Reviewerly/exHarmony huggingface dataset:
Elephant herding optimization technique based neural network for cancer prediction

Cancer is a kind of uncontrolled growth of abnormal cells in any part of the body. It can be of many types. Early prognosis of cancer is the only way to treat it in a better way for researchers. It is very important to classify cancer in high or low risk group and that can be done by applying different machine learning techniques. In this study, a nature-based machine learning technique is developed named as Elephant Herding Optimization algorithm (EHO) which is validated using some cancer datasets like lung cancer, breast cancer, and cervical cancer. Here, the feature selection algorithm like ANOVA and Kruskal-Wallis tests are used where the relevant number of features are selected. The performance of EHO is evaluated using Root Mean Square Error (RMSE) and Correct Classification Rate (CCR) with and without feature selection. The RMSE value of the EHO algorithm is compared with Local Linear Wavelet Neural Network (LLWNN) and Particle Swarm Optimization (PSO). EHO shows 0.9837 CCR in the breast cancer dataset, 0.9671 CCR in the cervical cancer dataset, and 0.8821 CCR in the lung cancer dataset using ANOVA test i.e. the best result in comparison to other optimization algorithms. According to the tables it is clear that classification techniques takes more time without feature selection techniques but less time with feature selection techniques.
Nighttime Defogging Using High-Low Frequency Decomposition and Grayscale-Color Networks

We address the problem of nighttime defogging from a single image by introducing a framework consisting of two modules: grayscale and color modules. Given an RGB foggy nighttime image, our grayscale module takes the grayscale version of the image as input, and decomposes it into high and low frequency layers. The high frequency layers contain the scene texture information, which is less affected by fog. While the low frequency layers contain the scene layout/structure information including fog and glow. Our grayscale module then enhances the visibility of the textures in the high frequency layers, and removes the presence of glow and fog in the low frequency layers. Having processed the high/low frequency information, it fuses the two layers to obtain a grayscale defogged image. Our second module, the color module, takes the original RGB image, and process it similarly to what the grayscale module does. However, to obtain fog-free high and low frequency information, the module is guided by the grayscale module. The reason of doing this is because grayscale images are less affected by multiple colors of atmospheric light, which are commonly present in nighttime scenes. Moreover, having the grayscale module allows us to have consistency losses between the outputs of the two modules, which is critical to our framework, since we do not have paired ground-truths for our real data. Our extensive experiments on real foggy nighttime images show the effectiveness of our method.
Speed optimal FPGA implementation of the encryption algorithms for telecom applications

The last two decades have seen a revolution in telecom technology with the evolution of three wireless mobile communication standards, namely, GPRS to 3G, 3G to 4G, and 4G to 5G. 5G offers faster download speeds and enables high connectivity between devices such as mobile phones, displays, smart homes, and smart cars because of its high reliability and high bandwidths (up to 10 Gbps). However, at the same time, data and personal information are also more susceptible to theft because of the high connectivity. Such threats can be addressed using electronic data encryption using the advanced encryption standard (AES). Because of their reconfigurable and parallel architectures, Field-Programmable Gate Arrays (FPGAs) are getting popular in VLSI design flows to enable the pre-silicon validation of designs faster data rates in real-time. FPGAs also serve as platforms for software development in the pre-silicon environment owing to their faster speeds. The design community is also heavily relying on High-Level Synthesis (HLS) tools in VLSI design flows. HLS platforms enable the new designs to improve the process with sustained authentication between two analytical selections from conventional functional specifications. We propose a high-throughput FPGA implementation based on high-level Synthesis for the AES algorithm. The implementation uses a 128-bit key and is highly suited for telecom applications such as 5G. Researchers have developed and tested the setup and then used the Vivado HLS tool to evaluate various HLS guidelines as per the implementation. The generated Verilog RTL was verified and implemented on Xilinx Kintex 7 and Virtex 6 FPGAs. Since using the same resources, we have seen significant results than existing methods achieved by individual investigators. We have also verified the design for functionality by checking the ciphertext output from our design against a reference design output for the same input plaintext.
Cross-Modality Multi-atlas Segmentation Using Deep Neural Networks

Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the intensity similarity between target and atlas images. However, such similarity can be problematic when target and atlas images are acquired using different imaging protocols. High-level structure information can provide reliable similarity measurement for cross-modality images when cooperating with deep neural networks (DNNs). This work presents a new MAS framework for cross-modality images, where both image registration and label fusion are achieved by DNNs. For image registration, we propose a consistent registration network, which can jointly estimate forward and backward dense displacement fields (DDFs). Additionally, an invertible constraint is employed in the network to reduce the correspondence ambiguity of the estimated DDFs. For label fusion, we adapt a few-shot learning network to measure the similarity of atlas and target patches. Moreover, the network can be seamlessly integrated into the patch-based label fusion. The proposed framework is evaluated on the MM-WHS dataset of MICCAI 2017. Results show that the framework is effective in both cross-modality registration and segmentation.
Chaotic circuit with OTA based memristor on image cryptology

