Reviewerly/exHarmony huggingface dataset:
Bayesian Neural Networks for Identification and Classification of Radio Frequency Transmitters Using Power Amplifiers’ Nonlinearity Signatures

The edge devices in an emerging Internet-of-Things (IoT) environment require comprehensive security measures that are within the power budget for ubiquitous computing. In this paper, a transmitter identification scheme consisting of a lightweight Bayesian neural network (BNN)-based classifier using raw time-domain data is presented. Evaluation is performed with data obtained in schematic-level simulation of high-efficiency CMOS power amplifier designs using a 65 nm process design kit (PDK). The Bayesian neural networks achieve 89.5% accuracy on the task of classifying six transmitters. Moreover, the BNN classifier is implemented on field-programmable gate array (FPGA) with parallel pseudo-Gaussian random number generators to achieve a throughput of more than 340,000 classifications per second, with average energy consumption for each classification task of 0.548 μJ. This low-power system enables comprehensive security for energy-constrained IoT devices and sensors.
Emotion Detection from Social Media Using Machine Learning Techniques: A Survey

The work carried out in this paper is to overview and compare various sentiment analysis methodologies and approaches in detail and also discuss the limitations of existing work and future direction about sentiment analysis methodologies. The main goal of sentiment analysis for market prediction is to recognize the customer's opinion about the available products. The work carried out in this paper is to overview and compare various sentiment analysis methodologies and approaches in detail with Sentiment Emotion Detection (SED) and also discuss the limitations of existing work and future direction about sentiment analysis methodologies on SED. The main goal of sentiment analysis for market prediction is to recognize the customer's opinion about the available products. It can pave the way for improvement and prevent future defects and flaws. The tools for identifying and classifying opinion communicated in a bit of text, in sound, or video formats indicate whether the creator's mood toward a specific issue, thread, item, and so on is positive, negative, or neutral. Human emotions are limited to being positive or negative. Still, it has more categories like happiness, sadness, joy, disgust, surprise, depression, frustration, anger, fear, confidence, trust, anticipation, shame, kindness, love, friendship, faith, and wonder. Analyzing people's comments/emotions is essential for the country, business, or individuals for their existence, which gives the researcher motivation on sentiment analysis on emotion detection.
Predicting the Performance of Solar Power Generation Using Deep Learning Methods

In recent years, many countries have provided promotion policies related to renewable energy in order to take advantage of the environmental factors of sufficient sunlight. However, the application of solar energy in the power grid also has disadvantages. The most obvious is the variability of power output, which will put pressure on the system. As more grid reserves are needed to compensate for fluctuations in power output, the variable nature of solar power may hinder further deployment. Besides, one of the main issues surrounding solar energy is the variability and unpredictability of sunlight. If it is cloudy or covered by clouds during the day, the photovoltaic cell cannot produce satisfactory electricity. How to collect relevant factors (variables) and data to make predictions so that the solar system can increase the power generation of solar power plants is an important topic that every solar supplier is constantly thinking about. The view is taken, therefore, in this work, we utilized the historical monitoring data collected by the ground-connected solar power plants to predict the power generation, using daily characteristics (24 h) to replace the usual seasonal characteristics (365 days) as the experimental basis. Further, we implemented daily numerical prediction of the whole-point power generation. The preliminary experimental evaluations demonstrate that our developed method is sensible, allowing for exploring the performance of solar power prediction.
Can attention enable MLPs to catch up with CNNs?

In the first week of May, 2021, researchers from four different institutions: Google, Tsinghua University, Oxford University and Facebook, shared their latest work [16, 7, 12, 17] on arXiv.org almost at the same time, each proposing new learning architectures, consisting mainly of linear layers, claiming them to be comparable, or even superior to convolutional-based models. This sparked immediate discussion and debate in both academic and industrial communities as to whether MLPs are sufficient, many thinking that learning architectures are returning to MLPs. Is this true? In this perspective, we give a brief history of learning architectures, including multilayer perceptrons (MLPs), convolutional neural networks (CNNs) and transformers. We then examine what the four newly proposed architectures have in common. Finally, we give our views on challenges and directions for new learning architectures, hoping to inspire future research.
Modelling and Mobile Learning with Math Trails

The chapter provides an orientation on the concept of mobile learning and how it can be pursued with mobile math trails. Math trails contain tasks that promote essential elements of mathematical modelling, such as mathematising. Research shows that, nowadays, math trails are more and more supported by digital media, and that this affects students’ motivation and achievements. The chapter collects existing findings on mobile learning with math trails and expands the findings with the results of a study on digital support of the modelling processes of 11th graders when doing math trails.
Clouds-Based Collaborative and Multi-Modal Mixed Reality for Virtual Heritage

