Reviewerly/exHarmony huggingface dataset:
Automated Journalism and the Freedom of Media: Understanding Legal and Ethical Implications in Competitive Authoritarian Regime

Today news organizations of many developed countries, being part of the digital world, are heralding new ways of news production through automated journalism and embracing new sort of technology-related challenges. The relationship between journalists and automation can vary significantly in different socio-cultural contexts, suggesting considering how automated journalism impacts media freedom through diverse legal and ethical problems. While the implications of automated journalism for media freedom in many Western democratic countries are in question, it is imperative to look at its impacts on media freedom in competitive authoritarian regimes. This study particularly looks at the case of Pakistan. Drawing on the new institutionalism theory, hence this study contributes an analysis of possible legal and ethical dilemmas that can arise from the practice of automated journalism in Pakistan’s journalism institution, affecting the state of media freedom in the country. This analysis is imperative for preparing the Pakistani journalists for automated journalism practice and for the development of a regulatory framework that can recognize transformations led by automation in journalism. Moreover, this study uses the qualitative method of in-depth interviews and uses thematic analysis to present the study’s findings.
Alert-driven Attack Graph Generation using S-PDFA

Ideal cyber threat intelligence (CTI) includes insights into attacker strategies that are specific to a network under observation.Such CTI currently requires extensive expert input for obtaining, assessing, and correlating system vulnerabilities into a graphical representation, often referred to as an attack graph (AG).Instead of deriving AGs based on system vulnerabilities, this work advocates the direct use of intrusion alerts.We propose SAGE, an explainable sequence learning pipeline that automatically constructs AGs from intrusion alerts without a priori expert knowledge.SAGE exploits the temporal and probabilistic dependence between alerts in a suffix-based probabilistic deterministic finite automaton (S-PDFA) -a model that brings infrequent severe alerts into the spotlight and summarizes paths leading to them.Attack graphs are extracted from the model on a per-victim, per-objective basis.SAGE is thoroughly evaluated on three open-source intrusion alert datasets collected through security testing competitions in order to analyze distributed multi-stage attacks.SAGE compresses over 330k alerts into 93 AGs that show how specific attacks transpired.The AGs are succinct, interpretable, and provide directly relevant insights into strategic differences and fingerprintable paths.They even show that attackers tend to follow shorter paths after they have discovered a longer one in 84.5% of the cases.
Morphology Model and Segmentation for Old Turkic Language

Old Turkic language is the basis of all modern Turkic languages. Its study is very important for Turkic peoples who possess modern Turkic languages. This is important both from a historical point of view and for the study of modern issues of neural machine translation, issues of the linguistic distance of modern Turkic languages from their progenitor. This paper proposes the development of a computational model of the morphology of Old Turkic language based on the CSE (Complete Set of Endings) – model of morphology and a study on this basis of the issue of morphological segmentation of the texts of Old Turkic language, which will subsequently be used for neural machine translation of Old Turkic language into modern Turkic languages. Since most of the modern Turkic languages, except for the Turkish language, belong to low-resource languages, the issues of developing computational models of morphology, developing models, algorithms and software for processing Turkic languages are relevant.
Masked Face Detection Algorithm in the Dense Crowd Based on Federated Learning

Wearing masks is an effective and simple method to prevent the spread of the COVID-19 pandemic in public places, such as train stations, classrooms, and streets. It is of positive significance to urge people to wear masks with computer vision technology. However, the existing detection methods are mainly for simple scenes, and facial missing detection is prone to occur in dense crowds with different scales and occlusions. Moreover, the data obtained by surveillance cameras in public places are difficult to be collected for centralized training, due to the privacy of individuals. In order to solve these problems, a cascaded network is proposed: the first level is the Dilation RetinaNet Face Location (DRFL) Network, which contains Enhanced Receptive Field Context (ERFC) module with the dilation convolution, aiming to reduce network parameters and locate faces of different scales. In order to adapt to embedded camera devices, the second level is the SRNet20 network, which is created by Neural Architecture Search (NAS). Due to privacy protection, it is difficult for surveillance video to share in practice, so our SRNet20 network is trained in federated learning. Meanwhile, we have made a masked face dataset containing about 20,000 images. Finally, the experiments highlight that the detection mAP of the face location is 90.6% on the Wider Face dataset, and the classification mAP of the masked face classification is 98.5% on the dataset we made, which means our cascaded network can detect masked faces in dense crowd scenes well.
Automated Feature Selection: A Reinforcement Learning Perspective

