Reviewerly/exHarmony huggingface dataset:
A Sentiment and Emotion Annotated Dataset for Bitcoin Price Forecasting Based on Reddit Posts

Cryptocurrencies have gained enormous momentum in finance and are nowadays commonly adopted as a medium of exchange for online payments.After recent events during which GameStop's stocks were believed to be influenced by WallStreetBets subReddit, Reddit has become a very hot topic on the cryptocurrency market.The influence of public opinions on cryptocurrency price trends has inspired researchers on exploring solutions that integrate such information in crypto price change forecasting.A popular integration technique regards representing social media opinions via sentiment features.However, this research direction is still in its infancy, where a limited number of publicly available datasets with sentiment annotations exists.We propose a novel Bitcoin Reddit Sentiment Dataset, a ready-to-use dataset annotated with state-ofthe-art sentiment and emotion recognition.The dataset contains pre-processed Reddit posts and comments about Bitcoin from several domainrelated subReddits along with Bitcoin's financial data.We evaluate several widely adopted neural architectures for crypto price change forecasting.Our results show controversial benefits of sentiment and emotion features advocating for more sophisticated social media integration techniques.We make our dataset publicly available for research.
Investigation of the next generation science standards including in the science book according to E-learn : analytical study

This article briefs educational, In recent years, interest has increased in including standards for the next generation in science books from (k-12), and the main goal of these suggested modifications is to match science instruction to current technological developments and support what students will need to learn in the future. by including NGSS in science textbooks. For the fifth grade of primary school in Iraq, our study analysis of the science book, using online questionnaires on primary school science instructors of the fifth grade before the stage of putting the analysis processes into practise. For this study, 40 replies from schools in waist City made up the data collection. According to the study, the majority of teachers lacked the knowledge and skills necessary to integrate the proposed standard modifications into the existing curricula and their lesson plans. Based on their assessments of the pupils' level of readiness, the teachers offered a number of recommendations.
A Comprehensive Review of Security Measures in Database Systems: Assessing Authentication, Access Control, and Beyond

This paper presents a comprehensive review of security measures in database systems, focusing on authentication, access control, encryption, auditing, intrusion detection, and privacy-enhancing techniques. It aims to provide valuable insights into the latest advancements and best practices in securing databases. The review examines the challenges, vulnerabilities, and mitigation strategies associated with database security. It explores various authentication methods, access control models, encryption techniques, auditing and monitoring approaches, intrusion detection systems, and data leakage prevention mechanisms. The paper also discusses the impact of emerging trends such as cloud computing, big data, and the Internet of Things on database security. By synthesizing existing research, this review aims to contribute to the advancement of database security and assist organizations in protecting their sensitive data.
Squeezed Superradiance Enables Robust Entanglement-Enhanced Metrology Even with Highly Imperfect Readout

Quantum metrology protocols using entangled states of large spin ensembles attempt to achieve measurement sensitivities surpassing the standard quantum limit (SQL), but in many cases they are severely limited by even small amounts of technical noise associated with imperfect sensor readout. Amplification strategies based on time-reversed coherent spin-squeezing dynamics have been devised to mitigate this issue, but are unfortunately very sensitive to dissipation, requiring a large single-spin cooperativity to be effective. Here, we propose a new dissipative protocol that combines amplification and squeezed fluctuations. It enables the use of entangled spin states for sensing well beyond the SQL even in the presence of significant readout noise. Further, it has a strong resilience against undesired single-spin dissipation, requiring only a large collective cooperativity to be effective.
KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction

The ability of knowledge graphs to represent complex relationships at scale has led to their adoption for various needs including knowledge representation, question-answering, and recommendation systems.Knowledge graphs are often incomplete in the information they represent, necessitating the need for knowledge graph completion tasks.Pre-trained and finetuned language models have shown promise in these tasks although these models ignore the intrinsic information encoded in the knowledge graph, namely the entity and relation types.In this work, we propose the Knowledge Graph Language Model (KGLM) architecture, where we introduce a new entity/relation embedding layer that learns to differentiate distinctive entity and relation types, therefore allowing the model to learn the structure of the knowledge graph.In this work, we show that further pretraining the language models with this additional embedding layer using the triples extracted from the knowledge graph, followed by the standard fine-tuning phase sets a new state-of-the-art performance for the link prediction task on the benchmark datasets.
A novel approach to minimal reservoir computing

