Reviewerly/exHarmony huggingface dataset:
3D Shape Temporal Aggregation for Video-Based Clothing-Change Person Re-identification

3D shape of human body can be both discriminative and clothing-independent information in video-based clothing-change person re-identification (Re-ID). However, existing Re-ID methods usually generate 3D body shapes without considering identity modelling, which severely weakens the discriminability of 3D human shapes. In addition, different video frames provide highly similar 3D shapes, but existing methods cannot capture the differences among 3D shapes over time. They are thus insensitive to the unique and discriminative 3D shape information of each frame and ineffectively aggregate many redundant framewise shapes in a videowise representation for Re-ID. To address these problems, we propose a 3D Shape Temporal Aggregation (3STA) model for video-based clothing-change Re-ID. To generate the discriminative 3D shape for each frame, we first introduce an identity-aware 3D shape generation module. It embeds the identity information into the generation of 3D shapes by the joint learning of shape estimation and identity recognition. Second, a difference-aware shape aggregation module is designed to measure inter-frame 3D human shape differences and automatically select the unique 3D shape information of each frame. This helps minimise redundancy and maximise complementarity in temporal shape aggregation. We further construct a Video-based Clothing-Change Re-ID (VCCR) dataset to address the lack of publicly available datasets for video-based clothing-change Re-ID. Extensive experiments on the VCCR dataset demonstrate the effectiveness of the proposed 3STA model. The dataset is available at https://vhank.github.io/vccr.github.io.
Cross-Architecture Knowledge Distillation

Transformer attracts much attention because of its ability to learn global relations and superior performance. In order to achieve higher performance, it is natural to distill complementary knowledge from Transformer to convolutional neural network (CNN). However, most existing knowledge distillation methods only consider homologous-architecture distillation, such as distilling knowledge from CNN to CNN. They may not be suitable when applying to cross-architecture scenarios, such as from Transformer to CNN. To deal with this problem, a novel cross-architecture knowledge distillation method is proposed. Specifically, instead of directly mimicking output/intermediate features of the teacher, partially cross attention projector and group-wise linear projector are introduced to align the student features with the teacher's in two projected feature spaces. And a multi-view robust training scheme is further presented to improve the robustness and stability of the framework. Extensive experiments show that the proposed method outperforms 14 state-of-the-arts on both small-scale and large-scale datasets.
Architectural Heritage Information Potential in Modern Commemorative Practices (On Latin America Example)

Emphasis is placed on the unique historical phenomenon of the formation of Latin American culture as an organic synthesis of European, Indian and African cultures with the complex application of commemorative practices in art genres. The complex of scientific approaches to the disclosure of the content and essence of the concept of “historical memory” in the European scientific discourse is analyzed. Emphasis is placed on the diversity of views of researchers in studying the features of socio-political processes of formation of individual and collective historical consciousness of man, society, society. A general description of the role and place of Latin American architecture in the world cultural and historical process is given. Peculiarities of formation, functioning and transformation of commemorative practices using the information potential of architectural monuments on the example of architecture tendencies development analysis of Latin America at the colonial period are considered. Peculiarities of application of dominant architectural styles in the process of urban planning as a reflection of the pan—European vector of art development are revealed. The dominant architectural forms, tendencies of architectural construction of different regions of Latin America and the influence of national practices of commemoration of the colonizing states on the formation of urban infrastructure are characterized. The role of cultures of the indigenous population of the Latin American cultural region in the formation of unique architectural schools in Latin America is indicated.
PS-ARM: An End-to-End Attention-Aware Relation Mixer Network for Person Search

Person search is a challenging problem with various real-world applications, that aims at joint person detection and re-identification of a query person from uncropped gallery images. Although, previous study focuses on rich feature information learning, it's still hard to retrieve the query person due to the occurrence of appearance deformations and background distractors. In this paper, we propose a novel attention-aware relation mixer (ARM) module for person search, which exploits the global relation between different local regions within RoI of a person and make it robust against various appearance deformations and occlusion. The proposed ARM is composed of a relation mixer block and a spatio-channel attention layer. The relation mixer block introduces a spatially attended spatial mixing and a channel-wise attended channel mixing for effectively capturing discriminative relation features within an RoI. These discriminative relation features are further enriched by introducing a spatio-channel attention where the foreground and background discriminability is empowered in a joint spatio-channel space. Our ARM module is generic and it does not rely on fine-grained supervisions or topological assumptions, hence being easily integrated into any Faster R-CNN based person search methods. Comprehensive experiments are performed on two challenging benchmark datasets: CUHK-SYSU and PRW. Our PS-ARM achieves state-of-the-art performance on both datasets. On the challenging PRW dataset, our PS-ARM achieves an absolute gain of 5% in the mAP score over SeqNet, while operating at a comparable speed. The source code and pre-trained models are available at https://github.com/mustansarfiaz/PS-ARM .
IoU-Enhanced Attention for End-to-End Task Specific Object Detection

