Reviewerly/exHarmony huggingface dataset:
Methods for matching English language addresses

Abstract Addresses occupy a niche location within the landscape of textual data, due to the positional importance carried by every word, and the geographic scope it refers to. The task of matching addresses happens every day and is present in various fields such as mail redirection, entity resolution, etc. Our work defines, and formalizes a framework to generate matching and mismatching pairs of addresses in the English language, and use it to evaluate various methods to automatically perform address matching. These methods vary widely from distance‐based approaches to deep learning models. By studying the Precision, Recall, and Accuracy metrics of these approaches, we obtain an understanding of the best suited method for this setting of the address matching task.
ASM: An Adaptive Secure Multicore for Co-located Mutually Distrusting Processes

With the ever-increasing virtualization of software and hardware, the privacy of user-sensitive data is a fundamental concern in computation outsourcing. Secure processors enable a trusted execution environment to guarantee security properties based on the principles of isolation, sealing, and integrity. However, the shared hardware resources within the microarchitecture are increasingly being used by co-located adversarial software to create timing-based side-channel attacks. State-of-the-art secure processors implement the strong isolation primitive to enable non-interference for shared hardware but suffer from frequent state purging and resource utilization overheads, leading to degraded performance. This article proposes ASM , an adaptive secure multicore architecture that enables a reconfigurable, yet strongly isolated execution environment. For outsourced security-critical processes, the proposed security kernel and hardware extensions allow either a given process to execute using all available cores or co-execute multiple processes on strongly isolated clusters of cores. This spatio-temporal execution environment is configured based on resource demands of processes, such that the secure processor mitigates state purging overheads and maximizes hardware resource utilization.
An end-to-end pipeline for historical censuses processing

Abstract Censuses are structured documents of great value for social and demographic history, which became widespread from the nineteenth century on. However, the plurality of formats and the natural variability of historical data make their extraction arduous and often lead to ungeneric recognition algorithms. We propose an end-to-end processing pipeline, based on optimization, in an attempt to reduce the number of free parameters. The layout analysis is based on semantic segmentation using neural networks for a generic recognition of the explicit column structure. The implicit row structure is deduced directly from the position of the text segments. The handwritten text detection is complemented by an intelligent framing method which significantly improves the quality of the HTR. In the end, we propose to combine several post-correction approaches, neural networks, and language models, to further improve the performance. Ultimately, our flexible methods make it possible to accurately detect more than 98% of the columns and 88% of the rows, despite the lack of graphical separator and the diversity of formats. Thanks to various reframing and post-correction strategies, HTR results reach the excellent performance of 3.44% character error rate on these noisy nineteenth century data. In total, more than 18,831 pages were extracted in 72 censuses over a century. This large historical dataset, as well as training data, is made open-access and released along with this article.
Reading Thoreau in the 21st Century

This essay offers a critical re-assessment of Henry David Thoreau’s life and legacy in the context of contemporary critiques of the mainstream environmental tradition. It takes seriously concerns raised about Thoreau’s status as a privileged white male, but calls for more nuance. In particular, the essay challenges those portraits of Thoreau that emphasize Thoreau’s status as a devotee of American nature religion to the exclusion of Thoreau’s significant contributions as an abolitionist and as a prescient critic of an emergent culture of consumption that relied on slavery for its success. The essay assesses the ambivalent legacies that Thoreau left in his wake, demonstrating those moments when Thoreau’s public life as a social critic chafed against his most deeply held spiritual orientations and practices. This essay also argues for the necessity of reading and teaching Thoreau side-by-side with the work of Black writers whose renderings of human-nature relationships both resonate with and depart from Thoreau’s Transcendentalist vision.
Adversarial Example Generation Method Based on Sensitive Features

As deep learning models have made remarkable strides in numerous fields, a variety of adversarial attack methods have emerged to interfere with deep learning models. Adversarial examples apply a minute perturbation to the original image, which is inconceivable to the human but produces a massive error in the deep learning model. Existing attack methods have achieved good results when the network structure is known. However, in the case of unknown network structures, the effectiveness of the attacks still needs to be improved. Therefore, transfer-based attacks are now very popular because of their convenience and practicality, allowing adversarial samples generated on known models to be used in attacks on unknown models. In this paper, we extract sensitive features by Grad-CAM and propose two single-step attacks methods and a multi-step attack method to corrupt sensitive features. In two single-step attacks, one corrupts the features extracted from a single model and the other corrupts the features extracted from multiple models. In multi-step attack, our method improves the existing attack method, thus enhancing the adversarial sample transferability to achieve better results on unknown models. Our method is also validated on CIFAR-10 and MINST, and achieves a 1%-3% improvement in transferability.
An Image Denoising Model via the Reconciliation of the Sparsity and Low-Rankness of the Dictionary Domain Coefficients

