Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Formal Storytelling Representation Models

This paper presents a comparative analysis of the various formal models that can be used to represent a story. The analysis focuses on two types of representation families: semantics-based representations, which use ontologies, and process-based representations. The aim is to provide a comparative overview of the models, analyzing their weaknesses and strengths, in order to determine the formal model that best lends itself to modeling a story by highlighting its main components in terms of the actors involved, events, actions, spatio-temporal relations, as well as cause and effect, in hopes of identifying the formal story representation model that can be used as the starting point for developing a framework that can perform automated storytelling generation. Finally, examples are given of the uses of these models to represent a mythological story.
METHODOLOGY FOR A KINEMATIC MODEL OF SEISMIC EARLY WARNING SYSTEM ABOUT CRITICAL INFRASTRUCTURE

The destructive earthquakes generated huge losses and human victims. As for the moment the time of impending strong seismic event is unpredictable, during the last years – new global innovation is under development – the seismic early warning systems (SEWS). All of them signalize the population and/or governmental bodies and after the earthquake starts just providing information that the seismic event is fact, to expect stronger shakings and some very fast measures to be performed – for example NPP’s reactors stop, as well as superfast trains, chemical production, gas and oil pipeline disconnection, etc. In lucky cases some people can react (especially in the areas of low buildings) and to evacuate effective in the time domain of few seconds, thus saving their lives. Such system is in operation in Japan since 2007 and some other countries also develop their own SEWS. The present investigation is targeted to the methodology development of so called kinematic SEWS. They are using a fundamental property of the body seismic waves P and S. As it is well known from the theory, the P-waves are faster and less destructive than S-waves. This property gives the possibility to use the detection of the P waves as important signal that after a while (the time domain depends on the distance between the seismic source and the investigated site) the slower (1.41 times) more destructive S waves will arrive and struck the infrastructure. Our previous experience of the developed SEWS (for example for Baku and Venice) gives the possibility to upgrade the modeling methodology extending to the formalized algorithm and accuracy estimations, especially for the critical infrastructure (for example NPP).
CREATING A “SMART” COMPUTER SCIENCE CLASSROOM AT UNIVERSITY

Abstract. This work aims to highlight issues related to the creation of "smart" microelectronics-based computer science classes in higher education institutions. The "smart" computer science classroom created is a fully automated educational environment that operates in its three modes: "standard", "automatic", "automatic power saving". "Samrt" cabinet can be controlled by smartphones, PCs and remote controls. Cabinet is equipped with various sensors, indicators and electronic parts based on ArduinoUNO, MEGA and ESP8266-12E WiFi modules. Measurements of built-in "smart" office sensors and indicators are used to display information on the status of the office and classroom microclimate for demonstration presentations and laboratory work. Designed for Iformatics, the Smart Cabinet consists of three modules: "Informational", "Executive" and "Demonstration", controlled by an ATMEL microcontroller. The demonstration module is designed to quickly and easily connect various sensors and components for solderless boards. Arduino open programming platform. Smart sensors in computer science classrooms can monitor the environment inside and outside the classroom (temperature, humidity, pressure, light levels, levels of carbon dioxide and other gases in the air); and remotely control peripherals: TVs, projectors, lamps, electrical outlets, curtains. All three modules are connected to a wireless local area network. "Star" topology with radio communication based on each module. The main components of the system are executive modules that have Internet access, equipment, technology and software tools. An overview of the educational system in technical educational institutions addresses the following issue: сreating "smart" computer science classrooms in higher education institutions.
Defending against Poisoning Attacks in Aerial Image Semantic Segmentation with Robust Invariant Feature Enhancement

The outstanding performance of deep neural networks (DNNs) in multiple computer vision in recent years has promoted its widespread use in aerial image semantic segmentation. Nonetheless, prior research has demonstrated the high susceptibility of DNNs to adversarial attacks. This poses significant security risks when applying DNNs to safety-critical earth observation missions. As an essential means of attacking DNNs, data poisoning attacks destroy model performance by contaminating model training data, allowing attackers to control prediction results by carefully crafting poisoning samples. Toward building a more robust DNNs-based aerial image semantic segmentation model, in this study, we proposed a robust invariant feature enhancement network (RIFENet) that can resist data poisoning attacks and has superior semantic segmentation performance. The constructed RIFENet improves the resistance to poisoning attacks by extracting and enhancing robust invariant features. Specifically, RIFENet uses a texture feature enhancement module (T-FEM), structural feature enhancement module (S-FEM), global feature enhancement module (G-FEM), and multi-resolution feature fusion module (MR-FFM) to enhance the representation of different robust features in the feature extraction process to suppress the interference of poisoning samples. Experiments on several benchmark aerial image datasets demonstrate that the proposed method is more robust and exhibits better generalization than other state-of-the-art methods.
Hierarchical Model Parallelism for Optimizing Inference on Many-core Processor via Decoupled 3D-CNN Structure

