Reviewerly/exHarmony huggingface dataset:
Leveraging commonsense reasoning towards a smarter Smart Home

The “smart” behaviour that characterises commercial Smart Home systems is due to the relations that users manually establish between devices and actions through automation rules. Therefore, the intelligence of these systems is entirely (or mostly) predefined by humans. To address this problem, several Artificial Intelligence techniques have been applied in this field to try to improve the smartness of Smart Home systems by enhancing features such as self-adaptation, self-evolution and self-awareness. This work in progress article presents an approach to provide smart homes with these features through a reasoning system able to deduce, define, monitor and update the automation rules that drive their behaviour, in an autonomous manner and with a knowledge management at different levels of abstraction. That is to say, the rules normally defined by humans are now created and modified automatically. The proposal is based in a commonsense knowledge model that represents how the world works and enables to infer what resources can be used to accomplish a specific objective. Automations are generated according to general, commonsense and context (topology, users, sensors, etc.) knowledge, and they are more or less sophisticated depending on the available devices. The set of behavioural rules is updated when any of these resources change. Scone is the high-performance and open-source knowledge base system where the knowledge model has been implemented. Finally, to evaluate and demonstrate the potential of the model, a set of automation rules is built for a concrete use case. Results are shown in a virtual smart home on the Home Assistant platform.
Albatross Optimization Algorithm: A Novel Nature Inspired Search Algorithm

The venture of meta-heuristic optimization algorithms into the field of biology has only accelerated growth in swarm intelligence and evolutionary algorithms. We present one such novel approach, inspired by several avian algorithms, mimicking the lifestyle and breeding pattern of the Laysan Albatross. The proposed algorithm is suitable for any nonlinear continuous function optimization task and proves to perform better for training neural networks, compared to gradient-based approaches such as back-propagation and other bio-inspired approaches such as the Whale Optimization Algorithm. We also show that it is also not as susceptible to overfitting. We tested our algorithm on a total of 12 datasets and obtained an average train accuracy of 0.785, average training standard deviation of 0.037, average test accuracy of 0.793 and an average test standard deviation of 0.035.
Gesture Recognition Method Based on Doppler Radar Technology

This paper presents a LSTM neural network gesture recognition method based on Doppler radar technology. Firstly, the velocity and distance of the gesture target are obtained by 2D-FFT, and the antenna array expansion is realized by using the high-order cumulant to estimate the azimuth of the recognition target. Then, the relationship among distance, velocity, azimuth and time is arranged to form a data set. At the same time, a multi-LSTM fusion network model is proposed. Finally, in order to improve the accuracy of recognition, the influence of initial learning rate and the number of hidden nodes on the performance of multi-LSTM fusion network model is analyzed. The experimental data show that when the initial learning rate is 0.005 and the hidden layer node is 128, the average correct rate of the six gestures can reach 96 %.
A Saliency Based Image Fusion Framework for Skin Lesion Segmentation and Classification

Melanoma, due to its higher mortality rate, is considered as one of the most pernicious types of skin cancers, mostly affecting the white populations. It has been reported a number of times and is now widely accepted, that early detection of melanoma increases the chances of the subject’s survival. Computer-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning techniques. In this work, we propose a framework that accurately segments, and later classifies, the lesion using improved image segmentation and fusion methods. The proposed technique takes an image and passes it through two methods simultaneously; one is the weighted visual saliency-based method, and the second is improved HDCT based saliency estimation. The resultant image maps are later fused using the proposed image fusion technique to generate a localized lesion region. The resultant binary image is later mapped back to the RGB image and fed into the Inception-ResNet-V2 pre-trained model–trained by applying transfer learning. The simulation results show improved performance compared to several existing methods.
Digital quantum simulation of NMR experiments

Simulations of nuclear magnetic resonance (NMR) experiments can be an important tool for extracting information about molecular structure and optimizing experimental protocols but are often intractable on classical computers for large molecules such as proteins and for protocols such as zero-field NMR. We demonstrate the first quantum simulation of an NMR spectrum, computing the zero-field spectrum of the methyl group of acetonitrile using four qubits of a trapped-ion quantum computer. We reduce the sampling cost of the quantum simulation by an order of magnitude using compressed sensing techniques. We show how the intrinsic decoherence of NMR systems may enable the zero-field simulation of classically hard molecules on relatively near-term quantum hardware and discuss how the experimentally demonstrated quantum algorithm can be used to efficiently simulate scientifically and technologically relevant solid-state NMR experiments on more mature devices. Our work opens a practical application for quantum computation.
Statistical Characteristics of Deep Representations: An Empirical Investigation

