Reviewerly/exHarmony huggingface dataset:
An Update on Medical Data Steganography and Encryption

As the medical field is moving toward the digital world, the security of the medical data had raised a concern to the people. The medical data like patient information, medical history being stored in digital image. Medical images are regarded as important and sensitive data in the medical informatics systems. For transferring medical images among physicians over an insecure network, developing a secure encryption algorithm is necessary. Steganography is the method of protection of files such as images, videos or text messages by concealing their information from unauthorized users using methods of encryption and masking of data and embedding them into different images or text files. Often medical records, contain specific images regarding diagnosis, videos of research and special authorized experiments, physical examination and other important visual details perhaps required for research. We apply steganography by encrypted image of any other medical image as a cover image and embedded images as a secret image with a private key. In receiver side, reverse algorithms are applied to retrieve the patients’ original data and image. This helps us to keep the medical data secure and at the same time leads to a fast and accurate treatment of the patient. In this chapter, we discuss the different types of algorithms securing the transmission of the medical images such as Goldreich Goldwasser Halevi (GGH) algorithm and encryption algorithm techniques such as Masking Algorithm Technique. The chapter is concluded by the various application and the recent advancements achieved in the medical field.
Robust canonical correlation analysis based on L1-norm minimization for feature learning and image recognition

Canonical correlation analysis (CCA) is a popular method that has been extensively used in feature learning. In nature, the objective function of CCA is equivalent to minimizing the distance of the paired data, and L2-norm is used as the distance metric. We know that L2-norm-based objective function will emphasize the large distance pairs and de-emphasizes the small distance pairs. To alleviate the aforementioned problems of CCA, we propose an approach named CCA based on L1-norm minimization (CCA-L1) for feature learning. To optimize the objective function, we develop an algorithm that can get a global optimized value. To maintain the distribution and the nonlinear characteristic respectively, we proposed two extensions of CCA-L1. Further, all of the aforementioned three proposed algorithms are extended to deal with multifeature data. The experimental results on an artificial dataset, real-world crop leaf disease dataset, ORL face dataset, and PIE face dataset show that our methods outperform traditional CCA and its variants.
Classification of Tissue Types in Histological Colorectal Cancer Images Using Residual Networks

Colorectal cancer (CRC) is one of the most prevalent cancer types, but until now only few studies have addressed the problem of automatic recognition of distinct tissue types in histological CRC images. In this paper, we employ two deep convolutional neural networks, the original 50-layer residual network ResNet-50 and a modified residual network ResNet-50-fla-drop, to automatically perform eight-class tissue separation in histological CRC images. Experimental results showed that the proposed modified residual network ResNet-50-fla-drop can achieve a classification accuracy of 94.4% for eight-class CRC tissue separation, and it outperforms other published machine learning methods, including the radial-basis function SVM and VGG-VD-16.
Cascade convolutional neural network‐long short‐term memory recurrent neural networks for automatic tonal and nontonal preclassification‐based Indian language identification

Abstract This work presents an automatic tonal/nontonal preclassification‐based Indian language identification (LID) system. Languages are firstly classified into tonal and nontonal categories, and then, individual languages are identified from the languages of the respective categories. This work proposes the use of pitch Chroma and formant features for this task, and also investigates how Mel‐frequency Cepstral Coefficients (MFCCs) complement these features. It further explores block processing (BP), pitch synchronous analysis (PSA)‐ and glottal closure regions (GCRs)‐based approaches for feature extraction, using syllables as basic units. Cascade convolutional neural network (CNN)‐long short‐term memory (LSTM) model using syllable‐level features has been developed. National Institute of Technology Silchar language database (NITS‐LD) and OGI‐Multilingual Telephone Speech Corpus (OGI‐MLTS) have been used for experimental validation. The proposed system based on the score combination of Cascade CNN‐LSTM models of Chroma (extracted from BP method), first two formants and MFCCs (both extracted from GCR method) reports the highest accuracies. In the preclassification stage, the observed accuracies are 91%, 87.3%, and 85.1% for NITS‐LD, for 30 s, 10 s, and 3 s test data respectively. For OGI‐MLTS database, the respective accuracies are 86.7%, 83.1%, and 80.6%. That amounts to absolute improvements of 11.6%, 12.3%, and 13.9% for NITS‐LD, and 12.5%, 11.9%, and 12.6% for OGI‐MLTS database with respect to that of the baseline system. The proposed preclassification‐based LID system shows improvements of 7.3%, 6.4%, and 7.4% for NITS‐LD and 6.1%, 6.7%, and 7.2% for OGI‐MLTS database over the baseline system for the three respective test data conditions.
FCM-Based Approach for Locating Visible Video Watermarks

