Reviewerly/exHarmony huggingface dataset:
EasyGaze3D: Towards Effective and Flexible 3D Gaze Estimation from a Single RGB Camera

Eye gaze can convey rich information of human intentions, which enables the social robots to comprehend the cognition and behavior of human targets. However, the existing 3D gaze estimation methods generally have high requirements either on the dedicated hardware or the quantity and quality of training databases, which largely limits their practical application values. This paper proposes EasyGaze3D, an effective 3D gaze estimation framework using a single RGB camera. First, the framework detects the 2D facial landmarks and recovers the 3D facial shape from the input image, and derives the required camera parameters with these features. Then, without loss of generality, the gaze direction can be regarded as the vector pointing from the eyeball center to the pupil center, which are derived respectively from the detected facial landmarks and the spherical fitting performed on the recovered 3D facial shape. Besides, we propose a flexible yet efficient calibration module, namely Easy-Cali, for deriving the subject-specific 3D facial shape and eyeball centers. The features calibrated by Easy-Cali can further boost the performance of EasyGaze3D. Experimental results show that our proposed method, being plug-and-play and without the need of training on large-scale dataset, can achieve superior performance against the existing methods based on deep models.
A Stable Adaptive Extended Kalman Filter for Estimating Robot Manipulators Link Velocity and Acceleration

One can estimate the velocity and acceleration of robot manipulators by utilizing nonlinear observers. This involves combining inertial measurement units (IMUs) with the motor encoders of the robot through a model-based sensor fusion technique. This approach is lightweight, versatile (suitable for a wide range of trajectories and applications), and straightforward to implement. In order to further improve the estimation accuracy while running the system, we propose to adapt the noise information in this paper. This would automatically reduce the system vulnerability to imperfect modelings and sensor changes. Moreover, viable strategies to maintain the system stability are introduced. Finally, we thoroughly evaluate the overall framework with a seven DoF robot manipulator whose links are equipped with IMUs.
Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores

Interactive reinforcement learning has shown promise in learning complex robotic tasks. However, the process can be human-intensive due to the requirement of a large amount of interactive feedback. This paper presents a new method that uses scores provided by humans instead of pairwise preferences to improve the feedback efficiency of interactive reinforcement learning. Our key insight is that scores can yield significantly more data than pairwise preferences. Specifically, we require a teacher to interactively score the full trajectories of an agent to train a behavioral policy in a sparse reward environment. To avoid unstable scores given by humans negatively impacting the training process, we propose an adaptive learning scheme. This enables the learning paradigm to be insensitive to imperfect or unreliable scores. We extensively evaluate our method for robotic locomotion and manipulation tasks. The results show that the proposed method can efficiently learn near-optimal policies by adaptive learning from scores while requiring less feedback compared to pairwise preference learning methods. The source codes are publicly available at https://github.com/SSKKai/Interactive-Scoring-IRL.
Mineral Resource Abundance: An Assessment Methodology for a Responsible Use of Mineral Raw Materials in Downstream Industries

The sustainability of mineral resources and, in particular, their abundance is a topic of growing interest. Nevertheless, the abundance of mineral raw materials is an extremely complex notion as it not only encompasses geological considerations but also environmental, technical, economic, and social constraints. In addition, to the best of our knowledge, no tools are currently available to allow a comprehensive evaluation of mineral raw material abundance. This research paper, therefore, aims to present an innovative and unique methodology to evaluate the abundance of non-energy mineral resources and determine a mineral abundance index (MAI). Based on a multicriteria analysis, MAI considers the natural abundance of a mineral raw material in the Earth’s crust and its availability on the market and integrates the influence of factors that could constrain or promote future market changes. This new index ranging from 0 (very scarce) to 100 (very abundant) aims to qualify the abundance of mineral resources in a simple and rapid manner based on published and reliable data. This new methodology could be a powerful decision-making support tool for any downstream industrials and end-users making use of mineral raw materials.
Cce-SpERT: A Span-Based Pre-trained Transformer Model for Chinese Civil Engineering Information Joint Extraction