Feature selection is a critical step in machine learning that selects the most important features for a subsequent prediction task. Effective feature selection can help to reduce dimensionality, improve prediction accuracy, and increase result comprehensibility. It is traditionally challenging to find the optimal feature subset from the feature subset space as the space could be very large. While much effort has been made on feature selection, reinforcement learning can provide a new perspective towards a more globally-optimal searching strategy. In the preliminary work, we propose a multi-agent reinforcement learning framework for the feature selection problem. Specifically, we first reformulate feature selection with a reinforcement learning framework by regarding each feature as an agent. Besides, we obtain the state of the environment in three ways, i.e., statistic description, autoencoder, and graph convolutional network (GCN), in order to derive a fixed-length state representation as the input of reinforcement learning. In addition, we study how the coordination among feature agents can be improved by a more effective reward scheme. Also, we provide a GMM-based generative rectified sampling strategy to accelerate the convergence of multi-agent reinforcement learning. Our method searches the feature subset space more globally and can be easily adapted to real-time scenarios due to the nature of reinforcement learning. In the extended version, we further accelerate the framework from two aspects. From the sampling aspect, we show the indirect acceleration by proposing a rank-based softmax sampling strategy. From the exploration aspect, we show the direct acceleration by proposing an interactive reinforcement learning (IRL)-based exploration strategy. Extensive experimental results show the significant improvement of the proposed method over conventional approaches.
Cost-sensitive ensemble learning: a unifying framework

Abstract Over the years, a plethora of cost-sensitive methods have been proposed for learning on data when different types of misclassification errors incur different costs. Our contribution is a unifying framework that provides a comprehensive and insightful overview on cost-sensitive ensemble methods, pinpointing their differences and similarities via a fine-grained categorization. Our framework contains natural extensions and generalisations of ideas across methods, be it AdaBoost, Bagging or Random Forest, and as a result not only yields all methods known to date but also some not previously considered.
Visibility and security in the smart home

Smart home, media and security systems intervene in the territory and boundaries of the home in a variety of ways. Among these are the capacity to watch the home from afar, and to record these observations over time, as well as using the home as a site of performance for those on the outside. In this paper, we map the meanings of the smart home and explore the tensions between security and visibility, adopting a cultural history and cultural analysis methodological approach. We make a contribution to the literature on the smart home, highlighting its connection to longer trajectories of media and cultural change, and to understanding the contemporary formations of technologised surveillance, with attention to practices that emerged in response to COVID-19. We focus on two aspects of our model of domestic smartification: Ludics (devices and systems for play or entertainment) and exteriorities (security and communication interfaces that remotely monitor and expose the home). We focus on these aspects relating them to ideas of haunting and the uncanny to explore the implications of making what was previously hidden visible and manipulable to others.
A proficient approach for face detection and recognition using machine learning and high‐performance computing

Summary The objective of this article is to increase the efficiency of face recognition in the aspect of a dynamic frame. Studies were carried out that focus on maintaining the high detection rate as well as increasing the accuracy. The study was conducted by considering the current recording of the dynamic frame of randomly selected people walking on a treadmill. Here, the variety of facial features was studied and extracted using different viewing angles on the subject. The existence of a huge amount of datasets and economical processing power is subject to value enhancement in the presence of a convolutional neural network (CNN) on different object identification and realization criteria. These methodologies with excellent knowledge of deep learning techniques that subjects to enhance the potential of the machine to learn the face values. CNN is capable to identify faces, positive positioning facial benchmarks, estimating postures and understanding faces in interconnected images and dynamic video frames. In this article, we present a novel phase detector technique that is rapid and has the potential of recognizing faces with huge changes. This is performed by a technique of using adopted CNN and recurrent neural network's subpart long‐short term memory technique in a particular way so that the objective of bringing improvement in the reorganization procedure can be fulfilled. And this was completed by getting 97.5%–98.1% of accuracy is sustained in this.
DNNAttention: A deep neural network and attention based architecture for cross project defect number prediction

