Reviewerly/exHarmony huggingface dataset:
PVBLiF: A Pseudo Video-Based Blind Quality Assessment Metric for Light Field Image

Going beyond traditional 2D imaging is not only an emerging trend of imaging technology, but also the key to a more immersive user experience. Light Field Image (LFI) is a typical high-dimensional imaging format, and the quality evaluation of which is very challenging but necessary. In this paper, we propose a novel Pseudo Video-based Blind quality assessment metric for Light Field image (PVBLiF). In contrast to most previous Light Field Image Quality Assessment (LF-IQA) metrics, in which different types of 2D representations derived from LFI are used for quality assessment indirectly, our metric exploits a more intuitive 3D representation, named Pseudo Video Block Sequence (PVBS), to evaluate the perceptual quality of LFI. For this purpose, we first divide the LFI into a massive number of non-overlapping PVBSs, which simultaneously contain spatial and angular information of LFI. Then, we propose a novel network (named PVBSNet) based on Convolutional Neural Networks (CNNs) to extract the spatio-angular features of PVBS and further evaluate the PVBS quality. The proposed PVBSNet consists of four stages: multi-information division, intra-feature extraction, cross-feature fusion, and quality regression. Finally, a Saliency- and Variance-guided Pooling (SVPooling) method is presented to integrate all the PVBS quality into the overall quality of LFI. The proposed PVBLiF metric has been extensively evaluated on three widely-used LFI datasets: Win5-LID, NBU-LF1.0, and SHU. Experimental results demonstrate that our proposed PVBLiF metric outperforms state-of-the-art metrics and is capable of highly approximating the performance of human observers. The source code of PVBLiF is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZhengyuZhang96/PVBLiF</uri> .
Optimisation of occlusion data based on graph neural networks with joint objectives

This paper proposes a data federation multiobjective tracking (MOT) method based on a graph neural network framework, where a graph-based approach merges the optimisation data between multiple objects in the same frame and between objects in the preceding and following frames into the feature learning task. This approach uses the data logic and topological properties of graphs, combined with detectors to encapsulate the neighbourhood information of each node, in order to construct an association graph between multi-objective multi-features with the inference logic of optimised occlusion data, which is then learned by linear filtering to constrain feature score similarity. Finally, this paper conducts comparative experiments on the MOT dataset. By comparing the experimental index data of MOT15/17/20, the outstanding performance of this paper’s method can be effectively demonstrated.
Problem-Based Learning Integrated Reading and Writing in Work and Energy Phenomena: Its Effectiveness on Problem-Solving Skills and Reading-Writing Literacy

Problem-solving Skills and Reading-Writing Literacy are two necessary things students must have to compete in the 21st century. This study aims at determining the effectiveness of Problem-based Learning with Integrated Reading and Writing tasks to improve students' problem-solving and reading-writing literacy skills. A quantitative method using a quasi-experimental design with a non-equivalent control group was employed in this study. The sampling technique used in this study was the purposive sampling technique. The sample used in this study was 36 students divided into two groups: the control group and the experimental group. The instrument used in this study was a physics problem-solving skill test instrument and a reading-writing literacy instrument. The data analysis technique in this study was a quantitative analysis technique. The results showed that the N-gain value of the physics problem-solving skill test for the experimental group was 0.67 in the medium category, and the control group was 0.65 in the medium category. In the reading-writing literacy test, the N-gain value for the experimental group was 0.55 in the medium category and 0.29 in the low category for the control group. The Problem-based Learning model with Integrated Reading and Writing Tasks has little impact on improving students' problem-solving skills. However, it has a significant effect on increasing students' reading-writing literacy.
Mathematical Model Simulation of Non-Linear Equations using MATLAB: Specific Volume of Gas with Van der Waals Equation

Computational physics is concerned with the application of numerical methods in solving physical problems. The van der Waals gas model is one of the most common non-linear models. This study simulated a mathematical model of a non-linear equation using MatLab for the case of the specific volume of gas in the equation of the state of van der Waals. This study aimed to determine the molar volume and compressibility factor, as well as describe the relationship between the compressibility factor and the reduced pressure. The method of the study is experimental. The independent variables are the reduced pressure and temperature values. The dependent variable is the determination of the value of the molar volume (V) and the compressibility factor (Z). The control variable, in the form of a function used in solving this case, is based on van der Waals equation with the gas used is ammonia. The fzero command can be used to solve f(x)=0 with a single variable. This program that has been running successfully can show various predictions in the form of reduction pressure, thus obtaining the values of the molar volume and compressibility factor using the ideal gas equation. There is a deviation in ammonia gas, the Z1 at high reduction pressures and Z1 at medium pressures. This study can provide contributions and benefits in the form of material enrichment of thermodynamics to understand how real gases behave. The ideal gas equation can be modified into the van der Waals equation.
Students’ Voice of Digital Literacy in Learning English

