Reviewerly/exHarmony huggingface dataset:
Applications of Knowledge Representation Learning

Knowledge representation learning (KRL) is one of the most important research topics in artificial intelligence, especial in natural language processing (NLP). After extracting entities and relations, some kinds of knowledge, KRL can efficiently calculate the semantics of the entities and the relations in a low-dimensional space, which effectively solve the problem of data sparsity, and can significantly improve the performance of knowledge acquisition, fusion and reasoning. Starting from the three common perspectives of KRL, scoring function, model coding type and additional information, this chapter introduces the overall framework of KRL and the specific model design. In addition, we introduce the corresponding experimental evaluation tasks, including the evaluation metrics and benchmark datasets of each model. Afterwards, we summarize how to apply KRL in various downstream NLP tasks.
MOODLE platform as a didactic support for the English language skills improvement

Introduction: higher education has experienced a number of changes in the teaching field due to the current situation. In this context, e-learning through educational platforms has taken on great value in the teaching-learning process. Objective: to apply an educational intervention through the Moodle platform to consolidate the skills of the English language in the students of the industrial maintenance career of the Escuela Superior Politécnica de Chimborazo during the special period April-September 2020. Methodology: this investigation was carried out in the context of mandatory teleworking due to the Covid-19 pandemic. This mixed research had a descriptive approach with a quasi-experimental focus. To achieve the main goal, 70 students from the third semester took an active part in it who were divided into two study groups: the control group and the experimental group, with 35 students respectively. Before the intervention phase, information was collected through a survey which yielded important general data related to their socio-demographic information and self-perception of their level of English. At the same time, a pre-test of knowledge of the language was taken from the students which was based on the Cambridge PET exam. With this exam, data were obtained for the development of the intervention plan that was implemented throughout the semester. Results: after the intervention phase, the students took a post-test and the corresponding means could be compared. They obtained 9.20 for the control group and 11.99 for the experimental group as final scores. Conclusion: it is concluded that the Moodle Platform is a valuable didactic support to improve English language skills.
Information processing capacity of diffractive surfaces

We analyze the information processing capacity of coherent optical networks formed by trainable diffractive surfaces to prove that the dimensionality of the solution space describing the set of all-optical transformations established by a diffractive network increases linearly with the number of diffractive surfaces, up to a limit determined by the size of the input/output fields-of-view. Deeper diffractive networks formed by larger numbers of trainable diffractive surfaces span a broader subspace of the complex-valued transformations between larger input/output fields-of-view, and present major advantages in terms of their function approximation power, inference accuracy and learning/generalization capabilities compared to a single diffractive surface.
Comparative Study on the Impact of Mobile Communication System on Social Life

Mobile technology has a tremendous capacity to connect people. It also brought a revolutionary change in day-to-day activities and in various sectors such as entertainment, health, agriculture, employment, revenue earning and social change. It has also some negative impacts like cellphone addiction, wasting time, distraction and radiation-related health issues. This paper focuses on the impacts of mobile technology in Bangladesh perspective. Both secondary and primary data have been used to conduct this survey. The result shows that mobile phone technology and their providers make a significant contribution to GDP and directly and indirectly generated 0.80 million jobs in Bangladesh. The most important finding is young people prefer internet or social sites to television, and the duration of spending leisure in mobile phone is increased significantly whether spending time with family, reading newspaper-books decreased remarkably. Result also shows that the internet has more negative impacts on younger people than on older ones. The effect of radiation from mobile device and mobile tower, and ways to reduce its risk have been discussed in this paper.
An Analytical Study of Code Smells

Software development process involves developing, building and enhancing high-quality software for specific tasks and as a consequence generates considerable amount of data. This data can be managed in a systematic manner creating knowledge repositories that can be used to competitive advantage. Lesson's learned as part of the development process can also be part of the knowledge bank and can be used to advantage in subsequent projects by developers and software practitioners. Code smells are a group of symptoms which reveal that code is not good enough and requires some actions to have a cleansed code. Software metrics help to detect code smells while refactoring methods are used for removing them. Furthermore, various tools are applicable for detecting of code smells. A Code smell repository organizes all the available knowledge in the literature about code smells and related concepts. An analytical study of code smells is presented in this paper which extracts useful, actionable and indicative knowledge.
Application-Based on Fuzzy Tsukamoto And Profile Matching for Combination Drugs Recommendations in Patients Hypertension with Complications