Recent technological advancements in immersive reality technologies have become a focus area in the virtual heritage (VH) domain. In this regard, this paper attempts to design and implement clouds-based collaborative and multi-modal MR application aiming at enhancing cultural learning in VH. The design and implementation can be adopted by the VH domain for various application themes. The application utilises cloud computing and immersive reality technologies. The use of cloud computing, collaborative, and multi-modal interaction methods is influenced by the following three issues. First, studies show that users’ interaction with immersive reality technologies and virtual environments determines their learning outcome and the overall experience. Second, studies also demonstrate that collaborative and multi-modal interaction methods enable engagement in immersive reality environments. Third, the integration of immersive reality technologies with traditional museums and cultural heritage sites is getting significant attention in the domain. However, a robust approach, development platforms (frameworks) and easily adopted design and implementation approaches, or guidelines are not commonly available to the VH community. This paper, therefore, will attempt to achieve two major goals. First, it attempts to design and implement a novel application that integrates cloud computing, immersive reality technology and VH. Second, it attempts to apply the proposed application to enhance cultural learning. From the perspective of cultural learning and users’ experience, the assumption is that the proposed approach (clouds-based collaborative and multi-modal MR) can enhance cultural learning by (1) establishing a contextual relationship and engagement between users, virtual environments and cultural context in museums and heritage sites, and (2) by enabling collaboration between users.
Bita-Net: Bi-temporal Attention Network for Facial Video Forgery Detection

Deep forgery detection on video data has attracted remarkable research attention in recent years due to its potential in defending forgery attacks. However, existing methods either only focus on the visual evidence within individual images, or are too sensitive to fluctuations across frames. To address these issues, this paper propose a novel model, named Bita-Net, to detect forgery faces in video data. The network design of Bita-Net is inspired by the mechanism of how human beings detect forgery data, i.e. browsing and scrutinizing, which is reflected by the two-pathway architecture of Bita-Net. Concretely, the browsing pathway scans the entire video at a high frame rate to check the temporal consistency, while the scrutinizing pathway focuses on analyzing key frames of the video at a lower frame rate. Furthermore, an attention branch is introduced to improve the forgery detection ability of the scrutinizing pathway. Extensive experiment results demonstrate the effectiveness and generalization ability of Bita-Net on various popular face forensics detection datasets, including FaceForensics++, CelebDF, DeepfakeTIMIT and UADFV.
3D Deep Learning for Anatomical Structure Segmentation in Multiple Imaging Modalities

Accurate, quantitative segmentation of anatomical structures in radiological scans, such as Magnetic Resonance Imaging (MRI) and Computer Tomography (CT), can produce significant biomarkers and can be integrated into computer-aided assisted diagnosis (CADx) systems to support the interpretation of medical images from multi-protocol scanners. However, there are serious challenges towards developing robust automated segmentation techniques, including high variations in anatomical structure and size, the presence of edge-based artefacts, and heavy un-controlled breathing that can produce blurred motion-based artefacts. This paper presents a novel computing approach for automatic organ and muscle segmentation in medical images from multiple modalities by harnessing the advantages of deep learning techniques in a two-part process. (1) a 3D encoder-decoder, Rb-UNet, builds a localisation model and a 3D Tiramisu network generates a boundary-preserving segmentation model for each target structure; (2) the fully trained Rb-UNet predicts a 3D bounding box encapsulating the target structure of interest, after which the fully trained Tiramisu model performs segmentation to reveal detailed organ or muscle boundaries. The proposed approach is evaluated on six different datasets, including MRI, Dynamic Contrast Enhanced (DCE) MRI and CT scans targeting the pancreas, liver, kidneys and psoas-muscle and achieves quantitative measures of mean Dice similarity coefficient (DSC) that surpass or are comparable with the state-of-the-art. A qualitative evaluation performed by two independent radiologists verified the preservation of detailed organ and muscle boundaries.
A Bitmap Approach for Mining Erasable Itemsets

Erasable-itemset mining is a valuable method of pattern extraction for helping the manager of a factory analyze production planning. The erasable itemsets derived can be considered important production information regarding how to plan the production of a factory during an economic depression or financial shortage for the manager. After the erasable-itemset mining was proposed in 2009, several efficient mining approaches for finding erasable itemsets have been developed. However, these methods require a considerable amount of execution time when the amount of product data is large. Especially, manufacturing small amounts of versatile products has been a trend today, and it will generate a large product database. This paper adopts a bitmap representation for itemsets in an erasable-itemset mining algorithm to speed up the execution. Unlike the traditional bitmap meaning for frequent itemsets, a bitmap for erasable-itemset mining here denotes the relationship that a product includes at least one material (item) in a specified itemset. Using the bitmap representation can easily find the desired products to check, thus decreasing the scans of a database. Experimental evaluation on synthesized and real datasets was used to compare the proposed approach with the other two under different parameter values. The experimental results show that the proposed approach can make a good trade-off between execution time and memory usage.
Assessment of Subjective and Objective Quality of Live Streaming Sports Videos

