Miracle-dz/UniFAR-FTUs huggingface dataset:
Density And Dynamic Time Warping Based Spatial Clustering For Appliance Operation Modes

Household demand response (DR) is an important research problem that aims to modify consumer’s energy consumption. One of the promising areas is clustering Appliance Operation Modes (AOMs) and inducing DR by promoting consumption patterns that use less energy-intensive modes. This work proposes a novel clustering approach (DDTWSC) which aims to cluster AOMs based on the similarity of the appliance load profiles (SUPs). DDTWSC leverages the power of the DensityBased Spatial Clustering of Applications with Noise (DBSCAN) algorithm to partition the appliance load profiles into clusters of similar profiles that share the same AOM. Within DBSCAN, to measure the similarity between SUPs, the Dynamic Time Warping (DTW) algorithm is used. The resulting clustering is evaluated against two publicly datasets, namely RAE and UK-DALE. The Silhouette score is used to measure the performance of the proposed technique in clustering SUPs. DDTWSC demonstrated a significant improvement in the results compared to similar previous work in the literature.
CARE: Enabling Hardware Performance Counter based Malware Detection Resilient to System Resource Competition

Hardware performance counter (HPC) has been widely used for learning based malware detection model because of its light-weight overhead and universality. Unfortunately, we find that such detection models are susceptible to the fluctuation of HPC caused by resource competition among programs. Hence, detecting malware based on HPC effectively is tricky in the actual multitasking environments. In this paper, we propose CARE, a framework to enable hardware performance counter based malware detection models resilient to resource competition. The key idea of CARE is to take full advantage of typical HPC-level behaviors (i.e., the behavior of the program itself stable and the resource competition always on) in resource competition environments to extract the invariant hidden behind HPC-level behaviors. We first design a benchmark based resource pressure generator to generate controlled resource competition environment for observing typical HPC-level behavior of a program in resource competition environments. We then train a behavior representation network to map HPC-level behaviors in any competition environment into a lowdimensional representations for malware detection. Utilizing three datasets collected with different resource competition types or levels in different application systems (e.g., server or desktop), we show that CARE helps detection models improve their malware detection performance in competition environment. We also demonstrate models with CARE outperform existing detection models and are not sensitive to different application systems. Finally, we demonstrate the strong robustness of CARE and its acceptable computational overhead.
A Privacy-Enhanced Federated Learning Scheme with Identity Protection

Federated learning (FL) is an emerging privacy-preserving machine learning framework, where users individually train a shared global model maintained by a central coordinating server without leaking local data. However, intrinsic privacy issue still exists, e.g., training samples could be revealed from inferring gradients. Moreover, identity privacy will be exposed when a participant performs a task. In this paper, we propose a privacy-preserving federated learning scheme (PPFL), which aims to enhance the privacy protection of participants, including data privacy and identity privacy. Specifically, we propose a lightweight linkable ring signature scheme to protect identity privacy, and we mask gradients via random number generator to protect data privacy. Moreover, compared with existing schemes, the communication overhead in unmasking phase remains constant regardless of the number of participants. Finally, we provide privacy and security analysis and experimental evaluations to corroborate the practical performance of the presented scheme with high efficiency.
Self-heating study of an AlGaN∕GaN-based heterostructure field-effect transistor using ultraviolet micro-Raman scattering

We report micro-Raman studies of self-heating in an AlGaN∕GaN heterostructure field-effect transistor using below (visible 488.0nm) and near (UV 363.8nm) GaN band-gap excitation. The shallow penetration depth of the UV light allows us to measure temperature rise (ΔT) in the two-dimensional electron gas (2DEG) region of the device between drain and source. Visible light gives the average ΔT in the GaN layer, and that of the SiC substrate, at the same lateral position. Combined, we depth profile the self-heating. Measured ΔT in the 2DEG is consistently over twice the average GaN-layer value. Electrical and thermal transport properties are simulated. We identify a hotspot, located at the gate edge in the 2DEG, as the prevailing factor in the self-heating.
A Deep Learning-based 3D-GAN for Glioma Subregions Detection and Segmentation in Multimodal Brain MRI volumes

