Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis. A Comparative of Machine Learning and Fuzzy Logic in the Study Case of Brexit Sentiment on Social Media

Sentiment analysis is a case of natural language processing that extracts the sentiment of people about any specific service, product, or the topic. Users spend a considerable amount of time scrolling through social media feeds to find something relatable, either emotionally or as a problem in their daily life that they want to solve. By analyzing the content produced by social media users, we can know if his opinion is relatively optimistic, neutral, or pessimistic about the subject – that varies from a service, news, a movie, etc. There are multiple approaches for sentiment analysis; first, there’s the Machine learning with its numerous methods that can be either supervised or unsupervised. Second, the Lexicon-based approach. Third, the combination of the two approaches – which is called a hybrid. Also, there are other approaches that we can add to get a more optimized result, such as working on improving text preprocessing techniques or by adding Fuzzy Logic approach and subjectivity detection. In this paper, we continued the same path as in our previous work [1]. We used the same ontologies, the same data collected about Brexit and also the same text preprocessing method. We applied a subjectivity detection using Long Short-Term Memory so that our model can predict subjectivity in a text. We did that by applying a fuzzy logic method based on the Lexicon based polarity and subjectivity classifier to classify the text into one of 5 categories. Finally, we come up with a comparison that summarizes every approach with their result.
Knowledge Distillation on Extractive Summarization

Large-scale pre-trained frameworks have shown state-of-the-art performance in several natural language processing tasks. However, the costly training and inference time are great challenges when deploying such models to real-world applications. In this work, we conduct an empirical study of knowledge distillation on an extractive text summarization task. We first utilized a pre-trained model as the teacher model for extractive summarization and extracted learned knowledge from it as soft targets. Then, we leveraged both the hard targets and the soft targets as the objective for training a much smaller student model to perform extractive summarization. Our results show the student model performs only 1 point lower in the three ROUGE scores on the CNN/DM dataset of extractive summarization while being 40% smaller than the teacher model and 50% faster in terms of the inference time.
Dynamics of a moving quantum charged particle

Quantum equations describing a moving charged quantum particle are derived. The electric nature is described by the current and charge densities, J→ and ρ; and the quantum nature by the particle mass, wavefunctions, ψ→ and ψ0. The vector wavefunction, ψ→ is found to act along the current direction. The energy of the matter fields concomitant with a particle is found to flow along the current direction. A fluid with a stationary mass density inherently exhibits a Thomas precession.
A Data Analytics Approach to Persona Development for The Future Mobile Office

The concept of using automated vehicles as mobile workspaces is now emerging. Consequently, the in- vehicle environment of automated vehicles must be redesigned to support user interactions in performing work-related tasks. During the design phase, interaction designers often use personas to understand target user groups. Personas are representations of prototypical users and are constructed from user surveys and interview data. Although data-driven, large samples of user data are typically assessed qualitatively and may result in personas that are not representative of target user groups. To create representative personas, this paper demonstrates a data analytics approach to persona development for future mobile workspaces using data from the occupational information network (O*NET). O*NET consists of data on 968 occupations, each defined by 277 features. The data were reduced using dimensionality reduction and 7 personas were identified using cluster analysis. Finally, the important features of each persona were identified using logistic regression.
Selection Method of Smart Grid Operation Index Based on Maximum Relevance and Minimum Redundancy

Smart grid operation data presents the characteristics of large amount of data, multiple types of data and rapid growth rate, which is not conducive to quantitative analysis of actual operation level of power grid. In order to quickly and effectively screen the smart grid operation indicators, simplify the operation index system and reduce the index redundancy, this paper proposes a quantitative analysis index to measure the effectiveness and redundancy of operation indicators based on the maximum relevance minimum redundancy criterion. Then, through quantitative analysis of indicators, the optimization model is constructed. Finally, the screening process is transformed into a 0-1 programming problem by coding, and the optimal index set is obtained by exhaustive method. The results show that the proposed screening method improves the information content of the index system and reduces the index redundancy, which verifies the rationality and effectiveness of the screening method.
Particle Physics Problems Addressed with Simple Mathematics Related to General Relativity

Existing particle physics models do not account for dark matter and neutrino mass, or explain the three generations of fundamental fermions.This analysis uses simple mathematics, related to general relativity, to address these problems.The paper does not address the very difficult problem of quantizing general relativity.
A Study of Data Storage for E –Learning in Cloud Computing

