Reviewerly/exHarmony huggingface dataset:
A Document Clustering Approach Using Shared Nearest Neighbour Affinity, TF-IDF and Angular Similarity

Quantum of data is increasing in an exponential order. Clustering is a major task in many text mining applications. Organizing text documents automatically, extracting topics from documents, retrieval of information and information filtering are considered as the applications of clustering. This task reveals identical patterns from a collection of documents. Understanding of the documents, representation of them and categorization of documents require various techniques. Text clustering process requires both natural language processing and machine learning techniques. An unsupervised spatial pattern identification approach is proposed for text data. A new algorithm for finding coherent patterns from a huge collection of text data is proposed, which is based on the shared nearest neighbour. The implementation followed by validation confirms that the proposed algorithm can cluster the text data for the identification of coherent patterns. The results are visualized using a graph. The results show the methodology works well for different text datasets.
Research on 3D Pose Measurement Algorithm Based on Binocular Vision

Abstract Based on binocular vision, this paper proposes an algorithm for fast and accurate three-dimensional measurement. The binocular 3D measurement system is mainly divided into four parts: binocular calibration, binocular correction, binocular matching and pose calculation. In this paper, an international softwareM is used to achieve camera calibration, the FAST algorithm is used to achieve the extraction of feature points, and the matching method that combines gray-level correlation coefficient (cc) and epipolar constraint is selected. Experiments have verified that this algorithm can accurately achieve three-dimensional pose measurement.
Parameter Estimation of Multivariate Adaptive Regression Spline (MARS) with Stepwise Approach to Multi Drug-Resistant Tuberculosis (MDR-TB) Modeling in Lamongan Regency

Abstract Tuberculosis (TB) is an infectious disease, caused by mycobacterium tuberculosis that affects various organs, especially the lungs. If TB treatment is not done thoroughly by the patient, then it can lead to death. Tuberculosis disease could not be healed cause TB bacteria to double immunity against anti-TB drugs, called multi-drug resistant (MDR). One identification issue toward TB infection chain is a TB case distribution analysis using mathematical modeling. The method used in this study was Multivariate Adaptive Regression Spline (MARS). Multivariate Adaptive Regression Spline is one type of non-parametric regression techniques, where the model does not assume the functional relationship between response and predictor variables, and has a flexible functional structure as well. Modeling aims to determine the factors that have the most significant influence on MDR-TB cases in Lamongan regency, as well as predict the incidence of MDR-TB in each sub-regency. The results show, the best model has a combination of BF = 28, MI = 2 and MO = 3, based on the minimum GCV, which is 5.26E-06. Furthermore, the model is statistically proper according to the criteria of APER and Press’s Q.
Communication-efficient k-Means for Edge-based Machine Learning

We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine learning computation to nearby edge servers. k-Means computation is fundamental to many data analytics, and the capability of computing provably accurate k-means centers by leveraging the computation power of the edge servers, at a low communication and computation cost to the data sources, will greatly improve the performance of these analytics. We propose to let the data sources send small summaries, generated by joint dimensionality reduction (DR) and cardinality reduction (CR), to support approximate k-means computation at reduced complexity and communication cost. By analyzing the complexity, the communication cost, and the approximation error of k-means algorithms based on state-of-the-art DR/CR methods, we show that: (i) in the single-source case, it is possible to achieve a near-optimal approximation at a near-linear complexity and a constant communication cost, (ii) in the multiple-source case, it is possible to achieve similar performance at a logarithmic communication cost, and (iii) the order of applying DR and CR significantly affects the complexity and the communication cost. Our findings are validated through experiments based on real datasets.
Unsupervised Open Set Recognition using Adversarial Autoencoders

We propose an unsupervised open set recognition approach that uses an Adversarial Autoencoder (AAE) for clustering and combines it with ii-loss to learn a representation that facilitates open set recognition. We evaluate our approach on two malware datasets. To show the applicability of the proposed method outside the malware domain we also evaluate it with an image dataset. Our proposed approach gives a statistically significant improvement over other evaluated approaches.
Research on the Innovative Practice Teaching Mode of Applied Undergraduate Digital Electronic Technology Major Based on the Computer Aid

Abstract Through a detailed analysis of the problems in the training of domestic applied undergraduate digital electronic technology majors, combined with the school’s digital electronic technology major practical teaching innovation mode and reform measures, to explore effective methods and approaches for applied undergraduate digital electronic technology major practical teaching, In order to build a practical teaching innovation model that can effectively train students’ performance analysis, design and application.
Space-Time Triplet Loss Network for Dynamic 3D Face Verification

