Reviewerly/exHarmony huggingface dataset:
Improvement of Non-Maximum Suppression in RGB-D Object Detection

Currently, the non-maximum suppression (NMS) algorithm is a commonly used method in the post-processing stage of object detection.However, the NMS algorithm cannot effectively eliminate missing and false object detection results because of the simple constraint condition.To solve the problem of the poor detection effect in highly overlapping dense object scenes in the traditional NMS algorithm, we design an RGB-D object detection network model based on the YOLO v3 framework, and using level-by-level metaphase fusion on the RGB and depth information, we propose an improved NMS algorithm which fuses depth characteristics.According to the depth of the object in the detection boxes, it is determined whether another object is the same object in highly overlapping detection boxes, and the average depth of the internal pixels in the detection boxes is calculated as a penalty term, then the penalty term is added to the detection box score to obtain a new constraint condition for non-maximum suppression.The experimental results on the NYU Depth V2 dataset show that the mean average precision (mAP) of the Depth Fusion NMS algorithm proposed in this paper is 0.8%, 0.5% and 0.3% higher than those of the Greedy-NMS, Soft NMS-L and Soft NMS-G methods, respectively.After comparison and analysis, our method can not only detect more overlapping objects but also achieve a better object localization accuracy.
Chatbot Implementation for ICD-10 Recommendation System

ICD-10 coding becomes an essential process to transform descriptions of medical diagnoses and procedures into universal medical code numbers. Because there are a large number of the ICD-10 codes, this process needs an expert or an experienced staff to proceed. However, insufficient of the staffs in this area makes this task become a difficulty for general public health staff. Therefore, a recommendation system by using chatbot technology is proposed in this work. This system is implemented by using a messaging application with auxiliary Natural Language Processing (NLP) library. The system was compared with the conventional ICD-10 application by using Analytic Hierarchy Process (AHP). The evaluation result shows that the proposed chatbot can perform an effective solution for selecting ICD-10 code. In addition, it also shows that the proposed chatbot is preferable to be applied in the ICD-10 application.
On the Concept of Meaningfulness in Constrained Type-2 Fuzzy Sets

Constrained type-2 fuzzy sets have been proposed as a tool to model type-2 fuzzy sets starting from a type-1 generator set with uncertainty. This constrained representation only defines as acceptable the embedded sets that have the same shape as the generator set in order to process only membership functions that are considered "meaningful" when using fuzzy operators such as centroid defuzzification. However, the idea of "meaningfulness" has never been clearly defined; at the same time there are some contexts in which a given concept (e.g. medium height) can be reasonably represented by multiple shapes, such as triangular and Gaussian. The aim of this paper is both to formally define the idea of meaningfulness of shapes and to extend the formal definitions of constrained interval type-2 fuzzy sets in order to allow the presence of multiple shapes among the acceptable embedded sets.
A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation

Obtaining large amounts of annotated biomedical data to train convolutional neural networks (CNNs) for image segmentation is expensive. We propose a method that requires only a few segmentation examples to accurately train a semi-automated segmentation algorithm. Our algorithm, a convolutional neural network method for boundary optimization (CoMBO), can be used to rapidly outline object boundaries using orders of magnitude less annotation than full segmentation masks, i.e., only a few pixels per image. We found that CoMBO is significantly more accurate than state-of-the-art machine learning methods such as Mask R-CNN. We also show how we can use CoMBO predictions, when CoMBO is trained on just 3 images, to rapidly create large amounts of accurate training data for Mask R-CNN. Our few-shot method is demonstrated on ISBI cell tracking challenge datasets.
Cyber Security Goal’s, Issue’s, Categorization &amp; Data Breaches

The tremendous increase in the computer users, internet cyber space is giving rise to more number to cyber crimes. Technocrats or popularly known as cyber criminals make use of technology, social engineering other techniques to extract the confidential information. so there is an emergent need to have comprehensive understanding of cyber attacks and its classification how one can get secured. Cyber security ensures the protection of information systems including software, hardware and information (data). The purpose of this paper is to give a review on cyber security, its goals, impacts issues. The paper also includes the brief description of various types of data breaches that have occurred in the past.
Learning-Based X-Ray Image Denoising Utilizing Model-Based Image Simulations

