Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Banking Chatbot using Graph-based Machine Learning Model

Many banking companies use a chatbot that can answer questions about their products. Knowing the sentiments of users can help companies evaluate the chatbots. However, the chatbot is made using a decision dialogue method so it has no knowledge of the sentiment of a sentence. Traditional data mining methods such as converting words to vectors are no longer efficient because of the limited representation of sentences. Therefore, we need a method that can connect words between words so that the model can understand it better, namely using graph-based representation. We represent a graph with words as nodes along with their node properties in the form of vectors and edges using a dependency parser to connect these nodes. With graphs we can represent text better such as being able to include the part of speech of the word, the number of letters, and so on. Data that has been converted to graph will be processed with a graph-based model. In this study, we compare traditional methods with graph-based methods in terms of the performance and inference time in detecting the sentiment of a sentence. The results of the experiment demonstrate that the graph-based model achieves a comparable level of accuracy (0.7173) to that of the tree-based model. However, when it comes to inference time, the graph-based models outperform the tree-based models, being three times faster.
A BERT-GCN-Based Detection Method for FBS Telecom Fraud Chinese SMS Texts

In the past decade, Fake Base Stations (FBS) have been consistently employed by criminals to target mobile users through spam text messages. Despite the introduction of several techniques to mitigate this problem, spam messages remain a persistent and challenging issue in some countries, such as China, resulting in billions of dollars in annual economic losses. Therefore, this paper proposes an algorithm named BERT-GCN that combines large-scale pre-training models with Graph Convolutional Networks (GCN) for multi-class detection of fraudulent base station telecommunications scams in Chinese text messages. First, BERT (Bidirectional Encoder Representation from Transformers) is used to encode the corpus and generate word embeddings. Subsequently, the generated word embeddings are fed into GCN for training. Finally, a Softmax layer is employed to perform multi-class detection on fraudulent base station telecommunications scams in Chinese text messages. Experimental results demonstrate that the proposed model achieves favorable performance on the FBS-SMS-Dataset and exhibits outstanding performance in the domain of multi-class detection.
Design of Active Disturbance Rejection Attitude Controller for Tethered UAV with Parameter Tuning

In this paper, active disturbance rejection control (ADRC) is designed for the characteristics of tethered UAV cable interference. Combined with the characteristics of active disturbance rejection control parameters difficult to tune, a parameter tuning method based on particle swarm optimization (PSO) algorithm is proposed. The kinematics model and dynamics model of the tether UAV are established, and then the attitude controller was designed for cable interference. The particle swarm optimization algorithm was designed with the integral of time and absolute error (ITAE) performance index as the objective function and as the fitness value of particle swarm. The parameter values obtained after optimization processing are substituted into the attitude controller. After experiment and simulation, comparing it with PID control, it is proved that it has feasibility and good anti-interference, and can track the track of unmanned vehicle very well.
Unveiling the Railway Traffic Knowledge in Tibet: An Advanced Model for Relational Triple Extraction

To address the deficiency of existing relation extraction models in effectively extracting relational triples pertaining to railway traffic knowledge in Tibet, this paper constructs a Tibet Railway Traffic text dataset and provides an enhanced relation extraction model. The proposed model incorporates subject feature enhancement and relational attention mechanisms. It leverages a pre-trained model as the embedding layer to obtain vector representations of text. Subsequently, the subject is extracted and its semantic information is augmented using an LSTM neural network. Furthermore, during object extraction, the multi-head attention mechanism enables the model to prioritize relations associated with the aforementioned features. Finally, objects are extracted based on the subjects and relations. The proposed method has been comprehensively evaluated on multiple datasets, including the Tibet Railway Traffic text dataset and two public datasets. The results on the Tibet dataset achieve an F1-score of 93.3%, surpassing the baseline model CasRel by 0.8%, indicating a superior applicability of the proposed model. On the other hand, the model achieves F1-scores of 91.1% and 92.6% on two public datasets, NYT and WebNLG, respectively, outperforming the baseline CasRel by 1.5% and 0.8%, which highlights the good generalization ability of the proposed model.
A non-contact interactive system for multimodal surgical robots based on LeapMotion and visual tags