In this paper, we apply a word encoding averaging approach to Transformer incorporating embedded codes of domain lexicons, and propose a Span-based joint information extraction model Cce-SpERT for Chinese civil engineering information specifications. In the proposed model, we study the multi-domain specialized vocabulary in the domain of Chinese civil engineering, and build a specific domain vocabulary embedding approach based on the word encoding average mechanism. With the aid of domain adaptive and task adaptive pre-trainings, we combine BERT and Transformer to generate a Span-based Transformer model Cce-SpERT for extraction of the entity and inter-entity relation at one-time. The experimental results demonstrate the potential of our model on pre-training, negative sampling, Span-based joint extraction, domain word embedding, and relative position embedding, and show that our model achieves an excellent result of joint information extraction on the regulatory text of Chinese civil engineering.
MENDELEY APPLICATION TRAINING TO IMPROVE STUDENT FINAL ASSIGNMENT WRITING

Every student who has entered the final study period must complete the final project/thesis as a graduation requirement. In order to produce quality and responsible writing, students must have the provision of knowledge and how to find, find and determine the correct references and write good citations to avoid plagiarism in thesis writing. The Mendeley application can assist in managing reference sources and writing citations, which can synchronize with the references menu in Microsoft Word. So the PKM team of the Mandarin Language Education Study Program, FKIP UNTAN, provided training on using the Mendeley Application to 20 semester VII students taking thesis writing. Based on the results of the training activities, it can see that 95% of the students who attended the training were able to master the Mendeley application. The obstacle faced in this training was the problem of student laptop devices still using a low version of the Windows operating system, resulting in students needing help installing the Mendeley application. However, this obstacle can be handled thanks to the direction and guidance of the lecturer in the PKM Team. The Mendeley application is relatively easy to learn, understand, and operate, so students will use the Mendeley application as support in completing their final project/thesis.
Archimedes Optimization Algorithm on a Structural Optimization Problem

Optimization is required in the design of engineering problems with various design variables and constraints since the main aim of an engineer is to find the best result that provides safety and economic conditions. In this study, a cantilever beam is optimized with Archimedes Optimization Algorithm (AOA) and the results are compared with Teaching-Learning-Based Optimization and Flower Pollination Algorithm. The optimization process aims to minimize the weight of the cantilever beam with five different cross-sections. After the optimization process with these three algorithms, the results were compared. The results showed that AOA is a competitive algorithm for the design problem.
A Large-scale 3D Object Dataset for 6-DoF Pose Estimation

Given the growing necessity of substantial human annotations in deep learning systems to enhance functionality and performance, it is imperative for researchers to scrutinize existing databases and develop their own datasets with custom labels, particularly for target applications such as object detection and pose estimation. This study introduces a large-scale 3D object dataset tailored for six degrees of freedom pose estimation in real-world scenarios. We describe the key features of our datasets available in the AI hub, emphasizing the expansive 3D object collection. Our methodology involves establishing a correspondence between eight points of an object cube in a 2D image, with the object’s pose determined using the conventional perspective-n-point (PnP) algorithm. To analyze the reprojection error, we employed a high-quality 3D mesh model and a binary mask of the target object in the RGB image. For database validation, all object categories were tested using a representative YOLO-like convolutional neural network architecture, such as real-time singleshot pose estimation. In addition, we conduct an in-depth analysis of the current database’s limitations. In the AI hub, we meticulously released all information regarding our new database, presenting it in a format consistent with our baseline database, LINEMOD. A comparative analysis against this baseline was conducted. To overcome the scalability concerns associated with unseen object categories, we explored an effective methodology that leverages vision and language knowledge distillation.
Similarity Property and Slide Attack of Block Cipher FESH

This paper focuses on similarity properties and extension of the classical slide property of block ciphers. Taking FESH, an award-winning block cipher of the National Cryptographic Algorithm Design Competition 2019, as an example, similarity properties of the encryption and key transformation are found, owing to the similar structures that the encryption and key transformation adopted, and the constants generation. Based on the similarity properties, extended slide properties can be constructed for FESH. Slide attacks of FESH are then proposed. The similarity properties and extended slide property are immune to the increasing of iterated rounds, i.e., it cannot be avoided by increasing the round number of FESH. Furthermore, extended slide property helps relaxing the strict requirements of the subkeys in slide attacks. Taking Feistel and SPN structures as examples, frameworks of slide attacks based on the extended slide properties are presented. Slide attack of FESH is exactly a concrete example of SPN structure.
A Chinese Cyberbullying Detection Model based on NEZHA Model and Res-BILSTM

