Reviewerly/exHarmony huggingface dataset:
Design of Real-time Semantic Segmentation Decoder for Automated Driving

Semantic segmentation remains a computationally intensive algorithm for embedded deployment even with the rapid growth of computation power. Thus efficient network design is a critical aspect especially for applications like automated driving which requires real-time performance. Recently, there has been a lot of research on designing efficient encoders that are mostly task agnostic. Unlike image classification and bounding box object detection tasks, decoders are computationally expensive as well for semantic segmentation task. In this work, we focus on efficient design of the segmentation decoder and assume that an efficient encoder is already designed to provide shared features for a multi-task learning system. We design a novel efficient non-bottleneck layer and a family of decoders which fit into a small run-time budget using VGG10 as efficient encoder. We demonstrate in our dataset that experimentation with various design choices led to an improvement of 10% from a baseline performance.
Toward Data Integrity Architecture for Cloud-Based AI Systems

AI has been implemented in many sectors such as security, health, finance, national defense, etc. However, together with AI’s groundbreaking improvement, some people exploit AI to do harmful things. In parallel, there is rapid development in cloud computing technology, introducing a cloud-based AI system. Unfortunately, the vulnerabilities in cloud computing will also affect the security of AI services. We observe that compromising the training data integrity means compromising the results in the AI system itself. From this background, we argue that it is essential to keep the data integrity in AI systems. To achieve our goal, we build a data integrity architecture by following the National Institute of Standards and Technology (NIST) cybersecurity framework guidance. We also utilize blockchain technology and smart contracts as a suitable solution to overcome the integrity issue because of its shared and decentralized ledger. Smart contracts are used to automate policy enforcement, keep track of data integrity, and prevent data forgery. First, we analyze the possible vulnerabilities and attacks in AI and cloud environments. Then we draw out our architecture requirements. The final result is that we present five modules in our proposed architecture that fulfilled NIST framework guidance to ensure continuous data integrity provisioning towards secure AI environments.
Distributed PageRank computation with improved round complexities

PageRank is a classic measure that effectively evaluates the importance of nodes in large graphs. It has been applied in numerous applications spanning data mining, Web algorithms, recommendation systems, load balancing, search and connectivity structures identification. Computing PageRank for large graphs is challenging and this has motivated the studies of distributed algorithms to compute PageRank. Previously, little works have been spent on the distributed PageRank algorithms with strong guarantees on both complexity and accuracy. In this paper, we focus on the theoretical aspect and study the complexity of distributed PageRank computation based on the well-known congested-clique model with a bandwidth generalization. An existing algorithm proposed by Sarma et al. (2015) can be applied in this model, which estimates PageRanks in an n-node graph using, with high probability, O(logn) communication rounds and a bandwidth of O((logn)3) bits. We present Radar-Push (RP), which is a distributed PageRank algorithm that is strictly better in round complexities. Specifically, Radar-Push uses O(loglogn) communication rounds and an edge bandwidth of O((logn)3) bits. We further show that Radar-Push can be adapted to efficiently compute an important variant of PageRank, namely, the batch one-hop personalized PageRank, in O(loglogn) communication rounds.
Vision‐based displacement measurement enhanced by super‐resolution using generative adversarial networks

Monitoring the deformation or displacement response of buildings is critical for structural safety. Recently, the development of computer vision has led to extensive research on the application of vision-based measurements in the structural monitoring. This enables the use of urban surveillance video cameras, which are widely installed and can produce numerous images and videos of urban scenes to measure the structural displacement. However, the structural displacement measurement may be inaccurate owing to the limited hardware resolution of the surveillance video cameras or the long distance from the cameras to the monitored targets. To this end, this study proposes a method to improve the displacement measurement accuracy using a deep learning super-resolution model based on generative adversarial networks. The proposed method achieves texture detail enhancement of low-resolution images or videos by supplementing high-resolution photographs of the target, thus improving the accuracy of the vision-based displacement measurement. The proposed method shows good accuracy and stability in both the static and dynamic experimental validations compared with the original low-resolution images/video and interpolation-based super-resolution images/video. In conclusion, the proposed method can support the displacement measurement of buildings and infrastructures based on urban surveillance video cameras.
Attention Responsive Technology and Ergonomics

This is a conference paper. The definitive version is available at: http://www.taylorandfrancis.co.uk/
A Design Framework of Virtual Reality Enabled Experiential Learning for Children with Autism Spectrum Disorder

