Reviewerly/exHarmony huggingface dataset:
An Upper Bound and Linear-Space Queries on the LZ-End Parsing

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)An Upper Bound and Linear-Space Queries on the LZ-End ParsingDominik Kempa and Barna SahaDominik Kempa and Barna Sahapp.2847 - 2866Chapter DOI:https://doi.org/10.1137/1.9781611977073.111PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Lempel–Ziv (LZ77) compression is the most commonly used lossless compression algorithm. The basic idea is to greedily break the input string into blocks (called “phrases”), every time forming as a phrase the longest prefix of the unprocessed part that has an earlier occurrence. In 2010, Kreft and Navarro introduced a variant of LZ77 called LZ-End, that additionally requires the previous occurrence of each phrase to end at the boundary of an already existing phrase. Due to its excellent practical performance as a compression algorithm and a compressed index, they conjectured that it achieves a compression that can be provably upper-bounded in terms of the LZ77 size. Despite the recent progress in understanding such relation for other compression algorithms (e.g., the run-length encoded Burrows–Wheeler transform), no such result is known for LZ-End. We prove that for any string of length n, the number ze of phrases in the LZ-End parsing satisfies , where z is the number of phrases in the LZ77 parsing. This puts LZ-End among the strongest dictionary compressors and solves a decade-old open problem of Kreft and Navarro. Using our techniques we also derive bounds for other variants of LZ-End and with respect to other compression measures. Our second contribution is a data structure that implements random access queries to the text in space and time. This is the first linear-size structure on LZ-End that efficiently implements such queries. All previous data structures either incur a logarithmic penalty in the space or have slow queries. We also show how to extend these techniques to support longest-common-extension (LCE) queries. 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
Application of the algebraic cryptanalysis method to the Kuznyechik encryption algorithm

This paper examines the application of the algebraic cryptanalysis method to the Kuznyechik encryption algorithm. The algebraic properties of reflections are studied, algorithms for the formation of a system of relative algebraic equations the formation of equations representing rounds are developed. When expressed using the proposed methods, it was found that the number of unknowns in the system of equations representing the 8-round Kuznyechik encryption algorithm is ≈2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">55</sup> , and the required memory size is ≃ 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">62</sup> .
Optimization Methods For Large-Scale Machine Learning

This paper mainly completes the binary classification of RCV1 text data set by logistic regression. Based on the established logistic regression model, the performance and characteristics of three numerical optimization algorithms–random gradient descent, Mini-Batch random gradient and L-BFGS are studied.
Toward Immersive Experience: Evaluation for Interactive Network Services

With the rapid advances of multimedia communications and networking technologies, immersive experience (IE) is gradually becoming a promising evaluation paradigm for interactive network services. In contrast with traditional quality of experience (QoE), which is generally applicable to regular audio/video services, IE is able to more precisely portray the extreme and vivid scene experiences brought by emerging interactive network services. To this end, this work first studies the differences between IE and traditional QoE from both theoretical and technical perspectives, including definitions, applications, and technical challenges. Then, we explore and classify influencing factors (IFs) on IE into various categories, which are the crucial basis of IE evaluation and management. Finally, to fulfill the timely IE evaluation under massive high-dimensional data, two lightweight evaluation approaches are proposed by exploiting artificial intelligence technology and exploring the mathematical relationship, respectively.
Performance modeling for I/O‐intensive applications on virtual machines

Abstract Models for virtual machines running on cloud computing systems. Modeling system behaviors of clouds is a grand challenge because the resource utilization in VMs is heterogeneous due to variability in workload conditions. We address this challenging issue by uniquely (1) objectifying the usage prediction of virtualized resources and (2) predicting the performance trends of programs running on clouds. At the heart of the modeling system, we pay particular attention to CPU cores, disk size, main memory space, and input data volume, which serve as important factors for the developed prediction module. We devise two resource‐utilization prediction algorithms driven by two distinctive sets of I/O and CPU intensive benchmarks, where one algorithm deals with execution time and the other one revolves around input data size. We investigate the correlation between CPU/disk utilization and VM live migrations. Our system aims at not only providing performance optimization for virtualized resources but also ensuring service level agreement (SLA) and Quality of Service (QoS). The model fits the curve quite well, thereby advocating for the efficiency of the algorithm. The case studies conducted in this project draw the comparisons between the performance of striped and monolithic disks as well as bringing forth the problem of cache coherence that causes hindrance to the experiment. We also deal with the cache‐coherence problem to improve the accuracy of our prediction algorithms
SGCNAX: A Scalable Graph Convolutional Neural Network Accelerator with Workload Balancing

