Reviewerly/exHarmony huggingface dataset:
Towards Post-disaster Damage Assessment using Deep Transfer Learning and GAN-based Data Augmentation

Cyber-physical disaster systems (CPDS) are a new cyber-physical application that collects physical realm measurements from IoT devices and sends them to the edge for damage severity analysis of impacted sites in the aftermath of a large-scale disaster. However, the lack of effective machine learning paradigms and the data and device heterogeneity of edge devices pose significant challenges in disaster damage assessment (DDA). To address these issues, we propose a generative adversarial network (GAN) and a lightweight, deep transfer learning-enabled, fine-tuned machine learning pipeline to reduce overall sensing error and improve the model's performance. In this paper, we applied several combinations of GANs (i.e., DCGAN, DiscoGAN, ProGAN, and Cycle-GAN) to generate fake images of the disaster. After that, three pre-trained models: VGG19, ResNet18, and DenseNet121, with deep transfer learning, are applied to classify the images of the disaster. We observed that the ResNet18 is the most pertinent model to achieve a test accuracy of 88.81%. With the experiments on real-world DDA applications, we have visualized the damage severity of disaster-impacted sites using different types of Class Activation Mapping (CAM) techniques, namely Grad-CAM++, Guided Grad-Cam, & Score-CAM. Finally, using k-means clustering, we have obtained the scatter plots to measure the damage severity into no damage, mild damage, and severe damage categories in the generated heat maps.
Periodic plane-wave electronic structure calculations on quantum computers

Abstract A procedure for defining virtual spaces, and the periodic one-electron and two-electron integrals, for plane-wave second quantized Hamiltonians has been developed, and it was validated using full configuration interaction (FCI) calculations, as well as executions of variational quantum eigensolver (VQE) circuits on Quantinuum’s ion trap quantum computers accessed through Microsoft’s Azure Quantum service. This work is an extension to periodic systems of a new class of algorithms in which the virtual spaces were generated by optimizing orbitals from small pairwise CI Hamiltonians, which we term as correlation optimized virtual orbitals with the abbreviation COVOs. In this extension, the integration of the first Brillouin zone is automatically incorporated into the two-electron integrals. With these procedures, we have been able to derive virtual spaces, containing only a few orbitals, that were able to capture a significant amount of correlation. The focus in this manuscript is on comparing the simulations of small molecules calculated with plane-wave basis sets with large periodic unit cells at the $$\Gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Γ</mml:mi> </mml:math> -point, including images, to results for plane-wave basis sets with aperiodic unit cells. The results for this approach were promising, as we were able to obtain good agreement between periodic and aperiodic results for an LiH molecule. Calculations performed on the Quantinuum H1-1 quantum computer produced surprisingly good energies, in which the error mitigation played a small role in the quantum hardware calculations and the (noisy) quantum simulator results. Using a modest number of circuit runs (500 shots), we reproduced the FCI values for the 1 COVO Hamiltonian with an error of 11 milliHartree, which is expected to improve with a larger number of circuit runs.
Complexity of Smart Home Setups: A Qualitative User Study on Smart Home Assistance and Implications on Technical Requirements

Setup and management of smart home systems is a complex task, and thus challenging for technically inexperienced users. We conducted a qualitative user study to evaluate whether an assistance system could empower users to make better and informed decisions regarding the selection of devices, their interoperability, the resulting set of features and their price. A group of 20 participants used our assistance app on a smartphone to configure a smart home while optimizing for features, interoperability, and the price-performance ratio. The results of our user study show that our assistance app can ease the problem of selecting useful devices and at the same time users become aware of new features resulting from the interoperation of selected devices. Furthermore, the assistance app can counteract the inherent interoperability problem between devices of different vendors or platforms. Finally, users are not only interested in individual device prices. They want to learn the cost of a certain feature set, including the cost of all devices necessary to realize this feature. Interestingly, none of the current smart home systems on the market offer a comparable assistance mechanism. Third-party solutions are not available either, because an assistance app requires meta data about features, interoperability, and usage of devices. This meta data is currently not available via APIs in state-of-the-art smart home systems and marketplaces. Therefore, we present a smart home architecture resulting from our research that can, among other benefits, provide the necessary meta data. Our research indicates that commercial smart home systems should invest more effort in user assistance to gain widespread adoption among technically inexperienced users. This in turn requires substantial changes to the meta data management in smart homes, because otherwise these assistance systems cannot be realized.
Impact of Redundancy on Resilience in Distributed Optimization and Learning

