Reviewerly/exHarmony huggingface dataset:
Image Retrieval Using Local Majority Intensity Patterns

The rapidly growing use of huge image database is becoming possible with the growth of multimedia technologies. Content-based image retrieval (CBIR) is observed as an efficient method for carrying out its management and retrieval. This paper embellishes the benefit of the image retrieval system based on the information as well as key technologies. Compared to the shortcoming that only one feature of the conventional method can be used, this paper proposes a technique for image retrieval, by analyzing a vigorous component descriptor named local majority intensity patterns (LMIP) for texture image retrieval. LMIP is the referenced pixel dependent on the encompassing lion's share pixels' conduct included in the image. The proposed LMIP have utilized the wager dominant part of odd and even pixels individually. The exploratory results have demonstrated that the proposed LMIP descriptor has accomplished a superior acknowledgment precision than existing methods by consuming less computation time.
CNN-Based Obstacle Avoidance Using RGB-Depth Image Fusion

In the last few years, deep learning has attracted wide interest and achieved great success in many computer vision related applications, such as image classification, object detection, object tracking, pose estimation and action recognition. One specific application that can greatly benefit from the recent advance of deep learning is robot vision-based obstacle avoidance. Vision-based obstacle avoidance systems are mostly based on classification algorithms. Most of these algorithms use either color images or depth images as the main source of information. In this paper, the aim is to investigate whether using information extracted from both types of images simultaneously would give better performance than using each one separately. To do this, we chose the convolutional neural network (CNN) as the classifier and HSV-based method to achieve the fusion. We tested this approach using two widely used pre-trained CNN architectures, namely Resnet-50 and GoogLeNet using a dataset locally collected. The results indicate that the image fusion-based classification algorithm achieve a higher accuracy (91.3%) than the one based on depth images (80.4%) but lower than the one based on color images (93.7%). These results can be partly explained by the fact that the used classifiers were pre-trained using color image datasets.
QRDF: An efficient RDF graph processing system for fast query

Abstract With the rapid growth of RDF (resource description framework) data volume, it is challenging to answer queries over dynamic RDF graphs of which the data frequently changes (by insertion/deletion) over time. This article presents QRDF , an efficient RDF graph processing system that supports not only efficient storage but also fast query of dynamic RDF graphs. QRDF (i) adopts a red‐black (RB) tree for quick RDF data insertion/deletion and (ii) asynchronously moves data from the RB tree into a vector for efficient RDF query. QRDF performs early pruning on its index structure for various RDF query patterns. Experimental results show that the query performance of QRDF over RDF graphs is about 2x higher than that of the state‐of‐the‐art RDF processing systems.
Performance of WebGL standard for displaying 3D applications on mobile devices

The development of 3D applications for consumption in browsers involves the use of the WebGL standard, whose performance determines the user experience and portability for mobile devices. In this context, this paper proposes to analyze the performance of a 3D application that uses the WebGL standard to run in browsers, taking into account mobile devices. Initial results show that the use of compression formats, connection speeds and browsers influence the loading times, performance and build size of the project. Thus, the user experience can be acceptable or deplorable.
Application of Blockchain Technology in Oil and Gas Industry

Distributed ledger, also known as blockchain technology, or BT for short, is an Internet database technology, which is characterized by decentralization, openness and transparency, allowing everyone to keep records in a database. It is an emerging technology that has set off a wave of global investment after big data, cloud computing, artificial intelligence, etc. As the underlying technology of Bitcoin, blockchain technology is an Internet database technology which is essentially to record data and contracts along the time axis. This technology has already brought tremendous changes in finance, security and other fields, and is expected to do the same in the oil and gas industry. With oil and gas exploration and development undergoing digital transformation, blockchain technology is one of the most promising digital technologies.
Stable Median Centre Clustering for Unsupervised Domain Adaptation Person Re-Identification

The current unsupervised domain adaptation person re-identification (re-ID) method aims to solve the domain shift problem and applies prior knowledge learned from labelled data in the source domain to unlabelled data in the target domain for person re-ID. At present, the unsupervised domain adaptation person re-ID method based on pseudolabels has obtained state-of-the-art performance. This method obtains pseudolabels via a clustering algorithm and uses these pseudolabels to optimize a CNN model. Although it achieves optimal performance, the model cannot be further optimized due to the existence of noisy labels in the clustering process. In this paper, we propose a stable median centre clustering (SMCC) for the unsupervised domain adaptation person re-ID method. SMCC adaptively mines credible samples for optimization purposes and reduces the impact of label noise and outliers on training to improve the performance of the resulting model. In particular, we use the intracluster distance confidence measure of the sample and its K-reciprocal nearest neighbour cluster proportion in the clustering process to select credible samples and assign different weights according to the intracluster sample distance confidence of samples to measure the distances between different clusters, thereby making the clustering results more robust. The experiments show that our SMCC method can select credible and stable samples for training and improve performance of the unsupervised domain adaptation model. Our code is available at https://github.com/sunburst792/SMCC-method/tree/master.
On Lowering Merge Costs of an LSM Tree

