Reviewerly/exHarmony huggingface dataset:
Automata-Derived Chaotic Image Encryption Scheme

This work introduces an encryption scheme for gray-scale plain-text images, which is based on a chaotic map. Initially, the proposed chaotic map, which is a modification of the Renyi map, is introduced and is utilized in defining a Pseudo-Random Bit Generator. Subsequently, a finite automaton is introduced. This, in combination with the aforementioned PRBG defines the encryption strategy, that is, the order in which the rows and columns are encrypted. The proposed method is subjected to a number of statistical tests, to prove its resistance against common attacks.
A Gender Recognition System Based on Facial Image

A gender recognition system (GRS) based on facial images can be embedded in different areas such as surveillance, human-robot interaction, targeted advertising, etc. Traditional facial feature-based recognition systems extract and analyze textures on a face. Although such approaches perform well under certain controlled situations, they may fail due to variations of faces in images, which is very common in real-life images. To overcome such problems, we need to have an effective combination of a feature descriptor, representation, and classifier providing better accuracy. Recently, many recognition problems are tackled by using the Deep Neural Network (DNN) such as Convolutional Neural Network (CNN). However, deep learning needs a large number of images, which is not usually available, to work as expected. We propose a model that combines handcrafted features with CNN to overcome the shortcomings including handling of variations in imaging, such as the illumination and pose variations, and the necessity of voluminous training sets. Experimental results also show that the proposed method performs better than the available gender recognition approaches.
High-Frequency Attention Residual GAN Network for Blind Motion Deblurring

The moving image deblurring method based on deep learning has achieved good results. However, some methods are not effective in restoring image texture detail information. Therefore, this paper proposes a High-Frequency Attention Residual Module (HFAR), which is used to guide the network to learn more high-frequency texture information in the image to improve the quality of image detail restoration. The designed attention residual module consists of two sub-modules, Fourier Channel Attention module (FCA) and Edge Spatial Attention module (ESA). The FCA module gives more weight to the feature maps that contain more high-frequency information in multiple channels. While the ESA module gives more weight to the areas in the feature maps which contain more high-frequency information to guide the network to learn image details and texture information. Extensive experiments on different datasets show that our method achieves state-of-the-art performance in motion deblurring.
Interleaved Predictive Control and Planning for an Unmanned Aerial Manipulator With on-the-Fly Rapid Re-Planning in Unknown Environments

Unmanned Aerial Manipulators (UAM) are gaining attention within the unmanned aerial systems research community. They can be used for aerial manipulation tasks such as object retrieval from confined and hard-to-reach spaces. The coupled dynamics between the arm and the base and also the limited flight time would require the development and implementation of optimal motion planning and robust flight control strategies. In this paper, we propose a novel integrated planning and control strategy for object retrieval. A new kinodynamic version of the Rapidly-exploring Random Tree (RRT*), called Lazy-Steering-RRT*, is developed for planning UAM’s motion from its start to a pre-grasp state, while keeping the motion of the arm to a minimum. This planning can be carried out on the fly by using Machine-Learning-based techniques to construct the edges in the search tree in a time-efficient way. This facilitates re-planning, as the environment is gradually sensed by limited-range sensors onboard. Once the UAM reaches the pre-grasp state at the end of the motion cycle, an RRT approach is then utilized, where motions of the base and the arm are coordinated for reaching and grasping the object. A novel partitioned control approach, composed of model predictive and PID controllers, is utilized for the UAM to track the planned trajectory. The overall motion planning and control algorithms have been implemented in simulation using Multibody Physics Engines and a number of representative simulation runs are presented. Our results show that the approach is effective in successfully executing the object retrieval task even in confined spaces. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This research was motivated by the problem of retrieving an object from a confined space such as that in a collapsed building using an unmanned aerial manipulator. Our approach has the following advantages over existing methods: a) Planning and re-planning can be carried out on the fly, b) No prior information about the environment is needed, c) High-frequency energy-optimal control of the UAM is carried out, and d) the planner and the controller are coupled. In this paper, we suggest some novel approaches on both planning and control aspects of the navigation and also on the integration of these two modules to provide a navigation package that meets all the requirements in the retrieval task. Furthermore, our algorithm is computationally-light, therefore, well suited for on the fly planning and control.
Deep ToA Mask Based Recursive Radar Pulse Deinterleaving

