Reviewerly/exHarmony huggingface dataset:
Attention adjacency matrix based graph convolutional networks for skeleton-based action recognition

Recent progress on human action recognition, fueled by the Graph Convolutional Network (GCN), has been substantial. However, two main problems are caused by the design strategy of graph convolution kernels: first, the partitioning strategy of neighbor set for graph vertices relies on the gravity center designed manually, which is limited in generalizability to diverse skeletons in action recognition; second, the existing GCN-based methods can only capture local physical dependencies among joints and result in missing implicit joint correlations due to over-smoothing. In this work, we present (1) a novel attention adjacency matrix (AAM) to design graph convolution kernels and (2) a dimension-attention block to improve the robustness of the model. Specifically, the proposed AAM is designed by a novel partitioning strategy for the neighbor set, through which an adjacency matrix is decomposed into several parametric matrices. Simultaneously, attention mechanism is introduced in the process to generate an attention matrix. Combining the matrix and the parametric matrices into an AAM through ResNet, we further exhibit the AAM based graph convolution network (AAM-GCN). The proposed dimension-attention block strengthens the important information in each dimension of skeleton data by extending the idea of channel-attention. Extensive experiments on two large-scale datasets, NTU-RGB+D and Kinetics, demonstrate that AAM-GCN achieves better performance than the state-of-the-art works.
Indicators for maturity and readiness for digital forensic investigation in era of industrial revolution 4.0

The introduction of Industrial Revolution 4.0 (IR 4.0) brings benefits to the industries and our daily life. Innovation such as the Internet of Things, cloud computing, and blockchain is not only confined to the manufacturing industry but covers the whole of human life. Notwithstanding the said innovation, it also gives rise to cybercrimes with these technologies’ assistance. The botnet called Mirai is one example of compromising the technology in IR 4.0 to launch large-scale cyberattacks through Internet access. It is therefore crucial for the digital forensic (DF) organization to be ready to handle this kind of incident. This paper aims to provide the indicators for DF organizations’ maturity and readiness in the era of IR 4.0. To establish the indicators, a systematic literature review (SLR) is conducted. It involves four phases in the SLR, where the focus is; (1) challenges of DF in IR 4.0, (2) chain of custody and DF readiness, (3) existing maturity model, and (4) benchmarking the maturity element, respectively. It covers the research studies taken from five databases. From the comparison analysis, this study has derived five indicators for the maturity and readiness of DF organization: (1) People and capacity development, (2) Organization, policy and cooperation, (3) Process, (4) Technology and technical, (5) Legislation and regulation. Finally the work outlines the DF practices based on the CMMI ver. 2 practice areas and potential governance and management objectives that can govern the DF organization.
Fast convergence rates of deep neural networks for classification

Abstract We derive the fast convergence rates of a deep neural network (DNN) classifier with the rectified linear unit (ReLU) activation function learned using the hinge loss. We consider three cases for a true model: (1) a smooth decision boundary, (2) smooth conditional class probability, and (3) the margin condition (i.e., the probability of inputs near the decision boundary is small). We show that the DNN classifier learned using the hinge loss achieves fast rate convergences for all three cases provided that the architecture (i.e., the number of layers, number of nodes and sparsity) is carefully selected. An important implication is that DNN architectures are very flexible for use in various cases without much modification. In addition, we consider a DNN classifier learned by minimizing the cross-entropy, and show that the DNN classifier achieves a fast convergence rate under the conditions that the noise exponent and margin exponent are large. Even though they are strong, we explain that these two conditions are not too absurd for image classification problems. To confirm our theoretical explanation, we present the results of a small numerical study conducted to compare the hinge loss and cross-entropy.
Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method

Long-tail and rare event problems become crucial when autonomous driving algorithms are applied in the real world. For the purpose of evaluating systems in challenging settings, we propose a generative framework to create safety-critical scenarios for evaluating specific task algorithms. We first represent the traffic scenarios with a series of autoregressive building blocks and generate diverse scenarios by sampling from the joint distribution of these blocks. We then train the generative model as an agent (or a generator) to search the risky scenario parameters for a given driving algorithm. We treat the driving algorithm as an environment that returns high reward to the agent when a risky scenario is generated. The whole process is optimized by the policy gradient reinforcement learning method. Through the experiments conducted on several scenarios in the simulation, we demonstrate that the proposed framework generates safety-critical scenarios more efficiently than grid search or human design methods. Another advantage of this method is its adaptiveness to the routes and parameters.
DeepSlicing: Collaborative and Adaptive CNN Inference With Low Latency

