Reviewerly/exHarmony huggingface dataset:
The Use and Exposure of Hate Speech Among Students: A Discourse Analysis Study

Using biased, hostile, and malicious language towards an individual or group of individuals due to their perceived innate characteristics is known as hate speech. It can cause harm to individuals, communities, and societies. In order to gain a better understanding of the prevalence and characteristics of hate speech within academic environments, we conducted a detailed survey of 79 carefully selected students. The survey was designed to determine the most frequently occurring types of hate speech, identify where these incidents commonly occur, and show whether or not students have personally experienced such instances. This study provides a comprehensive understanding of hate speech in educational settings. The findings revealed that a vast majority of the respondents had been exposed to hate speech, albeit unknowingly. However, a small percentage of the participants had not encountered any form of hate speech. These results highlight students' need for better education and awareness regarding the various forms of hate speech and where they can manifest. It is important for students always to be aware and knowledgeable about how to handle hateful speech and promote a culture of acceptance and kindness. This way, everyone can feel secure and comfortable in their surroundings and have the opportunity to flourish.
Context Matters: Cross-Domain Cell Detection in Histopathology Images via Contextual Regularization

Deep learning-based cell detectors have shown promise in automating cell detection in histopathology images, which can aid in cancer diagnosis and prognosis. Nevertheless, the color variation in stain appearance across histopathology images obtained from diverse locations might degrade the accuracy of cell detection. The limitations of a basic cell detector on a dissimilar target dataset are attributed to its emphasis on domain-specific features while overlooking domain-invariant features. Thus, a cell detector that is trained on a particular source dataset may not perform optimally on a different target dataset. In this work, we propose a domain generalization method contextual regularization (CR) for cell detection in histopathology images, which is derived from the basic object detector and focuses on domain-invariant features to enhance detection performance across varying datasets. Specifically, we involves a reconstruction task that involves masking the high-level semantic features either stochastically or adaptively. Then, a transformer-based reconstruction head is designed to recover the original features based on partial observations. Additionally, CR can be seamlessly integrated with various deep learning-based cell detectors without further modifications, and it does not request additional cost in the inference time. The proposed method was validated on a publicly available dataset that comprises histopathology images acquired at different sites, and the results show that our method can effectively improve the generalization of cell detectors to unseen domains.
Speaking Anxiety Experienced by The Fifth Semester Students of English Literature Department: A Case Study in Public Speaking Lesson

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This study is aimed to investigate the factors that contribute in speaking anxiety that experienced by the fifth semester students and the type of speaking anxiety in order to get the broader understanding about this phenomenon. This research is categorized as descriptive qualitative research. It involved the fifth semester students of English Literature Department 2022-2023 academic year as the participants. The data was collected through the interview. The findings of the research describe four factors that contribute in speaking anxiety and three types of anxiety. There were four factors based on Horwitz and Cope (1986) theory, the first factor is fear of negative evaluation (unconfident and overthinking) which made the student avoid do public speaking in English. Students were afraid because their mentality. The second is test anxiety (fear of performance in front of audience) which made the student avoid and afraid do the public speaking English. The third is communication apprehension (low speaking skill) which commonly happened on lack of vocabulary and unable to arrange the sentence and also stag in their mind. The fourth is new material which commonly happened on lack of understanding of new material. There were three types based on Horwitz (1986) theory, the first type of anxiety was trait anxiety it happened related when students fell unconfident and overthinking. The second type of anxiety was state anxiety. It happened relate on external factor and when someone feels being threatened. The third type was situation-specific anxiety. Due to low speaking skill seems like the students were afraid of being evaluated.&#x0D; &#x0D; &#x0D; &#x0D;
Multizone sound field reproduction based on equivalent source decomposition

Multizone sound field reproduction attempts to recreate a desired sound field on the bright zone and reduce sound field energy on the dark zone. Conventional pressure-matching based multizone reproduction method is only able to control sound pressures at limited number of measurement points on the bright zones which are equal to target sound field recording points number. The reproduction performance is strongly affected by the number of recording points. In this paper, a reproduction method based on the sparse plane wave decomposition method is proposed to improve the reproduction performance when the target sound field is generated by one or a small number of sources. The recorded target sound field is decomposed into a group of plane waves. Loudspeaker weights are calculated as a summation precalculated corresponding plane waves weights. A simulation is conducted in a room to verify the performance of the proposed method. Compared with the conventional method, the proposed method achieves better sound field reproduction accuracy on the bright zone and similar acoustic contrast performance.
Early Stage Design Methodology for Energy Efficiency in Buildings Using Asynchronous Distributed Task Queues Framework

