Reviewerly/exHarmony huggingface dataset:
Multi-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks

We use Hypergraph Attention Networks (HyperGAT) to recognize multiple labels of Chinese humor texts. We firstly represent a joke as a hypergraph. The sequential hyperedge and semantic hyperedge structures are used to construct hyperedges. Then, attention mechanisms are adopted to aggregate context information embedded in nodes and hyperedges. Finally, we use trained HyperGAT to complete the multi-label classification task. Experimental results on the Chinese humor multi-label dataset showed that HyperGAT model outperforms previous sequence-based (CNN, BiLSTM, FastText) and graph-based (Graph-CNN, TextGCN, Text Level GNN) deep learning models.
A Comprehensive Survey on Lossy Image Compression Methods

The collection and transmission of digital pictures is a huge problem in the current scenario. Image compression is an arena of research where, by eliminating certain redundancies, the size of the digital images is reduced at an optimum level. There are two forms, lossless and lossy, of image compression techniques. Some of the lossy image compression techniques are discussed by the authors in this paper and include a comparative study of these grayscale image compression techniques.Although lossy compression returns a very high bit rate reduction, as low-quality images are not preferred from the point of view of application, lossless compression is ideal for large application areas such as medical, deep sensing, and defence and research purposes. This paper provides a detailed review on various lossy picture compression methods that have been investigated over last decade in order to reduce the size of the images for efficient image transmission over communication media.
Implementation of Renewable Energy-based Public Wireless Internet Infrastructure in Rural Indonesia

The provision of the internet as an effort to encourage digitalization in rural areas is expected to provide opportunities for rural economic development, employment opportunities, trade, and other conveniences. Implementation of the internet in this rural area is a challenging task especially concerning geographic conditions such as hilly environments, lots of trees, and the location of the nearest broadcast station. Besides, the internet as a public infrastructure in rural areas must be combined with renewable energy to prevent environmental pollution and cost-efficiency. This study aims to analyze the provision of renewable energy-based public internet infrastructure in rural Indonesia using an applied research approach. Public internet infrastructure is developed with a wireless model using solar PV as a source of electrical energy. The results state that the provision of renewable energy-based internet has received a positive response from the village community because it provides easy wireless connectivity. However, there are still a few villagers who use the internet for productive purposes.
A New Iterative Algorithm for Estimating Parameters and Orders of Multiple-Input Single-Output Time Series Models

In this paper, we propose a new iterative algorithm for estimating the parameters and orders of a multiple-input single-output (MISO) time series model. This algorithm is based on a method suggested by Hannan and Rissanen (1982) for estimating an ARMA model. The key is the use of pseudo-linear regression techniques to derive the iterative nonlinear least-squares estimators by using the Gauss-Newton algorithm. Simulation results are presented to compare the new algorithm with the exact maximum likelihood method (EML) and the generalized least squares (GLS) method proposed by Sabiti (1997).
Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph  Embedding

Knowledge graph embedding models learn the representations of entities and relations in the knowledge graphs for predicting missing links (relations) between entities. Their effectiveness are deeply affected by the ability of modeling and inferring different relation patterns such as symmetry, asymmetry, inversion, composition and transitivity. Although existing models are already able to model many of these relations patterns, transitivity, a very common relation pattern, is still not been fully supported. In this paper, we first theoretically show that the transitive relations can be modeled with projections. We then propose the Rot-Pro model which combines the projection and relational rotation together. We prove that Rot-Pro can infer all the above relation patterns. Experimental results show that the proposed Rot-Pro model effectively learns the transitivity pattern and achieves the state-of-the-art results on the link prediction task in the datasets containing transitive relations.
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups

Group convolutional neural networks (G-CNNs) have been shown to increase parameter efficiency and model accuracy by incorporating geometric inductive biases. In this work, we investigate the properties of representations learned by regular G-CNNs, and show considerable parameter redundancy in group convolution kernels. This finding motivates further weight-tying by sharing convolution kernels over subgroups. To this end, we introduce convolution kernels that are separable over the subgroup and channel dimensions. In order to obtain equivariance to arbitrary affine Lie groups we provide a continuous parameterisation of separable convolution kernels. We evaluate our approach across several vision datasets, and show that our weight sharing leads to improved performance and computational efficiency. In many settings, separable G-CNNs outperform their non-separable counterpart, while only using a fraction of their training time. In addition, thanks to the increase in computational efficiency, we are able to implement G-CNNs equivariant to the $\mathrm{Sim(2)}$ group; the group of dilations, rotations and translations. $\mathrm{Sim(2)}$-equivariance further improves performance on all tasks considered.
Adversarial Examples Defense via Combining Data Transformations and RBF Layers

