Reviewerly/exHarmony huggingface dataset:
Parallel- and Cyclic-Iterative Variational Bayes for Fast Kalman Filtering in Large-Dimensions

This paper considers the linear-Gaussian filtering problem in large-dimensions, a framework in which the Kalman filter (KF) can be computationally prohibitive. As a remedy, a hybrid scheme combining KF with variational Bayes (VB), an approach that designs an approximation of the filtering probability density function (pdf) based on a separable form under the Kullback-Leibler divergence minimization criterion, has been proposed. Despite its approximate and cyclic-iterative characters, this VBKF algorithm can provide comparable performances to the KF, at reduced computational requirements. Here, we resort to the memory gradient subspace (MGS) optimization in the space of separable pdfs to derive a new variant of VBKF endowed with a parallel-iterative structure. At a given iteration of the proposed MGS-VBKF, all separate marginals of the approximate filtering pdf are updated in parallel, which could lead to significant computational time savings, especially when the state is split into small enough partitions. We establish the connection between VBKF and MGS-VBKF, showing that the latter can be derived from the former by iterating instead in parallel on its equations and inserting a correction term representing the contribution of the gradient and memory directions. We further extend these algorithms to the case of unsupervised systems with unknown observation noise uncertainties. The performances of the proposed filters are finally studied and compared to those of the KF and a deterministic ensemble Kalman filter through numerical experiments.
A Comparative study on state of art Cryptographic key distribution with quantum networks

Most online and network applications have sophisticated security features. The cost and volume of online transaction data is increasing due to network and internet-based applications tremendous growth. Encryption converts files to obtain information from the standard format in a format that cannot read aiding to security of data. Usually, single encryption algorithm is identified and ordered according to important characteristics such as fast, simple, yet able to protect data from attacks. However, Quantum cryptography, using basic quantum physics is an emerging technique to provide secured information exchange between authorities. Quantum dynamics are used by the Quantum Distribution Essential to ensure secure communication. This article discusses on classic and quantum based comparative analysis of cryptographic techniques to investigate the optimum algorithm in classical or quantum cryptography.
Finding High-quality Item Attributes for Recommendation

The sparse interactions between users and items on the web have aggravated the difficulty of their representations in recommender systems. Existing approaches leverage item attributes (e.g., item categories and tags) to alleviate the data sparsity problem, so as to enhance the performance and interpretability of recommendation. However, directly using all attributes of items cannot avoid the negative impacts of low-quality attributes, where manually labeling the quality of attributes is time-consuming. To this end, we propose HQRec to jointly measure the quality of attributes automatically and perform recommendation accurately. Specifically, we first analyze the different qualities among item attributes, and propose to leverage item categories to select high-quality tags via category-guided quality measurement and direction-aware optimization in an unsupervised fashion. Then, we propose to capture the complex relations among users and items based on the high-quality attributes, where a novel quality-aware embedding fusion and quality-aware embedding propagation mechanism for users and items is devised. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed HQRec framework, which constantly achieves an average of 14.73% improvement over the state-of-the-art baselines in terms of Recall and NDCG metrics. Insightful case studies also show that our automatic quality measurements are highly accurate and interpretable.
Unsupervised Deep Learning-based clustering for Human Activity Recognition

One of the main problems in applying deep learning techniques to recognize activities of daily living (ADLs) based on inertial sensors is the lack of appropriately large labeled datasets to train deep learning-based models. A large amount of data would be available due to the wide spread of mobile devices equipped with inertial sensors that can collect data to recognize human activities. Unfortunately, this data is not labeled. The paper proposes DISC (Deep Inertial Sensory Clustering), a DL-based clustering architecture that automatically labels multi-dimensional inertial signals. In particular, the architecture combines a recurrent AutoEncoder and a clustering criterion to predict unlabelled human activities-related signals. The proposed architecture is evaluated on three publicly available HAR datasets and compared with four well-known end-to-end deep clustering approaches. The experiments demonstrate the effectiveness of DISC on both clustering accuracy and normalized mutual infor-mation metrics.
A Recommendation Strategy Integrating Higher-Order Feature Interactions With Knowledge Graphs