Hypertension is still the number one killer disease in Indonesia, and hypertension sufferers have increased by 8.3% per year from 2013-2018. In 2018 the prevalence of hypertension in Indonesia reached 34.1% and affected about 20% of the world's population. Handling of hypertension sufferers can be done in two ways, namely modifying lifestyle and using drugs. Antihypertensive drugs that are given pay attention to age, history of the disease, smoking habits, obesity, and consider diseases such as diabetes, kidney, heart failure, and ischemic heart. Improper use of antihypertensive drugs often causes fever, diarrhea, diabetes, kidney failure, stress, breathing through the mouth, neurological disorders, radiation to the neck and head area, and localized disorders of the salivary glands. Doctors or medical personnel in providing antihypertensive drugs require good pharmacological knowledge. In general, not all hospitals have someone who is an expert in pharmacology. Therefore this study aims to develop a model based on Fuzzy Tsukamoto and Profile Matching for a recommendation of drug combinations, the suitability of dosage, and frequency in hypertensive patients. The results of the model evaluation show an accuracy of 97.5%
Modify alpha value of EMA method and brown method: A data forecasting comparison of COVID-19

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Syaharuddin, Habib Ratu Perwira Negara, Malik Ibrahim, Ahmad, Muhammad Zulfikri, Gilang Primajati, Via Yustitia, Suvriadi Panggabean, Rina Rohayu, Nurjannah Septyanun; Modify alpha value of EMA method and brown method: A data forecasting comparison of COVID-19. AIP Conf. Proc. 26 February 2021; 2329 (1): 060016. https://doi.org/10.1063/5.0042120 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Performance Evaluation of a Convolutional Neural Network for Handwritten Digit Recognition

Deep Learning involves learning multiple representation as well as abstraction levels that help make sense of image, audio, or text data. The MNIST dataset of handwritten digit images is frequently used for training, testing, and validating CNN models. This paper focuses on an efficient CNN model with a series of convolutions, ReLU and pooling layers. The model is developed and tested on MNIST data set with 99% accuracy. Further, similar versions of this model are subjected to various parameter variations and the corresponding impact on output parameters are studied.
The road not taken: manoeuvring through the Indian Companies Act to enable AI directors

Under the framework of the Indian Companies Act 2013, we seek to explore the possibilities of AI systems replacing directors in the board-room. We begin by examining the intractable debate in moral philosophy on the legal personhood of AI. Then, we go on to examine the current judicial and statutory understanding of the fiduciary duties of directors in s 166 of the Companies Act. We find that with the introduction of augmented AI into boardrooms, the existing construct of fiduciary duties is relatively sufficient, but needs some adaptation. Thereafter, we seek to build a case for dilution of the ‘natural person’ requirement for company directors in s 149 of the Companies Act and use Professors Bainbridge and Henderson’s concept of Board Service Providers towards this end. Drawing from Indian law provisions, the paper concludes by arguing that the proposed dilution is not as radical as it initially appears.
A COMPARATIVE STUDY OF ZERO KNOWLEDGE PROOF AND HOMOMORPHIC ENCRYPTION IN GUARANTEEING DATA PRIVACY IN BLOCKCHAIN APPLICATIONS

Homomorphic Encryption and Zero Knowledge Proofs are two trending concepts that are widely popular as data privacy preservation techniques in a wide variety of applications, especially in those associated with the newly evolved block chain technology which are immutable, distributed and secure. Zero knowledge proof is a cryptographic technique can provide proof that a certain statement is correct, without revealing any details about the statement, while homomorphic encryption allows to perform computations on encrypted data without decrypting it. This article explores the significance of the data privacy aspect provided by both ZKP and Homomorphic Encryption and how it can be effectively used to improvise the privacy of blockchain applications in various domains.
Management model to become a teacher during university education

The article reveals the problem of becoming a teacher, training of a teacher with a high intellectual level of self-awareness, capable of conceptual thinking, creative activity and ready for independent management of his own professional activity, which will allow him to interact with the learning environment more effectively, to be an active subject of professional and pedagogical activity.
Medical image denoising using wavelet transform and singular value decomposition