X-ray guidance is an integral part of interventional procedures, but the exposure to ionizing radiation poses a non-negligible threat to patients and clinical staff. Unfortunately, a reduction in the X-ray dose results in a lower signal-to-noise ratio, which may impair the quality of X-ray images. To ensure an acceptable image quality while keeping the X-ray dose as low as possible, it is common practice to use denoising techniques. However, at very low dose levels, the application of conventional denoising techniques may lead to undesirable artifacts or oversmoothing. On the other hand, supervised learning techniques have outperformed conventional techniques in producing suitable results, provided aligned pairs of associated high- and low-dose X-ray images are available. Unfortunately, it is neither acceptable nor possible to acquire such image pairs during a clinical intervention. To enable the use of learning-based methods for the denoising of X-ray images, we propose a novel strategy that involves the use of model-based simulations of low-dose X-ray images during the training phase. We utilize a data-driven normalization step that increases the robustness of the proposed approach to varying amounts of signal-dependent noise associated with different X-ray image acquisition protocols. A quantitative and qualitative analysis based on clinical and phantom data shows that the proposed strategy outperforms well-established conventional X-ray image denoising methods. It also indicates that the proposed approach allows for a significant dose reduction without sacrificing important image information.
SVTN: Siamese Visual Tracking Networks With Spatially Constrained Correlation Filter and Saliency Prior Context Model

Recently, Siamese network based trackers have been greatly developed and achieved state-of-the-art performance on multiple benchmarks. However, the decision-making mechanism needs to be studied more deeply in order to obtain higher accuracy. In this paper, we propose a novel Siamese network based visual tracking method, which enhances decision-making ability by Spatially Constrained Correlation Filter (SCCF) and Saliency Prior Context (SPC) model. We use the deep features extracted from Siamese networks to train the SCCF via the efficient Alternating Direction Method of Multipliers (ADMM), and our SCCF applies a penalizing matrix to suppress the boundary effect well. Meanwhile, we regard the end-to-end output of Siamese networks as a priori probability and utilize the spatio-temporal relationship to establish the SPC model. The SPC model can handle the various cases of feature distributions generated from different targets and their contexts. Further, we also take measures to solve some challenging problems in visual tracking, such as target scale change and target occlusion. We conduct extensive experiments to demonstrate the effectiveness of the proposed method, which obtains currently the best results on three large tracking benchmarks, including OTB-2013, OTB-2015, and VOT-2016.
RBPPFF for robust TAN

Terrain-aided navigation (TAN) is a technique for position estimation based on altimeter and digital elevation model measurements. To perform TAN, non-linear filters such as a particle filter (PF) or a point mass filter (PMF) are mainly used owing to the non-linearity of measurements and systems. In particular, owing to the degradation of the global positioning system or sensors and the non-linearity of maps and measurements, an initial state error or sudden state change may occur. The authors propose a Rao-Blackwellised particle-point mass fusion filter (RBPPFF) that performs robustly in the presence of such initial state errors or sudden state changes with low computational complexity in real time. The RBPPFF combines the advantages of a PF and PMF, and it employs Rao-Blackwellisation to efficiently increase the number of states. To develop the RBPPFF, the formulae of the RBPF and Rao-Blackwellised point mass fusion filter (RBPMF) are derived using the same conventions, and a method is presented for an effective fusion of the PF and the PMF. Finally, the performance of the proposed algorithm is demonstrated through Monte Carlo simulation across various environments with filters such as PF, PMF, RBPF, and RBPMF.
secGAN

Video streaming is one of the critical functionalities on edge devices, like IP cameras, because it enables many applications in mobile scenarios, such as live street viewing or surveillance video analytics. In the streaming pipeline, video transformations is commonly used to remove or change visual details for different purposes like privacy preservation. So far, many advanced full-frame transformations are empowered by deep learning technologies, especially the family of cycle-consistent Generative Adversarial Networks (cycGANs). However, such cycGANs cannot be directly applied to edge devices due to security issues raised in practice. If an active adversary is present, the video frame transformed by cycGANs may be recovered to its original form without any authorization. Besides, there is no mechanism to verify whether this frame is indeed transformed in a legitimate way. Therefore, we design a secure version of cycGANs by introducing a ''secret key'' concept to the model design. This new cycGAN, the secGAN, can protect its video transformation outputs and is also suitable to run on edge devices with considerations of running efficiency.
Deep Learning-Based Document Modeling for Personality Detection from Turkish Texts