The booming of Convolutional Neural Networks (CNNs) has empowered lots of computer-vision applications. Due to its stringent requirement for computing resources, substantial research has been conducted on how to optimize its deployment and execution on resource-constrained devices. However, previous works have several weaknesses, including limited support for various CNN structures, fixed scheduling strategies, overlapped computations, high synchronization overheads, etc. In this article, we present DeepSlicing, a collaborative and adaptive inference system that adapts to various CNNs and supports customized flexible fine-grained scheduling. As a built-in functionality, DeepSlicing has supported typical CNNs including GoogLeNet, ResNet, etc. By partitioning both model and data, we also design an efficient scheduler, Proportional Synchronized Scheduler (PSS), which achieves the trade-off between computation and synchronization. Based on PyTorch, we have implemented DeepSlicing on the testbed with real-world edge settings that consists of 8 heterogeneous Raspberry Pi's. The results indicate that DeepSlicing with PSS outperforms the existing systems dramatically, e.g., the inference latency and memory footprint are reduced up to 5.79× and 14.72×, respectively.
On the generalizability of Neural Program Models with respect to semantic-preserving program transformations

With the prevalence of publicly available source code repositories to train deep neural network models, neural program models can do well in source code analysis tasks such as predicting method names in given programs that cannot be easily done by traditional program analysis techniques. Although such neural program models have been tested on various existing datasets, the extent to which they generalize to unforeseen source code is largely unknown. Since it is very challenging to test neural program models on all unforeseen programs, in this paper, we propose to evaluate the generalizability of neural program models with respect to semantic-preserving transformations: a generalizable neural program model should perform equally well on programs that are of the same semantics but of different lexical appearances and syntactical structures. We compare the results of various neural program models for the method name prediction task on programs before and after automated semantic-preserving transformations. We use three Java datasets of different sizes and three state-of-the-art neural network models for code, namely code2vec, code2seq, and GGNN, to build nine such neural program models for evaluation. Our results show that even with small semantically preserving changes to the programs, these neural program models often fail to generalize their performance. Our results also suggest that neural program models based on data and control dependencies in programs generalize better than neural program models based only on abstract syntax trees. On the positive side, we observe that as the size of the training dataset grows and diversifies the generalizability of correct predictions produced by the neural program models can be improved too. Our results on the generalizability of neural program models provide insights to measure their limitations and provide a stepping stone for their improvement.
All-optical neuromorphic binary convolution with a spiking VCSEL neuron for image gradient magnitudes

All-optical binary convolution with a photonic spiking vertical-cavity surface-emitting laser (VCSEL) neuron is proposed and demonstrated experimentally for the first time, to the best of our knowledge. Optical inputs, extracted from digital images and temporally encoded using rectangular pulses, are injected in the VCSEL neuron, which delivers the convolution result in the number of fast ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mo form="prefix">&lt;</mml:mo> <mml:mn>100</mml:mn> <mml:mtext> </mml:mtext> <mml:mi>ps</mml:mi> </mml:mrow> </mml:math> long) spikes fired. Experimental and numerical results show that binary convolution is achieved successfully with a single spiking VCSEL neuron and that all-optical binary convolution can be used to calculate image gradient magnitudes to detect edge features and separate vertical and horizontal components in source images. We also show that this all-optical spiking binary convolution system is robust to noise and can operate with high-resolution images. Additionally, the proposed system offers important advantages such as ultrafast speed, high-energy efficiency, and simple hardware implementation, highlighting the potentials of spiking photonic VCSEL neurons for high-speed neuromorphic image processing systems and future photonic spiking convolutional neural networks.
Trends and Challenges in Network Covert Channels Countermeasures

Network covert channels are increasingly used to endow malware with stealthy behaviors, for instance to exfiltrate data or to orchestrate nodes of a botnet in a cloaked manner. Unfortunately, the detection of such attacks is difficult as network covert channels are often characterized by low data rates and defenders do not know in advance where the secret information has been hidden. Moreover, neutralization or mitigation are hard tasks, as they require to not disrupt legitimate flows or degrade the quality perceived by users. As a consequence, countermeasures are tightly coupled to specific channel architectures, leading to poorly generalizable and often scarcely scalable approaches. In this perspective, this paper investigates trends and challenges in the development of countermeasures against the most popular network covert channels. To this aim, we reviewed the relevant literature by considering approaches that can be effectively deployed to detect general injection mechanisms or threats observed in the wild. Emphasis has been put on enlightening trajectories that should be considered when engineering mitigation techniques or planning the research to face the increasing wave of information-hiding-capable malware. Results indicate that many works are extremely specialized and an effective strategy for taming security risks caused by network covert channels may benefit from high-level and general approaches. Moreover, mechanisms to prevent the exploitation of ambiguities should be already considered in early design phases of both protocols and services.
Variations in visual sensitivity predict motion sickness in virtual reality

