Reviewerly/exHarmony huggingface dataset:
Boundary delineation in transrectal ultrasound images for region of interest of prostate

Accurate and robust prostate segmentation in transrectal ultrasound (TRUS) images is of great interest for ultrasound-guided brachytherapy for prostate cancer. However, the current practice of manual segmentation is difficult, time-consuming, and prone to errors. To overcome these challenges, we developed an accurate prostate segmentation framework (A-ProSeg) for TRUS images. The proposed segmentation method includes three innovation steps: (1) acquiring the sequence of vertices by using an improved polygonal segment-based method with a small number of radiologist-defined seed points as prior points; (2) establishing an optimal machine learning-based method by using the improved evolutionary neural network; and (3) obtaining smooth contours of the prostate region of interest using the optimized machine learning-based method. The proposed method was evaluated on 266 patients who underwent prostate cancer brachytherapy. The proposed method achieved a high performance against the ground truth with a Dice similarity coefficient of 96.2% ± 2.4%, a Jaccard similarity coefficient of 94.4% ± 3.3%, and an accuracy of 95.7% ± 2.7%; these values are all higher than those obtained using state-of-the-art methods. A sensitivity evaluation on different noise levels demonstrated that our method achieved high robustness against changes in image quality. Meanwhile, an ablation study was performed, and the significance of all the key components of the proposed method was demonstrated.
Next-Word Prediction: A Perspective of Energy-Aware Distributed Inference

The pursuit of high-quality artificial intelligence generated contents (AIGC) with fast response has prompted the evolution of natural language processing (NLP) services, notably those enabled at the edge ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , edge NLP). For concreteness, we study distributed inference for next-word prediction which is a prevalent edge NLP service for mobile keyboards on user devices. Accordingly, we optimize coupled metrics, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , maximize prediction click-through rate (CTR) for improved quality-of-service (QoS), minimize user impatience for enhanced quality-of-experience (QoE), and keep energy consumption within budget for sustainability. Moreover, we consider the real-world setting where there is no prior knowledge of heterogeneous NLP models' prediction accuracy. Via an integration of online learning and online control, we propose a novel distributed inference algorithm for online next-word prediction with user impatience (DONUT) to estimate models' prediction accuracy and balance the trade-offs among coupled metrics. Our theoretical analysis reveals that DONUT achieves sub-linear regret (loss of CTR), ensures bounded user impatience, and maintains within-budget energy consumption. Through numerical simulations, we not only establish DONUT's superior performance over other baseline methods, but also demonstrate its adaptability to various settings.
Fuzzy Identity-Based Matchmaking Encryption and Its Application

Ateniese et al. introduced the primitive of matchmaking encryption (ME) at CRYPTO 2019 and left open several important questions, which include extending ME to fuzzy cases or giving an efficient ME in the identity-based setting without relying on random oracles. The main challenge is to achieve fuzzy bilateral access control while providing identity privacy of the sender and receiver, message confidentiality and authenticity without random oracles. In this work, we resolve the question by formalizing the first fuzzy identity-based ME (IB-ME) and presenting a concrete construction. Specifically, we propose the formal syntax definition of fuzzy IB-ME. In fuzzy IB-ME, the identities of senders and receivers are characterized by attribute sets. A ciphertext can be correctly decrypted if the overlaps between the attribute set of the sender or receiver and the attribute set specified by the other party are simultaneously greater than a threshold, which can be applied to many attractive applications such as fuzzy bilateral access control in online social dating. Then, we present concrete details of fuzzy IB-ME based on fuzzy identity-based encryption, which does not rely on other cryptographic tools such as two-input functional encryption and non-interactive zero-knowledge proof systems. In this process, fuzzy bilateral access control and identity privacy are achieved through the formalism of arranged ME and the splitting technique while message authenticity is provided through the authentication and binding of the encryption key. The identity privacy of the sender and receiver, confidentiality, and authenticity of messages are reduced to the decisional bilinear Diffie-Hellman, decision linear, and computational bilinear Diffie-Hellman assumptions in the selective model without random oracles. Finally, we implement the scheme and evaluate its performance through theoretical analyses and experiments to demonstrate its efficiency.
mentBot: A Repository for Mental Health Disorders*

Mental health disorders is one of critical illnesses that an individual tries to avoid talking about it. Thus, many people would seek information about mental health disorders by searching the information on Webs through available search engines. Despite the fact that the search engine is able to retrieve, these resources are normally scattered. The aim of this paper is to propose a chatbot so-called mentBot that is able to function as a repository for mental health disorders. Human users can chat and retrieve specific information from the mentBot without being judgmental. This paper presents an architecture of mentBot and a step by step process on how to design and implement mentBot.
Pincering SKINNY by Exploiting Slow Diffusion

