Reviewerly/exHarmony huggingface dataset:
FPGA-Based High-Performance Parallel Architecture for Homomorphic Computing on Encrypted Data

Homomorphic encryption is a tool that enables computation on encrypted data and thus has applications in privacy-preserving cloud computing.Though conceptually amazing, implementation of homomorphic encryption is very challenging and typically software implementations on general purpose computers are extremely slow.In this paper we present our year long effort to design a domain specific architecture in a heterogeneous Arm+FPGA platform to accelerate homomorphic computing on encrypted data.We design a custom co-processor for the computationally expensive operations of the well-known Fan-Vercauteren (FV) homomorphic encryption scheme on the FPGA, and make the Arm processor a server for executing different homomorphic applications in the cloud, using this FPGA-based co-processor.We use the most recent arithmetic and algorithmic optimization techniques and perform designspace exploration on different levels of the implementation hierarchy.In particular we apply circuit-level and block-level pipeline strategies to boost the clock frequency and increase the throughput respectively.To reduce computation latency, we use parallel processing at all levels.Starting from the highly optimized building blocks, we gradually build our multi-core multi-processor architecture for computing.We implemented and tested our optimized domain specific programmable architecture on a single Xilinx Zynq UltraScale+ MPSoC ZCU102 Evaluation Kit.At 200 MHz FPGA-clock, our implementation achieves over 13x speedup with respect to a highly optimized software implementation of the FV homomorphic encryption scheme on an Intel i5 processor running at 1.8 GHz.
Sentiment Analysis of News Articles: A Lexicon based Approach

Modern technological era has reshaped traditional lifestyle in several domains. The medium of publishing news and events has become faster with the advancement of Information Technology (IT). IT has also been flooded with immense amounts of data, which is being published every minute of every day, by millions of users, in the shape of comments, blogs, news sharing through blogs, social media micro-blogging websites and many more. Manual traversal of such huge data is a challenging job; thus, sophisticated methods are acquired to perform this task automatically and efficiently. News reports events that comprise of emotions - good, bad, neutral. Sentiment analysis is utilized to investigate human emotions (i.e., sentiments) present in textual information. This paper presents a lexicon-based approach for sentiment analysis of news articles. The experiments have been performed on BBC news dataset, which expresses the applicability and validation of the adopted approach.
Residents’ Reactions to Earthquake Early Warnings in Japan

This article empirically examines the effectiveness of earthquake early warning (EEW) in Japan based on experiences of residents who received warnings before earthquake shaking occurred. In Study 1, a survey (N = 299) was conducted to investigate residents' experiences of, and reactions to, an EEW issued in Gunma and neighboring regions on June 17, 2018. The main results were as follows. (1) People's primary reactions to the EEW were mental, not physical, and thus motionless. Most residents stayed still, not for safety reasons, but because they were focusing on mentally bracing themselves. (2) Residents perceived the EEW to be effective because it enabled them to mentally prepare, rather than take physical protective actions, before strong shaking arrived. (3) In future, residents anticipate that on receipt of an EEW they would undertake mental preparation as opposed to physical protective actions. In Study 2, a survey (N = 450) was conducted on another EEW issued for an earthquake offshore of Chiba Prefecture on July 7, 2018. Results were in line with those of Study 1, suggesting that the findings described above are robust. Finally, given people's lack of impetus to undertake protective action on receipt of an EEW, this article discusses ways to enhance such actions.
A knowledge based fuzzy-probabilistic roadmap method for mobile robot navigation

In this paper a novel knowledge based fuzzy control system with Probabilistic Roadmap (PRM) method is proposed for path planning and target seeking behaviour of mobile robot in presence of obstacles. The proposed method runs in two folds, first fold generates the shortest path between the start position to target position in a priori known cluttered environment, wherein the PRM is used to construct straight-lined path by connecting the intermediate nodes. However, the second fold steps help to convert the sharp corners into the smoothen curves throughout the path. The knowledge based fuzzy control system ensures the smooth turns by adjusting the suitable heading angle, there by correcting the position and orientation of the robots to find local/global targets. The presented methodology has been simulated and tested in various environments. Simulation results justify that, the method is not only capable to find an optimal or near-optimal robot path in complex obstacle present environments while avoiding any dead end situation, but also ensures a good control over robot velocity and smooth turning at the turning junctions. In all the simulation experiments it is found that, the method has shorten the path length by more than 5 percent while smoothening and subsequently navigation time was also reduced appreciably.
360proto

