Reviewerly/exHarmony huggingface dataset:
Three Approaches in the Study of Recurrent Markovian and Semi-Markovian Processes

We present three classical methods in the study of dynamic and stationary characteristic of processes of Markovian or Semi-Markovian type which possess points of regeneration. Our focus is on the stationary distributions and conditions of its existence and use. The first approach is based on detailed probability analysis of time dependent passages between the states of the process at a given moment. We call this approach Kolmogorov approach. The second approach uses the probability meaning of Laplace-Stieltjes transformation and of the probability generating functions/ Some additional arteficial excrement construction is used to show how derive direct relationships between these functions and how to find them explicitly. The third approach obtains relationships between the stationary characteristics of the process by use of so called “equations of equilibrium”. The input flow in each state must be equal to the respective output flow from that state. In such a way no accumulations should happen on each of that states when process gets its equilibrium. In all the illustrations of the these approaches we analyze a dynamic Marshal-Olkin reliability model with dependent components functioning in parallel. Results on this example are new.
Knowledge Life Cycle Management as a Key Aspect of Digitalization

Digital transformation of organizations became a significant research and engineering challenge worldwide. Implementing such a transformation requires not only a change in the technical equipment of the enterprise, but also developing new methods of knowledge life cycle management which include extraction of individual, interpersonal or organizational knowledge to explicit machine-readable forms and their conscious application during enterprise reengineering. Successful accomplishment of these tasks vitally relies on a rigorous scientific theory and formal methods. This work presents a new approach to knowledge life cycle management of different forms of knowledge based on combination of ontology engineering and evolvable domain-specific languages.
<i>INFORMS Journal on Data Science</i> (<i>IJDS</i>) Editorial #1: What Is an <i>IJDS</i> Paper?

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeINFORMS Journal on Data ScienceAhead of Print INFORMS Journal on Data Science (IJDS) Editorial #1: What Is an IJDS Paper?Galit Shmueli Galit Shmueli Published Online:11 Jan 2021https://doi.org/10.1287/ijds.2020.003 Previous Back to Top FiguresReferencesRelatedInformation Articles In Advance Article Information Metrics Downloaded 1,121 times in the past 12 months Information Published Online:January 11, 2021 Copyright © 2021, INFORMSCite asGalit Shmueli, (2021) INFORMS Journal on Data Science (IJDS) Editorial #1: What Is an IJDS Paper?. INFORMS Journal on Data Science 0(0). https://doi.org/10.1287/ijds.2020.003
Foreground color prediction through inverse compositing

In natural image matting, the goal is to estimate the opacity of the foreground object in the image. This opacity controls the way the foreground and background is blended in transparent regions. In recent years, advances in deep learning have led to many natural image matting algorithms that have achieved outstanding performance in a fully automatic manner. However, most of these algorithms only predict the alpha matte from the image, which is not sufficient to create high-quality compositions. Further, it is not possible to manually interact with these algorithms in any way except by directly changing their input or output. We propose a novel recurrent neural network that can be used as a post-processing method to recover the foreground and background colors of an image, given an initial alpha estimation. Our method outperforms the state-of-the-art in color estimation for natural image matting and show that the recurrent nature of our method allows users to easily change candidate solutions that lead to superior color estimations.
Real Time Human Activity Recognition Using Convolutional Neural Network and Deep Gated Recurrent Unit

Activity recognition on video is a challenging problem. To be used in the real application, the recognition approach should have good performance both in accuracy and speed. This research proposes a video-based human activity recognition method using a convolutional neural network (CNN) and a deep gated recurrent unit (GRU) network. First, selecting images in multiples of six on a sequence of frames is carried out to reduce redundant information and decrease computational complexity. Features extraction is then performed on the selected images using CNN with the MobileNetV2 architecture that has been trained in the ImageNet dataset. A collection of features extracted from a sequential frame in an activity video is fed to the GRU to analyze spatiotemporal features. Experiment on the YouTube 11 Actions dataset gives a promising result with an F1 score of 92.01% and 65.43 FPS speeds.
LibGuides: . Faculty Toolkit - Information + Data Literacy Resources: Home

This guide serves to link faculty to USF Libraries' Faculty Toolkit for Information + Data Literacy Resources.
Separable Four Points Fundamental Matrix

We present a novel approach for RANSAC-based computation of the fundamental matrix based on epipolar homography decomposition. We analyze the geometrical meaning of the decomposition-based representation and show that it directly induces a consecutive sampling strategy of two independent sets of correspondences. We show that our method guarantees a minimal number of evaluated hypotheses with respect to current minimal approaches, on the condition that there are four correspondences on an image line. We validate our approach on real-world image pairs, providing fast and accurate results.
A Transfer Machine Learning Matching Algorithm for Source and Target (TL-MAST)

