Reviewerly/exHarmony huggingface dataset:
Incompressible Cryptography

Incompressible encryption allows us to make the ciphertext size flexibly large and ensures that an adversary learns nothing about the encrypted data, even if the decryption key later leaks, unless she stores essentially the entire ciphertext. Incompressible signatures can be made arbitrarily large and ensure that an adversary cannot produce a signature on any message, even one she has seen signed before, unless she stores one of the signatures essentially in its entirety. In this work, we give simple constructions of both incompressible public-key encryption and signatures under minimal assumptions. Furthermore, large incompressible ciphertexts (resp. signatures) can be decrypted (resp. verified) in a streaming manner with low storage. In particular, these notions strengthen the related concepts of disappearing encryption and signatures, recently introduced by Guan and Zhandry (TCC 2021), whose previous constructions relied on sophisticated techniques and strong, non-standard assumptions. We extend our constructions to achieve an optimal “rate”, meaning the large ciphertexts (resp. signatures) can contain almost equally large messages, at the cost of stronger assumptions.
Timed Automata Learning via SMT Solving

Automata learning is a technique for automatically inferring models of existing systems, that enables formal verification of black-box systems. In this paper we propose a way of learning timed automata, extended final state machines that can measure the progress of time. We make use of SMT solving to learn timed automata consistent with the observations in a set of timed traces, which can be gathered via active testing or passive monitoring. By imposing a set of restrictions to the learnt models, we ensure that our solutions are not overly general. The presented SMT encoding of the problem allows for two ways of incremental solving and different search orders. We present a prototype implementation with results from case studies and randomly generated timed automata of varying size and complexity. We perform an extensive evaluation over six SMT solvers, using different theories and exploration strategies, as well as incremental and non-incremental solving.
Stateful Black-Box Fuzzing of Bluetooth Devices Using Automata Learning

Fuzzing (aka fuzz testing) shows promising results in security testing. The advantage of fuzzing is the relatively simple applicability compared to comprehensive manual security analysis. However, the effectiveness of black-box fuzzing is hard to judge since the internal structure of the system under test is unknown. Hence, in-depth behavior might not be covered by fuzzing. This paper aims at overcoming the limitations of black-box fuzzing. We present a stateful black-box fuzzing technique that uses a behavioral model of the system under test. Instead of manually creating the model, we apply active automata learning to automatically infer the model. Our framework generates a test suite for fuzzing that includes valid and invalid inputs. The goal is to explore unexpected behavior. For this, we test for conformance between the learned model and the system under test. Additionally, we analyze behavioral differences using the learned state information. In a case study, we evaluate implementations of the Bluetooth Low Energy (BLE) protocol on physical devices. The results reveal security and dependability issues in the tested devices leading to crashes of four out of six devices.
An Empirically Driven Guide on Using Bayes Factors for M/EEG Decoding

Bayes factors can be used to provide quantifiable evidence for contrasting hypotheses and have thus become increasingly popular in cognitive science. However, Bayes factors are rarely used to statistically assess the results of neuroimaging experiments. Here, we provide an empirically driven guide on implementing Bayes factors for time-series neural decoding results. Using real and simulated magnetoencephalography (MEG) data, we examine how parameters such as the shape of the prior and data size affect Bayes factors. Additionally, we discuss the benefits Bayes factors bring to analysing multivariate pattern analysis data and show how using Bayes factors can be used instead or in addition to traditional frequentist approaches.
Multi-keyword searchable encryption for smart grid edge computing

Searchable encryption scheme allows users to search for encrypted data containing some keywords without decrypting data. It protects the data privacy of data owners while promoting efficient data access. The searchable encryption scheme only supports a single keyword, which is easy to cause the problem of data redundancy. It makes users have to filter again in a large amount of data. In order to achieve the search goal more accurately, researchers proposed multi-keyword searchable encryption scheme. In addition, searchable encryption schemes that only support a single keyword are vulnerable to the disclosure of user access and search pattern. Based on these two reasons, we propose a searchable encryption scheme based on multi- keyword, which supports users to input more complex retrieval requests, and users can get a better sense of search experience. At the same time, it reduces the possibility of leakage of user access and search pattern to a certain extent. We also define the security model and prove that the scheme can resist the internal keyword guessing attack under the standard model. Finally, we also compare theoretical performance with other schemes and evaluate the experimental performance of our scheme.
Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-Based Abdominal Registration

