Reviewerly/exHarmony huggingface dataset:
ZigBee Security Vulnerabilities: Exploration and Evaluating

Secure communication is a necessity that must be considered in any smart application to guarantee the confidentiality of information. Recently, ZigBee has been utilized in different smart applications such as automatic metering, power fraud detection, industrial automation and many more. In this research paper, we evaluate the performance of ZigBee from a security perspective. We explore the ZigBee built-in security services, encryption techniques, security keys, and the trust center. The research findings serve as guidelines to focus on wireless communication security in general and on the ZigBee security issues in specific. There is a crucial need to fill in the gap where ZigBee has low-complexity processing capabilities and the need to integrate more robust enhancements and security controls.
Feature learning for fault detection in high-dimensional condition monitoring signals

Complex industrial systems are continuously monitored by a large number of heterogeneous sensors. The diversity of their operating conditions and the possible fault types make it impossible to collect enough data for learning all the possible fault patterns. The paper proposes an integrated automatic unsupervised feature learning and one-class classification for fault detection that uses data on healthy conditions only for its training. The approach is based on stacked Extreme Learning Machines (namely Hierarchical, or HELM) and comprises an autoencoder, performing unsupervised feature learning, stacked with a one-class classifier monitoring the distance of the test data to the training healthy class, thereby assessing the health of the system. This study provides a comprehensive evaluation of HELM fault detection capability compared to other machine learning approaches, such as stand-alone one-class classifiers (ELM and SVM), these same one-class classifiers combined with traditional dimensionality reduction methods (PCA) and a Deep Belief Network. The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data. Subsequently, the approach is evaluated on a real case study of a power plant fault. The proposed algorithm for fault detection, combining feature learning with the one-class classifier, demonstrates a better performance, particularly in cases where condition monitoring data contain several non-informative signals.
Single Network Panoptic Segmentation for Street Scene Understanding

In this work, we propose a single deep neural network for panoptic segmentation, for which the goal is to provide each individual pixel of an input image with a class label, as in semantic segmentation, as well as a unique identifier for specific objects in an image, following instance segmentation. Our network makes joint semantic and instance segmentation predictions and combines these to form an output in the panoptic format. This has two main benefits: firstly, the entire panoptic prediction is made in one pass, reducing the required computation time and resources; secondly, by learning the tasks jointly, information is shared between the two tasks, thereby improving performance. Our network is evaluated on two street scene datasets: Cityscapes and Mapillary Vistas. By leveraging information exchange and improving the merging heuristics, we increase the performance of the single network, and achieve a score of 23.9 on the Panoptic Quality (PQ) metric on Mapillary Vistas validation, with an input resolution of 640 × 900 pixels. On Cityscapes validation, our method achieves a PQ score of 45.9 with an input resolution of 512 × 1024 pixels. Moreover, our method decreases the prediction time by a factor of 2 with respect to separate networks.
Next generation pathology: artificial intelligence enhances histopathology practice

Abstract Deep learning algorithms have shown benefits for pathology in the context of risk stratification of tumors. Although the results are promising, several steps have to be made to confirm clinical utility. In a recent issue of The Journal of Pathology , Colling et al present a perspective manuscript providing a roadmap to routine use of artificial intelligence in histopathology. In this commentary, we aimed to put these key points in the context of recent findings of AI and digital image analysis studies. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley &amp; Sons, Ltd.
Blockchain-Based E-Vote-as-a-Service

This article aims at introducing a new configurable and multipurpose electronic voting service based on the blockchain infrastructure. The objective is to design an architecture to automatically translate service configuration defined by the end user into a cloud-based deployable bundle, automating business logic definition, blockchain configuration, and cloud service provider selection. The article presents the preliminary results of the system and a SOA-based services definition implemented with smart contracts.
Hierarchical Multi-Task Network For Race, Gender and Facial Attractiveness Recognition

Deep learning has powered many face related tasks and shown state-of-the-art performance. However, existing deep models are often trained separately for different problems, which results in heavy computational burden. To address this problem, we propose a novel multi-task network with fully convolutional architecture-Hierarchical Multi-task Network (HMT-Net), that simultaneously recognizes a person's gender, race and facial attractiveness from a given portrait image. Aiming to improve the robustness to outliers in facial beauty prediction task, a novel loss is introduced into HMTNet. Compared to existing deep approaches, the proposed HMTNet achieves state-of-the-art performance on several datasets, and it can learn more discriminative feature representation through joint training and feature aggregation. Extensive experiments evidence the effectiveness of HMTNet.
Novel Secure Outsourcing of Modular Inversion for Arbitrary and Variable Modulus

