Reviewerly/exHarmony huggingface dataset:
A Multi-view Confidence-calibrated Framework for Fair and Stable Graph Representation Learning

Graph Neural Networks (GNNs) are prone to adversarial attacks and discriminatory biases. The cutting-edge studies usually adopt a perturbation-invariant consistency regularization strategy without considering the inherent prediction uncertainties, which can lead to unsatisfactory overconfidence for incorrect prediction under intent graph topology or node features attacks. Besides, operating on the complete graph structure is biased towards global level graph noise and brings severe computational issues. In this work, we develop a multi-view confidence-calibrated framework, called MCCNIFTY, for unified fair and stable graph representation learning. At its core is a multi-view uncertainty-aware node embedding learning module derived from evidential theory, including an intra-view evidence calibration, an inter-view evidence fusion, and an uncertainty-aware message passing process in a GNN architecture, which simultaneously optimizes for counterfactual fairness and stability at the sub-graph level. Experimental results on three real-world datasets demonstrate that our method is capable of adequately capturing inherent uncertainties while improving the fairness and stability via subgraph-induced multiview confidence calibration.
Lossless medical image compression based on anatomical information and deep neural networks

Modern imaging modalities generate large volumes of medical data that place a heavy burden on both storage and transmission. Consequently, image data compression is a key research topic in the field of medical imaging. This paper proposes a compression technique that combines anatomical information and a custom deep neural network model to facilitate the transmission and storage of medical image data. This technique first divides the medical image data into specific regions based on anatomical features, namely, image density, relative position, and organ size. A deep neural network is then trained to generate a series of optimal predictors for each region. These predictors can be adaptively switched based on the characteristics of the region being compressed. The residuals are finally compressed with an entropy coding scheme. An evaluation of this compression technique shows that the proposed “divide and conquer” method indeed achieves high prediction accuracy and obtains a compression performance that is better than that of JPEG2000 by 38%.
MASCOT: A Quantization Framework for Efficient Matrix Factorization in Recommender Systems

In recent years, quantization methods have successfully accelerated the training of large deep neural network (DNN) models by reducing the level of precision in computing operations (e.g., forward/backward passes) without sacrificing its accuracy. In this work, therefore, we attempt to apply such a quantization idea to the popular Matrix factorization (MF) methods to deal with the growing scale of models and datasets in recommender systems. However, to our dismay, we observe that the state-of-the-art quantization methods are not effective in the training of MF models, unlike their successes in the training of DNN models. To this phenomenon, we posit that two distinctive features in training MF models could explain the difference: (i) the training of MF models is much more memory-intensive than that of DNN models, and (ii) the quantization errors across users and items in recommendation are not uniform. From these observations, we develop a quantization framework for MF models, named MASCOT, employing novel strategies (i.e., m-quantization and g-switching) to successfully address the aforementioned limitations of quantization in the training of MF models. The comprehensive evaluation using four real-world datasets demonstrates that MASCOT improves the training performance of MF models by about 45%, compared to the training without quantization, while maintaining low model errors, and the strategies and implementation optimizations of MASCOT are quite effective in the training of MF models. For the detailed information about MASCOT, we release the code of MASCOT and the datasets at: https://github.com/Yujaeseo/lCDM-2021_MASCOT.
A general framework for mining concept-drifting data streams with evolvable features

Mining feature evolvable streams has gained increasing attention in recent years. However, most existing approaches are designed for stationary data streams (i.e., data streams without concept drifts) and often work with high time complexity due to the time-consuming optimization procedure. The two deficiencies thus largely limit its applications to real-world data stream scenarios. In this paper, we consider a more difficult but practical streaming setting: a data stream with both concept drifts and evolvable features. To this end, we propose a general framework for mining concept-drifting data streams with evolvable features, called FEMC, based on an efficient Feature Evolvable streaming learning and dynamic Micro-Clusters maintenance. Specifically, we derive a closed-form solution to preserve the information in vanished features by learning a weight vector on survival features. The evolving concepts, are further learnt by dynamically maintaining a set of micro-clusters with varying feature space on-the-fly. Empirical results on real-world data sets have demonstrated the benefits of the proposed framework on both clustering and classification tasks by comparing with state-of-the-art algorithms.
Secure Remote Maintenance via Workflow-Driven Security Framework

