Reviewerly/exHarmony huggingface dataset:
Effects of Wearing Knee-tightening Devices and Presenting Shear Forces to the Knee on Redirected Walking

Natural and realistic walking experience in the virtual environment is critical in increasing immersion in virtual reality. Redirected walking (RDW) was proposed to improve the walking experience, and many studies were conducted to expand its detection threshold (DT). In this study, we investigated the effect on redirected walking when the knee was subjected to the hanger reflex, which creates the illusion of a strong force by applying a shearing force to the skin. The results suggest that the hanger reflex at the knee affects gait, but contrary to expectations, the hanger reflex at the knee did not affect the DT of RDW, and wearing the device on the knee itself expands the DT. Although the results are not what one would expect, the findings of this study provide an interesting perspective for more effective RDW in the future.
Using Gesture Recognition for AGV Control: Preliminary Research

In this paper, we present our investigation of the 2D Hand Gesture Recognition (HGR) which may be suitable for the control of the Automated Guided Vehicle (AGV). In real conditions, we deal with, among others, a complex background, changing lighting conditions, and different distances of the operator from the AGV. For this reason, in the article, we describe the database of 2D images created during the research. We tested classic algorithms and modified them by us ResNet50 and MobileNetV2 which were retrained partially using the transfer learning approach, as well as proposed a simple and effective Convolutional Neural Network (CNN). As part of our work, we used a closed engineering environment for rapid prototyping of vision algorithms, i.e., Adaptive Vision Studio (AVS), currently Zebra Aurora Vision, as well as an open Python programming environment. In addition, we shortly discuss the results of preliminary work on 3D HGR, which seems to be very promising for future work. The results show that, in our case, from the point of view of implementing the gesture recognition methods in AGVs, better results may be expected for RGB images than grayscale ones. Also using 3D imaging and a depth map may give better results.
Predict Inter-photo Visual Similarity via Pre-trained Computer Vision Models

There were three types of photo sets, 200 student life photos, 242 townscapes, and 100 garden landscapes. The student life photos included various photos, including landscapes, objects, people, and food. On the other hand, there were only garden photos in the garden landscapes. There was some variation of photos in the townscapes. The inter-photo visual similarity had been measured manually for each photo set. Dividing the photo set randomly into training and test data parts, the visual similarity was predicted via pre-trained computer vision models (CVMs), such as CNN, Vision Transformer (ViT), and CLIP. The prediction accuracy could be measured by the trace of the correlation matrix between the original and restored three dimensional MDS coordinates aligned by the Procrustes transformation. Three photo sets were used as a benchmark for the predictive power of CVMs. The image features by ViT models pre-trained on ImageNet-21K and by image encoders of CLIP showed high predictive power in any photo set. Combining different CVMs increased the predictive power. The MDS first axis was well restored for any photo set. For the student life photos with a large photo variation, the MDS from the visual similarity and the MDS from the best CVM was found almost the same, so there seemed no need to predict the visual similarity. For the garden landscapes with a small photo variation, the visual similarity prediction using CVMs was not so successful in the second and third axis of MDS.
Using Siamese Neural Networks for Efficient and Accurate IoT Device Identification

With the wide adoption of Internet of Things (IoT) devices, it becomes crucial to identify which IoT devices are connected to the network at a specific time. Previous studies have built machine learning models that can accurately identify IoT devices on a specific network based on their traffic characteristics. However, one limitation of such models is that whenever a new device joins the network, the model has to be retrained from scratch, which adds a lot of computation overhead. In this work, we propose the use of Siamese Neural Networks to reduce the retraining frequency of IoT device identification models. We use a public dataset containing traffic features from 10 devices. To validate the proposed idea, we first compare the performance of classical multi-class classification neural networks with Siamese Networks on the task at hand. We see that both networks perform similarly. Then, we build 10 separate models based on Siamese networks, and we train each of them to recognize a different combination of 9 devices. Then, we use each of the trained models to recognize the device that was not part of the training set. We assess the performance of each model, and we compare the results with the ones achieved by the multi-class classification network. We prove that with the proposed approach, similar or even better outcomes are achieved, with the main advantage of not having to retrain. Finally, we test the proposed approach against 2 other datasets: Aalto and UNSW. We compare the outcomes with previous works, and we prove that Siamese Networks achieve a better performance.
FLBCPS: Federated Learning based Secured Computation Offloading in Blockchain-Assisted Cyber-Physical Systems