Graph Convolutional Neural Networks (GCNs) have emerged as promising tools for graph-based machine learning applications. Given that GCNs are both compute- and memory-intensive, this constitutes a major challenge for the underlying hardware to efficiently process large-scale GCNs. In this paper, we introduce SGCNAX, a scalable GCN accelerator architecture for the high-performance and energy-efficient acceleration of GCNs. Unlike prior GCN accelerators that either employ limited loop optimization techniques, or determine the design variables based on random sampling, we systematically explore the loop optimization techniques for GCN acceleration and propose a flexible GCN dataflow that adapts to different GCN configurations to achieve optimal efficiency. We further propose two hardware-based techniques to address the workload imbalance problem caused by the unbalanced distribution of zeros in GCNs. Specifically, SGCNAX exploits an outer-product-based computation architecture that mitigates the intra-PE (Processing Elements) workload imbalance, and employs a group-and-shuffle approach to mitigate the inter-PE workload imbalance. Simulation results show that SGCNAX performs 9.2x, 1.6x and 1.2x better, and reduces DRAM accesses by a factor of 9.7x, 2.9x and 1.2x compared to HyGCN, AWB-GCN, and GCNAX, respectively.
Development of dual-arm mobile robot platform based on ROS

<ns4:p><ns4:bold>Background: </ns4:bold>With the increasing demand of mobile robots in warehousing, logistics and service fields, simple planar motion is difficult to meet the task requirements of complex environment. The combination of mobile robot and cooperative robot is helpful to improve the dexterity of robot movement and expand the application of robots.</ns4:p><ns4:p> <ns4:bold>Methods:</ns4:bold> Aiming at the application requirements of dual-arm robots and mobile robots in practical applications, this paper designed the hardware of a platform, built a simulation platform based on ROS (Robot Operating System), and designed the actual software control framework. Finally, the feasibility of the platform design was verified by the coupling motion experiment of the two robots.</ns4:p><ns4:p> <ns4:bold>Results:</ns4:bold> We have established a simulation of the dual-arm mobile platform in ROS, designed the actual software control framework, and verified the feasibility of the platform design through experiments.</ns4:p><ns4:p> <ns4:bold>Conclusions:</ns4:bold> The mobile platform can meet a variety of application requirements and lay the foundation for subsequent development.</ns4:p>
An Overview of Immersive Data Visualisation Methods Using Type by Task Taxonomy

The use of virtual reality (VR) technology in data visualisation and analytics introduces a new way for users to explore and understand the data through Immersive Analytics (IA). However, previous works evaluated the IA based on the traditional 2D visualisation tasks and thus reduced the VR benefits. The IA is also still in its infancy and lacks standard design guidelines for user tasks and evaluation. Hence, this paper aims to review the information exploration tasks in IA. This work used the systematic literature review methodology to search and analyse the literature, where there were eighteen studies identified for the systematic mapping based on type by task taxonomy. The timestep task is an additional user task proposed to support the IA function. The studies found that the IA supports various data visualisation fields and spatial tasks. Lastly, the limitation of this paper is discussed to identify potential future studies.
Adaptive Dense Ensemble Model for Text Classification

Text classification has been widely explored in natural language processing. In this article, we propose a novel adaptive dense ensemble model (AdaDEM) for text classification, which includes local ensemble stage (LES) and global dense ensemble stage (GDES). To strengthen the classification ability and robustness of the enhanced layer, we propose a selective ensemble model based on enhanced attention convolutional neural networks (EnCNNs). To increase the diversity of the ensemble system, these EnCNNs are generated by using two manners: 1) different sample subsets and 2) different granularity kernels. Then, an evaluation criterion that considers both accuracy and diversity is proposed in LES to obtain effective integration results. Furthermore, to make better use of information flow, we develop an adaptive dense ensemble structure with multiple enhanced layers in GDES to mitigate the issue that there may be redundant or invalid enhanced layers in the cascade structure. We conducted extensive experiments against state-of-the-art methods on multiple real-world datasets, including long and short texts, which has verified the effectiveness and generality of our method.
Public Key Encryption with Authorized Equality Test on Outsourced Ciphertexts for Cloud-Assisted IoT in Dual Server Model

