Reviewerly/exHarmony huggingface dataset:
YOLO v5 for SDSB Distant Tiny Object Detection

To reach SDSB (Self-Driving Sweeping Bot) in an efficient-sweeping manner, data collection of visual images regarding sweeping target must be conducted prior to analyze the required sweeping objects with other noises. In this work, three categorized target objects including, fallen leaves, speed bumps and manhole cover etc. are involved in training and validation phases. To reach SDSB with real-time object detection, the work further investigated one-stage of Yolo v5 learning approach of four version including, Yolo v5s, Yolo v5m, Yolo v51, and Yolo v5x, wherein Yolo v5s in terms of its benefits on lower frame rate, high-accuracy, and high-speed characteristics for real-time object detection. Furthermore, to detailed analyze the Yolo v5s performance on training and validation set, several indices including, box loss, objectness loss, classification loss, precision, recall and mean average precision (mAP) in terms of epoch number, are also reported in the experiments.
Multi-Reservoir Echo State Networks with Hodrick–Prescott Filter for nonlinear time-series prediction

The Echo State Network (ESN) is a representative model for reservoir computing, which is capable of high-speed model training for machine learning tasks with time series data. Extended models of the ESN, such as Multi-Reservoir ESNs (MRESNs), have been intensively studied for performance improvement in recent years. In this study, we propose a new model called an HP-MRESN by combining an MRESN with the Hodrick–Prescott (HP) filter for nonlinear time series prediction. The proposed HP-MRESN comprises three basic components: a time series decomposer, a reservoir state extractor, and an ensemble decoder. In the time series decomposer, we recursively leverage the HP filter to decompose original time-series data into multiple trend and cycle components. In the reservoir state extractor, each time series component is fed into a corresponding reservoir-state encoder for generating a reservoir state which is extracted as it is or through the principal component analysis. In the ensemble decoder, the states of multiple reservoirs are collected and processed to produce model outputs. Moreover, we propose a greedy algorithm to automatically find the best model architectures under designated hyperparameters for different prediction tasks. Experimental results on a total of 24 nonlinear time-series prediction tasks with 6 real-world datasets demonstrate that our proposed HP-MRESN not only can outperform some existing representative MRESN models and fully-trained RNN models but also can have relatively low training time. In addition, performance comparisons between the HP-MRESN and related MRESN models with other prepossessing methods show the benefit of time series decompositions using the HP filter. The codes of the proposed method are publicly available on https://github.com/Ziqiang-IRCN/HP-MRESN.
An Improved Lightweight User Authentication Scheme for the Internet of Medical Things

The Internet of Medical Things (IoMT) is used in the medical ecosystem through medical IoT sensors, such as blood glucose, heart rate, temperature, and pulse sensors. To maintain a secure sensor network and a stable IoMT environment, it is important to protect the medical IoT sensors themselves and the patient medical data they collect from various security threats. Medical IoT sensors attached to the patient's body must be protected from security threats, such as being controlled by unauthorized persons or transmitting erroneous medical data. In IoMT authentication, it is necessary to be sensitive to the following attack techniques. (1) The offline password guessing attack easily predicts a healthcare administrator's password offline and allows for easy access to the healthcare worker's account. (2) Privileged-insider attacks executed through impersonation are an easy way for an attacker to gain access to a healthcare administrator's environment. Recently, previous research proposed a lightweight and anonymity preserving user authentication scheme for IoT-based healthcare. However, this scheme was vulnerable to offline password guessing, impersonation, and privileged insider attacks. These attacks expose not only the patients' medical data such as blood pressure, pulse, and body temperature but also the patients' registration number, phone number, and guardian. To overcome these weaknesses, in the present study we propose an improved lightweight user authentication scheme for the Internet of Medical Things (IoMT). In our scheme, the hash function and XOR operation are used for operation in low-spec healthcare IoT sensor. The automatic cryptographic protocol tool ProVerif confirmed the security of the proposed scheme. Finally, we show that the proposed scheme is more secure than other protocols and that it has 266.48% better performance than schemes that have been previously described in other studies.
Energy Savings in Buildings Based on Image Depth Sensors for Human Activity Recognition

