Reviewerly/exHarmony huggingface dataset:
Human Activity Tracking System Using Pose Estimation Technique

Abstract: When some incident / any dangerous activity happens in the area the CCTV takes photos &amp; video pieces of evidence of what happened there, but there are only few such system that can capture evidence in real-time and process it to conduct further actions of an incident. It waits for the police to come and resolve the issue and what happens is when police come the evidence are vanished. The drawbacks are that when such an incident happens out of 10 people only 1 or 2 will try tocall the police or ambulance but this also takes a lot of time, no quick decisions are taken to resolve the issue until the police gets informed about it. Also, if the accident / suspicious activityhappens on road then that road routes get blocked in some cases, the system’s model should be accurate enough to observethe situation and by predictions should proceed with the decision-making to help police to resolve the issue smoothly and as early as possible. Our solution for such a problem is to develop a system that uses CCTV surveillance systems with an advanced technological model with which it can capture required evidence. Also, we had developed a system that can recognize gestures of a person to detect if he/she is beating someone in the Realtime footage with the help of CNN, Keras, YOLO, MediaPipe and TensorFlow libraries. Quick notifications are sent to alert the police control rooms. At Control room police will allot the nearby required FRVs clusters by sending notifications to them of incident happened in a particular area
Living without Beth and Craig: Definitions and Interpolants in Description and Modal Logics with Nominals and Role Inclusions

The Craig interpolation property (CIP) states that an interpolant for an implication exists iff it is valid. The projective Beth definability property (PBDP) states that an explicit definition exists iff a formula stating implicit definability is valid. Thus, the CIP and PBDP reduce potentially hard existence problems to entailment in the underlying logic. Description (and modal) logics with nominals and/or role inclusions do not enjoy the CIP nor the PBDP, but interpolants and explicit definitions have many applications, in particular in concept learning, ontology engineering, and ontology-based data management. In this article, we show that, even without Beth and Craig, the existence of interpolants and explicit definitions is decidable in description logics with nominals and/or role inclusions such as 𝒜ℒ𝒞𝒪, 𝒜ℒ𝒞ℋ, and 𝒜ℒ𝒞ℋ𝒪ℐ and corresponding hybrid modal logics. However, living without Beth and Craig makes these problems harder than entailment: the existence problems become 2ExpTime -complete in the presence of an ontology or the universal modality, and coNExpTime -complete otherwise. We also analyze explicit definition existence if all symbols (except the one that is defined) are admitted in the definition. In this case, the complexity depends on whether one considers individual or concept names. Finally, we consider the problem of computing interpolants and explicit definitions if they exist and turn the complexity upper bound proof into an algorithm computing them, at least for description logics with role inclusions.
Quantum image chaos encryption scheme based on quantum long-short term memory network

In recent years, the transmission security of image information has become an important research direction in the internet field. In this work, we propose a quantum image chaos encryption scheme based on quantum long-short term memory (QLSTM) network. We find that because the QLSTM network has a complex structure and more parameters, when the QLSTM network is used to improve the Lorenz chaotic sequence, its largest Lyapunov exponent is 2.5465% higher than that of the original sequence and 0.2844% higher than that the sequence improved by the classical long-short term memory (LSTM) network, while its result is closer to 1 and more stable in the 0–1 test. The improved sequence of QLSTM network has better chaotic performance and is predicted more difficultly, which improves the security of single chaotic system encryption. The original image is stored in the form of quantum states by using the NCQI quantum image representation model, and the improved sequence of QLSTM network is used to control the three-level radial diffusion, quantum generalized Arnold transform and quantum W-transform respectively, so that the gray value and pixel position of the quantum image are changed and the final encrypted image is obtained. The encryption scheme proposed in this work obtains the average information entropy of all three channels of RGB of greater than 7.999, the average value of pixel number change rate of 99.6047%, the average value of uniform average change intensity of 33.4613%, the average correlation of 0.0038, etc. In the test of statistical properties, the encryption scheme has higher security than some other traditional methods and can resist the common attacks.
Translating Politeness of Persadaan Tendi Texts in Karonese Language Into English

