Reviewerly/exHarmony huggingface dataset:
Baggage Image Retrieval with Attention-Based Network for Security Checks

Baggage image search is the task of finding the same baggage across several cameras, which can improve the efficiency of security check. Since existing state-of-the-art image retrieval requires a significant amount of training data, we aim to investigate the few-shot situation, utilizing only a few samples for each baggage. In this paper, the framework we introduced is called AttentionNet, exploring the weak region-wise annotations as attention clues to improve the retrieval performance. Specifically, we integrate the semantic and recognition tasks using shared convolutional neural networks by multi-task learning. Semantic attention mechanism is used to explicitly re-weight the feature map for emphasizing the foreground. To prevent overfitting in few-shot training, we adopt a variant of the triplet loss to perform deep metric learning with an online hard triplet mining strategy. Once trained, AttentionNet identifies a probe image by computing its embedding’s cosine distance with images in the gallery. Experiments show that our method achieves state-of-the-art accuracy in image search. For instance, we obtain 82.9% Rank-1 score on the MVB dataset, dramatically outperforming the currently existing methods.
HSEarch: Semantic Search System for Workplace Accident Reports

Semantic search engines, which integrate the output of text mining (TM) methods, can significantly increase the ease and efficiency of finding relevant documents and locating important information within them. We present a novel search engine for the construction industry, HSEarch ( http://www.nactem.ac.uk/hse/ ), which uses TM methods to provide semantically-enhanced, faceted search over a repository of workplace accident reports. Compared to previous TM-driven search engines for the construction industry, HSEarch provides a more interactive means for users to explore the contents of the repository, to review documents more systematically and to locate relevant knowledge within them.
Side-Channel Attack Counter-measure Analysis for Control and Diagnostics Applications

Many Side-Channel Attacks (SCAs) utilize power analysis to obtain sensitive material such as encryption keys and is one of the demonstrated threats to embedded systems. Masking is an effective Counter-measure against SCA; however, its implementation correctness is difficult to evaluate. Currently, most verification work is based on hardware electronics design and analysis tools. However, control and diagnostic applications typically are implemented using Simulink or similar tools. This paper explores the analysis of masking effectiveness using Simulink. Our method focuses on exploring the feasibility of random simulation and statistical analysis to achieve an approximate evaluation, to rule out the obvious unqualified designs. The paper also develops a solution for rigorous formal verification when the system is too complex to be handled by the verification tool, which addresses the state space explosion issue, one of the major obstacles for applying formal verification.
Implementation of Rivest Cypher 4 algorithm in Security Assertion Mark-up Language protocols on Single Sign-On services

Abstract Single Sign-On (SSO) is an authentication service that allows users to use a set of credential data to access multiple applications. The SSO can be implemented using Security Assertion Mark-up Language (SAML) which is recognized as a framework or standard for sending open messages that allow identity and security information to be shared to each entity. Credential data sent over the network enable unauthorized users to capture the data and this will be fatal since the data can be used to access all applications in the SSO entity. This study aims to propose a credible data security approach that is reliable so that the data is not easily read by unauthorized users even with the dictionary attacks. This security method is performed using the Rivest Cypher 4 (RC4) algorithm. This study is resulting in two major phenomena, (1) the utilization of the RC4 algorithm on SAML in SSO services has proven to be an effective data security approach for dictionary attacks, (2) the formulation of the RC4 algorithm on SAML has proven to not interfere with SSO services in the user authentication process.
Automation of Financial Services Using Artificial Intelligence with Human Touch

Artificial Intelligence is the latest in the series of disruptions manifested by computer science. AI has been rapidly transforming the dynamics of banking and financial services industry also. The established and emerging capabilities of AI are being combined, re- constituted and re-formulated in unexpected ways and are throwing up new opportunities and new challenges but at the same time posing new threats also. Apart from the ethico-neutral character of technology and its attendant threats like cyber-crimes and macro-financial risks, a major question to be inquired into is its sustainability as it tends to replace humans and the related personal touch which most often is the essence of financial services industry thriving on the art of customization and customer delight. The instant paper attempts to examine relatively under-explored perspective of AI replacing humans in the space of banking and financial services and unmindfully heralding the flight of personal touch and service customization which are the cornerstone of customer satisfaction and delight in industries like banking and financial services known for their fiduciary and responsible character.
SIFT Implementation based on LEON3 Processor