A smart city is a city that binds together technology, society, and government to enable the existence of a smart economy, smart mobility, smart environment, smart living, smart people, and smart governance in order to reduce the environmental impact of cities and improve life quality. The first step to achieve a fully connected smart city is to start with smaller modules such as smart homes and smart buildings with energy management systems. Buildings are responsible for a third of the total energy consumption; moreover, heating, ventilation, and air conditioning (HVAC) systems account for more than half of the residential energy consumption in the United States. Even though connected thermostats are widely available, they are not used as intended since most people do not have the expertise to control this device to reduce energy consumption. It is commonly set according to their thermal comfort needs; therefore, unnecessary energy consumption is often caused by wasteful behaviors and the estimated energy saving is not reached. Most studies in the thermal comfort domain to date have relied on simple activity diaries to estimate metabolic rate and fixed values of clothing parameters for strategies to set the connected thermostat’s setpoints because of the difficulty in tracking those variables. Therefore, this paper proposes a strategy to save energy by dynamically changing the setpoint of a connected thermostat by human activity recognition based on computer vision preserving the occupant’s thermal comfort. With the use of a depth sensor in conjunction with an RGB (Red–Green–Blue) camera, a methodology is proposed to eliminate the most common challenges in computer vision: background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance on human detection. Moreover, a Recurrent Neural Network (RNN) is implemented for human activity recognition (HAR) because of its data’s sequential characteristics, in combination with physiological parameters identification to estimate a dynamic metabolic rate. Finally, a strategy for dynamic setpoints based on the metabolic rate, predicted mean vote (PMV) parameter and the air temperature is simulated using EnergyPlus™ to evaluate the energy consumption in comparison with the expected energy consumption with fixed value setpoints. This work contributes with a strategy to reduce energy consumption up to 15% in buildings with connected thermostats from the successful implementation of the proposed method.
Federated Learning to Safeguard Patients Data: A Medical Image Retrieval Case

Healthcare data are distributed and confidential, making it difficult to use centralized automatic diagnostic techniques. For example, different hospitals hold the electronic health records (EHRs) of different patient populations; however, transferring this data between hospitals is difficult due to the sensitive nature of the information. This presents a significant obstacle to the development of efficient and generalizable analytical methods that require a large amount of diverse Big Data. Federated learning allows multiple institutions to work together to develop a machine learning algorithm without sharing their data. We conducted a systematic study to analyze the current state of FL in the healthcare industry and explore both the limitations of this technology and its potential. Organizations share the parameters of their models with each other. This allows them to reap the benefits of a model developed with a richer data set while protecting the confidentiality of their data. Standard methods for large-scale machine learning, distributed optimization, and privacy-friendly data analytics need to be fundamentally rethought to address the new problems posed by training on diverse networks that may contain large amounts of data. In this article, we discuss the particular qualities and difficulties of federated learning, provide a comprehensive overview of current approaches, and outline several directions for future work that are relevant to a variety of research communities. These issues are important to many different research communities.
Evaluation of deep unsupervised anomaly detection methods with a data-centric approach for on-line inspection

Anomaly detection methods are used to find abnormal states, instances or data points that differ from a normal sample from the data domain space. Industrial processes are a domain where predictive models are needed for finding anomalous data instances for quality enhancement and to advance zero defect manufacturing objectives. A main challenge in this domain, however, is absence of labels for training supervised models. The potentials of domain randomisation and synthetic data in-the-loop are illustrated by a use case from additive manufacturing for automotive components. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production by unsupervised anomaly detection. Based on a combination of WGAN and encoder CNN, adapted from f-AnoGAN, a solution with high potential for on-line anomaly detection is analysed. In an subsequent evaluation, we compare different metrics and neural network features for an automatic differentiation between normal or anomalous samples. We prove that using clustering methods with features generated by the discriminator yields better results than computing an anomaly score solely.
Real-time medical image encryption for H-IoT applications using improved sequences from chaotic maps

A new medical cryptosystem based on four chaotic maps is presented as a case study. The message queuing telemetry transport (MQTT) protocol is used in this research to propose an end-to-end chaotic encryption technique that would enhance security and secrecy to the transmission of medical images from any Healthcare Internet of Things (H-IoT) device connected to the Internet. The maximal Lyapunov exponent (LE), the analysis of bifurcation diagrams, the Statistical Test Suit from the National Institute of Standard and Technology in the Special Publication 800–22 (NIST SP 800–22), and TestU01 statistical tests are used to evaluate the randomness of the sequences. The proposed medical image encryption technique is confirmed to be strong enough to withstand most current assaults, including differential attacks, the correlation between adjacent pixels, statistical histograms, entropy, key-space, and key-space attacks. The suggested cryptosystem is suitable for edge computing devices and has throughputs of up to 10.53 Mbit/s on a Raspberry Pi 4 (RPi4) and 47.44 Mbit/s utilizing a 2.9 GHz CPU on a desktop PC with improved sequences from the Logistic 1D map. Because of this, the proposed embedded medical cryptosystem may be used to strengthen the security of real-time medical image transmission across WiFi networks and the Internet.
SA-SGRU: Combining Improved Self-Attention and Skip-GRU for Text Classification

