Reviewerly/exHarmony huggingface dataset:
Minimizing Classification Errors in Imbalanced Dataset Using Means of Sampling

Classification, a significant application of machine learning, labels each instance of the dataset into one of the predefined classes. Problems occur when the number of instances in the classes is not uniform. The exceptional lyuneven class distribution gives rise to class imbalancing issues which tend to demote the overall performance of the classifier. A set of data-level algorithms are available which are applied to adjust the class distribution. The class imbalancing emerges frequently in datasets from educational domains where the number of students with unsatisfactory performance general appears in low number comparing to the students with satisfactory outcomes. This paper applies a set of data-level sampling algorithms over a dataset taken from an educational domain. It underlines the consequences rising from classification with imbalanced dataset. This research confirms that a classification model achieving higher accuracy may not appear effective in correct identification of instances in minority class. Classification with an imbalance dataset may produce low recall, precision and F-Measure for classes with lower number of instances. The performance of classification model improves with application of data level algorithm. However, it highlights the supremacy of oversampling algorithm over undersampling algorithms.
Face Detection and Facial Feature Extraction with Machine Learning

Face detection is important part of surveillance systems and it has been widely used in computer vision and image processing. Face detection is also first step of the facial feature extraction. Facial feature extraction is a topic that has been focused on by many researchers in computer science, psychology, medicine and related fields and has become increasingly important in recent years. With the help of facial features, machine learning algorithms can estimate ages and classify genders of people. In this paper, face detection, facial feature extraction, age estimation and gender classification are presented. Firstly, face detection and extraction of facial features like eyes, eyebrows, mouth and nose are presented. Secondly, age estimation and gender classification based on the extracted facial features are explained. Experimental results prove that face detection algorithm efficiently detects human faces and facial feature algorithm accurately locates eyes, eyebrows, mouth and nose. Experimental results also show that, based on the extracted facial features, convolutional neural network architecture estimates ages of the people and classifies their gender.
From the Real World to the Virtual and Vice Versa: Recognition of Man-Made Objects and Structures and Augmented Reality as Tools for Digitalization of Natural Science Education

The use of neural networks-based recognition algorithms for toy objects and structures and augmented reality generation algorithms opens up new methodological possibilities in the teaching of natural sciences. Artificial intelligence methods make it possible to transfer to the material world the processes of compiling and executing control programs for real or virtual robots and the processes of generating environments (game fields) along which robots move. This technical, at first glance, achievement opens up the possibility of developing and practically introducing a methodology for consciously (in the sense of L. Vygotsky) mastering by the preschoolers of the system of scientific concepts of programming. Children master this system of scientific concepts by working with tangible instructional kits and composing programs for managing real and virtual robots in the “PiktoMir” textless programming environment.
Single Underwater Image Restoration By Multi-Scale Fusion In Multi-Color Space

Underwater imaging technology is a key technology for people to explore the world under the sea surface. However, the different attenuation of lights with different wavelengths and the scattering of light by suspended particles in water seriously affect the quality of underwater images. Most of the previous researches focused on eliminating blur solely or color cast solely in the image or these methods need a mass of image databases. To break through the limitations of hardware and the large image databases, we want to produce a new method that can be applied to more underwater scenes and can eliminate the image blur and color cast simultaneously. We propose a multi-scale fusion in multi-color space (MFMC) method, firstly, we obtain two images to be fused from one underwater image, one from stretching and shrinking the original underwater image in multiple color Spaces, and the other one from defogging the original underwater image based on the underwater imaging model. Secondly, we obtain four weight images from two images to be fused and then normalize the four weight images into two weight images. Thirdly, we decompose two images that are to be fused and two weight images into Laplace image pyramids and Gaussian image pyramids with the same level respectively. Finally, we fuse these pyramids and upsample the top image of the fusion image pyramid until the upsampled image has the same size as the original underwater image, and the final upsampled image is restored underwater image. we discuss two classical methods to eliminate blurring and color cast and an up to date underwater image restoration method. And we compare their performance with that of MFMC on three classic metrics and one up to date metric. Qualitative and quantitative analysss prove that our method was significantly effective in the restoration of underwater images.
Application of full-text indexed knowledge graph in Chinese address matching for hazardous materials transportation

