Reviewerly/exHarmony huggingface dataset:
Unusual Event Detection

Abstract: The protection of privacy against covert video recordings presents a considerable societal challenge. Our objective is to develop a computer vision system, such as a robotic device, that can identify human activities and improve daily life, all while ensuring that it does not capture video contentthat may violate individuals' privacy. In this paper, we propose a fundamental approach to reconcile these seemingly conflicting goals: the recognition of human activities using highly anonymized videodata with extremely low resolution (e.g., 16x12). We introduce a new concept called "inverse super resolution" (ISR), which entails learning the optimal set of image transformations to generate multiplelow-resolution (LR) training videos from a single highresolution source. Our ISR framework learns diverse sub-pixel transformations that are specifically optimized for activity classification, enabling the classifier to leverage high-resolution videos, such as those found on platforms like YouTube, by generating multiple LR training videos tailored to the specific activity recognition task. Through empirical experimentation, we demonstrate that the ISR paradigm significantly enhances activity recognition from extremely low-resolution video data.
Multimodal sentiment recognition: A comprehensive review of analysis techniques, applications, and challenges

This article offers a comprehensive review of multimodal emotion recognition. Initially, we provide a succinct overview of human emotions by delving into various emotion models. The focus then shifts to the representation of emotions through unimodal data sources, illustrating how these singular channels capture emotional cues. As we progress, the article underscores the advancements in multimodal fusion techniques that amalgamate multiple data sources for more accurate emotion detection. Three predominant architectures for multimodal fusion are then detailed, each showcasing its unique approach and benefits. Despite the leaps and bounds made in this field, several challenges persist. The latter section of this review highlights these lingering issues in multimodal emotion recognition, hinting at potential areas for further research and development. By weaving together the intricacies of emotion models, unimodal representations, fusion techniques, and existing challenges, this paper seeks to provide readers with a holistic understanding of the current landscape of multimodal emotion recognition.
Comparing the RSA and elgamal encryption methods

In the realm of computer science, the speed and efficiency of data processing vary depending on the chosen algorithms and programming languages. This article delves into an exploration of which combinations can yield the highest overall performance. Starting with an introduction to the selected algorithms and languages, the underlying mathematical foundations and principles of each algorithm are presented. The research then shifts its focus to the practical aspect translating these algorithms into executable code. Three specific combinations are tested: RSA algorithm implemented in Python, Elgamal algorithm implemented in Python, and RSA algorithm executed in C++. To ensure reliable results, each combination underwent 30 test iterations, with empirical data subsequently gathered. Two primary comparisons were conducted: the first pitting the Python-based RSA against the Python-based Elgamal, and the second contrasting the Python-based RSA with its C++ counterpart. From these tests, a compelling conclusion emerges. When it comes to transmitting encrypted messages and subsequently decrypting them, the RSA algorithm paired with Python emerges as the most optimal choice. Readers will gain a deeper understanding of the strengths and potential limitations of each pairing, equipping them to make more informed decisions in their cryptographic endeavors.
DEVELOPMENT OF COGNITIVE ACTIVITY OF PUPILS OF YOUNGER CLASSES IN THE EDUCATIONAL PROCESS ACTIVITIES

The article is devoted to the problem of the development of the cognitive activity of a junior high school student, which is becoming more and more relevant, since changes in life in the modern world require changes in the purpose and purpose of modern education. The article reveals the peculiarities of increasing the cognitive activity of younger schoolchildren. Attention is focused on the need for the harmonious development of the personality of a junior high school student, the implementation of an educational influence on him in order to reveal his inner potential, the study of needs and requests, and the creation of a comfortable educational environment on this basis. Approaches to the development of cognitive activity of schoolchildren are highlighted, since cognitive activity is the basis of almost any activity of a schoolchild and the determining basis of the organization of the educational process at school. The essence of the concepts "cognitive interests", "cognitive activity", "interactive methods", "interactive learning", "critical thinking" is revealed, the relationship between mental operations, education and practical influence on the personality in the process of forming the need to be active is demonstrated. The prerequisites for increasing students' cognitive interests are highlighted, the main requirements for the organization of a favorable educational, educational and developmental environment of an educational institution are revealed. The main aspects of using interactive methods in practice are demonstrated. Emphasis is placed on the main requirements for the teacher's practical activity, the main interactive techniques and methods that should be used to stimulate students' cognitive activity are revealed. The impact of various interactive forms and methods of working with primary school students is characterized. Scientific approaches to the given problem were analyzed, directions, forms and methods of practical implementation were established. It is outlined that cognitive activity is a primary condition for the formation of students' need for knowledge, mastering the skills of intellectual activity, independence, ensuring the depth and strength of knowledge.
Robust Frame Synchronization for Continuous-Variable Quantum Key Distribution with Quantum States

