Reviewerly/exHarmony huggingface dataset:
CNN-LSTM Enabled Prediction of Remaining Useful Life of Cutting Tool

To enhance production quality, productivity and energy consumption, it is paramount to predict the Remaining Useful Life (RUL) of a cutting tool accurately and efficiently. Deep learning algorithm-driven approaches have been actively explored in the research field though there are still potential areas to further enhance the performance of the approaches. In this research, to improve accuracy and expedite computational efficiency for predicting the RUL of cutting tools, a novel systemic methodology is designed to integrate strategies of signal partition and deep learning for effectively processing and analyzing multi-sourced sensor signals throughout the lifecycle of a cutting tool. In more detail, the methodology consists of two subsystems: (i) a Hurst exponent-based method is developed to effectively partition complex and multi-sourced signals along the tool wear evolution; (ii) a hybrid CNN-LSTM algorithm is designed to combine feature extraction, fusion and regression in a systematic means to facilitate the prediction based on segmented signals. The system is validated using a case study with a large set of databases using multiple cutting tools and with multi-sourced signals. Comprehensive comparisons between the proposed methodology and some other main-stream algorithms, such as CNN, LSTM, DNN and PCA, were carried out under the conditions of partitioned and un-partitioned signals. Benchmarks show that, based on the case study in this research, the prediction accuracy of the proposed methodology reached 87.3%, which are significantly better than those of the comparative algorithms.
Optimization Design Of Computer Information Processing System Based on Big Data Vision

Abstract With the development of computer technology and network technology, electronic technology has entered the information age. In the information age, the generation of a large number of data promotes the transformation of production and life style, and then brings human society into the era of big data. By analyzing the development of information processing system in the era of big data, this paper puts forward the core elements of information processing system and the optimization method of computer information system in the view of big data, aiming to strengthen the effective combination of big data technology and computer information system, and provide theoretical reference for related research.
Visualizing the Effect of Semantic Classes in the Attribution of Scene Recognition Models

The performance of Convolutional Neural Networks for image classification has vastly and steadily increased during the last years. This success goes hand in hand with the need to explain and understand their decisions: opening the black box. The problem of attribution specifically deals with the characterization of the response of Convolutional Neural Networks by identifying the input features responsible for the model’s decision. Among all attribution methods, perturbation-based methods are an important family based on measuring the effect of perturbations applied to the input image in the model’s output. In this paper, we discuss the limitations of existing approaches and propose a novel perturbation-based attribution method guided by semantic segmentation. Our method inhibits specific image areas according to their assigned semantic label. Hereby, perturbations are link up with a semantic meaning and a complete attribution map is obtained for all image pixels. In addition, we propose a particularization of the proposed method to the scene recognition task which, differently than image classification, requires multi-focus attribution models. The proposed semantic-guided attribution method enables us to delve deeper into scene recognition interpretability by obtaining for each scene class the sets of relevant, irrelevant and distracting semantic labels. Experimental results suggest that the method can boost research by increasing the understanding of Convolutional Neural Networks while uncovering datasets biases which may have been inadvertently included during the harvest and annotation processes. All the code, data and supplementary results are available at http://www-vpu.eps.uam.es/publications/SemanticEffectSceneRecognition/
Towards Drone-Sourced Live Video Analytics via Adaptive-yet-Compatible Compression

In this work, we present ACC (Adaptive-yet-Compatible Compression), one enabling technique for real-time drone-sourced edge-assisted video analytics built on top of the existing codec. ACC tackles an important technical problem to compress streamed video from the drone to the edge without scarifying accuracy and timeliness of video analytical tasks performed at the edge. ACC is inspired by the fact that not every bit in streamed video is equally valuable to video analytics, which opens new compression room over the conventional analytics-oblivious video codec technology. We exploit drone-specific context and intermediate hints from object detection to pursue adaptive fidelity needed to retain analytical quality. We have prototyped ACC in one showcase application of vehicle detection and validated its efficiency in representative scenarios. ACC has reduced transmission volume by average 9.53x with comparable detection accuracy.
Unsupervised Feature Selection using Encoder-Decoder Networks

Feature selection is one of the most important techniques for dimension reduction in a wide range of tasks. This paper presents a simple yet efficient method for unsupervised feature selection. To learn an encoder-decoder network on a huge number of training samples, the initial weights of such network must be regularly updated until the convergence reached. We interestingly investigated that the weights of an input neuron to the latent space neurons are highly correlated with the importance of them. We tailor an efficient measure as the importance of each feature value relies on the changing of connected weights to it. The experimental results confirm that the performance of proposed method is comparable, even better than other state-of-the-art methods.
WLAN-log-based superspreader detection in the COVID-19 pandemic

