Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Method for Forming Rideshare Groups

Nowadays, ridesharing has gained popularity as an alternative mode of transportation. It offers cost savings, convenience, environmental benefits, and social opportunities. This research aims at providing riders with the reliability of the route and the flexibility of the service. For this purpose, we mine the longest frequent movement from historical data. A novel algorithm for discovering rides hare groups based on the similarity of the users' longest frequent movements is proposed. In this way, the user does not have to search for a ride many times and does not have to wait too long as well because the group with a specific schedule and driver information can be excited and sent to the users in advance. The algorithm is evaluated based on synthetic datasets under different settings and compared with the existing ones. The experimental results of the proposed method is superior to the results of the previous algorithms in terms of execution time and the total travel distance.
High-level FPGA Design of Deep Learning Hyperspectral Anomaly Detection

Hyperspectral image Anomaly Detection (HSI-AD) has attracted considerable attention in the field of remote sensing. With improved detection accuracy, as a consequence of the increasing complexity of HSI-AD algorithms, more computing resources are required. In this paper, a hardware implementation utilising High-Level Synthesis (HLS) of Deep Belief Network (DBN) anomaly detector is proposed. The original sequential implementation is adapted into HLS through Vitis software and design optimisation to use on-chip memory for network weights and biases towards increased throughput are proposed. The addition of on-chip memory in the proposed design achieved a 72% reduction in time for performing auto-encoding and reconstruction error calculation compared to software.
Demonstration of High-overtone Bulk Acoustic Resonators using an Epitaxial ε-Ga2O3 Piezoelectric Film on SiC

In this work, we developed novel high-overtone bulk acoustic resonators (HBARs) with only top electrodes using an epitaxial ε-Ga<inf>2</inf>O<inf>3</inf> piezoelectric film on conductive SiC substrates. The HBARs with different ε-Ga<inf>2</inf>O<inf>3</inf> film thicknesses and electrode spacings were fabricated and investigated. The device with a 300-nm thick ε-Ga<inf>2</inf>O<inf>3</inf> film showed a broad frequency response from 1 to 8 GHz, and the free spectral range (FSR) between adjacent modes was 18.3 MHz. Other critical metrics of the device include quality factor (Q) > 5000 and f•Q > 2.5×10<sup>13</sup> Hz for operating frequencies up to 5 GHz. By narrowing the electrode spacing, the Q value of the HBARs with a 1.2-μm thick ε-Ga<inf>2</inf>O<inf>3</inf> film was improved to over 8000. The results demonstrate the great potential of the epitaxial ε-Ga<inf>2</inf>O<inf>3</inf> piezoelectric film for developing novel RF acoustic devices.
Preliminary Assessment of Two Simultaneous and Proportional Myocontrol Methods for 3-DoFs Prostheses Using Incremental Learning

Despite progressive developments over the last decades, current upper limb prostheses still lack a suitable control able to fully restore the functionalities of the lost arm. Traditional control approaches for prostheses fail when simultaneously actuating multiple Degrees of Freedom (DoFs), thus limiting their usability in daily-life scenarios. Machine learning, on the one hand, offers a solution to this issue through a promising approach for decoding user intentions but fails when input signals change. Incremental learning, on the other hand, reduces sources of error by quickly updating the model on new data rather than training the control model from scratch. In this study, we present an initial evaluation of a position and a velocity control strategy for simultaneous and proportional control over 3-DoFs based on incremental learning. The proposed controls are tested using a virtual Hannes prosthesis on two healthy participants. The performances are evaluated over eight sessions by performing the Target Achievement Control test and administering SUS and NASA-TLX questionnaires. Overall, this preliminary study demonstrates that both control strategies are promising approaches for prosthetic control, offering the potential to improve the usability of prostheses for individuals with limb loss. Further research extended to a wider population of both healthy subjects and amputees will be essential to thoroughly assess these control paradigms.
Optimization of AlN deposition parameters for a high frequency 1D pMUT Array

Aluminium Nitride (AlN) thin films have gained attention due to their exceptional electric, thermal properties, making them suitable for various electronic and optoelectronic applications. RF magnetron sputtering is a widely used technique for depositing AlN thin films, offering precise control over deposition parameters. This paper presents the optimization of RF magnetron sputtering parameters for a piezoelectric AlN thin film growth. The influence of key parameters, including gas ratio, RF power, substrate temperature is investigated. The results: FWHM of 2.4° - 2.6° and a surface roughness of 1.4 nm highlight the significance of process optimization to achieve high-quality piezoelectric AlN thin films, paving the way for their utilization in advanced electronic devices.
A Review on Secure Authentication Protocols in IOV: Algorithms, Protocols, and Comparisons