In this paper, we propose a new approach for 3D dynamic face verification exploiting 3D facial deformations. First, 3D faces are encoded into low-dimensional representations describing the local deformations of the faces with respect to a mean face. Second, the encoded versions of the 3D faces along a sequence are stacked into 2D arrays for temporal modeling. The resulting 2D arrays are then fed to a triplet loss network for dynamic sequence embedding. Finally, the outputs of the triplet loss network are compared using cosine similarity measure for face verification. By projecting the feature maps of the triplet loss network into attention maps on the 3D face sequences, we are able to detect the space-time patterns that contribute most to the pairwise similarity between different 3D facial expressions of the same person. The evaluation is conducted on the publicly available BU4D dataset which contains dynamic 3D face sequences. Obtained results are promising with respect to baseline methods.
The Xbox Series X System Architecture

The Xbox Series X console, released in November 2020, contains a System on Chip (SoC) created in partnership with AMD. This article describes its architecture including the intelligence for input processing, rendering game graphics and audio, managing storage, user services, and security, all under a tiered operating system.
TensorFIip: A Fast Fully-Decentralized Computational Lottery for Cryptocurrency Networks

Distributed ledger technology, and specifically blockchain protocols, have been leveraged to define decentralized lotteries to circumvent challenges in traditional lottery systems. Unfortunately, lottery consensus in proof-of-resource (such as proof-of-work, proof-of-stake) blockchains is provably biased towards lottery users that command a majority of the resource determining consensus in their respective cryptocurrency networks, resulting in partial-decentralization. Further, classical consensus protocols are inherently slow in achieving consensus, and thus cannot be considered for designing fast decentralized lotteries.We present a fast, fully-decentralized computational lottery TensorFlip, where the betting users and the lottery house are peers in the same cryptocurrency network, and the bet and winning per player is a consensus based on a rudimentary quantum and classical distributed computation. We employ a two-party verifiable random function construction for computing the lottery winnings. Our lottery operates in the presence of both fail-stop and Byzantine adversaries, under the synchronous network model. Although slower classical consensus based and partially-decentralized blockchain consensus based lotteries may exist, we believe we are the first to propose a quantum and classical computation based deterministic fully-decentralized lottery, with an expected constant round complexity protocol.
The Focusing Effect in Negotiations

Two players with preferences distorted by the focusing effect (Koszegi and Szeidl, 2013) negotiate an agreement over several issues and one transfer. We show that, as long as their preferences are differentially distorted, an issue will be inefficiently left out of the agreement or inefficiently included in the agreement whenever the importance of the other issues on the table is sufficiently large. Anticipating this possibility, the negotiating parties may negotiate in stages, by first signing an incomplete agreement and later finalizing the outcome of the negotiation. Negotiating in stages increases the efficiency of the negotiation, despite the fact that the players’ preferences are distorted by the focusing effect also when negotiating the incomplete agreement.
Design and Application of Intelligent Sensing Terminal for Distribution Transformer

In this paper, an intelligent sensing terminal for the distribution transformer is designed. The intelligent sensing terminal consists of the high-voltage sensing ontology and the edge computing terminal. The high-voltage sensing ontology is used for the voltage and current sensing measurement of the high voltage incoming line of distribution transformer. The edge computing terminal collects a variety of transformer (including the high-voltage sensing ontology) sensing information to the transformer loss calculation, fault detection and other edge computing based on the multi-information sensing. In this paper, the sensing principle of high-voltage sensing ontology is introduced in detail and the error principle of voltage sensing is analyzed. In addition, the accuracy of voltage and current sensing is tested, and several typical applications of using the sensing terminal are given. The sensing terminal is a typical device in the sensing layer of the Power Internet of Things, which is of great significance to achieve the safe, economic operation and efficient power consumption of distribution transformer.
On Signings and the Well-Founded Semantics

In this note, we use Kunen's notion of a signing to establish two theorems about the well-founded semantics of logic programs, in the case where we are interested in only (say) the positive literals of a predicate $p$ that are consequences of the program. The first theorem identifies a class of programs for which the well-founded and Fitting semantics coincide for the positive part of $p$. The second theorem shows that if a program has a signing then computing the positive part of $p$ under the well-founded semantics requires the computation of only one part of each predicate. This theorem suggests an analysis for query-answering under the well-founded semantics. In the process of proving these results, we use an alternative formulation of the well-founded semantics of logic programs, which might be of independent interest. Under consideration in Theory and Practice of Logic Programming (TPLP)
An Algorithm Based on Loop-Cutting Contribution Function for Loop Cutset Problem in Bayesian Network

