Reviewerly/exHarmony huggingface dataset:
Research on how to improve environmental protection during fire emergency rescue

Abstract With the rapid development of the urban economy, the flow of people in the city has gradually become active, and the amount of urban fires has gradually increased. The difficulty in preventing and extinguishing fires is gradually increasing. How to identify, judge, inspect, analyze and evaluate the factors that cause fires, carry out reasonable operations on the fires after the fires, optimize the firefighting capacity of the fire department, and effectively control Urban fires and the reduction of environmental pollution problems in fire rescue are the most important issues to be solved in the current situation. Briefly introduced the impact of fire accidents on the environment, systematically analyzed the sources of pollution and their hazards in fire accidents, and proposed the pollution control methods in fire accidents in combination with the existing firefighting methods, and made suggestions for improving the environmental protection of fire emergency rescue.
Correspondence Analysis of Child’s Play and Development through Wooden Toys

‘MOKUIKU’ is an educational activity to aim to deepen the experience with wood from infancy that can lead to a richer life, a better society, and an awareness for importance of the creation of new forests. ‘MOKUIKU-Toys’ is educational toys which have effect of encourage intellectual growth for young children. In our previous research of ‘Classification of Wooden Toys and Survey Analysis of the Effects’, we were able to verify the toys that were suitable for the development of children’s intelligence and physical ability. In addition, the relation between growth stage of children and the use of educational wooden toys was clarified in this research. Using a method of classifying 154 wooden toys for children from 0 years old to 3 years old in five categories, the relation between age group and play style was clarified using multivariate analysis. After that, using text mining analysis, we extracted useful information such as frequency of appearance of words in sentences, correlation of co-occurrence, appearance tendency. Finally, we created ‘Age correspondence table of effective use for children’s growth with MOKUIKU-Toys’. Using these results, we hope to create new production guidelines for MOKUIKU-Toys for preschool children.
Secure Visualization When Using Mobile Applications for Dementia Scenarios

Wandering is a common behavior during all stages of Alzheimer’s Disease (AD), but it can be distressing to both people with dementia and their caregivers. Ambient Assisted Living (AAL) solutions have been increasingly relevant to promote ageing in place, targeting both autonomous and dependent older adults. Despite the potentials of AAL solutions, their pervasiveness can raise several security and privacy challenges. With mobile mockups, this work presents a use-case with SoTRAACE - Socio-Technical Risk-Adaptable Access Control model, applied to the prevention of getting lost in AD. SoTRAACE functionalities are illustrated as embedded in an alarm navigation system aimed at monitoring the person’s geolocation and alerting when a predefined safety perimeter is crossed. The model, applied to a healthcare scenario, assesses security and privacy risks at each moment of interaction and provides an innovative way for improved adaptable security visualization in AAL environments.
An Improved Simulated Annealing Approach for Reconstructing Binary Images with Fixed Number of Strips

We consider the problem of reconstructing binary images from their horizontal and vertical projections and the given number of strips in each row and column. Knowing that the problem is NP-hard, in one of our recent papers (Szűcs, J., Balázs, P.: Variants of simulated annealing for strip constrained binary tomography, LNCS 10986, p. 82–92, 2019) we proposed variants of Simulated Annealing to solve this issue. However, in the same time we revealed different drawbacks of this approach. Here, we enhance the method in two ways: (1) we locally update the number of strips in each iteration, and (2) we apply the Curveball algorithm to avoid getting stuck in local minima. By experimental results we show that the new approach is faster than the former one, and it ensures also better solutions.
Hermes

Software maintenance is a costly task and error-prone for both software developers and users as well. By knowing how and what software requirements need to be changed, end users could perform maintenance assisted by tools. However, current literature lacks for tools that support automated maintenance in real-world scenarios and allow users interaction via natural language. Even worse, the current tools are unable to understand the semantic of requests, as well as perform the necessary transformations in the maintenance software. This paper, therefore, proposes Hermes, a natural language interface model for software transformation. It combines computational linguistics techniques and logic programming to perform automated maintenance requests in software. Hermes interacts with end user through state of the art language parsers and domain ontologies by interpreting the semantics of changes requests to build a typed graph that change the software. Hermes was evaluated through an empirical study with 8 participants to investigate its performance, the level of acceptance, and usability. The collected data show that Hermes was accurate, producing a high elevated correctness number of hits by finding correct transformations and has been highly accepted by the users. The results are encouraging and show the potential for using Hermes to properly produce software maintenance requests.
Automatic numerical differentiation by maximum likelihood estimation of a linear Gaussian state space model

