Reviewerly/exHarmony huggingface dataset:
Eye Movement Attention Based Depression Detection Model

Depression is a common mental illness. Unlike normal mood fluctuations which affect individuals only temporarily, depressed episodes can profoundly disrupt a person’s daily life and even lead to suicide. Eye movement data are commonly employed in depression identification because they are simple to collect and can show psychological processes. Given the above, we proposed EnSA, a novel model based on eye movement data. We established forward and reverse target stimuli to identify the subject’s saccade reaction and capture the subject’s eye movement data. The gathered eye movement data were entered into EnSA first, and the self-attention weights of each characteristic were calculated. To produce more expressive features, the self-attention features were convolved and then summed feature outputs. According to the results, our model performed well, with an accuracy of 93.5% and 95.5% in the prosaccade and antisaccade experiments, respectively.
GAHBT: Genetic Based Hashing Algorithm for Managing and Validating Health Data Integrity in Blockchain Technology

A method for managing, securing, and validating health data distribution records using a genetic-based hashing algorithm in a decentralized environment is presented in this research report. The rationale for choosing blockchain is to secure the transaction of health data and protect these data from manipulated fraudulent movement and corruption by a contributor to the chain, or any individual. Our approach uses technology that provides an efficient surveillance measure, including transparency of records, immunity from fraud, and protection from tampering, as well as sustaining the order of data. For medical research, the results here provide a genetic-based hashing algorithm for data security, which has lower computational complexity, low space coverage, higher security and integrity, and a high avalanche effect. The simulation will show the validity, immunity, and integrity of the data record. The technique modified in this secure decentralized network is a cryptographic hashing algorithm for 512 bits. In this study, a genetic algorithm (GA) is used to generate a key that must be used in the encryption and decryption of medical data. A GA is a metaheuristic approach inspired by the laws of genetics; and it is generally used to generate high-quality solutions for complex problems. Applications of GAs are possible in medical fields, such as radiology, oncology, cardiology, endocrinology, surgery, oncology, and radiotherapy in healthcare management.
RiC-O Converter: A Software to Convert EAC-CPF and EAD 2002 XML Files to RDF Datasets Conforming to Records in Contexts Ontology

RiC-O Converter is an open source command-line tool to convert EAD finding aids and EAC-CPF authority records to RDF files conforming to ICA Records in Contexts ontology (RiC-O) in a robust manner. It was developed for the Archives nationales of France but is aimed to be reused by other archival institutions, and to this aim is fully documented in English. It is based on XSLT stylesheets that consider the variability of EAD content. It enabled the Archives nationales of France to convert 15,400 EAC-CPF files and 31,000 EAD files into a homogeneous knowledge graph, and to start a more specific project aiming to provide end users with an intuitive search interface for a significant subset of this graph, opening new perspectives for navigating and linking from/to archival metadata.
Informatické myslenie na Slovensku a v Európe

The paper is focused on computational thinking as a new paradigm for Computer Science education introduced from early age. Its need has been made more evident during the COVID-19 pandemic. The teaching of Computer Science concepts in formal school education has increasingly been supported and implemented by the Ministries of Education in European countries. However, questions remain regarding its implementation, specifically what Computer Science concepts should be taught, when and how.
Implementing Artificial Intelligence to Reduce Marine Ecosystem Pollution

Industrial growth has a positive impact because it brings prosperity to humans. But on the other hand, it also has a negative effect, mainly due to industrial pollution produced. The pollution impacts environmental damage, one of which is the marine environment. This damage can be reduced by increasing understanding of the marine environment by monitoring it using technology. Although efforts have been made to monitor the marine environment, there are difficulties interpreting the large amount of data collected. To overcome these obstacles, we need technology that can process big data. One of the technologies that can be used is Artificial Intelligence which will be discussed in this study. This study aims to provide further understanding regarding the application of Artificial Intelligence to monitor the marine environment. This study uses a literature review method from several studies related to Artificial Intelligence. The final result of this study explains the potential and impact of applying Artificial Intelligence in reducing pollution of the marine environment sustainably. Although there have been many efforts to monitor the ocean for pollutants remotely, classifying the data is challenging because of the high volume of data. Therefore, the novelty of this research is to discuss a use case of a new approach to monitoring the ocean with the help of Artificial Intelligence. This research is expected to be motivated to develop better solutions in overcoming marine environment pollution using Artificial Intelligence technology.
Exploring quantum mechanical advantage for reservoir computing

