Reviewerly/exHarmony huggingface dataset:
Efficient Laconic Cryptography from Learning with Errors

Laconic cryptography is an emerging paradigm that enables cryptographic primitives with sublinear communication complexity in just two messages. In particular, a two-message protocol between Alice and Bob is called laconic if its communication and computation complexity are essentially independent of the size of Alice’s input. This can be thought of as a dual notion of fully-homomorphic encryption, as it enables “Bob-optimized” protocols. This paradigm has led to tremendous progress in recent years. However, all existing constructions of laconic primitives are considered only of theoretical interest: They all rely on non-black-box cryptographic techniques, which are highly impractical. This work shows that non-black-box techniques are not necessary for basic laconic cryptography primitives. We propose a completely algebraic construction of laconic encryption, a notion that we introduce in this work, which serves as the cornerstone of our framework. We prove that the scheme is secure under the standard Learning With Errors assumption (with polynomial modulus-to-noise ratio). We provide proof-of-concept implementations for the first time for laconic primitives, demonstrating the construction is indeed practical: For a database size of $$2^{50}$$ , encryption and decryption are in the order of single digit milliseconds. Laconic encryption can be used as a black box to construct other laconic primitives. Specifically, we show how to construct: All of the above have essentially optimal parameters and similar practical efficiency. Furthermore, our laconic encryption can be preprocessed such that the online encryption step is entirely combinatorial and therefore much more efficient. Using similar techniques, we also obtain identity-based encryption with an unbounded identity space and tight security proof (in the standard model).
Optimal Energy Consumption Path Planning for Unmanned Aerial Vehicles Based on Improved Particle Swarm Optimization

In order to enhance the energy efficiency of unmanned aerial vehicles (UAVs) during flight operations in mountainous terrain, this research paper proposes an improved particle swarm optimization (PSO) algorithm-based optimal energy path planning method, which effectively reduces the non-essential energy consumption of UAV during the flight operations through a reasonable path planning method. First, this research designs a 3D path planning method based on the PSO optimization algorithm with the goal of achieving optimal energy consumption during UAV flight operations. Then, to overcome the limitations of the classical PSO algorithm, such as poor global search capability and susceptibility to local optimality, a parameter adaptive method based on deep deterministic policy gradient (DDPG) is introduced. This parameter adaptive method dynamically adjusts the main parameters of the PSO algorithm by monitoring the state of the particle swarm solution set. Finally, the improved PSO algorithm based on parameter adaptive improvement is applied to path planning in mountainous terrain environments, and an optimal energy-consuming path-planning algorithm for UAVs based on the improved PSO algorithm is proposed. Simulation results show that the path-planning algorithm proposed in this research effectively reduces non-essential energy consumption during UAV flight operations, especially in more complex terrain scenarios.
Character Assassination

This chapter provides a unifying framework for the study of CA by identifying “character” and “character assassination” as distinguished from other related concepts. The authors highlight the strategic nature of the phenomenon, then introduce five pillars that are necessary to consider in a thorough analysis of a character attack. Finally, this chapter introduces some of its dynamics and methods.
Evaluating the Suitability of Several AR Devices and Tools for Industrial Applications

In recent years, there has been an increasing interest in Industrial Augmented Reality (IAR) due to its prominent role in the ongoing revolution known as Industry 4.0. For companies and industries it is essential to evaluate carefully which of the developed AR-based technologies to adopt, and when, for tasks such as training, maintenance, assistance, and collaborative design. There is also a wide array of hardware and software alternatives on the market, characterized by a significant heterogeneity in terms of functionalities, performance and cost. With this work, our objective is to study and compare some widely available devices and Software Development Kits (SDKs) for AR by leveraging a set of evaluation criteria derived from the actual literature which have been deemed capable to qualify the above assets as suitable for industrial applications. Such criteria include the operative range, robustness, accuracy and stability. Both marker-based and marker-less solutions have been considered, in order to investigate a wide range of possible use cases.
Bayesian Experimental Design for Finding Reliable Level Set Under Input Uncertainty

In the manufacturing industry, it is often necessary to repeat expensive operational testing of machine in order to identify the range of input conditions under which the machine operates properly.Since it is often difficult to accurately control the input conditions during the actual usage of the machine, there is a need to guarantee the performance of the machine after properly incorporating the possible variation in the input conditions.In this paper, we formulate this practical manufacturing scenario as an Input Uncertain Reliable Level Set Estimation (IU-rLSE) problem, and provide an efficient algorithm for solving it.The goal of IU-rLSE is to identify the input range in which the outputs smaller/greater than a desired threshold can be obtained with high probability when the input uncertainty is properly taken into consideration.We propose an active learning method to solve the IU-rLSE problem efficiently, theoretically analyze its accuracy and convergence, and illustrate its empirical performance through numerical experiments on artificial and real data.
Toward hardware-aware deep-learning-based dialogue systems

