Reviewerly/exHarmony huggingface dataset:
Low-Power Anomaly Detection and Classification System based on a Partially Binarized Autoencoder for In-Sensor Computing

This work proposes a new ultra low-power fault detection system, suitable for extreme edge or in-sensor computing. The system is composed of a hybrid HW/SW architecture: a hardware auto-encoder (AE) is always on at the edge for anomaly detection (AD), and of a software convolutional neural network (CNN) is activated only if the anomaly is detected for its classification. To achieve low area and energy requirements, the AE exploits an original partially binarization scheme, while the CNN shares the feature extraction module with the AE. The implementation of the AE on a Xilinx Artix-7 FPGA demonstrates that it is capable to manage in real-time sensors with a maximum Output Data Rate (ODR) of 365 kHz with a power dissipation of 122 mW. Best synthesis results with TSMC CMOS 65 nm standard cells show a power consumption of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$138\ \mu\mathrm{W}/\text{MHz}$</tex> and an area occupation of 0.49 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> when real-time operations are set, enabling the possibility to integrate the complete HW accelerator in the auxiliary circuitry that typically equips inertial MEMS and on the same die. Comparisons with the current literature show that the proposed system obtains state-of-the-art performances in terms of accuracy and compactness.
A New Blockchain Ecosystem for Trusted, Traceable and Transparent Ontological Knowledge Management

The Internet is becoming more centralized, more asymmetric in terms of knowledge and power distribution, more biased, less privacy-preserving and less trustworthy. Blockchain technologies already enable the safe and fair exchange of digital assets in a decentralized manner; however, its application to information exchange remains largely unexplored. This article exposes our vision for a semantically-enriched blockchain software ecosystem named ONTOCHAIN, that enables the development of trustworthy distributed applications that can empower users, guarantee both their privacy and high quality of service, and ultimately support pluralism and democracy. ONTOCHAIN aims primarily to attain trustworthy service exchange and trustworthy content handling by means of advanced knowledge management mechanisms for several domains such as health, economy, public services, energy and sustainability, news, media, entertainment, Industry 4.0 and tourism. We present the main components of the ONTOCHAIN architecture and their functionality. Finally, the validity of our approach is exemplified by describing how decentralized applications can be enabled by the ONTOCHAIN ecosystem.
Automotive Vulnerability Analysis for Deep Learning Blockchain Consensus Algorithm

In this study, future cars are attempting self-driving around the world. However, hacking, such as ECUs in automobiles, creates problems that are directly connected to human life. Therefore, this study wrote a paper that detects anomalies in such cars by field. As a related study, the study investigated the vulnerabilities of the automobile security committee and automobile security standards and investigated the detection of abnormalities in the hacking of geo-train cars using artificial intelligence’s LSTM and blockchain consensus algorithm. In addition, in automobile security, an algorithm was studied to predict normal and abnormal values using LSTM-based anomaly detection techniques on the premise that automobile communication networks are largely divided into internal and external networks. In the methodology, LSTM’s pure propagation malicious code detection technique was used, and it worked with an artificial intelligence consensus algorithm to increase security. In addition, Unity ML conducted an experiment by constructing a virtual environment using the Beta version. The LSTM blockchain consensus node network was composed of 50,000 processes to compare performance. For the first time, 100 Grouped Tx, 500 Channels were tested for performance. For the first time, the malicious code detection rate of the existing system was verified. Accelerator, Multichannel, Sharding, Raiden, Plasma, and Trubit values were verified, and values of approximately 15,000 to 50,000 were obtained. In this paper, we studied to become a paper of great significance on hacking that threatens human life with the development of self-driving cars in the future.
ASSP: An adaptive sample statistics-based pooling for full-reference image quality assessment

Most full-reference image quality assessment (IQA) models first compute local quality scores and then pool them into an overall score. In this paper, we develop an innovative pooling strategy based on sample statistics to adaptively make the IQA more consistent with human visual assessment. The innovation of this work is threefold. First, we identify that standard sample statistics and robust sample statistics could provide complementary information about the degree of degradation in distorted images. Second, an effective IQA metric is proposed by adaptively integrating robust sample statistics and standard sample statistics via excess kurtosis. Third, instead of using the statistics directly, we adjust them by taking into account the global change of image gradients to avoid exaggerating the degradation degree. Experiments conducted on five well-known IQA databases demonstrate the effectiveness of the proposed pooling strategy in terms of high prediction accuracy and monotonicity.
SCATE