The tremendous success of convolutional neural network (CNN) has made it ubiquitous in many fields of human endeavor. Many applications such as biomedical analysis and scientific data analysis involve analyzing volumetric data. This spawns huge demand for 3D-CNN. Although accelerators such as GPU may provide higher throughput on deep learning applications, they may not be available in all scenarios. CPU, especially many-core CPU with non-uniform memory access (NUMA) architecture, remains an attractive choice for deep learning inference in many scenarios. In this article, we propose a distributed inference solution for 3D-CNN that targets on the emerging ARM many-core CPU platform. A hierarchical partition approach is claimed to accelerate 3D-CNN inference by exploiting characteristics of memory and cache on ARM many-core CPU. Based on the hierarchical model partition approach, other optimization techniques such as NUMA-aware thread scheduling and optimization of 3D-img2row convolution are designed to exploit the potential of ARM many-core CPU for 3D-CNN. We evaluate our proposed inference solution with several classic 3D-CNNs: C3D, 3D-resnet34, 3D-resnet50, 3D-vgg11, and P3D. Our experimental results show that our solution can boost the performance of the 3D-CNN inference, and achieve much better scalability, with a negligible fluctuation in accuracy. When employing our 3D-CNN inference solution on ACL libraries, it can outperform naive ACL implementations by 11× to 50× on ARM many-core processor. When employing our 3D-CNN inference solution on NCNN libraries, it can outperform the naive NCNN implementations by 5.2× to 14.2× on ARM many-core processor.
Cognitive offloading benefits eye gaze interaction

Abstract For some people with strong motor impairments, controlling a computer with the eyes is the only possibility for human‐computer interaction. In addition, gaze control is becoming a new option as an input device for the general population as well because of the increasing availability of eye‐tracking technology. Yet, little is known about additional cognitive demands involved in gaze control and how to handle potentially high demands when using the eyes not only for perception but also for action. The present study shows that cognitive offloading improves performance when using gaze control. Memory for to‐be‐studied items benefitted significantly from saving another set of items just studied before for later restudy. Employing cognitive offloading in a targeted manner may be a useful means to assist in mastering eye‐gaze control.
Does financial inclusion catalyze trade

This study scrutinizes the effect of financial inclusion (FI) on trade by employing the Newey West standard approach, Feasible Generalized Least Square (FGLS), Pooled Ordinary Least Square (POLS) regression, Fixed Effect Model (FEM) and Random Effect Model (REM) on 24 developing economies from 2004 to 2020. Results found strong, significant and positive association linking FI and trade. Policy recommendations are developing countries should take serious measures to deepen FI which will boost the effect on trade. Furthermore, these economies should expand trade in new markets and use formal financial institutions as channels for carrying out trade transactions.
Malicious Address Identifier (MAI): A Browser Extension to Identify Malicious URLs

Many websites have emerged since the internet was introduced. Every day, people visit a variety of websites, and they unknowingly click on malicious URLs because they simply neglect to check the URLs they visit. Clicking on a malicious URL has significant consequences. People might become victims of a phishing attack, which aims to steal people’s money or identities by tricking them into disclosing their personal data such as passwords, credit card numbers, and bank account numbers on websites that portray themselves as legitimate. They might also have malware automatically installed on their computer devices, which can harm and destroy their systems. Therefore, if people don’t know whether a URL is malicious or not, something more dangerous might happen. In this paper, we present a browser extension that employs a two-layer model to identify malicious URLs on the web using Bi-LSTM and CNN algorithms. This extension not only identifies malicious URLs but also prevents them from being visited. Furthermore, it blocks ads so that users do not click on malicious URLs through ads.
A Survey Paper on Blockchain Technology and Consensus Algorithms

Blockchain is a decentralized technology. It is a public, immutable append only ledger. The data can be verified by all the participants which makes the system transparent and secured. This paper provides information about history of blockchain, various cyprtographic primitives including digital signature and public key cryptography, which are used in blockchain and different consensus algorithms. The consensus algorithms used in blockchain is a focus in this paper. Particularly we studied about consensus algorithms like proof of work, proof of stake, delegated proof of stake, proof of burn, proof of capacity, proof of activity, proof of elapsed time, byzantine fault tolerance, delegated byzantine fault tolerance, seieve and directed acrylic graph from various points of views like security, scalability, power consumption, fault tolerance, throughput, speed, node identity management, application platform etc. We also tabulated the comparison of various consensus algorithms.
Bengali Caption Generation for Images Using Deep Learning