This paper considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate with the server to find a minimum of the aggregate of the local cost functions. In the context of stochastic learning, the local cost of an agent is the loss function computed over the data at that agent. In this paper, we consider this problem in a system wherein some of the agents may be Byzantine faulty and some of the agents may be slow (also called stragglers). In this setting, we investigate the conditions under which it is possible to obtain an “approximate” solution to the above problem. In particular, we introduce the notion of (f, r;ϵ)-resilience to characterize how well the true solution is approximated in the presence of up to f Byzantine faulty agents, and up to r slow agents (or stragglers) – smaller ϵ represents a better approximation. We also introduce a measure named (f, r;ϵ)-redundancy to characterize the redundancy in the cost functions of the agents. Greater redundancy allows for a better approximation when solving the problem of aggregate cost minimization.
BandX : An Intelligent IoT-band for Human Activity Recognition based on TinyML

Human Activity Recognition (HAR) is used in several human-centric real-world situations. In many learning-based systems, sensors capture situational data and transfer it to adjacent computing devices for processing and analysis. This concerns user data privacy and network availability, as well as increasing response latency and unnecessary power consumption. To address these, we developed a device BandX that classifies human activities from wearable sensors using an on-device deep learning inference mechanism on a microcontroller unit (MCU) based on TinyML. BandX performs with an accuracy of around 94% and reduces significant network traffic in comparison to computational offloading methods. We have also designed a framework that manages BandX sensor/inference data and provides a logical layer for user activity-level inferences. This framework includes an interactive user interface that lets people track their activity patterns and other health statistics.
In-situ laser ablation of mineral fluid inclusions: Evaluating the effects of solid and liquid matrices, laser repetition rate and fluence on data quality

Fluids play an important role in many geological/ore genetic processes. The composition of the hydrothermal fluid is a critical but often unknown parameter that controls the mobilization of elements and their concentration into mineralized zones. In-situ analyses of fluid inclusions trapped in minerals using laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) is a powerful tool to constrain the composition of the mineralizing fluid. The LA-analysis of fluid inclusions is analytically challenging due to potential mismatch of matrix between fluid inclusions and solid NIST reference glasses commonly used as calibrating standards. We conducted experiments to evaluate the effects of using solid and liquid as calibrating standard on data quality during ablation of liquid/mineral fluid inclusions, as well as the effects of variable energy fluence and repetition rate on the time-dependent elemental fractionation. The LA-analyses of liquids (with NaCl matrix) when standardized against another solution has lower precision and accuracy compared to standardization with NIST reference glasses. The 193 nm laser has little matrix effect and causes negligible elemental fractionation. The trace element concentrations of liquids retrieved by external standardization with NIST reference glass has better accuracy and precision. Higher laser fluence (8.5 J/cm2) produces better sensitivity, lesser time-dependent elemental fractionation, lower detection limits, and better accuracy/precision and is recommended for ablation of natural fluid inclusions or liquids. We tested our preferred analytical protocol by analyzing the composition of quartz-hosted fluid inclusions from the Malanjkhand copper deposit in central India. The LA-ICPMS measurements of the fluid inclusions help to establish that the mineralizing fluid had high concentrations of K, Rb, and Cs. Fluid inclusions from the Malanjkhand copper deposit are trapped from fluids that are dominantly of magmatic origin, sourced from highly differentiated granitic magmas. The NIST glasses are appropriate calibrating standards for ablation of synthetic liquids as well as natural fluid inclusions.
Designing Web Based Proctoring System for Online Examination (SPIRIT 1.0) in Telkom University

The digital era, which is a jargon in welcoming the technological developments has given birth to a new form of learning that eliminates the requirement of physical presence of teachers and students within the same place and time, or it can be known as Online Learning. The possibility for the institution of education in supporting their business into a new level of services in supporting their ecosystem have met the challenges of the readiness in doing the transformation from the traditional learning within the context of human resources, technology, and learning concept into a syllabus with the purpose of achieving the same level or output. When the Corona Virus Diseases 19 (COVID 19) made drastic changes in every aspect that afterwards became a global pandemic, the implementation of online learning would ensure the survivability of the educational institution in keeping their business. Telkom University, as one of private university in Indonesia, with the student body almost reaches 30.000, uses Learning Management System (LMS) in enabling the learning activities as the primary solution but has a problem in ensuring no cheating activities during the examination. As the common approach in ensuring the integrity of online examination through LMS with the creation of question bank and randomize the question is seen as not efficient, the institution also tries to monitor and control with the implementation of video conference-based application like ZOOM and Safe Exam Browser (SEB), but raises another problem in cost, compatibility issues, and motivation issues. Proctoring has become another approach that is recently has continue evolving with the involvement from Artificial Intelligence (AI) in doing the facial recognition. This research will develop the proctoring tools that can be integrated with the LMS used in Telkom University as the strategy in preventing misconduct behavior that led to academic cheating in online examination.
LSA-T: The First Continuous Argentinian Sign Language Dataset for Sign Language Translation

