Reviewerly/exHarmony huggingface dataset:
BARRN: A Blind Image Compression Artifact Reduction Network for Industrial IoT Systems

Most industrial Internet of Things (IoT) devices reduce the capture image size using high-ratio JPEG compression, saving storage space and transmission bandwidth consumption. However, the resulting compression artifacts considerably affect the accuracy of subsequent tasks. Most artifact reduction algorithms do not consider the limitations of storage space and computing power of edge devices. In this study, a blind artifact reduction recurrent network (BARRN), which can reduce compression artifacts when the quality factors are unknown, is proposed. First, a structure based on recurrent convolution is designed for the specific requirements of industrial IoT image acquisition devices; the network can be scaled according to system resource constraints. Second, a more efficient convolution group, capable of adaptively processing different degradation levels, is proposed for optimal use of the limited computational resources. The experimental results demonstrate that the proposed BARRN can meet the needs of industrial systems with high computational efficiency.
SHERLock: Self-Supervised Hierarchical Event Representation Learning

Temporal event representations are an essential aspect of learning among humans. They allow for succinct encoding of the experiences we have through a variety of sensory inputs. Also, they are believed to be arranged hierarchically, allowing for an efficient representation of complex long-horizon experiences. Additionally, these representations are acquired in a self-supervised manner. Analogously, here we propose a model that learns temporal representations from long-horizon visual demonstration data and associated textual descriptions, without explicit temporal supervision. Our method produces a hierarchy of representations that align more closely with ground-truth human-annotated events (+15.3%) than state-of-the-art unsupervised baselines. Our results are comparable to heavily-supervised baselines in complex visual domains such as Chess Openings, YouCook2 and TutorialVQA datasets. Finally, we perform ablation studies illustrating the robustness of our approach. We release our code and demo visualizations in the Supplementary Material.
Comparison of Different Deep CNN Models for Leukemia Diagnosis

Deep learning has been gaining popularity as one of the most fascinating fields in the technology domain. Training deep learning models from scratch encounters several challenges in the real world. For one, it is not always possible to collect the necessary amount of data. In addition, one might not have access to high end hardware required for handling exceedingly large amounts of data. Transfer learning is a technique by which one can use the already trained models made available by the previous researchers and tweak them according to one’s specific needs. Here, we present a comparison of several of these pretrained models in order to diagnose whether a patient is suffering from Leukemia, a disease which can be fatal if left undetected or untreated, based on microscopic images of patient’s blood samples. The datasets used for performing said classification are the ALL IDB1 and ALL IDB2 datasets. Proposed classifier utilizes transfer learning in conjunction with fine tuning, a modification to the former which usually gives better results. Four of the most frequently applied pretrained models, namely VGG19, MobileNetV2, Resnet50, and InceptionV3, are used here and their accuracies are compared to see which one fits our problem statement the best. These models are compared on the basis of 4 different metrics, which are accuracy, precision, recall, and F1 score. The best model was obtained with InceptionV3, having an accuracy of 98.65%.
Technological collaborations model in Iranian automotive industry: developing an open source hardware platform

The present research seeks to answer the question of what are the variables and parameters to create a technological collaborations model in order to develop an open source hardware platform in automotive research and development. The research and development network in the Iranian automotive industry was selected as a statistical population that included different levels of developers, aggregators and automaker. The methods of fuzzy cognitive maps and mathematical modelling were used in two phases. Optimal relationships between different members were formed as within and between groups in terms of cost and value and according to the optimal model, the impacts of this interactions led to forming a common platform with minimum cost and maximum value. Technological collaborations in research and development networks are prevalent, but developing an open hardware platform in automotive research and development and determining an optimised community based on a multiplex network is new in this work.
Understanding Software Upgrade and Downgrade Processes in Data Centers

There has never before been such an eruption of information in human history. This could be attributed, among other digital artefacts, to the proliferation of cellphones, sensors, connected cars, and appliances. Today, a lot of the things we interact with and work with produce data automatically. Data centers are where all the data is kept. Data centers are essential to the continuity of everyday operations because they store the most important and proprietary assets of a business. Large Data Centers have been owned and run by businesses in recent years. These businesses are known as Hyperscalers, and examples include Google, Microsoft, Amazon, and Facebook. Routers, switches, firewalls, storage systems, servers, and application- delivery controllers are the main elements of a data center design. There will be hundreds of thousands of these networking devices, and they all require regular software upgrades to improve their security and efficiency. It is laborious to update the software on hundreds of devices in a data center. To automate this process, it is crucial to understand the basics of how a Data Center and Upgradation of software at a Data Center works. This work is aimed to address the same.
The Combination of Background Subtraction and Convolutional Neural Network for Product Recognition