In column stores, which ingest large amounts of data into multiple column groups, query performance deteriorates. Commercial column stores use log-structured merge (LSM) tree on projections to ingest data rapidly. LSM tree improves ingestion performance, but for column stores the sort-merge maintenance phase in an LSM tree is I/O-intensive, which slows concurrent queries and reduces overall throughput. In this paper, we present a simple heuristic approach to reduce the sorting and merging cost that arise when data is ingested in column stores. We demonstrate how a Min-Max heuristic can construct buckets and identify the level of sortedness in each range of data. Filled and relatively-sorted buckets are written out to disk; unfilled buckets are retained to achieve a better level of sortedness, thus avoiding the expensive sort-merge phase. We compare our Min-Max approach with LSM tree and production columnar stores using real and synthetic datasets.
Interaction designers’ perceptions of using motion-based full-body features

Movement-based full-body interactions are increasingly being used in the design of interactive spaces, computer-mediated environments, and virtual user experiences due to the development and availability of diverse sensing technologies. In this context, the role of interaction designers is to find systematic and predictable relationships between bodily actions and the corresponding responses from technology. Sensor-based interaction design relies on sensor data analysis and higher-level feature extraction to improve detection capabilities. However, understanding human movement to inform the design of motion-based interactions is not straightforward if the detection capabilities of interaction technologies are unknown. We aim at understanding the problems and opportunities that practitioners—regardless of their technical background—perceive in using different motion-based full-body features. To achieve this, we conducted four separate focus groups with experienced practitioners, with and without technical backgrounds. We used a framework for the analysis of focus group data in information systems research to identify content areas and draw conclusions. Our findings suggest that most interaction designers, regardless of their technical background, consider motion-based feature extraction to be challenging and time-consuming. However, participants acknowledge they might use designer-interpretable features as a potential tool to foster user behavior exploration. Understanding how practitioners link sensor-based interaction design with feature extraction technology is relevant to design computational tools and reduce the technical effort required from designers to characterize the user’s movement.
Brief Announcement

The popularity of permissioned blockchain systems demands BFT SMR protocols that are efficient under good network conditions (synchrony) and robust under bad network conditions (asynchrony). The state-of-the-art partially synchronous BFT SMR protocols provide optimal linear communication cost per decision under synchrony and good leaders, but lose liveness under asynchrony. On the other hand, the state-of-the-art asynchronous BFT SMR protocols are live even under asynchrony, but always pay quadratic cost even under synchrony. In this paper, we propose a BFT SMR protocol that achieves the best of both worlds -- optimal linear cost per decision under good networks and leaders, optimal quadratic cost per decision under bad networks, and remains always live.
The Application of Transformer Model Architecture for the Dependency Parsing Task

In this paper, authors discover the advantages of the attention-based neural network application to the natural language dependency-parsing task. The authors explain the architecture and show the results of comparison between attention-based neural network and long-short memory neural networks in relation to the dependency-parsing task.
Multi-touch Simulation System for Sand Painting

In this project we are aimed at developing a humanmachine interaction system of sand painting to make sand painting learning easily and to realize a more popular art form. Up to now, our sand painting system can completely achieve simulations of 10 types of common gesture techniques of sand painting. An all-in-one machine with multi-touch interaction is adopted for sand painting system to provide a better user experience. By selecting different sand painting simulating techniques and sand colors, users can carry out multi-touch interaction-based sand painting on the touch screen.
A Robust Document Identification Framework through f-BP Fingerprint

The identification of printed materials is a critical and challenging issue for security purposes, especially when it comes to documents such as banknotes, tickets, or rare collectable cards: eligible targets for ad hoc forgery. State-of-the-art methods require expensive and specific industrial equipment, while a low-cost, fast, and reliable solution for document identification is increasingly needed in many contexts. This paper presents a method to generate a robust fingerprint, by the extraction of translucent patterns from paper sheets, and exploiting the peculiarities of binary pattern descriptors. A final descriptor is generated by employing a block-based solution followed by principal component analysis (PCA), to reduce the overall data to be processed. To validate the robustness of the proposed method, a novel dataset was created and recognition tests were performed under both ideal and noisy conditions.
Matching Pragmatic Lithic Analysis and Proper Data Architecture