In order to tackle the difficulty associated with the ill-posed nature of the image registration problem, regularization is often used to constrain the solution space. For most learning-based registration approaches, the regularization usually has a fixed weight and only constrains the spatial transformation. Such convention has two limitations: (i) Besides the laborious grid search for the optimal fixed weight, the regularization strength of a specific image pair should be associated with the content of the images, thus the “one value fits all” training scheme is not ideal; (ii) Only spatially regularizing the transformation may neglect some informative clues related to the ill-posedness. In this study, we propose a mean-teacher based registration framework, which incorporates an additional temporal consistency regularization term by encouraging the teacher model’s prediction to be consistent with that of the student model. More importantly, instead of searching for a fixed weight, the teacher enables automatically adjusting the weights of the spatial regularization and the temporal consistency regularization by taking advantage of the transformation uncertainty and appearance uncertainty. Extensive experiments on the challenging abdominal CT-MRI registration show that our training strategy can promisingly advance the original learning-based method in terms of efficient hyperparameter tuning and a better tradeoff between accuracy and smoothness.
METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation

Knowing the reasoning chains from knowledge to the predicted answers can help construct an explainable question answering (QA) system.Advances on QA explanation propose to explain the answers with entailment trees composed of multiple entailment steps.While current work proposes to generate entailment trees with end-to-end generative models, the steps in the generated trees are not constrained and could be unreliable.In this paper, we propose METGEN, a Module-based Entailment Tree GENeration framework that has multiple modules and a reasoning controller.Given a question and several supporting knowledge, MET-GEN can iteratively generate the entailment tree by conducting single-step entailment with separate modules and selecting the reasoning flow with the controller.As each module is guided to perform a specific type of entailment reasoning, the steps generated by METGEN are more reliable and valid.Experiment results on the standard benchmark show that METGEN can outperform previous state-of-the-art models with only 9% of the parameters.
Radar Signals Intrapulse Modulation Recognition Using Phase-Based STFT and BiLSTM

The goal of Radar Emitter Recognition (RER) is to extract the features of a received emitter signal. It has become a critical issue as new radar types are emerging and the electromagnetic environment is becoming more dense and complex. Deep Neural Networks (DNNs) have recently proven effective for emitter identification, however, recognition of phase-coded waveforms at a low Signal to Noise Ratio (SNR) remains a challenge. In this paper, a novel phase-based RER approach using Short Time Fourier Transform (STFT) and Bidirectional Long Short Term Memory (BiLSTM) is proposed while enhancing ability of learning features from noisy signals. The phase spectrum of phase-coded signals is analyzed in contrast to the amplitude spectrum used in the state-of-the-art approaches in the literature. The derived phase-based features are directly provided as an input to the proposed BiLSTM architecture. A fully connected layer follows the BiLSTM layer. Finally, a softmax classifier is employed to accomplish the recognition task. Six distinct types of phase-coded waveforms degraded by Additive White Gaussian Noise (AWGN) with SNRs ranging from -8dB to 8dB are simulated. The suggested method in this research involves simple pre-processing and exhibits overall recognition accuracy of more than 90% at SNR of -2dB.
ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild

This paper investigates the task of 2D whole-body human pose estimation, which aims to localize dense landmarks on the entire human body including body, feet, face, and hands. We propose a single-network approach, termed ZoomNet, to take into account the hierarchical structure of the full human body and solve the scale variation of different body parts. We further propose a neural architecture search framework, termed ZoomNAS, to promote both the accuracy and efficiency of whole-body pose estimation. ZoomNAS jointly searches the model architecture and the connections between different sub-modules, and automatically allocates computational complexity for searched sub-modules. To train and evaluate ZoomNAS, we introduce the first large-scale 2D human whole-body dataset, namely COCO-WholeBody V1.0, which annotates 133 keypoints for in-the-wild images. Extensive experiments demonstrate the effectiveness of ZoomNAS and the significance of COCO-WholeBody V1.0.
Morphing Quantum Codes

We introduce a morphing procedure that can be used to generate new quantum codes from existing quantum codes. In particular, we morph the 15-qubit Reed-Muller code to obtain a [[10,1,2]] code that is the smallest-known stabilizer code with a fault-tolerant logical T gate. In addition, we construct a family of hybrid color-toric codes by morphing the color code. Our code family inherits the fault-tolerant gates of the original color code, implemented via constant-depth local unitaries. As a special case of this construction, we obtain toric codes with fault-tolerant multiqubit control-Z gates. We also provide an efficient decoding algorithm for hybrid color-toric codes in two dimensions and numerically benchmark its performance for phase-flip noise. We expect that morphing may also be a useful technique for modifying other code families such as triorthogonal codes.12 MoreReceived 29 December 2021Accepted 13 July 2022DOI:https://doi.org/10.1103/PRXQuantum.3.030319Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum error correctionQuantum gatesQuantum memoriesSurface code quantum computingTopological quantum computingQuantum Information
Rotate artificial potential field algorithm toward 3D real-time path planning for unmanned aerial vehicle