Mobile-edge computing (MEC) is a in demand method for improving the quality of computation experience on mobile devices (MD) since it helps MD’s to offload computing activities to MEC servers, which provide strong computing capabilities. However, there are certain unresolved concerns in present computation-offloading works: 1) safety issue; 2) joint computation offloading; and 3) flexible optimization. To solve safety and privacy concerns, we use Federated Learning-based blockchain technology, which provides data accuracy and irreversibility in MEC systems. Federated Learning (FL) is a promising technique towards effective machine learning while protecting privacy in dispersed situations such as the Internet of Things (IoT) and MEC. FL’s effectiveness is dependent on a network of participant nodes contributing their data and computational resources to the collective training of a globally model. As a result, preventing malicious nodes from interfering with model training while rewarding trustworthy nodes to assist to the learning process is critical for improved FL security and performance. We created an efficient resource allocation technique that optimizes computational offloading using a Blockchain-based Federated Learning (FL) method in order to add to the literature. The experimental findings show’s that the recommended FLBCPS technique improve system latency while maintaining consensus security.
Cloud Storage and Processing Service Selection considering Tiered Pricing and Data Regulations

Most countries have come up with varied data privacy regulations. Some countries have stringent data storage and processing regulations, while others allow conditional data transfer across borders. For enterprises with geographically distributed data sources (users), it becomes challenging to select cloud data centers that meet data regulations and have minimal overall operational costs. In this work, we propose a joint optimization model for the selection of storage and processing services from multiple cloud service providers, taking into practical consideration of data regulations and tiered pricing, which has not been addressed in the prior art. To solve this hard multi-objective combinatorial optimization problem, we propose a greedy cost-reductionbased algorithm. The proposed algorithm gives multiple nondominating solutions, which are on average 2.5% away from the optimal solution. Further, we demonstrate the implications of data regulations and the benefits of joint optimization.
On the Power Consumption of Serverless Functions: An Evaluation of OpenFaaS

The rapid growth in cloud-based technologies has introduced the need for very large data centres to meet the increasing demand for cloud services. One of the main challenges in managing these data centres is the sharp increase of power consumption. Research has therefore tackled the issue of power/energy efficiency in cloud data centres. Serverless computing is a cloud computing execution model that gives software developers the option to deploy their code without the need to configure servers, operating systems or runtime libraries, thus allowing them to invest less effort and capital in infrastructure management. This paper investigates whether serverless computing has the ability to support power efficiency. To this aim, a number of experiments are conducted to compare the power consumption of a serverless platform, OpenFaaS, against Docker containers with the consideration of applications and benchmarks. The experimental results show that OpenFaaS is more power efficient than Docker when the processor and memory are under stress.
Design of a Process and a Container-Based Cloud Architecture for the Automatic Generation of Storyline Visualizations

This paper addresses the goal of automated creation of intuitive graphical representations of textual descriptions, in particular utilizing the popular storyline visualization paradigm - a diagram that describes a temporal sequence of interactions among several actors. We propose a container-based architecture that integrates natural language processing and information visualization components, which is flexible enough to be deployed on cloud platforms, including Hybrid and MultiCloud ones. The methodology and the architecture that we propose are driven by a motivating example, focusing on the automatic creation of visualizations for textual descriptions of movie synopses. Anyway, the approach devised would be relevant in several domains, such as Cultural Heritage and Smart Tourism, e.g. to create an effective and easy to understand visualization of historical events.
Causal Discovery for Observational Sciences Using Supervised Machine Learning

Causal inference can estimate causal effects, but unless data are collected experimentally, statistical analyses must rely on pre-specified causal models. Causal discovery algorithms are empirical methods for constructing such causal models from data. Several asymptotically correct discovery methods already exist, but they generally struggle on smaller samples. Moreover, most methods focus on very sparse causal models, which may not always be a realistic representation of real-life data generating mechanisms. Finally, while causal relationships suggested by the methods often hold true, their claims about causal non-relatedness have high error rates. This non-conservative error trade off is not ideal for observational sciences, where the resulting model is directly used to inform causal inference: A causal model with many missing causal relations entails too strong assumptions and may lead to biased effect estimates. We propose a new causal discovery method that addresses these three shortcomings: Supervised learning discovery (SLdisco). SLdisco uses supervised machine learning to obtain a mapping from observational data to equivalence classes of causal models. We evaluate SLdisco in a large simulation study based on Gaussian data and we consider several choices of model size and sample size. We find that SLdisco is more conservative, only moderately less informative and less sensitive towards sample size than existing procedures. We furthermore provide a real epidemiological data application. We use random subsampling to investigate real data performance on small samples and again find that SLdisco is less sensitive towards sample size and hence seems to better utilize the information available in small datasets.
Information Security Risk Awareness Survey of Non-governmental Organization in Saudi Arabia