Sign language translation (SLT) is an active field of study that encompasses human-computer interaction, computer vision, natural language processing and machine learning. Progress on this field could lead to higher levels of integration of deaf people. This paper presents, to the best of our knowledge, the first continuous Argentinian Sign Language (LSA) dataset. It contains 14,880 sentence level videos of LSA extracted from the CN Sordos YouTube channel with labels and keypoints annotations for each signer. We also present a method for inferring the active signer, a detailed analysis of the characteristics of the dataset, a visualization tool to explore the dataset and a neural SLT model to serve as baseline for future experiments.
Blockchain and Crypto Exchange-traded Funds

This chapter examines the current state of crypto exchange-traded funds (ETFs). It focuses on issues preventing wider implementation and specific products. ETFs have become a popular investment vehicle that investors use to help achieve their long-term goals. A recurring theme is that regulators protect individual investors from direct exposure to cryptocurrency, which many view as highly speculative investments. Pressure from institutions and investors for a bitcoin-based ETF made progress in 2021 when Proshares, an ETF specialized investment company, debuted the first-ever bitcoin futures ETF in the United States. This event is the first-time investors could buy a fund on the New York Stock Exchange that tracks derivative futures contracts of bitcoin. This occurrence pushed this digital asset’s spot price to all-time highs, serving as a breakthrough in cryptocurrency history.
Central Bank Digital Currencies: Opening Pandora’s Box or Paving the Future of Money?

This chapter reviews developments concerning central bank digital currencies (CBDCs). It introduces, analyzes, and discusses the potential implications of CBDCs on the existing cryptoassets landscape. The chapter also provides an overview of the different approaches to adopting and implementing this new form of money. Additionally, it compares traditional cryptocurrencies, privately issued stablecoins, fiat currencies, and CBDCs. Although vastly divergent opinions exist on digital money's purpose, benefits, and use cases, CBDCs can provide opportunities for innovation and experimentation at a central bank and systemic level. CBDCs may pave the way for democratizing access to unbundled financial services while rethinking the overall purpose of money, monetary systems, and global business.
Power-Efficient VLSI Architecture of a New Class of Dyadic Gabor Wavelets for Medical Image Retrieval

Gabor wavelet is widely used in the analysis of texture features. It is found that Gabor wavelet filter bank (FB) requires infinite precision (due to irrational coefficients) to extract accurate directional textural features for which it needs numerous computations and external memory access. Due to this, there is a requirement for huge digital hardware, increased dynamic power dissipation, and increased processing time. In order to solve these issues, this article presents the first dyadic Gabor wavelet FB (DGWFB) based on the slight alteration in orientation parameter without disturbing the remaining Gabor wavelet parameters. In addition, this article introduces a separable VLSI architecture for the proposed DGWFB. The proposed VLSI architecture is implemented in field-programmable gate array (FPGA) through Xilinx Kintex 7 platform. It is observed that the proposed DGWFB has reduced processing time, dynamic power dissipation and digital hardware requirement significantly. The effectiveness of the designed DGWFB is evaluated in medical image retrieval application using three different publicly available medical databases namely NEMA, OASIS, and EXACT09. The proposed DGWFB achieved better performance as compared to existing Gabor wavelet FBs with significantly reduced digital hardware and processing time.
Detection of Water Changes in Plant Stems In Situ by the Primary Echo of Ultrasound RF with an Improved AIC Algorithm

The detection of water changes in plant stems by non-destructive online methods has become a hot spot in studying the physiological activity of plant water. In this paper, the ultrasonic radio-frequency echo (RFID) technique was used to detect water changes in stems. An algorithm (improved hybrid differential Akaike's Information Criterion (AIC)) was proposed to automatically compute the position of the primary ultrasonic echo of stems, which is the key parameter of water changes in stems. This method overcame the inaccurate location of the primary echo, which was caused by the anisotropic ultrasound propagation and heterogeneous stems. First of all, the improved algorithm was analyzed and its accuracy was verified by a set of simulated signals. Then, a set of cutting samples from stems were taken for ultrasonic detection in the process of water absorption. The correlation between the moisture content of stems and ultrasonic velocities was computed with the algorithm. It was found that the average correlation coefficient of the two parameters reached about 0.98. Finally, living sunflowers with different soil moistures were subjected to ultrasonic detection from 9:00 to 18:00 in situ. The results showed that the soil moisture and the primary ultrasonic echo position had a positive correlation, especially from 12:00 to 18:00; the average coefficient was 0.92. Meanwhile, our results showed that the ultrasonic detection of sunflower stems with different soil moistures was significantly distinct. Therefore, the improved AIC algorithm provided a method to effectively compute the primary echo position of limbs to help detect water changes in stems in situ.
Detecting dynamic visual attention in augmented reality aided navigation environment based on a multi-feature integration fully convolutional network

