Reviewerly/exHarmony huggingface dataset:
Explainable Deep Learning Methods for Medical Imaging Applications

This paper discusses explainable deep learning approaches for medical imaging applications. This includes evaluation of three different feature visualization approaches for visualizing deep neural network models, developed with medical domain images. The idea behind using these approaches is to unbox the internals of deep neural network, specifically convolutional neural networks (CNN) by providing a step by step learning of these models. These approaches could help clinicians to have confidence in complex deep learning models, as opposed to treating those as black boxes as these approaches can generate a meaningful view of the model layers and their feature maps. Also, these approaches can be adopted by data scientists to improve their model performances by looking at the model behavior in each layer. We have implemented three approaches namely Activation Map, Deconvolution and Grad-CAM localization in Keras with Tensorflow background and have validated the results with CNN models developed with natural images. We have also been able to generate these visualizations for models with even filter sizes with activation map and deconvolution approaches. These methods play crucial role in getting approval from regulatory authorities.
Informative path planner with exploration–exploitation trade-off for radiological surveys in non-convex scenarios

The risk toward human lives in situations involving chemical, biological, radiological, and nuclear (CBRN) threats can be mitigated or even neutralized by deploying carrying a suite of suitable sensors. Furthermore, mobile robots open up the possibility for automated radiological field surveys and monitoring operations, which have important applications in scenarios with CBRN threats. A path planner is one of the essential tools required for these robots to perform their tasks autonomously. Moreover, sophisticated path planners can greatly increase the efficiency of monitoring tasks by maximizing the information gathered in the minimum amount of time. This work proposes an informative path planner as an instrument to efficiently estimate maps of scalar quantities (e.g., radiation intensity, chemical concentration), motivated by applications in radiological inspection. The proposed path planner models the path with B-splines, enabling planning in continuous space. A Gaussian Process with a squared exponential kernel is used to model the underlying field. A modified form of mutual information, estimated from the Gaussian Process, is maximized to determine the most informative path, additionally rewarding observations made in regions where the field magnitude is large (e.g., near a radioactive source). A maximum likelihood estimator for source parameters is used to demonstrate that the proposed solution increases the accuracy of the estimated source positions. Simulation results show that the informative path planner adapts to non-convex environments and increases the number of observations made close to radioactive sources while avoiding obstacles.
E-Voting System using Hyperledger Sawtooth

E-Voting or electronic voting is a modern alternative for the traditional voting system involving paper ballots. For the large-scale implementation of electronic voting, the design of the proposed system has to support reliability as well as security. A secure, transparent and decentralized e- voting system is proposed using the Hyperledger Sawtooth blockchain framework. Restricted access of the system through election polling stations allows voters to cast their votes, which are recorded in the immutable blockchain state, thus instilling confidence in the voters regarding the fairness and reliability of the election procedure due to nil possibility of vote manipulation.
Disease Normalization with Graph Embeddings

The detection and normalization of diseases in biomedical texts are key biomedical natural language processing tasks. Disease names need not only be identified, but also normalized or linked to clinical taxonomies describing diseases such as MeSH®. In this paper we describe deep learning methods that tackle both tasks. We train and test our methods on the known NCBI disease benchmark corpus. We propose to represent disease names by leveraging MeSH® ’s graphical structure together with the lexical information available in the taxonomy using graph embeddings. We also show that combining neural named entity recognition models with our graph-based entity linking methods via multitask learning leads to improved disease recognition in the NCBI corpus.
Computing on quantum shared secrets for general quantum access structures

Quantum secret sharing is a method for sharing a secret quantum state among a number of individuals such that certain authorized subsets of participants can recover the secret shared state by collaboration and other subsets cannot. In this paper, we first propose a method for sharing a quantum secret in a basic (2, 3) threshold scheme, only by using qubits and the 7-qubit CSS code. Based on this (2, 3) scheme, we propose a new (n, n) scheme, and we also construct a quantum secret sharing scheme for any quantum access structure by induction. Secondly, based on the techniques of performing quantum computation on 7-qubit CSS codes, we introduce a method that authorized subsets can perform universal quantum computation on this shared state, without the need for recovering it. This generalizes recent attempts for doing quantum computation on (n, n) threshold schemes.
Lightweight block ciphers for resource-constrained devices

