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Many studies on mosquito biology rely on laboratory-reared colonies, emphasizing the need for standardized protocols to investigate critical aspects such as disease biology, mosquito behavior, and vector control methods. While much knowledge is derived from anthropophilic species from genera like Anopheles, Aedes, and Culex, there is a growing interest in studying mosquitoes that feed on non-human hosts. This interest stems from the desire to gain a deeper understanding of the evolution of diverse host range use and host specificity. However, there is currently a limited number of comprehensive protocols for studying such species. Considering this gap, we present a protocol for rearing Uranotaenia lowii, a mosquito species specialized in feeding on anuran amphibians by eavesdropping on host-emitted sound cues. Additionally, we provide instructions for successfully shipping live specimens to promote research on this species and similar ones. This protocol helps fill the current gap in comprehensive guidelines for rearing and maintaining colonies of anuran host–biting mosquitoes. It serves as a valuable resource for researchers seeking to establish colonies of mosquito species from the Uranotaeniini tribe. Ultimately, this protocol may facilitate research on the evolutionary ecology of Culicidae, as this family has recently been proposed to have originated from a frog-feeding ancestor. 

Abstract Sexual size variation in adult holometabolous insects may arise from selective pressures impacting ontogenetic stages associated with diverse habitats and resource use. In addition, scaling relations of these sexually dimorphic traits play an important role in morphological diversification. In mosquitoes, given the sexual differences in feeding strategies, investigations of the ontogeny of sexually dimorphic traits are of particular interest to understanding their reproductive biology and implementing early sex‐separating technologies for vector control. However, our current knowledge of the morphological scaling of body parts over development across sexes is centered around a few well‐known species of anthropophilic mosquitoes. In general, there is a noticeable gap in our understanding of the developmental biology of mosquitoes with limited medical consequences. One such mosquito isUranotaenia lowii(Diptera: Culicidae), a species of growing interest due to its unique host use of feeding exclusively on frogs by eavesdropping on their mating calls. This study takes a step forward toward filling this gap by investigating sexual size dimorphism during the ontogeny ofUr. lowii. We examined larval and pupal stages to focus on traits that allow sex identification to evaluate various sex‐sorting techniques that provide a foundation for experimental manipulation. We found that sex identification inUr. lowiiis possible during both larval and pupal stages. In the fourth larval instar, thorax length, abdomen length, and total body length differ significantly between the sexes, showing allometric scaling. In the pupal stage, the allometry of the head and thorax to body size remains consistent, as these parts fuse into the cephalothorax. Successful sorting based on cephalothorax length enables highly accurate pupal sex identification. This research sheds light on the biology ofUr. lowii,an understudied mosquito species, and lays the foundation for future studies on the developmental and reproductive biology of frog‐biting mosquitoes. 

Automatic recognition of bird behavior from long-term, un controlled outdoor imagery can contribute to conservation efforts by enabling large-scale monitoring of bird populations. Current techniques in AI-based wildlife monitoring have focused on short-term tracking and monitoring birds individually rather than in species-rich flocks. We present Bird-Collect, a comprehensive benchmark dataset for monitoring dense bird flock attributes. It includes a unique collection of more than 6,000 high-resolution images of Demoiselle Cranes (Anthropoides virgo) feeding and nesting in the vicinity of Khichan region of Rajasthan. Particularly, each image contains an average of 190 individual birds, illustrating the complex dynamics of densely populated bird flocks on a scale that has not previously been studied. In addition, a total of 433 distinct pictures captured at Keoladeo National Park, Bharatpur provide a comprehensive representation of 34 distinct bird species belonging to various taxonomic groups. These images offer details into the diversity and the behaviour of birds in vital natural ecosystem along the migratory flyways. Additionally, we provide a set of 2,500 point-annotated samples which serve as ground truth for benchmarking various computer vision tasks like crowd counting, density estimation, segmentation, and species classification. The benchmark performance for these tasks highlight the need for tailored approaches for specific wildlife applications, which include varied conditions including views, illumination, and resolutions. With around 46.2 GBs in size encompassing data collected from two distinct nesting ground sets, it is the largest birds dataset containing detailed annotations, showcasing a substantial leap in bird research possibilities. We intend to publicly release the dataset to the research community. The database is available at: https://iab-rubric.org/resources/wildlife-dataset/birdcollect 

