Reviewerly/exHarmony huggingface dataset:
FPGA Implementation using Novel Co-Design Approach of Real-Time Speech Chaos based Crypto-Watermarking Prototype

This paper presents a two Nexys FPGAs implementation of a Least Significant Bit (LSB) watermarking applied on real-time speech signals over a serial communication system. The watermarking technique has been strengthened by means of a chaos-based encryption scheme for the used watermark image. The speech acquisition is performed using FPGA inbuilt microphone while the recovery has been realized through a loudspeaker as output device for the watermarked signal and VGA monitor for the watermark image respectively. The transmission of the watermarked signal has been implemented using Universal Asynchronous Receiver Transmitter (UART) serial protocol between two FPGA boards. The LSB IP-Cores developed and integrated in the watermarking prototype have been synthesized by means of a co-design approach using Vivado-HLS. The realized prototype has required performances of 1132 LUT area using a speed of 130 Mhz and a power consumption of no more than 159 mW. Lastly, good results have been obtained of watermarking imperceptibility and security with low robustness performances against signal processing and geometric cryptanalysis attacks.
Role-playing software architecture styles

Software Architecture, from definition to maintenance and evolution, is a complex aspect of software development and, consequently, a challenging subject when it comes to teaching it, and learning it.Many research efforts have been devoted to designing teaching approaches, strategies and tools. Most of them, however, focus on the knowledge itself and the ways to convey it to students, rather than on the different learning styles of students themselves.Teaching methods which predominantly rely on verbal and written communication, are very well aligned with some learning styles. However, students with learning styles that benefit more from physical activity or first-hand experience, need to defer to cognitive processes that are less natural to them.In this work, we propose an innovative use of role-playing as teaching strategy for architecture models of reference (i.e. layered, pipe & filter, client-server, etc.). This role-playing of different software architectures, in which students play the part of specific components in the system, intends to complement other classical teaching materials, such as in-person or recorded lectures, lab assignments, or development projects.Addressing all learning styles within a classroom is key to ensure that we favour and foster the students’ different learning processes, and give everyone an even playfield in which to best develop their capabilities as Software Architects.
User Interface and Architecture Adaption Based on Emotions and Behaviors

This paper shows how emotions and behavior considerations in socio-technical systems lead to high-quality self-adaptations, both at application and architecture levels. In our approach, an interactive control system assesses the reconfigurations that enhance the quality of service (QoS) while considering humans’ quality of experience (QoE). We use a Model-Free Reinforcement Learning (MFRL) approach to self-adapt user interfaces (UIs) to users’ emotions. The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users’ QoE, i.e., task completion and satisfaction. If the control system detects a drop in QoS in emotion-based adaptations or other functions, another level of adaptation reconfigures the architecture towards better quality. We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in such potential danger. We performed experiments with a mobile application we developed that acts as a recommender system in emergency training. By taking contextual input of the users’ basic emotions from face recognition and their mobility behavior, the application intelligently adapts its UI to quickly lead people to safe areas while keeping them emotionally stable. In addition to UI adaptation, the system is capable of architecture-level adaptations to decrease response time if required. The evaluation process confirms the efficiency of the MFRL in iterations, as well as compared to other possible UI adaptation techniques. The emerging results also show that architecture-level adaptations positively impact the system performance and users’ emotions and performance.
A Pattern-Oriented Reference Architecture for Governance-Driven Blockchain Systems

Blockchain technology has been integrated into diverse software applications by enabling a decentralised architecture design. However, the defects of on-chain algorithmic mechanisms, and tedious disputes and debates in off-chain communities may affect the operation of blockchain systems. Accordingly, blockchain governance has received great interest for supporting the design, use, and maintenance of blockchain systems, hence improving the overall trustworthiness. Although much effort has been put into this research topic, there is a distinct lack of consideration for blockchain governance from the perspective of software architecture design. In this study, we propose a pattern-oriented reference architecture for governance-driven blockchain systems, which can provide guidance for future blockchain architecture design. We design the reference architecture based on an extensive review of architectural patterns for blockchain governance in academic literature and industry implementation. The reference architecture consists of four layers. We demonstrate the components in each layer, annotating with the identified patterns. A qualitative analysis of mapping two concrete blockchain architectures, Polkadot and Quorum, on the reference architecture is conducted, to evaluate the correctness and utility of proposed reference architecture.
Automatic Derivation of Vulnerability Models for Software Architectures

