Reviewerly/exHarmony huggingface dataset:
Variational Deep Logic Network for Joint Inference of Entities and Relations

Abstract Currently, deep learning models have been widely adopted and achieved promising results on various application domains. Despite their intriguing performance, most deep learning models function as black boxes, lacking explicit reasoning capabilities and explanations, which are usually essential for complex problems. Take joint inference in information extraction as an example. This task requires the identification of multiple structured knowledge from texts, which is inter-correlated, including entities, events, and the relationships between them. Various deep neural networks have been proposed to jointly perform entity extraction and relation prediction, which only propagate information implicitly via representation learning. However, they fail to encode the intensive correlations between entity types and relations to enforce their coexistence. On the other hand, some approaches adopt rules to explicitly constrain certain relational facts, although the separation of rules with representation learning usually restrains the approaches with error propagation. Moreover, the predefined rules are inflexible and might result in negative effects when data is noisy. To address these limitations, we propose a variational deep logic network that incorporates both representation learning and relational reasoning via the variational EM algorithm. The model consists of a deep neural network to learn high-level features with implicit interactions via the self-attention mechanism and a relational logic network to explicitly exploit target interactions. These two components are trained interactively to bring the best of both worlds. We conduct extensive experiments ranging from fine-grained sentiment terms extraction, end-to-end relation prediction, to end-to-end event extraction to demonstrate the effectiveness of our proposed method.
Authorized Data Secure Access Scheme With Specified Time and Relevance Ranked Keyword Search for Industrial Cloud Platforms

With the dramatically increase in the scale of Internet data, industrial cloud platforms have put forward higher requirements for data access control and keyword search. Although existing attribute-based encryption technology is suitable for realizing data access control, the problems of large number of matching contents of search results and high computational overhead remain exist. In light of this, an authorized data secure access scheme with specified time and relevance ranked keyword search is proposed to achieve accurate matching of the search results and reduce user’s cost. First, an authorization token on attribute-based access policy and the specified time is constructed. According to the token, the data meeting the access policy is selected within the specified time. Second, we leverage order-preserving index and extended keyword vector to construct keyword index, then the search results of user specified amount are sorted by relevance score. With the use of specified time and relevance for twice screening, the user can get the relevant search results accurately. Finally, the security proof and efficiency analysis show that the scheme is of feasibility.
Solar Irradiance Forecasting in Tropical Weather using an Evolutionary Lean Neural Network

Clean electricity system based on solar energy is rapidly growing. However, the intermittency of solar power remains an issue. An accurate solar irradiance forecast can mitigate the impact of variable solar power generation. In this paper, we present the Evolutionary Lean Neural Network (EVLNN) for time-series solar irradiance forecasting. The key novelty of EVLNN lies in incorporating a feedback structure to a partially connected neural network while using an improved genetic algorithm to optimize its architecture. We train the model with tropical weather data to provide a days-ahead forecast of solar irradiance at four different time-steps: 1-min, 15-min, 30-min, and hourly. We investigate the effects of fewer input features in model training and assess the model accuracy using a combination of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Adjusted Coefficient of Determination (Adjusted R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) metrics. We then compare the results with those obtained from neural networks modeled using other evolutionary algorithms (EA), namely the Particle Swarm Optimization, the Differential Evolution, and the classic Genetic Algorithm. We include the fully connected nonlinear time-delay backpropagation neural network as a benchmark against which to evaluate the EA-based models. The results demonstrated EVLNN's good generalization capability, specifically in the presence of a sparse dataset. Moreover, our proposed model trained with a single input variable achieved improved performance for the hourly, 30-min, and 15-min time-step predictions compared with those modeled using other learning algorithms.
New Time Horizon Based Classification of PV Power Generation Forecasting Techniques

Among the common renewable energy sources (RES), photovoltaic (PV) energy holds an increasing share in the global energy market. The depending on various environ mental aspects, weather conditions and non-linear characteristics create the main challenge in PV based installations specially on large farms level. Load variations and future expectations play a critical role in calculating the reserve stand-by units for vally-filling; a major challenge for grid-control authorities. Consequently, PV power generation forecasting is unavoidable for robust, reliable and cost-effective grid control from both operation and maintenance aspects. Despite the available review articles, recent forecasting techniques are not well classified or aggregated with solid benchmark assessments. This paper presents a novel criteria for classifying all the developed PV power generation forecasting techniques based on time horizon classification. In addition, inputs, outputs, forecasting methodology and performance metric are included in the proposed criteria. Detailed classification and comparison tables are concluded with detailed discussion and survey outcomes. The presented paper acts as a robust road-map for researchers concerned with this current topic as it accommodates all the available techniques with novel classification and comparison.
Research on the Development of Online Education in the Age of AI and 5G

