Reviewerly/exHarmony huggingface dataset:
Measuring Multimedia Content Proximity via Artificial Intelligence Methods

The paper describes existing and novel approaches to word sequence similarity measuring which uses image processing procedures to reveal proximity relations between words and their meaning. This is implemented due to the fact the each word sequence can be associated with a particular pictorial domain, and thus any textual information can be potentially presented via an image series. Theoretical results of rule based approach, genetic algorithms and neural networks application are analyzed in terms of solving the problem under study. The proposed information processing technology may be useful for automatic translation applications, known as CAT-tools by now, search engine optimization, etc. The proposed content analysis techniques are designed to boost translation efficiency in particular. Romano-germanic and east-slavonic language pairs have been used as an example of practical application. It aids translation experts in finding the best solution for a specific domain that he (or she) is usually unfamiliar with. Thus, eliminating human errors and decreasing time needed for contextual search and deep examining of the topic.
Research on payload distribution of UAV formation with constraints

In order to solve the problem of the distribution of limited combat power and payload in the multi-tasking cooperative scenario of the UAV formation, this paper proposes a decision method based on the NSGA-III algorithm. First, the NSGA-III algorithm is combined with the penalty function to solve the multi-objective optimization problem with constraints. Then, build a capability evaluation system for mission formations, and build a multi-objective optimization model with constraints for multi-tasking collaborative scenarios. Finally, the improved NSGA-III algorithm with the penalty function is utilized to solve the constrained multi-objective optimization problem. This method can propose a variety of non-inferior deployment schemes for multi-task collaborative scenarios with limited payload resources in a short time, effectively reduce the formulation time of schemes and improve the performance of tasks.
Game Description Logic with Integers: A GDL Numerical Extension

Many problems can be viewed as games, where one or more agents try to ensure that certain objectives hold no matter the behavior from the environment and other agents. In recent years, a number of logical formalisms have been proposed for specifying games among which the Game Description Language (GDL) was established as the official language for General Game Playing. Although numbers are recurring in games, the description of games with numerical features in GDL requires the enumeration from all possible numeric values and the relation among them. Thereby, in this paper, we introduce the Game Description Logic with Integers (GDLZ) to describe games with numerical variables, numerical parameters, as well as to perform numerical comparisons. We compare our approach with GDL and show that when describing the same game, GDLZ is more compact.
Fast Trilateral Filtering for Video Denoising

Video denoising is an important application of multimedia signal processing to enhance the interpretability of a noisy video sequence. Though several spatial and spatio-temporal filtering techniques are proposed in the literature to perform the task, these existing techniques are typically slow and fail to process video data in real-time. In this work, we present a video denoising technique in the form of a novel trilateral filter. The proposed trilateral filter is an extension of spatial bilateral filter considering the input video sequence as a 3-Dimensional spatiotemporal cube. The proposed nonlinear filtering technique smooths an image along with the preservation of edges and requires a lesser number of iterations for the process. This algorithm uses Fourier bases, the span of the input, and a local dynamic range to compute the trilateral filter output of a given input video. The approximation of the Gaussian range kernels is made by using raised cosines in the present work, however, the proposed algorithm supports any continuous kernel as a range kernel for the processing. The computation is relatively fast as compared to naive trilateral filtering and bilateral filtering, as a lesser number of iterations are involved.
Mobile App Third-Party Library traffic discovery method based on short text classification and clustering

Third-party libraries are used more and more in mobile applications, and their threats to mobile application security and privacy are increasing. Therefore, the identification of third-party library traffic is becoming more and more important for network security. The work on third-party libraries mainly focuses on the identification of third-party libraries and their security analysis. At present, there is very little work directly related to the identification of third-party library traffic. Based on the semi-supervised identification method of mobile application traffic based on short text classification and clustering, this paper proposes a third-party library traffic identification method using the information inside the cluster generated after clustering. Experimental results show that this method has a good effect on the identification of third-party library traffic.
Optimization of Fuzzy Membership Function based on the NCOS function method

This paper investigates the parameter optimization problem of membership functions for fuzzy-model-based control under imperfect premise matching. A novel frequency domain algorithm which can clearly relate the membership function parameters to the targeted control performance is developed, and consequently an optimization approach to the membership function parameters is then established based on the resulting nonlinear characteristic output spectrum function (nCOS). Compared to traditional search-based optimization approach, this method can give a more detailed result with less time consuming and an in-depth understanding of nonlinear influence rather than just optimal results. With this novel method, performance of the fuzzy-model-based controller is further enhanced. Finally, the fuzzy membership functions optimization method is applied to nonlinear systems to obtain improved system performance.
Utility Pattern Mining Based on the Rule of Utility Leverage

