The next generation of Internet services, such as Metaverse, rely on mixed reality (MR) technology to provide immersive user experiences. However, limited computation power of MR headset-mounted devices (HMDs) hinders the deployment of such services. Therefore, we propose an efficient informationsharing scheme based on full-duplex device-to-device (D2D) semantic communications to address this issue. Our approach enables users to avoid heavy and repetitive computational tasks, such as artificial intelligence-generated content (AIGC) in the view images of all MR users. Specifically, a user can transmit the generated content and semantic information extracted from their view image to nearby users, who can then use this information to obtain the spatial matching of computation results under their view images. We analyze the performance of full-duplex D2D communications, including the achievable rate and bit error probability, by using generalized small-scale fading models. To facilitate semantic information sharing among users, we design a contract theoretic AI-generated incentive mechanism. The proposed diffusion model generates the optimal contract design, outperforming two deep reinforcement learning algorithms, i.e., proximal policy optimization and soft actor-critic algorithms. Our numerical analysis experiment proves the effectiveness of our proposed methods.

Android malware attacks are increasing daily at a tremendous volume, making Android users more vulnerable to cyber-attacks. Researchers have developed many machine learning (ML)/ deep learning (DL) techniques to detect and mitigate android malware attacks. However, due to technological advancement, there is a rise in android mobile devices. Furthermore, the devices are geographically dispersed, resulting in distributed data. In such scenario, traditional ML/DL techniques are infeasible since all of these approaches require the data to be kept in a central system; this may provide a problem for user privacy because of the massive proliferation of Android mobile devices; putting the data in a central system creates an overhead. Also, the traditional ML/DL-based android malware classification techniques are not scalable. Researchers have proposed federated learning (FL) based android malware classification system to solve the privacy preservation and scalability with high classification performance. In traditional FL, Federated Averaging (FedAvg) is utilized to construct the global model at each round by merging all of the local models obtained from all of the customers that participated in the FL. However, the conventional FedAvg has a disadvantage: if one poor-performing local model is included in global model development for each round, it may result in an under-performing global model. Because FedAvg favors all local models equally when averaging. To address this issue, our main objective in this work is to design a dynamic weighted federated averaging (DW-FedAvg) strategy in which the weights for each local model are automatically updated based on their performance at the client. The DW-FedAvg is evaluated using four popular benchmark datasets, Melgenome, Drebin, Kronodroid and Tuandromd used in android malware classification research. The results show that our proposed approach is scalable, privacy preserved, and capable of outperforming the traditional FedAvg for android malware classification in terms of accuracy, F1 score, AUC score and FPR score.

Cellular networks are moving to fully automated schemes to minimize human intervention and maximize efficiency. This is what is called the Zero Touch networks paradigm. Until now, such management has been based on Drive Test results together with network-side metrics. However, this approach is clearly insufficient to achieve Zero Touch due to the complexity and high cost of collecting user side data in an increasingly vast and heterogeneous infrastructure. In this regard, the present work describes a novel framework for Zero Touch cellular networks that exploits the advantages of integrating User Equipment metrics. The suitability of the proposal has bee verified using data acquired from a commercial mobile network, achieving promising results through the use of traditional as well as User Equipment side metrics.

Understanding the success of augmentative exoskeletons is critical to their impact on society. Many exoskeletons are developed to address impairments stemming from neuromotor pathology, which  provides guidance for understanding their abilities; however, for augmentative exoskeletons that  assist able-bodied users, a clear metric of success remains an open question. In this study, we leverage  an approach from economics–the Vickrey second-price auction–to quantify the economic value  added by lower-limb exoskeletons during uphill walking. In our protocol, participants describe the  monetary compensation needed to continue walking uphill for consecutive bouts of two minutes. To incentivize truthful descriptions of participant’s “price to walk,” their compensation amounts were  provided as bids in Vickrey auctions before each bout. We compared these data across different  conditions to determine the marginal value of the exoskeleton weight, assistance, and weight +  assistance. Our approach found that the total value of the exoskeleton and its assistance was modest  ($3.40/hr, SD: $3.35/hr); while most participants found the assistance itself valuable ($19.76/hr, SD:  $19.43/hr), this value was nearly offset by the cost of wearing the exoskeleton weight (-$18.59/hr, SD:  $18.28/hr). Our approach and results demonstrate that economic value can be a powerful tool to  develop exoskeletons that maximize user benefit.

