Reviewerly/exHarmony huggingface dataset:
Digital competencies of music teachers under the ‘new normal’ in Hong Kong

The COVID-19 pandemic has posed challenges to education systems around the world, resulting in a ‘new normal’ for school music education in which teaching and learning can no longer rely only on conventional classroom practices. Concerns have also been raised over music teachers’ digital competencies, given the perceived importance of the use of technology within the new educational landscape. This article presents the results of a study that examined the challenges and experiences associated with the digital competencies of music teachers during the pandemic period. Semi-structured interviews were conducted with full-time music teachers (N = 18), all of whom taught music as a subject at mainstream primary or secondary schools in Hong Kong. The findings revealed challenges and upskilling of music teachers during the lockdown period, including the technological constraints imposed by the school and the online environment, how they made use of technology to teach music online, and the different ways they developed their digital competencies. Alongside with the catalyst effect of the pandemic that stimulated their upskilling, curriculum changes in teacher training programmes are necessary to better prepare music teachers for the digital transformation of music education and future challenges under the post-pandemic ‘new normal’ situation. 新型冠状病毒肺炎(COVID-19)對全球教育系統造成衝擊，導致學校的音樂教育進入了一種「新常態」，令教學不能只依賴傳統的面授課堂。鑒於「新常態」下的教學非常倚重科技的運用，音樂教師的數碼技能尤為重要。本研究旨在考察音樂教師在疫情期間運用數碼技能的經驗及其所面臨的挑戰，對18名在香港主流小學或中學教授音樂課程的全職教師進行了半結構化訪談，調查結果顯示在實施新冠肺炎封鎖措施期間音樂教師所面臨的挑戰和技能提升，包括線上教學環境及學校面授課堂的限制，科技的運用，以及他們學習數碼技能的方法。除了疫情的推動之外，課程改革及教師培訓也是必要的，以便音樂教師在數位化音樂教育和後疫情「新常態」下的未來挑戰做準備。
Quantum Logic Locking for Security

Having access to a unique quantum circuit that one wants to protect against use by others is a very likely scenario in industrial computing. However, currently, users rely on classical computer security schemes, which have known shortcomings. In this paper, we introduce a novel protection scheme along with a survey of other known methods to protect quantum information. In particular, we review physically unclonable functions (PUFs), obfuscation, and introduce quantum logic locking (QLL). The latter technique provisions end users to protect their circuit from an adversary through the use of a secret key.
Automated Classical Cipher Emulation Attacks via Unified Unsupervised Generative Adversarial Networks

Cryptanalysis has been studied and gradually improved with the evolution of cryptosystems over past decades. Recently, deep learning (DL) has started to be used in cryptanalysis to attack digital cryptosystems. As computing power keeps growing, deploying DL-based cryptanalysis becomes feasible in practice. However, since these studies can analyze only one cipher type for one DL model learning, it takes a lot of time to analyze multi ciphers. In this paper, we propose a unified cipher generative adversarial network (UC-GAN), which can perform ciphertext-to-plaintext translations among multiple domains (ciphers) using only a single DL model. In particular, the proposed model is based on unified unsupervised DL for the analysis of classical substitutional ciphers. Simulation results have indicated the feasibility and good performance of the proposed approach. In addition, we compared our experimental results with the findings of conditional GAN, where plaintext and ciphertext pairs in only the single domain are given as training data, and with CipherGAN, which is cipher mapping between unpaired ciphertext and plaintext in the single domain, respectively. The proposed model showed more than 97% accuracy by learning only data without prior knowledge of three substitutional ciphers. These findings could open a new possibility for simultaneously cracking various block ciphers, which has a great impact on the field of cryptography. To the best of our knowledge, this is the first study of the cryptanalysis of multiple cipher algorithms using only a single DL model
Two structures of extraposition in central dialects of Early New High German

Abstract Extraposition in OV Germanic languages is a complicated phenomenon, which has been analyzed in different ways—as a result of rightward movement, as base generation of extraposing items to the right, and as a result of raising of predicative elements. The most common type of item in extraposition is CP, but also PPs and heavy adjuncts are attested in extraposition in Modern German. Early New High German (ENHG) material adds complexity to this topic, since it shows much more widespread extraposition, allowed for arguments as well. In this article, I propose a twofold analysis of extraposition in ENHG. Based on DP-extraposition to the predicate, I argue that in certain cases extraposition can result from rightward movement of an extraposing XP, while in other cases it is raising of the particle to position adjoined to vP, which leaves XP in overt extraposition to the predicate.
Implementation of energy-efficient convolutional neural networks based on kernel-pruned silicon photonics