Software defect prediction (SDP) is the process of detecting fault-prone classes or modules in a software system. It helps in allocating resources before the testing phase more optimally. Due to a lack of an adequate dataset, defects can be predicted by employing data from different projects to train the classifier called cross-project defect prediction (CPDP). Cross-project defect number prediction (CPDNP) is one step ahead of CPDP, in which we can also estimate the number of defects in each module of a software system; we contemplate it as a regression problem. This article dealt with the CPDNP mechanism and suggested a CPDNP architecture by employing a deep neural network and attention layer called DNNAttention. We syntheses substantial data named cross-heap by utilizing an amalgamation of 44 projects from the PROMISE repository. We fed the cross-heap into DNNAttention to train and evaluate the performance over 44 datasets by applying transfer learning. We have also address class imbalance (CI) and overfitting problems by employing multi-label random over-sampling and dropout regularization, respectively. We compared the performance of the DNNAttention using mean squared error (MSE), mean absolute error (MAE), and accuracy over eight baseline methods. We found out of 44 projects, 19 and 20 have minimum MSE and MAE, respectively, and in 19 projects, accuracy yields by the proposed model surpasses exiting techniques. We also compared the performance in terms of Kendall and Fault-Percentile-Average with the recent unsupervised method and found DNNAttention significantly outperforms this method. Moreover, we found the improvement of the DNNAttention over other baseline methods in terms of MAE, MSE, and accuracy by inspecting 20% line of code are substantial. In most situations, the improvements are significant, and they have a large effect size across all 44 projects.
Orthogonal Ensemble Networks for Biomedical Image Segmentation

Despite the astonishing performance of deep-learning based approaches for visual tasks such as semantic segmentation, they are known to produce miscalibrated predictions, which could be harmful for critical decision-making processes. Ensemble learning has shown to not only boost the performance of individual models but also reduce their miscalibration by averaging independent predictions. In this scenario, model diversity has become a key factor, which facilitates individual models converging to different functional solutions. In this work, we introduce Orthogonal Ensemble Networks (OEN), a novel framework to explicitly enforce model diversity by means of orthogonal constraints. The proposed method is based on the hypothesis that inducing orthogonality among the constituents of the ensemble will increase the overall model diversity. We resort to a new pairwise orthogonality constraint which can be used to regularize a sequential ensemble training process, resulting on improved predictive performance and better calibrated model outputs. We benchmark the proposed framework in two challenging brain lesion segmentation tasks –brain tumor and white matter hyper-intensity segmentation in MR images. The experimental results show that our approach produces more robust and well-calibrated ensemble models and can deal with challenging tasks in the context of biomedical image segmentation.
GAN-Based Image Deblurring Using DCT Loss With Customized Datasets

In this paper, we propose a high quality image deblurring method that uses discrete cosine transform (DCT) and requires less computational complexity. We train our model on a new dataset which is customized to include images with large motion blurs. Recently, Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) based algorithms have been proposed for image deblurring. Moreover, multi-scale and multi-patch architectures of CNN restore blurred images clearly and suppress more ringing or blocking artifacts, but they take a longer time to process. To improve the quality of deblured images and reduce the computational time, we propose a method called "DeblurDCTGAN" that preserves texture and suppresses ringing artifacts in the restored image without multi-scale or multi-patch architecture using DCT based loss.This loss compares the restored image and the ground truth image in the frequency domain. With this loss, DeblurDCTGAN can reduce block noise and ringing artifacts while maintaining deblurring performance. Our experimental results show that DeblurDCTGAN gets the highest performances in terms of PSNR, SSIM, and running time compared with conventional methods. In terms of real image datasets, DeblurDCTGAN shows a better performance by using a customized training dataset made from GoPro, DVD, NFS and HIDE training datasets. Experimented code with pre-trained weights, datasets and results are available at https://github.com/Hiroki-Tomosada/DCTGAN-master.
Federated Few-Shot Learning with Adversarial Learning

We are interested in developing a unified machine learning framework for effectively training machine learning models from many small data sources such as mobile devices. This is a commonly encountered situation in mobile computing scenarios, where data is scarce and distributed while the tasks are distinct. In this paper, we propose a federated few-shot learning (FedFSL) framework to learn a few-shot classification model that can classify unseen data classes with only a few labeled samples. With the federated learning strategy, FedFSL can utilize many data sources while keeping data privacy and communication efficiency. To tackle the issue of obtaining misaligned decision boundaries produced by client models, we propose to regularize local updates by minimizing the divergence of client models. We also formulate the training in an adversarial fashion and optimize the client models to produce a discriminative feature space that can better represent unseen data samples. We demonstrate the intuitions and conduct experiments to show our approaches outperform baselines by more than 10% in learning benchmark vision tasks and 5% in language tasks.
APPLYING BLOCKCHAIN TECHNOLOGY TO RESHAPE THE SERVICE MODELS OF SUPPLY CHAIN FINANCE FOR SMES IN CHINA