Experiential learning is one of the most widely applied learning theories for virtual reality enabled learning, because virtual reality environment, especially immersive virtual reality environment, allows learners to actively experiment and reflectively observe in a safe and authentic environment. Our previous studies have shown that experiential learning could also be applied within a virtual reality learning environment for children with autism spectrum disorder (ASD) in various learning domains. However, there is a lack of in-depth discussion on how to systematically design, engineer and evaluate virtual reality enabled experiential learning activities for children with ASD and other complications (e.g., anxiety disorder, attention deficit hyperactivity disorder (ADHD), etc.). In this paper, we share our experience on applying experiential learning theory on the design of social competence learning in the immersive virtual reality environment for the children ASD, in order to address two fundamental questions. First, how different immersive virtual reality environments, such as the CAVE and head-mounted displays, affect the design of the virtual reality learning scenarios. Second, how the virtual reality learning contents can be designed to facilitate experiential learning in an immersive virtual reality environment for the ASD population. Our small-scale study shows that children with ASD often need facilitation during experiential learning in the immersive virtual reality environment and our design of providing in-VR facilitation can meet their learning needs.
WORST-CASE EVALUATION COMPLEXITY AND OPTIMALITY OF SECOND-ORDER METHODS FOR NONCONVEX SMOOTH OPTIMIZATION

We establish or refute the optimality of inexact second-order methods for unconstrained nonconvex optimization from the point of view of worst-case evaluation complexity, improving and generalizing the results of Cartis, Gould and Toint (2010,2011). To this aim, we consider a new general class of inexact second-order algorithms for unconstrained optimization that includes regularization and trust-region variations of Newton's method as well as of their linesearch variants. For each method in this class and arbitrary accuracy threshold $\epsilon \in (0,1)$, we exhibit a smooth objective function with bounded range, whose gradient is globally Lipschitz continuous and whose Hessian is $\alpha-$H\older continuous (for given $\alpha\in [0,1]$), for which the method in question takes at least $\lfloor\epsilon^{-(2+\alpha)/(1+\alpha)}\rfloor$ function evaluations to generate a first iterate whose gradient is smaller than $\epsilon$ in norm. Moreover, we also construct another function on which Newton's takes $\lfloor\epsilon^{-2}\rfloor$ evaluations, but whose Hessian is Lipschitz continuous on the path of iterates. These examples provide lower bounds on the worst-case evaluation complexity of methods in our class when applied to smooth problems satisfying the relevant assumptions. Furthermore, for $\alpha=1$, this lower bound is of the same order in $\epsilon$ as the upper bound on the worst-case evaluation complexity of the cubic and other methods in a class of methods proposed in Curtis, Robinson and samadi (2017) or in Royer and Wright (2017), thus implying that these methods have optimal worst-case evaluation complexity within a wider class of second-order methods, and that Newton's method is suboptimal.
Comment on “An image encryption algorithm based on modified logistic chaotic map”

A modified logistic map is presented by Han [1] that claims to overcome the issue of the small key space, by exhibiting infinite key space. This paper presents the analysis of the map for a large range of positive and negative control parameter. A strikingly divergent dynamic nature is observed, which is in contrast to the properties proposed in Han (2018). This paper has facilitated to identify the non-chaotic range in the modified logistic map with bifurcation diagram, Lyaponov exponent and phase portrait, which emphasizes the infeasibility of the key space. Therefore, a key which is randomly chosen from the modified logistic map may be in the non-chaotic band. Consequently, this leads to a weak image encryption, which makes it vulnerable to security attacks.
Secure File Sharing System Using Blockchain, IPFS and PKI Technologies

People are dependent on Trusted Third Party (TTP) administration based Centralized systems for content sharing having a deficit of security, faith, immutability, and clearness. This work has proposed a file-sharing environment based on Blockchain by clouting the Interplanetary File System (IPFS) and Public Key Infrastructure (PKI) systems, advantages for overcoming these troubles. The smart contract is implemented to control the access privilege and the modified version of IPFS software is utilized to enforce the predefined access-control list. An application framework on a secure decentralized file sharing system is presented in combination with IPFS and PKI to secure file sharing. PKI having public and private keys is used to enable encryption and decryption of every file transaction and authentication of identities through Metamask to cryptographically recognize account ownership in the Blockchain system. A gas consumption-based result analysis is done in the private Ethereum network and it attains transparency, security managed access, and quality of data indicating better efficacy of this work.
Two-layer integrated photonic architectures with multiport photodetectors for high-fidelity and energy-efficient matrix multiplications