Software architectures consist of more and more connections to different components or elements. With the increased connection and exchange between different elements also the attack surface increases, since each element might contain vulnerabilities. The vulnerabilities may be harmless on their own, but attackers could develop attack paths from the combination of different vulnerabilities. For a model-based attack propagation analysis, it is useful to have an annotated components model with vulnerabilities. However, depending on the size of the system, the manual annotation of these models is very time-consuming and error-prone. In this context, we present in this paper an approach that automatically annotates vulnerability information to the components of an architectural model. The goal here is to extract security information of source artifacts and transform them into an existing architecture-based security model to enable model-based security risk assessment. We evaluate our approach using three open-source case studies to demonstrate feasibility and accuracy. The results indicate high recall reading vulnerabilities.
Cross-sensor remote sensing imagery super-resolution via an edge-guided attention-based network

The deep learning based super-resolution (SR) methods have recently achieved remarkable progress in the reconstruction of ideally simulated high-quality remote sensing image datasets. However, due to the large variation in image quality caused by the complex degradation factors, their performance decreases dramatically on real-world images acquired by different satellite sensors. To this end, we propose a cross-sensor SR framework that consists of a cross-sensor degradation modeling strategy for bridging the gap between the images obtained by the source and target sensors, and an edge-guided attention-based SR (EGASR) network to promote the learning of high-frequency feature representation. Specifically, we build a degradation pool on the low-resolution (LR) target sensor to produce a degraded training dataset simulated from the high-resolution (HR) images obtained by the source sensor. Furthermore, the EGASR network, which employs the edge-guided residual attention block (EGRAB) to introduce implicit edge prior to enhance edge-related information, is embedded in the cross-sensor SR framework for reconstructing HR results with sharp details. The proposed method is applied on images from the Chinese Gaofen (GF) satellite sensors and compared to several representative SR methods. An ideally simulated GF-2 LR/HR image set with only downsampling considered is first used to evaluate the effectiveness of the proposed EGASR network. Moreover, GF-2/GF-1 and GF-2/GF-6 cross-sensor SR datasets are constructed by synthesizing GF-2 degraded image pairs with the degradation pools estimated from the GF-1 and GF-6 images, respectively. The results show that: 1) the proposed EGASR model shows superiority in reconstructing textural details and edge features, and achieves the best results among the state-of-art SR methods involved in comparison; 2) the cross-sensor SR framework significantly promotes the model’s ability to super-resolve real-world LR images acquired by the target satellite sensors, e.g., the NIQE values are improved by at least 30% and 34% on average with respect to other comparative methods for GF-2/GF-1 and GF-2/GF-6 datasets in the real experiments, respectively. Code is available at https://github.com/zhonghangqiu/EGASR.
Realistic performance evaluation and optimal energy management of a large-scale bifacial photovoltaic system

This paper introduces an optimal energy management and a comprehensive innovative comparison between on-grid monofacial and bifacial solar photovoltaic systems in terms of energy production, reliability, economic feasibility, ecological impact, footprint area, and other performance indicators. This is to satisfy the load demand of Al-Husainya city, Jordan. Realistic measured data are obtained from authoritative companies in Jordan. This includes ambient temperature, solar irradiance, and a utility-scale load demand. The optimal sizing and energy management strategy are achieved using Marine Predators optimization algorithm. This is done by minimizing Loss of Power Supply Probability. The decision variable is the number of photovoltaic modules. The unit investigation shows that the maximum power of the bifacial panel is increased compared with a monofacial module. So, this will highly affect system size and hence reliability, footprint required area, cost and ecological indicators are impacted too. Results show that the minimized values of the objective function of Loss of Power Supply Probability for monofacial and bifacial systems are 0.3534 % and 0.2511 %, respectively. The number of monofacial and bifacial panels are 12,940 and 11,584 respectively. In addition, the area required to install the bifacial array is decreased by 10.4791 % compared with the monofacial array. Finally, four additional optimization algorithms were used to validate and compare with the obtained results using the Marine Predators algorithm. The methodology described in this paper, which adopts the bifacial photovoltaic technology, is recommended and it can be applied anywhere to get highly reliable, feasible, and more environmentally friendly solar energy systems.
Do you get what you see? Insights of using mAP to select architectures of pretrained neural networks for automated aerial animal detection

