Reviewerly/exHarmony huggingface dataset:
Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data

Bayesian network (BN) structure learning from complete data has been extensively studied in the literature. However, fewer theoretical results are available for incomplete data, and most are based on the use of the Expectation-Maximisation (EM) algorithm. Balov (2013) proposed an alternative approach called Node-Average Likelihood (NAL) that is competitive with EM but computationally more efficient; and proved its consistency and model identifiability for discrete BNs. In this paper, we give general sufficient conditions for the consistency of NAL; and we prove consistency and identifiability for conditional Gaussian BNs, which include discrete and Gaussian BNs as special cases. Hence NAL has a wider applicability than originally stated in Balov (2013).
Viewer’s Sentiments on Game of Thrones: An Automated Lexicon-Based Sentiment Analysis on Real-Time YouTube Comments

Sentiment analysis (SA) is an integral domain of natural language processing. SA employs manifold natural language processing algorithms and methods for measuring the opinions of people via text analysis and computational linguistics. Due to prodigious soar in the text documents across social media, reviews, blogs, news, etc., sentiment analysis emerged out to be one of the significant data mining genres. The process in a whole of drawing out the sentiment polarity associated with some piece of text, i.e. positive, negative or neutral polarity, is known as sentiment analysis. The proposed work has automated the operation of fetching the real-time YouTube’s user comments. The objective is to ease the process of analysing thousands of YouTube’s user comments and finding how the users are thinking about video. In the proposed work, researchers have used six discrete data sets. In this work, the user comments are fetched from a YouTube review channel “Blind Wave”. These user comments have been collected from six discrete reaction videos of this channel. The channel members have given their live reaction on six episodes of “Game of Thrones” Season 8, i.e. first episode in first video and last episode in last. The user comments on each video have been analysed individually. This work helped in perceiving the sentimental feedback of users on each of the six episodes of “Game of Thrones” Season 8.
A Study of College Students' Lifestyle Regularity Based on Wearable Devices and Deep Learning

With the popularity of wearable devices, smart wearable devices containing various sensors have been widely adopted in healthcare applications. However, there is little research on the use of these devices to study lifestyle regularity, especially to study lifestyle regularity of college students using physiological or exercise data collected by smart wearable devices. In this work, we use the wrist wearable devices worn by students every day to collect college students' daily routine data, and establish models to analyze the regularity of the collected data and propose the use of MOE (Mixture of Experts) and transfer learning to improve the classification performance of the model. The experimental results show that the classification accuracy can be improved by 8.3% using MOE compared with not using it, and the accuracy can be further increased by 2.9% with Transfer Learning.
An Implementation of Single and Multiple Thresholds for Efficient Image Segmentation

The image segmentation is the process of finding and grouping the correlated pixels in the particular image. There are different types that are available to find the correlated pixels in the image. In this paper, the single and multiple thresholding are used for segmentation to observe the different image objects in an image. At first, the input samples are converted into grayscale images, after that those images are reshaped and processed by single and multiple thresholding to produce the segmented image output. The single and multiple thresholding are based on calculating the mean value of an image and fix the threshold value for object discriminations. Out of several algorithm for image segmentation, the conventional method of region-based segmentation will provide good results. The objects discrimination is purely based on the level set of pixels available in the image. Before processing, the image reshaping is also performed for better visualization of the segmented image.
Methods for ensuring the cognitive perception of students of educational materials with artistic and aesthetic content

The article discusses the methods of ensuring the cognitive perception of students of educational materials with artistic and aesthetic content. Cognitive perception is the most important component of a harmoniously developed personality, and its formation in a child can be considered as the most important task of modern society. The area of research carried out included the main levels and directions of art education. At the same time, the authors took into account those fundamental approaches to the definition of the subject of research itself and related concepts that have been tested recently.
Impact of Blockchain on E-Commerce

The current disruption has created many challenges for all businesses in the short run, but it has created many opportunities in the long time. By helping in crisis and recovery, blockchain can play a prominent role in accelerating post-crisis digital transformation initiatives. Trade has become more efficient, more inclusive, and less costly through blockchain. These innovative technologies in the fourth industrial revolution are transforming the world by making the processes more inclusive and efficient.
Zero-one laws for provability logic: Axiomatizing validity in almost all models and almost all frames

