Reviewerly/exHarmony huggingface dataset:
Uncertainty-Aware Graph-Guided Weakly Supervised Object Detection

Weakly supervised object detection is an important and challenging task in the computer vision community. In this paper, we treat weakly supervised object detection as a self-training learning task. Based on the framework of self-training, weakly supervised object detection has two uncertainties during training, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the uncertainty of the pseudo labels and the uncertainty of bounding box regression. To this end, we propose an uncertainty-aware graph-guided self-training framework to eliminate these uncertainties. First, we adopt a precise positive and negative sampling strategy to generate pseudo labels to solve the problem of pseudo label uncertainty. Then, we design a weighted location refinement branch based on Bayesian uncertainty modeling to overcome the bounding box regression uncertainty. Moreover, the imbalance between classification and localization tasks prevents the model from generating the task-aware feature map, and redundant proposals, if not handled properly, also introduce uncertainty to the detector. To overcome this problem, we design a graph-guided module that not only balances the two tasks from the perspective of features but also makes full use of proposals. Furthermore, the relation graph of proposals is constructed by clustering proposals, and then, the graph convolution network (GCN) is applied to propagate information on the graph. Thus, accurate feature representations of the objects are obtained through the graph-guided module, and the classification and localization tasks can promote each other. Extensive experiments on the PASCAL VOC 2007 and 2012 datasets demonstrate the effectiveness of our framework, and we obtain 55.2% and 52.0% mAPs on VOC2007 and VOC2012, respectively, showing its superiority over the state-of-the-art approaches by a large margin.
Privacy and Integrity Threats in Contact Tracing Systems and Their Mitigations

Several countries adopted the Google and Apple exposure notification system (GAEN) to slow the spread of the SARS-CoV-2 virus down. GAEN promised to guarantee security and privacy through a decentralized approach. In this article, we report several relevant privacy and integrity threats in GAEN, including new attacks. GAEN’s security issues are not inherent risks of contact tracing systems. Indeed, we also propose a system named Pronto-B2, which enjoys a much better resilience with respect to mass surveillance and replay attacks.
Chinese Named Entity Recognition Method Based on RoFormer-GlobalPointer and Improved R-drop

Aiming at solving the problems of blurred entity boundary and poor generalization ability of traditional named entity recognition, a named entity recognition method based on RoFormer-GlobalPointer and R-drop is proposed. The word vector with long-term distance information and semantic information is obtained through the RoFormer pre-training model with rotational position encoding; the entity recognition is carried out by considering the head and tail of the entity as a whole GlobalPointer method, and the proposed R-drop method improves the generalization of the model. Experiments show that the model achieves 98.6% values on the Resume datasets. Compared with the baseline BERT-CRF model, the method is improved by 2.8%, which is higher than the current mainstream models.
Dbux-PDG: An Interactive Program Dependency Graph for Data Structures and Algorithms

Understanding and debugging of data structures and algorithms (DSA) is one of the most common tasks in computer science. DSA tests have also become a standard threshold that software developers have to cross to "get the job". One major challenge in comprehending and debugging DSA implementations lies in establishing and maintaining mental models of the quintessentially complex and twisted networks of events that make up their dynamic runtime behavior. Despite the high level of difficulty of this crucial task, general purpose tools to help users understand or reason about DSA implementations still have very limited capabilities. In this work we present Dbux-PDG, a dynamic Program Dependency Graph extension for the Dbux omniscient debugger. It captures data and control flow, as well as data dependencies of a program’s execution for visualization and user interaction. To deal with the immense complexity of non-trivial programs, it offers multiple layers of summarization, that allow the user to explore either the graph as a whole or in parts, one step at a time, as they see fit. We present our findings from applying Dbux-PDG to 94 diverse algorithms and explore its utility in several case studies. All visual results are made available in an online gallery. Dbux-PDG is open source and one-click installable, making it a powerful, easy-to-use tool prototype for DSA comprehension.Video URL: https://youtu.be/dgXj3VoQJZQ
Towards an Integrated Evaluation Framework for XAI: An Experimental Study

Increasing prevalence of opaque black-box AI has highlighted the need for explanations of their behaviours, for example, via explanation artefacts/proxy models. The current paper presents a paradigm for human-grounded experiments to evaluate the relationship between explanation fidelity, human learning performance, understanding and trust in a black-box AI by manipulating the complexity of an explanatory artefact. Decision trees were used in the current experiment as exemplar interpretable surrogate models, providing explanations approximating black-box behaviour, by means of explanation by simplification. Consistent with our hypotheses: 1) explanatory artefacts brought about better learning, while greater decision tree depths led to greater interpretability of the AI's performance and greater trust in the AI; and 2) explanatory artefacts facilitated learning and task performance even after they were withdrawn. Findings are discussed in terms of the interplay between human understanding, trust and AI system performance, highlighting the simplifying assumption of a monotonic relationship between explanation fidelity and interpretability.
Cluster-guided Image Synthesis with Unconditional Models