Remote Maintenance in collaborative manufacturing provides a lot of benefits such as reduced downtime in manufacturing operations. But at the same time, it increases the attack-surface by opening new attack paths to strictly controlled network zones. In this paper, we analyse a real-world cross-organizational remote maintenance scenario by collecting security requirements (e.g., authentication, authorization, and auditability), and present a workflow-based approach to model and formally enforce access control for that scenario. The proposed approach leverages the Workflow-Driven Security Framework (WDSF) to enforce the least privilege principle; to ensure workflow integrity and separation of duties, (i.e., business process enforcement and compliance); to protect the confidentiality and integrity of sensitive information; and to provide traceability and non-repudiation in case of root-cause analysis. The WDSF uses Petri Nets (PN) to model and enforce the workflow, and blockchain and smart contracts to guarantee accountability and traceability of workflow events. The Petri Nets workflows are modelled and validated using the WoPeD tool.
Joint Scence Network and Attention-Guided for Image Captioning

Image captioning is an interesting and challenging task. The previously established image captioning approach is based mainly on the encoder-decoder architecture, but it suffers from problems such as inaccurate captioning information, and the generated captioning sentences are not sufficiently rich. This paper proposes a novel image captioning model that is based on a self-attention network and a scene graph relationship network. First, an improved self-attention network is added to the extraction of visual features to evaluate the effectiveness of image global information for image generation. Then, we design a visual intensity parameter to coordinate the strategies of visual features and language model for word generation. Finally, a graph convolutional network is designed to extract the relationships from the scene information to render the generated caption more exciting and to increase the accuracy of the fine-grained captioning. We demonstrated the satisfactory performance of the model on the MS-COCO and Flickr 30K datasets. The experimental results demonstrate that the proposed model realizes state-of-the-art performance.
Automatically Generating Release Notes with Content Classification Models

Release notes are admitted as an essential technical document in software maintenance. They summarize the main changes, e.g. bug fixes and new features, that have happened in the software since the previous release. Manually producing release notes is a time-consuming and challenging task. For that reason, sometimes developers neglect to write release notes. For example, we collect data from GitHub with over 1900 releases, and among them, 37% of the release notes are empty. To mitigate this problem, we propose an automatic release notes generation approach by applying the text summarization techniques, i.e. TextRank. To improve the keyword extraction method of traditional TextRank, we integrate the GloVe word embedding technique with TextRank. After generating release notes automatically, we apply machine learning algorithms to classify the release note contents (or sentences). We classify the contents into six categories, e.g. bug fixes and performance improvements, to represent the release notes better for users. We use the evaluation metric, e.g. ROUGE, to evaluate the automatically generated release notes. We also compare the performance of our technique with two popular extractive algorithms, e.g. Luhn’s and latent semantic analysis (LSA). Our evaluation results show that the improved TextRank method outperforms the two algorithms.
Weight-Identification Model of Cattle Using Machine-Learning Techniques for Anomaly Detection

Cattle raising is an important economic activity, where livestock entrepreneurs keep track of their production and investment costs, to measure production and business profitability, based on cattle weighing. However, it is complicated for the farmer to detect if the animals they are weighing have gained the right weight. This paper proposes a framework to identify the fattening process, which can be used to detect anomalies in cattle weight-gain over time. This framework used records of animals raised and fattened at “El Rosario” farm, located at the municipality of Montería (Córdoba-Colombia), to identify the fattening process. The performance of four machine-learning techniques to identify the ideal weight from real data was compared. The algorithms used were Decision Tree (DT), Gradient Boosting (GB), regression based on K-Nearest Neighbors (KNN), and Random Forest (RF). In addition, an outlier-detection process was performed to identify anomalous weights. In general, the results showed that the DT model was the one with the best performance with an average Mean Absolute Error (MAE) of 5.4 kg.
Composite Data Preparation Algorithm for SAR Imagery Object Recognition

The paper presents the approach to resolve the insufficient training data problem for object recognition on radar images. Article proposes the novel strategy of data preparation based on the fusion of objects and background images to form composite data. The composite samples were formed by the Poisson's image blending algorithm using open-source experimental database MSTAR and data synthesized by the pix2pix algorithm. It was shown that composite data is applicable in the conditions of a low number of initial data for training object detectors such as YOLOv5. According to mAP for the considered insufficient amount of training data case reaches 82%. It is shown that implementing training strategy based on the prepared composite dataset improves the accuracy recognition up to 3% of mAP. Generalization ability of trained detector was shown on delayed manually annotated samples from ICEYE database where model achieved mAP@0.5 of 62%.
Evaluation of Gender Bias in Facial Recognition with Traditional Machine Learning Algorithms

