Reviewerly/exHarmony huggingface dataset:
A New Approach for Image Classification Applying Reduction of Colored Keypoints

There are algorithms for feature extraction such as SIFT and Opponent-SIFT, which detect and describe keypoints. In image classification, it is common to have an image dataset. Therefore, when using an algorithm to detect and describe local features in a set of images, the number of keypoints detected by class can be disproportionate. This paper presents a novel approach to reduce the number of keypoints (and colored keypoints) in images after the feature extraction process so that computer vision techniques can be applied to image classification problems. This approach uses Zipf's Law and the Pareto Principle to conduct the new strategy to reduce keypoints. An experiment was conducted comparing four different strategies. Results are encouraging, and the proposal opens new paths for keypoints reduction and syntactical pattern recognition. The classification reached an F-Measure of 76,8%, and the computer performance (execution time) has increased from 9 to 1900 times.
Usability evaluation of an integrative exergaming system for the senior population

Cardiovascular diseases such as heart attack or stroke are one of the major causes of death worldwide. Physical activity plays a key role in the prevention and delay of such diseases. Configurable exergames have been suggested as a solution to promote physical exercise in the senior population. An integrative system was designed and implemented following human-centered design techniques involving the main prospective interactors with the system. The success of such a system can only be achieved if it is usable by the target audience. In this paper, we present the efforts towards evaluating the usability of an integrative system for exergames targeting the senior population. Results from the study revealed a high level of satisfaction and provided a list of improvements for future development iterations.
Monocular Vision-Based Real-Time Vehicle Detection at Container Terminals

We present a practical approach to vehicle detection at container terminals based on a single camera and prevailing convolutional neural network models in computer vision domain. Aiming at container terminal scenarios, we introduce a specialized data labelling strategy for network training, as well as an optimized setting of crucial hyperparameters, leading to a significant improvement on results. Our solution achieves 83% precision with 90% recall for semitrailer trunks within 30 m ahead of the vehicle-mounted monocular camera, at a speed of 32 frames per second (FPS) on a Nvidia Titan X for 416 × 416 image input, also providing more alternatives of fairly easy speed/accuracy trade-off. Compared to traditional lidar-based vehicle detection method for autonomous driving, our solution is more robust for particular container terminal scenarios while still maintaining a real-time performance by completely eliminating the region proposal stage.
Database Watermarking Scheme Based on Clustering with Size Constraint

The improved fuzzy C-means clustering algorithm is used in the process of selecting data to embed watermark. Firstly, aiming at the problem of unstable data size in the traditional fuzzy C-means clustering in the clustering results, the fuzzy C-means clustering algorithm was optimized. Moreover, the data category was further determined on basis of the addition conditional constraints to the clustering. Each tuple of the database can be preliminarily clustered due to the introduced algorithm for database watermark. Then, in each category, the user can select one or several types of data utilized the key control to embed into the watermark. Before embedding the watermark, the proportion parameter is set according to the sparse degree of the watermark, which reflects the principle that the user controls the copyright.
Recommendations for Personalized Keywords Combined with Word Vectors Clustering and Heat Sorting

In order to solve the problem of information overload in search engine, we propose a keyword recommendation method applied in search engine. Our method performs collaborative filtering according to the user's query log, combines word vector clustering to find the word classes that the user is interested in, and then recommends the keywords with the highest popularity among the word classes of interest to the user, assists the user to search and enhance the search engine efficiency. The method combines the user's behavior data and the heat sorting of keywords, and takes into account the association between keyword meanings. While retaining the advantages based on heat sorting recommendation, a certain degree of personalized recommendation is realized. We design two control experiments, comparing the recommendations for straightforward collaborative filtering and the recommendations based on heat sorting, our method achieve the best results in the experiment.
Differential Evolution Based on Learnable Evolution Model for Function Optimization

With the advance of technology, the generation of massive amounts of information grows every day, generating complex problems difficult to manage in an efficient way. Therefore, researchers have studied and modeled the way in which natural biological systems react and behave in certain situations, allowing to developed algorithms that exhibit a capacity to learn and/or adapt to new situations, obtaining better results than traditional approaches. In this article we present a new variant of the Differential Evolution (DE) algorithm inspired by the concept of the Learnable Evolution Model (LEM) to enhance the search capability through a selection mechanism based on machine learning to create a set of rules that allows the inferring of new candidates in the population that emerge not only the random scan. The proposed algorithm is tested and validated on a set of 23 bechmark test functions and its performance is compared with other metaheuristics. Results indicate that the proposed DE+LEM is competitive with other metaheuristic.
Multithreaded Evolutionary Computing