Visual attention detection, as an important concept for human visual behavior research, has been widely studied. However, previous studies seldom considered the feature integration mechanism to detect visual attention and rarely considered the differences due to different geographical scenes. In this paper, we use an augmented reality aided (AR-aided) navigation experimental dataset to study human visual behavior in a dynamic AR-aided environment. Then, we propose a multi-feature integration fully convolutional network (M-FCN) based on a self-adaptive environment weight (SEW) to integrate RGB-D, semantic, optical flow and spatial neighborhood features to detect human visual attention. The result shows that the M-FCN performs better than other state-of-the-art saliency models. In addition, the introduction of feature integration mechanism and the SEW can improve the accuracy and robustness of visual attention detection. Meanwhile, we find that RGB-D and semantic features perform best in different road routes and road types, but with the increase in road type complexity, the expressiveness of these two features weakens, and the expressiveness of optical flow and spatial neighborhood features increases. The research is helpful for AR-device navigation tool design and urban spatial planning.
Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

The ability to detect and localize the human eye is critical for use in security applications and human identification and verification systems.This is because eye recognition algorithms have multiple challenges, such as multi-pose variations, ocular parts, and illumination.Moreover, the modern security applications fail to detect facial expressions from eye images.In this paper, a Speeded-Up Roust Feature (SURF) Algorithm was utilized to localize the face images of the enrolled subjects.We highlighted on eye and pupil parts to be detected based on SURF, Hough Circle Transform (HCT), and Local Binary Pattern (LBP).Afterward, Deep Belief Neural Networks (DBNN) were used to classify the input features results from the SURF algorithm.We further determined the correctly and wrongly classified subjects using a confusion matrix with two class labels to classify people whose eye images are correctly detected.We apply Stochastic Gradient Descent (SGD) optimizer to address the overfitting problem, and the hyper-parameters are fine-tuned based on the applied DBNN.The accuracy of the proposed system is determined based on SURF, LBP, and DBNN classifier achieved 95.54% for the ORL dataset, 94.07% for the BioID, and 96.20% for the CASIA-V5 dataset.The proposed approach is more reliable and more advanced when compared with state-of-the-art algorithms.
On the similarities of representations in artificial and brain neural networks for speech recognition

In recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of performance, despite their huge differences in implementation, and so deep learning models can-in principle-serve as candidates for mechanistic models of the human auditory system.Utilizing high-performance automatic speech recognition systems, and advanced non-invasive human neuroimaging technology such as magnetoencephalography and multivariate pattern-information analysis, the current study aimed to relate machine-learned representations of speech to recorded human brain representations of the same speech.In one direction, we found a quasi-hierarchical functional organization in human auditory cortex qualitatively matched with the hidden layers of deep artificial neural networks trained as part of an automatic speech recognizer. In the reverse direction, we modified the hidden layer organization of the artificial neural network based on neural activation patterns in human brains. The result was a substantial improvement in word recognition accuracy and learned speech representations.We have demonstrated that artificial and brain neural networks can be mutually informative in the domain of speech recognition.
Digital Learning Activities at School-age Educare when Policy Reforms Calls for Educational Change

This study examines teachers’ accounts of what is happening in practices of the School-age Educare centre (SAEC) when faced with pressure from policy reforms to adopt digital technology and promote digital competence as both a requirement and a right for all children. The aim is to explain anticipated tensions that may produce the (im)possible digital practices of SAECs. The study is conducted with teachers from three SAECs in Sweden. Reflection conversations and interviews were used to produce data that was analysed using a constructivist grounded theory approach. The study contributes to understandings of teachers’ main concerns when SAECs are requested to adopt technology and align with reforms. It explains how tensions emerge, impact SAEC teachers’ actions towards revised curricula and affect the distribution of digital learning activities. Attention is asked to ensure that the rights of also young pupils are upheld in the digital world of today and tomorrow.