Reviewerly/exHarmony huggingface dataset:
Self-representation through Online Avatars in Major Online Contexts Among Chinese University Students

The feature of applying avatars is prevalent across different types of online activities. Avatar creation has inevitably become an important research topic due to its use in representing different identities, facilitating digital communication, and investigating body image problems. It has been studied that the standard and the idealization of avatar creation can differ across contexts. Besides, the research tools involved such as the Big Five Inventory and the research hypotheses around demographical characteristics strike the importance of cross-cultural factors. However, few studies of avatar creations have been conducted based on the Chinese population or referring to Chinese online platforms. Hence, the author addresses this research gap by examining (1) the self-representation and idealization of physical, demographical, and personality attributes through avatar creation, (2) differences in this self-representation and idealization across major online Chinese contexts, (3) the relationship between the idealization of avatars and body image attitudes among Chinese university students. The author analyzed data from 92 university students and found different levels of self-representation through avatars in the physical and demographic attributes. Moreover, a tendency for university students to idealize themselves through online avatars for both physical attributes and personality traits was observed. Furthermore, this self-representation and idealization mostly did not differ across contexts as previous studies concluded. Overall, this study reflects values in exploring avatar creation strategies across different cultures, providing pilot information and insights for further studies focusing on self-representation, avatar creation, and body images in virtual environments.
Analysis of the Differences between the Chinese and British General High School Education Systems

Due to the national conditions and historical background, China and the United Kingdom have many differences in education systems. Education system in China is mainly centered by teachers, emphasizing the leadership status of educators and paying attention to the basic knowledge with strict rules. British education system focuses on students' practice based on their own exploration with interest, paying attention to personality cultivation and advocating innovation ability. With the rapid development of international education, various problems in the process of education development in China and the United Kingdom gradually emerge. This paper mainly discusses the advantages and disadvantages of the general high schools education in China and the United Kingdom by comparison. Among them, the diversification of general high school education has the greatest impact on the current development of education, and effective measures need to be taken to improve it. Although there are many differences between China and the United Kingdom in both the concept and the mechanism of their education system, their same goal is to improve personal and social development. The two countries can complement each other's strengths and further improve education reform according to the comparison.
Rapid stabilization of stochastic quantum systems in a unified framework

Rapid stabilization of general stochastic quantum systems is investigated based on the rapid stability of stochastic differential equations. We introduce a Lyapunov–LaSalle-like theorem for a class of nonlinear stochastic systems first, based on which a unified framework of rapidly stabilizing stochastic quantum systems is proposed. According to the proposed unified framework, we design the switching state feedback controls to achieve the rapid stabilization of single-qubit systems, two-qubit systems, and N -qubit systems. From the unified framework, the state space is divided into two state subspaces, and the target state is located in one state subspace, while the other system equilibria are located in the other state subspace. Under the designed state feedback controls, the system state can only transit through the boundary between the two state subspaces no more than two times, and the target state is globally asymptotically stable in probability. In particular, the system state can converge exponentially in (all or part of) the state subspace where the target state is located. Moreover, the effectiveness and rapidity of the designed state feedback controls are shown in numerical simulations by stabilizing GHZ states for a three-qubit system.
A Sequence Labeling Method for First-order Logic Representation

Extracting logic knowledge is an important part of natural language understanding. This study introduces a new dataset for the purpose of measuring the model's ability to transfer natural language to logic form. Since logic represents how various concepts infer and reason from/to each other, it is essential to learn logical structure in any form of language while trying to comprehend its meaning of it. There have recently been many interests in deploying machine learning and deep learning methods for extracting logical knowledge and representing logic from natural language. The promising generative model that applied to recent sequence labeling work is used in the task of representing logic. Experiments have shown that generative methods can extract logic form from natural language texts. To be more specific, a sequence-to-sequence model is quite a convenient option when it deals with extraction tasks. The model provided in this study would be inspiring for future studies to work out more efficient solutions.
Shilling Attack Detection in User Based Recommendation System