Abstract Reservoir computers are powerful machine learning algorithms for predicting nonlinear systems. Unlike traditional feedforward neural networks, they work on small training data sets, operate with linear optimization, and therefore require minimal computational resources. However, the traditional reservoir computer uses random matrices to define the underlying recurrent neural network and has a large number of hyperparameters that need to be optimized. Recent approaches show that randomness can be taken out by running regressions on a large library of linear and nonlinear combinations constructed from the input data and their time lags and polynomials thereof. However, for high-dimensional and nonlinear data, the number of these combinations explodes. Here, we show that a few simple changes to the traditional reservoir computer architecture further minimizing computational resources lead to significant and robust improvements in short- and long-term predictive performances compared to similar models while requiring minimal sizes of training data sets.
ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models

Humor is a central aspect of human communication that has not been solved for artificial agents so far.Large language models (LLMs) are increasingly able to capture implicit and contextual information.Especially, OpenAI's ChatGPT recently gained immense public attention.The GPT3-based model almost seems to communicate on a human level and can even tell jokes.Humor is an essential component of human communication.But is ChatGPT really funny?We put ChatGPT's sense of humor to the test.In a series of exploratory experiments around jokes, i.e., generation, explanation, and detection, we seek to understand ChatGPT's capability to grasp and reproduce human humor.Since the model itself is not accessible, we applied prompt-based experiments.Our empirical evidence indicates that jokes are not hard-coded but mostly also not newly generated by the model.Over 90% of 1008 generated jokes were the same 25 Jokes.The system accurately explains valid jokes but also comes up with fictional explanations for invalid jokes.Joke-typical characteristics can mislead Chat-GPT in the classification of jokes.ChatGPT has not solved computational humor yet but it can be a big leap toward "funny" machines.
Experimental realization of convolution processing in photonic synthetic frequency dimensions

Convolution is an essential operation in signal and image processing and consumes most of the computing power in convolutional neural networks. Photonic convolution has the promise of addressing computational bottlenecks and outperforming electronic implementations. Performing photonic convolution in the synthetic frequency dimension, which harnesses the dynamics of light in the spectral degrees of freedom for photons, can lead to highly compact devices. Here, we experimentally realize convolution operations in the synthetic frequency dimension. Using a modulated ring resonator, we synthesize arbitrary convolution kernels using a predetermined modulation waveform with high accuracy. We demonstrate the convolution computation between input frequency combs and synthesized kernels. We also introduce the idea of an additive offset to broaden the kinds of kernels that can be implemented experimentally when the modulation strength is limited. Our work demonstrate the use of synthetic frequency dimension to efficiently encode data and implement computation tasks, leading to a compact and scalable photonic computation architecture.
Determinants Affecting Consumer Trust in Communication With AI Chatbots

This paper summarized the factors that influence consumers' trust in AI chatbots and divided it into chatbot-related factors (expertise, anthropomorphism, responsiveness, and ease of use), company-related factors (perceived risk, brand trust, human support), and consumer-related factors (privacy concerns). This research attempts to explore the mechanism of human-AI chatbots trust formation and answer the question of how to promote consumers' trust in AI chatbots. The results found that the chatbot-related factors (expertise, responsiveness, and anthropomorphism) positively affect consumers' trust in chatbots. The company-related factor (brand trust) positively affects consumers' trust in chatbots, and perceived risk negatively affect consumers' trust in chatbots. Privacy concerns have a moderating effect on company-related factors. This study helps deepen the understanding of human-AI chatbots communication trust, constructs a basic model of human-AI chatbots trust, and provides insights for e-commerce enterprises to improve chatbots and enhance consumer trust.
Denoised Self-Augmented Learning for Social Recommendation

Social recommendation is gaining increasing attention in various online applications, including e-commerce and online streaming, where social information is leveraged to improve user-item interaction modeling. Recently, Self-Supervised Learning (SSL) has proven to be remarkably effective in addressing data sparsity through augmented learning tasks. Inspired by this, researchers have attempted to incorporate SSL into social recommendation by supplementing the primary supervised task with social-aware self-supervised signals. However, social information can be unavoidably noisy in characterizing user preferences due to the ubiquitous presence of interest-irrelevant social connections, such as colleagues or classmates who do not share many common interests. To address this challenge, we propose a novel social recommender called the Denoised Self-Augmented Learning paradigm (DSL). Our model not only preserves helpful social relations to enhance user-item interaction modeling but also enables personalized cross-view knowledge transfer through adaptive semantic alignment in embedding space. Our experimental results on various recommendation benchmarks confirm the superiority of our DSL over state-of-the-art methods. We release our model implementation at: https://github.com/HKUDS/DSL.
Pathologists’ first opinions on barriers and facilitators of computational pathology adoption in oncological pathology: an international study

