Reviewerly/exHarmony huggingface dataset:
A Collaborative Elderly Care System using a Companion Robot and a Wearable Device

In this paper, we designed and implemented a collaborative elderly care system (CECAS) using a companion robot and a wearable device. First, we present the overall design of the system. Then, we describe its hardware and software design. Finally, we use three elderly care applications as examples to demonstrate the capability of the proposed system, which include: fall detection and response, conversational assistance, and dietary monitoring. In the fall detection and response application, a two-step method is proposed, which includes a motion data based preliminary detection on the wearable device and a video-based final confirmation on the companion robot. In the conversational assistance application, we implement weather, time, news and jokes skills to test the system. In the dietary monitoring application, in order to reduce the power consumption and improve the accuracy, we develop a wearable- robot-human collaboration mechanism to ensure that the camera captures the best food image at the right time. The results show that the wearable device significantly extended the sensing range of the robot and improved the robot’s capability.
An Improved Hunter-prey Optimization Algorithm and Its Application

Hunter-prey optimization (HPO) algorithm is a new optimization algorithm proposed to simulate the behavior of leopards, lions and other predators in hunting deer and antelope. In order to solve the problems such as insufficient global optimization capability of HPO, easy to fall into local optimization, and low optimization accuracy, an improved Hunter-prey optimization (IHPO) algorithm is proposed. Firstly, Tent chaotic map is used to generate the initial population and increase the diversity of individuals, Secondly, in order to balance the ability of global search in the early stage and local search in the late stage, the enhanced sine cosine algorithm (ESCA) is integrated to adaptively select the population location update mode according to the conversion probability, Finally, Cauchy mutation strategy is adopted in the later stage of iteration to disturb the population position and enhance the ability of the algorithm to jump out of precocity. The simulation results of benchmark functions show that IHPO algorithm has better convergence accuracy and convergence speed. The effectiveness of the improved algorithm is further verified by the simulation experiment of pipe routing examples
Sharing social media data: The role of past experiences, attitudes, norms, and perceived behavioral control

Social media data (SMD) have become an important data source in the social sciences. The purpose of this paper is to investigate the experiences and practices of researchers working with SMD in their research and gain insights into researchers' sharing behavior and influencing factors for their decisions. To achieve these aims, we conducted a survey study among researchers working with SMD. The questionnaire covered different topics related to accessing, (re)using, and sharing SMD. To examine attitudes toward data sharing, perceived subjective norms, and perceived behavioral control, we used questions based on the Theory of Planned Behavior (TPB). We employed a combination of qualitative and quantitative analyses. The results of the qualitative analysis show that the main reasons for not sharing SMD were that sharing was not considered or needed, as well as legal and ethical challenges. The quantitative analyses reveal that there are differences in the relative importance of past sharing and reuse experiences, experienced challenges, attitudes, subjective norms, and perceived behavioral control as predictors of future SMD sharing intentions, depending on the way the data should be shared (publicly, with restricted access, or upon personal request). Importantly, the TPB variables have predictive power for all types of SMD sharing.
The ‘Shift Left’ Principle

Why it’s so important to design and test for security early and continuously when it comes to embedded systems.
Time-sensitive Learning for Heterogeneous Federated Edge Intelligence

Real-time machine learning has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of application domains, including self-driving vehicles, intelligent transportation, and industry automation. We investigate real-time ML in a federated edge intelligence (FEI) system, an edge computing system that implements federated learning (FL) solutions based on data samples collected and uploaded from decentralized data networks. FEI systems often exhibit heterogenous communication and computational resource distribution, as well as non-i.i.d. data samples, resulting in long model training time and inefficient resource utilization. Motivated by this fact, we propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model. Training acceleration solutions for both TS-FL with synchronous coordination (TS-FL-SC) and asynchronous coordination (TS-FL-ASC) are investigated. To address straggler effect in TS-FL-SC, we develop an analytical solution to characterize the impact of selecting different subsets of edge servers on the overall model training time. A server dropping-based solution is proposed to allow slow-performance edge servers to be removed from participating in model training if their impact on the resulting model accuracy is limited. A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, local epoch number. We develop an analytical expression to characterize the impact of staleness effect of asynchronous coordination and straggler effect of FL on the time consumption of TS-FL-ASC. Experimental results show that TS-FL-SC and TS-FL-ASC can provide up to 63% and 28% of reduction, in the overall model training time, respectively.
Validation of classical modeling of single-photon pulse propagation

