Reviewerly/exHarmony huggingface dataset:
Efficient CNNs and Transformers for Video Understanding and Image Synthesis

In this talk, I will first discuss approaches that reduce the GFLOPs during inference for 3D convolutional neural networks (CNN) and vision transformers. While state-of-the-art 3D CNNs and vision transformers achieve very good results on action recognition datasets, they are computationally very expensive and require many GFLOPs. While the GFLOPs of a 3D CNN or vision transformer can be decreased by reducing the temporal feature resolution or the number of tokens, there is no setting that is optimal for all input clips. I will therefore discuss two differentiable sampling approaches that can be plugged into any existing 3D CNN or vision transformer architecture. The sampling approaches adapt the computational resources to the input video such that as much resources as needed but not more than necessary are used to classify a video. The approaches substantially reduce the computational cost (GFLOPs) of state-of-the-art networks while preserving the accuracy. In the second part, I will discuss an approach that generates annotated training samples of very rare classes. It is based on a generative adversarial network (GAN) that jointly synthesizes images and the corresponding segmentation mask for each image. The generated data can then be used for one-shot video object segmentation.
Image retrieval method based on k-means block

In order to improve the ability to describe image information, this paper proposes an image retrieval method based on kmeans block. Firstly, under Lab color space, the image is divided into 6 sub-regions using k-means algorithm. And the external rectangle determined by each sub-region is segmented on the original image to obtain 6 sub-blocks that can contain the main information of the image. Then, the color features of each sub-block are extracted separately. Finally, the degree of similarity is reflected by the Euclidean distance between the image feature vectors. The simulation experimental results show that the proposed algorithm in this paper has been improved compared with the comparison scheme.
Fine-Grained Feature Extraction from Indoor Data to Enhance Visual Question Answering

Current Visual Question Answering (VQA) models have limited contribution in the area of indoor images. In this paper, the proposed VQA model extracts image and question features from pre-trained VGG16 or ResNetl52v2 and Glove models respectively. Then these features are fed to the novel approach proposed in this paper where Stacked-Attention Network (SAN) which includes customized attention layers with Self-Focus Network (SFN) is used to extract fine-grained features of both the image and question. With these features, the answer prediction model generates an answer. This model is trained and tested on a subset of VQA v2 dataset. It gives a comparable performance when compared with other existing models.
Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio Masking

Self-supervised learning has achieved great success in both natural language processing and 2D vision, where masked modeling is a quite popular pre-training scheme. However, extending masking to 3D point cloud understanding that combines local and global features poses a new challenge. In our work, we present Point-LGMask, a novel method to embed both local and global contexts with multi-ratio masking, which is quite effective for self-supervised feature learning of point clouds but is unfortunately ignored by existing pre-training works. Specifically, to avoid fitting to a fixed masking ratio, we first propose multi-ratio masking, which prompts the encoder to fully explore representative features thanks to tasks of different difficulties. Next, to encourage the embedding of both local and global features, we formulate a compound loss, which consists of (i) a global representation contrastive loss to encourage the cluster assignments of the masked point clouds to be consistent to that of the completed input, and (ii) a local point cloud prediction loss to encourage accurate prediction of masked points. Equipped with our Point-LGMask, we show that our learned representations transfer well to various downstream tasks, including few-shot classification, shape classification, object part segmentation, as well as real-world scene-based 3D object detection and 3D semantic segmentation. Particularly, our model largely advances existing pre-training methods on the difficult few-shot classification task using the real-captured ScanObjectNN dataset by surpassing over 4% to the second-best method. Also, our Point-LGMask achieves 0.4% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$AP_{25}$</tex-math></inline-formula> and 0.8% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$AP_{50}$</tex-math></inline-formula> gains on 3D object detection task over the second-best method. 0.4% mAcc and 0.5% mIoU. Codes have been released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TangYuan96/Point-LGMask</uri> .
Facial expression recognition combining different resolution images

The key point of this thesis is to solve different resolution facial expression recognition. Previously, we used the Highresolution image generation module (HIG) and Coarse to fine the classification module to solve this problem. The effect of HIG is to convert low-resolution images to high-resolution images through superresolution. Coarse to fine classification module instead analyze the image step by step using a three-paragraph classification. Both methods can work well.
Model of Recommendation System to Enhance the Employability: Machine Learning Approach