Online education is a new educational pattern relying on netwok, Which adds flexibility and availability, regards of time, place, or pace of learning. Best of all, with online education, we can stick to our jobs and at the same time study and absorb the latest knowledge. AI, big data and 5G are the scinetific technology which lead the online education industry to decelop into a richer ecosystem and make Online education promote its advantages. In the second half of 2020, the COVID-19 pandemic in China has been under control, and China's online education will face both opportunities and challenges. This paper mainly adopts the method of questionnaire survey analysis, SPSS 20.0 statistical software and PLS to analyze the influencing factors of China's online education in the post-epidemic era. Finding that the education level, income level, education level and the purpose of the education have positive effect on online education, the education cost of online education has the reverse effect, the lower the price of online education, the larger the quantity of online education that will be demanded. At the same time, this paper finds that when students spend more time in online classes and the fewer exams they pass, the more students spend on online education platforms. According to the results, some countermeasures and suggestions are put forward.
Generalizing Floor Plans Using Graph Neural Networks

The proliferation of indoor maps is limited by the manual process of generalizing floor plans. Previous attempts at automating similar processes use rasterization for structure. With Graph Neural Networks (GNN) it is now possible to skip rasterization and rely on the inherent structures in CAD drawings. A core component in floor plan generalization is localization of doors. We show how floor plan graphs can be extracted directly from CAD primitives and how state-of the-art GNNs can be used to classify graph nodes as door or non-door. Generalization is represented by the creation of placeholder bounding boxes using the labelled graph nodes. Our graph-based approach completely outperforms the Faster R-CNN baseline, which fail to locate any doors with the desired localization accuracy. To support further development of graph-based methods and comparison with raster-based methods, we publish a new dataset that consists of both image and graph-based floor plan representations. Code and dataset is available at https://github.com/Chrps/MapGeneralization.
Optimization of Observations for a Limited Flow of Random Processes

Of great importance when creating complex information and measuring systems designed to measure the parameters of motion of one or several objects of observation, is the solution of problems of optimal control of measuring processes. Currently, approaches have been developed to determine the best observation strategies for a wide class of cases associated with the estimation of the parameters of motion of objects of observation in single-channel and multichannel, concentrated and spaced, discrete and discrete-continuous systems. A variety of approaches allows solving various problems of optimization of observation control. However, all the approaches considered do not take into account the random nature of the appearance and disappearance of information processes. Therefore, it is urgent to develop new models and methods that more fully describe the physical processes occurring in information systems for various purposes. In particular, it seems interesting to develop methods for optimizing observation, taking into account the random nature of the appearance and disappearance of information processes at the input of the measuring system. In this case, random flows can be used as a model describing the appearance and disappearance of information processes. This formulation of observation control problems, associated with the use of random streams as models describing the appearance and disappearance of information processes in space and time, is designed to remove a number of severe restrictions inherent in the classical observation control theory, which will improve the quality indicators of filtering information processes. This task is typical for the majority of multichannel detection and tracking systems (radar and radio navigation systems, trajectory measurement systems), in which information about the moments of the appearance of objects is of a probabilistic nature. The article is devoted to the development of a method for a priori planning of observations of a limited flow of random information processes. As a result, a method for optimization of observations has been developed, which makes it possible to take into account the random nature of the appearance and disappearance of information processes in accordance with the laws of a limited flow.
Blockchain-based Digital Locker using BigchainDB and InterPlanetary File System

Our identity as a human being is determined by the documents, not by appearance or physicality. The most important thing to prove the identity of humans is to show a government-issued document. Generally, from birth to death humans are recognized by documents because they are born with a birth certificate and they die with a death certificate. The main problem with these documents is that, they can be falsified or manipulated by others. Moreover in this digital era, they are stored in a centralized manner, which is prone to a cyber threat. This study aims to develop a blockchain environment to create, verify, and securely share documents in a decentralized manner. With the help of bigchainDB, interplanetary file system (IPFS), and asymmetric encryption, this research work will prototype the proposed solution called blockchain-based digital locker, which is similar to the DigiLocker released by the Department of Electronics and Information Technology (DeitY), Govt. of India. BigchainDB will help in treating each document as an asset by making it immutable with the help of IPFS and asymmetric encryption, where documents can not only be shared but also verified.
An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier

Handwritten digit recognition is one of the classical issues in the field of image grouping, a subfield of computer vision.The event of the handwritten digit is generous.With a wide opportunity, the issue of handwritten digit recognition by using computer vision and machine learning techniques has been a well-considered upon field.The field has gone through an exceptional turn of events, since the development of machine learning techniques.Utilizing the strategy for Support Vector Machine (SVM) and Principal Component Analysis (PCA), a robust and swift method to solve the problem of handwritten digit recognition, for the Kannada language is introduced.In this work, the Kannada-MNIST dataset is used for digit recognition to evaluate the performance of SVM and PCA.Efforts were made previously to recognize handwritten digits of different languages with this approach.However, due to the lack of a standard MNIST dataset for Kannada numerals, Kannada Handwritten digit recognition was left behind.With the introduction of the MNIST dataset for Kannada digits, we budge towards solving the problem statement and show how applying PCA for dimensionality reduction before using the SVM classifier increases the accuracy on the RBF kernel.60,000 images are used for training and 10,000 images for testing the model and an accuracy of 99.02% on validation data and 95.44% on test data is achieved.Performance measures like Precision, Recall, and F1-score have been evaluated on the method used.
A Color-Based Re-Ranking Process for People Re-Identification : Paper ID 21

People re-identification is a fundamental stage in video surveillance systems. This stage is used for determining the labels of the people's images usually based on their visual similarities. The appearance salience caused by wearing the clothes and carrying objects, with specific colors can be used for improving the performance of re-identification systems. To address the mentioned point, in this paper, we propose a color-based re-ranking process. In the proposed approach first, the input images are semantically segmented as: person's body, possible carried objects, and background. Then the most frequent colors of person's body and possible carried objects are used to re-order the ranked results obtained from two commonly used re-identification approaches i.e., Gaussian of Gaussian (GOG) and Hierarchical Gaussian Descriptors (HGDs). Experimental results on various people re-identification datasets show that our proposed re-ranking approach can improve the performance of re-identification systems.
OnSum: Extractive Single Document Summarization Using Ordered Neuron LSTM

A growing trend of extractive summarization research is to take the document structure into account which has been shown to correlate with the important contents in a text. However, building complex document structures such as Rhetorical Structure Theory (RST) is time-consuming and requires a large effort to prepare labeled training data. Therefore, how to effectively learn a document structure for summarization remains an open question. Recent findings in the language model area show that the syntactic distance of the basic semantic unit could be used to induce the syntactic structure without any extra labeled data. Inspired by these findings, we propose to extend the basic semantic units from words to sentences and extract the syntactic distance of each sentence in the document by building an ON-LSTM (Ordered Neuron LSTM) based model with the document-level language model objective. We then leverage these syntactic distances to evaluate whether a sentence could be extracted to the summary. Our model achieves state-of-the-art performance in terms of Rouge-1 (48.67) and Rouge-2 (26.32) on the CNN/Daily Mail data set.
Evaluation of a student-centered online one-to-one tutoring system

This paper introduces a system that supports student-centered online one-to-one tutoring and evaluates the practical value of the system by running an experiment with 64 experienced mathematics teachers and 810 students in Grade 7. The experiment lasted for 50 days. A comprehensive evaluation was performed using students' academic performance before and after usage of the system and the system log files. By classifying the students into active and inactive usage groups, it was determined that active students significantly outperformed inactive students on posttests, but with a small effect size. The results also suggested that high prior knowledge students tended to benefit more from using the system than low prior knowledge students. An explanation for this result was that students with a high level of prior knowledge were more likely to have good-quality interactions with their teachers. Therefore, although some advantages of this type of student-centered online one-to-one tutoring are observed, in this system, both the students and the teachers need to be further facilitated to produce more effective tutoring interactions.
An advanced hybrid algorithm for nonlinear function optimization with real world applications