The increased usage demand for digital multimedia has induced significant challenges regarding copyright protection, which is the copy control and proof of ownership. Digital watermarking serves as a solution to these kinds of problems. Among different types of digital watermarking, visible watermarking protects the copyrights effectively, since the approach not only prevents pirates but also visually proves the copyright of the broadcasted video. A visible watermark could be in any location on the frame (corner, center, diagonal, etc.). In addition, it could either completely or partially disappear for some frames. The same video also might have multiple watermarks. In order to strengthen the techniques of adding visible watermarks, there is a need to discover the weakness of the used watermarks. Since the major step of attacking a visible watermark is to locate it accurately, in this paper, a Fuzzy C-Means (FCM)-based approach is proposed to locate visible watermarks in video. Broadcasting channels are used to utilize video logos, which can be considered as a form of visible watermark that represents a trademark or symbol to declare the intellectual property ownership. In general, a high-standard video watermark has such properties as a clear background with distinctive shape without additional texture obscuring the watermark area. In addition, the probability of the logo appearing in the four corners of the video frames is higher than in the center. Based on these common properties of the video watermark, the proposed scheme locates the visible watermark using the Fuzzy C-Means technique without any prior information. The proposed technique has two stages: the first stage is positioning, and the second is masking (extracting the watermark mask). Due to real-world limitations such as noise, shadowing, and variations in cameras, the positioning stage is developed by employing gradient and Fuzzy C-Means classifier techniques. By using the dilation and erosion operators, the masking stage is developed to extract the watermark mask. Using a set of trademark videos, the proposed algorithm is tested and evaluated. A comparative study shows that the proposed FCM-based technique is able to achieve higher accuracy at a reasonable computational cost in comparison to the most related and recent published work. The proposed technique could locate different watermarks with high symmetry in their pattern, even if they appeared mutually in the same location. Still, it will be a challenge if the symmetry is low between used watermarks in the same location.
A Survey on Machine Learning Based Fault Tolerant Mechanisms in Cloud Towards Uncertainty Analysis

Cloud computing has the tendency to provide on-demand resources. Recently, there has been a large-scale migration of enterprise applications to the cloud. Any unexpected events that occur in cloud due to its dynamic nature is termed as uncertainty. The most cause of uncertainty can be the unexpected fault that arises in cloud environment. Hence the early detection and recovery of fault can abruptly reduce the uncertainty by enhancing the Quality of Service in cloud applications. This paper discusses the types of faults and failures present in cloud environment and it gives an overview on the existing fault handling mechanisms.
Aesthetics, Interaction and Machine Improvisation

Departing from the artistic research project Goodbye Intuition (GI) hosted by the Norwegian Academy of Music in Oslo, this article discusses the aesthetics of improvising with machines. Playing with a system such as the one described in this article, with limited intelligence and no real cognitive skills, will obviously reveal the weaknesses of the system, but it will also convey part of the preconditions and aesthetic frameworks that the human improviser brings to the table. If we want the autonomous system to have the same kind of freedom we commonly value in human players’ improvisational practice, are we prepared to accept that it may develop in a direction that departs from our original aesthetical ambitions? The analyses is based on some of the documented interplay between the musicians in a group in workshops and laboratories. The question of what constitutes an ethical relationship in this kind of improvisation is briefly discussed. The aspect of embodiment emerges as a central obstacle in the development of musical improvisation with machines.
Texture Ratio Vector Technique for the Classification of Breast Lesions Using SVM

Breast cancer is one of the most life frightening diseases in women. It arises due to the uncontrolled growth of cells in the breast. The area suffering from damage is known as a lesion that is classified as Benign and Malignant. This paper classifies the breast lesions using a ratio texture feature obtained from the texture features calculated inside the lesion (IAI) and the texture feature calculated on the upper side of the lesion (UAI). Statistical texture features like EDGE, SFM, NGTDM, FOS, GLCM, GLRLM, and GLDS are calculated. The SVM classifier is used to classify the lesions on the basis of ratio texture feature. The texture features calculated from IAI gains an overall accuracy of 62.2% with NGTDM texture feature whereas an overall accuracy of 82.2% is achieved in UAI using the GLCM texture feature. However, an overall accuracy of 86.6% is yielded with the FOS ratio texture vector having individual accuracies of 82% and 92.2% for benign and malignant class, respectively.
Diggi: A Secure Framework for Hosting Native Cloud Functions with Minimal Trust

The emerging serverless cloud paradigm, also known as Function as a Service (FaaS), enables auto-scalable cloud services on-demand, but requires complete trust in the surrounding infrastructure. Workloads hosting sensitive data, such as medical records or financial transactions, may not be able to use such cloud services due to these trust assumptions. This paper introduces Diggi, a native secure FaaS runtime for scalable function deployment requiring minimal trust in the underlying hardware/software stack. We demonstrate that Diggi is practical and that it holds comparable performance to conventional FaaS software.
Estimation of Number of Chewing Strokes and Swallowing Events by Using LSTM-CTC and Throat Microphone

