Reviewerly/exHarmony huggingface dataset:
A Novel Approach Based on Multi-Level Bottleneck Attention Modules Using Self-Guided Dropblock for Person Re-Identification

Person re-identification has inspired a lot of interest due to its significance in intelligent video surveillance.It is a difficult task due to the presence of critical challenges such as changes in appearance, misalignment, occlusion and background noise.Batch drop block layer (BDB) has been used recently in person re-identification by exploiting the feature erasing procedure.However, BDB drops a block of features randomly, resulting in the loss of contextual information, which makes the model difficult to train.Also, due to the random dropping of features, large area of discriminative information may lose during training, resulting in low efficiency and performance.To address this problem and to improve the model representation power, we propose a novel, lightweight, self-adaptive bottleneck attention module with a self-attention branch to improve the model performance by reducing the parameter overhead with negligible computation cost.The proposed approach entails bottleneck attention module (BAM) which is incorporated between ResNet layers to remove the background noise and to nominate the high-level semantic part.Further, dilated convolutions with batch normalization are used to tackle the contextual information loss problem and to avoid overfitting.In addition, an informative global branch is used which captures the global representation of the network, and the attention branch entails the multiscale local salient information.Two types of loss functions including softmax and batch hard triplet are used in the training process for each branch, forcing the network to encapsulate the common attribute within the similar identity and to maintain distance between distinct individuals.Compared with BDB, our network improves the mAp to 88.1% , and Rank-1 gets 96.3% for the market-1501 dataset.The results on Cuhk-03-Detected dataset showed 79.2% mAp score, with 81.4 %, Rank-1, whereas on Cuhk-03-labelled dataset, a mAP score of 81.3% and a Rank-1 score of 83.3% is achieved.Experiments reveal that ResNet model with addition of multiple BAM layers performs consistently over the state-of-the-art datasets using softmax and batch hard triplet loss.
EVA$^{2}$: Event-Assisted Video Frame Interpolation via Cross-Modal Alignment and Aggregation

We consider the problem of event-assisted video frame interpolation (VFI), a new track for VFI, by introducing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">event</i> data, a novel sensing modality, into the process of generating intermediate frames from low-frame-rate videos. This new track challenges existing methods in two aspects: (1) how to utilize the event data to align boundary keyframes to intermediate ones, especially when there are corruptions in scenes ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , non-uniform motion, object occlusions, and illumination changes); (2) how to effectively utilize and aggregate cross-modal information for further mitigating corruptions and refining details. In this paper, we propose a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> vent-assisted <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</u> FI method with cross-modal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> lignment and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> ggregation, termed <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EVA<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula></u> , to address these challenges. First, to handle corruptions during alignment, we devise the cross-modal Event-Guided Alignment (EGA) module, in which the intermediate frames are aligned at both the feature and the image levels. The alignment operation in the EGA module is guided by the offset maps generated from the event data and information extracted from the input boundary keyframes. Second, we propose the cross-modal Event-aware Dynamic Aggregation (EDA) module, in which the event-aware dynamic convolution operation is applied to aggregate the event data with the aligned results adaptively for further improvements. Extensive experiments on both synthetic and real-world datasets validate the effectiveness of our EVA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> .
ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo

Photo retouching finds many applications in various fields. However, most existing methods are designed for global retouching and seldom pay attention to the local region, while the latter is actually much more tedious and time-consuming in photography pipelines. In this paper, we propose a novel adaptive blend pyramid network, which aims to achieve fast local retouching on ultra high-resolution photos. The network is mainly composed of two components: a context-aware local retouching layer (LRL) and an adaptive blend pyramid layer (BPL). The LRL is designed to implement local retouching on low-resolution images, giving full consideration of the global context and local texture information, and the BPL is then developed to progressively expand the low-resolution results to the higher ones, with the help of the proposed adaptive blend module and refining module. Our method outperforms the existing methods by a large margin on two local photo retouching tasks and exhibits excellent performance in terms of running speed, achieving real-time inference on 4K images with a single NVIDIA Tesla P100 GPU. Moreover, we introduce the first high-definition cloth retouching dataset CRHD-3K to promote the research on local photo retouching. The dataset is available at https://github.com/youngLbw/crhd-3K.
IoT, AI and Digital Twin For Smart Campus