This paper aims to explore the politeness of persadaan tendi texts in Karonese language and translate them into English. Persadaan tendi or mukul is the process by which the bride and groom introduce themselves to their new relatives. This study was conducted using descriptive qualitative research and supported by translation and pragmatics analyses. There are three data sources of this study; from two books titled Adat Karo Si Rulo written by Ginting (2008) and Indahnya Perkawinan Adat Karo by Milala (2007); and from the researchers’ observations as participants at persadaan tendi ceremonies in Tigabinanga sub-district, Karo Regency and Tuntungan sub-district, Medan, North Sumatra. The results reveal that in persadaan tendi texts there are linguistic and cultural forms of politeness which are kalimbubu, kampil, and manok sangkep as cultural politeness. The second singular pronoun kam subject and the enclitic pronoun –ndu means ’your’, and endi or ‘here’ are linguistic politeness. Rebu (social avoidance to talk directly) among the rebu participants in Karonese society is cultural politeness. These examples of linguistic and cultural politeness in the source language (SL) are not found in the target language (TL). Molina and Albir’s (2002) translation technique of description, supported by critical discourse analysis and semantic analysis is workable to overcome untranslatable of the cultural and linguistic politeness in the SL.
EFL Undergraduate Students’ Politeness Strategy in Online Classroom Interaction

In an online classroom, the students rely on the textual message to construct and interpret meaning. They should select their sentences carefully to avoid misunderstanding by delivering them politely. This study aims to investigate the type of politeness strategies used by undergraduate students in online interaction in WhatsApp Groups. Using descriptive qualitative, with a case study design, this study used Documentation as the instrument. The data was collected from the screenshots of the interaction in the Class WhatsApp Group. The result showed that the undergraduate students used four types of politeness strategies with positive politeness shown to reflect their good relationship with classmates. The sub-category of politeness was found dominantly in giving gifts. The students used it to compliment and thank their classmates. It is a form of appreciation and acknowledgment to other students. This study was limited by the narrow focus of politeness strategy while it is possible to investigate further the different situations of the classroom and the platform.
Improving Stroke Trace Classification Explainability Through Counterexamples

Deep learning process trace classification is proving powerful in several application domains, including medical ones; however, classification results are typically not explainable, an issue which is particularly relevant in medicine. In our recent work we tackled this problem, by proposing trace saliency maps, a novel tool able to highlight what trace activities are particularly significant for the classification task. A trace saliency map is built by generating artificial perturbations of the trace at hand that are classified in the same class as the original one, called examples. In this paper, we investigate the role of counterexamples (i.e., artificial perturbations that are classified in a different class with respect to the original trace) in refining trace saliency map information, thus improving explainability. We test the approach in the domain of stroke.
Generating synthetic samples of chondrosarcoma histopathology with a denoising diffusion probabilistic model.

e13592 Background: The emergence of digital pathology — an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis — is changing the pathology ecosystem. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. However, creating digital pathology algorithms requires large volumes of training data, often on the order of thousands of histopathology slides. This becomes problematic for rare diseases, where imaging datasets of such size do not exist. This makes it impossible to train digital pathology models for these rare conditions. However, recent advances in generative deep learning models may provide a method for overcoming this lack of histology data for rare diseases. Pre-trained diffusion-based probabilistic models can be used to create photorealistic variations of existing images. In this study, we explored the potential of using a deep generative model created by OpenAI for the purpose of producing synthetic histopathology images, using chondrosarcoma as our rare tumor of interest. Methods: Our team compiled a dataset of 55 chondrosarcoma histolopathology images from the annotated records of Dr. Henry Jaffe, a pioneering authority in musculoskeletal pathology. We built a deep learning image-generation application in a Jupyter notebook environment, iterating upon OpenAI’s DALL-E application processing interface (API) with python programming language. Using the chondrosarcoma histology dataset and NVIDIA GPUs, we trained the deep learning application to generate multiple synthetic variations of each real chondrosarcoma image. Results: After several hours, the deep learning model successfully generated 1,000 images of chondrosarcoma from 55 original images. The synthetic histology images retained photorealistic quality and displayed characteristic cellular features of chondrosarcoma tumor tissue. Conclusions: Deep generative models may be useful in addressing issues of data scarcity in rare diseases, such as chondrosarcoma. For example, in situations where existing imaging data is insufficient for training diagnostic computer vision models, diffusion-based generative models could be applied to create training datasets. However, further exploration of ethical considerations and qualitative analyses of these generated data are needed.
PHYSIOTHERAPY IN ELDERLY PERSONS WITH CEREBROVASCULAR ACCIDENT (AVC) SEQUENCES

