Reviewerly/exHarmony huggingface dataset:
Research on integrated test and Evaluation of information system equipment

Abstract The integrated test of weapons and equipment is an urgent need for the development of weapons and equipment under the contemporary information conditions, because it can reduce the resource requirements, test cost and risk, as well as improve the test efficiency and the degree of confidence. This paper based on the objective requirements of integrated evaluation of information system equipment test, aims at performance test for information system equipment. For the same equipment testing, test means to analysis the factors that affect performance. The implementation is based on prior knowledge and expert experience response parameters, including regression model building, and the credibility of the weighted multi-source data fusion evaluation method research. It provides technical and theoretical support for the integrated test evaluation of information system equipment multi-source test.
IMPELEMENTASI METODE HARRIS BENEDICT PADA SISTEM INFORMASI PENGHITUNGAN GIZI REMAJA BERBASIS WEBSITE

The teenage period is the peak stage of the growth period of a person's weight and height. This growth process requires sufficient nutritional support. Teenagers who have adequate nutritional intake will have a healthy body condition, rarely experience pain so that activities at home and at school will run smoothly. Lack of adolescent knowledge to know the nutritional needs in the body, can result in inhibition of the growth process. There needs to be a nutrition education for teenagers. Adolescents need knowledge about the calculation of nutritional adequacy. Utilization of information technology is widely used as a tool of convenience and aids in daily activities. With the above problems, an information system is needed in providing knowledge to adolescents about nutritional needs in their bodies. Information systems are built with PHP and Mysql and counting nutritional needs using harris benedict methods. The Harris Benedict method is a way to count the number of calories a person needs. The programming languages used for the creation of this application are PHP and MySQL for databases. Based on the results of the study, this application can help parents to find out the nutritional needs of their children who are teenagers online.
Unsupervised Extractive Text Summarization Using Distance-Based Clustering Algorithms

Due to existence of numerous data in huge data sources, organizing and managing those is essential to extract required information according to user preference. Summarization plays a vital role in providing meaningful and precise information from long term document(s). Embedding of sentences is performed using BERT model, which makes the system capable for further processing. The vectors those lie closer in the n-dimensional space are grouped together. The number of clusters to be generated is estimated using gap statistics method. A novel approach is proposed in the sentences ranking and selection, which decides the quality of the summary. The clustering of sentences is carried out using three different clustering algorithms with three different distance metrics. The comparative performance analysis of the system is done for manually created dataset and the results are recorded.
Detecting Inertial Navigation System Outliers Using Long Short-Term Memory Recurrent Neural Networks

In order to improve the hitting accuracy of inertial-guided launch vehicle, the outliers in the output Euler angles and accelerations of Inertial Navigation System (INS) must be detected before launching. Current outlier detection methods in INS are mainly supervised approaches and may result in too many false positives. In this paper, we investigate and introduce the Long Short-Term Memory (LSTM) recurrent neural networks to forecast time sequences in INS. Due to its gate mechanism and capability to maintain internal state, our method could get relatively low prediction errors. Once the prediction model is trained, we propose a method to set a threshold to detect the outliers. We introduce the Exponentially Weighted Moving Average (EWMA) method to dampen spikes in prediction errors due to abrupt changes in the outputs of INS. Finally, we carried out experiments on the real outputs of INS using our method to detect outliers. The results demonstrate the suitability of LSTM networks for outlier detection.
Syntactic Dependency Constraint Based Graph Convolutional Network for Aspect Level Sentiment Classification

Aspect-Level Sentiment Classification (ALSC) aims to predict sentiment polarities of different aspects within sentences or documents. In the previous works, due to the problem of long-range gradient vanishing between aspect words and sentiment words, the syntactically irrelevant contextual words may be wrongly chosen for judging sentiment. To tackle this problem, we propose a novel Syntactic Dependency Constraint based Graph Convolutional Network for Aspect-level senti- ment classification (SDCGCN) network, which enhances sentiment words and aspect words. We build a Graph Convolutional Network (GCN) on the dependency tree of a sentence, and then use syntactic dependency constraint in the encoding and updating process of GCNs. In addition, considering the balance of short-range dependency and long-range dependency in the dependency tree, we use residuals to fuse the output of different layers of GCNs. And then we use the attention mechanism to capture the important dependency features. Experiments on three benchmarking collections illustrate that our proposed model has comparable effectiveness. Especially on the dataset LAPTOP, there is a 2% improvement compared to the state-of-the-art model. Finally, we conduct ablation experiments to prove the significance and effectiveness of our method.
Exploration of the Online and Offline Mixed Teaching Model in the College English Teaching

