Reviewerly/exHarmony huggingface dataset:
Hull Attacks on the Lattice Isomorphism Problem

The lattice isomorphism problem (LIP) asks one to find an isometry between two lattices. It has recently been proposed as a foundation for cryptography in two independent works [Ducas & van Woerden, EUROCRYPT 2022, Bennett et al. preprint 2021]. This problem is the lattice variant of the code equivalence problem, on which the notion of the hull of a code can lead to devastating attacks. In this work we study the cryptanalytic role of an adaptation of the hull to the lattice setting, namely, the s-hull. We first show that the s-hull is not helpful for creating an arithmetic distinguisher. More specifically, the genus of the s-hull can be efficiently predicted from s and the original genus and therefore carries no extra information. However, we also show that the hull can be helpful for geometric attacks: for certain lattices the minimal distance of the hull is relatively smaller than that of the original lattice, and this can be exploited. The attack cost remains exponential, but the constant in the exponent is halved. This second result gives a counterexample to the general hardness conjecture of LIP proposed by Ducas & van Woerden. Our results suggest that one should be very considerate about the geometry of hulls when instantiating LIP for cryptography. They also point to unimodular lattices as attractive options, as they are equal to their dual and their hulls, leaving only the original lattice to an attacker. Remarkably, this is already the case in proposed instantiations, namely the trivial lattice $$\mathbb Z^n$$ and the Barnes-Wall lattices.
POLKA: Towards Leakage-Resistant Post-quantum CCA-Secure Public Key Encryption

As for any cryptographic algorithm, the deployment of post-quantum CCA-secure public key encryption schemes may come with the need to be protected against side-channel attacks. For existing post-quantum schemes that have not been developed with leakage in mind, recent results showed that the cost of these protections can make their implementations more expensive by orders of magnitude. In this paper, we describe a new design, coined POLKA, that is specifically tailored to reduce this cost. It leverages various ingredients in order to enable efficient side-channel protected implementations such as: (i) the rigidity property (which intuitively means that the de-randomized encryption and decryption are injective functions) to avoid the very leaky re-encryption step of the Fujisaki-Okamoto transform, (ii) the randomization of the decryption thanks to the incorporation of a dummy ciphertext, removing the adversary's control of its intermediate computations and making these computations ephemeral, (iii) key-homomorphic computations that can be masked against side-channel attacks with overheads that scale linearly in the number of shares, (iv) hard physical learning problems to argue about the security of some critical unmasked operations. Furthermore, we use an explicit rejection mechanism (returning an error symbol for invalid ciphertexts) to avoid the additional leakage caused by implicit rejection. As a result, the operations of POLKA can be protected against leakage in a cheaper way than state-of-the-art designs, opening the way towards schemes that are both quantum-safe and leakage-resistant.
Transparent Batchable Time-lock Puzzles and Applications to Byzantine Consensus

Time-lock puzzles (TLP) are a fascinating type of cryptographic problem that is easy to generate, but takes a certain time to solve, even when arbitrary parallel speedup is allowed. TLPs have wide-ranging applications including fairness, round efficient computation, and more. To reduce the effort needed to solve large numbers of TLPs, prior work has proposed batching techniques to reduce the cost of solving. However, these proposals either require: (1) a trusted setup or (2) the puzzle size be linear in the maximum batch size, which implies setting an a priori bound on the maximum size of the batch. Any of these limitations restrict the utility of TLPs in decentralized and dynamic settings like permissionless blockchains. In this work, we demonstrate the feasibility and usefulness of a TLP that overcomes all the above limitations using indistinguishability obfuscation to show that there are no fundamental barriers to achieving such a TLP construction. As a main application of our TLP, we show how to improve the resilience of consensus protocols toward network-level adversaries in the following settings: (1) We show a generic compiler that boosts the resilience of a Byzantine broadcast protocol $$\varPi $$ as follows: if $$\varPi $$ is secure against $$t<n$$ weakly adaptive corruptions, then the compiled protocol is secure against $$t<n$$ strongly adaptive corruptions. Here, 'strong' refers to adaptively corrupting a party and deleting messages that it sent while still honest. Our compiler is round and communication preserving, and gives the first expected constant-round Byzantine broadcast protocol against a strongly adaptive adversary for the dishonest majority setting. (2) We adapt the Nakamoto consensus protocol to a weak model of synchrony where the adversary can adaptively create minority partitions in the network. Unlike prior works, we do not assume that all honest messages are delivered within a known upper bound on the message delay. This is the first work to show that it is possible to achieve consensus in the permissionless setting even after relaxing the standard synchrony assumption.
Hardening Signature Schemes via Derive-then-Derandomize: Stronger Security Proofs for EdDSA

