Reviewerly/exHarmony huggingface dataset:
Quality evaluation of government epidemic data openness based on cloud model and PSR theory

Government data opening has become one of the key measures of government emergency management in the big data era. Investigating deeply the quality of government data opening under public health emergencies can help grasp the current situation and provide experience for future work. This paper evaluates the 31 regional health commissions’ epidemic data openness using a framework based on PSR theory in China, and the cloud model was used to evaluate the quality of government data opening at the national and regional levels. The comprehensive evaluation level of government data openness under the epidemic situation in China was ordinary level and the level of data openness varies greatly among regions. Data state is the main factor restricting the level of data openness.
Augmented Aerial Reality: On Fusing Synthetic and Real Airborne Imagery for Object Detection

Object detection is a core task for image analysis and inter-pretation and is broadly applied in applications relying on space- and airborne imagery. Like all supervised deep learning methods, training an object detector generally requires a large amount of representative annotated data, which can be hard to acquire in practice. To overcome this challenge, generating synthetic data can be an option to alleviate a lack of real-world annotated data. One key influential factor for the quality of the synthetic data is the background. We show that the detectors' classifier especially depends severely on the background and has a large impact on the detection preci-sion. Using real background is a natural option, however, we show that this naive approach has drawbacks such as a sig-nificant drop in recall. In this paper, we demonstrate that by using style transfer to match the synthetic foreground to the real background, the detector can mitigate these drawbacks and achieve a more balanced result in terms of precision and recall.
AnANet: Association and Alignment Network for Modeling Implicit Relevance in Cross-Modal Correlation Classification

With the explosive increase of multimodal data, cross-modal correlation classification has become an important research topic and is in great demand in many cross-modal applications. A variety of classification schemes and predictive models have been built based on the existing cross-modal correlation categorization. However, these classification schemes typically follow the prior assumption that the paired cross-modal samples are strictly related, and thus pay great attention to the fine-grained relevant types of cross-modal correlation, ignoring the high volume of implicitly relevant data which are often wrongly classified into irrelevant types. Even more, previous predictive models fall short of reflecting the essence of cross-modal correlation according to their definitions, especially in the modeling of network structure. Thus in this paper, by comprehensively investigating the current image-text correlation classification research, we redefine a new classification scheme for cross-modal correlation based on the implicit and explicit relevance. To predict the types of image-text correlation based on our proposed definition, we further devise the Association and Alignment Network (namely AnANet) to model the implicit and explicit relevance, which captures both the implicit association of global discrepancy and commonality between image and text and explicit alignment of cross-modal local relevance. Experimental studies on our constructed new image-text correlation dataset verify the effectiveness of our proposed model.
Network Connectivity and Learning Performance on Restricted Boltzmann Machines

While Network Architecture Search (NAS) and network pruning have recently re-emerged as powerful techniques to design more effective networks with less parameters, their focus has been on improving deep neural network models with millions of parameters. However, the number of connections and the connectivity pattern between layers also influences the learning performance of shallow two-layer networks, such as the classic Restricted Boltzmann Machine (RBM). This work presents a comprehensive study of the connectivity space on the learning performance of RBMs using the bars and stripes (BAS) model and the MNIST dataset. For BAS, the main findings show that pattern and number of connections play a fundamental role on the learning performance, while for MNIST the number of connections is more important for the considered connection structures. Moreover, the fully connected network is outperformed by different patterns in both scenarios, indicating the importance of also designing more effective connectivity patterns for simple two-layer neural networks.
OCI Runtime Comparison and Analysis Study

Containers are one of the technologies required for cloud services. Because containers use resources directly on the host, they are lighter and faster than traditional virtual machines. However, it has the disadvantage of poor security and isolation. To solve this problem, security-oriented runtimes such as Kata, gVisor, and Firecracker have emerged. Each runtime is created differently, so performance and resource usage are different. In this study, performance and evaluation of each low-level runtime are conducted.
A Secure Encrypted Data Deduplication Scheme Based on Data Popularity Supporting Dynamic Ownership Management