Generative Adversarial Networks (GANs) are the driving force behind the state-of-the-art in image generation. Despite their ability to synthesize high-resolution photo-realistic images, generating content with on-demand conditioning of different granularity remains a challenge. This challenge is usually tackled by annotating massive datasets with the attributes of interest, a laborious task that is not always a viable option. Therefore, it is vital to introduce control into the generation process of unsupervised generative models. In this work, we focus on controllable image generation by leveraging GANs that are well-trained in an unsupervised fashion. To this end, we discover that the representation space of intermediate layers of the generator forms a number of clusters that separate the data according to semantically meaningful attributes (e.g., hair color and pose). By conditioning on the cluster assignments, the proposed method is able to control the semantic class of the generated image. Our approach enables sampling from each cluster by Implicit Maximum Likelihood Estimation (IMLE). We showcase the efficacy of our approach on faces (CelebA-HQ and FFHQ), animals (Imagenet) and objects (LSUN) using different pre-trained generative models. The results highlight the ability of our approach to condition image generation on attributes like gender, pose and hair style on faces, as well as a variety of features on different object classes.
Looks Like Magic: Transfer Learning in GANs to Generate New Card Illustrations

In this paper, we propose Magicstyle-ganand Magicstylegan-ada- both incarnations of the state-of-the-art models StyleGan2 and StyleGan2 ADA - to experiment with their capacity of transfer learning into a rather different domain: creating new illustrations for the vast universe of the game “Magic: The Gathering” cards. This is a challenging task especially due to the variety of elements present in these illustrations, such as humans, creatures, artifacts, and landscapes - not to mention the plethora of art styles of the images made by various artists throughout the years. To solve the task at hand, we introduced a novel dataset, named MTG, with thousands of illustration from diverse card types and rich in metadata. The resulting set is a dataset composed by a myriad of both realistic and fantasy-like illustrations. Although, to investigate effects of diversity we also introduced subsets that contain specific types of concepts, such as forests, islands, faces, and humans. We show that simpler models, such as DCGANs, are not able to learn to generate proper illustrations in any setting. On the other side, we train instances of Magicstylegan using all proposed subsets, being able to generate high quality illustrations. We perform experiments to understand how well pre-trained features from StyleGan2 can be transferred towards the target domain. We show that in well trained models we can find particular instances of noise vector that realistically represent real images from the dataset. Moreover, we provide both quantitative and qualitative studies to support our claims, and that demonstrate that Magicstylegan is the state-of-the-art approach for generating Magic illustrations. Finally, this paper highlights some emerging properties regarding transfer learning in GANs, which is still a somehow under-explored field in generative learning research.
The use of block chain technologies in smart power systems

This article aims to explore the benefits of blockchain technologies in regards to smart power systems. The diverse background of blockchain applications will be discussed to understand the potential of this technology. The article will address the driving factors for adoption of blockchain in power sector. The main aspects of blockchain technologies will be briefly summarized. Moreover, the prominent blockchain use cases and applications in the field of smart power systems will be explored along with the future challenges pertaining to the adoption of blockchain technologies in the mainstream.
Fast and Accurate Face Detection using Feature Pyramid with Grid Anchors

CNN-based face detection methods have achieved significant progress in recent years. However, making a good balance between time cost and detection accuracy is still a challenging problem. Those methods which can reach a very high detection accuracy always have complicated networks and rely on expensive GPUs for inference, while those methods which have shallow networks and can run on common devices always lose detection accuracy to a large extent. In this paper, we propose an effective anchor generation and bounding-box regression method which can improve the detection accuracy by modifying the detection head of the popular detection networks. With this effectiveness, we can reduce the trainable weights of the network to speed up the inference while maintaining high accuracy. As a result, our method can get a better speed-accuracy balance. In our method, we divide the input image into grids according to the sizes of the pyramid-like feature maps produced by CNN. In training, those grids close to the center of the ground-truth bounding-boxes are selected as anchors. After training, the regression mapping from the anchors to the ground-truth bounding-boxes can be acquired by the exponential transformation we designed. Our method explicitly and strictly use feature maps in different levels to detect faces of different sizes. The higher-level feature maps and larger grid anchors are responsible for detecting larger faces, while the lower-level feature maps and smaller grid anchors are dedicated to detecting smaller faces. Therefore, our method is effective for detecting multi-scale faces. The experiments on both GPU and CPU demonstrate that our method is effective. Our source code is publicly available on https://github.com/zhouliguo/GAFace.
Introduction