Identifying "superspreaders" of disease is a pressing concern for society during pandemics such as COVID-19. Superspreaders represent a group of people who have much more social contacts than others. The widespread deployment of WLAN infrastructure enables non-invasive contact tracing via people's ubiquitous mobile devices. This technology offers promise for detecting superspreaders. In this paper, we propose a general framework for WLAN-log-based superspreader detection. In our framework, we first use WLAN logs to construct contact graphs by jointly considering human symmetric and asymmetric interactions. Next, we adopt three vertex centrality measurements over the contact graphs to generate three groups of superspreader candidates. Finally, we leverage SEIR simulation to determine groups of superspreaders among these candidates, who are the most critical individuals for the spread of disease based on the simulation results. We have implemented our framework and evaluate it over a WLAN dataset with 41 million log entries from a large-scale university. Our evaluation shows superspreaders exist on university campuses. They change over the first few weeks of a semester, but stabilize throughout the rest of the term. The data also demonstrate that both symmetric and asymmetric contact tracing can discover superspreaders, but the latter performs better with daily contact graphs. Further, the evaluation shows no consistent differences among three vertex centrality measures for long-term (i.e., weekly) contact graphs, which necessitates the inclusion of SEIR simulation in our framework. We believe our proposed framework and these results can provide timely guidance for public health administrators regarding effective testing, intervention, and vaccination policies.
Transient Pattern Detection from Streaming Nature Data

A novel challenging for scientific discovery is finding an un-expected phenomenon from nature data. For such data-intensive discovery, we study online algorithms for detecting transient patterns in streaming nature data. There are two issues to be addressed. First, due to the limitation of memory resources, we need to process every incoming data by on-the-fly manner. Secondly, we need to avoid the false alert as far as possible, which adversely impacts on decisions. For tackling the first issue, we propose a fast detection algorithm based on the Chebyshev inequality. The Chebyshev inequality is a classic but general tail- bound inequality satisfied over any observed data. Together with adaptive windowing technique, the proposed algorithm can quickly decide if the prior data is in a normal or transient zone. We then introduce a two-phase alert mechanism into the baseline, which is effective for reducing the false alert rate. Consequently, we demonstrate the performance of our proposed method obtained by using both the artificial data and real data.
The potential of the blockchain for coordinated border management in developing countries

Globalization 4.0 offers new scenarios for the world economy, fueled by advanced technologies and the movement of ideas, people and goods. Faced with this situation of dynamic disruptions, border agencies have new opportunities to adapt their policies and business processes for secure, efficient, and scalable border management. Therefore, blockchain technology could help increase supply chain efficiency by providing a set of solutions that could offer new opportunities for the different actors at the border (information exchange, inter-agency cooperation, revenue collection, etc.). Because of the specificity of their issues and challenges, developing countries can exploit the potential offered by blockchain technology for better management of the international trade environment.
An Improved Particle Filter for UAV Passive Tracking Based on RSS

The increasing demand for unmanned aerial vehicle (UAV) supervision urgently requires accurate tracking algorithms as technical support. Particle filter (PF) is widely used for solving tracking problems for its fewer restrictions than Kalman filter (KF), but it needs to be optimized in received signal strength (RSS) based passive positioning scenario where the power measurement influenced by noise and antenna pattern. This paper proposes an improved PF algorithm. Firstly, probabilistic data association (PDA) is used to preprocess the measurement data to determine and eliminate outlier data. Secondly, the preliminary measurement data from offline experiments are used to modify the observation likelihood function to improve the weight distribution of the particles, so as to weaken the influence from non-ideal antenna pattern. In actual experiments, compared with the traditional PF algorithm, the results measured by USRP platform show that the proposed algorithm reduces the position RMSE from 5.55 m to 2.87 m when the UAV is moving at low speed. Owing to positioning accuracy improving, the velocity RMSE is also reduced by 5.1%.
Inductive Document Representation Learning for Short Text Clustering