In a practical continuous variable quantum key distribution (CVQKD) system, frame synchronization is crucial for its operation, especially in conditions of low signal-to-noise ratio (SNR) and phase drift. This paper introduces a robust frame synchronization scheme for CVQKD systems that only utilizes quantum signals. The proposed scheme effectively employs randomly selected segments of quantum signals to achieve frame synchronization, eliminating the need for additional modulation. The performance of this scheme applied in a local local oscillator scenario is thoroughly analyzed through numerical simulations. The results demonstrate that the proposed scheme is capable of withstanding low SNR and arbitrary slow phase drift, as well as fast phase drift originates from two independent lasers, while also slightly improving the secret key rate compared to the scheme using inserted synchronization frames. These findings validate the feasibility of implementing the proposed scheme for long-distance CVQKD in practical scenarios.
Deep Neural Network Based Spam Email Classification Using Attention Mechanisms

Spam emails pose a threat to individuals. The proliferation of spam emails daily has rendered traditional machine learning and deep learning methods for screening them ineffective and inefficient. In our research, we employ deep neural networks like RNN, LSTM, and GRU, incorporating attention mechanisms such as Bahdanua, scaled dot product (SDP), and Luong scaled dot product self-attention for spam email filtering. We evaluate our approach on various datasets, including Trec spam, Enron spam emails, SMS spam collections, and the Ling spam dataset, which constitutes a substantial custom dataset. All these datasets are publicly available. For the Enron dataset, we attain an accuracy of 99.97% using LSTM with SDP self-attention. Our custom dataset exhibits the highest accuracy of 99.01% when employing GRU with SDP self-attention. The SMS spam collection dataset yields a peak accuracy of 99.61% with LSTM and SDP attention. Using the GRU (Gated Recurrent Unit) alongside Luong and SDP (Structured Self-Attention) attention mechanisms, the peak accuracy of 99.89% in the Ling spam dataset. For the Trec spam dataset, the most accurate results are achieved using Luong attention LSTM, with an accuracy rate of 99.01%. Our performance analyses consistently indicate that employing the scaled dot product attention mechanism in conjunction with gated recurrent neural networks (GRU) delivers the most effective results. In summary, our research underscores the efficacy of employing advanced deep learning techniques and attention mechanisms for spam email filtering, with remarkable accuracy across multiple datasets. This approach presents a promising solution to the ever-growing problem of spam emails.
Finding Common Ground in the Field to Inform Science Policy

The Kansas Geological Survey’s annual Field Conference takes scientists, producers, legislators, and public officials around the state to spark conversation about natural resources.
Benchmarking NAS for Article Separation in Historical Newspapers

The digitization of historical newspapers is a crucial task for preserving cultural heritage and making it accessible for various natural language processing and information retrieval tasks. One of the key challenges in digitizing old newspapers is article separation, which consists of identifying and extracting individual articles from scanned newspaper images and retrieving the semantic structure. It is a critical step in making historical newspapers machine-readable and searchable, enabling tasks such as information extraction, document summarization, and text mining. In this work, we assess NewsEye Article Separation (NAS), a multilingual dataset for article separation in historical newspapers. It consists of scanned newspaper pages from the $$19^{th}$$ and $$20^{th}$$ centuries and annotation files in German, Finnish, and French. Moreover, the dataset is challenging due to the varying layouts and font styles, which makes it difficult for models to generalize to unseen data. Also, we introduce new metrics of article error rate, article coverage score, proper predicted article, and segmentation to evaluate the performance of the models trained on the NAS to highlight the relevance and challenges of this dataset. We believe that NAS, which is publicly available, will be a valuable resource for researchers working on historical newspaper digitization.
STRAS: A Semantic Textual-Cues Leveraged Rule-Based Approach for Article Separation in Historical Newspapers

The digitization of historical documents is a critical task for preserving cultural heritage and making vast amounts of information accessible to the wider public. One of the challenges in this process is separating individual articles from old newspaper images, which is significant for text analysis and information retrieval. In this work, we present a novel approach, Semantic Textual-cues leveraged Rule-based approach for Article Separation (STRAS) in historical newspapers. The presented approach, STRAS, involves utilizing textual information by extracting text region embeddings using scanned input images and their corresponding PAGE format files. The text regions with similar contextual embeddings are then categorized and articles are separated based on a defined rule set. The presented approach is tested on French and Finnish newspapers of the $$19^{th}$$ and early $$20^{th}$$ centuries. Besides this, novel metrics are introduced specifically for the article separation task: article error rate (AER), article coverage score (ACS), and proper predicted article (PPA). Our study evaluates the performance of various models, including skip-gram (sgSTRAS), continuous-bag-of-words (cbowSTRAS), FastText (ftSTRAS), and pre-trained SpaCy model (preSTRAS), and the results show that the sgSTRAS model achieves the highest mean ACS scores of 0.8343 and 0.8611 on the French and Finnish datasets, respectively, outperforming all other models. Our findings demonstrate that the semantic textual features contain valuable information, and the selection of an appropriate embedding method significantly influences the overall performance of the proposed approach to segment articles. To the best of our knowledge, this is the first study that applies a semantic textual similarity rule-based approach for article separation in historical newspapers, filling a gap in the existing literature and opening up new avenues for further research in this area.
Text Line Detection in Historical Index Tables: Evaluations on a New French PArish REcord Survey Dataset (PARES)