As one of the key technologies in the development of Unmanned Aerial Vehicle (UAV), the path planning method has an intuitive impact on the real-time performance of UAV. Most existing path planning algorithms have drawbacks in global optimization ability or optimization speed, and cannot meet the requirements of practical applications. To improve the effectiveness and practicability of the path planning algorithm for UAV obstacle avoidance, this paper proposes the Rotate Artificial Potential Field (R-APF) method based on the traditional artificial potential field method. By introducing a new potential field function and stimulating rotating repulsive force, the R-APF solves the problems of local minimum as well as inaccessibility to targets near obstacles, respectively. Convergence of the algorithm is discussed to demonstrate the feasibility of R-APF. This paper also designs and implements algorithm feasibility verification experiments, obstacle shuttle experiments, as well as running time, and efficiency comparison experiments. In addition, the improved Visual-Inertial Navigation System obstacle detection algorithm is introduced to carry out physical verification experiments. Theoretical proof and experimental results show the proposed R-APF method has higher obstacle avoidance capability, shorter running time, and higher success rate compared to other algorithms, and is promising in practical application.
Self-initialized active contours for microscopic cell image segmentation

Abstract Level set models are suitable for processing topological changes in different regions of images while performing segmentation. Active contour models require an empirical setting for initial parameters, which is tedious for the end-user. This study proposes an incremental level set model with the automatic initialization of contours based on local and global fitting energies that enable it to capture image regions containing intensity corruption or other light artifacts. The region-based area and the region-based length terms use signed pressure force (SPF) to strengthen the balloon force. SPF helps to achieve a smooth version of the gradient descent flow in terms of energy minimization. The proposed model is tested on multiple synthetic and real images. Our model has four advantages: first, there is no need for the end user to initialize the parameters; instead, the model is self-initialized. Second, it is more accurate than other methods. Third, it shows lower computational complexity. Fourth, it does not depend on the starting position of the contour. Finally, we evaluated the performance of our model on microscopic cell images (Coelho et al., in: 2009 IEEE international symposium on biomedical imaging: from nano to macro, IEEE, 2009) to confirm that its performance is superior to that of other state-of-the-art models.
Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

Abstract Recent years have witnessed the rapid development of service‐oriented computing technologies. The boom of Web services increases software developers' selection burden in developing new service‐based systems such as mashups. Timely recommending appropriate component services for developers to build new mashups has become a fundamental problem in service‐oriented software engineering. Existing service recommendation approaches are mainly designed for mashup development in the single‐round scenario. It is hard for them to effectively update recommendation results according to developers' requirements and behaviours (e.g. instant service selection). To address this issue, the authors propose a service bundle recommendation framework based on deep learning, DLISR, which aims to capture the interactions among the target mashup to build, selected (component) services, and the following service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending a candidate service. The authors also design two separate models for learning interactions from the perspectives of content and invocation history, respectively, and a hybrid model called HISR. Experiments on a real‐world dataset indicate that HISR can outperform several state‐of‐the‐art service recommendation methods to develop new mashups iteratively.
Fully Connected Generative Adversarial Network for Human Activity Recognition

Conditional Generative Adversarial Networks (CGAN) have shown great promise in generating synthetic data for sensor-based activity recognition. However, one key issue concerning existing CGAN is the design of the network architecture that affects sample quality. This study proposes an effective CGAN architecture that synthesizes higher quality samples than state-of-the-art CGAN architectures. This is achieved by combining convolutional layers with multiple fully connected networks in the generator’s input and discriminator’s output of the CGAN. We show the effectiveness of the proposed approach using elderly data for sensor-based activity recognition. Visual evaluation, similarity measure, and usability evaluation is used to assess the quality of generated samples by the proposed approach and validate its performance in activity recognition. In comparison to the state-of-the-art CGAN, the visual evaluation and similarity measure demonstrate that the proposed models’ synthetic data more accurately represents actual data and creates more variations in each synthetic data than the state-of-the-art approach respectively. The experimental stages of the usability evaluation, on the other hand, show a performance gain of 2.5%, 2.5%, 3.1%, and 4.4% over the state-of-the-art CGAN when using synthetic samples by the proposed architecture.
Research into influencing factors in user experiences of university mobile libraries based on mobile learning mode