The majority of the existing unsupervised methods for detecting shilling attacks are based on user rating patterns, ignoring the differences in rating behavior between legitimate users and attack users. These methods have low accuracy in detecting different shilling attacks without having any prior knowledge of the attack types. We provide a novel unsupervised shilling assault detection technique based on an examination of user rating behavior in order to overcome these constraints. By first examining the deviation of rating tendencies on each item, we are able to determine the target item(s) and the accompanying goals of the attack users. Based on the results of this study, a group of suspicious users is then created. Second, we examine the users' rating behaviors in terms of their rating and interest preferences. Finally, using measurements of user rating behavior, we determine the suspicious degree and identify attack users within the collection of suspicious users. The Movie Lens 1M dataset, the sampled Amazon review dataset, and the Netflix dataset all show how good the suggested detection model.
Data security in cloud computing using RSA Algorithm

The capacity of cloud computing, an emerging paradigm, to lower computer costs, has made it the hottest research area of the day. The most fascinating and alluring aspect of modern technology is what it is giving. The services to its customers over the internet on demand. Security has emerged as the primary challenge impeding the implementation of cloud settings because cloud computing stores data and distributes resources in an open environment. Although cloud computing is effective and promising, there are several issues with data security because the cloud user has no physical access to the data. We suggested a technique using the RSA algorithm to assure the security of the data.
Reflection on Set theory: Is the barber example a genuine illustration of Russell's paradox?

Since the formulation of Russell’s paradox, many people have created more accessible models while trying to understand and solve the paradox. The Barber paradox is the most famous one, but it is not the case that this paradox was not proposed by Russell. This paper will demonstrate the nature of both paradoxes through truth-functional language and propose possible solutions (theory of types) for Russell’s paradox. The reason why the Barber paradox is a pseudo paradox will also be illustrated with a possible solution. There is a huge difference between the paradoxes due to the fundamental difference in the set, and more reasons will be clarified in the paper.
Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples

Hyperspectral Imaging (HSI) is increasingly adopted in medical applications for the usefulness of understanding the spectral signature of specific organic and non-organic elements. The acquisition of such images is a complex task, and the commercial sensors that can measure such images is scarce down to the point that some of them have limited spatial resolution in the bands of interest. This work proposes an approach to enhance the spatial resolution of hyperspectral histology samples using super-resolution. As the data volume associated to HSI has always been an inconvenience for the image processing in practical terms, this work proposes a relatively low computationally intensive algorithm. Using multiple images of the same scene taken in a controlled environment (hyperspectral microscopic system) with sub-pixel shifts between them, the proposed algorithm can effectively enhance the spatial resolution of the sensor while maintaining the spectral signature of the pixels, competing in performance with other state-of-the-art super-resolution techniques, and paving the way towards its use in real-time applications.
Retinex‐inspired contrast stretch and detail boosting for lowlight image enhancement

Abstract Lowlight images with low brightness and contrast, blurry details usually bring us an uncomfortable visual experience. To promote the quality of these deviation images, this paper presents a new and efficient approach, named MFMR, for enhancing lowlight images in the hue‐saturation‐value (HSV) colour space. Concretely, the multi‐angle filter is first applied to estimate the artifact‐free illumination and reflection component of the V‐channel. Afterward, the adaptive bi‐interval histogram with human visual characteristics and morphological operations is employed to process the former, adaptive gamma correction to process the latter for generating various feature maps. In the end, these feature maps are united via adaptive multi‐scale fusion strategy to reconstruct high‐quality images, which are characterized by high contrast and brightness, vivid colour, and clearer details. Extensive experiments show that this method is a well‐proven low‐light image enhancement approach, which outperforms the state‐of‐the‐art comparison methods. Furthermore, the proposed method also can yield satisfying images in the heavy foggy, yellow sand, underwater, and other severe conditions.
Modal structures in groups and vector spaces

Abstract Vector spaces contain a number of general structures that invite analysis in modal languages. The resulting logical systems provide an interesting counterpart to the much better-studied modal logics of topological spaces. In this programmatic paper, we investigate issues of definability and axiomatization using standard techniques for modal and hybrid languages. The analysis proceeds in stages. We first present a modal analysis of commutative groups that establishes our main techniques, next we introduce a new modal logic of linear dependence and independence in vector spaces and, finally, we study a modal logic for describing full-fledged vector spaces. While still far from covering every basic aspect of linear algebra, our discussion identifies several leads for more systematic research.
Neural response generation for task completion using conversational knowledge graph