Cloud computing provides on demand services to its clients. Data storage is among one of the primary services provided by cloud computing. Cloud service provider hosts the data of data owner on their server and user can access their data from these servers. As data, owners and servers are different identities, the paradigm of data storage brings up many security challenges. An independent mechanism is required to make sure that data is correctly hosted in to the cloud storage server. In this paper, we will discuss the different techniques that are used for secure data storage on cloud. PC based research facilities are an essential part of the e-learning condition. With the presentation of Cloud Computing innovations an ever increasing number of labs move into the cloud, known as virtual labs. While there are numerous advantages for utilizing virtual labs, there is likewise an expansion unpredictability in regards to the use of them. There are numerous cloud administration frameworks (Cloud MS) which include an additional layer of multifaceted nature to the e-taking in encounter both from the point of view of the educator and of the understudy. The reason for the investigation is to recognize approaches to enhance the learning procedure using distributed computing advances, while lessening the unpredictability related with these advances the principle center is about the asking for, creation, arrangement, observing and administration of virtual labs utilizing Cloud Computing. IBM Tivoli Service Automation Manager and VMware Hypervisor are utilized for building a private cloud.
Natural Language Processing

Understanding and processing human language has always been a complicated task in computational theory. Natural language processing, a combination of artificial intelligence and computational linguistics, employs computational techniques to understand the structure of human language. The challenges in the field are increasing day by day with the explosive growth of text contents on the Internet, varying text forms used in social media, and handling conversational complexities associated with intelligent devices. Natural language processing ranges from analyzing natural language by tokenizing and parsing techniques to resolving ambiguities and co-references in language. This chapter explains the basics of natural language processing that includes text processing techniques, parsing, semantic analysis, and the latest trend in deep learning models, which promises excellent improvement in natural language processing tasks, with simple examples that give more comprehension about the concepts in brief. The chapter also includes a comparative study of long short-term memory and gated recurrent unit for the sequence to sequence modeling with step-by-step implementation details of opinion summarization.
Unseen Locomotion Mode Detection Problems using Inertial Sensors

Locomotion Mode Detection (LMD) is a predominating area of research from the past few years. It facilitates the convenience of estimating travel expenses, travel time, minimizing traffic congestion, etc. LMD incorporates sensory values of multiple inertial sensors, including accelerometer, gyroscope, magnetometer, and so on. The existing literature on LMD cover different aspect, but non-of-them emphasized on recognition of unseen class labels along with deployment of deep learning models on Resource Constraint Devices (RCDs). In addition, during inference using deep learning model deployed on RCDs, the dynamic resources variation is still unexplored. Therefore, we propose a deep learning-based mechanism that incorporates the concept of zero-shot learning for recognizing seen and unseen class labels (locomotion modes). Next, we solve an optimization problem to determine an optimal deep learning model to be deployed on RCDs. Further, we perform experimental analysis on publically available dataset to study the impact of seen classes, unseen classes, and RAM occupancy. The results justify the effectiveness of the proposed schemes in recognizing seen and unseen locomotion modes on RCDs.
Preventing DDoS Attacks on Bitcoin Memory Pool by the Dynamic Fee Threshold Mechanism

Blockchain is a well-known distributed technology which can be regarded as a decentralized database and combines the peer-to-peer architecture, cryptography and consensus mechanism. As the first and most famous application of blockchain and cryptocurrencies, Bitcoin also suffers from different kinds of attacks. Several studies have been done to analyze and solve these attacks. In this paper, we research the Distributed Denial-of-Service (DDoS) on Bitcoin’s memory pool (Mempool). We analyze the feasibility of this kind of attack based on the current mining and relay progress of Bitcoin, and propose the possible effects caused by this attack from some novel aspects of Bitcoin, including the confirmation time, market price and legitimate user. We further present the Dynamic Fee Threshold Mechanism to counter this attack. Our method uses several characteristics to distinguish the normal transactions in Bitcoin from the malicious spam transactions. We also make an analysis and experiments on the method, to demonstrate its effectiveness and accuracy of detecting the malicious transactions.
Construction Quality Evaluation Model of Concrete Pouring based on Support Vector Machine

