Miracle-dz/UniFAR-FTUs huggingface dataset:
A Certificateless Efficient and Secure Group Handover Authentication Protocol in 5G Enabled Vehicular Networks

In recent years, the world of vehicular communication is in full progress. With the emerge of the fifth-generation (5G) technology, the high bandwidth and low latency features in the 5G vehicle to everything (5G-V2X) network become possible. However, the current 5G mechanism specified by the Third Generation Partnership Project (3GPP) Release 16 incurs high signaling overhead over the radio access network and the core network when a vehicle platoon moves from a source base station to the target base station. Moreover, it also has several security problems in terms of the failure of key forward secrecy (KFS) and lack of mutual authentication. In this paper, we propose an efficient authentication protocol for vehicle platoons in all handover scenarios. The proposed protocol has been formally evaluated by the Scyther tool to show its ability to resist major typical malicious attacks. It has also been analysed on its security functionality. The performance evaluation demonstrates that the proposed protocol is efficient in terms of signaling and computational cost.
Privacy-preserving Trajectory Generation Algorithm Considering Utility based on Semantic Similarity Awareness

Location-based service recommendations usually need to collect and analyze the location information of trajectories generated by users with smart phones or wearable devices. It is easy to cause the location privacy leaks. The current location privacy protection methods usually confuse the adversary by adding fake locations into real trajectories to achieve the goal of privacy protection. However, these methods fail to consider the utility of user trajectories in service recommendations. In this paper, we propose a privacy-preserving trajectory generation algorithm based on service semantic similarity. To improve the utility of privacy-preserving trajectories for service recommendations, the algorithm constructs a series of service semantic grid maps and generates fake individuals with privacy-preserving trajectories considering both utility and privacy. Simulation results show that the proposed algorithm can effectively hide the locations in real trajectories of individuals and obtain higher effectiveness for service recommendations.
LiteFlow: towards high-performance adaptive neural networks for kernel datapath

Adaptive neural networks (NN) have been used to optimize OS kernel datapath functions because they can achieve superior performance under changing environments. However, how to deploy these NNs remains a challenge. One approach is to deploy these adaptive NNs in the userspace. However, such userspace deployments suffer from either high cross-space communication overhead or low responsiveness, significantly compromising the function performance. On the other hand, pure kernel-space deployments also incur a large performance degradation because the computation logic of model tuning algorithm is typically complex, interfering with the performance of normal datapath execution. This paper presents LiteFlow, a hybrid solution to build high-performance adaptive NNs for kernel datapath. At its core, LiteFlow decouples the control path of adaptive NNs into: (1) a kernel-space fast path for efficient model inference, and (2) a userspace slow path for effective model tuning. We have implemented LiteFlow with Linux kernel datapath and evaluated it with three popular datapath functions including congestion control, flow scheduling, and load balancing. Compared to prior works, LiteFlow achieves 44.4% better goodput for congestion control, and improves the completion time for long flows by 33.7% and 56.7% for flow scheduling and load balancing, respectively.
A state‐of‐art review on band‐notch characteristics in UWB antennas

Ultra‐wideband technology has experienced a rapid growth over the last decade for its contribution in different sectors of human society. Printed antennas are considered as preferred platform for implementing this technology because of its alluring characteristics like light weight, low cost, ease of fabrication, integration capability with other systems, etc. Antennas developed for ultra‐wideband applications are desired to have notch characteristics for avoiding interference with other existing radio communication systems. The techniques related to design and developments of printed band‐notched antennas are continuously upgraded for improving the antenna performance. In this article, a comprehensive review has been carried out on ultra‐wideband antennas with band notch characteristics proposed in around last decade. The band notched UWB antennas available in the literature have broadly been classified into five different categories based on their notch characteristics like single band‐notch, dual band‐notch, triple band‐notch, quad/multiple band‐notch, and reconfigurable/tunable band‐notch, respectively. This review exercise may be helpful for beginners working on ultra‐wideband band‐notched antennas and also such a review process is not available in the open literature to the best of author's knowledge.
Feasibility Study of Stress Detection with Machine Learning through EDA from Wearable Devices

The recent pandemic has brought tremendous changes to everyone’s life, causing stress about losing loved ones, losing jobs, and having changes in sleep or eating habits. This study investigates the feasibility of utilizing Electrodermal Activity (EDA) collected from wearable devices to detect people’s stress. EDA can quantify the changes in sympathetic dynamics by measuring sweat produced by our sweat glands. Currently, the adoption of EDA sensors to commercially off-the-shelf smart-watches is still in the infancy stage, and only a few brands have the EDA sensors implemented into their smartwatch. To facilitate our feasibility study, we need the datasets that contain the EDA signals collected from wearable devices. This paper uses two publicly available datasets containing the EDA signals collected from research-grade wearable devices. We cast the stress detection problem as a binary classification problem and trained the classifiers with three popular machine learning methods: K-Nearest Neighbor, Logistic Regression, and Random Forests. According to experimental results, Random Forests achieves an accuracy of 85.7% to classify stress from non-stress status. The results verified that wearable devices with EDA sensors have the potential to predict stress status.
A Model for Estimating Final Clutch Size from Follicle Counts in Plethodontid Salamanders