The popularity of cryptocurrencies has grown significantly in recent years, and they have become an important asset for internet trading. One of the main drawbacks of cryptocurrencies is the high volatility and fluctuation in value. The value of cryptocurrencies can change rapidly and dramatically, making them a risky investment. Cryptocurrencies are largely unregulated, which can exacerbate their volatility. The high volatility of cryptocurrencies has also led to a speculative bubble, with many investors buying and selling cryptocurrencies based on short-term price fluctuations rather than their underlying values. Therefore, how to reduce the fluctuation risk introduced by exchanges, transform uncertain prices to deterministic value, and promote the benefits of decentralized finance are critical for the future development of cryptos and Web 3.0. To address the issues, this paper proposes a novel theory as Automatic Increase Market Systems (AIMS) for cryptos, which could potentially be designed to automatically adjust the value of a cryptocurrency helping to stabilize the price and increase its value over time in a deterministic manner. We build a crypto, WISH (https://wishbank.wtf),  based on AIMS in order to demonstrate how the automatic increase market system would work in practice, and how it would influence the supply of the cryptocurrency in response to market demand and finally make itself to be a stable medium of exchange, ensuring that the AIMS is fair and transparent.

Nearly 15% of the global population is affected by disabilities impacting mobility. Monitoring foot pressure distribution during gait is a fundamental aspect of evaluating rehabilitation. Wearable systems provide a portable alternative to stationary equipment monitoring gait without laboratory space limitations. However, wearable sensors in some applications present challenges in the calibration, sensitivity, and human-sensor interface, requiring application-specific sensors. This study aimed to develop wearable sensors where the structural and material properties can characterise the sensitivity and range of measurement during the design phase. We developed wearable piezoresistive sensors using additive manufacturing to create mechanical metamaterials with embedded pressure-sensing capabilities. The sensors were fabricated in TPU using SLS and graphene ink infusion processes. Three structural designs were developed for different measuring ranges (0 – 50 N, 0 – 100 N, and 0 – 150 N) using body-centred cubic lattices constructed via pyramid unit cells. Two graphene infusion processes were evaluated. We tested the sensors’ mechanical and piezoresistive behaviour, measuring the compressive force, strain, and electrical resistance across the sensor. We analysed the influence of structural dimensions and the infusion process on the piezoresistive behaviour. The measuring range was affected mainly by tuneable structural dimensions. The infusion process influenced the piezoresistive sensitivity and affected the linearity response. The results indicate the characterisation of the sensitivity of piezoresistive sensors based on structural parameters and material properties. Mechanical metamaterials could embed pressure sensing in wearables, allowing for customisation based on design parameters using additive manufacture and graphene inks.

Efficient time-interleaved analog-to-digital converters (ADCs) that operate at high sample rates with wide input bandwidths are necessary to support increasing wireline transceiver data rates. This paper presents a 7-bit 38GS/s 32-way time-interleaved ADC that utilizes an 8-way interleaver architecture based on a speed-enhanced bootstrapped switch that increases input bandwidth. ADC sample rate and efficiency is improved with pipelined successive approximation register (SAR) unit ADCs that employ an output level shifting (OLS) settling technique in the dynamic residue amplifier to achieve settling in only 33% of the time required for a conventional current-mode logic (CML) amplifier. Using parallel comparators in the two 4-bit asynchronous pipeline stages allows for further improvements in ADC conversion speed. Fabricated in 22nm FinFET, the proposed ADC occupies 0.107mm2 area. Operating at 38GS/s, the ADC achieves 41.9fJ/conv.-step with low input frequencies, 64.1fJ/conv.-step at Nyquist, and has 20GHz 3dB input bandwidth.