In a complex electromagnetic environment, multiple radar signals of various modes are densely interleaved. In this environment, radar parameters overlap seriously and change continuously over time. Traditional radar pulse deinterleaving algorithms face severe challenges such as parameters missing, pulse jitter, and the increasing number of electronic countermeasure devices. In this paper, we propose a recursive deinterleaving algorithm based on blind signal separation and deep learning to cope with such a situation. The Recursive Deinterleaving Network (RDN) of Deep ToA Mask (DTM) maps the ToA train to a suitable feature space first. ToA coefficient masks of each radar emitter are estimated with the local and global context information of the radar pulse feature. Then the RDN sorts out several radar pulse trains recursively with the help of dual-path attention. It also predicts the number of emitters with nearly 100&#x0025; accuracy and handles the unknown Pulse Repetition Interval (PRI) situation. More accurate pulse deinterleaving results can be obtained if the DTM utilizes more radar parameters through proper pre-processing fine-tuning and post-processing re-clustering. The processing steps of the DTM are introduced in detail. The simulation shows it can achieve 97&#x0025; sorting accuracy for multi-pulse interleaved radar train with jitter PRI and pulse missing. The DTM algorithm can also deal with the interleaved radar signals of different PRI modulations by re-clustering with noisy PDW information. On the premise of knowing the modulation type or PRI information, the pulse train deinterleaving accuracy of multi-modulation emitters is higher.
Kinematic Orienteering Problem With Time-Optimal Trajectories for Multirotor UAVs

In many unmanned aerial vehicle (UAV) applications for surveillance and data collection, it is not possible to reach all requested locations due to the given maximum flight time. Hence, the requested locations must be prioritized and the problem of selecting the most important locations is modeled as an Orienteering Problem (OP). To fully exploit the kinematic properties of the UAV in such scenarios, we combine the OP with the generation of time-optimal trajectories with bounds on velocity and acceleration. We define the resulting problem as the Kinematic Orienteering Problem (KOP) and propose an exact mixed-integer formulation together with a Large Neighborhood Search (LNS) as a heuristic solution method. We demonstrate the effectiveness of our approach based on Orienteering instances from the literature and benchmark against optimal solutions of the Dubins Orienteering Problem (DOP) as the state-of-the-art. Additionally, we show by simulation \color{black} that the resulting solutions can be tracked precisely by a modern MPC-based flight controller. Since we demonstrate that the state-of-the-art in generating time-optimal trajectories in multiple dimensions is not generally correct, we further present an improved analytical method for time-optimal trajectory generation.
FalsifAI: Falsification of AI-Enabled Hybrid Control Systems Guided by Time-Aware Coverage Criteria

Modern Cyber-Physical Systems (CPSs) that need to perform complex control tasks (e.g., autonomous driving) are increasingly using AI-enabled controllers, mainly based on deep neural networks (DNNs). The quality assurance of such types of systems is of vital importance. However, their verification can be extremely challenging, due to their complexity and uninterpretable decision logic. Falsification is an established approach for CPS quality assurance, which, instead of attempting to prove the system correctness, aims at finding a time-variant input signal violating a formal specification describing the desired behavior; it often employs a search-based testing approach that tries to minimize the <i>robustness</i> of the specification, given by its quantitative semantics. However, guidance provided by robustness is mostly black-box and only related to the system output, but does not allow to understand whether the temporal internal behavior determined by multiple consecutive executions of the neural network controller has been explored sufficiently. To bridge this gap, in this paper, we make an early attempt at exploring the temporal behavior determined by the repeated executions of the neural network controllers in hybrid control systems and first propose eight time-aware coverage criteria specifically designed for neural network controllers in the context of CPS, which consider different features by design: the simple temporal activation of a neuron, the continuous activation of a neuron for a given duration, and the differential neuron activation behavior over time. Second, we introduce a falsification framework, named <inline-formula><tex-math notation="LaTeX">$\mathtt {FalsifAI}$</tex-math></inline-formula> , that exploits the coverage information for better falsification guidance. Namely, inputs of the controller that increase the coverage (so improving the <i>exploration</i> of the DNN behaviors), are prioritized in the <i>exploitation</i> phase of robustness minimization. Our large-scale evaluation over a total of 3 typical CPS tasks, 6 system specifications, 18 DNN models and more than 12,000 experiment runs, demonstrates 1) the advantage of our proposed technique in outperforming two state-of-the-art falsification approaches, and 2) the usefulness of our proposed time-aware coverage criteria for effective falsification guidance.
Abnormal Communication Signals Recognition Based on Image Enhancement and Improved Memory-Augmented Autoencoder