Multi-class retail product recognition is an important Computer Vision application for the retail industry. Track 4 of the AICITY challenge is introduced for the retail industry. This track focuses on the accuracy and efficiency of the automatic checkout process. However, due to the lack of training data for retail items in the real world, a synthetic data set is usually generated based on the 3d scanned items to produce training data for an automated checkout system. To overcome the difference informative representative appearance between training data and the real-world scenario in the test set provided by the AICITY organizer, our research focuses on analyzing and recognizing retail items by combining the traditional method and state-of-the-art Convolutional Neural Network (CNN) approach. This paper presents our proposed system for product counting and recognition for automated retail checkout. Our proposed method is ranked top 8 in the experimental evaluation in the 2022 AI City challenge Track-4 with an F1-score 0.4082.
Automatic Examination Paper Scores Calculation and Grades Analysis Based on OpenCV

How to automatically and accurately score the examination paper still remains a challenging task, due to different handwriting habits. In this article, a method based on OpenCV has been proposed that can automatically achieve examination paper scores calculation and grades analysis. Instead of turning pages manually, the paper pages are turned automatically by the device we assembled. When automatically turning the page, the high-speed photographic apparatus can capture images of the examination paper. Then the images are read in python and needed to accept a series of digital processing. After this, the location of the numbers can be located and cut. Moreover, the support vector machine (SVM) is used to construct a handwritten digit recognition system to identify the numbers cut from the images. Experimental results on several samples demonstrate the feasibility and superiority of this method in terms of efficiency.
Deep Hotel Recommender System Using Aspect-based Sentiment Analysis of Users’ Reviews

The value of search engines has increased with the digitalization of industries such as e-commerce and entertainment. There are numerous areas where recommendations are used and many different methods and technologies have been used to develop recommender systems. Deep learning-based methods are most effective as they improve the system’s overall performance by obtaining state-of-the-art predictive accuracy. However, collaborative filtering is the most famous technique used in the recommneder systems that rely on the user-item interaction. Besides, Aspect-Based Sentiment Analysis (ABSA) is a text analysis method that divides opinions into aspects and determines the sentiment associated with each. Unlike previous works, in this paper, we combine aspect-based sentiment analysis with multi-criteria collaborative filtering using deep learning. In this study, we use polarities obtained by aspect-based sentiment analysis from user reviews combined with the overall rating and sub-ratings in a deep neural network model with attention mechanism to predict user-item interactions. The model is composed of three parts: The first part includes Aspect-based Sentiment Analysis (ABSA) which is applied to the reviews, and polarities are extracted, then the final dataset is generated. In the second part, the user and item embeddings are concatenated and an attention layer is added after user and item embeddings. This layers provides the insights to user preferred words by the model by learning aspect weights. Then the obtained features are given as input to the Polarities and Sub-ratings Deep Neural Network (DNN) to predict the sub-ratings and polarities. Finally, in the third part, we give obtained criteria array as input to the Overall Score DNN to predict the overall score. Based on polarities the proposed approach leverages aspect-based user’s opinion. The obtained results indicate that proposed approach performs significantly better on the TripAdvisor dataset.
Smart Learning in the Ecosystem: Examines Smart Learning Structural Design Features Considering IoT and IoB

The Internet of Things (IoT), IoT-Education, and smartness are emerging technology used in Industry 4.0 to enable smarter education systems that can be adapted to different learners. Using IoT as an acceptable and useable infrastructure is one of the leaders' innovative strategies. It is an intelligence enabler that will be integrated into many essential parts of the future world. This study looks at the key elements of smart learning structural design, such as IoT and IoB (internet of behavior), as well as the major issues that must be addressed when creating smart educational environments that allow for personalisation. To incorporate smart learning environments into the learning ecosystem and educational contexts, IoT, IoB, and cloud services for a smart education ecosystem must be used to orchestrate formal and informal learning. This study emphasizes smart learning paradigms and smart learning environments and the importance of involving future users in the design process to broaden understanding of the design and implementation of innovative systems for smart learning.
Building Cyber Resilience: The Defensive Shield for the EU