Quantum reservoir computing is an emerging field in machine learning with quantum systems. While classical reservoir computing has proven to be a capable concept of enabling machine learning on real, complex dynamical systems with many degrees of freedom, the advantage of its quantum analogue is yet to be fully explored. Here, we establish a link between quantum properties of a quantum reservoir, namely entanglement and its occupied phase space dimension, and its linear short-term memory performance. We find that a high degree of entanglement in the reservoir is a prerequisite for a more complex reservoir dynamics that is key to unlocking the exponential phase space and higher short-term memory capacity. We quantify these relations and discuss the effect of dephasing in the performance of physical quantum reservoirs.
Self-learning Machines based on Hamiltonian Echo Backpropagation

A physical self-learning machine can be defined as a nonlinear dynamical system that can be trained on data (similar to artificial neural networks), but where the update of the internal degrees of freedom that serve as learnable parameters happens autonomously. In this way, neither external processing and feedback nor knowledge of (and control of) these internal degrees of freedom is required. We introduce a general scheme for self-learning in any time-reversible Hamiltonian system. We illustrate the training of such a self-learning machine numerically for the case of coupled nonlinear wave fields.
Automated Indian Sign Language Recognition System Using LSTM models

According to a survey by the World Federation of the Deaf, there are approximately 1.5 billion deaf people around the world, and more than 80 percent of them live in developing countries. In India, around 63 million people suffer from complete or partial deafness, and of these, at least 50 lakh are children. There is a separate community of people suffering from deafness because of the inability to communicate with the common people. Also, the common mass of the majority of developing nations isn’t aware of the different ways they can communicate with people suffering from deafness or dumbness due to which a communication gap is formed, which restricts deaf and dumb people’s ability to fit into society and contribute to the development of society to the best of their ability. To overcome this, many new sign language recognition models have been introduced. Hence, this inspired us to create a system that is especially for Indian Sign Language (ISL) users known as the Indian Sign Language Recognition System (ISLR). But the major challenge was the way to recognize the signs as Signs of Indian Sign language are a bit complex, it consists of signs involving both the hands of the person thus making it hard for an existing CNN based model to detect the signs. To overcome this mediapipe holistics landmarks are used, which gives value specifying the position of different landmarks of both the hands. For different signs these values will be different and the LSTM model is the best choice to work on these types of data. So a system working on Mediapipe and LSTM is created. Three LSTM models: simple LSTM, bidirectional LSTM models, and stacked LSTM models are being used in this system. A synthetic dataset consisting of seven gestures and 26 alphabets was created to train the model. The gestures taken are-Hello, Thanks, I love you, Thumbs up, Home, Help, Namaste. A dataset, inclusive of 30 videos of 30 frames each of certain gestures was recorded to train the model. Different performance measurement parameters like Accuracy, Precision, Recall, F1 Score, and Cohen Kappa Score are used in this work to compare the three LSTM models. As result, we have observed that simple LSTM and bidirectional LSTM models outperform stacked LSTM models.
Blockchain Technology for Financial Services &amp;amp; Globalization

Abstract Objectives: Modern blockchain technology has created unknown phenomena in developed countries. Countries like China, Japan, and India use the advantages of blockchain technology. This research work also highlighted the concept of blockchain technology eliminating corruption and fraud, which is the biggest problem in Bangladesh and Pakistan. Methods: Using data from two countries, Pakistan, and Bangladesh, blockchain technology for financial service and globalization. The researcher uses STATA 15.0 for data analysis. The techniques proposed by these methodologies simplify systematically and straightforwardly, which guarantees significant thoroughness, reliability, and regression. For the regression analysis, we use the OLS method. Findings: Blockchain is a technology that allows currency transactions to occur without a third party’s intervention. In Pakistan and Bangladesh, blockchain systems are very poor compared to other south Asian countries such as China, Alipay, and WeChat’s massive use of digital currency services. Blockchain technology-based currency and financial industries can be decentralized and accountable, beneficial for the digital economy. But Pakistan and Bangladesh are still too far away from blockchain technology. Novelty: There is growing interested in tracking actual assets on overseas shipments, where this technology can meet the needs of merchants, carriers, lenders, insurers, and regulators. Moreover, blockchain technology can control transaction costs and eliminate fraud. Blockchain systems are free from the middleman, which is why we can profit more and more without fraud and corruption in business. The digital economy plays a critical role in developing businesses and society in this fastest-growing situation.
Darijabert: a Step Forward in Nlp for the Written Moroccan Dialect

