Reviewerly/exHarmony huggingface dataset:
Improved Ensemble Classification for Evolving Data Streams

A major challenge for evolving data stream classification is feature evolution where features of stream instances are dynamically changing as they progress. Existing classification methods considered feature evolution either for fixed-size data or of limited degree with presumed dependence to history, making them unable to work effectively on evolving data streams of unbounded size and arbitrary feature evolution. Particularly, for evolving data streams containing instances of multiple labels, classification coping with feature evolution faces significant challenges. In this article, we present efficient ensemble methods for classifying evolving data streams of both single label and multiple labels through effective model coupling. For single-label classification, we present an improved unsupervised classification algorithm that applies multi-cluster feature selection (MCFS), which was originally proposed for static data classification, in the DXMiner framework to handle each window of instances in a dynamic stream. Our method generates an optimal feature subset and achieves a high classification accuracy. We further improve the time complexity of the feature selection process in MCFS by applying the Ball-tree searching technique. For multi-label classification, we propose an effective fixed-size ensemble classifier based on multi-label KNN, which works only for static multi-label data classification, by incorporating a weight adaptation strategy among the classifiers in the ensemble to dynamically update the model and cope with arbitrary feature evolution of stream instances as the stream progresses. Extensive experiment results on real-life data streams show that our algorithms outperform the existing results for single-label and multi-label classification in classification accuracy and efficiency.
Understanding performance concerns in the API documentation of data science libraries

The development of efficient data science applications is often impeded by unbearably long execution time and rapid RAM exhaustion. Since API documentation is the primary information source for troubleshooting, we investigate how performance concerns are documented in popular data science libraries. Our quantitative results reveal the prevalence of data science APIs that are documented in performance-related context and the infrequent maintenance activities on such documentation. Our qualitative analyses further reveal that crowd documentation like Stack Overflow and GitHub are highly complementary to official documentation in terms of the API coverage, the knowledge distribution, as well as the specific information conveyed through performance-related content. Data science practitioners could benefit from our findings by learning a more targeted search strategy for resolving performance issues. Researchers can be more assured of the advantages of integrating both the official and the crowd documentation to achieve a holistic view on the performance concerns in data science development.
Design Research on Vibration Tactile Feedback in Vehicle Navigation Information Application

Vehicles become the preferred means of transportation for people to travel, and the problems of intensified traffic congestion, frequent traffic accidents, and deterioration of the transportation environment have resulted. Therefore, the demand for in-vehicle navigation is more urgent, and the feedback of navigation information has also become an important influencing factor. The current research methods of information feedback are mainly visual and voice feedback that has advantages, but they also have significant shortcomings in terms of safety and experience. This research analyzes the situation through questionnaires and interviews. The method determines the body parts of the vibrotactile feedback and the driving problems and use scenarios that can be solved using the vibrotactile feedback. Based on the quantitative and qualitative analysis results, this study obtained the design opportunities of the vibrotactile feedback and built a prototype using the Arduino platform. According to the following verification tests, the test results show that the experiment has good usability, which can provide guidance and suggestions for future research.
Introduction to Blockchain Technology

Abstract This chapter gives an introduction to blockchain technology which was proposed by the pseudonymous Nakamoto (2008) with his well-known Bitcoin paper to enable online payments in a decentralized digital cash system, solving the double-spending problem where someone can make two payments with the same coin. The chapter starts with an informal outline of the application of a blockchain and a brief explanation of how a blockchain network is able to reach consensus about the common state of a transaction history. Furthermore, it introduces the Proof-of-Work (PoW) algorithmwhich needs to be donein order to extend the blockchain with new blocks. Since the PoW is a very costly computational algorithm, the transaction data is stored in previous blocks resistant to modification, and thus the PoW safeguards the order of blocks, forming a tamper-proof history of transactions. As a last step, it examines the double-spending attack, which is a fundamental attack to the integrity of a blockchain based system.
A Novel High-Precision Method Based on Sequence Weighted Adaptive Unscented Kalman Filter for GPS Disciplined Crystal Oscillator

