Reviewerly/exHarmony huggingface dataset:
Is an Object-Centric Video Representation Beneficial for Transfer?

The objective of this work is to learn an object-centric video representation, with the aim of improving transferability to novel tasks, i.e., tasks different from the pre-training task of action classification. To this end, we introduce a new object-centric video recognition model based on a transformer architecture. The model learns a set of object-centric summary vectors for the video, and uses these vectors to fuse the visual and spatio-temporal trajectory 'modalities' of the video clip. We also introduce a novel trajectory contrast loss to further enhance objectness in these summary vectors. With experiments on four datasets—SomethingSomething-V2, SomethingElse, Action Genome and EpicKitchens—we show that the object-centric model outperforms prior video representations (both object-agnostic and object-aware), when: (1) classifying actions on unseen objects and unseen environments; (2) low-shot learning of novel classes; (3) linear probe to other downstream tasks; as well as (4) for standard action classification.
Embedded Fusion: The Certainty of Blockchain Technology Complements the High Degree of Probability of Legal Presumption

This paper aims to analyze the technical basis and theoretical rationality of the People's Court Online Litigation Rules of China regarding the provision that blockchain-stored data has the effect of presumption of authenticity.The presumption is a legal technique to resolve facts proving difficulties.The traditional presumption of certainty is based on naive rules of thumb, while the presumption of certainty in information technology is based on the objective foundation of technological certainty.This paper adopts the research methods of case study and law analysis, analyzes in detail the characteristics of data certainty, rule certainty, and identity certainty of blockchain technology and argues that the certainty of blockchain technology has a complementary effect on the high probability of legal presumption.
Learnable Subspace Orthogonal Projection for Semi-supervised Image Classification

In this paper, we propose a learnable subspace orthogonal projection (LSOP) network for semi-supervised image classification. Although projection theory is widely used in various machine learning methods, solving projection matrix is a highly complex process. We employ an auto-encoder to construct a scalable and learnable subspace orthogonal projection network, thus enjoying lower computational consumption of subspace acquisition and smooth cooperation with deep neural networks. With these techniques, a promising end-to-end classification network is formulated. Extensive experimental results on real-world datasets demonstrate that the proposed classification algorithm achieves comparable performance with fewer training data than other projection methods.
Emphasizing Closeness and Diversity Simultaneously for Deep Face Representation

Recent years have witnessed remarkable progress in deep face recognition due to the advancement of softmax-based methods. In this work, we first provide the analysis to reveal the working mechanism of softmax-based methods from the geometry view. Margin-based softmax methods enhance the feature discrimination by the extra margin. Mining-based softmax methods pay more attention to hard samples and try to enlarge their diversity during training. Both closeness and diversity are essential for discriminative features learning; however, we observe that most previous works dealing with hard samples fail to balance the relationship between closeness and diversity. Therefore, we propose a novel approach to tackle the above issue. Specifically, we design a two-branch cooperative network: the Elementary Representation Branch (ERB) and the Refined Representation Branch (RRB). ERB employs the margin-based softmax to guide the network to learn elementary features and measure the difficulty of training samples. RRB employs the proposed sampling strategy in conjunction with two loss terms to enhance closeness and diversity simultaneously. Extensive experimental results on popular benchmarks demonstrate the superiority of our proposed method over state-of-the-art methods.
Neural Puppeteer: Keypoint-Based Neural Rendering of Dynamic Shapes

We introduce Neural Puppeteer, an efficient neural rendering pipeline for articulated shapes. By inverse rendering, we can predict 3D keypoints from multi-view 2D silhouettes alone, without requiring texture information. Furthermore, we can easily predict 3D keypoints of the same class of shapes with one and the same trained model and generalize more easily from training with synthetic data which we demonstrate by successfully applying zero-shot synthetic to real-world experiments. We demonstrate the flexibility of our method by fitting models to synthetic videos of different animals and a human, and achieve quantitative results which outperform our baselines. Our method uses 3D keypoints in conjunction with individual local feature vectors and a global latent code to allow for an efficient representation of time-varying and articulated shapes such as humans and animals. In contrast to previous work, we do not perform reconstruction in the 3D domain, but project the 3D features into 2D cameras and perform reconstruction of 2D RGB-D images from these projected features, which is significantly faster than volumetric rendering. Our synthetic dataset will be publicly available, to further develop the evolving field of animal pose and shape reconstruction.
Generating Multiple Hypotheses for 3D Human Mesh and Pose Using Conditional Generative Adversarial Nets

