Reviewerly/exHarmony huggingface dataset:
Learning Disentangled Features for Nerf-Based Face Reconstruction

The 3D-aware parametric face model named HeadNeRF achieved advantages in rendering photo-realistic face images. However, it has two limitations: (1) it uses single-image fitting reconstruction that is slow and prone to overfitting; (2) it lacks explicit 3D geometry information, making using semantic facial-parts-based loss challenging. This paper presents a 3D-aware face reconstruction learning framework tailored for HeadNeRF to address the limitations. We train a face encoder network that can directly learn the disentangled features for facial reconstruction to address the first limitation. For the second limitation, we introduce a lightweight semantic face segmentation network and facial-parts-based loss function to improve the reconstruction accuracy and quality. Our experiments show that the proposed method achieves a low reconstruction time consumption and enhanced reconstruction accuracy. Project page: https://peizhiyan.github.io/docs/headnerf+
An Alternative to Bilinear and Nearest-Neighbour Enlarging for Monitor Displays

This paper proposes a simple and effective algorithm for image enlargement, aims to improve upon the widely-used bi-linear and nearest-neighbour interpolations for monitor displays. The proposed method uses nearest-neighbour interpolation to generate preliminary enlarged images, and selectively modifies only diagonal edges to reduce blocking artifacts. Experiments demonstrate the proposed method produces clear images with fewer artifacts across different enlargement factors and scenes.
A YOLOV5-PF-based Approach to Visitor’s Interest Discrimination applied to Pavilion Tour-guide Robots

In the field of human-computer interaction, accurate proactive interaction behaviors by robots can effectively attract user attention and enhance user experience. In this work, a method called the visitor’s interest discrimination method is proposed for exhibition and museum guide robots to identify visitors in the environment who are suitable for initiating proactive interactions before initiating human-computer interaction requests. The proposed method analyzes the bounding boxes of targets based on YOLOv5-PF (YOLOv5-Poo1Former) object detection and combines a weight analysis algorithm that integrates the interaction area and interaction distance between visitor and showcases. This enables the estimation of visitor attention towards the showcases and inferring visitors’ interests in the showcases. Through experimental validation, the proposed method accurately discriminates visitors’ interests in the environment.
Curriculum Knowledge Switching for Pancreas Segmentation

Pancreas segmentation is challenging due to the small proportion and highly changeable anatomical structure. It motivates us to propose a novel segmentation framework, namely Curriculum Knowledge Switching (CKS) framework, which decomposes detecting pancreas into three phases with different difficulty extent: straightforward, difficult, and challenging. The framework switches from straightforward to challenging phases and thereby gradually learns to detect pancreas. In addition, we adopt the momentum update parameter updating mechanism during switching, ensuring the loss converges gradually when the input dataset changes. Experimental results show that different neural network backbones with the CKS framework achieved state-of-the-art performance on the NIH dataset as measured by the DSC metric. The code is available at https://github.com/kunzhan/CKS_Pancreas
Confidence-Aware Clustered Landmark Filtering For Hybrid 3D Face Tracking

The detection of facial landmarks in 2D images has received a great attention in the last decade, as it is a key step for several computer-vision-related applications. Most of the approaches are focused on still images, and are extended to videos by using a tracking-by-detection scheme. In this work, we propose a frame-to-frame tracking module based on grouped-landmark Kalman filters that can be integrated into existing deep-learning-based 3D face alignment pipelines. This method improves the landmark accuracy in cases with large occlusion, extreme head poses and blurriness that affect existing approaches. Our experiments on the Menpo 3DA-2D benchmark show improvements on model-free and 3D-model-based face alignment approaches.
QVRF: A Quantization-Error-Aware Variable Rate Framework for Learned Image Compression

Learned image compression has exhibited promising compression performance, but variable bitrates over a wide range remain a challenge. State-of-the-art variable rate methods compromise the loss of model performance and require numerous additional parameters. In this paper, we present a Quantization-error-aware Variable Rate Framework (QVRF) that utilizes a univariate quantization regulator a to achieve wide-range variable rates within a single model. Specifically, QVRF defines a quantization regulator vector coupled with predefined Lagrange multipliers to control quantization error of all latent representation for discrete variable rates. Additionally, a reparameterization method makes QVRF compatible with round quantizer and integer entropy coding. Exhaustive experiments demonstrate that existing fixed-rate VAE-based methods equipped with QVRF can achieve wide-range continuous variable rates within a single model without significant performance degradation. Furthermore, QVRF outperforms contemporary variable-rate methods in rate-distortion performance with minimal additional parameters. The code is available at https://github.com/bytedance/QRAF.
High-Precision Motion Vector Refinement for Bi-Directional Optical Flow