Video live streaming is gaining prevalence among video streaming services, especially for the delivery of popular sporting events. Many objective Video Quality Assessment (VQA) models have been developed to predict the perceptual quality of videos. Appropriate databases that exemplify the distortions encountered in live streaming videos are important to designing and learning objective VQA models. Towards making progress in this direction, we built a video quality database specifically designed for live streaming VQA research. The new video database is called the Laboratory for Image and Video Engineering (LIVE) Live stream Database. The LIVE Livestream Database includes 315 videos of 45 contents impaired by 6 types of distortions. We also performed a subjective quality study using the new database, whereby more than 12,000 human opinions were gathered from 40 subjects. We demonstrate the usefulness of the new resource by performing a holistic evaluation of the performance of current state-of-the-art (SOTA) VQA models. The LIVE Livestream database is being made publicly available for these purposes at https://live.ece.utexas.edu/research/LIVE_APV_Study/apv_index.html.
Application of modified wavelet and fractal modeling for detection of geochemical anomaly

Aim of this research is to propose a wavelet-fractal model for detection of geochemical anomaly based on a modified Morlet Wavelet Kernel (MMWK). This study was carried out for detection of lithium anomalies regarding stream sediment samples. Major Li anomalies commence from 51.1 ppm obtained by the wavelet-fractal modeling based on Decomposed Wavelet Transformation (DWT) using a conventional Morlet wavelet. A modified kernel for the Morlet wavelet was utilized for separation of the Li anomalies. The MMWK benefits from the use of L1 norm (instead of L2) which consequently provides more robustness and resistance to outliers for stream sediment data. Results derived via the modified Morlet kernel-fractal modeling show that there are principal lithium anomalies with Li ≥ 55.2 ppm. In addition, staged factor analysis was used and Li was placed with Rb, Cs and F in F2-2. Then, the F2-2 main anomalies commence from 2.7 and 2.9 using the conventional Morlet and MMWK, respectively. Subsequently, both methods were compared by a log-ratio matrix with an Overall Accuracy (OA) due to rock samples retaken from these anomalies. These rock samples were classified using a Concentration-Number (C-N) fractal model and high intensive anomalous samples with Li ≥ 126 ppm. This correlation indicated that the OA between the main obtained anomalies by the MMWK and the rock samples are higher than conventional Morlet-fractal modeling.
Reconfiguring governance: How cyber security regulations are reconfiguring water governance

Abstract Developments in improved monitoring, asset management, and resource efficiencies led to the water industry promising a step‐change in the design and operation of these facilities: the “blending” of traditional engineering equipment with digital technologies. These apparent benefits inevitably produce new challenges of regulating an emerging techno‐political landscape. One of the regulations is Europe's Network and Information Systems Security Directive, which aims to improve cyber security across critical infrastructure providers. This paper focuses on the implementation of Network and Information Systems in the context of the water sector in England. At the national and supranational levels, Network and Information Systems acts as a boundary object that gathers diverse communities of practice without the need to establish common goals. Further, in the process of transposing the Directive into the sectoral context, Network and Information Systems requires interpretation by expert communities. We show how translating the regulatory scope to the sectoral landscape involves prioritizing some water governance goals over others. As diverse expert communities converge in their collaboration practices, their priorities align or stand in tension with public interests. We argue that cyber security regulations have potential to reconfigure water governance by refocusing strategic priorities away from traditional concerns of environmental governance. We suggest ways to maintain diverse collaborations across engineering, computing, and water expertise that Network and Information Systems implementation remains aligned with the goals of water governance.
Demonstration of Single Photon Detection in Amorphous Molybdenum Silicide / Aluminium Superconducting Nanostrip

Single photon detectors (SPD) are largely used when the signal to be measured is particularly low or if high accuracy, timing resolution and low noise are required. About twenty years ago, a new type of SPD based on superconducting materials appeared, and today these devices present the best performances in terms of efficiency, noise, counting rate, detectable wavelength, dead time, and timing jitter. In this work, we first describe the working principle of Superconducting Nanostrips Single Photon Detectors (SNSPDs) and provide a brief overview of the main properties and applications. Then, we present the results of the fabrication and characterization of Molybdenum Silicide SNSPDs with a cover layer of a thin Aluminium film (MoSi/Al). MoSi has already been adopted to fabricate SNSPDs, and in this work, we prove that the additional Al layer enhances the operating conditions without compromising the detection likelihood and leaving unaltered the properties of the device, even over a temporal window of two years and after several thermal cycles.
Verifiable Semantic-Aware Ranked Keyword Search in Cloud-Assisted Edge Computing