Lightweight cryptography is a new branch of cryptography focused on providing security to resource-constraint devices such as wireless sensor networks (WSN), Radio-Frequency Identification (RFIDs) and other embedded systems. The factors considered in lightweight cryptography are mainly circuit area, memory requirement, processing time, latency, power, and energy consumption. This paper presents a discussion on common lightweight block ciphers in terms of different performance parameters, strength, design trends, limitations, and applications including the National Institute of Science and Technology (NIST) round 1 and 2 candidates. Analysis of these lightweight algorithms has offered an insight into this newly emerging field of cryptography.
Towards Causal Explanation Detection with Pyramid Salient-Aware Network

Causal explanation analysis (CEA) can assist us to understand the reasons behind daily events, which has been found very helpful for understanding the coherence of messages. In this paper, we focus on Causal Explanation Detection, an important subtask of causal explanation analysis, which determines whether a causal explanation exists in one message. We design a Pyramid Salient-Aware Network (PSAN) to detect causal explanations on messages. PSAN can assist in causal explanation detection via capturing the salient semantics of discourses contained in their keywords with a bottom graph-based word-level salient network. Furthermore, PSAN can modify the dominance of discourses via a top attention-based discourse-level salient network to enhance explanatory semantics of messages. The experiments on the commonly used dataset of CEA shows that the PSAN outperforms the state-of-the-art method by 1.8% F1 value on the Causal Explanation Detection task.
Multi-Scale Ship Tracking Based On Maritime Monitoring Platform

With the development of computer processing, network communication and other modern technology and equipment, the management of ships on the sea surface is becoming increasingly intelligent and automated. It is very important for navigation safety to obtain the behavior trajectory of the target ship. In this paper, a ship tracking method based on twin-neural network is proposed, which is mainly aimed at the deformation, blurring and blocking of ship movement at sea. The complex target ship in Marine environment around the real-time accurate detection of trace this research emphasis, In this paper, the ship tracking module based on correlation filtering method is firstly used to track the target ship, and then use the peak response than for water mist, and other vessels objects shade to what lead to the loss of the early warning, lost ship call based on the twin of the neural network for retrieving the lost ship detection module, continue to use the tracking module tracking target ship, the ship for a long time tracking under complex environment. Experimental results show that the tracking results of this algorithm are more accurate and real-time than those of typical algorithms.
Informative modeling of subjective reality for intellectual anthropomorphic robots

Abstract This research is aimed at solving the problem of safeguarding robot operation by modeling the subjective reality of an intellectual anthropomorphic robot. Analysis has been carried out on trends in designing advanced robot control systems and methods of improving the safety of robot usage. The following conclusions are drawn: the risk of causing damage to human life, health or property increases when people interact with robots; it is necessary to combine neural network control systems with expert control systems in order to enhance the intellect of a robot and raise the level of trust held by humans towards robots; developing norms and regulations, designing collaborative robots, drawing visualization diagrams for safety zones cannot really provide the depth of robot socialization sufficient for the human society; an informative model of the subjective reality could be integrated into an intellectual anthropomorphic robot to provide a safer ‘human-robot’ interaction. Such an approach should result in achieving the greatest trust of humans and their safety due to anthropomorphic conversion of both the external design of a robot and its internal control structure. By its nature, the research is interdisciplinary – it is run in the fields of artificial intelligence and philosophy of subjective reality.
re-OBJ: Jointly Learning the Foreground and Background for Object Instance Re-identification

Conventional approaches to object instance re-identification rely on matching appearances of the target objects among a set of frames. However, learning appearances of the objects alone might fail when there are multiple objects with similar appearance or multiple instances of same object class present in the scene. This paper proposes that partial observations of the background can be utilized to aid in the object re-identification task for a rigid scene, especially a rigid environment with a lot of reoccurring identical models of objects. Using an extension to the Mask R-CNN architecture, we learn to encode the important and distinct information in the background jointly with the foreground relevant to rigid real-world scenarios such as an indoor environment where objects are static and the camera moves around the scene. We demonstrate the effectiveness of our joint visual feature in the re-identification of objects in the ScanNet dataset and show a relative improvement of around 28.25% in the rank-1 accuracy over the deepSort method.
Progressive Colorization via Iterative Generative Models