Nowadays, the adoption of digital technology for NGOs (Non-Governmental Organizations) has become essential and unavoidable no matter how small the NGO are. Using technology come with its risk and opportunities; to control the risk an appropriate information security risk assessment methodology should be adopted. It helps to assess the risks and identify security requirements to protect information as well as maintain its confidentiality, integrity and availability. Furthermore, discovering vulnerabilities on the systems and defending threats help reduce risks and control the level of uncertainty NGOs facing. However, complying with information system risk assessments standards requires knowledge and experience on information security management which is a challenge for most of NGOs in Saudi Arabia as often lack resources. This paper contributes to demonstrates an analysis approach providing insight into the current awareness of information security risks in NGOs in Saudi Arabia. A survey was conducted on a sample of 168 NGOs accredited by the Ministry of Human Resources and Social Development (MHRSD) in Saudi Arabia were selected using a multi-stage stratified sampling approach. The results show a lack of security awareness in terms of protecting information security and the need for a straightforward tool to help assisting the information security risk with limited expertise and recourses.
Learning-Based Path Planning Under Co-Safe Temporal Logic Specifications

This paper presents a path planning algorithm for efficiently generating low-cost trajectories that meet mission requirements specified in Linear Time Logic (LTL), where cost functions are defined throughout the configuration space.The main idea of the paper is to increase efficiency by adding learningbased extensions to sampling-based path planning algorithms.The proposed method includes two layers: a high-level layer that determines how to expand the search tree to satisfy logical specifications and a low-level layer that extends the search tree to search for low-cost trajectories.To efficiently find low-cost trajectories, it is learned how to extend the search tree from the data via deep learning.By leveraging the conditional variational autoencoder, we learn the ideal search tree extension distribution in a given situation, which increases solution search efficiency.Simulations show that the proposed method finds a lowcost trajectory while meeting a given mission specification.Furthermore, it is confirmed that the proposed approach performs better than the existing methods.
Globally Optimal Robust Radar Calibration in Intelligent Transportation Systems

Radar is among the most popular sensors in modern Intelligent Transportation Systems (ITSs), enabling weather-robust perception. The orientation and position of the traffic radar relative to the ITS coordinate system are necessary for the perception fusion in ITSs. However, due to the unknown target association, sparseness and noisiness of traffic radar measurements, the robust and accurate extrinsic calibration of traffic radar is challenging. In this paper, we propose a targetless traffic radar calibration method based on GPS to overcome the inconvenience during ITS operation, because the installation of a dedicated calibration target on the highway is impractical and dangerous. On the other hand, the high-precision GPS device installed on the moving vehicle can provide traffic radar with accurate positioning information of the detection target. Furthermore, during the optimization process of extrinsic calibration, we propose a globally optimal registration method, which is robust to noise and outliers in radar measurements, and is called Gaussian Mixture Robust Branch and Bound (GMRBnB). Specifically, we first construct the robust objective function by utilizing the Gaussian Mixture Model (GMM). Then, we derive novel relaxation bounds and present the GMRBnB algorithm that overcomes the susceptibility to local minima and the dependence on initialization of traditional optimization methods. Compared with existing methods, extensive experiments in synthetic and real-world data demonstrate that our method is not only globally optimal, but also more accurate and robust.
Cloud Image Retrieval for Sea Fog Recognition (CIR-SFR) Using Double Branch Residual Neural Network

