Reviewerly/exHarmony huggingface dataset:
Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation

Despite the promise of machine learning (ML) to inform individualized medical care, the clinical utility of ML in medicine has been limited by the minimal interpretability and black box nature of these algorithms.The study aimed to demonstrate a general and simple framework for generating clinically relevant and interpretable visualizations of black box predictions to aid in the clinical translation of ML.To obtain improved transparency of ML, simplified models and visual displays can be generated using common methods from clinical practice such as decision trees and effect plots. We illustrated the approach based on postprocessing of ML predictions, in this case random forest predictions, and applied the method to data from the Left Ventricular (LV) Structural Predictors of Sudden Cardiac Death (SCD) Registry for individualized risk prediction of SCD, a leading cause of death.With the LV Structural Predictors of SCD Registry data, SCD risk predictions are obtained from a random forest algorithm that identifies the most important predictors, nonlinearities, and interactions among a large number of variables while naturally accounting for missing data. The black box predictions are postprocessed using classification and regression trees into a clinically relevant and interpretable visualization. The method also quantifies the relative importance of an individual or a combination of predictors. Several risk factors (heart failure hospitalization, cardiac magnetic resonance imaging indices, and serum concentration of systemic inflammation) can be clearly visualized as branch points of a decision tree to discriminate between low-, intermediate-, and high-risk patients.Through a clinically important example, we illustrate a general and simple approach to increase the clinical translation of ML through clinician-tailored visual displays of results from black box algorithms. We illustrate this general model-agnostic framework by applying it to SCD risk prediction. Although we illustrate the methods using SCD prediction with random forest, the methods presented are applicable more broadly to improving the clinical translation of ML, regardless of the specific ML algorithm or clinical application. As any trained predictive model can be summarized in this manner to a prespecified level of precision, we encourage the use of simplified visual displays as an adjunct to the complex predictive model. Overall, this framework can allow clinicians to peek inside the black box and develop a deeper understanding of the most important features from a model to gain trust in the predictions and confidence in applying them to clinical care.
Coverage Path Planning using Path Primitive Sampling and Primitive Coverage Graph for Visual Inspection

Planning the path to gather the surface information of the target objects is crucial to improve the efficiency of and reduce the overall cost, for visual inspection applications with Unmanned Aerial Vehicles (UAVs). Coverage Path Planning (CPP) problem is often formulated for these inspection applications because of the coverage requirement. Traditionally, researchers usually plan and optimize the viewpoints to capture the surface information first, and then optimize the path to visit the selected viewpoints. In this paper, we propose a novel planning method to directly sample and plan the inspection path for a camera-equipped UAV to acquire visual and geometric information of the target structures as a video stream setting in complex 3D environment. The proposed planning method first generates via-points and path primitives around the target object by using sampling methods based on voxel dilation and subtraction. A novel Primitive Coverage Graph (PCG) is then proposed to encode the topological information, flying distances, and visibility information, with the sampled via-points and path primitives. Finally graph search is performed to find the resultant path in the PCG to complete the inspection task with the coverage requirements. The effectiveness of the proposed method is demonstrated through simulation and field tests in this paper.
Using cognition to resolve duplicacy issues in socially connected healthcare for smart cities

Social web has transformed healthcare communication as patients reach out to seek support and advice by connecting with other patients, caregivers and healthcare professionals. The influx of health-related queries and the volume of answers within the medical forums is a testimony to this adaption. The scalability, natural interaction and dynamism of the continuously collected and connected user-generated social big data can support health assessment, intervention and provisioning to produce the best kind of cognitive smart city. On the flip side the use of social media for healthcare communication suffers from data deluge, lack of reliability and quality, confidentiality and privacy (location/personal) issues. Duplicate questions, that is, queries with similar semantics (meaning) corrupt the filtering mechanism, increase the response time and compromise with the quality of the answer too. This research puts forward solutions to resolve the key challenge of duplicacy within the medical community Question-Answering sites (Medical CQAs). We propose to solve the semantic question matching problem for duplicate question pair detection, using a hybrid deep learning model, which combines a Co-attention based Bi-Directional Long Short-Term Memory (Bi-LSTM) Siamese neural network and a Multi-layer perceptron classifier to output the probability of a similarity match between the two questions. Euclidean distance function is then used to compute the similarity between questions. The proposed model is validated on 100 question pairs which are scrapped from three featured groups, namely, ‘Irritable Bowel Syndrome’, ‘Anxiety Disorder’ and ‘Menopause’ of Patient.info community forum and an accuracy of 86.375% is observed. The results obtained are comparable to that of the Quora’s state-of-the-art results for duplicate detection.
Climate change impacts on potential solar energy production: A study case in Fukushima, Japan

