Reviewerly/exHarmony huggingface dataset:
Learning graph from graph signals: An approach based on sensitivity analysis over a deep learning framework

Utilizing a meaningful graph plays an essential part in the performance of graph-based algorithms. However, a ground-truth graph representing the relationships between data points is not readily available in many applications. This paper proposes a graph learning method based on sensitivity analysis over a deep learning framework called GL-SADL. The proposed method is composed of two steps. First, it estimates the signal value for each vertex using the signal values corresponding to the other vertices with a Deep Neural Network (DNN) block. Then a sensitivity analysis approach is applied to each DNN block to determine how the input signal values influence the DNN’s response. This procedure leads us to the underlying graph structure. The utilization of DNNs allows us to take advantage of the non-linearity characteristics of neural networks in modeling the observed graph signals. In addition, since the DNNs are considered as general approximators, there is no need to make any prior assumptions about the distribution of the observed graph signals. Experiments with synthetic and real-world datasets demonstrate that the proposed method can infer meaningful graph structures from observed graph signals.
Innovation in Education

Innovation in education is pursuing knowledge that will support novel and distinctive ideas. We can describe the term ‘innovation' in education as finding the most appropriate and productive way to get maximum outcome from educational institutes. It is concerned with unrevealing individual capabilities of the students and researchers, which can assist society in underlining and resolving many scientific and social problems and enables us to get more creative and critical thinkers. Innovation in education is thinking outside of the box, challenging the existing techniques and tools to acquire more valuable ways of learning. The chapter covers the essentials of innovation in education and aims to unveil the potential areas of innovation in existing education system; generally, it falls in the category of processes innovations, which include modern and meaningfully advanced techniques of classroom-based learning, assessment tools, and teaching techniques.
Some topological aspects of interval spaces

&lt;abstract&gt;&lt;p&gt;In previous papers, several $ T_{0} $, $ T_{2} $ objects, $ D $-connectedness and zero-dimensionality in topological categories have been introduced and compared. In this paper, we characterize separated objects, $ T_{0} $, $ {\bf{T_{0}}} $, $ T_{1} $, Pre-$ T_{2} $ and several versions of Hausdorff objects in the category of interval spaces and interval-preserving mappings and examine their mutual relationship. Further, we give the characterization of the notion of closedness and $ D $-connectedness in interval spaces and study some of their properties. Finally, we introduce zero-dimensionality in this category and show its relation to $ D $-connectedness.&lt;/p&gt;&lt;/abstract&gt;
Artificial Intelligence as the New Realm for Online Advertising

This exploratory study examines the magnitude of using artificial intelligence (AI) in the five-staged online advertising process: consumer insights, ad creation, media planning, buying, and finally, ad evaluation. It explores if there is a correlation between using AI in each stage and the next. It also investigates the impact of using AI on the overall perceived effectiveness. The study developed the conceptual framework titled “Process Model of AI Utilization in Online Advertising.” An online survey is conducted with a sample of 60 digital advertisers worldwide from both agency and client sides. The findings showed that AI is stepping progressively and differently in the four stages of the online advertising process. There is a relationship between using AI in each stage and the following one. AI utilization promotes the perceived effectiveness of the overall online ad process.
Motivation and Outline

The main purpose of this book is to describe a class of problem for which interval computations are not sufficient, and to design a physically reasonable extension of interval computations that would enable us to solve these problems. To describe these problems, let us first recall why we need interval computations in the first place.
X-ray dose profiles using artificial neural networks

This paper introduces a novel computational method to simulate and predict radiation dose profiles in a water phantom irradiated by X-rays of 6 and 15 MV at different depths and field sizes using Artificial Neural Networks within the error margin required by the code of practice 398 of the International Atomic Energy Agency (IAEA). Our method uses deep-learning Artificial Neural Networks as an alternative to the Monte Carlo methods usually used nowadays. It reproduces the radiation dose profiles for X-rays of 6 and 15 MV data reported in the British Journal of Radiology (Aird, 1996). Even more, our method reproduces data from other sources with acceptable errors. These simulations pave the way to enhance radiotherapy techniques in planning patient doses and calibrating ionizing radiation measurement instruments used in the fight against cancer.
A General Description of Measuring Devices: Second Step—Pairs of Compatible Outcomes

So, we have a set of possible outcomes. On this level, how do we describe uncertainty? Due to measurement uncertainty, when we apply the same measuring device to the same object twice, we may get different measurement results.
A Critical Analysis of Requirement Management in Agile Development

