Reviewerly/exHarmony huggingface dataset:
Image recognition in online monitoring of power equipment

The application of remote digital video surveillance and image recognition technology in online monitoring of power equipment is conducive to timely equipment maintenance and troubleshooting. In order to solve the problem of slow speed and large amount of computation of traditional template matching algorithm for power image recognition, a second template matching algorithm for fast recognition of target image is proposed in this article. Firstly, a quarter of the template data is taken and matched within a quarter of the source image, and a reasonable error threshold is given in the matching process. Then, the neighborhood of the minimum error point in rough matching is matched to get the final result. Finally, the algorithm is applied to identify the power equipment and detect the abnormal state of the power equipment. The experimental results show that the matching algorithm can not only accurately locate and identify power equipment and detect equipment faults, but also greatly improve the matching speed compared with other commonly used template matching algorithms.
Session-based recommendation via flow-based deep generative networks and Bayesian inference

We present a novel generative Session-Based Recommendation (SBR) framework, called VAriational SEssion-based Recommendation (VASER) – a non-linear probabilistic methodology allowing Bayesian inference for flexible parameter estimation of sequential recommendations. Instead of directly applying extended Variational AutoEncoders (VAE) to SBR, the proposed method introduces normalizing flows to estimate the probabilistic posterior, which is more effective than the agnostic presumed prior approximation used in existing deep generative recommendation approaches. We also combine the effectiveness of both stochastic and amortized variational inference to reduce the inference gaps and to alleviate the underfitting problem of variational recommendation. We propose two specific implementations of VASER, both of which explore soft attention mechanism to upweight the important clicks in a session and show that one of them, treating the attention vector as an auxiliary latent factor, can make the variational distribution more expressive, and thus improves the recommendation accuracy over the widely used deterministic attention approaches. Empirically, we show that the proposed models significantly outperform several state-of-the-art baselines, including the recently-proposed RNN/VAE-based approaches, on several real-world datasets.
PalmitoAR: The Last Battle of the U.S. Civil War Reenacted Using Augmented Reality

Various efforts are used to preserve American history including relying on formal education, distributing information (text, video or visual aids) on social channels, displaying artifacts in historical centers or more recently, virtual reality applications posted on a shared medium. However, many of the newly developed applications are designed specifically for dedicated hardware rather than for a broad audience, thus creating a barrier for disseminating cultural values. In this paper, we propose a web-based Augmented Reality (AR) application, namely PalmitoAR, which provides an intuitive way of observing one of the most significant historical Civil War battlefields, Palmito Ranch Battlefield located in Cameron County, Texas. The proposed AR application is designed to resurrect a series of events through (i) a printed map of Palmito Ranch with embedded markers that enables viewers to experience the battle without being present at the site, (ii) a mobile device with a WebGL supported browser that allows 3D contents to be rendered, and (iii) an AR library (A-Frame.io) that enables enthusiasts to recreate similar work. Our methodology strongly relies on the benefits of a simple, robust algorithm for AR marker recognition to position 3D models in a specific context and time. As a result, the proposed AR application is complementary to existing work and provides a seamless experience for a wide range of viewers. We evaluated and improved the application with the help of twenty-six users to gather perspectives on the specific benefits of employing AR in learning about battlefields and reenactment. The technology acceptance model was adapted to access an individual’s acceptance of information technology.
Implementasi K-Means Clustering Ujian Nasional Sekolah Menengah Pertama di Indonesia Tahun 2018/2019

Clustering is an activity that aims to group a data that has a similarity between one data with another data. K-Means clustering is a non-hierarchical data clustering method that attempts to partition existing data into one or more clusters / groups. In this study clustering was conducted using the K-Means algorithm using data on the achievements of the National Middle School National Examination in 2018 obtained from the official website of the Center for Education and Culture Assessment of the Ministry of Education and Culture of the Republic of Indonesia. The results of the cluster with the K-Means algorithm are obtained for cluster 1 there are 14 provinces, cluster 2 there are 5 provinces, and cluster 3 there are 15 provinces with cluster 1 level is a cluster with a high national test score, cluster 2 is a cluster with a low national test score and a cluster 3 is a cluster with moderate national examination scores. While the results of the evaluation of the K-Means algorithm with the number of clusters 3 produce an evaluation value of Connectivity 11,916, Dunn 0.246 and Silhouette 0.464.
A novel S-box-based postprocessing method for true random number generation

