Reviewerly/exHarmony huggingface dataset:
A Study of Interaction, Visual Canvas, and Immersion in AR Design: A DSR Approach

Augmented reality (AR) as an innovative technology has changed the way people use technology for interaction and communication.While researchers have studied the application of AR, research on AR as a communication medium remains scant.In this study, we investigate the effect of AR factors (namely, interaction, visual canvas/cues, and immersion) on AR-mediated communication.We apply design science research (DSR) guidelines to design, develop, and evaluate an AR artifact.We derive the design elements based on interactivity, media naturalness, and immersion theories and develop the AR artifact as a mobile app in an iterative manner.We evaluate the design product through the informed arguments and scenarios method, and the design process by assessing its conformance to DSR principles.We show that AR factors' design elements-interaction (user controls, contextual tasks, and ergonomics), visual canvas/cues (realistic 3D models, visual and audio cues, and aesthetics), and immersion (diverse components)-play a critical role in AR-mediated communication.Furthermore, high-quality product visuals and interactive user controls give users a good AR experience.From a practice perspective, AR app designers may incorporate the design process we used in our study and generate AR experiences that fully exploit AR media's communication affordance.We contribute to knowledge by using DSR guidelines for designing and developing AR as a communication medium.
Lion Based Butterfly Optimization with Improved YOLO-v4 for Heart Disease Prediction Using IoMT

The Internet of Medical Things (IoMT) has subsequently been used in healthcare services to gather sensor data for the prediction and diagnosis of cardiac disease. Recently image processing techniques require a clear focused solution to predict diseases. The primary goal of the proposed method is to use health information and medical pictures for classifying the data and forecasting cardiac disease. It consists of two phases for categorizing the data and prediction. If the previous phase's results are practical heart problems, then there is no need for phase 2 to predict. The first phase categorized data collected from healthcare sensors attached to the patient's body. The second stage evaluated the echocardiography images for the prediction of heart disease. A Hybrid Lion-based Butterfly Optimization Algorithm (L-BOA) is used for classifying the sensor data. In the existing method, Hybrid Faster R-CNN with SE-Rest-Net-101 is used for classification. Faster R-CNN uses areas to locate the item in the picture. The proposed method uses Improved YOLO-v4. It increases the semantic knowledge of little things. An Improved YOLO-v4 with CSPDarkNet53 is used for feature extraction and classifying the echo-cardiogram pictures. Both categorization approaches were used, and the results were integrated and confirmed in the ability to forecast heart disease. The LBO-YOLO-v4 process detected regular sensor data with 97.25% accuracy and irregular sensor data with 98.87% accuracy. The proposed improved YOLO-v4 with the CSPDarkNet53 method gives better classification among echo-cardiogram pictures.
AI-based Blockchain for the Metaverse: Approaches and Challenges

The recent advances in AI have enabled novel approaches and remarkable enhancements in the provision of existing services and applications. The metaverse is one of the emerging applications that got the advantages of this technological progression. Due to the cooperation between several cutting-edge technologies including IoT, digital twin (DT), and big data, the metaverse highly relies on the manipulation of an immense amount of gathered data. In this context, security is a major challenge, and as such, blockchain brings several advantages to the metaverse ecosystem as it allows secure and transparent data storage. However, with the dynamic nature of such an ecosystem and the fast variation in its AI-based services, the traditional blockchain features (e.g. smart contracts and consensus protocols) will face big challenges to meet the needed dynamic service requirements and scalability. This positioning paper proposes the concept of using a blockchain governed by AI to leverage the various features and advantages of blockchain services. The paper provides a review of the role of blockchain in the metaverse, challenges that arise, and then the role of AI in building intelligent blockchain features and their impact on the metaverse ecosystem.
Detecting Arabic Cyberbullying Tweets Using Machine Learning

The advancement of technology has paved the way for a new type of bullying, which often leads to negative stigma in the social setting. Cyberbullying is a cybercrime wherein one individual becomes the target of harassment and hatred. It has recently become more prevalent due to a rise in the usage of social media platforms, and, in some severe situations, it has even led to victims’ suicides. In the literature, several cyberbullying detection methods are proposed, but they are mainly focused on word-based data and user account attributes. Furthermore, most of them are related to the English language. Meanwhile, only a few papers have studied cyberbullying detection in Arabic social media platforms. This paper, therefore, aims to use machine learning in the Arabic language for automatic cyberbullying detection. The proposed mechanism identifies cyberbullying using the Support Vector Machine (SVM) classifier algorithm by using a real dataset obtained from YouTube and Twitter to train and test the classifier. Moreover, we include the Farasa tool to overcome text limitations and improve the detection of bullying attacks.
Spatial integration framework of solar, wind, and hydropower energy potential in Southeast Asia