In the past few years, the use of transformer-based models has experienced increasing popularity as new state-of-the-art performance was achieved in several natural language processing tasks. As these models are often extremely large, however, their use for applications within embedded devices may not be feasible. In this work, we look at one such specific application, retrieval-based dialogue systems, that poses additional difficulties when deployed in environments characterized by limited resources. Research on building dialogue systems able to engage in natural sounding conversation with humans has attracted increasing attention in recent years. This has led to the rise of commercial conversational agents, such as Google Home, Alexa and Siri situated on embedded devices, that enable users to interface with a wide range of underlying functionalities in a natural and seamless manner. In part due to memory and computational power constraints, these agents necessitate frequent communication with a server in order to process the users’ queries. This communication may act as a bottleneck, resulting in delays as well as in the halt of the system should the network connection be lost or unavailable. We propose a new framework for hardware-aware retrieval-based dialogue systems based on the Dual-Encoder architecture, coupled with a clustering method to group candidates pertaining to a same conversation, that reduces storage capacity and computational power requirements.
Few-shot learning for deformable image registration in 4DCT images

Objectives: To develop a rapid and accurate 4D deformable image registration (DIR) approach for online adaptive radiotherapy. Methods: We propose a deep learning (DL)-based few-shot registration network (FR-Net) to generate deformation vector fields from each respiratory phase to an implicit reference image, thereby mitigating the bias introduced by the selection of reference images. The proposed FR-Net is pretrained with limited unlabeled 4D data and further optimized by maximizing the intensity similarity of one specific four-dimensional computed tomography (4DCT) scan. Because of the learning ability of DL models, the few-shot learning strategy facilitates the generalization of the model to other 4D data sets and the acceleration of the optimization process. Results: The proposed FR-Net is evaluated for 4D groupwise and 3D pairwise registration on thoracic 4DCT data sets DIR-Lab and POPI. FR-Net displays an averaged target registration error of 1.48 mm and 1.16 mm between the maximum inhalation and exhalation phases in the 4DCT of DIR-Lab and POPI, respectively, with approximately 2 min required to optimize one 4DCT. Overall, FR-Net outperforms state-of-the-art methods in terms of registration accuracy and exhibits a low computational time. Conclusion: We develop a few-shot groupwise DIR algorithm for 4DCT images. The promising registration performance and computational efficiency demonstrate the prospective applications of this approach in registration tasks for online adaptive radiotherapy. Advances in knowledge: This work exploits DL models to solve the optimization problem in registering 4DCT scans while combining groupwise registration and few-shot learning strategy to solve the problem of consuming computational time and inferior registration accuracy.
Lean requirements traceability automation enabled by model-driven engineering

The benefits of requirements traceability, such as improvements in software product and process quality, early testing, and software maintenance, are widely described in the literature. Requirements traceability is a critical, widely accepted practice. However, very often it is not applied for fear of the additional costs associated with manual efforts or the use of additional tools.This article presents a "low-cost" mechanism for automating requirements traceability based on the model-driven paradigm and formalized by a metamodel for the creation and monitoring of traces and an integration process for traceability management. This approach can also be useful for information fusion in industry insofar that it facilitates data traceability.This article extends an existing model-driven development methodology to incorporate traceability as part of its development tool. The tool has been used successfully by several companies in real software development projects, helping developers to manage ongoing changes in functional requirements. One of those projects is cited as an example in the paper. The authors' current work leads them to conclude that a model-driven engineering approach, traditionally used only for the automatic generation of code in a software development process, can also be used to successfully automate and integrate traceability management without additional costs. The systematic evaluation of traceability management in industrial projects constitutes a promising area for future work.
A Novel Walking Activity Recognition Model for Rotation Time Series Collected by a Wearable Sensor in a Free-Living Environment