The traditional concrete pouring construction quality evaluation model has been difficult to meet the needs of the current pavement performance evaluation. Therefore, a concrete pouring construction quality evaluation model based on support vector machine is designed. First, we must improve the evaluation indicators. All indicators are aggregated through the pavement performance index, and 5 indicators are selected as the main evaluation indicators, and the calculation and conversion methods of the indicators are given. The support vector classification machine is designed to classify concrete Pouring construction projects are classified, and finally the evaluation process of the model is improved. According to the analysis results of actual calculation examples, compared with the traditional model, the evaluation result of the designed quality evaluation model is closer to the result of the standard specification, which verifies the validity of the design model.
Dynamic Information Needs Analysis: Understanding User Information Needs in Subterranean Warfare

In this practice-oriented paper for human factors research, we describe our experiences piloting an exercise to understand how a moment-by-moment input device can be utilized to help understand soldiers information needs while conducting subterranean (sub-t) warfare and facilitate development of Augmented Reality (AR) displays for sub-t use. We created a sub-t environment using the Unity game engine and utilized students as subjects to facilitate experiment setup and validation. Little research is being done on what information soldiers need in a subterranean environment. Here we begin to investigate these information needs.
Data Privacy Preservation Algorithm on Large-Scale Identical Generalization Hierarchy Data

In the data bursting era, an enormous amount of data can be collected. Though, such data can benefit both the data owners and the public, the privacy of collected data is an important concern. To guarantee the privacy of data, the k-anonymous method is applied before publishing the dataset. The dataset needs to have an identical value of at least k records in order to protect the data privacy which could cause data losses. The optimal k-anonymity is concerned with minimizing the data losses and also preserve data privacy. The generalization lattice is created to map all the generalization schemes and use them to find the optimal answer. The larger number of attributes means the larger number of nodes in the generalization lattice. Thus, in the larger number of attributes, the k-anonymous algorithm takes more computation resources and time to determine the answer. Although, due to the limited computation resources and time, the existing optimal k-anonymity algorithms only find the optimal answer on the small dataset. Therefore, in this paper, we design the optimal k-anonymity algorithm with the incremental attribute concept. At m attribute, our algorithm process only the nodes which previously satisfy the k-anonymity from the \(m-1\) attributes. Thus, our algorithm can find the optimal answer at a large number of attributes without reaching the memory limit problem compared with the existing k-anonymity algorithms due to it determines only some necessary nodes in the lattice.
User Aspiration and Design of Functional Architecture in Seafarer’s Certification Management

Abstract Maritime (kepelautan) is matters related to manning, education, certification, authority, rights and seafarer’s obligations. In marine transportation activities, there are several main instruments that must be in place, namely transportation facilities (ships), transportation facilities providers (shipping companies / government), ship operator service providers (seafarer), certified seafarers / producers of seafarers (training institutions) and infrastructure, such as ports, channels, warehouses, and so on. The relationship between seafarers and seafarer certification (education and training institutions / government) and shipping companies is a concern in this study, especially in relation to the impact of many falsification of seafarer certificates, despite the implementation of an online database certification system that is centralized in Directorate General of Sea Transportation (DJPL). There are 3 (three) major problems that are still popular at this time, for instance unoriginal of seafarer certificates, unpaid seafarers or late receiving wages for more than 1 (one) wage period, and seafarers have a difficult time to find jobs on board. One of the available instruments to solve this problem is the intelligent transport system (ITS) architecture which is liable to be adopted in mapping the aspirations of marine transportation stakeholders, blockchain technology, such as decentralizing (one of the pillars / characteristics of blockchain technology) seafarer certificate database. A cost benefit analysis will be used in this study to obtain how much does it cost to improve the system and to see the benefit of the product.
An LSTM model for extracting hierarchical relations between words for better topic modeling

Abstract Often when dealing with text data, there exists valuable information that determines the relationship between the words encountered in the corpus. The type of relationship which is sought after is the “has-a” and “is-a” relationship, with which one can build a hierarchical representation of words. Since each language has its own set of rules and syntax, extraction of the relationships ultimately boils down to understanding the syntax of the particular language and using relevant features in the process. The paper presents a machine-learning model for understanding the language syntax and deducing the relationships between the words encountered. To be specific, a sequence modeling approach if followed, where the model receives a sequence of words and makes use of the various properties of the words to build a hierarchical graph. The algorithm described will be independent of the language, and the model should be versatile enough to be trained for different languages. In addition, the paper also describes how this information can be used to build better topic models, given a corpus of text.
Graphical simulation of functions