It has been shown in the late 1960s that each formula of first-order logic without constants and function symbols obeys a zero-one law: As the number of elements of finite models increases, every formula holds either in almost all or in almost no models of that size. Therefore, many properties of models, such as having an even number of elements, cannot be expressed in the language of first-order logic. For modal logics, limit behavior for models and frames may differ. Halpern and Kapron proved zero-one laws for classes of models corresponding to the modal logics K, T, S4, and S5. They also proposed zero-one laws for the corresponding classes of frames, but their zero-one law for K-frames has since been disproved.In this paper, we prove zero-one laws for provability logic with respect to both model and frame validity. Moreover, we axiomatize validity in almost all irreflexive transitive finite models and in almost all irreflexive transitive finite frames, leading to two different axiom systems. In the proofs, we use a combinatorial result by Kleitman and Rothschild about the structure of almost all finite partial orders. On the way, we also show that a previous result by Halpern and Kapron about the axiomatization of almost sure frame validity for S4 is not correct. Finally, we consider the complexity of deciding whether a given formula is almost surely valid in the relevant finite models and frames.
P‐49: <i>Student Poster:</i> Efficient Direct‐Block‐Address Encoding for Single‐Shot‐Based 3D Reconstruction

We propose a new framework for single‐shot 3D reconstruction based on two‐dimensional tag embedding, where the Structured Light (SL) patterns are partitioned into a number of non‐overlapping blocks that each represent a codeword for explicitly encoding the projector's corresponding pixel address, resulting in an efficient computation for both constructing the SL images and for obtaining the projector‐camera pixel correspondences.
Exquisite Circuits: Collaborative Electronics Design through Drawing Games

We present Exquisite Circuits, a novel collaborative circuit design approach that remixes the surrealist Exquisite Corpse drawing game for paper circuits. In this pictorial, five participants played the game and documented their design, fabrication, and thought processes during gameplay. From these results, we contribute lessons learned on how game elements like surprise, ambiguous goals, and shared responsibility open new ways of thinking about the expressive and collaborative design of technology. Exquisite Circuits, through paper circuitry's hybrid of aesthetic and functional design affordances, helped reveal tensions between arts and technology cultures and approaches. We invite educators, designers, and technology creators to try their own variations of the Exquisite Circuits and share their results with the creative technology community.
Modeling of Aggregation Process Based on Feature Selection Extreme Learning Machine of Atomic Search Algorithm

Polymerization process is a process in the production of polyester fiber, and its reaction parameter intrinsic viscosity has an important influence on the properties of the final polyester fiber. In this paper, a feature selection extreme learning machine model based on binary encoding Atom Search Optimization algorithm is proposed and applied to the polymerization process of polyester fiber production. Firstly, the distance measure of K-NearestNeighbor algorithm, combined with binary coding, and Atom Search Optimization algorithm are used to select features of industrial data to obtain the optimal data set. According to the data set, atom search optimization algorithm is used to optimize the weight and threshold of extreme learning machine and the activation function of the improved extreme learning machine. A prediction model with root mean square error as fitness function was established and applied to polyester production process. The simulation results show that the model has good prediction accuracy, which can be used for reference in the follow-up industrial production.
Government Policy Toward Economic Development : An Analysis of Sung-Hee Jwa’s A General Theory of Economic Development

Sung-Hee Jwa’s A General Theory of Economic Development does an excellent job of explaining what produces economic development, and offers an insightful critique of mainstream theories of economic development. From a policy perspective, he concludes that governments should implement policies that discriminate in favor of firms that have demonstrated market success to promote development. He attributes the much more rapid economic growth of Japan, South Korea, and China, when compared to the slower growth of Western Europe and the United States, to such policies. This article offers several reasons to question this conclusion.
Towards an Automated Classification Approach for Software Engineering Research

The rapid growth of software engineering research publications forces an amount of scholarly knowledge that needs to be managed, organized and communicated in digital libraries and scientific search engines. Thus, there is a need for classified papers to accomplish these tasks, but the classification process is cumbersome. Moreover, in case of new schemas, one would need to reclassify previously published research. We propose to automate the classification and present different possible techniques for doing so: Using natural language models, a rule-based approach, or an approach based on topic-labeling. In this proposal paper, we initially implemented a prototype for text classification of software engineering research papers.
Monocular Stereo Vision of Image Feature-aware Interactive Generation

This paper presents an image feature extraction based monocular stereo vision method. According to the key feature-aware region of the image, the famous interactive segmentation method GrabCut is introduced. Combing with the image correction and affine transformation and stereoscopic generation, the method can get the resemble 3D vision effect include with the shadow generating and object folding from the original cut apart region in 2D space. The algorithm of image feature-aware monocular stereo vision is high effective and efficiency by experiment with several different background complexity samples. It’s readable and interesting in the usage of virtual reality and augment reality. Especially in the field of digital culture publication and naked eye 3D display, has great application value.
Entity-Based Knowledge Graph Information Retrieval for Biomedical Articles

