Reviewerly/exHarmony huggingface dataset:
Pattern Matching on Grammar-Compressed Strings in Linear Time

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Pattern Matching on Grammar-Compressed Strings in Linear TimeMoses Ganardi and Paweł GawrychowskiMoses Ganardi and Paweł Gawrychowskipp.2833 - 2846Chapter DOI:https://doi.org/10.1137/1.9781611977073.110PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract The most fundamental problem considered in algorithms for text processing is pattern matching: given a pattern p of length m and a text t of length n, does p occur in t? Multiple versions of this basic question have been considered, and by now we know algorithms that are fast both in practice and in theory. However, the rapid increase in the amount of generated and stored data brings the need of designing algorithms that operate directly on compressed representations of data. In the compressed pattern matching problem we are given a compressed representation of the text, with n being the length of the compressed representation and N being the length of the text, and an uncompressed pattern of length m. The most challenging (and yet relevant when working with highly repetitive data, say biological information) scenario is when the chosen compression method is capable of describing a string of exponential length (in the size of its representation). An elegant formalism for such a compression method is that of straight-line programs, which are simply context-free grammars describing exactly one string. While it has been known that compressed pattern matching problem can be solved in O(m + n log N) time for this compression method, designing a linear-time algorithm remained open. We resolve this open question by presenting an O(n + m) time algorithm that, given a context-free grammar of size n that produces a single string t and a pattern p of length m, decides whether p occurs in t as a substring. To this end, we devise improved solutions for the weighted ancestor problem and the substring concatenation problem. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Improving Novelty Detection Using the Reconstructions of Nearest Neighbours

We show that using nearest neighbours in the latent space of autoencoders and generative models significantly improves performance of semi-supervised novelty detection in both single and multi-class contexts. Our method harnesses a combination of reconstructions of the nearest neighbours and the latent-neighbour distances of a given input's latent representation. We demonstrate that our Nearest-Latent-Neighbours (NLN) algorithm is memory and time efficient, does not require significant data augmentation, nor is reliant on pretrained networks. Furthermore, we show that the NLN-algorithm is easily applicable to multiple datasets without modification. We validate our method across several standard datasets for a variety of different autoencoding architectures such as vanilla, adversarial and variational autoencoders using either reconstruction, residual or feature consistent losses. The results show that the NLN algorithm grants up to a 17% increase in Area Under the Receiver Operating Characteristics (AUROC) curve performance for the multi-class case and 8% for single-class novelty detection.
Revocable Large Universe Decentralized Multi-Authority Attribute-Based Encryption Without Key Abuse for Cloud-Aided IoT

Data confidentiality and access control are the key technologies of secure Internet of things (IoT) since the circulated application data on multiple different domains in IoT are generally confidential and privacy-sensitive. Large universe multi-authority attribute-based encryption (MA-ABE) is considered a promising technique to protect data confidentiality and achieve fine-grained access control for large-scale cross-domain applications. However, MA-ABE is facing the severe key abuse problem. Much research is devoted to using audit technologies and trace technologies to determine who should be responsible for the misused key which has a certain deterrent effect and prevents the key abuse to a certain extent. But they can’t solve the key abuse problem, since users still can leak the key and the leaked keys can still decrypt the ciphertext correctly. Moreover, they also cannot solve the key escrow problem. In this article, the author proposes the first revocable large universe decentralized MA-ABE without key abuse based on prime order bilinear groups. The proposed scheme allows for the dynamic capacity expansion of attributes, users, and authorities. It is not only static security in the random oracle model under the q-DPBDHE2 assumption but also secure against key abuse attacks launched by any party. Only the secret key owner can successfully decrypt the ciphertext with the secret key. The data user is unable to generate the available key different from her/his legal key by using her/his legal key. Neither CSP nor authority can generate the available decryption key or decrypt the ciphertext (even if the access policy is satisfied by the attributes it controls) using the keys it controls. An efficient user-attribute revocation mechanism is given and only a few operations are needed when decryption in the proposed scheme. The performance analysis results indicate that the proposed scheme is more efficient and suitable for the IoT.
Synthetic Data Generation and Multi-Task Learning for Extracting Temporal Information from Health-Related Narrative Text

