OpenRaiser/Intern-Atlas huggingface dataset:
PIMIP: An Open Source Platform for Pathology Information Management and Integration
Venue: arXiv cs.HC 2021
Year: 2021

Digital pathology plays a crucial role in the development of artificial intelligence in the medical field. The digital pathology platform can make the pathological resources digital and networked, and realize the permanent storage of visual data and the synchronous browsing processing without the limitation of time and space. It has been widely used in various fields of pathology. However, there is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections, as well as the management and structured description of relevant patient information. Most platforms cannot integrate image viewing, annotation and analysis, and text information management. To solve the above problems, we propose a comprehensive and extensible platform PIMIP. Our PIMIP has developed the image annotation functions based on the visualization of digital pathological sections. Our annotation functions support multi-user collaborative annotation and multi-device annotation, and realize the automation of some annotation tasks. In the annotation task, we invited a professional pathologist for guidance. We introduce a machine learning module for image analysis. The data we collected included public data from local hospitals and clinical examples. Our platform is more clinical and suitable for clinical use. In addition to image data, we also structured the management and display of text information. So our platform is comprehensive. The platform framework is built in a modular way to support users to add machine learning modules independently, which makes our platform extensible.
Secure and Reliable Transfer Learning Framework for 6G-enabled Internet of Vehicles
Venue: arXiv cs.NI 2021
Year: 2021

In the coming 6G era, Internet of Vehicles (IoV) has been evolving towards 6G-enabled IoV with super-high data rate, seamless networking coverage, and ubiquitous intelligence by Artificial Intelligence (AI). Transfer Learning (TL) has great potential to empower promising 6G-enabled IoV, such as smart driving assistance, with its outstanding features including enhancing quality and quantity of training data, speeding up learning processes and reducing computing demands. Although TL had been widely adopted in wireless applications (e.g., spectrum management and caching), its reliability and security in 6G-enabled IoV were still not well investigated. For instance, malicious vehicles in source domains may transfer and share untrustworthy models (i.e., knowledge) about connection availability to target domains, thus adversely affecting the performance of learning processes. Therefore, it is important to select and also incentivize trustworthy vehicles to participate in TL. In this article, we first introduce the integration of TL and 6G-enbaled IoV and provide TL applications for 6G-enabled IoV. We then design a secure and reliable transfer learning framework by using reputation to evaluate the reliability of pre-trained models and utilizing the consortium blockchain to achieve secure and efficient decentralized reputation management. Moreover, a deep learning-based auction scheme for the TL model market is designed to motivate high-reputation vehicles to participate in model sharing. Finally, the simulation results demonstrate that the proposed framework is secure and reliable with well-design incentives for TL in 6G-enabled IoV.
Reply to Comment on "TVOR: Finding Discrete Total Variation Outliers among Histograms"
Venue: arXiv cs.DM 2021
Year: 2021

In this paper, we respond to a critique of one of our papers previously published in this journal, entitled "TVOR: Finding Discrete Total Variation Outliers among Histograms". Our paper proposes a method for smoothness outliers detection among histograms by using the relation between their discrete total variations (DTV) and their respective sample sizes. In this response, we demonstrate point by point that, contrary to its claims, the critique has not found any mistakes or problems in our paper, either in the used datasets, methodology, or in the obtained top outlier candidates. On the contrary, the critique's claims can easily be shown to be mathematically unfounded, to directly contradict the common statistical theorems, and to go against well established demographic terms. Exactly this is done in the reply here by providing both theoretical and experimental evidence. Additionally, due to critique's complaint, a more extensive research on top outlier candidate, i.e. the Jasenovac list is conducted and in order to clear any of the critique's doubts, new evidence of its problematic nature unseen in other lists are presented. This reply is accompanied by additional theoretical explanations, simulations, and experimental results that not only confirm the earlier findings, but also present new data. The source code is at https://github.com/DiscreteTotalVariation/TVOR.
Structure from Silence: Learning Scene Structure from Ambient Sound
Venue: arXiv cs.SD 2021
Year: 2021

From whirling ceiling fans to ticking clocks, the sounds that we hear subtly vary as we move through a scene. We ask whether these ambient sounds convey information about 3D scene structure and, if so, whether they provide a useful learning signal for multimodal models. To study this, we collect a dataset of paired audio and RGB-D recordings from a variety of quiet indoor scenes. We then train models that estimate the distance to nearby walls, given only audio as input. We also use these recordings to learn multimodal representations through self-supervision, by training a network to associate images with their corresponding sounds. These results suggest that ambient sound conveys a surprising amount of information about scene structure, and that it is a useful signal for learning multimodal features.
Search in Imperfect Information Games
Venue: arXiv cs.AI 2021
Year: 2021