"It is well-known to those who know it" that single-photon interference experiments can be modeled classically [S. Barnett, arXiv:2207.14632 (2022)]. When a single-photon light pulse was split by a biprism good agreement with a classical fit was obtained and the photon was counted only once, consistent with a probabilistic interpr.etation [V. Jacques et al, Eur. Phys. J. D 35, 561 (2002)]. A justification for this "well know result of Quantum Optics" is implicit in [M. Hawton, Phys. Rev A 104, 052211 (2021)] where a real covariant field describing a single photon is first quantized. Here the theoretical basis of this result is reviewed and the theory is extended to multiphoton states and QED Fock space. The crucial role of the CPT theorem in coupling to charged matter and resolution of the photon localization problem is discussed.
Performance analysis for OFDM-based multi-carrier continuous-variable quantum key distribution with an arbitrary modulation protocol

Multi-carrier continuous-variable quantum key distribution (CV-QKD) is considered to be a promising way to boost the secret key rate (SKR) over the existing single-carrier CV-QKD scheme. However, the extra excess noise induced in the imperfect multi-carrier quantum state preparation process of N subcarriers will limit the performance of the system. Here, a systematic modulation noise model is proposed for the multi-carrier CV-QKD based on the orthogonal frequency division multiplexing (OFDM). Subsequently, the performance of multi-carrier CV-QKD with arbitrary modulation protocol (e.g. QPSK, 256QAM and Gaussian modulation protocol) can be quantitatively evaluated by combining the security analysis method of the single-carrier CV-QKD. Under practical system parameters, the simulation results show that the SKR of the multi-carrier CV-QKD can still be significantly improved by increasing the carrier number N even with imperfect practical modulations. Specifically, the total SKR of multi-carrier CV-QKD can be optimized by carefully choosing N. The proposed model provides a feasible theoretical framework for the future multi-carrier CV-QKD experimental implementation.
Named entity recognition in Odia language: a rule-based approach

NLP can be defined as a process of automatic handling of human spoken languages by computers. NLP is a vast research field linked to multiple areas like artificial intelligence, machine translation, information retrieval etc. NER is an information extraction (IE) process concerned with extracting named entities (NE) from raw data and categorising these NEs into some predefined classes. The process of recognising and extracting NEs from unstructured corpus or data is an essential task for solving different complications in various research fields such as, question answering, summarisation system, information extraction, machine learning, semantic web search, bio-informatics, video annotation and many more. For this research work a classical methodology, i.e., rule-based approach is used to construct a system for automatic identification of NEs from tourism domain data. The system considers words with their repetition and without their repetition and acquires 83% and 71% accuracy respectively.
Managing threats to health data and information: toward security

More than 2000 ransomware and malware attacks occurred in the United States during the first half of 2021. Many of these attacks targeted health systems and healthcare providers, making cybersecurity a top priority for the health industry. Health Information Exchange (HIE) networks face more complex security challenges than healthcare providers, because HIE networks serve as an interface between multiple health system actors, such as hospitals, pharmacies, and primary care doctors. Larger volumes of data could potentially make them bigger targets for cyber-criminals. HIE networks can further help healthcare facilities affected by cyberattacks by providing access to critical data. This means that HIE networks now play a more critical role than ever in ensuring that clinicians have access to the information they need to make decisions. The purpose of this chapter is to introduce information on cybersecurity as it relates to HIE networks. It also discusses several security frameworks, approaches, risk assessments, risk management plans, available resources, and emerging trends in HIE security. The end goal is to provide the reader with sufficient information to improve the security of their organization and serve as a reliable resource in an environment made uncertain by cyberattacks.
Mobius: Fine Tuning Large-Scale Models on Commodity GPU Servers

Fine-tuning on cheap commodity GPU servers makes large-scale deep learning models benefit more people. However, the low inter-GPU communication bandwidth and pressing communication contention on the commodity GPU server obstruct training efficiency.
Algorithmic Harm in Consumer Markets

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Use of Mixed Reality in Neuro-Oncology: A Single Centre Experience

(1) Background: Intra-operative neuronavigation is currently an essential component to most neurosurgical operations. Recent progress in mixed reality (MR) technology has attempted to overcome the disadvantages of the neuronavigation systems. We present our experience using the HoloLens 2 in neuro-oncology for both intra- and extra-axial tumours. (2) Results: We describe our experience with three patients who underwent tumour resection. We evaluated surgeon experience, accuracy of superimposed 3D image in tumour localisation with standard neuronavigation both pre- and intra-operatively. Surgeon training and usage for HoloLens 2 was short and easy. The process of image overlay was relatively straightforward for the three cases. Registration in prone position with a conventional neuronavigation system is often difficult, which was easily overcome during use of HoloLens 2. (3) Conclusion: Although certain limitations were identified, the authors feel that this system is a feasible alternative device for intra-operative visualization of neurosurgical pathology. Further studies are being planned to assess its accuracy and suitability across various surgical disciplines.
Empirical analysis of machine learning-based energy efficient cloud load balancing architectures: a quantitative perspective