Bi-directional Optical Flow (BDOF), is a very effective tool, developed based on the optical flow concept, which assumes that the motion of an object is smooth and is in a straight line in a short period of time. BDOF as a prediction adjustment tool for each sample, is included in VVC SW, where it derives parameters for each 4×4 block and adjusts each of its samples individually. The reference SW which is being developed for beyond VVC activity, i.e. ECM, has 2 BDOFs: One as Motion Vector (MV) refinement tool for each 8 × 8 subblock, and another one as sample adjustment BDOF similar to VVC one, but deriving the parameters for each sample. In this paper, a High-Precision BDOF is introduced which consists of the following parts: Firstly, a more accurate BDOF formula is used to derive the parameters for BDOF MV refinement. Next, a position-dependent weight is used to increase the focus of the parameter derivation. Finally, higher granularity as small as 4×4 is used for each subblock. For BDOF sample adjustment, another position-dependent weighted sum is used. The proposed method is implemented on top of ECM-7.0. Experimental results show that the proposed method provides up to –0.92% bitrate saving for some sequences compared to the latest ECM SW under random access configuration. The method explained in this paper is adopted into ECM-9.0.
Multi-Label Adversarial Attack Based on Label Correlation

The vulnerabilities of multi-label models concerning adversarial attacks have been paid much attention. In the multi-label model, the labels are not independent of each other. However, the existing multi-label adversarial attack works do not adequately consider label correlations, thus unable to cost the most minor disturbance while ensuring the attack success rate. To address this issue, we develop a method that uses the label correlation. For targeted attacks, we build a label correlation matrix using cosine distance and select the label with the highest correlation score with the attacked label as the target label. For untargeted attacks, we choose the attacked label with the lowest confidence because of the label correlation. The proposed method can achieve low attack costs with high success rates, as demonstrated in experimental results.
For District III director: Helen A. “Bonnie” Lawlor

Philadelphia Section. Retired, National Federation of Abstracting and Information Services, Philadelphia, Pennsylvania. Academic record: Chestnut Hill College, BS, 1966; Saint Joseph’s University, MS, 1976; University of Pennsylvania, Wharton School, MBA, 1989. Honors: ACS Fellow, 2013; ACS Division of Chemical Information Val Metanomski Meritorious Service Award, 2006; National Federation of Advanced Information Services Honorary Fellow, 2014; Alpha Epsilon Sigma, 1966; National Federation of Abstracting and Information Services Memorial Award, 1998; American Society for Information Science Achievement Award, 1996. Professional positions (for the past 10 years): National Federation of Advanced Information Services, executive director, 2002–14 (retired). Service in ACS national offices: Committee on Budget and Finance, 2018–, 2000–03; Committee on Committees, 2012–17, secretary, 2014–17; Council Policy Committee (voting), 2006–11, (nonvoting), 1997–99; Committee on Nominations and Elections, 2000–05, vice-chair, 2003, secretary, 2001; Committee on Divisional Activities, 1994–99, chair, 1997–99; Committee on Copyrights, 1989–98, chair, 1993–95; Committee on Committees, Task Force on Publications/Copyrights Inter-Committee
Evaluating Reification with Multi-Valued Properties in a Knowledge Graph of Licensed Educational Resources

This paper presents the construction of a Knowledge Graph (KG) of Educational Resources (ER), where RDF reification is essential. The ERs are described based on the subjects they cover considering their relevance. RDF reification is used to incorporate this subject’s relevance. Multiple reification models with distinct syntax and performance implications for storage and query processing exist. This study aims to experimentally compare four statement-based reification models with four triplestores to determine the most pertinent choice for our KG. We built four versions of the KG. Each version has a distinct reification model, namely standard reification, singleton properties, named graphs, and RDF-star, which were obtained using RML mappings. Each of the four triplestores (Virtuoso, Jena, Oxigraph, and GraphDB) was setup four times (except for Virtuoso, which does not support RDF-star), and seven different SPARQL queries were experimentally evaluated. This study shows that standard reification and named graphs lead to good performance. It also shows that, in the particular context of the used KG, Virtuoso outperforms Jena, GraphDB, and Oxigraph in most queries. The recent specification of RDF-star and SPARQL-star sheds light on statement-level annotations. The empirical study reported in this paper contributes to the efforts towards the efficient usage of RDF reification. In addition, this paper shares the pipeline of the KG construction using standard semantic web technologies.
Introduction To Statistics and Its Applications in Data Analytics and Artificial Intelligence