Solutions to assess walking deficiencies are widespread and largely used in healthcare. Wearable sensors are particularly appealing, as they offer the possibility to monitor gait in everyday life, outside a facility in which the context of evaluation biases the measure. While some wearable sensors are powerful enough to integrate complex walking activity recognition models, non-invasive lightweight sensors do not always have the computing or memory capacity to run them. In this paper, we propose a walking activity recognition model that offers a viable solution to this problem for any wearable sensors that measure rotational motion of body parts. Specifically, the model was trained and tuned using data collected by a motion sensor in the form of a unit quaternion time series recording the hip rotation over time. This time series was then transformed into a real-valued time series of geodesic distances between consecutive quaternions. Moving average and moving standard deviation versions of this time series were fed to standard machine learning classification algorithms. To compare the different models, we used metrics to assess classification performance (precision and accuracy) while maintaining the detection prevalence at the level of the prevalence of walking activities in the data, as well as metrics to assess change point detection capability and computation time. Our results suggest that the walking activity recognition model with a decision tree classifier yields the best compromise in terms of precision and computation time. The sensor that was used had purposely low computing and memory capacity so that reported performances can be thought of as the lower bounds of what can be achieved. Walking activity recognition is performed online, i.e., on-the-fly, which further extends the range of applicability of our model to sensors with very low memory capacity.
The Role of Human Operators' Suspicion in the Detection of Cyber Attacks

Despite the importance that human error in the cyber domain has had in recent reports, cyber warfare research to date has largely focused on the effects of cyber attacks on the target computer system. In contrast, there is little empirical work on the role of human operators during cyber breaches. More specifically, there is a need to understand the human-level factors at play when attacks occur. This paper views cyber attacks through the lens of suspicion, a construct that has been used in other contexts, but inadequately defined, in prior research. After defining the construct of suspicion, the authors demonstrate the role that suspicion plays as the conduit between computer operators' normal working behaviors and their ability to alter that behavior to detect and react to cyber attacks. With a focus on the user, rather than the target computer, the authors empirically develop a latent structure for a variety of types of cyber attacks, link that structure to levels of operator suspicion, link suspicion to users' cognitive and emotional states, and develop initial implications for cyber training.
Transformer Memory for Interactive Visual Navigation in Cluttered Environments

Substantial progress has been achieved in embodied visual navigation based on reinforcement learning (RL). These studies presume that the environment is stationary where all the obstacles are static. However, in real cluttered scenes, interactable objects (e.g. shoes and boxes) blocking the way of robots makes the environment non-stationary. Accordingly, the ego-centric visual agent will easily get stuck in the dilemma of finding the next waypoint as it struggles to decide whether to push the obstacles ahead. To handle the predicament, we formulate this interactive visual navigation as Partial Observed Markov Decision Process (POMDP). As the transformer encoder has demonstrated its superior ability to capture the spatial-temporal dependencies in natural language processing. We propose a transformer-based memory to empower the agents utilizing the historical interactive information. However, directly leveraging the transformer architecture in the RL settings is highly unstable. We further propose a surrogate objective to predict the next waypoint as the auxiliary task, which facilitates the representation learning and bootstraps the RL. We demonstrate our method in the iGibson environment and experimental results show a significant improvement over the interactive Gibson benchmark and the related recurrent RL policy both in the validation seen scenes and the test unseen scenes.
A task-independent design and development process for cognitive products in industrial applications

This paper motivates the employment of a cognitive headgear in the context of two concrete human-in-the-loop industrial use cases. However, the distinct design and implementation of such a headgear is strongly task-dependent and no one-fits-all solution exists. To overcome that, a computer-aided design process is proposed that allows the design of a device truly tailored to the individual needs. Further, the features and components required to support workers in the identified scenarios are depicted, including image-based worker localization, gaze-based skill-level detection, workflow recognition using head-worn sensors and multimodal assistance. Finally, early functional prototypes are illustrated.
Multi-robot Field Exploration in Hex-Decomposed Environments for Dubins Vehicles

Multi-robot systems can be useful in applications such as map building, surveillance, and search and rescue. To be efficient in practice, the robotic team should cooperatively explore a region of interest. However, when the environment is unknown, it is challenging to plan collision-free paths in real-time under both non-holonomic mobility constraints and energy constraints. We propose the Multi-robot Hex Decomposition Exploration (M-HDE) method for multiple Dubins vehicles to explore unknown environments. M-HDE ensures robots return to the initial (departure) position before energy runs out. Furthermore, when available energy level allow, M-HDE can achieve complete exploration of the environment. The proposed approach generates smooth, continuous paths for Dubins vehicles to follow at constant velocities, and offers geometric closed-form solutions for both team formation and paths. The performance is evaluated with a team of Turtlebots in simulated environments with obstacles in Gazebo.
Preexposure Prophylaxis Indication Criteria Underidentify Black and Latinx Persons and Require Revision

"Preexposure Prophylaxis Indication Criteria Underidentify Black and Latinx Persons and Require Revision." , 110(3), pp. 267–268
Discrete and Continuous Deep Residual Learning over Graphs

