Reviewerly/exHarmony huggingface dataset:
A New Design of Occlusion Invariant Face Recognition Using Optimal Pattern Extraction and CNN with GRU-Based Architecture

Deep learning networks are considered as an important technique for face recognition and image recognition. Convolutional Neural Networks (CNN) is regarded as a problem solver in face recognition challenges. To solve the challenges of occlusion and noise in the image, more clarification is needed to acquire high accuracy. Hence, a deep learning model is developed in this paper. The proposed model covers four main steps: (a) Data acquisition, (b) Pre-processing, (c) pattern extraction, and (d) classification. The benchmark datasets with occluded faces is gathered from public source. Further, the pre-processing of the images is performed by contrast enhancement and Gabor filtering. With these pre-processed images, pattern extraction is done by the optimal local mesh ternary pattern. By inputting the pattern extracted image, a deep learning model “CNN with Gated Recurrent Unit (GRU)” network performs the recognition process. The experimental results are obtained and the proposed model gives better classification accuracy.
Frame-Wise Action Recognition Training Framework for Skeleton-Based Anomaly Behavior Detection

We propose a novel training framework for frame-wise action recognition from a single video frame. Most existing action recognition methods employ sequence-wise features extraction, but these approaches constrain the setting of the camera's frame rate and are not always effective in the variety of existing network environments. To this end, our framework employs temporal attention obtained from a pretrained action recognition model, allowing training action recognition from a single video frame selected based on its "action-ness," even from existing training data without the need for additional data. In this paper, we demonstrate the effectiveness of our framework to address the challenging task of anomaly behavior detection from a single-frame skeleton. Our method is realized by using frame-wise features extracted from a skeleton-based action recognition model trained by our framework. By coupling our framework and our anomaly behavior detection method, we develop a powerful detector of anomaly behavior that humans can recognize from a single video frame. We evaluate our method with the "ShanghaiTech Campus" anomaly behavior detection benchmark dataset, and confirm its effectiveness when the input consists of single-frame skeletons.
A Storage Scheme for Access Control Record Based on Consortium Blockchain

Abstract The heterogeneous access control information scattered around different organizations or units is difficult to be gathered and audited, however, easy to be maliciously tampered. Aiming at the problems, this paper presents a blockchain-based storage scheme to store access control information, which can protect information privacy and facilitate the audit work. This is achieved by exploiting consortium blockchain, cryptography technology. Based on the scheme, we define the format of Access Control Record (ACR), design upload and download protocols, and realize the signature and encryption process for ACRs in a simulation environment. Theoretical analyses demonstrate that the proposed storage scheme needs lower storage cost and has higher efficiency compared with existing schemes, and can resist typical malicious attacks effectively.
Relevance Feedback for Human-in-The-Loop Person Re-Identification

Person re-identification is a challenging cross-camera matching problem, which makes it prone to domain shift issues. To mitigate them, supervised and unsupervised domain adaptation methods have been proposed, that exploit target data for model refinement, as well as direct transfer and domain generalisation methods, that do not make use of target data. In this work we show that the human-in-the-loop approach to person re-identification, that exploits user’s feedback on target data processed during system operation and does not require model re-training or fine-tuning, is a further effective solution against domain shift, although it was not originally devised to this aim. In particular, we consider human-in-the-loop implementations based on simple relevance feedback algorithms previously proposed for content-based image retrieval, together with novel versions of them we devised specifically for person re-identification. The considered human-in-the-loop solution is extensively evaluated and compared with existing approaches against domain shift on three benchmark data sets and a widely used deep learning baseline for person re-identification. Keywords: Person Re-Identification, Human in the Loop, Relevance Feedback, Online Domain Adaptation.
A Detailed Schematic Study on Feature Extraction Methodologies and Its Applications: A Position Paper

Feature extraction is stated to be one of the most important aspects in the field of machine learning, image processing and pattern recognition. In these fields, as the dimension of the data increases, it becomes highly important to provide a reliable analysis for growing. The process of feature extraction mostly starts from basic dataset, and slowly, it builds features derived from the data to make an informative learning step for human reading. In order to present this process of learning, we thought of providing a position paper which will discuss all the criteria, information, methodology and existing work in the area of feature extraction in different fields and domain. A clear, descriptive analysis is presented that discusses the feature extraction methods for selecting significant features that will improve the quality of the results.
Information Fusion Based on Metric Learning

Metric learning aims to measure the similarity or dissimilarity between each pair of samples. Until now, various types of metrics are studied and achieve satisfied performances in many applications. To comprehensively exploit the advantages of different metrics, this chapter proposes two metric fusion methods and applies them to classification and verification. After reading this chapter people can have preliminary knowledge on metric learning based fusion algorithms.
Object Tracking Using Siamese Network-Based Reinforcement Learning