Purpose The purpose of this study is to analyse and discuss the influencing factors of user experience in university mobile libraries and the improvement path of user experience in the context of mobile learning. Design/methodology/approach The study adopted the grounded theory research method, and the sample included 28 students from five universities, with mobile libraries as the research objects and semi-structured interview as data acquisition method. A step-by-step coding analysis of the original interview materials was conducted, which comprehensively identified the main concerns and problems encountered by users of the university mobile library apps especially in the mobile learning behaviour mode, and then a theoretical model of the influencing factors of the app user experience of the university mobile library was constructed. Findings A theoretical model of influencing factors was constructed, which determined that system quality, interaction quality, content quality, interface quality and function quality were the key elements of mobile library user experiences. Furthermore, based on the research results and user feedback obtained in the research process, the content and key points relating to the user experience can be elaborated in detail. In addition, this study was able to determine users' perspectives and their behavioural characteristics when engaging in mobile learning. Originality/value This study establishes a theoretical model of the factors influencing of the user experience of university mobile libraries based on mobile learning, which could provide a valuable reference for the design of other programs and strategies to promote user learning experiences of mobile library app in colleges and universities.
RecPack: An(other) Experimentation Toolkit for Top-N Recommendation using Implicit Feedback Data

RecPack is an easy-to-use, flexible and extensible toolkit for top-N recommendation with implicit feedback data. Its goal is to support researchers with the development of their recommendation algorithms, from similarity-based to deep learning algorithms, and allow for correct, reproducible and reusable experimentation. In this demo, we give an overview of the package and show how researchers can use it to their advantage when developing recommendation algorithms.
Self-Tuning Transmitter for Quantum Key Distribution Using Machine Intelligence

The development and performance of quantum technologies heavily relies on the properties of the quantum states, which often require careful optimization of the driving conditions of all underlying components. In quantum key distribution (QKD), optical injection locking (OIL) of pulsed lasers has recently been shown as a promising technique to realize high-speed quantum transmitters with efficient system design. However, due to the complex underlying laser dynamics, tuning such laser system is both a challenging and time-consuming task. Here, we experimentally demonstrate an OIL-based QKD transmitter that can be automatically tuned to its optimum operating state by employing a genetic algorithm. Starting with minimal knowledge of the laser operating parameters, the phase coherence and the quantum bit error rate of the system are optimized autonomously to a level matching the state of the art.
ET2FA: A Hybrid Heuristic Algorithm for Deadline-constrained Workflow Scheduling in Cloud

Cloud computing is an emerging computational infrastructure for cost-efficient workflow execution that provides flexible and dynamically scalable computing resources at pay-as-you-go pricing. Workflow scheduling, as a typical NP-Complete problem, is one of the major issues in cloud computing. However, in the cloud scenario with unlimited resources, how to generate an efficient and economical workflow scheduling scheme under the deadline constraint is still an extraordinary challenge. In this paper, we propose a hybrid heuristic algorithm called enhanced task type first algorithm (ET2FA) to solve deadline-constrained workflow scheduling in cloud with new features such as hibernation and per-second billing. The objectives to be minimized include the total cost and total idle rate. ET2FA involves three phases: 1) Task type first algorithm, which schedules tasks based on topological level and task types, and utilizes a compact-scheduling-condition based VM selection method to assign each task. 2) Delay operation based on block structure, which further optimizes total cost and total idle rate based on block structure properties. 3) Instance hibernate scheduling heuristic, which sets an instance to hibernate if idle for a duration. Extensive simulation experiments based on seven well-known real-world workflow applications show that ET2FA delivers better performance in comparison to the state-of-the-art algorithms.
E-Scooter Rider detection and classification in dense urban environments

Accurate detection and classification of vulnerable road users is a safety critical requirement for the deployment of autonomous vehicles in heterogeneous traffic. Although similar in physical appearance to pedestrians, e-scooter riders follow distinctly different characteristics of movement and can reach speeds of up to 45 kmph. The challenge of detecting e-scooter riders is exacerbated in urban environments where the frequency of partial occlusion is increased as riders navigate between vehicles, traffic infrastructure and other road users. This can lead to the non-detection or mis-classification of e-scooter riders as pedestrians, providing inaccurate information for accident mitigation and path planning in autonomous vehicle applications. This research introduces a novel benchmark for partially occluded e-scooter rider detection to facilitate the objective characterization of detection models. A novel, occlusion-aware method of e-scooter rider detection is presented that achieves a 15.93% improvement in detection performance over the current state of the art.