In cloud computing, the outsourced data face many privacy and security threats. To allow the cloud server to perform comparison, search, and classification on outsourced ciphertexts while simultaneously providing privacy guarantee, the encryption method that supports the ciphertext equality test is considered as a promising way. Users are able to authorize the cloud server to conduct the ciphertext equality test, so that two ciphertexts can be determined whether they encrypt the same message without being decrypted. In this process, users do not need to retrieve, decrypt, and then perform comparison on data; thus, the computing and communication efficiency can be greatly improved, and the privacy of user data can be guaranteed at the cloud server side. However, existing encryption schemes supporting authorized ciphertext equality test in the single server model cannot resist the keyword guessing attacks, and the solutions in the dual server model do not provide simultaneous authorization on two servers. To address these issues, this paper proposes a public key encryption scheme supporting authorized equality test on ciphertexts in the dual server model (PKE-AUT), where the primary server and secondary server must get the authorization from users before performing a sequential equality test on ciphertexts. Security and performance analysis demonstrate that the proposed PKE-AUT scheme not only guarantees the privacy of user data and authorization but also is practical in cloud-assisted IoT-related applications.
Padrões de metadados no arquivamento da Web

Introduction:Digital preservation in Web archiving will only be possible with the effective use of metadata standards. These standards are the ones that determine the persistence, consistency, comprehensibility, the access,and representation of selected sites, collected and stored in Web archives, besides defining the archivability of sites and the interoperability among systems. Objective:In this context, the objective of the article was to identify and define which metadata standards could be judged by memory institutions and universities so that they could enable digital preservation in Web archives.Methodology:For this, a qualitative, exploratory,and descriptive research was done, using the bibliographic method from a non-systematic inventory together with a review and analysis of the literature content. The Dublin Core, MODS, EAD, VRA Core, PREMIS, and METS standards were selected and analyzed. Results and Conclusion:The analysis of the results indicates that Dublin Core, MODS, EAD, and VRA Core supported METS and PREMIS in detecting and documenting technical aspects of sites and proving their authenticity, context, and origin. METS can manage archived sites by acting as OAIS information packages, while Dublin Core proved to be an exponent for Web archiving through its use in remarkable area initiatives.
Automatic Traffic Light Detection for Self-Driving Cars Using Transfer Learning

Self-driving vehicles are being tested to make them more road-ready and safer for a real traffic environment. Automobile giants: like Tesla, Waymo, Toyota, etc., are working exhaustively to cater to the needs of futuristic smart vehicles using deep learning methodologies. For a self-driving car to avoid collision situation, it must be able to accurately detect and classify traffic lights. After performing exhaustive experiments, we chose to compare the feature extraction capabilities of various pretrained CNN-based transfer learning models like VGG16, ResNet50, AlexNet, DenseNet121, InceptionV3, and Xception on freely available Lara & Lisa traffic light datasets. We segregated the Lisa traffic light dataset into day and night subsets and then manually separated the images into various traffic light classes like dayRed, dayYellow, nightYellow, nightGreen, road and traffic lights, LeftGreenArrow, and RightGreenArrow etc. We used the random forest classifier to identify the color of the detected traffic light. DenseNet121 achieved a top accuracy of 100% on the Lara traffic light dataset and the maximum accuracy of 99.88% on the Lisa Day-light dataset and 98.89% accuracy on the Lisa Night-light dataset.
Robust query processing for linked data fragments

Linked Data Fragments (LDFs) refer to interfaces that allow for publishing and querying Knowledge Graphs on the Web. These interfaces primarily differ in their expressivity and allow for exploring different trade-offs when balancing the workload between clients and servers in decentralized SPARQL query processing. To devise efficient query plans, clients typically rely on heuristics that leverage the metadata provided by the LDF interface, since obtaining fine-grained statistics from remote sources is a challenging task. However, these heuristics are prone to potential estimation errors based on the metadata which can lead to inefficient query executions with a high number of requests, large amounts of data transferred, and, consequently, excessive execution times. In this work, we investigate robust query processing techniques for Linked Data Fragment clients to address these challenges. We first focus on robust plan selection by proposing CROP, a query plan optimizer that explores the cost and robustness of alternative query plans. Then, we address robust query execution by proposing a new class of adaptive operators: Polymorphic Join Operators. These operators adapt their join strategy in response to possible cardinality estimation errors. The results of our first experimental study show that CROP outperforms state-of-the-art clients by exploring alternative plans based on their cost and robustness. In our second experimental study, we investigate how different planning approaches can benefit from polymorphic join operators and find that they enable more efficient query execution in the majority of cases.
AraBERT transformer model for Arabic comments and reviews analysis

