Reviewerly/exHarmony huggingface dataset:
Analisis Perbandingan Optical Character Recognition Google Vision dengan Microsoft Computer Vision pada Pembacaan KTP-el

In this era, the need of digital data is rapidly increasing. Electronic Residental Identity Card or KTP-el is the official identity card for resident of Indonesia. One fast way to extract information on an image is by using OCR/Optical Character Recognition. Competition between Google Vision API and Microsoft Computer Vision in providing OCR service encourage companies to choose the right provider. Method conducted in this research including literature review on both OCR service provider, identification and KTP-el sample image retrieval, data grouping, code implementation and accuracy testing, result analysis and discussion, and conclusion. The result of this research show that Microsoft Computer Vision have better accuracy in reading characters in KTP-el with an accuracy percentage of 0,81% to 15,8% difference to Google Vision. Google Vision has competitive accuracy, but suffers from deficiencies when reading KTP-el with blur and noise.
ASSESSMENT OF ROLL AND PITCH CONTROL OF UTM JAGADRONE

This paper aims to quantify the flying qualities of UTM JagaDrone using autonomous and semi-autonomous PID tuning. Drone technology has grown in popularity worldwide due to its applications, notably in security and surveillance. The JagaDrone is completely autonomous aerial surveillance, and this first aerial responder is equipped with an emergency stream video while flying to avoid obstacles and track a target. We conducted the tuning process using an open-source ArduPilot Mission Planner, and the tuned PID value was tested by carrying out numerous flight tests. In general, the flight testing of JagaDrone has unique aspects caused by the absence of a pilot onboard and the reduced reliability of the system. The flight test aims to ensure the JagaDrone is manufactured properly and in a condition for safe operations. This paper presents an experimental validation of multicopters to determine the desired tune method for the PID controller applied for multicopters. From the tuning, it was found that the significant overshoot and the settling time were reduced by 50%, which shows an improvement in the stability of a multicopter. Thus, the proposed method is valid and can be used as a reference for multicopter tuning. The result proves that the method used in this experiment produces a stable tune for a multicopter. The method proposed in this paper can be used to carry out both autonomous and semi-autonomous PID tuning.
Strike of Middle School Students Related to the June 10th Independence Movement Period

Immediately after the death of Emperor Yunghui on April 25, 1926, elementary school students first went on nationwide strike. After the death of Emperor Yunghui, schools, under the direction of the Japanese Government-General of Chosun, mainly led by Japanese teachers, held or suppressed students’ funeral and memorial ceremonies, and the students rebelled against the school and went on strike. After the June 10th Independence Movement took place, a series of students’ strike occurred in middle schools in various regions from mid-June 1926. When middle school students sympathized with the June 10th Independence Movement and went on strike, the school strengthened restrictions and punishments, and the students showed steps to advance the strike by ostracizing teachers. The death of Emperor Yunghui and the June 10th Independence Movement made elementary and middle school students realize that the national issue was not simply an issue within the school, but an issue related to Chosun independence.
A Study on the Continuous Textuality between Wangshu’s Tengtou Pavilion and a Chinese traditional garden, Liuyuan

This study analyzes the ‘continuous textuality’ between Wangshu’s Tengtou Pavilion and Liuyuan, a Chinese traditional garden. 7 principles of textuality were applied to analyze the Liuyuan-Tengtou text. For the first internal factors analysis such as ‘cohesion’ and ‘coherence’, 8 strategies of the Chinese traditional garden were used as a framework, and we found that the Liuyuan-Tengtou showed the continuous textuality with strong coherence and rather loose cohesion. In the following analyses of ‘situationality’ and ‘intertextuality’, it was explored that both Liuyuan and Tengtou are strongly influenced by Taoism and Shan-shui. In the analyses of ‘informativity’, ‘intentionality’, and ‘acceptability’, it was found that the Liuyuan and Tengtou used strategies such as information control, appropriateness, and conciseness in a similar manner for the recipients’ proper understanding of the producer’s intention. Although it cannot be an absolute judgment, it was an attempt to use the discussion of textual linguistics, which studies the human dialogue process, to create an integrated analysis that encompasses the internal and external, architectural, social, and user perspectives of the work.
Exploring Micro Aerial Vehicle Mechanism and Controller Design Using Webots Simulation

