Reviewerly/exHarmony huggingface dataset:
A Class of “Basic Media Design” Based on 3D-CAD for Beginners of Digital Processing

A basic subject of computer graphics (CG) based on three-dimensional (3D) computer-aided design (CAD) was provided in a class of “Basic Media Design” for undergraduate students in their first years. The 3D-CAD used here was the web browser-based 3D modeling tool Autodesk Tinkercad®. During a three-hour class, students are introduced to the following tasks: in the first hour they learn basic functions and skills about 3D design through basic functions of Tinkercad®; in the next hour, they operate the 3D design just by executing the procedures; in the remaining, they create their own 3D model. At the end of the class, they show their 3D models in augmented reality (AR). AR is a technology that allow the visualization of 3D models in the real world adding new information that can also be available on a smartphone. In this way, the students made remarks for what were displayed.
Compatible Certificateless and Identity-Based Cryptosystems for Heterogeneous IoT

Certificates ensure the authenticity of users’ public keys, however their overhead (e.g., certificate chains) might be too costly for some IoT systems like aerial drones. Certificate-free cryptosystems, like identity-based and certificateless systems, lift the burden of certificates and could be a suitable alternative for such IoTs. However, despite their merits, there is a research gap in achieving compatible identity-based and certificateless systems to allow users from different domains (identity-based or certificateless) to communicate seamlessly. Moreover, more efficient constructions can enable their adoption in resource-limited IoTs. In this work, we propose new identity-based and certificateless cryptosystems that provide such compatibility and efficiency. This feature is beneficial for heterogeneous IoT settings (e.g., commercial aerial drones), where different levels of trust/control is assumed on the trusted third party. Our schemes are more communication efficient than their public key based counterparts, as they do not need certificate processing. Our experimental analysis on both commodity and embedded IoT devices show that, only with the cost of having a larger system public key, our cryptosystems are more computation and communication efficient than their certificate-free counterparts. We prove the security of our schemes (in the random oracle model) and open-source our cryptographic framework for public testing/adoption.
Study of Deep Metric Learning on Character Classification

In this paper, traditional one-hot encoded network is compared with three deep metric learning methods by using contrastive loss, triplet loss, and quadruplet loss on character classification task. Experimental results show that deep metric learning methods provide similar performance to one-hot encoded network, but have more advantages and less label requirements. On the other hand, by comparing these well-known deep metric learning methods under the same number of parameters, their differences are also summarized.
The Conditional Perfect, A Quantitative Analysis in English-French Comparable-Parallel Corpora

The frequency of the conditional perfect in English and French was observed in a corpus of almost 12-million words corpus consisting of four 2.9-million-word comparable and parallel subcorpora, tagged by POS and lemma, and analyzed using regular expressions. Intralinguistically, authors and translators were compared using the Wilcoxon-Mann–Whitney test and Cliff's delta as a measurement of effect size, while potential interlinguistic influences were assessed by means of Spearman's correlation test. French-translated-from-English was found to be distinct from original French in its use of all conditional perfect forms, but significant differences between translators and authors were observed in both languages when COULD, MIGHT and POUVOIR were used as auxiliaries.
Multi-label Topic Classification of Patient Generated Content in a Breast-cancer Community Forum

The research community has been noticing the importance of the online forums in healthcare in understanding the nuances of health-related problems. Breast Cancer is the most common malignance among women worldwide and its survival rate is steadily increasing thanks to early diagnosis and timely and effective treatment. Still how they manage the disease and maintain the quality of daily life are worth understanding. And it is in this context that the online posts of patients with breast cancer, discussing several topics, become patient generated content. The multi-label nature with these posts arising out of the combined contents of these posts can bring out a multitude of divulging conclusions. The resulting classification issues of these online posts under various categories of topics is examined in the present work. A semi-supervised multi-label classification, followed by refinement of multiple assignments of labels based on fuzzy logic and a neighborhood technique is proposed in the paper. Multiplicity of labels, occurs during the assignment of labels to clusters based on proximity. While extending the refined label set to a greater number of unlabeled posts clustered on the basis of proximity, it is observed that the proposed method could bring out more information on the description of posts. The results thus convey that the most discussed topic in the posts is about diagnosis, along with tangential reference to adverse drug effect, presumably to offer support in terms of information or viewpoint. The results show how the diverse nature of multiple labels can be effectively harnessed to draw conclusion from the potential of social media posts of patients' experience in critical health problems.
Denoising of Magnetic Resonance Images with Deep Neural Regularizer Driven by Image Prior

