Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a Convolutional Neural Network on an Edge Computing Smartphone for Human Activity Recognition

Edge computing aims to integrate computing into everyday settings, enabling the system to be context-aware and private to the user. With the increasing success and popularity of deep learning methods, there is an increased demand to leverage these techniques in mobile and wearable computing scenarios. In this paper, we present an assessment of a deep human activity recognition system's memory and execution time requirements, when implemented on a mid-range smartphone class hardware and the memory implications for embedded hardware. This paper presents the design of a convolutional neural network (CNN) in the context of human activity recognition scenario. Here, layers of CNN automate the feature learning and the influence of various hyper-parameters such as the number of filters and filter size on the performance of CNN. The proposed CNN showed increased robustness with better capability of detecting activities with temporal dependence compared to models using statistical machine learning techniques. The model obtained an accuracy of 96.4% in a five-class static and dynamic activity recognition scenario. We calculated the proposed model memory consumption and execution time requirements needed for using it on a mid-range smartphone. Per-channel quantization of weights and per-layer quantization of activation to 8-bits of precision post-training produces classification accuracy within 2% of floating-point networks for dense, convolutional neural network architecture. Almost all the size and execution time reduction in the optimized model was achieved due to weight quantization. We achieved more than four times reduction in model size when optimized to 8-bit, which ensured a feasible model capable of fast on-device inference.
Organ at Risk Segmentation in Head and Neck CT Images Using a Two-Stage Segmentation Framework Based on 3D U-Net

Accurate segmentation of organs at risk (OARs) plays a critical role in the treatment planning of image-guided radiotherapy of head and neck cancer. This segmentation task is challenging for both humans and automated algorithms because of the relatively large number of OARs to be segmented, the large variability in size and morphology across different OARs, and the low contrast between some OARs and the background. In this study, we propose a two-stage segmentation framework based on 3D U-Net. In this framework, the segmentation of each OAR is decomposed into two subtasks: locating a bounding box of the OAR and segmenting the OAR from a small volume within the bounding box, and each subtask is fulfilled by a dedicated 3D U-Net. The decomposition makes each subtask much easier so that it can be better completed. We evaluated the proposed method and compared it to state-of-the-art methods using the Medical Image Computing and Computer-Assisted Intervention 2015 Challenge dataset. In terms of the boundary-based metric 95% Hausdorff distance, the proposed method ranked first for seven of nine OARs and ranked second for the other OARs. In terms of the area-based metric dice similarity coefficient, the proposed method ranked first for five of nine OARs and ranked second for the other three OARs with a small difference from the method that ranked first.
Chatbot for Disease Prediction and Treatment Recommendation using Machine Learning

Hospitals are the most widely used means by which a sick person gets medical check-ups, disease diagnosis and treatment recommendation. This has been a practice by almost all the people over the world. People consider it as the most reliable means to check their health status. The proposed system is to create an alternative to this conventional method of visiting a hospital and making an appointment with a doctor to get diagnosis. This research intends to apply the concepts of natural language processing and machine learning to create a chatbot application. People can interact with the chatbot just like they do with another human and through a series of queries, chatbot will identify the symptoms of the user and thereby, predicts the disease and recommends treatment. This system can be of great use to people in conducting daily check-ups, makes people aware of their health status and encourages people to make proper measures to remain healthy. According to this research, such a system is not widely used and people are less aware of it. Executing this proposed framework can help people avoid the time-consuming method of visiting hospitals by using this free of cost application, wherever they are.
Evolving Space-Time Neural Architectures for Videos

We present a new method for finding video CNN architectures that more optimally capture rich spatio-temporal information in videos. Previous work, taking advantage of 3D convolutions, obtained promising results by manually designing CNN video architectures. We here develop a novel evolutionary algorithm that automatically explores models with different types and combinations of layers to jointly learn interactions between spatial and temporal aspects of video representations. We demonstrate the generality of this algorithm by applying it to two meta-architectures. Further, we propose a new component, the iTGM layer, which more efficiently utilizes its parameters to allow learning of space-time interactions over longer time horizons. The iTGM layer is often preferred by the evolutionary algorithm and allows building cost-efficient networks. The proposed approach discovers new diverse and interesting video architectures that were unknown previously. More importantly they are both more accurate and faster than prior models, and outperform the state-of-the-art results on four datasets: Kinetics, Charades, Moments in Time and HMDB. We will open source the code and models, to encourage future model development.
How integration of cyber security management and incident response enables organizational learning