Lightweight cryptography is an emerging field where designers are testing the limits of symmetric cryptography. We investigate the resistance against sidechannel attacks of a new class of lighter blockciphers, which use a classic substitution–permutation network with slow diffusion and many rounds.Among these ciphers, we focus on SKINNY, a primitive used up to the final round ofNIST’s recent lightweight standardisation effort. We show that the lack of diffusion in the key scheduler allows an attacker to combine leakage from the first and the last rounds, effectively pincering its target. Furthermore, the slow diffusion used by its partial key-absorption and linear layers enable, on both sides, to target S-Boxes from several rounds deep.As some of these S-boxes leak on the same part of the key, full key recovery exploiting all leakage requires a clever combining strategy. We introduce the use of cluster graph inference (an established tool from probabilistic graphical model theory) to enhance both unprofiled or profiled differential power analysis, enabling us to handlethe increase of S-Boxes with their intertwined leakage.We evaluate the strength of our attack both in the Hamming weight model and against two implementations running on an STM32F303 ARM Cortex-M4 hosted on a ChipWhisperer target board, showing that our attack reduces the number of traces required to attack SKINNY by a factor of around 2.75.
Masked Graph Neural Networks for Unsupervised Anomaly Detection in Multivariate Time Series

Anomaly detection has been widely used in grid operation and maintenance, machine fault detection, and so on. In these applications, the multivariate time-series data from multiple sensors with latent relationships are always high-dimensional, which makes multivariate time-series anomaly detection particularly challenging. In existing unsupervised anomaly detection methods for multivariate time series, it is difficult to capture the complex associations among multiple sensors. Graph neural networks (GNNs) can model complex relations in the form of a graph, but the observed time-series data from multiple sensors lack explicit graph structures. GNNs cannot automatically learn the complex correlations in the multivariate time-series data or make good use of the latent relationships among time-series data. In this paper, we propose a new method—masked graph neural networks for unsupervised anomaly detection (MGUAD). MGUAD can learn the structure of the unobserved causality among sensors to detect anomalies. To robustly learn the temporal context from adjacent time points of time-series data from the same sensor, MGUAD randomly masks some points of the time-series data from the sensor and reconstructs the masked time points. Similarly, to robustly learn the graph-level context from adjacent nodes or edges in the relation graph of multivariate time series, MGUAD masks some nodes or edges in the graph under the framework of a GNN. Comprehensive experiments are conducted on three public datasets. According to the experimental findings, MGUAD outperforms state-of-the-art anomaly detection methods.
Hydrodynamic Response of Mocean Wave Energy Converter in Extreme Waves

The design of moored floating wave energy converters (WECs) must take into account extreme responses and mooring line loads in order to ensure their survival and continued wave power generation in the ocean environment. This study focuses on Mocean Energy's hinged raft WEC and aims to provide a comprehensive understanding of its hydrodynamic characteristics in survival wave conditions. To achieve this, a physical model study was conducted on a Froude scale of 1 in 50 at the FloWave Ocean Energy Research Facility, University of Edinburgh.&#x0D; The experiments involved the use of NewWaves focusing of crest and trough at the model hinge location, as well as long irregular waves. Motion responses of the fore and aft bodies of the WEC were measured using a Qualisys camera, and single component load cells were used to measure the forces in the 3-point catenary mooring line. The hydrodynamic characteristics of the WEC were evaluated in terms of response amplitude operators and non-dimensional mooring line loads.&#x0D; Results indicate that the fore and aft bodies of the WEC exhibit similar motion responses, except for the pitch motion. The aft body has a pitch response 2 to 3 times higher than the fore body. Concerning the moorings, the wave load on the mooring line in line with the wave direction was found to be higher than the other two mooring lines which were arranged at an angle to the wave direction.&#x0D; In this paper, a brief discussion of the model set-up, parameters, test procedure, analysis of results, and discussion will be reported. The results will provide insight into the behaviour of the Mocean device in survival wave conditions and will aid with the determination of appropriate design parameters for optimal performance and survival in the ocean environment.
A new method based on semi-tensor product of matrices for solving reduced biquaternion matrix equation $ \sum\limits_{p = 1}^l A_pXB_p = C $ and its application in color image restoration

&lt;abstract&gt;&lt;p&gt;In this paper, semi-tensor product of real matrices is extended to reduced biquaternion matrices, and then some new conclusions of the reduced biquaternion matrices under the vector operator are proposed using semi-tensor product of reduced biquaternion matrices, so that the reduced biquaternion matrix equation $ \sum\limits_{p = 1}^l A_pXB_p = C $ can be transformed into a reduced biquaternion linear equations, then the expression of the least squares solution of the equation is obtained using the $ \mathcal{L}_\mathcal{C} $-representation and Moore-Penrose inverse. The necessary and sufficient conditions for the compatibility and the expression of general solutions of the equation are obtained, and the minimal norm solutions are also given. Finally, our proposed method of solving the reduced biquaternion matrix equation is applied to color image restoration.&lt;/p&gt;&lt;/abstract&gt;
Dynamic Loss for Robust Learning