To construct a long-term energy plan for renewable energies such as photovoltaics (PV), the influence of meteorological conditions on energy yield must be considered. It is necessary to understand how climate change impacts energy generation. For that purpose, a method to estimate PV energy generation considering climate change scenarios was developed. The developed method can estimate hourly generation amounts with spatial resolutions of 1-km mesh. It was applied to Fukushima Prefecture, located in the northeast region of Japan. To establish future scenarios three Representative Concentration Pathways (RCP) and seven Global Climate Models (GCM) were analyzed, and uncertainties caused by differences in future scenarios were investigated. The results show that annual generation of PV energy was estimated to increase on average by 1.7% in 2030, 3.9% in 2050, and 4.9% in 2070 due to climate change. Energy plans and adaptation actions are expected to be performed so the country is prepared for future impacts.
Malaria Detection Using Multiple Deep Learning Approaches

With about 200 million global instances and over 400,000 fatalities a year, malaria continues an enormous strain on global health. Modern information technology plays a major part in many attempts to combat the disease, along with biomedical research and political efforts. In specific, insufficient malaria diagnosis was one of the obstacles to a promising mortality decrease. The paper offers an outline of these methods and explores present advancement in the field of microscopic malaria detection and we have ventured into utilization of deep learning for detection of Malaria Parasite. Deep Learning over the years has proven to be much faster and much more accurate as it automates feature extraction of the dataset. In this research paper, we investigated various models of Deep Learning and monitored which of these models provided a better accuracy and faster resolution than previously used deep learning models. Our results show that Resnet 50 model gave the highest accuracy of 0.975504.
Towards Explainable Shared Control using Augmented Reality

Shared control plays a pivotal role in establishing effective human-robot interactions. Traditional control-sharing methods strive to complement a human's capabilities at safely completing a task, and thereby rely on users forming a mental model of the expected robot behaviour. However, these methods can often bewilder or frustrate users whenever their actions do not elicit the intended system response, forming a misalignment between the respective internal models of the robot and human. To resolve this model misalignment, we introduce Explainable Shared Control as a paradigm in which assistance and information feedback are jointly considered. Augmented reality is presented as an integral component of this paradigm, by visually unveiling the robot's inner workings to human operators. Explainable Shared Control is instantiated and tested for assistive navigation in a setup involving a robotic wheelchair and a Microsoft HoloLens with add-on eye tracking. Experimental results indicate that the introduced paradigm facilitates transparent assistance by improving recovery times from adverse events associated with model misalignment.
Social Trust Network Embedding

Developing effective network embedding methods for social trust networks (STNs) is a non-trivial problem because two key pieces of information need to be preserved simultaneously: a user's relations to latent factors and the trust transfer patterns that govern what type of relationship will form. In this study, we propose a novel social trust network embedding method (STNE) to address these issues. Specifically, we present a modified Skip-Gram model with negative sampling to jointly learn latent factor features, along with the trust transfer pattern features. Moreover, we define a flexible notion about a user's latent relationships with other users, which generates reliable negative samples for optimization. Extensive experiments on several real-world networks demonstrate the efficacy of the proposed STNE.
Quantum Pure State Tomography via Variational Hybrid Quantum-Classical Method