Extracting temporal information is critical to process health-related text.Temporal information extraction is a challenging task for language models because it requires processing both texts and numbers.Moreover, the fundamental challenge is how to obtain a largescale training dataset.To address this, we propose a synthetic data generation algorithm.Also, we propose a novel multi-task temporal information extraction model and investigate whether multi-task learning can contribute to performance improvement by exploiting additional training signals with the existing training data.For experiments, we collected a custom dataset containing unstructured texts with temporal information of sleep-related activities.Experimental results show that utilising synthetic data can improve the performance when the augmentation factor is 3.The results also show that when multi-task learning is used with an appropriate amount of synthetic data, the performance can significantly improve from 82. to 88.6 and from 83.9 to 91.9 regarding micro-and macro-average exact match scores of normalised time prediction, respectively.
Considerations of providing patent and trademark assistance services at one's library

Purpose The authors' goal in writing this article was to provide background information and detailed considerations to assist those wishing to provide patent and trademark assistance at their libraries. The major considerations include staffing, spaces and resources, with the time commitment from the staff being the most significant. Design/methodology/approach This paper combined the experiences of an author relatively new to patent and trademark librarianship with one who has years of experience. These were used in tandem with knowledge gained from a decade of attendance at annual week-long seminars at the United States Patent and Trademark Office (USPTO) as well as by way of a comprehensive literature review. Findings The main commitment needed in providing patent and trademark services to the public is not money but the investment of time, which includes professional development, staffing, teaching classes and workshops, outreach and consultations. Originality/value The information in this paper should serve as guidance to anyone new to providing patent and trademark services within their libraries, including those at Patent and Trademark Resource Centers (PTRCs), Patent Information Centres (PATLIBs) and beyond. Although articles have been published on various aspects of intellectual property (IP) and libraries, a comprehensive guide to providing patent and trademark services has yet to be published.
Prediction of Human Motion with Motion Optimization and Neural Networks

Human motion prediction is a crucial foundation for efficient and safe human-robot cooperation. In order to improve the accuracy of long-term motion prediction, an approach combining initial trajectory prediction and motion optimization is proposed. The neural network model is applied to model the human hand motion for initial prediction. Then, we utilize the Gradient Descent method to optimize the motion considering the target set constraints. The motion's target point position is obtained from K-Nearest Neighbor (KNN) classification model. Experiments on real reaching motion data demonstrate that the proposed algorithm achieves a higher prediction accuracy compared with the traditional single neural network model.
A Study on Main Factors Influencing Major Choice of Nanjing Audit University Students

Backgrounds — Students who graduated from high school and went to university have to consider and make a decision about their choice of university major, an extremely important decision to each of them, which can greatly influence their studying road, future occupation and even whole life. Purpose — The purpose of the research is to find out the main factors influencing NAU students’ choice of major. In addition, this research also aims to determine how Personal Factors, Family Factors and Profession Factors influence NAU students’ choice of major respectively. Methodology — We based on the previous research and narrowed three main factors including Personal Factor, Family Factor and Profession Factor as the main variables, then we conducted a questionnaire survey among all NAU students of different majors and grades, finally we analyzed the data that we collected and drew some conclusions. Results — We find that students have some knowledge of their major before they entering the university rather than knowing nothing, the Profession Factor is the top factor which is the biggest influence on the students’ choice of major, however, though some minor factors which belong to the Profession Factor have an important place, the dominant factor is still the Gaokao Score which belongs to the Personal Factor. As for those students who have changed their major, they more tend to choose Auditing, Accounting and Finance because of the professional advantages, only a few students are still dissatisfied with their chosen.
Medical Imaging System

The MD Image System, a true-color image processing system that serves as a diagnostic aid and tool for storage and distribution of images, was developed by Medical Image Management Systems, Huntsville, AL, as a spinoff from a spinoff. The original spinoff, Geostar 8800, developed by Crystal Image Technologies, Huntsville, incorporates advanced UNIX versions of ELAS (developed by NASA's Earth Resources Laboratory for analysis of Landsat images) for general purpose image processing. The MD Image System is an application of this technology to a medical system that aids in the diagnosis of cancer, and can accept, store and analyze images from other sources such as Magnetic Resonance Imaging.
Sequential Randomized Smoothing for Adversarially Robust Speech Recognition

While Automatic Speech Recognition has been shown to be vulnerable to adversarial attacks, defenses against these attacks are still lagging.Existing, naive defenses can be partially broken with an adaptive attack.In classification tasks, the Randomized Smoothing paradigm has been shown to be effective at defending models.However, it is difficult to apply this paradigm to ASR tasks, due to their complexity and the sequential nature of their outputs.Our paper overcomes some of these challenges by leveraging speech-specific tools like enhancement and ROVER voting to design an ASR model that is robust to perturbations.We apply adaptive versions of stateof-the-art attacks, such as the Imperceptible ASR attack, to our model, and show that our strongest defense is robust to all attacks that use inaudible noise, and can only be broken with very high distortion.
Exploiting Multi-scale Fusion, Spatial Attention and Patch Interaction Techniques for Text-Independent Writer Identification

