Reviewerly/exHarmony huggingface dataset:
Delay-weight plasticity-based supervised learning in optical spiking neural networks

We propose a modified supervised learning algorithm for optical spiking neural networks, which introduces synaptic time-delay plasticity on the basis of traditional weight training. Delay learning is combined with the remote supervised method that is incorporated with photonic spike-timing-dependent plasticity. A spike sequence learning task implemented via the proposed algorithm is found to have better performance than via the traditional weight-based method. Moreover, the proposed algorithm is also applied to two benchmark data sets for classification. In a simple network structure with only a few optical neurons, the classification accuracy based on the delay-weight learning algorithm is significantly improved compared with weight-based learning. The introduction of delay adjusting improves the learning efficiency and performance of the algorithm, which is helpful for photonic neuromorphic computing and is also important specifically for understanding information processing in the biological brain.
CapsNet regularization and its conjugation with ResNet for signature identification

• We proposed a regularized version of CapsNet aiming for more generalization and overfitting avoidance. • Adding regularized term leads to removing the decoder in the baseline CapsNet that causes a noticeable reduction of learning parameters and fast convergence. • We also proposed a conjugation of regularized CapsNet with ResNet dealing with the small number of training samples or the input images with a large size. • We evaluated our approach over three well-known publicly available datasets. We propose a new regularization term for CapsNet that significantly improves the generalization power of the original method from small training data while requiring much fewer parameters, making it suitable for large input images. We also propose a very efficient DNN architecture that integrates CapsNet with ResNet to obtain the advantages of the two architectures. CapsNet allows a powerful understanding of the objects’ components and their positions, while ResNet provides efficient feature extraction and description. Our approach is general, and we demonstrate it on the problem of signature identification from images. To show our approach superiority, we provide several evaluations with different protocols. We also show that our approach outperforms the state-of-the-art on this problem with thorough experiments on three publicly available datasets CEDAR, MCYT, and UTSig.
A deep hybrid neural network for single image dehazing via wavelet transform

Image dehazing is a fast-growing research area in image processing and computer vision. Due to the extreme fog, haze, and air dispersion within an environment, the hazy image raises several challenges in retrieving original image information type. However, past techniques endure massive computation complexity and even the distortion of original images such as halos and over-saturation. In this research work, a new wavelet Hybrid (Local-Global Combined) Network is proposed for single image dehazing using a convolution neural network (CNN) in the wavelet domain (WH-Net). It is observed that low-level features such as edges are more important than high-level features such as texture. So we have used 2-DWT to decompose the single image model into the frequency subbands, which performs more quickly. It is demonstrated that the estimation of wavelet sub-bands reformulates the trainable end-to-end learning with a special architecture where DWT and IDWT are the feature extraction layers instead of Conv and Deconv, distinguishing it from classical CNN networks. The WHNet method is designed to achieve multi-level representations of hazy images to provide local and global information. The proposed network prominent features are designed with fewer convolution layers without decreasing performance relative to the commonly observed deeper learning models. Compared to several state-of-the-art algorithms, our proposed WHNet neural network surpasses in visual and quantitative performances on three public datasets.
Blockchain architecture for automotive traceability

The automotive value chain comprises a complex manufacturing network, which encompasses several departments, companies, and even countries. In the age of electrification and autonomous driving, the quality and transparency requirements are rising, in particular for electric and electronic components, which contribute to safety-critical functionalities. In this research, we analyze the traceability requirements and liability implications for the automotive manufacturing network. A key component in preventing safety and quality deficiencies in manufacturing processes is a state-of-the-art traceability system. Accordingly, we propose a blockchain-based traceability architecture to achieve the aspired level of transparency and process security. The proposed blockchain architecture processes manufacturing data entries and defines blockchain nodes, access, as well as consensus algorithm and transaction logic. An Ethereum-based blockchain demonstrator validates the architecture by implementing the logic for automotive product configuration and process sequencing based on the semi-fungible ERC 1155 token, the proof of authority consensus, and an automotive product token smart contract.
High-Speed and Unified ECC Processor for Generic Weierstrass Curves over GF(p) on FPGA