A person's eating behavior, such as, chewing and swallowing can represents the health conditions of that person. From that viewpoint, we were trying to develop a system that can monitor a person's chewing and swallowing, simply as well as in detail by using sound information recorded near the oropharynx. However, its accuracy was not good enough because of lack of well-labeled training data. In this paper, we propose a technique to gather a large amount of weak-labeled data easily, and leverage it to train the Long Short Term Memory-Connectionist Temporal Classification (LSTM-CTC) model for estimating the number of chewing and swallowing.
SmartOverlays: A Visual Saliency Driven Label Placement for Intelligent Human-Computer Interfaces

In augmented reality (AR), the computer generated labels assist in understanding a scene by addition of contextual information. However, naive label placement often results in clutter and occlusion impairing the effectiveness of AR visualization. For label placement, the main objectives to be satisfied are, non-occlusion to the scene of interest, the proximity of labels to the object, and, temporally coherent labels in a video/live feed. We present a novel method for the placement of labels corresponding to objects of interest in a video/live feed that satisfies the aforementioned objectives. Our proposed framework, SmartOverlays, first identifies the objects and generates corresponding labels using a YOLOv2 [28] in a video frame; at the same time, Saliency Attention Model (SAM) [7] learns eye fixation points that aid in predicting saliency maps; finally, computes Voronoi partitions of the video frame, choosing the centroids of objects as seed points, to place labels for satisfying the proximity constraints with the object of interest. In addition, our approach incorporates tracking the detected objects in a frame to facilitate temporal coherence between frames that enhances the readability of labels. We measure the effectiveness of SmartOverlays framework using three objective metrics: (a) Label Occlusion over Saliency (LOS), (b) temporal jitter metric to quantify jitter in the label placement, (c) computation time for label placement.
Optimizing e-KYC Process Using Distributed Ledger Technology and Smart Contracts

The current know-your-customer (KYC) process in banks needs the customer to go through the entire process of KYC in every bank. This process is redundant, time consuming and costly. To address this problem, we propose a an optimized solution for e-KYC process using distributed ledger technology and smart contracts, which aims at reducing the overall cost of KYC verification process in banks. With the help of the proposed smart contracts, the KYC verification needs to do once for a customer. The result of this KYC verification process is securely shared among other banks with the customer’s consent. It ensures that the relation between a customer and a bank is kept hidden from other banks. The proposed e-KYC solution offers efficiency, overall cost reduction and the improved trust with the increased transparency and throughput. The proposed solution is implemented on hyperledger fabric blockchain, and throughput and latency are analyzed to test its feasibility.
Multiview Co-segmentation for Wide Baseline Images using Cross-view Supervision

This paper presents a method to co-segment an object from wide baseline multiview images using cross-view self-supervision. A key challenge in the wide baseline images lies in the fragility of photometric matching. Inspired by shape-from-silhouette that does not require photometric matching, we formulate a new theory of shape belief transfer-the segmentation belief in one image can be used to predict that of the other image through epipolar geometry. This formulation is differentiable, and therefore, an end-to-end training is possible. We analyze the shape belief transfer to identify the theoretical upper and lower bounds of the unlabeled data segmentation, which characterizes the degenerate cases of co-segmentation. We design a novel triple network that embeds this shape belief transfer, which is agnostic to visual appearance and baseline. The resulting network is validated by recognizing a target object from realworld visual data including non-human species and a subject of interest in social videos where attaining large-scale annotated data is challenging.
A Deep Learning Approach for Face Hallucination Guided by Facial Boundary Responses

Face hallucination is a domain-specific super-resolution (SR) problem of learning a mapping between a low-resolution (LR) face image and its corresponding high-resolution (HR) image. Tremendous progress on deep learning has shown exciting potential for a variety of face hallucination tasks. However, most deep-learning–based methods are limited to handle facial appearance information without paying attention to facial structure priors. In this article, we propose an open source 1 Boundary-aware Dual-branch Network (BDN) for face hallucination, which simultaneously extracts face features and estimates facial boundary responses from LR inputs, ultimately fusing them to reconstruct HR results. Specifically, we first upsample LR face images to HR feature maps, and then feed the upsampled HR features into a memory unit and an attention unit synchronously to obtain the refined features and predict facial boundary responses. Next, they are fed into a feature map fusion unit to combine facial appearance and structure information by a spatial attention mechanism. Moreover, we employ a series of stacked units to boost performance before recovering HR face images. Finally, a discriminative network is developed to improve visual quality by introducing adversarial learning strategy. Extensive experiments show that the proposed approach achieves superior face hallucination results against the state-of-the-art ones.
Active Learning for High-Dimensional Binary Features

