Reviewerly/exHarmony huggingface dataset:
Dilated CNN Based Human Verifier for Intrusion Detection

This paper proposes an intrusion detection algorithm for intelligent surveillance systems. The algorithm detects an intrusion threat via a dual-stage computer vision algorithm. In the first stage, the input of video sequences passes through a probabilistic change detector based on Gaussian Mixture Models to segment intruders from the background. The extracted foreground region is then passed through the second stage to verify if it is human. The second stage is based on a shallow convolutional neural network (CNN) employing dilated convolution. The system sends an alert if there is intrusion detected. The algorithm is validated and compared with a top-ranked change detection algorithms. It outperformed the compared algorithm on the i-LIDS dataset of sterile zone monitoring.
A Novel Budget-based Collaborative Recommendation System for Tourism (BCRS)

It is customary for people to visit and rate a place, hotel, or restaurant in online traveling apps. The travel history of people reflects the type of places, hotels, or restaurants they like and indicates their future likings. In most traveling apps, the app tries to recommend the places, hotels, and restaurants based on ratings. These apps use filters that do not take the consideration of user preferences and budget under which a user would want to explore the places, hotels or restaurants of a particular unknown destination. In this paper, we have developed a generative model that will not only take into consideration individual liking but also connects it with the budget in which they want to explore a destination. Our proposed model is Budget based Collaborative Recommendation System (BCRS). The BCRS is intuitive and highly interpretable. We have applied this model on real-world datasets, and the results show the advantage of uncovering user’s budgets for the recommendation of places, hotel, or restaurants which will be a boon for tourism as it will increase the reachability of users to many places and user would able to explore places of their preferences in their budget.
Design of IE<sup>3</sup> Laboratory Dynamic Website

The laboratory is an important base for experimental teaching, scientific research and technical services in colleges and universities, and one of the basic conditions for running a good school. In response to the repeated setting of teaching resources in the construction and management of university laboratories, the lack of sharing of resources, and the occlusion of information, I have improved and designed the IE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> laboratory dynamic website.This paper adopts two kinds of plans for the front page information display and background management: 1. Front page: user login registration, online consultation, real-time weather, personnel information changes, etc; 2. Background management: personnel flow, material purchase and reimbursement Management and review of updates, permissions, and message information for the front desk.This paper proposes two methods: (1) Browser/Server, B/S and Model View Controller, MVC mode, (2) the server uses Tomcat and Nginx, and uses Spring+SpringMVC+MyBatis, SSM Framework) programming technology, MySQL database, compared to using only traditional C / S (Client/Server) mode and Tomcat, it is efficient.
Misère Tic-Tac-Toe on Projective Binary Steiner Triple Systems

Imagine playing tic-tac-toe to lose. Two players, Xavier and Olivia, alternate marking squares as usual. As soon as one player owns three squares in a row, they lose. The combinatorial game “misère tic-tac-toe” generalizes this idea. The two players must first agree on a board made from points and lines, which are subsets of the points—but this need not be a traditional tic-tac-toe board. In this article, we study misère tic-tac-toe on projective binary Steiner triple systems. We provide an explicit winning strategy for the second player, Olivia. This winning strategy relies on the nested geometric structure of these systems, as well as the structure of caps within them. This article completes the final case for misère tic-tac-toe on the “geometric” Steiner triple systems, with the surprising result that the winning strategy belongs to different players on affine versus projective Steiner triple systems.
DEMO Enhanced Method Engineering

In this chapter, a number of contributions are presented wherein DEMO is used in combination with an existing and well-accepted approach or activity in the broad field of enterprise engineering, all resulting in improving the quality of the other approach or activity. The selected contributions are: (1) DEMO enhanced Agile Software Development, (2) DEMO enhanced Lean Six Sigma (3) DEMO enhanced BPMN, (4) DEMO enhanced software testing, and (5) DEMO enhanced mining.
Learning Graphs from Noisy Epidemic Cascades