Despite recent successes in 3D human mesh/pose recovery, the human mesh/pose reconstruction ambiguity is a challenging problem that can not be avoided as lighting, occlusion or self-occlusion in scenes happens. We argue that there could be multiple 3D human meshes corresponding a single image from a view point, because we really do not know what happens in extreme lighting or behind occlusion/self occlusion. In this paper, we address the problem using Conditional Generative Adversarial Nets (CGANs) to generate multiple hypotheses for 3D human mesh and pose from a single image under the condition of 2D joints and relative depth of adjacent joints. The initial estimation of 2D human skeletons, relative depth and features is taken as input of CGANs to train the generator and discriminator in the first stage. Then the generator of CGANs is used to generate multiple human meshes via different conditions which are consistent with human silhouette and 2D joint points in the second stage. Selecting and clustering are utilized to eliminate abnormal and redundant human meshes. The number of hypothesis is not unified for each single image, and it is dependent on 2D pose ambiguity. Unlike the existing end-to-end 3D human mesh recovery methods, our approach consists of three task-specific deep networks trained separately to mitigate the training burden in terms of time and datasets. Our approach has been evaluated not only on the datasets of laboratory and real scenes but also on Internet images qualitatively and quantitatively, and experimental results demonstrate the effectiveness of our approach.
A Compressive Prior Guided Mask Predictive Coding Approach for Video Analysis

In real-world scenarios, video analysis algorithms are conducted for visual signals after compression and transmission. Generally speaking, most codecs introduce irreversible distortion due to coarse quantization during compression. The distortion may lead to significant perception degradation in terms of video analysis performance. To tackle this problem, we propose an efficient plug-and-play approach to preserve the essential semantic information in video sequences explicitly. The proposed approach could boost the video analysis performance with a little extra bit cost. Specifically, we employ the proposed approach on an emerging video analysis task, video object segmentation (VOS). Massive experimental results prove that the our work outperforms the existing coding approaches over multiple VOS datasets. Concretely, it could improve the analysis performance by up to 13% at similar bitrates. Additional experiments also verifies the flexibility of our scheme because there is no dependency on any specific VOS model or encoding method. Essentially, the proposed approach provides novel insights for the emerging Video Coding for Machine (VCM) standard.
Boundary-Aware Temporal Sentence Grounding with Adaptive Proposal Refinement

Temporal sentence grounding (TSG) in videos aims to localize the temporal interval from an untrimmed video that is relevant to a given query sentence. In this paper, we introduce an effective proposal-based approach to solve the TSG problem. A Boundary-aware Feature Enhancement (BAFE) module is proposed to enhance the proposal feature with its boundary information, by imposing a new temporal difference loss. Meanwhile, we introduce a Boundary-aware Feature Aggregation (BAFA) module to aggregate boundary features and propose a Proposal-level Contrastive Learning (PCL) method to learn query-related content features by maximizing the mutual information between the query and proposals. Furthermore, we introduce a Proposal Interaction (PI) module with Adaptive Proposal Selection (APS) strategies to effectively refine proposal representations and make the final localization. Extensive experiments on Charades-STA, ActivityNet-Captions and TACoS datasets show the effectiveness of our solution. Our code is available at https://github.com/DJX1995/BAN-APR .
Bright as the Sun: In-depth Analysis of Imagination-Driven Image Captioning

Existing studies on image captioning mainly focus on generating “literal” captions based on visual entities in images and their basic properties such as colors and spatial relationships. However, to describe images, humans use not only literal descriptions but also “imagination-driven” descriptions that characterize visual entities by some different entities; they are often more vivid, precise, and visually comprehensible by readers/hearers. Nonetheless, none of the existing studies seriously consider captions of this type. This study presents the first comprehensive analysis of the generation and evaluation of imagination-driven captions. Specifically, we first analyze imagination-driven captions in existing image captioning datasets. Then, we present the comprehensive categorizations of imagination-driven captions and their usage, discussing the (potential) issues with the current image captioning models to generate such captions. Next, compiling these captions extracted from the existing datasets and synthesizing fake captions, we create a dataset named IdC-I and -II. Using this dataset, we examine nine existing metrics of image captioning about how accurately they can evaluate imagination-driven caption generation. Last, we propose a baseline model for imagination-driven captioning. It has a built-in mechanism to select which to generate between literal and imagination-driven captions, which existing image captioning models cannot do. Experimental results demonstrate that our model performs better than six existing models, especially for imagination-driven caption generation. Dataset and code will be publicly available at: https://github.com/TranHuyen1191/Imagination-driven-Image-Captioning .
Design as Inquiry. Critical Design in Tourism

