While unmanned aerial vehicles (UAVs) are increasingly utilized in many domains, there is a growing concern about location integrity for securely deploying and managing the vehicles. A body of studies tackled this problem, e.g., using hardware sensors, cryptographic mechanisms, and machine learning (ML) approaches, but they concentrate primarily on GPS-related information (e.g., jamming and noise). In this study, we take a different approach that performs the checks by analyzing actual movement information. This new approach keeps track of location updates across the flight path (‘trajectory’) rather than relying only on point-wise GPS-specific features to test the validity of the location information. To this end, we develop a deep sequence method that takes a sequence of flight data samples with a minimal set of attributes capturing location movement over time. Our extensive experimental results support the feasibility of our approach, showing up to 98.9% accuracy for ensuring location consistency (even without referring to any of the GPS-specific features).

As we embrace the dawn of the 5G era, the fusion of Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies has heralded a new epoch in network architecture. This provides indispensable capabilities and virtualized infrastructure to meet the burgeoning demands of modern networks. Yet, with each stride forward in mobile communication technology, security threats loom larger, casting a shadow over the very fabric of our networks. In this context, identifying and addressing these threats is crucial as it plays a key role in enhancing the security of a 5G-Advanced and a next 6G mobile communication technologies. Therefore, this paper undertakes an investigative journey to explore security threats introduced by SDN/NFV technologies in the 5G-Advanced core networks. Through our simulations, we unveil a need for fortified security technologies within the 5G-Advanced core networks, illuminating a path towards the creation of bespoke datasets tailored for the trends of 5G-Advanced security research.

Detection and analysis of viral genomes with Nanopore sequencing has shown great promise in the surveillance of pathogen outbreaks. However, the number of virus detection pipelines supporting Nanopore sequencing is very limited. Here, we present VirPipe, a new pipeline for the detection of viral genomes from Nanopore or Illumina sequencing input featuring streamlined installation and customization. AVAILABILITY AND IMPLEMENTATION: VirPipe source code and documentation are freely available for download at https://github.com/KijinKims/VirPipe, implemented in Python and Nextflow.

Capturing human annotators' subjective responses in image annotation has become crucial as vision-based classifiers expand the range of application areas. While there has been significant progress in image annotation interface design in general, relatively little research has been conducted to understand how to elicit reliable and cost-efficient human annotation when the nature of the task includes a certain level of subjectivity. To bridge this gap, we aim to understand how different sampling methods in image batch labeling, a design that allows human annotators to label a batch of images simultaneously, can impact human annotation performances. In particular, we developed three different strategies in forming image batches: (1) uncertainty-based labeling (UL) that prioritizes images that a classifier predicts with the highest uncertainty, (2) certainty-based labeling (CL), a reverse strategy of UL, and (3) random, a baseline approach that randomly selects images. Although UL and CL solely select images to be labeled from a classifier's point of view, we hypothesized that human-side perception and labeling performance may also vary depending on the different sampling strategies. In our study, we observed that participants were able to recognize a different level of perceived cognitive load across three conditions (CL the easiest while UL the most difficult). We also observed a trade-off between annotation task effectiveness (CL and UL more reliable than random) and task efficiency (UL the most efficient while CL the least efficient). Based on the results, we discuss the implications of design and possible future research directions of image batch labeling.

We propose improved SNR through on-chip Differentially Modulated Signaling with High Voltage (HV) based on Time-Interleaved Sensing Method (TISM) in fingerprint sensing. Tx driver transmits the 0 to 20V-pulse output with proposed signaling into Fingerprint Sensor (FPS), in order to increase the signal to noise ratio (SNR). When capacitance difference of valley-ridge is 0.4fF, the proposed differentially modulated TISM (DM-TISM) signaling is achieved to greater SNR (3.3dB) than that of conventional TISM in performance of fingerprint sensor readout IC architecture, when noise transient is included. Tx driver consists of 4 identical channels. A chip size is 1.3 × 1.1 mm2and prototype is fabricated in 0.25-um BCDMOS process.