We consider a transform, called Derive-then-Derandomize, that hardens a given signature scheme against randomness failure and implementation error. We prove that it works. We then give a general lemma showing indifferentiability of a class of constructions that apply a shrinking output transform to an MD-style hash function. Armed with these tools, we give new proofs for the widely standardized and used $$\textsf{EdDSA}$$ signature scheme, improving prior work in two ways: (1) we give proofs for the case that the hash function is an MD-style one, reflecting the use of SHA512 in the NIST standard, and (2) we improve the tightness of the reduction so that one has guarantees for group sizes in actual use.
Security Analysis of RSA-BSSA

In a blind signature scheme, a user can obtain a digital signature on a message of her choice without revealing anything about the message or the resulting signature to the signer. Blind signature schemes have recently found applications for privacy-preserving web browsing and ad ecosystems, and as such, are ripe for standardization. In this paper, we show that the recent proposed standard of Denis, Jacobs and Wood [16, 17] constitutes a strongly one-more-unforgeable blind signature scheme in the random-oracle model under the one-more-RSA assumption. Further, we show that the blind version of RSA-FDH proposed and analyzed by Bellare, Namprempre, Pointcheval and Semanko [6] does not satisfy blindness when the public key is chosen maliciously, but satisfies a weaker notion of a blind token.
Robotics in the Fifth Industrial Revolution

The utilization and development of robotic systems directly correlate with the series of industrial revolutions. An investigation on robotics systems across the industrial revolutions showed how robotic systems evolve and be integrated into manufacturing and hospitality industries. In preparation for the fifth industrial revolution, the paper presents the factors for adoption, initiatives, and focus areas for the 5 countries that have adopted robotic systems. It can be observed that initiatives were done to invest in the development and utilization of Collaborative Robots in manufacturing workspaces. Countries like Japan and South Korea made strong initiatives to support service robots for the medical and logistical industries. The adoption of these technologies has been shown to promote a more efficient and safer workspace. Finally, the paper presents the current position of the Philippines and presents recommendation to facilitate the adoption of robotic systems in the Philippines. As discussions are being conducted on the fifth industrial revolution, its approach and implementation are imminent, the paper aims to contribute to the preparation for the fifth industrial revolution in the Philippines.
Unlocking Signal Processing With Image Detection: A Frequency Hopping Detection Scheme for Complex EMI Environments Using STFT and CenterNet

Accurate detection and parameter estimation of frequency hopping (FH) signals remain challenging in FH signal-based transmission systems. This study proposes a scheme combining time-frequency analysis (TFA) and deep learning (DL)-based image processing algorithms to alleviate the degradation of detection accuracy and estimation performance caused by complex electromagnetic interference (EMI). A short-time Fourier transform (STFT) was used to obtain the signal spectrogram, which reflects the signal energy in a concentration-dependent manner. Then, a CenterNet-based deep network was employed to identify each FH hop’s shape and position, reducing the computational burden via a lightweight neural network while maintaining high recognition accuracy. Inverse mapping from the coordinates to the spectrogram was used to perform parameter estimation in the time-frequency (TF) domain. The estimation error was reduced by precisely locating the centroid of the signal energy using CenterNet. The simulation results demonstrate that the proposed scheme can accurately estimate the FH signal at a low signal-to-noise ratio (SNR) with complex EMI. Furthermore, appropriately determining the optimal parameters of CenterNet to ensure the estimator performance provides a novel approach for integrating DL into signal detection and estimation in complex EMI environments.
Mask Detection and Classification in Thermal Face Images

Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify the type of mask on the face. The previously proposed dataset of thermal images was extended and annotated with the description of a type of mask and a location of a mask within a face. Different deep learning models were adapted. The best model for face mask detection turned out to be the Yolov5 model in the "nano" version, reaching mAP higher than 97% and precision of about 95%. High accuracy was also obtained for mask type classification. The best results were obtained for the convolutional neural network model built on an autoencoder initially trained in the thermal image reconstruction problem. The pretrained encoder was used to train a classifier which achieved an accuracy of 91%.
Efficient Method for Mining High Utility Occupancy Patterns Based on Indexed List Structure