College English is one of the important basic courses in colleges and universities.The traditional teaching has been adopted in English teaching for many years, which is not ideal and there are some limitations to some extent.With the development of Internet, the traditional teaching should be changed, and the blended teaching concept should be introduced into teaching to improve teaching methods, optimize teaching content, and make use of rich teaching resources to achieve the best blended teaching online and offline and improve the teaching effect of English courses.
Growing Neural Gas Based Topological Environmental Map Building and Path Planning in Unknown Environment

An autonomous mobile robot needs to many tasks such as a self-localization, collision detection, and path planning to a target position in an unknown environment. Therefore, it is important for the robot to build environmental maps with different resolutions in each work space. In addition, the robot requires the path planning capability in the unknown environment for applying the robot to various environment such as a disaster site and commercial construction. This research proposes a Growing Neural Gas based topological environmental map building method from a metric map with high resolution map for using the self-localization. Our proposed method enables to build the topological map with occupancy information of the metric map and preserve the geometric feature of the map simultaneously. Next, the path planning method in the unknown environment is proposed by utilizing the occupancy information of the topological map. Finally, we conduct on several experiments for evaluating our proposed method by comparing to other conventional approaches, and discuss the effectiveness of our proposed method.
AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models

Attention-based pre-trained language models such as GPT-2 brought considerable progress to end-to-end dialogue modelling. However, they also present considerable risks for task-oriented dialogue, such as lack of knowledge grounding or diversity. To address these issues, we introduce modified training objectives for language model finetuning, and we employ massive data augmentation via back-translation to increase the diversity of the training data. We further examine the possibilities of combining data from multiples sources to improve performance on the target dataset. We carefully evaluate our contributions with both human and automatic methods. Our model substantially outperforms the baseline on the MultiWOZ data and shows competitive performance with state of the art in both automatic and human evaluation.
Beyond the Bias Variance Trade-Off: A Mutual Information Trade-Off in Deep Learning

The classical bias variance trade-off cannot accurately explain how over-parameterized Deep Neural Networks (DNNs) avoid overfitting and achieve good generalization. To address the problem, we alternatively derive a Mutual Information (MI) trade-off based on the recently proposed MI explanation for generalization. In addition, we propose a probabilistic representation of DNNs for accurately estimating the MI. Compared to the classical bias variance trade-off, the MI trade-off not only accurately measures the generalization of over-parameterized DNNs but also formulates the relation between DNN architecture and generalization.
A Critical Study on Violation of a Decent Burial/Cremation Right of a Dead Person During COVID-19: Special Reference to India

This chapter broadly focuses on the rights of a dead person relating to his/her burial or cremation. Recently the ongoing pandemic has brought to the forefront many appalling incidences in India that have forced us to think that whether even a dead person has any right after death? In this chapter, the author is going to discuss about the law relating to the rights of a dead person, giving special attention to the right for a decent burial or cremation in the context of both Indian and international legal systems, as well as in such pandemic situations. This chapter further focuses on the issues of violation of a dead person’s right to a decent burial/cremation in India through various case studies and their psychologic impact on the society. This discussion will also shed light on the pros and cons of the laws and guidelines relating to dead body management during a pandemic as prescribed by the WHO and the Indian government for the benefit of the society as a whole.
COVID-Vention: From Physical to Virtual Open Day, UOW Malaysia KDU Perspective

COVID-19 pandemic has affected everyone in terms of the life style, the way of carrying out work, meetings, shopping, teaching and learning and many more. Many economy sectors were also affected. This paper is reporting on how the institute of higher learning transforming the usual physical open day events to the virtual environment due to the Movement Control Order (MCO) imposed by the Malaysian Government in the fight to contain the spreading of the COVID-19 virus among the community. The virtual open day platform was created entirely by 3D visualized rendering software. It features plenty of stunning and conceptual virtual spaces such as the Grand Lobby and up to 16 program booths. The platform is also integrated with interactive and immersive features for viewers to navigate, explore and click on any hotspot. Clicking on a hotspot triggers an action such as opening a photo, poster, video, text file, or URL link. Google Analytics is being integrated to analyze the statistics and demographic of the viewers/visitors. The virtual platform is highly customizable and can be used to host other commercialized events such as virtual exhibitions, virtual fair, virtual shopping experience, and virtual travel tours. This is why virtual tours are considered the future of creativity with control in the palm of our hands.
Physics of Complex Present: Properties of Action Strategy Cloud