A linear Gaussian state-space smoothing algorithm is presented for off-line estimation of derivatives from a sequence of noisy measurements. The algorithm uses numerically stable square-root formulas, can handle simultaneous independent measurements and non-equally spaced abscissas, and can compute state estimates at points between the data abscissas. The state space model's parameters, including driving noise intensity, measurement variance, and initial state, are determined from the given data sequence using maximum likelihood estimation computed using an expectation maximisation iteration. In tests with synthetic biomechanics data, the algorithm is found to be more accurate compared to a widely used open source automatic numerical differentiation algorithm, especially for acceleration estimation.
Visual Analytics and IR Experimental Evaluation

We investigate the application of Visual Analytics (VA) techniques to the exploration and interpretation of Information Retrieval (IR) experimental data. We first briefly introduce the main concepts about VA and then we present some relevant examples of VA prototypes developed for better investigating IR evaluation data. Finally, we conclude with an discussion of the current trends and future challenges on this topic.
Accurate single image super-resolution using cascading dense connections

In recent years, Convolutional Neural network (CNN) has achieved great success in the field of Single-Image SuperResolution (SISR) tasks. In order to improve the SISR performance, this paper proposes an accurate SISR method by introducing cascading dense connections in a very deep CNN. In detail, we construct the Cascading Dense Network (CDN) to fully make use of the features from input low resolution image and all the convolutional layers, which implements a cascading mechanism upon the dense connected convolutional layers. In addition, the global feature fusion in the CDN enables both short- and long- paths to be built directly connection from the input to each layer, alleviating the vanishing-gradient problem of very deep CNN. Extensive experiments show that our CDN achieves state-of-the-art performance on traditional SISR metrics (i.e. PSNR and SSIM). In addition, we introduce the object recognition as the additional evaluation metric for SISR, which further demonstrates the effectiveness of our method.
Construction and Optimization of Feature Descriptor Based on Dynamic Local Intensity Order Relations of Pixel Group

With the prevalence of smart embedded systems, the amount of images being captured and processed on mobile devices have grown significantly in recent years. Image feature descriptors which play crucial roles in detection or recognition tasks are expected to exhibit robust matching performance while at the same time maintain reasonable storage requirement. Among the local feature descriptors that have been proposed previously, local intensity order patterns (LIOP) demonstrated superior performance in many benchmark studies. As LIOP encodes the ranking relation in a point set (with N elements), however, its feature dimension increases drastically (N!) with the number of a neighboring sampling points around a pixel. To alleviate the dimensionality issue, this paper presents a local feature descriptor by considering pairwise intensity relation in a pixel group, thereby reducing feature dimension to the order of $$C^{N}_{2}$$ . In the proposed method, the threshold for assigning order relation is set dynamically according to local intensity distribution. Different weighting schemes, including linear transformation and Euclidean distance, have also been investigated to adjust the contribution of each pairing relation. Ultimately, dynamic local intensity order relations (DLIOR) pattern is devised to effectively encode intensity order relation of each pixel group. Experimental results indicate that DLIOR consumes less storage space than LIOP but achieves comparable or superior feature matching performance using benchmark data set.
Search Property of Nonlinear Map Optimization

We proposed Nonlinear Map Optimization (NMO) to improve the solution search capability of particle swarm optimization (PSO) algorithm. NMO is one of PSO-based swarm intelligence algorithms. We have previously proposed a canonical deterministic system PSO (CD-PSO) that is removed probabilistic factors from PSO to analyze its solution search behavior and is extracted only the essential dynamics of the solution search property of PSO. Although the dynamics of CD-PSO describe the basic dynamics of PSO, the solution search performance of CD-PSO is very poor than PSO. One of the causes is the distribution of search points. Although the search point distribution of PSO has a normal distribution shape depending on stochastic factors, the search point distribution of CD-PSO which is a deterministic system is not similar to the normal distribution. In order to improve the search point distribution of CD-PSO, we proposed a modified CD-PSO. Based on the modified CD-PSO, we proposed an NMO algorithm whose search points are derived by a nonlinear map. In this article, we clarify that the solution search property of NMO. The distribution of search points is similar to a normal distribution shape. Also, the nonlinear mapping of NMO derives complex behavior due to nonlinearity depended on parameters. These properties lead to the local search capability of NMO which is improved compared to PSO. Also, information exchange within the swarm similar to PSO is related to the global search ability. Since NMO can consider local search and global search separately, the solution search capability can be improved with unimodal functions than conventional PSO.
Consumption behavior evaluation of college students based on PSO-DTSVM