This paper proposes a new method of implementation of the part of SIFT (Scale-Invariant Feature Transform) algorithm used to extract the feature of an image of a size 256256 of pixels, which is mainly based on the using the LEON3 soft core processor .With this method it is possible to detect points of interest and so perform matching.This process allows several real time applications as robotic navigation, stereovision, object recognition etc. Obtained results show a very robustness in rotation, scale invariant as well as luminosity change.SIFT algorithm saw big success in various applications of computer vision.However, its high computation complexity has been a challenge for the most part of embedded implementations.This paper presents a partial implementation of the SIFT algorithm, which is to implement just the extraction of the characteristics that is based on the LEON3 processor.This implementation method overcomes the existing problems, in particular, the high dependence of existing implementations on the hardware architecture used.Indeed, the high flexibility of the processor allows the possibility to develop the application independent of the target board.
Grammatical Structure and Long Memory

The previous chapters in Part IV mainly considered the relation between the statistical universals and elements of language. This chapter instead considers the relation between long memory and the structure of sentences. Part III explained the long memory underlying language, which is characterized by clustering phenomena among linguistic events, such that words occur in a clustered manner. Such clustering phenomena have been reported in many complex systems, and their appearance in language is intuitively comprehensible as being caused by shifts in context.
Breast Ultrasound Image Analysis based on Transfer Learning

Breast cancer is the most common cancer in women, and ultrasound diagnosis is one of the most common diagnostic methods. This paper adopted the transfer learning method to classify benign and malignant breast tumours by ultrasound images and predict molecular subtypes. To study the effect of the transfer learning model on breast cancer diagnosis in the benign and malignant tumour classification, we tested whether different models were transferred learning. After transfer learning, the Xception model reached 87.75% in tumour classification accuracy, superior to other models without transfer learning. In predicting and diagnosing molecular subtypes, the multiple-transfer learning Xception model's recognition accuracy reached 74.3%, and the AUC value reached 0.8801. Compared with the Xception model without transfer learning, the accuracy was improved by 4.02%, and the single-transfer learning Xception model was improved by 3.22%, achieving a relatively excellent classification effect. Our results show that the application of transfer learning can improve breast cancer ultrasound images' diagnostic accuracy. Multiple transfer learning can improve the accuracy of molecular subtypes prediction in a small sample of breast cancer.
Unraveling the Semantic Evolution of Core Nodes in a Global Contribution Network

The analysis of social structures is important in many contexts, especially in Global Software Development, where various developers with diverse skills and knowledge are involved. In this sense, searching for essential members is a valuable task since they are fundamental to the network's evolution. With the main goal of identifying and monitoring these individuals, we propose a temporal analysis approach that explores syntactic and semantic network aspects. We describe experiments based on popular projects on Github that consider a network modeled over consecutive time periods. We also propose an ontology to represent the domain knowledge and explore the network by investigating its semantic context. We provide evidence that our approach can detect individuals considered essential based on their role in the network.
How to Make a Query in Image Retrieval with Partial Information Extracted from Multiple Image Samples?

The existing image retrieval methods generally require at least one complete image as a query sample. From the practical point of view, a user may not have an image sample in hand for query. Instead, partial information from multiple image samples would be available. This paper therefore attempts to deal with this problem by presenting a novel framework that allows a user to make an image query composed of several partial information extracted from multiple image samples via Boolean operations (i.e. AND, OR and NOT). Based on the request from the query, a Descriptor Cluster Label Table (DCLT) is designed to efficiently find out the result of Boolean operations on partial information. Experiments show the promising result of the proposed framework on commodity query and criminal investigation, respectively, although it is essentially applicable to different scenarios as well by changing descriptors.
Academic Writing with Corpora: A Resource Book for Data‐Driven Learning, TatyanaKarpenko‐Seccombe. Routledge: 2020. 244 pp., ISBN 978‐0‐367‐18178‐9

International Journal of Applied LinguisticsVolume 31, Issue 2 p. 306-308 BOOK REVIEW Academic Writing with Corpora: A Resource Book for Data-Driven Learning, Karpenko-Seccombe, Tatyana. Routledge: 2020. 244 pp., ISBN 978-0-367-18178-9 Kanglong Liu, Kanglong Liu The Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, ChinaSearch for more papers by this authorMuhammad Afzaal, Muhammad Afzaal orcid.org/0000-0003-4649-781X The Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, Hung Hom, Hong Kong, Hong KongSearch for more papers by this author Kanglong Liu, Kanglong Liu The Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, ChinaSearch for more papers by this authorMuhammad Afzaal, Muhammad Afzaal orcid.org/0000-0003-4649-781X The Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, Hung Hom, Hong Kong, Hong KongSearch for more papers by this author First published: 01 April 2021 https://doi.org/10.1111/ijal.12342Read 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 Share a linkShare onFacebookTwitterLinked InRedditWechat Open Research PEER REVIEW The peer review history for this article is available at https://publons.com/publon/10.1111/ijal.12342. Volume31, Issue2Supplement: Corpus‐based language teaching and learning: applications and implicationsJuly 2021Pages 306-308 RelatedInformation
Single orbit affine generators for extended BCH codes with designed distance three

