Reviewerly/exHarmony huggingface dataset:
Real-time Face Recognition System Using Deep Learning Method

Face recognition is one of the most popular methods currently used for biometric systems. The selection of a suitable method greatly affects the reliability of the biometrics system. This research will use Deep learning to improve the reliability of the biometric system and will compare it with the SVM method. The Deep Learning method will be adopted using the Siamese Network with the YoloV5 detection method as a real-time face detector. There are two stages in this research: the registration process and the recognition process. The registration process is image acquisition using YoloV5. The image result will be saved in the storage folder, and the preprocessing and training process will use the Siamese Network. The face feature model will be stored in the database. The recognition process is the same as the registration, but the feature extraction result will be embedded and compared with the already trained models. The accuracy rate using the Siamese model was 94%.&#x0D;
Identify Reviews of Pedulilindungi Applications using Topic Modeling with Latent Dirichlet Allocation Method

The emergence of Covid-19 in December 2019 has disrupted life worldwide, including Indonesia. The government has made various efforts to control the pandemic, one of which is the development of an application called PeduliLindungi. This app aims to be a reliable tool for the government and the entire community during the pandemic. As a new regulation, the use of PeduliLindungi has prompted numerous reviews assessing its quality and performance. With the app's emergence and growth, various topics have emerged and become trending among the public. These topics were identified through user reviews of the PeduliLindungi app, using the Latent Dirichlet Allocation (LDA) algorithm. The data, consisting of 15,522 reviews, was collected from the Google Play Store and underwent pre-processing, including dictionary and corpus creation, determining the number of topics, and modeling with LDA. The resulting topic modeling process generated the ten most prominent topics. The outcomes were visualized using word clouds and topic distribution graphs, representing the most discussed aspects of the PeduliLindungi app among users. These topics are considered diverse since each issue has no relation or similarity to one another.
Application of multimodal perception scenario construction based on IoT technology in university music teaching

In the contemporary landscape of diversified talent cultivation, enhancing education through intelligent means and expediting the process of talent development stand as paramount pursuits. Within the domain of instrumental music education, beyond merely listening to student performances, it becomes imperative to assess their movements, thus furnishing additional insights to fuel their subsequent growth. This article introduces a novel multimodal information fusion evaluation approach, combining sound information and movement data to address the challenge of evaluating students' learning status in college music instruction. The proposed framework leverages Internet of Things (IoT) technology, utilizing strategically positioned microphones and cameras within the local area network to accomplish data acquisition. Sound feature extraction is accomplished through the employment of Mel-scale frequency cepstral coefficients (MFCC), while the OpenPose framework in deep learning and convolutional neural networks (CNN) is harnessed to extract action features during students' performances. Subsequently, the fusion of feature layers is achieved through CNN, culminating in the evaluation of students' academic efficacy, facilitated by a fully connected network (FCN) and an activation function. In comparison to evaluations conducted by the teacher in the class, this approach achieves an impressive accuracy of 95.7% across the three categories of Excellent, Good, and Failed students' evaluation processes. This breakthrough offers novel insights for the future of music teaching and interactive class evaluations while expanding the horizons of multimodal information fusion methods' applications.
An Analysis of Grammatical Errors in Chinese EFL Learners' Oral English

Compared with reading, writing, listening, Chinese English as Foreign Language (EFL) learners oral English ability is usually far weaker and ignored in classroom language teaching. In terms of oral English teaching and practicing, most of the emphasis is on pronunciation, vocabulary, intonation, while little attention has been given to the grammatical errors in the oral English. The present research focuses on analyzing grammatical errors in Chinese EFL learners oral English. In order to identify the most prevalent grammatical error types and determine the relationship between grammatical errors and oral English proficiency levels, the study collects recordings of subjects discussing a particular topic and transcribing them for error recognition, classification, frequency analysis, and correlation analysis. It is found that Chines EFL learners commit preposition and article errors most frequently; no obvious correlation between error numbers and English-speaking levels is shown, while two error types omission auxiliary be and noun for adjective have significant positive correlation with learners English-speaking scores as indicators of their oral English levels. Based on that, it is concluded that differences between the mother tongue and English, as well as the complexity of some grammatical rules are main causes for errors, and teachers should be aware that students are likely to commit some certain types of errors more as they make progress and try to use more complicated grammatical patterns.
KC-GEE: knowledge-based conditioning for generative event extraction