This chapter provides an overview of critical design and extends key notions from the critical design tradition to tourism design. Critical design opposes design’s naturalised association with the creation of viable and profitable artefacts or services, and instead emphasises how design can be used as a means for crafting questions and doing social critique. By proving examples and detailing a number of key characteristics of the critical design approach, the chapter suggests research opportunities associated with the turn towards design as a form of inquiry.
Emotional Information-Based Hybrid Recommendation System

In this technology-driven times, recommender systems play a crucial role in providing better user experience and attracting as many users as possible on the website. We propose a hybrid approach for selecting movies that incorporates emotional data in this study. The model will include the benefits of both content-based filtering and collaborative filtering to make up a hybrid model, and then, an additional parameter which is emotions further enhances the accuracy and efficiency of the model. The model is evaluated and compared with some other approaches, and it appears to perform better in a real-time environment. The model also tries to eliminate some of the existing limitations up to some extent.
Feature Selection Using Hybrid Black Hole Genetic Algorithm in Multi-label Datasets

In multi-label classification, each instance is assigned to a group of labels. Due to its expanding use in applications across domains, multi-label classification has gained prominence in recent years. Feature selection is the most common and important preprocessing step in all machine learning and data mining tasks. Removal of highly correlated, irrelevant, and noisy features increases the performance of an algorithm and reduces the computational time. Recently, a Black Hole metaheuristic algorithm, inspired by the phenomenon of Black Holes, has been developed. In this study, we present a modified standalone Black Hole algorithm by hybridizing the standard Black Hole algorithm with two genetic algorithm operators, namely crossover and mutation operators. We carried out experiments on several benchmarking datasets across domains. The modified feature selection algorithm was also employed on a multi-label epitope dataset. The results were compared with the standalone Black Hole and other most common evolutionary algorithms such as Ant Colony, Advance Ant Colony, genetic algorithms, and Binary Gravitational Search Algorithm. The results showed that our improved hybrid algorithm outperforms the existing evolutionary algorithms on most datasets. The synergistic combination of Black Hole and Genetic Algorithms can be used to solve multi-label classification problems in different domains.
Support Vector Regression-Based Hybrid Models for Multi-day Ahead Forecasting of Cryptocurrency

After the introduction of Bitcoin in 2008, the cryptocurrency rose to popularity at an exponential rate, and currently, it is one of the most traded financial instruments worldwide. The nature of cryptocurrency is not only complicated but is also a bemusing financial instrument due to its high volatility. In this study, we present a novel hybrid machine learning model with a goal to perform multi-day ahead price forecasting of cryptocurrency. This study proposes four hybrid models that combine random forest (RF) with four variants of support vector regression (SVR): LSSVR, PSVR, $$\epsilon $$ -TSVR, and GEPSVR. The performance of these models is evaluated over six popular cryptocurrencies employed over a large scale of technical indicators based on two performance metrics, RMSE, and $$R^2$$ Score. The empirical results obtained over various cryptocurrency dataset show that hybrid models outperform the basic variants of SVR.
Current and Future Trends on Smart Home Technology: Including SoS Perspective

Ambient intelligence is a system technology which embeds sensors, computers, and actuators into the environment without making the user aware. The system estimates the situation and behavior of the user in the environment and provides useful support to the user through interaction with the system. Ambient intelligence can be applied to various environments, one of which is considered a house environment called smart home. This report introduces overseas and Japan commercialization and research cases on smart home technology. Smart home technology is evolving rapidly every year due to the miniaturization, embedding, and high-speed processing of computers and sensors. Furthermore, the technology is considered an example familiar to us as a concrete one of SoS, System of Systems. Here is a follow-up to the similar article published in 2021.
Reusable Self-attention-Based Recommender System for Fashion

A large number of empirical studies on applying self-attention models in the domain of recommender systems are based on offline evaluation and metrics computed on standardized datasets, without insights on how these models perform in real-life scenarios. Moreover, many of them do not consider information such as item and customer metadata, although deep learning recommenders live up to their full potential only when numerous features of heterogeneous types are included. Also, typically recommendation models are designed to serve well only a single use case, which increases modeling complexity and maintenance costs, and may lead to inconsistent customer experience. In this work, we present a reusable attention-based fashion recommendation algorithm (AFRA) that utilizes various interaction types with different fashion entities such as items (e.g., shirt), outfits and influencers, and their heterogeneous features. Moreover, we leverage temporal and contextual information to address both short- and long-term customer preferences. We show its effectiveness on outfit recommendation use cases, in particular: (1) personalized ranked feed; (2) outfit recommendations by style; (3) similar item recommendation, and (4) in-session recommendations inspired by most recent customer actions. We present both offline and online experimental results demonstrating substantial improvements in customer retention and engagement.
Design and Development of an Indexing Drill Jig for Currency Counting Machine

