Reviewerly/exHarmony huggingface dataset:
Discovering Top-k Profitable Patterns for Smart Manufacturing

In the past, many studies were developed to discover useful knowledge from rich data for decision-making in wide applications in Internet of Things and Web of Things, such as smart manufacturing. Utility-driven pattern mining (UPM) technology is famous in knowledge discovering domain. However, one of the biggest issues of UPM is the setting of a suitable minimum utility threshold (minUtil). The higher minUtil is, the fewer interesting patterns are obtained. Conversely, the lower minUtil is, the more useless patterns are discovered. In this paper, we propose a solution for discovering top-k profitable patterns with average-utility measure, which can be applied to manufacturing. The average-utility of a pattern, w.r.t its corresponding length, can be used to fairly measure the pattern. The proposed new upper-bounds on average-utility are tighter than previous upper-bounds. Moreover, based on these upper-bounds, the novel PPT algorithm utilizes merging and projection techniques to greatly reduce the search space. By adopting several threshold raising strategies, the PPT algorithm can discover correct top-k patterns in a short time. We also implemented the efficiency and effectiveness of the algorithm on real and synthetic datasets. The experimental results reveal that the algorithm not only get a complete set of top-k interesting patterns, but also works better than the state-of-the-art algorithm in terms of runtime, memory consumption and scalability. Especially, the proposed algorithm performs very well on dense datasets.
Can Deep Learning Models Learn the Vulnerable Patterns for Vulnerability Detection?

Deep learning has been widely used for the security issue of vulnerability prediction. However, it is confusing to explain how a deep learning model makes decisions on the prediction, although such a model achieves a good performance. Meanwhile, it is also difficult to discover which part of the source code is concentrated on by this black-box model. To this end, we present an empirical evaluation to explore how the deep learning model works on predicting vulnerability and whether it precisely captures the critical code segments to represent the vulnerable patterns. First of all, we build a new vulnerability dataset, called Juliet+, in which vulnerability-related code lines of both positive (bad) and negative (good) samples are labeled manually with substantial efforts, based on the Juliet Test Suite. After that, four deep learning models by leveraging attention mechanisms are empirically implemented to detect vulnerability through mining vulnerable patterns from the source code. We conduct extensive experiments to evaluate the effectiveness of such four models and to analyze the interpretability with evaluation metrics such as Hit@k. The empirical experiment results reveal that the deep learning models with attention, to some extent, can focus on the vulnerability-related code segments that are profitable to interpret the result of vulnerability detection, especially when we adopt the graph neural network model. We further investigate what factors affect the interpretability of models including the class distribution, the number of samples, and the differences of sample features. We find the graph neural network model performs better on part of the dataset which contains balanced and sufficient samples with obvious differences between vulnerable and non-vulnerable patterns.
Dynamic state feedback controller and observer design for dynamic artificial neural network models

Artificial neural networks are black-box models that can be used to model nonlinear dynamical systems. This article presents a synthesis method for full dynamic state feedback controllers and state and output observers that have guaranteed properties for systems approximated by dynamic artificial neural networks. The resulting control designs are applicable to the practical situation in which the steady-state values for the control input are not known. Dynamic artificial neural networks are written in the standard nonlinear operator form, also known in the literature as the Luré formulation. A generalized form of the Luré formulation is adopted to allow for the representation of deep ℓ-layer networks, ℓ≥1. Sufficient conditions for controller synthesis and observer design are derived in the form of linear matrix inequalities, using a quadratic Lyapunov function. The synthesis method is demonstrated for the control of pH in two tanks in series and a numerical example.
Only Header: a reliable encrypted traffic classification framework without privacy risk

Encrypted traffic classification plays a critical role in network management, providing appropriate Quality-of-Service and Network Intrusion Detection. Conventional port-based and deep packet inspection approaches cannot classify encrypted traffic effectively. Methods based on machine learning can classify encrypted traffic by extracting statistical features of the flow. However, they require manual extraction of features. Recent studies show that the approaches based on deep learning are compelling for the task. They can automatically learn raw traffic features without manual feature extraction. However, these studies still take the payload of encrypted traffic as the model input, which may cause privacy risks. Besides, a massive encrypted payload causes great storage pressure on traffic classification. In this paper, we propose a reliable encrypted traffic classification framework by only using the flow header called Only Header, which avoids privacy risks and achieves lightweight storage. Firstly, we introduce a twice segmentation mechanism to dilute the interference traffic and increase the weight of effective traffic. Then, we use capsule neural networks (CapsNet) to learn spatial and byte features of the flow header. The Only Header’s effectiveness is compared with other methods using two public datasets, including ISCX VPN-nonVPN and ISCX Tor-nonTor datasets. The experimental results demonstrate that the Only Header outperforms the state-of-the-art encrypted traffic classification methods.
The Application of VR/AR Technology in Graphic Design Based on zSpace