In this paper, we present a high-speed, unified elliptic curve cryptography (ECC) processor for arbitrary Weierstrass curves over GF(p), which to the best of our knowledge, outperforms other similar works in terms of execution time. Our approach employs the combination of the schoolbook long and Karatsuba multiplication algorithm for the elliptic curve point multiplication (ECPM) to achieve better parallelization while retaining low complexity. In the hardware implementation, the substantial gain in speed is also contributed by our n-bit pipelined Montgomery Modular Multiplier (pMMM), which is constructed from our n-bit pipelined multiplier-accumulators that utilizes digital signal processor (DSP) primitives as digit multipliers. Additionally, we also introduce our unified, pipelined modular adder/subtractor (pMAS) for the underlying field arithmetic, and leverage a more efficient yet compact scheduling of the Montgomery ladder algorithm. The implementation for 256-bit modulus size on the 7-series FPGA: Virtex-7, Kintex-7, and XC7Z020 yields 0.139, 0.138, and 0.206 ms of execution time, respectively. Furthermore, since our pMMM module is generic for any curve in Weierstrass form, we support multi-curve parameters, resulting in a unified ECC architecture. Lastly, our method also works in constant time, making it suitable for applications requiring high speed and SCA-resistant characteristics.
Cluster adaptation networks for unsupervised domain adaptation

Domain adaptation is an important technology for transferring source domain knowledge to new, unseen target domains. Recently, domain adaptation models are applied to learn domain invariant representations by minimizing distribution distance or adversarial training in the feature space. However, existing adversarial domain adaptation methods fail to preserve the data structure in the feature space. In this paper, we propose a novel domain adaptation method called Cluster adaptation Networks (CAN). CAN decreases the domain shift by aligning the category centers of source representations and the cluster centers of target representations in the feature space, which preserves the class-level structure and facilitates the classification of the target domain. Experiments on Digits, Office-Home and ImageCLEF-DA datasets validate the effectiveness of the structure preservation in our model.
VERITAS: Accurately Estimating the Correct Output on Noisy Intermediate-Scale Quantum Computers

Noisy Intermediate-Scale Quantum (NISQ) machines are being increasingly used to develop quantum algorithms and establish use cases for quantum computing. However, these devices are highly error-prone and produce output, which can be far from the correct output of the quantum algorithm. In this paper, we propose VERITAS, an end-to-end approach toward designing quantum experiments, executing experiments, and correcting outputs produced by quantum circuits post their execution such that the correct output of the quantum algorithm can be accurately estimated.
The Role of Employees’ Information Security Awareness on the Intention to Resist Social Engineering

Social engineering is a form of attack trying to manipulate employees to make them disclose confidential information or perform actions that threatens the security of organizations. The goal of this paper is to study both individual and organizational factors that affect information security awareness of employees and how this leads to intention to resist social engineering attacks. The proposed research model is validated using survey data of 136 employees. The empirical results suggest that leadership and the tendency toward risky behavior are influencing information security awareness of employees. Information security awareness was confirmed as a central factor for information security, whereby the promotion of awareness for information security is indicated as an important aspect to protect a company from potential attacks. The impact of information security awareness on attitude, perceived behavior control and subjective norm in addition to the indirect effect on the intention to resist social engineering, underline the importance of this factor.
Introduction: ways of machine seeing

How do machines, and, in particular, computational technologies, change the way we see the world? This special issue brings together researchers from a wide range of disciplines to explore the entanglement of machines and their ways of seeing from new critical perspectives.This 'editorial' is for a special issue of AI & Society, which includes contributions from: Maria Jesus Schultz Abarca, Peter Bell, Tobias Blanke, Benjamin Bratton, Claudio Celis Bueno, Kate Crawford, Iain Emsley, Abelardo Gil-Fournier, Daniel Chavez Heras, Vladan Joler, Nicolas Maleve, Lev Manovich, Nicholas Mirzoeff, Perle Mohl, Bruno Moreschi, Fabian Offert, Trevor Paglan, Jussi Parikka, Luciana Parisi, Matteo Pasquinelli, Gabriel Pereira, Carloalberto Treccani, Rebecca Uliasz, and Manuel van der Veen.
A Convolutional Neural Network-based Ancient Sundanese Character Classifier with Data Augmentation

With an increasing interest in the digitization effort of ancient manuscripts, ancient character recognition becomes one of the most important areas in the automated document image analysis. In this regard, we propose a Convolutional Neural Network (CNN)-based classifier to recognize the ancient Sundanese characters obtained from a digital collection of Southeast Asian palm leaf manuscripts. In this work, we utilize two different preprocessing techniques for the dataset. The first technique involves the use of geometric transformations, noise background addition, and brightness adjustment to augment the imbalanced samples to be fed into the classifier. The second technique makes use of the Otsu's threshold method to binarize the characters and only uses the usual geometric transformations for the data augmentation. The proposed network with different data augmentation processes is trained on the training set and tested on the testing set. Image binarization from the second technique can outperform the performance of the CNN-based classifier upon the first technique by achieving a testing accuracy of 97.74%.
Looking for the Detail and Context Devils: High-Resolution Salient Object Detection