Knowledge graphs (KG) are efficient auxiliary information in recommender systems.However, in knowledge graph feature learning, the learning is mainly for triples rather than recommendations, which may bring difficulty in large improvement for recommendation performance.One of the problems in the existing methods is that they cannot uncover the deep interaction information of users in a simple way, and this motivates effective learning the potential embedded information through the knowledge graph.The Graph Convolutional Network (GCN) can be useful for learning information about graph structured data.This paper proposes a method that fuses higher order feature interactions and knowledge graphs and uses them for recommendation.For users, they uses gated recurrent units (GRU) to focus on their preferences so that the ability of convolutional neural networks in processing user preference features is enhanced; for items, the cross-learning module is used to learn higher order features between items and entities; for users and entities, KG and user-item interaction information are combined followed by feature extraction of graph structured data by Light GCN, allowing the model to learn potential user-entity associations in the graph structured data.These procedures enable the model to learn potential user-entity associations in the graph data.Extensive experiments on two real datasets show that the proposed model performs better than the state-of-the-art model.
A Survey of Procedural Content Generation for Games

With the continuous development of video game, the complexity of video game is gradually increasing. Besides, the cost of manually creating video game content keeps increasing although it is already very expensive. Nowadays, the cost of Game development is gradually prohibitive. In the process of a 3A game development, tens of millions development budget is spent on creating game content. In order to curb the increasing consumption of time and money, Procedural Content Generation for Games (PCG-G) has become a popular way to solve these problems by automatic game content generation. This paper reviews the PCG-G research. Firstly, we introduce what PCG-G is and its development. Then we classify PCG-G into three groups which include traditional methods, search-based methods and machine learning methods. Finally, we will show cases of PCG-G application in video games, introduce what effect PCG-G has on the game player experience and some problem of PCG-G.
Implementation of agile management and business agility

Since the appearance of the Agile Manifesto for more successful software development, the agile concept of thinking in management has evolved and today we have far more developed forms of agile management, agile tools and methods that have helped many teams and companies to adopt and apply agile practices. The aim of this paper is to review the different levels of implementation of agile management and show the main advantages that the implementation of agile management has. Through the literature review, the concept of agile project management, organizational agility and business agility as the highest stage of implementation of the agile concept in companies are presented. The paper analyzes the classic and agile concepts of project management, shows the process of agile project management as well as the main roles and benefits. Organizational agility is shown through roles and organizational structure, as well as through the most common ways of agile work management. Finally, business agility is explained and the main areas where changes need to be made to establish it at the company level are discussed.
Mixed Reality Meets Procedural Content Generation in Video Games

The use of artificial intelligence and procedural content generation algorithms in mixed reality games is an unexplored space. We posit that these algorithms can enhance the gameplay experience in mixed reality games. We present two prototype games that use procedural content generation to design levels that make use of the affordances in the player’s physical environment. The levels produced can be tailored to a user, customizing gameplay difficulty and affecting how the player moves around the real-world environment.
BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster

Most AI projects start with a Python notebook running on a single laptop; however, one usually needs to go through a mountain of pains to scale it to handle larger dataset (for both experimentation and production deployment). These usually entail many manual and error-prone steps for the data scientists to fully take advantage of the available hardware resources (e.g., SIMD instructions, multi-processing, quantization, memory allocation optimization, data partitioning, distributed computing, etc.). To address this challenge, we have open sourced BigDL 2.0 at https://github.com/intel-analytics/BigDL/ under Apache 2.0 license (combining the original BigDL [19] and Analytics Zoo [18] projects); using BigDL 2.0, users can simply build conventional Python notebooks on their laptops (with possible AutoML support), which can then be transparently accelerated on a single node (with up-to 9.6x speedup in our experiments), and seamlessly scaled out to a large cluster (across several hundreds servers in real-world use cases). BigDL 2.0 has already been adopted by many real-world users (such as Mastercard, Burger King, Inspur, etc.) in production.
Illumination Analysis of Deep Face Recognition