Address matching is one of the key technologies of origin-destination analysis of hazardous materials transportation. In order to solve the matching problem of complex non-standard Chinese addresses of hazardous material transportation, a self-expanding full-text indexed address data knowledge graph was proposed. A weighted Pinyin full-text search mechanism was incorporated to improve the matching accuracy of misspelled addresses. Combined with the online geographic searching engines, a multi-mode matching mechanism of addresses for hazardous materials was constructed, and a semi supervised matching method was performed for a small number of unresolved addresses. The simulation calculation of electronic waybill addresses for hazardous material transportation showed that the matching accuracy of extended knowledge graph reached 67.5% and the matching accuracy reached 91.57% under unsupervised multi-mode conditions. Besides, the proposed algorithm outperformed the online geographic search engines in both matching accuracy and matching precision when facing the issues of hard-to-parse addresses.
Answer Set Programming Made Easy

We take up an idea from the folklore of Answer Set Programming (ASP), namely that choices, integrity constraints along with a restricted rule format is sufficient for ASP. We elaborate upon the foundations of this idea in the context of the logic of Here-and-There and show how it can be derived from the logical principle of extension by definition. We then provide an austere form of logic programs that may serve as a normalform for logic programs similar to conjunctive normalform in classical logic. Finally, we take the key ideas and propose a modeling methodology for ASP beginners and illustrate how it can be used.
On Johnson’s “Sufficientness” Postulates for Feature-Sampling Models

In the 1920s, the English philosopher W.E. Johnson introduced a characterization of the symmetric Dirichlet prior distribution in terms of its predictive distribution. This is typically referred to as Johnson’s “sufficientness” postulate, and it has been the subject of many contributions in Bayesian statistics, leading to predictive characterization for infinite-dimensional generalizations of the Dirichlet distribution, i.e., species-sampling models. In this paper, we review “sufficientness” postulates for species-sampling models, and then investigate analogous predictive characterizations for the more general feature-sampling models. In particular, we present a “sufficientness” postulate for a class of feature-sampling models referred to as Scaled Processes (SPs), and then discuss analogous characterizations in the general setup of feature-sampling models.
Actuator and Sensor Fault Estimation of Quadrotor UAV Based on Adaptive Two-Stage Central Difference Kalman Filter

This paper studies the problem of fault estimation against actuator and sensor fault of quadrotor UAV. Combined with the adaptive mechanism and the idea of the separate bias, an adaptive two-step central difference Kalman filter is presented to estimate UAV fault accurately. Firstly, the general fault models of UAV are given, in which the actuator bias fault and efficiency loss are considered. For the type of sensor faults, two typical cases are considered, namely bias fault and drift. Secondly, to reduce the computation of state augmentation strategy, the idea of separate bias is introduced, and the detailed derivation process of standard two-stage central difference Kalman filter is given. However, in case of actuator faults or sensor faults, the characteristics of noise will change and no longer match the prior. To this end, the adaptive scale matrix and a novel Kalman innovation-based adaptive estimation with forgetting factor optimization are proposed to real-time update noise covariance on the estimation process. Finally, simulation examples and actual flight data of UAV are carried out to confirm the merits of the proposed fault estimation algorithm.
<i>Spaz!</i> The Effects of Local Latency on Player Actions in a Desktop-Based Exergame

Technological advancement has decreased network latencies while simultaneously increasing local latencies. This may impact exergames—video games that incorporate exercise—the most since exergames tend to have complicated platforms to capture player actions. This article presents a study using a custom desktop-based exergame that controls for local latency and measures player performance and quality of experience (QoE). Analysis of the results from a 37-person user study shows that while player performance and QoE degrade with latency, exergame actions are fairly tolerant of even hundreds of milliseconds of latency. Our data point toward a crucial tipping point at latency values of approximately 400 ms.
Unstructured Big Data Information Extraction Techniques Survey: Privacy Preservation Perspective

Big data analytics has given us the ability to analyze massive amounts of data, but it has also put privacy at a risk. Personal information may be present in data processed by big data analytics, which must be preserved. Existing privacy preservation strategies, such as anonymization, necessitate dividing a dataset into a set of sensitive, quasi-identifiers and non-sensitive properties. It may be conceivable to have such a distribution with structured data, but it is difficult to detect sensitive features and quasi-identifiers in unstructured data. Big data generates massive amounts of data that are heterogeneous in nature and includes a range of different sorts of data, including text, image, audio and video. Traditional data extraction technologies are ineffective in the context of this massive influx of unstructured big data. This paper present a survey of various information extraction techniques for unstructured big data analytics with a context of privacy preservation.
Natural language processing and information retrieval system based on BP neural network