Object tracking is a technique for tracking a specific object appearing in a video sequence while observing its features or changes.Recently, many algorithms showing high performance have emerged by applying the Siamese network to the object tracking field.A Siamese network is designed to learn the similarity between two images.In object tracking, the Siamese network tracks the object by finding the location most similar to the target image in the search image.Algorithms based on Siamese networks are vulnerable to partial and total occlusion of objects.In addition, since the object is tracked using only the similarity with the image obtained using the ground-truth bounding box of the first frame, if an object is missed once, then errors are accumulated, and a situation where the object drifts away from the object of interest frequently occurs.Therefore, in this paper, we propose a reinforcement learning model that can maximize the reward for tracking success after partial and total occlusion of an object.We also propose a dynamic template exchange method using a template that has been successfully tracked in a recent frame to solve the drift problem.When the proposed model is applied to the existing tracking models to evaluate the quantitative performance in representative object tracking benchmarks VOT2018 and OTB50, it is confirmed that the accuracy is improved, and the number of tracking failures decreases compared to the existing method.As a result, an accuracy of 0.618, robustness of 0.234, and expected average overlap (EAO) of 0.416 are achieved in VOT2018, and success of 0.673 and precision of 0.881 are achieved in OTB50.
Adversarial Examples Are Closely Relevant to Neural Network Models - A Preliminary Experiment Explore

Neural networks are fragile because adversarial examples can readily assault them. As a result of the current scenario, academics from various countries have paid close attention to adversarial examples: many research outcomes, e.g., adversarial and defensive approaches and algorithms. However, numerous people are still baffled about how adversarial examples affect neural networks. We present hypotheses and devise extensive experiments to acquire more information about adversarial examples to verify this notion. By experiments, we investigate the neural network's sensitivity to adversarial examples in diverse aspects, e.g., model architectures, activation functions, and loss functions. The consequence of the experiment shows that adversarial examples are closely related to them. Peculiarly, sensitivity's property could help us distinguish the adversarial examples from the data set. This work will inspire the research of adversarial examples detection.
DeepSTAPLE: Learning to Predict Multimodal Registration Quality for Unsupervised Domain Adaptation

While deep neural networks often achieve outstanding results on semantic segmentation tasks within a dataset domain, performance can drop significantly when predicting domain-shifted input data. Multi-atlas segmentation utilizes multiple available sample annotations which are deformed and propagated to the target domain via multimodal image registration and fused to a consensus label afterwards but subsequent network training with the registered data may not yield optimal results due to registration errors. In this work, we propose to extend a curriculum learning approach with additional regularization and fixed weighting to train a semantic segmentation model along with data parameters representing the atlas confidence. Using these adjustments we can show that registration quality information can be extracted out of a semantic segmentation model and further be used to create label consensi when using a straightforward weighting scheme. Comparing our results to the STAPLE method, we find that our consensi are not only a better approximation of the oracle-label regarding Dice score but also improve subsequent network training results.
PrivTDSI: A Local Differentially Private Approach for Truth Discovery via Sampling and Inference

Truth discovery is an effective way to identify the aggregated truth of each task among multiple observed data drawn from different workers of varying reliabilities. However, existing studies are insufficient to protect individuals’ privacy, as they either just guarantee the weaker versions of local differential privacy (LDP) or potentially assume that the tasks are independent. In this paper, we, for the first time, investigate the problem of truth discovery while achieving the rigorous LDP for each worker with continuous inputs without the independence assumption. We present a locally differentially private truth discovery approach called <i>PrivTDSI</i> based on sampling and inference with solid privacy and utility guarantees. In <i>PrivTDSI</i> , the server first determines which values of each worker should be sampled according to a sample proportion and sends the indexes of these values to each worker. Then, each worker adds noise into the sampled values for privacy protection and uploads them to the server. After receiving the noisy sampled values from all the workers, the server first infers the unsampled values and then conducts truth discovery based on both the noisy sampled values and the inferred values. In particular, to determine the sample proportion, we formulate a <i>constrained nonlinear programming</i> problem and give a closed-form solution to this problem. Moreover, to determine which values of each worker should be sampled while avoiding the situation where the values of some workers or tasks might not be sampled at all, we develop a two-stage sampling method called <i>TOSS</i> . Furthermore, to infer the unsampled values accurately, we design a quality-aware inference method based on matrix factorization called <i>QualityMF</i> . Experimental results on two real-world datasets and a synthetic dataset demonstrate the effectiveness of <inline-formula><tex-math notation="LaTeX">${PrivTDSI}$</tex-math></inline-formula> .
A Combined Feature Encoding Network with Semantic Enhancement for Image Tampering Forensics

