Reviewerly/exHarmony huggingface dataset:
Content Based Image Retrieval Based on Color String Coding and Genetic Algorithm

Content based image retrieval is a technique which, by dint of a query image, can provide similar results to those eventuated by a query into a large images base. The traditional methods use the visual contents of the image; the requested image goes through a phase of extracting color, shape, or texture. The same goes for all of the images in the base. The images in question are then subjected to a direct comparison. In this work, we develop a content based image retrieval system using a method based on color features coded in the form of strings and genetic algorithms (color string coding and genetic algorithms). Herein, the problem lies in the selection of similar strings by means of meta-heuristic algorithms and the search methods based on the evolutionary concept using natural operations: selection, crossover and mutation. The aim is to select the fittest individuals in a given large population. Afterwards, we compare the results obtained by modifying at intervals the size of the images to assess the influence of the size on our method, and therefore, to decrease the computation time. This approach purports to reduce the costs of extracting other features and to increase the retrieval precision. Finally, our system is evaluated against the other systems based on two measures: recall and precision.
GDTM: Graph-based Dynamic Topic Models

Abstract Dynamic Topic Modeling (DTM) is the ultimate solution for extracting topics from short texts generated in Online Social Networks (OSNs) like Twitter. It requires to be scalable and to be able to account for sparsity and dynamicity of short texts. Current solutions combine probabilistic mixture models like Dirichlet Multinomial or Pitman-Yor Process with approximate inference approaches like Gibbs Sampling and Stochastic Variational Inference to, respectively, account for dynamicity and scalability of DTM. However, these methods basically rely on weak probabilistic language models, which do not account for sparsity in short texts. In addition, their inference is based on iterative optimizations, which have scalability issues when it comes to DTM. We present GDTM, a single-pass graph-based DTM algorithm, to solve the problem. GDTM combines a context-rich and incremental feature representation method with graph partitioning to address scalability and dynamicity and uses a rich language model to account for sparsity. We run multiple experiments over a large-scale Twitter dataset to analyze the accuracy and scalability of GDTM and compare the results with four state-of-the-art models. In result, GDTM outperforms the best model by $$11\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>11</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on accuracy and performs by an order of magnitude faster while creating four times better topic quality over standard evaluation metrics.
Gradient-based Interpretation on Convolutional Neural Network for Classification of Pathological Images

Digital pathological images have been developing very fast with the recent progress in imaging hardware. It provides tons of imaging data for diagnostic purposes in medical applications. Data-driven algorithms, especially convolutional neural networks (CNN), have been successfully applied in various image analysis tasks. However, neural network models are regarded as black box model and they are supposed to be more interpretable and transparent for real-life applications, especially in the field of medical image analysis. In this work, we trained a CNN model on digital pathological images from lymph nodes sections to classify metastatic tissue, achieving F1 score of over 86%. Furthermore, we investigate the possible ways to understand how the model makes decisions using gradient-based method.
Learning across views for stereo image completion

Stereo image completion (SIC) is to fill holes existing in a pair of stereo images. SIC is more complicated than single image repairing, which needs to complete the pair of images while keeping their stereoscopic consistency. In recent years, deep learning has been introduced into single image repairing but seldom used for SIC. The authors present a novel deep learning‐based approach for SIC. In their method, an X‐shaped fully convolutional network (called SICNet) is proposed and designed to complete stereo images, which is composed of two branches of convolutional neural network layers to encode the context of the left and right images separately, a fusion module for stereo‐interactive completion, and two branches of decoders to produce completed left and right images, respectively. In consideration of both inter‐view and intra‐view cues, they introduce auxiliary networks and define comprehensive losses to train SICNet to perform single‐view coherent and cross‐view consistent completion simultaneously. Extensive experiments are conducted to show the state‐of‐the‐art performances of the proposed approach and its key components.
Exploring the Effects of a Virtual Companion on Solitary Jogging Experience

Group exercise with superior partners is more effective for gaining motivation and improving athletic performance than when exercising alone, but it can be difficult to always find such partners. In this paper, we propose a novel experience of jogging with a virtual companion instead of a human partner. We report on qualitative analysis results from a controlled experiment to investigate how joggers feel and how their behavior changes when they jog alone, with a human partner, or with a virtual companion. The virtual companion was represented as either a full-body, limb-only, or a point-light avatar using smart glasses.
XR Framework for Collaborating Remote Heterogeneous Devices