Illumination variation significantly affects the performance of face recognition algorithms. However, it is difficult to construct a database that meets the demand of illumination analysis with current face image database construction technology. The influence of illumination variation on deep face recognition based on 3D face models is investigated. First a new method is presented to produce face images under arbitrary illuminations by using a 3D face model based on face basis image representation theory proposed. Then the database used for illumination analysis is constructed. Afterwards, an optimal way is explored to select illuminations used for constructing a high quality face image database by analyzing the impacts of different illumination sampling strategies on the performances of face recognition models. With the advantage of accurate illumination labeling of virtual data, the performances of different illumination labeling methods on the training of face recognition network are tested on a multi-task learning framework. Through experiments on the effects of different lighting sampling schemes and labeling methods on the performances of face recognition models carried out on virtual data and real data, it is concluded that using an appropriate amount of lightings of basis images to build a database is an effective lighting sampling scheme, and accurate lighting annotation can help further improve the face recognition rate, the corresponding recognition models have face recognition accuracy rates of more than 98% on virtual and real monitoring datasets which contain face images captured under very challenging lighting conditions. Proposed work improve the performance of deep face recognition models, and facilitate the construction of high quality face image databases with various illuminations.
ESMAC: Efficient and Secure Multi-Owner Access Control With TEE in Multi-Level Data Processing

Traditional data access control schemes only prevent unauthorized access to private data with a single owner. They are not suitable for application in a Multi-Level Data Processing (MLDP) scenario, where data are processed by a series of parties who also insert new data. Hence, the accumulated dataset should be protected through access control handled by hierarchically-structured parties who are at least partial data owners in MLDP. Existing multi-owner access control schemes mainly focus on controlling access to co-owned data of multiple entities with the equal ownership, but seldom investigates how to apply access control in MLDP. In this paper, we base the off-the-shelf Trusted Execution Environment (TEE), Intel SGX, to propose an Efficient and Secure Multi-owner Access Control scheme (ESMAC) for access authorization in MLDP. Moreover, to prevent unauthorized data disclosure by non-root data owners aiming to gain extra profits, we further introduce undercover polices to supervise their behaviors. Specifically, we design a data protection scheme based on game theory to decide the payoffs and punishments of honest and dishonest data owners, which motivates data owners to behave honestly when claiming ownership over data. Through comprehensive security analysis and performance evaluation, we demonstrate ESMAC's security and effectiveness.
Machine Reading Comprehension Model in RuNNE Competition

The paper studies machine reading comprehension model (MRC) (Li et al., 2020) in its application to extracting nested named entities (nested NER) in the RuNNE-2022 evaluation (Artemova et al., 2022). The model transforms named entity recognition tasks to a question-answering task. In this paper we compare several approaches to formulating ”questions” for the MRC model such as entity type names (keywords), entity type definitions, most frequent examples for the train set, combinations of definitions and examples. We found that using two most frequent examples from the training set is comparable in quality of nested NER with gathering qualitative definitions from different dictionaries, which is much more complicated. In the RuNNE evaluation, the MRC model obtained the best results among models without any manual work (rules or additional manual annotation of texts).
SpOT: Spatiotemporal Modeling for 3D Object Tracking

3D multi-object tracking aims to uniquely and consistently identify all mobile entities through time. Despite the rich spatiotemporal information available in this setting, current 3D tracking methods primarily rely on abstracted information and limited history, e.g. single-frame object bounding boxes. In this work, we develop a holistic representation of traffic scenes that leverages both spatial and temporal information of the actors in the scene. Specifically, we reformulate tracking as a spatiotemporal problem by representing tracked objects as sequences of time-stamped points and bounding boxes over a long temporal history. At each timestamp, we improve the location and motion estimates of our tracked objects through learned refinement over the full sequence of object history. By considering time and space jointly, our representation naturally encodes fundamental physical priors such as object permanence and consistency across time. Our spatiotemporal tracking framework achieves state-of-the-art performance on the Waymo and nuScenes benchmarks.
Research on personalized learning space in educational metaverse

Educational applications of metaverses should be considered as a medium for sustainable education. In this paper, we propose a general personalized learning space in the educational metaverse (PLSEM) framework, which incorporates VR and AR technologies to simulate the complex learning interactions. This paper analyzes the effects of the metaverse on learning from the perspectives of teaching principles, learning activity design and technology application. Learners&apos; emotional input and cognitive level were effectively improved through the convergence of various technologies. The relationship between learning motivation and the environment was examined by analyzing previous studies. Furthermore, PLSEM can be used to provide online learning space similar to an offline classroom for students by creating innovative educational environments.
Localization Method Based on Multi-QR Codes for Mobile Robots

