Reviewerly/exHarmony huggingface dataset:
Neural Image Representations for Multi-image Fusion and Layer Separation

We propose a framework for aligning and fusing multiple images into a single view using neural image representations (NIRs), also known as implicit or coordinate-based neural representations. Our framework targets burst images that exhibit camera ego motion and potential changes in the scene. We describe different strategies for alignment depending on the nature of the scene motion—namely, perspective planar (i.e., homography), optical flow with minimal scene change, and optical flow with notable occlusion and disocclusion. With the neural image representation, our framework effectively combines multiple inputs into a single canonical view without the need for selecting one of the images as a reference frame. We demonstrate how to use this multi-frame fusion framework for various layer separation tasks. The code and results are available at https://shnnam.github.io/research/nir .
Efficient maximum <i>k</i> -plex computation over large sparse graphs

The k -plex model is a relaxation of the clique model by allowing every vertex to miss up to k neighbors. Designing exact and efficient algorithms for computing a maximum k -plex in a graph has been receiving increasing interest recently. However, the existing algorithms are still inefficient due to having major limitations. We in this paper design a new algorithm kPlexS for the maximum k -plex problem, with three novel contributions. Firstly, we propose a new framework for computing maximum k -plex over large sparse graphs, by iteratively extracting small dense subgraphs from it and then solving each of the extracted dense subgraphs by a branch-and-bound search. Secondly, we propose an efficient reduction algorithm CTCP to reduce the input graph size by exhaustively conducting vertex reduction and edge reduction. CTCP computes a smaller reduced graph and also has a lower time complexity than the existing techniques. Moreover, we iteratively invoke CTCP to reduce the input graph once a vertex has been processed and removed from it. Thirdly, we develop a branch-and-bound algorithm BBMatrix specifically targeting the dense subgraphs that are extracted from the input graph. BBMatrix represents its input graph by an adjacency matrix, and utilizes both first-order (i.e., individual vertices) and second-order information (i.e., pairs of vertices) for reduction and upper bounding. In addition, incremental techniques are proposed to efficiently apply the reduction and upper bounding during the recursion. Extensive empirical studies on large real graphs demonstrate that our algorithm kPlexS outperforms the state-of-the-art algorithms BnB, Maplex, and KpLeX.
Learning to Memorize Feature Hallucination for One-Shot Image Generation

This paper studies the task of One-Shot image Generation (OSG), where generation network learned on base dataset should be generalizable to synthesize images of novel categories with only one available sample per novel category. Most existing methods for feature transfer in oneshot image generation only learn reusable features implicitly on pre-training tasks. Such methods would be likely to overfit pre-training tasks. In this paper, we propose a novel model to explicitly learn and memorize reusable features that can help hallucinate novel category images. To be specific, our algorithm learns to decompose image features into the Category-Related (CR) and Category-Independent(CI) features. Our model learning to memorize class-independent CI features which are further utilized by our feature hallucination component to generate target novel category images. We validate our model on several benchmarks. Extensive experiments demonstrate that our model effectively boosts the OSG performance and can generate compelling and diverse samples.
A survey on the use of artificial intelligence (AI)-enabled techniques in digital marketing

It is no doubt that now a day's Artificial intelligence (AI) is anemergent technology in the digital marketing industry. Commercial Artificial Intelligence (AI) is an important topic for research. Artificial intelligence is growing quickly more sensitive and popular in the digital marketing field due to the capacity of advertisers to employ it efficiently. In order to provide the capability of the highest level of originality and efficiency in digital marketing ground, artificial intelligence techniques are more fruitful for customers, marketers and, retailers. This paper depicts the impact of artificial intelligence techniques on advertising on a diminutive and average scale in the digital marketing field. Ad targeting, predictive analysis, voice search are different artificial intelligence techniques for digital marketing purposes throughout the consumer life cycle. The topic of Artificial Intelligence (AI) in digital marketing itself can be outsized enough to verifyeliminating the area of research into precise application and application frameworks.However, our objective in this paper is to provide a comparative analysis of the digital marketing field using artificial intelligence techniques and to propose some specific guidelines based on different parameters, tools, and frameworks.Through this paper, we have observed thelast five years (2016-2021) research trends which reflect that out of different parameters, customer relationship and promotional activity are the most widely used parameter in the digital marketing field. Another significant point is, most of the research identifies that artificial intelligence is a compulsory technique used in the modern digital marketing field. Some data collection and data analysis tools are also being used in this field using artificial intelligence techniques. Our research methods try to get a complete view on artificial intelligence (AI) Marketing were multifaceted but were very focused on secondary research sources. Finally in this paper, we propose some recommendationsthat will offer better Artificial Intelligence (AI) implementation in the digital marketing field.
Double Articulation Analyzer With Prosody for Unsupervised Word and Phone Discovery