Automatic caption generation from images has evolved into an active research topic that requires Natural Language Processing (NLP) and Computer Vision (CV) to comprehend the image input and represent it in text. This can assist visually impaired people by generating text captions of images to understand their surroundings. In this study, we have presented a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) approach, which can generate natural language for an image. A dataset containing 8,000 images and a total of 37611 captions are utilized for training our model. Besides, VVG16 is employed to extract features from images. Finally, performance is evaluated, which shows an accuracy of 66% and BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores of 0.40, 0.18, 0.11, and 0.03, respectively.
Analyzing Traffic-Complaint Tweets Based on Time-Location Context Analysis to Develop A Traffic Urgency Model

Intelligent transportation systems in urban areas in developing countries are still being developed. In this process, there are plenteous reports from society that complain about traffic jams, accidents, and other conditions that require immediate handling. For instance, in Surabaya, one of the urban areas in Indonesia, there were 7,975 complaints via @e100ss Twitter's account, the transportation media in Surabaya, in December 2022. This research aims to find contexts from the traffic complaint text to determine the urgency factor from the text. Traffic urgency factors are used to determine the complaint's handling priority. Therefore, the main steps in this research are as follows. The first step is preprocessing and tokenizing the text data. The second step is calculating the term frequency (TF) from the tokenized word. Furthermore, the words with the highest TF values were categorized according to their context. In this context, a model to determine urgency was developed in this research. This research suggests that traffic urgency is moderated by the context of location, context of a group people that being reported, context of the object that mentioned in the complaint text, and context of the condition. This research also found that a specific location has the most frequent reported time.
A Novel end-to-end Framework for Occluded Pixel Reconstruction with Spatio-temporal Features for Improved Person Re-identification

Person re-identification is vital for monitoring and tracking crowd movement to enhance public security. However, re-identification in the presence of occlusion substantially reduces the performance of existing systems and is a challenging area. In this work, we propose a plausible solution to this problem by developing effective occlusion detection and reconstruction framework for RGB images/videos consisting of Deep Neural Networks. Specifically, a CNN-based occlusion detection model classifies individual input frames, followed by a Conv-LSTM and Autoencoder to reconstruct the occluded pixels corresponding to the occluded frames for sequential (video) and non-sequential (image) data, respectively. The quality of the reconstructed RGB frames is further refined and fine-tuned using a Conditional Generative Adversarial Network (cGAN). Our method is evaluated on four well-known public data sets of the domain, and the qualitative reconstruction results are indeed appealing. Quantitative evaluation in terms of re-identification accuracy of the Siamese network showed an exceptional Rank-1 accuracy after occluded pixel reconstruction on various datasets. A comparative analysis with state-of-the-art approaches also demonstrates the robustness of our work for use in real-life surveillance systems.
Sequence-Labeling RoBERTa Model for Dependency-Parsing in Classical Chinese and Its Application to Vietnamese and Thai

The author and his colleagues have been developing classical Chinese treebank using Universal Dependencies. We also developed RoBERTa-Classical-Chinese model pre-trained with classical Chinese texts of 1.7 billion characters. In this paper we describe how to finetune sequence-labeling RoBERTa model for dependency-parsing in classical Chinese. We introduce “goeswith”-labeled edges into the directed acyclic graphs of Universal Dependencies in order to resolve the mismatch between the token length of RoBERTa-Classical-Chinese and the word length in classical Chinese. We utilize [MASK]token of RoBERTa model to handle outgoing edges and to produce the adjacency-matrices for the graphs of Universal Dependencies. Our RoBERTa-UDgoeswith model outperforms other dependency-parsers in classical Chinese on LAS / MLAS / BLEX benchmark scores. Then we apply our methods to other isolating languages. For Vietnamese we introduce “goeswith”-labeled edges to separate words into space-separated syllables, and finetune RoBERTa and PhoBERT models. For Thai we try three kinds of tokenizers, character-wise tokenizer, quasi-syllable tokenizer, and SentencePiece, to produce RoBERTa models.
Crowdfunding using Blockchain

Crowdfunding is a process of raising small amounts of cash from groups of individuals. Businesses and products that may have otherwise not been capable of raising capital are often the most successful on crowdfunding platforms. It provides a platform for businesses, entrepreneurs, NGOs, and customary people to host online fundraising campaigns with the assistance of blockchain technology which may be of any nature like non-profit cause, personal cause, business related. This can be an excellent option for non-profit organizations and businesses that have service-based initiatives. People always have been insecure about the risks crowdfunding platforms come with. The most common problems they face are most of the campaigns around the world are not regulated and some of the crowdfunding campaigns turned out to be fraud. Besides the completion of some projects was significantly delayed. Blockchain technology is considered a perfect solution for solving the problems listed above, and cryptocurrency is used to make donations over a crowdfunding platform.
Classification of News Texts from Different Languages with Machine Learning Algorithms