Energy consumption in the building sector is about 40% of total energy consumed globally and is trending upwards, along with its contribution to greenhouse gas (GHG) emissions. Given the adverse impacts of GHG emissions, it’s crucial to integrate energy efficiency into building designs. The most significant opportunities for enhancing energy performance are present during the initial phases of building design, which are less impacted by other design constraints. Various tools exist for simulating different design options, providing feedback in terms of energy consumption and comfort parameters. These simulation outputs must then be analyzed to derive design solutions. This paper presents an innovative approach that utilizes user input parameters, processes them through cloud computing, and outputs easily understandable strategies for energy-efficient building design. The methodology employs Asynchronous Distributed Task Queues (DTQ)-a scalable and reliable alternative to conventional speedup techniques-for conducting parametric energy simulations in the cloud. The goal of this approach is to assist design teams in identifying, visualizing, and prioritizing energy-saving design strategies from a range of possible solutions for each project.
Design and Development of a Chatbot for Personalized Learning in Higher Education

Technology now plays a more significant role in our everyday lives, impacting how students learn and gain knowledge. The use of artificial intelligence allows educators to provide students with a personalized learning environment. Systematically detecting whether or not students are grasping the material has been created. Based on the feedback, we can also determine the student's starting point. Conversational agents or chatbots could be used to supplement teacher instruction and reduce the costs of informal education. The development and training of an intelligent conversational agent have long been a goal of humanity and a significant challenge for scientists and programmers. It was still just a dream until recently. On the other hand, language teachers have a new hope thanks to cutting-edge technologies like deep learning and neural networks, which provide intelligent chatbots that can learn through communication just like humans. His research aims to create an emotionally realistic chatbot system using artificial intelligence to improve the chatbot's believability. This study aims to create a chatbot that can be integrated into a personalized teaching–learning process. The goal of the study is to determine the efficacy of the developed chatbot in personalized learning. Its developed system enables it to provide a suitable personalized learning solution.
Research on Path Planning of Improved RRT Algorithm Under Narrow Channel

In complex environments where narrow passageways exist, there are problems such as low success rate of path finding in the path planning of mobile robots based on the traditional fast expanding random tree (RRT) algorithm. To address the above problems, an RRT algorithm for autonomous narrow channel finding is proposed, using an entrance finding algorithm to identify the entrance of the channel and a bias strategy to rationalize the sampling point selection to improve the success rate of path planning. In addition, a greedy algorithm is introduced to optimize the initial path and improve the quality of the planned path. Through experiments, it is shown that the proposed algorithm improves 59.6%, 56%, 9.3%, and 40% in four aspects compared with the RRT algorithm with bias in narrow channel environment in terms of iteration time, number of iterations, path length, and path planning success rate, respectively.
Optimized Real-Time Object Detection and Tracking Using a Refined YOLOV4 Algorithm

The subsequent disquisition unveils an innovative tracking stratagem, specifically tailored for automotive monitoring situations. The recommended tactic aspires to perfect the blueprint, augmenting the detection briskness via a judicious synthesis of YOLOv4 and DeepSORT. The conventional convolution integral within YOLOv4 succumbs to depthwise separable convolution, culminating in a diminished computational exertion and accentuated recognition speed. In addition, the CSPNet framework nestled within YOLOv4 undergoes a tweaking process to pare down the parameter quantity of the blueprint. Alterations in the attribute extraction network of the Deep SORT algorithm facilitate effective re-identification, rendering it ideally malleable for automotive pursuit situations. Experimental excursions on custom datasets signify that despite a slight depreciation in average precision, the detection frame rate skyrockets to an impressive 132.4 FPS, thereby catering to tracking task prerequisites on gadgets with finite performance aptitude. As a result, this academic endeavor proffers a convenient resolution for real-time automotive tracking in settings operating under resource restrictions.
Building Funding-Standardized Research Algorithms Based on Big Data Analysis