This chapter is the final part of our constructions of mathematical formalism for human goal-oriented actions within the complex present. The proposed concept of action strategy cloud is the gist of formalism. The developed description comprises several constituent components: temporal extension of the complex present; fuzziness of the complex present; 2D-time dynamics of action strategy clouds. It includes: The appendix presents the dynamics of systems with higher-order time-derivatives and the construction of the corresponding Hamiltonian description. This appendix is added to the book because, on the one hand, the dynamics of systems with higher-order time-derivatives plays a crucial role in our theory of human temporality, on the other hand, this formalism does not belong to the standard curriculum of university education.
Application of Cloud Computing Assisted Instruction in English Teaching

Abstract College English teachers focus on the training of students’ English listening and reading ability and English writing ability. They lack the awareness of training students to form critical thinking logic and cultivate students’ innovative ability. Therefore, it has a great impact on cultivating students’ autonomous learning habits and improving students’ learning ability. Listening and speaking, reading, translation and writing are the basic skills that students must master in the process of learning English courses. In the past, most of the students passively received the English knowledge content instilled by English teachers, which seriously restricted the development of students’ thinking logic, and the students’ subjective initiative in learning could not be fully exerted. Nowadays, the teaching concept of the new curriculum reform is now With the continuous deepening and implementation, modern science and technology and new education theory will be organically integrated in the field of education, so as to make the course teaching more efficient and students get better education. From the continuous updating and adjustment of teaching mode, modern science and technology has a great influence and promotion on the field of education.
A Robust Unscented Kalman Filter for Tracking Star-Convex Extended Target with the Non-uniform Scattering Sources

Conventional extended target tracking algorithms rely upon the hypothesis of the uniformly distributed scattering sources. However, the change of radar azimuth makes the statistics characteristic of the scattering source changed. In this paper, a robust star-convex extended target tracking algorithm is proposed to deal with this issue. Uniform distributed pseudo-measurement is generated by using the Centroid Contour Distance method (CCD) and Acceptance-Rejection sampling method, and then Double Layer Unscented Kalman filter (UKF) is derived to calculate the posterior intensity of kinematic state and shape parameters. Simulation results confirm the superiority of the proposed methods in comparison with classic UKF.
Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm

Word sense disambiguation (WSD) refers to determining the right meaning of a vague word using its context. The WSD intermediately consolidates the performance of final tasks to achieve high accuracy. Mainly, a WSD solution improves the accuracy of text summarisation, information retrieval, and machine translation. This study addresses the WSD by assigning a set of senses to a given text, where the maximum semantic relatedness is obtained. This is achieved by proposing a swarm intelligence method, called firefly algorithm (FA) to find the best possible set of senses. Because of the FA is based on a population of solutions, it explores the problem space more than exploiting it. Hence, we hybridise the FA with a one-point search algorithm to improve its exploitation capacity. Practically, this hybridisation aims to maximise the semantic relatedness of an eligible set of senses. In this study, the semantic relatedness is measured by proposing a glosses-overlapping method enriched by the notion of information content. To evaluate the proposed method, we have conducted intensive experiments with comparisons to the related works based on benchmark datasets. The obtained results showed that our method is comparable if not superior to the related works. Thus, the proposed method can be considered as an efficient solver for the WSD task.
Image Recognition System Using Neural Network Techniques: an Overview

The huge demand for image recognition systems has led to an increased requirement for raising the efficiency of this technology and finding solutions to reduce the deficiencies and weaknesses of image recognition systems. This field of research confronts many challenges and issues that can affect the accuracy of systems. This paper reviews several studies and approaches to the framework for system optimization for both object and face recognition system. The main difference between these areas is that the object recognition system is more concerned with class objects rather than a specific case as the face recognition system. The review thereafter illustrates the Convolutional Neural Network (CNN) aspects. It analyzes biometric technology and compares several methods of solving based on a (CNN) taxonomies. Finally, it presents some of the challenges and potential future opportunities for objects and faces recognition systems research field. The main objective is to highlight the important technologies used to build a robust image recognition system.
Implementation of a WGAN-GP for Human Pose Transfer using a 3-channel pose representation