Infants acquire words and phonemes from unsegmented speech signals using segmentation cues, such as distributional, prosodic, and co-occurrence cues. Many pre-existing computational models that represent the process tend to focus on distributional or prosodic cues. This paper proposes a nonparametric Bayesian probabilistic generative model called the prosodic hierarchical Dirichlet process-hidden language model (Prosodic HDP-HLM). Prosodic HDP-HLM, an extension of HDP-HLM, considers both prosodic and distributional cues within a single integrative generative model. We conducted three experiments on different types of datasets, and demonstrate the validity of the proposed method. The results show that the Prosodic DAA successfully uses prosodic cues and outperforms a method that solely uses distributional cues. The main contributions of this study are as follows: 1) We develop a probabilistic generative model for time series data including prosody that potentially has a double articulation structure; 2) We propose the Prosodic DAA by deriving the inference procedure for Prosodic HDP-HLM and show that Prosodic DAA can discover words directly from continuous human speech signals using statistical information and prosodic information in an unsupervised manner; 3) We show that prosodic cues contribute to word segmentation more in naturally distributed case words, i.e., they follow Zipf's law.
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network

Adversarial examples provoke weak reliability and potential security issues in deep neural networks. Although adversarial training has been widely studied to improve adversarial robustness, it works in an over-parameterized regime and requires high computations and large memory budgets. To bridge adversarial robustness and model compression, we propose a novel adversarial pruning method, Masking Adversarial Damage (MAD) that employs second-order information of adversarial loss. By using it, we can accurately estimate adversarial saliency for model parameters and determine which parameters can be pruned without weakening adversarial robustness. Furthermore, we reveal that model parameters of initial layer are highly sensitive to the adversarial examples and show that compressed feature representation retains semantic information for the target objects. Through extensive experiments on three public datasets, we demonstrate that MAD effectively prunes adversarially trained networks without loosing adversarial robustness and shows better performance than previous adversarial pruning methods.
Interpretable Open-Set Domain Adaptation via Angular Margin Separation

Open-set Domain Adaptation (OSDA) aims to recognize classes in the target domain that are seen in the source domain while rejecting other unseen target-exclusive classes into an unknown class, which ignores the diversity of the latter and is therefore incapable of their interpretation. The recently-proposed Semantic Recovery OSDA (SR-OSDA) brings in semantic attributes and attacks the challenge via partial alignment and visual-semantic projection, marking the first step towards interpretable OSDA. Following that line, in this work, we propose a representation learning framework termed Angular Margin Separation (AMS) that unveils the power of discriminative and robust representation for both open-set domain adaptation and cross-domain semantic recovery. Our core idea is to exploit an additive angular margin with regularization for both robust feature fine-tuning and discriminative joint feature alignment, which turns out advantageous to learning an accurate and less biased visual-semantic projection. Further, we propose a post-training re-projection that boosts the performance of seen classes interpretation without deterioration on unseen classes. Verified by extensive experiments, AMS achieves a notable improvement over the existing SR-OSDA baseline, with an average 7.6% increment in semantic recovery accuracy of unseen classes in multiple transfer tasks. Our code is available at AMS .
Towards mining OSS skills from GitHub activity

Open source software (OSS) development relies on diverse skill sets. However, to our knowledge, there are no tools which detect OSS-related skills. In this paper, we present a novel method to detect OSS skills and prototype it in a tool called Disko. Our approach relies on identifying relevant signals, which are measurable activities or cues associated with a skill. Our tool detects how contributors 1) teach others to be involved in OSS projects, 2) show commitment towards an OSS project, 3) have knowledge in specific programming languages, and 4) are familiar with OSS practices. We then evaluate the tool by administering a survey to 455 OSS contributors. We demonstrate that Disko yields promising results: it detects the presence of these skills with precision scores between 77% to 97%. We also find that over 54% of participants would display their high-proficiency skills. Our approach can be used to transform existing OSS experiences, such as identifying collaborators, matching mentors to mentees, and assigning project roles. Given the positive results and potential impact of our approach, we outline future research opportunities in interpreting and sharing OSS skills.
Multi-Level Structure-Enhanced Network for 3D Single Object Tracking in Sparse Point Clouds