Image tampering forensics is performed by analyzing images to locate the tampered regions. However, most image tampering detection methods lack locational accuracy and are effective only for specific types of tampering. To address these problems, this chapter proposes a method that employs an encoder-decoder network structure with combined multiple feature encoding to segment tampered regions of an image from untampered regions. Three features, obtained using constrained convolution, steganalysis rich model filtering and common convolution, are combined. During the encoding stage, ring residual units are used to extract features. The combination of multiple features and the ring residual units makes the proposed method most suitable for image tampering detection. Channel attention with a soft threshold function is used to reinforce semantic information in the decoding stage. Experiments with three image forensic datasets, NIST16, COVERAGE and CASIA, demonstrate that the proposed method exhibits strong performance in terms of the F1 score and localization of tampered regions.
Sequential obfuscation and toxic argumentation

We study an information design problem where two competing senders move sequentially. The second mover can either reveal more information (constructive argumentation) or obfuscate the first mover's information (toxic argumentation). We show that sequential obfuscation of an initially disclosed state never reveals more, and sometimes reveals strictly less information than sequential disclosure of an initially hidden state. Sequential obfuscation is completely uninformative when the senders are risk averse or risk neutral, or when they have zero-sum preferences. By contrast, sequential disclosure generally reveals some information, and it is fully revealing when the senders are risk neutral or have zero-sum preferences.
The Technological Adaptation of SMES Through Fuzzy Cognitive Maps

SMEs are the fundamental base of the economy of Latin America and the Caribbean since they are an ideal element for the generation of jobs. However, in most of these companies, the administrative and production processes are carried out manually, which generates daily problems that, in many cases, cause them an economic imbalance that can rise to a level that can lead to failure. This work dynamic is reason enough to guide these companies towards a digital transformation, hoping to make them more competitive to stay longer in the commercial field. This research proposes novel strategies for implementing technologies based on a fuzzy logic model, where a vague cognitive map shows how each action benefits these companies. This analysis starts with a neural network simulation that learns and confirms four proposed hypotheses through specialized software created to streamline and specify the hypothesis verification operations; thus, this is available to any company, variable or, idea. The computer simulator was developed in a high-level multiplatform language with free access, allowing technology to automate processes, generate the increase or decrease of the selected variables, and improve the operation of the companies. The interpretation of the results issued by the simulator showed that process automation increases with the introduction of technology to the company. Consequently, many aspects will facilitate achieving its primary objective, which is to increase its profits in multiple elements and decrease the actions that cause problems to the company as a waste of time and money.
Towards the Evaluation of Action Reversibility in STRIPS Using Domain Generators

Robustness is a major prerequisite for using AI systems in real world applications. In the context of AI planning, the reversibility of actions, i.e., the possibility to undo the effects of an action using a reverse plan, is one promising direction to achieve robust plans. Plans only made of reversible actions are resilient against goal changes during plan execution. This paper presents a naive implementation of a non-deterministic theoretical algorithm for determining action reversibility in STRIPS planning. However, evaluating action reversibility systems turns out to be a difficult challenge, as standard planning benchmarks are hardly applicable. We observed that manually crafted domains and in particular those obtained from domain generators easily contain bias. Based on an existing domain generator, we propose two slight variations that exhibit a completely different search tree characteristics. We use these domain generators to evaluate our implementation in close comparison to an existing ASP implementation and show that different generators indeed favor different implementations. Thus, a variety of domain generators is a necessary foundation for the evaluation of action reversibility systems.
Insulator Detection Study Based on Improved Faster-RCNN

Now, unmanned aerial vehicles are used widespreadly in insulators detection. We aimed at the problems of large errors in the identification and positioning of insulators in complex environments during aerial photography, and inaccurate detection of defective insulators, and difficulty in identifying small targets. This paper improved Faster Region-based CNN (Faster-RCNN) to improve the insulator positioning accuracy and defect detection precision. Firstly, the improved Region Proposal Network increased the accuracy of the identification and positioning of insulators with designing the residual module and performed multi-scale training. Secondly, the Intersection over Union (IoU) aware module improved the detection accuracy of defective insulators. Finally, compared with the unimproved Faster-RCNN, this study improved the positioning accuracy of insulators by 4.88% and the detection of defective insulators by 8.91%.
Understanding Decentralization in Proof-of-Stake Blockchains: An Agent-Based Simulation Approach