From the very dawn of the field, search with value functions was a fundamental concept of computer games research. Turing's chess algorithm from 1950 was able to think two moves ahead, and Shannon's work on chess from $1950$ includes an extensive section on evaluation functions to be used within a search. Samuel's checkers program from 1959 already combines search and value functions that are learned through self-play and bootstrapping. TD-Gammon improves upon those ideas and uses neural networks to learn those complex value functions -- only to be again used within search. The combination of decision-time search and value functions has been present in the remarkable milestones where computers bested their human counterparts in long standing challenging games -- DeepBlue for Chess and AlphaGo for Go. Until recently, this powerful framework of search aided with (learned) value functions has been limited to perfect information games. As many interesting problems do not provide the agent perfect information of the environment, this was an unfortunate limitation. This thesis introduces the reader to sound search for imperfect information games.
Multimodal End-to-End Group Emotion Recognition using Cross-Modal Attention
Venue: arXiv cs.CV 2021
Year: 2021

Classifying group-level emotions is a challenging task due to complexity of video, in which not only visual, but also audio information should be taken into consideration. Existing works on multimodal emotion recognition are using bulky approach, where pretrained neural networks are used as a feature extractors and then extracted features are being fused. However, this approach does not consider attributes of multimodal data and feature extractors cannot be fine-tuned for specific task which can be disadvantageous for overall model accuracy. To this end, our impact is twofold: (i) we train model end-to-end, which allows early layers of neural network to be adapted with taking into account later, fusion layers, of two modalities; (ii) all layers of our model was fine-tuned for downstream task of emotion recognition, so there were no need to train neural networks from scratch. Our model achieves best validation accuracy of 60.37% which is approximately 8.5% higher, than VGAF dataset baseline and is competitive with existing works, audio and video modalities.
Graph Neural Network Training with Data Tiering
Venue: arXiv cs.LG 2021
Year: 2021

Graph Neural Networks (GNNs) have shown success in learning from graph-structured data, with applications to fraud detection, recommendation, and knowledge graph reasoning. However, training GNN efficiently is challenging because: 1) GPU memory capacity is limited and can be insufficient for large datasets, and 2) the graph-based data structure causes irregular data access patterns. In this work, we provide a method to statistical analyze and identify more frequently accessed data ahead of GNN training. Our data tiering method not only utilizes the structure of input graph, but also an insight gained from actual GNN training process to achieve a higher prediction result. With our data tiering method, we additionally provide a new data placement and access strategy to further minimize the CPU-GPU communication overhead. We also take into account of multi-GPU GNN training as well and we demonstrate the effectiveness of our strategy in a multi-GPU system. The evaluation results show that our work reduces CPU-GPU traffic by 87-95% and improves the training speed of GNN over the existing solutions by 1.6-2.1x on graphs with hundreds of millions of nodes and billions of edges.
The Decidability and Complexity of Interleaved Bidirected Dyck Reachability
Venue: arXiv cs.PL 2021
Year: 2021

Dyck reachability is the standard formulation of a large domain of static analyses, as it achieves the sweet spot between precision and efficiency, and has thus been studied extensively. Interleaved Dyck reachability (denoted $D_k\odot D_k$) uses two Dyck languages for increased precision (e.g., context and field sensitivity) but is well-known to be undecidable. As many static analyses yield a certain type of bidirected graphs, they give rise to interleaved bidirected Dyck reachability problems. Although these problems have seen numerous applications, their decidability and complexity has largely remained open. In a recent work, Li et al. made the first steps in this direction, showing that (i) $D_1\odot D_1$ reachability (i.e., when both Dyck languages are over a single parenthesis and act as counters) is computable in $O(n^7)$ time, while (ii) $D_k\odot D_k$ reachability is NP-hard. In this work we address the decidability and complexity of all variants of interleaved bidirected Dyck reachability. First, we show that $D_1\odot D_1$ reachability can be computed in $O(n^3\cdot α(n))$ time, significantly improving over the existing $O(n^7)$ bound. Second, we show that $D_k\odot D_1$ reachability (i.e., when one language acts as a counter) is decidable, in contrast to the non-bidirected case where decidability is open. We further consider $D_k\odot D_1$ reachability where the counter remains linearly bounded. Our third result shows that this bounded variant can be solved in $O(n^2\cdot α(n))$ time, while our fourth result shows that the problem has a (conditional) quadratic lower bound, and thus our upper bound is essentially optimal. Fifth, we show that full $D_k\odot D_k$ reachability is undecidable. This improves the recent NP-hardness lower-bound, and shows that the problem is equivalent to the non-bidirected case.
A study on Channel Popularity in Twitch
Venue: arXiv cs.SI 2021
Year: 2021