Sparse coding has achieved great success in various image restoration tasks. However, if the sparse representation coefficients of the structure (low-frequency information) and texture (high-frequency information) components of the image are under the same penalty constraint, the restoration effect may not be ideal. In this paper, an image denoising model combining mixed norm and weighted nuclear norm as regularization terms is proposed. The proposed model simultaneously exploits the group sparsity of the high frequency and low-rankness of the low frequency in dictionary-domain. The mixed norm is used to constrain the high frequency part and the weighted nuclear norm is used to constrain the low frequency part. In order to make the proposed model easy to solve under the framework of alternative direction multiplier method (ADMM), iterative shrinkage threshold method and weighted nuclear norm minimization method are used to solve the two sub-problems. The validity of the model is verified experimentally through comparison with some state-of-the-art methods.
News Recommendation System Based on Topic Embedding and Knowledge Embedding

News recommendation system is designed to deal with massive news and provide personalized recommendations for users. Accurately capturing user preferences and modeling news and users is the key to news recommendation. In this paper, we propose a new framework, news recommendation system based on topic embedding and knowledge embedding (NRTK). NRTK handle news titles that users have clicked on from two perspectives to obtain news and user representation embedding :1) extracting explicit and latent topic features from news and mining users' preferences for them in historical behaviors; 2) extracting entities and propagating users' potential preferences in the knowledge graph. Experiments in a real-world dataset validate the effectiveness and efficiency of our approach.
RNNCon: Contribution Coverage Testing for Stacked Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are applied in safety-critical fields such as autonomous driving, aircraft collision detection, and smart credit. They are highly susceptible to input perturbations, but little research on RNN-oriented testing techniques has been conducted, leaving a threat to a large number of sequential application domains. To address these gaps, improve the test adequacy of RNNs, find more defects, and improve the performance of RNNs models and their robustness to input perturbations. We aim to propose a test coverage metric for the underlying structure of RNNs, which is used to guide the generation of test inputs to test RNNs. Although coverage metrics have been proposed for RNNs, such as the hidden state coverage in RNN-Test, they ignore the fact that the underlying structure of RNNs is still a fully connected neural network but with an additional "delayer" that records the network state at the time of data input. We use the contributions, i.e., the combination of the outputs of neurons and the weights they emit, as the minimum computational unit of RNNs to explore the finer-grained logical structure inside the recurrent cells. Compared to existing coverage metrics, our research covers the decision mechanism of RNNs in more detail and is more likely to generate more adversarial samples and discover more flaws in the model. In this paper, we redefine the contribution coverage metric applicable to Stacked LSTMs and Stacked GRUs by considering the joint effect of neurons and weights in the underlying structure of the neural network. We propose a new coverage metric, RNNCon, which can be used to guide the generation of adversarial test inputs. And we design and implement a test framework prototype RNNCon-Test. 2 datasets, 4 LSTM models, and 4 GRU models are used to verify the effectiveness of RNNCon-Test. Compared to the current state-of-the-art study RNN-Test, RNNCon can cover a deeper decision logic of RNNs. RNNCon-Test is not only effective in identifying defects in Deep Learning (DL) systems but also in improving the performance of the model if the adversarial inputs generated by RNNCon-Test are filtered and added to the training set to retrain the model. In the case where the accuracy of the model is already high, RNNCon-Test is still able to improve the accuracy of the model by up to 0.45%.
Improving Text-to-SQL with a Hybrid Decoding Method

Text-to-SQL is a task that converts natural language questions into SQL queries. Recent text-to-SQL models employ two decoding methods: sketch-based and generation-based, but each has its own shortcomings. The sketch-based method has limitations in performance as it does not reflect the relevance between SQL elements, while the generation-based method may increase inference time and cause syntactic errors. Therefore, we propose a novel decoding method, Hybrid decoder, which combines both methods. This reflects inter-SQL element information and defines elements that can be generated, enabling the generation of syntactically accurate SQL queries. Additionally, we introduce a Value prediction module for predicting values in the WHERE clause. It simplifies the decoding process and reduces the size of vocabulary by predicting values at once, regardless of the number of conditions. The results of evaluating the significance of Hybrid decoder indicate that it improves performance by effectively incorporating mutual information among SQL elements, compared to the sketch-based method. It also efficiently generates SQL queries by simplifying the decoding process in the generation-based method. In addition, we design a new evaluation measure to evaluate if it generates syntactically correct SQL queries. The result demonstrates that the proposed model generates syntactically accurate SQL queries.
An In-depth Guide to Cross-chain Protocols under a Multi-chain World