We show that for any prime p, p ≠ 2, 3 and any m, m ≥ 3 the narrow-sense BCH codes over GF (p) of length p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> - 1 with designed distance three are not spanned by theirs minimum nonzero weight codewords. We prove that the extensions of these codes are spanned by their codewords of weight five and the bases could be chosen in the affine orbits of explicit codewords.
Work-sensitive Dynamic Complexity of Formal Languages

Abstract Which amount of parallel resources is needed for updating a query result after changing an input? In this work we study the amount of work required for dynamically answering membership and range queries for formal languages in parallel constant time with polynomially many processors. As a prerequisite, we propose a framework for specifying dynamic, parallel, constant-time programs that require small amounts of work. This framework is based on the dynamic descriptive complexity framework by Patnaik and Immerman.
Detection Based Multipath Correlation Filter for Visual Object Tracking

This paper presents a novel detection based multipath correlation filter (DT-MPT) for moving single object tracking from Unmanned Aerial Vehicles (UAVs). Most object trackers suffer under complicated situations such as the fast motion of objects, camera motion, similar objects, and occlusions, usually found in UAV videos. This paper utilizes a correlation filter based tracking algorithm along with a motion estimation block, a novel detection block, and a new multipath block to cope with those challenges. The detection block uses the concept of the difference image to detect moving objects, while the multipath block considers multiple positions for a few looks ahead frame for accurate localization of an object position. The performance analysis on three challenging benchmark datasets UAV123@10fps, UAV20L and DTB70 show that the proposed tracker DT-MPT outperforms its base tracker as well as most of the other top-performing state of art correlation filter-based trackers while running in almost real-time.
Review on Data Analysis of Cryptocurrency

Crypto currencies have fascinated all over the world since past few decades. It generally works on the principle of block chain. With its rising value in the market the demands have also risen. In this paper it is tried further to reanalyze the values for a range of crypto currencies. From the raw data it has been analyzed with various perspectives as per the requirement for the evaluation. Afterwards the various concepts of the data designed a statistical model of some crypto currencies with different perspective of the data. Also from the statistical data it can be seen a variety of ups & downs of an assortment of crypto currencies in todayâ€™s market as per the data.
Diversity-Aware Top-N Recommendation: A Deep Reinforcement Learning Way

The increasing popularity of the recommender system deeply influences our decisions on the Internet, which is a typical continuous interaction process between the system and its users. Most previous recommender systems heavily focus on optimizing recommendation accuracy while neglecting the other important aspects of recommendation quality, such as diversity of recommendation list. In this study, we propose a novel recommendation framework to optimize the recommendation list for the Top-N task, named Collaborative Filtering-based Deep Reinforcement Learning (CFDRL), which promotes the diversity of recommendation results without sacrificing the recommendation accuracy. More specifically, to effectively capture the continuous user-item interaction for recommendations, we adopt the deep reinforcement learning (DRL) to update the recommendation strategy dynamically according to the user's real-time feedback. Meanwhile, to generate diverse and complementary items for recommendation, we design a diversity-aware reward function that can lead to maximizing reward with the trade-off between diversity and accuracy. Besides, to alleviate the disadvantage of DQN that directly picking the recommendations with the highest Q-values from the unselected items, we define a modified $$\varepsilon$$ -greedy explore policy with jointly CF model. It firstly utilizes CF model to sort the items and divide them into two part according to the item similarity, then with a probability the agent selects from them and generates an action list with the modified $$\varepsilon$$ -greedy explore policy. The experimental results conducted on two real-world e-commerce datasets demonstrate the effectiveness of the proposed model.
Characterization of "tecnologías de la información y comunicación" faculty of "Universidad Tecnológica de la Selva"

Currently, national education policies of coverage, quality and relevance impose challenges to higher education institutions. To face them, universities must place the curricular update, student motivation and improvement of the teaching-learning processes as primary focus of attention. To effectively transform these axes, it is necessary to have relevant information that allows to assertively guide decision making. Thus, this project characterizes the Information and Communication Technologies Division of the Universidad Tecnológica de la Selva through the analysis of information obtained from students and population of the area of influence, combined with the review of repositories indicators of official information. In this sense, this paper provides an instrument to evaluate students’ expectations and opinions regarding the teaching-learning process and incorporates an instrument aimed at the population of the area of influence to measure their level of knowledge of the University and the general opinion of it. This new aspect is relevant given that the population of the area of influence is the first promoter or detractor of the institution’s work due to its coexistence with the university community and it is not usually considered in this type of studies.
Recognition of Tibetan Maximal-length Noun Phrases Based on Syntax Tree