Agile software development (agile SD) has evolved and originated to solve the issues of industries due to several changes in the requirements. Industries have also recognized and acknowledged this reality. In addition, with the suitable process alignment and control, changing or evolving requirements could be efficiently solved and managed for satisfying the project stakeholders through agile method. Hence, this study attempts to review requirement management (RM) in agile environment which is a significant part that confirms the successful accomplishment of goals pertaining to the product development. Requirement prioritization (RP) is important to assure RM as it improvises planning, scheduling and budget control. This study analyses RP in agile. The study also discusses the uses of agile requirement engineering. A comparative analysis is carried out to analyse various RM steps pursued in different agile software development (SD) methods and quality requirement (QR) management practices relying on agile environment. Several agile SD methods considered for analysis include SCRUM, lean software development (LSD), Kanban, dynamic system development method (DSDM), extreme programming (XP), feature driven development (FDD), adaptive software development (ASD) and agile unified process (AUP). On the other hand, various QR management practices considered for analysis include client engagement, direct interaction, provisioning user stories, uninterrupted planning, combining the requirement analysis, review meetings, etc. This study also discusses the RM challenges in agile methodology by analysing various existing methods. Hence, this study affords detailed information associated with current traditional RM practices that will assist the software practitioners to choose appropriate methods in agile SD for handling persistent requirement changes.
Novel Exotic Projected Quantum Kernel Features-Enabled Neural Network for Handwritten Digit Image Classification

The handwritten digit recognition provides ability to identify human handwritten characters to the machine with the constant practice. Generally, handwritten digits are not perfect that varies from person to person and come in a variety of flavors. So identifying the exact digit with this various typo remains a challenging task for the programmers to train the machine for digit recognition. With this review, the objective of this work is to predict the handwritten digit by incorporating the quantum computing features with MNIST handwritten digit database retrieved from the Kaggle machine learning repository. The proposed projected quantum kernel feature-enabled neural network is designed with the following steps. The MNIST handwritten digit database contains 2 classes for the digit ‘0’ and ‘3’. The handwritten digit is downscaled with the component as 10. The downscaled 10 component data is splitted with training and testing pixels of 10,000 and 500, respectively. The downscaled 10 component data is relabeled, and projected quantum kernel feature is computed. The projected quantum kernel feature-enabled neural network model is created and data is fitted. The data is also applied with traditional fully connected neural network. The testing data performance is compared with projected quantum kernel feature-enabled neural network and traditional fully connected neural network with the performance metrics as precision, recall, f score and accuracy. Experimental results show that the proposed projected quantum kernel feature-enabled neural network exhibits the accuracy and f score with 96%, precision with 95% and recall with 97% when compared to traditional fully connected neural network model.
Improving ​conversational ​search with ​query ​reformulation ​using ​selective ​contextual ​history

Automated responses to questions for conversational agents, known as conversation passage retrieval, is challenging due to omissions and implied context in user queries. To help address this challenge, queries can be re-written using pre-trained sequence-to-sequence models based on contextual clues from the conversation's history to resolve ambiguities. In this research, we use the TREC conversational assistant (CAsT) 2020 dataset, selecting relevant single sentences from conversation history for query reformulation to improve system effectiveness and efficiency by avoiding topic drift. We propose a practical query selection method that measures clarity score to determine whether to use response sentences during reformulation. We further explore query reformulation as a binary term classification problem and the effects of rank fusion using multiple retrieval models. T5 and BERT retrievals are inventively combined to better represent user information need. Using multi-model fusion, our best system outperforms the best CAsT 2020 run, with an [email protected] of 0.537. The implication is that a more selective system that varies the use of responses depending on the query produces a more effective conversational reformulation system. Combining different retrieval results also proved effective in improving system recall.
Predicting ICU Admissions for Hospitalized COVID-19 Patients with a Factor Graph-based Model

This paper presents a factor graph-based model that takes comorbidities and clinical measurements as inputs and predicts intensive care unit (ICU) admissions 3 days and 7 days in advance for hospitalized COVID-19 patients. We applied the proposed model on a COVID-19 cohort from a large medical center in Chicago (with records from March 2020 to August 2021). We used the first occurrence of the Delta variant in the U.S., February 2021, as the threshold to divide the dataset into pre-Delta data (533 patients) and post-Delta data (56 patients). Our model demonstrated 0.82 AUC on the pre-Delta data and 0.87 AUC on the post-Delta data in 7-day predictions. Our contribution is a model that (i) explains relationships between different clinical features and provides interpretations for ICU admissions, (ii) outperforms existing methods for 7-day predictions, and (iii) maintains more robustness than existing models in predictions under the influence of the Delta variant. The proposed model could be used as a predictive tool in clinical practice to help clinicians in decision-making by predicting which patients will need ICU support in the future.
Secure storage scheme of trajectory data for digital tracking mechanism