Abstract Computational pathology (CPath) algorithms detect, segment or classify cancer in whole slide images, approaching or even exceeding the accuracy of pathologists. Challenges have to be overcome before these algorithms can be used in practice. We therefore aim to explore international perspectives on the future role of CPath in oncological pathology by focusing on opinions and first experiences regarding barriers and facilitators. We conducted an international explorative eSurvey and semi-structured interviews with pathologists utilizing an implementation framework to classify potential influencing factors. The eSurvey results showed remarkable variation in opinions regarding attitude, understandability and validation of CPath. Interview results showed that barriers focused on the quality of available evidence, while most facilitators concerned strengths of CPath. A lack of consensus was present for multiple factors, such as the determination of sufficient validation using CPath, the preferred function of CPath within the digital workflow and the timing of CPath introduction in pathology education. The diversity in opinions illustrates variety in influencing factors in CPath adoption. A next step would be to quantitatively determine important factors for adoption and initiate validation studies. Both should include clear case descriptions and be conducted among a more homogenous panel of pathologists based on sub specialization.
Multi-attention-based approach for deepfake face and expression swap detection and localization

Abstract Advancements in facial manipulation technology have resulted in highly realistic and indistinguishable face and expression swap videos. However, this has also raised concerns regarding the security risks associated with deepfakes. In the field of multimedia forensics, the detection and precise localization of image forgery has become essential tasks. Current deepfake detectors perform well with high-quality faces within specific datasets, but often struggle to maintain their performance when evaluated across different datasets. To this end, we propose an attention-based multi-task approach to improve feature maps for classification and localization tasks. The encoder and the attention-based decoder of our network generate localized maps that highlight regions with information about the type of manipulation. These localized features are shared with the classification network, improving its performance. Instead of using encoded spatial features, attention-based localized features from the decoder’s first layer are combined with frequency domain features to create a discriminative representation for deepfake detection. Through extensive experiments on face and expression swap datasets, we demonstrate that our method achieves competitive performance in comparison to state-of-the-art deepfake detection approaches in both in-dataset and cross-dataset scenarios. Code is available at https://github.com/saimawaseem/Multi-Attention-Based-Approach-for-Deepfake-Face-and-Expression-Swap-Detection-and-Localization .
Implementing Support Vector Machine Algorithm for Early Slum Identification in Yogyakarta City, Indonesia Using Pleiades Images

Slums are one of the urban problems that continue to get the attention of the government and the city of Yogyakarta. Over time, cities continue to experience changes in land use due to population growth and migration. Therefore, it is necessary to monitor the existence of slums continuously. The objectives of this study are to conduct early identification of the slum using the Support Vector Machine (SVM) Algorithm, which is applied to the Pleiades Image in parts of Yogyakarta City, to test the accuracy of the slum mapping results generated from the SVM compared to the Slum Map of the KOTAKU Program. The data used are Pleiades Image, administrative maps, and existing slum maps of the KOTAKU Program, which are used to test the accuracy. The method used is Machine Learning with a Support Vector Machine Algorithm. The parameters used for early identification of the slums are the characteristics of the object (characteristics of buildings), settlement (density and shape), and the environment (location and its proximity to rivers and industries). We separate slum and non-slum based on texture, morphology, and spectral approaches. Based on the accuracy test results between the SVM classification results map of the slum and the map from the KOTAKU Program, the accuracy is 86.25% with a kappa coefficient of 0.796.
Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation

Standard Unsupervised Domain Adaptation (UDA) methods assume the availability of both source and target data during the adaptation. In this work, we investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific case of UDA where a model is adapted to a target domain without access to source data. We propose a novel approach for the SF-UDA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels. The classification loss is reweighted based on the reliability of the pseudo-labels that is measured by estimating their uncertainty. Guided by such reweighting strategy, the pseudo-labels are progressively refined by aggregating knowledge from neighbouring samples. Furthermore, a self-supervised contrastive framework is leveraged as a target space regulariser to enhance such knowledge aggregation. A novel negative pairs exclusion strategy is proposed to identify and exclude negative pairs made of samples sharing the same class, even in presence of some noise in the pseudo-labels. Our method outperforms previous methods on three major benchmarks by a large margin. We set the new SF-UDA state-of-the-art on VisDA-C and DomainNet with a performance gain of + 1.8% on both benchmarks and on PACS with + 12.3% in the single-source setting and +6.6% in multi-target adaptation. Additional analyses demonstrate that the proposed approach is robust to the noise, which results in significantly more accurate pseudo-labels compared to state-of-the-art approaches.
Adaptive Sparse Pairwise Loss for Object Re-Identification