The Internet of Vehicles (IoV) is an emerging technological advancement with the potential to significantly transform the automotive industry and transportation networks. Its primary objective is to facilitate communication among vehicles, infrastructure, and a wide range of networked devices. The increasing number of connected vehicles has led to a growing worry among researchers on the security of the Internet of Vehicles (IoV). This topic has become a prominent area of study due to the unresolved difficulties around security and privacy in the IoV. The primary focus lies on security because to the diverse range of security and privacy risks that have the potential to jeopardize the well-being, privacy, and lives of individuals within vehicles. The development of communication protocols for autonomous cars presents novel issues in the exploration and enhancement of IoV networks' performance. These challenges aim to bolster security and privacy measures, while also ensuring the integrity, validity, and secrecy of the sent data. This study presents a comprehensive examination of various symmetric and asymmetric encryption and decryption methods, as well as lightweight cryptographic protocols employed in the context of the Internet of Vehicles (IoV). This paper provides a comprehensive examination and elucidation of security and privacy issues, along with the corresponding remedial measures that have been recently recommended. The content is provided in a manner that is easily comprehensible to the reader. The present study offers a comprehensive analysis of risks, attacks, protocols, and solutions within the realm of collaborative cryptographic algorithms. Through a systematic examination, distinct categories are provided to facilitate a clear understanding of these key components.
BookMate: Leveraging Deep Learning to Empower Caregivers of People with ASD in Generation of Social Stories

People with Autism Spectrum Disorder (ASD) have difficulties in social communication and interaction. Their caregivers help them in overcoming these challenges. Social stories are tools largely adopted to improve the interaction and communication capabilities of people with ASD. The creation of a social story is not an easy task. Several guidelines have been defined to build them. In this paper, we propose an interactive mobile application aimed at empowering caregivers of people with ASD in generating social stories. The application integrates AI processing and AI-based audio transcription for story generation, effective audio data extraction, and data processing. The stories are presented in a slide-show format to the learner in different modalities according to the learner's capabilities. A preliminary usability study of the application has been performed and the first results are encouraging.
Phase Distribution Efficiency of cm-Scale Ultrasonically Powered Receivers

In the domain of ultrasonically powered biomedical implants, there is an increasing interest in cm-scale ultrasonic receivers (RX). However, when a single-element transducer is used as the RX transducer, an uneven phase distribution across the RX area can significantly reduce the harvestable power. In this paper, we investigate the impact of lateral and angular misalignment on the acoustic field phase distribution across the RX surface. We show that, for a single-element RX transducer, lateral misalignment has minimal effect on the harvestable power, whereas even small angular misalignments can cause a considerable reduction, especially for larger RX sizes. We present a potential solution that consists of subdividing a large RX transducer (e.g. 20 × 20 mm2) into smaller elements, which significantly improves power transfer efficiency by taking advantage of the smaller phase variation across the surface of each element. The trade-offs between achieving a minimum acceptable power transfer efficiency and managing the increased complexity in packaging and matching circuitry are also discussed.
An In-Depth Analysis of Android’s Java Class Library: its Evolution and Security Impact

Android is an operating system widely deployed especially on devices such as smartphones. In this paper, we study the evolution of OpenJDK Java Class Library (JCL) versions used as the basis of the Dalvik Virtual Machine (DVM) and the Android Runtime (ART). We also identify vulnerabilities impacting OpenJDK JCL versions and analyze their impact on Android. Our results indicate that the complexity of the Android JCL code imported from OpenJDK increases because: (1) there is an increase in the number of classes imported from OpenJDK, (2) there is an increase in the fragmentation of the JCL code in Android as code is increasingly imported from multiple OpenJDK versions at the same time, and (3) there is an increase in the distance between the JCL code in Android and OpenJDK as, for instance, Android developer introduce customizations to the imported code. We also observe that most OpenJDK vulnerabilities (80%) are not impacting Android because the vulnerable classes are not imported in Android. Nevertheless, Android does import vulnerable code and little is done to patch this vulnerable code which is only “patched” when a newer version of the vulnerable code is imported. This means that the code can stay vulnerable in Android for years. Most of the vulnerabilities impacting Android (77%) have a security impact on the availability of the system. By developing a proof-of-concept, we show that OpenJDK vulnerabilities imported in Android do have a security impact. We suggest to seriously take into account public information available about OpenJDK vulnerabilities to increase the security of the Android development pipeline.
Spinal Cord Penumbra Restoration Monitoring during Chronic Phase Using Ultrasound Localization Microscopy