Abstract Blind and reckless consumption of the college students has become a common phenomenon. To give a correctly guiding, a decision tree support vector machine (DTSVM) based on particle swarm optimization (PSO) used to evaluate college students consumption behaviour is proposed in this paper. Firstly, the classification standard of college students consumption was derived with the investigation and data analysis. Then, the consumption ability and habits for different students are divided into four categories. Lastly, the targeted consumption index is fed back for the corresponding students. Experiment results show that a classification accuracy of 99.79% can be achieved, which will provide better guidance for the college students’ consumption behaviour.
Niche Radius Adaptation in Bat Algorithm for Locating Multiple Optima in Multimodal Functions

Evolutionary algorithms (EAs) are often used for multimodal optimization which is modeled as real-world problem. However, most EAs still not enough to find multiple local optima because of the concept of the solution movement between nearest neighbor solutions. This paper proposes the niche radius-based bat algorithm (NRBA), which is designed to find multiple local optima in multimodal optimization. We focus on bat algorithm (BA) which deals with the trade-off between exploration and exploitation in the evolutionary process and extend it with niche radius which can control and modify the search space of solutions to avoid overlapping the found optima. In detail, the proposed BA consists of three search phases: (i) the movement from neighbors for avoiding overlapping the same found optima; (ii) the exploitation for searching nearby the best solution of its domain with Niche Radius; (iii) the exploration for searching randomly in all domain of the radius. In order to evaluate the performance of NRBA, this paper employs some test-bed multimodal functions and compare NRBA with BA and NSBA. The experimental results suggest that NRBA is able to provide the better search performance than BA and NSBA to find multiple global optima in most of benchmark functions.
AN INDIVIDUAL'S COGNITIVE ACTIVITY PROBLEM IN HISTORY RETROSPECTIVE

The article studies the aspects of cognitive activity and the activity in history retrospection. The cognition and its study is not something unchanged, once and for all given, but is something that develops under certain laws. It has long history with the sources in the most ancient philosophy. At every stage of its development the knowledge is the result of the history of knowledge, the essence of all human activity forms. Scientific knowledge has its historically altered morphology. Its historical path analysis makes it possible to argue its origin from ancient philosophical schools, philosophers of Ancient India, China, Egypt, the countries of Mesopotamia. The phenomenon of an individual’s cognitive activity is complex and multifaceted. Scientists argue that cognitive activity is a complicated process of transition from ignorance to knowledge, from inability to ability, from random observations to systematic knowledge of the material world, to mastering the scientific truths. At the same time, a man, mastering new knowledge, affects the world, which, in turn, changes human life. The article analyses the influence of historical conditions on developing scientific thought about cognition, cognitive activity. The effectiveness of studying students’ educational and cognitive activity depends on how thoroughly the cognitive process is studied and the way its laws and features are used. The authors emphasise that taking into account the cognition theory possibilities, its Klondike was set in the Ancient world era and filled with new content rising to a new level in each generation .
Some comments on the constrained trajectory generation for UAV systems

This paper presents a trajectory generation procedure for unmanned vehicles (UAVs) which makes use of the flat-output representation of the UAV model to derive the desired reference trajectory. Furthermore, a corrective mechanism whose goal is to minimize the trajectory tracking error in the presence of bounded disturbances is implemented. Exploiting the flat description of the relevant states/inputs and a B-spline parametrization of the flat output, sufficient formulations for input and state magnitude constraints which involve only the B-splines' control points are given. The theoretical results are illustrated over a UAV model in simulation.
Aliasing Error Upper Bounds in Case of Digital Signals Uniform Sampling

The development of modern systems, such as the automotive radar or the satellite sensors, working at increased frequencies, brings new challenges for the design of the sampling systems. Taking into account the huge volume of data acquired by these systems, the data reduction techniques based on sampling become of great interest nowadays. In this context, the aliasing error is a very useful indicator for the quality of data reduction procedure. We introduce in this paper some upper bounds of the aliasing error in case of digital signals uniform sampling.
PoliDOM