Photonic integrated circuits (PICs) are emerging as a promising tool for accelerating matrix multiplications in deep learning. Previous PIC architectures, primarily focusing on the matrix-vector multiplication (MVM), have large hardware errors that increase with the device scale. In this work, we propose a novel PIC architecture for MVM, which features an intrinsically small hardware error that does not increase with the device scale. Moreover, we further develop this concept and propose a PIC architecture for the general matrix-matrix multiplication (GEMM), which allows the GEMM to be directly performed on a photonic chip with a high energy efficiency unattainable by parallel or sequential MVMs. This work provides a promising approach to realize a high fidelity and high energy efficiency optical computing platform.
Tsallis-Entropy Segmentation through MRF and Alzheimer anatomic reference for Brain Magnetic Resonance Parcellation

Quantifying the intracranial tissue volume changes in magnetic resonance imaging (MRI) assists specialists to analyze the effects of natural or pathological changes. Since these changes can be subtle, the accuracy of the automatic compartmentalization method is always criticized by specialists. We propose and then evaluate an automatic segmentation method based on modified q-entropy (Mqe) through a modified Markov Random Field (MMRF) enhanced by Alzheimer anatomic reference (AAR) to provide a high accuracy brain tissues parcellation approach (Mqe-MMRF). We underwent two strategies to evaluate Mqe-MMRF; a simulation of different levels of noise and non-uniformity effect on MRI data (7 subjects) and a set of twenty MRI data available from MRBrainS13 as patient brain tissue segmentation challenge. We accessed eleven quality metrics compared to reference tissues delineations to evaluate Mqe-MMRF. MRI segmentation scores decreased by only 4.6% on quality metrics after noise and non-uniformity simulations of 40% and 9%, respectively. We found significant mean improvements in the metrics of the five training subjects, for whole-brain 0.86%, White Matter 3.20%, Gray Matter 3.99%, and Cerebrospinal Fluid 4.16% (p-values < 0.02) when Mqe-MMRF compared to the other reference methods. We also processed the Mqe-MMRF on 15 evaluation subjects group from MRBrainS13 online challenge, and the results held a higher rank than the reference tools; FreeSurfer, SPM, and FSL. Since the proposed method improved the precision of brain segmentation, specifically, for GM, and thus one can use it in quantitative and morphological brain studies.
Tecnologías digitales como recurso pedagógico y su integración curricular en la formación inicial docente en Chile

The objective of this study was to characterize the modalities of integration of digital technologies into Chilean initial teacher training and to identify the variables associated with the adoption of a particular modality, based on the study programs of all Elementary and Secondary Pedagogy programs currently admitting students. A content analysis of the information available from all the elementary and secondary pedagogy programs, informed by the Ministry of Education of Chile (n = 225), was conducted in order to identify ICT content in the curriculum and the graduate profiles. We found that there were four modalities of integration: only in graduate profiles (16%), only in subjects (26.2%), in profiles and subjects (22.7%), and no integration (35.1%). We also found that these profiles were distributed homogenously, without differentiation in terms of tuition fees, years of accreditation, proportion of lecturers with a doctorate, specialty (elementary or secondary education), membership of the Council of Rectors of Chilean Universities, the ratio of computers to students, and the proportion of full-time lecturers. It was only possible to identify a significant difference by geographical location, where the north of the country shows a significantly higher proportion of non-integration. These results indicate that a high proportion of study programs do not integrate ICT at all, even at an introductory level, which, in the light of the discussion on public ICT policy in schools, can be interpreted as an inadequate starting point for the initiation of plans that require both advanced ICT skills and the development of skills for implementation in the classroom.
INTEGRATION OF AUGMENTED REALITY IN LEARNING CHEMISTRY: A PATHWAY FOR REALIZATION OF INDUSTRIAL REVOLUTION 4.0 GOALS

The rapid development and adoption of digital and recent technologies in every aspect of human endeavour and the transformation of industries call for industrial revolution 4.0 where industries strive to establish an intelligent system in order to realize the best business results. This industrial revolution is underway focusing on digital transformation and creating an internet of things. The augmented reality is a computer-based technology where digital information, contents, and graphics are incorporated and overlaid which is in line with education 4.0 as a reflection of industry 4.0. The technology may serve as a pathway for realizing both education and industry 4.0 as many authors recognized it, one of the nine key components for this revolution. To achieve this, integrating augmented reality in teaching chemistry play significant roles as it is expected to enhance the 21st-century skills such as critical thinking, problem-solving, creativity and collaboration skills. It was in light of this, this paper discusses how the integration of augmented reality in learning chemistry empowers students to perform various tasks and functions in industrial automation through the use of those skills. The paper concludes by describing chemistry knowledge combined with the 21st-century skills as the driving force for transforming raw materials into required products and services for attaining industrial revolution 4.0 goals. Some challenging issues that may be faced in integrating the augmented reality were raised for proper precautions.
A Decentralized Secure Mailbox System based on Blockchain