GPS disciplined crystal oscillator technology has been widely used to obtain high-precision and low-cost frequency standards. But there are two factors that would affect the accuracy of the frequency correction. The first is the random error and outliers contained in the one pulse per second (PPS) outputted by the GPS. The second factor is the frequency nonlinear drift of crystal oscillator because of the aging and the temperature. To solve these problems, a mathematical model of the frequency difference between IPPS signal and crystal oscillator signal is established in this paper. And then a novel high-precision method based on the sequence weighted adaptive Unscented Kalman filter (UKF) algorithm is proposed on the basis of the mathematical model. To be more specific, the UKF is used to address the problem of random error and frequency nonlinear drift. And the effect of outliers is eliminated by weighing sequence in the UKF algorithm. The adaptive filtering technology can reduce the impact of the variable and random system noise. Numerical simulation and verification are described. The results show that the proposed method can make frequency standards higher stability and precision.
The Tip of the Iceberg

In this article, we investigate a fundamental question regarding software security: Is the security of SW releases increasing over time? We approach this question with a detailed analysis of the large body of open-source software packaged in the popular Debian GNU/Linux distribution. Contrary to common intuition, we find no clear evidence that the vulnerability rate of widely used software decreases over time: Even in popular and “stable” releases, the fixing of bugs does not seem to reduce the rate of newly identified vulnerabilities. The intuitive conclusion is worrisome: Commonly employed development and validation procedures do not seem to scale with the increase of features and complexity—they are only chopping pieces off the top of an iceberg of vulnerabilities. To the best of our knowledge, this is the first investigation into the problem that studies a complete distribution of software, spanning multiple versions. Although we can not give a definitive answer, we show that several popular beliefs also cannot be confirmed given our dataset. We publish our Debian Vulnerability Analysis Framework (DVAF) , an automated dataset creation and analysis process, to enable reproduction and further analysis of our results. Overall, we hope our contributions provide important insights into the vulnerability discovery process and help in identifying effective techniques for vulnerability analysis and prevention.
User Taste-Aware Image Search

Pinterest as a popular image search platform has been widely adopted by users. Every day, people come to Pinterest searching for fashion- and home decor-related content. In these domains, users exhibit stable personal tastes. In this paper, we propose a novel search algorithm which can infer user tastes from their past engagement history and tailor the search results to fit their preferences. The online and offline experiments show that our method can efficiently improve user experience and increase user engagements.
Neural Bayesian Information Processing

Deep learning is developed as a learning process from source inputs to target outputs where the inference or optimization is performed over an assumed deterministic model with deep structure. A wide range of temporal and spatial data in language and vision are treated as the inputs or outputs to build such a complicated mapping in different information systems. A systematic and elaborate transfer is required to meet the mapping between source and target domains. Also, the semantic structure in natural language and computer vision may not be well represented or trained in mathematical logic or computer programs. The distribution function in discrete or continuous latent variable model for words, sentences, images or videos may not be properly decomposed or estimated. The system robustness to heterogeneous environments may not be assured. This tutorial addresses the fundamentals and advances in statistical models and neural networks, and presents a series of deep Bayesian solutions including variational Bayes, sampling method, Bayesian neural network, variational auto-encoder (VAE), stochastic recurrent neural network, sequence-to-sequence model, attention mechanism, end-to-end network, stochastic temporal convolutional network, temporal difference VAE, normalizing flow and neural ordinary differential equation. Enhancing the prior/posterior representation is addressed in different latent variable models. We illustrate how these models are connected and why they work for a variety of applications on complex patterns in language and vision. The word, sentence and image embeddings are merged with semantic constraint or structural information. Bayesian learning is formulated in the optimization procedure where the posterior collapse is tackled. An informative latent space is trained to incorporate deep Bayesian learning in various information systems.
Image Pattern Verification Based On Seller's Batik Solo Product Name Using SURF As A Texture Based Image Retrieval

Sellers in online marketplace who use batik Solo's pattern name as their product title, but mistaken it with product image that doesn't contain the pattern, could causes the buyer to wear batik Solo in wrong event. Every pattern in batik Solo has crucial meaning and specialize to be worn in different event such as wedding ceremony or funeral. Therefore this research has the purpose to build a system that can verify batik Solo's product image depending on the batik pattern name that the seller wants to use as their product title in online marketplace. First, the research input batik Solo pattern name in the four biggest online marketplaces in Indonesia, which are Tokopedia, Bukalapak, Shopee and Lazada. Second, it scrapped some of the images result from every marketplace. Then the research makes a system that can identify the image's texture from marketplace and identify the image's texture from the data set that was prepared before. This process could be completed using the SURF method. Next, an image from an online marketplace that contains a specific pattern will input to the system to find if it retrieves image from data set's image with a similar intended pattern or not. The system verification will label an image as true if it can retrieve some images from data set, and label an image as false if it can't retrieve image from data set. The output label from the system will be compared with the label from human judgement to measure the accuracy of the system. The results are, the highest accuracy is 74.76% and the highest recall is 94.55%.
Microservice Architecture for Multistore Database Using Canonical Data Model