In this study, a new chaotic system and its analog circuit design have been introduced. The circuit of chaotic system has only a memristor, an inductor, a capacitor and a negative resistor. The proposed memristor emulator circuit is based on Operational Transconductance Amplifier (OTA) and it consists of multipliers, resistors, a capacitor and a current conveyor apart from OTAs. We have shown the chaotic behavior of our system with both numerical analyses done in MATLAB environment and an analog circuit analyses using LTspice simulation software. In addition to chaotic (pseudo-periodic) behavior, the periodic spiking behavior of our proposed system by changing a system parameter is also presented. The image cryptology method is carried out to prevent the unauthorized distribution of information and data. In image encryption part, an algorithm is suggested to provide confusion (pixel position permutation) and diffusion (pixel value transformation) operations on the image. A pixel swap method for both row and column is preferred as confusion operation and a logical XOR method for both row and column is done as diffusion operation on the pixels of the image. In order to decrypt the encrypted image, the reversed swap and the reversed XOR operations have been performed. In this way, image encryption and decryption schemes using a chaotic system with OTA based memristor are successfully carried out. Moreover, the security analyses with the help of histogram analysis, correlation coefficient analysis of two adjacent pixels, information entropy analysis and initial condition sensitivity analysis have been done in order to prove that our suggested algorithm is robust against different statistical attacks and exhaustive attack.
A hybrid recommendation model in social media based on deep emotion analysis and multi-source view fusion

Abstract The recommendation system is an effective means to solve the information overload problem that exists in social networks, which is also one of the most common applications of big data technology. Thus, the matrix decomposition recommendation model based on scoring data has been extensively studied and applied in recent years, but the data sparsity problem affects the recommendation quality of the model. To this end, this paper proposes a hybrid recommendation model based on deep emotion analysis and multi-source view fusion which makes a personalized recommendation with user-post interaction ratings, implicit feedback and auxiliary information in a hybrid recommendation system. Specifically, the HITS algorithm is used to process the data set, which can filter out the users and posts with high influence and eliminate most of the low-quality users and posts. Secondly, the calculation method of measuring the similarity of candidate posts and the method of calculating K nearest neighbors are designed, which solves the problem that the text description information of post content in the recommendation system is difficult to mine and utilize. Then, the cooperative training strategy is used to achieve the fusion of two recommended views, which eliminates the data distribution deviation added to the training data pool in the iterative training. Finally, the performance of the DMHR algorithm proposed in this paper is compared with other state-of-art algorithms based on the Twitter dataset. The experimental results show that the DMHR algorithm has significant improvements in score prediction and recommendation performance.
Requirements Dependency Extraction by Integrating Active Learning with Ontology-Based Retrieval

Context: Incomplete or incorrect detection of requirement dependencies has proven to result in reduced release quality and substantial rework. Additionally, the extraction of dependencies is challenging since requirements are mostly documented in natural language, which makes it a cognitively difficult task. Moreover, with ever-changing and new requirements, a manual analysis process must be repeated, which imposes extra hardship even for domain experts. Objective: The three main objectives of this research are: 1) Proposing a new dependency extraction method using a variant of Active Learning (AL). 2) Evaluating this AL and Ontology-based Retrieval (OBR) as baseline methods for dependency extraction on the two industrial data sets. 3) Analyzing the value gained from integrating these diverse approaches to form two hybrid methods. Method: Building on the general AL, ensemble and semi-supervised machine learning, a variant of AL was developed, which was further integrated with OBR to form two hybrid methods (Hybrid1, Hybrid2) for extracting three types of dependencies (requires, refines, other): Hybrid1 used OBR as a substitute for human expert; Hybrid2 used dependencies extracted through the OBR as an additional input for training set in AL. Results: For two industrial case studies, AL extracted more dependencies than OBR. Hybrid1 showed improvement for both data sets. For one of them, F1 score increased to 82.6% compared to the AL baseline score of 49.9%. Hybrid2 increased the accuracy by 25% to the level of 75.8% compared to the AL baseline accuracy. OBR also complemented the AL approach by reducing 50% of the human effort.
Blockchain for Secure-GaS: Blockchain-Powered Secure Natural Gas IoT System With AI-Enabled Gas Prediction and Transaction in Smart City

