesting prior for natural objects is the Euler elastical model, which involves the squared curvature. In other areas of science, researchers have noticed that some physical binarization processes, like emulsion unmixing, can be well-approximated by curvature-related flow like the Willmore flow. However, curvature-related flows are not easy to compute because curvature is difficult to estimate accurately, and the underlying optimization processes are not convex. In this article, we propose to formulate a digital flow that approximates an Elastica-related flow using a multigrid-convergent curvature estimator, within a discrete variational framework. We also present an application of this model as a post-processing step to a segmentation framework.
Discriminativeness-Preserved Domain Adaptation for Few-Shot Learning

Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples. However, this assumption is often invalid in practice - the target classes could come from a different domain. This poses an additional domain adaptation (DA) challenge with few training samples. In this article, the problem of cross-domain few-shot learning (CD-FSL) is approached, which requires solving FSL and DA in a unified framework. To this end, we propose a novel discriminativeness-preserved domain adaptive prototypical network (DPDAPN) model. It is designed to address a specific challenge in CD-FSL: the DA objective means that the source and target data distributions need to be aligned, typically through a shared domain adaptive feature embedding space, but the FSL objective dictates that the target domain per-class distribution must be different from that of any source domain class, meaning aligning the distributions across domains may harm the FSL performance. How to achieve global domain distribution alignment while maintaining source/target per-class discriminativeness thus becomes the key. Our solution is to explicitly enhance the source/target per-class separation before domain adaptive feature embedding learning in DPDAPN to alleviate the negative effect of domain alignment on FSL. Extensive experiments show that our DPDAPN outperforms the state-of-the-art FSL and DA models, as well as their naive combinations.
A Two-Layer 3-D Reconstruction Method and Calibration for Multicamera-Based Volumetric Positioning and Characterization

A 3-D reconstruction method and multicamera calibration using multiple artificial reference markers have been used for precise volumetric surveillance of fast-flying objects. The method uses a two-layer 3-D reconstruction that integrates two multicamera stereo nodes. The fields of view of stereo nodes are directed at an acute angle to each other to provide greater coverage with the given constraints and to determine the flight characteristics of objects in 3-D. The object's flight reconstruction includes a “rough” estimation of its positions relative to selected artificial reference points in both stereo nodes separately and subsequent “refinement” of calculated positions. In this article, we describe the proposed method and calibration technique using a multicamera system to measure the object characteristics in 3-D. The proposed method applies to volumetric surveillance in situations where it is necessary to count, track, and analyze the activities of flying objects, especially birds, using high spatial resolution.
Nonparametric tests for circular regression

No matter the nature of the response and/or explanatory variables in a regression model, some basic issues such as the existence of an effect of the predictor on the response, or the assessment of a common shape across groups of observations, must be solved prior to model fitting. This is also the case for regression models involving circular variables (supported on the unit circumference). In that context, using kernel regression methods, this paper provides a flexible alternative for constructing pilot estimators that allow to construct suitable statistics to perform no-effect tests and tests for equality and parallelism of regression curves. Finite sample performance of the proposed methods is analyzed in a simulation study and illustrated with real data examples.
HPA “Lost Lederhosen” Short Movie: From Camera to Cloud — From Production Through Post-Production and Finishing in 1 Day

For the 2020 Hollywood Professional Association (HPA) Tech Retreat on 18 February 2020, which also happens to be the 25th anniversary of Tech Retreat, we wanted to exhibit technology quite differently from the earlier ones. All HPA engineering award winners of the last three years were invited to participate, and the HPA Supersession Technology demonstration was designed as a film shoot and post-production demonstration. Additionally, we also invited prospective candidates of HPA awards, using the latest trends in technology available to the Hollywood community in 2020.
A Novel Approach to Enhance the Generalization Capability of the Hourly Solar Diffuse Horizontal Irradiance Models on Diverse Climates

Solar radiation data is essential for the development of many solar energy applications ranging from thermal collectors to building simulation tools, but its availability is limited, especially the diffuse radiation component. There are several studies aimed at predicting this value, but very few studies cover the generalizability of such models on varying climates. Our study investigates how well these models generalize and also show how to enhance their generalizability on different climates. Since machine learning approaches are known to generalize well, we apply them to truly understand how well they perform on different climates than they are originally trained. Therefore, we trained them on datasets from the U.S. and tested on several European climates. The machine learning model that is developed for U.S. climates not only showed low mean absolute error (MAE) of 23 W/m2, but also generalized very well on European climates with MAE in the range of 20 to 27 W/m2. Further investigation into the factors influencing the generalizability revealed that careful selection of the training data can improve the results significantly.
Differential Privacy at Risk: Bridging Randomness and Privacy Budget