Utility pattern mining is one of the important problem in the field of data mining. However, the current utility mining techniques is to discover high utility patterns, that is, the pattern whose utility exceeds the user-specified threshold, but does not further explore the correlation between different patterns. Therefore, this paper introduces the concept of confidence and lift in high-utility pattern mining, and proposes the conception of Utility Leverage Pattern. In order to effectively discover relevant profitable itemsets, an algorithm called ULPM (Utility Leverage pattern mining) for mining utility pattern based on the utility leverage rule is proposed. The experimental results show that the ULPM algorithm is efficient in mining utility leverage patterns.
Toward an Ensemble of Object Detectors

The field of object detection has witnessed great strides in recent years. With the wave of deep neural networks (DNN), many breakthroughs have achieved for the problems of object detection which previously were thought to be difficult. However, there exists a limitation with DNN-based approaches as some architectures are only suitable for particular types of object. Thus it would be desirable to combine the strengths of different methods to handle objects in different contexts. In this study, we propose an ensemble of object detectors in which individual detectors are adaptively combine for the collaborated decision. The combination is conducted on the outputs of detectors including the predicted label and location for each object. We proposed a detector selection method to select the suitable detectors and a weighted-based combining method to combine the predicted locations of selected detectors. The parameters of these methods are optimized by using Particle Swarm Optimization in order to maximize mean Average Precision (mAP) metric. Experiments conducted on VOC2007 dataset with six object detectors show that our ensemble method is better than each single detector.
Error Heuristic Based Text-Only Error Correction Method for Automatic Speech Recognition

With the fast development of deep learning, automatic speech recognition (ASR) has achieved significant improvement. However, there still exists some errors in ASR transcriptions. They will greatly interfere with the downstream tasks, which take the transcribed text as source data. Obviously, it is necessary to set a corrector to reduce errors in the ASR transcriptions. For various downstream tasks, a text-only based corrector would be more adorable because of its minimal requirements for recognizer. However, the limitation of ASR decoding information exerts considerable influence on the performance of corrector. Correcting a correctly recognized word into a wrong one is one of the most common problems. To relieve this situation, we propose to adopt error knowledge from an error detection model as a heuristic to train a sequence-to-sequence (Seq2seq) correction model by transfer learning. In this way, the corrector can focus on correcting the wrong words with a soft label. The experiment shows our method can effectively correct ASR errors, with a 4.35% word error rate (WER) reduction for the transcription. It outperforms the state of the art Seq2seq baseline with a 1.27% WER reduction.
Robust Neural Networks Learning via a Minimization of Stochastic Output Sensitivity

this article, we propose Sensitivity Minimization Learning (SML) to overcome the performance degradation problem caused by features corruption at the testing phase by using the stochastic sensitivity measure (STSM) as a regularizer. The STSM measures output deviations between each training sample and its noisy versions generated by a feature perturbation strategy. The feature perturbation strategy is user defined to simulate noises that our model target to defense at the testing phase. The SML is computational efficiency in training and testing phase and minimizes the generalization error on both training samples and its noise version set with feature perturbations. Models regularized by the STSM can be trained by the stochastic gradient descent algorithm efficiently and applied to very large scale applications. Experiment results on eight grayscale image, one color image, and two face databases show that the SML significantly outperforms several regularization techniques and yields much lower classification error when testing sets are contaminated with noises.
A conversation about linked data in the library and publishing ecosystem

During the inaugural 2020 NISO+ conference, the “Ask the Experts about… Linked Data” panel included discussion of the transition of library metadata from legacy, record-based models to linked data structures. Panelists John Chapman (OCLC, Inc.) and Philip Schreur (Stanford University) were the speakers; NISO Board of Directors member Mary Sauer-Games (OCLC, Inc.) was the facilitator. The event was an open-ended conversation, with topics driven by questions and comments from the audience.
Gesture Recognition Method with Illumination Robustness for Wheelchairs