Frequently corresponding to syntactic components, the Maximal-length Noun Phrase (MNP) possesses abundant syntactic and semantic information and acts a certain semantic role in sentences. Recognition of MNP plays an important role in Natural Language Processing and lays the foundation for analyzing and understanding sentence structure and semantics. By comparing the essence of different MNPs, this article defines the MNP in the Tibetan language from the perspective of syntax tree. A total of 6,038 sentences are extracted from the syntax tree corpus, the structure type, boundary feature, and frequency of MNPs are analyzed, and the MNPs are recognized by applying the sequence tagging model and the syntactic analysis model. The accuracy, recall, and F1 score of the recognition results of applying sequence tagging model are 87.14%, 84.72%, and 85.92%, respectively. The accuracy, recall, and F1 score of the recognition results of applying syntactic analysis model are 87.66%, 87.63%, and 87.65%, respectively.
Over Fifty Years of Hydrogeological Practice and Geoethics: An Intergenerational View of a Changing World

The exercise of professional hydrogeology is a privilege that requires sound scientific and technical knowledge, field training and interpersonal skills. A skilled hydrogeologist is required to have ethics, deontology, integrity, eco-responsibility, leadership, and behaviour but also the acceptance of the high-standards and codes of ethics and boundaries to avoid discrimination and harassment. In addition, the hydrogeology practice must encompass the geoethical approach. Water supply and infrastructure design and construction for groundwater monitoring are not popular nowadays around the scientific and technical community. However, thousands and thousands of wells and boreholes are drilled worldwide annually, without appropriate hydrogeological support. A professional hydrogeologist cannot support, for example, policies of abandon of fieldwork, leaving the aquifer exploitation restrained to high levels of management and governance. Those practices must be developed through proper conceptual site modelling based on field and laboratory data, complemented by GIS mapping, geovisualization techniques, numerical tools to predict scenarios and climate change understanding. On the ethical point of view, this systematic methodology underpins personal scientific integrity but also a comprehensive understanding of the problem to solve. That includes the moral decisions to be made regarding the undertake of the pure and applied hydrogeology in the practice, as well as the professional appropriate use of the scientific advancements in groundwater science, technology, and management.
Developing a Novel Recurrent Neural Network Architecture with Fewer Parameters and Good Learning Performance

Recurrent neural networks (RNNs) are among the most promising of the many artificial intelligence techniques now under development, showing great potential for memory, interaction, and linguistic understanding. Among the more sophisticated RNNs are long short-term memory (LSTM) and gated recurrent units (GRUs), which emulate animal brain behavior; these methods yield superior memory and learning speed because of the excellent core structure of their architectures. In this study, we attempted to make further improvements in core structure and develop a novel, compact architecture with a high learning speed. We stochastically generated 30000 RNN architectures, evaluated their performance, and selected the one most capable of memorizing long contexts with relatively few parameters. This RNN, YamRNN, had fewer parameters than LSTM and GRU by a factor of two-thirds or better and reduced the time required to achieve the same learning performance on a sequence classification task as LSTM and GRU by 80% at maximum. This novel RNN architecture is expected to be useful for addressing problems such as predictions and analyses on contextual data and also suggests that there is room for the development of better architectures.
Physical generation of random numbers using an asymmetrical Boolean network*

Autonomous Boolean networks (ABNs) have been successfully applied to the generation of random number due to their complex nonlinear dynamics and convenient on-chip integration. Most of the ABNs used for random number generators show a symmetric topology, despite their oscillations dependent on the inconsistency of time delays along links. To address this issue, we suggest an asymmetrical autonomous Boolean network (aABN) and show numerically that it provides large amplitude oscillations by using equal time delays along links and the same logical gates. Experimental results show that the chaotic features of aABN are comparable to those of symmetric ABNs despite their being made of fewer nodes. Finally, we put forward a random number generator based on aABN and show that it generates the random numbers passing the NIST test suite at 100 Mbits/s. The unpredictability of the random numbers is analyzed by restarting the random number generator repeatedly. The aABN may replace symmetrical ABNs in many applications using fewer nodes and, in turn, reducing power consumption.