Filtering noise to recreate a high-quality image in medical image processing is an important task. During acquisition, transmission, and retrieval from storage devices, generally images are getting corrupted. So, for further analysis images must get denoised. The noises can be categorised into different types based on their nature and origin. Researchers are still looking for the effective denoising technique. Wavelet Transform (WT) is an effective transform method for denoising. Similarly Singular Value Decomposition (SVD) is also an important tool for denoising. Combining WT with SVD results in further reduction of noise. This paper proposes use of WT along with SVD for medical image denoising. Performance of image denoising is evaluated on the basis of Signal to Noise Ratio (SNR) and Peak Signal-Noise Ratio (PSNR). In the proposed approach, experimental results of WT-SVD combination gives better SNR and PSNR values than WT and SVD, if used independently.
U‐FPNDet: A one‐shot traffic object detector based on U‐shaped feature pyramid module

In the field of automatic driving, identifying vehicles and pedestrians is the starting point of other automatic driving techniques. Using the information collected by the camera to detect traffic targets is particularly important. The main bottleneck of traffic object detection is due to the same category of targets, which may have different scales. For example, the pixel-level of cars may range from 30 to 300 px, which will cause instability of positioning and classification. In this paper, a multi-dimension feature pyramid is constructed in order to solve the multi-scale problem. The feature pyramid is built by developing a U-shaped module and using a cascade-method. In order to verify the effectiveness of the U-shaped module, we also designed a new one-shot detector U-FPNDet. The model first extracts the basic feature map by using the basic network and constructs the multi-dimension feature pyramid. Next, a pyramid pooling module is used to get more context information from the scene. Finally, the detection network is run on each level of the pyramid to obtain the final result by NMS. By using this method, a state-of-the-art performance is achieved on both detection and classification on commonly used benchmarks.
Location probability learning in 3-dimensional virtual search environments

When a visual search target frequently appears in one target-rich region of space, participants learn to search there first, resulting in faster reaction time when the target appears there than when it appears elsewhere. Most research on this location probability learning (LPL) effect uses 2-dimensional (2D) search environments that are distinct from real-world search contexts, and the few studies on LPL in 3-dimensional (3D) contexts include complex visual cues or foraging tasks and therefore may not tap into the same habit-like learning mechanism as 2D LPL. The present study aimed to establish a baseline evaluation of LPL in controlled 3D search environments using virtual reality. The use of a virtual 3D search environment allowed us to compare LPL for information within a participant's initial field of view to LPL for information behind participants, outside of the initial field of view. Participants searched for a letter T on the ground among letter Ls in a large virtual space that was devoid of complex visual cues or landmarks. The T appeared in one target-rich quadrant of the floor space on half of the trials during the training phase. The target-rich quadrant appeared in front of half of the participants and behind the other half. LPL was considerably greater in the former condition than in the latter. This reveals an important constraint on LPL in real-world environments and indicates that consistent search patterns and consistent egocentric spatial coding are essential for this form of visual statistical learning in 3D environments.
Mobile-Assisted Mind Mapping (MAMMAT) as a Tool to Support University Students’ Argumentative Writing

&lt;p class="0abstract"&gt;This study examined the use of a mobile-based technological tool known as Mobile-assisted Mind Mapping Technique (MAMMAT) to support ESL university students’ argumentative writing skills performance. The participants of this study were 45 ESL university students from two lecture groups at a public university campus in Peninsular Malaysia. The study employed a quasi-experimental research design. The experimental group was taught utilizing the MAMMAT whilst the control group was taught utilizing the conventional method. The pre-test and post-test scores were analysed through analysis of covariance (ANCOVA). The analysis of covariance (ANCOVA) results showed that there were statistically significant differences between the groups when test items were measured. As a conclusion, the MAMMAT has supported ESL university students’ argumentative writing skills performance. The study implicates that the use of MAMMAT can support students’ argumentative writing in ESL classroom.&lt;/p&gt;
Pipelined Backpropagation at Scale: Training Large Models without Batches.

New hardware can substantially increase the speed and efficiency of deep neural network training. To guide the development of future hardware architectures, it is pertinent to explore the hardware and machine learning properties of alternative training algorithms. In this work we evaluate the use of small batch, fine-grained Pipelined Backpropagation, an asynchronous pipeline parallel training algorithm that has significant hardware advantages. We introduce two methods, Spike Compensation and Linear Weight Prediction, that effectively mitigate the downsides caused by the asynchronicity of Pipelined Backpropagation and outperform existing techniques in our setting. We show that appropriate normalization and small batch sizes can also aid training. With our methods, fine-grained Pipelined Backpropagation using a batch size of one can match the accuracy of SGD for multiple networks trained on CIFAR-10 and ImageNet. Simple scaling rules allow the use of existing hyperparameters for traditional training without additional tuning.
Address Name System: Wallet Ownership Resolution Based on Reliable Self-Declaration

