Reviewerly/exHarmony huggingface dataset:
A New Coarse-To-Fine 3D Face Reconstruction Method Based On 3DMM Flame and Transformer: CoFiT-3D FaRe

The process of 3D face reconstruction has the remarkable capability to recover 3D information from a single facial image, enabling its utilization in accelerated 3D modeling, facial animation, and enhanced face recognition performance. The prevailing technique employs deep learning Convolutional Neural Networks (CNNs) to forecast intermediary 3D facial structures, such as 3D Morphable Model (3DMM) parameters or UV maps. While these methods demonstrate promise, they still encounter limitations with occlusions and depend heavily on pre-existing linear 3DMM, which restricts the utilization of input image data. In this paper, we introduce CoFiT-3D FaRe, a novel Coarse-to-Fine methodology for reconstructing 3D human faces. This approach makes use of the Dual Vision Transformer network (DaViT) and 3DMM Flame. The DaViT proficiently captures both local features and global context within the images, effectively overcoming limitations imposed by CNNs. In the initial coarse stage, the DaViT is employed to regress 3DMM parameters from the input image, facilitating the reconstruction of 3D human face’s shape and texture at a coarse level. While the 3DMM provides valuable three-dimensional information, its linear nature imposes constraints on achieving a realistic representation. To tackle this issue, the subsequent fine reconstruction stages extract more intricate information from the image and refine the outcomes of the initial coarse reconstruction. The paper employs detail reconstruction block of DECA (Detailed Expression Capture and Animation) for precise shape reconstruction and introduces an innovative approach for accurate texture reconstruction. This enables the restoration of occluded regions and the retention of intricate facial features. Our experiments on the NoW evaluation set demonstrate competitive outcomes in both shape and texture reconstruction when compared to state-of-the-art (SOTA) solutions.
Handling of unbalanced LC-MS medicinal plant data using Near-Miss Undersampling tested with Gaussian Naive Bayes and K-Nearest Neighbors

Imbalanced data refers to data with classes that have extreme majority and minority data. Such data can lead to inaccurate results. The dataset used in this study came from LC-MS data of medicinal plants that had previously been labeled using the webscraping method and unbalance. There are several resampling algorithms to balance the data. This study used nearmiss undersampling with consideration for being more robust against overfitting. The balanced data was split for training and testing with a ratio of 70:30, which will be tested using Gaussian Nave Bayes and K-Nearest Neighbors classification algorithms. The results showed that Near Miss version 1 sampling with the Gaussian Naive Bayes algorithm provided better accuracy and faster execution time.
Probability Forecast of Photo Voltaic Power with an Adaptive Discretization Algorithm in Higher-Order Markov Chain

The higher-order Markov chain (HMC) model is most common photovoltaic (PV) output probability prediction method. The HMC model requires to determine the number of discretization states subjectively, a technique to optimize the selection of the state number, however, is missing. To address this issue, an adaptive discretization method based on information entropy theory is proposed in this paper, which considers the PV output volatility and enhances prediction accuracy and uncertainty capturing ability. Experimental results using real-world PV output datasets validates the superiority of the proposed method over traditional HMC models by achieving higher prediction accuracy and better capture the uncertainty.
Underwater Target Detection Based on Improved Yolov5

Remotely Operated Vehicle (ROV) has been widely used for underwater rescue, underwater detection, underwater fishing and other underwater missions. Underwater target detection can obtain the class information and position information of the target. The vision-based underwater target detection algorithm can enhance the underwater observation capability of ROV, which is crucial for ROV to achieve high precision underwater grasping and underwater fishing. Based on this, this paper improves the original network architecture of YOLO v5 to make it better adapted to the requirements of underwater target detection tasks, including: replacing the SPPF layer in the original backbone network using the MSPPF structure; introducing a new loss function Alpha-SIOU. These operations can better detect the small and dense characteristics of underwater targets and enable the detector to capture features more accurately. In this paper, experiments are conducted based on underwater biological datasets. The results show the improved Y olov5 achieves an average accuracy of 78.1%, which is a significant improvement over the baseline of YOLO v5.
Data Trust in Logistics: Price Prediction for Collaboration

The logistics industry is facing the challenge of handling the increasing number of parcel shipments, which requires more drivers or alternative approaches. One possible solution is to improve collaboration between logistics providers through the use of a data trust. This paper proposes the implementation of a price prediction model to automate the comparison of potential partners and thus facilitate collaboration. The concepts of data trust, access control and policies, as well as the fundamentals of artificial intelligence in price formation and prediction are explained. The proposed model aims to simplify the pricing process and incorporate it into a global model. The study highlights the importance of data privacy and security, regulatory compliance, the use of access control for enforce access policies and machine learning methods in data trust environments. The findings highlight the potential benefits of AI-based pricing models in the logistics industry and the need for further research and development in this area.
Adaptive Nonlinear Dimensionality Reduction with a Local Metric