Sequentially transferring machine learning (ML) models in a row over several data sets can result in an improvement of performance. Considering an increasing number of data sets, the possible number “transfer paths” of a transfer grows exponentially. Thus, in this paper, we present TL-MAST, a matching algorithm for identifying the optimal transfer of ML models to reduce the computational effort across a number of data sets—determining their transferability. This is achieved by suggesting suitable source data sets for pairing with a target data set at hand. The approach is based on a layer-wise, metric-supported comparison of individually trained base neural networks through meta machine learning as a proxy for their performance in a crosswise transfer scenario. We evaluate TL-MAST on two real-world data sets: a unique sales data set composed of two restaurant chains and a publicly available stock data set. We are able to identify the best performing transfer paths and therefore, drastically decrease computational time to find the optimal transfer.
Task-Assisted Domain Adaptation with Anchor Tasks

Some tasks, such as surface normals or single-view depth estimation, require per-pixel ground truth that is difficult to obtain on real images but easy to obtain on synthetic. However, models learned on synthetic images often do not generalize well to real images due to the domain shift. Our key idea to improve domain adaptation is to introduce a separate anchor task (such as facial landmarks) whose annotations can be obtained at no cost or are already available on both synthetic and real datasets. To further leverage the implicit relationship between the anchor and main tasks, we apply our HeadFreeze technique that learns the cross-task guidance on the source domain with the final network layers, and use it on the target domain. We evaluate our methods on surface normal estimation on two pairs of datasets (indoor scenes and faces) with two kinds of anchor tasks (semantic segmentation and facial landmarks). We show that blindly applying domain adaptation or training the auxiliary task on only one domain may hurt performance, while using anchor tasks on both domains is better behaved. Our HeadFreeze technique outperforms competing approaches, reaching performance in facial images on par with a recently popular surface normal estimation method using shape from shading domain knowledge.
How to Detect Benign Domains Based on “Lonesome” DNS Traffic

There is a fatal weakness in lots of previous domain classification methods based on DNS traffic. Almost all of the previous methods must get many duplicate domains' information in their DNS traffic for extracting statistical features. However, there are lots of domains that appear only few times in the DNS traffic (e.g. newly registered domains). This leads to the detection difficulty using previous domain detection methods. In this paper, we first define a new term, "lonesome" DNS traffic, which only has few duplicate domain request records and whose period is quite short. And then, we conduct experiments based on real-world lonesome DNS traffic to explore which features are the most effective for detecting benign domains based on lonesome DNS traffic and the corresponding suitable machine learning method in this situation. The average AUC reaches 95.64% and the average false positive rate is 0.542%.
Open Set Domain Adaptation by Extreme Value Theory

Common domain adaptation techniques assume that the source domain and the target domain share an identical label space, which is problematic since when target samples are unlabeled we have no knowledge on whether the two domains share the same label space. When this is not the case, the existing methods fail to perform well because the additional unknown classes are also matched with the source domain during adaptation. In this paper, we tackle the open set domain adaptation problem under the assumption that the source and the target label spaces only partially overlap, and the task becomes when the unknown classes exist, how to detect the target unknown classes and avoid aligning them with the source domain. We propose to model the tail of the entropy distribution with Extreme Value Theory for unknown class detection, which has shown promising performance on conventional domain adaptation datasets, and the proposed method outperforms the state-of-the-art models in domain adaptation.
The Study of Advanced Scientific Experiments in Avatar- A Postcolonial Perspective

Abstract Scientific advancement is used for the welfare of many people across the world. Even in the times of pandemic, the advancement of science has paved way for digital communication. But in this paper, an analysis is going to be made on how the humans had misused the scientific advancement to conquer a planet called Pandora in the movie Avatar to destroy the inhabitants of the planet who are referred to as Na’vi and to plunder a valuable natural mineral called unobtanium. This movie is an indirect adaptation of science versus nature, the argument to be made in this research paper is that nature is the base for everything, but people are deconstructing it with the scientific advancements, it is arguably like placing our own lives into misery. The question is that do we really need science for a happy life but the indigenous people in Pandora lived happily before the invasion of science in their planet, this is going to be further analysed in this research paper. The title emphasizes on the scientific advancement because this paper aims to discuss the impact of the advanced science that is depicted in the movie.
Towards a Complete Perspective on Labeled Tree Indexing: New Size Bounds, Efficient Constructions, and Beyond