The quality of randomness in numbers generated by true random number generators (TRNGs) depends on the source of entropy. However, in TRNGs, sources of entropy are affected by environmental changes and this creates a correlation between the generated bit sequences. Postprocessing is required to remove the problem created by this correlation in TRNGs. In this study, an S-box-based postprocessing structure is proposed as an alternative to the postprocessing structures seen in the published literature. A ring oscillator (RO)-based TRNG is used to demonstrate the use of an S-box for postprocessing and the removal of correlations between number sequences. The statistical properties of the numbers generated through postprocessing are obtained according to the entropy, autocorrelation, statistical complexity measure, and the NIST 800.22 test suite. According to the results, the postprocessing successfully removed the correlation. Moreover, the data rate of the bit sequence generated by the proposed postprocessing is reduced to $2/3$ of its original value at the output.
Scaling Camouflage: Content Disguising Attack Against Computer Vision Applications

Recently, deep neural networks have achieved state-of-the-art performance in multiple computer vision tasks, and become core parts of computer vision applications. In most of their implementations, a standard input preprocessing component called image scaling is embedded, in order to resize the original data to match the input size of pre-trained neural networks. This article demonstrates content disguising attacks by exploiting the image scaling procedure, which cause machine's extracted content to be dramatically dissimilar with that before scaled. Different from previous adversarial attacks, our attacks happen in the data preprocessing stage, and hence they are not subject to specific machine learning models. To achieve a better deceiving and disguising effect, we propose and implement three feasible attack approaches with L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> - and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -norm distance metrics. We have conducted a comprehensive evaluation on various image classification applications, including three local demos and two remote proprietary services. We also investigate the attack effects on a YOLO-v3 object detection demo. Our experimental results demonstrate successful content disguising against all of them, which validate our approaches are practical.
Special Issue on Visual Sensors

Visual sensors have characteristics that make them interesting as sources of information for any process or system [...]
Efficient two-stage cryptography scheme for secure distributed data storage in cloud computing

Cloud computing environment requires secure access for data from the cloud server, small execution time, and low time complexity. Existing traditional cryptography algorithms are not suitable for cloud storage. In this paper, an efficient two-stage cryptography scheme is proposed to access and store data into cloud safely. It comprises both user authentication and encryption processes. First, a two-factor authentication scheme one-time password is proposed. It overcomes the weaknesses in the existing authentication schemes. The proposed authentication method does not require specific extra hardware or additional processing time to identity the user. Second, the plaintext is divided into two parts which are encrypted separately using a unique key for each. This division increases the security of the proposed scheme and in addition decreases the encryption time. The keys are generated using logistic chaos model theory. Chaos equation generates different values of keys which are very sensitive to initial condition and control parameter values entered by the user. This scheme achieves high-security level by introducing different security processes with different stages. The simulation results demonstrate that the proposed scheme reduces the size of the ciphertext and both encryption and decryption times than competing schemes without adding any complexity.
Regularized Multimodal Hierarchical Topic Model for Document-by-Document Exploratory Search

In the exploratory search paradigm of information retrieval, the user has a complicated search demand that can not be formulated in a short query. The user collects thematically relevant information iteratively in a “query-browse-refine” process being motivated by learning, understanding, and knowledge acquisition purposes. We consider an elementary step of this scenario in which the search intent can be expressed by a long text query. For this case, we develop an exploratory search engine based on probabilistic topic modeling. Topic model gives a low-dimensional sparse interpretable vector representation (topical embedding) of a text. The search engine uses these embeddings for ranking documents by their similarity to the query. We show that performing only one query, the topic-based search engine achieves better precision and recall that human assessors do spending up to one hour in a conventional browse-refine loop. We use additive regularization for topic modeling (ARTM) to make the model simultaneously sparse, decorrelated, n-gram, multimodal and hierarchical. We show experimentally that each of these features of the model is important to achieve precision and recall higher than 90%. Topical hierarchy emulates a natural human strategy to focus on subtopics gradually discarding unnecessary information. Also we show that increasing the number of levels in the hierarchy improves the search quality and makes it possible to enrich the model with a larger number of topics. We use the fast parallel implementation of the regularized EM-algorithm from BigARTM open source project. We use crowdsourcing in order to collect relevance assessments for the search quality evaluation.
Super-Resolution Reconstruction Algorithm for Infrared Image with Double Regular Items Based on Sub-Pixel Convolution