Label noise and class imbalance are common challenges encountered in real-world datasets. Existing approaches for robust learning often focus on addressing either label noise or class imbalance individually, resulting in suboptimal performance when both biases are present. To bridge this gap, this work introduces a novel meta-learning-based dynamic loss that adapts the objective functions during the training process to effectively learn a classifier from long-tailed noisy data. Specifically, our dynamic loss consists of two components: a label corrector and a margin generator. The label corrector is responsible for correcting noisy labels, while the margin generator generates per-class classification margins by capturing the underlying data distribution and the learning state of the classifier. In addition, we employ a hierarchical sampling strategy that enriches a small amount of unbiased metadata with diverse and challenging samples. This enables the joint optimization of the two components in the dynamic loss through meta-learning, allowing the classifier to effectively adapt to clean and balanced test data. Extensive experiments conducted on multiple real-world and synthetic datasets with various types of data biases, including CIFAR-10/100, Animal-10N, ImageNet-LT, and Webvision, demonstrate that our method achieves state-of-the-art accuracy.
PROOF SYSTEMS FOR TWO-WAY MODAL MU-CALCULUS

Abstract We present sound and complete sequent calculi for the modal mu-calculus with converse modalities, aka two-way modal mu-calculus. Notably, we introduce a cyclic proof system wherein proofs can be represented as finite trees with back-edges, i.e., finite graphs. The sequent calculi incorporate ordinal annotations and structural rules for managing them. Soundness is proved with relative ease as is the case for the modal mu-calculus with explicit ordinals. The main ingredients in the proof of completeness are isolating a class of non-wellfounded proofs with sequents of bounded size, called slim proofs, and a counter-model construction that shows slimness suffices to capture all validities. Slim proofs are further transformed into cyclic proofs by means of re-assigning ordinal annotations.
Automatically Prepare Training Data for YOLO Using Robotic In-Hand Observation and Synthesis

Deep learning methods have recently exhibited impressive performance in object detection. However, such methods needed much training data to achieve high recognition accuracy, which was time-consuming and required considerable manual work like labeling images. In this paper, we automatically prepare training data using robots. Considering the low efficiency and high energy consumption in robot motion, we proposed combining robotic in-hand observation and data synthesis to enlarge the limited data set collected by the robot. We first used a robot with a depth sensor to collect images of objects held in the robot’s hands and segment the object pictures. Then, we used a copy-paste method to synthesize the segmented objects with rack backgrounds. The collected and synthetic images are combined to train a deep detection neural network. We conducted experiments to compare YOLOv5x detectors trained with images collected using the proposed method and several other methods. The results showed that combined observation and synthetic images led to comparable performance to manual data preparation. They provided a good guide on optimizing data configurations and parameter settings for training detectors. The proposed method required only a single process and was a low-cost way to produce the combined data. Interested readers may find the data sets and trained models from the following GitHub repository: github.com/wrslab/tubedet <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The background of this study is a requirement in lab automation – Using robots to arrange randomly placed tubes automatically. Before sending test tubes to an examination machine for gradient tests, humans need to categorize and organize the tubes into specific patterns to fit the machine’s internal design. Employing humans is difficult as the tube arrangement requirements are time-varying. A preferred solution is using robots to replace humans. The robots should have a vision system to detect the tubes and a manipulation system to perform physical arranging actions. They will be used in busy seasons while deployed for other tasks in leisure time. Deep neural networks like YOLO are effective for the tube detection task. However, preparing the training data is challenging and unsuitable for lab end users. Pre-trained neural networks are options but have limited tube detection ability and cannot deal with newly included tube types. The method developed in this work helps solve the training data preparation problem. With its support, the robot can automatically prepare training data that has comparable quality to manually labeled ones in a single-process and low-cost way.
Enhancing Accessibility of Cultural Heritage: Extended Reality and Tactile Prints for an Inclusive Experience of the Madonna Dell’Itri Church in Nociglia

This paper presents the partial development and implementation results of an ongoing project aimed at enhancing the accessibility of the Madonna dell’Itri Church in Nociglia (Italy), by means of different smart fruition tools. Thanks to a collaboration between the Department of Engineering for Innovation and the Department of Cultural Heritage of the University of Salento, along with the Municipality of Nociglia, the project aims to study and monitor the conservation status of the cultural asset, to enhance a smart fruition by means of eXtended Reality (XR) technologies and to improve accessibility of the site. This paper specifically focuses on the XR applications developed to achieve these goals, showcasing their potential to provide accessible solutions and improve the overall visitor experience, especially for individuals with special needs.
Automatic Generation of Semantic Parts for Face Image Synthesis