Distributed ledger technologies have become popular through the advent of cryptocurrencies, especially Bitcoin, bringing new applicabilities and new challenges. Even though privacy and anonymity are desirable attributes of their users, there is ample evidence that, in some cases, such attributes may be dispensable and digital wallet ownership could be known. In this paper, we introduce a wallet ownership resolution mechanism based on reliable self-declaration of wallet address holders, called Address Name System (ANS). The address-entity mapping public service proposed here relies on special certificates voluntarily populated and verifiable in an independent way. A prototype of this service was implemented as a proof of concept of the proposal and a functional validation of its operations was carried out.
Assess the knowledge regarding breast self-examination among medical students in IGIMS, Patna, Bihar

Breast Self-Examination (BSE) is a screening method used in an attempt to detect early breast cancer. The method involves the women herself looking at and feeling each breast for possible lumps, distortions or swelling. Breast Self-Examination (BSE) was once promoted heavily as a means of finding cancer at a more curable stage. So this study was aimed at assessing knowledge regarding Breast Self-Examination among Medical students in IGIMS, Patna, Bihar.
See clearly on rainy days: Hybrid multiscale loss guided multi-feature fusion network for single image rain removal

Abstract The quality of photos is highly susceptible to severe weather such as heavy rain; it can also degrade the performance of various visual tasks like object detection. Rain removal is a challenging problem because rain streaks have different appearances even in one image. Regions where rain accumulates appear foggy or misty, while rain streaks can be clearly seen in areas where rain is less heavy. We propose removing various rain effects in pictures using a hybrid multiscale loss guided multiple feature fusion de-raining network (MSGMFFNet). Specially, to deal with rain streaks, our method generates a rain streak attention map, while preprocessing uses gamma correction and contrast enhancement to enhanced images to address the problem of rain accumulation. Using these tools, the model can restore a result with abundant details. Furthermore, a hybrid multiscale loss combining L 1 loss and edge loss is used to guide the training process to pay attention to edge and content information. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of our method.
Explaining Black-box Classification Models with Arguments

Two approaches for explaining black-box classification models have been studied: a global approach which aims at stressing when classes are predicted independently of instances, and a local approach which looks for justifying individual predictions. Besides, different types of local explanations have been studied in the recent literature, however their links to global explanations remain unclear.The present paper proposes a unified setting for global explanations and local ones. It is based on dual concepts that provide global explanations: arguments in favour of predictions and arguments against predictions. The former justify why a class is suggested by a black-box classifier and the latter state why a class is not. We investigate the properties of both types of arguments, and provide ways for generating arguments pro a class from arguments con the class and vice versa. Finally, we define various notions of local explanations from the literature by arguments pros/con, characterizing formally their relationships and differences, and also their relations with global explanations.
The Study of Quality Evaluation Model for the Real-Time Interactive Online Teaching

The massive-scale real-time interactive online teaching that emerged during the COVID-19 pandemic is more intangible and interactive than traditional recording and broadcasting teaching and flipped class, whose quality is more difficult to measure. At present, the epidemic around the world is not optimistic, and universities worldwide are still in the online teaching mode. Constructing a scientific multi-dimensional teaching quality evaluation system is the key to ensuring the quality of real-time interactive teaching in institutions of higher education, and mixed teaching in the post-epidemic era. This study introduces the SERVQUAL service quality measurement method, with students as the majority of evaluation, aiming to construct a real-time interactive online teaching quality evaluation model in universities. This method consists of five dimensions--Tangibles, Reliability, Responsiveness, Assurance and Empathy. Based on this model, this study conducted data analysis on the online teaching quality of universities under the epidemic situation, pointed out the content of service quality that needs to be improved in online teaching, and put forward countermeasures and suggestions for improvement.
End‐to‐End Multi‐Domain and Multi‐Step Jamming Prediction in Wireless Communications

In this letter, the problem of jamming data prediction in wireless communications is investigated. Both time and frequency domains are considered to construct the multi-domain historical jamming data tensor. Besides, due to the perceiver's limited ability, false alarm data and missing detection data are considered. Two neural network prediction models are proposed to predict the jammers' future actions based on deep learning techniques. One is the multi-variate long-short-term-memory (multi-variate LSTM) model, and the other is the 2-D convolutional long-short-term-memory model. Simulation results show that the proposed models have better prediction accuracy and robustness than the benchmark method.