Without densely tiled anchor boxes or grid points in the image, sparse R-CNN achieves promising results through a set of object queries and proposal boxes updated in the cascaded training manner. However, due to the sparse nature and the one-to-one relation between the query and its attending region, it heavily depends on the self attention, which is usually inaccurate in the early training stage. Moreover, in a scene of dense objects, the object query interacts with many irrelevant ones, reducing its uniqueness and harming the performance. This paper proposes to use IoU between different boxes as a prior for the value routing in self attention. The original attention matrix multiplies the same size matrix computed from the IoU of proposal boxes, and they determine the routing scheme so that the irrelevant features can be suppressed. Furthermore, to accurately extract features for both classification and regression, we add two lightweight projection heads to provide the dynamic channel masks based on object query, and they multiply with the output from dynamic convs, making the results suitable for the two different tasks. We validate the proposed scheme on different datasets, including MS-COCO and CrowdHuman, showing that it significantly improves the performance and increases the model convergence speed. Codes are available at https://github.com/bravezzzzzz/IoU-Enhanced-Attention .
The Use of AI in Metaverse

The benefits of the technological advances developing with the Internet are constantly increasing recently. These developments, which have grown at almost the speed of light, have made it convenient for users to leave the real world and live in the virtual world. The first phase of this breakthrough started with introducing social networks into our lives. There are many services and applications in this sector. The second phase brought by social networks over users has been virtual environments and virtual game worlds. At this point, the term metaverse, which is a combination of the words meta and universe, has emerged. Apart from the word's meaning, it refers to a virtual environment shared by more than one person. The development of technologies has occurred in terms of the increasing number or quality of applications and artificial intelligence; serious developments are taking place today. In this study, in addition to the artificial intelligence applications and topics taking place in the metaverse universe, research has been done on the artificial intelligence theories that are predicted to be realized as a concept.
The burial of Education in Telangana State Politics (2014 onwards)

The above title of the article stands to pleasing that not to harm and destroying "knowledge society in India". However the delight and held in reverence to moral dedicated politicians in our country. Meanwhile there shall be healthy criticism but finding out who the culprits are those who planned behind the curtain to destroying education in newly formed Telangana glorious State. Set of issues about education destruction, Tracing out the ground reality of deliberate decisions which spoiled completely the beautiful quality environment of education in our Telangana state. These worst things happened in Telangana state due to the miscreant attitudes that expose their villainous traits in so called democratic rule. In spite of all evils against education in Telangana state, we all should give a big thanks to God who established immovable solid constitutional foundation through the incredible man Dr. B.R. Ambedkar. In this connection, most valuable caution to teachers of state and our country India. Teaching is noble profession that can establish the development of society. If teachers don’t realise and repent for quality teaching which they can gain by commitment and dedication, they are to become ‘one of the major reasons’ for society destruction. There is an inspiring statement ‘Good teachers matter more than the curriculum and administration. There are millions of dedicated teachers which produced by India. Thanks to mother India. But few are to bring bad name and sluggish by losing their valuable opportunity. The gist of this article is the criminal politics and "lawbreaking allocation of budget funds" for education in Telangana Government fell shocked since 2014. This is the exact core reason the destruction schemed started!
Generalized bent functions with large dimension

By a fundamental result by Mesnager et al. in 2018, a generalized bent function (originally defined as a class of functions from an $ n $-dimensional vector space $ \mathbb{V}_n^{(p)} $ into a cyclic group), is a bent function $ g: \mathbb{V}_n^{(p)}\rightarrow \mathbb{F}_p $ with a partition $ \mathcal{P} $ of $ \mathbb{V}_n^{(p)} $, such that for every function $ C $ which is constant on the sets of $ \mathcal{P} $, the function $ g + C $ is bent. The set of these bent functions forms then an affine space of dimension $ |\mathcal{P}| \le p^{n/2} $. This characterization of generalized bent functions is much more comprehensive than any earlier description.In this article, we analyse some classes of bent functions under this perspective. As shown earlier, Maiorana-McFarland bent functions permit the largest possible partitions, giving rise to $ p^{n/2} $-dimensional affine bent function spaces. The reason behind is their characterization as the bent functions, which are affine restricted to the $ n/2 $-dimensional affine subspaces of a trivial cover of $ \mathbb{V}_n^{(p)} $. We will show that maximal possible partitions can also be obtained for other classes of (regular) bent functions. Most notably, these classes are described as bent functions which are affine restricted to non-trivial covers of $ \mathbb{V}_n^{(p)} $. We investigate (largest) partitions for other types of bent functions, including weakly regular and non-weakly respectively non-dual bent functions. We round off the article giving partitions for bent functions obtained from bent partitions, which includes the partial spread class, and partitions for Carlet's class $ \mathcal{C} $ and $ \mathcal{D} $.
Double/Debiased Machine Learning for Static Games with Incomplete Information