Abstract Event extraction is an important, but challenging task. Many existing techniques decompose it into event and argument detection/classification subtasks, which are complex structured prediction problems. Generation-based extraction techniques lessen the complexity of the problem formulation and are able to leverage the reasoning capabilities of large pretrained language models. However, they still suffer from poor zero-shot generalizability and are ineffective in handling long contexts such as documents. We propose a generative event extraction model, KC-GEE, that addresses these limitations. A key contribution of KC-GEE is a novel knowledge-based conditioning technique that injects the schema of candidate event types as the prefix into each layer of an encoder-decoder language model. This enables effective zero-shot learning and improves supervised learning. Our experiments on two benchmark datasets demonstrate the strong performance of our KC-GEE model. It achieves particularly strong results in the challenging document-level extraction task and in the zero-shot learning setting, outperforming state-of-the-art models by up to 5.4 absolute F1 points.
HOLOTWIN: A Modular and Interoperable Approach to Holographic Telepresence System Development

In the field of holographic-type communication (HTC), this paper presents a comprehensive exploration of existing technologies and proposes a novel, modular architecture for holographic telepresence systems (HTPSs). We substantiate our architectural framework through a practical implementation, demonstrating its modularity, interoperability, and versatility. Quantitative and qualitative assessments reveal both the promise and areas for improvement within our platform. Our findings reinforce the premise that the key to unlocking HTC's future lies in modularity and interoperability, serving as critical pillars for efficient standardization and the development of minimal viable products.
tex2net: A Package for Storytelling Using Network Models

As the volume of textual data grows at a fast pace, there is an increasing need for effective techniques to analyze and present this data meaningfully. Traditional methods of summarizing text data, such as word clouds or tag clouds may not provide a comprehensive narrative overview. In contrast, visual representations, such as graphs, arguably allow the visualization of more complex information. In this paper, we propose a text-to-graph conversion technique that allows the visualization of a story's main characters and relationships. Although visualizing text data through graphs is becoming increasingly popular, existing graph tools generally depend on structured data representations and are unable to comprehensively visualize a narrative and its entities (characters). Our proposed text-to-graph conversion technique addresses this gap, by providing a valuable tool for storytelling visualization, along with relevant guidelines. To this end, we propose a methodology to learn expressive graphs (stories) by extracting relevant relationships between focal entities (characters) from a text document. Graph representation is subsequently refined to communicate the flow of sample narratives. The methodology is provided as a software library, termed tex2net. The acquired results indicate that the proposed approach is able to summarize the story, complementing the use of traditional text summarization techniques. Additionally, we found the graphical summaries more engaging and easier to understand.
Utilization of Webcam Cameras as X-Ray Image Capture (kV Settings and Shutter Sensors)

The result of x-ray imagery is generally in the form of film sheets. To obtain an image, the film must pass through the process of processing with chemicals. Departing from these problems, efforts were made to develop digital x-ray detectors using much cheaper devices. By using voltage variations and also sensors that aim to find out the difference in the quality of the resulting image. The contribution of this study is that the system can display negative image results from webcam camera captures that have previously passed the image processing process with the Matrix Laboratory (MATLAB) Application. The measurement ranges used are 60, 65, and 70 kV. with a set mA of 25mA, a duration of irradiation of 1 second, and a moderate intensity of illumination light. From the measurement results, it shows that the X-ray Image Capture Tool can be compared with the image results from the Philip brand DR with the MSE value obtained, which is 34.8775 with parameters Phototransistor BPT1331, 70kV, and 25mA. And the lowest MSE value is 61.7615 with parameters LDR, 66kV, 25mA. The results of this study indicate that the tool can be used to capture X-rays.
Technologies of indirect influence in patriotic education of students of Russian higher educational institutions

The article describes indirect influence technologies that can be used for education (in particular, patriotic) both in group and individual work with students of Russian universities. The first one, in particular, includes game methods (quizzes, heuristic games, debates, discussions, group drawing), the second one – working with texts, writing compositions, essays and letters. The thesis is substantiated that indirect influence is in many cases more effective than the direct one, since it affects not only the consciousness and intellect, but also the feelings and subconsciousness of the trainees. In the context of the increased importance of educational work in modern conditions, the article will be useful to all teachers of the Academy.
Semantic-Aware Efficient Multi-Keyword Top K-Similarity Search Over Encrypted Cloud Data

Outsourcing data storage in infrastructure has been a popular solution for organizations and individual users since it offers numerous advantages over traditional on-premises storage choices. Data encryption before outsourcing data to infrastructure is a general strategy to safeguard data confidentiality. It is challenging to search for the specified keywords in encrypted datasets in cloud computing settings, and it is obviously impracticable to download all the data from the cloud and decode it locally. The focus of current search technique is on exact matches and simple pattern matching, which result in incomplete or irrelevant. The approach uses 4D hyperchaotic mapping and a powerful deoxyribonucleic acid (DNA) encryption mechanism to make it very difficult to decrypt the encrypted data without the proper key. The proposed approach helps create an effective and safe encryption. Global vector word embedding is taken into consideration while generating semantically aware search results in a semantically conscious top-k multi-keyword retrieval-supporting searchable encryption technique.
A Distributed Virtual-Machine Placement and Migration Approach Based on Modern Portfolio Theory