Erbium-doped fiber amplifier (EDFA) is an optical amplifier/repeater device used to boost the intensity of optical signals being carried through fiber optic communication networks. A highly accurate EDFA model - to predict the signal gain for each channel - is required because of its crucial role in optical network management and optimization. EDFA channel inputs (i.e. features) either carry signal or are idle, therefore they can be treated as binary features. However, channel outputs (and the corresponding signal gains) are continuous values. Labeled training data is very expensive to collect for EDFA devices, therefore we devise an active learning strategy suitable for binary features to overcome this issue. We propose to take advantage of sparse linear models to simplify the predictive model. This approach improves signal gain prediction and accelerates active learning query generation. We show the performance of our proposed active learning strategies on simulated data and real EDFA data.
Using Machine Learning for Predicting Efficiency in Manufacturing Industry

The overall equipment effectiveness (OEE) is a performance measurement metric widely used. Its calculation provides to the managers the possibility to identify the main losses that reduce the machine effectiveness and then take the necessary decisions in order to improve the situation. However, this calculation is done a-posterior which is often too late. In the present research, we implemented different Machine Learning algorithms namely; Support vector machine, Optimized Support vector Machine (using Genetic Algorithm), Random Forest, XGBoost and Deep Learning to predict the estimate OEE value. The data used to train our models was provided by an automotive cable production industry. The results show that the Deep Learning and Random Forest are more accurate and present better performance for the prediction of the overall equipment effectiveness in our case study.
Salient Contour-Aware Based Twice Learning Strategy for Saliency Detection

Fully convolutional neural networks (FCNs) have shown outstanding performance in many computer vision tasks including salient object detection. However, most deep learning-based saliency detection models are too complicated. They cause difficulties in training. Additionally, the performance of those overly complex deep learning models is limited, and the price performance ratio of those complex models is very low. To address the problems of existing deep-learning-based methods, we introduce a new research field called saliency contour detection and design a new dataset for saliency contour detection. Inspired by the human sketching process, we propose a novel contour-aware algorithm using FCNs with a twice learning strategy for saliency detection, which imitates and dissects the process of human cognition. Extensive experimental evaluations demonstrate the effectiveness of our proposed method against other outstanding methods.
Machine Learning Classification on Traffic of Secondary Encryption

Encrypted traffic classification plays an important role in network management. In this paper, we take as an example of the web browsing application, and propose a machine learning classification scheme, Bali, that can identify the encrypted traffic from various websites. We employ packet length statistics as discriminative features of encrypted traffic. In order to further investigate the differences among encrypted traffic from various websites, we develop a clustering method based on an observation that the first outgoing and incoming packets with specific flags from the same website have similar features. The above two techniques can be incorporated into typical machine learning models (e.g., random forests, SVM, kNN) for traffic classification. Experiment results using real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods.
Transliteration of text input from Kannada to Braille and vice versa

People who are working with blind and do not understand Braille, need conversion of Braille document to a language representation. Our proposed method defines an efficient algorithm for conversion of Kannada text to Braille and Braille script to Kannada. We have taken the input from Kannada online website “Kendasampige” and from the online Kannada newspaper “Udayavani”, It gives no mapping errors and works for all Kannada compound characters. The Braille output that is generated is given as input to the embosser to produce the hardcopy on a Braille paper which can be read by the visually impaired. Braille text is not available in the internet and hence converting text from Kannada to Braille helps a lot for visually impaired people.
Electric Tower Target Identification Based on High-resolution SAR Image and Deep Learning

Abstract With regard to the electric tower identification problem in the high-resolution SAR image under complicated background, it is difficult for the existing target detection algorithm to reach balance in identification precision and efficiency. Therefore, this paper combines the advantages of high YOLOv2 calculation efficiency and high VGG classification precision, and proposes a kind of two-stage target detection algorithm of YOLOv2 and VGG cascade connection by virtue of transfer learning. At the Stage-1, the sliding window and the non-maximum suppression algorithms are combined, and the YOLOv2 is utilized to conduct rapid electric power detection to the whole SAR image, thus obtaining the target detection result of high recall rate; at the Stage-2, the VGG classification model is utilized to conduct secondary classification to the target and background with regard to the target detection result at the Stag-1, thus further eliminating the false positive. This algorithm can be used to enhance the accuracy of only using YOLO v2 f or target identification, thus reducing the false alarm rate of model effectively. The electric towers in the mountainous belt and the plain belt are used as samples for training, thus enhancing the robustness of algorithm. After conducting algorithm testing to the COSMO image in Zhijiang City, Hubei Province, the result shows that the electric tower recall rate can reach 73.8%. The method adopted in this paper can identify the electric tower target of the whole SAR image more accurately through model transfer and two-stage deep learning, thus being further promoted into actual application scene.