The present work aims to report the importance of physiotherapy in the rehabilitation process in elderly people with sequelae of stroke, a disease that is responsible for many deaths and leaves many sequels. Physiotherapy after stroke should start as soon as possible, as the faster the patient is stimulated, the faster his recovery will be, with a view to improving the quality of life and recovering lost movements. It will be used for the recovery of various physiotherapeutic techniques such as electrothermal therapy, kinesiotherapy, hydrotherapy aimed at recovering strength and mobility in order to make the elderly more independent in their activities, helping to maintain mental health and returning to their routine. Physiotherapy can obtain several benefits depending also on the patient’s will, family support and the work of a multidisciplinary team composed of a doctor, nurse, physiotherapist, speech therapist, nutritionist and psychologist.
A Study on the geography of Korean Folktales described in Foreign Languages in 1900s: Rewritten and Translated Korea

This study focuses on a collection of Korean tales written by foreign languages published in the late 19th and early 20th centuries: to analyze the dual aspects of translation and to examine the translated Korea embodied in the tales. When conducting oral literature research centered on the late 19th and early 20th centuries, it is still a major problem to specify the scope and methodology. One of key feature of this period is the publication of collections in foreign languages other than Korean and Hanmun. This is a clear indication of the heterogeneity of the period, but it also proves the need for the finalization of the field and the diversification of methodologies. In this study, I will interpret these texts from the perspective of Korean literary studies. From a perspective distinct from that of Korean tales, foreign language tales are described in another language, and the concept of their description, or translation, must be examined. This can be seen as a process of dual translation, in which Korean tales described in foreign languages first undergo a semantic and discursive shift in which Korean texts are transformed into foreign languages, and at the same time, a process of universal transformation based on the identity of the orality itself. Based on this, the related foreign language tales can be chosen and categorized, which is not only a classification of texts but also a possibility of categorizing the research area. Also, in this essay, translation encompasses the rewriting of tales. It is through the rewriting of tales that the intercommunication between Korean tales and the early 1900s context that surrounded them is revealed. Focusing on one of the most prolific tales in the collection, The Rabbit and Turtle, I will examine how Korea has been translated through the story world.
Exploring the Effects of Flipped Learning Classes for Pre-service Early Childhood Teachers: Focusing on Play Guidance Classes

Purpose: The purpose of this study was to examine the effect of pre-service Early Childhood teachers'flipped learning classes and the students' perception of the class, and to seek the direction of teachingand learning for the training of pre-service teachers. Methods: This study involved twenty-one students taking a play guidance class at E University. Theplay guidance class was operated as an intensive course for eleven weeks, and flipped learning wasutilized for seven weeks. First, a paired-sample t-test was conducted to verify the effectiveness of theflipped learning class. Next, text network analysis using the Netminer program and qualitative analysisof the contents of the reflection diary were conducted to examine the perception of the pre-serviceearly childhood teachers' classes. Results: First, the flipped learning classes of pre-service Early Childhood teachers were found to havepositive effects on self-directed learning, learning motivation, and cooperative self-efficacy. Second,because of text network analysis, play appeared as the most important word, followed by infant, class,learning, discussion, observation, teacher, and support as important words. As a result of qualitativelyanalyzing the reflection diary, it was confirmed that it was an opportunity for ‘reinforcing learning’,‘expanding experience’, and ‘experience of recalling the field’. Conclusion: It was confirmed that the flipped learning class is a learner-led class that reflects therecent changes in the educational field, and that it is a meaningful teaching and learning method totrain pre-service teachers in the field of Early Childhood Education.
Malay Traditional Motifs Character in Jewellery Design

This study determines the traditional Malay motifs design that was discovered in the jewellery artefact collections from the National Museum Kuala Lumpur. The visit and support with the observation of the jewellery collection assisted in the process of digital compilation. Then the categorisation of types of jewellery was then divided into three categories of traditional accessories. The identification of motifs clearly showed how the formation of flora, fauna, abstract, cosmos and geometric characters was used and developed. This compilation of this study significantly sustains the tradition of this unique traditional precious metal heritage towards the contemporary design sources for future.
Analisis penentuan daerah rawan banjir di Kota Kupang terhadap kerentanan fisik dan ekonomi menggunakan Geographic Information System