A labeled tree (or a trie) is a natural generalization of a string which can also be seen as a compact representation of a set of strings. This paper considers the labeled tree indexing problem, and provides a number of new results on space bound analysis and on algorithms for efficient construction and pattern matching queries. Kosaraju [FOCS 1989] was the first to consider the labeled tree indexing problem and he proposed the suffix tree for a backward trie, where the strings in the trie are read in the leaf-to-root direction. In contrast to a backward trie, we call an ordinary trie as a forward trie. Despite a few follow-up works after Kosaraju's paper, indexing forward/backward tries is not well understood yet. In this paper, we show a full perspective on the sizes of indexing structures such as suffix trees, DAWGs, CDAWGs, suffix arrays, affix trees, affix arrays for forward and backward tries. Some of them take O(n) space in the size n of the input trie, while the others can occupy O(n2) space in the worst case. In particular, we show that the size of the DAWG for a forward trie with n nodes is Ω(σn), where σ is the number of distinct characters in the trie. This becomes Ω(n2) for an alphabet of size σ =Θ(n). Still, we show that there is a compact O(n)-space implicit representation of the DAWG for a forward trie, whose space requirement is independent of the alphabet size. This compact representation allows for simulating each DAWG edge traversal in O(log σ) time, and can be constructed in O(n) time and space over any integer alphabet of size O(n). In addition, this readily extends to the first indexing structure that permits bidirectional pattern searches over a trie within linear space in the input trie size. We also discuss the size of the DAWG built on a labeled DAG or on an acyclic DFA, and present a quadratic lower bound for its size.
Educational Technologies based on Artificial Intelligence: some pros and cons

The idea to improve education by applying tools developed in the area of Artificial Intelligence dates back to at least the 1970's and by now there exists a huge number of publications on the topic. The present paper briefly discusses some pros and cons of educational technologies based on our current capabilities in Artificial Intelligence.
Comparative Analysis of ICT in Education Between China and CEECs

China is located in the east of Asia and the west coast of the Pacific Ocean. Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech, Estonia, Greece, Hungary, Latvia, Lithuania, Montenegro, North Macedonia, Poland, Romania, Serbia, Slovak, Slovenia are located in central and eastern Europe (hereinafter referred to as CEECs)
Design of Artificial Intelligence for Smart Risk Pre-review System at the KC EMC

The world’s major countries use Free Trade Agreements and protectionism to politicize non-tariff barriers along with tariffs that benefit their own industries. When it comes to world trade, there are a total of 79 legally mandatory certification systems in Korea. As part of the non-tariff barrier policy, electrical and electronic products are subject to pre-evaluation of the “Broadcasting and Communication Equipment Conformity Assessment” system. In this paper, we design Artificial Intelligence, Big Data, and Cloud Computing platforms to improve existing document evaluation. It analyzes big data in the form of electric and electronic products, and judges whether the pre-evaluation for the suitability of KC Electromagnetic waves intended to be sold in Korea is valid through AI machine learning. In addition, if it is not suitable, we intend to design an AI deep learning system that provides a complementary measure and provide it in the form of a Cloud Computing platform. We intend to develop a semi-automatic AI connection system that can perform KC conformity assessment for electric and electronic products to be sold in Korea.
A Primer on Word Embedding

The current research on the topic of machine learning and especially the domain of natural language processing has gained much popularity in the modern era. One such framework for attaining NLP tasks is word embedding, which represents data as vectors, i.e., real numbers rather than words of natural language because neural networks do not understand them naturally. Word embeddings try to capture both syntactic and semantic information of words and capture relationships according to context and morphology. This paper reviews each word embedding technique available in the contemporary world ranging from traditional embeddings based on the frequency of terms to pre-trained embeddings like prediction-based embeddings. The goal of this paper is to present the myriad methods available for word embedding, classify their working patterns, also identify their pros and cons for working on text classification and detect their hegemony over the traditional methods of NLP.
Computer Simulation of Percolation in a Two-dimensional Square Network

The article presents a model of computer simulation of the percolation phenomenon in a two-dimensional square network. The analyzed type is site percolation. The concentration value for which the percolation threshold occurs was statistically determined and the distribution of points forming the conductive clusters in the matrix was analyzed. Based on the simulation, the created percolation channels, non-conducting clusters and coordinates of critical points were visualized.
Exogenous Parameters in Solar Forecasting