Digital literacy competence played an essential role in the growth of University students to achieve their goals of learning. This study was carried out to define the students’ perception of the use of digital literacy in learning English. The participant consists of the students of the Accounting and Information system program, at Bina Sarana Informatika University. 50 students were chosen by purposive sampling technique. The students had a distinctive response regarding the use of digital literacy competence and how they perceive it. This study was a qualitative case study. The data was gathered through open-ended questionnaires and classroom observation. The findings showed that the students had a positive voice regarding the use of digital literacy competence in learning English, they argued that digital literacy need willingness and critical thinking skills.
The Efficacy of Mobile-Assisted Language Learning in Improving Learners’ Reading Skills in Relation to Metacognitive Strategy

Mobile-assisted language learning (MALL) is a mobile device-based approach that is utilized in language learning to enhance students' reading proficiency. As mobile devices have become the dominant mode of learning due to prevailing conditions, it has become increasingly necessary to employ adaptable learning processes that aim to improve students' reading skills through the use of metacognitive strategies. Although reading is one of several skills essential to learning English, in the current pandemic situation, it is impractical to teach it directly. Therefore, the MALL approach is imperative to adapt to the new teaching-learning process, particularly in the current pandemic scenario. This study endeavors to assess the application of MALL and its impact on students' reading skills by conducting both qualitative and quantitative research. Data collection methods in this study include observation, documentation, and field notes. The study involved one problematic class, to which researchers implemented the MALL application, followed by tests and interviews at the end of the study. The results indicate a significant increase in students' reading skills, along with ease of learning during the pandemic. Thus, this study highlights the efficacy of the MALL approach in improving students' reading skills and adapting to the pandemic-induced new teaching-learning process.
Addressing Trust Issues in Supply-Chain Management Systems Through Blockchain Software Patterns

Blockchain technology is a decentralized and distributed ledger that allows for secure, transparent, and immutable tracking of transactions. However, it is not a one-size-fits-all solution for addressing trust issues in the supply chain. In software engineering, design patterns provide a blueprint that developers can follow to solve a specific problem in a structured and efficient manner. In this paper, we identify and discuss the reusable blockchain software patterns that can be applied to design trustworthy solutions in supply chain management (SCM). Based on the literature analysis, we define a comprehensive taxonomy of SCM-specific trust issues. Then we apply requirement engineering technique to translate these issues into trust requirements and demonstrate how these requirements can be met by the specific blockchain software patterns.
The Emerging Media Art Performance of Eine Kleine Nachtmusik

This performance aims to transform the traditional classical music experience through emerging media technology. Generative visualization will analyze and use not only musicians' audio but also audience-generated noise and ambient sound. This will make the audience's expression part of the performance, and the visual outcomes will become nonfungible experiences.
Illusory Land

The core idea is that fragments of memory echo through the meta-universe. It is a 360-degree immersive art work using the latest real-time animation technology. It can be displayed in VR or Dome projection. Allowing the audience to immerse themselves in a surreal Oriental landscape fantasy, experience dreams and memories of the past, present, and future growing, changing, disintegrating, and reorganizing in a chaotic meta-universe.
Design a effective parallel structure clustering approach for adaptive RGB image segmentation

In a pattern recognition and image understanding applications, one of the most significant processes considered is the colour image segmentation.In this paper, an effective parallel structure clustering approach for adaptive unsupervised based bottom up red-green-blue (RGB) colour histogram search approach is proposed to achieve colour image segmentation.The RGB histogram is processed first by usinga double-scan procedure for the determination of important modes in every histogram.The bottom-up histogram search approach is used in the nest step to process the each mode for the RGB triplet formation.These triplets are used as the cluster centroids which cluster the pixels into regions and produce the resulting segmented image.In addition, a parallel processing structure is designed and implemented for the proposed scheme on agraphics processing unit (GPU) to reduce computational complexity.Experimental results show that the proposed algorithm outperforms state-of-the art algorithms both in terms of computational complexity and execution speed.For computational complexity, the proposed scheme running on a GPU provided19 and 15 times lower complexity than the proposed scheme running on a CPU and the L0-based scheme, respectively.
Spy Robot

Abstract: The major reason for creating this robot is so that it can monitor combatants' movements and maybe take the position of guards who patrol at night. The Robots are equipped with night vision wireless cameras that may send videos of the battlefield to minimise damage and human casualties. Military personnel that enter an uncharted area run a serious risk to their lives. The robot will function as an effective piece of military equipment to reduce the loss of human life and to halt criminal activity. It will make it possible for all military personnel and members of the armed forces to comprehend the situation.
Sketch-Based Image Retrieval System for Criminal Records Using Deep Learning