The application of digital tracking mechanism introduces a series of leakage problems of users' personal sensitive information related to the trajectory. Therefore, we propose a secure storage scheme for trajectory data. Firstly, four-dimensional spatiotemporal clustering of the trajectory data is performed to reduce the spatiotemporal complexity of data storage. Secondly, the privacy level of the trajectory data in the clusters is measured individually, which ensures the needs for personalized privacy protection are met. Finally, a noise trajectory (NTR) tree based on differential privacy is constructed, and the allocation of privacy budget and noise addition are optimized. Extensive simulations show that our scheme improves in terms of time efficiency, and achieves a flexible and effective balance between data accuracy and privacy.
Latent Representation Weights Learning of the Indefinite Length of Views for Conception Diagnosis

Deep learning has great prevalence in various medical diagnosis tasks. Existing methods can tackle the issue of multiviews very well. However, these methods cannot process indefinite lengths of multiviews, especially with a “dimension gap” between them, such as blood flow ultrasound images. In this work, we propose Latent Representation Weight Learning (LRWL) to learn the latent representative weight of each image or view and then integrate the views with the weights and the diagnostic indexes as part of the input data to DL to predict successful conception. This method can describe the role of each view accurately. We perform thorough experiments on a real reproduction dataset to evaluate LRWL. The results show that our proposed method achieves the top performances with higher accuracy and good convergence.
Learning Domain Ethical Principles from Interactions with Users

Abstract Drawing from practical philosophy, we argue that AI-based systems could develop ethical decision-making and judgment capabilities by learning from experience. This has inspired our work which combines answer set programming and inductive logic programming to learn domain ethical principles from the interactions with users in the context of a dialogue system.
Multiscale laplacian learning

Machine learning has greatly influenced many fields, including science. However, despite of the tremendous accomplishments of machine learning, one of the key limitations of most existing machine learning approaches is their reliance on large labeled sets, and thus, data with limited labeled samples remains a challenge. Moreover, the performance of machine learning methods often severely hindered in case of diverse data, usually associated with smaller data sets or data associated with areas of study where the size of the data sets is constrained by high experimental cost and/or ethics. These challenges call for innovative strategies for dealing with these types of data. In this work, the aforementioned challenges are addressed by integrating graph-based frameworks, semi-supervised techniques, multiscale structures, and modified and adapted optimization procedures. This results in two innovative multiscale Laplacian learning (MLL) approaches for machine learning tasks, such as data classification, and for tackling data with limited samples, diverse data, and small data sets. The first approach, multikernel manifold learning (MML), integrates manifold learning with multikernel information and incorporates a warped kernel regularizer using multiscale graph Laplacians. The second approach, the multiscale MBO (MMBO) method, introduces multiscale Laplacians to the modification of the famous classical Merriman-Bence-Osher (MBO) scheme, and makes use of fast solvers. We demonstrate the performance of our algorithms experimentally on a variety of benchmark data sets, and compare them favorably to the state-of-art approaches.
Attribute De-biased Vision Transformer (AD-ViT) for Long-Term Person Re-identification

Person re-identification (re-ID) aims to retrieve images of the same identity from a gallery of person images across cameras and viewpoints. However, most works in person re-ID assume a short-term setting characterized by invariance in appearance. In contrast, a high visual variance can be frequently seen in a long-term setting due to changes in apparel and accessories, which makes the task more challenging. Therefore, learning identity-specific features agnostic of temporally variant features is crucial for robust long-term person Re-ID. To this end, we propose an Attribute De-biased Vision Transformer (AD-ViT) to provide direct supervision to learn identity-specific features. Specifically, we produce attribute labels for person instances and utilize them to guide our model to focus on identity features through gradient reversal. Our experiments on two long-term re-ID datasets - LTCC and NKUP show that the proposed work consistently outperforms current state-of-the-art methods.
Learning Occlusion-Aware Dense Correspondences for Multi-Modal Images

We introduce a scalable multi-modal approach to learn dense, i.e., pixel-level, correspondences and occlusion maps, between images in a video sequence. The problems of finding dense correspondences and occlusion maps are fundamental in computer vision. In this work we jointly train a deep network to tackle both, with a shared feature extraction stage. We use depth and color images with ground truth optical flow and occlusion maps to train the network end-to-end. From the multi-modal input, the network learns to estimate occlusion maps, optical flows, and a correspondence embedding providing a meaningful latent feature space. We evaluate the performance on a dataset of images derived from synthetic characters, and perform a thorough ablation study to demonstrate that the proposed components of our architecture combine to achieve the lowest correspondence error. The scalability of our proposed method comes from the ability to incorporate additional modalities, e.g., infrared images.
Distance-dependent Feature Alignment and Selection for Imbalance 3D Point Cloud Object Detection

