Reviewerly/exHarmony huggingface dataset:
Discussion on Propositional Logic Incorporating Set Thought into Discrete Mathematics

Abstract Discrete mathematics is a basic core course for computer and big data majors. It includes four parts: mathematical logic, set theory, algebraic structure, graph theory. They are relatively independent branches respectively, of which propositional logic is the first part of discrete mathematics teaching materials in mathematical logic. This chapter is difficult for students in the second half of this chapter because the basic concepts are not easy to understand thoroughly. In order to solve this problem, this paper integrates the set theory into the understanding and mastery of the connectives, truth tables and important equivalent formulas in propositional logic, thus breaking through the learning difficulties in this chapter of propositional logic, enabling students to find a sense of achievement in learning from the first chapter of discrete mathematics and stimulating students’ interest in learning this course. Discrete mathematics is a mathematical discipline that studies the structure of discrete quantities and their interrelationships. It is an important branch of modern mathematics, as well as a basic core course for computer major and big data major. Its theory is strong [2]. Learning this course can cultivate students’ logical reasoning ability, abstract thinking ability and meticulous generalization ability, and lay a foundation for other professional courses. Discrete mathematics includes four learning parts: mathematical logic, set theory, algebraic structure and graph theory. Among them, mathematical logic mainly studies the reasoning and formalization methods of discrete objects. Propositional logic is the first chapter in mathematical logic, and is also the first part of most discrete mathematics textbooks. This chapter is characterized by many concepts, abstraction, rigorous logic and many proof, which makes it difficult for students to learn [3]. Based on many years of teaching experience, this paper summarizes the root of the students’ key difficulties in learning this chapter, integrates the collective thought to teach students to compare and understand, and then solves all the learning difficulties in this chapter.
Smart Contract for Real Estate Using Blockchain

The commercial real estate (CRE) industry appears to take pride in keeping several aspects of its operations private, such as comparable lease rental rates, property prices, and valuations, to create a possible competitive advantage. However, secrets are hard to keep and may not even be desired in today’s hyperconnected and digitized world. In response to greater demand for ​transparency, ​technology advancements and the disintermediation by startups are gradually making some of this information public. As a result, property-related information is increasingly available in digital and paper form. However, a significant portion of the digitized information is hosted on disparate systems, which results in a lack of transparency and efficiency, and a higher incidence of inaccuracies that creates a greater potential for fraudulence. This paper aims to present the Blockchain and smart contract for a specific domain which is real estate. Currently, the real estate business online is at risk of fraudulence. The main objective of this project is to create a platform to maintain transparency in the real estate world so that no fraudulent activities can happen due to false contracts. Goal is to create tamper proof systems and to remove third party reliability for transactions. Consensus algorithms such as Proof of Work and Proof of Authority can be used to achieve consensus in the network. Blockchain uses Proof of work concept to ensure transaction denial thus helping remove denial of services. Proof of authority on the other hand ensures only authorized smart contracts pass the system.
Feeling the Pain of Others in Need: Studying the Effect of VR on Donation Behavior Using EEG

Virtual reality (VR) enables people to engage in experiences that reach far beyond physical reality. This has inspired humanitarian organizations (among others the United Nations) to use VR technology to raise the awareness of humanitarian crises by virtually transporting people to the regions affected. As a consequence, these immersive experiences may lead to a change in the readiness to donate. As scientific evidence for this effect is still rare we propose an experimental design which aims at investigating how immersion affects donation behavior. In particular, neurophysiological measurement (EEG) shall shed light on the influence of immersion on emotional and motivational processes. First results from a convenient sample of young men indicate that donation behavior is linked to the dynamics of frontal alpha asymmetry changes.
A Vision Towards A Conceptual Basis for the Systematic Treatment of Uncertainty in Goal Modelling

Goal modelling is one the most important early activities in requirements engineering. Here, we describe a vision for a conceptual basis for the systematic identification and treatment of uncertainty in goal modelling. We aim to characterize the wide variety of uncertainty in goal modelling and to provide a theoretical framework for systematic uncertainty analysis. We thus adopt Walker's taxonomy which distinguishes among three dimensions of uncertainty: location, level, and nature. In addition, we propose to adapt Walker's uncertainty matrix as a heuristic tool to categorize various dimensions of uncertainty in goal modelling to serve as a conceptual framework for improving comprehension and communication of uncertainty between modellers and stakeholders and among modellers themselves. Understanding the various dimensions of uncertainty is a vital step towards the sufficient recognition and treatment of uncertainty in goal modelling activities. This in turn will help identify and prioritize critical uncertainties, which affect the goal modelling process in its entirety. We thus propose a long-term research agenda and urge community contributions in this research direction.
Boosting Tricks for Word Mover’s Distance