Detecting increasing anomalous signals is critical to effective spectrum management due to the complexity of the use of electromagnetic spectrum. The anomaly recognition approach based on autoencoder (AE) heavily relies on the assumption that the reconstruction error of normal signals is generally lower than that of abnormal signals. Unfortunately, such an assumption does not necessarily hold due to the excessive generalization ability of the AE. The memory-augmented autoencoder model (MemAE) has been recently proposed to address this issue by introducing memory modules. Still, the standard MemAE model performs poorly on complex image datasets, and its performance for abnormal communication signals recognition is unknown. Therefore, we propose an image enhancement and improved memory-augmented autoencoder model (IIMemAE) to recognize abnormal communication signals. Specifically, we consider two key factors, i.e., the existence of redundant information in the time-frequency spectrogram and the low recognition accuracy of normal signals of the standard MemAE model. We introduce an image enhancement module and an anomaly determination module compared with the standard MemAE model. The proposed IIMemAE model can address the issue that the performance of anomaly recognition may be degraded due to the imbalanced communication signals in the real world. The simulation results show that IIMemAE can effectively recognize synthetic abnormal communication signals even at low signal-to-noise ratio (SNR) and jamming-to-signal ratio (JSR) conditions and outperform the standard MemAE model. Besides, the parametric Pauta criterion proposed can balance the recognition accuracy of normal and abnormal signals to meet the need for diverse recognition tasks.
FrameAugment: A Simple Data Augmentation Method for Encoder–Decoder Speech Recognition

As the architecture of deep learning-based speech recognizers has recently changed to the end-to-end style, increasing the effective amount of training data has become an important issue. To tackle this issue, various data augmentation techniques to create additional training data by transforming labeled data have been studied. We propose a method called FrameAugment to augment data by changing the speed of speech locally for selected sections, which is different from the conventional speed perturbation technique that changes the speed of speech uniformly for the entire utterance. To change the speed of the selected sections of speech, the number of frames for the randomly selected sections is adjusted through linear interpolation in the spectrogram domain. The proposed method is shown to achieve 6.8% better performance than the baseline in the WSJ database and 9.5% better than the baseline in the LibriSpeech database. It is also confirmed that the proposed method further improves speech recognition performance when it is combined with the previous data augmentation techniques.
Understanding online behavior towards community water user participation: A perspective of a developing country

The social network is a network of virtual relationships that can facilitate the development of a new society in which everyone can use online communication effectively. This article investigates and identifies the fundamental influences on the social network system, as well as the online behavior of the community users. This study was designed by any social network to help improve efficiency and offer people with services that match the needs of their communities. Furthermore, it increases participation in the equitable distribution of social benefits. This study investigates the critical factors that impact a community's view of community water user participation. The researcher sent a questionnaire on a five-point Likert scale to 1,000 community water customers and collected 627 valid replies. Data from 14 villages were sampled using a simple random sampling strategy to acquire the data. Subsequently, descriptive statistics are used to describe the data (frequency distributions, percentages, averages, medians, and standard deviation). Furthermore, PLS-SEM was used to examine the relationships between factors and to launch the conceptual model using PLS route modeling. This study reveals that digital technologies are crucial to increasing the expectations and happiness of the community through social networks. Multiple causes contribute to its expansion. In addition, this research provides an outstanding case study technique based on TAM and ECT to assess people's social networking and community participation habits. Additionally, community water providers participate in social networks by certifying that their expectations are met.
Preface to the Focus Section on Big Data Problems in Seismology

Research Article| July 28, 2022 Preface to the Focus Section on Big Data Problems in Seismology Daniel T. Trugman; Daniel T. Trugman 1Nevada Seismological Laboratory, University of Nevada, Reno, Nevada, U.S.A. https://orcid.org/0000-0002-9296-4223 Search for other works by this author on: GSW Google Scholar Lihua Fang; Lihua Fang 2Institute of Geophysics, China Earthquake Administration, Beijing, China Search for other works by this author on: GSW Google Scholar Jonathan Ajo‐Franklin; Jonathan Ajo‐Franklin 3Department of Earth, Environmental, and Planetary Sciences, Rice University, Houston, Texas, U.S.A. Search for other works by this author on: GSW Google Scholar Avinash Nayak; Avinash Nayak 4Energy Geosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, U.S.A. https://orcid.org/0000-0001-7913-7189 Search for other works by this author on: GSW Google Scholar Zefeng Li Zefeng Li * 5School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China *Corresponding author: zefengli@ustc.edu.cn https://orcid.org/0000-0003-4405-8872 Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2022) 93 (5): 2423–2425. https://doi.org/10.1785/0220220219 Article history first online: 28 Jul 2022 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Daniel T. Trugman, Lihua Fang, Jonathan Ajo‐Franklin, Avinash Nayak, Zefeng Li; Preface to the Focus Section on Big Data Problems in Seismology. Seismological Research Letters 2022;; 93 (5): 2423–2425. doi: https://doi.org/10.1785/0220220219 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search Seismology has long been a data‐driven science, with the field propelled inexorably forward as new datasets became available and their contents were studied in detail. From the first teleseismic recordings with modern seismometers in the late 1800s (Dewey and Byerly, 1969) to the rollout of the Worldwide Standardized Seismic Network (WWSSN) in the 1960s (Sykes, 1967) to the recent deployments of dense nodal arrays (Lin et al., 2013; Ben‐Zion et al., 2015; Brenguier et al., 2016; Catchings et al., 2020) and Distributed Acoustic Sensing technology (DAS; Zhan,... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Language Bias-Driven Self-Knowledge Distillation with Generalization Uncertainty for Reducing Language Bias in Visual Question Answering