In recent years, China’s education standardization work has been continuously strengthened, and a series of educational standards have been formulated and implemented, which have played an important role in regulating, leading and guaranteeing them.at the same time, Compared with the practice of education reform and development and the needs of education modernization, Education standardization work still exists the formulation of standards is not scientific and standard, the organization and implementation of standards are not used to often and other problems, The main performance is: the standard consciousness is not strong, The concept of standards has not yet been established, Has not yet formed everything has a standard, according to the standard of handling habits; The standard system is not yet perfect, There is still a gap in the standard supply, Lack of standards in some key areas; The standard formulation mechanism is not perfect, The standardization of standardization work should be further improved; The standard quality still needs to be improved, Dynamic adjustment mechanism is not sound, Some standards have aging problems; Implementation of the standards needs to be increased, The implementation mechanism is still not yet perfect; The international influence of educational standards is not yet strong, International recognition is not high, This paper uses deep learning algorithm to merge the basic information of college students, The accuracy prediction and analysis of the poor students, It plays a very good auxiliary role in the subsequent identification of poor students.
A Multi-Dimensional Text Sentiment Analysis Method Based on Joint Network

Text sentiment analysis in social media has problems such as irregular structure, short length and sparse features. In this paper, a text sentiment analysis method combining emotional symbols is proposed. Based on the BiGRU and capsule network joint network model, this method fully considers the influence of emotional emoji in the text to be analyzed on the sentiment analysis tendency. Secondly, the BiGRU network was used to extract the long-term dependent features of the text context, and the capsule network was used to deal with the problem of losing feature information in the CNN pooling layer to better extract the local features of the text. Finally, the Softmax classifier was used to output the sentiment tendency. Experimental results show that the proposed model is superior to the current mainstream models in accuracy, recall rate and F1 value.
Min-Leader Optimal Scheduling Algorithm in Kubernetes Clusters

The development of containerization and virtualization technologies has made Kubernetes a popular container orchestration platform that allows multiple replicas for scalability and availability. For stateful applications, each replica requires persistent storage, and a leader must be elected among the replicas to handle client requests and ensure data consistency. However, leaders for multiple stateful applications can become unbalanced across nodes, leading to inefficient resource utilization and load imbalance. This paper proposes the Min-Leader optimal scheduling algorithm that balances leader distribution across nodes during the scheduling phase of Pods. The algorithm considers the number of leaders on each node and prioritizes nodes with the least number of leaders with the goal of improving cluster availability and stability. The algorithm is integrated into the default Kubernetes scheduler as a plugin and validated using experiments. The algorithm effectively balances workload between nodes, improving cluster availability and stability.
Cultural Development and the Imperatives of a New Reality

At present, cardinal shifts are taking place due to the changing phase of civilization development and a whole complex of global problems faced by mankind. Technology and the emerging challenges require post-industrial development of society, new meanings and values, and the design of the future, while both the management apparatus and the cultural sphere are lagging behind, seeking to use the mechanisms and standards of industrial society. The key to solving these problems is a new paradigm of cultural development. It is based on the triad of ratio – emotio – intuitio: not an opposition, but a deep connection between natural science and humanitarian cultures. Instead of the repetitiveness of investigated phenomena and averages typical of modern science, the emphasis is on paradoxical, unique, surprising entities that change our relationship to the world and our place in it. There is a need for redundancy, originality, paradoxicality, which is more characteristic of art than of science. This path was foreseen by V.I. Vernadsky, the time of his ideas has come. In the article these problems are comprehended from the standpoint of the theory of self-organization or synergetics, as well as general ideas about time. Speaking in the language of Greek myths, to Chronos and Cyclos we should add Kairos, the god of the happy moment and luck, indicating the favorable moment when one should act to achieve success and transformation.
IR-Setnet: Sparsity Aware Ensemble Network for Infrared Imaging Based Drone Localization and Tracking In Distorted Surveillance Videos

Although Unmanned Aerial Vehicles (UAVs/drones) have been extensively deployed for various applications, unauthorized operations cause serious security threats and infringement of privacy. Target (drone) localization plays a crucial role in military operations that is extremely challenging under low visibility conditions, uneven illumination, weak contrast, background distortions and close resemblances to other flying entities such as birds. In such cases, infrared imaging outperforms RGB imaging since the images in the former case are based on temperature differences and they help identify and track objects that are not visible in RGB images. In this paper, a sparse ensemble network is proposed for infrared drone localization combined with a CLIP-based zero shot tracking network for tracing the trajectory of the target. Static pruning and model quantization are adopted for introducing sparsity in the ensemble network. This is further combined with a Vision Transformer (ViT) for multi-class distortion classification in real-time surveillance videos. The end-to-end pipeline is termed as IR-SETNET which exhibits 25% improvement in target localization with a mean average precision (mAP) score of 99.90%, drone tracking accuracy of 95.60%, mean distortion classification accuracy of 89.75% and a five-fold improvement in inference speed on introduction of sparsity.
Implementasi Algoritma Floyd Warshall Pada Aplikasi Dewan Masjid Indonesia (Dmi) Kota Semarang Untuk Menentukan Masjid Terdekat