Abstract The performance of existing transformer-based language models in providing state-of-the-art results on many downstream tasks is well established. However, these models tend to be limited to high-resource languages or are multilingual in nature. The availability of models dedicated to Arabic dialects is limited, and even those that exist primarily support dialects written in Arabic script. This study presents the first BERT models for Moroccan Arabic dialect, also known as Darija, called DarijaBERT, DarijaBERT-arabizi, and DarijaBERT-mix. These models are trained on the largest Arabic monodialectal corpus, supporting both Arabic and Latin character representations of the Moroccan dialect. The models' performance is evaluated and compared to existing multidialectal and multilingual models on four distinct downstream tasks, demonstrating state-of-the-art results. The data collection methodology and pre-training process are described, and the Moroccan Topic Classification Dataset (MTCD) is introduced as the first dataset for topic classification in the Moroccan Arabic dialect. The pre-trained models and MTCD dataset are available to the scientific community.
Reliable Natural Language Understanding with Large Language Models and Answer Set Programming

Humans understand language by extracting information (meaning) from sentences, combining it with existing commonsense knowledge, and then performing reasoning to draw conclusions. While large language models (LLMs) such as GPT-3 and ChatGPT are able to leverage patterns in the text to solve a variety of NLP tasks, they fall short in problems that require reasoning. They also cannot reliably explain the answers generated for a given question. In order to emulate humans better, we propose STAR, a framework that combines LLMs with Answer Set Programming (ASP). We show how LLMs can be used to effectively extract knowledge -- represented as predicates -- from language. Goal-directed ASP is then employed to reliably reason over this knowledge. We apply the STAR framework to three different NLU tasks requiring reasoning: qualitative reasoning, mathematical reasoning, and goal-directed conversation. Our experiments reveal that STAR is able to bridge the gap of reasoning in NLU tasks, leading to significant performance improvements, especially for smaller LLMs, i.e., LLMs with a smaller number of parameters. NLU applications developed using the STAR framework are also explainable: along with the predicates generated, a justification in the form of a proof tree can be produced for a given output.
Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms

This paper introduces a novel approach to leveraging features learned from both supervised and self-supervised paradigms, to improve image classification tasks, specifically for vehicle classification. Two state-of-the-art self-supervised learning methods, DINO and data2vec, were evaluated and compared for their representation learning of vehicle images. The former contrasts local and global views while the latter uses masked prediction on multiple layered representations. In the latter case, supervised learning is employed to finetune a pretrained YOLOR object detector for detecting vehicle wheels, from which definitive wheel positional features are retrieved. The representations learned from these self-supervised learning methods were combined with the wheel positional features for the vehicle classification task. Particularly, a random wheel masking strategy was utilized to finetune the previously learned representations in harmony with the wheel positional features during the training of the classifier. Our experiments show that the data2vec-distilled representations, which are consistent with our wheel masking strategy, outperformed the DINO counterpart, resulting in a celebrated Top-1 classification accuracy of 97.2% for classifying the 13 vehicle classes defined by the Federal Highway Administration.
Streaming Data Processing