The arrival of the information age has promoted the rapid development of information technology, and the birth of VR and AR technology has brought many changes to the emerging technology fields. R&amp;D and progress of VR and AR technology highlight outstanding achievements in the information age. In the future, VR and AR technologies will bring huge economic benefits to related industries, and at the same time, they will lead the future of related industries. Virtual reality technology has evolved for decades since its birth. As a new technology, VR has greatly changed people’s daily life. No matter in the field of graphic design or interior design, it can be used as a special technical means to effectively penetrate between abstract thinking and entity, thus promoting the change of people’s design thinking. Therefore, VR technology has been paid more and more attention in recent years, so it is widely used. With the strengthening of China’s national strength, VR/AR technology based on zSpace has been more and more applied to the field of graphic design. Therefore, this paper takes VR/AR technology based on zSpace as the research object, analyzes the application of VR/AR technology in graphic design in detail, sorts out the advantages and existing problems of VR/AR technology in graphic design field, and discusses the role of emerging technologies in graphic design field, hoping to provide reference and help for the improvement of virtual reality technology in graphic design field.
Expanding Queries with Maximum Likelihood Estimators and Language Models

The employment of various language modelling techniques in the area of information retrieval is gaining wide adoption in the state of the art methods. The precision of the language model enables the solution of the issue of information retrieval in a huge corpus of texts. To accomplish this, these techniques begin by estimating a probabilistic linguistic model for each article in the collection that is capable of generating a ranking of relevant texts in response to a query. One of the difficulties that this family of methods faces is a shortage of data. As a result, smoothing methods capable of changing the maximum likelihood estimator are required to account for the imprecision created. This paper highlights its use surpasses established approaches, such as tf-idf, for creating rankings of documents sorted by relevance. Finally, we examine various ideas related to query expansion by utilizing such methods.
Coherent VCSEL Network Computing

We use a 2D array of injection-locked VCSELs to implement a spatiotemporally multiplexed optical neural network and demonstrate image classification at high accuracy with GHz line rates.
Two-stage cost-sensitive local models for heterogeneous cross-project defect prediction

Software defect prediction is an active topic in the field of software engineering. Cross-project defect prediction (CPDP) adopts the defect data set of the source project to predict the defects of the target project. However, the metrics of the source project and those of the target project are often different, and the traditional CPDP has certain limitations at this time. To address the inconsistency of source and target metrics, researchers propose heterogeneous cross-project defect prediction (HCPDP). To improve the performance of the HCPDP, we propose new Two-stage Cost-sensitive Local Models (TCLM). TCLM aims to improve on the problem of feature selection, linear inseparability of heterogeneous data, class imbalance and data adoption problems in HCPDP. Firstly, in the feature selection stage, we add cost information to improve the feature selection algorithm. Then, KCCA (Kernel Canonical Correlation Analysis) is used to project and map the heterogeneous data into a common feature space so as to mitigate the problem of inconsistent feature sets of the source and the target projects. Secondly, in the model training stage, we adopt local models to improve the performance, and introduce cost information to deal with the class imbalance problem. To verify the effectiveness of the TCLM method, we conduct large-scale empirical study on 24 projects in the AEEEM, PROMISE, NASA, and Relink datasets. Experimental results show that TCLM indeed outperforms the previous work. Therefore, we recommend using the TCLM method to build an HCPDP model.
Hate Speech Detection in Social Media for the Kurdish Language

With the rapid growth of technology over the world, especially, on the internet, people enormously use social media freely to express their ideologies. Sometimes the freedom of media is caught up and the rights of others are beaten down by hate speech. Moreover, social media is an easy and vague way to desecrate people, groups, and parties since there is no any way to recognize anonymous users over social media. Testing human speech is common for English, Arabic, and Turkish languages while there is no attempt for the Kurdish language. For that reason, the Kurdish hate speech dataset is collected from comments on the Facebook application as an effort for detecting hate speech and removing them. The raw dataset consists of 6882 comments which are divided into hate and hot hate classes. Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bays (NB) algorithms are implemented and compared. Based on the results, the SVM is found most excellent with the F1 measure being 0.687.
Efficient graph neural architecture search using Monte Carlo Tree search and prediction network