Word embeddings have opened a new path in creating novel approaches for addressing traditional problems in the natural language processing (NLP) domain. However, using word embeddings to compare text documents remains a relatively unexplored topic—with Word Mover’s Distance (WMD) being the prominent tool used so far. In this paper, we present a variety of tools that can further improve the computation of distances between documents based on WMD. We demonstrate that, alternative stopwords, cross document-topic comparison, deep contextualized word vectors and convex metric learning, constitute powerful tools that can boost WMD.
Evaluating Automatic Parameter Control Methods for Locomotion in Multiscale Virtual Environments

Virtual environments with a wide range of scales are becoming commonplace in Virtual Reality applications. Methods to control locomotion parameters can help users explore such environments more easily. For multi-scale virtual environments, point-and-teleport locomotion with a well-designed distance control method can enable mid-air teleportation, which makes it competitive to flying interfaces. Yet, automatic distance control for point-and-teleport has not been studied in such environments. We present a new method to automatically control the distance for point-and-teleport. In our first user study, we used a solar system environment to compare three methods: automatic distance control for point-and-teleport, manual distance control for point-and-teleport, and automatic speed control for flying. Results showed that automatic control significantly reduces overshoot compared with manual control for point-and-teleport, but the discontinuous nature of teleportation made users prefer flying with automatic speed control. We conducted a second study to compare automatic-speed-controlled flying and two versions of our teleportation method with automatic distance control, one incorporating optical flow cues. We found that point-and-teleport with optical flow cues and automatic distance control was more accurate than flying with automatic speed control, and both were equally preferred to point-and-teleport without the cues.
Classification of Article Knowledge Field using Naive Bayes Classifier

Scientific articles are articles that contain systematic reports on the results of a study or research, reviewing the articles in the publishing process will be more effective if the articles being reviewed are in accordance with the scientific field of the reviewer. Therefore, a classification process is needed for the article before it is directed to the reviewer. The classification carried out in this study is using the Naive Bayes Classifier method. The study was conducted using training data as many as 150 articles that already have categories and test data as many as 100 articles. 71 classified test data outputs the appropriate category, while the rest are still inaccurate. Thus, the accuracy value obtained is 71.00%, this result is influenced by the amount of training data used, and the process at the preprocessing stage carried out.
Towards Cleaner Environments by Automated Garbage Detection in Images

The environment protection is becoming, now more than ever, a serious consideration of all government, non-government, and industrial organizations. The problem of littering and garbage is severe, particularly in developing countries. The problem of littering is that it has a compounding effect, and unless the litter is reported and cleaned right away, it tends to compound and become an even more significant problem. To raise awareness of this problem and to allow a future automated solution, we propose developing a garbage detecting system for detection and segmentation of garbage in images. For this reason, we use deep semantic segmentation approach to train a garbage segmentation model. Due to the small dataset for the task, we use transfer learning of pre-trained model that is adjusted to this specific problem. For this particular experiment, we also develop our own dataset to build segmentation models. In general, the deep semantic segmentation approaches combined with transfer learning, give promising results. They show great potential towards developing a garbage detection application that can be used by the public services and by concerned citizens to report garbage pollution problems in their communities.
Specifying Framing Conditions for Smart Contracts

Smart contracts are programs which run in conjunction with distributed ledgers. They often manage valuable assets, but, like all programs, they contain errors which can be exploited by an attacker. This makes them are a prime target for formal methods. Many formal analysis methods require the contracts’ program code to be annotated with formal specifications. In this paper, we propose an approach and a formalism to enrich specifications with frame conditions, i.e., a specification of what a smart contract function cannot resp. will not do. We discuss the storage models of two smart contract platforms, Ethereum and Hyperledger Fabric, and propose languages for specifying frame conditions for both of them, based on the theory of dynamic frames.
SharedNet: A Novel Efficient Convolutional Architecture Based on Group Sharing Convolution