Amid its massive increase in energy demand, Southeast Asia has pledged to increase its use of renewable energy by up to 23% by 2025. Geospatial technology approaches that integrate statistical data, spatial models, earth observation satellite data, and climate modeling can be used to conduct strategic analyses for understanding the potential and efficiency of renewable energy development. This study aims to create the first spatial model of its kind in Southeast Asia to develop multi-renewable energy from solar, wind, and hydropower, further broken down into residential and agricultural areas. The novelty of this study is the development of a new priority model for renewable energy development resulting from the integration of area suitability analysis and the estimation of the amount of potential energy. Areas with high potential power estimations for the combination of the three types of energy are mostly located in northern Southeast Asia. Areas close to the equator, have a lower potential than the northern countries, except for southern regions. Solar photovoltaic (PV) plant construction is the most area-intensive type of energy generation among the considered energy sources, requiring 143,901,600 ha (61.71%), followed by wind (39,618,300 ha; 16.98%); a combination of solar PV and wind (37,302,500 ha; 16%); hydro (7,665,200 ha; 3.28%); a combination of hydro and solar PV (3,792,500 ha; 1.62%); and a combination of hydro and wind (582,700 ha; 0.25%). This study is timely and important because it will inform policies and regional strategies for transitioning to renewable energy, with consideration of the different characteristics present in Southeast Asia.
Analysis of large-language model versus human performance for genetics questions

Abstract Large-language models like ChatGPT have recently received a great deal of attention. To assess ChatGPT in the field of genetics, we compared its performance to human respondents in answering genetics questions (involving 13,636 responses) that had been posted on social media platforms starting in 2021. Overall, ChatGPT did not perform significantly differently than human respondents, but did significantly better on memorization-type questions versus critical thinking questions, frequently provided different answers when asked questions multiple times, and provided plausible explanations for both correct and incorrect answers.
Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis

Histopathological images are commonly used imaging modalities for breast cancer. As manual analysis of histopathological images is difficult, automated tools utilizing artificial intelligence (AI) and deep learning (DL) methods should be modelled. The recent advancements in DL approaches will be helpful in establishing maximal image classification performance in numerous application zones. This study develops an arithmetic optimization algorithm with deep-learning-based histopathological breast cancer classification (AOADL-HBCC) technique for healthcare decision making. The AOADL-HBCC technique employs noise removal based on median filtering (MF) and a contrast enhancement process. In addition, the presented AOADL-HBCC technique applies an AOA with a SqueezeNet model to derive feature vectors. Finally, a deep belief network (DBN) classifier with an Adamax hyperparameter optimizer is applied for the breast cancer classification process. In order to exhibit the enhanced breast cancer classification results of the AOADL-HBCC methodology, this comparative study states that the AOADL-HBCC technique displays better performance than other recent methodologies, with a maximum accuracy of 96.77%.
Back to MLP: A Simple Baseline for Human Motion Prediction

This paper tackles the problem of human motion prediction, consisting in forecasting future body poses from historically observed sequences. State-of-the-art approaches provide good results, however, they rely on deep learning architectures of arbitrary complexity, such as Recurrent Neural Networks(RNN), Transformers or Graph Convolutional Networks(GCN), typically requiring multiple training stages and more than 2 million parameters. In this paper, we show that, after combining with a series of standard practices, such as applying Discrete Cosine Transform(DCT), predicting residual displacement of joints and optimizing velocity as an auxiliary loss, a light-weight network based on multi-layer perceptrons(MLPs) with only 0.14 million parameters can surpass the state-of-the-art performance. An exhaustive evaluation on the Human3.6M, AMASS, and 3DPW datasets shows that our method, named siMLPe, consistently outperforms all other approaches. We hope that our simple method could serve as a strong baseline for the community and allow re-thinking of the human motion prediction problem. The code is publicly available at \url{https://github.com/dulucas/siMLPe}.
Self-Supervised Image Denoising for Real-World Images With Context-Aware Transformer