Short text clustering (STC) is an important task that can discover topics or groups in the fast-growing social networks, e.g., Tweets and Google News. Different from the long texts, STC is more challenging since the word co-occurrence patterns presented in short texts usually make the traditional methods (e.g., TF-IDF) suffer from a sparsity problem of inevitably generating sparse representations. Moreover, these learned representations may lead to the inferior performance of clustering which essentially relies on calculating the distances between the presentations. For alleviating this problem, recent studies are mostly committed to developing representation learning approaches to learn compact low-dimensional embeddings, while most of them, including probabilistic graph models and word embedding models, require all documents in the corpus to be present during the training process. Thus, these methods inherently perform transductive learning which naturally cannot handle well the representations of unseen documents where few words have been learned before. Recently, Graph Neural Networks (GNNs) has drawn a lot of attention in various applications. Inspired by the mechanism of vertex information propagation guided by the graph structure in GNNs, we propose an inductive document representation learning model, called IDRL, that can map the short text structures into a graph network and recursively aggregate the neighbor information of the words in the unseen documents. Then, we can reconstruct the representations of the previously unseen short texts with the limited numbers of word embeddings learned before. Experimental results show that our proposed method can learn more discriminative representations in terms of inductive classification tasks and achieve better clustering performance than state-of-the-art models on four real-world datasets.
The Effectiveness of Digital Media for Students’ Writing Abilities: A Literature Review

Writing is one of the skills difficult for students to achieve and requires the right media in the process of transferring the knowledge. Learning in today's era demands learning media that supports and bridges the learning process so that it is easier conveying to students. The use of digital media based on to the students’ needs is one solution to problems related to students' writing skills. This study aims to determine the effectiveness of digital media for students' writing abilities. It conducted a literature review with two steps; database search and thematic analysis. The results of this study indicate that learning using digital media for students' writing ability is very effective in use. Furthermore, this study suggests the use of digital media to suit the specific writing skills.
Fast Segmentation of Markov Random Fields Corrupted by Correlated Noise

Markov Random fields (MRF) represent a powerful mathematical model and they are used in several areas, but it is almost impossible to perform exact analytical calculations when using MRF and we must use approximations and iterative methods that are greedy in terms of time and computing resources. In the literature, proposed MRF methods for Bayesian and parameters estimation are complicated for implementation and represent many disadvantages in practice.We propose in this work, and in order to remedy the problems mentioned above, a very simple MAP-MRF framework based mainly on local conditional probabilities, contrary to the existing solutions in literature where we rely on the energy function model.Two powerful models based on the proposed framework are then presented. They will be compared with two recent works to show how they are more efficient with respect to classical models when it comes to the unsupervised segmentation of corrupted data with correlated noise.
Small Magnitude PMU Bad Data Detection Based on Data Mining Technology

This paper presents a method to detect bad data from small amplitude Phasor Measurement Units (PMUs). First of all, the characteristics of poor PMU data with small amplitude are analyzed. Then, the advantages of long short-term memory (LSTM) network in processing time series data are analyzed, and the characteristics of time series data selection memory are used to extract the characteristic quantities of PMU amplitude data. Next, a method of using the phase angle slope to amplify the characteristics of the bad phase angle data is proposed. Finally, using the characteristics of the amplitude and phase angle of the PMU data, and using the density-based spatial clustering (DBSCAN) algorithm of the noise-based application for clustering analysis, the effective detection of small-amplitude PMU bad data is realized. Simulation results show that this method can improve the quality of PMU data.
Parity-swap state comparison amplifier for Schrödinger cat states

We propose a postselecting parity-swap amplifier for Schr\"odinger cat states that does not require the amplified state to be known a priori. The device is based on a previously-implemented state comparison amplifier for coherent states. It consumes only Gaussian resource states, which provides an advantage over some cat state amplifiers. It requires simple Geiger-mode photodetectors and works with high fidelity and approximately twofold gain.
An Emphasis on Quantum Cryptography and Quantum Key Distribution

The application of internet has spiked up in the present-day scenario, as the exchange of information made between two parties happens in public environment. Hence privacy of information plays an important role in our day to day life. There have been incredible developments made in the field of cryptography resulting in modern cryptography at its zenith. Quantum computers are one among them creating fear into security agencies across the world. Solving the complex mathematical calculations is uncomplicated using quantum computers which results in breaking the keys of modern cryptography, which cannot be broken using classical computers. The concept of quantum physics, into the cryptographic world has resulted in the advancement of quantum cryptography. This technique utilizes the idea of key generation by photons, and communicates between peer entities by secured channel. Quantum cryptography adapts quantum mechanical principles like Heisenberg Uncertainty principle and photon polarization principle to provide secure communication between two parties. This article focuses on generation of a secret shared key, later converted into Quantum bits (Qbits) and transmitted to the receiver securely.
Evaluation of Interpretable Association Rule Mining Methods on Time-Series in the Maritime Domain