In this paper we propose the use of continuous residual modules for graph kernels in Graph Neural Networks. We show how both discrete and continuous residual layers allow for more robust training, being that continuous residual layers are those which are applied by integrating through an Ordinary Differential Equation (ODE) solver to produce their output. We experimentally show that these residuals achieve better results than the ones with non-residual modules when multiple layers are used, mitigating the low-pass filtering effect of GCN-based models. Finally, we apply and analyse the behaviour of these techniques and give pointers to how this technique can be useful in other domains by allowing more predictable behaviour under dynamic times of computation.
Organizing The World's Largest Hardware Security Competition

The recent trend of providing fast and flexible hardware platforms as-a-service coupled with the advancements in hardware design tools have significantly reduced the effort of designing new hardware. Additionally, with the advent of open-source Instruction Set Architectures (ISAs) such as OpenRISC and RISC-V, we witness the rise of a wide variety of open-source and commercial processor cores and System-On-Chip (SoC) designs in a short time.
Effective Soil Type Classification Using Convolutional Neural Network

Soil classification is a growing research area in the current era. Various studies have proposed different techniques to deal with the issues, including rule-based, statistical, and traditional learning methods. However, the plans remain drawbacks to producing an accurate classification result. Therefore, we propose a novel technique to address soil classification by implementing a deep learning algorithm to construct an effective model. Based on the experiment result, the proposed model can obtain classification results with an accuracy rate of 97% and a loss of 0.1606. Furthermore, we also received an F1-score of 98%.
Transfer Learning, Style Control, and Speaker Reconstruction Loss for Zero-Shot Multilingual Multi-Speaker Text-to-Speech on Low-Resource Languages

Deep neural network (DNN)-based systems generally require large amounts of training data, so they have data scarcity problems in low-resource languages.Recent studies have succeeded in building zero-shot multi-speaker DNN-based TTS on high-resource languages, but they still have unsatisfactory performance on unseen speakers.This study addresses two main problems: overcoming the problem of data scarcity in the DNN-based TTS on low-resource languages and improving the performance of zero-shot speaker adaptation for unseen speakers.We propose a novel multi-stage transfer learning strategy using a partial network-based deep transfer learning to overcome the low-resource problem by utilizing pretrained monolingual single-speaker TTS and d-vector speaker encoder on a high-resource language as the source domain.Meanwhile, to improve the performance of zero-shot speaker adaptation, we propose a new TTS model that incorporates an explicit style control from the target speaker for TTS conditioning and an utterance-level speaker reconstruction loss during TTS training.We use publicly available speech datasets for experiments.We show that our proposed training strategy is able to effectively train the TTS models using a limited amount of training data of low-resource target languages.The models trained using the proposed transfer learning successfully produce intelligible natural speech sounds, while in contrast using standard training fails to make the models synthesize understandable speech.We also demonstrate that our proposed style encoder network and speaker reconstruction loss significantly improves speaker similarity in zero-shot speaker adaptation task compared to the baseline model.Overall, our proposed TTS model and training strategy has succeeded in increasing the speaker cosine similarity of the synthesized speech on the unseen speakers test set by 0.468 and 0.279 in native and foreign languages respectively.
A Review: Image Processing Techniques’ Roles towards Energy-Efficient and Secure IoT

The goal of this review paper is to highlight the image processing techniques’ role in the Internet of Things (IoT), aiming to attain an energy-efficient and secure IoT. IoT-dependent systems (IoTSs) cause heavy usage of energy. This is one of the biggest issues associated with IoTSs. Another issue is that the security of digital content is a big challenge and difficulty. Image processing has recently played an essential role in resolving these difficulties. Several researchers have made efforts to improve future IoTSs, which are summarized in this article. Day-by-day, proposed methods are developed, and thus IoT deployment has been plainly engaged in our everyday activities. Several efficient image-processing techniques that can be utilized by IoTSs to overcome such issues have been proposed. This review paper aims to highlight those proposed methods that can make contributions in this direction. Thus, this study aims to review numerous research studies on this subject. This study looks at 36 publications relevant to image-processing techniques utilized by several types of IoTSs. The innovative work of this review paper is to provide readers with a map of suitable image processing techniques to be used with certain types of IoT systems (i.e., scenarios). Both methodology and analysis have come out with a suggested mind map highlighting a number of proposed solutions (i.e., image processing techniques) that can be suitable to help design an energy-efficient, secure, and intelligent IoT system. We have made some conclusions and projections for future research work.