Object re-identification (ReID) aims to find instances with the same identity as the given probe from a large gallery. Pairwise losses play an important role in training a strong ReID network. Existing pairwise losses densely exploit each instance as an anchor and sample its triplets in a mini-batch. This dense sampling mechanism inevitably introduces positive pairs that share few visual similarities, which can be harmful to the training. To address this problem, we propose a novel loss paradigm termed Sparse Pair-wise (SP) loss that only leverages few appropriate pairs for each class in a mini-batch, and empirically demonstrate that it is sufficient for the ReID tasks. Based on the proposed loss framework, we propose an adaptive positive mining strategy that can dynamically adapt to diverse intra-class variations. Extensive experiments show that SP loss and its adaptive variant AdaSP loss outperform other pairwise losses, and achieve state-of-the-art performance across several ReID benchmarks. Code is available at https://github.com/Astaxanthin/AdaSP.
Role of Legal Regulation in Preventing Corruption in the Market of Digital Financial Assets in the Context of Sustainable Development

Objective: The study considers the main trends in the legal regulation of cryptocurrency in different countries. The authors conduct a systematic analysis of the impact of digital financial assets on corruption. Method: The authors have analyzed legal acts and compared data from different countries to study the legal regulation of digital financial assets. Result: The authors have shown that there is no legal regulation of digital financial assets, in particular cryptocurrencies. There are no clear definitions and rules regarding these assets, which causes problems with taxation, money laundering, and the identification of parties to transactions. Conclusion: Based on the data obtained, the authors have substantiated the need for the proper legal regulation of digital financial assets, including cryptocurrencies. The state shall resolve issues related to terminology, legal status, taxation, and cryptocurrency exchange. In addition, the study highlights the importance of KYC and AML mechanisms to prevent corruption and money laundering with the use of digital financial assets. A comprehensive legal framework will provide certainty and protect both individual and institutional investors dealing with digital financial assets.
Adapting science education during crises: first lessons from the COVID-19 pandemic

The COVID-19 pandemic has disrupted science education, forcing teachers and students to switch to online and hybrid modes of learning. This paper reviews recent research on how to cope with these challenges using various innovative teaching strategies. These include online platforms and tools, historical and philosophical perspectives, and holistic approaches to science education. The paper discusses the implications of these findings for the future of science education.
A Deep Recurrent Learning-Based Region-Focused Feature Detection for Enhanced Target Detection in Multi-Object Media

Target detection in high-contrast, multi-object images and movies is challenging. This difficulty results from different areas and objects/people having varying pixel distributions, contrast, and intensity properties. This work introduces a new region-focused feature detection (RFD) method to tackle this problem and improve target detection accuracy. The RFD method divides the input image into several smaller ones so that as much of the image as possible is processed. Each of these zones has its own contrast and intensity attributes computed. Deep recurrent learning is then used to iteratively extract these features using a similarity measure from training inputs corresponding to various regions. The target can be located by combining features from many locations that overlap. The recognized target is compared to the inputs used during training, with the help of contrast and intensity attributes, to increase accuracy. The feature distribution across regions is also used for repeated training of the learning paradigm. This method efficiently lowers false rates during region selection and pattern matching with numerous extraction instances. Therefore, the suggested method provides greater accuracy by singling out distinct regions and filtering out misleading rate-generating features. The accuracy, similarity index, false rate, extraction ratio, processing time, and others are used to assess the effectiveness of the proposed approach. The proposed RFD improves the similarity index by 10.69%, extraction ratio by 9.04%, and precision by 13.27%. The false rate and processing time are reduced by 7.78% and 9.19%, respectively.
Closed-book Question Generation via Contrastive Learning

Question Generation (QG) is a fundamental NLP task for many downstream applications. Recent studies on open-book QG, where supportive answer-context pairs are provided to models, have achieved promising progress. However, generating natural questions under a more practical closed-book setting that lacks these supporting documents still remains a challenge. In this work, we propose a new QG model for this closed-book setting that is designed to better understand the semantics of long-form abstractive answers and store more information in its parameters through contrastive learning and an answer reconstruction module. Through experiments, we validate the proposed QG model on both public datasets and a new WikiCQA dataset. Empirical results show that the proposed QG model outperforms baselines in both automatic evaluation and human evaluation. In addition, we show how to leverage the proposed model to improve existing question-answering systems. These results further indicate the effectiveness of our QG model for enhancing closed-book question-answering tasks.