In this study, the effects of eight representation regularization methods are investigated, including two newly developed rank regularizers (RR). The investigation shows that the statistical characteristics of representations such as correlation, sparsity, and rank can be manipulated as intended, during training. Furthermore, it is possible to improve the baseline performance simply by trying all the representation regularizers and fine-tuning the strength of their effects. In contrast to performance improvement, no consistent relationship between performance and statistical characteristics was observable. The results indicate that manipulation of statistical characteristics can be helpful for improving performance, but only indirectly through its influence on learning dynamics or its tuning effects.
Data information gain method of accounting computerization based on Data Mining

In view of the poor effect of traditional accounting computerization data information gain, this paper proposes a data mining based accounting computerization data information gain method. Based on the entropy weight correlation analysis method, the feature proportion of accounting computerization is calculated, and on this basis, the statistics between accounting computerization data are determined. The data mining method is used to mine the accounting computerization data, determine the accounting computerization data information, and process the corresponding information gain. The experimental results show that the improved information gain method has higher gain accuracy.
Research on Network Data Storage Technology Based on Autonomous Controllable System

With the popularization of network applications, more and more devices can be connected to the network, and the amount of data generated is increasing. The problem of secure data storage is becoming more and more prominent. The storage of data is becoming larger and more complex. It has moved from simple independent storage to the cloud. The storage system has become an important network component independent of the server. The performance of the storage system directly affects the performance of the network and directly affects the user experience of network applications. This article discusses the mainstream storage technology solutions, namely Direct Attached Storage (DAS), Storage Area Network (SAN) and Network Attached Storage (NAS). The three solutions have their own characteristics and are suitable for different application scenarios and requirements. In this paper, network attached storage (NAS) is taken as an example, and a specific storage application deployment plan is given in combination with application requirements.
Of Leaders and Directors: A visual model to describe and analyse persistent visual cues directing to single out-of view targets

Researchers have come up with many visual cues that can guide Virtual (VR) and Augmented Reality (AR) users to out of view objects. The paper provides a classification of cues and tasks and visual model to describe and analyse cues to support their design.
Finet: Using Fine-grained Batch Normalization to Train Light-weight Neural Networks

To build light-weight network, we propose a new normalization, Fine-grained Batch Normalization (FBN). Different from Batch Normalization (BN), which normalizes the final summation of the weighted inputs, FBN normalizes the intermediate state of the summation. We propose a novel lightweight network based on FBN, called Finet. At training time, the convolutional layer with FBN can be seen as an inverted bottleneck mechanism. FBN can be fused into convolution at inference time. After fusion, Finet uses the standard convolution with equal channel width, thus makes the inference more efficient. On ImageNet classification dataset, Finet achieves the state-of-art performance (65.706 % accuracy with 43M FLOPs, and 73.786% accuracy with 303M FLOPs), Moreover, experiments show that Finet is more efficient than other state-of-art lightweight networks.
Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings

Reliability of machine learning (ML) systems is crucial in safety-critical applications such as healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of ML systems in deployment. Sequential and parallel ensemble techniques have shown improved performance of ML systems in multi-modal settings by leveraging the feature sets together. We propose an uncertainty-aware boosting technique for multi-modal ensembling in order to focus on the data points with higher associated uncertainty estimates, rather than the ones with higher loss values. We evaluate this method on healthcare tasks related to Dementia and Parkinson's disease which involve real-world multi-modal speech and text data, wherein our method shows an improved performance. Additional analysis suggests that introducing uncertainty-awareness into the boosted ensembles decreases the overall entropy of the system, making it more robust to heteroscedasticity in the data, as well as better calibrating each of the modalities along with high quality prediction intervals. We open-source our entire codebase at https://github.com/usarawgi911//Uncertainty-aware-boosting.
Bayesian Framework-Based Adaptive Hybrid Filtering for Speckle Noise Reduction in Ultrasound Images Via Lion Plus FireFly Algorithm

