Reviewerly/exHarmony huggingface dataset:
A Non-Local Means Based Multiplicative Denoising Method for Image Processing

Image denoising is one of the most important and critical issues in the field of digital image processing. Its purpose is to keep the edge and detail information as much as possible while removing the noise in the image. Numerous current denoising methods have poor performance when dealing with multiplicative noise, and they usually cannot well preserve the detail and edge information in the image. For this reason, we propose a Non-Local Means (NLM) based multiplicative denoising method. Firstly, the image is logarithmically transformed to convert the noise into multiplicative additive noise that is easier to handle. Secondly, a new square root kernel function is used to replace the original exponential kernel function for Non-Local Means filtering. Finally, the denoised image is obtained through the inverse exponential transformation. By comparing with a variety of classical denoising algorithms, we make the conclusions that the proposed method can not only effectively remove the multiplicative noise, but also better preserve the detail and edge information in the image, which will lay the foundation for more advanced image processing.
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information

In linguistics, a sememe is defined as the minimum semantic unit of languages.Sememe knowledge bases (KBs), which are built by manually annotating words with sememes, have been successfully applied to various NLP tasks.However, existing sememe KBs only cover a few languages, which hinders the wide utilization of sememes.To address this issue, the task of sememe prediction for Ba-belNet synsets (SPBS) is presented, aiming to build a multilingual sememe KB based on BabelNet, a multilingual encyclopedia dictionary.By automatically predicting sememes for a BabelNet synset, the words in many languages in the synset would obtain sememe annotations simultaneously.However, previous SPBS methods have not taken full advantage of the abundant information in BabelNet.In this paper, we utilize the multilingual synonyms, multilingual glosses and images in Ba-belNet for SPBS.We design a multimodal information fusion model to encode and combine this information for sememe prediction.Experimental results show the substantial outperformance of our model over previous methods (about 10 MAP and F1 scores).All the code and data of this paper can be obtained at https://github.com/thunlp/MSGI.
A Quality of Experience Illustrator User Interface for Cloud Provider Recommendations

Cloud infrastructures handle processing and storage options for a multitude of applications and services. Expert users are tasked to verify assigned resources and select optimal combinations to accommodate the infrastructure operations. For the technical users (engineers) in this specialised environment, user intent is not modelled in the traditional HCI application sense, but rather by intentionally combining the functional and non-functional requirements of the infrastructure through provider recommendations that are used as features. This work reports on the design, development and evaluation of a user interface that enable intent transfer from the specialised technical level of the expert user to the provider recommendation evaluation by the same users.
Key sequence security by Charles Bennett protocol

This paper proposes an efficient and novel technique for assessment of the direction of switched capacitor bank as well as estimating its distance from the monitoring location in real distribution systems. At first, the proposed. The work considered a number of issues arising in the creation of quantum fiber-optic systems of quantum key distribution. A study of the basic principles of the work of Protocol B92 was carried out, distinguishing features from BB84 were noted. In conclusion, there are a number of drawbacks that impose restrictions when working on long routes.
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue

Scaling dialogue systems to a multitude of domains, tasks and languages relies on costly and time-consuming data annotation for different domain-task-language configurations. The annotation efforts might be substantially reduced by the methods that generalise well in zero- and few-shot scenarios, and also effectively leverage external unannotated data sources (e.g., Web-scale corpora). We propose two methods to this aim, offering improved dialogue natural language understanding (NLU) across multiple languages: 1) Multi-SentAugment, and 2) LayerAgg. Multi-SentAugment is a self-training method which augments available (typically few-shot) training data with similar (automatically labelled) in-domain sentences from large monolingual Web-scale corpora. LayerAgg learns to select and combine useful semantic information scattered across different layers of a Transformer model (e.g., mBERT); it is especially suited for zero-shot scenarios as semantically richer representations should strengthen the model’s cross-lingual capabilities. Applying the two methods with state-of-the-art NLU models obtains consistent improvements across two standard multilingual NLU datasets covering 16 diverse languages. The gains are observed in zero-shot, few-shot, and even in full-data scenarios. The results also suggest that the two methods achieve a synergistic effect: the best overall performance in few-shot setups is attained when the methods are used together.
Machine Learning-Based Scheduling and Resources Allocation in Distributed Computing

In this work we study a promising approach for efficient online scheduling of job-flows in high performance and distributed parallel computing. The majority of job-flow optimization approaches, including backfilling and microscheduling, require apriori knowledge of a full job queue to make the optimization decisions. In a more general scenario when user jobs are submitted individually, the resources selection and allocation should be performed immediately in the online mode. In this work we consider a neural network prototype model trained to perform online optimization decisions based on a known optimal solution. For this purpose, we designed MLAK algorithm which implements 0–1 knapsack problem based on the apriori unknown utility function. In a dedicated simulation experiments with different utility functions MLAK provides resources selection efficiency comparable to a classical greedy algorithm.
Fuzzy Logic Framework for Ontology Instance Alignment