This research presents an empirical analysis on the realities of feature extraction and generalization of images of human faces. Experiments performed using principal component analysis (PCA) and histogram of oriented gradients (HOG) provided new insights on the variance of classification accuracy based on the quantity of data, and the actuality of data captured by specific principal components. HOG features provided greater classification accuracy for exact matches, and eigen faces of PCA were more accurate for generalization to previously unseen data. The results presented offer practical insights that can assist in the selection and design of algorithms that utilize PCA or HOG for data analysis. This is of particular significance in avoiding errors when data may undergo variations in sample size.

Rich variations in gait are generated according to several attributes of the individual and environment, such as age, athleticism, terrain, speed, personal ”style”, mood, etc. The effects of these attributes can be hard to quantify explicitly, but relatively straightforward to sample. We seek to generate gait that expresses these attributes, creating synthetic gait samples that exemplify a custom mix of attributes. This is difficult to perform manually, and generally restricted to simple, human-interpretable and handcrafted rules. In this manuscript, we present neural network architectures to learn representations of hard to quantify attributes from data, and generate gait trajectories by composing multiple desirable attributes. We demonstrate this method for the two most commonly desired attribute classes: individual style and walking speed. We show that two methods, cost function design and latent space regularization, can be used individually or combined. We also show two uses of machine learning classifiers that recognize individuals and speeds. Firstly, they can be used as quantitative measures of success; if a synthetic gait fools a classifier, then it is considered to be a good example of that class. Secondly, we show that classifiers can be used in the latent space regularizations and cost functions to improve training beyond a typical squared-error cost.

In this letter, a new dissipative energy flow (DEF) method is derived for source detection of oscillations associated with inverter-based resources (IBRs). The conventional DEF method encounters difficulty in correctly locating the oscillation source in an IBR-penetrated system simulated in electromagnetic transient (EMT) environment. By examining the fundamental assumption, we derived a new DEF suitable for IBRs acting as current sources. The detection results show the IBR oscillations’ origins can be correctly located using the proposed DEF.

This paper presents a survey of FL and blockchain for financial security.

Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations under this framework.

Organometal halide perovskites have recently attracted plenty of attention in solar cell applications due to their high charge-carrier mobilities, high photoconversion efficiencies and ease of fabrication. Herein, we demonstrate the broadband characteristics of CH3NH3PbI3-xClx thin film photodetectors that were fabricated using ambient processing conditions. Two different photodetector devices were designed and fabricated. The first device (architecture 1) involves spinning a thin perovskite film between the top and bottom electrodes, while the second (architecture 2) includes additional electron and hole transport layers between the contacts. The achieved responsivity was 2 A/W at 2 V for architecture 1 compared to 0.42 A/W at -2 V for architecture 2. We have observed that the perovskite film formed on top of the PEDOT: PSS layer is non-uniform with large crystalline domains. In some parts, the crystal domains were as large as 1.76 μm, whereas better surface coverage was achieved on top of the ITO, which had smaller crystal domains (1.4 μm). This resulted in the dark current of architecture 1 being one order of magnitude higher in comparison to architecture 2. This new fabrication method allows for low-cost optoelectronic device fabrication in uncontrolled laboratories. Contrary to recommendations in the literature, our findings show that simple fabrication with the PEDOT:PSS layer enables good photoconduction properties in room temperature conditions.

A reconfigurable partially reflective surface (PRS) antenna can offer low-cost and low-complexity beam steering, traditionally implemented using a phased array with a bulky feeding network. This paper utilizes ray-tracing to prove analytically that an inductive nonuniform PRS facilitates larger beam deflection and lower sidelobe level (SLL) than a capacitive one, given the same reflection phase difference. With a suitable tradeoff in the beam deflection angle and gain variation between the reconfigurable states, an inductive reconfigurable PRS is designed for a ±13° steering range. To enhance the steering range with minimal complexity, two parasitic elements loaded with PIN diodes are added to the feeding source, resulting in the overall steering range of ±30°. The fabricated prototype verified that the pencil-shape beam can be steered towards 0º and ±30º with SLLs of less than -19dB at 5.5GHz. The antenna achieved peak realized gains of 9.5-10.4dBi for the three states with gain variation of less than 0.9dBi. The measured overlapped impedance band covers 5.41-5.63 GHz for all states.