Aims: The data which is continuously being produced by hundreds of thousands of data sources is recognized as streamed data. The data which is processed via this kind of source is relatively smaller in size and is being sent at the same time it is generated.&#x0D; Study Design: In streaming data, the data range is so wide like the telemetry from interconnected devices or other such forms of data with the inclusion of certain web applications. This information should be handled consecutively and steadily on a record-by-record premise or throughout sliding time windows and utilized for a wide assortment of examinations including relationships, totals, separating, and inspecting.&#x0D; Place and Duration of Study: Service usage (for metering and billing), server activity, website clicks, and the geo-location of devices, people, and physical goods are just a few of the many aspects of a company's business and customer activity that can be seen through this type of analysis. It also enables companies to respond quickly to new arising situations.&#x0D; Methodology: The research methodology is used for the current research work is the qualitative method through which the research studies of a similar domain are studied thoroughly. It has been analyzed that the specified changes in the large volumes of data can better be managed through stream data processing.&#x0D; Results: The flaws of batch data processing are better dealt with through the usage of streaming data processing agenda. Real-time monitoring as well as response functionality are the keys to success in the given method of data processing.&#x0D; Conclusion: Stream data processing connects analytics and applications. Because multiple systems can be constructed using the same architecture, this makes the construction of the infrastructure a similar architecture. It additionally allows designers to fabricate applications that utilize scientific outcomes to straightforwardly answer information experiences and make a move.
Full Communication Memory Networks for Team-Level Cooperation Learning

Abstract Communication in multi-agent systems is a key driver of team-level cooperation, for instance allowing individual agents to augment their knowledge about the world in partially-observable environments. In this paper, we propose two reinforcement learning-based multi-agent models, namely FCMNet and FCMTran. The two models both allow agents to simultaneously learn a differentiable communication mechanism that connects all agents as well as a common, cooperative policy conditioned upon received information. FCMNet utilizes multiple directional recurrent neural networks to sequentially transmit and encode the current observation-based messages sent by every other agent at each timestep. FCMTran further relies on the encoder of a modified transformer to simultaneously aggregate multiple self-generated messages sent by all agents at the previous timestep into a single message that is used in the current timestep. Results from evaluating our models on a challenging set of StarCraft II micromanagement tasks with shared rewards show that FCMNet and FCMTran both outperform recent communication-based methods and value decomposition methods in almost all tested StarCraft II micromanagement tasks. We further improve the performance of our models by combining them with value decomposition techniques; there, in particular, we show that FCMTran with value decomposition significantly pushes the state-of-the-art on one of the hardest benchmark tasks without any task-specific tuning. We also investigate the robustness of FCMNet under communication disturbances (i.e., binarized messages, random message loss, and random communication order) in an asymmetric collaborative pathfinding task with individual rewards, demonstrating FMCNet’s potential applicability in real-world robotic tasks.
Exploring GPT-3 Model's Capability in Passing the Sally-Anne Test A Preliminary Study in Two Languages

This study aimed to investigate the capability of GPT-3 in passing the Sally-Anne test in two languages: Chinese and English. Three experiments were conducted to evaluate the model's performance under different questioning prompts. The results showed that with appropriate prompts, the model was able to consistently pass the test. The findings highlight the sensitivity of GPT-3 to different prompts and demonstrate the potential for using large language models as research subjects. This study sheds light on the similarities between human and AI reasoning and could contribute to further advancements in human-like AI development. It also encourages psychologists to expand their research interests to include AI.
The Surprising Effectiveness of Deep Orthogonal Procrustes Alignment in Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a labeled source domain to an unlabeled target domain. Traditionally, geometry-based alignment methods, e.g., Orthogonal Procrustes Alignment (OPA), formed an important class of solutions to this problem. Despite their mathematical tractability, they rarely produce effective adaptation performance with the recent benchmarks. Instead, state-of-the-art approaches rely on sophisticated distribution alignment strategies such as adversarial training. In this paper, we show that, conventional OPA, when coupled with powerful deep feature extractors and a novel bi-level optimization formulation, is indeed an effective choice for handling challenging distribution shifts. When compared to existing UDA methods, our approach offers the following benefits: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">computational efficiency</i> : Through the isolation of alignment and classifier training steps during adaptation, and the use of deep OPA, our approach is computationally very effective (typically requiring only 700 K parameters more than the base feature extractor as compared to millions of extra parameters required by state-of-the-art UDA baselines); (ii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data efficiency</i> : Our approach does not require updating our feature extractor during adaptation and hence can be effective even with limited target data; (iii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">improved generalization</i> : The resulting models are intrinsically well-regularized and demonstrate effective generalization even in the challenging partial DA setting, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., target domain contains only a subset of the classes observed in the source domain.; and (iv) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incremental training</i> : Our approach allows progressive adaptation of models to novel domains (unseen during training) without requiring retraining of the model from scratch.