The computational problem of Human Pose Transfer (HPT) is addressed in this paper. HPT in recent days have become an emerging research topic which can be used in fields like fashion design, media production, animation, virtual reality. Given the image of a human subject and a target pose, the goal of HPT is to generate a new image of the human subject with the novel pose. That is, the pose of the target pose is transferred to the human subject. HPT has been carried out in two stages. In stage 1, a rough estimate is generated and in stage 2, the rough estimate is refined with a generative adversarial network. The novelty of this work is the way pose information is represented. Earlier methods used computationally expensive pose representations like 3D DensePose and 18-channel pose heatmaps. This work uses a 3-channel colour image of a stick figure to represent human pose. Different body parts are encoded with different colours. The convolutional neural networks will now have to recognize colours only, and since these colours encode body parts, eventually the network will also learn about the position of the body parts.
Probabilistic logic in computer science

The article examines probabilistic logic as applied to computer science and its problems. Probabilistic logic is treated as a generalization of formal logic. In many cases, the application of probabilistic logic occurs under conditions of uncertainty. Probabilistic logic uses a model approach. Computer science also uses a model approach. Probabilistic logic describes uncertainty using quantitative values of probability. Computer science describes uncertainty using the concept of information entropy. The article introduces the concept of informational logical uncertainty. The article finds a connection between probabilistic logic and entropy. This connection makes it possible to build a probabilistically logical model. This connection makes it possible to create probabilistically logical modeling, which is in many ways an analogue of information modeling. The purpose of probabilistically logical modeling is to overcome information and logical uncertainty.
The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization

Theoretical results show that neural networks can be approximated by Gaussian processes in the infinite-width limit. However, for fully connected networks, it has been previously shown that for any fixed network width, $n$, the Gaussian approximation gets worse as the network depth, $d$, increases. Given that modern networks are deep, this raises the question of how well modern architectures, like ResNets, are captured by the infinite-width limit. To provide a better approximation, we study ReLU ResNets in the infinite-depth-and-width limit, where both depth and width tend to infinity as their ratio, $d/n$, remains constant. In contrast to the Gaussian infinite-width limit, we show theoretically that the network exhibits log-Gaussian behaviour at initialization in the infinite-depth-and-width limit, with parameters depending on the ratio $d/n$. Using Monte Carlo simulations, we demonstrate that even basic properties of standard ResNet architectures are poorly captured by the Gaussian limit, but remarkably well captured by our log-Gaussian limit. Moreover, our analysis reveals that ReLU ResNets at initialization are hypoactivated: fewer than half of the ReLUs are activated. Additionally, we calculate the interlayer correlations, which have the effect of exponentially increasing the variance of the network output. Based on our analysis, we introduce Balanced ResNets, a simple architecture modification, which eliminates hypoactivation and interlayer correlations and is more amenable to theoretical analysis.
Disease Inference using LSTM and Sparse Neural Network – Health Data

Despite the fact that high-quality solutions for solving biomedical problems utilizing artificial intelligence and deep learning algorithms are available, there is no precise way to reach end-users. Bots can deliver health-care solutions, but they can’t be totally trusted unless users provide an accurate description of a medical condition. The difficulty of terminology is rooted here in the fact that not all users have the medical knowledge to provide an accurate description of their ailment, and expecting the same is also nonsensical, thus this issue is critical. Strong NLU (natural language understanding) is required, which can’t be accomplished with basic RNNs. Solving graph-based problems is an art form. For resolving such a challenge, NLP has spawned a new avenue. This paper considers the words in the dataset as vertices and use dense subgraphs to uniquely identify the disease using medical dictionary data. But identifying the the dataset containing medical descriptions and language used by common users has been the tough part. Fortunately, health care forums all across the world had the exactly required information, along with valuable solutions/answers offered by authorized medical specialists. With the data publicly available, the only thing left to do was develop our language model, which will translate community data into valid medical data, which was made possible by the extraction of medical signatures from Snomed via global data mining. This is the first study to combine recurrent language modeling with graph-based language modeling.
Algorithmic Procedure for the Reduction of the Classifier Based on the ε-, δ-criterion in the Method of Logical Analysis of Data

The selection of logical patterns for a new classifier by reducing the original classifier based on the ε-, δ-criterion is proposed. The implementation of the algorithmic procedure for the reduction of the classifier consists of a number of steps: the formation of the initial classifier from a set of partial patterns with the application of the increasing procedure to them, the assessment of the suitability of each pattern in the set using the ε-, δ-criterion, the reduction of the classifier based on the assessment of suitability. Numerical experiments on real problems which make it possible to assess the feasibility of the proposed algorithmic procedure are presented.