We consider the problem of learning the weighted edges of a graph by observing the noisy times of infection for multiple epidemic cascades on this graph. Past work has considered this problem when the cascade information, i.e., infection times, are known exactly. Though the noisy setting is well motivated by many epidemic processes (e.g., most human epidemics), to the best of our knowledge, very little is known about when it is solvable. Previous work on the no-noise setting critically uses the ordering information. If noise can reverse this -- a node's reported (noisy) infection time comes after the reported infection time of some node it infected -- then we are unable to see how previous results can be extended. We therefore tackle two versions of the noisy setting: the limited-noise setting, where we know noisy times of infections, and the extreme-noise setting, in which we only know whether or not a node was infected. We provide a polynomial time algorithm for recovering the structure of bidirectional trees in the extreme-noise setting, and show our algorithm matches lower bounds established in the no-noise setting, and hence is optimal. We extend our results for general degree-bounded graphs, where again we show that our (poly-time) algorithm can recover the structure of the graph with optimal sample complexity. We also provide the first efficient algorithm to learn the weights of the bidirectional tree in the limited-noise setting. Finally, we give a polynomial time algorithm for learning the weights of general bounded-degree graphs in the limited-noise setting. This algorithm extends to general graphs (at the price of exponential running time), proving the problem is solvable in the general case. All our algorithms work for any noise distribution, without any restriction on the variance.
Set-Membership Filtering with Quadratic Inequality Constraints

This paper investigates the problem of set-membership filtering for nonlinear dynamic systems with general nonconvex inhomogeneous quadratic inequality constraints. We propose an ellipsoidal state bounding estimation in the setting of unknown but bounded noise. To guarantee the on-line usage, at each time step, the nonlinear function is linearized by Taylor expansion, where the bounding ellipsoid of the remainder is updated on-line based on the current state bounding ellipsoid. Moreover, based on the remainder bounds and the constraints, both the state prediction and measurement update of the filtering can be transformed to a semidefinite programming problem which can be solved efficiently. A typical numerical example demonstrates the effectiveness of this filtering.
Scene Classification Using Deep Learning Technique

In recent years, deep learning techniques have been of more focus due to its remarkable results, especially in computer vision. Computer vision involves processing and understanding image data which is well represented and processed by deep learning techniques. It has overcome the traditional methods of image classification where handcrafted features were used to classify images. With the advent of deep learning, researchers drew their focus on automated feature extraction for image classification. Deep learning is a promising approach to perform classification-oriented tasks. In this paper, we have focused on classification of classroom scenes using deep learning techniques by automatically extracting features and classifying the scenes into meaningful labels. This novel approach tends to ease the task of monitoring students in the classroom by automatically extracting information and analyzing it. At first, we introduce some of the traditional techniques and their limitations. We then introduce ResNet architecture of 152 layers pretrained with ImageNet data to accomplish our institute’s classroom scene classification. Further, we demonstrate the results of our classroom data classified using ResNet architecture pretrained with ImageNet dataset.
Classification of Imbalanced Data: Addressing Data Intrinsic Characteristics

Misclassification of skewed datasets is one of the major problems in data mining. In the case of skewness, the number of instances presenting an important class are very short as compared to other classes. Many real world applications suffer from this skewed distribution. Depending on application to application, misclassification of the minority class instances could lead to huge losses. Some of the recent investigations have suggested that there are some issues related to data intrinsic characteristics which heavily affects the performance of the classifier. The major issues are: presence of small disjuncts, lack of density, overlap between classes, presence of noise, borderline instances and the problem of data shift. Through this study, we have discussed an insight into these issues and proposed potential algorithmic solutions that helped us in minimising these problems. For this study we have considered a binary classification model, one being the minority class and other being the majority class. The results induced by the study shows that, this can be use as a basis for future algorithms in addressing the issues of data intrinsic characteristics. The paper is divided into following sections- 1. Introduction 2. Related Work 3. Nature of the problem 4. Proposed Solutions 5. Experiments & Results 6. Conclusion
The Implementation of Green Industry through Innovative Approach at PT. Tirta Investama of Pandaan

Abstract Industrial companies in both Indonesia and the world are now making green environment as a part of their business strategy. Various companies have proven that green environment will be able to increase the competitiveness of its companies. Through government policy, companies in Indonesia have initiated and created certain methods and approaches to realize green industry as a form of concern for the environment, one of which is carried out by PT. Tirta Investama (TIV) of Pandaan. The company engaged in Bottled Drinking Water (AMDK) has carried out various innovative approaches in the field of energy efficiency through the Juminten Program, emission reduction through re-setting the pressure of 1,500 Line-2 SBO machines, reducing B3 waste with the WyPall Tissue Utilization approach, and Non B3 by developing the Integrated Waste Management approach based on Purwosari Industrial Estate - Pandaan, water conservation through the Improvement Setting Final Rinse 5 Gallon, the approach of optimizing the wastewater recycling system in the Water Pollution Load Program, biodiversity program through Wareh Sapen, and community development through the Integrated Community Develoment.
Synchronization of the Quantum Key Distribution System with Priori Information About the Fiber-Optic Line Length

