Reviewerly/exHarmony huggingface dataset:
Meta-Parameter Selection for Embedding Generation of Latency Spaces in Auto Encoder Analytics

Picking an appropriate parameter setting (meta-parameters) for visualization and embedding techniques is a tedious task. However, especially when studying the latent representation generated by an autoencoder for unsupervised data analysis, it is also an indispensable one. Here we present a procedure using a cross-correlative take on the meta-parameters. This ansatz allows us to deduce meaningful meta-parameter limits using OPTICS, DBSCAN, UMAP, t-SNE, and k-MEANS. We can perform first steps of a meaningful visual analysis in the unsupervised case using a vanilla autoencoder on the MNIST and DeepVALVE data sets.
RECONSTRUCTED TEETH IMAGE FROM BRACES WITH GAN

Recent advances in deep learning models have shown promising potential in object removal, which is the task of replacing undesired objects with appropriate pixel values using known context. Object removal-based deep learning can commonly be solved by modeling it as the Img2Img (image to image) translation or Inpainting. Instead of dealing with a large context, this paper aims at a specific application of object removal, that is, erasing braces trace out of an image having teeth with braces (called braces2teeth problem). We solved the problem by three methods corresponding to different datasets. Firstly, we use the CycleGAN model to deal with the problem that paired training data is not available. In the second case, we try to create pseudo-paired data to train the Pix2Pix model. In the last case, we utilize GraphCut combining generative inpainting model to build a user-interactive tool that can improve the result in case the user is not satisfied with previous results. To our best knowledge, this study is one of the first attempts to take the braces2teeth problem into account by using deep learning techniques and it can be applied in various fields, from health care to entertainment.
In-Place VM Migration for Fast Update of Hypervisor Stack

It is challenging for cloud vendors to update data center software without affecting the performance of tenants’ virtual machines that are running in their clouds. Update methods of Linux/KVM based cloud hypervisors are explored by using QEMU virtual machine in-place live migration during the update. Newer VMMs (virtual machine managers) such as QEMU do not require file-backed memory copying operations during migration. This feature can be used to instrument in-place live migration, allowing a VM to be suspended without copying its memory content to backing storage, and yet being able to resume from a new version of a hypervisor and VMM. Using VM in-place migration enables several faster update methods of a hypervisor OS that could be used by cloud vendors to update their infrastructure without severely impacting tenants. This work explores several update methods that can be built on top of in-place live migration.
Massive Open Online Courses as the Initial Stage of Development of a Digital University

The purpose of this study is to identify the conditions (including pedagogical) for effective development and operation of MOOCs to meet the interests of all participants in the educational process and national innovation (educational) policy. The present paper is based on the classical methodology of scientific research, including a set of scientific methods such as a logical consideration of historical events, a dialectical way of studying the world around us, considering concepts in the interrelation of their contradictory aspects, identifying the causes and relations between phenomena, abstracting and specifying results, and the method of multiple thematic comparative research. The study uses the method of sociological surveys to collect empirical data. Further, it uses methods of statistical and graphic data processing. Other methods applied include analysis of relevant modern scientific literature and the synthesis of promising new directions for the development of universities using digital educational technologies. Relevant questionnaires were developed to interview students and teachers on the research topic set forth. In general, students have a positive attitude toward digital educational technologies and MOOCs. The main effects of MOOCs in the pedagogical process are as follows: The educational material becomes easier to perceive, more visual and diverse, the cognitive activity of students grows, the quality of information transmission and knowledge transfer to students improves, and the controllability of the acquired skills and abilities by students and teachers increases. To improve the effectiveness of MOOCs, it is necessary to reduce the academic load of teachers, simplify paper workflow, and increase the practical orientation of educational materials.
Examining forms of inductive bias towards 'simplicity' in genetic algorithms to enhance evolvability of boolean functions

Recent work has looked at the evolvability of biological networks by representing them as graphs or functions, and studying the conditions under which certain structures are feasibly reachable. One key factor in enabling evolvability appears to be some form of prior constraint or inductive bias on the evolutionary landscape, which typically represents selectivity for low-complexity structures. Here we examine two different approaches for incorporating this kind of inductive bias in genetic algorithms, and propose a simulation framework allowing us to compare them and to relate the notion of a function class to that of a function's algorithmic complexity.
The Role of Letter Anatomy in Type Design: An Eye-Tracking Study of Bengali Letters