In 2021, public blockchains have made remarkable progress with the support of investors and developers, ushering in the era of the multi-chain world, which represents a paradigm shift that is unlikely to be reversed. Cross-chain protocols have become an essential infrastructure in this new world, referring to a standardized set of rules and processes that enable the secure transfer of assets or information between different blockchain networks. To help researchers and industry practitioners better understand the concepts, advantages, and limitations of cross-chain technologies, this paper presents a comprehensive guide to cross-chain protocols. We cover a range of topics, including message transmission protocols, leading cross-chain bridges, and other cross-chain applications. Additionally, we discuss potential risks and challenges associated with cross-chain protocols and explore the future of cross-chain protocol development. Our work is intended to offer valuable insights and references for anyone looking to delve deeper into cross-chain protocols, whether they are academic researchers or industry professionals.
Design and Implementation of an Integrated Control System for Omnidirectional Mobile Robots in Industrial Logistics

The integration of intelligent robots in industrial production processes has the potential to significantly enhance efficiency and reduce human adversity. However, for such robots to effectively operate within human environments, it is critical that they possess an adequate understanding of their surroundings and are able to navigate through narrow aisles while avoiding both stationary and moving obstacles. In this research study, an omnidirectional automotive mobile robot has been designed for the purpose of performing industrial logistics tasks within heavy traffic and dynamic environments. A control system has been developed, which incorporates both high-level and low-level algorithms, and a graphical interface has been introduced for each control system. A highly efficient micro-controller, namely myRIO, has been utilized as the low-level computer to control the motors with an appropriate level of accuracy and robustness. Additionally, a Raspberry Pi 4, in conjunction with a remote PC, has been utilized for high-level decision making, such as mapping the experimental environment, path planning, and localization, through the utilization of multiple Lidar sensors, IMU, and odometry data generated by wheel encoders. In terms of software programming, LabVIEW has been employed for the low-level computer, and the Robot Operating System (ROS) has been utilized for the design of the higher-level software architecture. The proposed techniques discussed in this paper provide a solution for the development of medium- and large-category omnidirectional mobile robots with autonomous navigation and mapping capabilities.
An Offline EP Test Tube Positioning Tilt Correction Algorithm Based on Lightweight Yolov4

As an infrastructure of biochemical laboratories, EP tube label plays a significant role in information extraction to meet the limitations of computing power in offline devices and solve the problem that the EP tube label cannot be accurately identified before identification because the label belongs to multi-angle random placement. This paper proposes a light-weight neural network YOLOv4-tiny-ECA to position tubes and a tilt correction method based on Hough transform. First, the EP tube rack is roughly positioned based on the diffuse filling algorithm combined with digital morphological corrosion, and then the EP tubes in the rack are precisely positioned using the light-weight YOLO target detection algorithm combined with the attention mechanism. Next, the baseline is added to the label as the basis for determining the tilt angle. For the valid target, the baseline is extracted using the Hough transform and the tilt angle is calculated by vector fork multiplication. Finally, baseline is removed using image processing algorithm for better recognition results. Our results show that the light-weight YOLO algorithm reduces the network parameters by 56% and computation by 55% while keeping the accuracy rate largely unchanged, the offline positioning tilt correction method can achieve 98.8% accuracy and 0.076[Formula: see text]s processing speed for a single test tube on average, which meets the real-time requirement.
Code quality improvement using Aquila Optimizer

Abstract The software industry always requires high‐quality software. Code quality has been improved via refactoring and re‐refactoring and removing code smells. Refactoring is a technique to improve existing code by modifying inner structure, while code smells are indication that might have a wrong in code that causes a negative impact on the code quality. According to previous studies, the accuracy of code smell detection was not stable and could be improved by advanced algorithms. Additionally, the impact of refactoring on each of the quality attributes is unclear. This research proposes the use of Aquila Optimizer to detect code smells in addition to investigate the refactoring and re‐refactoring impact through ISO 9126 model. The experiment is conducted on five open‐sources Java projects to detect nine code smell types and measure the values of the ISO 9126 before and after the refactoring and re‐refactoring techniques to show the impact on code quality. The results showed that the average of detection accuracy is 95.18% and 95.68% for precision and recall, respectively. Moreover, the results in large projects are better than in the medium projects. The findings also show improvement in the values of the ISO 9126 model after applying refactoring and re‐refactoring.
Explainable artificial intelligence envisioned security mechanism for cyber threat hunting