Convolutional Neural Networks (CNNs) are vulnerable to adversarial attacks. By adding imperceptible perturbations to the input images, adversarial attack methods can fool CNN models with a high confidence. The main reason is that existing CNN models usually use softmax-like linear classifiers. Recent researches indicate that Radial Basis Function (RBF) network can effectively improve the nonlinearity classification capability and demonstrates robustness against white-box attacks, while data transformations can smooth the classification boundary and show high efficacy for countering black-box attacks. We propose to incorporate data transformations and RBF together to simultaneously enhance the robustness of CNNs against white-box and black-box attacks. However, applying RBF to a very deep CNN will lead to a difficult convergence during training, while data transformations might reduce classification accuracy due to introducing noises. To solve these issues, we further propose a deep supervision strategy and a novel dual loss function. Experiments on two public available datasets demonstrate that applying the proposed methods to the existing CNN models greatly improve their abilities against adversarial attacks while keeping their original recognition performance.
3D-OES: Viewpoint-Invariant Object-Factorized Environment Simulators

We propose an action-conditioned dynamics model that predicts scene changes caused by object and agent interactions in a viewpoint-invariant 3D neural scene representation space, inferred from RGB-D videos. In this 3D feature space, objects do not interfere with one another and their appearance persists over time and across viewpoints. This permits our model to predict future scenes long in the future by simply moving 3D object features based on cumulative object motion predictions. Object motion predictions are computed by a graph neural network that operates over the object features extracted from the 3D neural scene representation. Our model's simulations can be decoded by a neural renderer into2D image views from any desired viewpoint, which aids the interpretability of our latent 3D simulation space. We show our model generalizes well its predictions across varying number and appearances of interacting objects as well as across camera viewpoints, outperforming existing 2D and 3D dynamics models. We further demonstrate sim-to-real transfer of the learnt dynamics by applying our model trained solely in simulation to model-based control for pushing objects to desired locations under clutter on a real robotic setup
A Survey on Deduplication Checking In Cloud Computing

Cloud Computing has been one of the hottest buzzwords over the last few years but it is surprisingly known that the people have been using it for more than 10 years. Gmail, Facebook, Dropbox, Skype, PayPal, and Salesforce.com are all examples of cloud solutions which was not thinking about them in these terms. The main idea behind the cloud is that the information can be accessed over the internet without having any exhaustive familiarity of the communications used to enable it. The major services existing in Cloud computing is the Cloud storage. With the cloud storage, data can be stored on multiple third party servers which is not cared by the user and no one knows where exactly data saved. With the increase in size of the data every day, there is a need to handle, manage and mainly to store data, is a major problem faced by the people or organization. This article specifies about the study on space occupied by duplicate data over cloud. Where the data is increasing day by day , at the same time one thing to be noticeable that enough space at cloud is occupied by duplicate data so there is a need to check the data for de duplication at cloud before uploading.
Implementing a virtual presentation in EFL classroom for engineering students

COVID-19 Pandemic brings many changes in human life, especially teaching-learning process. In Covid-19 pandemic, teaching learning process is conducted by online learning. This article described one of the strategies used in online learning. Presentation is one of the strategies used to deliver materials and the English lecturer in one of engineering campuses in Surabaya applied this strategy. The students explained the materials using a video. The students must make their video presentation to help audience understand the materials.  The lecturer did not restrict the application used to make video presentation. By applying presentation in delivering materials, this way would help the students understand the materials and they were more confident in using English. They could practice their communication with the audiences. The participants of this study were 60 students who took English 1 as their subject. The students were from civil engineering. This research would use quantitative method and the data would be taken from observation, survey and students' video presentation. Presentation motivated the students to speak English better and they also learned how to communicate with their friends using English. Besides, they learned how the students edited the video which could be understood.
Significance of Open Access Resources: A Study

Benefits include lower costs, greater accessibility, and better prospects for long-term preservation of scholarly works.Open access literature is defined as “digital
A Robust Audio Watermarking in Cepstrum Domain Composed of Sample's Relation Dependent Embedding and Computationally Simple Extraction Phase