We propose a novel efficient deep convolutional neural networks model for low-computational equipment, referred to as SharedNet. And on ImageNet, Our SharedNet has achieved the best accuracy and efficiency in similar convolutional neural networks. It is due to a new efficient convolution called group sharing convolution which applies parameter sharing to reduce computational cost. Referring to other efficient network design methods, we design our network units which called SharedNet unit by combining group convolution with group shared convolution. The experimental results show that the accuracy and efficiency of this unit are superior to the depthwise separable convolution. Interestingly, SharedNet also shows flexibility according to application requirements that the number of groups of a unit could be adjusted to balance accuracy and efficiency. Besides, for both neural architecture search and manually-designed CNNs, a model unit plays an import role and, therefore, we stack SharedNet units to build models referring to ResNet164 to test accuracy and efficiency of SharedNet units on CIFAR10 and CIFAR100.
Security and Performance Considerations of Improved Password Authentication Algorithm, Based on OTP and Hash-Chains

The paper presents an approach for password authentication based on one-time password and hash function that improves the security of the P2P authentication because no passwords are stored. Back in the years, Leslie Lamport, in his paper from 1981, proposes a method for password authentication, based on sequential hash calculations starting from a given initial value. Calculate and store the hashes locally in a file. We use this idea to measure the authentication time, and we go further with improving the security and the speed of authentication. The primary purpose of this research is to enhance the security around passwords authentication. We base this authentication on the improvement of the process of password authentication with two-factor authentication - one-time password (OTP) and hash chains. Preparation of the initial value uses the OTP password (also for the first hash calculation in the hash-chain), and it indicates/indexes exact hash from the hash-chain. Every time a client initiates an authentication process, the password will be different. The second purpose of the paper is to compare two different scenarios – with local files, used to store the passwords (Lamport’s idea), and the proposed approach – without storing the passwords. The time duration of the authentication between two nodes gives the comparison between both methods. The suggested approach can be applied in various immutable and distributed applications based on docker containers and microservices.
Semi-Supervised Learning for Multi-Speaker Text-to-Speech Synthesis Using Discrete Speech Representation

Recently, end-to-end multi-speaker text-to-speech (TTS) systems gain success in the situation where a lot of high-quality speech plus their corresponding transcriptions are available. However, laborious paired data collection processes prevent many institutes from building multi-speaker TTS systems of great performance. In this work, we propose a semi-supervised learning approach for multi-speaker TTS. A multi-speaker TTS model can learn from the untranscribed audio via the proposed encoder-decoder framework with discrete speech representation. The experiment results demonstrate that with only an hour of paired speech data, no matter the paired data is from multiple speakers or a single speaker, the proposed model can generate intelligible speech in different voices. We found the model can benefit from the proposed semi-supervised learning approach even when part of the unpaired speech data is noisy. In addition, our analysis reveals that different speaker characteristics of the paired data have an impact on the effectiveness of semi-supervised TTS.
Analog Gauge Reader based on Image Recognition

Abstract Analog dial are widely used in modern production and life. In the fields of meter detection and meter scale value recording, the demand for automatic reading of analog dial is increasing. This paper proposes an improved method of fast reading of images, which refines the scale circle and uses the branch point extraction algorithm to extract the intersection of the scale line and the scale circle to express the scale line indirectly. Finally, the pointer reading is calculated based on the ratio of the deflection angle of the pointer to the total angle of the range.
Marine Three-dimensional Simulation Animation Design Based on Computer Rapid Simulation Technology

Wang, Y., 2020. Marine three-dimensional simulation animation design based on computer rapid simulation technology. In: Li, L. and Huang, X. (eds.), Sustainable Development in Coastal Regions: A Perspective of Environment, Economy, and Technology. Journal of Coastal Research, Special Issue No. 112, pp. 386-389. Coconut Creek (Florida), ISSN 0749-0208.In order to realize the three-dimensional integrated visualization of marine scalar data with characteristics such as massive, multi-source, heterogeneous, multi-dimensional, multi-format, dynamic, etc., a three-dimensional virtual visualization architecture based on service ideas and oriented to general scalar data formats is designed, and ocean three-dimensional Visualized the prototype system, and explained its architecture and functional design in detail; using a visualization method based on geometric objects, combined with key technologies such as LOD dynamic scheduling, surface texture and fast query, realized the efficient and fast 3D ocean scalar field data Simulation animation visualization, element value query and time sequence analysis functions. This research can provide ideas and technical reference for the visualization and analysis of other marine environmental element data.
Blended learning: a strategy of current mathematics learning