3D single object tracking is the task of localizing a target object in a search point cloud frame. In this letter, we present a multi-level structure-enhanced tracking model to improve the tracking performance in sparse 3D point clouds. Towards this end, we first encode the target and the search point clouds efficiently in two near-neighbors graphs, which allows structural information to flow between neighbors along graph edges. We then design a cross-graph attention mechanism, which associates similar nodes across the target graph and the search graph, and further dissimilar graph nodes for apart. Integrating the proposed mechanism into the above Siamese feature learning of both the target and the search frame, we strengthen the structural correlation between the target and the search frame. In that case, distinguishing the potential target from the background in the search frame would be much simpler. Finally, we design a U-shaped sparse convolutional block to aggregate the structural features of the potential target in the search frame. Integrating the proposed block into an existing target localization module from (Hui et al., 2021), we localize target centers accurately. Experiments on the KITTI benchmark demonstrate that our method outperforms some state-of-the-art models, achieving at least a 3.2% improvement in terms of average tracking precision.
Abstractive Text Summarization Using BART

In the last recent years, there's a huge amount of data available on the internet, and is generated very rapidly. It is very difficult for human beings to analyze and extract useful information from huge data especially when the text is large in size and longer documents which increases the time to process and analyze the data, further it also increases the time taken to summarize it. To address this issue automatic text summarization is used. Text summarization is defined as creating a short, accurate, and fluent summary of a longer document. It summarizes the larger text without any human intervention. This paper will provide a mechanism where it does the text summarization quickly and effectively even for large data. We can use this model to summarize and extract important information from a large document or text based on our input. Here a concept of the Deep Learning model is used for text summarization which is called BART (Bidirectional and Auto- Regressive Transformer). it consists of both an encoder and decoder. The encoder and decoder are merged to form the BART algorithm. BERT is trained with a huge amount of unlabeled data to achieve the state of art results. The use of an attention mechanism in each layer of BERT makes the model much more popular as it highlights the important features of the input data. BERT performs the masked language modeling with the help of its several bidirectional transformer layers and predicts the missing values. On the other hand, decoder is used to predict the next token in a sentence. Merging both the encoder and decoder will form the BART model. Here we will compare the BART model with BERT, T5, and Roberta.
A Cohesive Relation Between Cybersecurity and Information security

This article argues the fact that cybersecurity and information security overlaps considerably however the concepts are entirely different. Knowledge is needed to perform their daily activities safely. Achieving cybersecurity in today's population and ensuring continuity in future populations requires a belief in “self-renewal” that influences behavior. In an organizational context, this need is met by promoting information security cultures (ISCs). Similarly, the Cyber Security Culture (CSC) should be promoted in a social context. Cybersecurity has a great impact on current society. Almost all of our daily lives rely on certain information and communication technologies that can be threatened in some way. This article argues that cybersecurity relies on a combination of the effectiveness of information security measures and a clear background check that they are working and effective. With this in mind, this article provides an overview of how information processing has a relationship with trust and information security, and how they are used to combat the highly persistent threats and exploits currently raging in cyberspace. Therefore, in response to the subsequent turmoil, this text analyzes the characteristics of the theoretical heritage, as well as the destructive elements inside and outside the organization, and examines the determinants and interchangeable meanings of the phrase. This article will help clarify the scope, origin, and impact of cybersecurity, primarily based on academic literature reviews of previous studies.
Camera Pose Estimation using Implicit Distortion Models

Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric model with a regularization term that ensures the latent distortion map varies smoothly throughout the image. The proposed model is effectively parameter-free and allows us to optimize the 6 degree-of-freedom camera pose without explicitly knowing the intrinsic calibration. We show that the method is applicable to a wide selection of cameras with varying distortion and in multiple applications, such as visual localization and structure-from-motion.
Affective Dynamics and Cognition During Game-Based Learning