Data deduplication technology can save a lot of storage space for the cloud server. However, how to use data deduplication technology to save storage space while ensuring data security has always been a difficult problem. To find a balance between data deduplication technology and data security, some studies introduce the concept of data popularity. In the encrypted data deduplication scheme based on data popularity, the popular data is encrypted by simple and efficient convergent encryption, and the unpopular data is encrypted by semantically secure symmetric encryption. However, the existing schemes do not consider the problem of dynamic ownership management. If the revoked user obtains the ciphertext of popular data, he/she also can decrypt the ciphertext to obtain the plaintext data. In this paper, we propose a secure encrypted data deduplication scheme supporting dynamic ownership management, which can prevent the revoked user from obtaining plaintext data. We use proxy re-encryption algorithm to encrypt convergent ciphertext which is able to prevent ciphertext data from leaking information of popular data. Besides, we design a proof of ownership protocol to prevent the revoked user from regaining access to the data. In this way, the revoked user will not be able to obtain plaintext data.
Associations between Categorization Rules and Categorical Visual Search

One of the tasks of modern categorization theories is the search for cognitive functions associated with categorical learning. Foreign research has demonstrated an association between visual search and continuum category learning (categorical representation): success of the visual search based on single-feature categorical rules does not change with increasing number of distractors, but collapses when the search is supported by information integration categorical rules. The purpose of the current study was to identify links between learning discrete categories and the success of visual search in order to test the previously obtained effect on another type of categories — verbal rules (explicit type) and prototypes (implicit type). It was assumed that, since the representation based on prototypes would have a lower level of awareness, its support for the visual search based (success) would be lower than while forming verbal rules. Participants (N = 121) completed a task where they learnt a new artificial category belonging to one of two types of rules and immediately after that performed a categorical visual search task where they were asked to search for a target relevant to the category they learnt. We found that after learning the verbal rule as well as forming prototypes, visual search success did not collapse with increasing number of distractors. We also found that the higher the success rate in learning a new category, the more effective the visual search was, regardless of the type of rule. Thus, we have shown for the first time that visual search can be supported by different types of categories, both explicit and implicit. We explain the present results and their difference from the results of the previous study by the fact that categories based on discrete features (as opposed to continuums) allows to create more robust representations that are easier to use in non-categorical tasks such as visual search.
An Adapted GRASP Approach for Hyperparameter Search on Deep Networks Applied to Tabular Data

The robustness and resilience of the deep learning models offer consistent and competitive results in real-world applications. Despite its adaptability, the training and adjustment of the hyperparameters still demand knowledge and time from the designer. This paper proposes a simple and effective approach based on the Greedy Randomized Adaptive Search Procedure (GRASP) algorithm that we adapt to optimize deep neural networks models. We evaluated the performance of the proposed approach using the models Deep Feedforward Neural Network (DFNN) and TabNet, considering the Tabu Search algorithm as a baseline in five tabular datasets. Both optimization algorithms showed high performance regarding the (i) quality of the best solution, (ii) convergence, and (iii) local search. However, the adapted GRASP approach showed better results, optimizing the deep models in all datasets with statistical significance.
Maximal Closed Substrings

A string is closed if it has length 1 or has a nonempty border without internal occurrences. In this paper we introduce the definition of a maximal closed substring (MCS), which is an occurrence of a closed substring that cannot be extended to the left nor to the right into a longer closed substring. MCSs with exponent at least 2 are commonly called runs; those with exponent smaller than 2, instead, are particular cases of maximal gapped repeats. We show that a string of length n contains $$\mathcal O(n^{1.5})$$ MCSs. We also provide an output-sensitive algorithm that, given a string of length n over a constant-size alphabet, locates all m MCSs the string contains in $$\mathcal O(n\log n + m)$$ time.
RealPatch: A Statistical Matching Framework for Model Patching with Real Samples

Machine learning classifiers are typically trained to minimise the average error across a dataset. Unfortunately, in practice, this process often exploits spurious correlations caused by subgroup imbalance within the training data, resulting in high average performance but highly variable performance across subgroups. Recent work to address this problem proposes model patching with CAMEL. This previous approach uses generative adversarial networks to perform intra-class inter-subgroup data augmentations, requiring (a) the training of a number of computationally expensive models and (b) sufficient quality of model’s synthetic outputs for the given domain. In this work, we propose RealPatch, a framework for simpler, faster, and more data-efficient data augmentation based on statistical matching. Our framework performs model patching by augmenting a dataset with real samples, mitigating the need to train generative models for the target task. We demonstrate the effectiveness of RealPatch on three benchmark datasets, CelebA, Waterbirds and a subset of iWildCam, showing improvements in worst-case subgroup performance and in subgroup performance gap in binary classification. Furthermore, we conduct experiments with the imSitu dataset with 211 classes, a setting where generative model-based patching such as CAMEL is impractical. We show that RealPatch can successfully eliminate dataset leakage while reducing model leakage and maintaining high utility. The code for RealPatch can be found at https://github.com/wearepal/RealPatch .
An Adaptive PCA-Like Asynchronously Deep Reservoir Computing for Modeling Data-Driven Soft Sensors