With the advent of high-speed 5G networks, establishing a remote space-sharing XR environment that supports real-time interaction based on the latest VR/AR/MR technology is receiving attention as a promising new research topic. With that aim, we have developed a framework that allows users with different types of devices, in different physical spaces, to effectively build a virtual world where collaborative work can be performed. By using this technology, new types of content can be effectively produced that can interact in real time with various commercial VR/MR head-mounted displays, smart mobile devices, and even beam projectors. In this paper, we demonstrate the effectiveness and potential of this framework through the example of game content development where MR and VR head-mounted display users interact in virtual space.
On Solving a Generalized Constrained Longest Common Subsequence Problem

Given a set of two input strings and a pattern string, the constrained longest common subsequence problem deals with finding a longest string that is a subsequence of both input strings and that contains the given pattern string as a subsequence. This problem has various applications, especially in computational biology. In this work we consider the $$\mathcal {NP}$$ –hard case of the problem in which more than two input strings are given. First, we adapt an existing A $$^*$$ search from two input strings to an arbitrary number m of input strings ( $$m \ge 2$$ ). With the aim of tackling large problem instances approximately, we additionally propose a greedy heuristic and a beam search. All three algorithms are compared to an existing approximation algorithm from the literature. Beam search turns out to be the best heuristic approach, matching almost all optimal solutions obtained by A $$^*$$ search for rather small instances.
Semantic Segmentation of color images via Feature Extraction Techniques

Abstract In this research semantic segmentation (SS) by deep neural network is used. Segmentation of contextual information with the use of patch-patch between the regions and background. The patch-patch context is done with the help of ANNs (Artificial Neural Networks) and Adaboost to identify the semantic correlation of adjacent patches. Efficient piece-wise training of deep model is then assigned to eschew continuous inference through background propagation. For Patch-background context capturing the network is designed with the help of multi-scale image input and slide Pyramid pooling for the efficient performance. In this model we have used both ANNs and Adaboost. The simulation results are verified in the semantic segmentation dataset like MATLAB contextual modeling. The proposed technique will gave better accuracy. The evaluation metrics of invented method has been done in terms pixel accuracy. The model achieves outperforming innovation than the other algorithms on semantic segmentation.
Evidence accumulation clustering using combinations of features

Evidence accumulation clustering (EAC) is an ensemble clustering algorithm that can cluster data for arbitrary shapes and numbers of clusters. Here, we present a variant of EAC in which we aimed to better cluster data with a large number of features, many of which may be uninformative. Our new method builds on the existing EAC algorithm by populating the clustering ensemble with clusterings based on combinations of fewer features than the original dataset at a time. Our method also calls for prewhitening the recombined data and weighting the influence of each individual clustering by an estimate of its informativeness. We provide code of an example implementation of the algorithm in Matlab and demonstrate its effectiveness compared to ordinary evidence accumulation clustering with synthetic data.•The clustering ensemble is made by clustering on subset combinations of features from the data•The recombined data may be prewhitened•Evidence accumulation can be improved by weighting the evidence with a goodness-of-clustering measure.
Trading Cost and Throughput in Geo-Distributed Analytics With A Two Time Scale Approach

In the era of global-scale services, analytical queries are performed on datasets that span multiple data centers (DCs). Such geo-distributed queries generate a large amount of inter-DC data transfers at run time. Due to the expensive inter-DC bandwidth, various methods have been proposed to reduce the traffic cost in geo-distributed data analytics. However, current methods do not attempt to address the throughput issue in geo-distributed analytics. In this article, we target at characterizing and optimizing a cost-throughput tradeoff problem in geo-distributed data analytics. Our objectives are two-fold: (1) we minimize the inter-DC traffic cost when serving geo-distributed analytics with uncertain query demand, and (2) we maximize the system throughput, in terms of the number of query requests that can be successfully served with guaranteed queuing delay. Specifically, we formulate a stochastic optimization problem that seamlessly combines these two objectives. To solve this problem, we take advantage of Lyapunov optimization techniques to design and analyze a two-timescale online control framework. Without prior knowledge of future query requests, this framework makes online decisions on input data placement and admission control of query requests. Rigorous theoretical analyses show that our framework can achieve a near-optimal solution and maintain system stability and robustness as well. Extensive trace-driven simulation results further demonstrate that our framework is capable of reducing inter-DC traffic cost, improving system throughput, and guaranteeing a maximum delay for each query request.
Approach for Selecting and Integrating Cloud Services to Construct Hybrid Cloud