Understanding the structure of multidimensional patterns, especially in unsupervised cases, is of fundamental importance in data mining, pattern recognition and machine learning. Several algorithms have been proposed to analyze the structure of high dimensional data based on the notion of manifold learning. These algorithms have been used to extract the intrinsic characteristics of different types of high dimensional data by performing nonlinear dimensionality reduction. Most of these algorithms rely on the Euclidean metric and manually choosing a neighborhood size. They cannot recover the intrinsic geometry of data manifold automatically and accurately for some data sets. In this paper, we propose an adaptive version of ISOMAP that integrates the advantages of local and global manifold learning algorithms. Faster convergence rate of this algorithm is obtained by replacing the Euclidean metric with the arc length metric as the sum of second-order approximation to the geodesic distance. Our experiments on synthetic data as well as real world images demonstrate that our proposed algorithm can achieve better performance than that of ISOMAP and solve some harder data sets that are impossible for existing global methods.
Dialogue Attributes' Zero-Shot Classification Based Anime Scene's Matching for Japanese Listening Test

This paper proposes a method to provide Anime dialogue scenes for Japanese listening test training through Zero-shot classification. In this study, listening test dialogues and anime dialogue scenes were categorized by three attributes: speakers’ relationship, dialogue location, and dialogue style. We collected 90 listening dialogues from each level of the past Japanese Language Proficiency Test (JLPT) and manually labeled the attributes of each dialogue. After testing the effect of the zero-shot model on listening dialogues’ classification with 2,143 different sets of label keywords, nine sets with the highest accuracy were identified. We classified 247,645 anime dialogue scenes’ attributes using the nine sets of label keywords and counted the number of anime scenes, word cover rate, and text similarity between anime dialogues and 90 listening tests when different kinds of attributes were matched. The results show that as the number of matched attributes increases, the range of selected anime scenes continues narrowing and being precise while keeping basically the same word cover rate and text similarity. The average number of anime scenes when matching single, double, and triple attributes were 140,988, 70,834, and 29,362, while the word cover rate of matched anime scenes to the input listening dialogues were 96.18%, 95.22%, and 94.51%.
Possibilities of using smart technologies in the higher education system for high-quality training of specialists

The goal of Smart-education of the XXI century is written out – the development of skills in the conditions of a digital society, which are necessary for the successful professional activity of specialists; provision of an environment that creates competitive specialists due to a high level of development of the skills and knowledge of modern society among students of higher education. The concept of Smart-education is described, which involves the creation of an intellectual environment for the application of Smart-technologies in the higher education system with the aim of quality training of specialists, and continuous development of phase competencies in participants of the educational process. The main ideas that form the basis of the Smart concept are highlighted. The reason for the necessary implementation of Smart-learning is proven. Positions unifying the educational process in the Smart environment are shown, the vision of the Smart-education strategy is presented, and its ultimate goal is described. In the process of our search, we conducted a survey of students and university teachers in order to compare the answers of the respondents and their actual attitude to the implementation of Smart technologies in the educational process.
An Improved Attention Mechanism Fusion for Multi-Label Image Classification

Multi-label image classification (MLIC) is a practical and challenging task, the main goal is to correctly identify each object from an image containing multiple objects. Since the labels in the image have a certain correlation, the graph convolutional network (GCN) is used to model label dependence with word embedding as the prior knowledge, which significantly improves the accuracy of MLIC. However, the introduction of the GCN promotes the label word to be referenced as an information different from the image modality, which requires the model to require two modalities for information fusion. Due to the natural semantic gap between different modal information, the direct fusion method leads to information mismatch. We propose a Dynamic Fusion Image and Word Attention Module (DFIW) to solve information mismatch. The DFIW module consists of two basic attention units. By simulating the self-attention of label words and images, the information of the two modes is fully fused. The effectiveness of our model (DFIW-GCN) is evaluated on public datasets. Experiments show that DFIW-GCN can achieve better performance.
Classifying respondent comments from the 2021 Canadian Census of Population using machine learning methods1