In this paper, we present an information retrieval system on a corpus of scientific articles related to COVID-19 and biomedical.Prasad, Vikash Kumar Bharti, Shashvat Koganti, Nishanth We build a heterogeneous entity-based knowledge graph network, where edges are shared between biomedical entities and paper names, where entities appear in abstract of the paper. The biomedical entities are derived from the abstract of the scientific articles using a fine-tuned Bio-BERT model. For a user query, entities are derived using a fine-tuned Bio-BERT model and then semantic similarity to query is employed for the return of the top-most relevant papers on the titles. We also provide a small set of results for the information retrieval system.
The price mechanism

We explore how prices of minerals react to increasing geological scarcity and formulate a hypothesis on the correlation between geological scarcity and price trend. The long-term development of prices of 35 mineral resources is presented. We compare the price trends of these materials with the hypothetical price development. The conclusion is that until 2020 there is no significant difference in price trend between geologically scarce minerals and geologically abundant minerals.
Adapting Travelling Salesmen Problem for Real-Time UAS Path Planning Using Genetic Algorithm

Route preparation for unmanned aerial vehicle (UAV) or unmanned aerial system (UAS) is one of the biggest challenges when it comes to carrying out any mission. Analysis of UAS path planning has gained significant attention from researchers as it is an essential aspect of the UAS collaboration project mission. With the objective of solving the path planning issue, we adapt the travelling salesmen problem (TSP) and propose to solve it by operating an improved Genetic Algorithm (GA) that can find out the path within the shortest distance and period. In this paper, we first develop an algorithm using the concept of GA to solve TSP as per the requirements of UAS path planning. The UAS is considered the travelling salesman in the TSP concept and the mission objective is regarded as the visiting city, since TSP is a complex NP-hard problem, this paper performs optimization algorithm as GA to solve TSP, which can be both successfully and expertly planned. Generally, the algorithm used to find the lowest possible distance to visit all of the TSP’s access points. The optimal solution to various challenges tends to require the use of GA. Some conditions and operators that depend on this algorithm are population size, selection method, mutation and crossover. We will present an improved way of the GA operator in this paper. In contrast with several other techniques, the suggested approach for TSP was more successful in the application of UAS path planning. This paper deals with GA as a part of the simulation to represent the TSP model for UAS route planning. The outcomes show that the approach of the GA is relatively effective in finding the minimum path direction. GA is efficient to solve TSP for UAS path planning problem within a reasonable period of less than 1 min for simple problem and less that 5 min for complex problem.
Model-based Ensemble Reinforcement Learning with Soft Proximal Policy Optimization

At present, model-free reinforcement learning has been widely used in games, robot control and other fields, and has achieved good results. However, these algorithms require a large number of samples to achieve good performance, which limits the application of model-free reinforcement learning methods in real-world domains. Model-based reinforcement learning can use fewer samples, but requires careful tuning. In this article, we use a neural network ensemble model to learn the dynamics, and incorporate model predictive control as the basic control framework. The dynamic model is also used to train an initial model-free neural network to achieve a combination of sampling efficiency and performance. We evaluated our method on the MuJoCo experimental platform. The results show that, compared with other model-free and model-based methods, our approach achieves better task performance while having excellent sample efficiency.
Ethical Hacking, Threats, and Vulnerabilities in Cybersecurity

This chapter will discuss the important topic of ethical hacking, also known as penetration testing. It will start by explaining the constituents of ethical hacking: scope and goal setting, exploitation, and documentation. The authors will define and explain the reasons for the rapid rise in cyber-crimes and their socio-economic impact. It will further discuss the steps involved in ethical hacking, who is allowed to conduct ethical hacking, its importance, and the role it plays in deterring future and potential hackers. The chapter will analyze the various types of malware and the steps to follow to become an ethical hacker. It will further describe social engineering, the types of cyber-attacks, the phases of attack, testing for vulnerabilities, and it will put forward a list of countermeasures. The chapter will end by detailing the steps to be taken in the documentation process and crafting the executive summary.
Detecting Overlapping Pharmaceutical Infusion Bags using Convolutional Neural Network