In this chapter, we will introduce the history of neural networks and complex-valued neural networksComplex-valued neural networks, and provide detailed recent progress and advances of complex-valued neural networks with emphasis on complex-valued neural networks with time delayTime delay, which include multiple dynamical behaviors achievements and analysis techniques and synthesis methods applied on them. Finally, we will state the purpose, scope, intended audience and organization of the book.
VSSum: A Virtual Surveillance Dataset for Video Summary

Video summary can greatly reduce the size of video while retaining most of the content, which is a very promising video analysis technology, especially for surveillance. However, few datasets can be used for video summaries because of the privacy and verbosity of surveillance videos. In order to improve the performance of video summary technology in the surveillance video area, we introduce VSSum, a Virtual Surveillance video dataset, which currently contains 1,000 simulated videos in virtual scenarios with a length of 5 minutes. Each video contains one predefined abnormal action to be summarized and multiple normal actions. Moreover, randomness is introduced from various aspects, such as character models, camera angles, action times, and positions. The dataset has the characteristics of controllability, diversity, and large-scale. Considering the characteristics of surveillance video, we propose a baseline model for surveillance video summary called VSSumNet. It uses the padding and threshold method instead of the center-cropping and proportion method separately, and the 1D convolution is used to enhance the continuity of output. Experimental results show that the baseline outperforms previous methods.
Agile Mindset Competencies for Project Leaders

The most common interpretation of ‘agile’ or even ‘key message’ is “mindset, set of principles and values” [1]. Various research has shown that management generally lacks involvement in agile projects. However, the role of a project manager in agile projects is constantly evolving and needs to be redefined accordingly. The authors aimed to define which meta-components of the project leader profile are key and which agile mindset competencies are relevant to agile environments. In this study, a project leader's agile mindset competency profile is elaborated on. The agile mindset competency profile of project leaders that resulted from the literature review and comprised 14 competencies was evaluated by practitioners. Moreover, four meta-components of the project leader profile in agile projects were defined, and their importance was evaluated, too.
ScaleNet: Searching for the Model to Scale

Recently, community has paid increasing attention on model scaling and contributed to developing a model family with a wide spectrum of scales. Current methods either simply resort to a one-shot NAS manner to construct a non-structural and non-scalable model family or rely on a manual yet fixed scaling strategy to scale an unnecessarily best base model. In this paper, we bridge both two components and propose ScaleNet to jointly search base model and scaling strategy so that the scaled large model can have more promising performance. Concretely, we design a super-supernet to embody models with different spectrum of sizes (e.g., FLOPs). Then, the scaling strategy can be learned interactively with the base model via a Markov chain-based evolution algorithm and generalized to develop even larger models. To obtain a decent super-supernet, we design a hierarchical sampling strategy to enhance its training sufficiency and alleviate the disturbance. Experimental results show our scaled networks enjoy significant performance superiority on various FLOPs, but with at least $$2.53\times $$ reduction on search cost. Codes are available at https://github.com/luminolx/ScaleNet .
Analytical Study of Content-Based and Collaborative Filtering Methods for Recommender Systems

Recommender systems (RSs) are an integral part of daily life. The main purpose of these systems is to suggest relevant items to the users and the use of recommender systems are critical in industries. RS is broadly classified into content-based filtering and collaborative filtering. This paper analyzes the aforementioned recommendation techniques. Content-based filtering system recommends relevant items to the user based on feedback or previous actions. Contrarily, collaborative filtering uses similarities between users or items to suggest relevant items. This study uses MovieLens 20 M dataset which has 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users. The study analyzes the issue of content-based filtering in which the system is unable to suggest different genres to the user and primarily focuses on the user’s interest which uses one-hot encoding approach and a dot product of the input user ratings vector and movies feature matrix for generating a user profile on which the system recommends suggestions to the user. Collaborative filtering technique overcomes this issue and provides robust suggestions which finds suggestions based on top neighbors, and similarity score between users is calculated by Pearson correlation method. Both the systems have advantages and disadvantages; content-based systems are unable to recommend movies of the genres which the user has not rated, which restricts the scope of recommendation. The paper analyzes the issue of limited exploration in content-based filtering and depicts how collaborative filtering provides generic solution.
A Task Design for Studying Referring Behaviors for Linguistic HRI