With the development of the Internet of Things technology, as an important source of information perception, QR codes are widely used in various fields. In the visual localization of mobile robots based on QR codes, there are intermittent problems in the visual localization of mobile robots based on individual QR code identification, and the accuracy and reliability of positioning need to be improved. Based on multi-QR codes, a localization scheme for mobile robots is designed, which determines the direction and actual position of the mobile robot by the location of multi-QR codes detected and the encoding information identified, and achieves higher precision and more reliable localization. Besides, through the construction of the experimental scene for localization testing, the high precision localization of the proposed multi-QR codes localization algorithm is verified, which can be used to estimate the position of the mobile robot effectively. It provides more accurate localization information for autonomous navigation.
Database Management System Design Improvement for Child Stunting Data Collection in Multiple Observation Areas

The launch of StuntingDB is believed to enliven the role of database management systems (DBMS) in Indonesia's stunting research. However, a novelty in stunting data management that enables parallel project activation in multiple observation areas is necessary. Several improved functionalities in StuntingDB must be designed to prevent the high demand for accessibility in the middle of stunting research. Connolly and Begg's principle of database design is adopted along with Software Development Life Cycle's Waterfall model for creating a continuous development of the system. This research produces a new database design in the form of Entity Relationship Diagram (ERD) and Use Case Diagram to help system developers realizing a new version of StuntingDB for stunting research in several observation areas.
Exploiting CLIP-Based Multi-modal Approach for Artwork Classification and Retrieval

Given the recent advantages in multimodal image pretraining where visual models trained with semantically dense textual super- vision tend to have better generalization capabilities than those trained using categorical attributes or through unsupervised techniques, in this work we investigate how recent CLIP model can be applied in several tasks in artwork domain. We perform exhaustive experiments on the NoisyArt dataset which is a collection of artwork images collected from public resources on the web. On such dataset CLIP achieve impressive results on (zero-shot) classification and promising results in both artwork-to-artwork and description-to-artwork domain.
Tackling Long-Tailed Category Distribution Under Domain Shifts

Machine learning models fail to perform well on real-world applications when 1) the category distribution P(Y) of the training dataset suffers from long-tailed distribution and 2) the test data is drawn from different conditional distributions P(X|Y). Existing approaches cannot handle the scenario where both issues exist, which however is common for real-world applications. In this study, we took a step forward and looked into the problem of long-tailed classification under domain shifts. We designed three novel core functional blocks including Distribution Calibrated Classification Loss, Visual-Semantic Mapping and Semantic-Similarity Guided Augmentation. Furthermore, we adopted a meta-learning framework which integrates these three blocks to improve domain generalization on unseen target domains. Two new datasets were proposed for this problem, named AWA2-LTS and ImageNet-LTS. We evaluated our method on the two datasets and extensive experimental results demonstrate that our proposed method can achieve superior performance over state-of-the-art long-tailed/domain generalization approaches and the combinations. Source codes and datasets can be found at our project page https://xiaogu.site/LTDS .
News-RO-Offense - A Romanian Offensive Language Dataset and Baseline Models Centered on News Article Comments

The use of offensive language can lead to uncomfortable situations, psychological harm, and in particular cases even to violence.Social networks and websites struggle to reduce the prevalence of these types of messages by using an automated detector.In this paper, we propose a novel Romanian language dataset for offensive message detection.We manually annotated 4,052 comments on a Romanian local news website into one of the following classes: non-offensive, targeted insults, racist, homophobic, and sexist.In addition, we establish a baseline of five automated classifiers, out of which the model based on RoBERT and two layers of CNN achieves the highest performance with an average F1-score of .74.
Blokzincir Tabanlı E-Ticaret: Bir Değerlendirme

The e-commerce sector has grown beyond predictions with the help of the most recent technological developments, changes in consumer preferences, and the COVID-19 pandemic. E-commerce activities assume a subsidiary role of brick-and-mortar shops with the benefits it offers to all parties, which have also resulted in new problems such as privacy, security, transparency, and costs, unlike traditional businesses. Blockchain technology is among the promising technologies in solving the problems of the e-commerce sector with its decentralized structure that does not require third-party intermediaries, and its features (such as privacy, immutability, security, transparency, and auditability). In this study—which examines, under five titles, the benefits that blockchain can offer to e-commerce—it is concluded that blockchain can reshape e-commerce activities by enabling integrated e-commerce systems that include all parties and offering low transaction costs, high transaction speed, traceability of transactions, strong security standards, and low risks. In this direction, blockchain-based e-commerce platforms can push existing e-commerce platforms into the background.