Hiện nay, các cơ quan chức năng đang đẩy mạnh tuyên truyền, giải thích và xử lý quyết liệt đối với những trường hợp chuyển nhượng nhà đất không trung thực (giao dịch hai giá) để nhằm mục đích giảm bớt tiền thuế, phí phải nộp cho Nhà nước. Tuy nhiên, dưới góc độ của quan hệ dân sự thì việc chuyển nhượng nhà đất lại có thể tự do, tự nguyện, bình đẳng, thỏa thuận nên không thể khẳng định mọi giao dịch nhà đất với giá thấp hơn mức bình thường trên thị trường đều là giao dịch hai giá. Vì vậy, đây là vấn đề rất cần có sự nghiên cứu, tìm hiểu để có những giải pháp phù hợp.
Trong bài viết này, tác giả trình bày tổng quan về hiện trạng chế định tính giá trong chính sách tài chính về tài nguyên nước ở Việt Nam, kinh nghiệm quốc tế cũng như chủ trương, chính sách của Đảng, Nhà nước ta về vấn đề này và đưa ra kiến nghị sửa đổi Luật Tài nguyên nước hiện hành.
Thuốc lá là mặt hàng không được khuyến khích tiêu thụ vì những tác hại rất lớn đối với sức khỏe con người. Một số quốc gia, trong đó có Việt Nam, đã và đang tích cực thực hiện các biện pháp kiểm soát việc tiêu thụ thuốc lá. Trong đó, biện pháp tăng thuế đối với thuốc lá thường được sử dụng như một biện pháp phổ biến vì biện pháp này có tác dụng giảm tiêu thụ thuốc lá, mang lại các lợi ích khác cho xã hội và có thể tăng thu cho ngân sách nhà nước.
This study presents a reinforced techniquefor mining frequent fuzzy sets based on cellular learning automata (CLA). The results demonstratethat frequent set mining can be accomplished with less running time when the proposed method iscompared to iMFFP and NPSFF methods.
In this paper, perform an in-depth evaluation of the FA-CFS model, both quantitative results for system performance evaluation and qualitative results for user experience on mobile device usage. The experiments show that FA-CFS can reduce the rate of interface frame time peaks by up to 40% in terms of quantitative results and obtains a quantifiable impact on the quality of user experience with a quicker, more responsive interface.
This study focus on the image captioning problem in Vietnamese. Indetail, an empirical study of grid-based and region-based feature extraction approaches using currentstate-of-the-art object detection methods is investigated to explore the suitable way to represent theimages in the model space. Each feature type represents images, and the image captioning task istrained using the Transformer-based model. The effectiveness of different feature types is exploredon two Vietnamese datasets: UIT-ViIC and VieCap4H, the two standard benchmark datasets. Theexperimental results show crucial insight into the feature extraction task for image captioning inVietnamese.
Internet of Things technology was introduced to allow many physical devices to connectover the Internet. The data and tasks generated by these devices put pressure on the traditionalcloud due to high resource and latency demand. Vehicular Fog Computing (VFC) is a concept thatutilizes the computational resources integrated into the vehicles to support the processing of end-user-generated tasks. This research first proposes a bag of tasks offloading framework that allowsvehicles to handle multiple tasks and any given time step. We then implement an evolution-basedalgorithm called Time-Cost-aware Task-Node Mapping (TCaTNM) to optimize completion time andoperating costs simultaneously. The proposed algorithm is evaluated on datasets of different tasksand computing node sizes. The results show that our scheduling algorithm can save more than60%ofmonetary cost than the Particle Swarm Optimization (PSO) algorithm with competitive computationtime. Further evaluations also show that our algorithm has a much faster learning rate and can scaleits performance as the number of tasks and computing nodes increases.
MDS (maximum distance separable) matrices are ones that come from MDS codes that have been studied for a long time in error correcting code theory and have many applications in block ciphers. To improve the security of block ciphers, dynamic block ciphers can be created. Using MDS matrix transformations is a method used to make block ciphers dynamic. Direct exponentiation is a transformation that can be used to generate dynamic MDS matrices to create a dynamic diffusion layer of the block ciphers. However, for cryptographic algorithms that use an MDS matrix as a component of them, the implementation of matrix multiplication is quite expensive, especially when the matrix has a large size. In this paper, the mathematical basis for quick calculation of direct exponentiation of an MDS matrix will be presented. On that basis, it is to suggest how to apply that fast calculation to dynamic algorithms using the direct exponentiation. This result is very meaningful in software implementation for MDS matrices, especially when implementing dynamic block ciphers to increase execution speed.
In this paper, present a different approach applying a machine learning model that combines many algorithms in two phases, and propose a feature reduction method. Specifically, our research is focused on the combination of different regression and classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extra Tree Regressor (extraTree), Light Gradient Boosting Machine (LGBM), Logistic Regression (LR) and Linear Regression (LiR) to create a new data set and models that can be used in the training phase. These proposed models are tested on the UJIIndoorLoc 1 dataset. Our experimental results show a prediction accuracy of 98.73% by floor, and an estimated accuracy of 99.62% and 99.52% respectively by longitude and latitude. When compared with the results of the models in which we use independent algorithms, and of other researches that have different models using the same algorithms and on the same dataset, most of our results are better.