Watermark bits embedded in audio signals considering the sample’s relative state in a frame may strengthen the attack-invariant features of audio watermarking algorithm. In this work, we propose to embed watermarks in an audio signal considering the relation between the mean values of consecutive groups of samples which shows robustness by overcoming common watermarking challenges. Here, we divide the host audio signal into equal-sized non-overlapping frames which in turn is split into four equalsized non-overlapping sub-frames. After, transforming these sub-frames in cepstrum domain we finally use the relation between the differences of first two sub-frames and last two sub-frames to embed watermarks. Depending on the watermark bit (either 0 or 1) to be embed, our embedding technique either interchange or update the differences between these groups of samples by distorting the sample values in sub-frames selectively. Thus, watermarks are embedded by making a little or no distortion of the sub-frames which helps our scheme to be imperceptible in nature. Moreover, use of such embedding technique lead our watermarking scheme to a computationally less complex extraction method. Simulation results also justify our claim of the proposed scheme to be both robust and imperceptible.
Evaluating the Public Space Transformation Through Virtual Museum Practices

With the Covid-19 pandemic, people's lives, working and living conditions, social relations are completely transformed, while social relations and public spaces are also changing. Before the pandemic, spaces such as parks, gardens, museums, cinemas and theaters were defined as public spaces, while publicity required people to be together physically. After the pandemic, communication between people shifts from physical space to virtual space, due to the limitation of the use of social space, and the concept of public space is reinterpreted. With the pandemic period, platforms are created where cities can be visited virtually, online access to events such as theaters, concerts and festivals is provided, and virtual visits are offered to museums all over the world. In this study, the concept of changing publicity and transformations in public space are evaluated through museums. A survey is conducted to reveal the differences between the physical museum and the virtual museum experience. Through the survey, four different types of experiences (social, cognitive, introspective and object experiences) are questioned, revealing the perception of space and how users interpret the change in public space. Examining the transformation of public space through museums allows for new inferences about people's perception and experience of space. The essence of a public space detached from the physical space is lost and the concept of publicity becomes shallower.
사회복지전공생의 윤리적 민감성과 인권감수성

The purpose of this study was to investigate human rights sensitivity and ethical sensitivity of undergraduates majoring social work. For this, we surveyed 103 of undergraduates of the 3 universities. The results of the study were summarized as follows. Their had normal level of human rights sensitivity and low level of ethical sensitivity. About 5 cases of human rights sensitivity, the number of people who first considered human rights values was small. This means that the value of human rights is wavering depending on the situation or subject. And they could cognize ethical issues of situation, but didn’t know background knowledge and discuss for appropriate measures. Human rights sensitivity and ethical sensitivity were correlated. And we confirmed that attending a related subjects and practical experience are very important. Based on the results, practical suggestions were discussed as follows. The opening of ethicsrelated subjects should be considered once again, or special lectures of it should be prepared in the university. And supervisors of social work field practicum should be able to present ethical situations and issues that can be experienced.
Taming Core Data

Core Data is one of the most famous frameworks known to iOS developers. Core Data has been available since the release of iOS 3, and it has evolved a lot since then. A common misconception is that Core Data is a database or a database wrapper. Though persisting data is one of its features, Core Data is much more than that. The essence of Core Data is that it manages our application’s object graph. An object graph is basically a collection of objects connected with relationships. Core Data manages the objects in these graphs, and we can use Core Data to persist the graph on disk if we want. In addition to that, the framework has multiple other features such as data validation and undo/redo management.
Mental Health Status and Influencing Factors of College Counselors Based on Social Psychology

Objectives: In order to further improve the mental health of college counselors, a recommendation algorithm is proposed based on social psychology to study the mental health status and influencing factors of college counselors. Methods: This algorithm analyzes the influencing factors of the user’s mental health by calculating the feature vector on the basis of user’s preference and similarity for the social environment resource. The matrix vector of resource similarity is constructed by the calculated cosine similarity, and the context modeling hierarchical model is constructed. Results: The user’s preference matrix for resources is calculated, so that a personalized recommendation algorithm based on label and collaborative filtering is proposed. Finally, the algorithm and model proposed are validated. Experiments show that when the value of N is 10 or 15, the recommendation algorithm by label can improve the recommendation accuracy and recall rate, which indicates that the proposed algorithm can significantly improve the quality of recommendation results. Conclusion: Therefore, the algorithm proposed can effectively improve the recommendation quality of college counselors’ choice of colleges to alleviate psychological stress.
Middle-level Distillation Method Based on Multi-source Information Matching Guided by Auxiliary Classification