The loop cutset solving algorithm in the Bayesian network is particularly important for Bayesian inference. This paper proposes an algorithm for solving the approximate minimum loop cutset based on the loop-cutting contribution index. Compared with the existing algorithms, the algorithm uses the loop-cutting contribution index of nodes and node-pairs to analyze nodes from a global perspective, and select loop cutset candidates with node-pair as the unit. The algorithm uses the parameter μ to control the range of node-pairs, and the parameter ω to control the selection conditions of the node-pairs, so that the algorithm can adjust the parameters according to the size of the Bayesian networks, which ensures computational efficiency. The numerical experiments show that the calculation efficiency of the algorithm is significantly improved when it is consistent with the accuracy of the existing algorithm; the experiments also studied the influence of parameter settings on calculation efficiency using trend analysis and two-way analysis of variance. The loop cutset solving algorithm based on the loop-cutting contribution index uses the node-pair as the unit to solve the loop cutset, which helps to improve the efficiency of Bayesian inference and Bayesian network structure analysis.
A Survey on Decentralized Crowdsourcing Using Blockchain Technology

Many of the emerging crowd sourcing platforms find central repositories as single point of failure, and are related to the shortcomings of conventional trust-based models. Thanks to involvement of malicious users, they are also vulnerable to distributed denial of service (DDoS) and Sybil attacks. Additionally, high service fees from the community sourcing platform can hinder crowd sourcing growth. It has both research and substantial value for tackling these potential problems. In this article, CrowdBC, a blockchain-based cooperative crowd sourcing system through which a community of employees may solve the requester’s challenge without investing in some third intuition. The safety of consumers is ensured and allows less traction costs. In specific, the new system architecture will be adopted. Implementation of a software model on the public test network Ethereum with real-world dataset. Experiment outcome reveals the viability, efficiency, and scalability of the crowd sourcing method.
Construction Material Concrete Quality Detection Method based on Ultrasonic Tomography Technology

The current quality detection methods for building materials concrete are inaccurate in the processing results of sound waves and amplitude signals, resulting in low accuracy of the propagation velocity and wave velocity dispersion in the detected concrete, and inaccurate quality strength detection results, so based on ultrasonic tomography technology The characteristics of the design of construction materials concrete quality inspection methods. Collect ultrasonic velocity and amplitude signals, and eliminate the abnormal signals in the collected sound velocity and amplitude; make full use of the ultrasonic tomography technology in the internal propagation of the medium, and the characteristics of digital imaging, complete the image transformation, establish the concrete mathematical model, output the internal image of concrete, and complete the concrete quality detection. Experimental results: The proposed construction material concrete quality inspection method, when inspecting the construction material concrete, the dispersion of the propagation velocity and the wave velocity in the detected concrete, compared with other inspection methods, the construction material concrete quality inspection method The result is more accurate.
Illumination Normalization of Face Image Based-on Multi-stage Feature Maps

Nowadays, illumination normalization of facial image based on Generative Adversarial Networks (GANs) has made noticeable progress. However, the image quality and the recognition accuracy are unsatisfactory when the faces encounter severe illumination. For these reasons, we put forward a new scheme for normalizing face illumination. Moreover, feature maps with multiple sizes are extracted by different convolutional layers of pretrained feature network, and then use these feature maps to compute loss, we call it multi-stage feature maps (MSFM) loss. Qualitative and quantitative experimental results show that the proposed method achieves favorable illumination normalization results against previous models under various illumination challenges.
Classification criteria for data deduplication methods

Data deduplication refers to size reduction of data by eliminating data redundancy due to duplication. Possibility of duplication is high when size of data is huge. As the data especially digital data is growing drastically on the Internet due to emerging online ways of communication and interaction in various areas such as social media, banking, and marketing, the problem of duplicate data has become serious. There are various data deduplication techniques that can be used to reduce its size. Apart from reducing the required storage space this reduction may result into different adjoining benefits. For example, it saves device cost and time required for backup and archive when data is to be stored on secondary storage. In case of primary storage, it eliminates duplicate disk I/Os and thus reduce the time of program execution. When data is meant for cloud storage, deduplication reduces time for data uploading on WAN. When data is to be stored on virtual machine, it saves time for its migration. When data is on network, its size reduction reduces time of transmission and reduces redundancy for WAN optimization. Different techniques for data deduplication may be categorized based on various aspects of algorithm design and scenarios it is designed for such as location, time, unit of deduplication, and so on. Based on location, they can be categorized as source or client depending on whether this operation is performed on client side or server side during communication of data. Whereas, based on time it depends on whether the process is performed while the data was being stored or after the data was stored. The categorization based on unit of deduplication is popularly done as file chunking, fixed block chunking and variable block chunking. Data deduplication consists of primarily identification of the duplicate data elements and replacing them by links such that all such duplicate data elements may be located physically at single position on physical media. Various steps involved in the process of data deduplication are chunking and fingerprinting, hashing, indexing, and compression.
WITHDRAWN: An energy efficient and improved language translator with cnn based deep encoder and decoder

