Reviewerly/exHarmony huggingface dataset:
An Adaptive Steganography Approach for Live Video Streams Based on Edge Analysis and Frame Variability

With the rapid evolution of digital video communication, the field of steganography has gained more attention. Video steganography has vital advantage over other steganographic techniques due to its utilization of video as the cover medium for data hiding. In this paper the advantages of using live video as a cover medium for steganography is explored. This paper presents an adaptive steganography which uses live video as cover medium. The proposed method uses content analysis and frame heterogeneity to dynamically select several steganographic techniques. Live frames are processed by initially applying Canny edge detection, yielding an edge map. Edge density is then calculated, enabling the identification of smooth frames. Depending on the calculated edge density, the algorithm chooses between Canny and Sobel edge detection techniques to adapt to diverse content complexities. Additionally, each frame's standard deviation is calculated to determine variation in content of the frame. If the computed standard deviation surpasses a predetermined threshold, LSB is selected as embedding technique. Otherwise, the Discrete Cosine Transform (DCT) is favored for its computational efficiency. The findings suggest that our methodology is apt for real-time video applications, as indicated by the time taken parameter. Additionally, the proposed method's superiority is substantiated by the MSE and PSNR metrics.
Anemia Identification from Blood Smear Images Using Deep Learning: An XAI Approach

Anemia is a prevalent hematologic disorder characterized by a reduction in the number of red blood cells or a decrease in their oxygen-carrying capacity. Accurate and timely diagnosis of anemia is crucial for effective patient management and improved health outcomes. In this proposed work, we developed a CNN-based model to classify individuals as having symptoms of Iron-deficiency Anemia (IDA) or not, using blood smear images. The CNN model will be trained and fine-tuned using this dataset to maximize the classification accuracy. This helps healthcare professionals to understand the system's decisions and assist in the diagnosis and management of anemia. Despite a relatively limited number of data samples, our proposed work showed promising results by leveraging augmentation techniques. We achieved an accuracy of 0.95, with precision and recall values of 0.9 and 0.89, respectively. The area under the Receiver Operating Characteristic (ROC) curve is reported as 0.98.
Design of Hybrid Model Based on Chaotic Map to Secure the Images

Secure multimedia transmission is one of the challenging conditions in the age of communication technology. To achieve a secure multimedia transaction encryption can be used which preserves the confidentiality, integrity, and authenticity of multimedia content (such as images, videos, and audio) during digital transactions. Encryption techniques that transform original image into an unintelligible or scrambled form, known as a cipher image, are one way to achieve secure image transmission over the network. This way, even if the attacker gains access to the cipher, they would not be able to retrieve the real image. Chaos-based encryption is a cryptographic technique that uses chaotic systems inherent unpredictability and sensitivity to beginning conditions to generate encryption algorithms. This study enables the implementation and analysis of chaos-based picture encryption algorithms for image encryption. A chaotic system, such as the logistic map or the Lorenz system, is used to generate chaotic value sequences. These systems are extremely sensitive to beginning conditions, making it difficult to predict their future states. As a native chaotic and hybrid chaotic techniques, Arnold cat map, Henon map and Tent map are utilised in security models this offers an option for improving the protection of multimedia information in an increasingly linked environment. The experimental results shows that the proposed system work satisfactorily.
GNN Based Trainable Dependency Parser

Semantic relations between words in a sentence are identified using a technique called dependency parsing. Algorithms called dependency parsers are used to map the words in a sentence to the said semantic roles and to identify the syntactic relations between words. Transition-based dependencies on Indian languages have been relatively less explored, partially attributed to lack of high quality treebanks. Graph Neural Network based parsing techniques have shown relatively high accuracy levels on English language and languages with similar syntactic structures (eg. Spanish), and have potential to show good accuracy with other language syntaxes. Hindi is a language with an extremely rich morphology and a FreeWord order. Such a language, also referred to as a Morphologically Rich, FreeWord Order (MoR-FWO) language, is immensely difficult to parse using traditional methods. Using Graph Neural Networks, we present a state-of-the-art dependency parser for Hindi. We compare performance and efficiency of two approaches in this project- a Machine Learning model and a Neural Network model.
ClothFormer - A Boundary Aware Self-Attention Network for Human Outfit Parsing

Advanced deep learning-based networks have redefined state-of-the-art performance on parsing tasks such as human parsing, face parsing etc., owing to their potential to extract deep features from significant datasets. One such task in the succession of parsing tasks is human outfit or cloth parsing. The semantic disparity between identical features led to inefficient parsing even after adopting heavy and dense architectures involving deep convolutional neural networks (DCNN), notably at border levels. Feature extraction with variations in pose and illumination had also become some of the challenges of cloth parsing. To address these problems, ClothFormer - An Boundary Aware Self-Attention Network for Human Outfit Parsing - is introduced which explores the use of multi-scale techniques to enhance the per-pixel frames' reliability and accuracy. The self-attention module in ClothFormer is incorporated with a lightweight backbone to achieve striking metrics on UTFPR-SBD3 and CFPD datasets.
A Framework for Encryption and Decryption Using Image Steganography