Location Based Service (LBS) is a service on smartphones that functions as a navigation device based on the user's position to determine the location where the user is. LBS utilizes GPS capabilities in finding geolocation information and sometimes using Google maps to display a complete map of the location. But the results of previous research studies Google Map does not give shortest and accessible routes. Furthermore, to improve work of LBS, Floyd Warshall algorithm is used because the algorithm has the principle of optimality in calculating the total of all routes optimally. According to data recorded by the Ministry of Religion of the Republic of Indonesia there have been 1,304 Mosques in the City of Semarang, but with this much data it should be easier to find places of worship for Muslims. Most mosques that are visited are mosques on the highway because it is more visible even though there are many other mosques that can be accessed. By using the White Box and Black Box tests, finding shortest path to find places of worship in the city of Semarang can be given accurately. The result was the Floyd Warshall algorithm could provide shortest path route and it was more accessible better than Google Map navigation.
3D Reconstruction of Reflective Objects Based on Local Grating Completion

A reconstruction technique using local grating complement is suggested in order to address the issues of phase error and decreased reconstruction accuracy brought on by grating deformation brought on by strong reflection on the surface of the object during three-dimensional reconstruction of structured light. First, it is investigated how the measured object's material characteristics and reflection parameters like the specular reflection angle contribute to the missing phenomenon in the 3D reconstructed point cloud model. Second, a local grating completion approach is added before phase unwrapping to finish the missing portion of the grating in the reflective area in order to increase the phase unwrapping accuracy. This method is based on the phase shift method and complementary Gray code reconstruction method. The reflective rectangular aluminum alloy plate was constructed with a monocular structured light 3D reconstruction technique that had a flatness inaccuracy of less than 0.5 millimeters. After testing, it has been found that the improved method described in this paper can effectively compensate for the reconstruction loss brought on by the object surface's reflection. In addition, the phase decoding information is more complete, the reconstruction rate is 25% higher than with the traditional method, and the flatness error rate is reduced by 28.2%.
Emitter Recognition Method Based on Feature Fusion

In order to solve the problem that the fingerprint features of communication emitter extracted based on the traditional mechanism model are not detailed enough, which leads to the low identification rate of individual communication emitter, a method for individual identification of communication emitter based on feature fusion is proposed. The method extracts the fingerprint features based on traditional mechanism models, such as statistical features of the signal, and then constructs a deep neural network to extract deep and subtle features from the original signal. The extracted multi-dimensional features are combined, and the fused features are used to identify the individual communication emitter through the random forest classification algorithm. The research results show that compared with the emitter individual identification method based on the traditional mechanism model, the feature fusion method proposed in this paper makes full use of the traditional fingerprint features of the communication emitter signal, and combines the powerful subtle feature extraction ability of the neural network to achieve In order to better identify the individual communication emitter under the condition of low signal-to-noise ratio, the experimental results show that this method can significantly improve the accuracy of individual emitter identification.
Assessment of Soil Contamination Using Remote Sensing and Spatial Techniques

Soil contamination poses an enormous challenge to environmental and human health, necessitating effective assessment and management. Assessing the contamination of soil using remote sensing and spatial techniques has become an important area, as it provides rapid and accurate information about the extent and distribution of contaminants in soil. This chapter aims to present some of the current state of knowledge and advancements in this field. Remote sensing techniques, including hyperspectral remote sensing, thermal remote sensing, and radar remote sensing, offer valuable tools for mapping and monitoring soil contamination over large areas, enabling efficient decision-making and resource allocation. Spatial techniques usually involve the analysis and integration of spatially referenced data to assess and visualize soil contamination patterns and include geographic information systems, geostatistics, geospatialGeospatial modelling techniques, machine learning, and data mining. Advancements in remote sensing and spatial techniques have enhanced the accuracy and efficiency of soil contamination assessment and include the development of high-resolution satellite sensors, advanced image processing algorithms, and the integration of multisource data. However, several challenges persist in the form of spectral unmixing, the scale of mapping, the resolution of data, ground validation, and data integration. Remote sensing and spatial techniques thus provide valuable tools for assessing soil contamination by offering the ability to map contamination patterns over large areas, identify hotspots, and support decision-making processes. Recent advancements in the assessment of soil contamination using remote sensing and spatial techniques have contributed to improved accuracy, efficiency, and applicability of these methods.