Natural language processing is an important means to realize the communication between man and machine using natural language, and help computers quickly understand the meaning expressed by natural language. The most common application system using natural language processing technology is information retrieval system. On this basis, an information processing model based on BP neural network (BPNN) and statistical method is discussed, and the principle of BPNN is explained in detail. After analyzing these phenomena, researchers think that natural language processing is more suitable for tasks requiring accurate results, and the understanding level of natural language processing is divided into seven levels from low level to high level: pronunciation level → morphology level → vocabulary level → syntax level → semantics level → pragmatics level → context level. On this basis, the application of natural language processing in information retrieval system is discussed.
Particle dual averaging: optimization of mean field neural network with global convergence rate analysis*

Abstract We propose the particle dual averaging (PDA) method, which generalizes the dual averaging method in convex optimization to the optimization over probability distributions with quantitative runtime guarantee. The algorithm consists of an inner loop and outer loop: the inner loop utilizes the Langevin algorithm to approximately solve for a stationary distribution, which is then optimized in the outer loop. The method can thus be interpreted as an extension of the Langevin algorithm to naturally handle nonlinear functional on the probability space. An important application of the proposed method is the optimization of neural network in the mean field regime, which is theoretically attractive due to the presence of nonlinear feature learning, but quantitative convergence rate can be challenging to obtain. By adapting finite-dimensional convex optimization theory into the space of measures, we analyze PDA in regularized empirical/expected risk minimization, and establish quantitative global convergence in learning two-layer mean field neural networks under more general settings. Our theoretical results are supported by numerical simulations on neural networks with reasonable size.
Secure Software Immune Receptors from SQL Injection and Cross Site Scripting Attacks in Content Delivery Network Web Applications

In our proposed work the web security has been enhanced using additional security code and an enhanced frame work. Administrator of site is required to specify the security code for particular date and time. On user end user would be capable to login and view authentic code allotted to them during particular time slot. This work would be better in comparison of tradition researches in order to prevent sql injection attack and cross script because proposed work is not just considering the security, it is also focusing on the performance of security system. This system is considering the lot of security dimensions. But in previous system there was focus either on sql injection or cross script. Proposed research is providing versatile security and is available with low time consumption with less probability of unauthentic access.
Photovoltaic Solar Power Plant Maintenance Management based on IoT and Machine Learning

Photovoltaic solar energy requires novel algorithms to ensure suitable maintenance management. Supervisory control and data acquisition system, combined with machine learning techniques, is required to obtain reliable information about the real state of photovoltaic systems. This paper introduces an Internet of Things platform for photovoltaic maintenance management based on classification algorithms to detect patterns, where performance ratio decreases significantly in time series. A real case study is presented with SCADA data from a photovoltaic solar plant located in Spain. The classification algorithms employed are Shapelets and K-nearest neighbors. The results prove the robust performance of both algorithms in pattern recognition, whereas K-nearest neighbors is preferable for implementation on the Internet of Things platform due to the reduced execution time. The application of the platform developed in this paper improve photovoltaic maintenance management detecting performance ratio reductions.
Digital Didactical Resources as Innovative Multimedia Methods in the Work of a Modern and Reflective Teacher

The demand for innovation in school and teaching is a reaction to what is perceived as the traditional school. Innovation in teaching mostly depends on the teachers themselves and how much they are ready to change and improve their own work, to be a reflective practitioner. The subject of our research is the role of innovative methods in improving students’ school achievement. Innovative multimedia methods can influence student motivation, and consequently their achievements can be even more pronounced. With this in mind, the following questions were asked in the research: Do teachers use innovative teaching methods; and which innovative methods do they use the most? For what purposes do teachers use a computer in teaching? Do students create PowerPoint presentations and use the Internet to generate content? The research considered the attitudes of teachers regarding the use of educational software in teaching, as well as the role of innovative methods on students’ school achievements. The following variables were considered in the research: how many years the teachers had served (up to 10 years; from 11 to 20 years; and over 20 years of work experience) and the type of school (primary-secondary school). For the purposes of this research, a questionnaire with a Likert-type scale instrument consisting of 31 items was used. The research included 105 teachers of different subjects who attended seminars on Innovative Methods through ICT. The research shows that multimedia has not been fully implemented in primary and secondary schools.
kMatrix: A Space Efficient Streaming Graph Summarization Technique