Abstract Disabled elderly people have to rely on wheelchair life. And the wheelchairs are used in daily life with different illumination conditions. This paper proposes a hand gesture recognition method to control wheelchairs based on dynamic colour clustering. As dynamic clustering skin detection algorithm is used, the gesture recognition accuracy is improved. Using skin detection, arm removal and polygon fitting methods, the gesture images collected by a Kinect V2, are recognized as control commands to drive the wheelchair. Experiments show that the proposed method can be applied to different illumination conditions with better recognition accuracy.
Identifying Key Factors that Inform Project Delivery Method Selection in the Water Industry

It is vital for water utilities to select the most appropriate project delivery method for the design and construction of their infrastructure projects. Alternative project delivery methods (APDM), such as construction management at-risk (CMAR) and design-build (DB), are now being added to design-bid-build (DBB) as part of a more comprehensive project delivery method toolbox for these utilities. The objective of this study is to identify key project delivery method selection factors for the water industry to support utilities in selecting the most suitable delivery method for their treatment plant projects. The authors reviewed about 40 relevant academic research papers and 15 decision-models that highlighted key project delivery method selection factors. As a result, 44 selection factors were identified and ranked in terms of frequency. An industry workshop was conducted with 20 industry experts and resulted in the identification of 13 key selection factors, seven primary factors, and six secondary factors, that inform project delivery method selection for the water industry. The identified factors in this paper will support the development of a decision-support tool that will assist water utilities with their project delivery method selection decisions.
Automated construction technology of the government agencies knowledge graph based on the topical crawler

Abstract Government agencies are the skeleton of the knowledge graph in the field of government affairs; it is the starting and ending point of various government affairs. This paper proposes an automated construction technology of the government agencies knowledge graph based on the topical crawler, which collects government and department information from relevant government websites and constructs the knowledge graph automatically. Sampling verification shows that the knowledge precision of this government agencies knowledge graph exceeds 96%, and it covers nearly 99% of municipal government information and more than 92% of government department information across the country. Furthermore, through the tracking of government websites, the knowledge graph of government agencies can be automatically updated. As far as our understanding is concerned, this is the first time to explore the technology of automated construction of knowledge graph in the related fields of government affairs combined with the collection system of the web crawler.
WITHDRAWN: Image recognition of ice and snowsports based on FPGA and neural network

Abstract The natural, social, and cultural background of the development of the ice and snow sports industry. Ice hockey is a major winter sport played in the western countries, m mainly in one of the coldest countries in the globally. With increasing popularity in Western countries, good pass rates and ice hockey have been used as inspiration in the entertainment industry. In the previous method Image Recognition-based Ice and Snow Sports for Machine learning and IoT (Internet of thing). The previous method is difficulty in software management, large complexity of network structure. So in the proposed method Image Recognition of Ice and Snow Sports Based on FPGA (Field Programmable Gate Arrays) and Neural Network. Image processing is the process of converting an image into a digital format and obtaining a better image or performing certain operations on it to gather some useful information. It is a type of signal distribution that can be an image for pattern recognition using (Field Programmable Gate Arrays) based upon input image, output image, or its related features such as video frames or photos. Image recognition can be defined as data classification based on already acquired knowledge and models and/or statistical information gathered from its representation. One of the key elements of model recognition is its applicability. The system and adjust it to provide accurate results (An application-specific integrated circuit). Training depends on the algorithm used for the data, which is an important step in how many operations the system performs on the data provided to the system.
A Blacklisting Smart Contract

The traditional financial system has the ability to facilitate forensic investigations into crimes relating to financial fraud. The same cannot be said about the blockchain-based financial system that has come to exist over the past few years. This financial system does not tolerate third parties impeding transactions and so presents the problem of not being able to facilitate investigations. The concept of a blacklist is considered as a potential solution to this issue, specifically, a blockchain-centric blacklist. This paper describes the implementation and evaluation of such a blacklist as a potential solution for facilitating investigations involving blockchain-based financial systems.
Research on Robot Path Planning Method Based on Tangent Intersection Method

Aiming at the path planning problem of robots in complex field environment, this paper proposes a new method of local path planning-tangent intersection method, and the selected object is a foot robot. Firstly, the environmental information is described by constructing a new obstacle expression model; secondly, a visual tangent is constructed on this model to form an intersection network; finally, an evaluation function is used to construct a safe passage with minimum cost from the intersection network, and the simulation experiment results show that the use of the robot moving path planned by the method has high efficiency and good safety.
A Strategy for the Linear Time Implementation of the SMVQ Algorithm