Abstract Virtual machine placement and migration (VMPM) are key operations for managing cloud resources. Considering the large scale of cloud infrastructures, several proposals still fail to provide a comprehensive and scalable solution. A variety of approaches have been used to address this issue, e.g., the modern portfolio theory (MPT). Originally formulated for financial markets, MPT enables the construction of a portfolio of financial assets in order to maximize profit and reduce risk. This paper presents a novel VMPM approach applying MPT and incremental statistics computation for VMPM decision-making so as to maximize resource usage while minimizing under and overload. Extensive simulation experiments were conducted using CloudSim Plus, relying on synthetic data, PlanetLab and Google Cluster traces. Results show that the proposal is highly scalable and largely reduces computational complexity and memory footprint, making it suitable for large-scale cloud service providers.
Web3 and the future of applied ecosystem and conservation science

Web3, the third generation of the internet, aims to deliver decentralized and democratic control of information, addressing issues of data privacy, incentivization, and trust through transparency, participation in decision-making and the distribution of value using digital assets. Web3 technologies are starting to change the higher education research and development landscape, offering potential solutions to many of the most entrenched challenges facing applied science, including inefficiency, inequity, bias, exploitation, and exclusion. In this Roadmap, I sketch the various components of Web3 that are likely to be most relevant to the future of decentralized ecological and conservation sciences and encourage greater participation by established scientists to ensure that Web3 technologies genuinely address the problems facing the incumbent science model.
FortiRx: Distributed Ledger Based Verifiable and Trustworthy Electronic Prescription Sharing

A paper-based prescription signed by the prescriber to authorize dispensing of medication is typically used in traditional healthcare. Such systems are prone to many issues like medication errors, latency, and lack of integration with other healthcare systems. Hence, Electronic prescription (E-prescription) systems are being used as alternatives to overcome these issues. Even though E-prescription systems provide the advantage of recording and maintaining patient medication history but still face issues such as system crashes, latency due to their centralized architectures, prone to many security threats like identity theft and unauthorized patient record access and modifications. Lack of standardization can also make such E-prescription systems not interoperable, which may lead to information fragmentation or delays in the processing of prescriptions. Hence, there is still a need for making these E-prescription systems more secure, reliable, and cost-effective for wide-range adaptation. Blockchain is one such technology that can add additional layers of security to the existing E-prescription systems by providing tamper-proof records of all transactions which will help in ensuring the authenticity and integrity of prescriptions. Blockchain can also help in better management of patients' privacy while patients still have full control over their health data. Blockchain usage can also enhance interoperability and reduce prescription abuse. The proposed application FortiRx makes use of the Ethereum blockchain platform and leverages smart contracts for implementing business logic. Cyphertext-Policy Attribute-Based Encryption (CP-ABE) is used in the proposed application to create and manage access-control mechanisms and ensure Health Insurance Portability and Accountability Act (HIPPA) compliance. The proposed system has been implemented and analyzed for security, reliability, and adaptability in a real-time environment.
Improvement in Multi-resident Activity Recognition System in a Smart Home Using Activity Clustering

Human Activity Recognition (HAR) integrates ambient assisted living (AAL), leading to smart home automation for monitoring activities, healthcare, fall detection, etc. Various researchers have proposed a single-resident HAR system for ambient-sensor based smart home data, which is simple, and single-resident is not always the case. Multi-resident recognition is slightly complex and time-consuming. The researchers have made several efforts to generate benchmark datasets, such as CASAS, ARAS, vanKasteren, etc., for baseline comparison and performance analysis. However, these datasets have certain limitations, such as data association, annotation scarcity, computational cost, and even with data collection itself. This paper profoundly analyzed these limitations and manually clustered the activity labels to record the improvement in the performance of the system in terms of both recognition rate and computational time on the ARAS dataset.
Unsupervised Text Embedding Space Generation Using Generative Adversarial Networks for Text Synthesis