In recent years, the development of deep learning has been pushing image denoising to a new level. Among them, self-supervised denoising is increasingly popular because it does not require any prior knowledge. Most of the existing self-supervised methods are based on convolutional neural networks (CNN), which are restricted by the locality of the receptive field and would cause color shifts or textures loss. In this paper, we propose a novel Denoise Transformer for real-world image denoising, which is mainly constructed with Context-aware Denoise Transformer (CADT) units and Secondary Noise Extractor (SNE) block. CADT is designed as a dual-branch structure, where the global branch uses a window-based Transformer encoder to extract the global information, while the local branch focuses on the extraction of local features with small receptive field. By incorporating CADT as basic components, we build a hierarchical network to directly learn the noise distribution information through residual learning and obtain the first stage denoised output. Then, we design SNE in low computation for secondary global noise extraction. Finally the blind spots are collected from the Denoise Transformer output and reconstructed, forming the final denoised image. Extensive experiments on the real-world SIDD benchmark achieve 50.62/0.990 for PSNR/SSIM, which is competitive with the current state-of-the-art method and only 0.17/0.001 lower. Visual comparisons on public sRGB, Raw-RGB and greyscale datasets prove that our proposed Denoise Transformer has a competitive performance, especially on blurred textures and low-light images, without using additional knowledge, e.g., noise level or noise type, regarding the underlying unknown noise.
SKIER: A Symbolic Knowledge Integrated Model for Conversational Emotion Recognition

Emotion recognition in conversation (ERC) has received increasing attention from the research community. However, the ERC task is challenging, largely due to the complex and unstructured properties of multi-party conversations. Besides, the majority of daily dialogues take place in a specific context or circumstance, which requires rich external knowledge to understand the background of a certain dialogue. In this paper, we address these challenges by explicitly modeling the discourse relations between utterances and incorporating symbolic knowledge into multi-party conversations. We first introduce a dialogue parsing algorithm into ERC and further improve the algorithm through a transfer learning method. Moreover, we leverage different symbolic knowledge graph relations to learn knowledge-enhanced features for the ERC task. Extensive experiments on three benchmarks demonstrate that both dialogue structure graphs and symbolic knowledge are beneficial to the model performance on the task. Additionally, experimental results indicate that the proposed model surpasses baseline models on several indices.
A Novel Hybrid Decision Making Framework for Comparing Market Performance of Metaverse Crypto Assets

With proliferation of smartphone usage and rapidly increasing internet access, the financial sectors has been witnessing a transformational change in the last few years, leading to the digital economy. The cryptocurrencies (digital assets) and the metaverse assets that use blockchain technology extensively have emerged as an attractive investment option to the investors. The review of the extant literature shows a scantiness of research in comparing the performance of metaverse crypto assets based on market indicators. In this regard, the current work aims to provide a framework to compare the metaverse crypto assets based on their market performance. To this end, the present paper proposes a novel hybrid framework such as Logarithmic Percentage Change driven Compromise Solution based Appraisal (LOPCCSA). To compare the alternatives the variables like return, momentum of the daily closing price, market capitalization, trading volume and risk (in terms of historical volatility realized over the study period 2022). From the result we observe that the momentum of the closing prices and volatility of the price movements hold the higher importance as derived by calculation of objective weights. Theta Network (A10) comes out as the top performer ahead of ApeCoin (A1) or MANA (A3) or Internet Computer (A6). The comparison of the results obtained by LOPCCSA with other MCDM models show considerable consistency. The sensitivity analysis indicates that LOPCCSA provides a stable solution. The present paper is a distinct attempt that shall attract the decision makers and investors to carry out a deeper and comprehensive analysis of the metaverse crypto assets in near future.
IDENTIFICATION AND DISTRIBUTION OF MINERALS IN SOILS FROM AL-AHRAR AREA, WAIST PROVINCE, IRAQ

The clay mineralogy of soils and of the main calcareous sedimentary soils of middle of Iraq were investigated to determine the presence of different clay minerals and their distribution pattern in soils. Six pedons were chosen and total of 24 soil horizons were collected. The results revealed that the soil minerals were primary and others were secondary minerals. Generally, the results of the semi-quantitative distribution of the clay minerals in clay fractions showed the dominance of the montmorillonite minerals and illite alternate with chlorite in being main and kaolinite are major, while, the presence of palygorskite mineral were trace except P5 may be miner (saline soils). The origin of the montmorillonite minerals in these soil may be inherited in origin from the mica. Soil chlorite was found in some soils due to chloritization of expanding clay minerals. The mineralogical distribution in bulk soils were dominated by calcite, and quartz, while other minerals were identified feldspar, hematite and pyroxene in addition to the clay minerals in all the soil samples.
ChatGPT broke the Turing test — the race is on for new ways to assess AI

Large language models mimic human chatter, but scientists disagree on their ability to reason. Large language models mimic human chatter, but scientists disagree on their ability to reason.
ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning

Over the last few years, large language models (LLMs) have emerged as the most important breakthroughs in natural language processing (NLP) that fundamentally transform research and developments in the field. ChatGPT represents one of the most exciting LLM systems developed recently to showcase impressive skills for language generation and highly attract public attention. Among various exciting applications discovered for ChatGPT in English, the model can process and generate texts for multiple languages due to its multilingual training data. Given the broad adoption of ChatGPT for English in different problems and areas, a natural question is whether ChatGPT can also be applied effectively for other languages or it is necessary to develop more language-specific technologies. The answer to this question requires a thorough evaluation of ChatGPT over multiple tasks with diverse languages and large datasets (i.e., beyond reported anecdotes), which is still missing or limited in current research. Our work aims to fill this gap for the evaluation of ChatGPT and similar LLMs to provide more comprehensive information for multilingual NLP applications. In particular, we evaluate ChatGPT on 7 different tasks, covering 37 diverse languages with high, medium, low, and extremely low resources. Compared to the performance of previous models, our extensive experiments demonstrate the worse performance of ChatGPT for different NLP tasks and languages, calling for further research to develop better models and understanding for multilingual learning.
Probabilistic Alternating-Time Temporal Logic of Incomplete Information and Synchronous Perfect Recall

A probabilistic variant of ATL* logic is proposed to work with multi-player games of incomplete information and synchronous perfect recall. The semantics of the logic is settled over probabilistic interpreted system and partially observed probabilistic concurrent game structure. While unexpectedly, the model checking problem is in general undecidable even for single-group fragment, we find a fragment whose complexity is in 2-EXPTIME. The usefulness of this fragment is shown over a land search scenario.
Intrinsic motivation and mental replay enable efficient online adaptation in stochastic recurrent networks

Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial. In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network. By using learning signals which mimic the intrinsic motivation signal cognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds. We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given way points.
On the Economics of Offline Password Cracking

We develop an economic model of an offline password cracker which allows us to make quantitative predictions about the fraction of accounts that a rational password attacker would crack in the event of an authentication server breach. We apply our economic model to analyze recent massive password breaches at Yahoo!, Dropbox, LastPass and AshleyMadison. All four organizations were using key-stretching to protect user passwords. In fact, LastPass' use of PBKDF2-SHA256 with $10^5$ hash iterations exceeds 2017 NIST minimum recommendation by an order of magnitude. Nevertheless, our analysis paints a bleak picture: the adopted key-stretching levels provide insufficient protection for user passwords. In particular, we present strong evidence that most user passwords follow a Zipf's law distribution, and characterize the behavior of a rational attacker when user passwords are selected from a Zipf's law distribution. We show that there is a finite threshold which depends on the Zipf's law parameters that characterizes the behavior of a rational attacker -- if the value of a cracked password (normalized by the cost of computing the password hash function) exceeds this threshold then the adversary's optimal strategy is always to continue attacking until each user password has been cracked. In all cases (Yahoo!, Dropbox, LastPass and AshleyMadison) we find that the value of a cracked password almost certainly exceeds this threshold meaning that a rational attacker would crack all passwords that are selected from the Zipf's law distribution (i.e., most user passwords). This prediction holds even if we incorporate an aggressive model of diminishing returns for the attacker (e.g., the total value of $500$ million cracked passwords is less than $100$ times the total value of $5$ million passwords). See paper for full abstract.
Machine Learning With Observers Predicts Complex Spatiotemporal Behavior

Chimeras and branching are two archetypical complex phenomena that appear in many physical systems; because of their different intrinsic dynamics, they delineate opposite non-trivial limits in the complexity of wave motion and present severe challenges in predicting chaotic and singular behavior in extended physical systems. We report on the long-term forecasting capability of Long Short-Term Memory (LSTM) and reservoir computing (RC) recurrent neural networks, when they are applied to the spatiotemporal evolution of turbulent chimeras in simulated arrays of coupled superconducting quantum interference devices (SQUIDs) or lasers, and branching in the electronic flow of two-dimensional graphene with random potential. We propose a new method in which we assign one LSTM network to each system node except for "observer" nodes which provide continual "ground truth" measurements as input; we refer to this method as "Observer LSTM" (OLSTM). We demonstrate that even a small number of observers greatly improves the data-driven (model-free) long-term forecasting capability of the LSTM networks and provide the framework for a consistent comparison between the RC and LSTM methods. We find that RC requires smaller training datasets than OLSTMs, but the latter requires fewer observers. Both methods are benchmarked against Feed-Forward neural networks (FNNs), also trained to make predictions with observers (OFNNs).