The elementary unit of letter construction is the basic anatomical features of letterforms. The letter identification is a process of decoding and encoding of the anatomical information of the letters. They are the fundamental components of letter identification. This paper explains the process of Bengali letter identification and reveals the crucial anatomical features for design purposes. Here, an eye-tracking study has been conducted to identify the involvement of letter features in reading. In order to reveal the letter features, we exposed the letters under low contrast conditions to establish the consequence of anatomical features. The objective of the experiment is to identify the important letters features during the reading process that aids the letter identification task. A total number of six participants had performed the task under low contrast condition, and data was captured using an eye-tracking device. The aim of the paper is to understand the role of anatomical features in the letter identification process, so that such information can be useful for the Type designers. Type designers are designers who design typefaces and develop fonts for everyday use. The saccades, fixation, and heat-signature data from eye-tracking study have provided two major sets of information—(1) a list of letter features (individually) involved in the letter identification process, (2) a list of common and unique letter features across all Bengali letters. The result indicates that there is an active involvement of letter-parts and features in the letter identification process, and the list of common and distinctive letter features has been prepared to guide the type designers.
Constructing Micro Knowledge Graphs from Technical Support Documents

Short technical support pages such as IBM Technotes are quite common in technical support domain. These pages can be very useful as the knowledge sources for technical support applications such as chatbots, search engines and question-answering (QA) systems. Information extracted from documents to drive technical support applications is often stored in the form of Knowledge Graph (KG). Building KGs from a large corpus of documents poses a challenge of granularity because a large number of entities and actions are present in each page. The KG becomes virtually unusable if all entities and actions from these pages are stored in the KG. Therefore, only key entities and actions from each page are extracted and stored in the KG. This approach however leads to loss of knowledge represented by entities and actions left out of the KG as they are no longer available to graph search and reasoning functions. We propose a set of techniques to create micro knowledge graph (micrograph) for each of such web pages. The micrograph stores all the entities and actions in a page and also takes advantage of the structure of the page to represent exactly in which part of that page these entities and actions appeared, and also how they relate to each other. These micrographs can be used as additional knowledge sources by technical support applications. We define schemas for representing semi-structured and plain text knowledge present in the technical support web pages. Solutions in technical support domain include procedures made of steps. We also propose a technique to extract procedures from these webpages and the schemas to represent them in the micrographs. We also discuss how technical support applications can take advantage of the micrographs.
Analyze the Problems Existing in English Information Teaching and Study the Countermeasures

Informatization of education is the inevitable development of the times.The rapid development of information technology is constantly changing people's life, work and thinking methods, and at the same time changing the way of teaching English.Modern information technology is undergoing a transition from assisted teaching to leading teaching, and the existing problems in English classroom teaching will become more prominent.This article starts with the analysis of the existing problems in English informatization teaching, and systematically summarizes the existing problems in four aspects: Students' information literacy is low, and their independent learning ability needs to be improved; Teachers' information literacy is insufficient, and their ability to apply information technology urgently needs to be strengthened; Insufficient innovation of English informatization teaching model and poor teaching methods; The structure of English informatization teaching content needs to be improved.It further analyzes the three main reasons for the existing problems: The quality and learning ability of students vary; Teachers have insufficient knowledge and use of informatization teaching; The infrastructure construction and basic conditions of English informatization teaching are relatively weak.Finally, a solution based on the strategies of intensive training to continuously improve teachers' informatization teaching technology, integrating resources to effectively improve English informatization teaching content, and innovative ideas to optimize the school's English informatization teaching environment, provides theoretical support for promoting the healthy development of English education.
Hyperstate Space Graphs