There are described different methods of usage of the standard.NET class Task for the creation of multithreaded applications in C#. For instance, to separate the thread which performs expensive calculations from the one, which supports the Windows application's user interface. It is given an example of the usage of described techniques in the application, which solves the traveling salesman problem for a large number of cities using the genetic algorithm. It is presented the results of the experimental performance evaluation of the algorithm, depending on the number of parallel calculation execution threads.
Rapid Detection of Individual Cells Based on K-means Clustering and Regional Connectivity

The methods of automatic recognition, segmentation and counting methods based on image recognition technology and artificial intelligence technology are the core issues to achieve standardization of digital pathological diagnosis. This paper proposes a rapid method based on K-means clustering and regional connectivity to infer the complete contour of individual cells. Rapid detection of individual cells is realized for bone marrow cell images. Firstly, K-means clustering and regional connectivity are used to detect individual nucleus region, and analyze the geometric characteristics of cells. Over-segmented individual cells are judged by area, roundness and shortest diameter of cell. Secondly, using optimized segmentation for over-segmented individual nucleus region based on the constraints of center distance. Finally, the method infers the complete contour of individual cells. Sixty-one bone marrow cell images were tested, the detection precision of granulocytes is more than 90%, and the precision of overall individual cells detection is significantly improved.
Adaptive Ensemble

In this paper we share our solution to the Biomedia ACM MM Grand Challenge 2019, which focuses on the gastrointestinal tract with an aim to detect and classify abnormalities. We firstly identify the challenges in this task, including the scarce and imbalanced data, and the subtle inter-category variances. Based on these analysis, we propose a solution which leverages the 10-fold cross validation approach to alleviate the over-fitting problem, and design a model to adaptively ensemble all sub-models belonging to all component models. Based on extensive offline evaluations, we verify the performance of the proposed technique under various settings. In the competition, we eventually receive the MCC(Matthews correlation coefficient) score of 0.9480.
Optimization Design of Wind Turbine Tunnel Based On Fluid Analysis Results

Abstract At present, the problem of wind turbines affecting flying organisms has been highly valued. Wind turbines will not only attack flying creatures through rotating blades, but also the flying organisms will affect the habitat of ground creatures because of the reduction of flying height. In this study, a fan with protection function is designed by designing a wind turbine system with a wind tunnel design that can be smoothly adopted by birds. Because the blade is not in direct contact with the wind, it is introduced into the blade through the wind tunnel. Therefore, in the design of the wind tunnel, not only the wind speed of the introduction is not weakened, but also the wind speed can be fastened through the structural angle. Therefore, the blades can be rotated as scheduled to achieve the purpose of wind power generation. In the design of the wind tunnel, the study conducted the wind speed test of the structure through continuous fluid analysis. In the results obtained, correct all structures that may affect wind speed and re-analyze to ensure that the wind speed can successfully to achieve the goal. At the same time, the maximum wind speed obtained will be used to test the torque and power generation by the formula to see if the design is able to reach the goal of power generating.
Impact of the Integral Evaluation on the Professional Training Process of the Students of the Technology Professional Programs

Training is the evaluation. What, how and how much to evaluate are aspects to be taken into account. Within the framework of the educational proposal of integral education of an emerging university in Lima Norte, integral evaluation has been implemented, which is a strategy that consists of not only assessing cognitive aspects but also research aspects, aspects of the conative-volitional process. This evaluation is done to the students and teachers (who are the main actors of the professional training process) through rubrics communicated appropriately by the academic department to the teachers, which communicate to the students. The result of this experience is that students have made progress in their research skills, in their interpersonal relationships; also in the teachers has allowed us to detect weaknesses in the research aspect and therefore generate policies to advance in that aspect. We believe it is important to socialize this experience so that it can be taken into account by other educational institutions since the integral formation is now a necessity and therefore the integral evaluation as well.
Features and Connection Based Network Alignment Method