Colorization is the process of coloring monochrome images. It has been widely used in photo processing and scientific illustration. However, colorizing grayscale images is an intrinsic ill-posed and ambiguous problem, with multiple plausible solutions. To address this issue, we develop a novel progressive automatic colorization via iterative generative models (iGM) that can produce satisfactory colorization in an unsupervised manner. In particular, the generative model is exploited in multi-color spaces (e.g., RGB, YCbCr) jointly and enforced with linearly autocorrelative constraint. This is regarded as the key prior information to pave the way for producing the most probable colorization in high-dimensional space. Experiments on indoor and outdoor scenes reveal that iGM produces more realistic and finer results, compared to state-of-the-arts.
Deep Learning Based Valid Bug Reports Determination and Explanation

Bug reports are widely used by developers to fix bugs. Due to the lack of experience, reporters may submit numerous invalid bug reports. Manually determining valid bug reports is a laborious task. Automatically identifying valid bug reports can save time and effort for bug analysis. In this paper, we propose a deep learning-based approach to determine and explain valid bug reports using only textual information i.e., summaries and descriptions of bug reports. Convolutional neural network (CNN) is applied to capture their contextual and semantic features. Moreover, by analyzing the spatial structure of CNN, we backtrack the trained CNN model to get phrases that can explain valid bug reports determination. After inspecting the phrases manually, we summarize some valid bug report patterns. We evaluate our approach on five large-scale open-source projects containing a total of 540491 bug reports. On average, across the five projects, our approach achieves 0.85, 0.80, 0.69 and improves the state-of-the-art approach by 8.97%, 9.59%, 9.52% in terms of AUC, F1-score for valid bug reports, and F1-score for invalid bug reports, respectively. From the summarized patterns, we can find that determining valid bug reports is mainly due to three categories of patterns: Attachment, Environment, and Reproduce.
Healthcare Transformation in Singapore With Artificial Intelligence

OPINION article Front. Digit. Health, 17 November 2020Sec. Connected Health Volume 2 - 2020 | https://doi.org/10.3389/fdgth.2020.592121
Facial Image Quality Assessment using an Ensemble of Pre-Trained Deep Learning Models (EFQnet)

Facial recognition is a type of biometric that deals with the facial region of a human image. The low recognition accuracy of existing Facial Recognition Systems (FRS) is due to the low quality of captured images. The assessment of image quality, therefore, becomes a requirement to be taken before passing such an image through the FRS. This study presents a proposed Facial Image Quality Assessment (FIQA) model using an ensemble of pre-trained deep learning models (EFQnet). The system known as EFQnet is an ensemble of ResNet-50, DenseNet, and Inception-Net CNN pre-trained models. It utilizes a performance-based ground truth that forecasts a quality score for the input image between 0 to 1. The three models are ensemble using full fully connected Feedforward Neural Network and AMSGrad stochastic gradient Descent algorithm. When trained and fully implemented EFQnet will be evaluated on standard IQA databases and finally deployed in a Personal Identity Verification (PIV) scenario.
On Architectural Support for Instruction Set Randomization

Instruction Set Randomization (ISR) is able to protect against remote code injection attacks by randomizing the instruction set of each process. Thereby, even if an attacker succeeds to inject code, it will fail to execute on the randomized processor. The majority of existing ISR implementations is based on emulators and binary instrumentation tools that unfortunately: (i) incur significant runtime performance overheads, (ii) limit the ease of deployment, (iii) cannot protect the underlying operating system kernel, and (iv) are vulnerable to evasion attempts that bypass the ISR protection itself. To address these issues, we present the design and implementation of ASIST, an architecture with both hardware and operating system support for ISR. ASIST uses our extended SPARC processor that is mapped onto a FPGA board and runs our modified Linux kernel to support the new features. In particular, before executing a new user-level process, the operating system loads its randomization key into a newly defined register, and the modified processor decodes the process’s instructions with this key. Besides that, ASIST uses a separate randomization key for the operating system to protect the base system against attacks that exploit kernel vulnerabilities to run arbitrary code with elevated privileges. Our evaluation shows that ASIST can transparently protect both user-land applications and the operating system kernel from code injection and code reuse attacks, with about 1.5% runtime overhead when using simple encryption schemes, such as XOR and Transposition; more secure ciphers, such as AES, even though they are much more complicated for mapping them to hardware, they are still within acceptable margins,with approximately 10% runtime overhead, when efficiently leveraging the spatial locality of code through modern instruction cache configurations.
Unconditionally secure quantum bit commitment based on the uncertainty principle