The amount of collected information on data repositories has vastly increased with the advent of the internet. It has become increasingly complex to deal with these massive data streams due to their sheer volume and the throughput of incoming data. Many of these data streams are mapped into graphs, which helps discover some of their properties. However, due to the difficulty in processing massive streaming graphs, they are summarized such that their properties can be approximately evaluated using the summaries. gSketch, TCM, and gMatrix are some of the major streaming graph summarization techniques. Our primary contribution is devising kMatrix, which is much more memory efficient than existing streaming graph summarization techniques. We achieved this by partitioning the allocated memory using a sample of the original graph stream. Through the experiments, we show that kMatrix can achieve a significantly less error for the queries using the same space as that of TCM and gMatrix.
Differentially Private Federated Learning in Multi-Cell Networks

Federated learning (FL) is a distributed learning method where multiple users train and upload their local models or gradients to an edge server for artificial intelligence (AI) model training. However, the local model uploading causes information leakage on local data. Differential privacy (DP) is a random mechanism that adds uncertainty to protect privacy for dataset. In this paper, we study a multi-cell FL network where each cell is a FL system. Each user adds artificial noise in its uploaded local gradient, and the multi-cell interference is exploited to enhance the DP levels. The studied problem is formulated as a mean square error (MSE) minimization problem subject to the DP and power constraints, by controlling the transmission power on local gradient and artificial noise of each user. Our results show that multi-cell interference is beneficial to DP.
Force-Directed Timelines: Visualizing &amp; Exploring Temporal Patterns

Visualization has shown to be a valuable tool in the analysis of large and complex temporal datasets, aided by the emergence of new models such as Time Curves, which distorts timelines to position time points based on their similarity with each other, reflecting changes in the data over time. In this paper, we further explore time-series functionally and aesthetically by presenting an interactive and parameter-based implementation of the Time Curves model, complemented with the addition of supporting visualizations and data analysis methods. In our implementation we introduce Time Paths, a force-directed layout that can dynamically transform the original model to not only smoothen the transitions between time points, but also reduce visual noise in favor of portraying overall patterns. The proposed addition of visual elements to the model includes temporal glyphs and a supporting timeline graph which help discover and better understand temporal patterns across complex datasets. Through interactive exploration, we demonstrate how these methods can be used to analyze and identify the main agents at the source of significant instances in three biological datasets. These methods are presented within CroP, a data visualization tool with coordinated multiple views aimed at the analysis of biological datasets.
Dimensionality reduction methods for Impedance Spectroscopy data of biological materials

Abstract Electrical impedance spectroscopy combined with Neural Networks can be a powerful combination to identify biological materials. This paper utilizes a data set containing two biological samples taken from different species and applies the most popular methods of dimensionality reduction. This is done in order to find out which method is able to minimize computational demand and maximize accuracy in the classification test. This paper proposes that the classic PCA method is the fastest and the most accurate under the configurations used.
Efficient Architecture for Long Integer Modular Multiplication over Solinas Prime

Modular multiplication of very long integers is a key building block of fully homomorphic encryption and elliptic curve cryptography. The Karatsuba algorithm reduces the multiplication complexity by decomposing the operands into shorter segments. However, in the case of long numbers, adding up the segment products to derive the final product and then carrying out modular reduction as in previous designs can take many clock cycles. This paper focuses on moduli in the format of Solinas prime and proposes to integrate modular reduction into every segment product of the Karatsuba integer multiplication. As a result, the length of the intermediate results is further reduced and they can be added up simultaneously by using a carry-save adder at the cost of small area increase. Additionally, the computation scheduling are optimized to reduce the required number of registers and multiplexers. Complexity analysis shows that, for decomposition factors of 2, 3 and 4, our design requires on average 18.5% less clock cycles with only 5.9% area overhead and similar critical path compared to carrying out the modular reduction on the final product.