Design of energy efficient load balancing models in cloud environments requires in-depth analysis of the cloud architecture and nature of requests served by the cloud. Depending upon these parameters, machine learning models are designed which aim at assigning best possible resource combination to serve the given tasks. This assignment varies w.r.t. multiple task and cloud parameters; which include task time, virtual machine (VM) performance, task deadline, energy consumption, etc. In order to perform this task, a wide variety of algorithms are developed by researchers cloud designers. Each of these algorithms aim at optimizing certain load balancing related parameters; for instance, a Genetic Algorithm (GA) designed for optimization of VM utilization might not consider task deadline before task allocation to the VMs. While, algorithms aimed at performing deadline aware load balancing might not provide effective cloud-to-task-mapping before allocation of tasks. Thus, it becomes difficult for researchers to select the best possible algorithms for their cloud deployment. In order to reduce this ambiguity, the underlying text compares different energy efficient cloud load balancing algorithms; and evaluates their performance in terms of computational complexity, and relative energy efficiency. This performance evaluation is further extended via inter architecture comparison; in order to evaluate the most optimum load balancer implementation for a given energy efficient application. Thus, after referring this text, researchers and cloud system designers will be able to select optimum algorithmic implementations for their given deployment. This will assist in reducing cloud deployment delay, and improving application specific load balancer performance.
A CNN-SVM hybrid model for the classification of thyroid nodules in medical ultrasound images

The thyroid nodule is one of the endocrine issues which is caused by the formation of irregular cells in the thyroid region. The recent success of machine and deep learning techniques in image recognition task leads to solve challenges in diagnostic imaging. An effective Convolutional Neural Network-Support Vector Machine (CNN-SVM) hybrid model is proposed using hinge loss function to achieve better results and stable convergence. The efficiency of the proposed model has been evaluated on public and collected data sets having 1180 and 2616 thyroid USG images after data augmentation. The proposed model has achieved an accuracy of 94.57%, specificity of 91.89%, sensitivity of 96.70% and f-measure of 95.64% on dataset-1 and an accuracy of 96%, specificity of 93.93%, sensitivity of 97.80% and f-measure of 98.33% on dataset-2. It has shown an improvement of 3% to 5% on dataset-1 and 4% to 6% on dataset-2 in comparison with state-of-the-art models.
Cost-effective data replication mechanism modelling for cloud storage

Replication increases data availability and reduces access latency in cloud storage. Solutions prioritise system performance over replica production and storage costs. Under a pay-as-you-go model, cloud users incur rising fees for replica storage and consistency management. Before expanding node copies, we outline a dynamic replication technique. The suggested technique concentrates resources for effective distribution across all nodes. The cost-effectiveness of the proposed model is determined using threshold values; the proposed system reduced cloud replicas, reducing costs. Azure's $6 static solution has three nodes. The adaptive data replication management system reduces copies from three to two, saving memory and reducing price from $6 to $4. MATLAB and other programming languages are used. Azure is compared to Date Replication and Reproducer (DRR). The proposed solution is cheaper than Azure with three replicates.
Are All Combinations Equal? Combining Textual and Visual Features with Multiple Space Learning for Text-Based Video Retrieval

In this paper we tackle the cross-modal video retrieval problem and, more specifically, we focus on text-to-video retrieval. We investigate how to optimally combine multiple diverse textual and visual features into feature pairs that lead to generating multiple joint feature spaces, which encode text-video pairs into comparable representations. To learn these representations our proposed network architecture is trained by following a multiple space learning procedure. Moreover, at the retrieval stage, we introduce additional softmax operations for revising the inferred query-video similarities. Extensive experiments in several setups based on three large-scale datasets (IACC.3, V3C1, and MSR-VTT) lead to conclusions on how to best combine text-visual features and document the performance of the proposed network. (Source code is made publicly available at: https://github.com/bmezaris/TextToVideoRetrieval-TtimesV )
Interrelated effects of chromosome size, mechanics, number, location-orientation and polar ejection force on the spindle accuracy: a 3D computational study

The extension of the 3D computational model presented sheds light on some questions about spindle assembly’s speed and accuracy, such as the effect of chromosome size, number, geometry, and polar ejection force.
SPANet: Spatial Adaptive Convolution Based Content-Aware Network for Aerial Image Semantic Segmentation