As a result of the developments in technology, the internet is accepted as one of the most important sources of information today. Although it is possible to access a large number of data in a short time thanks to the Internet, it is critical to analyze this data correctly. The need for text mining is increasing day by day by processing and analyzing the increasingly irregular text type data in the digital environment and classifying them in a meaningful way. In this study, news texts obtained from online German, Spanish, English and Turkish news sites were separated according to predetermined world, sports, economy and politics categories. The data set consisting of 4000 news texts was classified using 41 different machine learning algorithms in the Weka program. The highest successful classification was obtained with Naive Bayes Multinominal and Naive Bayes Multinominal Updateable algorithms, and 93.5% for German news texts, 93.3% for English news texts, 82.8% for Spanish news texts and 88.8% for Turkish news texts.
Emoticontrol: Emotions-based Control of User-Interfaces Adaptations

Emotions are integral to human nature, and their existence, duration, and evolution could lead to specific behaviors. If emotions and behaviors are ignored in the design of socio-technical systems, they will fail or cause discomfort. User interfaces (UIs) are elements of interactive systems able to trigger or moderate emotions. UIs are increasingly designed adaptive to users' various characteristics, intending to improve their satisfaction, performance, and decisions. However, previous adaptation supervising approaches are not effectively adopted in real life since they neglect the dynamic behaviors of humans or systems. This paper proposes Emoticontrol, a quality-driven approach to adapting UIs to users' emotions using Model-Free Reinforcement Learning (MFRL). The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users' enhanced quality of experience (QoE). The approach also considers improving the software quality of service (QoS) by designing software architecture alternatives. We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in potential danger. We performed experiments with a mobile application we developed that acts as a recommender system in evacuation training. By taking contextual input of the users' basic emotions from face recognition, the application intelligently adapts its UI to quickly lead people to safe areas while keeping them emotionally controlled. We consider software performance a crucial QoS; thus, we adopt and test architectures that facilitate an acceptable level of performance. The evaluation process confirms the efficiency and effectiveness of the MFRL in iterations, as well as compared to other UI adaptation techniques.
UAV Remote-Sensing Image Semantic Segmentation Strategy Based on Thermal Infrared and Multispectral Image Features

The availability of high-resolution imagery resources for semantic segmentation research has expanded significantly due to the rapid development of remote-sensing technology utilizing unmanned aerial vehicles (UAVs). These images provide researchers with a more accurate view of the region of interest and allow for more detailed analysis and interpretation of the images. However, semantic segmentation based on UAV remote-sensing imagery still faces new challenges in deriving ground objects. In contrast to the commonly used multispectral (MS) imagery, thermal infrared (TIR) imagery can record the emission of ground objects, making the temperature characteristics of TIR imagery and the color characteristics of MS imagery complementary. These two approaches can be used synergistically to provide more comprehensive image information. On this basis, we propose a strategy for semantic segmentation of UAV images by utilizing both TIR and MS image features. The approach combines principal component analysis (PCA) transformation with a deep learning semantic segmentation network, namely, Deeplv3. The effectiveness of the proposed strategy is evaluated by comparing it with both traditional supervised classification algorithms and deep learning algorithms. According to the results, the proposed strategy exhibits greater robustness, achieving a mean pixel accuracy (MPA) of 92.8% and a mean intersection over union (MIOU) of 73.5%. These results outperform several classical deep learning semantic segmentation algorithms that were also evaluated. The proposed strategy would be beneficial to promote the development of semantic segmentation technology for UAV remote-sensing images.
A Novel Radar Signals Sorting Method via Residual Graph Convolutional Network

The dense, complex and variable electromagnetic environment poses a serious challenge to radar signal sorting (RSS) in modern electronic reconnaissance systems. In order to improve RSS performance, this letter proposes a semi-supervised learning framework-based RSS method via a residual graph convolutional network (ResGCN-RSS) to effectively improve the generalization ability of the signal sorting models in small data scenarios. Firstly, the graph structure construction of intercepted radar signals is performed via K-nearest neighbor algorithm. Then, the three-layer ResGCN is designed to adaptively improve the features learning. Finally, RSS can be effectively and efficiently implemented through an end-to-end ResGCN with small labeled graph data of interleaved radar signals. The simulation experimental results show that our proposed method can achieve better average accuracy with little computational cost increasing when the labeled data is very small, compared to some existing methods.