In the recent era, various types of data are available on the Web in various forms such as text, images, audio, and video. Extracting information of interest from this web content is one of the applications of Web Content Mining. Computer Science is one of the most demanding fields where students can get the best recruitment in the IT (Information Technology) industry through job portals. Still, there is a gap of skills between academia and industry. One of the prominent factors is non-competent syllabus. This Research Paper introduces the Industry Institute Interaction Recommendation System-IIIRS model of a Web Mining technique. It is based on a recommendation system to enhance the employability of students, by bridging the gap between the institution's curriculum and the required skills set at the industry end. The proposed model includes application of data extraction, data pre-processing, classification, and clustering. The model presents the state of the art skills required by the software industry, and gives recommendations about required updates in the syllabus of academic institute. The model serves the purpose of both industry and academia by bridging the gap of skill sets due to incompetent syllabus.
Effective and light weight security system for highly confidential cloud data such as PHR

The server in a cloud storage system can hold a very large amount of Personal health records (PHR) data or information. The cloud platform's storage servers provide archival services for a lengthy time frame. The third party basically functions as an administrator for the efficiency of cloud storage. This is why we're starting up our cloud storage service. One of the biggest difficulties with the cloud is that it is vulnerable to hacking. Ordinary methods of encryption are used to safeguard the information from prying eyes. All of the secret messages' code words are kept in a system of varying symbols. Deletion coding is carried out in a manner analogous to that which is used to calculate the unequal code word cyphers required for a communication in a distributed setting. When the message symbols are stored in different servers in a dispersed environment, the cryptographic term signs are also calculated independently and stored. For this reason, we introduce and include a threshold proxy re-encryption scheme. Fully Homomorphic Encryption is a promising approach to securing sensitive PHR data by limiting who can view it. When sending encrypted PHR data, the proxy re-encryption mechanism re-encrypts the PHR data again before sending it on to the recipient or storage server. Allocation is completed when secure access control has maximised performance. In light of this, we expect to see the Schmidt-Samoa Public Key Encryption (SSPKE) method developed on the Enhanced v Boosting Algorithm (EBA) by PHR data Hiding Architecture. Additionally, in this initiative, we employ a procedure of multi-party protocol admission control to operate and access the user's PHR data without jeopardising the sensitive cloud PHR data privacy. The results of the experiments show the beneficial effect when various metrics, such as total processing time, server response time, and PHR data decomposition rate, are taken into account for the application of PHR.
Evolution of deformation and stress during necking in uniaxial tension

In this investigation, we study experimentally the evolution of both the deformation and the stress during the necking process of a thin metal sheet subject to uniaxial tension. The deformation over the sample surface is obtained using the optical technique of Digital Image Correlation (DIC), which maps out full-field displacement over a two-dimensional (2D) domain. The stress field associated with the measured deformation is determined using the technique developed by Liu [18], where if the deforming material remains isotropic, the stresses can be computed based on the measured deformation by solving the equation of equilibrium together with appropriate traction boundary conditions. The deformation measurement indicates that after the initiation of the neck the deformation in areas outside the neck is frozen, confirming what Bridgman [2] has speculated, and the deformation within the neck area continues to increase till final failure. There is never a reversal in deformation and, as a result, all material particles in the necking zone only experience softening but not unloading. The results also reveal some unique and interesting patterns of the stress field over the necking zone. Their implications on the analysis and modeling of the necking process are discussed.
On Bisimilarity for Polyhedral Models and SLCS

The notion of bisimilarity plays an important role in concurrency theory. It provides formal support to the idea of processes having “equivalent behaviour” and is a powerful tool for model reduction. Furthermore, bisimilarity typically coincides with logical equivalence of an appropriate modal logic enabling model checking to be applied on reduced models. Recently, notions of bisimilarity have been proposed also for models of space, including those based on polyhedra. The latter are central in many domains of application that exploit mesh processing and typically consist of millions of cells, the basic components of face-poset models, discrete representations of polyhedral models. This paper builds on the polyhedral semantics of the Spatial Logic for Closure Spaces (SLCS) for which the geometric spatial model checker PolyLogicA has been developed, that is based on face-poset models. We propose a novel notion of spatial bisimilarity for face-poset models, called ±-bisimilarity. We show that it coincides with logical equivalence induced by SLCS on such models. The latter corresponds to logical equivalence with respect to SLCS on polyhedra which, in turn, coincides with simplicial bisimilarity, a notion of bisimilarity for continuous spaces.
The Effect of Innovative Leadership, Employees’ Innovative Knowledge and Skills on the Innovative Work Behavior of Employees