Automatically analyzing games is an important challenge for automated game design, general game playing, and cocreative game design tools. However, understanding the nature of an unseen game is extremely difficult due to the lack of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> design knowledge and heuristics. In this article, we formally define <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hyperstate space graphs</i> , a compressed form of state-space graphs, which can be constructed without any prior design knowledge about a game. We show how hyperstate space graphs produce compact representations of games, which closely relate to the heuristics designed by hand for search-based artificial intelligence (AI) agents; we show how hyperstate space graphs relate to modern ideas about game design; we report on exploratory uses of hyperstate space graphs as an analytical tool, and we point toward future applications for hyperstates across game AI research.
Introduction to Sequential Heteroscedastic Probabilistic Neural Networks

This paper is dedicated to analyzing the performance of an online classification algorithm called sequential heteroscedastic probabilistic neural network (SHPNN). The aforementioned algorithm is a variant of probabilistic neural networks (PNNs). This algorithm has the advantage of being structurally flexible to match the complexities of the data space. Another distinctive feature of this algorithm is the fact that it can achieve roughly the same level of accuracy compared to its counterparts while having an acceptable speed in training phase. But perhaps its most important quality is the fact that SHPNN’s structure contains valuable information about the underlying statistics of the data. In this paper the derivation of SHPNN formulation is presented and discussed. The following analysis includes comparison between the performance of SHPNN and other similar algorithms. Furthermore, the interpretability of this network is analyzed by visualizing the learning process.
Improved Green Cloud Computing with Reduce Fault in the Network: A Study

With the rise in technology, cloud computing has become an important part in every aspect of a forum. With respect to the need, Green Cloud computing is attractive in Cloud Computing. A brief review about green cloud computing is presented on how the energy consumption can be reduced. It studies different techniques and the parameters through which reduction in cost with less carbon emission is achieved. It demonstrates the experiences where it attempts for environment safe and reduced fault or error in the cloud network. Also, it explains various factors like reliability, scalability, availability and many more by which one can signify the effective fault tolerance, making normal cloud, a "Green cloud" which cannot harm the environment.
NiaClass: Building Rule-Based Classification Models Using Nature-Inspired Algorithms

Searching for a set of rules, with which the knowledge hidden in data is extracted, can also be applied for multi-class classification. In line with this, a collection of nature-inspired algorithms are selected for determining the set of rules capable of classifying the samples into three or more classes. This set is encoded into representation of individuals and undergoes acting the variation operators. The results of various nature-inspired algorithms, obtained after their application on more UCI classification databases, are compared with each other, and revealed that some of them can be potential candidates for real-world applications.
Continuous Wavelet Transforms for Vector-Valued Functions

We consider continuous wavelet transforms associated to unitary representations of the semi-direct product of a vector group with a linear Lie group realized on the Hilbert spaces of square-integrable vector-valued functions. In particular, we give a concrete example of an admissible vector-valued function (vector field) for the 3-dimensional similitude group.
A Support Interface for Remembering Events in Novels by Visualizing Time-Series Information of Characters and Their Existing Places

The style of reading books has shifted. People use electronic devices to read e-books. People often read books in a short time because it is difficult to have enough time for reading books at one time. If there is a time gap between readings, they may forget what has happened in the already read parts of books. Especially if people read novels where many characters appear, it is more difficult to remember what has happened for each character. Reading several stories in parallel should increase the number of rereads. The more times they reread books, the more difficult they enjoy reading. This paper proposes a support interface for remembering events in novels by visualizing time-series information of characters and their existing places. The proposed interface consists of two parts: (a) displaying of a novel text and (b) visualizing of time-series information of character names and their existing places. The proposed interface uses a historical chart as a metaphor for the visualization. If a novel text is given, the proposed interface extracts character names and places with the Conditional Random Fields (CRFs). The proposed interface sets the horizontal axis as the sentence's number and the vertical axis as character names. The existing places are mapped as labelled dots on the plane given by the two axes. We experimented the proposed interface with participants and verified that the proposed interface could support them to remember events in novels.
Archiving the Sonic Ephemeral: Towards a classification of sound installation documentations through spatial audio features