The study involves the designing of a custom drill jig to manufacture a crucial component of currency counting machines. A drill jig is a work-holding device that holds, supports and locates the workpiece and guides the drill bit to perform the drilling operation. The holes to be drilled are equidistantly located along the lateral curved surface of the cylindrical workpiece. Indexing refers to a process of quick and accurate location of a workpiece or fixture in a number of specific positions. An indexing plate type indexing mechanism was developed to drill all five holes without the need of clamping and unclamping the workpiece multiple times. The components were designed, assembled and drafted using Solidworks. Height gauges were used to determine the positioning of the drilled holes with respect to the face of the workpiece. Go/no-go gauge was utilized to verify the diameter of the drilled holes. Implementing indexing drill jigs significantly improved the accuracy of the position at which the holes were being drilled. The consistency of the manufactured components and the rate of production of the housing were also significantly improved.
International Business and Block-Chain Ventures

This research discusses International Business and Block-chain ventures consequences of block-chain ventures. The author points out all the advantages block-chain ventures can generate with businesses searching for buyers, clients, staff, and distributors to access international markets. Besides, the international existence of original coin offerings, including their business models, indicates some conventional methods through internationalization. This analysis seeks to examine the effect on international business of the block-chain ventures as well as the resulting problems and repercussions for international governance. Design/Technique/Strategy To international finance, banking and insurance, supply chain management, and logistics, the study of various block-chain applications, marketing, and advertising illustrates that the use of block-chain in corporate business has various implications on global governance. Outcomes although it is possible to enhance land ownership security or reduce transaction costs, their impact on certain global governance roles is much more indifferent. Limitations/implications: A need for further multidisciplinary or observational research becomes indicated as a guide for future studies. When technology interrupts corporate practices, practical effects often impact organizations’ governance on even an international scale. Recommendations have been created for both the potential control of International Business and Block-chain ventures industry. Therefore, the technology of an originality/meaning block-chain extends well beyond the crypt currency frontier and has developed itself through numerous areas of international business. The research study seeks to examine the effect on both the block-chain's international business and the resulting problems’ potential repercussions for international governance. And conclude its influences on global policy; the use of block-chain technology in foreign relations across several fields is now being examined. The blocking strategy is based on offering global governance-specific challenges and rewards.
Amalgamation of Cloud computing with big data

As technology advances, massive amounts of data in many different formats are produced everywhere. The industries that have fueled the growth of data, including retail, media, finance, healthcare, and education, have produced a tremendously large and complicated collection of data that is known as "big data." On the other hand, a virtual service called cloud computing is utilised for processing, data storage, and data mining in order to maximise flexibility and cut costs. It is assisting society in addressing upcoming issues, including large data management, cyber security, and quality control. Additionally, cutting-edge technologies like distributed ledger technology, artificial intelligence, and many other capabilities are becoming accessible as services thanks to cloud computing. The two most important techniques in the field of information technology. These two IT efforts have the potential to transform every aspect of the company and have an impact on how we analyse data. It’s become popular in research to use the cloud environment for large data analysis. This work attempts to comprehend the Cloud Computing technology in Big Data so as to obtain efficient and quick results. Furthermore, the paper sheds light on the challenges faced in the domain and concludes with the future scope of these technologies.
Aspect Based Sentiment Classification Using Deep Memory Network

Our main aim is to target and identify the polarity of the sentiments using a Deep memory network for the classification. And for emerging new technologies there is a drastic need for new services. Even though there are sequential neural methods like Long Short-Term Memory (LSTM) networks and a few other feature-based Support Vector Machine (SVM), they mostly approach focus on encapsulating the importance of every word in the sentence and at the same time interferes the aspect sentiment polarity. Hence with this growing number of services, the polarity is not enough to help people who seek these technologies. And using a deep memory network, provided new possibilities to go deeper into the research and helped to carry out Aspect based sentimental analysis. Through multiple computational layers, these degrees are equally given importance and have been calculated which makes it a neural attention model instead of maintaining focus on the external memory. This method proves that it is more sustainable than the other models, LSTM and SVM, and is also 15 times quicker with 9 computational layers.