Reviewerly/exHarmony huggingface dataset:
A Pre-Training Based Personalized Dialogue Generation Model with Persona-Sparse Data

Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue contexts by encoding speakers' personas together with dialogue histories. Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights. Our model can utilize persona-sparse dialogues in a unified manner during the training process, and can also control the amount of persona-related features to exhibit during the inference process. Both automatic and manual evaluation demonstrates that the proposed model outperforms state-of-the-art methods for generating more coherent and persona consistent responses with persona-sparse data.
HIN: Hierarchical Inference Network for Document-Level Relation Extraction

Document-level RE requires reading, inferring and aggregating over multiple sentences. From our point of view, it is necessary for document-level RE to take advantage of multi-granularity inference information: entity level, sentence level and document level. Thus, how to obtain and aggregate the inference information with different granularity is challenging for document-level RE, which has not been considered by previous work. In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level. Translation constraint and bilinear transformation are applied to target entity pair in multiple subspaces to get entity-level inference information. Next, we model the inference between entity-level information and sentence representation to achieve sentence-level inference information. Finally, a hierarchical aggregation approach is adopted to obtain the document-level inference information. In this way, our model can effectively aggregate inference information from these three different granularities. Experimental results show that our method achieves state-of-the-art performance on the large-scale DocRED dataset. We also demonstrate that using BERT representations can further substantially boost the performance.
Boundary-aware context neural network for medical image segmentation

Medical image segmentation can provide a reliable basis for further clinical analysis and disease diagnosis. With the development of convolutional neural networks (CNNs), medical image segmentation performance has advanced significantly. However, most existing CNN-based methods often produce unsatisfactory segmentation masks without accurate object boundaries. This problem is caused by the limited context information and inadequate discriminative feature maps after consecutive pooling and convolution operations. Additionally, medical images are characterized by high intra-class variation, inter-class indistinction and noise, extracting powerful context and aggregating discriminative features for fine-grained segmentation remain challenging. In this study, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information, which incorporates encoder-decoder architecture. In each stage of the encoder sub-network, a proposed pyramid edge extraction module first obtains multi-granularity edge information. Then a newly designed mini multi-task learning module for jointly learning segments the object masks and detects lesion boundaries, in which a new interactive attention layer is introduced to bridge the two tasks. In this way, information complementarity between different tasks is achieved, which effectively leverages the boundary information to offer strong cues for better segmentation prediction. Finally, a cross feature fusion module acts to selectively aggregate multi-level features from the entire encoder sub-network. By cascading these three modules, richer context and fine-grain features of each stage are encoded and then delivered to the decoder. The results of extensive experiments on five datasets show that the proposed BA-Net outperforms state-of-the-art techniques.
Data citizenship: rethinking data literacy in the age of disinformation, misinformation, and malinformation

In this paper we examine what 'data literacy' -under various definitions -means at a time of persistent distribution of 'dis-/mis-/mal-information' via digital media.The paper first explores the definition of literacies (written, media, information, digital and data literacies) considering the various parameters and considerations they have gone through.We then examine the intersection of dis-/mis-/mal-information and 'fake-news' and these literacies.The paper explores what types of literacies are needed today and the important role of variations in citizens' social context.We highlight three main gaps in current data literacy frameworks -1.going beyond the individual; 2. critical thinking of the online ecosystem; and 3. designing skills for proactive citizens.We discuss these gaps while highlighting how we integrated these into our survey of UK citizens' data literacies as part of our Nuffield Foundation funded project -Me and My Big Data.By discussing our theoretical and methodological challenges we aim to shed light on not only how the definition of data literacy changes but also how we can develop education programmes that take into account information distortions and put proactive citizens at the centre.
Joint Resource Allocation and Incentive Design for Blockchain-Based Mobile Edge Computing