Reservoir computing (RC) is a promising tool to build data-driven models, which has exhibited excellent performance in dynamical modeling area. Asynchronously deep reservoir computing (ADRC) is an improved version of RC. It generates more diverse dynamics in the reservoir than traditional RCs because of multi-layered structure and asynchronous information process. Reservoir size is a key factor to affect the performance of ADRC. However, the reservoir size of ADRC is very difficult to be determined. To solve this problem, an adaptive PCA (principal component analysis)-like method is proposed. This method promotes the useful reservoir neuron signals while suppresses the useless reservoir neuron signals. It is very similar to the function of PCA in data process. The training way of the adaptive PCA-like ADRC (APCA-ADRC) is derived based on ridge regression technique. The validity of the APCA-ADRC is tested by modeling the SO2 concentration in sulfur recovery unit. Experimental results show it is prominent in building soft sensors with high performance.
LAMANet: A Real-Time, Machine Learning-Enhanced Approximate Message Passing Detector for Massive MIMO

Model-driven machine learning for signal detection in the physical layer of mobile communication systems combines well-known detector structures with learned parameters. Recent work has shown high detection performance in massive multiple-input–multiple-output (MIMO) detection; however, thorough complexity analysis and real-time processing hardware are lacking. This work proposes a novel machine learning enhanced approximate message passing (AMP) algorithm named LAMANet and its hardware implementation. The algorithm solves some major challenges of previous proposals such as the complete loss of performance in untrained detectors and the still high computational complexity compared to traditional massive MIMO detection methods. We provide a comprehensive complexity comparison, simulations of the symbol error rate (SER) performance over realistic channel models, and a field-programmable gate array (FPGA) implementation capable of processing LAMANet in real time. The results show a similar detection performance of LAMANet to previous machine learning-enhanced algorithms, while the computational effort is reduced to a level where real-time computation in hardware becomes comparable to traditional detection methods.
Working Backwards: Creating a Character Backstory Generation System Using Idealized Creative Writing Outputs

This paper is a reflection on the process of designing and developing a character backstory generation system prototype following literary analysis of idealized textual outputs. An overview of previous generative systems (both academic and commercial) and some of the design priorities associated with these systems is described in order to set a context for the project. The design process is then described, with particular focus on the creation of the idealized outputs and their purpose. Finally, the learning outcomes following initial generative texts created by the prototype engine are shared, weighing the pros and cons of both the design approach, the resulting generator and its outputs.
Pedagogical conditions for the formation of the legal culture of bachelors of non-legal specialties

The purpose of the study is to reveal the pedagogical conditions for the formation of the legal culture of bachelors of non-legal specialties through the analysis of the essence of the basic concepts of research from the position of humanistic and professional orientation. The scientific novelty consists in substantiating the essence and content of pedagogical conditions, as well as their direct impact on the process of improving the forms, methods, means of the educational process, creating optimal conditions for the development of the system of legal knowledge and skills of bachelors of non-legal specialties. As a result, the pedagogical conditions for the development of the legal culture of bachelors of non-legal specialties are determined and justified.
Multi-Agent Local Information Reconstruction for Situational Cognition

Learning an effective strategy is challenging for agents in partially observable environment, where the agents can only observe a part of environment information and make decisions based on local information. Hence, how to effectively utilize local information to achieve efficient cooperation among the agents is particularly important. The agents can establish an understanding of themselves and their surrounding environment based on their historical observation information. However, the understanding lacking global information is local or limited, resulting in low performance in some complex tasks. To solve the problem, a situational cognition learning framework is proposed for each agent based on local information, with which each agent can reconstruct a cognition about itself and its surrounding environment and map it into a high dimensional representation space. In particular, situational cognition is modeled as a random variable under the condition of local trajectory. In addition, an information regularizer is introduced to ensure that the situational cognition is complete and accurate through maximizing the mutual information between the situational cognition and global information, conditioned on the local trajectory of the agent. Various simulations are conducted and show that the proposed framework significantly promotes cooperation among the agents and improves performance.
Analysis of Threats for Ambient Assisted Living (AAL) Wireless Sensor Network (WSN)-Based System