Although pillar-based 3D object detection methods can balance the performance and inference speed, the inconsistent object features caused by dramatic sparsity drops of LiDAR point clouds sabotage the detection accuracy. We present a novel and efficient plug-in method, SVDnet, to improve the state-of-the-art pillar-based models. First, a novel low-rank objective loss is introduced to extract distance-aware vehicle features and suppress the other variations. Next, we alleviated the remaining feature inconsistency caused by object positions with two strategies. One is a Distance Alignment Ratio-generation Network (DARN), which fuses multi-scale features by distance-adaptive ratios. The other is a position attention network that modulates features based on positions. Our results on the KITTI dataset show that SVDnet improves the pillar methods and outperforms the other plug-in strategies in accuracy and speed.
Graph Structure Learning Boosted Neural Network for Image Segmentation

Although Convolutional Neural Networks have made significant progress in image segmentation, it remains inadequate for exploring the structural relationships between image components and how graphs can be employed to guide image segmentation. To explore the structural relationships inherent in image components, the Graph Structure Learning Boosted Neural Network was proposed, which takes the contextual information generated by the CNN as features of the nodes and then uses a self-supervised graph generator to generate an adjacency matrix representing the image components connectivity. Then a Graph Neural Network (GNN) uses the adjacency matrix to fuse information between components according to their connectivity, thus transforming the CNN’s pixel classification problem into the GNN’s pixel classification problem. The whole model is lightweight and scalable, and extensive experiments have demonstrated the scalability of the model alongside the effectiveness of the method.
Robust Unseen Video Understanding for Various Surveillance Environments

Automated video-based applications are a highly demanding technique from a security perspective, where detection of moving objects i.e., moving object segmentation (MOS) is performed. Therefore, we have proposed an effective solution with a spatio-temporal squeeze excitation mechanism (SqEm) based multi-level feature sharing encoder-decoder network for MOS. Here, the SqEm module is proposed to get prominent foreground edge information using spatio-temporal features. Further, a multi-level feature sharing residual decoder module is proposed with respective SqEm features and previous output features for accurate and consistent foreground segmentation. To handle the foreground or background class imbalance issue, we propose a region of interest-based edge loss. The extensive experimental analysis on three databases is conducted. Result analysis and ablation study proved the robustness of the proposed network for unseen video understanding over SOTA methods.
Simultaneous Localization and Anomaly Detection from First-Person Video Data through a Coupled Dynamic Bayesian Network Model

This paper proposes a method to localize a moving agent - such as an autonomous surveillance vehicle - inside a known environment using First Person Viewpoint video data. Anomalies w.r.t. expected vehicle motion and image content are extracted to guide the localization, signal when the localization results are not trustworthy and explain the reason for the failure. During the training phase, a Dynamic Bayesian Network model is learned, which couples positional and video data. To learn it, clustering is performed on the odometry data, and a modified Kalman Variational Autoencoder is built over the video data. During the testing phase, a Coupled Markov Jump Particle Filter leverages the learned Dynamic Bayesian Network to extract anomalies and to estimate the vehicle’s position, given only camera data. The proposed method is evaluated on two real-world datasets of a vehicle performing perimeter monitoring of a closed environment and of a shopping cart moving in a supermarket.
Tracking Failure Prediction for Siamese Trackers Based on Channel Feature Statistics

Failure prediction has rarely been studied for Siamese trackers due to a lack of meaningful analysis of tracking failing cases. In this paper, we provide a meaningful analysis of tracking failure in Siamese trackers. Our analysis includes the statistics of the channel-wise feature correlation between the exemplar and tracked target patches. We observe that the correlation statistics (max, mean, and std) are highly related to the overlapping ratio between tracked and ground-truth bounding boxes. Based on this observation, we devise a tracking failure prediction model that extracts more plentiful factors than simple statistics. The proposed tracking failure prediction model is validated on most-popular tracking benchmark datasets through extensive experiments.
Deformable Modules for Flexible Feature Sampling on Vision Transformer

Vision transformers have shown that the self-attention mechanism performs well in the computer vision field. However, since such transformers are based on data sampled from fixed areas, there is a limit to efficiently learning the important features in images. To compensate, we propose two modules based on the deformable operation: deformable patch embedding and deformable pooling. Deformable patch embedding consists of a hybrid structure of standard and deformable convolutions, and adaptively samples features from an image. The deformable pooling module also has a similar structure to the embedding module, but it not only samples data flexibly after self-attention but also allows the transformer to learn spatial information of various scales. The experimental results show that the transformer with the proposed modules converges faster and outperforms various vision transformers on image classification (ImageNet-1K) and object detection (MS-COCO).