Severity of motion sickness varies across individuals. While some experience immediate symptoms, others seem relatively immune. We explored a potential explanation for such individual variability based on cue conflict theory. According to cue conflict theory, sensory signals that lead to mutually incompatible perceptual interpretations will produce physical discomfort. A direct consequence of such theory is that individuals with greater sensitivity to visual (or vestibular) sensory cues should show greater susceptibility, because they would be more likely to detect a conflict. Using virtual reality (VR), we assessed individual sensitivity to a number of visual cues and subsequently induced moderate levels of motion sickness using stereoscopic movies presented in the VR headset. We found that an observer’s sensitivity to motion parallax cues predicted severity of motion sickness symptoms. We also evaluated evidence for another reported source of variability in motion sickness severity in VR, namely sex, but found little support. We speculate that previously reported sex differences might have been due to poor personalization of VR displays, which default to male settings and introduce cue conflicts for the majority of females. Our results identify a sensitivity-based sensory predictor of motion sickness, which can be used to personalize VR experiences and mitigate discomfort.
Penalty methods for a variational quantum eigensolver

The variational quantum eigensolver (VQE) is a promising algorithm to compute eigenstates and eigenenergies of a given quantum system that can be performed on a near-term quantum computer. Obtaining eigenstates and eigenenergies in a specific symmetry sector of the system is often necessary for practical applications of the VQE in various fields ranging from high energy physics to quantum chemistry. It is common to add a penalty term in the cost function of the VQE to calculate such a symmetry-resolving energy spectrum, but systematic analysis on the effect of the penalty term has been lacking, and the use of the penalty term in the VQE has not been justified rigorously. In this work, we investigate two major types of penalty terms for the VQE that were proposed in the previous studies. We show a penalty term in one of the two types works properly in that eigenstates obtained by the VQE with the penalty term reside in the desired symmetry sector. We further give a convenient formula to determine the magnitude of the penalty term, which may lead to the faster convergence of the VQE. Meanwhile, we prove that the other type of penalty terms does not work for obtaining the target state with the desired symmetry in a rigorous sense and even gives completely wrong results in some cases. We finally provide numerical simulations to validate our analysis. Our results apply to general quantum systems and lay the theoretical foundation for the use of the VQE with the penalty terms to obtain the symmetry-resolving energy spectrum of the system, which fuels the application of a near-term quantum computer.
Trajectory Optimization of 5-Link Biped Robot Using Beetle Antennae Search

In this brief, we presented a framework for the trajectory optimization of the 5-link Biped Robot using the Beetle Antennae Search (BAS) algorithm. The biped robot is highly non-linear and has complex dynamical modeling. It is challenging to obtain a closed-form solution for the Robot. The robot modeling has two stages, i.e., the optimal trajectory generation and the robust control of a robot. The conventional methodologies treat both problems separately and are computationally expensive. This brief presented an optimization problem that combines the trajectory generation and control problem of a 5-link biped robot. We employed Beetle Antennae Search (BAS) for the problem’s online-solution, an intelligent online optimization approach. BAS is a single-particle searching algorithm, which makes it robust, computationally, and memory-wise efficient. We applied BAS on two non-linear systems, i.e., inverted pendulum and 5-link biped robot, and convincing results are obtained with BAS.
ICT dynamics for gender inclusive intermediary education: Minimum poverty and inequality thresholds in developing countries

This study examines linkages between information and communication technology (ICT) dynamics, inequality and poverty in order to establish critical masses of poverty and inequality that should not be exceeded in order for ICT dynamics to promote gender inclusive education in 57 developing countries for the period 2012–2016. Poverty is measured with the poverty headcount ratio at national poverty lines (% of the population) while inequality is proxied by the Gini coefficient, the Atkinson index and the Palma ratio. The ICT dynamics are measured with 'internet access in school', 'virtual social network', 'personal computers' 'mobile phone penetration", 'internet penetration' and 'fixed broadband subscriptions'. The empirical evidence is based on interactive Generalized Method of Moments estimators from which thresholds are computed contingent on the validity of tested hypotheses. First, the Gini coefficient should not exceed 0.5618 in order for 'internet access in school' to positively affect inclusive education. Second, the poverty headcount ratio at national poverty lines (% of the population) should remain below 33.6842% in order for 'internet access in school' to favorably influence inclusive education. Third, the Palma ratio should not exceed 3.3766 in order for internet penetration to favorably affect inclusive education. Fourth, for personal computers to increase inclusive education, the Gini coefficient, Palma ratio and poverty headcount (% of the population) should not exceed 0.4781, 3.5294 and 17.7272, respectively. The study confirms the significant role technological deepening plays in advancing inclusive education by means of policies that reduce poverty and income inequality, with potentially wider applicability to other developing economies. The study has provided poverty and inequality levels that should not be exceeded in order for personal computers, internet penetration and 'internet access in school' to promote gender inclusive education.
Knowledge-Based Economy Capacity Building for Developing Countries: A Panel Analysis in Southern African Development Community