In the past few decades, there has been an increasing need for Internet users to host real time events online and to share their experiences with live, interactive audiences. Online streaming services like Twitch have attracted millions of users to stream and to spectate. There have been few studies about the prediction of streamers' popularity on Twitch. In this paper, we look at potential factors that can contribute to the popularity of streamers. Streamer data was collected through consistent tracking using Twitch's API during a 4 weeks period. Each user's streaming information such as the number of current viewers and followers, the genre of the stream etc., were collected. From the results, we found that the frequency of streaming sessions, the types of content and the length of the streams are major factors in determining how much viewers and subscribers streamers can gain during sessions.
The Application of Digital Technology and the Learning Characteristics of Generation Z in Higher Education
Venue: arXiv cs.CY 2021
Year: 2021

The Generation Z (Gen Z), or the digital natives have never experienced a life without the internet. In addition, the advancement of digital technologies such as social media, smart mobile technologies, cloud computing, and the Internet-of-things has transformed how individuals perform their day-to-day activities. Especially for Gen Z, the use of digital technology has become an essential part of their daily routine, as a result, challenging the norm. As such, Gen Z displays unique learning characteristics which are different from previous generations. This change opens new avenues for exploring the impact of digital technology on the learning characteristics of Gen Z and possible applications to the higher education environment. By conducting a literature review of 80 studies, this paper presents a comprehensive framework for understanding the influence of digital technologies on the learning characteristics of Gen Z in higher education.
Cybersecurity Issues and Practices in a Cloud Context: A Comparison Amongst Micro, Small and Medium Enterprises
Venue: arXiv cs.CR 2021
Year: 2021

The advancement and the proliferation of information systems among enterprises have given rise to understanding cybersecurity. Cybersecurity practices provide a set of techniques and procedures to protect the systems, networks, programs and data from attack, damage, or unauthorised access. Such cybersecurity practices vary and are applied differently to different types of enterprises. The purpose of this research is to compare the critical cybersecurity threats and practices in the cloud context among micro, small, and medium enterprises. By conducting a survey among 289 micro, small and medium-sized enterprises in Australia, this study highlights the significant differences in their cloud security practices. It also concludes that future studies that focus on cybersecurity issues and practices in the context of cloud computing should pay attention to these differences.
What Does the Post-Moore Era Mean for Research Software Engineering?
Venue: arXiv cs.SE 2021
Year: 2021

We are entering the post-Moore era where we no longer enjoy the free ride of the performance growth from simply shrinking the transistor features. However, this does not necessarily mean that we are entering a dark era of computing. On the contrary, sustaining the performance growth of computing in the post-Moore era itself is cutting-edge research. Concretely, heterogeneity and hardware specialization are becoming promising approaches in hardware designs. However, these are paradigm shifts in computer architecture. So what does the post-Moore era mean for research software engineering? This position paper addresses such a question by summarizing possible challenges and opportunities for research software engineering in the post-Moore era. We then briefly discuss what is missing and how we prepare to tackle such challenges and exploit opportunities for the future of computing.
Agent Spaces
Venue: arXiv cs.AI 2021
Year: 2021

Exploration is one of the most important tasks in Reinforcement Learning, but it is not well-defined beyond finite problems in the Dynamic Programming paradigm (see Subsection 2.4). We provide a reinterpretation of exploration which can be applied to any online learning method. We come to this definition by approaching exploration from a new direction. After finding that concepts of exploration created to solve simple Markov decision processes with Dynamic Programming are no longer broadly applicable, we reexamine exploration. Instead of extending the ends of dynamic exploration procedures, we extend their means. That is, rather than repeatedly sampling every state-action pair possible in a process, we define the act of modifying an agent to itself be explorative. The resulting definition of exploration can be applied in infinite problems and non-dynamic learning methods, which the dynamic notion of exploration cannot tolerate. To understand the way that modifications of an agent affect learning, we describe a novel structure on the set of agents: a collection of distances (see footnote 7) $d_{a} \in A$, which represent the perspectives of each agent possible in the process. Using these distances, we define a topology and show that many important structures in Reinforcement Learning are well behaved under the topology induced by convergence in the agent space.
Kronecker Factorization for Preventing Catastrophic Forgetting in Large-scale Medical Entity Linking
Venue: arXiv cs.CL 2021
Year: 2021