Supply chain finance refers to the financial service model in which banks rely on core enterprises to manage the capital flow and logistics of upstream and downstream small- and medium-sized enterprises (SMEs). It adopts the self-testing and closed-loop credit model to control funds and risks. It is an efficient route for SMEs to solve the problem of financing. However, at present, the market of supply chain finance in China still faces problems such as the inability of credit disassembly of core enterprises, which seriously hinder the development of the supply chain finance industry. Blockchain technology featuring decentralization, tamper-proofing and traceability had been widely adopted in the field of finance and provided a new vision to solve the bottleneck for the development of supply chain finance. In this paper, with regard to the characteristics of supply chain finance, we propose a novel blockchain-driven architecture to reshape the business process of supply chain finance, and we introduce the underlying technical implementations in detail. Our contributions in this paper include (1) proposing a novel technical architecture of blockchain-driven supply chain finance management system and detailing its underlying implementations; (2) designing the mechanism of the credit disassembly, which is implemented by blockchain technology, to improve financing efficiency; and (3) exploring the impact and potential of blockchain technology in traditional business models (e.g., receivables financing, inventory financing and prepayment financing) in supply chain finance.
Hierarchical Bayesian LSTM for Head Trajectory Prediction on Omnidirectional Images

When viewing omnidirectional images (ODIs), viewers can access different viewports via head movement (HM), which sequentially forms head trajectories in spatial-temporal domain. Thus, head trajectories play a key role in modeling human attention on ODIs. In this paper, we establish a large-scale dataset collecting 21,600 head trajectories on 1,080 ODIs. By mining our dataset, we find two important factors influencing head trajectories, i.e., temporal dependency and subject-specific variance. Accordingly, we propose a novel approach integrating hierarchical Bayesian inference into long short-term memory (LSTM) network for head trajectory prediction on ODIs, which is called HiBayes-LSTM. In HiBayes-LSTM, we develop a mechanism of Future Intention Estimation (FIE), which captures the temporal correlations from previous, current and estimated future information, for predicting viewport transition. Additionally, a training scheme called Hierarchical Bayesian inference (HBI) is developed for modeling inter-subject uncertainty in HiBayes-LSTM. For HBI, we introduce a joint Gaussian distribution in a hierarchy, to approximate the posterior distribution over network weights. By sampling subject-specific weights from the approximated posterior distribution, our HiBayes-LSTM approach can yield diverse viewport transition among different subjects and obtain multiple head trajectories. Extensive experiments validate that our HiBayes-LSTM approach significantly outperforms 9 state-of-the-art approaches for trajectory prediction on ODIs, and then it is successfully applied to predict saliency on ODIs.
Prediction of IoT Traffic Using the Gated Recurrent Unit Neural Network- (GRU-NN-) Based Predictive Model

Prediction of IoT traffic in the current era has attracted noteworthy attention to utilize the bandwidth and channel capacity optimally. In this paper, the problem of IoT traffic prediction has been studied, and solutions have been proposed by using machine learning method ARIMA and learning time series algorithms such as LSTM and gated recurrent unit (GRU-NN) based on neural networks. The proposed GRU-NN predicts the traffic on the basis of transfer learning. The advantage of the GRU-NN over LSTM is also highlighted by solving the problem of gradient disappearance. The proposed GRU-NN memorizes the traffic characteristics of the IoT environment for a long time which eventually helps the system to forecast the upcoming traffic from the existing traces of the traffic. The proposed GRU-NN makes use of the transfer learning technique to handle the problem of insufficient IoT traffic data along with the gradient boosting training method for achieving better accuracy in predicting the network traffic in the IoT environment. The results reveal that the proposed GRU-NN model outperforms the other traffic predictors in terms of statistical performance evaluation parameters such as MAE, RMSE, MRE, and MSE. The results show that the GRU-NN provides the most accurate predictions followed by the LSTM predictor and then ARIMA and other approaches taken up for the comparative study.
Robust triple extraction with cascade bidirectional capsule network

Recent approaches have witnessed the success of neural models for triple extraction. However, we empirically observe that previous approaches may fail for those disambiguate text expressed in a similar context and generate triples that contradict the commonsense. Such issues severely hinder the generalization of triple extraction in real-world applications. Motivated by the capsule networks’ power of modeling latent structures and the implicit entity-relation schema, we propose a novel Cascade Bidirectional Capsule Network (CBCapsule) to address those issues. We firstly introduce a cascade capsule network to dynamically aggregate context representations and then propose a bidirectional routing mechanism to encourage interaction between the high level (e.g., relations) and low level (e.g., entities) capsules. Experimental results on three benchmarks show that our proposed approach is more efficient than baselines and has a more robust generalization ability with complex surface forms.