In this paper, we address the challenge of document image analysis for historical index table documents with handwritten records. Demographic studies can gain insight from the use of automatic document analysis in such documents through the study of population movements. To evaluate the efficacy of automatic layout analysis tools, we release the PARES dataset [6], which contains 250 labeled index table images originating from French archives. Also, we run state-of-the-art algorithms (U-FCN, R-CNN and Transformers) in order to detect the lines within index tables, a common prerequisite for handwritten text recognition (HTR). Our results indicate that text line extraction works well with the U-FCN model, while also indicating that Transformer architectures show promise for accurate text line detection in such historical documents with great efficiency. This is a encouraging step towards a Transformer-based architecture for both layout and content detection. This process and dataset represent a first step to automatically analyze handwritten and historical index tables. In addition to this paper and the PARES [6] dataset of historical index tables of 250 images, we release segmentation masks, the code we used to train and test the models, and the models themselves.
Pemanfaatan Cloud Computing dalam Revolusi 4.0

Technology has changed the way people communicate, interact and act in various tasks and activities. One of them is how people store digital data in a storage medium. Conventional storage media such as hard drives, flash disks, CD/DVDs have begun to be abandoned. One of the reasons it has started to be abandoned is that important data is often lost along with the loss of the media. Technology, especially the internet, has become one of the solutions in the world of media storage, namely the presence of cloud technology through strong cloud. In addition to being the answer, the internet is also a means of communication through weblogs or often abbreviated as blogs. Weblogs can be an effective medium of information about an organization's activities. These two things are behind the plan for community service activities at SMKN 2 Bengkulu Tengah. For teachers, strong cloud technology has been introduced through lectures and direct training, while for students they have been trained how to design a weblog according to the needs of the school, especially the students. Teachers can take advantage of the many advantages of cloud storage for various purposes. Students of SMKN 2 Bengkulu Tengah can have a means of communication and a creative arena with webloging activities.
Politeness Strategies in Performing Commissive Illocutionary Acts in Joseph R. Biden's Inauguration Speech

This research aims to explore the use of politeness strategies in the performance of commissive illocutionary acts in Joseph R. Biden’s inauguration speech. The politeness strategies were classified based on the theory of politeness strategies by Brown and Levinson (1987). The commissive illocutionary acts performed are promise, guarantee, and refusal. The result showed that there are 39 performances of commissive illocutionary acts that are found in the speech. The classification and analysis showed that out of 39 utterances, there are 17 occurrences of bald on record, 10 occurrences of positive politeness, and 12 occurrences of off-record. The result shows that, in delivering his inauguration speech, Biden tends to use the most straightforward way of speaking to issue an act.
Teknik ALBA untuk Meningkatkan Kemampuan Membaca dalam Pembelajaran Daring

This study aimed to determine the effect of using ALBA techniques online learning on students' reading skills. This research method used was quasi-experimental with treatment for the control and experimental classes. Data analysis descriptive and inferential statistics. In descriptive statistics to see the average test, the highest, and lowest scores. Inferential statistics are performed to test for normality, homogeneity, and average difference. The increase in the score was analyzed using a score gain. The results of the data analysis included (1) the increase in the score for the control class of 0.1 is included in the high classification, (2) the experimental class is 0.14 high, and (3) the increase in the post-test for the control and experimental class is 0.12 which is in the high classification. Based on the results of data analysis, it can be concluded that there are differences in the results of the ability test in the control class, experimental class in posttest in the control and experimental class. Learning to read the beginning using the ALBA technique in online learning has a good effect according to the results of interviews. This technique makes it easier for students to accelerate reading primary school students.
The Influence of Consumption of Carbohydrate to Adequacy Nutrition for Student of Elementary School Class 6 Tunas Karya Desa Karyawangi