In many domains, robots must be able to commu-nicate to humans through natural language. One of the core capabilities needed for task-based natural language communication is the ability to refer to objects, people, and locations. Existing work on robot referring expression generation has focused nearly exclusively on generation of definite descriptions to visible objects. But humans use many other linguistic forms to refer (e.g., pronouns) and commonly refer to objects that cannot be seen at time of reference. Critically, existing corpora used for modeling robot referring expression generation are insufficient for modeling this wider array of referring phenomena. To address this research gap, we present a novel interaction task in which an instructor teaches a learner in a series of construction tasks that require repeated reference to a mixture of present and non-present objects. We further explain how this task could be used in principled data collection efforts.
Automatic Curriculum Learning for Large-Scale Cooperative Multiagent Systems

Recently, a lot of works have been devoted to researching how agents can learn efficient cooperation in multiagent systems. However, it still remains challenging in large-scale multiagent systems (MASs) due to the complex dynamics between the agents and environment and the dimension explosion of state-action space. In this paper, we propose a novel MultiAgent Automatic Curriculum Learning method (MA-ACL) to solve learning problems of large-scale cooperative MASs by beginning from learning on a multiagent scenario with a few agents and automatically progressively increasing the number of agents. An evaluation mechanism based on self-supervised learning is innovatively designed to automatically generate appropriate curricula with a progressively increasing number of agents. Moreover, since the observation state dimension of agents varies across curricula and the learned policy knowledge needs to be effectively encoded, we design a new Distributed Transferable Relation-modeling Policy network structure (DTRP) to handle the dynamic size of the network input and model relational knowledge between agents and their surrounding environment. Simulation results show that the proposed MA-ACL using DTRP can significantly improve the performance of large-scale multiagent learning compared with manual or non curriculum learning methods, and DTRP greatly boosts the performance of MA-ACL.
Internet of Things and Healthcare 4.0 Based on a Real-time model Study in the Smart Retirement Village

Due to the increase of life expectancy in recent years, the elderly population is expected to increase significantly. In addition, due to the recent outbreak of infectious diseases and the consequent occupation of hospitals, as well as the increasing cost of treatment on the one hand, and the need for medical staff on the other, the issue of caring for the elderly in need of health care has become a main challenge. Health issues have accordingly been raised as one of the main problems that directly affect the quality of life and development of countries. For improving the life quality of elderly people, an integrated smart retirement village (SRV) or a smart home system (ISHS) can be helpful in the monitoring and caring of the elderly. The SV with its health 4.0 generation has provided the monitoring of the elderly population without limiting their movements and daily life. The present paper first introduces studies on elderly people being monitored in the smart village system through the Internet. Finally an architecture is proposed for a higher degree of comfort and the sending of real-time sensor data.
Economic Perspectives on the Governance of Blockchains

The structure and operation of blockchains are dynamic, which means that mechanisms must exist for implementing changes. The New Institutional Economics (NIE), with its emphasis on how rules govern the performance of any complex organization or network, provides an especially useful framework to consider governance of blockchains. We consider how NIE has been applied to blockchain and future applications. Our analysis is divided into consideration of blockchain networks as institutional technologies, blockchain networks as knowledge commons and polycentric enterprises, and the ways to empirically research blockchain networks. The Institutional Analysis and Design (IAD) framework developed by Elinor Ostrom is particularly useful to develop an empirical research agenda for comparing the institutional features of blockchains and, ultimately, to comparing their performance.
Entanglement at Avoided Level Crossings

We introduce various bipartite entanglement indicators that are defined entirely from optical and spin tomograms, circumventing reconstruction of the density matrix. Some of these indicators are adapted from the manner in which the distance between two classical probability distributions is estimated. We also indicate how spin squeezing can be related to the tomographic entanglement indicators. A comparison is made of the performance of these tomographic indicators with standard entanglement measures based on the density matrix, with two objectives: to gauge the utility of specific indicators in different situations both in bipartite continuous variable systems and in hybrid quantum systems; and to assess how far ideas from classical probability theory and its role in information processing can be used to understand quantum entanglement. These investigations are conducted specifically in systems in which, with the variation of an appropriate parameter, some of the energy levels move close to each other and then move apart, avoiding level crossing. We summarize known results pointing out that standard quantum entanglement measures extremize at avoided energy-level crossings. Hence such systems provide an ideal platform for our investigations on the utility and limitations of tomographic entanglement indicators.