In a microservice architecture, solutions are created by teams focused on specific domains and needs. They independently develop and deploy distributed services in the network. One characteristic of microservices is decentralized data management. Each microservice may use different data management technology which best fit its needs. Hence, it is an issue to integrate data of heterogenous microservices to come up with consolidate data views. Flexible and efficient solutions in this scenario are needed. This work is based on the use of a canonical data model as the mechanism for data integration in microservices. The canonical data model is the reference for query specifications and data integration. This work proposes and implements a microservice architecture based on this strategy and it is composed by nodes that intercommunicate through several mechanisms (e.g., SPARQL, GraphQL and JDBC queries, calls to REST services and proprietary APIs). The solution was analyzed in a proof of concept in a fictitious scenario but using real services available at DBPedia and Twitter. The evaluation goal was to qualitatively analyze the use of the architecture in the design, development and execution of microservices in order to identify the characteristics one should consider when using the canonical data model strategy. The evaluation employed the criteria of ISO/IEC 25010 model that most relate to the SOA challenges, which were: usability; performance; compatibility; and, maintainability. The identified advantages and disadvantages of using the architecture (i.e., the strategy) can be used by architects and developers to make their development decisions.
Using CNN With Handcrafted Features for Prostate Cancer Classification

Prostate cancer has been a leading cause of death among males for a long time. Currently, with the help of computer-aided detection systems, prostate cancer can be detected in a relatively early stage, thus improving the patients' survival rate. In this paper, we propose a computer-aided system based on deep learning method to help classify prostate cancer. Our model combines both convolutional neural network (CNN) extracted features and handcrafted features. In our model, the input data is sent into two subnets. One is a modified ResNet with an improved spatial transformer (ST) for high dimension feature extraction. The other subnet extracts three handcrafted features and processes them with a simple CNN. After those two subnets, the output features of the two subnets are concatenated and then sent into the final classifier for prostate cancer classification. Experimental results show that our model achieves an accuracy of 0.947, which is better than other state-of-the-art methods.
Smart bikes: Gradual update of IoT systems

Automated consistent software updating is challenging for distributed internet-of-things applications. We are developing a solution based on allowing several versions of the software to coexist on the same device, so that new and old software can be gradually phased in and out. We demonstrate our solution using a smart-city application for preventing thefts of bike batteries.
A Reversible Meaningful Image Encryption Scheme Based on Block Compressive Sensing

An efficient and reversible meaningful image encryption scheme is proposed in this paper. The plain image is first compressed and encrypted simultaneously by Adaptive Block Compressive Sensing (ABCS) framework to create a noise-like secret image. Next, Least Significant Bit (LSB) embedding is employed to embed the secret image into a carrier image to generate the final meaningful cipher image. In this scheme, ABCS improves the compression and efficiency performance, and the embedding and extraction operations are absolutely reversible. The simulation results and security analyses are presented to demonstrate the effectiveness, compression, secrecy of the proposed scheme.
The Random Neural Network with a Genetic algorithm in Intelligent Buildings

The Random Neural Network with a Genetic algorithm and its integration into an Intelligent Building: iBuilding is proposed in this paper. The presented biological method, founded on the Genome, codifies and transmits the information from the Intelligent Building. Furthermore, it also multiplexes its data entirely to generate Clusters of Buildings that are interconnected with each other. The key concept proposed in this paper is that the learned information obtained by iBuilding after its interaction with the environment is never lost when the building is decommissioned or retrofitted but transmitted to future iBuilding generations as distributed organisms. Data is codified in the network weights instead of the neurons, similar as the Genome, in order to enable an Artificial Intelligence evolution in iBuilding. The presented biological algorithm is inserted into an iBuilding model where sensorial neurons distributed within the Intelligent Building collect measurements about its environment and select relevant information. This proposed model has been validated with several research datasets that cover several key scenarios; experimental results demonstrate that the Random Neural Network Genetic Algorithm codifies, transmits and multiplexes iBuilding information to future generations with insignificant error, therefore, successfully creating a cluster of buildings.
SURF Algorithm with Convolutional Neural Network as Face Recognition Technique