The widely addressed topic of ontology alignment to this day contains several open research questions that remain either unanswered or only vaguely tackled. One of them is designating alignments of concept instances, which according to the literature are addressed in a handful of publications. Therefore, in this paper we propose a formal framework based on fuzzy logic that can be used to determine such mappings. We provide several similarity functions and a set of inference rules for combining them. The approach has been experimentally verified using widely accepted datasets provided by the Ontology Alignment Evaluation Initiative, yielding promising results.
Faster Post-Quantum TLS Handshakes Without Intermediate CA Certificates

Traditionally, the most data-heavy part of a (D)TLS handshake has been authentication which includes a handshake signature and digital certificates. Although most common (D)TLS usecases are not significantly affected, some constrained ones such as low bandwidth environments or delay sensitive applications can see drastic performance degradation due to big certificates or certificate chains. That has led the security community to seek options to alleviate the issue. Post-quantum signatures and keys, on the other hand, have been proven to noticeably slow down handshakes even for common Internet (D)TLS or QUIC applications due to the significantly higher amounts of post-quantum authentication data they include. In this work, we quantify the size issue of post-quantum certificates in (D)TLS and QUIC and make the case for speeding up (D)TLS and QUIC handshakes by omitting the intermediate certificate authority certificates in the handshake. We present how that can be achieved along with the usecases that will mostly benefit from such a mechanism. We offer quantitative analyses to show that this approach is relatively straightforward, backwards compatible and with little overhead introduced for caching the certificates. We also discuss caching mechanisms based on different optimization goals.
Logarithmic Equal-Letter Runs for BWT of Purely Morphic Words

In this paper we study the number $$r_{\texttt {bwt}}$$ of equal-letter runs produced by the Burrows-Wheeler transform (BWT) when it is applied to purely morphic finite words, which are words generated by iterating prolongable morphisms. Such a parameter $$r_{\texttt {bwt}}$$ is very significant since it provides a measure of the performances of the BWT, in terms of both compressibility and indexing. In particular, we prove that, when BWT is applied to whichever purely morphic finite word on a binary alphabet, $$r_{\texttt {bwt}}$$ is $$\mathcal {O}(\log n)$$ , where n is the length of the word. Moreover, we prove that $$r_{\texttt {bwt}}$$ is $$\varTheta (\log n)$$ for the binary words generated by a large class of prolongable binary morphisms. These bounds are proved by providing some new structural properties of the bispecial circular factors of such words.
BB84 quantum key distribution protocol

The main objects of the use of promising quantum information transmission networks in the telecommunications environment are presented. The development of a new direction of the protected method of information transmission is considered. The main tasks in the use ofquantum networks in the educational environment in the training of information security specialists are formulated.
LegionARius - Beyond Limes

LegionARius is a real-time strategy game using Augmented Reality (AR) to let players experience Roman and Germanic history in their own home. Being rooted in the tradition of Serious Games (SG), LegionARius aims at providing historically accurate knowledge about ancient everyday life in an entertaining manner. The players have to decide the destiny of a Roman cohort or a Germanic tribe living alongside the fortifications of Limes during the second century AD. As Roman commander or chief of a Germanic tribe they build, produce, trade and use their environment to choose a path between peaceful coexistence and war. With the help of AR the player can view and inspect the historically accurate 3D models of Roman or Germanic buildings. In addition, the player gets to know the historical context and background in a playful way. This knowledge is not only expanded in the course of the game, but is also a key element for success.
NewsKVQA: Knowledge-Aware News Video Question Answering

Answering questions in the context of videos can be helpful in video indexing, video retrieval systems, video summarization, learning management systems and surveillance video analysis. Although there exists a large body of work on visual question answering, work on video question answering (1) is limited to domains like movies, TV shows, gameplay, or human activity, and (2) is mostly based on common sense reasoning. In this paper, we explore a new frontier in video question answering: answering knowledge-based questions in the context of news videos. To this end, we curate a new dataset of $$\sim $$ 12K news videos spanning across $$\sim $$ 156 h with $$\sim $$ 1M multiple-choice question-answer pairs covering 8263 unique entities. We make the dataset publicly available ( https://tinyurl.com/videoQAData ). Using this dataset, we propose a novel approach, NewsKVQA (Knowledge-Aware News Video Question Answering) which performs multi-modal inferencing over textual multiple-choice questions, videos, their transcripts and knowledge base, and presents a strong baseline.
Ai/Ml Approach to Lithology Quantification from Rock Chips Analysis