Abstract The documentation and analysis of archaeological lithics must navigate a basic tension between examining and recording data on individual artifacts or on aggregates of artifacts. This poses a challenge both for artifact processing and for database construction. We present here an R Shiny solution that enables lithic analysts to enter data for both individual artifacts and aggregates of artifacts while maintaining a robust yet flexible data structure. This takes the form of a browser-based database interface that uses R to query existing data and transform new data as necessary so that users entering data of varying resolutions still produce data structured around individual artifacts. We demonstrate the function and efficacy of this tool (termed the Queryable Artifact Recording Interface [QuARI]) using the example of the Stelida Naxos Archaeological Project (SNAP), which, focused on a Paleolithic and Mesolithic chert quarry, has necessarily confronted challenges of processing and analyzing large quantities of lithic material.
3D Reconstruction and Understanding of Indoor Scene Based on Single Image

Given a photo of a room, through our automated system, try to reconstruct a three-dimensional scene similar to the photo. Among them, the indoor objects come from the objects in the ShapeNet model library, which enables our system to produce a high-quality output result. The method we use is to use a full convolutional neural network to train images and compare indicators, use Faster-RCNN to perform multitasking object recognition and target detection, and then iteratively optimize the position and proportion of objects in the room, so that the scene renders a three-dimensional map Best match with input photo.
Requirement Elicitation and Prototype Development of an Intelligent Environment to Support People With Early Dementia

In an aging population, the prevalence of people with dementia is increasing. Intelligent environments can offer a solution to support people in the early stages of dementia, who often experience problems with maintaining their day-night rhythm. The focus of this work is developing an Intelligent Environment for assisting these people with their daily structure while living independently at home. Three co-creation methods with several stakeholder groups were used to elicit requirements and develop a prototype of an Intelligent Environment. Participants were involved in sessions using Mind Maps (N=55), Idea Generation Cards (N=35), and in a multidisciplinary design team with representatives of eight organizations. The result of these sessions was a set of 10 requirements for the Intelligent Environment on the following topics: context-awareness, pattern recognition, adaptation, support, personalization, autonomy, modularity, dementia proof interaction, costs, data and privacy. Based on these requirements, several scenarios were developed to explicate such an environment for supporting people with early dementia. The design team translated the requirements to a real-life prototype of an Intelligent Environment integrated into a demonstration home. The prototype serves as a basis for further development of Intelligent Environments to enable people with dementia to live longer independently at home.
Smart contracts based supply chain resource management system in the industrial internet

In order to address the inconvenience and opaqueness issues existing in the scenario of ordering resources for manufacturing companies, in this paper, we design a supply chain resource management system based on the blockchain technology. A deserving resource smart contract (DRSC) and a punishment smart contract (PSC) are developed to deal with the environment of transactions, and a resource allocation method is proposed to obtain an optimal strategy. Finally, simulation is carried out to verify the feasibility of the designed system and illustrate a final allocation strategy maximizing the overall profits.
Investigating the performance of multi-objective optimization when learning Bayesian Networks

Bayesian Networks have been widely used in the last decades in many fields, to describe statistical dependencies among random variables. In general, learning the structure of such models is a problem with considerable theoretical interest that poses many challenges. On the one hand, it is a well-known NP-complete problem, practically hardened by the huge search space of possible solutions. On the other hand, the phenomenon of I-equivalence, i.e., different graphical structures underpinning the same set of statistical dependencies, may lead to multimodal fitness landscapes further hindering maximum likelihood approaches to solve the task. Despite all these difficulties, greedy search methods based on a likelihood score coupled with a regularizator score to account for model complexity, have been shown to be surprisingly effective in practice. In this paper, we consider the formulation of the task of learning the structure of Bayesian Networks as an optimization problem based on a likelihood score, without complexity terms to regularize it. In particular, we exploit the NSGA-II multi-objective optimization procedure in order to explicitly account for both the likelihood of a solution and the number of selected arcs, by setting these as the two objective functions of the method. The aim of this work is to investigate the behavior of NSGA-II and analyse the quality of its solutions. We thus thoroughly examined the optimization results obtained on a wide set of simulated data, by considering both the goodness of the inferred solutions in terms of the objective functions values achieved, and by comparing the retrieved structures with the ground truth, i.e., the networks used to generate the target data. Our results show that NSGA-II can converge to solutions characterized by better likelihood and less arcs than classic approaches, although paradoxically characterized in many cases by a lower similarity with the target network.
FPGA Acceleration of Protein Back-Translation and Alignment