Effective dialogue generation for task completion is challenging to build. The task requires the response generation system to generate the responses consistent with intent and slot values, have diversity in response and be able to handle multiple domains. The response also needs to be context relevant with respect to the previous utterances in the conversation. In this paper, we build six different models containing Bi-directional Long Short Term Memory (Bi-LSTM) and Bidirectional Encoder Representations from Transformers (BERT) based encoders. To effectively generate the correct slot values, we implement a copy mechanism at the decoder side. To capture the conversation context and the current state of the conversation we introduce a simple heuristic to build a conversational knowledge graph. Using this novel algorithm we are able to capture important aspects in a conversation. This conversational knowledge-graph is then used by our response generation model to generate more relevant and consistent responses. Using this knowledge-graph we do not need the entire utterance history, rather only the last utterance to capture the conversational context. We conduct experiments showing the effectiveness of the knowledge-graph in capturing the context and generating good response. We compare these results against hierarchical-encoder-decoder models and show that the use of triples from the conversational knowledge-graph is an effective method to capture context and the user requirement. Using this knowledge-graph we show an average performance gain of 0.75 BLEU score across different models. Similar results also hold true across different manual evaluation metrics.
NorMASS: A normative MAS-based modeling approach for simulating incentive mechanisms of Q&amp;A communities

Incentive mechanisms steer users in Q&A communities to achieve community goals, which need to be cautiously reviewed and revised before actual industrial application. Simulating incentive mechanisms is significant for predicting how changes in incentive mechanisms will affect community emergence, such as user answering patterns. However, due to the complexity of Q&A communities, the challenge faced by simulating incentive mechanisms lies in the difficulty of establishing micro-macro connections in the communities to simulate their emergence. To fill this gap, this paper proposes a Normative Multi-Agent System based Simulation (NorMASS) approach to simulate community emergence. The NorMASS models a Q&A community as a normative multi-agent system and adopts agents to formally express community users. Moreover, the approach provides an open-source simulator with a data generator to simulate community emergence. An evaluation of the NorMASS comparing simulation emergence with the counterpart of an actual community demonstrates that the proposed approach provides an effective solution for simulating incentive mechanisms of Q&A communities, with a similarity of 80% or above.
Probabilistic coherence, logical consistency, and Bayesian learning: Neural language models as epistemic agents

It is argued that suitably trained neural language models exhibit key properties of epistemic agency: they hold probabilistically coherent and logically consistent degrees of belief, which they can rationally revise in the face of novel evidence. To this purpose, we conduct computational experiments with rankers: T5 models [Raffel et al. 2020] that are pretrained on carefully designed synthetic corpora. Moreover, we introduce a procedure for eliciting a model's degrees of belief, and define numerical metrics that measure the extent to which given degrees of belief violate (probabilistic, logical, and Bayesian) rationality constraints. While pretrained rankers are found to suffer from global inconsistency (in agreement with, e.g., [Jang et al. 2021]), we observe that subsequent self-training on auto-generated texts allows rankers to gradually obtain a probabilistically coherent belief system that is aligned with logical constraints. In addition, such self-training is found to have a pivotal role in rational evidential learning, too, for it seems to enable rankers to propagate a novel evidence item through their belief systems, successively re-adjusting individual degrees of belief. All this, we conclude, confirms the Rationality Hypothesis, i.e., the claim that suitable trained NLMs may exhibit advanced rational skills. We suggest that this hypothesis has empirical, yet also normative and conceptual ramifications far beyond the practical linguistic problems NLMs have originally been designed to solve.
MEMBANGUN KARAKTER GENERASI MUDA HINDU DENGAN MENINGKATKAN KECERDASAN SOSIALMELALUI AJARAN TRI PARARTHA (Refleksi Perayaan Hari Raya Saraswati di Era Disrupsi)