The synchronization is explored for the quantum key distribution (QKD), where photon pulses are utilized as synchronized signals. The analyzed two-stage synchronization algorithm is based on the truth that legitimate users of the QKD system have a priori information with respect to the length of the fiber-optic line. Explanatory expressions are gotten that build up the functional relationship of the energy, probability and time parameters of the single-photon synchronization subsystem with the parameters of the optical fiber, fiber optical transmitter, single-photon avalanche photodiode (SPAD). The necessities for the selection of the least number of frames at the search stage and the allowable number of tests at the testing stage are formulated.
Machine Learning based Presaging Technique for Multi-user Utility Pattern Rooted Cloud Service Negotiation for Providing Efficient Service

In recent years, Cloud computing is emerging as an indispensable technology in handling the increasing number of users and data generated by them. Despite the hype, the increasing data rate proportionally increases the workload of the cloud service providers in order to meet the consumer demands. Moreover this situation results in developing many cloud service providers in order deliver the required and efficient services to the users. But the way to select the appropriate service provider is still remaining as a bottleneck. To solve the increasing server negotiation challenges, a pattern-based service negotiation method has been utilized in this research work. This method has been implemented with an improved suggestion feature, which provides users with similar patterns recognized form their past entries to assist the users with their current selection. By incorporating implicit tracking approach, a sequence of the services accessed by the users have been maintained and this can be leveraged as suggestion to the users. Based on the pattern, the most important services are ranked according to the users' activity and the data has been observed using machine learning algorithm. The ranked list is given to the user to select the required service. The proposed technique increases the performance of the cloud environment and also the strain handled by the service provider in suggesting the utility pattern for the user is reduced by using the machine learning concept.
Coding structure of JMVDC along saliency mapping: a prespective compression technique

Summary Summarizing of multi‐see video and penetrating information depicts an augmentation of the high‐performance video coding (HPVC) criterion. Multi‐see recordings taken by numerous cameras of the same scene from various positions and edges progress towards becoming feasible since little estimated and less value cameras are accessible. Multi‐see video enables the customers to unreservedly change their viewpoints whenever they need to see the scene from any position. For profundity coding, new intracoding modes, an altered movement pay, and movement vector coding and also the idea of movement parameter legacy are a piece of the HPVC expansion. A novel encoder control utilizes see blend improvement, which ensures that amazing middle of the road perspectives can be produced in view of the decoded information. The bit stream arrange bolsters the extraction of halfway piece streams, with the goal that traditional latest video standards like 2D, stereo, multi‐see in addition to profundity organization can be de‐transmit from a solitary piece stream. We proposed a novel saliency detection technique for stereoscopic pictures, which depends on local‐global differentiation highlights, trailed by encompassing district improvement and stereo focus earlier upgrade and furthermore we have proposed an algorithm that effectively chooses a near‐optimalinterview PS and related texture and profundity QPs, at GOP level, when the MVD information organize is utilized for IMVS frameworks. Objective and subjective outcomes are introduced, exhibiting that the proposed approach gives half piece rate investment funds in correlation with JMVDC simulcast and 22% in examination with a direct multi‐see expansion of JMVDC without the recently created coding instruments.
What to See in a Scientific Event? A Social Recommendation Model of Technical Sessions

Scientific/Academic events promote the meeting of researchers for the dissemination of their work to a scientific community. These events are dynamic, where several sessions can happen simultaneously and participants may have difficulty choosing which sessions to attend. Recommender Systems can support participants in this choice by offering recommendations based on information from sessions, participants’ profile and social relationships. This paper presents a Social Recommendation Model of Technical Sessions for Scientific Events. The model was partially implemented and was applied on the IHC 2017 and IHC 2018 events, considering co-authoring relationships. The application of the model was evaluated via a questionnaire and triangulated through captured usage data, where users provided their perceptions about the utility of coauthoring network to recommend sessions. Results suggest the model is promising and capable of supporting the implementation of relevant social recommendation mechanisms.
What Happened in Turkey After Booking.com Limitation: Sentiment Analysis of Tweets via Text Mining

Twitter is one of the most popular applications for sharing feelings and opinions. Sentiment analysis also known as opinion mining is basically used to classify text into three or more categories: positive, negative and neutral sentiments. In this study, sentiment analysis is tested on tweets about www.booking.com in Turkey after the court decided to stop the activities of Booking.com. Moreover, after the date that Booking.com stops its services, traffic data of other major Web sites serving in this sector has been obtained and how they are influenced by this activity is also interpreted. As a result of the literature, sentiment analysis on English texts is a highly popular and well-studied topic; however, it has been observed that the study of text mining in Turkish language is limited. The data is obtained on Twitter from starting the date that Booking.com closures in Turkey. The Twitter messages in Turkish were manually obtained from the Internet because of being expensive of old tweet data. The data has been passed through the preprocessing, attribute selection and classification stages. At the end of these processes, the data is analyzed using various text mining algorithms so the success rates achieved are compared and interpreted.
Semi-automatic 2D-to-3D video conversion based on background sprite generation