Abstract This article aims to provide an overview of a mathematical learning strategy that can be used in facing the industrial revolution 4.0 in the education world of Indonesia. The background of this article is to explain one of today’s learning strategies, Blended Learning. This article uses analytical and literature review methods. That is, in this case, the increased information and communications technology usage in the learning process in Indonesia is analyzed following the existing circumstances based on relevant books, articles, journals, and other sources. This article examines the characteristics of the blended learning strategy, the procedures for implementing blended learning, and the philosophical foundation of blended learning in mathematics learning. The results from reviewing the articles obtained show that the blended-learning strategy is suitable to use in mathematics learning since students, by use blended-learning strategy, may evaluate their skills themselves as well as determine their learning speed.
Exploring Lexicon-Free Modeling Units for End-to-End Korean and Korean-English Code-Switching Speech Recognition

As the character-based end-to-end automatic speech recognition (ASR) models evolve, the choice of acoustic modeling units becomes important. Since Korean is a fairly phonetic language and has a unique writing system with its own Korean alphabet, it's worth investigating modeling units for an end-to-end Korean ASR task. In this work, we introduce lexicon-free modeling units in Korean, and explore them using a hybrid CTC/Attention-based encoder-decoder model. Five lexicon-free units are investigated: Syllable-based Korean character (with English character for a code-switching task), Korean Jamo character (with English character), sub-word on syllable-based character (with sub-word in English), sub-word on Jamo character (with sub-words in English), and finally byte unit, which is a universal one across language. Experiments on Zeroth-Korean (51.6 hrs) and Medical Record (2530 hrs) are done for Korean and Korean-English code-switching ASR tasks, respectively. Sequence-to-sequence learning with sub-words based on Korean syllables (and sub-words in English) performs the best for both tasks without lexicon and an extra language model integration.
Patentability of inventions created by AI—the DABUS claims from an Indian perspective

... The conception of human-like machines is quite old—as early as in the early 17th century, René Descartes theorized a machine that could ‘reply appropriately to everything that may be said in its presence’.1 The modern term ‘artificial intelligence’ (‘AI’) was coined in 1956 by computer scientist John McCarthy as part of a Dartmouth College workshop that is now regarded as the founding of AI studies.2 Following this beginning, it was only a matter of time until the advent of AI machines that could, by themselves, without any contribution by humans, create patentable inventions (‘computational invention/s’). However, the first computational invention to seek patent protection with the AI system listed as an inventor were claims made in 2018/19 on behalf of the Device for the Autonomous Bootstrapping of Unified Sentience (DABUS) in the UK, the European Patent Office (EPO) and the USA. Prior to this, neither did patent laws take computational inventions into consideration, nor had AI machines sought to apply for patents. The applications, therefore, marked a watershed moment in the crossroads between patent law and AI history.
Dynamic calibration and compensation method of a large-scale laser beam based on specular reflection for a nonorthogonal shaft laser theodolite measurement system

The nonorthogonal shaft laser theodolite (N-theodolite) measurement system is a new kind of instrument that can be applied to large-scale metrology. Different from traditional theodolites, the three axes of the N-theodolite have no requirements on the orthogonality and intersection. For the measurement in laser scale, the parameters of the system on a large scale must be known. A calibration method of the laser beam based on specular reflection and its compensation are proposed, which are at a range of 12 m. Through the experiments, the results show that the straightness accuracy of the laser beam of the left N-theodolite is 0.12 mm at a range of 12 m, and the right one is 0.14 mm. Through the measurement experiments, the maximum measurement deviation of the system is 0.27 mm, which is verified to be a feasible method to calibrate the laser beam in large-scale space.
Infinite Walking in Three Dimensions in Virtual Reality

In virtual environments, users can move almost infinitely while actually staying within a small area in real space. Several techniques such as Infinite Walking and Redirected Walking have already been developed for this purpose, but in order to overcome their respective limitations, several of them must be combined with visual effects and tricks to create the impression of a realistic environment and realistic movement. In this paper, our graduation class presents the prototype of a shopping mall simulator game realizing infinite motion in 3D on 639 m² within a 5 x 5 meter VR sensor range, even allowing the player to move freely on multiple floors of a beautifully designed shopping mall.