Graph Neural Networks (GNNs) have emerged recently as a powerful way of dealing with non-Euclidean data on graphs, such as social networks and citation networks. Despite their success, obtaining optimal graph neural networks requires immense manual work and domain knowledge. Inspired by the strong searching capability of neural architecture search in CNN, a few attempts automatically search optimal GNNs that rival the best human-invented architectures. However, existing Graph Neural Architecture Search (GNAS) approaches face two challenges: (1) Sampling GNNs across the entire search space results in low search efficiency, particularly in large search spaces. (2) It is pretty costly to evaluate GNNs by training architectures from scratch. To overcome these challenges, this paper proposes an Efficient Graph Neural Architecture Search (EGNAS) method based on Monte Carlo Tree Search (MCTS) and a prediction network. Specifically, EGNAS first uses MCTS to recursively partition the entire search space into good or bad search regions. Then, the reinforcement learning-based search strategy (also called the agent) is applied to sample GNNs in those good search regions, which prevents overly exploring complex architectures and bad-performance regions, thus improving sampling efficiency. To reduce the evaluation cost, we use a prediction network to estimate the performance of GNNs. We alternately use ground-truth accuracy (by training GNNs from scratch) and prediction accuracy (by the prediction network) to update the search strategy to avoid inaccuracies caused by long-term use of the prediction network. Furthermore, to improve the training efficiency and stability, the agent is trained by a variant of Proximal Policy Optimization. Experiments show that EGNAS can search for better GNNs in the promising search region in a shorter search time, with an accuracy of 83.5%, 73.3%, 79.6%, and 94.5% on Cora, Citeseer, Pubmed, and Photo datasets, respectively In particular, compared to the most popular GNAS algorithm, our EGNAS-NP without using the prediction network achieves an accuracy of 83.6% on Cora, 73.5% on Citeseer, 79.9% on Pubmed, and 94.6% on Photo, with a relative improvement of 0.6%, 0.2%, 0.7%, and 0.6%.
Dual branch parallel steganographic framework based on multi-scale distillation in framelet domain

The intuition behind image steganography is to hide secret image into cover image, thereby analysis and retrieval are avoided. Image steganography methods based on deep learning have recently gained popularity in comparison to traditional methods. However, most steganography methods have poor perceptibility and restoration robustness, resulting in weak quality of stego images and revealed images. To address this gap, we propose DBPSNet, an original dual branch parallel steganographic network based on knowledge distillation in framelet domain. Wavelet frame transform performed on cover image is used to migrate the steganographic framework in spatial domain to framelet domain. Afterwards, we establish the steganographic framework based on knowledge distillation, where the teacher branch produces feature maps layer-by-layer to guide student branch in learning how to generate reliable and realistic stego image. In addition, the random noise layer is used in reconstruction network, which helps improve the robustness of model by attempting to reveal image from the noisy stego image. Extensive controlled experiments on BOSSBase show that DBPSNet is ahead of some mainstream image steganography methods in terms of steganography and reconstruction effects, algorithm security and model robustness.
Privacy Preservation of Medical Health Records using Symmetric Block Cipher and Frequency Domain Watermarking Techniques

Digital watermarking is a growing paradigm in information science and communication systems that can be applied in the medical field. In general, a digital watermark is a mechanism for protecting digital information's copyright. Various algorithms have been developed till date to implement watermarking in order to safeguard data. The Discrete Cosine Transform is one such well-known algorithm (DCT). This algorithm is a frequency domain technique that provides security against attacks. ECG signals can also be compressed using DCT in electrocardiography. The proposed method primarily focuses on the application of digital watermarking techniques on patient medical records. The solution's goal is to preserve patients' medical records by integrating them in their MRI scan reports. As a result, the attacker will only be able to see the MRI scan report, while the health information will be buried. Singular value decomposition is used to compress the MRI scan report in this case (SVD). The medical health record, on the other hand, is encrypted using Advanced Encryption Standard (AES) algorithm, and the cipher picture obtained is employed in the embedding procedure. The DCT technique is used to embed the compressed MRI scan (cover image) and cipher image (watermark image). The embedded medical health record can be extracted by reverse application of DCT. The extracted cipher image is then decrypted using AES decryption algorithm. The produced watermark image’s (health record) quality is then assessed using a set of standards referred to as the Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE).
Hierarchical fuzzy deep learning for image classification