Abstract: An overview to the Sketch Based Image Retrieval for Criminal Record where the user provides a sketch as input to the system to retrieve relevant images from the database. It is seen that traditional methods to draw the face sketch are still difficult and time consuming. This system is developed so that the identification of criminals is done faster than the traditional method. Therefore, the paper presents a simple and effective deep learning framework where user can create the sketch of the suspect and can be matched to the database to get the relevant criminal images. It mainly uses Histogram Oriented Graph, Support Vector Machine, Deep Convolutional Neural Networks machine learning algorithms for face landmarks estimation, feature extraction and pattern matching. SBIR proves to be more efficient and faster to process the criminal face in real-time, as time plays an important role in immediate action in the crime branch.
Design of Low Vision Electronic Glasses with Image Processing Capabilities Using Raspberry Pi

Poor vision is one of the most common eye health issues worldwide. Low vision patients are typically treated with optical devices or by substituting hearing or touch for visual capabilities. Head-mounted displays are currently the most promising form of low-vision assistive technology since they utilize the user's remaining natural visual capabilities. In this work, a prototype head-mounted display-based low-vision tool in the form of electronic glasses was designed utilizing a Raspberry Pi computer. The prototype was created using a Raspberry Pi 4 B coupled with cameras to allow real-time video acquisition. The LCD on the electronic eyewear frame as the camera showed the video recording. The prototype also included software utilizing five image processing modes—magnification, brightness enhancement, adaptive contrast enhancement, edge enhancement, and text detection and recognition- to help persons with limited vision acquire visual information more effectively. OpenCV was used with Python to create the software system. Average framerate measurements of 30–40 FPS for brightness and contrast improvement modes, 20 FPS for zooming and edge enhancement modes, and 1.3 FPS for text identification modes showed that the concept of electronic spectacles was successfully implemented in this research.
Comparative Study of RSA Asymmetric Algorithm and AES Algorithm for Data Security

There are many ways to ensure data security, one of the classic way but still effective is to use encryption. Encryption itself has two techniques or algorithms: symmetric-key (also called secret-key) and asymmetric-key encryption (also called public key). In this paper, we proposed an analysis of two algorithm of encryption: RSA and AES algorithm in term of securing digital data. The method used in this research are: RSA and AES analysis, then retrieving the result. The two algorithm is deeply and thoroughly analyzed to discover the effectiveness to secure the data. The Technological Readiness Level (TKT) is at level 6, which means demonstration of a model or prototype or the analysis result of a system or subsystem or a study in a relevant environment. The result concluded that the application of the AES encryption algorithm is more optimal than RSA encryption in digital data security. Because the encryption and decryption process of using the AES algorithm is faster, although the difference in testing time of the two encryption algorithms is not too significant. The entropy value of 4.96 in AES encryption is greater than that of RSA proving that the even distribution of characters in the chiper text code does not accumulate on certain characters so that it will be difficult to attack using frequency analysis.
A Pragmatic Study of Turn Taking and Adjacency Pairs in Online Conversations

This study tries to investigate one type of synchronous online communication, which is Internet Relay Chat, in terms of conversational analysis, particularly turn-taking and adjacency pairs. Synchronous online communication can be viewed as a novel medium that combines spoken, written and electronic properties. There may be a direct causal link between the lack of coherence in synchronous online communication and its propensity for language play that leads to the conversational analysis of synchronous online communication, which is associated with a reduction of coherence, disruption of turn adjacency and phantom turn adjacency. In synchronous online communication, there is a difficulty in interpreting messages in their sequential context that arises from the fact that turn sequencing is partly user-controlled and partly system-controlled. This leads to disrupted turn-taking and adjacency pairs as other stands of conversation get inserted between their parts.
Malignant Detection of Breast Nodules On BIRADS-Based Ultrasound Images Margin, Orientation, And Posterior