High utility pattern mining has been proposed to improve the traditional support-based pattern mining methods that process binary databases. High utility patterns are discovered by effectively considering the quantity and importance of items. Recently, high utility occupancy pattern mining studies have been conducted to extract high-quality patterns by utilizing both the occupancy utility and frequency measure. Although the previous approaches provide worthy information in terms of utility occupancy, they require time-consuming tasks because of numerous comparison operations in exploring entries in global data structures. This results in significant performance degradation when the database is large, or a pre-defined threshold is low. An indexed list structure improves the inefficiency of the list-based approach by structurally connecting each tuple. In this paper, we propose an efficient high utility occupancy mining approach based on novel indexed list-based structures. The two newly designed data structures maintain index information on items or patterns and facilitate rapid pattern extension. Our approach improves the cost of generating long patterns of list-based ones by reducing a large number of comparison overheads. In addition, we devise novel constructing and mining methods that are suitable for the proposed data structures and utility occupancy functions. To narrow the wide search space, efficient pruning techniques apply to the designed methods. Thorough performance experiments using real and synthetic datasets show that our method is more efficient than state-of-the-art methods in environments where given thresholds change.
A Holistic Approach Towards Side-Channel Secure Fixed-Weight Polynomial Sampling

The sampling of polynomials with fixed weight is a procedure required by round-4 Key Encapsulation Mechanisms (KEMs) for Post-Quantum Cryptography (PQC) standardization (BIKE, HQC, McEliece) as well as NTRU, Streamlined NTRU Prime, and NTRU LPRrime . Recent attacks have shown in this context that side-channel leakage of sampling methods can be exploited for key recoveries. While countermeasures regarding such timing attacks have already been presented, still, there is no comprehensive work covering solutions that are also secure against power side channels. To close this gap, the contribution of this work is threefold: First, we analyze requirements for the different use cases of fixed weight sampling. Second, we demonstrate how all known sampling methods can be implemented securely against timing and power/EM side channels and propose performance-enhancing modifications. Furthermore, we propose a new, comparison-based methodology that outperforms existing methods in the masked setting for the three round-4 KEMs BIKE, HQC, and McEliece . Third, we present bitsliced and arbitrary-order masked software implementations and benchmarked them for all relevant cryptographic schemes to be able to infer recommendations for each use case. Additionally, we provide a hardware implementation of our new method as a case study and analyze the feasibility of implementing the other approaches in hardware.
Pose-Invariant Face Recognition via Facial Landmark Based Ensemble Learning

In recent years, pose-invariant face recognition has been mainly approached from a holistic insight. DCNNs (ArcFace, Elastic Face, FaceNet) are used to compute a face image embedding, which is used later to perform face recognition. This paper presents a novel approach to pose-invariant face recognition through the use of ensemble learning and local feature descriptors. The proposed method trains a base learner for each person’s face recognition ensemble system, based on feature vectors (SIFT, GMM, LBP) extracted from image regions surrounding specific facial landmarks. Three different classification models (SVM, Naive Bayes, GMM) are exclusively used as base learners, and the training procedure for each of these models is detailed. The proposed methodology includes a novel face pose descriptor referred to as the Face Angle Vector (FAV) which is utilized by a head pose classification model to determine the pose class of a face image. This model works together with a Base Learner Selection (BLS) block, to determine a set of facial landmarks to extract local feature descriptors, and uses them as the input to their corresponding base learners. Experimental results show a better performance over state-of-the-art methods using the CMU-PIE as the testing dataset, and face poses within ±90°.
Discriminating chert origins using machine‐learning approaches

Automatic techniques have not been applied in the geochemical discrimination of marine, terrestrial and hydrothermal cherts, which is critical to retrieving the paleoenvironmental information of ancient sedimentary basins. Here, we applied three supervised machine‐learning methods to analyse the labelled geochemical compositional data (32 major and trace elements) of cherts, including the support vector machine (SVM), sparse multinomial regression and linear discrimination analysis. Our results show that these machine‐learning methods can successfully discriminate different chert types, as the lowest classification score is 79%. Among the methods, the SVM is the most powerful approach (classification score 88%, on average), even though it is hard to be geochemically interpreted. Sparse multinomial regression and linear discrimination analysis are also efficient approaches (mean classification score 84%), which can be used to extract the important elements and the most representative sample for each chert type. Especially, the linear discrimination analysis can construct binary discrimination diagram superposed with biplot rays and posterior probability‐based decision boundaries, which could be quickly applied and explained by other researchers. Furthermore, the successful discrimination of different chert types indicates that cherts derived from different silica sources have different geochemical signatures, albeit mixing between different sources of silica during chert formation is common. Last, the general geochemical features of each chert type were summarised based on geochemical information extracted by machine‐learning methods and previous geochemical studies.
Mobile Bot Application for Identification of Trypanosoma evansi Infection through Thin-Blood Film Examination Based on Deep Learning Approach