Abstract. Estimates of clutch sizes are essential for modeling population dynamics, yet for many species of amphibian, clutches can be difficult to observe or methodologically problematic to measure. Clutch sizes for direct-developing Plethodontidae are regularly estimated from counts of ovarian follicles. Because many more follicles begin to develop in an ovary than will ultimately reach full size and be deposited, follicle counts change dramatically over an individual female's follicle development cycle, and a high level of subjectivity is inherent in the process of estimating clutch sizes from follicle counts. Many published studies are not transparent in how they determine clutch sizes from follicle counts. Some investigators address this bias using threshold sizes or other characteristics to separate those follicles that will ultimately mature and be deposited from those that will not, but our experience indicates that such approaches still likely overestimate clutch sizes. To move beyond the subjectivity inherent in estimation of final clutch size from follicle counts, we modeled large Plethodon clutch size as a function of female body size (snout–vent length, SVL) and follicle diameter, then used that model to predict the likely number of mature eggs deposited. We propose that this approach provides reasonable estimates of clutch sizes and variances for use in demographic models.
Single Radio-Over-Fiber Link and RF Chain-Based 60 GHz Multi-Beam Transmission

An efficient multi-user transmission scheme at 60 GHz using a single-feed leaky wave antenna (LWA) and hence requiring only a single radio-over-fiber link and single RF chain is presented. A subcarrier multiplexed signal carrying different users’ data is transported over 2.2 km of optical fiber and then upconverted to the 60 GHz band for transmission to multiple spatially separated users through the beam steering characteristics of the LWA. An overall sum data rate, the combined rate from all users, of 10.6 Gb/s using 16-QAM modulation serving ten users over a transmission bandwidth of 3.05 GHz or 20 users with QPSK over 6.1 GHz span, is achieved experimentally. The theoretical sum data rates for 6.1 GHz bandwidth for different numbers of users are calculated, considering the SNR degradation due to the angularly dispersed LWA beam, showing that data rates over 30 Gb/s can be obtained. Finally, a system design that improves coverage and spectrum efficiency through operating multiple LWAs with a single RF chain is demonstrated.
Fusion Algorithm of Laser-Point Clouds and Optical Images

—The fusion of laser-point clouds and optical images has been increasingly used in the field of environmental sensing and intelligent devices. It has boosted the development of areas such as entertainment, online house viewing, virtual reality and driverless cars. Traditional fusion algorithms of laser-point clouds and images require a lot of interactive operations such as artificial calibration of sensors and setting of control points. To solve the above problems, this paper proposed an automatic fusion algorithm of laser-point clouds and optical images based on depth images. In this algorithm, the laser-point clouds are converted into depth images, and the mapping relationship between depth images and optical images will be solved. Then the laser-point clouds and the optical images can be fused. The experimental results show that this method can not only ensure the accuracy, but also realize the automation.
Potential Add-On Effects of Manual Therapy Techniques in Migraine Patients: A Randomised Controlled Trial

Objective: To ascertain whether the combination of soft tissue and articulatory manual techniques is more effective than either one of these techniques alone for reducing migraine impact; Methods: Seventy-five participants with migraine were randomly divided into three groups (n = 25 per group): (i) soft tissue (STG), (ii) articulatory (AG), and (iii) combined treatment (STAG). Pain, frequency of occurrence, duration, disability and impact, depression and anxiety levels, and perception of change were analysed at baseline, post intervention (T2) and at four-week follow-up (T3); Results: STAG showed a significantly greater reduction in pain versus STG and AG at T2 (p < 0.001; p = 0.014) and at T3 (p < 0.001; p = 0.01). Furthermore, STAG achieved a significantly greater reduction in pain duration versus STG at T2 (p = 0.020) and T3 (p = 0.026) and a greater impression of change versus STG (p = 0.004) and AG (p = 0.037) at T3. Similar effects were observed in all groups for frequency of occurrence, migraine disability, impact, and depression and anxiety levels; Conclusions: A combined manual therapy protocol including soft tissue and articulatory techniques yields larger improvements on pain and perception of change than either technique alone, yet the three therapeutic approaches show similar benefits for reducing pain, disability and impact caused by the migraine, depression or anxiety levels.
GCN-based stock relations analysis for stock market prediction