The research aimed to investigate how the innovative leadership of administrators, as well as the innovative knowledge and skills of employees, impact their innovative work behavior. The study utilized a descriptive assessment and correlational research design, with data collected from employees at two colleges (DWCL & DWCV) using validated research questionnaires. Results showed that the innovative leadership of administrators, innovative knowledge, innovative skills, and innovative work behavior of employees were all assessed as high, and there was a significant correlation between them. These findings contribute to the discourse on innovative work behavior among employees.
Research on the Optimization of Teacher Performance Management System in Private Colleges and Universities

Private colleges and universities are an important part of the domestic higher education system.For a long time, they have made outstanding achievements in talent training.In recent years, the number of private colleges and universities in China has increased from time to time.It should
Effective Strategies for Developing Reading Habits in Elementary School Students

The importance of reading for the growth of a person's life is self-evident. In order for children to hold books and fall in love with reading, the formation of good reading habits is essential, and primary school is particularly important for the formation of a person's habits. Starting from the importance of cultivating primary school students' reading habits, this article discusses effective strategies for cultivating primary school students' reading habits from the aspects of creating a good reading environment, stimulating primary school students' interest in reading, and guiding primary school students to form correct reading habits.
The Effectiveness of Developing E-Book Learning Media on Bilingual Learning Course

The purpose of this study was to determine the effectiveness of e-book learning media that was developed.The type of this research is research and development with ADDIE model.The subject of this research is test subject.The data collection technique is test.The result of this research and development is the increase in college students' cognitive learning outcomes is in the medium category.This shows that the use of e-book learning media can improve college students' cognitive learning outcomes.
Virtual Campuses with Social Learning Environments: A Future Alternative for Traditional Campuses

Covid 19 has been a tremendous impact on all the modern society, the 21st century has been shaped by its implication, digital transformation of all business have been accelerated and stress, but what is the Higher Education reactions and how its processes will be transform. It has been more than two decades since the concept of distance learning was implemented, there have been several online Universities in the world. A few year ago, there were big arguments about the credibility and quality of remote education with systems. E learning platforms were on the rise, and MOOCs, Massive online Courses were given all over the place, then some sort of disbelief and other factors were slowing the advocacy of this emerging educational technologies. Then Covid came, and suddenly all Higher Education in the World is remote, with e-learning platforms and has been for the last two years, half of the education of all professionals has been remote. One of the aspects of distance learning is their more isolated learning mode and little social interaction, an experiment of three different student exchange programs were conducted making groups of students from different universities work together on a virtual campus with avatars. The results were a very positive response to having virtual social interaction tools and activities, the level of technology has advance enough to use these virtual worlds and students satisfaction is very high, however big efforts on customization of programs and environments must be made.
Science to Life: User Experience in Children’s Science Book with Augmented Reality (AR)

This paper proposes an interactive Science book called Science to Life that is supported by Augmented Reality (AR), based on Malaysian Science textbooks from Year 1 to Year 6. The AR application is developed using Unity 3D with the support of the Vuforia engine. The evaluation showed favorable feedback from the users on the roles and capacity of Science to Life in educating young students on the Science subject. This AR book is hoped to spark interest among primary school students to learn and master the Science subject.
CIRC Model Based on Audiovisual Local Wisdom on Ability to Write Malay Pantun

One of the factors causing the low ability of students in writing literature is because learning is centered on the teacher (teacher center) and not focused on students (student center). Generally, teachers use conventional methods and models so that it affects the low learning achievement of students. The purpose of this paper is to describe the effect of the Cooperative Integrated Reading Composition (CIRC) model based on audiovisual local cultural wisdom on the ability to write Sambas Malay pantun for students of Indonesian Language and Literature Education. By using audiovisual media, it is hoped that it can arouse students’ imagination in expressing their expressions in pantun. Learning in general follows standard steps from the CIRC model, by providing innovations complemented by audiovisual media so that learning is easier and more enjoyable. This research is a quantitative research with experimental method. The data source was 24 first semester students of the Indonesian Language Education Study Program at STKIP Singkawang who were formed into 6 heterogeneous groups and assigned to compose 3 pantun for each group. The results showed that writing pantun in groups using the audiovisual-based CIRC model provided more motivation and creativity for students so that the pantun created were maximally successful.
Blockchain enabled dual level security scheme with spiral shuffling and hashing technique for secret video transmission