Multi-task learning is useful in NLP because it is often practically desirable to have a single model that works across a range of tasks. In the medical domain, sequential training on tasks may sometimes be the only way to train models, either because access to the original (potentially sensitive) data is no longer available, or simply owing to the computational costs inherent to joint retraining. A major issue inherent to sequential learning, however, is catastrophic forgetting, i.e., a substantial drop in accuracy on prior tasks when a model is updated for a new task. Elastic Weight Consolidation is a recently proposed method to address this issue, but scaling this approach to the modern large models used in practice requires making strong independence assumptions about model parameters, limiting its effectiveness. In this work, we apply Kronecker Factorization--a recent approach that relaxes independence assumptions--to prevent catastrophic forgetting in convolutional and Transformer-based neural networks at scale. We show the effectiveness of this technique on the important and illustrative task of medical entity linking across three datasets, demonstrating the capability of the technique to be used to make efficient updates to existing methods as new medical data becomes available. On average, the proposed method reduces catastrophic forgetting by 51% when using a BERT-based model, compared to a 27% reduction using standard Elastic Weight Consolidation, while maintaining spatial complexity proportional to the number of model parameters.
The Other Side of Black Screen: Rethinking Interaction in Synchronous Remote Learning for Collaborative Programming
Venue: arXiv cs.CY 2021
Year: 2021

Collaborative learning environments such as programming labs are crucial for learning experiential hands-on skills such as critical thinking and problem solving, and peer discussion. In a traditional laboratory setting, many of these skills can be practiced through natural interaction (verbal, facial) and physical co-location. However, during and after a global pandemic, these learning practices cannot be exercised safely in in-person settings any longer and thus need to be re-imagined for a remote learning environment. As discussions spur about effective remote learning practices, there is an urgency for identifying the unique needs demanded by both students and instructors under different learning environments. How can we design remote learning to offer broadly accessible learning, by drawing in-person practices and combining them with the power of remote learning solutions? In this case study, we present observations of in-person and online versions of 2 introductory programming courses offered before and during the COVID-19 pandemic. Our observations reveal certain user needs and interaction practices under 5 themes that are unique to students' prior experience with the curriculum and academic level. We find that the current online video-conferencing platforms cannot foster collaborative learning among peers, lacks learning ambiance and spontaneous engagement between students and instructors. Based on our findings, we propose design recommendations and intervention strategies to improve current practices in synchronous remote learning that can facilitate a better learning environment, particularly for introductory lab courses.
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series
Venue: arXiv cs.LG 2021
Year: 2021

The Levenberg-Marquardt (LM) optimization algorithm has been widely used for solving machine learning problems. Literature reviews have shown that the LM can be very powerful and effective on moderate function approximation problems when the number of weights in the network is not more than a couple of hundred. In contrast, the LM does not seem to perform as well when dealing with pattern recognition or classification problems, and inefficient when networks become large (e.g. with more than 500 weights). In this paper, we exploit the true power of LM algorithm using some real world aircraft datasets. On these datasets most other commonly used optimizers are unable to detect the anomalies caused by the changing conditions of the aircraft engine. The challenging nature of the datasets are the abrupt changes in the time series data. We find that the LM optimizer has a much better ability to approximate abrupt changes and detect anomalies than other optimizers. We compare the performance, in addressing this anomaly/change detection problem, of the LM and several other optimizers. We assess the relative performance based on a range of measures including network complexity (i.e. number of weights), fitting accuracy, over fitting, training time, use of GPUs and memory requirement etc. We also discuss the issue of robust LM implementation in MATLAB and Tensorflow for promoting more popular usage of the LM algorithm and potential use of LM optimizer for large-scale problems.
Fine-Grained Image Analysis with Deep Learning: A Survey
Venue: arXiv cs.CV 2021
Year: 2021

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas -- fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community.