In decision critical domains, the results generated by black box models such as state of the art deep learning based classifiers raise questions regarding their explainability. In order to ensure the trust of operators in these systems, an explanation of the reasons behind the predictions is crucial. As rule-based approaches rely on simple if-then statements which can easily be understood by a human operator they are considered as an interpretable prediction model. Therefore, association rule mining methods are applied for explaining time-series classifier in the maritime domain. Three rule mining algorithms are evaluated on the classification of vessel types trained on a real world dataset. Each one is a surrogate model which mimics the behavior of the underlying neural network. In the experiments the GiniReg method performs the best, resulting in a less complex model which is easier to interpret. The SBRL method works well in terms of classification performance but due to an increase in complexity, it is more challenging to explain. Furthermore, during the evaluation the impact of hyper-parameters on the performance of the model along with the execution time of all three approaches is analyzed.
A Intelligent CNN-BiLSTM Approach for Chinese Sentiment Analysis on Spark

Short text Chinese sentiment classification has become an important task in sentiment analysis fields. In recent years, deep learning-based methods have been widely used in sentiment classification. However, with the complexity of deep learning models, the number of model parameters has increased. The effectiveness of the model depends largely on the choice of hyperparameter combinations. The optimization of model parameters becomes a tricky problem. In this paper, we propose an intelligent neural network sentiment classification approach that uses particle swarm optimization algorithm to optimize the parameters of the neural network model (CNN-BiLSTM). Meanwhile, in order to improve the calculation efficiency of the algorithm, we propose a parallel variant of particle swarm optimization algorithm, which can be iteratively calculated on the Spark distributed platform (big data technology). Experiments on Chinese sentiment analysis dataset validate the effectiveness of our approach.
Predictive Bi-clustering Trees for Hierarchical Multi-label Classification

In the recent literature on multi-label classification, a lot of attention is given to methods that exploit label dependencies. Most of these methods assume that the dependencies are static over the entire instance space. In contrast, here we present an approach that dynamically adapts the label partitions in a multi-label decision tree learning context. In particular, we adapt the recently introduced predictive bi-clustering tree (PBCT) method towards multi-label classification tasks. This way, tree nodes can split the instance-label matrix both in a horizontal and a vertical way. We focus on hierarchical multi-label classification (HMC) tasks, and map the label hierarchy to a feature set over the label space. This feature set is exploited to infer vertical splits, which are regulated by a lookahead strategy in the tree building procedure. We evaluate our proposed method using benchmark datasets. Experiments demonstrate that our proposal (PBCT-HMC) obtained better or competitive results in comparison to its direct competitors, both in terms of predictive performance and model size. Compared to an HMC method that does not produce label partitions though, our method results in larger models on average, while still producing equally large or smaller models in one third of the datasets by creating suitable label partitions.
Predicting Future Classifiers for Evolving Non-linear Decision Boundaries

In streaming data applications, the underlying concept often changes with time which necessitates the update of employed classifiers. Most approaches in the literature utilize the arriving labeled data to continually update the classifier system. However, it is often difficult/expensive to continuously receive the labels for the arriving data. Moreover, in domains such as embedded sensing, resource-aware classifiers that do not update frequently are needed. To tackle these issues, recent works have proposed to predict classifiers at a future time instance by additionally learning the dynamics of changing classifier weights during the initial training phase. This strategy bypasses the need to retrain/relearn the classifiers, and thus the additional labeled data is no longer required. However, the current progress is limited to the prediction of linear classifiers. As a step forward, in this work, we propose a probabilistic model for predicting future non-linear classifiers given time-stamped labeled data. We develop a variational inference based learning algorithm and demonstrate the effectiveness of our approach through experiments using synthetic and real-world datasets.
Robust and Atomic Technique for Edge Detection to Identify Edges Present in Digital Image Using DSCTHS Method

The goal of this research paper is to examine the process and effects of Edge Detection and to propose an effective and standard algorithm for the same Edges are a suburb of honed discontinuity of pixel worth and Edge detection is about discerning those edges. Edge detection is used to extricate the intelligence an image i.e., in both image inspection and managing. To discern edges in an image there are assorted expertise or methodologies unearthed by distinct scientists. Among them some are Robert cross edge detector, Sobel Edge detector, Canny edge detection, Prewitt Edge detection, Palladian of Gaussian, Zero cross edge detection. Each methodology has different Shortcomings like for Robert Edge Detection technique the main drawback is it is much vulnerable to noise, like this, we scrutinized all the edge detection techniques and tried to vanquish the drawbacks in the algorithm that is Executed in this paper. In this paper, we have implemented a newly cultivated edge detection technique and filtration blueprint for detecting edges present in the digital image and removal of noise.