The attempt to identify the text in natural language processing is considered to a confronting task based on the variations encountered on the language formats like textures, fonts and illuminations. English is a common language used on Arab countries with Arabic scripts. To provide better understanding towards the tough Arabic language; this work introduces an English-Arabic text recognition with the available dataset. With the available data evaluation has been made for recognizing the text and provide with a proper meaning. To show an extensive research perspective to the investigators, experimentation is carried over the text features and classification is done with Deep learning approaches. This work presents a novel approach to extract invariant features from the input data and to detect appropriate work. So as to choose appropriate features, weight of the invariant feature is determined and provided for validation. Here, an encoder and a decoder are placed with the layer of CNN to perform computation. The work has been compared with existing approaches SVM, NB classifier to project the advantages of the proposed model. Simulation is done with MATLAB environment and the proposed model shows better trade off in identifying the machine based language translation for analyzing text.
WITHDRAWN: Lung nodule image quality assessment under iterative model reconstruction

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Towards an Internal Evaluation Measure for Arbitrarily Oriented Subspace Clustering

In the setting of unsupervised machine learning, especially in clustering tasks, the evaluation of either novel algorithms or the assessment of a clustering of novel data is challenging. While mostly in the literature the evaluation of new methods is performed on labelled data, there are cases where no labels are at our disposal. In other cases we may not want to trust the “ground truth” labels. In general there exists a spectrum of so called internal evaluation measures in the literature. Each of the measures is mostly specialized towards a specific clustering model. The model of arbitrarily oriented subspace clusters is a more recent one. To the best of our knowledge there exist at the current time no internal evaluation measures tailored at assessing this particular type of clusterings. In this work we present the first internal quality measures for arbitrarily oriented subspace clusterings namely the normalized projected energy (NPE) and subspace compactness score (SCS). The results from the experiments show that especially NPE is capable of assessing clusterings by considering archetypical properties of arbitrarily oriented subspace clustering.
Research on Performance of Asymmetric Polling System Based on Blockchain

To solve the problem of how different customers can achieve load balancing and improve the efficiency of information transmission under high concurrency, it is proposed to add polling to the underlying technology of the blockchain to achieve system decentralization. First, a blockchain-based asymmetric polling system model is proposed; then a clustering algorithm is proposed to analyze the system, use Markov chains and probability generating functions to establish a data model, and derive the asymmetry Gated and asymmetry Exhaustive the average queue length and the average waiting time; finally, compare the average queue length and the average waiting time to verify the correctness of the scheme. Experiments have proved that the new model system can speed up information processing and improve system performance.
Multi-modal Steganography Based on Semantic Relevancy

Traditional steganography embeds confidential information by modifying the carrier at the symbol level, e.g., the pixels of an image or the words of a text. Since modification traces will inevitably be left on the carrier, it is hard to resist the detection of the steganalysis algorithms. To address this problem, this paper proposes a novel steganographic framework called multi-modal steganography, which hides secret messages at the semantic level. In this framework, multi-modal covers are projected into a common semantic space, in which their relevancy can be measured. The confidential information can be embedded in the semantic relevancy among the covers with a relevancy-message mapping algorithm. By choosing and sending a series of original multi-modal covers, the secret messages are transmitted to the receiver. In this paper, we adopt text and image as the two modalities. A visual semantic embedding model is utilized to measure the relevancy between the texts and images. Both the theoretical analysis and experiments demonstrate that the proposed multi-modal steganography has good resistance to the existing steganalysis methods and high quality of concealment.
Deblurring Method for Motion Blurred Images based on GAN

The purpose of image motion deblur is to recover the underlying clear image from the corresponding blur image. In most traditional methods, the image recovery task is formulated as a problem of blur core estimation and use a priori to calculate. In this paper we proposes a generative adversarial network(GAN) model based on the mobilenet-V3 network structure to meet the needs of motion blurred image recovery on mobile devices. Based on traditional evaluation indicators, we propose a new evaluation metric on mobile device. Extensive experiments show that our method is superior to the competing methods.