This paper presents a technique for semi-automatic 2D-to-3D stereo video conversion, which is known to provide user intervention in assigning foreground/background depths for key frames and then get depth maps for non-key frames via automatic depth propagation. Our algorithm treats foreground and background separately. For foregrounds, kernel pixels are identified and then used as the seeds for graph-cut segmentation for each non-key frame independently, resulting in results not limited by objects’ motion activity. For backgrounds, all video frames, after foregrounds being removed, are integrated into a common background sprite model (BSM) based on a relay-frame-based image registration algorithm. Users can then draw background depths for BSM in an integrated manner, thus reducing human efforts significantly. Experimental results show that our method is capable of retaining more faithful foreground depth boundaries (by 1.6–2.7 dB) and smoother background depths than prior works. This advantage is helpful for 3D display and 3D perception.
Sketch Worksheets in Science, Technology, Engineering, and Mathematics Classrooms: Two Deployments

Sketching is a valuable but underutilized tool for science education. Sketch worksheets were developed to help change this, by using artificial intelligence technology to give students immediate feedback and to give instructors assistance in grading. Sketch worksheets use automatically computed visual representations combined with conceptual information to give feedback to students, by computing analogies between students' sketches and an instructor's solution sketch. This enables domain experts to develop sketch worksheets, to facilitate dissemination. We describe our experiences in deploying them in geoscience and artificial intelligence classes. The geoscience worksheets, authored by geoscientists at University of Wisconsin‐Madison, were used at both Wisconsin and Northwestern University. The artificial intelligence worksheets were developed and used at Northwestern. Our experience indicates that sketch worksheets can provide helpful on‐the‐spot feedback to students, and significantly improve grading efficiency, to the point where sketching assignments can be more practical to use broadly in science, technology, engineering, and mathematics education.
Survey on Cross-Age Face Comparison

Cross-Age Face Comparison has become a popular research topic in computer vision and biometric because of its ability to deal with facial changes that occur during the aging process. This problem has many applications including finding a missing person, identifying criminals, government document verification, image matching, image retrieval, etc. Traditionally, different models such as Hidden Factor Analysis, Maximum Entropy Feature Descriptor, and Cross-Age Reference Coding were used by researcher to solve the problem. These models need feature extraction using Local Binary Pattern and Histogram of Oriented Gradient. On the other hand deep learning based techniques do not require feature extraction and are being widely used for facial analysis. This paper focuses on the broad literature survey of state-of-the-art researches attempting Cross-Age Face Recognition/Retrieval and Cross-Age Face verification. Major contributions of the paper include (1) analysis of various Face comparison challenges with their solutions given by researchers and (2) analysis of various traditional/deep learning based models used to solve the Age invariant problem.
Using thermal infrared surveying to commission utility-scale, commercial ground-mounted, and rooftop solar installations (Conference Presentation)

Thermal infrared surveying is a great way to commission solar installations in-situ. After analyzing and commissioning over three gigawatts of solar panels in the past seven years, we have figured out what works and importantly, what does not work. All solar fields are not the same. Before planning a site visit, the thermographer must take into account the size and layout of the field, the types of panels, how the panels are wired, and then the best timing of the survey. First, the platform and methodology for scanning needs to be chosen, then scheduling can take place. Correct timing of the survey is the most important factor because the amount of solar insolation is absolutely critical to successful testing. To successfully accomplish the testing under good conditions, many factors need to be predicted with accuracy. These include the layout of the field, the ambient conditions (such as rain, sun, clouds, wind speed), and the angle of the Sun preceding and during the survey.
Image Classification on Low Resolution Images using Dilated Dense Pyramid Convolutions

Deep neural networks has the tendency to deploy a high image classification accuracy for high resolution images. Classifying the images captured by low resolution cameras are always a challenging task. Because of low resolution the extraction of good features from this type of image is still remaining as a challenging task for researchers. Moreover high resolution images have more layers to train that means they take more time to train. As the layers go deeper the problem of vanishing gradient increases. In this research work, low resolution images from scratch are trained using dilation kernels. By adding dilation the receptive field of the image gets increased. This boost the classification accuracy in Tiny ImageNet data set. This improved model shows top 1 accuracy of about 63.45% without using any ensemble approaches.