Abstract With the popularization of cloud computing, various cloud services have emerged, and hybrid clouds that can take advantage of combining public and private clouds are attracting attention. However, because of their variety, determining a combination of cloud services suited to the user’s current environment and requirements is expensive when deploying a hybrid cloud. Even if the required services are available, there is a lack of tools to connect them, manage them in batches, and utilize the integrated environment. To solve these problems, this paper proposes a cloud selection and integration process (C-SIP), which selects and integrates a combination of cloud services through a hybrid cloud service broker (hybrid CSB), which is an automation solution supporting hybrid cloud deployment. Moreover, the proposed method is realized using a script including the application programming interface of each cloud service. The proposed C-SIP will be used as a core approach toward the hybrid CSB, which is expected to facilitate the introduction of hybrid clouds and the acquisition of cloud strategies.
An Unsupervised Multiple Word-Embedding Method with Attention Model for Cross Domain Aspect Term Extraction

Aspect-based sentiment analysis is mainly a domain-dependent problem. In recent years, the existing approaches have the problem of domain adaptation for the poor performance when the domain on which the model is applied is other than the one on which the aspect extraction model is trained. However, a large sample corpus for all domains is required to train an accurate model to extract the most relevant domain-specific aspects. Furthermore, the manual annotation of large labels for the supervised model for the different domain is expensive and time-consuming. In this paper, first, we used an unsupervised method using multiple word embeddings to extract domain-specific aspects belonging to different aspects and then use these aspects as label data to train an attention-based cross-domain model for better prediction. The proposed method is evaluated on SemEval-14 and SemEval-16 datasets and competitive results are shown for baseline and most recent approaches.
Simulation and Design of Quantum Circuits

Currently, there is an ongoing “race” to build the first practically useful quantum computer that provides substantial speed-ups for certain problems compared to conventional computers. In addition to the development of such devices, this also requires the development of automated tools and methods that provide assistance in the simulation and design of corresponding applications. Otherwise, a situation might be reached where we have powerful quantum computers but hardly any proper means to actually use them. This work provides an overview of corresponding solutions for the task of quantum circuit simulation, the task of quantum circuit design, as well as corresponding mapping tasks. The covered solutions utilise expertise on efficient data structures and algorithms gained in the design of conventional circuits and systems over the last decades. While the respective descriptions are kept brief and mainly convey the general ideas, references to further readings are provided for a more detailed treatment.
Information network security construction based on depth learning and modulus algorithm

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218.
An Individual model for Human Activity Recognition Using Transfer Deep Learning

Deep transfer learning. In this work, we present an individual model that efficiently recognizes the characteristics of each of 6 activities of each user. We started using the network structure from our previous work, 1D-CNN, which has already shown resistance to abnormal data. This experiment was started by pre-train network from 30 users. After that, after receiving the main model from the main data source, each test data was examined, the main model prediction performance. We found that there is a high rate of error prediction. We point to this problem and therefore attempt to solve it by using a transfer learning approach using parameter sharing from the main model to train a small set of data from the same test set. In addition, the researchers have used the data to test some of the main models for learning efficiency in each of the users. Then we will demonstrate the process of learning which is an improvement on relearning.
How do conversational case-based reasoning systems interact with their users: a literature review

Conversational case-based reasoning (CCBR) systems retrieve past cases that are similar to a current problem by eliciting situation descriptions in interactive dialogues with their users. To find out how such human-machine cooperation is put into practice, the present article reviews the CCBR literature and extracts a list of dialogue principles – interaction techniques by means of which CCBR systems communicate with their users. Seven dialogue principles are identified and explained: mixed initiative, question selection and ordering, dealing with abstraction and expertise, explanations, visualisation and highlighting, dialogue termination, and evaluation support. The results reveal that current CCBR systems already make great efforts to put user needs into the centre of the interaction. At the same time, the current implementation of dialogue principles that adjust CCBR systems to user needs raise questions about who should be in control of these adjustments, what levels of human-computer interaction should be adjusted, and what goals should guide adjustment decisions. Moreover, the present review highlights a number of limitations concerning the methodology and contents of CCBR research, and points out questions for future research on human-computer interaction in CCBR systems.
Multi-User VR Cooperative Puzzle Game