The objective of this research is to Present the use of Digital Twin. Internet of Things and Intelligence Technology to Intelligently Develop The Energy Management Potential of Campuses By using Digital Twin to manage energy consumption, it enables the creation of work and life on campus, whether it is personal. Teachers and students, including those with disabilities, have a better quality of life. Living on campus is more comfortable Collect energy consumption data, organizations to manage power management systems, reduce energy consumption, simplify management in all departments, and reduce campus costs. It can transparently monitor energy consumption and use educational equipment on campus, especially cost and expenditure management, and sustainable energy consumption, as well as management. Control usage data, energy consumption results, and budget allocations related to educational institutions can create reliability in managing information about electrical energy and energy competency assessment results, and can be recorded and controlled in Digital Twin that can be as if it were with a control center. IoT and Cloud Computing are also integrated with AI systems embedded in state-of-the-art equipment for use in environmental management and intelligent energy management. As well as creating new energy models and management that can resolve future emergencies by controlling virtual energy within the organization from outside anytime, anywhere.
Integrating Blockchain with ERP systems for better supply chain performance

Technology evolution has long allowed for the better supply chain management. Now, the world is experiencing the emergence of new technologies: for example, blockchain technology. This technology has already proven itself in several areas, including the financial sector. It is now a question of exploiting it with Enterprise Resource Planning systems (ERP) to improve the supply chain performance. Thanks to a study of the literature, we were able to present how blockchain can improve the supply chain management by integrating it with ERP systems and the problems it can solve.
Effect of Distance Measures on K-Nearest Neighbour Classifier

Machine learning classifiers vary concerning their learning functions and approach. K–nearest neighbour (K-NN) is a versatile instance-based machine learning algorithm used for classification without building a learning model. Varying distance measures in K-NN for computing distance between instances affect the classification accuracy. Commonly used distance metrics are Euclidean, Manhattan, Minkowski and Mahalanobis. In this work, extensive experiments were carried out on diverse datasets. These datasets vary in terms of the domain, number and types of features. The classification accuracy results obtained by applying different distance measures in the K-NN classifier were compared and analyzed. The results show that the Mahalanobis distance performed better with more than 90% accuracy, than the Euclidean and other measures when applied on most datasets. The other two most significant inferences from this analysis are: first, the Mahalanobis distance considered the correlation between the features and the deviation of instances from the mean, and second, it is scale-invariant.
Improving Few-Shot Image Classification with Self-supervised Learning

Few-Shot Image Classification (FSIC) aims to learn an image classifier with only a few training samples. The key challenge of few-shot image classification is to learn this classifier with scarce labeled data. To tackle the issue, we leverage the self-supervised learning (SSL) paradigm to exploit unsupervised information. This work builds upon two-stage training paradigm, to push the current state-of-the-art (SOTA) in solving FSIC problem further. Specifically, we incorporate the traditional self-supervised learning method (TSSL) into the pre-training stage and propose an episodic contrastive loss (CL) as an auxiliary supervision for the meta-training stage. The proposed bipartite method, called FSIC-SSL, can SOTA task accuracies on two mainstream FSIC benchmark datasets. Our code will be available at https://github.com/SethDeng/FSIC_SSL .
Joint Workload Scheduling in Geo-Distributed Data Centers Considering UPS Power Losses

Recent years have witnessed the rapid growth in the electricity consumption of internet data centers (IDC) and consequently their high operational cost. Fortunately, geo-distributed multiple data centers could be jointly optimized to reduce the total operational cost using the flexible computational workload scheduling in terms of local electricity price and power consumption level of data center facilities, where in particular the power losses of uninterruptible power supply (UPS) contribute to around 5%. However, the nonlinear characteristics of UPS depending on load ratio have not been fully considered in the joint operation of multiple data centers. In view of this, this paper proposed a new joint workload scheduling method by carefully regulating the UPS power losses within multiple geo-distributed data centers. To precisely formulate the optimization problem, a comprehensive model of multiple geo-distributed data centers is built by considering local electricity price, renewable generation, conventional generator units, energy storage system (ESS), scheduling flexibility of different types of computational workload and especially the characteristics of UPSs. Furthermore, to guarantee the feasibility of the joint workload scheduling method when the renewable power and electricity varies, a day-ahead and intraday scheduling method is proposed for the first time. In consequence, six cases are simulated and solved by the Gurobi software, whose results have illustrated that the proposed method can reduce the operational cost by up to 4.69% under different scenarios.
Multiple Genetic Syndromes Recognition Based on a Deep Learning Framework and Cross-Loss Training