This paper presents estimation and inference methods for static games with incomplete information using modern machine learning tools. Conventional econometric methods in Bajari et al. (2010), can only be applied with low-dimensional covariates compared to the sample size N. However, when the data includes high-dimensional covariates, using machine learning estimators to recover the nuisance parameters can lead to biases in the structural parameter of interest, θ0, due to regularization and over-fitting. The proposed estimator addresses these biases by characterizing Neyman orthogonal moments and using cross-fitting algorithm. This allows researchers to use machine learning methods like Lasso, Random Forest, or Neural Networks to estimate the first-stage nuisance parameters, leading to a parameter of interest that is √N-consistent and asymptotically normal. Monte Carlo experiments show that without correction of the moment condition, the nuisance parameters estimated from machine learning estimators can cause significant downward biases (toward zero) in the estimates of θ0. The proposed estimator based on orthogonal moment effectively removes these biases, resulting in estimates that are unbiased and centered on the true value.
Mobile Android-based application as an instructional media for understanding vehicles’ air conditioner system

Instructional media as part of teaching and learning activities has an important role in supporting students to be able to understand the lesson. This study aims to; (1) creating a mobile Android-based application as instructional media for automotive air conditioner systems, (2) determining the level of feasibility of the application, and (3) formulating the students’ and teachers’ perspectives toward the application. This research used a quantitative methodology. Sugiyono's research and development (R&D) model was used in this study. This method consists of 10 steps which are defining problems, data collection, design product, design validation, design revision, design trial, design revision, implementation, and production. This study was taken place in the Automotive Engineering department of SMK N 1 Seyegan, Indonesia. The findings reveal that; (1) the application has already been produced, (2) this application is feasible that has a level of “highly decent” based on the judgment of educational media and subject matter experts, and (3) based on the perspective assessment from students and teachers, it could be concluded that this application is highly decent and worthy to be used as an instructional media in SMK teaching and learning activities.
Comprehensive review of state-of-the-art intelligent methods for videosequences analysis

The article deals with the problem of creating and improving various methods and approaches for analyzing video sequences in the aerospace field. Within the framework of this article, an analysis of the methods for searching for an object was carried out, which showed that these methods must satisfy the invariance to projective transformations of the object's image and the computational complexity must be the minimum achievable for the application of solving problems in real time. The Viola-Jones method was considered in detail, which will later be used to monitor the process of soldering waveguide paths. In the future, it is planned to develop a program based on the Viola-Jones method, which will be used to monitor the process of soldering waveguide paths.
M-health digital evidence taxonomy system (MDETS): Enabling digital forensics readiness with knowledge sharing approach

Enabling a forensically ready environment in an organization may include sharing the knowledge of digital forensics tools, procedures, and guidelines between practitioners. This paper presents a M-health Digital Evidence Taxonomy System (MDETS) as a proof of concept that facilitates the forensic readiness in terms of people, process and technology through knowledge sharing approach. Using mobile health (m-health) applications as a case study, MDETS comprises a knowledge warehouse of forensic artefacts from 34 m-health apps. We adopted four knowledge sharing criteria that are articulation, awareness, access, and guidelines. The effectiveness of MDETS was evaluated using people, process, and technology elements. Interviews were conducted with 7 digital forensics practitioners in Malaysia to examine respondents’ perception about the effectiveness of integrating the digital evidence taxonomy with knowledge sharing approach. Interview findings indicated that sharing the knowledge of digital evidence taxonomy is significantly enables the digital forensics readiness practices from people, process, and technology elements. Furthermore, the use of MDETS could beneficial to provide trainings for new digital forensics investigators.
The implementation of sentiment analysis on Indonesian sexual violation bill using Naïve Bayes algorithm