Summary This article presents an advanced hybrid algorithm ( h aDEPSO) for nonlinear constrained function optimization problem. It consists of the advised advanced differential evolution, that is, aDE (wherein a novel mutation, crossover, and selection strategy is introduced, to avoid premature convergence) and particle swarm optimization, namely, aPSO (in which novel gradually varying parameters is utilized, to escape stagnation). The proposed h aDEPSO achieve better results as aDE and aPSO provides diverse convergence characteristic to the solution space. Moreover, in h aDEPSO distinct population is merged with other in a predefined way, to balance between exploration and exploitation. Efficiency of the proposed hybrid h aDEPSO along with its integrating component aDE and aPSO are inspected on IEEE CEC'2006 constrained benchmark functions and IEEE CEC'2011 real world problems. According to the comparison results, the proposed methods achieved better results than traditional DE and PSO with their hybrids as well as over many state‐of‐the‐art algorithms.
DEVELOPING ANIMAKER-BASED ANIMATION VIDEOS ON THE THEME OF “DAERAH TEMPAT TINGGALKU” AT GRADE IV SDN BANJARSARI 2 SERANG

Media is something that cannot be separated from a learning process. Variations in the use of media are necessary to make students motivated to learn. This research aimed to produce a product of animaker-based animation video. The type of this research was Research and Development (R&amp;D) using the Borg and Gall model which had been modified by Sugiyono. This model consisted of six stages: potential and problem analysis, data collection, product design, product design validation, product revision, and product testing. The subjects of this study were 39 grade IV students of SDN Banjarsari 2 Serang. This product was tested to 20 students in a limited trial. Based on the results of data analysis, it was found that 1) the percentage of the average score from expert validation was 84.7% which was included in "very feasible" category; 2) the percentage of the average score from students’ responses was 92.72% which was included the "very good" category.
An Immuno-Inspired Transfer Learning Paradigm

Transfer learning in Artificial Neural Networks (ANN) is performed by transferring some or all the layers of neurons from within an already learned source ANN, to a target ANN which is then made to learn a new domain. Though such transfers are known to accelerate the convergence of the target ANN, there is no hard and fast method to ascertain which of the neurons or layers need to be transferred. While most methods advocate the transfer of complete layers of neurons, this paper describes an Immuno-inspired transfer learning paradigm that aids in ascertaining the pertinent or Hot neurons which when transferred facilitate faster convergence of a target ANN. The method uses the concept of an Idiotypic Network to evolve the temperatures associated with neurons within the various layers of the source ANN. Temperatures of those neurons which contribute more to the learning, increase making them hotter. This paper also presents results obtained from experiments performed on dissimilar datasets using this method, which clearly authenticate its efficacy.
Generalization Bound for Imbalanced Classification

The oversampling approach is often used for binary imbalanced classification. We demonstrate that the approach can be interpreted as the weighted classification and derived a generalization bound for it. The bound can be used for more accurate re-balancing of classes. Results of computational experiments support the theoretical estimate of the optimal weighting.
Zero Power Crop Water-Stress Detector Based on a Micromechanical Photoswitch Monitoring Leaf Transmittance Change

This paper reports on the first micromechanical photoswitch (MP) capable of detecting water-stress in a plant by monitoring its leaf's infrared transmittance change without consuming any electrical power in standby. The detection mechanism relies on the dependence of shortwave infrared (SWIR) transmittance on the leaf water relative content (RWC): lower the RWC, higher the transmitted IR power through the leaf. The detector selectively harvests this transmitted SWIR radiation to detect the water stress and generate a digitized output-bit. To achieve this, the proposed detector employs an MP with an integrated high-efficiency narrowband IR plasmonic absorber ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\eta\sim 93\%$</tex> , bandwidth ∼10%) matched to one of the water's spectral absorption bands ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda=1.47\mu m$</tex> ). Here, we show that the MP can be tuned to detect different levels of water stress (RWC from 88% to 55%) by varying the applied bias voltage (between 5.91V and 5.76V), while keeping it dormant with zero-power consumption when the plant is healthy/non-water stressed.
Image Classification Using Deep Neural Networks

Facial expression recognition is an activity that is performed by every human in their day-to-day lives. Each one of us analyses the expressions of the individuals we interact with to understand how people interact and respond with us. The malicious intentions of a thief or a person to be interviewed can be recognized with the help of his facial features and gestures. Face recognition from picture or video is a well-known point in biometrics inquiry. Numerous open places, for the most part, have reconnaissance cameras, and these cameras have their noteworthy security incentives. It is generally recognized that face recognition has assumed a significant job in reconnaissance framework. The genuine favorable circumstances of face-based distinguishing proof over different biometrics are uniqueness. Since the human face is a unique item having a high level of inconstancy in its appearance, face location is a troublesome issue in computer vision. This chapter explores emotion detection using facial images.