In recent years, Salient Object Detection (SOD) has shown great success with the achievements of large-scale benchmarks and deep learning techniques. However, existing SOD methods mainly focus on natural images with low-resolutions, e.g., $400\times 400$ or less. This drawback hinders them for advanced practical applications, which need high-resolution, detail-aware results. Besides, lacking of the boundary detail and semantic context of salient objects is also a key concern for accurate SOD. To address these issues, in this work we focus on the High-Resolution Salient Object Detection (HRSOD) task. Technically, we propose the first end-to-end learnable framework, named Dual ReFinement Network (DRFNet), for fully automatic HRSOD. More specifically, the proposed DRFNet consists of a shared feature extractor and two effective refinement heads. By decoupling the detail and context information, one refinement head adopts a global-aware feature pyramid. Without increasing too much computational burden, it can boost the spatial detail information, which narrows the gap between high-level semantics and low-level details. In parallel, the other refinement head adopts hybrid dilated convolutional blocks and group-wise upsamplings, which are very efficient in extracting contextual information. Based on the dual refinements, our approach can enlarge receptive fields and obtain more discriminative features from high-resolution images. Experimental results on high-resolution benchmarks (the public DUT-HRSOD and the proposed DAVIS-SOD) demonstrate that our method is not only efficient but also performs more accurate than other state-of-the-arts. Besides, our method generalizes well on typical low-resolution benchmarks.
What are the main drivers of Blockchain Adoption within Supply Chain? – an exploratory research

This paper aims to identify the main drivers for Blockchain adoption within Supply Chains, following the Design Science Research (DSR) methodology and focusing on defining, validating, and reducing a myriad of factors with the aid of experts on both fields. Based on the literature review, and through means of questionnaires, factors were rated and refuted according to the relevance given. Two rounds narrowed the results, and upon consensus nine drivers were identified fitting under two categories: Challenges and Incentives. Considering the results are based on a limited number of factors, and the impact of firm-level influences were disregarded, respondent’s perceptions could have slightly varied if the sample of participants differed. Overall, it provides academics with a theoretical framework combining existing literature into a set of drivers. Providing insights to vendors on how their reputation may influence clients’ adoption, whilst fulfilling a literature gap in the supply chain area.
Metrology-Based Design of a Wearable Augmented Reality System for Monitoring Patient’s Vitals in Real Time

In this work, an augmented-reality (AR) system for monitoring patient's vitals in real time during surgical procedures is proposed and metrologically characterized in terms of transmission error rates and latency. These specifications, in fact, are crucial to ensure real-time response which is a critical requirement for Health applications. The proposed system automatically acquires the vitals from the operating room instrumentation, and shows them in real time directly on a set of wearable AR glasses. In this way, the surgical team has a real-time visualization of a comprehensive set of patient's information, without constantly looking at the instrumentation. The system was designed to ensure modularity, flexibility, ease of use and, most importantly, a reliable communication. The system was designed, implemented and validated experimentally through on-field tests carried out at the Academic Hospital of Federico II University, using instrumentation typically available in the operating room: namely, a respiratory ventilator and a patient monitor. Results of the experimental validation highlight the need for the metrological approach in conceiving advanced monitoring surgical procedures in operating room.
Inspecting Edge Data Integrity With Aggregate Signature in Distributed Edge Computing Environment

In the edge computing environment, app vendors can cache their data on a large number of geographically distributed edge servers to serve their users. However, those cached data are particularly vulnerable to both intentional and accidental corruption, which makes data security a major concern in the EC environment. Given limited computing resources of edge servers, how to effectively and efficiently inspect those data over tremendous edge servers is a critical and open problem. To tackle this edge data integrity (EDI) problem, we first study the entities, threats, system objectives, and the inspection mechanism, then propose a novel approach named EDI-S for inspecting the integrity of edge data and localizing the corrupted ones. Based on the elliptic curve cryptography, EDI-S generates one digital signature as the integrity proof for each replica. Then, multiple integrity proofs can be inspected altogether via an aggregate verification. This allows the integrity of tremendous cache data on multiple edge servers can be inspected more efficiently. EDI-S also provides two methods for localizing the corrupted data on edge servers, one for small-scale scenarios and the other for large-scale scenarios. Both theoretical analysis and experimentally evaluation demonstrate that EDI-S can solve the EDI problem effectively and efficiently.
Privacy preservation of electronic health records with adversarial attacks identification in hybrid cloud