The vast amount of images generated by aerial imagery in the context of regular wildlife surveys nowadays require automatic processing tools. At the top of the mountain of different methods to automatically detect objects in images reigns deep learning's object detection. The recent focus given to this task has led to an influx of many different architectures of neural networks that are benchmarked against standard datasets like Microsoft's Common Objects in COntext (COCO). Performance on COCO, a large dataset of computer vision images, is given in terms of mean Average Precision (mAP). In this study, we use six pretrained networks to detect red deer from aerial images, three of which have never been used, to our knowledge, in a context of aerial wildlife surveys. We compare their performance along COCO's mAP and a common test metric in animal surveys, the F1-score. We also evaluate how dataset imbalance and background uniformity, two common difficulties in wildlife surveys, impact the performance of our models. Our results show that the mAP is not a reliable metric to select the best model to count animals in aerial images and that a counting-focused metric like the F1-score should be favored instead. Our best overall performance was achieved with Generalized Focal Loss (GFL). It scored the highest along both metrics, combining most accurate counting and localization (with average F1-score of 0.96 and 0.97 and average mAP scores of 0.77 and 0.89 on both datasets respectively) and is therefore very promising for future applications. While both imbalance and background uniformity improved the performance of our models, their combined effect had twice as much impact as the choice of architecture. This finding seems to confirm that the recent data-centric shift in the deep learning field could also lead to performance gains in wildlife surveys.
A Survey on the Integration of Blockchains and Databases

The success of blockchain technology in cryptocurrencies reveals its potential in the data management field. Recently, there is a trend in the database community to integrate blockchains and traditional databases to obtain security, efficiency, and privacy from the two distinctive but related systems. In this survey, we discuss the use of blockchain technology in the data management field and focus on the fusion system of blockchains and databases. We first classify existing blockchain-related data management technologies by their locations on the blockchain-database spectrum. Based on the taxonomy, we discuss three types of fusion systems and analyze their design spaces and trade-offs. Then, by further investigating the typical systems and techniques of each type of fusion system and comparing the solutions, we provide insights of each fusion model. Finally, we outline the unsolved challenges and promising directions in this field and believe that fusion systems will take a more important role in data management tasks. We hope this survey can help both academia and industry to better understand the advantages and limitations of blockchain-related data management systems and develop fusion systems that meet various requirements in practice.
FEED2SEARCH: a framework for hybrid-molecule based semantic search

Adopting semantic technologies has proven several benefits for enabling a better representation of the data and empowering reasoning capabilities over it. However, there are still unresolved issues, such as the shift from heterogeneous documents to semantic data models and the representation of search results. Thus, in this paper, we introduce a novel FramEwork for hybrid molEcule-baseD SEmantic SEARCH, entitled FEED2SEARCH, which facilitates Information Retrieval over a heterogeneous document corpus. We first propose a semantic representation of the corpus, which automatically generates a semantic graph covering both structural and domain-specific aspects. Then, we propose a query processing pipeline based on a novel data structure for query answers, extracted from this graph, which embeds core information together with structural-based and domain-specific context. This provides users with interpretable search results, helping them understand relevant information and track cross document dependencies. A set of experiments conducted using real-world construction projects from the Architecture, Engineering and Construction (AEC) industry shows promising results, which motivates us to further investigate the effectiveness of our proposal in other domains.
Breast lesion classification using features fusion and selection of ensemble <scp>ResNet</scp> method

Abstract Medical Imaging with Deep Learning has recently become the most prominent topic in the scientific world. Significant results have been obtained in the classification of medical images using deep learning methods, and there has been an increase in studies on malignant types. The main reason for choosing breast cancer is that breast cancer is one of the critical malignant types that increase the death rate in women. In this study, 1236 ultrasound images were collected from Elazig Fethi Sekin City Hospital, and three different ResNet CNN architectures were used for feature extraction. Data were trained with an SVM classifier. In addition, the three ResNet architectures were combined, and novel fused ResNet architecture was used in this study. In addition, these features were used with three different feature selection techniques, MR‐MR, NCA, and Relieff. These results are 89.3% obtained from ALL‐ResNet architecture and the feature selected with NCA in normal and lesion classification. Normal, malignant, and benign classification best accuracy is 84.9% with ALL‐ResNet NCA. Experimental studies show that MR‐MR, NCA, and Relieff feature selection algorithms reduce features and give more results that are successful. This indicates that the proposed method is more successful than classical deep learning methods.
Delocalized and dynamical catalytic randomness and information flow