In traditional KD, the output of the large model (pseudolabel) is usually used to supervise the small model, andthe small model conforms to the real label and the teacher's pseudo label. Later work proposed feature loss as amiddle level supervisor to further mine teacher information. However, the performance of feature loss is not asstrong as the output KD, and the definition and implementation of feature loss are more comp licated, and it is notrobust to different T model structures. A simple, novel and effective auxiliary middle level distillation (AID)middle level supervision method is raised, effectively enhancing the performance of the learner/S model (S modelfor shor t) under the lecturer/ T model (T model for short). Specifically, we use the auxiliary branch as thetransformation of the student network, and use the feature distance, logits distance, sample pair relationship andother multi source information matching to shrink the difference between the S model features and the T model'sadvanced features. Use orthogonalization and logical normalization techniques to make auxiliary branches bettertransfer feature knowledge. Our very novel method is the first in the KD research field to use multi sourceinformation to match middle level supervision. We have achieved excellent results on common benchmarks. InCIFAR100 and CIFAR10, the accuracy of 11 models increased by 5.46% and 2.49% respectively on average. InImageNet , AID achieves 1.57 times compression and 1.81 times acceleration without loss of accuracy. As teachersfor many models that perform well, we can improve student performance more effectively under the training of ourmethods.
JEDAI Explains Decision-Making AI.

This paper presents JEDAI, an AI system designed for outreach and educational efforts aimed at non-AI experts. JEDAI features a novel synthesis of research ideas from integrated task and motion planning and explainable AI. JEDAI helps users create high-level, intuitive plans while ensuring that they will be executable by the robot. It also provides users customized explanations about errors and helps improve their understanding of AI planning as well as the limits and capabilities of the underlying robot system.
Capsule Graph Neural Networks with EM Routing

To effectively classify graph instances, graph neural networks need to have the capability to capture the part-whole relationship existing in a graph. A capsule is a group of neurons representing complicated properties of entities, which has shown its advantages in traditional convolutional neural networks. This paper proposed novel Capsule Graph Neural Networks that use the EM routing mechanism (CapsGNNEM) to generate high-quality graph embeddings. Experimental results on a number of real-world graph datasets demonstrate that the proposed CapsGNNEM outperforms nine state-of-the-art models in graph classification tasks.
Provability Games for Non-classical Logics

Game semantics provides an alternative view on basic logical concepts. Provability games, i.e., games for the validity of a formula provide a link between proof systems and semantics. We present a new type of provability games, namely the Mezhirov game, for minimal propositional logic and two modal systems: the logic of functional frames and the logic of serial frames, i.e., \(\mathbf {KD}\) and prove their adequacy. The games are finite resulting in a finite search for winning strategies.
Advantages of the Introduction of Digital Technologies into the Educational Process

In this article we will talk about the changes in the educational system, the effective tools in this process. In addition, the process of introducing various forms of digital technologies into the educational process, as well as the convenience of the process of applying these technologies were mentioned.
Minimax Combined with Machine Learning to Cope with Uncertainties in Medical Application

The work considers ML problems in medical application and presents a minimax approach for developing ML models that would be resistant to aleatoric and epistemic uncertainties. Aleatoric uncertainties are presented with the help of different resampling strategies whereas epistemic ones are a variety of models. The main methods applied are based on linear regression, SVM, random forest for ML, PCA for dimension reduction, and cross-validation as a resampling strategy. The approach which is offered is presented with the help of the flowchart which includes the basic steps of ML model development under uncertainties, including import and primary processing of the clinical data, the statement of task, resampling strategy including the dimension reduction, the choice of methods (learners), tuning their parameters, and models comparison on the basis of minimax criterion. As a clinical example, we consider the problem of the development of learners for lifetime prediction for cardiac patients. The software implementation of the ML model development under uncertainties is offered in the package mlr with the help of the benchmark of learners.Nakonechnyi, Oleksandr Martsenyuk, Vasyl Klos-Witkowska, Aleksandra Zhehestovska, Diana