Abstract In scientific research, it is normal practice to arrive at a description of the analyzed process with the function of one independent variable and the participation of several parameters, which are also assumed to be independent quantities, but in the study they have relatively constant values. These parameters are usually used to adjust the process, ie when choosing their specific values, the process meets the requirements of the researcher for optimality according to pre-selected criteria. The present work aims to develop a graphical user interface for managing parameters involved in an analytically presented function. The possibilities of the Korelia program for function research are considered. The function is entered as a string. After the lexical and syntactic analysis, a table of identifiers and an reverse polish notation of the function are formed. By modifying the values of the parameters of the function, the user tracks the changes in its graph. In this way, the role of each of them can be assessed.
Effects of Virtual Reality Learning Platforms on Usability and Presence

The effectiveness of the learning process in the virtual reality (VR) environment and the presence and immersion components of the VR environment are among the most important variables for students to feel as if they are part of the 3D environment and function in the environment. The objective of this chapter is to determine and compare the presence and usability levels of primary school students participating in VR environments with different immersion characteristics (immersive and non-immersive). According to the findings, there was no significant difference between immersive and non-immersive VR environments in terms of presence and usability. It was also determined that the level of presence of students in both groups did not vary depending on usability. The results are regarded to be useful to educators, researchers, and instructional designers who want to integrate VR technology into their educational environments.
Soft Labels Transfer with Discriminative Representations Learning for Unsupervised Domain Adaptation

Domain adaptation aims to address the challenge of transferring the knowledge obtained from the source domain with rich label information to the target domain with less or even no label information. Recent methods start to tackle this problem by incorporating the hard-pseudo labels for the target samples to better reduce the cross-domain distribution shifts. However, these approaches are vulnerable to the error accumulation and hence unable to preserve cross-domain category consistency. Because the accuracy of pseudo labels cannot be guaranteed explicitly. To address this issue, we propose a Soft Labels transfer with Discriminative Representations learning (SLDR) framework to jointly optimize the class-wise adaptation with soft target labels and learn the discriminative domain-invariant features in a unified model. Specifically, to benefit each other in an effective manner, we simultaneously explore soft target labels by label propagation for better condition adaptation and preserve the important properties of inter-class separability and intra-class compactness for reducing more domain shifts. Extensive experiments are conducted on several unsupervised domain adaptation datasets, and the results show that SLDR outperforms the state-of-the-art methods.
An unsupervised methodology for online drift detection in multivariate industrial datasets

Slight deviations in the evolution of measured parameters of industrial machinery or processes can signal performance degradations and upcoming failures. Therefore, the timely and accurate detection of these drifts is important, yet complicated by the fact that industrial datasets are often multivariate in nature, inherently dynamic and often noisy. In this paper, a robust drift detection approach is proposed that extends a semi-parametric log-likelihood detector with adaptive windowing, allowing to dynamically adapt to the newly incoming data over time. It is shown that the approach is more accurate and can strongly reduce the computation time when compared to non-adaptive approaches, while achieving a similar detection delay. When evaluated on an industrial data set, the methodology can compete with offline drift detection methods.
Blind motion deconvolution for binary images

Binary images are prevalent in digital systems and have a wide range of applications including texts, fingerprint recognition, handwritten signatures, stellar astronomy, barcodes, and vehicle license plates. The recorded binary images are often degraded by blur and additive noise due to environmental effects and imperfections in the imaging system. In this paper, we study the problem of recovering the sharp binary image and the blur kernel from the motion degraded observation. We propose a new minimization model by using the binary prior of image pixel and the l0 norm of image gradient to enforce the estimated image to be binary and the image gradient to be sparse respectively. An effective numerical optimization algorithm is applied for solving the proposed model. Extensive experiments for blind binary image deconvolution demonstrate that the proposed method outperforms some existing state-of-the-art methods in terms of visual quality and peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).
On P-Cancellation Modules

In this paper, the concept of cancellation modules is introduced, where is a prime ideal of a commutative ring with identity, also the effect of localization on cancellation modules is studied and some properties of this type of modules are given, furthermore some conditions are given which make cancellation modules as a cancellation and conversely. Also, the effect of ascending chain conditions and descending chain conditions on the cancellation modules are studied and different characterizations of this type of cancellation modules are given.