Inability to regulate affective states can impact one's capacity to engage in higher-order thinking like scientific reasoning with game-based learning environments. Many efforts have been made to build affect-aware systems to mitigate the potentially detrimental effects of negative affect. Yet, gaps in research exist since accurately capturing and modeling affect as a state that changes dynamically over time is methodologically and analytically challenging. In this paper, we calculated multilevel mixed effects growth models to assess whether seventy-eight participants’ ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> = 78) time engaging in scientific reasoning (via logfiles and eye gaze) were related to time facially expressing confused, frustrated, and neutral states (via facial recognition software) during game-based learning with Crystal Island. The fitted model estimated significant positive relations between the time learners facially expressed confusion, frustration, and neutral states and time engaging in scientific-reasoning actions. The time individual learners facially expressed frustrated, confused, and neutral states explained a significant amount of variation in time engaging in scientific reasoning. Our finding emphasize that individual differences and agency may play a important role on relations between affective states, their dynamics, and higher-order cognition during game-based learning. Designing affect-aware game-based learning environments that track the dynamics within individual learners’ affective states may best support cognition.
Multi-Agent Task Allocation Techniques for Harvest Team Formation

With increasing demands for soft fruit and shortages of seasonal workers, farms are seeking innovative solutions for efficiently managing their workforce. The harvesting workforce is typically organised by farm managers who assign workers to the fields that are ready to be harvested. They aim to minimise staff time (and costs) and distribute work fairly, whilst still picking all ripe fruit within the fields that need to be harvested. This paper posits that this problem can be addressed using multi-criteria, multi-agent task allocation techniques. The work presented compares the application of Genetic Algorithms (GAs) vs auction-based approaches to the challenge of assigning workers with various skill sets to fields with various estimated yields. These approaches are evaluated alongside a previously suggested method and the teams that were manually created by a farm manager during the 2021 harvesting season. Results indicate that the GA approach produces more efficient team allocations than the alternatives assessed.
Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels

Deep models trained with noisy labels are prone to over-fitting and struggle in generalization. Most existing solutions are based on an ideal assumption that the label noise is class-conditional, i.e. instances of the same class share the same noise model, and are independent of features. While in practice, the real-world noise patterns are usually more fine-grained as instance-dependent ones, which poses a big challenge, especially in the presence of inter-class imbalance. In this paper, we propose a two-stage clean samples identification method to address the aforementioned challenge. First, we employ a class-level feature clustering procedure for the early identification of clean samples that are near the class-wise prediction centers. Notably, we address the class imbalance problem by aggregating rare classes according to their prediction entropy. Second, for the remaining clean samples that are close to the ground truth class boundary (usually mixed with the samples with instance-dependent noises), we propose a novel consistency-based classification method that identifies them using the consistency of two classifier heads: the higher the consistency, the larger the probability that a sample is clean. Extensive experiments on several challenging benchmarks demonstrate the superior performance of our method against the state-of-the-art. Code is available at https://github.com/uitrbn/TSCSI_IDN .
Online Hyperparameter Optimization for Streaming Neural Networks

Neural networks have enjoyed tremendous success in many areas over the last decade. They are also receiving more and more attention in learning from data streams, which is inherently incremental. An incremental setting poses challenges for hyperparameter optimization, which is essential to obtain satisfactory network performance. To overcome this challenge, we introduce Continuously Adaptive Neural networks for Data streams (CAND). For every prediction, CAND chooses the current best network from a pool of candidates by continuously monitoring the performance of all candidate networks. The candidates are trained using different optimizers and hyperparameters. An experimental comparison against three state-of-the-art stream learning methods, over 17 benchmark streaming datasets con-firms the competitive performance of CAND, especially on high-dimensional data. We also investigate two orthogonal heuristics for accelerating Cand,which trade-off small amounts of accuracy for significant run-time gains. We observe that training on small mini-batches yields similar accuracy to single-instance fully incremental training, even on evolving data streams.
Pedestrian Trajectory Prediction in Graph Representation Using Convolutional Neural Networks

Predicting the future trajectories of pedestrian in real-world contexts, including video surveillance, self-driving, and robotic systems, is a challenging task due of different trajectory patterns. In this task there are two primary problems: (1) advanced interaction modeling among pedestrians and (2) the specific motion pattern extraction. Pedestrian trajectories are affected not only by another pedestrian, but also by interactions with the environment. To obtain the interactions of pedestrian movements and the active change trend of the environment, there are two components in the proposed method: encoder with a spatial graph neural network for interaction modeling and decoder with a temporal graph neural network for motion pattern extraction. The investigated approach is more compact and efficient than the based method, with a reduced variable size and better accuracy. Furthermore, utilizing two publicly available datasets (ETH and UCY), our model achieves better experimental results considering final displacement error (FDE) and average displacement error (ADE) metrics and predicts more socially appropriate trajectories.