Magnetic resonance imaging (MRI) is an important medical diagnosis technique in clinical diagnosis, while the quality of MR images is always damaged by the noise which is caused in the image acquisition process. In the classic image denoising methods, how to design an excellent regularizer with the prior knowledge of image is the key to solve the denoising problem. In this work, we introduce the deep neural regularizer for the MRI denoising tasks, the deep neural regularizer is made up of neural network structure and objective function, similar to the classic regularizer, both of these two parts are designed with the prior knowledge of image. The proposed neural network has three main parts: encoder network, decoder network and skip connections, the encoder network which consists of five down-sampling blocks is enforced to deeply extract low-resolution or highly-abstract MR image features, similar to the encoder network architecture, the decoder network is made up of five up-sampling blocks and is enforced to restore high-resolution MR image features. To generate more finer image features, we also use skip connections to transmit the abstract information from encoder to decoder directly. The objective function consists of data fidelity term and image quality penalty term, specifically, to enforce the capability of data fidelity term, we add the self-designed image structural consistency calculation to data fidelity term besides only calculating the image consistency over image pixels with mean squared error. Meanwhile, to guide the network generate more clearer image and reduce noise information, with the prior knowledge of image sharpness, an image quality penalty term which calculates the MR image sharpness is also added to the objective function. Experimental results over the simulated MRI data and real clinical data demonstrate the proposed network can achieve superior performance compared with other methods in terms of peak signal to noise ratio, structure similarity index, image average gradient and image information entropy.
Discriminative Adversarial Domain Adaptation Based on Circle Loss

Transfer learning can solve some problems that the lack of data and labeled data is unavailable in deep learning. The domain adaptation method in transfer learning can solve the problem of domain-shift, which is the main direction of current research. The generative adversarial network is popular in the generative model now, So the combination of them will obtain more respectable performance. While previous approaches only use it as a data augmentation tool. Even though the overall distribution is aligned, some samples will still be misclassified, and the corresponding classifier is trained on the source samples and cannot perfectly generalize to the target. We propose a novel generative discriminative domain adaptation method based on the reverse gradient by combining the generative adversarial method to reverse gradient. The method uses unlabeled target domain data to make the target and source domains distributions get closer in the feature space. At the same time, the auxiliary class's generation adversarial network is used to generate consistent-class representation, and the corresponding class's distribution is aligned across domains. Besides, we use circle loss as the classification function. Circle loss proposed a unified formula for deep feature learning features. It is useful for our classification task in our method. Our proposed method achieves better performance than others in most experimental settings and has shown to work well across different datasets such as digits and Office.
An Efficient Accelerator for Deep Convolutional Neural Networks

Convolutional neural networks (CNN) in deep learning have become popular in many of the latest applications from speech recognition to image classification and object detection. Among them, YOLO (You only look once) is a well-known algorithm in object detection. YOLO convolutional neural networks require a lot of multiplication and accumulation calculations. On the edge, special hardware needs to be designed to speed up the calculation. In order to reduce hardware costs, a new distributed arithmetic (DA) architecture similar to NEDA is proposed. The multipliers is replaced by adders. The purpose is to reduce the cost of power consumption and area while maintaining high speed and high precision. Mathematical analysis proves that DA can only use addition to achieve multiplication in the form of two's complement, and then perform data shift at the end to implement the operation of the adder, not the multiplier. In addition, in this paper, after convolution, maximum pooling is performed to reduce the bandwidth. Finally, the biggest feature of this article is that PE can perform 1.78 MAC operations in one clock cycle.
A New Medical Images Encryption algorithm Based on Gold Code: Futures Trends Towards Telemedicine

Abstract The rising necessity for telemedicine has made the healthcare sector an urgent and unlimited necessity to secure important data sent between medical centers for treatment purposes. The medical image encryption is a significant technique to attain a medical images security (MIS). There are numerous researchers utilized advanced encryption standard (AES) to guarantee of MIS. The direct application of AES encryption technology on medical images led to a long-time of processing; also it leads to a clear background regions, which are considered defects. This article includes the application of information theory (IT) to detect regions of the medical image that include, ROI (regions of interest) and ROB (regions of background). To reduce processing time and protect the medical image by utilizing AES with a higher degree of protection, a hybrid encryption is applied to ROI, and a method of coding such as gold code (GC) is applied to ROB after creation. The proposed method involves less time processing for the entire medical image of the AES application and has the best possible security as described in the entropy and link calculations.
A Novel INS/CNS/GNSS Integrated Navigation Algorithm