Abstract In the process of designing a system, it is necessary to identify potential threats to that system and to include appropriate countermeasures. The process that helps in finding problems in the initial phase of the system design is the threat modeling. Creating a threat model consists of several steps and there are many tools that can help in automating the procedure. This article deals with the threat analysis for an Ambient Assisted Living (AAL) system based on Wireless Sensor Network (WSN), whose main function is to provide information for carers in order to provide the best possible help to the elderly and disabled. We have identified and analysed 99 potential threats to the considered system, where Denial of Service (DoS) resulted in being the most represented class of threats. After conducting the threat assessment, the obtained results showed that the majority of threats are high-risk and justified the use of threat modeling in the design phase, since they can be utilized in further system development stages.
Authorship Attribution on Kannada Text using Bi-Directional LSTM Technique

is the field of deducing the author of an unknown textual source based on certain characteristics inherently present in the author's style of writing.Author attribution has a ton of useful applications which help automate manual tasks.The proposed model is designed to predict the authorship of the Kannada text using a sequential neural network with Bi-Directional Long Short Term Memory layers, Dense layers, Activation function and Dropout layers.Based on the nature of the data, we have used stochastic gradient descent as an optimizer that improves the learning of the proposed model.The model extracts Part of the speech tags as one of the semantic features using the N-gram technique.A Conditional random fields model is developed to assign Part of the speech tags for the Kannada text tokens, which is the base for the proposed model.The parts of the speech model achieve an overall 90% and 91% F1 score and accuracy respectively.There is no state-of-art model to compare the performance of our model with other models developed for the Kannada language.The proposed model is evaluated using the One Versus Five (1 vs 5) method and overall accuracy of 77.8% is achieved.
Deep Learning Techniques for Biomedical Image Analysis in Healthcare

Biomedical image analysis is very relevant to public health and welfare. Deep learning is quickly growing and has shown enhanced performance in medical applications. It has also been widely extended in academia and industry. The utilization of various deep learning methods on medical imaging endeavours to create systems that can help in the identification of disease and the automation of interpreting biomedical images to help treatment planning. New advancements in machine learning are primarily about deep learning employed for identifying, classifying, and quantifying patterns in images in the medical field. Deep learning, a more precise convolutional neural network has given excellent performance over machine learning in solving visual problems. This chapter summarizes a review of different deep learning techniques used and how they are applied in medical image interpretation and future directions.
Ex-Cit XR: Expert-elicitation of XR Techniques for Disengaging from IVEs

This research explores visualisation and interaction techniques to help disengage users from immersive virtual environments (IVEs) and transition them back to the real world. To gain such insight, we invited eleven Extended Reality (XR) experts to participate in an elicitation study to design the techniques for disengagement. We elicited a total of 132 techniques for four different scenarios of IVEs and classified them into six categories. This led us to discover key design patterns in disengagement strategies. Finally, we summarise key findings and illustrate how the disengagement techniques can be strategically escalated in the exemplary use cases of Ex-Cit XR.
Analysis on Generative Adversarial Networks: Application in Graphics and Image Processing

General Adversarial Network, or GAN, a novel algorithm that is efficient and not too sophisticated for most computer engineers with often greater precision than other existing algorithms, has exhibited vast amounts of merits and conveniences after dominating the major computer domains. So far, the method has been applied to and made some singular achievements in statistical and medical and other similar domains. In today’s and future’s fast paced world, this paper has seen greater demands for such a method to provide more reliable data with fewer resources and time, inspiring us to uncover the potential and possible applications of this algorithm in the future.
Context-Awareness in Human-Robot Interaction: Approaches and Challenges

To be seamlessly integrated in human-centered environments, robots are expected to have intelligent social capabilities on top of their physical abilities. To this end, research in artifi-cial intelligence and human-robot interaction face two major challenges. Firstly, robots need to cope with uncertainty during interaction, especially when dealing with factors that are not fully observable and hard to infer (latent variables) such as the states representing the dynamic environment and human behavior (e.g., intents, goals, preferences). Secondly, robots need to communicate their behaviors to agents (humans and other robots in the environment) in a clear and understandable manner. Therefore, robots need to be context-aware: being able to perceive and understand their surroundings, and adapt their functionalities accordingly.