The traditional cyberbullying detection method based on keyword filtering has great limitations, including relying heavily on manual intervention, which has a waste of manpower and time to a certain extent. In addition, the traditional detection method cannot effectively judge the meaning of the words, due to the subjectivity and context dependence of the expression of bullying texts and some words show different meanings in different contexts. This paper proposes a Chinese cyberbullying detection method based on neural context representation model and residual network composed of BILSTM. This is a neural context representation model for Chinese understanding, which can effectively solve the above problems. The experimental results show that the proposed method has better detection effect than the comparison model on the same Chinese dataset.
Topic Knowledge Based Controlled Generation for Long Documents Using Retrieval-Based Language Models

Current LLM summarization systems Produce broad overviews which are disconnected from people specific interests and expectations. Basically, people preferences (topics) can be expressed by a collection of semantic keywords. Previous work exploit these keywords as extra input to generate summary. That requires additional human annotations. To tackle these constraints, we propose a novel framework, Topic Knowledge based Controlled Generation (TKCG), to control generated summaries through a set of topic keywords that are extracted automatically from source documents. First, as large language models (LLMs) are limited by context window length, we need to split the documents into small pieces like chapters acccording to the document format, as one chapter is a semantically complete section. Secondly we extract some topic keywords from source documents with a transformer-based model. These topic keywords are used to retrieve the chapters that are related to the topic. We then input the combination of topic keywords and chapters as prompts into LLM to get conditional summaries. We also demonstrate the effectiveness of TKCG on two standard datasets, MACSum and arXiv.
Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence (XAI) has emerged as a critical facet in the realm of machine learning and artificial intelligence, responding to the increasing complexity of models, particularly deep neural networks, and the subsequent need for transparent decision making processes. This research paper delves into the essence of XAI, unraveling its significance across diverse domains such as healthcare, finance, and criminal justice. As a countermeasure to the opacity of intricate models, the paper explores various XAI methods and techniques, including LIME and SHAP, weighing their interpretability against computational efficiency and accuracy. Through an examination of real-world applications, the research elucidates how XAI not only enhances decision-making processes but also influences user trust and acceptance in AI systems. However, the paper also scrutinizes the delicate balance between interpretability and performance, shedding light on instances where the pursuit of accuracy may compromise explain-ability. Additionally, it navigates through the current challenges and limitations in XAI, the regulatory landscape surrounding AI explain-ability, and offers insights into future trends and directions, fostering a comprehensive understanding of XAI's present state and future potential.
A Blockchain Patient-Centric Records Framework for Older Adult Healthcare

Patient-centric medical record systems provide patients control over their health data versus electronic health record (EHR) systems that are health provider based and typically geared around bill presentment and payment. There are several limitations in current EHR systems, such as details of healthcare not making it into the system, the loss of out of network healthcare and potential for malicious cyber exploitations. This research effort posits the potential of utilizing blockchain to support a patient-centered personal health record (PHR) system focused on the healthcare needs of older adults. Such a system expands the data collected to include every source of healthcare provider from optometrists to chiropractors to oncologists. Blockchain technologies would provide architecture and security for such a system. Specifically, we present the framework geared to track older adult health records including modules that provide early disease detection and drug-drug interaction for the top chronic diseases experienced by older adults using various machine learning classification algorithms. The algorithms evaluate the entirety of diagnoses and symptoms to find co-morbidities that may be an indicator of latent disease such as early signs of dementia and Alzheimer’s diseases. The patient’s health information is interpreted by a nurse practitioner or hospitalist who can determine if a specialist needs to be involved to evaluate the predicted disease. The proposed approach will provide a secure way to have a comprehensive view of the patient’s health data and arm the patient with the most inclusive set of information for doctors to provide the best health care.
Secure Communication Through Quantum Channels: A Study of Quantum Cryptography

Quantum communication and quantum cryptography are two rapidly growing fields that aim to utilize the principles of quantum physics for secure communication and data encryption. In this paper, we provide an overview of the current state of research in quantum communication and quantum cryptography. We discuss the basic principles and concepts of quantum communication and cryptography, including quantum key distribution, quantum teleportation, and quantum digital signatures. We also review the various physical implementations of quantum communication systems, such as fiber-optic and free-space links, and their applications in practical scenarios, such as secure financial transactions and military communications. We analyze the security of quantum communication and cryptography protocols and compare them with classical encryption methods. We highlight the advantages of quantum communication and cryptography over traditional methods, such as its ability to provide unconditional security and its resistance to attacks from quantum computers.
Adaptive Instance Object Style Transfer