Trypanosomiasis caused Trypanosoma evansi is current public health concern especially, in south Asia and Southeast Asia. Although polymerase chain reaction is currently used as a standard method, the techniques required skilled personnel, were performed in multiple steps, and required expensive instruments. Fundamental microscopic approach also has limitation in use by facing both inter- and intra-variability of interpretation by examiners. New automatic tool with the microscopic examination is needed. The study aimed to develop the mobile application-based YOLO neural network algorithms to predict T. evansi blood stages from thin-blood film examination. YOLO v4 tiny model is outperformed to localize and classify unseen images with the best performance at 95% of sensitivity, specificity, precision, accuracy and F1 score, respectively, with less misclassification rate than 5%. Simulation implementation platform, calling CiRA bot, give the empirical result and reliably comparable to that from the computational experiment studied with the area under ROC and precision-recall curves as 0.964 and 0.962, respectively. The result obtained from the CIRA bot platform is good enough for further distribution in field site. In the future, the study could contribute human and animal public health staff to simply identify the unicellular parasitic flagellate infection and also benefit them for designing the strategy in prevention and treatment of the disease.
Litho-structural and hydrothermal alteration mapping for mineral prospection in the Maider basin of Morocco based on remote sensing and field investigations

Landsat 8 OLI images were processed using band ratio (RB), principal component analysis (PCA), composite ratios (CR) 4/2–6/7-6/5 were used to map lithology, geological structures, and hydrothermal alteration zones. The studied area, Jbel Boulachrhal, is characterized by copper, barite, and amethyst mineralization and is in the Maider basin at the level of Morocco's eastern Anti-Atlas, which is formed by Paleozoic landfills. This vein-type mineralization, which oriented NE-SW, NNE-SSW, to E-W corresponds to the main orientations of mapped faults using remote sensing and confirmed by the field study, and is revealed by the lineaments extracted from the processing of Landsat 8 Oli. The mineralization is polymetallic, with copper oxide and sulfide dominating, as well as quartz, amethyst, lead, and barite. Remote sensing techniques combined with field research have showed that high lineament density areas that frequently correlate with areas of hydrothermal alteration are highly related to the occurrences of these mineralizations. These findings indicate that satellite image processing techniques (lineament density and hydrothermal alteration zones) will most likely be used in mining exploration.
Building an image set for modeling image re-targeting using deep learning

The displays of various devices such as TVs, laptops, and mobile phones vary greatly in size, aspect ratio, and resolution. Creating different versions of the same media for these different systems can be costly in terms of storage. A novel solution to this problem is image re-targeting, which adapts an image to fit any device display, preserving the main content of the image without wasting additional storage. Re-targeting can be performed on the device itself or on an intermediate device such as an edge server. The main re-targeting techniques found in literature are cropping, seam carving, and scale and stretch, but it is not easy to determine the best technique for a given image. In this paper, we propose using deep learning to model the decision of which re-targeting technique to use for which target size. To achieve this, we built and annotated a large image dataset using a team of 10 people, including 2750 original images representing 6 categories. We applied the most commonly used four re-targeting techniques (cropping, scaling, Seam Carving, and optimized scale and stretch) to the original image set to create five different sizes (HDP (1600 × 900), HD (1280 × 720), SVGA (800 × 600), WVGA (852 × 480), and NTSC (720 × 480)). The resulting image set, which includes the re-targeted images, contains 46750 images. We examined and summarized the annotators’ perceptual point of view for reference and comparison purposes. Additionally, we created a deep learning model and trained it using the newly generated image set to recommend the best re-targeting technique based on an input image and a target size. The model consists of 30 independent sub-models (five models for each image category). All these sub-models are constructed using pre-trained ResNet50. The choice of the sub-model to use for a particular image is based on both the input image category and target size. To accelerate the training process and avoid overfitting of the sub-models, we utilize transfer learning and froze the weights of the first 49 layers of the pre-trained model used. We then substituted the last fully connected layer with three fully connected layers, with the last one being a classification layer. The results demonstrated that our image dataset is suitable for training deep learning models for image re-targeting.