Accurate, fast, and automatic gliomas segmentation from brain magnetic resonance imaging (MRI) scans is crucial for brain cancer diagnosis and therapy, but it remains, until nowadays, challenging because of variations in location, form, and imaging intensity of gliomas. This paper presents an automatic high- and low-grade glioma (HGG and LGG) sub-regions segmentation technique using deep learning-based three-dimensional generative adversarial network (3D-GAN) that localizes and segments the whole gliomas and their intra-regions, comprising necroses, edemas and active tumors, in multimodal brain MRI volumes of BraTS'2022 Datasets. Experimental tests and evaluations of the proposed GAN model have been realized on both, BraTS'2022 training and validation datasets, containing 5880 brain MRIs corresponding to 1470 different subjects with HGG and LGG of various sizes, forms, locations, and intensities. Dice coefficients for enhancing tumor (ET), whole tumor (WT), and tumor core (TC) reached 0.770, 0.872 and 0.832 respectively on 20 % of the training dataset. On the challenge validation dataset, our model achieved an average ET, WT, and TC Dice score of 0.774, 0.907 and 0.829 respectively.
Prediction-based Congestion Avoidance MAC Protocol for IoV (PCA-MAC)

An Internet of Vehicles (IoV) network consists in a combination of IoT and VANET. It is facing the problem of network congestion. The Medium Access Control (MAC) protocol in the IoV protocol, which is based on the standard 802.11p, gives unsatisfactory performance especially in various traffic conditions. Congestion usually occurs when network capacity is less than channel load. This can block the message exchange and cause additional latency and more collisions. To alleviate the situation, we propose an efficient congestion avoidance MAC scheme for IoV. The aim of this study is to reduce network congestion and messaging overhead. Based on deep reinforcement learning technique, our proposed scheme uses several MAC parameters to avoid or manage detected network congestion. It adjusts the MAC parameters periodically by supervising the behavior of the communication in the vicinity of each vehicle. In this way, it can decrease delay and collision rate, and increase throughput. The efficiency of our scheme is evaluated through extensive simulations, and the simulation results show the effectiveness of the PCA algorithm.
Dynamic Neural Network Accelerator for Multispectral detection Based on FPGA

Multispectral detection is an important and challenging task in computer vision. Two-stream architecture is commonly used in multispectral detection, which requires much more computational resources, more on-chip memory, and higher off-chip bandwidth than traditional CNNs. Compared to static neural networks, dynamic networks have advantages in terms of computational efficiency, adaptiveness, etc. In this paper, we propose an efficient dynamic neural network for multispectral detection. Compared with the unoptimized static model, our proposed model reduces computation by 41% and off-chip memory access by 77% on average with less than 1% decrease in mAP. We design cross-layer scheduling for dynamic networks that can fine-tune the fusion model at runtime. We implement a flexible FPGA-based hardware accelerator to evaluate our design. Experiments demonstrate that our dynamic neural network accelerator achieves a maximum 2.7× inference speedup.
IoT Based Smart Home Appliance Control for detecting the speed using Wifi 802.11a in Comparison with Wi-Max

The purpose of this research is to investigate the viability of utilising Wi-Fi 802.11a for the control of household appliances and to evaluate its performance in comparison to that of WiMAX. The sample size is two, and there are two separate groups for each of them is one hundred. When evaluating the efficiency of the Internet of Things-based household appliances, 200 representative samples in total are used, with a type I error rate of 0.05 and a pertest power of 80%. utilised. WiMAX and WIFI 802.11a are contrasted with regard to how quickly they transfer data. It has been determined that the mean speed of WIFI 802.11a can now reach up to 54 Mbps, while the mean speed of WiMAX is 10 Mbps. WiMAX is not nearly as good as Wi-Fi 802.11a-based home appliance solutions for the Internet of Things (IoT).
Estimating Physical Activity From Incomplete Accelerometer Data in Field Studies