Abstract The calibration of noise for a privacy-preserving mechanism depends on the sensitivity of the query and the prescribed privacy level. A data steward must make the non-trivial choice of a privacy level that balances the requirements of users and the monetary constraints of the business entity. Firstly, we analyse roles of the sources of randomness, namely the explicit randomness induced by the noise distribution and the implicit randomness induced by the data-generation distribution, that are involved in the design of a privacy-preserving mechanism. The finer analysis enables us to provide stronger privacy guarantees with quantifiable risks. Thus, we propose privacy at risk that is a probabilistic calibration of privacy-preserving mechanisms. We provide a composition theorem that leverages privacy at risk. We instantiate the probabilistic calibration for the Laplace mechanism by providing analytical results. Secondly, we propose a cost model that bridges the gap between the privacy level and the compensation budget estimated by a GDPR compliant business entity. The convexity of the proposed cost model leads to a unique fine-tuning of privacy level that minimises the compensation budget. We show its effectiveness by illustrating a realistic scenario that avoids overestimation of the compensation budget by using privacy at risk for the Laplace mechanism. We quantitatively show that composition using the cost optimal privacy at risk provides stronger privacy guarantee than the classical advanced composition. Although the illustration is specific to the chosen cost model, it naturally extends to any convex cost model. We also provide realistic illustrations of how a data steward uses privacy at risk to balance the trade-off between utility and privacy.
Investigation and Research on the Status Quo of Professional Training of Preschool Education Majors in Secondary Vocational Schools

At present, China is in a period of rapid development of comprehensive national strength and science and technology, and people have put forward higher requirements for preschool education. In our country's basic education curriculum system, preschool education is still in a relatively weak link. With the opening of the second child policy, the number of teachers in kindergartens is insufficient, and the degree of specialization of teachers is not high, which not only fails to meet the actual needs of society, but also Unable to meet the country's requirements for the development of preschool education. This study, through the discussion of the training of preschool education professionals in secondary vocational and technical schools, is supported by relevant theories, and analyzes the current situation of the training of preschool education professionals in secondary vocational schools, and puts forward corresponding implementation suggestions.
Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images

Unprecedented data collection and sharing have exacerbated privacy concerns and led to increasing interest in privacy-preserving tools that remove sensitive attributes from images while maintaining useful information for other tasks. Currently, state-of-the-art approaches use privacy-preserving generative adversarial networks (PP-GANs) for this purpose, for instance, to enable reliable facial expression recognition without leaking users' identity. However, PP-GANs do not offer formal proofs of privacy and instead rely on experimentally measuring information leakage using classification accuracy on the sensitive attributes of deep learning (DL)-based discriminators. In this work, we question the rigor of such checks by subverting existing privacy-preserving GANs for facial expression recognition. We show that it is possible to hide the sensitive identification data in the sanitized output images of such PP-GANs for later extraction, which can even allow for reconstruction of the entire input images, while satisfying privacy checks. We demonstrate our approach via a PP-GAN-based architecture and provide qualitative and quantitative evaluations using two public datasets. Our experimental results raise fundamental questions about the need for more rigorous privacy checks of PP-GANs, and we provide insights into the social impact of these.
Improved Highway Network Block for Training Very Deep Neural Networks

Very deep networks are successful in various tasks with reported results surpassing human performance. However, training such very deep networks is not trivial. Typically, the problems of learning the identity function and feature reuse can work together to plague optimization of very deep networks. In this paper, we propose a highway network with gate constraints that addresses the aforementioned problems, and thus alleviates the difficulty of training. Namely, we propose two variants of highway network, HWGC and HWCC, employing feature summation and concatenation respectively. The proposed highway networks, besides being more computationally efficient, are shown to have more interesting learning characteristics such as natural learning of hierarchical and robust representations due to a more effective usage of model depth, fewer gates for successful learning, better generalization capacity and faster convergence than the original highway network. Experimental results show that our models outperform the original highway network and many state-of-the-art models. Importantly, we observe that our second model with feature concatenation and compression consistently outperforms our model with feature summation of similar depth, the original highway network, many state-of-the-art models and even ResNets on four benchmarking datasets which are CIFAR-10, CIFAR-100, Fashion-MNIST, SVHN and imagenet-2012 (ILSVRC) datasets. Furthermore, the second proposed model is more computationally efficient than the state-of-the-art in view of training, inference time and GPU memory resource, which strongly supports real-time applications. Using a similar number of model parameters for the CIFAR-10, CIFAR-100, Fashion-MNIST and SVHN datasets, the significantly shallower proposed model can surpass the performance of ResNet-110 and ResNet-164 that are roughly 6 and 8 times deeper, respectively. Similarly, for the imagenet dataset, the proposed models surpass the performance of ResNet-101 and ResNet-152 that are roughly three times deeper.
Hindi TimeBank: An ISO-TimeML Annotated Reference Corpus

ISO-TimeML is an international standard for multilingual event annotation, detection, categorization and linking. In this paper, we present the Hindi TimeBank, an ISO-TimeML annotated reference corpus for the detection and classification of events, states and time expressions, and the links between them. Based on contemporary developments in Hindi event recognition, we propose language independent and language-specific deviations from the ISO-TimeML guidelines, but preserve the schema. These deviations include the inclusion of annotator confidence, and an independent mechanism of identifying and annotating states such as copulars and existentials) With this paper, we present an open-source corpus, the Hindi TimeBank. The Hindi TimeBank is a 1,000 article dataset, with over 25,000 events, 3,500 states and 2,000 time expressions. We analyze the dataset in detail and provide a class-wise distribution of events, states and time expressions. Our guidelines and dataset are backed by high average inter-annotator agreement scores.