In recent years, the integration of robots in minimally invasive surgery has gained significant traction in clinical practice. However, conventional contact-based human-computer interaction poses the risk of bacterial infection, significantly limiting the role of robots in surgery. To address this limitation, we propose an innovative interaction method rooted in gestures and visual tags, allowing surgeons to control and fine-tune surgical robots without physical contact with the environment. By encoding the six gestures collected using LeapMotion, we can effectively control the surgical robot in a non-contact manner. Moreover, utilizing Aruco technology, we have accurately identified the 3D spatial position of the visual label, and developed 12 fine-tuning operations to refine surgical instruments. To evaluate the applicability of our proposed system in surgery, we designed a relevant experimental setup. In the experiment, we achieved enough precision. These results demonstrate that our system meets the clinical standard, providing doctors with a non-contact and flexible means of interacting with robots during surgery.
Meaningful control of intelligent systems

The rapid evolution of AI technology is well on its way to being able to support and even replace humans in various tasks. Exploiting the application potential, with simultaneous mitigation of the (systemic) risks to our society is proving to be a major challenge. How do we tell (autonomous) AI systems effectively what to do? How do we make them function in a responsible and morally acceptable way? To what extent can we demand explainability?
Immersive Unit Visualization with Augmented Reality

Immersive Unit Visualization is an emergent form of visualization that arose from Immersive Analytics where, unlike traditional visualizations, each data point is represented by an individual visual mark in an immersive virtual environment. This practice has focused almost exclusively on virtual reality, excluding augmented reality (AR). This article develops and tests a prototype of an Immersive Unit Visualization (Floating Companies II) with two AR devices: head-mounted display (HMD) and hand-held display (HHD). Results from the testing sessions with 20 users were analyzed through qualitative research analysis and thematic coding indicating that, while the HHD enabled a first contact with AR visualization on a familiar device, HMD improved the perception of hybrid space by supporting greater stability of virtual content, wider field of view, improved spatial perception, increased sense of immersion, and more realistic simulation, which had an impact on information reading and sense-making. The materialization of abstract quantitative values into concrete reality through its simulation in the real environment and the ludic dimension stand out as important opportunities for this type of visualization. This paper investigates the aspects distinguishing two experiences regarding data visualization in hybrid space, and characterizes ways of seeing information with AR, identifying opportunities to advance information design research.
Underwater Image Enhancement Algorithm Based on UWGAN and U-Net

The underwater environment presents unique characteristics that often result in defects like low contrast and blurred edges. To address these issues and improve underwater target recognition for vehicles, a new method that combines UWMAN and U-Net algorithms has been developed. One key advantage of this algorithm is its ability to counteract the impact of underwater environmental factors. It achieves this by enhancing the color of the images and improving various details, leading to higher scores in evaluations of underwater color image quality and overall image quality. This means that the algorithm effectively enhances the clarity and visual appeal of underwater images. Additionally, the enhanced images obtained through this method demonstrate improved matching effects in feature point matching experiments. This indicates that the algorithm enhances the identification and alignment of crucial features within the images, resulting in more accurate target recognition. These capabilities are vital for underwater vehicles operating in challenging underwater environments. Overall, the combination of UWMAN and U-Net algorithms represents a significant advancement in enhancing image clarity and improving the accuracy of underwater target recognition. The algorithm’s ability to mitigate the impact of underwater environmental factors and produce visually pleasing images holds great potential for various applications related to underwater exploration, marine research, and inspection tasks beneath the water’s surface.
Ideational Meanings in Results and Discussion Section of English Students’ Final Project Reports Viewed from SF-MDA

The purpose of this study is to investigate how knowledge is construed by integrating multimodal texts in English Students’ Final Project Reports, especially in the results and discussion section. Therefore, the qualitative data were obtained from the results and discussion section of English Students’ Final Project Reports published in 2022, Politeknik Negeri Bandung. In analyzing the data, Systemic Functional-Multimodality Discourse Analysis (SF-MDA) was employed. The SFL framework was used in investigating verbal texts that were supported by visual texts. The visual texts were studied by applying MDA. Both frameworks were applied to find out the intersemiosis relation of verbal and visual modes. The results show that the students used tables and graphs to support their claims in the verbal texts. However, the intersemiosis of the modes is divergent. It is implied that the students were not aware yet of the function of the visual mode in their final project reports. This study can be a reference to improve students’ academic writing.
The Colorization Based on Self-Attention Mechanism and GAN

Grayscale image colorization is a process of adding reasonable color information to an image, and converting grayscale images into color images is an important and difficult image processing task. The process of colorization is to predict the color information corresponding to the grayscale image by the colorization model. In this paper, the proposed multi-scale input adversarial generative network coloring model with multi-scale input is the colorization scheme based on self-attention mechanism and GAN is proposed in this paper. The experimental result on the colorization of grayscale cartoon images shows the effectiveness of the proposed scheme.
Temporal Relationship Extraction of Conflict Events in Open Source Military Journalism