Silicon-based optical neural networks offer the prospect of high-performance computing on integrated photonic circuits. However, the scalability of on-chip optical depth networks is restricted by the limited energy and space resources. Here, we present a silicon-based photonic convolutional neural network (PCNN) combined with the kernel pruning, in which the optical convolutional computing core of PCNN is a tunable micro-ring weight bank. Our numerical simulation demonstrates the effect of weight mapping accuracy on PCNN performance and we find that the performance of PCNN decreases significantly when the weight mapping accuracy is less than 4.3 bits. Additionally, the experimental demonstration shows that the accuracy of the PCNN on the MNIST dataset has a slight loss compared to the original CNN when 93.75 % of the convolutional kernels are pruned. By making use of kernel pruning, the energy saved by a convolutional kernel removal is about 202.3 mW, and the overall energy saved has a linear relationship with the number of kernels removed. The methodology is scalable and provides a feasible solution for implementing faster and more energy-efficient large-scale optical convolutional neural networks on photonic integrated circuits.
Measuring and Understanding Crowdturfing in the App Store

Application marketplaces collect ratings and reviews from users to provide references for other consumers. Many crowdturfing activities abuse user reviews to manipulate the reputation of an app and mislead other consumers. To understand and improve the ecosystem of reviews in the app market, we investigate the existence of crowdturfing based on the App Store. This paper reports a measurement study of crowdturfing and its reviews in the App Store. We use a sliding window to obtain the relationship graph between users and the community detection method to binary classify the detected communities. Then, we measure and analyze the crowdturfing obtained from the classification and compare them with genuine users. We analyze several features of crowdturfing, such as ratings, sentiment scores, text similarity, and common words. We also investigate which apps crowdturfing often appears in and reveal their role in app ranking. These insights are used as features in machine learning models, and the results show that they can effectively train classifiers and detect crowdturfing reviews with an accuracy of up to 98.13%. This study reveals malicious crowdfunding practices in the App Store and helps to strengthen the review security of app marketplaces.
An immersive multisensory virtual reality approach to the study of human-built environment interactions: Technical workflows

Virtual Reality technology has gained increased attention due to its capacity to provide immersive and interactive experiences to its users. Although increasing evidence has suggested that incorporating multisensory components in VR can promote the sense of presence and improve user performance, most of the current VR applications are limited to visual and auditory senses. In this article, a novel method of integrating thermal-related devices (heat lamps and fans) into Virtual Reality was developed. Automated interaction with the thermal-related devices was achieved using Arduino-based control module with its program embedded into the VR platform-Unity. The functions, hardware and software requirements of the multisensory Virtual Reality system as well as the step-by-step procedures are detailed to provide a reproducible workflow for future applications.•A practical workflow to integrate thermal apparatus into Virtual Reality.•Dynamic airflow and radiative heating incorporated into Virtual Reality.•Automated process to allow user interaction with the thermal components in Virtual Reality.
ADEFGuard: Anomaly detection framework based on Ethereum smart contracts behaviours

Smart contract is the building block of blockchain systems that enables automated peer-to-peer transactions and decentralized services. Smart contracts certainly provide a powerful functional surplus for maintaining the consistency of transactions in applications governed by blockchain technology. Smart contracts have become lucrative and profitable targets for attackers because they can hold a large amount of money. Formal verification and symbolic analysis have been employed to combat these destructive scams by analyzing the codes and function calls, yet each scam's vulnerability should be discreetly predefined. In this work, we introduce ADEFGuard, a new anomaly detection framework based on the behavior of smart contracts, as new features. We design a learning and monitoring module to determine fraudulent smart contract behaviors. Our framework is advantageous over basic algorithms in three aspects. First, ADEFGuard provides a unified solution to different genres of scams, relieving the need for code analysis skills. Second, ADEFGuard's inference is orders of magnitude faster than code analysis. Third, experimental results show that ADEFGuard achieves high accuracy (85%), precision (75%) and recall (90%) for malicious contracts and is potentially useful in detecting new malicious behaviors of smart contracts.
Citizen Science and The University of Queensland Seismograph Stations (UQSS)—A Study of Seismic T Waves in S-W Pacific Ocean