Most stock price predictive models merely rely on the target stock’s historical information to forecast future prices, where the linkage effects between stocks are neglected. However, a group of prior studies has shown that the leverage of correlations between stocks could significantly improve the predictions. This article proposes a unified time-series relational multi-factor model (TRMF), which composes a self-generating relations (SGR) algorithm that can extract relational features automatically. In addition, the TRMF model integrates stock relations with other multiple dimensional features for the price prediction compared to extant works. Experimental validations are performed on the NYSE and NASDAQ data, where the model is compared with the popular methods such as attention Long Short-Term Memory network (Attn-LSTM), Support Vector Regression (SVR), and multi-factor framework (MF). Results show that compared with these extant methods, our model has a higher expected cumulative return rate and a lower risk of return volatility.
A GAN-Bert Based Fault Diagnosis Model for CBTC Data Communication Systems Using Edge-to-edge Collaboration Training

Communication-Based Train Control (CBTC) systems use wireless communication to confirm safe train operation for train-ground transmission. CBTC consists of four subsystems, and the Data Communication System (DCS) is one of the most important parts, which plays a vital role in train-ground transmission. Many devices are exposed to the environment in DCS, trains operation in bad weather easily leads to a series of hardware failures. When DCS occurs one mistake, it will threaten the efficiency of CBTC and even the safety of passengers. Since DCS occurs failures inevitably, it is necessary to analyze and identify the mistakes more quickly and accurately. This paper proposes a GAN-Bert based edge-to-edge model to identify DCS faults. Bidirectional Encoder Representations from Transformers (BERT) extracts text features from fault diagnosis with Masked Language Model(MLM) and Next Sentence Prediction(NSP). Meanwhile, Generative Adversarial Network (GAN) is used to perform DCS fault diagnosis. At the same time, edge-to-edge collaboration contributes to training a better model. We use raw DCS logs and compare prediction results between the GAN-Bert and Bert-only models. The simulation results represent that the GAN-Bert based model presents higher accuracy.
The Benefits of Interdisciplinary Collaboration in Community-Engaged Research: Insights From a Study of Digital Storytelling With Marginalized Youth

This article draws on qualitative data from a long-term partnership to exemplify the unique advantages of interdisciplinary and collaborative approaches in community-engaged research. We demonstrate how the differing foci and intersecting concerns of our scholarly fields, social work and media studies, benefited our work with marginalized communities to promote youth voice through digital storytelling. This effort was grounded in the shared view that digital storytelling offers an excellent opportunity to engage creatively with young people’s memories and experiences and that such storytelling can support young people in their healing, identity formation, agency development, and engagement with the public. By working together across disciplines, we were able to surface and address concerns related to vulnerability, privacy, and advocacy among young people experiencing marginalization in ways that would not have been possible in a project involving only social work or media studies. We illustrate this process by describing three critical incidents that exposed our disciplinary overlaps and differences in ways that helped us navigate complex issues related to young people shared their stories with the public. Our findings therefore have implications for others working with vulnerable communities to amplify counternarratives with the goal of bringing about positive systemic change.
Reconfigurable Intelligent Surface Assisted Millimeter Wave Indoor Localization Systems

Reconfigurable intelligent surfaces (RISs) are regarded as one of the most promising techniques in the sixth-generation (6G) mobile communication networks. With the feature of smartly tuning the electromagnetic environment, RISs provide a possibility for ubiquitous and high-precision localization in 6G. However, proper system models for large indoor RIS-assisted networks and high-precision localization algorithms are still missing. In this paper, we propose a RIS-assisted downlink millimeter-wave (mmWave) indoor localization framework based on segment-by-segment far-field assumption. In addition, a brand new coarse-to-fine localization algorithm with low-complexity grid design is provided. Numerical results show that millimeter-level localization precision is achieved under the RIS-assisted indoor scenarios, which reveals that RIS can provide a solid support for accurate localization in the 6G era.
Cost-based Compute Cluster Formation in Edge Computing

Edge Computing (EC) is a promising computing paradigm that can foster a wide spectrum of delay-sensitive and/or data-intensive applications. As opposed to cloud computing, which relies on remote cloud servers, EC brings the computing service closer to the end-users, which can significantly reduce the delay. The concept of EC has recently expanded to include harvesting the computation resources of the Extreme Edge Devices (EEDs), such as smartphones, autonomous vehicles, tablets, etc. However, the cost of recruiting EEDs for resource allocation in such EC environments is mostly overlooked. In this paper, we propose the Price-based Compute Clusters Recruitment (PCCR) scheme. In PCCR, we minimize the cost of recruiting the EEDs required to perform a given set of tasks, where each task is satisfied by the collaborative effort of a group of EEDs forming a compute cluster. PCCR strives to minimize the total recruitment cost while keeping the delay below a certain threshold by forming the optimal set of compute clusters from a pool of heterogeneous EEDs available in a given geographical area. We formulate the optimization problem as a Mixed Integer Quadratically Constrained Quadratic Program (MIQCQP). We then derive an analytical solution using the KKT conditions and Lagrangian analysis. Extensive simulations show that PCCR significantly outperforms a prominent baseline approach in terms of recruitment cost.
Association of Muscle Mass, Muscle Strength, and Muscle Function with Gait Ability Assessed Using Inertial Measurement Unit Sensors in Older Women