Semantic image synthesis (SIS) refers to the problem of generating realistic imagery given a semantic segmentation mask that defines the spatial layout of object classes. Most of the approaches in the literature, other than the quality of the generated images, put effort in finding solutions to increase the generation diversity in terms of style i.e. texture. However, they all neglect a different feature, which is the possibility of manipulating the layout provided by the mask. Currently, the only way to do so is manually by means of graphical users interfaces. In this paper, we describe a network architecture to address the problem of automatically manipulating or generating the shape of object classes in semantic segmentation masks, with specific focus on human faces. Our proposed model allows embedding the mask class-wise into a latent space where each class embedding can be independently edited. Then, a bi-directional LSTM block and a convolutional decoder output a new, locally manipulated mask. We report quantitative and qualitative results on the CelebMask-HQ dataset, which show our model can both faithfully reconstruct and modify a segmentation mask at the class level. Also, we show our model can be put before a SIS generator, opening the way to a fully automatic generation control of both shape and texture. Code available at https://github.com/TFonta/Semantic-VAE .
A Self-Labeling Method for Adaptive Machine Learning by Interactive Causality

Learning from unlabeled data, or self-learning, can substantially reduce the complexity of machine learning (ML) utilization in real-time deployment. While the development of un/ semi-supervised algorithms shows promising results in learning with reduced labels, the fundamental assumption of data smoothness restricts its scope of application, especially with non-stationary data distributions in different domains. Leveraging trans-domain invariant causal relationships, causality has recently been employed to foster robust ML. In this paper, we have developed a generic method for self-labeling data that relies on known causality among interactive objects and learned temporal relations among causal events for identifying and associating labels and input data. The causal time-interval between asynchronous cause and effect events is studied to achieve self-labeling. We utilize dynamical system theory in a 1- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d$</tex-math></inline-formula> setting to demonstrate that our proposed method outperforms traditional feature similarity based semi-supervised learning. A computer simulation experiment was conducted to generate high-dimensional data, and the comprehensive results reveal the potential of learning adaptation in dynamic environments to improve ML robustness against shifts in data distribution.
Comprehensive Vulnerability Evaluation of Face Recognition Systems to Template Inversion Attacks via 3D Face Reconstruction

In this paper, we comprehensively evaluate the vulnerability of state-of-the-art face recognition systems to template inversion attacks using 3D face reconstruction. We propose a new method (called GaFaR) to reconstruct 3D faces from facial templates using a pretrained geometry-aware face generation network, and train a mapping from facial templates to the intermediate latent space of the face generator network. We train our mapping with a semi-supervised approach using real and synthetic face images. For real face images, we use a generative adversarial network (GAN)-based framework to learn the distribution of generator intermediate latent space. For synthetic face images, we directly learn the mapping from facial templates to the generator intermediate latent code. Furthermore, to improve the success attack rate, we use two optimization methods on the camera parameters of the GNeRF model. We propose our method in the whitebox and blackbox attacks against face recognition systems and compare the transferability of our attack with state-of-the-art methods across other face recognition systems on the MOBIO and LFW datasets. We also perform practical presentation attacks on face recognition systems using the digital screen replay and printed photographs, and evaluate the vulnerability of face recognition systems to different template inversion attacks. The project page is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.idiap.ch/paper/gafar</uri> .
Point-to-multipoint quantum key distribution against two-mode collective attack

We report the security analysis of point-to-multipoint quantum key distribution against two-mode collective attacks, showing the stability of the protocol facing all kinds of two-mode attack strategies.
Adaptable Decision Making Chatbot System: Unlocking Interoperability in Smart Manufacturing

In the context of Industry 4.0, the training of operators can be improved through the use of real-time chatbot interfaces that provide expedient and focused information. This paper explores the incorporation of a chatbot-based interface within an industrial context, specifically through the development and implementation of an innovative immersive augmented reality (AR) consultation chatbot system. This system utilizes smart glass interfaces and different interoperable industrial internet of Things (IIoT) devices to empower operators with real-time information and enhance decision-making in manufacturing processes. The system emphasizes interoperability and has the potential for application across diverse production floor settings. Initial feedback from a pilot study is presented, and suggestions for widespread integration of such interfaces in smart manufacturing environments are put forward. The presented proof of concept demonstrates the potential for chatbot-based interfaces to enhance operator training applications in Industry 4.0.