Kupang city, as the representative of East Nusa Tenggara Province, is the first most populous city in the province with the density of 63 peoples/km. It is the centre of trading, government, education, and other typical urban activities within the province. However, the unintegrated drainage system and the high level of land conversion has caused several sub-districts in Kupang city become prone to flooding. This research aims to map the flood-prone locations based on Geographic Information System by exploring thematic maps (rainfall, estimation of seawater inundation, historical of flooding, distance from river, slope, land aquifer, and land use) which can visualize flood zone in Kupang city. The result of this study indicates that Kelapa Lima is the most flood-prone sub-district compared to others, due to the dominance of settlement density, trade in services, and the existence of primary arterial road access which are not supported by an optimal irrigation.
Method for Frame Removal Detection in Static Camera Surveillance Video

A method is proposed for passive protection of a surveillance camera video from a video fragment deletion attack. The method is based on the construction of local features for the samples of two consecutive frames, followed by a multilayer neural network classification. Post-processing and calculation of statistics based on the results of the classification make it possible to decide whether a given pair of frames is sequential or a number of frames were cut between them. Experiments show the efficiency of detecting the fact of a fragment removal even from stationary scenes, when such a deletion is visually imperceptible.
Generalization of Machine Learning-Based Image Compression Methods for Video Compression

The article explores the adaptation of digital image compression methods based on machine learning to the case of video data compression. The generalized image compression method applies digital image generation and segmentation, pyramid-based digital image coding, and interpolation on hierarchically organized arrays of pixels based on machine learning. Image compression uses artificial convolutional neural networks and generative adversarial neural networks, super-resolution artificial neural network algorithms and autoencoders to implement the basic steps. The proposed generalization approach uses interframe dependencies to reduce information redundancy through a video frame approximator based on machine learning. Approximation can significantly reduce the entropy and variance of the encoded data, which results in a reduction in the size of data. The results of computational experiments on real video sequences prove the high efficiency of the approach proposed in this paper to generalize digital image coding methods based on machine learning for the case of video compression.
Time-Segment Photovoltaic Prediction Method Based on Multi-objective Slime Mold Algorithm to Improve Support Vector Machine

A time-sharing photovoltaic (PV) prediction method, which obtains the high-resolution solar irradiance forecast value of a certain region through WRF model, corrects the forecast value using the model output statistics method, and processes it in the same type of historical data of the corresponding period, is put forward in this paper. In addition, a new algorithm is proposed based on multi-objective slime mold algorithm (MOSMA) to improve support vector machine (SVM). Besides, the MOSMA-SVM prediction model is constructed and applied to the Yulara Solar System in central Australia. Through comparison of the prediction models of SVM and BPNN, it can be observed that the mean absolute percentage error (MAPE) is reduced from (1.9373% - 28.935%) to (1.096% - 3.6563%), indicating that the proposed algorithm has the higher accuracy.
The Security Constructions and Enhancements of Smart Wearable Devices in Modern Technologies and Health Monitoring System

At present, the use of smart devices is increasing. The number of devices used in daily life is increasing faster than factories. In which the advanced technology of smart wearable devices has created a new dimension. Most people wear these types of wearable devices to make their daily life better and much easier. But the security of those wearable devices needs to be further improved. This paper discusses in detail the application, configuration, technical strategies, and security vulnerabilities of the wearable devices mentioned above. It also contains information on the extent of security vulnerabilities and how serious problems can be a threat to that user. The future growth of the smart wearable market will focus on fitness and healthcare applications. Also discussed are how to fix these vulnerabilities and how to improve the security of wearable devices. Finally, future improvements and enhancements to wearable devices are proposed.
Constrained Structure Learning for Scene Graph Generation

As a structured prediction task, scene graph generation aims to build a visually-grounded scene graph to explicitly model objects and their relationships in an input image. Currently, the mean field variational Bayesian framework is the de facto methodology used by the existing methods, in which the unconstrained inference step is often implemented by a message passing neural network. However, such formulation fails to explore other inference strategies, and largely ignores the more general constrained optimization models. In this paper, we present a constrained structure learning method, for which an explicit constrained variational inference objective is proposed. Instead of applying the ubiquitous message-passing strategy, a generic constrained optimization method - entropic mirror descent - is utilized to solve the constrained variational inference step. We validate the proposed generic model on various popular scene graph generation benchmarks and show that it outperforms the state-of-the-art methods.