Social media platforms have democratized the publication process resulting into easy and viral propagation of information. Oftentimes this misinformation is accompanied by misleading or doctored images that quickly circulate across the internet and reach many unsuspecting users. Several manual as well as automated efforts have been undertaken in the past to solve this critical problem. While manual efforts cannot keep up with the rate at which this content is churned out, many automated approaches only leverage concatenation (of the image and text representations) thereby failing to build effective crossmodal embeddings. Architectures like this fail in many cases because the text or image doesn't need to be false for the corresponding text, image pair to be misinformation. While some recent work attempts to use attention techniques to compute a crossmodal representation using pretrained text and image embeddings, we show a more effective approach towards utilizing such pretrained embeddings to build richer representations that can be classified better. This involves several challenges like how to handle text variations on Twitter and Weibo, how to encode the image information and how to leverage the text and image encodings together effectively. Our architecture, SCATE (Shared Cross Attention Transformer Encoders), leverages deep convolutional neural networks and transformer-based methods to encode image and text information utilizing crossmodal attention and shared layers for the two modalities. Our experiments with three popular benchmark datasets (Twitter, WeiboA and WeiboB) show that our proposed methods outperform the state-of-the-art methods by approximately three percentage points on all three datasets.
Integrated Fabry–Perot filter with wideband noise suppression for satellite-based daytime quantum key distribution

Spectral filtering is essential in daytime quantum key distribution (QKD), which can suppress the strong background noise caused by scattered solar irradiation. An integrated Fabry–Perot filter is implemented based on a scheme that combines a Fabry–Perot etalon and a dense-wavelength-division-multiplex filter for narrow linewidth filtering and broad-spectrum noise suppression, respectively. This filter is integrated into a butterfly package with single-mode fibers for optical input and output, thereby enhancing high robustness and ease of use. The measurement results show that the filter has a linewidth of 25.6 pm, a noise suppression of over 44.7 dB ranging between 1380–1760 nm, an optical efficiency of 74.5% with variation less than 0.9% in 120 min, and a polarization fidelity after compensation exceeding 99.9%. The ability of fine-tuning the central wavelength with 9.5 pm/°C makes it very suitable for satellite-based applications under the Doppler effect. Further analysis is also given to demonstrate the prospects of applying this filter in future satellite-based daytime QKD applications.
AttENT: Domain-Adaptive Medical Image Segmentation via Attention-Aware Translation and Adversarial Entropy Minimization

Due to the intrinsic domain shift among different modalities, it is nontrivial to directly apply a well-trained model into other cross-modality medical images. Unsupervised domain adaptation (UDA) has the potential to reduce such domain shift. However, existing UDA methods try to align domains in either image level or in feature level, failing to consider the unified relationship between cross-modality images and their corresponding features. In this paper, we propose a novel UDA framework for domain adaptive medical image segmentation. The proposed framework synergizes both pixel space and entropy space for domain alignment. Specifically, in the pixel space, we introduce the attention mechanism into CycleGAN, and enhance the semantic and geometric consistency of the target organs during the image style transformation. In entropy space, we utilize entropy minimization principle to force consistent image segmentation between well-annotated source domain and non-annotated target domain. The aligned ensemble of two representation spaces enables a well-trained segmentation model to effectively transfer from source domain to target domain. The experimental results demonstrate the effectiveness of the proposed method. For the task of multi-organs segmentation from cross-modality medical images, our proposed framework achieves state-of-the-art performance, with some specific metric even superior to those of supervised methods. The code is available at https://github.com/lichen14/AttENT.
Deep Learning in Barcode Recognition: A Systematic Literature Review

The use of deep learning (DL) for barcode recognition and analysis has achieved remarkable success and has attracted great attention in various domains.Unlike other barcode recognition methods, DL-based approaches can significantly improve the speed and accuracy of both barcode detection and decoding.However, after almost a decade of progress, the current status of DL-based barcode recognition has yet to be thoroughly explored.Specifically, summaries of key insights and gaps remain unavailable in the literature.Therefore, this study aims to comprehensively review recent applications of DL methods in barcode recognition.We mainly conducted a well-constructed systematic literature review (SLR) approach to collect relevant articles and evaluate and summarize the state of the art.This study's contributions are threefold.First, the paper highlights new DL approaches' applicability to barcode localization and decoding processes and their potential to either reduce the time required or provide higher quality.Second, another main finding of this study signifies an increasing demand for public and specific barcode datasets that allow DL methods to learn more efficiently in the big data era.Finally, we conclude with a discussion on the crucial challenges of DL with respect to barcode recognition, incorporating promising directions for future research development.
Modulation Recognition Algorithm based on Digital Communication Signal Time-Frequency Image