In cryptography and algorithmic number theory, modular inversion is viewed as one of the most common and time-consuming operations. It is hard to be directly accomplished on resource-constrained clients (e.g., mobile devices and IC cards) since modular inversion involves a great amount of operations on large numbers in practice. To address the above problem, this paper proposes a novel unimodular matrix transformation technique to realize secure outsourcing of modular inversion. This technique makes our algorithm achieve several amazing properties. First, to the best of our knowledge, it is the first secure outsourcing computation algorithm that supports arbitrary and variable modulus, which eliminates the restriction in previous work that the protected modulus has to be a fixed composite number. Second, our algorithm is based on the single untrusted program model, which avoids the non-collusion assumption between multiple servers. Third, for each given instance of modular inversion, it only needs one round interaction between the client and the cloud server, and enables the client to verify the correctness of the results returned from the cloud server with the (optimal) probability 1. Furthermore, we propose an extended secure outsourcing algorithm that can solve modular inversion in multi-variable case. Theoretical analysis and experimental results show that our proposed algorithms achieve remarkable local-client&#x2019;s computational savings. At last, as two important and helpful applications of our algorithms, the outsourced implementations of the key generation of RSA algorithm and the Chinese Reminder Theorem are given.
Stateful functions as a service in action

In the serverless model, users upload application code to a cloud platform and the cloud provider undertakes the deployment, execution and scaling of the application, relieving users from all operational aspects. Although very popular, current serverless offerings offer poor support for the management of local application state , the main reason being that managing state and keeping it consistent at large scale is very challenging. As a result, the serverless model is inadequate for executing stateful, latency-sensitive applications. In this paper we present a high-level programming model for developing stateful functions and deploying them in the cloud. Our programming model allows functions to retain state as well as call other functions. In order to deploy stateful functions in a cloud infrastructure, we translate functions and their data exchanges into a stateful dataflow graph. With this paper we aim at demonstrating that using a modified version of an open-source dataflow engine as a runtime for stateful functions, we can deploy scalable and stateful services in the cloud with surprisingly low latency and high throughput.
Unbalanced private set intersection cardinality protocol with low communication cost

Private set intersection cardinality (PSI-CA) allows two parties, the sender and receiver, to compute the cardinality of the intersection, without revealing anything more to the other party. This paper focuses on the unbalanced private data sets case, where two parties hold sets of private data items, such as the users' identifiers; and where the size of the receiver's private data set is significantly smaller than the size of the sender's private data set. Two parties want to learn the cardinality of the intersection, but nothing else. The commutative encryption inspires authors to develop a novel protocol to solve the problem. Furthermore, by the application of the Bloom filter, the receiver can compute the output more easily than by the method that the encryption is carried out on the sender's private data set when low-power mobile IoT devices are used. In the semi-honest model, we can prove the security of our protocol when the sender's data set is big enough. The experiment shows the deviation of our protocol is negligible and the computation costs of our protocol.
Squeeze-and-Excitation Wide Residual Networks in Image Classification

The depth and width of the network have been investigated to influence the performance of image classification during the resent research. Wide residual networks (WRNs) have proved that the performance of classification can be improved by the width of the networks. With consideration of the significance, expanding the width is to increase the number of channels. However, not all the channels are needed. Meanwhile, much channel information will be lost while exploiting the global average pooling at the end of WRNs for image representations because the mean value is only related to the first order information. With the two considerations stated above, we propose squeeze-and-excitation WRNs which are based on the global covariance pooling (SE-WRNs-GVP). A residual Squeeze-and-Excitation block (rSE-block) can make up for the lost information due to global average pooling in SE-block. Then, informative channels of WRNs will be utilized. Finally, the global covariance pooling at the end of WRNs characterizes the correlations of feature channels for more discriminative representations. A SE-block with dropout is proposed to avoid over-fitting. We conduct experiments on CIFAR10 and CIFAR100 datasets and achieve a better performance without increasing the model complexity.
Natural Language Processing based Jaro-The Interviewing Chatbot

Recently, recruiters have find the taxing to communicate with all their candidates about the interview process and it results in the hassle of conducting interviews. Also, in cases, where there are large volumes of applicants, communicating with thousands of candidates and conducting other screening duties add to the heap of recruitment problems. The proposed system, JARO addresses the common concerns that a candidate faces when it comes to attend the mass interviews. Some of the challenges faced are inconsistency in questions, different days, different times of the day, interviewer's mood, venue of the interview and the list goes on. Therefore, JARO accelerates the interview process towards an unbiased decision-making process by proposing a chatbot that would conduct interviews by analyzing the candidates Curriculum Vitae (CV), based on which, it then prepares a set of questions to be asked to the candidate. The system will consist of features like resume analysis and automatic interview processes. The software would also ask questions based on the previous responses of the candidate by utilizing a Natural Language Processing (NLP) model which is very helpful in this process. After the interview process, the software would analyze the data collected to determine the right choice for the position offered. Thus, the project, JARO chatbot mainly intends to streamline the process of hiring employees.
IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction

The majority of the existing methods for non-rigid 3D surface regression from a single 2D image require an object template or point tracks over multiple frames as an input, and are still far from real-time processing rates. In this work, we present the Isometry-Aware Monocular Generative Adversarial Network (IsMo-GAN) - an approach for direct 3D reconstruction from a single image, trained for the deformation model in an adversarial manner on a light-weight synthetic dataset. IsMo-GAN reconstructs surfaces from real images under varying illumination, camera poses, textures and shading at over 250 Hz. In multiple experiments, it consistently outperforms multiple approaches in the reconstruction accuracy, runtime, generalisation to unknown surfaces and robustness to occlusions. In comparison to the state-of-the-art, we reduce the reconstruction error by 10-30% including the textureless case and our surfaces evince fewer artefacts qualitatively.
FeatherCNN: Fast Inference Computation with TensorGEMM on ARM Architectures

Deep Learning is ubiquitous in a wide field of applications ranging from research to industry. In comparison to timeconsuming iterative training of convolutional neural networks (CNNs), inference is a relatively lightweight operation making it amenable to execution on mobile devices. Nevertheless, lower latency and higher computation efficiency are crucial to allow for complex models and prolonged battery life. Addressing the aforementioned challenges, we propose FeatherCNN- a fast inference library for ARM CPUs - targeting the performance ceiling of mobile devices. FeatherCNN employs three key techniques: 1) A highly efficient TensorGEMM (generalized matrix multiplication) routine is applied to accelerate Winograd convolution on ARM CPUs, 2) General layer optimization based on custom high performance kernels improves both the computational efficiency and locality of memory access patterns for non-Winograd layers. 3) The framework design emphasizes joint layer-wise optimization using layer fusion to remove redundant calculations and memory movements. Performance evaluation reveals that FeatherCNN significantly outperforms state-ofthe-art libraries. A forward propagation pass of VGG-16 on a 64-core ARM server is 48, 14, and 12 times faster than Caffe using OpenBLAS, Caffe2 using Eigen, and NNPACK, respectively. In addition, FeatherCNN is 3.19 times faster than the recently released TensorFlow Lite library on an iPhone 7 plus. In terms of GEMM performance, FeatherCNN achieves 14.8 and 39.0 percent higher performance than Apple's Accelerate framework on an iPhone 7 plus and Eigen on a Samsung Galaxy S8, respectively. The source code of FeatherCNN library is publicly available at https://github.com/tencent/feathercnn.
GestEar

We present GestEar, a gesture recognition method for sound-emitting gestures, such as snapping, knocking, or clapping, using only a simple smartwatch. Besides the motion information from the built-in accelerometer and gyroscope, we exploit audio data recorded by the smartwatch microphone as input. We propose a lightweight convolutional neural network architecture for gesture recognition, specifically designed to run locally on resource-constrained devices, which achieves a user-independent recognition accuracy of 97.2% for nine distinct gestures. We further show how to incorporate gesture detection and gesture classification in the same network, compare different network designs, and showcase a number of different applications built with our method. We find that the audio input drastically reduces the false positive rate in continuous recognition compared to using only motion.
The Software/Hardware Co-Design and Implementation of SM2/3/4 Encryption/Decryption and Digital Signature System

The security of smart devices is facing great challenges. This article presents a new hybrid cipher framework suitable for such devices. Using software/hardware (SW/HW) co-design method, an efficient encryption/decryption and digital signature scheme based on SM2, SM3, and SM4 algorithms is implemented. First, the framework is partitioned into software and hardware parts based on the analysis result of a pure software solution and the hardware overhead under the conditions of design constraints. Second, the flow of the whole scheme and embedded algorithms are realized by SW/HW co-design. Finally, an improved implementation of SM2/3/4 algorithms is proposed to achieve higher efficiency. In this implementation, some SW/HW modules are parallelized to reduce the running time and enhance the performance. The proposed design is safe and can resist simple power analysis (SPA) attacks. Especially, the AHB bus interface IP and software scheduling approach are adopted in data transfer and manipulation. The design is taped-out on a silicon chip with SMIC 110-nm technology process. The chip uses about 199K logic gates and 1-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> areas. The operating frequency of the design is 36 MHz and the chip consumes 23-mW power. The comparison with similar previous works shows our proposed design is more efficient with speed increasing of more than 10%.
Sentiment Analysis Using Machine Learning Classifiers: Evaluation of Performance