The usage of social media is increasing exponentially since it has been the easiest and fastest way to share information between people or organizations. As a result of this broad usage and activity of people on social networks, considerable amount of data is generated continuously. The availability of user generated data makes it possible to analyze personality of people. Personality is the most distinctive feature for an individual. The results of these analyses can be utilized in several ways. They provide support for human resources recruitment units to consider suitable candidates. Similar products and services can be offered to people who share the similar personality characteristics. Personality traits help in diagnosis of certain mental illnesses. It is also helpful in forensics to use personality traits on suspects to clarify the forensic case. With the rapid dissemination of online documents in many different languages, the classification of these documents has become an important requirement. Machine Learning (ML) and Natural Language Processing (NLP) methods have been used to classify these digitized data. In this study, current ML techniques and methodologies have been used to classify text documents and analyze person characteristics from these datasets. As a result of classification, detailed information about the personality traits of the writer could be obtained. It was seen that the frequency-based analysis and the use of the emotional words at the word level are crucial in the textual personality analysis.
An Animal Classification based on Light Convolutional Network Neural Network

Designing an accurate and efficient model for animal recognition is a challenging task. It needs to consider many aspects including the accuracy of the model, the number of parameters, the complexity of calculation and so on. Therefore, we propose a novel convolutional network, called Bilateral Convolutional Network (BCNet), which aims at achieving a trade-off between recognition accuracy and model size, so that it can be better feasible to the mobile devices. It consists of two components, namely the feature extraction module and the classification module, respectively. The feature extraction module adopts a bilateral structure, which contains two sub-networks. A feature network for extracting image features and a location network for identifying object locations. The classification modeule adopts a two fully connected layer as a classifier. We tested on the Animals with Attributes animal (AWA) dataset, and the top-1 accuracy of the model was 85.6%. The model size of Bilateral Convolutional Network is 67.14 M, and computation complexity is only 1.12 GFLOPs. Compared with other methods, it achieves a good balance between recognition accuracy and model size.
Fast and Accurate 3D Measurement Based on Light-Field Camera and Deep Learning

The precise combination of image sensor and micro-lens array enables light-field cameras to record both angular and spatial information of incoming light, therefore, one can calculate disparity and depth from one single light-field image captured by one single light-field camera. In turn, 3D models of the recorded objects can be recovered, which means a 3D measurement system can be built using a light-field camera. However, reflective and texture-less areas in light-field images have complicated conditions, making it hard to correctly calculate disparity with existing algorithms. To tackle this problem, we introduce a novel end-to-end network VommaNet to retrieve multi-scale features from reflective and texture-less regions for accurate disparity estimation. Meanwhile, our network has achieved similar or better performance in other regions for both synthetic light-field images and real-world data compared to the state-of-the-art algorithms.
A Reinforcement One-Shot Active Learning Approach for Aircraft Type Recognition

Target recognition is an important aspect of air traffic management, but the study on automatic aircraft identification is still in the exploratory stage. Rapid aircraft processing and accurate aircraft type recognition remain challenging tasks due to the high-speed movement of the aircraft against complex backgrounds. Active learning, as a promising research topic of machine learning in recent decades, can use less labeled data to obtain the same model accuracy as supervised learning, which greatly reduces the cost of labeling a dataset. Instead of manually developing policies of accessing the labels of desired instances, an improved active learning approach, which can not only learn to classify samples using small supervision but additionally capture a relatively optimal label query strategy, was developed by employing the reinforcement learning in the process of decision-making. The proposed model was first tested with the Amsterdam Library of Object Images (ALOI) dataset and then used to perform aircraft type recognition on one-month real-world flight track data. Our method offers a satisfactory solution for learning new concepts rapidly from a small amount of data, which well meets the needs of aircraft type recognition task in practical application.
A New Bound on Quantum Wielandt Inequality

A new bound on quantum version of Wielandt inequality for positive (not necessarily completely positive) maps has been established. Also bounds for entanglement breaking and PPT channels are put forward which are better bound than the previous bounds known. We prove that a primitive positive map E acting on M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> that satisfies the Schwarz inequality becomes strictly positive after at most 2(d-1) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> iterations. This is to say that after 2(d-1) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> iterations, such a map sends every positive semidefinite matrix to a positive definite one. This finding does not depend on the number of Kraus operators as the map may not admit any Kraus decomposition. The motivation of this work is to provide an answer to a question raised by Sanz-García-Wolf and Cirac in their work on quantum Wielandt bound.
A Survey on Task Scheduling using Intelligent Water Drops Algorithm in Cloud Computing