For improving the navigation ability of the INS/CNS/GNSS system in the non-Gaussian noise environment, a novel navigation algorithm based on maximum correntropy generalized high-degree CKF (MCHCKF) is proposed. First, the INS/GNSS and INS/CNS systems obtain the sub-states by the MCHCKF algorithm, respectively. Then, according to the minimum variance criterion and cubature rule, the sub-state estimations are fused to get the global state. Finally, the superior performance of the proposed method is proved in the missile-borne navigation simulation system.
Fair and Sound Secret Sharing from Homomorphic Time-Lock Puzzles

Achieving fairness and soundness in non-simultaneous rational secret sharing schemes has proved to be challenging. On the one hand, soundness can be ensured by providing side information related to the secret as a check, but on the other, this can be used by deviant players to compromise fairness. To overcome this, the idea of incorporating a time delay was suggested in the literature: in particular, time-delay encryption based on memory-bound functions has been put forth as a solution. In this paper, we propose a different approach to achieve such delay, namely using homomorphic time-lock puzzles (HTLPs), introduced at CRYPTO 2019, and construct a fair and sound rational secret sharing scheme in the non-simultaneous setting from HTLPs. HTLPs are used to embed sub-shares of the secret for a predetermined time. This allows to restore fairness of the secret reconstruction phase, despite players having access to information related to the secret which is required to ensure the soundness of the scheme. Key to our construction is the fact that the time-lock puzzles are homomorphic so that players can compactly evaluate sub-shares. Without this efficiency improvement, players would have to independently solve each puzzle sent from the other players to obtain a share of the secret, which would be computationally inefficient. We argue that achieving both fairness and soundness in a non-simultaneous scheme using a time delay based on CPU-bound functions rather than memory-bound functions is more cost-effective and realistic in relation to the implementation of the construction.
A Novel Coarse-to-Fine Segmentation Method for Pancreatic Cancer

Pancreatic cancer is one of the most deadly cancers, with a 5-year survival rate of only 8%, but early diagnosis can increase the survival rate to about 20%. Therefore, by examining abdominal CT scans, early and accurate diagnosis of pancreatic tumors is critical to treatment decisions. In CT images, pancreatic cancer faces imbalanced classification, background interference, and non-rigid geometric features, making it difficult for even experienced radiologists to diagnose. Therefore, we propose a novel method to detect pancreatic cancer more accurately. In order to make the positioning more accurate, we use a rough segmentation network to get a rough positioning, and then get a more accurate segmentation effect based on this positioning. We trained and tested on a public data set containing 281 cases, and the dsc coefficient reached 63.4±23.67%. This experiment proves that our proposed new method is effective and shows potential clinical impact.
Analysis of Hotspot Data for Drought Clustering Using K-Means Algorithm

Drought is a disaster that is often experienced in Indonesia. This disaster occurred because Indonesia's geographical location is on the equator. Drought has had a major impact on the community such as crop failure, forest fires, soil damage, the emergence of disease outbreaks, and the extinction of animals and plants. Based on data from the Ministry of Environment of the Republic of Indonesia, the distribution of Riau's hotspots is quite unique. It is said so, because in this distribution, Riau has increased in every February and March as many as 277 and 248 hotspots in the last two years, namely between 2018 and 2019. To anticipate the drought that occurred in Riau, the clustering of drought-prone areas was conducted based on the analysis of hotspots data. This clustering of vulnerable areas is done by the K-Means algorithm. In determining the number of clusters of vulnerable areas, the elbow method is used as a determinant and produces as many as 4 cluster. The results of these method were analyzed by the silhouette coefficient. The result of analyzed is 0.388632163 and were classified as well-clustered. From these results, Rokan Hilir, Bengkalis, Kota Dumai are the dangerous district with 3106, 2361, and 117 point of dangerous distribution, respectively.
Camouflage is NOT easy: Uncovering adversarial fraudsters in large online app review platform

Given users and products that he/she reviews, can we recognize fake reviews just using the text information, or determine whether a reviewer is a fraud or not? Automatically detecting fake reviews and reviewers is an urgent problem and lots of work attempts for discovering linguistics, behaviors and graph patterns. However, in reality, there are new kinds of fraudsters who can change their behaviors to camouflage as genuine reviewers to avoid detection systems. With the fraudsters become distributed, dynamic, and adversarial, anti-spam tasks face a new challenge. In this paper, we tackle the challenge of adversarial fraudsters in online app review platform and propose a system called DDF (Detect, Defense, and Forecast) to uncover camouflage accounts. Firstly, we select a small set of seed with high-precision based on text and behavior features; Secondly, we build our graph-based detection model for uncovering hidden (distant) users who serve structurally similar to the seed by utilizing Graph Convolutional Network (GCN) algorithm. Thirdly, we evaluate DDF using real-world data set from Tencent APP Store and analyze the potential fraudsters detected by DDF. It is worth mentioning that precision can achieve 0.95+. Finally, we validate the efficiency and scalability of DDF and show that it can be well transferred to other anti-spam tasks.
A Statistical Test of The Effect of Learning Rate and Momentum Coefficient of Sgd and Its Interaction on Neural Network Performance