This paper proposes a linear time implementation of the Side Match Vector Quantization (SMVQ) algorithm. It maps the side match distortion values between codewords and a boundary vector onto an order-preserving integer sequence. Hence the SMVQ algorithm’s state codebook generation step can exploit any integer sorting algorithm to improve its overall runtime complexity. The proposed SMVQ implementations utilize the bucket sort algorithm to demonstrate this concept. The experimental results show a percentage change of −84.26% for N = 1024 (46.052–7.247 s) and −94.82% for N = 4096 (998.355–51.710 s) in the average execution time of the SMVQ algorithm.
Generation of quasi-rectangle-states of the vibrational motion of an ion

We show how to generate quasi-rectangle-states of the vibrational motion of an ion, this is, states that have the same probability in a given position interval. We produce those states by repeated ion-laser interactions followed by conditional measurements that generate a superposition of squeezed states. The squeeze parameter of the initial state may be tuned in order to obtain such highly localized rectangle states.
Development of functional fault-tolerant system

Abstract Before you begin to develop a fault-tolerant system, you should specify the system requirements. Besides high fault tolerance and performance, the computing system shall be highly available. Such systems include NUMA systems with non-uniform memory access, SMP systems with symmetrical multiprocessing and MPP mass-parallel systems. The ultimate choice of architecture largely depends on whether maximum performance, scalability, or fault tolerance and availability are needed.
A Color Adjustment Method for HDR Display of Video Content Received Over Wireless Multimedia Networks

Bandwidth limitations in wireless networks may be prohibitive for transmitting High Dynamic Range (HDR) video content to end users to take advantage of the capabilities of HDR displays. Instead, the Standard Dynamic Range (SDR) version of the content may be transmitted, which is inverse tone mapped to the visually rich HDR format at the receiver end. One of the challenges in this approach is that the mapping process causes color shifts. Failing to address this color change, degrades the overall visual quality of the generated HDR video. In this paper, we propose a perception-based color adjustment method that is capable of preserving the hue of colors and produces HDR colors that closely follow their SDR counterparts, while causing negligible luminance change. Performance evaluations show that our method outperforms existing state-of-the-art color adjustment methods.
A Blockchain and Defensive Deception Co-design for Webcam Spyware Detection

Recently there have been rapid developments in the progression of advanced malware attacks, requiring the need for robust malware detection methods. We show that the integration of access control with defensive deception is more efficient than approaches from those categories of tools running alone, since malware operate under the heavy influence of deception mechanisms while trying to evade access control. We facilitate the identification of second-order webcam spyware through an approach that is larger than ever in terms of behavior. We propose to integrate defensive deception mechanisms in a series of interactions that lead to the use of a decoy I/O device and decoy server, in this case, a decoy smart contract and decoy key management server case study. Our approach combines blockchain concepts, such as smart contracts, with key management methods, to create confusion in malware's operations to benefit their detection.
Task-Guided Context-Path Embedding in Temporal Heterogeneous Networks

Network embedding maps the nodes of a network to a continuous vector space, which can then be used as the input to downstream tasks, such as node classification, node clustering, link prediction, and similarity search. To learn network embedding more effectively, many technologies adopt the approach of random walk to obtain the network structure. As the meta-path of heterogeneous networks emerges, network embedding will be equipped with more semantic interpretation. Consequently, various random walks, based on meta-path strategies, have been proposed for network embedding. However, the combination of semantic and structure in a heterogeneous network cannot achieve ideal results. To overcome this challenge, we start from a task-guided issue by combining the timestamps information in the heterogeneous network, and then employing the method of temporal segmentation to decompose the network into a continuous temporal sequence. Finally, the set of context-paths between nodes is calculated in a continuous vector by the depth-first meta-path search algorithm. More precisely, we propose a Temporal Sliding Density Walk (TSDW) algorithm by combining network semantics and structure effectively. Empirical results for network data show that TSDW could significantly outperform the state-of-the-art representation learning models, including DeepWalk, LINE, Node2vec, PTE, Meapath2vec, HIN2vec, HTNE, and CTDNE by 3.02% to 44.9% of Macro-F1, 0.9% to 18.92% of Micro-F1 in multi-class node classification and 21% to 47% of NMI in node clustering.