The current generation of DOM (Document Object Model) Cross-Site Scripting (DOM-XSS) filters are mostly browser-based tools, and do not allow the web developers to control authorized or unauthorized modifications of the web page's DOM. In this work, we propose a policy-based and browser-based protection mechanism to detect and prevent unauthorized tampering of the DOM. To examine the efficiency and feasibility of our approach, we implement the proposed solution in an open source web browser, Chromium. Our proposed approach has little performance overhead and effectively detects malicious modifications of the DOM. We also conduct a thorough analysis of the current state-of-the-art policy-based MutationObserver API and uncover its limitations.
Recognizing Video Resolution by Monitoring Memory Metrics in Mobile Clients

Content delivery is one of the most successful applications in mobile networks. Mobile edge servers are significantly contributing to the improvement of this type of applications, by also performing resolution adaptations as a function of the radio link quality observed by the users. In this paper, we face the problem of dynamically tracking the perceived video resolution by mobile clients in a mobile edge environment to guarantee an agreed Service Level Agreement (SLA). To this purpose, we propose a protocol-agnostic approach, based on monitoring some memory metrics of a running video streaming process at the mobile client, for identifying the time-varying resolution of the video content.
Real-Time Scale Adaptive Visual Tracking with Context Information and Correlation Filters

Visual tracking has been studied for several decades but continues to draw significant attention because of its critical role in many applications. However, there still need to improve the overall tracking capability to counter various tracking issues, including large scale variation, occlusion, and deformation. This paper presents an appealing tracker with robust scale adaptive, which applies the discriminative correlation filter for scale estimation as an independent part after finding the optimal translation based on the kernelized correlation filter and context information. Compared to an exhaustive scale space search scheme, our approach provides improved performance while being computationally efficient. In order to reveal the effectiveness of our approach, we use benchmark sequences annotated with 11 attributes to evaluate how well the tracker handles different attributes. Numerous experiments demonstrate that the proposed algorithm performs favorably against several state-of-the-art algorithms.
A process partitioning technique for constructing decentralized web service compositions

Summary Web service compositions have been widely applied in different applications. A service composition is usually implemented in either a centralized or decentralized manner. Compared with the centralized service composition, the decentralized composition has no central control component, and components interact with each other directly, thereby achieving better performance. Process partitioning is a technique to divide a process into multiple parts and has been shown that it can be successfully applied to decentralizing process‐driven service compositions. This paper proposes a new process partitioning technique for constructing decentralized service compositions. The proposed technique, which is based on typed digraphs and a graph transformation technique, is used for exploring available process partitioning solutions. For applications, this paper discusses the topology and interaction features about the partitioning solutions and summarizes a ranking method for them. Three experiments are conducted to evaluate the proposed methods in this paper. Experimental results show that the proposed methods can be applied in constructing decentralized service compositions effectively. In addition, the results also show that the decentralized compositions can have lower average response times and higher throughputs than the corresponding centralized compositions in the experiments.
Prediction of Data Stream Based on Gaussian Process Regression with Online Free Variational Inference Approximation

Abstract Online prediction of data stream is currently being used in various fields. The prediction method based on Gaussian Process Regression has obvious advantages since its outputs have a probabilistic significance, which is suitable for dealing with nonlinear and complex regression problem. However, the characteristics of the data stream are real-time and online. If only using the historical data of the previous time as the training sample to construct the prediction model, the model will not be accurately predicted once the distribution of the new data changes. To this end, we employ the Online Free Variational Inference approximation to build the prediction model. The key idea is to introduce a variational distribution and maximizing the Kullback-Leibler Divergence between the variational distribution and the true value of the output’s posterior distribution. Further, we use telemetry data to verify the validity of the Online Free Variational Inference approximation and the experiment shows that the online data stream can be predicted well by the method employed in the paper.
Experimental Study on the Effect of Motion Sickness on 4D Movie Viewing Comfort

At present, academic research on 4D movies mainly focuses on cinema equipment and projection technology. This paper takes the viewing comfort as the research object. Through questionnaire survey, it is found the main factor affecting the comfort of viewing is the motion sickness, and through theoretical research, it is found that there is a correlation between motion sickness and motion sickness caused by watching 4D movies. So try to ease the motion sickness caused by watching 4D movies by solving the problem of motion sickness, and to carry out experimental verification, finally get a conclusion that psychological comfort can effectively alleviate the user’s motion sickness.