To improve the analysis of respondent comments from the Canadian Census of Population, data scientists at Statistics Canada compared and evaluated traditional machine learning, deep learning and transformer-based techniques. Cross-lingual Language Model-Robustly Optimized Bidirectional Encoder Representations from Transformers (XLM-R), a cross-lingual language model, fine-tuned on census respondent comments yield the best result of 89.91% F1 score overall despite language and class imbalances. Following the evaluation, the fine-tuned model was implemented successfully to objectively categorize comments from the 2021 Census of Population, with high accuracy. As a result, feedback from respondents was directed to the appropriate subject matter analysts, for them to analyze post-collection.
Socio-demographic determinants of Filipino school leaders’ digital leadership

The field of digital leadership in the Philippines is limited, despite the country's growing regard for school digitization. This study investigated the socio-demographic factors such as age, gender, years of service, educational attainment, location of school, and type of school, that influenced Filipino school leaders' digital leadership. A cross-sectional survey was conducted with 386 school leaders, selected through convenience sampling technique. The data was collected through an invitation link to a questionnaire survey distributed through various social media platforms. Descriptive data using frequency and percentage, and independent t-test and one-way analysis of variance (ANOVA) were employed to examine the variables of the study. The study found that younger male leaders working in urban or private schools, or those who were relatively new in their leadership roles, with higher educational qualifications tend to have higher levels of digital leadership. This meant that socio-demographic factors of school leaders can influence their digital leadership. The study also emphasizes the critical need for in-depth research into the nature and implications of digital leadership in the Philippine educational landscape, focusing on how socio-demographic factors influenced the adoption of digital leadership skills. The study's findings implied that government and educational institutions need to take a more proactive role in supporting the development of digital leadership skills among all school leaders, regardless of their socio-demographic backgrounds. This includes providing training and support, and creating a more supportive environment for digital leadership.
Research of Differences in Chinese and Western Thinking in Linguistic and Custom

In the situation of gradual globalization, communication and interaction between various languages and conventions have become more frequent. The reflection of the difference between Chinese and Western thinking in linguistic and cultural diversity has become more apparent and essential. By doing this, people can have a further comprehension of the values, cognitive styles, and social habits behind different cultures and languages. In recent research, it has been shown that there are differences in language expression between China and the West, as well as differences in personal values and time. The author would like to research the differences between Chinese and Western ways of thinking in language, the differences in social etiquette and ways of interacting with the different values and cognitive styles. The purpose of the research is to assist people better cope with the challenges of cultural conflicts and exchanges and to foster mutual cooperation among different cultures. The research method used in this paper is the literature review, and the significance of the research is that it not only can help people better cope with and conform to this difference in thinking between China and the West, but it also improves the international communication capacity of future students.
StaTI: Protecting against Fault Attacks Using Stable Threshold Implementations

Fault attacks impose a serious threat against the practical implementations of cryptographic algorithms. Statistical Ineffective Fault Attacks (SIFA), exploiting the dependency between the secret data and the fault propagation overcame many of the known countermeasures. Later, several countermeasures have been proposed to tackle this attack using error detection methods. However, the efficiency of the countermeasures, in part governed by the number of error checks, still remains a challenge.In this work, we propose a fault countermeasure, StaTI, based on threshold implementations and linear encoding techniques. The proposed countermeasure protects the implementations of cryptographic algorithms against both side-channel and fault adversaries in a non-combined attack setting. We present a new composable notion, stability, to protect a threshold implementation against a formal gate/register-faulting adversary. Stability ensures fault propagation, making a single error check of the output suffice. To illustrate the stability notion, first, we provide stable encodings of the XOR and AND gates. Then, we present techniques to encode threshold implementations of S-boxes, and provide stable encodings of some quadratic S-boxes together with their security and performance evaluation. Additionally, we propose general encoding techniques to transform a threshold implementation of any function (e.g., non-injective functions) to a stable one. We then provide an encoding technique to use in symmetric primitives which encodes state elements together significantly reducing the encoded state size. Finally, we used StaTI to implement a secure Keccak on FPGA and report on its efficiency.
Imitation Is the Most Sincere Form of Mockery: Mock Jewish English in Online Extremist Communities

Abstract This article examines the use of Mock Jewish English ( MJE ) among members of the modern far right as a means of perpetuating ideologies centered around antisemitic canards originating from the 19th and 20th centuries. In order to investigate MJE as an act of language crossing, I examine the 900+ million token Unicorn Riot subcorpus of the Corpus of Digital Extremism and Conspiracies (CoDEC). Following this analysis, I describe the linguistic features of MJE when it is used as ventriloquation, specifically the lexical and phonological features employed by the white supremacist parody advice show, Dear Rabbi . In my analysis, I find that these two strategies of MJE are used by members of the far right to spread antisemitic ideologies, further the semantic pejoration of Jewish lexical borrowings, and covertly affiliate themselves with one another in public spaces (via language crossing) or distance themselves from Jewishness (via ventriloquation).
Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic

Computational intelligence and nature-inspired computing have changed the way biologically and linguistically driven computing paradigms are made. In the last few decades, they have been used more and more to solve optimisation problems in the real world. Computational linguistics has its roots in linguistics, but most of the studies being done today are led by computer scientists. Data-driven and machine-learning methods have become more popular than handwritten language rules, which shows this shift. This study uses a new method called Computational Linguistics-based mood Analysis using Enhanced Beetle Antenna Search with deep learning (CLSA-EBASDL) to tackle the important problem of mood analysis during the COVID-19 pandemic. We sought to determine how people felt about the COVID-19 pandemic by studying social media texts. The method is made up of three main steps. First, data pre-processing changes raw data into a shape that can be used. After that, word embedding is done using the ‘bi-directional encoder representations of transformers (BERT) process. An attention-based bidirectional long short-term memory (ABiLSTM) network is at the heart of mood classification. The Enhanced Beetle Antenna Search (EBAS) method, in particular, fine-tunes hyperparameters so that the ABiLSTM model works at its best. Many tests show that the CLSA-EBASDL method works better than others. Comparative studies show that it works, making it the best method for analysing opinion during the COVID-19 pandemic.
Application of Fuzzy Time Series Method Cheng Model in Forecasting Stock Prices PT Bukit Asam Tbk

Investment in stocks is one type of investment that can get huge profits, but there are also great risks. So it is necessary to analyze in advance before starting an investment in stocks, in order to avoid losses. One way is to forecast the stock price using fuzzy time series Cheng. The data used is weekly period stock price data from PTBA in January 2020 - December 2022, which can be categorized as a form of time series. From this research, the forecasting value for the next period is Rp. 3797. Which results in a MAPE of 4.2%, which means that FTS Cheng method is very good to use in forecasting the share price of PT Bukit Asam Tbk, because it produces a MAPE value &lt;10%, and produces an RMSE of 158 rupiah, which means the average of the difference between actual and forecast values.
MEDIA PEMBELAJARAN BAHASA MELALUI MEMBACA UJARAN BAGI PENYANDANG TUNARUNGU BERBASIS ANDROID

The communication system for the deaf is non-verbal (non-verbal) communication using signs, either hand movements or body signals and facial expressions. Deafness can be defined as a condition of hearing loss. Deaf children are children who experience hearing deficiencies and therefore require special services. Deaf children have difficulty observing sounds through their hearing, therefore they have to perceive sounds or sounds or someone's expressions through their sight. In this research the author examines and analyzes the problem of Android-based language learning media through speech reading for deaf people. The purpose of writing is as a tool or alternative medium to be able to learn how to read your opponent's lip movements in the communication process. This application uses the research and development (R&amp;D) method and in designing this learning media application, it uses the ADDIE Model (Analysis, Design, Development, Implementation, and Evaluation). The result of this research is an Android-based language learning media application through speech reading for deaf people that has been created to help the learning process, especially between teachers and deaf students in learning at SLB Muhamadiyah Kelayu.
The Phenomenon Of Code Switching And Code Mixing In English Learning Classroom

The diversity of languages in Indonesia has sundry errors such as gaps, errors in interpreting other languages, and others. It means, it also applies when in the process of learning foreign languages at school, especially English. Thus, learning English is difficult to build a positive spirit in learning, making students active and responsive in the learning process. Therefore, this study focuses on examining the types of code-switching and code-mixing in learning English in schools. This study uses a qualitative paradigm, using a descriptive case study type of research. Therefore, the researcher sets the goal to describe the data as it occurs in the phenomenon under study. This study is the occurrence of code-switching and code-mixing events in learning English at school. In this study, the researcher used guided semi structured interviewed as a data collection technique. The data from this study was analyzed based on the steps used, namely identifying the data, classifying the data, and describing the data according to the research objectives. The results of this study are the determinants of code-switching and code-mixing from teachers or students. Then, there are types of each between code-mixing and code-switching.
Research on augmented reality navigation of in vitro fenestration of stent-graft based on deep learning and virtual-real registration

Objectives In vitro fenestration of stent-graft (IVFS) demands high-precision navigation methods to achieve optimal surgical outcomes. This study aims to propose an augmented reality (AR) navigation method for IVFS, which can provide in situ overlay display to locate fenestration positions.