In almost all proposed computer science algorithms, success rates are directly related to the data set. Recommendation and link prediction methods are the most critical methods in which the success of the repository directly affects the success of the method. The cold-start problem can be classified as basically a missing data problem. Since there are dozens of social networks in the world, not being able to collect all data in one source can be called as missing data problem. The fact that the users' Twitter pages cannot be used as a single source without other social networks like Instagram for collecting data about users. Instead of this, it can be more successful if data from both networks to connect. It is not usual for users to link accounts across different social networks or merge them into a single account. This study aimed to find the same users in different social networks by looking at the attributes of the profile, their connections, and shares on the network. In this study, based on the status and shares of the users on the network, the proposed method has been successfully found in the profiles of the person in different networks.
Power Integration Mall Recommendation Model Based on Reverse Reward Feedback Learning Optimization

At present, the recommendation algorithm of power integration mall is often limited to users' personal preferences and historical choices, and it is difficult to capture the change information of users' behavior exchange trend, so it is easy to lead the recommendation model into local optimum. In order to solve the above problems, a method of power integral merchandise recommendation based on inverse reward learning optimization is proposed. The algorithm constructs a latent customer mining model to cluster users in multi-dimension, extracts the relevant feature information of different user groups, and implements the convertible merchandise recommendation for different user groups with three-dimensional rating recommendation algorithm. Subsequently, an optimal recommendation model based on inverse reward feedback learning is adopted to guide the next recommendation behavior according to the reward plastic function formed by historical exchange records. On the premise of satisfying users' individual exchange preferences, on the one hand, it avoids the limitation of the traditional recommendation algorithm which leads to the recommendation of goods, on the other hand, it enlarges the range of users' choice of exchange by the direction of recommendation formed by the overall users' exchange preferences. The experimental results show that the model can improve the success rate of customer converting commodities, and illustrate its feasibility and effectiveness.
Utilizing User Values to Generate System Requirements for a Geographically-dispersed Workforce

A geographically-dispersed user base presents challenges for requirements engineering in enterprise systems, with problems including limited access to end users, a lack of trust between users and developers, and poor informal communication. Not only must these standard organizational and personnel difficulties be taken into consideration, but geographic dispersal also complicates the steps of the requirements engineering process. This paper investigates whether a method can be created to develop requirements for just such geographically-distributed outposts within the same organization. A psychological instrument that measures basic values was coupled with individual interviews to collect the value profiles for frontline security guards throughout Sweden. The results were used to generate requirements for field equipment, with goal- and feature models generated as a final result.
Improved Document Filtering by Multilevel Term Relations

Humans tend to organize information in documents in a logical and intentional way. This organization, which we call textual structure, is commonly in terms of sections, chapters, paragraphs, or sentences. This structure facilitates the understanding of the content that we want to transmit. However, such structure, in which we usually encode the semantic content of information, is not usually exploited by the filtering methods for the construction of user profile. In this work, we propose the use of term relations considering different context levels for enhancing document filtering. We propose methods for obtaining the representation, considering the existence of imbalance between the documents that satisfy the information needs of users, as well as the Cold Start problem (having scarce information) during the initial construction of the user profile. The experiments carried out allowed to assess the impact on the filtering task of the proposed representation.
Universal Industrial-applicable Calibration Method of Optical-electronic Triangulation Systems for 3D Geometry Measurements

Abstract Universal industrial-applicable calibration method for optical-electronic triangulation systems using structured illumination for 3D geometry measurements is proposed. An analytical and experimental estimations of the measurement error due to the method of phase steps and system calibration were obtained. The method has a simple implementation when measuring the 3D geometry of large objects. It is shown, that proposed method can be effectively used in measurement systems constructed on a standard low-cost element base.
Independent component analysis with reference multifeature integration for visual object tracking via adaptive update

Tracking methods based on correlation filter have been showing great advantages and huge potential. However, it will lead to tracking drift when the target is disturbed, such as occlusion, illumination variation, deformation, and others. In the pursuit of tracking accuracy and robustness, a feature integration model and an adaptive update method for robust tracking are proposed. First, the fine-tuned convolutional neural networks medium is employed to obtain the deep features. The deep features are integrated with traditional manual features by combining with the independent component analysis with reference method to obtain more discriminative features. Second, in the model update phase, an adaptive updating strategy based on the center shift Euclidean distance of image patch is proposed to reduce the unnecessary calculation in model training so as to speed up the tracking speed. Finally, the tracker proposed is evaluated on online tracking benchmark (OTB-2015) and visual object tracking benchmark (VOT-2016). The experimental results show that the method that integrates deep features with traditional manual features can distinguish background and target better. Thus the proposed VOT algorithm performs better robustness and accuracy against state-of-the-art methods when dealing with challenging environments.
Random S-boxes in Symmetric Ciphers