Abstract Cyber threat hunting proactively searches for cyber threats, which are undetected by the traditional defense mechanisms. It scans deep to identify malicious programs (ie, malware) that escape from detection. It is important because sophisticated cyber threats can bypass the cyber security mechanisms. The performance of the cyber threat hunting can be improved through artificial intelligence (AI), especially, explainable AI (XAI), which adds trust component to the cyber threat hunting process. Due to the inclusion of XAI, the security experts get the full explanations of the detected threats as the working of the detection model in XAI is known. Information, like, which one is a threat, how it has been detected, and why it has been detected, can be obtained very easily due to the inclusion of XAI in the cyber threat hunting. Therefore, an XAI‐envisioned mechanism for cyber threat hunting has been proposed (in short, XAISM‐CTH). The network and threat models of XAISM‐CTH are designed and discussed. The conducted security analysis proves the security of XAISM‐CTH against various potential attacks. XAISM‐CTH also performs better than the other existing schemes. At the end, a practical implementation of XAISM‐CTH has been provided to observe its impact on the performance of the system.
Cognitive CAPTCHA Password Reminder

In recent years, the number of personal accounts assigned to one business user has been constantly growing. There could be as many as 191 individual login credentials used by an average employee, according to a 2017 study. The most recurrent problems associated with this situation faced by users are the strength of passwords and ability to recall them. Researchers have proven that "users are aware of what constitutes a secure password but may forgo these security measures in terms of more convenient passwords, largely depending on account type". Reusing the same password across multiple platforms or creating one with dictionary words has also been proved to be a common practice amongst many. In this paper, a novel password-reminder scheme will be presented. The goal was that the user creates a CAPTCHA-like image with a hidden meaning, that only he or she can decode. The image must be in some way related to that individual's memory or her/his unique knowledge or experience. With this image, being presented each time during logging in, the user is asked to associate a password consisting of two or more words and a number. If the image is selected properly and strong association with a person's visual memory has been linked to it, the chances of recalling a lengthy password he/she created should not present a problem.
DESIGNING ENGLISH: TEXT‐BASED INSTRUCTION WITH PRINCIPLED ECLECTICISMBy AnitaTriastuti, SuwarsihMadya, and PhilipChappell<scp>UNY</scp> Press. <scp>ISBN</scp> 978–60249–8333‐8. x + 282 pages

TESOL JournalEarly View e713 MEDIA REVIEW DESIGNING ENGLISH: TEXT-BASED INSTRUCTION WITH PRINCIPLED ECLECTICISM By Anita Triastuti, Suwarsih Madya, and Philip ChappellUNY Press. ISBN 978–60249–8333-8. x + 282 pages Elih Sutisna Yanto, Elih Sutisna Yanto [email protected] orcid.org/0000-0003-0701-6454 Universitas Singaperbangsa KarawangSearch for more papers by this author Suwarno, Corresponding Author Suwarno [email protected] orcid.org/0000-0002-8639-6127 Politeknik Negeri Batam Correspondence Email: [email protected]Search for more papers by this author Elih Sutisna Yanto, Elih Sutisna Yanto [email protected] orcid.org/0000-0003-0701-6454 Universitas Singaperbangsa KarawangSearch for more papers by this author Suwarno, Corresponding Author Suwarno [email protected] orcid.org/0000-0002-8639-6127 Politeknik Negeri Batam Correspondence Email: [email protected]Search for more papers by this author First published: 17 March 2023 https://doi.org/10.1002/tesj.713Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. REFERENCES Derewianka, B. (2015). The contribution of genre theory to literacy education in Australia. In J. Turbill, G. Barton, & C. Brock (Eds.), Teaching writing in today's classrooms: Looking back to looking forward (pp. 69– 86). Crows Nest: Australian Literacy Educators’ Association. Derewianka, B., & Jones, P. (2016). Teaching language in context ( 2nd ed.). Oxford, England: Oxford University Press. Gao, L. (2011). Eclecticism or principled eclecticism. Creative Education, 2, 363– 369. https://doi.org/10.4236/ce.2011.24051 Halliday, M.A.K., (1978). Language as social semiotic: The social interpretation of language and meaning. London: Edward Arnold. Halliday, M. A. K. (1994). An introduction to functional grammar ( 2nd ed.). London, England: Edward Arnold. Halliday, M. A. K., & Hasan, R. (1989). Language, context, and text: Aspects of language in a social-semiotic perspective. Oxford, England: Oxford University Press. Early ViewOnline Version of Record before inclusion in an issuee713 ReferencesRelatedInformation