Ranked keyword search has gained Ranked keyword search has gained traction due to its attractive properties such as flexibility and accessibility. However, most existing ranked keyword search schemes ignore the semantic associations between the documents and queries. To solve this challenging issue in cloud-assisted edge computing, we first design the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> emantic-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> anked <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti-keyword <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> earch (SRMS) scheme by adopting the Latent Dirichlet Allocation (LDA) topic model and the Chinese Remainder Theorem (CRT)-based secret sharing mechanism. Considering that the cloud server may be malicious, we implement a basic verification mechanism in SRMS to verify the correctness and completeness of search results and extend this verification mechanism in cloud-assisted edge computing scenarios. Formal security analysis proves that SRMS and extended result verification mechanisms are secure in both the known ciphertext model and the known background model. Extensive experiments using the real-world dataset demonstrate that SRMS is efficient and practical.
Monitoring the Health and Residence Conditions of Elderly People, Using LoRa and the Things Network

The rapid development and widespread use of information and telecommunication technologies do not mitigate, in many situations, information exclusion, nor the physical isolation of people—mainly that of the elderly living in remote locations, whose mobile network coverage is deficient or non-existent, preventing them from accessing health care, be it routine follow-up procedures or emergencies. Addressing this, we raise the question that guides our study: how can we monitor the elderly’s residence and health conditions, detect falls, and track their movement in the vicinity of their homes in a non-intrusive manner? To answer this question, we present a system prototype that uses affordable, low-cost, and low-energy equipment with media and data processing, supported by LoRa (Long Range) and ESP32 microcontrollers, coupling several sensors. As a result, it is possible to monitor sensors that predict and detect falls or other risk events for the user, e.g., fire, with authorized persons and entities, family members, civil protection, and security forces accessing the gathered data, assuring their security. We conclude that the system could decisively improve people’s quality of life, particularly those of the elderly who live in remote places with greater vulnerability.
Smoking Cessation System for Preemptive Smoking Detection

Smoking cessation is a significant challenge for many people addicted to cigarettes and tobacco. Mobile health-related research into smoking cessation is primarily focused on mobile phone data collection either using self-reporting or sensor monitoring techniques. In the past 5 years with the increased popularity of smartwatch devices, research has been conducted to predict smoking movements associated with smoking behaviors based on accelerometer data analyzed from the internal sensors in a user's smartwatch. Previous smoking detection methods focused on classifying current user smoking behavior. For many users who are trying to quit smoking, this form of detection may be insufficient as the user has already relapsed. In this paper, we present a smoking cessation system utilizing a smartwatch and finger sensor that is capable of detecting pre-smoking activities to discourage users from future smoking behavior. Pre-smoking activities include grabbing a pack of cigarettes or lighting a cigarette and these activities are often immediately succeeded by smoking. Therefore, through accurate detection of pre-smoking activities, we can alert the user before they have relapsed. Our smoking cessation system combines data from a smartwatch for gross accelerometer and gyroscope information and a wearable finger sensor for detailed finger bend-angle information. We compare the results of a smartwatch-only system with a combined smartwatch and finger sensor system to illustrate the accuracy of each system. The combined smartwatch and finger sensor system performed at an 80.6% accuracy for the classification of pre-smoking activities compared to 47.0% accuracy of the smartwatch-only system.
Manipulating Avatars for Enhanced Communication in Extended Reality

Avatars are common virtual representations used in Extended Reality (XR) to support interaction and communication between remote collaborators. Recent advancements in wearable displays provide features such as eye and face-tracking, to enable avatars to express non-verbal cues in XR. The research in this paper investigates the impact of avatar visualization on Social Presence and user's preference by simulating face tracking in an asymmetric XR remote collaboration between a desktop user and a Virtual Reality (VR) user. Our study was conducted between pairs of participants, one on a laptop computer supporting face tracking and the other being immersed in VR, experiencing different visualization conditions. They worked together to complete an island survival task. We found that the users preferred 3D avatars with facial expressions placed in the scene, compared to 2D screen attached avatars without facial expressions. Participants felt that the presence of the collaborator's avatar improved overall communication, yet Social Presence was not significantly different between conditions as they mainly relied on audio for communication.