In this paper, an adaptive dual-regularization super-resolution reconstruction algorithm based on sub-pixel convolution (MPSR) is proposed. There are two novel features of the algorithm: First, the traditional regularization algorithm and sub-pixel convolution algorithm are combined to enrich the details; then, a regularization function with two adaptive parameters and two regularization terms is proposed to enhance the edge. MPSR firstly enhances the multi-scale detail of low-resolution images; then, regular processing and feature extraction are carried out; finally, sub-pixel convolution is used to fuse the extracted features to generate high-resolution images. The experimental results show that the subjective and objective evaluation indexes (PSNR/SSIM) of the algorithm have achieved satisfactory results.
Disease Prediction Model Based on BiLSTM and Attention Mechanism

Electronic health records are digital records of patients' medical history, diagnosis, medication, treatment plans. EHRs not only contain the patients' medical and treatment history, but also systematically collect patients' clinical data. Therefore, it is very valuable to improve the patient's health care management by mining the information in the EHRs. However, due to the irregularities and sparsity of EHRs, EHRs mining is very challenging. In this paper, the laboratory data, physiological indicators and diagnosis time during the patients' hospitalization period are extracted from the MIMIC-III database. Then, the extracted features are used to generate the patients' representation vector. Finally, we propose a prediction model based on BiLSTM and attention mechanism, which is called Bi-Attention. The BiLSTM is adopted to learn the forward and backward timing information in the patient's representation vectors and to predict the patient's disease by utilizing the specific clinical information in the timed medical record with the attention mechanism. The experimental results show that compared with other methods, the proposed model can effectively improve the prediction performance.
New pseudo-random number generator based on improved discrete-space chaotic map

In this paper, a new pseudo-random number generator (PRNG) based on improved onedimensional discrete-space chaotic map is proposed. Like the original, the improved map relies on bijective mapping of permutations and natural numbers. Instead of using standard Lehmer code, we use a mapping computable in linear time, which significantly speeds up the PRNG. Results of NIST 800-22 test suite and TestU01 test suite confirm that the proposed approach can be used for generation of pseudo-random numbers. Due to discrete nature of used chaotic map, the proposed PRNG is not influenced by dynamical degradation and has virtually unlimited key space. Proposed approach has much better ratio between required memory and security level than previous secure one-dimensional discrete-space chaotic PRNGs. Also, proposed PRNG is much faster than other secure PRNGs of the same type. Satisfactory speed and small memory requirements indicate that proposed PRNG has properties desirable for use in devices with limited memory space, such as wireless sensor networks.
Improving Sentence Representations via Component Focusing

The efficiency of natural language processing (NLP) tasks, such as text classification and information retrieval, can be significantly improved with proper sentence representations. Neural networks such as convolutional neural network (CNN) and recurrent neural network (RNN) are gradually applied to learn the representations of sentences and are suitable for processing sequences. Recently, bidirectional encoder representations from transformers (BERT) has attracted much attention because it achieves state-of-the-art performance on various NLP tasks. However, these standard models do not adequately address a general linguistic fact, that is, different sentence components serve diverse roles in the meaning of a sentence. In general, the subject, predicate, and object serve the most crucial roles as they represent the primary meaning of a sentence. Additionally, words in a sentence are also related to each other by syntactic relations. To emphasize on these issues, we propose a sentence representation model, a modification of the pre-trained bidirectional encoder representations from transformers (BERT) network via component focusing (CF-BERT). The sentence representation consists of a basic part which refers to the complete sentence, and a component-enhanced part, which focuses on subject, predicate, object, and their relations. For the best performance, a weight factor is introduced to adjust the ratio of both parts. We evaluate CF-BERT on two different tasks: semantic textual similarity and entailment classification. Results show that CF-BERT yields a significant performance gain compared to other sentence representation methods.
Improvement of Ancient Shui Character Recognition Model Based on Convolutional Neural Network