Abstract Digital assets of organizations are under constant threat from a wide assortment of nefarious actors. When threats materialize, the consequences can be significant. Most large organizations invest in a dedicated information security management (ISM) function to ensure that digital assets are protected. The ISM function conducts risk assessments, develops strategy, provides policies and training to define roles and guide behavior, and implements technological controls such as firewalls, antivirus, and encryption to restrict unauthorized access. Despite these protective measures, incidents (security breaches) will occur. Alongside the security management function, many organizations also retain an incident response (IR) function to mitigate damage from an attack and promptly restore digital services. However, few organizations integrate and learn from experiences of these functions in an optimal manner that enables them to not only respond to security incidents, but also proactively maneuver the threat environment. In this article we draw on organizational learning theory to develop a conceptual framework that explains how the ISM and IR functions can be better integrated. The strong integration of ISM and IR functions, in turn, creates learning opportunities that lead to organizational security benefits including: increased awareness of security risks, compilation of threat intelligence, removal of flaws in security defenses, evaluation of security defensive logic, and enhanced security response.
Wearable Fall Detector Using Recurrent Neural Networks

Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls' risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time.
Diverse Image Synthesis From Semantic Layouts via Conditional IMLE

Most existing methods for conditional image synthesis are only able to generate a single plausible image for any given input, or at best a fixed number of plausible images. In this paper, we focus on the problem of generating images from semantic segmentation maps and present a simple new method that can generate an arbitrary number of images with diverse appearance for the same semantic layout. Unlike most existing approaches which adopt the GAN framework, our method is based on the recently introduced Implicit Maximum Likelihood Estimation (IMLE) framework. Compared to the leading approach, our method is able to generate more diverse images while producing fewer artifacts despite using the same architecture. The learned latent space also has sensible structure despite the lack of supervision that encourages such behaviour.
Scene Graph Prediction with Limited Labels

Visual knowledge bases such as Visual Genome power numerous applications in computer vision, including visual question answering and captioning, but suffer from sparse, incomplete relationships. All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each. Hiring human annotators is expensive, and using textual knowledge base completion methods are incompatible with visual data. In this paper, we introduce a semi-supervised method that assigns probabilistic relationship labels to a large number of unlabeled images using few labeled examples. We analyze visual relationships to suggest two types of image-agnostic features that are used to generate noisy heuristics, whose outputs are aggregated using a factor graph-based generative model. With as few as 10 labeled examples per relationship, the generative model creates enough training data to train any existing state-of-the-art scene graph model. We demonstrate that our method outperforms all baseline approaches on scene graph prediction by5.16 recall@100 for PREDCLS. In our limited label setting, we define a complexity metric for relationships that serves as an indicator (R^2 = 0.778) for conditions under which our method succeeds over transfer learning, the de-facto approach for training with limited labels.
Radial basis function neural network aided adaptive extended Kalman filter for spacecraft relative navigation

This paper presents a novel technique, combining neural network and Kalman filter, for state estimation. The proposed solution provides the estimates of the system states while also estimating the uncertain or unmodeled terms of the process dynamics. The developed algorithm exploits a Radial Basis Function Neural Network that outputs an estimate of the disturbances that are included in the prediction step of an Adaptive Extended Kalman Filter. A recursive form of adaptation is used to limit the computational burden. The proposed solution is compared to classical navigation filter implementations. A realistic spacecraft relative navigation scenario is selected to test the filter performance. Simulations are performed with accurate tuning and also in off-nominal conditions to test the filter robustness.
Towards Interpretable Face Recognition

Deep CNNs have been pushing the frontier of visual recognition over past years. Besides recognition accuracy, strong demands in understanding deep CNNs in the research community motivate developments of tools to dissect pre-trained models to visualize how they make predictions. Recent works further push the interpretability in the network learning stage to learn more meaningful representations. In this work, focusing on a specific area of visual recognition, we report our efforts towards interpretable face recognition. We propose a spatial activation diversity loss to learn more structured face representations. By leveraging the structure, we further design a feature activation diversity loss to push the interpretable representations to be discriminative and robust to occlusions. We demonstrate on three face recognition benchmarks that our proposed method is able to achieve the state-of-art face recognition accuracy with easily interpretable face representations.
Quantitative Verification of Neural Networks and Its Security Applications