Mobile edge computing (MEC), as a promising technology, provides proximate and prompt computing service for mobile users on various applications. With appropriate incentives, profit-driven users can offload multi-task requests across heterogeneous edge servers. However, such incentive trade lacks a trustworthy platform. Due to the decentralized nature of MEC, trading information from players is easily tampered with by edge servers, which poses a threat to cross-server resource allocation. In this paper, we jointly consider incentives and cross-server resource allocation in blockchain-driven MEC, where the blockchain prevents malicious edge servers from tampering with player information by maintaining a continuous tamper-proof ledger database. Particularly, we propose two double auction mechanisms, namely a double auction mechanism based on breakeven (DAMB) and a more efficient breakeven-free double auction mechanism (BFDA), in which users request multi-task service with claimed bids and edge servers cooperate with each other to serve users. A delegated proof of stake (DPoS) based blockchain technology is leveraged to realize decentralized, untampered, safe and fair resource allocation consensus mechanism. The simulation results show that the proposed DAMB and BFDA can significantly improve the system efficiency of MEC.
Exploring the intersection between software industry and Software Engineering education - A systematic mapping of Software Engineering Trends

Software has become ubiquitous in every corner of modern societies. During the last five decades, software engineering has also changed significantly to advance the development of various types and scales of software products. In this context, Software Engineering Education plays an important role in keeping students updated with software technologies, processes, and practices that are popular in industries. We investigate from literature the extent Software Engineering Education addresses major Software Engineering Trends in the academic setting. We conducted a systematic mapping study about teaching major Software Engineering Trends in project courses. We classified 126 papers based on their investigated Software Engineering Trends, specifically Software Engineering processes and practices, teaching approaches, and the evolution of Software Engineering Trends over time. We reveal that Agile Software Development is the major trend. The other Trends, i.e., Software Implementation, Usability and Value, Global Software Engineering, and Lean Software Startup, are relatively small in the academic setting, but continuously growing in the last five years. System of Systems is the least investigated among all Trends. The study points out the possible gaps between Software Industry and Education, which implies actionable insights for researchers, educators, and practitioners.
Correlating photons using the collective nonlinear response of atoms weakly coupled to an optical mode

Photons in a nonlinear medium can repel or attract each other, resulting in strongly correlated quantum many-body states1,2. Typically, such correlated states of light arise from the extreme nonlinearity granted by quantum emitters that are strongly coupled to a photonic mode2,3. However, unavoidable dissipation (such as photon loss) blurs nonlinear quantum effects when such approaches are used. Here, we generate strongly correlated photon states using only weak coupling and taking advantage of dissipation. An ensemble of non-interacting waveguide-coupled atoms induces correlations between simultaneously arriving photons through collectively enhanced nonlinear interactions. These correlated photons experience less dissipation than the uncorrelated ones. Depending on the number of atoms, we experimentally observe strong photon bunching or antibunching of the transmitted light. This realization of a collectively enhanced nonlinearity may turn out to be transformational for quantum information science and opens new avenues for generating non-classical light, covering frequencies from the microwave to the X-ray regime. Strongly correlated photon states are achieved using only weak coupling thanks to an ensemble of non-interacting waveguide-coupled atoms and collectively enhanced nonlinear interactions.
Federated Learning on Clinical Benchmark Data: Performance Assessment

Background Federated learning (FL) is a newly proposed machine-learning method that uses a decentralized dataset. Since data transfer is not necessary for the learning process in FL, there is a significant advantage in protecting personal privacy. Therefore, many studies are being actively conducted in the applications of FL for diverse areas. Objective The aim of this study was to evaluate the reliability and performance of FL using three benchmark datasets, including a clinical benchmark dataset. Methods To evaluate FL in a realistic setting, we implemented FL using a client-server architecture with Python. The implemented client-server version of the FL software was deployed to Amazon Web Services. Modified National Institute of Standards and Technology (MNIST), Medical Information Mart for Intensive Care-III (MIMIC-III), and electrocardiogram (ECG) datasets were used to evaluate the performance of FL. To test FL in a realistic setting, the MNIST dataset was split into 10 different clients, with one digit for each client. In addition, we conducted four different experiments according to basic, imbalanced, skewed, and a combination of imbalanced and skewed data distributions. We also compared the performance of FL to that of the state-of-the-art method with respect to in-hospital mortality using the MIMIC-III dataset. Likewise, we conducted experiments comparing basic and imbalanced data distributions using MIMIC-III and ECG data. Results FL on the basic MNIST dataset with 10 clients achieved an area under the receiver operating characteristic curve (AUROC) of 0.997 and an F1-score of 0.946. The experiment with the imbalanced MNIST dataset achieved an AUROC of 0.995 and an F1-score of 0.921. The experiment with the skewed MNIST dataset achieved an AUROC of 0.992 and an F1-score of 0.905. Finally, the combined imbalanced and skewed experiment achieved an AUROC of 0.990 and an F1-score of 0.891. The basic FL on in-hospital mortality using MIMIC-III data achieved an AUROC of 0.850 and an F1-score of 0.944, while the experiment with the imbalanced MIMIC-III dataset achieved an AUROC of 0.850 and an F1-score of 0.943. For ECG classification, the basic FL achieved an AUROC of 0.938 and an F1-score of 0.807, and the imbalanced ECG dataset achieved an AUROC of 0.943 and an F1-score of 0.807. Conclusions FL demonstrated comparative performance on different benchmark datasets. In addition, FL demonstrated reliable performance in cases where the distribution was imbalanced, skewed, and extreme, reflecting the real-life scenario in which data distributions from various hospitals are different. FL can achieve high performance while maintaining privacy protection because there is no requirement to centralize the data.
Explainable Automated Fact-Checking for Public Health Claims

Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence.The vast majority of fact-checking studies focus exclusively on political claims.Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required.We present the first study of explainable fact-checking for claims which require specific expertise.For our case study we choose the setting of public health.To support this case study we construct a new dataset PUBHEALTH of 11.8K claims accompanied by journalist crafted, gold standard explanations (i.e., judgments) to support the fact-check labels for claims 1 .We explore two tasks: veracity prediction and explanation generation.We also define and evaluate, with humans and computationally, three coherence properties of explanation quality.Our results indicate that, by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise.
Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering

Grouping the objects based on their similarities is an important common task in machine learning applications. Many clustering methods have been developed, among them k-means based clustering methods have been broadly used and several extensions have been developed to improve the original k-means clustering method such as k-means ++ and kernel k-means. K-means is a linear clustering method; that is, it divides the objects into linearly separable groups, while kernel k-means is a non-linear technique. Kernel k-means projects the elements to a higher dimensional feature space using a kernel function, and then groups them. Different kernel functions may not perform similarly in clustering of a data set and, in turn, choosing the right kernel for an application could be challenging. In our previous work, we introduced a weighted majority voting method for clustering based on normalized mutual information (NMI). NMI is a supervised method where the true labels for a training set are required to calculate NMI. In this study, we extend our previous work of aggregating the clustering results to develop an unsupervised weighting function where a training set is not available. The proposed weighting function here is based on Silhouette index, as an unsupervised criterion. As a result, a training set is not required to calculate Silhouette index. This makes our new method more sensible in terms of clustering concept.
Object Detection and Classification Based on YOLO-V5 with Improved Maritime Dataset

SMD (Singapore Maritime Dataset) is a public dataset with annotated videos, and it is almost unique in the training of deep neural networks (DNN) for the recognition of maritime objects. However, there are noisy labels and imprecisely located bounding boxes in the ground truth of the SMD. In this paper, for the benchmark of DNN algorithms, we correct the annotations of the SMD dataset and present an improved version, which we coined SMD-Plus. We also propose augmentation techniques designed especially for the SMD-Plus. More specifically, an online transformation of training images via Copy &amp; Paste is applied to solve the class-imbalance problem in the training dataset. Furthermore, the mix-up technique is adopted in addition to the basic augmentation techniques for YOLO-V5. Experimental results show that the detection and classification performance of the modified YOLO-V5 with the SMD-Plus has improved in comparison to the original YOLO-V5. The ground truth of the SMD-Plus and our experimental results are available for download.
Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification

Breast cancer is a crucial reason for death in females. Early recognition of this disease with the assistance of mammography reduces the death rate. Deep learning (DL) is an approach being utilized and requested by radiologist that assists them in making an accurate diagnosis and helps to improve outcome predictions. This paper includes a new approach, applied on the Mini-MIAS dataset of 322 images, involving a pre-processing method and inbuilt feature extraction using K-mean clustering for Speed-Up Robust Features (SURF) selection. A new layer is added at the classification level, which carries out a ratio of 70% training to30% testing of the deep neural network and Multiclass Support Vector Machine (MSVM). The outcome described here demonstrates that the accuracy rate of the proposed automated DL method using K-mean clustering with MSVM is better than using a decision tree model. Experimental results show that the average accuracy (ACC) rates of the three classes, i.e., normal, benign and malignant cancer, using the proposed method are95%, 94% and 98%, respectively. The increased sensitivity rate is 3%, specificity is 2%, and Receiver Operating Characteristics (ROC) area is 0.99 using SVM compared to the Multi-Layer Perception (MLP) and J48+K-mean clustering WEKA manual approach. A 10-fold cross validation was used, and the obtained results for the Support Vector Machine (SVM), K-nearest neighbour (KNN), linear discriminant analysis (LDA) and Decision Tree were 96.9%, 93.8%, 89.7% and 88.7%, respectively.
On the Multiple Sources and Privacy Preservation Issues for Heterogeneous Defect Prediction