Summary A large high-resolution image dataset of rock chips obtained from offshore wells, the North West Shelf of Australia, is obtained. The dataset consists of images of 158 rock chip samples that comprise single-lithology samples of 21 carbonates, 57 sandstones, 46 mudstones, and 34 volcanics. The images are used to create a labelled dataset of rock chips of different lithologies using manual labelling using LabelMe software and a newly developed automatic approach. This procedure is implemented as a python code that uses JPEG images as input data and produces JSON files with grain boundaries and labels as an output. Cascade Mask R-CNN, an object detection algorithm, is used for grain detection and classification. Our study shows that recent progress in AI/ML approaches allows automation of manual and time-consuming tasks of lithology classification.
UAV Path Planning Based on Hybrid Differential Evolution with Fireworks Algorithm

This paper considers the unmanned aerial vehicle (UAV) global path planning as an optimization problem with constraints and proposes a hybrid differential evolution with firework algorithm (HDEFWA) to generate the optimal feasible path. The multiple constraints based on the realistic scenarios are taken into account, including terrain and threat area constraints. The hybrid algorithm integrates the differential evolution operator into the mechanism of optimizing the fireworks algorithm (FWA) and uses the ideas of mutation, crossover, and selection to transform the spark particles generated by the explosion. The source of the differential mutation operator is the excellent particles in the iterative population. This mechanism makes up for the basic firework algorithm's neglect of the excellent solution resources in the population, which greatly improves the information sharing among the solutions. Experiments show that the proposed hybrid algorithm is superior to other intelligent algorithms in UAV path planning.
A Concise Survey on Solving Feature Selection Problems with Metaheuristic Algorithms

In machine learning, feature selection is a crucial and important operation. The basic goal of the feature selection problem is to lower the size of the feature set while preserving performance accuracy. To classify the datasets, many approaches have been devised. Metaheuristic algorithms, on the other hand, have received a lot of interest for handling a variety of optimization problems. As a result, this paper gives a comprehensive assessment of the literature on employing metaheuristic algorithms to solve feature selection problems. Furthermore, based on their behavior, metaheuristic algorithms have been divided into four types. A classified list of over a hundred metaheuristic algorithms is also provided. Only binary variations of metaheuristic algorithms have been evaluated and categorized in order to address the feature selection problem, and a comprehensive explanation of each has been provided. The binary classification, name of the classifier used, datasets, and evaluation metrics for metaheuristic algorithms used to solve feature selection problems are all included. After analyzing the articles, problems and issues in getting the optimal feature subset using various metaheuristic methods are identified. Finally, several research needs are identified for researchers who want to continue their work on building or refining metaheuristic categorization algorithms. A case study is presented for an application in which UCI datasets are adopted and a number of metaheuristic algorithms are used to achieve the optimum functionality.
Vision based Human Activity Recognition using Deep Neural Network Framework

Human Activity Recognition (HAR) has become a well-liked subject in study as of its broad application.With the growth of deep learning, novel thoughts have emerged to tackle HAR issues.One example is recognizing human behaviors without exposing a person's identify.Advanced computer vision approaches, on the other hand, are still thought to be potential development directions for constructing a human activity classification approach from a series of video frames.To solve this issue, a deep learning neural network technique using Depthwise Separable Convolution (DSC) with Bidirectional Long Short-Term Memory (DSC-BLSTM) is proposed here.The redeeming features of the proposed network system comprises a DSC convolution that helps to reduce not only the number of learnable parameters but also computational cost in together training and testing method The bidirectional LSTM process can combine the positive and the negative time direction.The proposed method comprises of three phases, which includes Video data preparation, Feature Extraction using Depthwise Separable Convolution Neural Network algorithm and DSC-BLSTM algorithm.The proposed DSC-BLSTM method obtains high accuracy, F1-score when compared to other HAR algorithms like MC-HF-SVM, Baseline LSTM Bidir-LSTM algorithms.
Real-Time Magnetic Sensor Anomaly Detection Using Autoencoder Neural Networks on the DIII-D Tokamak

Magnetic diagnostics in tokamaks are key to plasma equilibrium control (plasma current, plasma shape, and position) and amelioration of plasma instabilities. Thus, real-time identification of the anomalous sensor is mandatory. A new real-time system based on autoencoder (AE) neural networks (NNs) for anomaly detection in magnetics signals, including both flux loops and magnetic probes, has been successfully implemented on the plasma control system (PCS) of the DIII-D tokamak. The AE NN is trained on over 4000 plasma discharges, with an optimized latent space representation of the input signals while minimizing the reconstruction loss. An algorithm determining anomalous sensors based on excessive deviations from accurate reconstruction from the trained NNs is constructed in a MATLAB/Simulink environment and is deployed on the real-time PCS using the embedded MATLAB coder (EMC) environment. The reconstruction performance of the algorithm is quantified in hardware-in-the-loop (HIL) simulation by injecting artificially common fault types, including stuck-at-zero fault, saturated fault, noise fault, and drift fault. The AE NN algorithm has been successfully commissioned on the DIII-D PCS.
A Generic Construction for Crossovers of Graph-Like Structures