This research introduces a comprehensive framework that integrates encryption and steganography techniques for secure communication using digital photographs. Encryption obscures the content, while steganography hides encrypted data within selected image pixels, imperceptible to human observers. A pseudorandom number generator aids secure pixel selection. Retrieving the concealed message involves steganography's data extraction followed by decryption using the appropriate key. This approach safeguards sensitive data in various applications, ensuring the confidentiality and authenticity of digital communication.
An Interactive Framework for Querying Data from Large Pdf Files

With the evolution of huge language models, it is now possible to converse with robots on any topic. However, unstructured data, such as PDF files, must still be converted into a format that these models can easily analyze. Through this study, we present a framework for creating conversational PDF applications that are powered by language models, specifically OpenAI's GPT-3 architecture. Our framework, enables users to connect their language model APIs to other data sources, such as PDF files, and allows the language model to interact with the data source. We show our framework's usefulness by breaking a big PDF document into smaller chunks, transforming each chunk into embeddings, and constructing a knowledge base that users may query. The results obtained suggest that employing embeddings rather than comparing text directly can greatly reduce data size while improving search result accuracy.
The new editorial team at CYIL: continuity and change

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Social Media User Opinion Analysis Using Deep Learning and Machine Learning Methods: A Case Study on Airlines

ABsTRACT. The rapid surge in social media usage has augmented the significance and value of data available on these platforms. As a result, analyzing community sentiment and opinions related to various topics and events using social media data has become increasingly crucial. However, the sheer volume of data produced on social media platforms surpasses human processing capabilities. Consequently, artificial intelligence-based models became frequently employed in social media analysis. In this study, deep learning (DL) and machine learning (ML) methods are applied to assess user opinions regarding airlines, and the effectiveness of these methods in social media analysis is comparatively discussed based on the performance results obtained. Due to the imbalanced nature of the dataset, synthetic data is produced using the Synthetic Minority Over-Sampling Technique (SMOTE) to enhance model performance. Before the SMOTE process, the dataset containing 14640 data points expanded to 27534 data points after the SMOTE process. The experimental results demonstrate that Support Vector Machines (SVM) achieved the highest performance among all methods with accuracy, precision, recall, and F-score values of 0.79 in the pre-SMOTE (imbalanced dataset). In contrast, Random Forest (RF) obtained the best performance among all methods, with accuracy, precision, recall, and F-score values of 0.88 in the post-SMOTE (balanced data set). Moreover, experimental findings demonstrate that SMOTE led to performance improvements in ML and DL models, ranging from a minimum of 3% to a maximum of 24% increase in F-Score metric.
Comparison of Leading Language Parsers – ANTLR, JavaCC, SableCC, Tree-sitter, Yacc, Bison

Software engineering applications in domains like embedded systems and health care have increased exponentially during the last few years. Developing, analyzing, and customization of languages is one of the core software engineering aspects. This usually involves lexical, syntactical, and semantic operations, technically termed parsing. For this, several parsers have been introduced in state-of-the-art. However, due to diverse features, selecting a parser for a particular operation during software engineering applications is always problematic. In this article, we identified six leading parsers (i.e., ANTLR, JavaCC, SableCC, Tree-sitter, Yacc, and Bison) from the state-of-the-art. Furthermore, we also identified significant parser features to perform meaningful comparative analysis. Results indicate that ANTLR and JavaCC provide enhanced parsing features, such as the parsing algorithm and the extended grammar notation. However, JavaCC is suitable for simple grammar definition, whereas ANTLR allows specifying complex grammar with multiple alternative paths. The findings of this article are highly beneficial for researchers and practitioners while selecting the right parser to perform specific software engineering tasks.
A Performance Evaluation on the Blockchain-based Traceability Application of Pharmaceutical Supply Chain: A Case Study

Blockchain is a disruptive technology known for its security features and continuous development. Despite blockchain’s notable features, such as immutability, it is still a compelling solution for various industry use cases. Albeit technical hitches such as network overload are still of concern to use this technology in an enterprise context. Some factors to consider include high transaction throughput and low latency. This study focuses on developing a blockchain-based web application that focuses on a specific industry use case, in this case, the distribution and recall transactions of the pharmaceutical supply chain. Since the pharmaceutical supply chain suffers from frequent visibility problems, blockchain is a suitable solution for this use case. After developing the application, performance testing was done to determine the system’s throughput, latency, and scalability performance. Three use cases were tested explicitly by different transaction rates (tps), the total number of transactions, and the number of simultaneous transactions in the simulated test environment. For every performance test, two transaction types were done: the invoke and query transactions. The study showed that a blockchain system’s hardware configuration heavily impacted the system’s overall performance through the blockchain’s latency, throughput, and scalability. These factors depend on the configuration of the system, the network design of the blockchain, and the complexity of the operations of the smart contract.
Learning Concept Credible Models for Mitigating Shortcuts.