Multi-user virtual reality (VR) games are at the cutting edge of interpersonal interactions, and are therefore uniquely geared towards real-time interactive games between human players. This paper describes the process of designing a cooperative game where the obstacles are designed to encourage collaboration between players in a dynamic VR environment. This is done using the Unity game engine and the Blender graphics modeling tool. We demonstrate the progress of our scheme in a multi-player cooperative game, as well as the importance of the VR interface for encouraging cooperation. The VR experience provides a realistic human-human interaction improving on generic game-play, as our system utilizes the real-time interface to create an entertaining VR experience.
Data collection based on multi-agent modeling for intelligent and precision farming in lokoss region morocco

Precision Agriculture (PA) is an approach to farm management that aims to develop a sustainable agriculture that optimizes crop growth decisions by increasing yields and profitability of production for the farmer. Research in this context has confronted several challenges: dynamism of crop growth management, which makes it a complex problem and difficult to model, lack of qualified agronomic services and absence of site-specific fertilizer recommendations. The proposed solution in this paper aims to provide crop recommendations for the use of fertilizers, irrigation duration... by the adoption of multi-agent systems (MAS) that will overcome the complexity of the problem. In addition, we opted for wireless sensor network (WSN) to collect data on the agricultural environment, such as temperature, moisture, humidity and light. This solution divided into four parts, starting with receiving data from the sensor, filtering these data, visualizing and controlling components and finally arriving at the mining stage as a decision support system. In this article, our simulation was in Lokoss region in order to establishing a predictive analysis on the impact of climatic change on red fruits.
Cognitive Diagnostic Modelling for Mathematical Modelling Assessment

In a professional development workshop, we measured mathematical modelling as a performance assessment representing a multidimensional construct where dimensions are established with the performance indicators provided in a rubric. The study showed that a multidimensional non-compensatory psychometric model for scoring mathematical modelling performance provided a statistical model that fit to the assumptions of the mathematical modelling assessment. Empirical results are presented from a mathematical modelling task. We demonstrated that we can identify distinct attribute mastery profiles and provide fine-grained individualized feedback. Performing such analysis for teachers helped us identify professional development needs for teaching mathematical modelling and applications.
Clustering Algorithm of Density Difference Optimized by Mixed Teaching and Learning

Abstract Density peak clustering (DPC) algorithm is to find clustering centers by calculating the local density and distance of data points based on the distance between data points and the cutoff distance ( d c ) set manually. Generally, the attribute calculation between data points is simply obtained by Euclidean distance. However, when the density distribution of data points in data sets is uneven, there are high-density and low-density points, and the d c value is set artificially and randomly, this will seriously affect the clustering results of DPC algorithm. For this reason, a clustering algorithm which combines teaching and learning optimization algorithm and density gap is proposed (NSTLBO-DGDPC). First, in order to consider the influence of data point attributes and neighborhoods, the density difference distance is introduced to replace the Euclidean distance of the original algorithm. Secondly, because manual selection of clustering centers may produce incorrect clustering results, the standard deviation of high-density distance is used to determine the clustering centers of clustering algorithm. Finally, using the teaching and learning optimization algorithm (TLBO) to find the optimal value, in order to avoid the algorithm falling into local optimum. When the population density reaches a certain threshold, the niche selection strategy is introduced to discharge the similarity value, and then the nonlinear decreasing strategy is used to update the students in the teaching stage and the learning stage to obtain the optimal d c solution. In this paper, the accuracy and convergence of the improved TLBO algorithm (NSTLBO) are verified by ten benchmark functions. Simulation experiments show that the NSTLBO algorithm has better performance. Clustering algorithm integrating teaching and learning optimization algorithm and density gap proposed in this paper are validated by using eight synthetic data sets and eight real data sets. The simulation results show that the algorithm has better clustering quality and effect.