Seismologists listen to Earth’s noise as it rips apart (faulting), exudes (volcanoes) and swallows (subducts) large volumes of rock. Your mobile phone is most likely detecting such noise, right now! This paper is about one such specific noise, the T wave. It summarises an early and successful piece of citizen science, performed within The University of Queensland Seismograph Stations (UQSS) observatory, in cooperation with colleagues at CSIRO. It was designed to encourage young STEM students from Brisbane high schools to engage in “real” research, back in 1995. Bear in mind, this is a time period when science is changing considerably from analog to digital media and operational recording methods. The citizen science students used a pre-prepared decadal collection (1980–1990) of T waves, derived from the Brisbane seismograph (BRS) observatory data catalogue. BRS has been operating since 1937 and is part of the global World-Wide Seismograph Station Network (WWSSN). Fortunately, seismology is a very collaborative field. There is a lot of data analysis involved in the science of recording earthquake signals, with auxiliary definitive catalogues, observers logbooks, housing of the recordings themselves (analog and digital) and the software mediums that change over time. It equally tests housekeeping proficiency, where a maze of record-keeping problems can be encountered in a longitudinal data collection study such as this. Having completed the project report, Earthquake generated T phases on BRS Seismograph (Brisbane, Q’ld) a predictor for Tasman Sea Tsunamis? their (analog) results sat in a cupboard until recently. The project was re-analysed in 2022 for a higher-degree student, discovering a timely climate change implication for the study. The original research question has now been amplified with a brief literature review. We observe that currently in Australia, university and government earth science observatories have diminished, and in their place, public seismic networks (PSN) have evolved, either in backyard sheds or school science labs. We now additionally propose here that the level of expertise required ideally fits the role of advancing citizen science, for a real science advantage. This is already a topical citizen disaster preparedness action area, and we propose that it has applications as a possible educational strategy for citizen engagement in today’s climate emergency. In addition, we are hopeful that other researchers in oceanography will read this paper and decide to explore the ocean’s temperature rise phenomenon through the eyes of seismological observers.
ORCNN-X: Attention-Driven Multiscale Network for Detecting Small Objects in Complex Aerial Scenes

Currently, object detection on remote sensing images has drawn significant attention due to its extensive applications, including environmental monitoring, urban planning, and disaster assessment. However, detecting objects in the aerial images captured by remote sensors presents unique challenges compared to natural images, such as low resolution, complex backgrounds, and variations in scale and angle. Prior object detection algorithms are limited in their ability to identify oriented small objects, especially in aerial images where small objects are usually obscured by background noise. To address the above limitations, a novel framework (ORCNN-X) was proposed for oriented small object detection in remote sensing images by improving the Oriented RCNN. The framework adopts a multiscale feature extraction network (ResNeSt+) with a dynamic attention module (DCSA) and an effective feature fusion mechanism (W-PAFPN) to enhance the model’s perception ability and handle variations in scale and angle. The proposed framework is evaluated based on two public benchmark datasets, DOTA and HRSC2016. The experiments demonstrate its state-of-the-art performance in aspects of detection accuracy and speed. The presented model can also represent more objective spatial location information according to the feature visualization maps. Specifically, our model outperforms the baseline model by 1.43% mAP50 and 1.37% mAP12 on DOTA and HRSC2016 datasets, respectively.
Cyber oriented digital engineering

Abstract The purpose of cyber oriented digital engineering (CODE) is to provide a repeatable systems‐engineering process for systems to resist Stuxnet‐class advanced persistent threat (APT) cyberattacks. CODE integrates cyber thinking with systems thinking. CODE accomplishes this by extending the US Department of Defense (DoD) digital engineering (DE) framework with functional mission analysis for cyber (FMA‐C), hybrid cloud architecture, zero trust (ZT) principles, threat analysis, and hardware cyber hardening (HCH). The lack of success of red team attacks conducted in our laboratory against an exemplar system demonstrates how following the CODE systems engineering process actually does “bake cybersecurity into the system”, making the resulting systems architecture and implementations more resilient. In a recent Pilot Project, CODE enhanced the systems requirements document of the top left side of the systems engineering Vee. CODE Pilot requirements embodied ZT principles, including machine to machine (M2M) credential exchanges and internal self‐checking. We anticipate working with International Council on Systems Engineering (INCOSE), the object management group (OMG,) and others towards standardizing CODE's cyber‐systems engineering processes for broader use of the global systems engineering community.
Gaze Point Tracking Based on a Robotic Body–Head–Eye Coordination Method

When the magnitude of a gaze is too large, human beings change the orientation of their head or body to assist their eyes in tracking targets because saccade alone is insufficient to keep a target at the center region of the retina. To make a robot gaze at targets rapidly and stably (as a human does), it is necessary to design a body-head-eye coordinated motion control strategy. A robot system equipped with eyes and a head is designed in this paper. Gaze point tracking problems are divided into two sub-problems: in situ gaze point tracking and approaching gaze point tracking. In the in situ gaze tracking state, the desired positions of the eye, head and body are calculated on the basis of minimizing resource consumption and maximizing stability. In the approaching gaze point tracking state, the robot is expected to approach the object at a zero angle. In the process of tracking, the three-dimensional (3D) coordinates of the object are obtained by the bionic eye and then converted to the head coordinate system and the mobile robot coordinate system. The desired positions of the head, eyes and body are obtained according to the object's 3D coordinates. Then, using sophisticated motor control methods, the head, eyes and body are controlled to the desired position. This method avoids the complex process of adjusting control parameters and does not require the design of complex control algorithms. Based on this strategy, in situ gaze point tracking and approaching gaze point tracking experiments are performed by the robot. The experimental results show that body-head-eye coordination gaze point tracking based on the 3D coordinates of an object is feasible. This paper provides a new method that differs from the traditional two-dimensional image-based method for robotic body-head-eye gaze point tracking.
Energy‐efficient load balancing for divisible tasks on heterogeneous clusters