To answer questions, visual question answering systems (VQA) rely on language bias but ignore the information of the images, which has negative information on its generalization. The mainstream debiased methods focus on removing language prior to inferring. However, the image samples are distributed unevenly in the dataset, so the feature sets acquired by the model often cannot cover the features (views) of the tail samples. Therefore, language bias occurs. This paper proposes a language bias-driven self-knowledge distillation framework to implicitly learn the feature sets of multi-views so as to reduce language bias. Moreover, to measure the performance of student models, the authors of this paper use a generalization uncertainty index to help student models learn unbiased visual knowledge and force them to focus more on the questions that cannot be answered based on language bias alone. In addition, the authors of this paper analyze the theory of the proposed method and verify the positive correlation between generalization uncertainty and expected test error. The authors of this paper validate the method’s effectiveness on the VQA-CP v2, VQA-CP v1 and VQA v2 datasets through extensive ablation experiments.
Certificateless linkable ring signature‐based blockchains for securing cognitive radio networks

Summary Rapid advancement in industrial applications of Internet of Things (IoT) has dramatically increased the number of Internet connected devices resulting in big challenges of scalability, adaptability, connectivity, and accessibility. Cognitive radio is efficient in handling spectrum allocation problem by facilitating the allocation of unlicensed spectrum dynamically to the IoT applications. However, cognitive radio network (CRN) capability of spectrum sensing is highly vulnerable to diversified number of security attacks. Malicious user disturbs the spectrum sensing potential and minimizes its accuracy by launching attacks during the detection of primary signal. In this article, certificateless linkable ring signature‐based blockchains (CLRSB) is proposed for securing CRNs by achieving reliable discrimination between trustworthy users through cryptographic keys. It initially transformed the complete set of users such as PUs and SUs into the structure of blocks representing the smart contracts‐based traditional public ledger blockchain that gets integrated to form a decentralized network for attaining improvement in efficient spectrum sensing. It adopted a mayfly optimization algorithm (MFOA)‐improved CNN (MFOA‐ICNN)‐based spectrum sensing method for preventing the interference between PUs and SUs during spectrum access. It also used MFOA for optimizing the features with balanced trade‐off between exploration and exploitation to identify the remarkable features that attributes toward efficient spectrum sensing. It specifically used CLRS for protecting information about the transaction initiator for the objective of protecting blockchain privacy. Simulation experiments of this CLRSB confirmed better response time and minimized frame loss on an average by 18.13% and 17.81%, better than the baseline schemes used for investigation.
Fundamental Research on Detecting Contradictions in Requirements: Taxonomy and Semi-Automated Approach

Requirements documents can contain several thousand individual requirements. They must be error-free to avoid unnecessary complications and costs in the later product development stages. An important part of this is to identify contradictions between two requirements. The first step is therefore to define what contradictions are and in what form they can occur in requirement documents. In this paper the scientific theories regarding contradictions are discussed, concerning to their usefulness for the topic. In doing so, the Aristotelian Logic proved to provide the best basis for an application in the Requirements Engineering context. Based on this theory, we have created specific subtypes of contradictions to match them to the requirements engineering field. The identification of these subtypes is done by a formalization of the requirement sentences and a subsequent analysis by means of simple questions. To validate the method, industrial requirement documents were searched for contradictions. For each detected type of contradiction, we present an example of the detection process. Thereby, we show that the method is easy to apply and may also be used by non-specialists. Thus, our method provides a taxonomy as a basis for further research on automated contradiction detection as well as on automated quality analysis of requirements documents.