Monitoring and analysis of wildlife are key to conservation planning and conflict management. The widespread use of camera traps coupled with AI-based analysis tools serves as an excellent example of successful and non-invasive use of technology for design, planning, and evaluation of conservation policies. As opposed to the typical use of camera traps that capture still images or short videos, in this project, we propose to analyze longer term videos monitoring a large flock of birds. This project, which is part of the NSF-TIH Indo-US joint R&D partnership, focuses on solving challenges associated with the analysis of long-term videos captured at feeding grounds and nesting sites, among other such locations that host large flocks of migratory birds. We foresee that the objectives of this project would lead to datasets and benchmarking tools as well as novel algorithms that would be instrumental in developing automated video analysis tools that could in turn help understand individual and social behavior of birds. The first of the key outcomes of this research will include the curation of challenging, real-world datasets for benchmarking various image and video analytics algorithms for tasks such as counting, detection, segmentation, and tracking. Our recent efforts towards this outcome is a curated dataset of 812 high-resolution, point-annotated, images (4K - 32MP) of a flock of Demoiselle cranes (Anthropoides virgo) taken from their feeding site at Khichan, Rajasthan, India. The average number of birds in each image is about 207, with a maximum count of 1500. The benchmark experiments show that state-of-the-art vision techniques struggle with tasks such as segmentation, detection, localization, and density estimation for the proposed dataset. Over the execution of this open science research, we will be scaling this dataset for segmentation and tracking in videos, as well as developing novel techniques for video analytics for wildlife monitoring. 

Bitslicing is a software implementation technique that treats an N-bit processor datapath as N parallel single-bit datapaths. Bitslicing is particularly useful to implement data-parallel algorithms, algorithms that apply the same operation sequence to every element of a vector. Indeed, a bit-wise processor instruction applies the same logical operation to every single-bit slice. A second benefit of bitsliced execution is that the natural spatial redundancy of bitsliced software can support countermeasures against fault attacks. A k-redundant program on an N-bit processor then runs as N/k parallel redundant slices. In this contribution, we combine these two benefits of bitslicing to implement a fault countermeasure for the number-theoretic transform (NTT). The NTT eiciently implements a polynomial multiplication. The internal symmetry of the NTT algorithm lends itself to a data-parallel implementation, and hence it is a good candidate for the redundantly bitsliced implementation. We implement a redundantly bitsliced NTT on an advanced 667MHz ARM Cortex-A9 processor, and study the fault coverage for the protected NTT under optimized electromagnetic fault injection (EMFI). Our work brings two major contributions. First, we show for the irst time how to develop a redundantly bitsliced version of the NTT. We integrate the protected NTT into a full Dilithium signature sequence. Second, we demonstrate an EMFI analysis on a prototype implementation of the Dilithium signature sequence on ARM Cortex-M9. We perform a detailed EM fault-injection parameter search to optimize the location, intensity and timing of injected EM pulses. We demonstrate that, under optimized fault injection parameters, about 10% of the injected faults become potentially exploitable. However, the redundantly bitsliced NTT design is able to catch the majority of these potentially exploitable faults, even when the remainder of the Dilithium algorithm as well as the control low is left unprotected. To our knowledge, this is the irst demonstration of a bitslice-redundant design of the NTT that offers distributed fault detection throughout the execution of the algorithm. 

ABSTRACT Most mosquito and midge species use hearing during acoustic mating behaviors. For frog-biting species, however, hearing plays an important role beyond mating as females rely on anuran calls to obtain blood meals. Despite the extensive work examining hearing in mosquito species that use sound in mating contexts, our understanding of how mosquitoes hear frog calls is limited. Here, we directly investigated the mechanisms underlying detection of frog calls by a mosquito species specialized on eavesdropping on anuran mating signals: Uranotaenia lowii. Behavioral, biomechanical and neurophysiological analyses revealed that the antenna of this frog-biting species can detect frog calls by relying on neural and mechanical responses comparable to those of non-frog-biting species. Our findings show that in Ur. lowii, contrary to most species, males do not use sound for mating, but females use hearing to locate their anuran host. We also show that the response of the antennae of this frog-biting species resembles that of the antenna of species that use hearing for mating. Finally, we discuss our data considering how mosquitoes may have evolved the ability to tap into the communication system of frogs. 