In model-driven optimization (MDO), domain-specific models are used to define and solve optimization problems with evolutionary algorithms. Models are typically evolved using mutations, which can be formally specified as graph transformations. So far, only mutations have been used in MDO to generate new solutions from existing ones; a crossover mechanism has not yet been elaborated. In this paper, we present a generic crossover construction for graph-like structures that can be used to implement crossover operators in MDO. We prove basic properties of our construction and show how it can be used to implement a whole set of crossover operators that have been proposed for specific problems and situations on graphs.
Intuitionistic Fuzzy Generalized Net Model of the Humanoid Service Robot Functionalities

This paper presents an approach to model the humanoid service robot functionalities based on the apparatuses of one of the extensions of the Generalized Nets (GNs) and the Intuitionistic Fuzzy Sets (IFSs), called Intuitionistic Fuzzy GNs of the first type (IFGN1). Here, an IFGN1-model of humanoid robot is developed, which describes the main functionalities of the service and the feedback of the embedded sensors the intuitionistic fuzzy estimations of the IFGN1-transition condition predicates give information for the possible risks to the robot when performing various tasks. The so described IFGN1-model will permit the development of a more detailed and complex model allowing optimization and improvement of the robot functioning in different environment.
Image Forgery Detection Using Multi-Layer Convolutional Neural Network

Image forgery detection is among the most appealing research domains in the field of multimedia forensics. Distinguishing tampered images from pristine images has become increasingly difficult due to growing number of sophisticated manipulation techniques. Digital images can be easily tampered using copy-move or splicing techniques and the traces of forgery can be hidden using post-processing operations. The field of digital image forensics majorly aims at detecting image forgery and ensuring the authenticity of digital visual media. In this manuscript, we propose a passive approach for digital image forgery detection. It is a non-block-based functional framework that utilizes deep convolutional neural network to distinguish tampered and pristine images. To build an efficient and robust model, extensive experiments have been evaluated on COVERAGE dataset and the results suggest that it attains state-of-the-art performance with greater robustness to post-processing tampering operations. The comparative results have also been included for better evaluation.
Enhanced Security Layer for Hardening Image Steganography

Akshita, Pregada Amritha, P. P.Steganography is a technique for securing data by hiding data into data or data behind data. Modern steganography uses a variety of formats or types, including text, picture, audio, video, and protocol, but digital images are the most extensively utilised due to their prevalence on the internet For example, secret information binary code can be hidden in images binary code, causing the image to be slightly altered. Some enable invisibility of information, while others supply a large secret message that must be kept hidden. This paper proposes a new method of image steganography based on the LSB substitution, with Base64 Encoding added to make it more secure. We are going to build an authentication application, which will add one more layer of security for image steganography. This legitimate email application can be used for communication between the sender and the receiver.
An Overview of Blockchain and IoT in e-Healthcare System

Blockchain technologies and Internet of Things (IoT) are being tremendously applied in many fields, especially for e-healthcare. For the safe, secure delivery of healthcare data management, there must be miraculous support for applying applications of blockchain. Patients’ privacy and security are becoming a worry as the number of IoT devices in the healthcare system grows at an exponential rate. IoT devices can provide real-time sensory data like clinical trials, device tracking, health insurance details of patients for better tracking and pharmaceutical tracing. In IoT, the problem arises when there is manipulation of data or tampering of data or any point of failure, especially in healthcare. A blockchain is a form of the ledger; which consists of distributed records that are unmodifiable and transparent through replicating among public/private networks. Because of its competency and convenience for people’s lifestyles, the mobile healthcare system is receiving a lot of attention. This paper examines an overview of the blockchain-based authentication technologies, as well as answers to security concerns and developments in healthcare via blockchain and IoT integration. Also, discuss the applications of blockchain in e-healthcare to design decentralized IoT-based e-healthcare systems, methods, statistics, and success cases applied. Further, this paper prepares the challenges of blockchain-based smart healthcare systems.
A Deontic Modal SFP in Chengdu Chinese

This study identifies the SFP təu in Chengdu Chinese as a C-element encoding deontic modality, such as commission or permission, depending on the person(s) of the subject. Moreover, it conveys conditionality in that the implementation of an action requires the completion of another action. It is conditionality that gives rise to a temporally sequential reading, which is dominant enough to earn təu a term as “antecedent aspect marker”. Syntactically, conditionality occurs in an adjunct CP, and this CP is further adjoined to an elided IP, in which deontic modality is located. This IP further projects to a higher CP accommodating the deontic modal SFP təu.