Style transfer is the procedure to regenerate a given target image in the style of another style image. In this work, a new instance object style transfer scheme is proposed which uses GrabCut segmentation along with a single-image super-resolution (SISR) network to preprocess the inputs and perform the transformation using a traditional style transfer network. A complete analysis is done with various combinations of target and style image pairs taking users' opinions. For more effective comparison, the contour of the output images is extracted and also similarity is measured. Experimental outcomes show that the resulting images are better in visual quality, less distorted, and preserve more semantic information from the target image than the existing schemes.
Green Banking Through Blockchain-Based Application for Secure Transactions

Blockchain technology is a foundational, supporting technology with fascinating financial implications. Today, blockchain technology can be used in banking and other sectors of the economy. Blockchains have the potential to update and revolutionize the core technology of credit information and payment clearing processes in banks. We are developing a Blockchain-based solution to secure bank transaction operations. The research aimed to determine the potential outcomes of shifting from traditional banking to decentralized banking and the impact of this environment. In this work, we illustrated a blockchain-based application. We demonstrated how the impact on the environment of the blockchain-based application for secured transactions and decreased fraud, reduced human efforts, reduced media, and physical component usage, and how these reduced expenses, energy consumption, carbon emission, and so on.
Revitalisasi Perpustakaan untuk Meningkatkan Literasi Siswa SD Negeri 2 Babussalam

Libraries that do not function optimally result in very low student literacy interest and concentration so that revitalizing the library is mandatory to increase student literacy interest. The method carried out is by diffusion of science and technology, namely rebuilding the library by rearranging reading books, changing the atmosphere of the library. The library can regain its role as a source of information for students, based on student visits to the library to increase so that student literacy increases. The involvement of various parties plays an important role in the implementation of revitalization and there needs to be continuous action taken to continue to maintain the condition and function of the library. The result of the service is the improvement of literacy of SD Negeri 2 Babussalam students and the ability to manage libraries by the school
Crop Yield Prediction using GELM (Gaussian Extreme Learning Machine)

Accurate prediction of crop yield is crucial for optimizing agricultural practices and ensuring food security. This project presents a novel approach, titled "Soil Factors-Driven Crop Yield Prediction with Optimized GELM (Gaussian Extreme Learning Machine)", aimed at improving crop yield prediction by leveraging soil factors. In this study, we propose an optimized version of the Gaussian Extreme Learning Machine (GELM) algorithm to effectively model the complex relationship between soil factors and crop yield.
HINDI TEXT CATEGORIZATION &amp; TRANSLATION

The Hindi Text Translation and Categorization aims to develop a comprehensive system for translating and categorizing Hindi text content. This project addresses the growing need for effective language processing tools in the context of Hindi, one of the most widely spoken languages globally. The project consists of two primary components: translation and categorization. The translation module employs state-of-the-art machine translation techniques to provide accurate and contextually relevant translations between Hindi and other languages. This functionality is essential for breaking down language barriers and facilitating communication across diverse linguistic communities. The categorization module focuses on organizing and classifying Hindi text content into relevant categories or topics. Leveraging advanced natural language processing (NLP) algorithms, the system can analyze the semantic meaning of text and assign it to appropriate categories. This categorization enhances content management, information retrieval, and overall user experience, particularly in applications such as content recommendation systems and information filtering.
Literatur Review: Penerapan Metode Analytic Hierarchy Process Sebagai Teknik Prioritas Kebutuhan Perangkat Lunak

AHP is one of the popular techniques used in prioritizing software requirements. This study aims to determine the application of AHP as a software requirements priority technique by reviewing thirty literature related to the application of AHP as a software requirements prioritization technique pub-lished in 2017 to 2023. The results of the study explain that the application of AHP has several ad-vantages, but on the other hand AHP also has some drawbacks. Over time, a number of literatures have discussed the development of AHP which aims to minimize weaknesses in the application of AHP. The development of AHP is by integrating AHP with Planning Game, MoSCoW, QFD, and Fuzzy. In addition, development is also carried out by adding new algorithms, namely ReDCCahp and Enhanced AHP. The results of this study provide a useful reference for researchers and practitioners in the field of requirements engineering in prioritizing software requirements using AHP.