The existence of speckle noise in ultrasound (US) image processing distorts the image quality and also hinders the development of systematic approaches for US images. Numerous de-speckling schemes were established to date that concern speckle reduction; however, the models suffer from demerits like computational time, computational complexity, etc., that are to be rectified as soon as possible. This compulsion takes to the introduction of a new de-speckling model via an adaptive hybrid filter model that includes four filters like guided filter (GF), speckle-reducing bilateral filter (SRBF), rotation invariant bilateral nonlocal means filter (RIBNLM), and median filter (MF) respectively. Moreover, the novelty goes under the selection of optimal filter coefficients that make the process effective. Bayesian-based neural network is used to predict the appropriate filter coefficients, where the training library is constructed with the optimal coefficients. Along with this, the selection of optimal filter coefficients is done under the defined objective function using a new hybrid algorithm termed as Randomized FireFly (FF) update in Lion Algorithm (RFU-LA) that hybrids the concept of both LA and FF, respectively. Finally, the performance of the proposed de-speckling model is compared over that of other conventional models with respect to different performance measures. Accordingly, from the analysis, the mean MAPE of the proposed method are 39.13% and 49.28% higher than those of the wavelet filtering and hybrid filtering schemes for a noise variance of 0.1.
Robust Deep Facial Attribute Prediction against Adversarial Attacks

Face recognition has always been a hot topic in research, and has also widely been applied in industry areas and daily life. Nowadays, face recognition models with excellent performance are mostly based on deep neural networks (DNN). However, recently researchers find that images added invisible perturbations could successfully fool neural networks, which is known as the so-called adversarial attack. The perturbed images, also known as adversarial examples, are almost the same as the original images, but neural network could give different and wrong predictions with high confidence on these adversarial examples. Such a phenomenon indicates the vulnerable robustness of neural network and thus casts a shadow on the security of DNN-based face recognition models. Therefore, in this paper, we focus on the facial attribute prediction task in face recognition, investigate the influence of adversarial attack on facial attribute prediction and give a solution on improving the robustness of facial attribute prediction models. Extensive experiment results illustrate that the solution could indeed produce much more robust results in facial attribute prediction against adversarial attacks.
Participatory Action Research and Open Source Hardware Appropriation for Large Scale In-The-Wild Studies

With the rise of ubiquitous computing, the use of research in-the-wild approach in both Ubicomp and HCI context has become more viable, but not without its challenges. End-users and communities can be great beneficiaries of such an approach if actively involved in the process from an early stage and throughout the whole process. We start by presenting a future case study, the Cue Band study, which revolves around the creation of a wristband cueing device for people with Parkinson’s that experience drooling. We present an approach for research in-the-wild, which draws on participatory action research theory, that places the end-user at the centre of the process, aiming to first and for most to create a workable product for the end-user, before engaging in a formal study. In the last section, we explore the appropriation of existing open-source hardware for in-the-wild research, by describing problems and solutions associated with developing Ubicomp technologies for large-scale studies.
Equivalence Checking for Orthocomplemented Bisemilattices in Log-Linear Time

Abstract Motivated by proof checking, we consider the problem of efficiently establishing equivalence of propositional formulas by relaxing the completeness requirements while still providing certain guarantees. We present a quasilinear time algorithm to decide the word problem on a natural algebraic structures we call orthocomplemented bisemilattices, a subtheory of Boolean algebra. The starting point for our procedure is a variation of Aho, Hopcroft, Ullman algorithm for isomorphism of trees, which we generalize to directed acyclic graphs. We combine this algorithm with a term rewriting system we introduce to decide equivalence of terms. We prove that our rewriting system is terminating and confluent, implying the existence of a normal form. We then show that our algorithm computes this normal form in log linear (and thus sub-quadratic) time. We provide pseudocode and a minimal working implementation in Scala.
Weight-Based Nakamoto-Style Blockchains

We propose a framework for building Nakamoto-style proof-of-work blockchains where blocks are treated differently in the "longest chain rule". The crucial parameter is a weight function assigning different weights to blocks according to their hash value. Our framework enables the analysis of different weight functions while proving all statements at the appropriate level of abstraction. This allows us to quickly derive protocol guarantees for different weight functions. We exemplify the usefulness of our framework by capturing the classical Bitcoin protocol as well as exponentially growing functions as special cases. We show the typical properties—chain growth, chain quality and common prefix—for both, and further show that the latter provide an additional guarantee, namely a weak form of optimistic responsiveness. More precisely, we prove for a certain class of exponentially growing weight functions that in periods without corruption, the confirmation time only depends on the unknown actual network delay instead of the known upper bound.
Deep Reinforcement Learning for Quadrotor Path Following and Obstacle Avoidance