Text independent writer identification is a challenging problem that differentiates between different handwriting styles to decide the author of the handwritten text. Earlier writer identification relied on handcrafted features to reveal pieces of differences between writers. Recent work with the advent of convolutional neural network, deep learning-based methods have evolved. In this paper, three different deep learning techniques - spatial attention mechanism, multi-scale feature fusion and patch-based CNN were proposed to effectively capture the difference between each writer’s handwriting. Our methods are based on the hypothesis that handwritten text images have specific spatial regions which are more unique to a writer’s style, multi-scale features propagate characteristic features with respect to individual writers and patch-based features give more general and robust representations that helps to discriminate handwriting from different writers. The proposed methods outperforms various state-of-the-art methodologies on word-level and page-level writer identification methods on three publicly available datasets - CVL, Firemaker, CERUG-EN datasets and give comparable performance on the IAM dataset.
SOME RESULTS AND ANALYZES FROM THE TEACHING OF ARTIFICIAL INTELLIGENCE IN HIGH SCHOOL

In the last few years, various schools in Bulgaria have introduced the study of elements of artificial intelligence as an innovation in specialized, professional and innovative classes, as well as in schools and clubs of interest. During this school year in Plovdiv district two schools introduced the study of artificial intelligence as an optional module in the profiled preparation of the profile „Software and Hardware Sciences“: High School „St. St. Cyril and Methodius „- Asenovgrad and High School“ Prof. Dr. Asen Zlatarov “- town of Parvomay. The training in these schools is conducted according to the curriculum approved by the Ministry of Education and Science, using the developed textbooks on the first topic „Solving problems through search“. The article presents some results and makes an analysis of the conducted training and the conducted test and survey. Conclusions and approaches for improving training are proposed.
Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

Age estimation models can be employed in many applications, including soft biometrics, content access control, targeted advertising, and many more. However, as some facial images are taken in unrestrained conditions, the quality relegates, which results in the loss of several essential ageing features. This study investigates how introducing a new layer of data processing based on a super-resolution generative adversarial network (SRGAN) model can influence the accuracy of age estimation by enhancing the quality of both the training and testing samples. Additionally, we introduce a novel convolutional neural network (CNN) classifier to distinguish between several age classes. We train one of our classifiers on a reconstructed version of the original dataset and compare its performance with an identical classifier trained on the original version of the same dataset. Our findings reveal that the classifier which trains on the reconstructed dataset produces better classification accuracy, opening the door for more research into building data-centric machine learning systems.
Information and communication technologies, their impact on the teaching-learning process

Information and communication technologies allow human beings to develop their usual activities in less time, especially allowing teachers to carry out innovative educational activities. The objective of this research is to analyze the technological tools and their impact on the teaching-learning process of the “Aurelio Salazar” Educational Unit. The qualitative method was applied to study the depth levels of the teaching process, the inductive-deductive method to be able to deduce from what was studied the behavior of this methodology and its incidence in education, the bibliographic review. As an innovation of the teacher, there is the adoption of new technologies as an educational tool to achieve the realization of activities, managing to develop new knowledge and significant experiences for the student. It was obtained as a result that the PBL is one of the methodologies most used by teachers, which allows them to specify activities and projects with the help of technological tools, allowing them to effectively develop the skills of students, acquiring experiences that allow them to solve problems in your community in a responsible way, always practicing ethical and moral values.
Academic prose across countries