In April 2021 the European Union (EU) published its new Cybersecurity Strategy which reflects the organisation’s ambition to be in the lead for digital economy, invest more in technology, and remain the frontrunner in maintaining a high level of protection for the whole society. The key for being able to effectively address cyber threats lies in cyber resilience: the ability to mitigate the damage of an incident and continue operation. This chapter explains in a nutshell the EU’s main regulatory and policy aims in achieving increased resilience in cyberspace. In particular, the chapter focuses on the role of disruptive technologies in building a defensive shield for the EU and recent developments within the domain of EU cyber defence. Finally, the chapter will illustrate how cooperation with trusted partners such as South Korea will be mutually beneficial for enhancing cyber resilience.
Evolutionary Edge Association and Auction in Hierarchical Federated Learning

To reduce node failures and device dropouts, the Hierarchical Federated Learning (HFL) framework has been proposed whereby cluster heads are designated to support the data owners through intermediate model aggregation. This decentralized learning approach reduces the reliance on a central controller, e.g., the model owner. However, the issues of resource allocation and incentive design are not well-studied in the HFL framework. In this chapter, we consider a two-level resource allocation and incentive mechanism design problem. In the lower level, the cluster heads offer rewards in exchange for the data owners’ participation, and the data owners are free to choose which cluster to join. Specifically, we apply the evolutionary game theory to model the dynamics of the cluster selection process. In the upper level, each cluster head can choose to serve a model owner, whereas the model owners have to compete among each other for the services of the cluster heads. As such, we propose a deep learning based auction mechanism to derive the valuation of each cluster head’s services. The performance evaluation shows the uniqueness and stability of our proposed evolutionary game, as well as the revenue maximizing properties of the deep learning based auction.
Incorporating Constraints in Reinforcement Learning Assisted Energy System Decision Making: A Selected Review

With the widespread use of reinforcement learning (RL) in the energy system, the damage to the system caused by the agents' stochastic exploration is beginning to be appreciated. We need to ensure the safety of agents during the whole training process, even at each time step. Therefore, we need to consider how to incorporate safety constraints into RL. In this paper, we summarize two approaches to soft constraints and three approaches to hard constraints, respectively, and provide a comparative analysis of their advantages and disadvantages. Finally, we conclude approaches to how to incorporate constraints in RL, and give an outlook on future research directions.
Transferring multiple text styles using CycleGAN with supervised style latent space

Text style transfer is a relevant task, contributing to theoretical and practical advancement in several areas, especially when working with non-parallel data. The concept behind non-parallel style transfer is to change a specific dimension of the sentence while retaining the overall context. Previous work used adversarial learning to perform such a task. Although it was not initially created to work with textual data, it proved very effective. Most of the previous work has focused on developing algorithms capable of transferring between binary styles, with limited generalization capabilities and limited applications. This work proposes a framework capable of working with multiple styles and improving content retention (BLEU) after a transfer. The proposed framework combines supervised learning of latent spaces and their separation within the architecture. The results suggest that the proposed framework improves content retention in multi-style scenarios while maintaining accuracy comparable to state-of-the-art.
Research on Multi-Model Fusion for Named Entity Recognition Based on Loop Parameter Sharing Transfer Learning

The biggest problem confronted by named entity recognition (NER) in practical applications is that the number of labeled corpora in most application domains is small due to the high labeling cost; Only a few domains have a large number of well labeled corpora. The lack of labeled corpora is the biggest problem that restricts the application of NER in reality. To address the above problems, we propose T-NER, a model based on transfer learning, in this paper. Structurally, T-NER is of multi-model fusion architecture: the multi-dimensional and multi-level fusion of the sequence learning models BiLSTM and BiGRU is performed; In terms of method, T-NER is a transfer learning based on parameter sharing, which uses the model to train the richly labeled corpora in the source domain, and shares the model parameters of the trained corpora in the source domain to the target domain, so as to transfer the knowledge of the source domain to the target domain. The implementation of model parameter sharing is based on the loop structure of T_NER. After the corpus in the source domain is trained, its model parameters will return to the initial part of the model by utilizing the loop structure of the model to participate in the training of the target domain. In the testing process, the training speed and training results of T_NER in small sample experiments are better than the base model, which shows the strong superiority of the T_NER model in the NER of small samples; In large sample experiments, the effect of the T_NER model is not inferior to the base model. As far as the current experimental results are concerned, the T_NER model can realize the training of small-sample and fast transferable NER model.
A Volterra Process Model for River Water Temperature