Arabic language is rich and complex in terms of word morphology compared to other Latin languages. Recently, natural language processing (NLP) field emerges with many researches targeting Arabic language understanding (ALU). In this context, this work presents our developed approach based on the Arabic bidirectional encoder representations from transformers (AraBERT) model where the main required steps are presented in detail. We started by the input text pre-processing, which is, then, segmented using the Farasa segmentation technique. In the next step, the AraBERT model is implemented with the pertinent parameters. The performance of our approach has been evaluated using the ARev dataset which contains more than 40,000 comments-remarks records relate to the tourism sector such as hotel reviews, restaurant reviews and others. Moreover, the obtained results are deeply compared with other relevant states of the art methods, and it shows the competitiveness of our approach that gives important results that can serve as a guide for further improvements in this field.
Recognition of handwritten characters using deep convolution neural network

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2021 Impact Factor: 0.682The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
Question answering with deep neural networks for semi-structured heterogeneous genealogical knowledge graphs

With the rising popularity of user-generated genealogical family trees, new genealogical information systems have been developed. State-of-the-art natural question answering algorithms use deep neural network (DNN) architecture based on self-attention networks. However, some of these models use sequence-based inputs and are not suitable to work with graph-based structure, while graph-based DNN models rely on high levels of comprehensiveness of knowledge graphs that is nonexistent in the genealogical domain. Moreover, these supervised DNN models require training datasets that are absent in the genealogical domain. This study proposes an end-to-end approach for question answering using genealogical family trees by: (1) representing genealogical data as knowledge graphs, (2) converting them to texts, (3) combining them with unstructured texts, and (4) training a transformer-based question answering model. To evaluate the need for a dedicated approach, a comparison between the fine-tuned model (Uncle-BERT) trained on the auto-generated genealogical dataset and state-of-the-art question-answering models was performed. The findings indicate that there are significant differences between answering genealogical questions and open-domain questions. Moreover, the proposed methodology reduces complexity while increasing accuracy and may have practical implications for genealogical research and real-world projects, making genealogical data accessible to experts as well as the general public.
Sea-Surface Target Visual Tracking with a Multi-Camera Cooperation Approach

Cameras are widely used in the detection and tracking of moving targets. Compared to target visual tracking using a single camera, cooperative tracking based on multiple cameras has advantages including wider visual field, higher tracking reliability, higher precision of target positioning and higher possibility of multiple-target visual tracking. With vast ocean and sea surfaces, it is a challenge using multiple cameras to work together to achieve specific target tracking and detection, and it will have a wide range of application prospects. According to the characteristics of sea-surface moving targets and visual images, this study proposed and designed a sea-surface moving-target visual detection and tracking system with a multi-camera cooperation approach. In the system, the technologies of moving target detection, tracking, and matching are studied, and the strategy to coordinate multi-camera cooperation is proposed. The comprehensive experiments of cooperative sea-surface moving-target visual tracking show that the method used in this study has improved performance compared with contrapositive methods, and the proposed system can meet the needs of multi-camera cooperative visual tracking of moving targets on the sea surface.
Pre-Demosaic Graph-Based Light Field Image Compression

An unfocused plenoptic light field (LF) camera places an array of microlenses in front of an image sensor in order to separately capture different directional rays arriving at an image pixel. Using a conventional Bayer pattern, data captured at each pixel is a single color component (R, G or B). The sensed data then undergoes demosaicking (interpolation of RGB components per pixel) and conversion to an array of sub-aperture images (SAIs). In this paper, we propose a new LF image coding scheme based on graph lifting transform (GLT), where the acquired sensor data are coded in the original captured form without pre-processing. Specifically, we directly map raw sensed color data to the SAIs, resulting in sparsely distributed color pixels on 2D grids, and perform demosaicking at the receiver after decoding. To exploit spatial correlation among the sparse pixels, we propose a novel intra-prediction scheme, where the prediction kernel is determined according to the local gradient estimated from already coded neighboring pixel blocks. We then connect the pixels by forming a graph, modeling the prediction residuals statistically as a Gaussian Markov Random Field (GMRF). The optimal edge weights are computed via a graph learning method using a set of training SAIs. The residual data is encoded via low-complexity GLT. Experiments show that at high PSNRs-important for archiving and instant storage scenarios-our method outperformed significantly a conventional light field image coding scheme with demosaicking followed by High Efficiency Video Coding (HEVC).
Democratizing Data-Driven Agriculture Using Affordable Hardware

The world needs to sustainably grow more food to feed the growing population of the planet.Data-driven agriculture is a promising technique that can help farmers be more productive, reduce costs, and enable adoption of sustainable agricultural practices.However, the adoption of data-driven agriculture is limited by a lack of affordable technologies, for broadband, sensing, imaging, and insights.In this article, we present an overview of Project FarmBeats, a research project that started in 2014 to increase the adoption of data-driven agricultural practices.We provide an overview of the various components of the FarmBeats architecture, and details of the hardware innovations.