The cases of women's sexual violations in Indonesia annually increase, and due to this fact, reforming the sexual violation bill for protecting Indonesian women is critical. The Indonesian government is currently launching a draft of the Indonesian sexual violation bill to refine the previous sexual violation bill. This study focuses to identify the sentiment of Twitter users in Indonesia about women's sexual violence bill using the Naïve Bayes algorithm. The data is taken from Twitter from October to December 2020 via Twitter API. The data collected is 1132 tweets divided into 90% of training data and 10% of testing data using Python. The several results of this study are, the accuracy score is 96%, the precision score is 100%, the recall score is 35% and the F1 score is 0,67. It means that 35% of the Indonesian Twitter users support the Indonesian sexual violation bill draft.
Semi-supervised atmospheric component learning in low-light image problem

Ambient lighting conditions play a crucial role in determining the perceptual quality of images from photographic devices. In general, inadequate transmission light and undesired atmospheric conditions jointly degrade the image quality. If we know the desired ambient factors associated with the given low-light image, we can recover the enhanced image easily. Typical deep networks perform enhancement mappings without investigating the light distribution and color formulation properties. This leads to a lack of image instance-adaptive performance in practice. On the other hand, physical model-driven schemes suffer from the need for inherent decompositions and multiple objective minimizations. Moreover, the above approaches are rarely data efficient or free of postprediction tuning. Influenced by the above issues, this study presents a semisupervised training method using no-reference image quality metrics for low-light image restoration. We incorporate the classical haze distribution model to explore the physical properties of the given image to learn the effect of atmospheric components and minimize a single objective for restoration. We validate the performance of our network for six widely used low-light datasets. Experimental studies show that our proposed study achieves a competitive performance for no-reference metrics compared to current state-of-the-art methods. We also show the improved generalization performance of our proposed method which is efficient in preserving face identities in extreme low-light scenarios.
An Optimal Reinforced Deep Belief Network for Detection of Malicious Network Traffic

Network traffic analysis is referred to as the method to monitor the availability of the network and identify the anomaly activities like operational issues and security. Network traffic mainly occurs due to the transmission of a large amount of data over the computer network at the same time. The grouping and classification process can evaluate the network traffic and the endless determination of network traffic patterns is the most important challenge during network traffic classification. The existing approaches have some limitations because the time consumption was not able to reduce and it has low classification accuracy in the network traffic analysis. To overcome these problems, the Dove Swarm Optimized Reinforced Deep Belief Network (DSO-RDBN) is proposed which is utilized for optimizing the hyperparameters. There are no effectual training algorithms that can optimize the different hyperparameters associated with the RDBN architecture such as momentum coefficients, learning rate, number of hidden nodes, and number of hidden layers without manual tuning. The manual selection of hyperparameters leads to some delay in the network traffic classification process so the DSO algorithm is implemented for addressing the hyperparameter tuning problem. For network traffic classification analysis, the USTC-TFC2016 test dataset is used and this dataset has two categories Benign and Malicious. The performance metrics such as accuracy, precision, sensitivity, F-measure, and false alarm range (FAR) are applied for improving the performance compared to state-of-art methods. The performance rate of accuracy, precision, sensitivity, F-measure and FAR is 97.2%, 97.6%, 97.5%, 97.6% and 68% respectively.
<scp>Electric</scp> locomotive electrical control training based on virtual reality technology

Abstract The recent rapid development of the computer‐aided design technologies has provided concrete support for the education and research in the field of railway transportation. Virtual reality (VR) technology is becoming particularly important in the field of experimental teaching due to its low cost, low restriction and immersion. In this work, we have designed a virtual simulation untried system of the electric joint control in electric locomotives based on VR technology. Based on the user's understanding of the three circuit principles in the experiment, together with the recognition and operation ability to control appliances and equipment, the system assessment system has been scored and evaluated accordingly. The tests of the virtual simulation system have shown the experimental teaching can reduce the cost while effectively improving the learning effect.
An architectural approach to modeling artificial general intelligence

This study presents an architectural approach for building a conceptual model of artificial general intelligence (AGI). The architectural approach is generally used to model information systems (IS) of enterprises and can be also used as part of a system-wide approach to describe other complex open systems. The paper suggests three layers and five levels of the AGI model. Two levels (entropy and process) are at the technological layer of AI functioning, two more levels (social and linguistic ones) are at the relationship layer responsible for the behavior of AI, and, finally, the uppermost level (actualization) supposes general intelligence. All the components of each upper layer are connected to the components of the lower layers forming the AGI model. The feature of the social layer is determined by the requirements to the subjectivity of the intellect, its ability to make decisions independently and be responsible for them. The task of the upper layer is self-identification of AGI and understanding its place. The hypothesis has been put forward that the limitation of the life cycle is an important condition for the actualization of intelligence.