Sea fog is a common weather phenomenon at sea, which reduces visibility and causes tremendous hazards to marine transportation, marine fishing and other maritime operations. Traditional sea fog monitoring methods have enormous difficulties in characterizing the diversity of sea fog and distinguishing sea fog from low-level clouds. Thus, we propose a cloud image retrieval method for sea fog recognition (CIR-SFR) in a deep learning framework by combining the advantages of metric learning. CIR-SFR includes the feature extraction module and the retrieval-based sea fog recognition module. The feature extraction module adopts the Double Branch Residual Neural Network (DBRNN) to comprehensively extract the global and local features of cloud images. By introducing local branches and using activation masks, DBRNN can focus on regions of interest in cloud images. Moreover, cloud image features are projected into the semantic space by introducing Multi-Similarity Loss, which effectively improves the discrimination ability of sea fog and low-level clouds. For the retrieval-based sea fog recognition module, similar cloud images are retrieved from the cloud image dataset according to the distance in the feature space, and accurate sea fog recognition results are obtained by counting the percentage of various cloud image types in the retrieval results. To evaluate the sea fog recognition system, we establish a dataset of 2544 cloud images including clear sky, low-level cloud, medium high cloud and sea fog. Experimental results show that the proposed method outperforms the traditional methods in sea fog recognition, which provides a new way for sea fog recognition.
S-QRD-ELM: Scalable QR-Decomposition-Based Extreme Learning Machine Engine Supporting Online Class-Incremental Learning for ECG-Based User Identification

User identification enables secure access to data and machines in smart factories. Compared with other modalities, ECG-based user identification is rising due to its intrinsic liveness proof and invulnerability to spoofing without contact. On the other hand, as new employees are registered at the factory, the ECG-based user identification system needs to be updated based on the new coming data. This scenario can be defined as an online class-incremental learning (O-CIL) problem. By exploiting hardware-software co- design, this work presents a Scalable QR-decomposition-based extreme learning machine (S-QRD-ELM) engine that can effectively and efficiently support O-CIL for ECG-based user identification. At the software level, we apply the concept of “the others” class and inversion-free QR-decomposition (QRD) recursive least squares to the S-QRD-ELM. This makes S-QRD-ELM achieve 79.7% higher accuracy in the O-CIL scenario compared with the neural network trained with back-propagation (BP-NN). At the hardware level, a one-dimensional diagonally-mapped linear array (1D-DMLA) is proposed to efficiently compute the QRD and back-substitution (BS) operations inside the S-QRD-ELM, reducing 98.5% of the silicon area. Moreover, the integrated processing element (PE) design with the unified COordinate Rotation DIgital Computer (u-CORDIC) further reduces 15.3% of the area and 22.4% of the power consumption. This engine is fabricated in 40nm CMOS technology with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.33\times 1.33$ </tex-math></inline-formula> mm2 die area. The chip achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.02\mu \text{J}$ </tex-math></inline-formula> /sample and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.47\mu \text{J}$ </tex-math></inline-formula> /sample inferencing and learning energy efficiency, respectively, which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.4\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$28.5\times $ </tex-math></inline-formula> than the state-of-the-art. To the best of our knowledge, the proposed highly energy-efficient S-QRD-ELM engine is the first chip to meet the requirements of O-CIL for ECG-based user identification.
HybridMatch: Semi-Supervised Facial Landmark Detection via Hybrid Heatmap Representations

Facial landmark detection is an essential task in face-processing techniques. Traditional methods however require expensive pixel-level labels. Semi-supervised facial landmark detection has been explored as an alternative but previous approaches only focus on training-oriented issues (e.g., noisy pseudo-labels in the semi-supervised learning), neglecting task-oriented issues (i.e., the quantization error in the landmark detection). We argue that semi-supervised landmark detectors should resolve the two technical issues simultaneously. Through a simple experiment, we found that task- and training-oriented solutions may negatively influence each other, thus eliminating their negative interactions is important. To this end, we devise a new heatmap regression framework via hybrid representation, namely HybridMatch.We utilize both 1-D and 2-D heatmap representations. Here, the 1-D and 2-D heatmap help alleviate the task-oriented and the training-oriented issues, respectively. To exploit the advantages of our hybrid representation, we introduce curriculum learning; relying more on the 2-D heatmap at the early training stage and gradually increasing the effects of the 1-D heatmap. By resolving the two issues simultaneously, we can capture more precise landmark points than existing methods with only a few annotated data. Extensive experiments show that HybridMatch achieves state-of-the-art performance on three benchmark datasets, especially showing 26.3% NME improvement over the existing method in the 300-W <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">full</i> set at 5% data ratio. Surprisingly, our method records a comparable performance, 5.04 ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">challenging</i> set in the 300-W) to the fully-supervised facial landmark detector 5.03. The remarkable performance of HybridMatch shows its potential as a practical alternative to the fully-supervised model.