The prevalent commercial deployment of automated facial analysis systems such as face recognition as a robust authentication method has increasingly fueled scientific attention. Current machine learning algorithms allow for a relatively reliable detection, recognition, and categorization of face images comprised of age, race, and gender. Algorithms with such biased data are bound to produce skewed results. It leads to a significant decrease in the performance of state-of-the-art models when applied to images of gender or ethnicity groups. In this paper, we study the gender bias in facial recognition with gender balanced and imbalanced training sets using five traditional machine learning algorithms. We aim to report the machine learning classifiers which are inclined towards gender bias and the ones which mitigate it. Miss rates metric is effective in finding out potential bias in predictions. Our study utilizes miss rates metric along with a standard metric such as accuracy, precision or recall to evaluate possible gender bias effectively.
VAULT: A Scalable Blockchain-Based Protocol for Secure Data Access and Collaboration

Data sharing is as vital as data storage. Existing centralized data sharing and access systems provide less transparency and traceability as the users have to trust a centralized authority and its decision making for the entire system. There is a need for decentralized distributed data storage and access without a central authority. Blockchain provides promising solutions to such needs. However, the existing decentralized blockchain-based solutions are complex and involve financial incentives, which limits their applications. We propose a secure permissioned blockchain-based decentralized system, VAULT, with a novel quorum-based consensus. We store encrypted files using Interplanetary File System (IPFS) and the references to the files in the blockchain. VAULT is designed for applications involving collaboration from multiple permissioned parties, and users can store, access, and share data as well as manage projects through blockchain. Our experimental results show that our quorum selection is fair, and the VAULT protocol is scalable.
Fog removal and enhancement method for UAV aerial images based on dark channel prior

The existing UAV aerial image de-fog methods have low image contrast after de-fog, the difference between light and dark image is not obvious, leading to poor de-fog effect. Therefore, an aerial image de-fog enhancement method based on dark channel a priori is proposed. The image variance and absolute gradient mean are combined to get the weight coefficients, and the edge pixels are smoothed by using the multiple decomposition form. The image intensity is calculated and the noise is reduced. A convolution neural network is introduced to calculate the atmospheric transmittance in haze. Based on this, dark channel prior algorithm is used to enhance the light and shade difference of aerial photography image and realise the de-fog enhancement of aerial photography image. To verify the performance of the proposed method, simulation experiments are designed which were compared with the existing methods results in better fog-removing effect, higher contrast and shorter time.
Automated game testing using computer vision methods

Video game development is a growing industry nowadays with high revenues. However, even if there are many resources invested in the software development process, many games still contain bugs or performance issues that affect the user experience. This paper presents ideas on how computer vision methods can be used to automate the process of game testing. The goal is to replace the parts of the testing process that require human users (testers) with machines as much as possible, in order to reduce costs and perform more tests in less time by scaling with hardware resources. The focus is on solving existing real-world problems that have emerged from several discussions with industry partners. We base our methods on previous work in this area using intelligent agents playing video games and deep learning methods that interpret feedback from their actions based on visual output. The paper proposes several methods and a set of open-source tools, independent of the operating system or deployment platform, to evaluate the efficiency of the presented methods.
Using Linked Data to create provenance-rich metadata interlinks: the design and evaluation of the NAISC-L interlinking framework for libraries, archives and museums

Abstract Linked data (LD) have the capability to open up and share materials, held in libraries, archives and museums (LAMs), in ways that are restricted by many existing metadata standards. Specifically, LD interlinking can be used to enrich data and to improve data discoverability on the Web through interlinking related resources across datasets and institutions. However, there is currently a notable lack of interlinking across leading LD projects in LAMs, impacting upon the discoverability of their materials. This research describes the Novel Authoritative Interlinking for Semantic Web Cataloguing in Libraries (NAISC-L) interlinking framework. Unlike existing interlinking frameworks, NAISC-L was designed specifically with the requirements of the LAM domain in mind. The framework was evaluated by Information Professionals (IPs), including librarians, archivists and metadata cataloguers, via three user-experiments including a think-aloud test, an online interlink creation test and a field test in a music archive. Across all experiments, participants achieved a high level of interlink accuracy, and usability measures indicated that IPs found NAISC-L to be useful and user-friendly. Overall, NAISC-L was shown to be an effective framework for engaging IPs in the process of LD interlinking, and for facilitating the creation of richer and more authoritative interlinks between LAM resources. NAISC-L supports the linking of related resource across datasets and institutions, thereby enabling richer and more varied search queries, and can thus be used to improve the discoverability of materials held in LAMs.
A k-Anonymised Federated Learning Framework with Decision Trees