During training, models can exploit spurious correlations as shortcuts, resulting in poor generalization performance when shortcuts do not persist. In this work, assuming access to a representation based on domain knowledge (i.e., known concepts) that is invariant to shortcuts, we aim to learn robust and accurate models from biased training data. In contrast to previous work, we do not rely solely on known concepts, but allow the model to also learn unknown concepts. We propose two approaches for mitigating shortcuts that incorporate domain knowledge, while accounting for potentially important yet unknown concepts. The first approach is two-staged. After fitting a model using known concepts, it accounts for the residual using unknown concepts. While flexible, we show that this approach is vulnerable when shortcuts are correlated with the unknown concepts. This limitation is addressed by our second approach that extends a recently proposed regularization penalty. Applied to two real-world datasets, we demonstrate that both approaches can successfully mitigate shortcut learning.
Synergizing Sustainability in the Age of AI and Blockchain Technology

In the nexus of innovation and responsibility, the technological realm is witnessing a dynamic intersection between the United Nations’ Sustainable Development Goals (SDGs) and frontier technologies, i.e. Artificial Intelligence (AI) and Blockchain. This technical keynote seeks to illuminate the architectures and algorithms powering these technologies, contextualized within the framework such as UI GreenMetric smart building standard developed at Universitas Indonesia. We will discuss computational capabilities of AI and the cryptographic robustness of Blockchain, revealing their potential in shaping sustainable solutions. We will introduce cutting-edge research, underscoring the methodologies in which AI and Blockchain are advancing sustainable initiatives. Furthermore, a foresight into emerging trends will be presented to oversee the next wave of smart technologies geared towards ecological and socio-economic sustainability.
Offline-Online Actor-Critic for Partially Observable Markov Decision Process

Offline-online reinforcement learning combines the advantages of offline learning in terms of sample efficiency and online learning in terms of exploration and trial-and-error. On the other hand, the partial observability of the environment is a challenging issue in reinforcement learning. This paper proposes a Partially Observable Offline-Online Actor-Critic (PO3AC) algorithm. In PO3AC, during both the offline and online training phases, different weighted behavior cloning regularization terms are considered to alleviate the distribution shift. During training, the actor and critic are trained separately, and a recurrent neural network is used to train a state prediction model that predicts the mapping of observation to belief states. Experimental results conducted on the PyBullet physics engine demonstrate that the proposed algorithm outperforms existing algorithms.
Theoretical and methodological basis of studying the topic «word problems of practical content» in the school course of mathematics

In modern schools, we often encounter the problem that students lose interest in learning. Tasks of practical content are designed to increase interest in school mathematics education.&#x0D; The orientation of the research of problems of practical content involves the orientation of methods in the process of solving problems and the formation of strong skills of independent activity in schoolchildren and the education of persistent interest in the subject. Also, practical tasks help to highlight interdisciplinary connections, make you delve deeper into theoretical aspects, and form a complete picture. In the process of solving such problems, students develop cognitive, informational and social competence. Learning becomes useful and, at the same time, exciting and interesting for students.&#x0D; Word problems in the school mathematics course play a very important role and are divided by functions, by the requirements of the task, by types. The article is devoted to the analysis of the theoretical and methodological foundations of problems of practical content and their solution.&#x0D; The purpose of the work is the classification of text problems according to various parameters based on the analysis of scientific and methodological literature on this topic. Word problems of such types as movement problems, work problems, buying and selling problems, interest problems, mixtures and alloys are selected for consideration. The main results of the publication include recommendations for creating a mathematical model of the problem condition using tables, depending on its type.&#x0D; Problems of various types are presented, which are solved by constructing a corresponding mathematical model of the problem condition. This approach to studying the topic "Word problems" is used in my own experience of teaching mathematics
Upaya Meningkatkan Kemampuan Kognitif Dalam Kegiatan Mengenal Lambang Bilangan Melalui Penerapan Metode Demonstrasi Berbantuan Media Gambar