Many patients with genetic syndromes have special facial features, which boast significant potential value for clinical diagnosis.Deep learning and computer vision technology can be employed to diagnose genetic diseases by analyzing facial features of patients.As a matter of fact, the application of deep learning technology in the area of genetic diseases is confined owing to the difficulties of patient data acquisition.This study develops BioFace, a deep learning framework that can recognize multiple genetic diseases facial attributes based on limited datasets.BioFace is a deep neural network architecture designed premised on Resnet.To increase the weight of effective features and weaken the weight of invalid or unobvious features during extraction of facial features, we add Squeeze-and-Excitation (SE) blocks in the network.In combination with this network architecture, we designed a cross-loss training method based on transfer learning.This method can transfer the ability learned from the task of face identification to the task of recognition of genetic diseases facial attribute, and improve the inter-class distance of different genetic diseases and the intra-class distance of similar genetic diseases simultaneously.These render it possible for deep learning to be applied to recognition of multiple genetic diseases facial attribute with very small amount of data.In this research, we tested 10 syndromes with our framework and the Top-1 accuracy was 93.5%, which is the state-of-the-art in multiple genetic syndromes recognition research.In practical clinical applications, our framework and methods can be extended to the disease identification of more small datasets, potentially offering valuable assistance for the auxiliary clinical application of genetic diagnosis and other related genetic research.
Semantic Knowledge Graphs for Distributed Data Spaces: The Public Procurement Pilot Experience

This paper presents the experience gained in the context of a European pilot project funded by the ISA2 programme. It aims at constructing a semantic knowledge graph that establishes a distributed data space for public procurement. We describe the results obtained, the follow up actions and the main lessons learnt from the construction of the knowledge graph. This latter requires to support different data governance scenarios: some partners control, with their own tools, the building process of their portion of the knowledge graph. Other partners participate in the pilot by providing only their open CSV/XML/JSON datasets, in which case transformations are required. These are performed on the infrastructure made available by the European Big Data Test Infrastructure (BDTI). The paper introduces the design and implementation of the knowledge graph construction process within such a BDTI infrastructure. By instantiating an OWL ontology created for this purpose, we are able to provide a declarative description of the whole workflow required to transform input data into RDF output data, which form the knowledge graph. The declarative description is therefore used to provide instructions to a workflow engine we use (Apache Airflow) for knowledge graph construction purposes.
A Systematic Review on Language Identification of Code-Mixed Text: Techniques, Data Availability, Challenges, and Framework Development

The mix of native language with other languages (code-mixing) in social media has posed a severe challenge for language identification (LID) systems.It has encouraged research on code-mixed LID solutions.This study investigated the techniques, challenges, and dataset availability with corresponding quality criteria and developed a comprehensive framework for code-mixed LID.This study addressed four research issues to identify gaps and future work opportunities in tackling code-mixed LID challenges.Based on our analysis of reviewed studies, we outlined key points for future research in code-mixed LID.We demonstrated a taxonomy of applied techniques for code-mixed LID and highlighted the different technique variants.In code-mixed LID tasks, we discovered four significant challenges: ambiguity, lexical borrowing, non-standard words, and intra-word code-mixing.This systematic literature review recognised 32 code-mixed datasets available for LID.We proposed five features to describe the quality criteria dataset.The features are the number of instances or sentences, percentage of code-mixed types in the data, number of tokens, number of unique tokens, and average sentence length.Finally, we synthesised the methodologies and proposed a conceptual framework for subsequent studies through our literature analysis.
Transformer-Based Self-Supervised Monocular Depth and Visual Odometry