Background: The purpose of this study was to develop a data-driven approach for analyzing incomplete accelerometer data from field-base studies. Methods: Multiple days of accelerometer data from the Stanford Girls health Enrichment Multi-site Studies (N = 294 African American girls) were summed across each minute of each day to produce a composite weekday and weekend day. Composite method estimates of physical activity were compared with those derived from methods typically described in the literature (comparison methods). Results: The composite method retained 99.7% and 100% of participants in weekday and weekend-day analysis, respectively, versus 84.7% to 94.2% and 28.6% to 99.0% for the comparison methods. Average wearing times for the composite method for weekday and weekend day were 99.6% and 98.6%, respectively, 91.7% to 93.9% and 82.3% to 95.4% for the comparison methods. Composite-method physical activity estimates were similar to comparison-methods estimates. Conclusion: The composite method used.
Edge-assisted Federated Learning in Vehicular Networks

Given the plethora of sensors with which vehicles are equipped, today's automated vehicles already generate large amounts of data, and this is expected to increase in the case of autonomous vehicles, to enable data-driven solutions for vehicle control, safety and comfort, as well as to effectively implement convenience applications. It is expected that a crucial role in processing such data will be played by machine learning models, which, however, require substantial computing and energy resources for their training. In this paper, we address the use of cooperative learning solutions to train a Neural Network (NN) model while keeping data local to each vehicle involved in the training process. In particular, we focus on Federated Learning (FL) and explore how this cooperative learning scheme can be applied in an urban scenario where several cars, supported by a server located at the edge of the network, collaborate to train a NN model. To this end, we consider an LSTM model for trajectory prediction - a task that is an essential component of many safety and convenience vehicular applications, and investigate the performance of FL as the number of vehicles contributing to the learning process, and the data set they own, vary. To do so, we leverage realistic mobility traces of a large city and the FLOWER FL platform.
Correlation between Neurophysiological Measures of Consciousness and BCI Performance in a Locked-in Patient

This exploratory study searched for systematic correlations between measures of consciousness in resting-state EEG and the performances reached in subsequently performed P300-based BCI tasks. We opted for measures of consciousness (LZC & power-law exponent) which are reflective for two different properties of brain dynamics, i.e., the complexity and criticality of neural signals. To unravel systematic relationships, we performed correlation analyses on a small, clinical dataset from a locked-in ALS-patient and on the publicly accessible dataset from Won et al. [1] containing data from 55 healthy participants. We detected opposing correlation patterns for the two samples. Whereas increased brain criticality and complexity seemed to be related to higher performance in the healthy participants, strong correlations between both our measures of consciousness and BCI performance in the ALSpatient indicate that decreased brain criticality and decreased brain complexity seemed to be advantageous to reach higher BCI performance in ALS-patients. We interpret this pattern regarding the known increases in functional connectivity in ALS-patients and put up for discussion if the increased functional connectivity with simultaneously decreasing structural connectivity in ALS-patients induces a brain dynamic of higher or even super-criticality, which is disadvantageous for information processing. In this case the brain criticality must be reduced to reenter a range of criticality in brain dynamics to facilitate the performance of tasks in need of a high amount of information processing – a pattern which we observed here.
Redesign of Indonesia E-commerce Online Review using User Information Behavior and Build-Learn-Measure Approaches