The traditional natural gas Internet-of-Things (IoT) system has many problems, such as centralized management of resources, noncirculation of data between stations, insecurity of transaction information or account books, and lack of contract consensus. In order to ensure data security and reliable transaction, this article introduces artificial intelligence (AI) and blockchain technology and constructs an AI-enabled and blockchain-powered natural gas IoT system in a smart city. In this article, the natural gas output prediction model based on temporal pattern attention-based LSTMs (TPA-LSTMs) is used to enable the system to sense the change of natural gas deliverability. In addition, we establish a blockchain-based secure natural gas transaction scheme, which dynamically matches the purchase contract and sale contract to maximize the interests of the buyer and the seller and obtain a transaction contract. The experimental results show that our model can predict the output value of natural gas in real time and select the appropriate transaction matching scheme according to the dynamic demand for sales.
<i>TaxThemis</i>: Interactive Mining and Exploration of Suspicious Tax Evasion Groups

Tax evasion is a serious economic problem for many countries, as it can undermine the government's tax system and lead to an unfair business competition environment. Recent research has applied data analytics techniques to analyze and detect tax evasion behaviors of individual taxpayers. However, they have failed to support the analysis and exploration of the related party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where a group of taxpayers is involved. In this paper, we present TaxThemis, an interactive visual analytics system to help tax officers mine and explore suspicious tax evasion groups through analyzing heterogeneous tax-related data. A taxpayer network is constructed and fused with the respective trade network to detect suspicious RPTTE groups. Rich visualizations are designed to facilitate the exploration and investigation of suspicious transactions between related taxpayers with profit and topological data analysis. Specifically, we propose a calendar heatmap with a carefully-designed encoding scheme to intuitively show the evidence of transferring revenue through related party transactions. We demonstrate the usefulness and effectiveness of TaxThemis through two case studies on real-world tax-related data and interviews with domain experts.
Joint Raindrop and Haze Removal From a Single Image

In a recent study, it was shown that, with adversarial training of an attentive generative network, it is possible to convert a raindrop degraded image into a relatively clean one.However, in real world, raindrop appearance is not only formed by individual raindrops, but also by the distant raindrops accumulation and the atmospheric veiling, namely haze.Current methods are limited in extracting accurate features from a raindrop degraded image with background scene, the blurred raindrop regions, and the haze.In this paper, we propose a new model for an image corrupted by the raindrops and the haze, and introduce an integrated multi-task algorithm to address the joint raindrop and haze removal (JRHR) problem by combining an improved estimate of the atmospheric light, a modified transmission map, a generative adversarial network (GAN) and an optimized visual attention network.The proposed algorithm can extract more accurate features for both sky and non-sky regions.Experimental evaluation has been conducted to show that the proposed algorithm significantly outperforms stateof-the-art algorithms on both synthetic and real-world images in terms of both qualitative and quantitative measures.
Temporal filtering networks for online action detection

Online action detection aims to detect a current action from an untrimmed, streaming video, where only current and past frames are available. Recent methods for online action detection have focused on how to model discriminative representations from temporally partial information. However, they overlook the fact that the input video contains background as well as actions. To overcome this problem, in this paper, we propose a novel approach, named Temporal Filtering Network, to distinguish between relevant and irrelevant information from a partially observed, untrimmed video. Specifically, we present a filtering module to learn relevance scores indicating how relevant the information is to a current action. Our filtering module emphasizes the relevant information to a current action, while it filters out the information of background and unrelated actions. We conduct extensive experiments on THUMOS-14 and TVSeries datasets. On these datasets, the proposed method outperforms state-of-the-art methods by a large margin. We also show the effectiveness of the filtering module through comprehensive ablation studies.
Black-box attack against handwritten signature verification with region-restricted adversarial perturbations

Handwritten signature verification is used to verify the identity of individuals through recognizing their signatures. Adversarial examples can induce misclassification, hence posing a severe threat to signature verification. At present, a variety of adversarial example attacks have been developed for image classification, but they are not that useful for attacking signature verification due to two main reasons. First, adversarial perturbations are likely to be imposed on the background of signature images, making them perceptible to human eyes. Second, perfect knowledge about signature verification systems is actually unavailable to attackers. Therefore, how to generate effective and stealthy signature adversarial examples is still an open issue. To shed insights on this challenging problem, we propose the first black-box adversarial example attack against handwritten signature verification in this paper. Our method has two key designs. First, its perturbations are intentionally restricted to the foreground (i.e., strokes) of signature images, which reduces the risk of being recognized by humans. Second, a gradient-free method is developed to achieve the desired perturbations through iteratively updating their positions and optimizing their intensity. Extensive experiments confirm the three advantages of our method. First, the adversarial perturbations generated by our method are almost invisible, while those generated by existing methods are more well-marked. Second, our method defeats the state-of-the-art signature verification method with a surprisingly high success rate of 92.1%. Last, our method breaks through the defense of background cleaning, although this defense can deactivate almost all the existing adversarial example attacks towards signature verification.
BISON: Brain tissue segmentation pipeline using T<sub>1</sub>‐weighted magnetic resonance images and a random forest classifier