Self-supervised monocular depth and visual odometry (VO) are often cast as coupled tasks. Accurate depth contributes to precise pose estimation and vice versa. Existing architectures typically exploit stacking convolution layers and long short-term memory (LSTM) units to capture long-range dependencies. However, their intrinsic locality hinders the model from getting the expected performance gain. In this article, we propose a Transformer-based architecture, named Transformer-based self-supervised monocular depth and VO (TSSM-VO), to tackle these problems. It comprises two main components: 1) a depth generator that leverages the powerful capability of multihead self-attention (MHSA) on modeling long-range spatial dependencies and 2) a pose estimator built upon a Transformer to learn long-range temporal correlations of image sequences. Moreover, a new data augmentation loss based on structural similarity (SSIM) is introduced to constrain further the structural similarity between the augmented depth and the augmented predicted depth. Rigorous ablation studies and exhaustive performance comparison on the KITTI and Make3D datasets demonstrate the superiority of TSSM-VO over other self-supervised methods. We expect that TSSM-VO would enhance the ability of intelligent agents to understand the surrounding environments.
Sign Language to Text Conversion in Real Time using Transfer Learning

The people in the world who are hearing impaired face many obstacles in communication and require an interpreter to comprehend what a person is saying. There has been constant scientific research and the existing models lack the ability to make accurate predictions. So we propose a deep learning model trained on the ASL i.e the American Sign Language which will take action in the form of American Sign Language as input and translate it into text. To achieve the former a Convolution Neural Network based VGG16 architecture is used as well as a Tensorflow model for image classification and we have improved the accuracy of the latter by over 4%. The dataset which has been used is the ASL dataset which has over 87000 images and has been used to train and test the video. There has been an improvement in accuracy from 94% of CNN to 98.7% by Transfer Learning. An application with the deep learning model integrated has also been built.
Saliency Hierarchy Modeling via Generative Kernels for Salient Object Detection

Salient Object Detection (SOD) is a challenging problem that aims to precisely recognize and segment the salient objects. In ground-truth maps, all pixels belonging to the salient objects are positively annotated with the same value. However, the saliency level should be a relative quantity, which varies among different regions in a given sample and different samples. The conflict between various saliency levels and single saliency value in ground-truth, results in learning difficulty. To alleviate the problem, we propose a Saliency Hierarchy Network (SHNet), modeling saliency patterns via generative kernels from two perspectives: region-level and sample-level. Specifically, we construct a Saliency Hierarchy Module to explicitly model saliency levels of different regions in a given sample with the guide of prior knowledge. Moreover, considering the sample-level divergence, we introduce a Hyper Kernel Generator to capture the global contexts and adaptively generate convolution kernels for various inputs. As a result, extensive experiments on five standard benchmarks demonstrate our SHNet outperforms other state-of-the-art methods in both terms of performance and efficiency.
Abstract and Concrete Materials: What to use for Visualization Onboarding for a Treemap Visualization?

Visual exploration of large and complex data is becoming increasingly important in different domains. However, non-experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them. To make new forms of visualizations understandable and interpretable for a broad range of audiences, visualization onboarding methods can support users. However, it is unclear whether concrete or abstract materials yield better results to foster learning. In order to answer this question, we conducted a within-subject study with 40 students to compare abstract and concrete onboarding messages for a treemap visualization. The results show that (1) concrete onboarding messages are more helpful than abstract, whereas the length of the abstract messages is ranked higher; (2) abstract onboarding messages lead to more valuable descriptions; and (3) either concrete or abstract onboarding messages can lead to high valuable insights.
Temporal Saliency Query Network for Efficient Video Recognition

Efficient video recognition is a hot-spot research topic with the explosive growth of multimedia data on the Internet and mobile devices. Most existing methods select the salient frames without awareness of the class-specific saliency scores, which neglect the implicit association between the saliency of frames and its belonging category. To alleviate this issue, we devise a novel Temporal Saliency Query (TSQ) mechanism, which introduces class-specific information to provide fine-grained cues for saliency measurement. Specifically, we model the class-specific saliency measuring process as a query-response task. For each category, the common pattern of it is employed as a query and the most salient frames are responded to it. Then, the calculated similarities are adopted as the frame saliency scores. To achieve it, we propose a Temporal Saliency Query Network (TSQNet) that includes two instantiations of the TSQ mechanism based on visual appearance similarities and textual event-object relations. Afterward, cross-modality interactions are imposed to promote the information exchange between them. Finally, we use the class-specific saliencies of the most confident categories generated by two modalities to perform the selection of salient frames. Extensive experiments demonstrate the effectiveness of our method by achieving state-of-the-art results on ActivityNet, FCVID and Mini-Kinetics datasets. Our project page is at https://lawrencexia2008.github.io/projects/tsqnet .