Spinal cord injury (SCI) often results in a lifelong sensation loss or even paralysis. A major mechanism is no timely intervention on vasculature disruption after primary insult, which causes further expansion of infarction core and irreversible injury of penumbra zone. Techniques used foe brain penumbra including Positron Emission Tomography (PET), Diffusion-Weighted Imaging (DWI), Perfusion-Weighted Imaging (PWI), and Computed Tomography (CT) are limited in resolution. Digital subtraction angiography (DSA) and magnetic resonance angiography (MRA) are still the standard methods in clinics. Recently developed ultrasound localization microscopy (ULM) enables to map the microvasculature of small animal in deep tissue with a resolution of tens of microns. With the advantages of non-radiation, portability and super-resolution, it has become an attractive alternative for angiography and could potentially meet the needs of comprehensive intraoperative examination and prognosis tracking. In this study, ULM was performed on a standardized rat model of spinal cord compression for in vivo penumbra imaging. Basso, Beattie, Bresnahan (BBB) locomotor rating scale was applied for model validation. The high consistency between ultrasonic vascular quantification including morphology and density and animal behavioral tests was observed during penumbra development.
Classification with Explanation for Human Trafficking Networks

On a worldwide scale, an increasing number of victims of human trafficking were observed these last years, covering a majority of countries and territories. Among them, a large portion of women and girls are recruited primarily for sexual exploitation. United Nations Office on Drugs and Crime (UNODC) highlights the difficulties of access to justice which deprive victims of protection, a central issue behind our work. Our contribution is part of an emerging research trend, combining Artificial Intelligence (AI), Humanities and Social Sciences (HSS). It makes an original use of legal database to identify Human Trafficking Networks (HTNs), involving both sexual abuse victims and exploiters. First, a reformulation of the legal database as a numerical database is proposed, using new features expressing relationships between people involved in the same court case, likely to better reveal HTNs. Secondly, six machine learning algorithms, including Decision Tree, Random Forest, Gradient Boosting, Logistic Regression, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) are used to train on numerical database and learn to classify the input court case into one of the three classes: Not suspicious, Suspicious, or Probably suspicious. We in details discuss knowledge-based feature engineering, dataset balancing, parameters tuning, and best models selection. The comparative empirical evaluations between those classification algorithms have been conducted in order to highlights the relevance of our HTNs detection approach. To help the end-users, to better understand the displayed HTNs, for Decision Tree and Random Forest, we also provide explanations of why such court case can be classified. Those results were finally discussed with experts in the field of human trafficking, providing us with interesting feedback shedding light to this multidimensional form of modern-day slavery problem.
Spatial Inequality in the Southeast Asian Intercity Transport Network

Abstract Spatial inequality in transport access is both the driver and outcome of rising economic inequality in Southeast Asia. Unlike many regional disparity studies that focus on national economic indicators, this paper takes an urban network approach to assess the spatial inequality in Southeast Asian intercity transport network. We analyze urban connectivity in intercity road, rail, and air networks for a total of 47 Southeast Asian cities. Spatial inequality at the city and network level is revealed via centrality measures and community detection, respectively. Gini coefficients for individual centrality rankings point to a hierarchical degree distribution, a rather even distribution of closeness centrality, and a highly concentrated distribution of betweenness centrality. Four network communities are identified, reflecting the influences of entrenched uneven development, fragmented geography, and economic and political policies.
Physics-informed neural networks for modeling acoustic radiation force-induced shear wave propagation and the reconstruction of material parameters from observations

Physics-informed neural networks (PINNs) solve differential equations and give compressed, analytic solutions free of discretization. Previous work used PINNs to model shear wave propagation from a spherical Gaussian impulse in an incompressible, transversely isotropic (ITI) material. Here, we extend this method to model the more complex geometry of an acoustic radiation force impulse (ARFI) and subsequent 3D shear wave propagation in both isotropic and ITI materials. We find that PINNs produce high-fidelity 3D solutions across different material models. PINN solutions qualitatively matched paired finite element method (FEM) simulations and without artifacts seen in FEM related to spatial discretization. The frequency content of PINN solutions differed slightly from the FEM, depending on the initial excitation geometry. PINNs have the potential to help rapidly explore material model parameter spaces. We also investigate the feasibility of solving the inverse problem to reconstruct material parameters from observed data by setting these parameters as variable outputs of the PINN model. PINNs were able to reconstruct the shear modulus using data measured in an isotropic elastic phantom to within 3 percent. Lastly, we show that PINNs can reconstruct all three independent parameters of an ITI material model from data.
RPCover: Recovering gRPC Dependency in Multilingual Projects