This paper proposes the application of audio feature extraction for the classification of sonic documentations of the works presented during the Singuhr Hoergalerie Project from 1996 to 2014. More specifically, I utilize the Stereo Panning Spectrum Features to discern some of the relationships between the artworks based on the spatial characteristics of said documentations. The first part of this paper addresses the theoretical concerns that emerge from documenting a form of art that champions ephemerality as one of its core characteristics. Audio recordings force sound installations into an ontological reality and effectively positions them in the musical realm, thus subjecting them to a one-dimensional interpretation. An approach to overcome this paradox is built upon Kersten Glandien’s proposal to analyze sound art. The second part describes the technical processes of examining the Singuhr recordings through the proposed algorithm. The results present a novel form of approaching sonic documentations by analyzing spatial relations between audio sources, and how to incorporate them into additional documentary and archival material.
Cursory View of IoT-Forensic Readiness Framework Based on ISO/IEC 27043 Recommendations

The Internet of things (IoT) technology has been adopted by many organisations as a way of improving business operations and discovering new business opportunities. However, the threat-related landscape and security ramifications that comes with this adoption has become a subject of focus. As a result, there is a need to significantly explore IoT readiness across organisations. The authors in this paper propose an IoT Forensic Readiness (IoT-FR) Framework for organisations that leverages on ISO/IEC 27043 International Standard. The IoT-FR framework benefits organisations that intend to adopt and implement IoT.
A Federated Multi-Task Learning Model Based on Adaptive Distributed Data Latent Correlation Analysis

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task’s similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.
Immersive Learning About IC-Engine Using Augmented Reality

Augmented reality (AR) is a technology that overlay’s an image generated by the computer on a user’s view of real world, thus providing a composite view. Applications on AR includes navigation, marketing, entertainment, tourism, construction, industrial training and maintenance, training for public safety, medical training, interactive teaching and learning, and many more. The chapter’s focus is in the field of education where Internal Combustion Engine (IC-Engine) are considered, they are the most widely used heat engines in vehicles like boats, ships, airplanes, and trains. It is a complicated engine to understand as the leaner has to imagine all the internal parts of the engine and the cost of the engine is also very high. Using AR application, the learner will be able to have a 3D view of all the parts of the IC-Engine, interactively acquire a detailed knowledge about it with visual and audio information given with a real environment interaction.
The Museum of Dreams: Exploring a “Dreaming” Visual Experience via Machine Vision and Visual Synthesis

In this paper, we will be introducing an art installation titled "The Museum of Dreams,"—which is an interactive system incorporating AI and machine learning techniques to create a "dreaming" experience for the participant and the audience. It's a screen-based installation connected to a webcam placed on top of the screen and facing towards the participants. When the participants approach the installation, the system will read their movement and then translate the data, synchronize and generate images with varying shapes with vivid colors and smooth gradients in real-time.
Artificial Intelligence-Based Automatic Evaluation Engine

Automatic evaluation of transcripts deals with comparing texts and evaluating them using the best available methods in NLP. The core part of this project is to enable the machine to evaluate descriptive transcripts all by itself. This reduces the human effort correcting huge answers in educational institutions and crowd examination platforms. Finding syntax and semantics out of text has been of great effort since years. As a part of this research, many NLP models like entity recognition, POS tagging, and topic modeling like LSA and LDA have come into existence. We are using a combination of these to get the semantics out of a transcript to eventually evaluate it.
Deep Neural Networks Based Object Detection for Road Safety Using YOLO-V3

Traffic safety plays a pivotal role in the society, and hence, there is a grave need for streamlining the protocols and measures taken in our country. This paper gives a comprehensive solution to the situation at hand, a new approach to ‘road safety’ based on seatbelt and helmet object detection using deep neural networks. The pipeline consists of using the deep learning frameworks and ‘You Only Look Once V3 (YOLO-V3)’, which is a convolutional neural network-based algorithm, for a hassle-free “real-time object detection.” Detection systems in the past re-engineer classifiers or localizers to perform detection. The paper focuses on a single neural network applied to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region. Popular image data sources such as the UCI Machine Learning Repository and Google Open Image Datasets were explored, and a dataset was created, which underwent preprocessing. The developed model is very versatile, compact, and was tested on various edge cases after which the model achieved an overall accuracy of 86.3% which was on a higher note compared to the previous works. The program can be readily deployed on CCTV, Webcams, or local computers.