Unconditionally secure quantum bit commitment (QBC) was considered impossible. But the no-go proofs are based on the Hughston-Jozsa-Wootters (HJW) theorem (a.k.a. the Uhlmann theorem). Recently it was found that in high-dimensional systems, there exist some states which can display a chaos effect in quantum steering, so that the attack strategy based on the HJW theorem has to require the capability of discriminating quantum states with very subtle difference, to the extent that is not allowed by the uncertainty principle. With the help of this finding, here we propose a simple QBC protocol which manages to evade the no-go proofs.
Classifying Breast Density in Mammographic Images Using Wavelet-Based and Fine-Tuned Sensory Neural Networks

In this modern world of biomedical medicine, the classification of breast density has been considered a very important part of the process of breast diagnosis. Furthering the same research, this research aims to determine the patient’s breast density by mammogram image with the help of modern techniques such as computerized devices and machine learning algorithms, which will greatly help the radiologist. To carry out this process, this research paper introduces a Convolutional Neural Network (CNN) model of deep learning that will work as a basis for waveform conversion and fine-tune. This deep learning model will prove effective in automatically classifying a patient’s breast density. With the help of this method, the last two layers which are fully connected are removed and joined with two newly formed layers. This would have helped in addressing a pre-trained AlexNet model that further improved the classification process. In this model, the original or preprocessed images are used at level 1 of the input (which is in sharp contrast to the usual methods based on the CNN model), which also makes the model compatible with the use of redundant wavelet coefficients. Because in the field of radiologists it is very important to underline the difference between scattered density and heterogeneous density, so the main objective of this research is focused on this end. As the proposed method has an accuracy of 82.2%, it shows a better performance. This research paper further compares the effectiveness and performance of the proposed method to traditional fine-tuning CNN models, with satisfactory results. The comparative results of the proposed method suggest that the proposed method is in the field of radiologists representing a helpful tool. This method may be intended to act as a second eye for doctors in the medical field with the intention of classifying the categories of breast density in the patient during breast cancer screening.
Computation Offloading for Machine Learning in Industrial Environments

Industrial applications, such as real-time manufacturing, fault classification and inference, autonomous cars, etc., are data-driven applications that require machine learning with a wealth of data generated from industrial Internet of Things (IoT) devices. However, conventional approaches of transmitting this rich data to a remote data center to learn may be undesired due to the non-negligible network transmission delay and the sensitiveness of data privacy. By deploying a number of computing-capable devices at the network edge, edge computing supports the implementation of machine learning close to the industrial environment. Considering the heterogeneous computing capability as well as network location of edge devices, there are two types of feasible edge computing based machine learning models, including the centralized learning and federated learning models. In centralized learning, a resource-rich edge server aggregates the data from different IoT devices and performs machine learning. In federated learning, distributed edge devices and a federated server collaborate to perform machine learning. The features that data should be offloaded in centralized learning while it is locally trained in federated learning make centralized learning and federated learning quite different. We study the computation offloading problem for edge computing based machine learning in an industrial environment, considering the abovementioned machine learning models. We formulate a machine learning-based offloading problem with the goal of minimizing the training delay. Then, an energy-constrained delay-greedy (ECDG) algorithm is designed to solve the problem. Finally, simulation studies based on the MNIST dataset have been conducted to illustrate the efficiency of the proposal.
Transformation of FASTA files into feature vectors for unsupervised compression of short reads databases

FASTA data sets of short reads are usually generated in tens or hundreds for a biomedical study. However, current compression of these data sets is carried out one-by-one without consideration of the inter-similarity between the data sets which can be otherwise exploited to enhance compression performance of de novo compression. We show that clustering these data sets into similar sub-groups for a group-by-group compression can greatly improve the compression performance. Our novel idea is to detect the lexicographically smallest k-mer (k-minimizer) for every read in each data set, and uses these k-mers as features and their frequencies in every data set as feature values to transform these huge data sets each into a characteristic feature vector. Unsupervised clustering algorithms are then applied to these vectors to find similar data sets and merge them. As the amount of common k-mers of similar feature values between two data sets implies an excessive proportion of overlapping reads shared between the two data sets, merging similar data sets creates immense sequence redundancy to boost the compression performance. Experiments confirm that our clustering approach can gain up to 12% improvement over several state-of-the-art algorithms in compressing reads databases consisting of 17–100 data sets (48.57–197.97[Formula: see text]GB).