Blockchain systems allow for securely keeping shared records of transactions in a decentralized way. This is enabled by consensus mechanisms. Proof-of-work is the most prominent consensus mechanism but has major shortcomings. In this paper, we focus on proof-of-stake, its best-known alternative. Importantly, decentralization is not an inherent feature of blockchain systems, but a technological possibility. Numerous security incidents illustrate that decentralization cannot be taken for granted. We therefore study how key parameters affect the degree of decentralization in proof-of-stake blockchains. Based on a real-world implementation of a proof-of-stake blockchain system, we conduct an agent-based simulation to study how a range of parameter manipulations impact the degree of decentralization. The results suggest that a high number of initial potential validator nodes, large transactions, and a high number of transactions increase the degree of decentralization. They also suggest that a very high or very low positive validator network growth rate increases the degree of decentralization. We only find weak support for an impact of changes in transaction fees and initial stake distributions. Our findings contribute to understanding the mechanisms that lead to decentralization in proof-of-stake blockchains as well as to designing proof-of-stake blockchains that are prone to decentralization and therefore more secure.
Towards Class-Balanced Privacy Preserving Heterogeneous Model Aggregation

Heterogeneous model aggregation (HMA) is an effective paradigm that integrates on-device trained models heterogeneous in architecture and target task into a comprehensive model. Recent works adopt knowledge distillation to amalgamate the knowledge of learned features and predictions from heterogeneous on-device models to realize HMA. However, most of them ignore that the disclosure of learned features exposes on-device models to privacy attacks. Moreover, the aggregated model may suffer from the imbalanced supervision caused by the uneven distribution of amalgamated knowledge about each class and show class bias. In this article, to address these issues, we propose a response-based class-balanced heterogeneous model aggregation mechanism, called CBHMA. It can effectively achieve HMA in a privacy-preserving manner and alleviate class bias in the aggregated model. Specifically, CBHMA aggregates on-device models by using only their response information to reduce their privacy leakage risk. To mitigate the impact of imbalanced supervision, CBHMA quantitatively measures the imbalanced supervision level for each class. Based on that, CBHMA customizes fine-grained misclassification costs for each class and utilizes such costs to adjust the importance of each class (more importance to classes with weaker supervision) in the response-based HMA algorithm. Extensive experiments on two real-world datasets demonstrate the effectiveness of CBHMA.
Optimization Design of Multi-vehicle Urban Logistics Distribution Based on Ant Colony Optimization

In order to provide reasonable and effective decision support for logistics enterprises in vehicle distribution path planning, the problem of multi-model urban logistics distribution path was studied according to the distribution mode of single distribution center, and a mathematical model aiming at minimizing the total distribution cost was established. According to the characteristics of the model, a solution method based on ant colony algorithm is proposed. MATLAB was used to carry out the simulation experiment, and 159 supermarket logistics distribution path optimization cases in the city were used to carry out the simulation experiment. The results show that the model and algorithm in this paper are feasible and advantageous in solving the problem of multi-vehicle distribution routes in cities.
Future Challenges: Enhancement Techniques

This chapter discusses one of the critical future challenges, which is video quality measurement for video quality enhancement techniques. Enhancement techniques have already found their place in the market, with new TVs capable of improving the quality by super-resolution models or new codecs that aim at quality enhancement, e.g., LCEVC. In the gaming context, NVIDIA introduces the Dynamic Super Resolution (DSR) model that runs on Maxwell architectures. NVIDIA claims that it can offer a 4k resolution video gaming experience for 1080p generated (rendered) content. Therefore, this section will discuss the new enhancement codec (LCEVC) and enhancement techniques that might impact the quality assessment models for gaming streaming services. This section is written based on the results of two recent publications (Avanaki et al. Quality enhancement of gaming content using generative adversarial networks. In 2020 twelfth international conference on quality of multimedia experience (QoMEX). IEEE, New York, pp. 1–6, 2020; Barman et al., Evaluation of MPEG-5 Part 2 (LCEVC) for live gaming video streaming applications. In ACM Mile-High Video, 2022, submitted).
A Family of Sorkin Parameters to Test Born’s Rule Using Mutually Coherent Sources

Now that we know how to describe the states, the detection process, and the spatial evolution of electromagnetic fields or matter waves in terms of the spatial propagator, we are ready to tackle interference phenomena for an arbitrary number of accessible modes and an arbitrary number of particles. Starting with the mode transformation of an initial single-particle state as in Sect. 3.4, we adapt it to the notation of Chap. 4 and generalize it to an arbitrary number of interfering paths. Afterwards, we develop the concept of interference orders—as proposed initially by Sorkin in 1994 [1]—in the framework of first and also second quantization. After recapitulating the current state of the experiments in single-particle correlations, we pass over to two-particle states, where we explicitly build the two-particle interference hierarchy order by order, and then to general M-particle states.