When reading texts for text classification tasks, a large number of words are irrelevant, and in text classification tasks, the traditional self-attention mechanism has the problem of weight distribution limitations. Therefore, a text classification model that combines an improved self-attention mechanism with a Skip-GRU (Skip-grate recurrent unit) network (SA-SGRU) is proposed in this paper. Firstly, Skip-GRU, the enhanced model of GRU (Grate Recurrent Unit), is used to skip the content that is not important for text classification when reading texts and only capture effective global information. Then, the improved self-attention mechanism is introduced to redistribute the weight of the deep text sequences. Secondly, the optimized CNN (convolutional neural network) is combined to bring up the local features of texts. Finally, a Softmax classifier is used to obtain the classification results of sample labels. Experimental results show that the proposed method can achieve better performance on three public datasets compared with other baseline methods. The ablation experiments also demonstrate the effectiveness of each module in the proposed model.
Time to Think “Meta”: A Critical Viewpoint on the Risks and Benefits of Virtual Worlds for Mental Health

The metaverse is gaining traction in the general population and has become a priority of the technological industry. Defined as persistent virtual worlds that exist in virtual or augmented reality, the metaverse proposes to afford a range of activities of daily life, from socializing and relaxing to gaming, shopping, and working. Because of its scope, its projected popularity, and its immersivity, the metaverse may pose unique opportunities and risks for mental health. In this viewpoint article, we integrate existing evidence on the mental health impacts of video games, social media, and virtual reality to anticipate how the metaverse could influence mental health. We outline 2 categories of mechanisms related to mental health: experiences or behaviors afforded by the metaverse and experiences or behaviors displaced by it. The metaverse may benefit mental health by affording control (over an avatar and its virtual environment), cognitive activation, physical activity, social connections, and a sense of autonomy and competence. However, repetitive rewarding experiences may lead to addiction-like behaviors, and high engagement in virtual worlds may facilitate and perpetuate the avoidance of challenges in the offline environment. Further, time spent in virtual worlds may displace (reduce) other determinants of mental health, such as sleep rhythms and offline social capital. Importantly, individuals will differ in their uses of and psychological responses to the metaverse, resulting in heterogeneous impacts on their mental health. Their technological motivations, developmental stage, sociodemographic context, and prior mental health problems are some of the factors that may modify and frame the positive and negative effects of the metaverse on their mental health. In conclusion, as the metaverse is being scaffolded by the industry and by its users, there is a window of opportunity for researchers, clinicians, and people with lived experience to coproduce knowledge on its possible impacts on mental health and illness, with the hope of influencing policy-making, technological development, and counseling of patients.
A Metamodeling Approach for IoT Forensic Investigation

The Internet of Things (IoT) Investigation of Forensics (IoTFI) is one of the subdomains of Digital Forensics that aims to record and evaluate incidents involving the Internet of Things (IoT). Because of the many different standards, operating systems, and infrastructure-based aspects that make up the Internet of Things industry, this sector is extremely varied, ambiguate, and complicated. Many distinct IoTFI models and frameworks were developed, each one based on a unique set of investigation procedures and activities tailored to a particular IoT scenario. Because of these models, the domain becomes increasingly complicated and disorganized among those who perform domain forensics. As a result, the IoTFI domain does not have a general model for managing, sharing, and reusing the processes and activities that it offers. With the use of the metamodeling development process, this work aims to create an Internet of Things Forensic Investigation Metamodel (IoTFIM) for the IoTFI domain. Utilizing the metamodeling development process allows for the construction and validation of a metamodel and the verification that the metamodel is both comprehensive and consistent. The IoTFIM is divided into two phases: the first phase identifies the problem, and the second phase develops the IoTFIM. It is utilized to structure and organize IoTFI domain knowledge, which makes it easier for domain forensic practitioners to manage, organize, share, and reuse IoTFI domain knowledge. The purpose of this is to detect, recognize, extract, and match various IoTFI processes, concepts, activities, and tasks from various IoTFI models in an IoTFIM that was established, facilitating the process of deriving and instantiating solution models for domain practitioners. Utilizing several metamodeling methodologies, we were able to validate the generated IoTFMI’s consistency as well as its applicability (comparison against other models, frequency-based selection). Based on the findings, it can be concluded that the built IoTFIM is consistent and coherent. This makes it possible for domain forensic practitioners to simply instantiate new solution models by picking and combining concept elements (attribute and operations) based on the requirements of their models.
A new method for high dynamic range 3D measurement combining adaptive fringe projection and original-inverse fringe projection