The advent of microservice architecture has led to a significant shift in the development of service-oriented software. In particular, the use of Remote Procedure Call (RPC), a mode of Inter-Process Communication (IPC) prevalent in microservices, has noticeably increased. To figure out the relationships between services and obtain a high-level understanding of service-oriented software, a line of recent work focuses on the dynamic construction of service call graphs, which relies on the preliminary deployment of services and only captures the calling relationships within a specific time frame. Meanwhile, static methods avoid the need for pre-deployment and often provide a more stable and complete graph compared to dynamic techniques. However, research and practical applications of static call graph construction remain relatively unexplored. This paper introduces RPCover, a novel gRPC dependency recovery framework that facilitates the interconnection of services across various programming languages using their static gRPC calls. In addition, due to the lack of a multilingual microservice benchmark that uses gRPC, we build the first multilingual benchmark RPCoverBench that contains complex gRPC call relations. RPCover has been evaluated on a single language benchmark (DeathStarBench) and our multilingual benchmark (RPCoverBench). The results show that RPCover effectively recovers 99.33% of the use cases of gRPC calls with less than 200% of the overhead compared with a single-language semantic dependency analyzer.
Analyzing C++ Stream Parallelism in Shared-Memory when Porting to Flink and Storm

Stream processing plays a crucial role in various information-oriented digital systems. Two popular frameworks for real-time data processing, Flink and Storm, provide solutions for effective parallel stream processing in Java. An option to leverage Java's mature ecosystem for distributed stream processing involves porting legacy C++ applications to Java. However, this raises considerations on the adequacy of the equivalent Java mechanisms and potential degradation in throughput. Therefore, our objective is to evaluate programmability and performance when converting stream processing applications from C++ to Java while also exploring the parallelization capabilities offered by Flink and Storm. Furthermore, we aim to assess the throughput of Flink and Storm on shared-memory manycore machines, a hardware architecture commonly found in cloud environments. To achieve this, we conduct experiments involving four different stream processing applications. We highlight challenges encountered when porting C++ to Java and working with Flink and Storm. Furthermore, we discuss throughput, latency, CPU, and memory usage results.
Academic Career Assistance Services for Student-Athletes in South Tyrol: A Study Protocol

Career assistance programmes for student-athletes have seen increasing interest in Europe in the last decade, with beneficial impacts on athletes' sporting and academic careers, future employments, and personal lives. This research topic is rooted in the literature on dual career (DC) and career transitions and focuses on the fact that professional and semi-professional athletes face relevant stressors when transitioning from junior to senior competitive categories and from high school to university. Failures in these transitions often correspond in dropping out from sport or studies, but this can be prevented by adequate supporting services for student-athletes. The present paper outlines the protocol for a PhD research project aiming at enhancing or implementing academic career assistance services in South Tyrol. The project will be based on a mixed-method study design, consisting of: (Study 1) literature review, examination of national and international guidelines and of existing DC programmes, census of student-athletes enrolled at the Free University of Bozen-Bolzano (UniBz); (Study 2) investigation through quantitative and qualitative methods to examine the links between student-athletes' identities, their well-being, and their academic and sporting performance; and (Study 3) evaluation of pros and cons of existing academic career assistance services at UniBz, that will be discussed within focus groups including student-athletes, coaches, scholars and other academic staff and stakeholders. Results from this project will inform the enhancement of existing services, with specific regard to student-athletes' careers.
Comparative Analysis of Deep Learning Models for Detecting Jamming Attacks in Wi-Fi Network Data

Jamming attacks presents a significant challenge to the security and reliability of wireless communication networks, especially in the context of IoT applications. This study introduces a novel approach to detecting jamming attacks by utilizing the upper-layer network parameters from the application and transport layers. An experimental testbed is developed consisting of a Wi-Fi-based IoT server-client application to collect data. The network parameters are gathered from both noiseless and noisy environment conditions to examine the performance variations of different deep-learning models in diverse environments. The performance of various deep learning models is systematically compared, employing evaluation metrics such as accuracy, F1 score, precision, recall, model complexity, and training time. The findings of this research contribute to the development of effective techniques for jamming detection. Moreover, this study provides valuable insights into the selection and adaptation of appropriate models based on system requirements and specifications, enabling efficient detection and mitigation of jamming attacks in wireless communication systems.