In recent years, Cloud computing emerges as an innovative platform that provides a high-performance computing environment to the users and also provides the delivery of on-demand computing resources. Efficiency of cloud system is enhanced by using various resource management methodologies. Despite the hype, Scheduling is still remaining as a challenging task. The main aim of this research paper is to discuss about the comparison of IWDs based algorithm with many familiar scheduling algorithms, such as MIN-MIN, MAX-MIN algorithm, Round Robin algorithm, FCFS algorithm, MCT algorithm, PSO algorithm and C-PSO algorithm where the IWD algorithms presents enhancement in the functional output and economy in many circumstances.
New Hybrid AES Static S-Box Algorithm Using Chaotic Maps

Methods of security need to be improved to face new techniques of data-stealing. The substitution table is the essence of the block ciphers encryption A good designing of S-Box increases the encryption algorithm security. This paper proposes two algorithms to generate modified S-Boxes; the S-Box depend on key and plaintext using the RC4 algorithm. And the S-Box depend on key and plaintext using RC4 chaotic algorithm This paper aims to expound the qualification of the proposed S-Boxes compared to the existing AES and Dynamic S-Boxes.
A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis

Mobile app development in recent years has resulted in new products and features to improve human life. Mobile telematics is one such development that encompasses multidisciplinary fields for transportation safety. The application of mobile telematics has been explored in many areas, such as insurance and road safety. However, to the best of our knowledge, its application in gender detection has not been explored. This paper proposes a Choquet fuzzy integral vertical bagging classifier that detects gender through mobile telematics. In this model, different random forest classifiers are trained by randomly generated features with rough set theory, and the top three classifiers are fused using the Choquet fuzzy integral. The model is implemented and evaluated on a real dataset. The empirical results indicate that the Choquet fuzzy integral vertical bagging classifier outperforms other classifiers.
Group Announcement Logic with Distributed Knowledge

Public announcement logic (PAL) is an extension of epistemic logic with dynamic operators that model the effects of all agents simultaneously and publicly acquiring the same piece of information. One of the extensions of PAL, group announcement logic (GAL), allows quantification over (possibly joint) announcements made by agents. In GAL, it is possible to reason about what groups can achieve by making such announcements. It seems intuitive that this notion of coalitional ability should be closely related to the notion of distributed knowledge, the implicit knowledge of a group. Thus, we study the extension of GAL with distributed knowledge, and in particular possible interaction properties between GAL operators and distributed knowledge. The perhaps surprising result is that there in fact are no interaction properties, contrary to intuition. We make this claim precise by providing a sound and complete axiomatisation of GAL with distributed knowledge.
IoTLogBlock: Recording Off-line Transactions of Low-Power IoT Devices Using a Blockchain

For any distributed system, and especially for the Internet of Things, recording interactions between devices is essential. At first glance, blockchains seem to be suitable for storing these interactions, as they allow multiple parties to share a distributed ledger. However, at a closer look, blockchains require heavy computations, large memory capacity, and always-on communication to the cloud; these are three properties that are challenging for IoT devices with limited resources. In this paper, we present IoTLogBlock to address these challenges. IoTLogBlock connects resource-constrained IoT devices to the blockchain, and it consists of three building blocks jointly enabling recording transactions: a lightweight contract signing protocol, a blockchain network, and a smart contract. The contract signing protocol allows devices to interact locally to perform transactions, even if no communication to the cloud and the blockchain exists at that moment. At a later time, devices forward the stored transactions to the blockchain, where a smart contract ultimately verifies the transactions. We evaluate our design on low-power devices and quantify the performance in terms of memory, computation, and energy consumption. Our results show that a constrained device can create and sign a transaction within 3 s on average. Finally, we expose the devices to network scenarios with edge connections ranging from 10 s to over 2 h.
Task Adaptive Metric Space for Medium-Shot Medical Image Classification

In the medical domain, one challenge of deep learning is to build sample-efficient models from a small number of labeled data. In recent years, meta-learning has become an important approach to few-shot image classification. However, current research on meta-learning focuses on learning from a few examples; we propose to extend few-shot learning to medium-shot to evaluate medical classification tasks in a more realistic setup. We build a baseline evaluation procedure by analyzing two representative meta-learning methods through the lens of bias-variance tradeoff, and propose to fuse the two techniques for better bias-variance equilibrium. The proposed method, Task Adaptive Metric Space (TAMS), fine-tunes parameters of a metric space to represent medical data in a more semantically meaningful way. Our empirical studies suggest that TAMS outperforms other baselines. Visualizations on the metric space show TAMS leads to better-separated clusters. Our baselines and evaluation procedure of the proposed TAMS opens the door to more research on medium-shot medical image classification.