Breast cancer has the largest prevalence in the world in 2020, with 2,261,419 cases or 11.7%. It is also the leading cause of cancer death, accounting for 6.9% of all cancer deaths. Asia and Indonesia have the greatest prevalence and mortality rates. This is an urgent issue that must be addressed. Ultrasonography (USG) is advised for assessing the features of breast nodules. Breast nodules on ultrasound pictures are interpreted using the Breast Imaging, Reporting, and Data System (BIRADS) category, which has five features. Yet, the probability of a False Positive Result (FPR) on ultrasound imaging is relatively high. Computer Aided Diagnosis (CAD) was created to reduce FPR. However, CAD research based on many BIRADS traits is currently margined. As a result, based on three BIRADS characteristics, namely the margin, posterior, and orientation aspects, this study aims to proposed the methode for diagnosing breast nodule malignancy. The proposed method consists of 4 stages, namely, pre-processing, automatic segmentation, features extraction, and classification. Pre-processing adaptive median filter maximum window size is 11 pixels, linear histogram normalizing, and Reduction Anisotropic Diffusion (SRAD) filter were used to construct the method. The neutrosophic watershed method was used in the suggested automatic segmentation. Based on the nodule's margin, orientation, and posterior, 10 features were proposed: nodule width, gradient, slenderness, margin sharpness, shadow indicators, skewness, energy, entropy, dispersion, and solidity. MLP is a classification approach. The test used 94 nodule pictures and yielded an accuracy of 88.30%, a sensitivity of 82.35%, a specificity of 91.67%, a Kappa of 0.7449, and an AUC of 0.865. As a result, it is feasible to conclude that the proposed method is capable of detecting malignancy in breast nodules in ultrasound images. To make the proposed method more reliable in the future, automatic RoI can be developed.
Flutter Based Chatbot for Tourism

Kanyakumari, a popular tourist destination in India, attracts tourists from around the world with its scenic beauty, cultural heritage, and historical significance. However, tourists often face challenges in finding accurate information, making bookings, and planning their itinerary, leading to a less-than-optimal experience. In this technology intervention, we propose the development of a chatbot using JavaScript to address these challenges and provide a seamless and memorable tourism experience in Kanyakumari. The chatbot will be designed to engage with tourists in a conversational manner, providing information about tourist attractions answering frequently asked questions, assisting with bookings for accommodations and tours, and offering personalized recommendations based on user preferences. The chatbot will leverage JavaScript libraries for natural language processing (NLP) to understand user inputs, integrate with relevant APIs to fetch real-time data about Kanyakumari's tourist offerings, and implement backend functionality for managing user sessions and storing data. We anticipate that the development of a chatbot as a technology intervention will enhance the tourism experience in Kanyakumari making it hassle-free and memorable for tourists. The chatbot will provide accurate information, streamline bookings, and offer personalized recommendations resulting in improved tourist satisfaction and increased engagement with the destinatio
Exact Calculations for One Photon Micromaser with Coherent Inputs II

We introduce results for the second part of our calculations for the action of a single atom and one photon micromaser when atoms are coherently injected into a microwave cavity, and for the case when the atomic probabilities |α|≠0 and |β|≠0. During the presence of the atom inside the cavity the field couples to a heat bath at temperature T. The results of this second part and those of the first part of our work give a complete information for the behaviour of the micromaser field for the two cases of injected atoms namely|α|=1, |β|=0 and |α|≠0 , |β|≠0 . We have found that the evolution of the field after a sufficient number of atoms had passed the cavity shows a steady state when the repetition time T_pis much greater than the interaction time t_int. We also found that the trapping state at n = 3 plays an important role in the early dynamics of the field within the cavity. Finally, we found that the field evolution towards a mixed state and not to a pure state. This prevention of purity because of: the presence of decay of the field inside the cavity, the presence of the trapping state at n = 3 (centered between the two trapping states at photon number n = 0 and at a photon number n = 15) prevents the field from developing towards a pure state, and finally the presence of the black body radiation field initially in the cavity at temperature T.
The Role Macrosystems as Main Proponent of the Ecology Approach in Scientific Writing Learning

The ecological approach is very appropriate for understanding mental, psychosocial, and linguistic development. The aims of this study is to describe the perceptions of sociocultural ecology in agro-industrial societies and their impact on scientific writing competence. Data were obtained by observation and questionnaires on 90 students at the University of Jember as student representatives with an agro-industry environment and their involvement in research and learning texts about agro-industry in the field of writing. The results show that there are groups with high sociocultural ecological perception and knowledge values and groups with low perception and knowledge values. To see the urgency of perception and knowledge of scientific writing competence, these students took a writing test and then the results were examined in terms of reasoning, text organization skills, and procedural knowledge. After the statistical tests were carried out, it was concluded that the perception and knowledge of the agro-industry macrosystem had a significant effect on students' reasoning abilities, significance (sig) 0.032 &lt;0.05; does not have a significant effect on text organization competence with a significance value (sig) 0.067 &gt; 0.05; and has a significant effect on procedural knowledge with a significance value (sig) 0.032 &lt;0.05. These results indicate that the scientific productive competence of language with an ecological approach from the macrosystem aspect needs to pay attention to creativity. Greater efforts are needed to improve the development of writing on the components of unity, continuity, completeness, sequence, and point of view consistency.