Recent developments in technology need the methods to become more efficient in various conditions. We can see this situation is much visible in multimedia and verification, where biometrics are used to recognize somebody from images, both as detections of suspicious behaviors and user verification. The paper presents proposed technique for face verification by the use of hybrid method based on SURF and neural network classifier. On the input image SURF is searching for potential key points, for which it creates special maps. These descriptors are forwarded to neural network for analysis and verification. Proposed solution works well and experimental results show good efficiency.
Moving Target Tracking with Robot Based on Laser Range Finder

The moving target detection and tracking of mobile robots have always been a difficulty and hot spot in the field of robot research. This paper focuses on the detection and tracking algorithm of the robot moving target based on the Laser Range Finder. Firstly, the algorithm uses a Laser Range Finder to obtain the relative distance and direction information between the robot and the moving object in real-time. Then, according to the current distance and azimuth information, the instantaneous movement speed and acceleration information of the robot and the moving target, and the physical parameters of the robot itself, the movement state of the movement target is calculated and predicted, and the control function is fitted. The control strategy enables the robot to track the moving target in real-time. Experiments were conducted on the AS-R robot platform. The experimental results show that the control algorithm can effectively track the moving target in real-time, and the algorithm has good robustness.
Technique for Enhancing the Robustness, Imperceptibility, and the Security of Digital Audio Watermarking

This chapter describes the results produced by the new algorithm and also performance analysis by comparing all the results with the existing algorithms has been discussed.
Content Based Unsupervised Video Summarization Using Birds Foraging Search

Video summarization is the process of making summary of a video by extracting the key frames. Our content based video summarization approach is based on birds foraging search optimization algorithm. In this proposed method, windowing rule is used to retrieve the candidate frames of the video. Contours of different angles with the horizontal axis are detected from several blocks of the candidate frames. We group the blocks according to the angles. Color moments are used as color features of each group. This contour group based features represent contents of the candidate frames. It is suitable because of its ability to group the features of the same portions of an object irrespective of its location. We adapt population-based birds foraging search optimization algorithm to determine the key frames from the candidate frames. It is evident from the experiment that our proposed method produce better result than some related works.
INTERNALISASI PENDIDIKAN KARAKTER DALAM GERAKAN LITERASI BERBASIS KEARIFAN LOKAL

The purpose of research is to describe the character education in the local wisdom-based literacy movement using text that is close to the environment of students and the implementation of character education in the local wisdom-based literacy movement. The research method uses a descriptive qualitative approach. Data collection techniques using observation, interviews, documentation. Data were analyzed by reducing data, presenting data and drawing conclusions. The results showed that character education in the literacy movement based on local wisdom is curiosity, communicative, fond of reading, discipline, creative and responsibility. The internalization of character educators in the literacy movement based on local wisdom is to use a variety of strategies such as utilizing texts from environmental observations that exist near students, regional saga texts and folklore. Evaluation is carried out by writing notes in the student's reading record book and data values ​​to find out the competencies that have been mastered by students.
A Spectrum Sensing Algorithm to Improve Detection Rates in Cognitive Radio Systems

To solve the problem of low detection rate of primary user signal under low signal-to-noise ratio in wireless channel environment, a spectrum sensing method of cognitive radio network based on quantum particle swarm optimization extreme learning machine (ELM) algorithm is proposed. According to the characteristics of extreme learning machine algorithm, quantum particle swarm optimization (QPSO) is employed to optimize parameters of extreme learning machine, and QPSO-ELM model with structural risk idea is constructed. The model which reduces the empirical risk of the algorithm improves the generalization ability of the model and improves the spectrum sensing performance of the algorithm. Simulation experiments show that compared with the three machine learning algorithms of artificial neural network (ANN), support vector machine (SVM) and extreme learning machine (ELM), the spectrum sensing performance of the algorithm is improved by 16%, 28% and 9% respectively when the signal-to-noise ratio is -15dB. And the simulation experiments proves that the algorithm proposed in this paper has higher performance under the condition of low signal-to-noise ratio and can effectively realize the spectrum sensing of the main user signal.
Multi-agent Architecture for Internet of Medical Things

The technological advancements in recent years has enabled the creation of the Internet of Medical Things, i.e. solutions where medical devices can communicate with each other and exchange data. The guiding idea is to model solutions that can reduce the amount of expected time for analysis of examination results, quick response in the case of diseases as well as assist doctors. In this paper, we propose a solution based on a multi-agent system, where agents are adapted to use classifiers based on artificial intelligence techniques. In the proposed model, we also analyze patient data security and describe the solution so that it is possible to use data in training classifiers without affecting their patient identity. Our approach has been tested on solution simulations based on the most popular technique of artificial intelligence – convolutional neural network.