Quadrotor Micro Aerial Vehicle (MAV) is one of the Unmanned Aerial Vehicle (UAV) which very versatile with their capabilities for specific purposes. Understanding and controlling MAV is important for various applications, from surveillance and delivery services to disaster response and entertainment. In engineering education, providing a hardware platform for understanding MAV mechanism and the controller design is very challenging in terms of cost and safety issue. This paper presents a study on drone mechanisms and controller design using Webots, a versatile robot simulation environment. The primary objectives of this research were to simulate and analyze the behavior of drones in a line following task, employing PID controller as the control strategies, and to evaluate the suitability of Webots as a simulation tool for drone-related teaching and learning as well as research. In the methodology section, the setup of the Webots-based drone simulation, including the selection of a suitable drone model, sensor configurations, and the implementation of control algorithms will be explained. Line following task will be focused to analyze the drone mechanism and the controller design. Data on the drone performance will be collected through the experiment and will be analyzed rigorously. The findings reveal the effectiveness of Webots as a platform for simulating drone behavior. The advantages of using simulation tools like Webots, including cost-effectiveness, safety, and the ability to test and refine control algorithms before deployment on physical drones was proved in this study. It is shown that Webots can be leveraged to teach and train individuals in drone technology without the need for physical hardware.
The New Age of Artificial Intelligence Regards Bharat and World Affairs

There is a flood of modern machines in the world, whose form has been taken by AI, it is entering the life of every person, whether it is your TV or fridge, it is visible in every field, from automobile to mobiles phone. Everyone has become accustomed to AI. The special thing is that in this era of modernity, the help of AI is being taken to increase the yield of agriculture. Farmers are able to spray their crops through drones. So in the same world, AI is also being used in large quantities in the fields of war like drone missile, guided missile, satellite missile etc. This AI is moving towards the new future of the world by which either peace will be established in the future or a devastating war will be fought.&#x0D; Although AI is likely to be operated by humans only because if this does not happen then the future of the world will be in the hands of AI.&#x0D; But given the various AI stages, this can only be imagined The Russia-Ukraine war is now at a more disastrous stage, in such a situation, what role AI can play in the search for peace remains to be seen. Due to differences with you and hunger for expansion, today the world is once again at a disastrous stage, among them China - Taiwan, Isreal-Iran etc. countries are standing on the front-line regarding the war against each other. Which cannot be seen normally. The related research paper attracts more attention to this and also questions the utility of AI. Although it will prove useful in some areas, can Ai be used for peace instead of war?&#x0D; it remains to be known
The Application of Mind Mapping in the Concept Teaching of Junior Middle School

Mind mapping; Concept teaching; Junior high school acoustics
Practice Spotlight: All We Need Is … The Endosymbiotic Love Calendar

This spotlight reflects on Endosymbiotic Love Calendar 2021, an art-meets-science project supported by Arts Council England and the Microbiological Society and completed in 2020. The project combined scientific experimentation, art-making, post-human ecology, and queer approaches through the format of a familiar domestic object: a wall calendar for 2021. The project reframed symbiotic microorganisms as radically queer beings. They literally brought this queer-science-art discourse into people’s kitchens in the form of a calendar. The objective was to explore identity, performativity, and social justice through a queer ecology that reimagined aesthetically these symbiotic relationships.
An Empirical Analysis of Text Segmentation for BERT Classification in Extended Documents

In the domain of natural language processing and text analysis, the Bidirectional Encoder Representations from Transformers (BERT) has emerged as a powerful tool for discerning the intricate nuances of textual data. Nonetheless, BERT’s inherent token limit of 512 tokens presents a noteworthy challenge when confronted with exceedingly long documents. Lengthy documents are commonly encountered during legal document review and often exceed the 512-token constraint. In response to this challenge, this study empirically compares two distinct applications of BERT, leveraging real-world, construction industry legal data. The approach compares applications of BERT to the entire document and on segmented text portions from each document. In the latter approach, the highest-scoring text segment from each document represents the document’s score. This research offers practical insights for effectively utilizing BERT in scenarios where document length exceeds the token limit. Our results allow practitioners and researchers to make informed choices when confronted with documents of significant length, thus contributing to a more effective and insightful application of BERT for text analysis.
Investigating Initial Access Strategies and Malware Deployment Tactics Used by Iranian Advanced Persistent Threat Groups