Water temperature as a key indicator of river environments evolves dynamically as well as stochastically. We introduce a new mathematical framework for modeling river water temperature at an observation station based on a Volterra process: a non-Markovian model. The non-Markovian nature arises from a non-exponential autocorrelation function, which is effectively captured by a completely monotone kernel. Parameters of the model are identified for a study site in Japan where the water temperature has been measured at a 10-min interval. We show that the observed time series of the water temperature is separated into seasonal and stochastic parts, and that the non-exponential autocorrelation function is fitted well by the proposed model. A brief application of the model to statistical evaluation of the river water temperature subject to misspecification is finally presented.
A Graph Convolutional Network with Early Attention Module for Skeleton-based Action Prediction

This paper addresses the problem of skeleton-based action prediction, which aims to predict the action label when the skeleton sequence is partially observed. The action prediction task is more challenging compared to the after-the-fact action recognition since it needs to make decisions according to the beginning part of action executions. The existing methods improve the action prediction performance by taking advantage of the global action knowledge in full sequences, and some of them require the correspondence between a partial sequence and its associated full sequence. In this paper, we step towards a new direction by exploiting the discriminative information in early observations of actions as much as possible. We propose a Graph Convolutional Network with Early Attention Module (GCN-EAM), which employs a series of spatial-temporal graph convolution blocks to extract features from skeletons. In order to infer the action category as fast as possible, we introduce an early attention module to adaptively emphasize discriminative observations at the beginning stage of actions. The proposed method is evaluated on the large-scale NTU-RGB+D dataset and achieves excellent performance for action prediction.
Debugging Procedural Level Designs with Mental Maps

Procedural Level Generation provides tools and techniques for generating many game levels from a single specification. Instead of creating levels by hand, level designers make use of generators that automate the creation process. However, iteratively improving a level’s design requires encoding generators of adventures, puzzles and encounters in notations that bear little resemblance to generated content. Raising the level quality is difficult, because it is hard to reason about bugs that can manifest inside generated content.
Virtual Tasks but Real Gains: Improving Multi-Task Learning

In supervised multi-task learning, the choice of auxiliary tasks is usually decided manually. This immediately raises the issue of how to choose suitable tasks, which in turn requires the need to measure the similarity between tasks. In this paper, we propose a task-similarity metric that depends solely on the task labels, not on the machine learning models. We show that auxiliary tasks may be synthetically generated from the main tasks with any desired similarity. Labels for these virtual tasks are also generated. Finally, we show that learning these virtual tasks along with the main task leads to real performance gains.
Query-UAP: Query-Efficient Universal Adversarial Perturbation for Large-Scale Person Re-Identification Attack

The performance of person re-identification (ReID) has improved significantly with the development of deep learning. And we find that even the state-of-the-art ReID models are vulnerable to universal adversarial perturbation (UAP) attack under white-box attack. However, the white-box setting that the adversary has full access to model is not suitable for practical application. This situation inspires us to explore the UAP attack in more realistic black-box setting. In this paper, we propose a novel query-based black-box UAP attack algorithm for large-scale ReID attacking, which adopts the coordinate-wise gradient estimation method combined with importance sampling for gradient estimation. In particular, the UAP attack can be more easily applied to large-scale attacks, compared with the commonly used image-specific attack in the literature. What’s more, in order to speed up the convergence of attack, we propose a coordinate-wise MI-FGSM with spatial momentum prior to update UAP. Meanwhile, our UAP updating method avoids the undesirable spread of inaccurate gradient estimation in iterations. Extensive experiments show that, at a very low average number of queries per image, the attack success rate and visual quality of the adversarial samples generated by our attack algorithm are very close to white-box attack. For example, when attacking AP-Net, one of the best ReID models at present, only an average of 297 queries per image can significantly reduce the mAP from 0.89 to 0.03 under Market1501. The code is available at https://github.com/HWliiu/QueryUAPReidAttack .