Stochastic Gradient Descent (SGD) is a well-received algorithm for large-scale optimization in neural networks for its low iteration cost. However, due to Gradient variance, it often has difficulty in finding optimal learning rate and thus suffers from slow convergence. Using momentum is proven to be a simple effective way of overcoming the slow convergence problem of SDG as long as momentum is properly set. According to the performance metrics, this paper proposes a novel statistical model for analyzing the performance of neural networks. The model takes into account learning rate and momentum, and the method can be used to evaluate and verify their interaction effects on neural network performance. Our study shows that the interaction effects are significant. When momentum has a value smaller than 0.5, the impact on the training time is not statistically noticeable.
Semiparametric imputation using conditional Gaussian mixture models under item nonresponse

Abstract Imputation is a popular technique for handling item nonresponse. Parametric imputation is based on a parametric model for imputation and is not robust against the failure of the imputation model. Nonparametric imputation is fully robust but is not applicable when the dimension of covariates is large due to the curse of dimensionality. Semiparametric imputation is another robust imputation based on a flexible model where the number of model parameters can increase with the sample size. In this paper, we propose a new semiparametric imputation based on a more flexible model assumption than the Gaussian mixture model. In the proposed mixture model, we assume a conditional Gaussian model for the study variable given the auxiliary variables, but the marginal distribution of the auxiliary variables is not necessarily Gaussian. The proposed mixture model is more flexible and achieves a better approximation than the Gaussian mixture models. The proposed method is applicable to high‐dimensional covariate problem by including a penalty function in the conditional log‐likelihood function. The proposed method is applied to the 2017 Korean Household Income and Expenditure Survey conducted by Statistics Korea.
Parallel methods for linear systems solution in extreme learning machines: an overview

Abstract This paper aims to present an updated review of parallel algorithms for solving square and rectangular single and double precision matrix linear systems using multi-core central processing units and graphic processing units. A brief description of the methods for the solution of linear systems based on operations, factorization and iterations was made. The methodology implemented, in this article, is a documentary and it was based on the review of about 17 papers reported in the literature during the last five years (2016-2020). The disclosed findings demonstrate the potential of parallelism to significantly decrease extreme learning machines training times for problems with large amounts of data given the calculation of the Moore Penrose pseudo inverse. The implementation of parallel algorithms in the calculation of the pseudo-inverse will allow to contribute significantly in the applications of diversifying areas, since it can accelerate the training time of the extreme learning machines with optimal results.
An Estimation Method of Candidate Region for Superimposing Information Based on Gaze Tracking Data in Soccer Videos

A novel method estimating candidate regions for superimposing information in soccer videos based on gaze tracking data is presented in this paper. The proposed method generates a likelihood map based on visual attention regions based on the gaze tracking data and detection results of objects such as players and soccer goals in soccer videos. Candidate regions for superimposing information are estimated by using the likelihood map. Experimental results show that the proposed method realizes effective candidate region estimation.
Data visualization case studies for high‐dimensional data validation

Microsimulation and synthetic data are often high dimensional, requiring extensive validation and exploration to compare results against certain benchmarks. In both cases, validation is necessary to ensure that the many univariate distributions and multivariate relationships in the new data are similar to the many univariate distributions and multivariate relationships in the underlying data. This article illustrates some data visualization techniques for comparing a generated sample or population against a known reference sample or population. For implementation ease, we also outline an iterative workflow built with R Markdown that can be shared publicly on GitHub or privately with Amazon Web Services S3. The lessons learned from this work apply to any analysis that compares multiple data sets, deals with high‐dimensional data, or involves summarizing iterations of analyses.
3D Kinematics of Upper Limb Functional Assessment Using HTC Vive in Unreal Engine 4

The purpose of research in this paper is to quantify the accuracy and precision of HTC Vive by making upper limb assessment measurements and performing functional tasks in the Unreal Engine 4. Thirty healthy males performed daily aim functional tasks, and arm length measurement and assessment were made. Each participant attended two testing sessions and one arm length measurement session. The upper limb length was measured using HTC Vive after making three types of hand posture exercises. The arm assessment included the minimum and maximum angle of shoulder adduction, abduction, flexion and extension. The experiment showed all the upper limb measurements collected from the functional tasks as well as the position and rotation of the upper limb could be estimated correctly.