This description is about a new 256-bit block symmetric substitution-permutation cipher, called a managed substitution cipher. This is a cipher with single-layer permutated transformations in each cycle. The management of substituted transformations occur by including them in a chain so that the output value of the previous SL transform is fed to the input of the current fortified 32-bit substituted transformation (SL conversion) along with the current value of the input data block through the adder for modulo 2. It is shown that such construction of cycled function allows to use random substitutions without any selection in a cipher while not reducing its strength.
A Resilient Behavior Approach Based on Non-monotonic Logic

In this article we present an approach for representing a resilient system which has the capability of absorb perturbations and overcome a disaster. A framework called KOSA is depicted, which is a world that contains a set of knowledge describing objectives, states and actions, linked by a set of rules. This link is expressed by a default theory. First, we define resilience as a relation among states and objectives. Secondly, from a given state, extensions are calculated, which provides information where to go to the future state. The connection, among two or more states creates different configurations that we call trajectories. These connections represent an evolution of the knowledge. Consequently, this reveals the existence of a resilient trajectory. Examples of piloting an airplane are concerned through this paper. Eventually, we present a discrete theoretical behavior of the complete model. Finally the notion of distance among extensions is introduced.
Mathesis Universalis and Homotopy Type Theory

The present paper investigates the use of impredicative methods for the construction of inductive types in homotopy type theory. Inductive types have been constructed impredicatively in other systems of type theory in the past, but these fail to have the correct rules. Using new methods, the paper shows how to repair these prior constructions, and extend the impredicative methodology to include also the newly discovered higher inductive types that form the basis of the recent applications in homotopy theory. This present work refines and extends the traditional logistic approach to foundations of mathematics to encompass both arithmetic and geometry in a comprehensive logistic system that also admits a computational implementation on modern computing machines. The connection to the idea of mathesis universalis is thus quite direct.
On Quantizing the Mental Image of Concepts for Visual Semantic Analyses

With the rise of multi-modal applications, the need for better understanding of the relationship between language and vision becomes prominent. While modern applications often consider both text and image, human perception is often only of secondary consideration. In my doctoral studies, I research the quantization of visual differences between concepts regarding human perception. Initially, I looked at local visual differences between concepts and their subordinate concepts, measuring the variety gap between images of, e.g. car and vehicle. In the following study, I applied data-mining on Web-crawled images to estimate psycholinguistics metrics like the imageability of words. In this way, the tendency of low- vs. high-imageability can be estimated on a dictionary-level, defining the gap between words like peace and car. Going forward, I want to create visualization demos to analyze psycholinguistic relationships in image datasets.
Semiautomated Ontology Learning to Provide Domain-Specific Knowledge Search in Marathi Language

In this research work, our goal is to build a self-sustainable, reproducible, and extensive domain-specific ontology for the purposes of creating a knowledge search engine. We have used online data as the primary information store using which we construct ontology by identifying concepts (nodes) and relationships between concepts. The project encompasses preestablished ideas gathered from successful NLP trials and presents a new variation to the task of ontology creation. The system, for which the ontology is being created, is a knowledge search engine in Marathi. This aims at building semiautomated ontology whose target demographic is primary school children and the selected domain is science domain. This project proposes a method to build semiautomated ontology. We use a combination of natural language processing method and machine learning method to automate the ontology learning task. Automatically learned ontology is further modified by language and domain experts to enrich the contents of ontology. Unlike, standard search engines, our knowledge search engine attempts to provide learned resources directly to the user rather than website links. This approach enables the user to directly get information without having to spend time on browsing indexed links.
Deployment in Cloud Computing

Although clouds have adopted common communication protocols such as HTTP and SOAP, interoperability, integration, and coordination of all clouds remain a concern. Instead, companies are looking for solutions to deploy an infrastructure that spans multiple instances of public and private clouds. Each of the proposed cloud solutions has its own limitations, management APIs, and development cycles that must be monitored and managed to provide a consistent set. The objective of the article is to answer the question: Is there a platform to deploy, run and manage applications in a multi-cloud environment and to ensure their availability, performance, and optimal use of resources?