In the modern digital era, data is considered to be the most valuable asset. Multimedia data includes audio, image and video forms which grabs attention and vulnerable for hacking. Transmission of multimedia data in open network is always unsecure. In this research paper, a novel method to transmit secret video is proposed. First, keyframes are detected by processing the ROI (region of interest) to classify segments. Spiral Shuffling and SHA512 hashing technique are applied to build the cipher blocks. Blockchain technique enhances strength of the Dual Level (Crypto-Stegano) Security Scheme. Discrete cosine transformation with normalization is applied to embed cipher blocks in audio files without any traces of hidden data. Combining the major security techniques such as Cryptography, Steganography and Blockchain enhances the strength of the proposed BDLSS (Blockchain enabled Dual Level Security Scheme). Experimental analysis is carried out with PSNR, SSIM, MSE, NPCR and Correlation coefficient values to prove the efficiency and the proposed method outperforms well over existing methods.
Research on a DS-class X-ray film digitisation device and an image enhancement algorithm with 12-bit RAW data

Automatic evaluation of the X-ray films of welds is an important area of study in the field of non-destructive testing (NDT). A digitising device for the X-ray film is the key to realising automatic evaluation and its image quality directly determines the quality of the automatic evaluation system. At present, the imaging quality of most mature digitising systems on the market cannot meet the DS-class requirements of international standard EN 14096. In order to solve the problems of low visual contrast, detailed information that is difficult to distinguish and low visual resolution in existing digitising systems, a high-quality embedded X-ray film digitising device, processing films with adjustable width, is developed in this paper. The device uses a 12-bit camera and has the characteristics of small size, simple operation and easy maintenance. In order to improve the imaging quality, an optical visual enhancement model is constructed for the generated 12-bit RAW data, which can improve the display of the data. The MTF20% values of the device in the horizontal and vertical directions are 7.544 and 7.460, respectively, and the optical density range is 0.5-4.5 D, the comprehensive index of which can reach DS class. The device has been initially applied by non-destructive testing companies.
MULTISCALE MODELING OF NEUROMORPHIC SYSTEMS

Multiscale approaches, models and algorithms for designing neuromorphic devices for the memory of new generation computers are presented. The developed approaches make it possible to solve problems associated with simulating modeling of the work of neuromorphic networks in intellectual analysis modes of data and machine learning. The issues related to the construction of a computing model of formation/destruction of conductive channels in memristive elements underlying neuromorphic networks are considered. New algorithms for modeling the work of the neuromorphic network have been created, taking into account stochastic effects, as well as original methods and means of simulating modeling of teaching a neuromorphic network. The main approaches are presented when creating software for simulating modeling of neuromorphic networks based on the integration platform of multiscale modeling, combining information flows at various large -scale levels, including the level of a new resistive memory element, the level of neuromorphic network and the level of simulation of the neuromorphic network on precedents . The book is intended for scientists, specialists in the field of computing electronics, senior students and graduate students of technical universities.
Security Analysis of Blockchain Layer-One Sharding Based Extended-UTxO Model

Blockchain technology facilitates the transfer of digital assets, accomplished through the distributed storage of a transaction ledger, allowing peer-to-peer participant nodes to agree on valid transactions based on their local records without the reliance on centralised infrastructure or trusted participants. Distributed ledgers are increasing in public adoption, which can be attributed to the permissionless infrastructure and a rise in decentralised finance (DeFi) protocols. In this growth, shortcomings in throughput and latency have been highlighted, especially when compared to traditional payment channels. The extended-UTXO (eUTXO) model offers the untapped potential to support a functionally scalable infrastructure by adopting qualities of both the account model, and directed acyclic graph-structured UTXO model. We identified the unique benefits of eUTXO as: the ability to bundle the transaction processing of non-conflicting input states, achieving parallelism at the validator nodes; and the ability to implement complex off-chain scaling solutions through smart contracts. This research examines the security impact of sharding when applied alongside an eUTXO ledger. To illustrate this we introduce S-EUTO, a novel proof-of-concept state-sharding protocol. It leverages distributed randomness to ensure unbiased node-to-shard distribution and introduces an input/output cross-shard transaction architecture to maintain global state synchronisation. Our model demonstrated the potential of sharding alongside eUTXO without compromising security.