The Southern African Development Community is lagging behind in terms of knowledge economy relative to other regions worldwide. This dramatically reduces the chances of keeping up with their economically established counterparts in terms of sustainable development. This paper therefore, applies multivariate panel data analysis which is predicted on the Cobb–Douglas production function to analyze the affiliation flanked by knowledge-based economy pillars and economic growth from 1998–2018. The World Bank knowledge-based economy framework is employed. To achieve the study goal, the long-run effect regarding proxies of each pillar in the knowledge-based economy on economic growth is first estimated. Afterwards, the average impact of each pillar is examined using the average impact index (AII). Employment of both conventional unit root and co-integration tests showed all observed series are stationary and co-integrated. Further estimation of the long-run relationship using both static and dynamic models (fixed effect and generalized method of moment) portrayed that government effectiveness, adjusted savings on education expenditure, tertiary enrollment, scientific and technical journals, and mobile cellular subscriptions have significant positive impact on economic growth. Finally, the AII estimation unveiled that the innovation pillar is the most impactful aspect on economic growth followed by education and skills with the least being information and communication technology infrastructure. Feasible policy recommendations are further suggested.
Classification by ordinal sums of conjunctive and disjunctive functions for explainable AI and interpretable machine learning solutions

We propose a novel classification according to aggregation functions of mixed behaviour by variability in ordinal sums of conjunctive and disjunctive functions. Consequently, domain experts are empowered to assign only the most important observations regarding the considered attributes. This has the advantage that the variability of the functions provides opportunities for machine learning to learn the best possible option from the data. Moreover, such a solution is comprehensible, reproducible and explainable-per-design to domain experts. In this paper, we discuss the proposed approach with examples and outline the research steps in interactive machine learning with a human-in-the-loop over aggregation functions. Although human experts are not always able to explain anything either, they are sometimes able to bring in experience, contextual understanding and implicit knowledge, which is desirable in certain machine learning tasks and can contribute to the robustness of algorithms. The obtained theoretical results in ordinal sums are discussed and illustrated on examples.
Mobile Augmented Reality Heritage Applications: Meeting the Needs of Heritage Tourists

As competition intensifies, value co-creation with tourists is essential for successful implementation of mobile augmented reality (AR) heritage applications. This study therefore aims to identify heritage tourists’ needs and involvement when developing mobile AR heritage applications using a grounded theory approach. Since AR applications are still in their infancy in the tourism industry the grounded theory approach was employed. Fifty in-depth interviews were conducted in Macau’s World Heritage Sites. The interview transcripts were analyzed by the open coding method with the NVivo software, the process of axial coding and the selective coding method. This study generated new requirements for mobile AR heritage applications that reflect the needs of the Asian tourist market, which are different from those of the European tourist market. The characteristic of tourist empowerment and the association between co-creation and tourism were also identified. This study contributes to provide a theoretical framework for designing mobile AR heritage applications and has implications for mobile AR application developers and tourism practitioners.
Efficient State Preparation for Quantum Amplitude Estimation

Quantum Amplitude Estimation (QAE) can achieve a quadratic speed-up for applications classically solved by Monte Carlo simulation. A key requirement to realize this advantage is efficient state preparation. If state preparation is too expensive, it can diminish the quantum advantage. Preparing arbitrary quantum states has exponential complexity with respect to the number of qubits, thus, is not applicable. Currently known efficient techniques require problems based on log-concave probability distributions, involve learning an unknown distribution from empirical data, or fully rely on quantum arithmetic. In this paper, we introduce an approach to simplify state preparation, together with a circuit optimization technique, both of which can help reduce the circuit complexity for QAE state preparation significantly. We demonstrate the introduced techniques for a numerical integration example on real quantum hardware, as well as for option pricing under the Heston model, i.e., based on a stochastic volatility process, using simulation.