Statistics is a field of study that deals with the collection, analysis, interpretation, and presentation of data. It is used to make inferences about populations based on samples, to test hypotheses, and to develop models. Applications of statistics in data analytics: Data analytics is the process of using data to extract insights and make better decisions. Statistics is a key tool in data analytics, and it is used for a variety of tasks, such as: Data cleaning: This is the process of removing errors and inconsistencies from data. Data exploration: This is the process of understanding the data by summarizing it and looking for patterns. Data modeling: This is the process of building models that can be used to predict future outcomes. Data visualization: This is the process of displaying data in a way that is easy to understand. Applications of statistics in artificial intelligence: Artificial intelligence (AI) is a field of computer science that deals with the creation of intelligent agents, which are systems that can reason, learn, and act autonomously. Statistics is a key tool in AI, and it is used for a variety of tasks, such as: Machine learning.
Effects of Data Augmentation Methods on YOLO v5s: Application of Deep Learning with Pytorch for Individual Cattle Identification

In this paper, we investigate the performance of the YOLO v5s (You Only Look Once) model for the identification of individual cattle in a cattle herd. The model is a popular method for real-time object detection, accuracy, and speed. However, since the videos obtained from the cattle herd consist of free space images, the number of frames in the data is unbalanced. This negatively affects the performance of the YOLOv5 model. First, we investigate the model performance on the unbalanced initial dataset obtained from raw images, then we stabilize the initial dataset using some data augmentation methods and obtain the model performance. Finally, we built the target detection model and achieved excellent model performance with an mAP (mean average precision) of 99.5% on the balanced dataset compared to the model on the unbalanced data (mAP of 95.8%). The experimental results show that YOLO v5s has a good potential for automatic cattle identification, but with the use of data augmentation methods, superior performance can be obtained from the model.
Navigating Horizons - A Technical Exploration of Autonomous Drone Guidance

Our project focuses on creating a cutting-edge tool that recognizes barriers and dynamically maps out alternate routes for drone navigation. We've developed a virtual environment to simulate the drone's operations using Microsoft AirSim and Unreal Engine, enabling us to hone our strategy in a safe environment. A large sensor that is carefully positioned at the heart of our virtual drone acts as a watchful observer to catch movement along the intended path With the help of a voxel grid and the Informed RRT* algorithm, we have improved the drone's ability to navigate through various terrains without running into obstacles. The establishment of an autonomous drone system is our ultimate objective. The drone can hover and navigate on its own without direct piloting thanks to the integration of sophisticated large-scale cameras and sensors. This game-changing development in drone technology opens the door to more adaptable and productive aerial operations.
Counteraction to the Involvement of Minors in the Illicit Traffic in Narcotic Drugs, Psychotropic Substances and Their Analogues, Сommitted by the Information and Telecommunication Technologies

The subject of this article is countering to the involvement of minors in the illicit traffic in narcotic drugs, psychotropic substances and their analogues, committed by information and telecommunication technologies. The authors investigate the state of the problem, the means of criminal legal counteraction, the main causes and conditions of the commission of the crimes under consideration. It is noted that, in general, the means of criminal legislation are adequate to the state of the problem, however, a complex of reasons has formed that contribute to the increase in the involvement of minors in illicit trafficking of narcotic drugs, psychotropic substances and their analogues, committed through the use of information and telecommunication technologies. The authors cite and characterize these reasons. It is noted that the basic determinants are problems in the socio-economic organization of society, and the main catalyst for this negative social phenomenon is the high latency of this type of crime, due to the technical component – the availability of means of operational and anonymous commission of crimes. The complex of the main counteraction measures taken is analyzed, their effectiveness, problems, prospects are determined. Separate issues of the relevant experience of foreign states are considered. Some aspects of the problem and the measures taken are illustrated by statistics and examples of law enforcement activities. The measures that, according to the authors, will contribute to the fight against this type of crime are proposed.
Cryptocurrencies from Islamic perspective

Purpose This paper aims to derive a compatible Shariah opinion on the permissibility of using cryptocurrencies by Muslims by reviewing the opinions expressed by Shariah scholars on the permissibility of cryptocurrencies. Design/methodology/approach This is a qualitative desk review research where the opinions expressed by the Shariah scholars on the permissibility of cryptocurrencies and the issues related to it have been analyzed using the literature. All the Shariah parameters checked pertaining to currencies have been studied and assessed to derive the Shariah opinion. Findings The research findings suggest that cryptocurrencies do not fully meet the characteristics of money according to Shariah principles. Scholars debate their classification as a medium of exchange due to concerns about volatility, intrinsic value and governance. The treatment of cryptocurrencies varies, and their decentralized nature prevents monopolization. Governance and resistance to manipulation are facilitated by blockchain technology. Classifying cryptocurrencies as hard money and their recognition as the primary unit of account face challenges. While they can be a store of value, price volatility and regulations must be considered. The network effect is crucial for their success, and their supply is controlled through complex protocols. These findings have implications for policymakers in Islamic finance. Originality/value The differences in Shariah opinions on using cryptocurrencies have been a major debate in the Islamic financial industry. A clear and comprehensive study is not found on the differences in the Shariah opinions on their reasonings, which is important for researchers and professionals in the field. Therefore, this research provides valuable insights for policymakers, scholars and practitioners in Islamic finance, contributing to the understanding of applying Islamic principles to cryptocurrencies.
Proof of Optimum (PoO): Consensus Model Based on Fairness and Efficiency in Blockchain