Identifying genome functionality changes our understanding of humans and helps us in disease diagnosis; as well as drug, bio-material, and genetic engineering of plants and animals. Comparing the structure of the protein sequences, when only sequence information is available, against a database with known functionality helps us to identify and recognize the functionality of the unknown sequence. The process of predicting the possible RNA sequence that a specific protein has originated from is called back-translation. Aligning the back-translated RNA sequence against the database locates the most similar sequences, which is used to predict the functionality of the unknown protein sequence. Providing massive parallelism, FPGAs can accelerate bioinformatics applications substantially. In this paper, we propose, FabP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> FabP is also the name of a family of proteins, “Fatty-Acid-Binding Proteins”., an optimized FPGA-based accelerator for aligning a back-translated protein sequence against a database of DNA/RNA sequences. FabP is deeply optimized to fully utilize the FPGA resources and the DRAM memory bandwidth to maximize the performance. FabP on a mid-range FPGA provides 8.1 % and 23.3× (24.8× and 266.8 ×) speedup and higher energy efficiency as compared to the GPU-based implementation on a high-end NVIDIA GPU (state-of-the-art CPU implementation), respectively.
Projections with Logarithmic Divergences

In information geometry, generalized exponential families and statistical manifolds with curvature are under active investigation in recent years. In this paper we consider the statistical manifold induced by a logarithmic $$L^{(\alpha )}$$ -divergence which generalizes the Bregman divergence. It is known that such a manifold is dually projectively flat with constant negative sectional curvature, and is closely related to the $$\mathcal {F}^{(\alpha )}$$ -family, a generalized exponential family introduced by the second author [16]. Our main result constructs a dual foliation of the statistical manifold, i.e., an orthogonal decomposition consisting of primal and dual autoparallel submanifolds. This decomposition, which can be naturally interpreted in terms of primal and dual projections with respect to the logarithmic divergence, extends the dual foliation of a dually flat manifold studied by Amari [1]. As an application, we formulate a new $$L^{(\alpha )}$$ -PCA problem which generalizes the exponential family PCA [5].
Herding an Adversarial Swarm in Three-dimensional Spaces

This paper presents a defense approach to safeguard a protected area against an attack by a swarm of adversarial agents in three-dimensional (3D) space. We extend our 2D `stringNet Herding' approach, in which a closed formation of string-barriers is established around the adversarial swarm to confine their motion and herd them to a safe area, to 3D spaces by introducing 3D-StringNet. 3D-StringNet is a closed 3D formation of triangular netlike barriers. We provide a systematic approach to generate three types of 3D formations that are used in the 3D herding process and modifications to the finite-time convergent control laws developed in our earlier work. Furthermore, for given initial positions of the defenders, we provide conditions on the initial positions of the attackers for which the defenders are guaranteed to gather as a specified formation at a position on the shortest path of the attackers to the protected area before attackers reach there. The approach is investigated in simulations.
The American Library Association: The landmarks of international library and bibliographic cooperation (1876–1976)

During the first 100 years of its existence, the American Library Association (ALA), the oldest national library association in the world, contributed much to the international library and bibliographic cooperation. The author describes the key areas of ALA international activity during the period under review. He identifies the following areas: support of building national library associations of other countries, projects and programs of international information exchange, promotion of basic democratic principles of libraries’ work, shared cataloguing, building the professional library education system, creation of the library periodicals and others. Based on the facts revealed in the periodicals, the author claims that national library associations are among the key instruments of library development both on the national and international levels. The fact that the American Library Association played a critical role in interlibrary relations strengthening during the described period evidences the association’s importance in the process of international library cooperation development.
A Decentralized Ontology Versioning Model Designed for Inter-operability and Multi-organizational Data Exchange

Current information systems operated in a multi-organization context use centralized or hierarchical data models proven inefficient as the network’s scale rapidly increases. The introduction of blockchain and decentralized specific mechanisms stimulate the use of ontology-based data models. While the adoption of blockchain as a communication medium facilitates the communication and data exchange in a more reliably and securely fashion, this opens new challenges regarding the evolution and versioning of the ontology. This paper proposes a versioning mechanism tailored to the decentralized environment. It provides the conceptual building blocks allowing an ontology to evolve under the supervision of a community of individuals and organizations proposing progressive refinements to the ontology elements. Also, it details the aggregation mechanism used to select the changes for the new version.