In the domain of cyber threats, Iran is listed among the top four concerns for the United States. Beyond its nuclear potential, Iran commands a substantial cyber force linked to at least 11 Advanced Persistent Threats (APTs), responsible for a wide range of cyber attacks targeting government, healthcare, infrastructure, and essential services like power. Mapping APT threats is crucial in the Cyber Threat Intelligence (CTI) field, as network defenders must have actionable knowledge to implement defenses. Linking these Tactics, Techniques, and Procedures (TTPs) and common attack vectors is one step toward this goal. There is a gap in existing research identifying similarities and common tactics shared among Iran APT malware and initial access vectors. This research examines the Advanced Persistent Threats (APTs) attributed to Iran, focusing on historically documented and currently active groups, focusing on their malware and TTPs.
Graph Neural Networks for Entity Matching

Data integration still remains largely rule-driven and lacks universal automation. In this work, we propose a general approach to modeling and integrating entities from structured data, such as relational databases, as well as unstructured sources, such as free text from news articles. Our approach is designed to explicitly model and leverage relations between entities, thereby using all available information and preserving as much context as possible. This is achieved by combining siamese and graph neural networks to propagate information between connected entities. We evaluate our method on the task of integrating data about business entities and demonstrate that it outperforms standard rule-based systems, as well as other deep-learning approaches that do not leverage any inductive bias towards the use of graph-based representations.
Reed-Solomon Codes Over Ring with Lower Computational Complexity

Reed-Solomon (RS) codes are widely used in storage systems to provide high data reliability. The existing RS codes are constructed over finite field with field size larger than the code length that have high encoding/decoding complexity. In this paper, we propose a new construction for RS codes over a cyclic ring instead of finite field. Our new RS codes only incurs XOR and cyclic-shift operation in the encoding/decoding processes, and therefore have lower encoding/decoding complexity than the existing RS codes. In addition, we show that we can employ the efficient Reed-Muller (RM) transform in our new RS codes that reduces the encoding/decoding complexity. Moreover, we implement our new RS codes by C++ and show that our new RS codes have better encoding/decoding performance than the existing RS codes for the evaluated parameters.
Temporal Knowledge Graph Question Answering Models Enhanced with GAT

Temporal Knowledge Graph Question Answering (TKGQA) task aims to find an entity or timestamp from a temporal knowledge graph to answer temporal reasoning questions. However, most existing models fail to capture the implicit temporal information in the questions, resulting in weak performance when handling complex temporal reasoning tasks. To address this issue, this paper proposes a novel TKGQA model called GATQR, which integrates graph attention mechanism. The model utilizes a pre-trained temporal knowledge base in the form of quadruples and introduces Graph Attention Network (GAT) to effectively capture the implicit temporal information in the questions. By integrating with relation representations trained by the RoBERTa, it further enhances the temporal relationship representation in the queries. Finally, this representation is combined with the pre-trained TKG embeddings to predict the entity or timestamp with the highest score as the answer. Experimental results on the largest benchmark dataset CronQuestion demonstrate that compared to baseline models such as CronKGQA, EntityQR, and TempoQR-Soft, the GATQR achieves significant improvements in Hits@l results for handling complex and temporal question types, with increases of 35% and 13%, 18% and 9%, and 9% and 3%, respectively. These results validate the effectiveness and superiority of the GATQR model in capturing implicit temporal information and enhancing complex reasoning capabilities.
Online Closed Episode Mining with Root-Order Decomposition

We present an online method for frequent episode mining of streaming data. Although the extraction of frequent patterns has been actively studied in the field of episode mining, an optimal method suitable for streaming data has yet to be determined. In this study, we decompose an arbitrary sequence into a set of simple pre- and post-event relations called root-orders. This decomposition facilitates utilizing arbitrary, efficient methods for closed itemset mining in the online setting. Consequently, the proposed method is the first and most complete algorithm for extracting the so-called closed sequences with distance. We empirically verified the availability of the extracted output in a sequence prediction problem. We also investigated its ability to recover the original solution derived using existing methods for episode mining.
Robust category recognition based on deep templates for educational mobile applications

Popularity of mobile vision applications constantly increases. These Artificial Intelligence technology-driven solutions exhibit high potential for the dynamically-changing landscape of education, in which they could be used as powerful tools to engage and motivate learners. The integration of game-like elements into educational software can be a very effective approach to capture the attention of students by providing interactive and immersive learning experiences. That is why in this study we present a category recognition method for computationally-limited devices such as smartphones to be used in a cost-efficient manner in the areas connected to interactive education. We use small and efficient ImageNet-trained Convolutional Neural Networks and Deep Template Matching to create a classification solution. To test our solution, we construct a specialized image dataset with different toys and items that could be suitable for early-stage education to engage young learners. We perform an extensive study on how the number of images used to create templates and different similarity/distance measures applied for matching impact the accuracy to provide practical observations that can be used to create real-life game-like elements for educational software. Our results indicate that utilizing more templates and similarity measures results in better generalization, representativeness, and robustness, particularly when it comes to rotation, which is crucial for mobile applications. We release our data and code via our GitHub repository at https://github.com/iitis/EduToyz.
Multi-Task Learning Using Facial Features for Mental Health Screening