Online review is a critical feature in e-commerce to increase users' decisions to buy products. This research found that an e-commerce platform in Indonesia lacked design aspects in its online review and recommender system results that should be improved. This research combined user information behavior (UIB) with a lean startup approach (build-measure-learn). To achieve the expected user goals, designing an online review in e-commerce must consider how users interact with the online review and recommender system. It also should consider the e-commerce users' and owners' perspectives. This research improved and evaluated the design of the existing e-commerce in Indonesia. Firstly, it adopted the UIB approach to detail the design of the e-commerce online review and recommender system's results related to the review features. Then, it performed the initial usability testing to get rapid feedback from ten study participants using the maze tools to measure a usability task and heatmap tracing. Lastly, this research followed up and evaluated the results of the initial usability testing using the build-measure-learn approach. The results revealed that UIB implementation for designing the online review and recommender system results as follows. The redesign had better misclick rate (31.7% compared to original design 41.7%), better average success (46.2% compared to original design 33.3%), easier to use (4.3 in 5 likert scale compared to original design 3.7) and all participants chosen the multiple filter feature compared to single filter in original design and participants chosen to the addition of like and dislike feature in the proposed design. These results were captured from the participants study through rapid feedback based on the user information behavior when using the online review. From a design perspective, it can help an e-commerce designer to improve other aspects of the e-commerce features.
Firewall Management: Rapid Anomaly Detection

The detection of anomalies in large firewall rule sets is a commonly agreed challenge. Several approaches have been proposed in literature but have not been adapted broadly due to their limited performance. In this work, we present an approach that adapts the concept of Header Space Analysis (HSA) to the static analysis of firewall rule sets. The proposed Domain oriented Header Space Analysis (DHSA) enables a domain-specific modeling and the characterization of anomalies which, in turn, can be transferred to classic HSA. Hence, the approach benefits from HSA's efficient solving techniques. Our evaluation demonstrates the scalability of our approach checking very large realistic rule sets within few seconds. In comparison with other state of the art tools from literature, our approach shows at least 115-fold performance gain for real world workloads.
Towards Developing Novel and Intuitive Brain-based Control Interface via On-skin Input

Input is a significant challenge for current wear-able systems in the context of extended reality and metaverse environment due to lack of expressive power, naturalness, intuitiveness and social acceptability. In this paper, we propose a novel control interface based on skin touch that exhibits reasonable compatibility with miniaturized wearable systems and alleviates memorability issues by adding a touch dimension to the repertoire of motor and gestural input patterns. This onskin touch-motion interface allows the body’s surface to be annexed as an input surface of the wearable system with limited input surface, without the need for the skin to be invasively instrumented with sensors, tracking markers, or other on-skin devices. We designed a deep learning-based method to decode the user’s intentional skin-touch using multi-scale temporal and spatial convolution modules for efficient extraction of touch and motor-related features from our challenging dataset, along with a conditional affine transformation module for capturing the inter-relationship between touch and motion features. The proposed method achieved an average classification accuracy of 57.82 (±1.79) % for the touch-motion four classes and 48.12 (±2.05) % for the motion four classes across twelve subjects. Our end-to-end model shows superior performance compared to conventional deep neural networks in terms of classification performance, class confusion, and feature discriminability, but with fewer parameters, demonstrating considerable potential for real-life applications.
Magnetospheric interaction with Triton's ionosphere

The large electron densities measured by the Voyager radio occultation experiment are attributed to the precipitation of magnetospheric electrons with energy > 10 keV. Because the ionospheric electric Pedersen conductivity of Triton is {approximately} (1-2) {times} 10{sup 4} mho and the Alfven conductance is {approximately} 3.5 mho, direct convective flow of plasma into the essentially infinitely conducting ionosphere is negligible. Magnetospheric electrons are transported to Triton's ionopause by curvature drift as a result of weak magnetic field line draping in a sub-Alfvenic plasma interaction with Triton. At the ionopause energetic and inelastic scattering and precipitate into the upper atmosphere. The average power dissipation is estimated to be {approximately} (2-3) {times} 10{sup 8} W.
Piezoelectric Properties and Densification Based on Control of Volatile Mass of Potassium and Sodium in (K0.5Na0.5)NbO3 Ceramics