This paper is a reflection and meaning of the Saraswati day celebration in the midst of rapidly changing times. The occurrence of a generational change that results in a change in the way of thinking and a new way of working, demands also a quick adjustment. Speed of response is the key to success in the 21st century. Achieve success it requires another dimension besides the intellectual intelligence dimension. The dimension of social intelligence is one of the key components. Social intelligence is a person's ability to understand his environment optimally and react appropriately for social success. The point is to be able to win global competition, it is necessary to have a character that refers to aspects of social intelligence. One source of character values comes from the value of religious teachings. Hinduism has many important teachings that can be used as a reference in shaping the character of the younger generation, one of which is the teachings of Tri Parartha. This teaching describes three main attitudes and behaviors and aims to glorify others in the context of social interaction.
Graph learning-based generation of abstractions for reinforcement learning

Abstract The application of reinforcement learning (RL) algorithms is often hindered by the combinatorial explosion of the state space. Previous works have leveraged abstractions which condense large state spaces to find tractable solutions. However, they assumed that the abstractions are provided by a domain expert. In this work, we propose a new approach to automatically construct abstract Markov decision processes (AMDPs) for potential-based reward shaping to improve the sample efficiency of RL algorithms. Our approach to constructing abstract states is inspired by graph representation learning methods , it effectively encodes the topological and reward structure of the ground-level MDP. We perform large-scale quantitative experiments on a range of navigation and gathering tasks under both stationary and stochastic settings. Our approach shows improvements of up to 8.5 times in sample efficiency and up to 3 times in run time over the baseline approach. Besides, with our qualitative analyses of the generated AMDPs, we are able to visually demonstrate the capability of our approach to preserve the topological and reward structure of the ground-level MDP.
An Improved Steganographic Scheme Using the Contour Principle to Ensure the Privacy of Medical Data on Digital Images

With the improvement of current online communication schemes, it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate.Traditional steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before transmission.Both of the techniques mentioned above aid in the confidentiality of feature content.The proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red, Green, and Blue.The private Content originated from a medical client and was forwarded to a medical practitioner on the server end through the internet.The K-Means clustering principle uses the contouring approach to frame the pixel clusters on the image layers.The content embodiment procedure is performed on the selected pixel groups of all layers of the image using the Least Significant Bit (LSB) substitution technique to build the secret Content embedded image known as the stego image, which is subsequently transmitted across the internet medium to the server end.The experimental results are computed using the inputs from "Open-Access Medical Image Repositories (aylward.org)"and demonstrate the scheme's impudence as the Content concealing procedure progresses.
An Unsupervised Writer Identification Based on Generating Clusterable燛mbeddings

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems.Due to its importance, numerous studies have been conducted in various languages.Researchers have established several learning methods for writer identification including supervised and unsupervised learning.However, supervised methods require a large amount of annotation data, which is impossible in most scenarios.On the other hand, unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted.This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features.A pairwise architecturebased Autoembedder was applied to generate clusterable embeddings for handwritten text images.Furthermore, the trained baseline architecture generates the embedding of the data image, and the K-means algorithm is used to distinguish the embedding of individual writers.The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks.In addition, traditional evaluation metrics are used in the proposed model.Finally, the proposed model is compared with a few unsupervised models, and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.
Comparative Analysis of Execution of CNN-Based Sanguine Data Transmission with LSB-SS and PVD-SS

The intact data transmission to the authentic user is becoming crucial at every moment in the current era.Steganography; is a technique for concealing the hidden message in any cover media such as image, video; and audio to increase the protection of data.The resilience and imperceptibility are improved by choosing an appropriate embedding position.This paper gives a novel system to immerse the secret information in different videos with different methods.An audio and video steganography with novel amalgamations are implemented to immerse the confidential auditory information and the authentic user's face image.A hidden message is first included in the audio from the multimedia file; using LSB Technique.The Stego-video is created in the second stage by merging the authorized user's face into the frame of the video; by using PVD technology.Stego-audio is linked again with the stego-video in the third stage.The incorporated perspective techniques (LSB-SS and PVD-SS algorithms) with more significant data immersing capacity, good robustness and imperceptibility are proposed in this research work.The spread spectrum approach is used to increase the complexity of secret data recognition.Two different video files are tested with different voice files with the results such as PSNR, SSIM, RMSE and MSE as 52.3, 0.9963, 0.0024 and 0.0000059, respectively.