This study uses deep learning theory into the character recognition technology for Shui characters in ancient books, with the objectives of overcoming the instability of the high-pixel ancient Shui characters generative model and the need for large scale handwritten text data annotation among other issues. By constructing a multilayer adversarial neural network with a Laplacian structure, a clear generative model is established for original image data of Shui characters and a stable adversarial network model with multiple mapping relationships from coarse to fine is formed. Based on the analysis of the feature distance of Shui character image samples, the minimum inter-class spacing value and the optimal number of clusters are calculated. Combined with feedback from the classifier model, the optimal number of clusters in the clustering model is adjusted, an evaluation function with information entropy adjustment and clustering threshold convergence is constructed for the unsupervised labelling of Shui character image samples. In this paper, the feedback from the convolutional neural network is used to determine the algorithmic model of the hyperparameters for clustering annotation, and this structure is also designed to improve the recognition rate of handwritten Shui characters in ancient books.
Cipherchain: A Secure and Efficient Ciphertext Blockchain via mPECK

Most existing blockchain schemes are based on the design concept "openness and transparency" to realize data security, which usually require transaction data to be presented in the form of plaintext.However, it inevitably brings the issues with respect to data privacy and operating performance.In this paper, we proposed a novel blockchain scheme called Cipherchain, which can process and maintain transaction data in the form of ciphertext while the characteristics of immutability and auditability are guaranteed.Specifically in our scheme, transactions can be encrypted locally based on a searchable encryption scheme called multi-user public key encryption with conjunctive keyword search (mPECK), and can be accessed by multiple specific participants after appended to the globally consistent distributed ledger.By introducing execution-consensus-update paradigm of transaction flow, Cipherchain cannot only make it possible for transaction data to exist in the form of ciphertext, but also guarantee the overall system performance not greatly affected by cryptographic operations and other local execution work.In addition, Cipherchain is a promising scheme to realize the technology combination of "blockchain+cloud computing" and "permissioned blockchain+public blockchain".
SDAE-GAN: Enable high-dimensional pathological images in liver cancer survival prediction with a policy gradient based data augmentation method

High-dimensional pathological images produced by Immunohistochemistry (IHC) methods consist of many pathological indexes, which play critical roles in cancer treatment planning. However, these indexes currently cannot be utilized in survival prediction because joining them with patients' clinicopathological features (e.g., age and tumor size) is challenging due to their high dimension and sparse features. To address this problem, we propose a novel two-stage survival prediction model named ICSPM to join the IHC images and clinicopathological features. For the first stage, our proposed SDAE-GAN compresses high-dimensional IHC images to flat, compact and representative feature vectors by compressing and reconstructing them. For the first time, SDAE-GAN integrates dense blocks, the stacked auto-encoder and the GAN architecture to maximize the ability to detect patterns in IHC images. In addition, we propose a novel policy gradient based data augmentation method to involve the diversity in IHC images without breaking patterns inside them. For the second stage, ICSPM adopts a DenseNet to join feature vectors and clinicopathological features for survival prediction. Experimental results demonstrate that ICSPM reached a state-of-the-art prediction accuracy of 0.72 on the five-year survival. ICSPM is the first work to enable high-dimensional IHC images in cancer survival prediction. We prove that high-dimensional IHC images and clinicopathological features provide valuable and complementary information in survival prediction.
Feature selection for DDoS detection using classification machine learning techniques

Computer system security is a factor that needs to be considered in the era of industrial revolution 4.0, namely by preventing various threats to the system, as well as being able to detect and repair any damage that occurs to the computer system. DDoS attacks are a threat to the company at this time because this attack is carried out by making very large requests for a site or website server so that the system becomes stuck and cannot function at all. DDoS attacks in Indonesia and developed countries always increase every year to 6% from only 3%. To minimize the attack, we conducted a study using Machine Learning techniques. The dataset is obtained from the results of DDoS attacks that have been collected by the researchers. From the datasets there is a training and testing of data using five techniques classification: Neural Network, Naïve Bayes and Random Forest, KNN, and Support Vector Machine (SVM), datasets processed have different percentages, with the aim of facilitating in classifying. From this study it can be concluded that from the five classification techniques used, the Forest random classification technique achieved the highest level of accuracy (98.70%) with a Weighted Avg 98.4%. This means that the technique can detect DDoS attacks accurately on the application that will be developed.