For fringe projection profilometry (FPP), high reflections on metal, plastic, and ceramic surfaces cause intensity saturation, resulting in three dimensional (3D) measurement errors. The measurement efficiency using the multiple exposure method (MEM) is low and the exposure time is difficult to quantify. The original and inverse fringe projection method (OIFPM) suffers from saturation of the opposite fringes on highly reflective surfaces. The adaptive fringe projection method (AFPM) is still limited by image dynamic range and pixel mapping accuracy. To this end, this paper proposes a new scheme combining OIFPM and AFPM, called the original and inverse adaptive fringe projection method (OIAFPM). Firstly, an adaptive intensity template in the camera coordinate system is generated. Secondly, a pixel mapping between the camera and projector images is established. Thirdly, original and inverse adaptive fringes are generated and projected. Finally, 72 wrapped phase calculation equations are given in detail. Experiments confirm that the 3D measurement employing the proposed OIAFPM is much better than that employing AFPM and OIFPM. Although the 3D measurement employing MEM is similar to that employing OIAFPM, the measurement efficiency employing OIAFPM is much higher than that employing MEM.
FECANet: Boosting Few-Shot Semantic Segmentation With Feature-Enhanced Context-Aware Network

Few-shot semantic segmentation is the task of learning to locate each pixel of the novel class in the query image with only a few annotated support images. The current correlation-based methods construct pair-wise feature correlations to establish the many-to-many matching because the typical prototype-based approaches cannot learn fine-grained correspondence relations. However, the existing methods still suffer from the noise contained in naive correlations and the lack of context semantic information in correlations. To alleviate these problems mentioned above, we propose a Feature-Enhanced Context-Aware Network (FECANet). Specifically, a feature enhancement module is proposed to suppress the matching noise caused by inter-class local similarity and enhance the intra-class relevance in the naive correlation. In addition, we propose a novel correlation reconstruction module that encodes extra correspondence relations between foreground and background and multi-scale context semantic features, significantly boosting the encoder to capture a reliable matching pattern. Experiments on PASCAL-$5^i$ and COCO-$20^i$ datasets demonstrate that our proposed FECANet leads to remarkable improvement compared to previous state-of-the-arts, demonstrating its effectiveness.
Joint contrastive triple-learning for deep multi-view clustering

Deep multi-view clustering (MVC) is to mine and employ the complex relationships among views to learn the compact data clusters with deep neural networks in an unsupervised manner. The more recent deep contrastive learning (CL) methods have shown promising performance in MVC by learning cluster-oriented deep feature representations, which is realized by contrasting the positive and negative sample pairs. However, most existing deep contrastive MVC methods only focus on the one-side contrastive learning, such as feature-level or cluster-level contrast, failing to integrating the two sides together or bringing in more important aspects of contrast. Additionally, most of them work in a separate two-stage manner, i.e., first feature learning and then data clustering, failing to mutually benefit each other. To fix the above challenges, in this paper we propose a novel joint contrastive triple-learning framework to learn multi-view discriminative feature representation for deep clustering, which is threefold, i.e., feature-level alignment-oriented and commonality-oriented CL, and cluster-level consistency-oriented CL. The former two submodules aim to contrast the encoded feature representations of data samples in different feature levels, while the last contrasts the data samples in the cluster-level representations. Benefiting from the triple contrast, the more discriminative representations of views can be obtained. Meanwhile, a view weight learning module is designed to learn and exploit the quantitative complementary information across the learned discriminative features of each view. Thus, the contrastive triple-learning module, the view weight learning module and the data clustering module with these fused features are jointly performed, so that these modules are mutually beneficial. The extensive experiments on several challenging multi-view datasets show the superiority of the proposed method over many state-of-the-art methods, especially the large improvement of 15.5% and 8.1% on Caltech-4V and CCV in terms of accuracy. Due to the promising performance on visual datasets, the proposed method can be applied into many practical visual applications such as visual recognition and analysis. The source code of the proposed method is provided at https://github.com/ShizheHu/Joint-Contrastive-Triple-learning.