Neural networks are increasingly employed in safety-critical domains. This has prompted interest in verifying or certifying logically encoded properties of neural networks. Prior work has largely focused on checking existential properties, wherein the goal is to check whether there exists any input that violates a given property of interest. However, neural network training is a stochastic process, and many questions arising in their analysis require probabilistic and quantitative reasoning, i.e., estimating how many inputs satisfy a given property. To this end, our paper proposes a novel and principled framework to quantitative verification of logical properties specified over neural networks. Our framework is the first to provide PAC-style soundness guarantees, in that its quantitative estimates are within a controllable and bounded error from the true count. We instantiate our algorithmic framework by building a prototype tool called NPAQ that enables checking rich properties over binarized neural networks. We show how emerging security analyses can utilize our framework in 3 applications: quantifying robustness to adversarial inputs, efficacy of trojan attacks, and fairness/bias of given neural networks.
Tornado: Enabling Blockchain in Heterogeneous Internet of Things Through a Space-Structured Approach

With the widespread applications of the Internet of Things (IoT), e.g., smart city, business, healthcare, etc., the security of data and devices becomes a major concern. Although blockchain can effectively enhance the network security and achieve fault tolerance, the huge resource consumption and limited performance of data processing restrict its deployments in IoT scenarios. Observing the heterogeneity and resource constraints, we intend to make blockchain accommodate both wimpy and brawny IoT devices. In this article, we present Tornado, a high-performance blockchain system based on space-structured ledger and corresponding algorithms, to enable blockchain in IoT. Specifically, we first design a space-structured chain architecture with novel data structures for promoting the network scalability. To address the huge heterogeneity of IoT, a novel consensus mechanism named collaborative-proof of work is developed. Moreover, we propose the space-structured greedy heaviest-observed subtree (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GHOST) protocol for improving the resource efficiency of IoT devices. Additionally, a dynamic weight assignment mechanism in S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GHOST contributes to reflect the trustworthiness of data and devices. Extensive experiments demonstrate that Tornado can achieve a maximum throughput of 3464.76 transactions per second. The optimizations of propagation latency and resource efficiency are 68.14% and 30.56%, respectively.
Quisquis: A New Design for Anonymous Cryptocurrencies

Despite their usage of pseudonyms rather than persistent identifiers, most existing cryptocurrencies do not provide users with any meaningful levels of privacy. This has prompted the creation of privacy-enhanced cryptocurrencies such as Monero and Zcash, which are specifically designed to counteract the tracking analysis possible in currencies like Bitcoin. These cryptocurrencies, however, also suffer from some drawbacks: in both Monero and Zcash, the set of potential unspent coins is always growing, which means users cannot store a concise representation of the blockchain. Additionally, Zcash requires a common reference string and the fact that addresses are reused multiple times in Monero has led to attacks to its anonymity. In this paper we propose a new design for anonymous cryptocurrencies, Quisquis, that achieves provably secure notions of anonymity. Quisquis stores a relatively small amount of data, does not require trusted setup, and in Quisquis each address appears on the blockchain at most twice: once when it is generated as output of a transaction, and once when it is spent as input to a transaction. Our result is achieved by combining a DDH-based tool (that we call updatable keys) with efficient zero-knowledge arguments.
Human activity recognition from UAV-captured video sequences

This research paper introduces a new approach for human activity recognition from UAV-captured video sequences. The proposed approach involves two phases: an offline phase and an inference phase. A scene stabilization step is performed together with these two phases. The offline phase aims to generate the human/non-human model as well as a human activity model using a convolutional neural network. The inference phase makes use of the already generated models in order to detect humans and recognize their activities. Our main contribution lies in adapting the convolutional neural networks, normally dedicated to the classification task, to detect humans. In addition, the classification of human activities is carried out according to two scenarios: An instant classification of video frames and an entire classification of the video sequences. Relying on an experimental evaluation of the proposed methods for human detection and human activity classification on the UCF-ARG dataset, we validated not only these contributions but also the performance of our methods compared to the existing ones.
Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentation

Existing weakly supervised semantic segmentation (WSSS) methods usually utilize the results of pre-trained saliency detection (SD) models without explicitly modelling the connections between the two tasks, which is not the most efficient configuration. Here we propose a unified multi-task learning framework to jointly solve WSSS and SD using a single network, i.e. saliency and segmentation network (SSNet). SSNet consists of a segmentation network (SN) and a saliency aggregation module (SAM). For an input image, SN generates the segmentation result and, SAM predicts the saliency of each category and aggregating the segmentation masks of all categories into a saliency map. The proposed network is trained end-to-end with image-level category labels and class-agnostic pixel-level saliency labels. Experiments on PASCAL VOC 2012 segmentation dataset and four saliency benchmark datasets show the performance of our method compares favorably against state-of-the-art weakly supervised segmentation methods and fully supervised saliency detection methods.
Different Medical Image Registration Techniques: A Comparative Analysis

Image registration provides major role in real world applications and classic digital image processing. Image registration is carried out for more than one image and this image was captured from a different location, different sensors, different time and different viewpoints.This paper deals with the comparative analysis of various registration techniques and here six registration techniques depending upon intensity, phase correlation, image feature, area, control points and mutual information are compared. Comparative analysis for different methodologies shows the advantages of one method over the other methods. The foremost objective of this paper is to deliver a complete reference source for the scholars interested in registration, irrespective of specific application extents.Finally performance analyses are evaluated for the medical datasets and comparison is graphically shown with the MATLAB simulation tool.
Variance-Reduced Decentralized Stochastic Optimization With Accelerated Convergence

This paper describes a novel algorithmic framework to minimize a finite-sum of functions available over a network of nodes. The proposed framework, that we call GT-VR, is stochastic and decentralized, and thus is particularly suitable for problems where large-scale, potentially private data, cannot be collected or processed at a centralized server. The GT-VR framework leads to a family of algorithms with two key ingredients: (i) local variance reduction, that enables estimating the local batch gradients from arbitrarily drawn samples of local data; and, (ii) global gradient tracking, which fuses the gradient information across the nodes. Naturally, combining different variance reduction and gradient tracking techniques leads to different algorithms of interest with valuable practical tradeoffs and design considerations. Our focus in this paper is on two instantiations of the GT-VR framework, namely GT-SAGA and GT-SVRG, that, similar to their centralized counterparts (SAGA and SVRG), exhibit a compromise between space and time. We show that both GT-SAGA and GT-SVRG achieve accelerated linear convergence for smooth and strongly convex problems and further describe the regimes in which they achieve non-asymptotic, network-independent linear convergence rates that are faster with respect to the existing decentralized first-order schemes. Moreover, we show that both algorithms achieve a linear speedup in such regimes compared to their centralized counterparts that process all data at a single node. Extensive simulations illustrate the convergence behavior of the corresponding algorithms.
SenseChain: A blockchain-based crowdsensing framework for multiple requesters and multiple workers

In this paper, we propose a decentralized crowdsensing framework for multiple requesters with multiple workers built on Ethereum blockchain- SenseChain. Crowdsensing frameworks utilize workers’ sensing capabilities to fulfill requesters’ sensing tasks. While crowdsensing is typically managed by a centralized platform, the centralized management entails various challenges such as reliability in workers’ selection, fair evaluation for payment distribution, potential breach of users’ privacy, and high deployment cost. The proposed solution, SenseChain, is a decentralized crowdsensing framework developed to run on Ethereum blockchain to mitigate said challenges while increasing users’ engagement, with reasonable cost. SenseChain is developed around three smart contracts: (1) User Manager Contract (UMC), (2) Task Manager Contract (TMC), and (3) Task Detailed Contract (TDC). These contracts implement the platform features such as maintaining users’ information, publishing tasks from multiple requesters, accepting reservations and solutions from multiple workers, and evaluating solutions to calculate proportional payments. The framework is implemented using Solidity and Web3.js, where a real publicly available dataset is used. The framework performance is compared to a centralized greedy selection framework to demonstrate its comparability while mitigating tackled challenges. The results in terms of solutions quality, time cost, and workers traveled distance confirm its viability as a solution for crowdsensing.