In this paper, we present a tunable bandpass frequency-selective surface (FSS) with wide tuning range. The FSS is composed of a metallic rectangular waveguide array implanted in the double-sided printed circuit board (DSPCB) and an array of embedded varactors with bias network in the backside of the DSPCB. The waveguide array has a frequency-selective transmission peak due to tunneling effect and the transmission frequency is tuned by changing the capacitance of varactors through different bias voltages. One advantage of this configuration is that the varactors together with the bias network are designed on the same layer, which can effectively reduce the overall thickness into only 0.006λc (λc is the free-space wavelength at the highest transmission frequency). On the other hand, the insertion loss associated with the parasitic resistance of varactors is greatly decreased, owing to the novel integrated design of biasing lines with varactors and the nearly-full-metal waveguide array. A prototype is fabricated and measured, showing that by changing the varactor capacitance from 0.12 pF to 1 pF, the passband exhibits a 2.32:1 frequency tuning range from 5.8 GHz to 2.5 GHz with an insertion loss of 0.2 dB-2.6 dB. A polarization-insensitive FSS is also studied in simulations.

Liver vessels generated from computed tomography are usually pretty small, which poses big challenges for satisfactory vessel segmentation, including 1) the scarcity of high-quality and large-volume vessel masks, 2) the difficulty in capturing vessel-specific features, and 3) the heavily imbalanced distribution of vessels and liver tissues. To advance, a sophisticated model and an elaborated dataset have been built. The model has a newly conceived Laplacian salience filter, highlighting vessel-like regions and suppressing other liver regions, to shape the vessel-specific feature learning and to balance vessels against others. It is further coupled with a pyramid deep learning architecture to capture various levels of features, hence the enhancement of feature formulation. Experiments show that this model markedly outperforms the state-of-the-art approaches, achieving a relative improvement of Dice score by at least 1.63% compared to the existing best model on available datasets. More promisingly, the averaged Dice score produced by existing models on the newly con- structed dataset is as high as 0.728 ± 0.067, which is at least 19.1% higher than that obtained from the existing best dataset under the same settings. These observations suggest that the proposed Laplacian salience, along with the elaborated dataset, can be helpful for liver vessel seg- mentation.

The temporal answer grounding in instructional video (TAGV) is a new task naturally derived from temporal sentence grounding in general video (TSGV). Given an untrimmed instructional video and a text question, this task aims at locating the frame span from the video that can semantically answer the question. Existing methods tend to formulate the TAGV task with a visual span-based predictor by matching the video frame span queried by the text question. However, due to the weak correlations of the semantic features between the textual question and visual answer, existing methods adopting the visual span-based predictor perform poorly in the TAGV task. In this paper, we propose a visual-prompt text span localizing (VPTSL) method, which introduces the timestamped subtitles to perform the text span localization. Specifically, we design the text span-based predictor, where the input text question, video subtitles, and visual prompt features are jointly learned with the pre-trained language model for enhancing the joint semantic representations. As a result, the TAGV task is reformulated with the visual-prompt subtitle span prediction matching the visual answer. Extensive experiments on three instructional video datasets, namely MedVidQA, TutorialVQA, and VehicleVQA, show that the proposed method outperforms several state-of-the-art (SOTA) methods by a  large margin in terms of mIoU score, which demonstrates the effectiveness of the proposed visual prompt and text span-based predictor. Besides, all the experimental codes and datasets are open-sourced on the website https://github.com/wengsyx/VPTSL.

This article provides a targetless extrinsic calibration method between camera and low-resolution 3D LiDAR. We carried out real-world experiments on a multi-sensor platform containing a Velodyne VLP-16 LiDAR and a camera. Ground-truth extrinsic matrix is generated by a state-of-the-art target-based method. Regarding the calibration error, the rotation and translation RMSE of our method are 0.59° and 3cm respectively. Codes are avaible at https://github.com/gitouni/Targetless-LiDAR-camera-calibration. NB: This article has been accepted for publication in IEEE Sensors Journal.  Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Citation Information: 10.1109/JSEN.2023.3263833