Heterogeneous defect prediction (HDP) refers to predicting defect-proneness of software modules in a target project using heterogeneous metric data from other projects. Existing HDP methods mainly focus on predicting target instances with single source. In practice, there exist plenty of external projects. Multiple sources can generally provide more information than a single project. Therefore, it is meaningful to investigate whether the HDP performance can be improved by employing multiple sources. However, a precondition of conducting HDP is that the external sources are available. Due to privacy concerns, most companies are not willing to share their data. To facilitate data sharing, it is essential to study how to protect the privacy of data owners before they release their data. In this paper, we study the above two issues in HDP. Specifically, to utilize multiple sources effectively, we propose a multi-source selection based manifold discriminant alignment (MSMDA) approach. To protect the privacy of data owners, a sparse representation based double obfuscation algorithm is designed and applied to HDP. Through a case study of 28 projects, our results show that MSMDA can achieve better performance than a range of baseline methods. The improvement is 3.4-15.3 percent in g-measure and 3.0-19.1 percent in AUG.
Multi-sensor fusion methodology for enhanced land vehicle positioning

It is a main challenge for land vehicles to achieve reliable and low-cost navigation solution in various situations, especially when Global Positioning System (GPS) is not available. To address this challenge, we propose an enhanced multi-sensor fusion methodology to fuse the information from low-cost GPS, MEMS Inertial Measurement Unit (IMU), and digital compass in this paper. First, a key data preprocessing algorithm based on Empirical Mode Decomposition (EMD) interval threshold filter is developed to remove the noises in inertial sensors so as to offer more accurate information for subsequent modeling. Then, a Least-Squares Support Vector Machine (LSSVM)-based nonlinear autoregressive with exogenous input (NARX) model (LSSVM-NARX) is designed and augmented with Kalman filter (KF) to construct a novel LSSVM-NARX/KF hybrid strategy. In case of GPS outages, the recently updated LSSVM-NARX is adopted to predict and compensate for the INS position errors. Finally, the performance of proposed methodology was evaluated with real-world data collected in urban settings including typical driving maneuvers. The results indicate that the proposed methodology can achieve remarkable enhancement in positioning accuracy in GPS-denied environments.
Optimal orientation angles for maximizing energy yield for solar PV in Saudi Arabia

This paper uses research-quality, ground measurements of irradiance and temperature that are accurate to ±2% to estimate the electric energy yield of fixed solar modules for utility-scale solar power plants at 18 sites in Saudi Arabia. The calculation is performed for a range of tilt and azimuth angles and the orientation that gives the optimum annual energy yield is determined. A detailed analysis is presented for Riyadh including the impact of non-optimal tilt and azimuth angles on annual energy yield. It is also found that energy yield in March and October are higher than in April and September, due to milder operating temperatures of the modules. A similar optimization of tilt and azimuth is performed each month separately. Adjusting the orientation each month increases energy yield by 4.01% compared to the annual optimum, but requires considerable labour cost. Further analysis shows that an increase in energy yield of 3.63% can be obtained by adjusting the orientation at five selected times during the year, thus significantly reducing the labour requirement. The optimal orientation and corresponding energy yield for all 18 sites is combined with a site suitability analysis taking into account climate, topography and proximity to roads, transmission lines and protected areas. Six sites are selected as having high suitability and high energy yield: Albaha, Arar, Hail, Riyadh, Tabuk and Taif. For these cities the optimal tilt is only slightly higher than the latitude, however the optimum azimuth is from 20° to 53° west of south due to an asymmetrical daily irradiance profile.