Generative Adversarial Networks (GAN) is a model for data synthesis, which creates plausible data through the competition of generator and discriminator. Although GAN application to image synthesis is extensively studied, it has inherent limitations to natural language generation. Because natural language is composed of discrete tokens, a generator has difficulty updating its gradient through backpropagation; therefore, most text-GAN studies generate sentences starting with a random token based on a reward system. Thus, the generators of previous studies are pre-trained in an autoregressive way before adversarial training, causing data memorization that synthesized sentences reproduce the training data. In this paper, we synthesize sentences using a framework similar to the original GAN. More specifically, we propose Text Embedding Space Generative Adversarial Networks (TESGAN) which generate continuous text embedding spaces instead of discrete tokens to solve the gradient backpropagation problem. Furthermore, TESGAN conducts unsupervised learning which does not directly refer to the text of the training data to overcome the data memorization issue. By adopting this novel method, TESGAN can synthesize new sentences, showing the potential of unsupervised learning for text synthesis. We expect to see extended research combining Large Language Models with a new perspective of viewing text as an continuous space.
MPEG Column: 139th MPEG Meeting (Virtual/Online)

The original blog post can be found at the Bitmovin Techblog and has been modified/updated here to focus on and highlight research aspects.
Joint Variational Auto-Encoder for Anomaly Detection in High Energy Physics

Despite providing invaluable data in the field of High Energy Physics, the LHC may encounter challenges in obtaining interesting results through conventional methods applied in previous run periods. Our proposed approach involves a Joint Variational Autoencoder (JointVAE) model, trained on known physics processes to identify anomalous events corresponding to previously unidentified physics signatures. By doing so, this method does not rely on any specific new physics signatures, and it can detect anomaly events in an unsupervised manner, complementing the traditional LHC search tactics relying on model-dependent hypothesis testing. This paper also presents a study on the implementation feasibility of the JointVAE model for real-time anomaly detection in general-purpose experiments at CERN LHC. Low latency and reduced resource consumption are among the challenges faced when implementing machine learning models in fast applications, such as the trigger system of the LHC experiments. Therefore, the JointVAE model has been studied for its implementation feasibility in Field-Programmable Gate Arrays (FPGAs), utilizing a tool based on High-Level Synthesis named HLS4ML. The code that supports this work is publicly available at https://github.com/LorenzoValente3/JointVAE4AD.
W-Li Potentials in the Tailing Dumps of Rewat Hill, Degana, Rajasthan, NW India; Constraints from Petrography and Geochemistry

The largest tungsten deposit at Degana in NW India, hosted in Neoproterozoic (ca. 827±8.2 Ma) peraluminous granite emplaced within Delhi meta-sediments, characterized as vein and greisen types hydrothermal multi-metal deposit. A number of NW-SE trending quartz and pegmatite veins, intruded in granite, host tungsten and lithium of economic significance. Extensive mining for tungsten ore in the past resulted in huge amount of tailing dumps on hill slope of Rewat hill all over. These tailing dumps mainly contain greisenised granite, pegmatite and quartz-mica-wolframite veins which host anomalous W-Li-Sn-Mo-Ta multi-metals. In this study, we provide first-hand information on W-Li and associated rare metals potential of tailing dumps of Rewat hill using petrographic study and geochemical analysis. A total 7 samples from tailing dumps were analysed by ICP-MS indicate significant W values which ranges between 201.47 ppm to 2647.83 ppm with an average of 748.51 ppm, Li (1006.92 ppm to 6701.43 ppm, avg. 2743.64 ppm), Sn (22.77 ppm to 438.87 ppm, avg. 96.49 ppm), Mo (3.43 ppm to 254.21 ppm, avg. 63.0 ppm) and Ta up to 53.09 ppm. Ore beneficiation study on tungsten ore from tailing dumps by NML indicated that at 5% WO3, the grade of the pre-concentrate; a recovery of > 60% could be achieved whereas at 30% WO3 grade the recovery is about 46%. However, IBM results indicated a recovery of 59.8% with 66.30–66.50% WO3 grade of final concentrate from tailing dumps having assay value between 0.14%–0.15% WO3.
Personalized Recommendation System Based on Meta-learning for Human-centric Consumer Services in Gig Economy

The gig economy has facilitated the growth of customized services through digital platforms that connect consumers with service providers. However, the surge in service providers has led to a “cold-start problem", which limits the effectiveness of personalized task recommendation systems. To address this challenge, this paper purposed a personalized recommendation system for human-centric consumer services in the gig economy. It addresses the problem by using meta-learning to generate suitable preference embeddings for workers with limited bidding history, interests, and working competence. The system includes a competence module with self-attention and interest modules to capture workers’ personalized preferences. The model is evaluated on real-world datasets from Freelancer.com, and the results demonstrate that it outperforms state-of-the-art models in accurately recommending suitable personalized tasks to both new and existing workers with skill-evolving. The proposed system can reduce task completion times and improve task quality by ensuring that tasks are assigned to the most suitable workers.