Nowadays, the communication services provided by the e-mail system are used all over the world for formal communication and business negotiation. The traditional e-mail systems provide services based on their centralized servers. If the centralized servers are attacked, the confidential message and the private information will be leaked to the attacker. To this end, this paper uses blockchain technology to build a decentralized e-mail system with trust and accountability. To improve the PBFT algorithm which is widely-used for blockchain consensus and reduce the bad influence brought by the abnormal blockchain nodes, this work proposes a node quality control strategy for blockchain consensus nodes. Moreover, this paper designs the blockchain-based evidence storage module and dispute resolution module to serve our mailbox users. The experiment results show that the blockchain-based e-mail system proposed in this paper can achieve a high-quality consensus mechanism with the node quality control strategy and reduce the transaction delay time.
Evaluation of clear-sky and satellite-derived irradiance data for determining the degradation of photovoltaic system performance

Knowing the degradation in performance of a photovoltaic (PV) system over time is important for estimating the lifetime energy produced and the financial return. A key parameter for normalizing performance and determining degradation is the plane-of-array (POA) irradiance. Because accurate long-term POA measurements are not always readily available, three methods of providing irradiance data for determining the degradation rate of PV systems were evaluated—a method using irradiance data modeled with the Ineichen clear-sky model and monthly Linke turbidity coefficients, a method using the supplemental clear-sky irradiance data from the National Solar Radiation Data Base (NSRDB), and a method using the NSRDB solar irradiance data for both cloudy and clear-sky conditions (all-sky). The irradiance data from the three methods were evaluated using measured irradiance data from 1998 through 2018 for the seven-station SURFRAD network and for 3-, 5-, and 10-year periods that might be used for evaluating PV system performance. Only the two clear-sky methods for the 10-year periods had less uncertainty with respect to determining PV system degradation than the expected median degradation rate for PV systems of −0.5%/year to −0.6%/year. Shorter periods and the all-sky method had larger uncertainties, making their use questionable for determining the degradation rates of PV systems.
Generative Adversarial Network for Text-to-Face Synthesis and Manipulation with Pretrained BERT Model

This work proposes a cyclic generative adversarial network with spatial-wise and channel-wise attention modules for text-to-face synthesis and manipulation. Then, we explore the pre-trained transformer-based BERT model to obtain text embedding. Furthermore, dual-layer perceptual loss and SSIM loss are introduced to reinforce the delicate features and preserve facial identity during the manipulation task. Additionally, we adopt a novel Flickr-Faces-HQ with Text descriptions (FFHQ-Text) dataset with numerous facial attribute annotations to advance the development of the text-to-face task. In particular, by introducing the StyleGAN encoder for learning latent representations to our proposed post-processing method, we demonstrate that even training on a smaller text-to-face dataset can synthesize more realistic images. Experimental results reveal the effectiveness of our approach, which generates photo-realistic facial images, edits the specific facial attribute with the correlated keywords manipulation, outperforms previous state-of-the-art methods both in quality and quantity, and suggests promising future directions.
Incremental Learning in Semantic Segmentation from Image Labels

Although existing semantic segmentation approaches achieve impressive results, they still struggle to update their models incrementally as new categories are uncovered. Furthermore, pixel-by-pixel annotations are expensive and time-consuming. This paper proposes a novel framework for Weakly Incremental Learning for Semantic Segmentation, that aims at learning to segment new classes from cheap and largely available image-level labels. As opposed to existing approaches, that need to generate pseudolabels offline, we use a localizer, trained with image-level labels and regularized by the segmentation model, to obtain pseudo-supervision online and update the model incrementally. We cope with the inherent noise in the process by using soft-labels generated by the localizer. We demonstrate the effectiveness of our approach on the Pascal VOC and COCO datasets, outperforming offline weakly-supervised methods and obtaining results comparable with incremental learning methods with full supervision. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code can be found at https://github.com/fcd194/WILSON.