We explore 360 paper prototyping to rapidly create AR/VR prototypes from paper and bring them to life on AR/VR devices. Our approach is based on a set of emerging paper prototyping templates specifically for AR/VR. These templates resemble the key components of many AR/VR interfaces, including 2D representations of immersive environments, AR marker overlays and face masks, VR controller models and menus, and 2D screens and HUDs. To make prototyping with these templates effective, we developed 360proto, a suite of three novel physical--digital prototyping tools: (1) the 360proto Camera for capturing paper mockups of all components simply by taking a photo with a smartphone and seeing 360-degree panoramic previews on the phone or stereoscopic previews in Google Cardboard; (2) the 360proto Studio for organizing and editing captures, for composing AR/VR interfaces by layering the captures, and for making them interactive with Wizard of Oz via live video streaming; (3) the 360proto App for running and testing the interactive prototypes on AR/VR capable mobile devices and headsets. Through five student design jams with a total of 86 participants and our own design space explorations, we demonstrate that our approach with 360proto is useful to create relatively complex AR/VR applications.
A novel paradigm for managing the product development process utilising blockchain technology principles

The product conceptualisation, design and manufacturing phases are becoming increasingly complex, since more available resources, stakeholders and sophisticated technologies are involved during product development. The exchange and management of product-related information is often a challenging task, affecting significantly the intellectual property protection process as well as the distinction of roles among stakeholders. This paper proposes a conceptual framework that utilises blockchain technology principles for managing product development information and processes with the goal of providing new approaches to extending the functionality of product data management systems. A test case focusing on products developed with additive manufacturing technologies is presented.
Beyond Dyadic Interactions

Chatbots have grown as a space for research and development in recent years due both to the realization of their commercial potential and to advancements in language processing that have facilitated more natural conversations. However, nearly all chatbots to date have been designed for dyadic, one-on-one communication with users. In this paper we present a comprehensive review of research on chatbots supplemented by a review of commercial and independent chatbots. We argue that chatbots' social roles and conversational capabilities beyond dyadic interactions have been underexplored, and that expansion into this design space could support richer social interactions in online communities and help address the longstanding challenges of maintaining, moderating, and growing these communities. In order to identify opportunities beyond dyadic interactions, we used research-through-design methods to generate more than 400 concepts for new social chatbots, and we present seven categories that emerged from analysis of these ideas.
A New Reversible Data Hiding in Encrypted Image Based on Multi-Secret Sharing and Lightweight Cryptographic Algorithms

Reversible data hiding in encrypted images (RDHEI) has been introduced for preserving image privacy and data embedding. RDHEI usually involves three parties, namely, the image provider, data hider, and receiver. On the security with key setting, there are three categories: share independent secret keys (SIK), shared one key (SOK), and share no secret keys (SNK). In SIK, the image provider and data hider must respectively and independently share secret keys with the receiver, whereas in SNK, no secret key is shared. However, the literature works proposed SNK-type schemes by using homomorphic encryption (with exorbitant computation cost). In this paper, we address the SOK setting, where only the image provider shares a secret key with the receiver, and the data hider can embed a secret message without any knowledge of this key. To realize our SOK scheme in a simple manner, we propose a new technique by using multi-secret sharing as the underlying encryption, which indeed induces a blow-up issue of the key size. For preserving the efficiency of the key size, we apply a compression by using lightweight cryptographic algorithms. Then, we demonstrate our SOK scheme based on the proposed techniques, and show effectiveness, efficiency, and security by experiments and analysis.
Holistic resource management for sustainable and reliable cloud computing: An innovative solution to global challenge

Minimizing the energy consumption of servers within cloud computing systems is of upmost importance to cloud providers toward reducing operational costs and enhancing service sustainability by consolidating services onto fewer active servers. Moreover, providers must also provision high levels of availability and reliability, hence cloud services are frequently replicated across servers that subsequently increases server energy consumption and resource overhead. These two objectives can present a potential conflict within cloud resource management decision making that must balance between service consolidation and replication to minimize energy consumption whilst maximizing server availability and reliability, respectively. In this paper, we propose a cuckoo optimization-based energy-reliability aware resource scheduling technique (CRUZE) for holistic management of cloud computing resources including servers, networks, storage, and cooling systems. CRUZE clusters and executes heterogeneous workloads on provisioned cloud resources and enhances the energy-efficiency and reduces the carbon footprint in datacenters without adversely affecting cloud service reliability. We evaluate the effectiveness of CRUZE against existing state-of-the-art solutions using the CloudSim toolkit. Results indicate that our proposed technique is capable of reducing energy consumption by 20.1% whilst improving reliability and CPU utilization by 17.1% and 15.7% respectively without affecting other Quality of Service parameters.
IoT-based predictive maintenance for fleet management