Military conflict is a global concern, and as global conflicts continue to grow in number and complexity, the need to quickly and accurately understand the temporal relationships of conflict events is becoming more pressing. Analyses of the course of conflict events by military analysts, intelligence officers are crucial in war prevention, intervention and conflict resolution. Extracting event timing relationships from a large number of conflict-related texts in open source journalism can help researchers and policy makers better understand the evolution of conflicts and the causal relationships behind them. However, there are fewer current studies on event temporal relationship extraction from Chinese texts, while the accuracy of current temporal relationship studies is low. In this paper, we construct a chapter-level Chinese military conflict event temporal relationship dataset. In order to extract the event relationships in the document, the whole document is coded using Roberta, contextual extraction of multi-featured Chinese military conflict event trigger words is performed with additional classification headers, and contextual repetitions of different event pairs corresponding to the location of event triggers are sent as inputs, and then the model is fine-tuned. classification of event pairs labels using a linear layer. This method is more effective for the task of event temporal relationship extraction. The text constructs a Chinese military event conflict event dataset, and the experimental results show that the recall F1 based on this method reaches 0.63, which is slightly higher than the current mainstream benchmark model.
Spark Parameter Optimization Based on HC2-SA Algorithm

Spark, as an efficient big data processing framework, the selection of its parameters is crucial for the performance and efficiency of the application. Inappropriate parameter configurations can seriously affect the performance of Spark jobs. This article proposes a Spark parameter optimization method based on the HC2-SA algorithm to improve the performance and efficiency of Spark applications. A series of Spark applications were experimented with different parameters, and the running time data was collected. Multiple machine learning algorithms were used to train and test the data to establish a performance model for Spark jobs. Then, the HC2-SA algorithm was used to search in the parameter space and obtain optimized parameters for Spark applications. Three different parameter space search algorithms, including hill climbing, simulated annealing, and HC2-SA algorithm, were compared in the experiment. The experimental results showed that the HC2-SA algorithm performed the best in Spark parameter optimization, and it could find the optimized parameter combination in a shorter time.
Meinungsfreiheit und Moralismus

Abstract In Germany, like in many other liberal countries, there is an extensive public discourse about freedom of speech being at risk. It will be insisted in this contribution that on the one hand, freedom of speech as a subjective right of basic law is not endangered. On the other hand, it should be acknowledged that in public discourse moralistic exaggerations are quite common. Against this background, the paper asks when different opinions are to be respected and at what point it is permitted or even required to condemn them as immoral. The basic argument advanced here is that opinions should be respected as long as they are based in or at least supported by reasonable moral theories. Those opinions that do not have such a support can be labelled as properly immoral and criticized as such.
Sistem Pengarsipan Digital pada Dinas Kelautan Provinsi NTT

The Digital Archiving System at the Marine Service of East Nusa Tenggara Province in archiving incoming mail, outgoing mail active and inactive archives is still manual by being recorded in the agenda book which makes it difficult to find files. It aims to create a filing system for incoming mail, outgoing mail, active archives and inactive archives to facilitate archiving and searching. The research method used is the method of interview, observation, literature study and internet browsing, while for application development the method used is the Waterfall method. The software used is windows 10, Visual Studio code, Xampp Control Panel v3.2.2 PHP, mysql. The results of the application that are built are expected to help the Department and also for website visitors.&#x0D; &#x0D; Keywords: Archiving system, incoming mail, outgoing mail, PHP active and inactive archives, mysql
HISTORY OF EDUCATION IN THE SCIENTIFIC RESEARCH OF IVAN FILYPCAK (1871–1945)

The article reveals the figure of the historian Ivan Filipchak (1871–1945) as a researcher of education in Eastern Galicia and, partially, Lemkiv region. For the first time, the study of I. Filipchak's studies in the history of education is analyzed. It is claimed that most of them are devoted mainly to the history of education and educational institutions: rural primary schools in the villages of Strilbychi and Hordynia in Sambir Region, the monastery school of the village of Lavriv, Sambir Teacher's Seminary. Two works of the researcher are singled out, which have a generalizing nature and are dedicated to the development of folk schooling in the ethnographic regions of Ukraine – Lemkivshchyna and Boykivshchyna. It is proved that some of the works were written on the basis of extensive source material (primarily the study of the history of the main school in Lavrov), while some others (school in Hordyna, education in Lemkiv region) were built on a rather limited source base. It was noted that the researcher actively used the memories of teachers and students, personal memories, documents from the school archives and funds of the «Boikivshchyna» Society in Sambor, of which he was an active member. A feature of many of his works has been established – a noticeable antiquarian approach to historical sources, the presence of archeographic essays in the studios, which is characteristic of novice historians and local historians. It was revealed that in his writings the author very clearly traces, in his opinion, the main factors of the development of education – the policy of the authorities, representatives of the church, the community and individual educational figures. It is proven that the scientist paid considerable attention to little-known figures in the field of public education, in particular to teachers P. Buschakovsky, H. Gordynskyi, Fr. M. Maksymovich, Y. Kerekyarto, T. Bilenko, and others. It was established that the scientist's works were characterized by special attention to native-language education, as well as ethnographic observations about the life of Boiks and Lemks. Keywords: history of education, Ivan Filipchak, historiography, Roman Lukan, historiographical analysis, Sambir region.
An Effective Reference-Point-Set (RPS) Based Bi-Directional Frequent Itemset Generation