This background of this research is the low ability of children to recognize number symbols, one of the alternatives offered by researchers is the introduction of number symbols by applying the demonstration method. The research method used was classroom action research conducted in 2 cycles. Data collection methods used are observation, assignment and documentation. The results showed that the children's cognitive abilities in recognizing number symbols which developed very well in each indicator showed a good increase after the action was carried out in cycle II, where children could recognize number symbols 1-10 with a percentage of 71.4%, children could show number symbols 1-10 in sequence with a percentage of 62.0%, children paired symbol numbers 1-20 with picture media with a percentage of 76.1%, and children could name symbols of numbers 1-10 in sequence with a percentage of 76.1%.&#x0D; Keywords: Cognitive Abilities, Recognize Number Symbols, Demonstration Learning Methods
Cohesive Ties Found in Writing English Text at SMA Dharma Wanita 2023/2024 Medan

Cohesive is a concept applied to text linguistics and discourse analysis to determine if the written text's properties. Cohesive ties are divided into five categories: reference, substitution, ellipsis, conjunction and lexical ties. This research was conducted to look at the types of lexical cohesive ties in student description writing and the dominant cohesive ties found in the writing of SMA Dharma Wanita students. This research is limited by two types of cohesive ties, namely reference and conjunction. This study used the qualitative data techniques. Based on the results of student writing through descriptive text, there were 114 cohesive ties used by students with 32 cohesive tie reference types and 82 conjunctions.
Tinjauan Awal Naskah Sujarah Penatah (Sejarah Penatah Wayang): Naratif, Kreativitas, dan Legitimasi

The names of wayang puppet makers or penatah (in Javanese) are well known among puppetry aficionados in Indonesia, but little tends to be known about their biographies. It is as if their personalities are overwhelmed by the greatness of their art work. In Yogyakarta’s wayang tradition, a number of names such Resapenatas, Maraguna, Kertiwanda, Prawirasucitra, Prayitnawiguna and Bundhu are very well known. All are considered great wayang makers or artists, with distinct aesthetic styles, but almost all details of their lives are unknown. The only substantial written text to concern penatah is a manuscript titled Sujarah Panatah (A History of Wayang Carvers)written in the early 20th century in Yogyakarta. Tales about carverstake the form of wayang plays. In one play, Ki Mangunwiguna teaches his son how to make a wayang based on prior exemplars. The author of the text tries to connect factual stories and myths about the gods. As in babad stories about kings of yore, wayang makers are presented as if they are also not just ordinary people. This is a means to establish their legitimacy. They are chosen, because they are able to communicate with gods. Alongside the text are wayang-style illustrations, supplementing the creativity of the narrative. This paper will consider the factual basis and validity of stories collected in the Sujarah Penatah, the way the text understands the origins of creativity and its mode for legitimising wayang makers and the continuity of their craft.
Peningkatan Hasil Belajar IPS Dengan Menerapkan Model Picture And Picture Siswa Sekolah Dasar

The aim of this researchis to describe the application of picture and picture model to improvethe learning outcomes of the knowing currency history at class III SDN 009 Ganting Salo district. The formulation of the problem in this research is how the application of picture and picture model to improve learning outcomes on the material of knowing currency history at class III SDN 009 Ganting Salo district. This research was conducted in class III SDN 009 Ganting Salo district. This research was conducted in two cycles and each cycle was held twice. The subject of this research is the students of class III SDN 009 Ganting Salo district which amounted to 18 people, with the number of male students 9 people, and female students 9 people. The techniques of collection Data used in this research are of observation, documentation, and evaluation. Based on the research results can be concluded that in the first cycle is still quite good with an average of 65,28. Then from 18 students only 11 students or 61,11% who achieve mastery individually. Students' learning outcomes in cycle II is quite good with an average of 71,94, and 15 students complete or 83,33% who achieve mastery individually. Thus it can be concluded that with the implementation of picture and picture model can be improve the learning outcomes of the knowing currency history at class III SDN 009 Ganting Salo Kampar district.&#x0D;
Pengaruh metode Inkuiri Terhadap Hasil Belajar Siswa (Studi Kuasi Eksperimen Pada Mata Pelajaran Matematika Kelas IV di Sekolah Dasar Negeri Indrapuri Kecamatan Tapung Kabupaten Kampar)

The purpose of the research was to determine and analyze the effect of the inquiry method on student learning outcomes in grade IV mathematics at Indrapuri Public Elementary School, Tapung District, Kampar Regency. The research was d0ne on 64 students as research objects which were divided into an experimental class and a control class. The research method uses a quasi-experimental method with a data collection tool in the form of an objective test. The data obtained were analyzed using a paired sample t-test. The results of the study concluded that the application of the inquiry method had an effect on student learning outcomes in math class IV at Indrapuri State Elementary School (SDN), Tapung District, Kampar Regency. Thus it can be suggested that the inquiry method can be applied as an alternative learning method in elementary schools.&#x0D;