An increasing trend in healthcare organizations to outsource EHRs’ data to the cloud highlights new challenges regarding the privacy of given individuals. Healthcare organizations outsource their EHRs data in a hybrid cloud that elevates the problem of security and privacy in terms of EHRs’ access to an unlimited number of recipients in a hybrid cloud environment. In this paper, we investigated the need for a privacy-preserving access control model for the hybrid cloud. A comprehensive and exploratory analysis of privacy-preserving solutions with the help of taxonomy for cloud-based EHRs is described in this work. We have formally identified the existence of internal access control and external privacy disclosures in outsourcing system architecture for hybrid cloud. Then, we proposed a privacy-preserving XACML based access control model (PPX-AC) that supports fine-grained access control with the multipurpose utilization of EHRs alongside state-of-the-art privacy mechanism. Our proposed approach invalidates the identified security and privacy attacks. We have formally verified the proposed privacy-preserving XACML based access control model (PPX-AC) with the invalidation of identified privacy attacks using High-Level Petri Nets (HLPN). Moreover, property verification of the proposed model in SMT-lib and Z3 solver and implementation of the model proves its effectiveness in terms of privacy-aware EHRs access and multipurpose usage.
The Impact of the Virtual Learning Platform EDUKA on the Academic Performance of Primary School Children

The modern teaching/learning environment is, like never before, rich with digital teaching/learning technologies and tools that are becoming part of children’s daily lives. Background: In Lithuania, virtual teaching/learning platforms (environments for mathematics, knowledge of nature, history, and language practice) in primary education became more widely used approximately three years ago after the implementation and application of the virtual teaching/learning platform EDUKA. The purpose of this study was to establish the effect of the virtual teaching/learning platform EDUKA on the learning outcomes of primary-grade students in the subject of mathematics. Methods: In this study, a pre-test/middle-test/post-test experimental strategy was used to avoid any disruption of educational activities due to the random selection of children in each group. Mathematical diagnostic progress tests (MDPTs) are an objective way to measure skills and abilities. The MDPTs were divided into two sections: the tasks were allocated according to performance levels and the content, as well as fields of activity and cognitive skills. The assessment of all areas of activity was based on the primary school children’s performance (i.e., unsatisfactory, satisfactory, basic, and advanced). Results: An analysis of the results of the MDPTs showed that, across the seven possible tasks, both male and female seven-year-old children achieved satisfactory results (results were observed between groups) (post-test: control gathering (CG) 5.10; test gathering (EG) 5.04; p = 0.560), basic results (post-test: CG 6.28; EG 6.42; p = 0.630), and advanced results (post-test: CG 1.90; EG 2.27; p = 0.025). The differences between the pre-test and post-test advanced (p = 0.038) and the pre-test and post-test basic (p = 0.018) levels were found to increase. Conclusions: It was found that intensively integrating the virtual learning platform EDUKA into formal education—specifically in the subject of mathematics—had a significant impact on primary school children’s mathematical performance. In addition, after the experiment, a statistically significant difference was found (p &lt; 0.05) in primary school children with higher levels. The intervention in the experimental group (i.e., integration of the virtual learning platform into the formal mathematics learning process) had a positive impact on access to mathematics. Students’ math learning achievements were positive in progressive mathematics.
The Machine Learning Machine: A Tangible User Interface for Teaching Machine Learning

Machine Learning (ML) is often used invisibly in everyday applications with little opportunity for consumers to investigate how it works. In this paper, we expand recent efforts to unfold what students should know about ML and how to design tools and activities allowing them to engage with ML. To do so, we explore how to make processes and aspects of ML tangible through the design of the Machine Learning Machine (MLM); a tangible user interface which enables students to create their own data-sets using pen and paper and to iteratively build and test ML models using this data. Based on insights from the design process and a preliminary pilot study with the MLM, we discuss how a tangible approach to engaging with ML can spur curiosity in students and how the iterative process of improving ML models can encourage students to reflect on the relation between data, model and predictions.
PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference

In the present paper, we propose an extension of the Deep Planning Network (PlaNet), also referred to as PlaNet of the Bayesians (PlaNet-Bayes). There has been a growing demand in model predictive control (MPC) in partially observable environments in which complete information is unavailable because of, for example, lack of expensive sensors. PlaNet is a promising solution to realize such latent MPC, as it is used to train state-space models via model-based reinforcement learning (MBRL) and to conduct planning in the latent space. However, recent state-of-the-art strategies mentioned in MBRR literature, such as involving uncertainty into training and planning, have not been considered, significantly suppressing the training performance. The proposed extension is to make PlaNet uncertainty-aware on the basis of Bayesian inference, in which both model and action uncertainty are incorporated. Uncertainty in latent models is represented using a neural network ensemble to approximately infer model posteriors. The ensemble of optimal action candidates is also employed to capture multimodal uncertainty in the optimality. The concept of the action ensemble relies on a general variational inference MPC (VI-MPC) framework and its instance, probabilistic action ensemble with trajectory sampling (PaETS). In this paper, we extend VI-MPC and PaETS, which have been originally introduced in previous literature, to address partially observable cases. We experimentally compare the performances on continuous control tasks, and conclude that our method can consistently improve the asymptotic performance compared with PlaNet.