Signal automatic modulation recognition stands for the modulation modes that can classify and recognize various communication signals automatically. Modulation recognition, as the midway between the detection and demodulation for signals, is critical in both military and civilian applications. Electronic countermeasures and software radio, for example. Although modulation recognition technique has advanced significantly in the past few years, there are still various challenges to be overcome as the complex engineering environment and wireless communication channel requirements become more sophisticated. This paper proposes an approach based on the Convolutional Neural Network (CNN) feature fusion to overcome the problem of numerous modulation modes failing to examine the correlation in distinct characters. Moreover, a pseudo Wigner-Ville distribution is appeared in this paper to turn a one-dimensional signal into an image, the image features are extracted by a kind of Convolutional Neural Network model, and the features extracted by CNN are combined with artificial features. The modulation recognition performance is further enhanced by the fusion feature. Simulation findings suggest the technique proposed in this paper enhances the ability especially when the SNR is low, implying this mode of building fusion features is more successful.
MTGK: Multi-source cross-network node classification via transferable graph knowledge

The main idea of multi-source cross-network node classification (CNNC) is to promote the target network’s node classification accuracy by borrowing knowledge from multi-source networks. However, the source networks and the target network often have no intersection on the nodes and links. Thus, the main challenge of multi-source CNNC is to select the most transferable source networks and identify network-invariant patterns between networks to promote cross-network learning. Therefore, targeting the problem of CNNC, in this study, we introduce a multi-source knowledge transfer method, which aims to use a large number of transferable graph patterns in multiple source networks to assist the target network’s unlabeled nodes to be correctly classified. Specifically, we propose to select the most transferable source networks to learn network-invariant common sub-graph patterns and utilize such patterns to build new structural features for the target network. In addition, we devise an iterative classification algorithm, MTGK, which jointly classifies the nodes of the target network based on the attribute features and the transferable structural features of the nodes in multiple source networks. A large number of experiments conducted on real-world social networks and citation networks demonstrate that MTGK outperforms the most advanced methods in CNNC performance.
Two studies of the perceptions of risk, benefits and likelihood of undertaking password management behaviours

Passwords remain the most common form of authentication in the digital world. People have increasing numbers of passwords, and many people undertake risky password management behaviours such as re-using passwords, writing them down and sharing them. It is unclear whether people persist in these behaviours because they do not understand the risks involved or the benefits of the behaviour outweigh the risk. Two studies investigated the relationships between people's perceptions of the risks and benefits of a range of password management behaviours and their self-reports of their likelihood of undertaking them. The first study involved 120 MTurkers in the U.S.A., the second study involved 128 respondents from 26 different countries including approximately 50% students. In both studies, respondents took rated the risks, benefits and likelihood of undertaking 15 password management behaviours. The two very different samples provided very similar results, validating our classification of password management behaviours into four components: Storing, Sharing, Loggingn and Change behaviours. Somecomponents were more affected by perceptions of the benefits, others were equally affected by the perceptions of the risks and the benefits. These results have implications for how information about risky password behaviours is presented to users and general education about password security.
S2TD: A Tree-Structured Decoder for Image Paragraph Captioning

Image paragraph captioning, a task to generate the paragraph description for a given image, usually requires mining and organizing linguistic counterparts from abundant visual clues. Limited by sequential decoding perspective, previous methods have difficulty in organizing the visual clues holistically or capturing the structural nature of linguistic descriptions. In this paper, we propose a novel tree-structured visual paragraph decoder network, called Splitting to Tree Decoder (S2TD) to address this problem. The key idea is to model the paragraph decoding process as a top-down binary tree expansion. S2TD consists of three modules: a split module, a score module, and a word-level RNN. The split module iteratively splits ancestral visual representations into two parts through a gating mechanism. To determine the tree topology, the score module uses cosine similarity to evaluate the nodes splitting. A novel tree structure loss is proposed to enable end-to-end learning. After the tree expansion, the word-level RNN decodes leaf nodes into sentences forming a coherent paragraph. Extensive experiments are conducted on the Stanford benchmark dataset. The experimental results show promising performance of our proposed S2TD.
Data Level Privacy Preserving: A Stochastic Perturbation Approach Based on Differential Privacy