The ability to predict solar radiation reliably is crucial in optimizing solar energy integration, ensuring grid stability and regulating energy markets. One way to improve accuracy in forecasting solar radiation with time series modeling is to use exogenous variables (e.g. temperature, humidity, pressure, wind speed, and direction) in addition to solar radiation measurements. Evidence from existing studies indicates that the extent to which such exogenous variables can improve solar forecasting is largely dependent on the type of algorithm used. Our results indicate that the scope of the prediction target (lag duration, number of steps ahead) also plays an important role in determining the ability of exogenous variables to improve solar forecasting results. More specifically, the accurate pairing of exogenous variables and forecasting algorithms can help achieve accuracy improvements with longer lags at diverse horizons. These results argue in favor of a multi-modeling approach where specific forecasting configurations are determined dynamically for each choice of time series input.
Weight Determining of Attributes in Multicriterial Decision Making for Software Selection

Selection the true software packages is very important for growing any company and have a significant influence on company competitiveness. There is a different affecting factor in the selection of software for company and to solve this problem was used multi-criteria decision-making problem. An important issue of a decision-making problem is determining the weights of attributes that are given by experts. Since different experts have different importance in decision-making, it is important to find a set of appropriate weights. In this paper, a pair of most compromising and least compromising solutions is derived from individual judgments of decision-makers and then, these solutions are applied as the bases to determine the amount of individual alignment with the experts opinion by using a closeness coefficient approach. Determining the weights of decision-makers, decision-making problem of software selection is then solved.
Analyzing Privacy of Time Series Data Using Substitute Auto-encoder Neural Network

Nowadays people are very concerned about their health. For example, people want to keep track of how often they are active throughout the day. Hence many smart devices are equipped with sensors which help to keep track of the activities. Users who use these sensors want to share the information to be analyzed for their own health benefits. However, at the same time the users do not want to share the sensitive information. In this paper, we propose a privacy preserving predictive model. We used the concept of denoising Auto-encoder to hide the sensitive attributes of a user. We divided the data sets into three different subsets: desired, sensitive, and non sensitive subsets. The output of the denoising Auto-encoder will only be the desired and non-sensitive subsets. The sensitive subsets are hidden by the non-sensitive subsets. We evaluated the efficacy of our predictive model using three different flavors of Auto-encoders (CNN, Deep, and LSTM). To retain the accuracy as similar as possible compared to the original data set, we used a convolutional neural network for classification. We measured the Fl-score of our model against each of the three Auto-encoders.
A Method for Welding Track Correction Based on Emulational Laser and Trajectory

In this paper, a method for welding track correction based on emulational laser and trajectory are introduced. The proposed method is divided into two parts: seam tracking and trajectory correction. In the seam tracking method, by using the prior information of emulational laser stripes which are generated by the simulation software and affine transformation of emulational laser stripes, the real seam point can be detected. And then the trajectory correction method mainly consists of three steps: pre-processing, coarse matching and presice matching by using Iterative Closest Point Matching (ICP). For various conditions and workpieces, the corresponding experiments are conducted in this paper. Experimental results demonstrate that the method can meet the requirement of the internal seams tracking of workpiece and accuracy of welding track correction.
Vitality of Technological Aid in Mastering English

English language as we all know is the most important language as it is a common language through which people from different parts of the world can communicate with each other. Hence it is very important for the people to learn English language. So, why not use internet and other technologies to learn English Language, after all, we human beings are living in the digital age, where Technology plays a very vital role in our day to day lives, where almost every person in the world has his or her own smartphones, where everyone can access the Internet by smartphones, or computers, for various countless purposes. This paper attempts to shed light on how exactly people can learn English Language through Internet and the other technologies that are available today. Not only that, this paper also shows how many, teachers and Students on an average take the assistance of the Internet to learn English Language and the benefits of using technology by learners as well as teachers across the globe.
Evaluation and Augmentation of Köppen-Geiger Climate Zone Based off of Real-World Satellite Weather Data

Photovoltaic (PV) power is a critical resource for meeting future energy demands. As integration of photovoltaic systems increases it is important to categorize climate parameters by conditions that are known to affect the viability of PV. A widely used classification is Köppen Geiger Climate zone which divides the world into five main climates and subcategories based on humidity and average temperature. Despite Köppen Geiger climate classifications widespread popularity it was not designed with parameters that determine the performance or degradation of modules. This paper presents an evaluation of the Köppen Geiger climate classification using real-world weather data from 357 PV sites. Two types of clustering algorithms were applied. The clustering algorithms had difficulty distinguishing between temperature categories. Furthermore, machine learning algorithms were used to predict the climate zone classification. The random forest model had the highest prediction accuracy of 76%. Both clustering and machine learning failed to capture the seasonality of data resulting in poor performance.