Purpose Tissue segmentation from T 1 ‐weighted (T1W) MRI is a critical requirement in many neuroscience and clinical applications. However, accurate tissue segmentation is challenging because of the variabilities in tissue intensity profiles caused by differences in scanner models, acquisition protocols, and age. In addition, many methods assume healthy anatomy and fail in the presence of pathology such as white matter hyperintensities (WMHs). We present BISON (Brain tISsue segmentatiON), a new pipeline for tissue segmentation using a random forest classifier and a set of intensity and location priors based on T1W MRI. Methods BISON was developed and cross‐validated using multiscanner manual labels of 72 subjects aged 5 to 96 years. We also assessed the test–retest reliability of BISON on two data sets: 20 subjects with scan/rescan MR images and manual segmentations and 90 scans from a single individual. The results were compared against Atropos, a state‐of‐the‐art commonly used tissue classification method from advanced normalization tools (ANTs). Results BISON cross‐validation dice kappa values against manual segmentations of 72 MRI volumes yielded κ GM = 0.88, κ WM = 0.85, κ CSF = 0.77, outperforming Atropos (κ GM = 0.79, κ WM = 0.84, κ CSF = 0.64), test–retest values on 20 subjects of κ GM = 0.94, κ WM = 0.92, κ CSF = 0.77 outperforming both manual (κ GM = 0.92, κ WM = 0.91, κ CSF =0.74) and Atropos (κ GM = 0.87, κ WM = 0.92, κ CSF = 0.79). Finally, BISON outperformed Atropos, FAST (fast automated segmentation tool) from the FMRIB (Functional Magnetic Resonance Imaging of the Brain) Software Library, and SPM12 (statistical parametric mapping 12) in the presence of WMHs. Conclusion BISON can provide accurate and robust segmentations in data from various age ranges and scanner models, making it ideal for performing tissue classification in large multicenter and multiscanner databases.
Adversarial Attacks on Link Prediction Algorithms Based on Graph Neural Networks

Link prediction is one of the fundamental problems for graph-structured data. However, a number of applications of link prediction, such as predicting commercial ties or memberships within a criminal organization, are adversarial, with another party aiming to minimize its effectiveness by manipulating observed information about the graph. In this paper, we focus on the feasibility of mounting adversarial attacks against link prediction algorithms based on graph neural networks. We first propose a greedy heuristic that exploits incremental computation to find attacks against a state-of-the-art link prediction algorithm, called SEAL. We then design an efficient variant of this algorithm that incorporates the link formation mechanism and Υ-decaying heuristic theory to design more effective adversarial attacks. We used real-world datasets and performed an extensive array of experiments to show that the performance of SEAL is negatively affected by a significant margin. More importantly, our experimental results have shown that our adversarial attacks mounted based on SEAL can be readily transferred to several existing link prediction heuristics in the literature.
Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-class Segmentation

Over the past decade, deep learning has become the gold standard for automatic medical image segmentation. Every segmentation task has an underlying uncertainty due to image resolution, annotation protocol, etc. Therefore, a number of methods and metrics have been proposed to quantify the uncertainty of neural networks mostly based on Bayesian deep learning, ensemble learning methods or output probability calibration. The aim of our research is to assess how reliable the different uncertainty metrics found in the literature are. We propose a quantitative and statistical comparison of uncertainty measures based on the relevance of the uncertainty map to predict misclassification. Four uncertainty metrics were compared over a set of 144 models. The application studied is the segmentation of the lumen and vessel wall of carotid arteries based on multiple sequences of magnetic resonance (MR) images in multi-center data.
A learning-based method of attack on optical asymmetric cryptosystems

The so-called Optical Asymmetric Cryptosystems (OACs) have attracted more and more attention due to the unique mechanism of encryption/decryption and higher security level resulted from the involved nonlinear operations. Meanwhile, several attack methods have also been reported to analyze the security risk of typical OACs. In this work, we first stated our basic views about two famous OACs and then demonstrated that they are both vulnerable to a deep-learning-based strategy. Thanks to a carefully designed and trained (by known plaintext-ciphertext pairs) generative adversarial network (GAN), an attacker could intercept enough high-frequency components of subsequent plaintext leading to successful retrieval. Compared with the previous amplitude-phase-retrieval-based methods, the proposed learning-based scheme has a major advantage of retrieving plaintexts with high quality in real-time. Numerical simulations demonstrate the feasibility and effectiveness of the proposed learning-based attack method.