In recent years, the Internet of Things (IoT) and big data have been hot topics. With all this data being produced, new applications such as predictive maintenance are possible. Consensus self-organized models approach (COSMO) is an example of a predictive maintenance system for a fleet of public transport buses, which attempts to diagnose faulty buses that deviate from the rest of the bus fleet. The present work proposes a novel IoT architecture for predictive maintenance and proposes a semi-supervised machine learning algorithm that attempts to improve the sensor selection performed in COSMO. With the help of the Société de Transport de l’Outaouais, a minimally viable prototype of the architecture has been deployed and J1939 sensor data have been acquired.
An overview and comprehensive comparison of ensembles for concept drift

Online learning is about extracting information from large data streams which may be affected by changes in the distribution of the data, events known as concept drift. Concept drift detectors are small programs that try to detect these changes and make it possible to replace the base classifier, improving the overall accuracy. Ensembles of classifiers are also common in this application area and some of them are configurable with a drift detector. This article summarizes a large-scale comparison of six ensemble algorithms, configured with 10 different drift detectors, for learning from fully labeled data streams, using a large number of artificial datasets and two popular base learners in the area: Naive Bayes and Hoeffding Tree. In addition, the code of one the ensembles (Leveraging Bagging) was modified to permit its configuration with any drift detector: its original implementation only uses ADWIN. The goal is to assess how good the existing ensemble algorithms configurable with detectors really are and also to verify and challenge a common belief in the area. The results of the experiments suggest that, in most datasets, the choice of ensemble algorithm has much more impact on the final accuracy than the choice of drift detector used in its configuration. They also suggest the best auxiliary detectors to configure the ensembles, i.e. those that maximize the accuracy of the ensembles, are only marginally different from the best detectors in the same datasets in terms of their accuracies (recently reported in another article).
Detail-Enhanced Multi-Scale Exposure Fusion in YUV Color Space

It is recognized that existing multi-scale exposure fusion algorithms can be improved using edge-preserving smoothing techniques. However, the complexity of edge-preserving smoothing-based multi-scale exposure fusion is an issue for mobile devices. In this paper, a simpler multi-scale exposure fusion algorithm is designed in YUV color space. The proposed algorithm can preserve details in the brightest and darkest regions of a high dynamic range (HDR) scene and the edge-preserving smoothing-based multi-scale exposure fusion algorithm while avoiding color distortion from appearing in the fused image. The complexity of the proposed algorithm is about half of the edge-preserving smoothing-based multi-scale exposure fusion algorithm. The proposed algorithm is thus friendlier to the smartphones than the edge-preserving smoothing-based multi-scale exposure fusion algorithm. In addition, a simple detail-enhancement component is proposed to enhance fine details of fused images. The experimental results show that the proposed component can be adopted to produce an enhanced image with visibly enhanced fine details and a higher MEF-SSIM value. This is impossible for existing detail enhancement components. Clearly, the component is attractive for PC-based applications.
An improved Elman neural network with piecewise weighted gradient for time series prediction

Time series prediction is an important tool for system analysis. Traditional forecasting methods usually achieve prediction by establishing static model or only mining the information from the sequence itself, but without considering the dynamics of the system or its triggering factors. Elman neural network (ENN) is a dynamical model that can remember historical states. In order to better establish the real multivariate time series model and improve the single-step prediction accuracy, this study proposes an improved regularized ENN method based on piecewise time weighted gradient (PWRENN). Each time the parameters of PWRENN model are updated, the weighted gradient is calculated by weighing the current gradient and the previous gradients according to a monotonically increasing temporal function. By considering both the current gradient and previous gradients, PWRENN is able to simulate the time-lag effect in the time series prediction problem. Moreover, the system environment may change with time, and the historical data of the same slot at a different time should have different effects on the predicted data. This work uses a piecewise time function with different parameters at different time periods to more accurately model the real system. In PWRENN, L2 regularization is adopted to solve the overfitting problem and enhance the generalization performance. Furthermore, the effectiveness of the proposed method is verified in an artificial Mackey-Glass time series prediction and three landslide displacement predictions.
SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization

In this paper we present a novel and general method to accelerate convolutional neural network (CNN) inference by taking advantage of feature map sparsity. We experimentally demonstrate that a highly quantized version of the original network is sufficient in predicting the output sparsity accurately, and verify that leveraging such sparsity in inference incurs negligible accuracy drop compared with the original network. To accelerate inference, for each convolution layer our approach first obtains a binary sparsity mask of the output feature maps by running inference on a quantized version of the original network layer, and then conducts a full-precision sparse convolution to find out the precise values of the non-zero outputs. Compared with existing work, our approach avoids the overhead of training additional auxiliary networks, while is still applicable to general CNN networks without being limited to certain application domains.
Character Recognition in Air-Writing Based on Network of Radars for Human-Machine Interface

Radar technology plays a vital role in contact-less detection of hand gestures or motions, which forms an alternate and intuitive form of human-computer interface. Air-writing refers to the writing of linguistic characters or words in free space by hand gesture movements. In this paper, we propose an air-writing system based on a network of millimeter wave radars. We propose a two-stage approach for extraction and recognition of handwriting gestures. The extraction processing stage uses a fine range estimate combined with the trilateration technique to detect and localize the hand marker, followed by a smoothening filter to create a trajectory of the character through the hand movement. For the recognition stage, we explore two approaches: one extracts the time-series trajectory data and recognizes the drawn character using long short term memory (LSTM), bi-directional LSTM (BLSTM), and convolutional LSTM (ConvLSTM) with connectionist temporal classification (CTC) loss function, and the other approach reconstructs a 2D image from the trajectory of drawn character and uses deep convolutional neural network (DCNN) to classify the alphabets drawn by the user. ConvLSTM-CTC performs best among LSTM variants on time-series trajectory data achieving 98.33% classification accuracy similar to DCNN over the chosen character set. This paper employs real data using a network of three 60-GHz millimeter wave radar sensor to demonstrate the success of the proposed setup and associated algorithm with design consideration.
Linear and Nonlinear Regression-Based Maximum Correntropy Extended Kalman Filtering

The extended Kalman filter (EKF) is a method extensively applied in many areas, particularly, in nonlinear target tracking. The optimization criterion commonly used in EKF is the celebrated minimum mean square error (MMSE) criterion, which exhibits excellent performance under Gaussian noise assumption. However, its performance may degrade dramatically when the noises are heavy tailed. To cope with this problem, this paper proposes two new nonlinear filters, namely the linear regression maximum correntropy EKF (LRMCEKF) and nonlinear regression maximum correntropy EKF (NRMCEKF), by applying the maximum correntropy criterion (MCC) rather than the MMSE criterion to EKF. In both filters, a regression model is formulated, and a fixed-point iterative algorithm is utilized to obtain the posterior estimates. The effectiveness and robustness of the proposed algorithms in target tracking are confirmed by an illustrative example.
Surface and Deep Features Ensemble for Sentiment Analysis of Arabic Tweets

Sentiment analysis (SA) of Arabic tweets is a complex task due to the rich morphology of the Arabic language and the informal nature of language on Twitter.Previous research on the SA of tweets mainly focused on manually extracting features from the text.Recently, neural word embeddings have been utilized as less labor-intensive representations than manual feature engineering.Most of these word-embeddings model the syntactic information of words while ignoring the sentiment context.In this paper, we propose to learn sentiment-specific word embeddings from Arabic tweets and use them in the Arabic Twitter sentiment classification.Moreover, we propose a feature ensemble model of surface and deep features.The surface features are manually extracted features, and the deep features are generic word embeddings and sentimentspecific word embeddings.The extensive experiments are performed to test the effectiveness of the surface and deep features ensemble, pooling functions, embeddings size, and cross-dataset models.The recent language representation model BERT is also evaluated on the task of SA of Arabic tweets.The models are evaluated on three different datasets of Arabic tweets, and they outperform the previous results on all these datasets with a significant increase in the F-score.The experimental results demonstrate that: 1) the highest performing model is the ensemble of surface and deep features and 2) the approach achieves the state-of-the-art results on several benchmarking datasets.
Once a Kind Friend is Now a Thing

The number of households using stand-alone conversational agents is rapidly increasing. However, recent research revealed that in some of these households, use of agents decreases over time, and we know little about why. Therefore, we aim to understand how people utilize such devices in their daily lives and to explore the associated obstacles or difficulties. We conducted a long-term (12-week) user study in which we followed eight households, examining their use of Amazon Echo at home. From a series of diaries, surveys, and interviews with eight first-time users, we identified how their experiences changed over time and how conversational agents lose their presence at home. We found that voice interface, physical form, and at-home installation affect users' perceptions and expectations of smart speakers. Based on these findings, we discuss challenges and design opportunities for future at-home conversational agents.