We propose a privacy-preserving framework using Mondrian k-anonymity with decision trees in a Federated Learning (FL) setting for the horizontally partitioned data. Data heterogeneity in FL makes the data non-IID (Non-Independent and Identically Distributed). We use a novel approach to create non-IID partitions of data by solving an optimization problem. In this work, each device trains a decision tree classifier. Devices share the root node of their trees with the aggregator. The aggregator merges the trees by choosing the most common split attribute and grows the branches based on the split values of the chosen split attribute. This recursive process stops when all the nodes to be merged are leaf nodes. After the merging operation, the aggregator sends the merged decision tree to the distributed devices. Therefore, we aim to build a joint machine learning model based on the data from multiple devices while offering k-anonymity to the participants.
A Comprehensive Survey on Face Detection Techniques

The need for automatic understanding and examination of data increased with the tremendous growth of video and imaging databases. The change of identity, feelings and attitudes of a person's face always play a key role in terms of social communication. It is difficult for human beings to distinguish and identify various faces. Hence, we can say that in face recognition, the automatic computer-aided face detection system plays an important role. It also plays a significant role in determining the facial expressions and their recognition, estimation of head pose and interaction of humans and computers, etc. The size and location of the human face in a digital image are determined by face detection. For face detection in digital images, this paper brings forward a detailed and comprehensive survey of various important techniques. In this paper, different challenges and applications regarding face detection are also discussed. The standard databases for the detection of the face are mentioned along with various other features. Along with this, special discussions are provided regarding highly practical aspects for the robustness of the system for face detection. In the end, there ¬are some highly promising directions for the research and investigation to be conducted in the future.
Local Model Update for Blockchain Enabled Federated Learning: Approach and Analysis

Federated learning (FL) has been considered as a promising distributed learning tool in massive data mining for different local devices. Addressing in the trust risk of centralized model aggregation and the challenge of data heterogeneity in traditional FL, this paper proposes an enhancement FL approach in a blockchain network. By analyzing the shortcakes of the classic FL that is widely used in the blockchain enabled FL networks, we propose a novel local parameter update approach, where the information of the last-round global model is utilized to reduce the local performance drift caused by data heterogeneity. The convergence of the proposed FL approach is then proved and the convergence rate is revealed to be linear to the training time. Finally, extensive experiments are carried out with a public dataset to validate the effectiveness of the proposed approach with comparisons of two classic baseline approaches.
Option compatible reward inverse reinforcement learning

Reinforcement learning in complex environments is a challenging problem. In particular, the success of reinforcement learning algorithms depends on a well-designed reward function. Inverse reinforcement learning (IRL) solves the problem of recovering reward functions from expert demonstrations. In this paper, we solve a hierarchical inverse reinforcement learning problem within the options framework, which allows us to utilize intrinsic motivation of the expert demonstrations. A gradient method for parametrized options is used to deduce a defining equation for the Q-feature space, which leads to a reward feature space. Using a second-order optimality condition for option parameters, an optimal reward function is selected. Experimental results in both discrete and continuous domains confirm that our recovered rewards provide a solution to the IRL problem using temporal abstraction, which in turn are effective in accelerating transfer learning tasks. We also show that our method is robust to noises contained in expert demonstrations.
User Experience and Information Architecture of Selected National Library Websites: A Comparative Content Inventory, Heuristic Evaluation, and Usability Investigation

National library websites are essential vehicles for the general public to access national information and resources. The purpose of this study is to examine the usability, user experience (UX), and information architecture (IA) of a sample of national library websites. Through a three-phased multi-methods investigation featuring (1) a content inventory of 28 websites, (2) a web IA and usability evaluation of five websites, and (3) usability tests for three websites, the study results revealed statistically significant gender differences in participants’ task performance. Significant correlations were found between participants’ satisfaction and the content, visual design, and IA, as well as between heuristic evaluation scores and participants’ ratings. Several factors affected participants’ satisfaction: quality of information, the trustworthiness of the content, the perceived credibility of the content producers/providers, the website’s overall structure, and the visual design. The findings of the research also helped to identify common content elements on website homepages and to present a practical procedure for assessing national library websites.