Considerable interest has been shown over the last several decades for fuzzy logic and its application. The intelligent and deep learning systems are gaining breakthroughs in all walks of life to solve real-life problems for the future. The conventional fuzzy has the constraint to work with limited rule dimensions, whereas deep neural networks are unable to handle uncertain and imprecise data implicitly in the system. The objective of this paper is to develop a generalized algorithm for intelligent systems that can handle uncertainty and imprecise behavior especially for processing of large image datasets. In this paper, the hierarchical fuzzy approach is suggested, as it is gaining attention to tackle large real-life problems. The strategy used is to partition a large image dataset into small data samples and connect all the fuzzy subsystems in a hierarchical manner. In the literature, as far as authors know, no one has developed a hierarchical fuzzy approach to handle a large image dataset of real images. The algorithm for hierarchical fuzzy logic for a large image data using image thresholding has been discussed. To make the assessment, the real image database has been considered. The image classification has attained the potential applications to defense and security especially for target identification and classification. The accuracy and computational time comparisons of hierarchical fuzzy systems with existing methodologies such as deep neural networks have been discussed.
Fuzzy granular deep convolutional network with residual structures

In recent years, the deep neural network technology has developed rapidly and has been effective in processing and analyzing images, videos, sounds and many other aspects. However, traditional neural networks only learn the samples themselves and ignore the differences between samples. When the network depth is too deep, network degradation phenomenon may occur. To solve these drawbacks, we propose the fuzzy granular deep convolutional network with residual structures. Firstly, we define the concept, operation and correlation measures of fuzzy granules, and construct the granulation reference system by random sampling for fuzzy granulation. Furthermore, modules of granular neuron, granular activation function, granular convolution, and granular residual are defined, and the fuzzy granular deep convolutional network with residual structures is built. The loss functions and learning algorithm are designed for the granular neural network, and the fuzzy granular neural network is successfully trained. The fuzzy granular neural network owns the characteristics of multi-granularity, multi-angle and structured, which has good generalization performance. Finally, experiments are conducted on the Cifar100 and Tiny Imagenet datasets. The experimental results show the effectiveness of the residual granular neural network. We further found that the granular neural network could alleviate the degradation problem. Moreover granular neural network with fewer hidden layers can achieve the function of a neural network with more hidden layers.
A Fast and Flexible FPGA-based Accelerator for Natural Language Processing Neural Networks

Deep neural networks (DNNs) have become key solutions in the natural language processing (NLP) domain. However, the existing accelerators customized for their narrow target models cannot support diverse NLP models. Therefore, naively running complex NLP models on the existing accelerators often leads to very marginal performance improvements. For these reasons, architects are now in dire need of a new accelerator that can run various NLP models while taking its full performance potential. In this article, we propose FlexRun, an FPGA-based modular accelerator to efficiently support diverse and complex NLP models. First, we identify key components commonly used by NLP models and implement them on top of a current state-of-the-art FPGA-based accelerator. Next, FlexRun conducts an in-depth design space exploration to find the best accelerator architecture for a target NLP model. Last, FlexRun automatically reconfigures the accelerator based on the exploration results. Our FlexRun design outperforms the current state-of-the-art FPGA-based accelerator by 1.21×–2.73× and 1.15×–1.50× for BERT and GPT2, respectively. Compared to Nvidia’s V100 GPU, FlexRun achieves 2.69× higher performance on average for various BERT and GPT2 models.
Combined particle and smooth innovation filtering for nonlinear estimation

In this paper, a new state and parameter estimation method is introduced based on the particle filter (PF) and the sliding innovation filter (SIF). The PF is a popular estimation method, which makes use of distributed point masses to form an approximation of the probability distribution function (PDF). The SIF is a relatively new estimation strategy based on sliding mode concepts, formulated in a predictor-corrector format. It has been shown to be very robust to modeling errors and uncertainties. The combined method (PF-SIF) utilizes the estimates and state error covariance of the SIF to formulate the proposal distribution which generates the particles used by the PF. The PF-SIF method is applied on a nonlinear target tracking problem, where the results are compared with other popular estimation methods.