Motivated by the rise of quantum computers, existing public-key cryptosystems are expected to be replaced by post-quantum schemes in the next decade in billions of devices. To facilitate the transition, NIST is running a standardization process which is currently in its final Round. Only three digital signature schemes are left in the competition, among which Dilithium and Falcon are the ones based on lattices. Besides security and performance, significant attention has been given to resistance against implementation attacks that target side-channel leakage or fault injection response. Classical fault attacks on signature schemes make use of pairs of faulty and correct signatures to recover the secret key which only works on deterministic schemes. To counter such attacks, Dilithium offers a randomized version which makes each signature unique, even when signing identical messages. In this work, we introduce a novel Signature Correction Attack which not only applies to the deterministic version but also to the randomized version of Dilithium and is effective even on constant-time implementations using AVX2 instructions. The Signature Correction Attack exploits the mathematical structure of Dilithium to recover the secret key bits by using faulty signatures and the public-key. It can work for any fault mechanism which can induce single bit-flips. For demonstration, we are using Rowhammer induced faults. Thus, our attack does not require any physical access or special privileges, and hence could be also implemented on shared cloud servers. Using Rowhammer attack, we inject bit flips into the secret key s1 of Dilithium, which results in incorrect signatures being generated by the signing algorithm. Since we can find the correct signature using our Signature Correction algorithm, we can use the difference between the correct and incorrect signatures to infer the location and value of the flipped bit without needing a correct and faulty pair. To quantify the reduction in the security level, we perform a thorough classical and quantum security analysis of Dilithium and successfully recover 1,851 bits out of 3,072 bits of secret key $$s_{1}$$ for security level 2. Fully recovered bits are used to reduce the dimension of the lattice whereas partially recovered coefficients are used to to reduce the norm of the secret key coefficients. Further analysis for both primal and dual attacks shows that the lattice strength against quantum attackers is reduced from 2128 to 281 while the strength against classical attackers is reduced from 2141 to 289. Hence, the Signature Correction Attack may be employed to achieve a practical attack on Dilithium (security level 2) as proposed in Round 3 of the NIST post-quantum standardization process. 

Advancements in smartphone applications have empowered even non-technical users to perform sophisticated operations such as morphing in faces as few tap operations. While such enablements have positive effects, as a negative side, now anyone can digitally attack face (biometric) recognition systems. For example, face swapping application of Snapchat can easily create “swapped” identities and circumvent face recognition system. This research presents a novel database, termed as SWAPPED - Digital Attack Video Face Database, prepared using Snapchat’s application which swaps/stitches two faces and creates videos. The database contains bonafide face videos and face swapped videos of multiple subjects. Baseline face recognition experiments using commercial system shows over 90% rank-1 accuracy when attack videos are used as probe. As a second contribution, this research also presents a novel Weighted Local Magnitude Pattern feature descriptor based presentation attack detection algorithm which outperforms several existing approaches. 

The widespread use of smartphones has spurred the research in mobile iris devices. Due to their convenience, these mobile devices are also utilized in unconstrained outdoor scenarios. This has necessitated the development of reliable iris recognition algorithms for such uncontrolled environment. At the same time, iris presentation attacks pose a major challenge to current iris recognition systems. It has been shown that print attacks and textured contact lens may significantly degrade the iris recognition performance. Motivated by these factors, we present a novel Mobile Uncontrolled Iris Presentation Attack Database (MUIPAD). The database contains more than 10,000 iris images that are acquired with and without textured contact lenses in indoor and outdoor environments using a mobile sensor. We also investigate the efficacy of textured contact lens in identity impersonation and obfuscation. Moreover, we demonstrate the effectiveness of deep learning based features for iris presentation attack detection on the proposed database.