Recently lead-free ceramic systems have been intensively studied in order to fabricate environmentally friendly piezoelectric ceramics and replace widely used lead-free piezoelectric ceramics. The (K0.5Na0.5)NbO3 (KNN) piezoelectric ceramics with non-lead piezoelectric ceramics were manufactured by conventional solid-state synthesis, and we obtained sintered bodies with relative densities over 97.4% by adding excess K (0.44 mol %)/Na (0.22 mol %) to improve sinterability. The composition of the sintered bodies was close to stoichiometric compounds. The electromechanical coupling factor (kp) and piezoelectric constant (d33) of KNN + excess K (0.44 mol %)/Na (0.22 mol %) composition ceramics showed optimal values of 0.341 and 126 pC/N, respectively.
REMOTE RECOIL AND WAVE CAPTURE: WAVE-VORTEX INTERACTIONS IN ATMOSPHERE-OCEAN MODELS

Summary In a recent paper [1] we described a fundamentally new wave-mean or wave-vortex interaction effect able to force persistent, cumulative change in mean flows in the absence of wave breaking or other kinds of wave dissipation. It is associated with the refraction of nondissipating waves by inhomogeneous mean (vortical) flows. The simplest relevant case is that of a narrow beam of sound waves, or shallow-water gravity waves, weakly refracted by a single vortex in two dimensions. An effective recoil force arises. This accords with expectation from a naive photon analogy or “pseudomomentum rule” except that the force acts not where the waves refract, but at the vortex core, even if the core is spatially separated from the refracting beam of waves. Strong refraction brings further phenomena including catastrophic “wave capture”, a nontrivial variant of classical critical-layer absorption; see the more detailed presentation on this by Buhler in this Minisymposium. One implication is that there are missing forces not yet accounted for in atmospheric climate and weather-prediction models. Connections with the “pseudomomentum rule” and the “wave momentum myth” are discussed.
Interactive and Reliable Graph Processing via the Edge-Cloud Collaboration Framework

Due to the limitations of cloud computing on latency, bandwidth and data confidentiality, edge computing has emerged as a novel location-aware paradigm to provide the capacity constrained portable terminals with more processing capacity to improve the performance and quality of service (QoS) in several typical domains of the human activity in smart society, such as social networks, medical diagnosis, transportation, Internet of Things (IoT), etc. These domains often handle a vast collection of entities with various relationships which can be naturally represented by graph data structures. Graph processing is a powerful tool to model and optimize the complex problems where the graph-based data is involved. In view of the relatively insufficient resource provision of the edge devices, for the first time to our knowledge, we propose a reliable edge-cloud collaboration framework facilitating the shortest path search (SPS) operations based on a lightweight interactive graph processing library. One practical case is presented as well to show the typical application scenarios of our graph processing strategy. Experimental evaluation is conducted to indicate that the proposed strategy is able to efficiently implement the relevant application tasks with considering user-friendliness, low-latency response, collaborations between edge and cloud, interactions among edge devices, and privacy protection at an acceptable overhead.
Comparison of CNN Models in Non-small Lung Cancer Diagnosis

Since the traditional manual method of classifying lung cancer CT photos is very time-consuming, a new automated, highly accurate classification method is urgently needed. This has led to the use of the Convolutional Neural Network (CNN) for image classification and recognition as the best choice in the medical field. However, due to the problem of vanishing gradient when the layer depth of these models is too deep, the gradient will be vanishingly small and this leads to extremely low learning efficiency, which may even be close to 0. In this paper, a reimplemented CNN model based on ResNet is used to improve low learning efficiency. Three different CNNs are also compared and illustrate that the deeper neural networks have better learning efficiency and higher accuracy for classifying lung CT images. Specifically, LeNet, AlexNet, and ResNet are reimplemented, where the first two CNNs represent traditional models. By comparing the accuracy of the three models, we conclude that the traditional model is sufficient for the task of classifying lung cancer models and has an acceptable accuracy rate when the number of training sessions reaches a certain level, but it is not competitive in learning efficiency and accuracy for the same number of training sessions compared to the subsequent models with more neurons.