The modern era internet is full of information pertaining to opinion of people through applications such as social media, micro blogging sites, review sites, personal blogs etc. Sentimental analysis is an area in text mining where opinion of people can be analyzed and classified into positive, negative or neutral. In this work, the sentiments of the tweets or reviews published in the twitter is identified by searching for the particular keyword in tweets and then evaluate the polarity of the tweets as positive and negative. The sentiments of the tweets that are tweeted on a twitter evaluated based on feature selection of each score words. In order to select the best features Naive Bayes Classifier (NBC) is used for training and testing the features of a words and also evaluating the sentiment polarity of each tweets. Performance evaluation parameters such as accuracy, precision and time is taken into consideration and compared with three machine learning classifiers, namely, Random Forest, Naive Bayes and Support Vector Machine (SVM).
Effects of Depth Layer Switching between an Optical See-Through Head-Mounted Display and a Body-Proximate Display

Optical see-through head-mounted displays (OST HMDs) typically display virtual content at a fixed focal distance while users need to integrate this information with real-world information at different depth layers. This problem is pronounced in body-proximate multi-display systems, such as when an OST HMD is combined with a smartphone or smartwatch. While such joint systems open up a new design space, they also reduce users' ability to integrate visual information. We quantify this cost by presenting the results of an experiment (n=24) that evaluates human performance in a visual search task across an OST HMD and a body-proximate display at 30 cm. The results reveal that task completion time increases significantly by approximately 50 % and the error rate increases significantly by approximately 100 % compared to visual search on a single depth layer. These results highlight a design trade-off when designing joint OST HMD-body proximate display systems.
Raspberry Pi based voice-operated personal assistant (Neobot)

This research work aims to build up a personal assistant by using Raspberry Pi as a processing chip and underlying architecture. It emphasizes the substitution of screen-based interaction by utilizing ambient technologies, Robotics and IoT, means the user interface is integrated with the physical gadget. It comprises of components, for example, IR sensors, Pi camera [6], Mic and Motor driver. It is a voice-controlled personal assistant whose movements will be controlled through voice directions and it has the capacity to peruse the content from pictures and then articulate the equivalent to the client by utilizing the inbuilt speaker. It can help the outwardly disabled to connect with the world by giving them the access to informative sources like Wikipedia, Calculator, and so on by using their voice as the command.
Partial Adversarial Training for Neural Network-Based Uncertainty Quantification

Currently available uncertainty quantification (UQ) neural networks (NNs) are trained through the statistical error minimization. Therefore, NNs perform poorly for critical input patterns. Some input patterns have lower coverage probabilities than others. Such input dependent performance is evident in electricity price prediction where different input features are coming from heterogeneous monitoring sources. This paper proposes a prediction interval (PI) based UQ of the electricity price with the proposed partial adversarial training to achieve the input-domain independent performance. The proposed training consists of initial training, adversarial sample generations from critical samples and a final training with the combined datasets of critical samples and initial training samples. Critical situations are situations where prediction systems struggle to predict and make higher statistical errors. Multiple NNs are first trained with different initializations. Each uncovered sample to the NN pair in the training set generates an adversarial sample. The adversarial dataset is concatenated with the initial samples. The final NN training is performed with the combined dataset. The technique is visualized with rough sketches in both time and input domain. The feasibility and performance are examined on experimental electricity market price datasets.
Joint optimization of service request routing and instance placement in the microservice system

Microservice architecture is a promising architectural style. It decomposes monolithic software into a set of loosely coupled containerized microservices and associates them into multiple microservice chains to serve service requests. The new architecture creates flexibility for service provisioning but also introduces increased energy consumption and low service performance. Efficient resource allocation is critical. Unfortunately, existing solutions are designed at a coarse level for virtual machine (VM)-based clouds and not optimized for such chain-oriented service provisioning. In this paper, we study the resource allocation optimization problem for service request routing and microservice instance placement, so as to jointly reduce both resource usage and chains' end-to-end response time for saving energy and guaranteeing Quality of Service (QoS). We design detailed workload models for microservices and chains and formulate the optimization problem as a bi-criteria optimization problem. To address it, a three-stage scheme is proposed to search and optimize the trade-off decisions, route service requests into instances and deploy instances to servers in a balanced manner. Through numerical evaluations, we show that while assuring the same QoS, our scheme performs significantly better than and faster than benchmarking algorithms on reducing energy consumption and balancing load.