Data Mining (DM) is a combination of several fields that effectively extracts hidden patterns from vast amounts of historical data. One of the DM activities used to produce association rules is Association Rule Mining (ARM). To significantly reduce time and space complexities, the proposed method utilizes an effective bi-directional frequent itemset generation approach. The dataset is explicitly bifurcated into dense and sparse regions in the process of mining frequent itemset. One more feature is proposed in this paper which sensibly predetermines a candidate subset called, Reference-Points-Set (RPS), to reduce the complexities associated with mining of frequent itemsets. The RPS helps to reduce the number of scans over the actual dataset. The novelty is to look at possible candidates during the initial database scans, which can cut down on the number of additional database scans that are required. According to experimental data, the average scan count of the proposed method is respectively, 24% and 65%, lower than that of Dynamic Itemset Counting (DIC) and M-Apriori, across different support counts. The proposed method typically results in a 10% reduction in execution time over DIC and is three times more efficient than M-Apriori. These results significantly outperform those of their predecessors, which strongly supports the proposed approach when creating frequent itemsets from large datasets
LWE Based Quantum-Resistant Pseudo-Random Number Generator

In the realm of cryptography, computational statistics, gaming, simulation processes, gambling, and other related fields, the design of Cryptographically Secure Pseudo-Random Number Generators (CSPRNGs) poses a significant challenge. With the rapid advancement of quantum computing, the imminent "quantum-threat" looms closer, posing a risk to our current cryptographically secure PRNGs. Consequently, it becomes crucial to address these threats seriously and develop diverse tools and techniques to ensure that cryptographically secure Pseudo-Random Number Generators (PRNGs) remain unbreakable by both classical and quantum computers. this paper presents a novel approach to constructing an effective Quantum-Resistant Pseudo-Random Number Generator (QRPRNG) using the principles of lattice-based Learning with Errors (LWE). LWE is considered quantum-resistant due to its reliance on the hardness of problems like the Shortest Vector Problem and Closest Vector Problem. Our work focuses on developing a QRPRNG that utilizes a Linear Feedback Shift Register (LFSR) to generate a stream of pseudo-random bits. To construct a secure seed for the QRPRNG, LWE is employed. The proposed QRPRNG incorporates a secure seed input to the LFSR, and employs a Homomorphic function to protect the security of the finite states within the LFSR. NIST statistical tests are conducted to evaluate the randomness of the generated output by the constructed QRPRNG. The proposed QRPRNG achieves a throughput of 35.172 Mbit/s.
The transformation of online teaching in higher education institutions in the post-pandemic period

The relevance of this study is due to several factors. On the one hand, there is an active technical development of modern online education; on the other hand, there is a need that has developed due to the pandemic, which has forced many educational processes to go online. The year 2020 has seen a dramatic change in the work of university teachers, who have had to urgently integrate themselves into an online environment that is unfamiliar to many of them. Two years have passed, but online education is still relevant and in demand. What are the positive and negative aspects of teaching in an online environment? And how has the attitude towards online education changed over the past two years? We have tried to look at these issues from the perspective of teachers of higher educational institutions. The aim of our research is to analyze the current perception of online teaching in higher education. As a result of the sociological research conducted in 2020 and 2022 (using the online survey method), the peculiarities of the transformation of online teaching in the activities of higher education teachers were revealed. It was concluded that the attitude of teachers towards online teaching has improved. However, according to teachers, the quality of online education is still inferior to offline education and there are many problems and shortcomings. In many ways, the success of online education is due to the ability of both teachers and students to master new technical tools and to adapt to new conditions. The conclusions highlight the problems of online education and the prospects for its development. The characteristics of modern technologies are analyzed, their impact on improving the effectiveness of online education and possible options for further use are predicted.