Major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are prevalent mental health conditions with severe physical and social impacts. They are expensive and detection is difficult, requiring substantial time from trained mental professionals. To alleviate this issue, recent studies explore the diagnostic potential of deep learning models trained on modalities extracted from clinical interview videos, conducted by a virtual agent. However, deep learning models are challenging to train because of the long sequences and small number of participants common in the mental health community. To combat these challenges, we leverage multi-task learning, using temporal facial features as input, to screen for MDD and PTSD. The multi-task architecture is based on a bidirectional GRU model with self-attention. We evaluate our multi-task model on temporal facial features extracted from responses to 15 clinical interview questions conducted by a virtual agent. The results suggest that multi-task learning increases the generalization performance compared to single-task learning. For MDD screening, multi-task learning improved the balanced accuracy over single-task learning for 11 of the 15 datasets. In fact, our multi-learning model increased the MDD screening ability by 25 percent to a balanced accuracy of 0.87 in some scenarios. This work provides valuable findings for the future of mental screening applications leveraging temporal facial features.
Combining Transformers and Tree-based Decoders for Solving Math Word Problems

Solving math word problems is a popular topic in natural language processing. We not only need to classify the grammatical structures in the questions, but also understand the mathematical logic expressed between words. Errors in semantic understanding may lead to the failure to generate correct solution equations. Thus, the correct answer cannot be calculated. Previous studies mainly used sequence-to-sequence recurrent neural networks (RNNs) to obtain meaning in words, or combined graph neural networks to capture more information in questions to achieve better results. In addition, recent studies also showed that, a tree-based decoder leads to better results than a decoder of RNNs. In this paper, we propose to combine transformers and tree-based decoders for solving math word problems. Firstly, we use a transformer encoder to read math word problems, whose outputs are given to two different decoders, including Transformer decoder, and a treebased decoder. Secondly, from the answer equations generated from the two decoders, the better solution is selected. The experimental results on the two commonly used math word problem datasets, MAWPS and ASDiv-A, show that our model achieves 89.3% and 81.9% accuracy, which are 2.2% and 4.2% higher than the vanilla transformer model, respectively. For the MAWPS dataset the performance is comparable to state-of-the-art model Graph2Tree. This shows the effectiveness of our proposed method.
Digitization of Physical Notes: A Comprehensive Approach Using OCR, CNN, RNN, and NMF

2.3 trillion gigabytes of data is generated every day but that's today's world for you. In this technological era where we can use technology in almost all sectors and regular and constant day-to-day activities, these huge digital copies and data are communicated and accessed. Hence even though handwritten texts have their purpose and application they are of no use in the digital era. Questioning the same problem, innovations and key-to-note digitization and topic identification of this huge digital database carry inestimable merits and value. Before fitting execution, we went on exploring different methods, algorithms, and systems with our own which lead us to many trials and errors, and mistakes. Ultimately we came up with a system integrating Topic Detection and Identification. Intending to integrate both processes into a single system, we examine various algorithms involving neural networks like RNN, CNN, and ANN, and methods such as Tesseract, KNN, and LSTM that are used for implementing OCR while techniques such as K means clustering, TF-IDF, LDA, and LINGO have been employed to perform topic detection and identification. Based on our study and results from various papers, we have decided to use CNN for OCR.
Interpretable Unsupervised Log Anomaly Detection

Modern software systems’ increasing complexity and scale makes it challenging to accurately detect system issues and outages, which have been tackled as an anomaly detection task. Conventionally, such anomalous events barely happen, and annotating them is time-consuming and impractical in big data streams. Even with automated anomaly detection, resolving issues promptly is a remaining challenge that can only be done by providing specific contexts such as root causes, target/affected services, and more. To address these fundamentally important problems, we present Grid Transformer (GT), a framework designed to detect and explain $\log$ anomalies in an unsupervised setting. We first train an Auto-Encoder model to generate pseudo labels. Then, we train the proposed grid transformer that not only predicts anomalies but also generates why a particular instance is an anomaly. Through extensive experiments, we demonstrate the effectiveness of our approach where it is shown to outperform the other $\log$ anomaly detection models by 20% while also able to generate time-wise and message-wise explanations of the anomalies.