Abstract With the growing power consumption of heterogeneous clusters, energy‐aware resource management has been a hot research topic recently. Among various candidate techniques, load balancing is one of the software schemes for energy optimization through mapping tasks to servers properly, especially for divisible tasks like web and database queries. However, most practical load balancing schemes are lack of energy awareness, while recent energy‐aware schemes usually have high time complexities. In this article, we present an energy‐efficient load balancing (EELB) scheme that consists of two‐phase heuristics, including server provisioning and load balancing . Based on prior knowledge of power models of servers in the cluster, the proposed scheme is able to make wise decisions on selecting power‐on servers as well as scheduling tasks to servers. To validate EELB, we conduct extensive experiments on both simulated and real‐world testbeds. Experimental results reveal that the proposed method outperforms competitors in terms of energy saving, and is more applicable to heterogeneous clusters.
Development of Methodology for the Evaluation of Solar Energy through Hybrid Models for the Energy Sector

The forecast of the generation of electrical energy from the solar resource is associated with its uncertainty due to the meteorological variations that it presents. Solar power generation forecasts are important for the efficient operation of solar plants. This article shows a methodology entailing a multilayer neural network with backpropagation and input data from a model with time lag coordinates for a horizon of 24 h and beyond. The neural network model was compared with statistical and prediction models numerical time, resulting in a MAPE of 0.57% and a MAE of 69.29 W.
Image Retrieval Using Fusion of Sauvola and Thepade’s Sorted Block Truncation Coding-Based Color Features

Because of the tremendous growth in digital imaging, enhanced communication and storage technology, billions of images are captured, stored, and exchanged daily. Finding and searching for an image in a large collection is becoming challenging. The query by reference image retrieval (IR) technique aims to close the semantic gap between the query and retrieve images while improving performance. The primary goal of the work proposed here is to develop discriminative and descriptive features of the image with the minimum possible size. Here, the weighted feature fusion-based IR technique is proposed using Sauvola local thresholding (SLT) and Thepade’s Sorted Block Truncation Coding (SBTC) methods. The proposed technique is tested using two standard datasets with mean square error (MSE) as a distance measure and average retrieval accuracy (ARA) as a performance metric. The technique has contributed to the enhancement of ARA with the small and fixed-size image feature vector. The feature vector generated is much smaller than the image dimension and is used as a feature vector to represent the image for retrieval. Results prove that the proposed technique of SBTC 8-ary with 0.1 weight and SLT with 0.9 weight feature fusion gives better ARA than other techniques studied.
Data Sharing Mechanism and Strategy for Multi-Service Integration for Smart Grid

The new power system is an energy interconnection network based on renewable energy generation. Information interconnection, data security, and reliability are the basis for the digital transformation of the power grid. Data sharing, lifecycle management, security, and user information privacy are issues that need to be addressed urgently. This paper analyzes the characteristics of the multi-combination of power grid data across services and introduces smart contract, cross-chain, and security encryption-related technologies. Based on the effective combination of smart contract and CP-ABE, data sharing schemes, including data sharing mechanism and data access control model are designed. Given this scheme, the blockchain system’s overall architecture is proposed, including the main chain, side chain, data sharing, and cross-chain information interaction. Finally, the underlying blockchain service platform is built using the Hyperledger open-source framework. We deploy the platform to verify the feasibility of the scheme according to the requirements of the data center, trust center, and blockchain-distributed nodes.
Enabling Insights by Long-Term Evaluation of Social Impact Indicators of Engineered Products for Global Development Using In Situ Sensors and Deep Learning

Abstract Remotely measuring social impact indicators of products in developing countries can enable researchers and practitioners to make informed decisions relative to the design of products, improvement of products, or social interventions that can help improve the lives of individuals. Collecting data for determining social impact indicators for long-term periods through manual methods can be cost prohibitive and preclude collection of data that could provide valuable insights. Using in situ sensors remotely deployed and paired with deep learning can enable practitioners to collect long-term data that provide insights that can be as beneficial as data collected through manual observation but with the cost and continuity made possible by sensor devices. Postulates related to successfully developing and deploying this approach have been identified and their usefulness demonstrated through an example application related to a water hand pump in Uganda in which sensor data were collected over a five-month span. Following these postulates can help researchers and practitioners avoid potential issues that could be encountered without them.