We generalize the theory of catalytic quantum randomness to delocalized and dynamical settings. First, we expand the resource theory of randomness (RTR) by calculating the amount of entropy catalytically extractable from a correlated or dynamical randomness source. In doing so, we show that no entropy can be catalytically extracted when one cannot implement local projective measurement on randomness source without altering its state. The RTR, as an archetype of the `concave' resource theory, is complementary to the convex resource theories in which the amount of randomness required to erase the resource is a resource measure. As an application, we prove that quantum operation cannot be hidden in correlation between two parties without using randomness, which is the dynamical generalization of the no-hiding theorem. Second, we study the physical properties of information flow. Popularized quotes like "information is physical" or "it from bit" suggest the matter-like picture of information that can travel with the definite direction while leaving detectable traces on its region of departure. To examine the validity of this picture, we focus on that catalysis of randomness models directional flow of information with the distinguished source and recipient. We show that classical information can always spread from its source without altering its source or its surrounding context, like an immaterial entity, while quantum information cannot. We suggest an approach to formal definition of semantic quantum information and claim that utilizing semantic information is equivalent to using a partially depleted information source. By doing so, we unify the utilization of semantic and non-semantic quantum information and conclude that one can always extract more information from an incompletely depleted classical randomness source, but it is not possible for quantum randomness sources.
Deterministic quantum search with adjustable parameters: Implementations and applications

Grover's algorithm provides a quadratic speedup over classical algorithms to search for marked elements in an unstructured database. The original algorithm is probabilistic, returning a marked element with bounded error. There are several schemes to achieve the deterministic version, by using the generalized Grover's iteration $G(\alpha,\beta):=S_r(\beta)\, S_o(\alpha)$ composed of phase oracle $S_o(\alpha)$ and phase rotation $S_r(\beta)$. However, in all the existing schemes the value range of $\alpha$ and $\beta$ is limited; for instance, in the three early schemes $\alpha$ and $\beta$ are determined by the proportion of marked states $M/N$. In this paper, we break through this limitation by presenting a search framework with adjustable parameters, which allows $\alpha$ or $\beta$ to be arbitrarily given. The significance of the framework lies not only in the expansion of mathematical form, but also in its application value, as we present two disparate problems which we are able to solve deterministically using the proposed framework, whereas previous schemes are ineffective.
Unsupervised Multi-Scale-Stage Content-Aware Homography Estimation

Homography estimation is a critical component in many computer-vision tasks. However, most deep homography methods focus on extracting local features and ignore global features or the corresponding relationship between features from two images or video frames. These methods are effective for alignment of image pairs with small displacement. In this paper, we propose an unsupervised Multi-Scale-Stage Content-Aware Homography Estimation Network (MS2CA-HENet). In the framework, we use multi-scale input images for different stages to cope with different scales of transformations. In each stage, we consider local and global features via our Self-Attention-augmented ConvNet (SAC). Furthermore, feature matching is explicitly enhanced using feature-matching modules. By shrinking the error residual of each stage, our network achieves coarse-to-fine results. Experiments show that our MS2CA-HENet achieves better results than other methods.
Civil Conflict and Cross-Border Lending: A Sectoral, Micro Lender-Level Analysis of Syndicated Loans

ABSTRACTThis study examines empirically the impact of civil conflict on cross-border lending. Our sample covers 165 countries over the period 1984-2019 with loan data disaggregated at the economic sectoral level (primary, secondary and tertiary sectors), an analysis that is absent in existing literature. Our results indicate that cross-border lending to the primary sector is not significantly influenced by civil conflict, whereas cross-border lending to secondary and tertiary sectors is negatively impacted by the outbreak of civil conflict, leading to a decreased volume of loans or reversals of existing loans.KEYWORDS: Cross-border lendingcivil conflicteconomic sectorssyndicated loansvolume of loansJEL CLASSIFICATION: D74F34F51 AcknowledgmentsWe would wish to thank the editor and the anonymous referees for many helpful comments. Any remaining errors remain solely ours.Disclosure StatementNo potential conflict of interest was reported by the authors.Data Availability StatementThe data that support the findings of this study are available from the corresponding author, C. Li, upon reasonable request.Notes1. Also see the summary of the definitions and data sources for each variable in Table A1 of the Appendix.2. As suggested by Cameron and Miller (2015), the multi-way clustering strategy entails clustering at progressively higher levels and stop clustering when the standard errors vary relatively little. Therefore, we cluster at both the borrower and lender levels for the robustness check.3. Select findings from the robustness tests and an additional analysis have been relegated to the Appendix for reference (refer to Tables A5-A8) to save space.Additional informationFundingThis work was supported by the University Research Projects of Philosophy and Social Sciences in Jiangsu Province (Grant no. 2020SJA1210) and the Humanities and Social Sciences Youth Foundation of Ministry of Education in China (Grant no. 22YJCGJW004).