When producing pharmaceutical infusion bags, manual inspection of the production of the bags is very important. We set out to see if convolutional neural networks (CNNs) can be used to make one of these inspection steps easier. The task was to detect where infusion bags placed on a tray overlapped. The problem with detecting this overlap is that the infusion bags are made of transparent plastic and filled with transparent fluid, so it is hard to see the bag placements. Several different setups of CNNs were explored, as well as one image processing technique. All the neural networks tested were able to find the infusion bags. The U-Net CNN for the whole image seemed to be especially well suited this specific inspection problem, as this made it possible to make a direct input-to-output neural network with the least limitations. This paper describes the various methods and setups used, including their advantages and limitations. How to implement the selected setup in the actual inspection process and some further work we would like to do is also mentioned.
Designing a Blockchain-Based Pemilu E-Voting Information System

Data is an important thing as a base of an analytic or a hypothesis for concluding participant vote data in the Indonesian General Election. The data needs to be processed and secured, so the integrity of the data is in good condition. It also needs to protect the participant voting rights, so the information is correctly displayed. The research problems include creating a system that protects the integrity of election data and creating a system that protects election rights for each voter. Based on the problems, this research discusses a blockchain-based electronic voting information system that would secure the integrity of data and also protecting the participant voting rights in a General Election. The system uses Ethereum as a blockchain with Solidity as a programming language to build a smart contract and is built in Microsoft Windows platform. In this research, consortium blockchain and biometric fingerprint authentication are used as a problem-solving method, and waterfall steps is used as a system development method. The result of this research is a proposed design of the e-voting system. The conclusion based on this research is a blockchain-based e-voting system that secures the integrity of the data in a selection process and ensures protection to each vote right.&#x0D; &#x0D; Index Terms—Authentication; Blockchain; Data; Integrity; E-Voting; System
Productive Programming of Distributed Systems with the SHAD C++ Library

High-performance computing (HPC) is often perceived as a matter of making large-scale systems (e.g., clusters) run as fast as possible, regardless the required programming effort. However, the idea of "bringing HPC to the masses" has recently emerged. Inspired by this vision, we have designed SHAD, the Scalable High-performance Algorithms and Data-structures library [1][6]. SHAD is open source software, written in C++, for C++ developers. Unlike other HPC libraries for distributed systems, which rely on SPMD models, SHAD adopts a shared-memory programming abstraction, to make C++ programmers feel at home. Underneath, SHAD manages tasking and data-movements, moving the computation where data resides and taking advantage of asynchrony to tolerate network latency. At the bottom of his stack, SHAD can interface with multiple runtime systems: this not only improves developer's productivity, by hiding the complexity of such software and of the underlying hardware, but also greatly enhance code portability. Thanks to its abstraction layers, SHAD can indeed target different systems, ranging from laptops to HPC clusters, without any need for modifying the user-level code.We have prototyped and open-sourced the implementation of (a subset of) the C++ standard library (STL) targeting multi-node HPC clusters. Our work allows plain STL-based C++ code to scale on HPC systems, with no need for rewriting the code to exploit the complex hardware. SHAD is available under Apache v2 License at https://github.com/pnnl/SHAD. In this paper we overview the design of the SHAD library, depicting its main components: runtime systems abstractions for tasking; parallel and distributed data-structures; STL-compliant interfaces and algorithms.
The Environment and Body-Brain Complexity

An open question for both natural and artificial evolutionary systems is how, and under what environmental and evolutionary conditions complexity evolves. This study investigates the impact of increasingly complex task environments on the evolution of robot complexity. Specifically, the impact of evolving body-brain couplings on locomotive task performance, where robot evolution was directed by either body-brain exploration (novelty search) or objective-based (fitness function) evolutionary search. Results indicated that novelty search enabled the evolution of increased robot body-brain complexity and efficacy given specific environment conditions. The key contribution is thus the demonstration that body-brain exploration is suitable for evolving robot complexity that enables high fitness robots in specific environments.
Different Techniques Used in Smart Traffic Light Management System Using CCTV

In India, traffic congestion has become a vicinity of concern. Heavy hold-ups and tiring roads lead to waste in an ample amount of time and fuel. Smart traffic light systems using CCTV cameras are introduced for the intelligent functioning of traffic lights. Different image processing algorithms, vehicle detection techniques are used for the advancing system. A review of the latest trends and technologies used in researches related to the respective field is brought to light. Briefing of smart mode and manual mode traffic light systems is done in the paper with an aim at providing knowledge to researchers about the preexisting work done in this area. This study review gives the latest possible techniques useful for working on smart traffic lights using image processing, in a real-time environment. Also, the limitation of the algorithms, approaches, and researches is keenly observed, and practical ideas to improve and overcome them are generalized.