Aging-related muscle atrophy is associated with decreased muscle mass (MM), muscle strength (MS), and muscle function (MF) and may cause motor control, balance, and gait pattern impairments. This study determined associations of three speed-based gait variables with loss of MM, MS, and MF in older women. Overall, 432 older women aged ≥65 performed appendicular skeletal muscle, handgrip strength, and five times sit-to-stand test to evaluate MM, MS, and MF. A gait test was performed at three speeds by modifying the preferred walking speed (PWS; slower walking speed (SWS); faster-walking speed (FWS)) on a straight 19 m walkway. Stride length (SL) at PWS was significantly associated with MM. FWS and coefficient of variance (CV) of double support phase (DSP) and DSP at PWS showed significant associations with MS. CV of step time and stride time at SWS, FWS, and single support phase (SSP) at PWS showed significant associations with MF. SL at PWS, DSP at FWS, CV of DSP at PWS, stride time at SWS, and CV of SSP at PWS showed significant associations with composite MM, MS, and MF variables. Our study indicated that gait tasks under continuous and various speed conditions are useful for evaluating MM, MS, and MF.
Finite-time energy-to-peak control for Markov jump systems with pure time delays

This study investigates finite-time energy-to-peak control for pure-time-delay Markov jump systems. The main objective is to obtain some theorems such that the corresponding pure-time-delay Markov jump systems are finite time energy-to-peak stable or stabilizable. First, based on mathematical transformation, the pure-time-delay Markov jump systems are described in a model description that includes the current system state and several distributed time-delay items. Second, according to linear matrix inequality (LMI) theory, a positive energy functional is constructed, which includes a triple integral item. Then, after some mathematical operations, some sufficient conditions are obtained for Markov jump systems to be finite-time energy-to-peak stable or stabilizable. The obtained results are expressed in LMIs, which can be conveniently solved by computers. Finally, examples are given to show the usefulness of the obtained theorems.
A BEGS-SQP method to optimize the droop controller parameters in island microgrid

In this study, an optimization model containing a small-signal stability constraint is proposed to improve the microgrid’s stable operation. Besides, the optimization model considers the multiple conditions to guarantee the microgrid can operate in different conditions. However, the parameter optimization of the droop controller in this optimization model is a nonsmooth optimization problem. The heuristic algorithm is hard to obtain a robust optimal solution. Therefore, a Broyden-Fletcher-Goldfarb-Shanno combined with sequential quadratic programming (BFGS-SQP) method is proposed to tackle this weakness. This proposed method is a mathematical method that can obtain a robust solution during repeat simulation. A case study on the microgrid system demonstrates the effectiveness of the BFGS-SQP method.
The Development of Cybersecurity Information Sharing Framework for National Critical Information Infrastructure in Indonesia

The increase of cyber attacks in the Critical Information Infrastructure (CII) requires every organization to collaborate through Cybersecurity Information Sharing (CIS). To support the implementation of the CIS, governance support is needed in the form of a framework that can be used as a reference. This study focuses on developing a CIS framework for the CII sector in Indonesia which consists of three main outputs, namely the proposed ecosystem, the proposed framework and the recommendations for the implementation of the framework. The proposed framework is based on standards including ISO/IEC 27032, NIST SP 800-150 and ENISA ISAC in a Box, based on best practices for implementing CIS and best practices for implementation in other countries including the United States, Australia, United Kingdom, Singapore and Canada. To validate, the expert judgment method was used to obtain suggestions for improvement. The expert judgment method was also carried out quantitatively to measure interrater reliability between experts using Fleiss Kappa Statistics. The measurement results show a kappa value of 0.938, which means that the proposed framework implementation recommendation gets an agreement from the experts at the almost perfect agreement level. statistic to measure interrater reliability between experts with Fleiss Kappa Statistics and shows a Kappa Value of 0.938 which means it is acceptable or is in the almost perfect agreement range. The results of this research can be used by BSSN, Ministries/Institutions as IPPS and owners CII as a guide in organizing the CIS group, especially in the CII sector in Indonesia. To develop this research, it can be tested by applying the framework that has been prepared in a CIS group that is national or sectoral or can develop a framework so that it can also be used for the already formed CIS group.