Study this aim for knowing the influence of carbohydrate consumption to adequate nutrition of students grade 6 Tunas Karya Elementary School students Desa Karyawangi. Research conducted to 12 students, consisting of 7 boys and 5 girls with a range ages 10-12 years. As for the stages of research carried out, is (i) deployment pre-test questionnaire, (ii) giving education about the importance of carbohydrates for the body via powerpoint, and (iii) deployment post-test questionnaire. Analysis results show the mean pretest score was 66.7 and the post-test mean was 84.6. Comparison results from results between pre-test and post-test showed existence enhancement knowledge for students after holding education. Result of N-Gain calculation is known to have an average N-Gain value of 0.53 which states education about importance carbohydrate for body this not enough effective for student school base. Remember activity education is conducted only at meetings only, implications obtained are not effective and still many students who haven't once got education or eye lessons like this. from what we hope with existence study this education about importance carbohydrate or nutrition balanced more echoed return especially in children student school the basis for them understands importance consume carbohydrate for sufficient needs nutrition.
Explainable Mobile Traffic Classification: the Case of Incremental Learning

The surge in mobile network usage has contributed to the adoption of Deep Learning (DL) techniques for Traffic Classification (TC) to ensure efficient network management. However, DL-based classifiers still face challenges due to the frequent release of new apps (making them outdated) and the lack of interpretability (limiting their adoption). In this regard, Class Incremental Learning and eXplainable Artificial Intelligence have emerged as fundamental methodological tools. This work aims at reducing the gap between the DL models' performance and their interpretability in the TC domain. In this study, we examine from different perspectives the differences between classifiers when trained from scratch and incrementally. Using Deep SHAP, we derive global explanations to emphasize disparities in input importance. We comprehensively analyze base classifiers' behavior to understand the starting point of the incremental process and examine updated models to uncover architectures' features resulting from the incremental training.
Parallel Quantum Annealing: A Novel Approach to Solving Multiple NP-Hard Problems Concurrently

This study introduces an innovative application of Parallel Quantum Annealing (PQA) to concurrently solve multiple NP-hard problems on a quantum annealer, leveraging unused qubits on D-Wave's Pegasus architecture to enhance efficiency and accuracy. While parallel execution of different problems on the same quantum annealing device has been previously explored, our application extends the current PQA method, which typically solves a single problem multiple times or different problems with minor embedded combinatorial optimization. This study primarily focuses on three NP-hard problems relevant to integrated circuit design: the Graph Coloring Problem (GCP), the Minimum Vertex Cover Problem (MVCP), and the Graph Partitioning Problem (GPP). After formulating these problems as Quadratic Unconstrained Binary Optimization (QUBO), they were embedded into the D-Wave quantum annealing machine, and the annealing process was run on the combined QUBO. The results were subsequently decoded into individual problems. Our results, quantified via the Time-To-Target (TTT) metric, showcased enhanced efficiency and accuracy in reaching target solutions, indicating a substantial improvement over traditional Quantum Annealing (QA) and Simulated Annealing (SA) methods. The research illustrates the potential of quantum computing for solving complex real-world problems more efficiently and emphasizes the importance of leveraging all available qubits in quantum annealers. The findings can significantly contribute to the development and optimization of quantum computing methods and have broad application implications, particularly in areas where NP-hard problems are prevalent.
Predicting Expressibility of Parameterized Quantum Circuits Using Graph Neural Network

Parameterized Quantum Circuits (PQCs) are essential to quantum machine learning and optimization algorithms. The expressibility of PQCs, which measures their ability to represent a wide range of quantum states, is a critical factor influencing their efficacy in solving quantum problems. However, the existing technique for computing expressibility relies on statistically estimating it through classical simulations, which requires many samples. In this work, we propose a novel method based on Graph Neural Networks (GNNs) for predicting the expressibility of PQCs. By leveraging the graph-based representation of PQCs, our GNN-based model captures intricate relationships between circuit parameters and their resulting expressibility. We train the GNN model on a comprehensive dataset of PQCs annotated with their expressibility values. Experimental evaluation on a four thousand random PQC dataset and IBM Qiskit's hardware efficient ansatz sets demonstrates the superior performance of our approach, achieving a root mean square error (RMSE) of 0.03 and 0.06, respectively.
Adapting the DisCoCat-Model for Question Answering in the Chinese Language

We introduce quantum natural language processing for the Chinese language. Our approach focuses on Question-answering on a set of sentences, whether a particular sentence is truthful with respect to the whole corpus of sentences. We employ the Categorical Distributional Compositional (DisCoCat) model to translate sentences to valid quantum circuits. We achieve a fitting score of 97% on the test set. Our sentence set is also significantly larger than previous experiments. The results show general applicability of the framework to other languages. We also show that it can be used to introspect natural language models and provide new approaches to model explainability.