Semantic segmentation of remote sensing images encounters four significant difficulties: 1) complex backgrounds, 2) large-scale differences, 3) numerous small objects, and 4) extreme foreground-background imbalance. However, the existing generic semantic segmentation models mainly focus on the modeling context information and rarely focus on these four issues. This article presents an enhanced remote sensing image semantic segmentation framework to solve these problems through the Hierarchical Atrous Pyramid module (HASP) and spatial-adaptive convolution based FPN decoder framework (SPA-FPN). On the one hand, HASP solved the problem of complex backgrounds and large-scale differences by further enlarging the receptive field of the network through the cascade of atrous convolution with various rates. On the other hand, spatial adaptive convolution is embedded in FPN decoder framework step by step to solve the problems of numerous small objects and extreme foreground-background imbalance. Besides, a boundary-based loss function is constructed to help the network optimize the relevant segmentation results. Extensive experiments over iSAID and ISPRS Vaihingen datasets reflect the superiority of the presented structure to conventional state-of-the-art semantic segmentation approaches. The code will be accessible.
Dynamic N:M Fine-Grained Structured Sparse Attention Mechanism

Transformers are becoming the mainstream solutions for various tasks like NLP and Computer vision. Despite their success, the high complexity of the attention mechanism hinders them from being applied to latency-sensitive tasks. One opportunity to accelerate the attention mechanism is leveraging the sparsity in the attention weight matrix. However, due to the dilemma between "dynamic" and "fine-grained", previous studies fail to achieve speedup on GPUs under moderate sequence lengths. They also require costly retraining to recover accuracy. In this paper, we present DFSS, the first GPU-friendly dynamic fine-grained pruning mechanism, to address this dilemma. DFSS dynamically prunes the full attention score matrix to N:M fine-grained structured sparse pattern. Our key insight is that on the dynamic side, N:M sparsity is friendly to pruning and encoding the sparse matrix on GPU. On the fine-grained side, it always preserves the dominant entries in each row. We develop a dynamic sampled dense-dense matrix multiplication kernel, first of its kind, that multiplies the query and key matrices, prunes the result, and encodes the compressed sparse matrix without overhead. Compared with previous studies, DFSS achieves speedup in arbitrary sequence lengths. It only takes a few fine-tuning epochs to reach on-par accuracy with full attention mechanism. We provide both theoretical and empirical evidence to demonstrate DFSS is a good approximation of the full attention mechanism. We evaluate the 1:2 and 2:4 sparsity under different settings and achieve 1.38 ~ 1.86× speedups over the full-attention on A100 GPU. On tasks from various domains with sequence lengths from 384 to 4096, its accuracy is on par with the full attention after only a couple of finetuning epochs from the dense pre-trained model.
Technological pedagogical content knowledge: Exploring new perspectives

Recognising the challenges involved in understanding the knowledge that teachers need to develop to use technology in their teaching dynamics, we examined the prior research that has not clearly revealed strategic changes for teacher preparation in the digital age. The goal was to expand on the current understandings of the nature of technological pedagogical content knowledge (TPCK/TPACK) and provide a launchpad for future research by teacher educators as they contemplate revisions in the education of teachers to better prepare them for teaching in the digital age. To do so, we conducted qualitative meta-synthesis research. Within this context, we identified interpretations and comprehensions that pushed us forward in defence of new perspectives on the nature of this knowledge, regarding the comprehension of TPCK/TPACK as a transformative and homogenous knowledge; TPCK/TPACK’s levels of development, including a new first level; and the need for and challenges of redesigning teacher education. Implications for practice or policy: To better prepare teachers to teach, teachers’ educators should understand TPCK/TPACK as a homogeneous and transformative knowledge. Teachers’ training programmes should realize that often teachers are not aware of the of the possibilities of using technologies to teach. Teachers should have access to continuous learning to keep developing their own TPCK/TPACK during their entire career. Teachers and teachers’ educators should understand TPCK/TPACK developing as a continuous and individual process and not as something standardised.
Moliverse: Contextually embedding the microcosm into the universe

We present Moliverse, an integration of the molecular visualization framework VIAMD into the astronomical visualization software OpenSpace, allowing us to bridge the two extreme ends of the scale spectrum to show, for example, the gas composition in a planet's atmosphere or molecular structures in comet trails and can empower the creation of educational exhibitions. For that purpose we do not use a linear scale traversal but break the scale continuity and show molecular simulations as focus in the context of celestial bodies. We demonstrate the application of our concept in two storytelling scenarios and envision the application both for science presentations to lay audiences and for dedicated exploration, potentially also in a molecule-only environment.