With the great amount of available data, especially collecting from the ubiquitous Internet of Things (IoT), the issue of privacy leakage arises increasingly concerns recently. To preserve the privacy of IoT datasets, traditional methods usually calibrate random noises on the data values to achieve differential privacy (DP). However, the amount of the calibrating noises should be carefully designed and a heedless value will definitely degrade the availability of datasets. Thus, in this work, we propose a stochastic perturbation method to sanitize the dataset, where the perturbation is obtained from the rest samples in the same dataset. In addition, we derive the expression of the utility level based on its unique framework and prove that the proposed algorithm can achieve the <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -DP. To show the effectiveness of the proposed algorithm, we conduct extensive experiments on real-life datasets by various functions, such as query answers and machine learning tasks. By comparing with the state-of-the-art methods, our proposed algorithm can achieve a better performance under the same privacy level.
Where to Start: Studying Type Annotation Practices in Python

Dynamic programming languages have been embracing gradual typing, which supports optional type annotations in source code. Type-annotating a complex and long-lasting codebase is indeed a gradual and expensive process, where two issues have troubled developers. First, there is few guidance about how to implement type annotations due to the existence of non-trivial type practices; second, there is few guidance about which portion of a codebase should be type-annotated first. To address these issues, this paper investigates the patterns of non-trivial type-annotation practices and features of type-annotated code files. Our study detected six patterns of type-annotation practices, which involve recovering and expressing design concerns. Moreover, we revealed three complementary features of type-annotated files. Besides, we implemented a tool for studying optional typing practice. We suggest that: 1) design concerns should be considered to improve type annotation implementation by following at least six patterns; 2) files critical to software architecture could be type-annotated in priority. We believe these guidelines would promote a better type annotation practice for dynamic languages.
Arabic Handwriting Classification using Deep Transfer Learning Techniques

Arabic handwriting is slightly different from the handwriting of other languages; hence it is possible to distinguish the handwriting written by the native or non-native writer based on their handwriting. However, classifying Arabic handwriting is challenging using traditional text recognition algorithms. Thus, this study evaluated and validated the utilisation of deep transfer learning models to overcome such issues. Hence, seven types of deep learning transfer models, namely the AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101, VGG16, and VGG19, were used to determine the most suitable model for classifying the handwritten images written by the native or non-native. Two datasets comprised of Arabic handwriting images were used to evaluate and validate the newly developed deep learning models used to classify each model’s output as either native or foreign (non-native) writers. The training and validation sets were conducted using both original and augmented datasets. Results showed that the highest accuracy is using the GoogleNet deep learning model for both normal and augmented datasets, with the highest accuracy attained as 93.2% using normal data and 95.5% using augmented data in classifying the native handwriting.
Special Aspects of Errors Definition via Sum Codes within Embedded Control Schemas Being Realized by Means of Boolean Complement Method

Within present article characteristics of errors definition are being analyzed within coded words of conventional sum codes (Berger Codes). Unrevealed features of earlier unknown errors detection based on self-multiplicity and forms (unidirectional, symmetrical either asymmetrical), were discovered by us, which may be helpful while synthesis of embedded control schemas, for example, by means of Boolean Complement Method. Here we demonstrated, that while implementation of Berger Codes, certain amount of symmetrical as well as asymmetrical plus unidirectional faults within coded words are not being spotted, which considered as a difference compares to prior defined characteristics of malfunction identification within data vectors of Berger Codes only (regarding our abovementioned case, any symmetrical errors are not the matter of identification, but at the same time, any of unidirectional or asymmetrical mistakes are being found, which is useful while errors definition systems synthesis). The share of not traced errors from the entire quantity per Berger Codes with data vectors dimensions <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r=4\ldots 7$</tex> is composed of less than 2%, as for Berger Codes with vectors lengths <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r=8\ldots 15$</tex> , it is even less than 0.5%. The abovementioned factor has significant influence on probabilistic characteristics of errors detection into coded words of Berger Codes: for instance, within prospect of failure absence in single digit order number <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p=0.9$</tex> , chance of error identification in coded words Pm is above the value of 0.99 for total noted lengths of data vectors. Application of conventional codes with summation is well thought-out as effective case for embedded control systems synthesis including via Boolean Complement Method, where, data and check digits of coded words are being evaluated by means of diagnostic object itself.