While academic prose has been extensively investigated, there is a dearth of analyses across different countries. The present study addresses this research gap through an exploration of academic writing in ten countries (i.e. Great Britain, Hong Kong, India, Ireland, Jamaica, Kenya, New Zealand, the Philippines, Singapore, and Tanzania). To this end, we draw on the Humanities and Technology printed academic writing in International Corpus of English (ICE). The equivalence of texts in any category across ICE corpora has not been challenged to date, and this is the second gap - a methodological one - that is explored in the present chapter. Our study examines ten ICE corpora contrastively with a view to (1) identifying the lexical-semantic characteristics of the Humanities and Technology printed academic writing in each ICE corpus; and (2) checking the potential homogeneity of these two disciplinary domains across ICE corpora. Our research employs the key key word method: each text in one disciplinary domain in one ICE corpus was compared with all the other printed academic writing texts in the other three disciplinary domains in that same ICE corpus. The aim was to foreground the lexical indices which are specific of the focal disciplinary domain. Following their automatic identification, key key words were analysed qualitatively through concordance lines, and they were clustered into semantic groups. These groups were first considered on a country- by-country basis and then contrasted across the ten countries. The findings reveal not only what is characteristic of Humanities and Technology printed academic writing in each of the ten national ICE corpora but also whether these features are idiosyncratically found in one ICE corpus or dispersed across several of them. While, from the perspective of corpus design, there is an assumption of comparability among the texts which make up each of the four disciplinary domains in ICE corpora, our investigation shows that this does not seem to be the case. We hold that the availability of corpus documentation is therefore a must to allow for comparative studies across ICE corpora.
Application of the K-Means algorithm to determine poverty status in Hulu Sungai Tengah

Abstract Poverty is a condition of living in an inability to meet the minimum needs of life or basic needs. In Indonesia, poverty is one of the main problems that still need an optimal solution. Several government programs to address the problem of poverty have been carried out, but not infrequently the implementation is not right on target. The importance of this assistance is expected to improve the welfare of the community so it is very unfortunate if the assistance has not been right on target. This study aims to determine the status of poverty in Hulu Sungai Tengah Regency. By observing a problem above, it can be necessary to use a grouping method in determining poverty status. so that in this study using the cluster method, namely K-Means in clustering population data. Based on the results of data analysis using 353 head of family in the population data of HST Regency, it can be concluded that there are three poverty status clusters, namely low-level poverty (cluster 3) with a total of 130 head of family, medium-level poverty (cluster 2) with a total of 130 head of family. 111 head of family, and high poverty level (cluster 1) with a total of 112 head of family.
Towards post-quantum security for cyber-physical systems: Integrating PQC into industrial M2M communication1

The threat of a cryptographically relevant quantum computer contributes to an increasing interest in the field of post-quantum cryptography (PQC). Compared to existing research efforts regarding the integration of PQC into the Transport Layer Security (TLS) protocol, industrial communication protocols have so far been neglected. Since industrial cyber-physical systems (CPS) are typically deployed for decades, protection against such long-term threats is needed. In this work, we propose two novel solutions for the integration of post-quantum (PQ) primitives (digital signatures and key establishment) into the industrial protocol Open Platform Communications Unified Architecture (OPC UA): a hybrid solution combining conventional cryptography with PQC and a solution solely based on PQC. Both approaches provide mutual authentication between client and server and are realized with certificates fully compliant to the X.509 standard. We implement the two solutions and measure and evaluate their performance across three different security levels. All selected algorithms (Kyber, Dilithium, and Falcon) are candidates for standardization by the National Institute of Standards and Technology (NIST). We show that Falcon is a suitable option – especially – when using floating-point hardware provided by our ARM-based evaluation platform. Our proposed hybrid solution provides PQ security for early adopters but comes with additional performance and communication requirements. Our solution solely based on PQC shows superior performance across all evaluated security levels in terms of handshake duration compared to conventional OPC UA but comes at the cost of increased handshake sizes. In addition to our performance evaluation, we provide a proof of security in the symbolic model for our two PQC-based variants of OPC UA. For this proof, we use the cryptographic protocol verifier ProVerif and formally verify confidentiality and authentication properties of our quantum-resistant variants.
Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses.

3D human pose estimation from a single image is an inverse problem due to the inherent ambiguity of the missing depth. Several previous works addressed the inverse problem by generating multiple hypotheses. However, these works are strongly supervised and require ground truth 2D-to-3D correspondences which can be difficult to obtain. In this paper, we propose a weakly supervised deep generative network to address the inverse problem and circumvent the need for ground truth 2D-to-3D correspondences. To this end, we design our network to model a proposal distribution which we use to approximate the unknown multi-modal target posterior distribution. We achieve the approximation by minimizing the KL divergence between the proposal and target distributions, and this leads to a 2D reprojection error and a prior loss term that can be weakly supervised. Furthermore, we determine the most probable solution as the conditional mode of the samples using the mean-shift algorithm. We evaluate our method on three benchmark datasets -- Human3.6M, MPII and MPI-INF-3DHP. Experimental results show that our approach is capable of generating multiple feasible hypotheses and achieves state-of-the-art results compared to existing weakly supervised approaches. Our source code is available at the project website.