This chapter revises the path following and obstacle avoidance problems of a quadrotor vehicle based on deep reinforcement learning theory. The aim of this chapter is to develop a system capable of accurately following a path by adapting the vehicle's velocity to the path's shape, while avoiding obstacles that may appear in the vehicle's route. Both the path following and obstacle avoidance problems are addressed with the Deep Deterministic Policy Gradient (DDPG) algorithm. For the path following problem three different approaches are presented. Each approach emerges as an improved version of the preceding one. The first approach uses only instantaneous information of the path for solving the problem. The second approach includes a structure that makes it possible for the agent to anticipate the curves to come. The third agent includes a strategy that allows it to compute the optimal velocity according to the path's shape. For the obstacle avoidance problem, a combination of a DDPG agent that avoids obstacles and another one that follows the path is presented. The obstacle avoidance approach uses the LIDAR information to detect obstacles around the vehicle. LIDAR data is processed to eliminate ground detections. If an obstacle is detected in the vehicle's route, the agent modifies the reference path to avoid it. The developed agent communicates with the path following agent by sending it the modified reference path. A detailed description of the process of defining the state vector, the reward function and the action of the agent is given. A training framework that combines the tensorflow-python environment with Gazebo-ROS using the RotorS simulator is built. The agents are tested in RotorS and experimentally with the Asctec Hummingbird quadrotor (on the path following problem). Experimental results prove the validity of the agents, which are able to achieve a generalized solution of both problems.
A Pilot Study of Students’ Behavioral Intention to Use AI for Language Learning in Higher Education

Artificial Intelligence (AI) has been used for language learning in higher education to strengthen teaching practice and meet learning needs. However, studies on the behavior of university students using artificial intelligence for language learning are still insufficient. The study involved 129 university students and investigated factors inherent in their behavior of using AI to learn languages. Exploratory factor analysis was used to validate the questionnaire consisting of five factors: (i) knowledge of AI-enabled language apps, (ii) attitude to use AI, (iii) perceived ease of use, (iv) subjective norm, and (v) behavioral intention. Results of this study indicated that the above factors were associated with students’ behavioral intention for language learning positively.
Investigating Page Turning Methods for Sheet Music during Piano Play

Musical pieces composed for the piano are registered on paper sheets. Since the space for sheets on the piano is limited, pianists have to turn pages while playing the piano to get access to the registered without having to stop playing. In this work, we present two methods to support page turning during piano play: 1) turning by touch and 2) turning by pedal in combination with two types of note layouts in (1) DIN A4 and (2) an adaptive display size. We compared these page turning methods to the baseline of using paper pages with 10 participants in a controlled lab experiment. Our results show that the pedal-adaptive turning method leads to the lowest interruption time while playing and pedal-based methods have a high usability and low cognitive load.
Innovation of Supply Chain Finance Model Based on Blockchain Technology

Taking the whole industry as the main body and providing financial services to the upstream and downstream of the whole supply chain (SC) is the main feature of SC finance. However, there are some problems in the process, such as poor information communication for financing enterprises and backward management mode, which pose certain risks to banks and other financial institutions. In recent years, blockchain technology has been developed rapidly, especially in financial services. However, there is little research on it in China. Therefore, this paper puts forward the innovation mode of blockchain technology in SC finance. This paper makes an in-depth research and Analysis on the blockchain technology and SC finance, and believes that the main problems in the current SC finance can be better solved with the assistance of blockchain technology, which has a positive role in improving the overall service capacity of China’s SC finance. In this paper, the characteristics of the application of financial blockchain technology are analyzed in detail. In order to further study, this paper investigates the current situation of the application and development of blockchain technology in China. Through sorting out the public data, it can be seen that by 2019, the number of supporting policies for blockchain in China has reached 135, and the number of relevant blockchain entrepreneurship activities has also reached 176, which shows that blockchain technology has a good prospect for the use of SC finance. This paper studies the innovation mode of blockchain technology in SC finance, and analyzes that blockchain technology is an indispensable important tool in the development of SC finance and plays a key role in the development of SC finance.
A Federated Parallel Data Platform for Trustworthy AI

Traditional data analytics approaches often ignore data security and privacy issues, and are not efficient for dynamic analysis and decision-making. The parallel data and federated data provide new ideas for addressing these issues. However, how to design the analytics applications of parallel data and federated data is challenging and calls for comprehensive tool support. This paper proposes a federated parallel data platform (FPDP) that provides an end-to-end data analytics pipeline, such as virtual data generation, federation model construction, and parallel data deduction. We implement a proof-of-concept prototype and evaluate the feasibility of the platform design using a fault diagnosis case study. The evaluation results show that the proposed approach is effective and efficient to help developing trustworthy AI applications.