Blockchain systems are popular technologies that have recently emerged. As a decentralized system, blockchain technology has provided many solutions and many problems associated with these solutions. One of its most important problems is that while performing hash calculations very intensively to create a new consensus block, it reduces its efficiency depending on the duration. In this study, a new model to avoid Proof of Work (PoW), which directs the computations made to create blocks to optimization algorithms, is proposed. The proof mechanism proposed in this study is called Proof of Optimization (PoO). A traveling salesman problem (TSP) is entered into the designed system to solve the optimization algorithms. Nodes are asked to solve the TSP in certain iterations and populations. As a result, nodes are asked to create blocks with the fitness, density and time values obtained. PoO and PoW consensus algorithms were subjected to an experimental comparison in the system. The test results show that the block generation time of the PoO consensus model varies between 2 s according to the dataset solution with the least cities (ulysses22) and 60 s according to the dataset solution with the most cities (gr666). Additionally, as a result of experimental analyses, it was determined that decentralization, which is the percentage of block creation among miners in the blockchain, reached a more stable value and the fairness index rose above 0.90 on average. When the obtained values were compared with PoW, it was observed that the block time was more stable and the decentralization of the blockchain was higher. In this way, high-equipped nodes in the blockchain system are prevented from dominating the network. Thus, it is ensured that low-equipped nodes have the right to create blocks in the blockchain. The difficulty levels of the problems can be adjusted by changing the number of cities in the TSP evaluated in this study. In this way, the problem of creating blocks in the network can be made more difficult or easier at any time.
GABBROIDS AND MONZONITOIDS OF THE TIGRINY ORE CLUSTER (SIKHOTE-ALIN): COMPOSITION, AGE, SETTINGS OF FORMATION, AND RELATION WITH MINERALIZATION.

The article presents the results of the study of mineralogical and petrographic characteristics, composition and age of gabbroids and monzonitoids of the Burelomny Creek massif located in the Central Sikhote-Alin and spatially associated with the Tigriny rare-metal-granite intrusion, and Sn-W deposit. It is shown that the gabbroids and monzonitoids of the massif correspond to the high-potassium units of the normal and moderately alkaline series, and have low concentrations of titanium, high concentrations of phosphorus, moderate concentrations of HFSE and REE elements. The geochemical characteristics of the rocks indicate that their source combines lithospheric and sublithospheric mantle substrates and that they formed in the setting of a transform continental margin. The age of the gabbroids, determined by the Ar-Ar dating of a biotite monofraction, is 101+1.5 Ma and is synchronous with the "peak" of basite and granitoid magmatism in the Sikhote-Alin. A significant difference in age (more than 20 Ma) and discrepancy in geochemical specialization indicate that the Tigriny massif rare-metal granites, and associated mineralization have no genetic relationship with gabbroids and monzonitoids of the Burelomny Creek massif.
An e-Infrastructure for FAIR Data Management of Underwater Observatories

The study of global phenomena such as the impact of the oceans on climate change requires extensive and high-quality data. Over the last decade underwater observatories became a mature technology, allowing the collection of high-resolution data on a continuous basis. However, the management of such large volumes of heterogeneous data proves to be a challenge. This work presents the OBSEA’s underwater observatory data management e-Infrastructure. This infrastructure leverages existing open-source tools and community-accepted standards to build a modular yet robust data management framework, supporting the storage and exchange of multi-parametric marine data following the FAIR principles.
Advertising in the Metaverse

As the metaverse grows, leading businesses are already searching the metaverse for potential advertising partners. This article explores whether advertising in the real world can be directly applied to the metaverse. Through the method of literature review, this article summarizes some extant readings and proposes that with the characteristics of decentralization, shared environment, and immersion in the metaverse, advertising will be different from the real world. Moreover, the challenges and problems that brands will face are also raised. This article provides a reference on advertising strategies for brands who want to enter the metaverse, and also lays the groundwork for further in-depth research.