To obtain a complete description of a quantum system, one usually employs standard quantum state tomography, which, however, requires an exponential number of measurements to be performed and hence is impractical when the system's size grows large. In this paper, we introduce a self-learning tomographic scheme based on the variational hybrid quantum-classical method. The key part of the scheme is a learning procedure, in which we learn a control sequence capable of driving the unknown target state coherently to a simple fiducial state, so that the target state can be directly reconstructed by applying the control sequence in reverse. In this manner, the state tomography problem is converted to a state-to-state transfer problem. To solve the latter problem, we use a closed-loop-learning control approach. Our scheme is experimentally tested using techniques of four-qubit nuclear magnetic resonance. The experimental results indicate that the proposed tomographic scheme can handle a broad class of states, including entangled states in the field of quantum information, as well as dynamical states of quantum many-body systems common to condensed-matter physics.
Composable finite-size effects in free-space continuous-variable quantum-key-distribution systems

Free-space channels provide the possibility of establishing continuous-variable quantum key distribution (CV-QKD) in global communication networks. However, the fluctuating nature of transmissivity in these channels introduces an extra noise which reduces the achievable secret key rate. We consider two classical post-processing strategies, post-selection of high-transmissivity data and data clusterization, to reduce the fluctuation-induced noise of the channel. We undertake the first investigation of such strategies utilising a composable security proof in a realistic finite-size regime against both collective and individual attacks. We also present an efficient parameter estimation approach to estimate the effective Gaussian parameters over the post-selected data or the clustered data. Although the composable finite-size effects become more significant with the post-selection and clusterization both reducing the size of the data, our results show that these strategies are still able to enhance the finite-size key rate against both individual and collective attacks with a remarkable improvement against collective attacks--even moving the protocol from an insecure regime to a secure regime under certain conditions.
Graph neural entity disambiguation

Entity Disambiguation (ED) aims to automatically resolve mentions of entities in a document to corresponding entries in a given knowledge base. State-of-the-art ED methods typically utilize local contextual information for obtaining mention embeddings which will be compared to candidate entity embeddings and then apply Conditional Random Field (CRF) for collective ED, considering global coherence. An inherent drawback of these methods is that, the global semantic relationships among the candidate entities in the same document are not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the global coherence effect. In this paper, to address the issue, we propose a novel end-to-end graph neural entity disambiguation model which fully exploits the global semantic information. In particular, a heterogeneous entity-word graph is first constructed for each document to model the global semantic relationships among candidate entities in a same document. Then graph convolutional network (GCN) is applied on the entity-word graph to generate enhanced entity embeddings encoding global semantics, which are fed to a CRF for collective ED. Extensive experiments have demonstrated the efficiency and effectiveness of our method over a few state-of-the-art ED methods.
Deep Learning with Convolutional Neural Network and Long Short-Term Memory for Phishing Detection

Phishers sometimes exploit users' trust of a known website's appearance by using a similar page that looks like the legitimate site. In recent times, researchers have tried to identify and classify the issues that can contribute to the detection of phishing websites. This study focuses on design and development of a deep learning based phishing detection solution that leverages the Universal Resource Locator and website content such as images and frame elements. A Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) algorithm were used to build a classification model. The experimental results showed that the proposed model achieved an accuracy rate of 93.28%.
Enabling cloud storage auditing with key-exposure resilience under continual key-leakage

Cloud storage auditing is a service that is usually provided to enable clients to verify the integrity of their data stored in the cloud. However, clients risk exposing their secret key. To address the problem of key exposure, researchers have provided “Forward Security” by dividing the entire lifetime of the secret key into several periods and updating the secret key within each of these periods. Forward security can ensure the validity of authenticators before the period in which the secret key is fully exposed. However, the security of these protocols can be broken by launching side-channel attacks to leak the secret key partially rather than fully. In this study, we focus on implementing measures in cloud storage auditing to protect against side-channel attacks in practice. We formalize the definition and security model of a cloud storage auditing protocol, which supports forward security under continual key-leakage, and construct the first protocol. Our protocol remains secure even if an adversary obtains partial leakage of the secret key during a period. In addition, if the secret key were to be fully disclosed in a certain period, our protocol would maintain forward security. Therefore, the proposed protocol provides stronger security compared with existing protocols.
A Multi-Label Classification Method Using a Hierarchical and Transparent Representation for Paper-Reviewer Recommendation

The paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. It aims to recommend appropriate experts in a discipline to comment on the quality of papers of others in that discipline. How to effectively and accurately recommend reviewers for the submitted papers is a meaningful and still tough task. Generally, the relationship between a paper and a reviewer often depends on the semantic expressions of them. Creating a more expressive representation can make the peer-review process more robust and less arbitrary. So the representations of a paper and a reviewer are very important for the paper-reviewer recommendation. Actually, a reviewer or a paper often belongs to multiple research fields, which increases difficulty in paper-reviewer recommendation. In this article, we propose a Multi-Label Classification method using a HIErarchical and transPArent Representation named Hiepar-MLC . First, we introduce HIErarchical and transPArent Representation (Hiepar) to express the semantic information of the reviewer and the paper. Hiepar is learned from a two-level bidirectional gated recurrent unit based network applying the attention mechanism. It is capable of capturing the two-level hierarchical information (word-sentence-document) and highlighting the elements in reviewers or papers to support the labels. This word-sentence-document information mirrors the hierarchical structure of a reviewer or a paper and captures the exact semantics of them. Then we transform the paper-reviewer recommendation problem into a multi-level classification issue, whose multiple research labels exactly guide the learning process. It is flexible in that we can select any multi-label classification method to solve the paper-reviewer recommendation problem. Further, we propose a simple multi-label-based reviewer assignment (MLBRA) strategy to select the appropriate reviewers. It is interesting in that we also explore the paper-reviewer recommendation in the coarse-grain granularity. Extensive experiments on the real-world dataset consisting of the papers in the ACM Digital Library show that Hiepar-MLC achieves better label prediction performance than the existing representation alternatives. In addition, with the MLBRA strategy, we show the effectiveness and the feasibility of our transformation from paper-reviewer recommendation to multi-label classification.
Different "Intelligibility" for Different Folks

Many arguments have concluded that our autonomous technologies must be intelligible, interpretable, or explainable, even if that property comes at a performance cost. In this paper, we consider the reasons why some property like these might be valuable, we conclude that there is not simply one kind of 'intelligibility', but rather different types for different individuals and uses. In particular, different interests and goals require different types of intelligibility (or explanations, or other related notion). We thus provide a typography of 'intelligibility' that distinguishes various notions, and draw methodological conclusions about how autonomous technologies should be designed and deployed in different ways, depending on whose intelligibility is required.
Cyberbullying Detection Using Deep Learning and Word Embeddings

Cyberbullying detection has become a pressing need in Internet usage governance due to its harmful consequences. Different approaches have been proposed to tackle this problem, including deep learning. In this paper, an empirical study is conducted to evaluate the effectiveness and efficiency of deep learning algorithms, coupled with word embeddings in detecting cyberbullying texts. Three deep learning algorithms were experimented, namely GRU, LSTM and BLSTM. Data pre-processing steps, including oversampling were performed on the selected social media datasets. For feature representations, four different word embeddings models were explored, including word2vec, GloVe, Reddit and ELMO models. Elmo cares of word context by capturing information from the word surroundings which eliminates some of the shortcomings of pre-trained word embeddings models. For more accurate results, 10-fold cross-validation technique was implemented. The experimental results show that BLSTM performs best with ELMO in detecting cyberbullying texts. The efficiency of each model is also measured by calculating the average time taken for training each model. GRU outperforms in terms of time efficiency. Based on the analysis done on false negative cases, three observations were made, which highlight the limitations of word embeddings models on top of GRU algorithm in cyberbullying detection.
Development and evaluation of a real-time pedestrian counting system for high-volume conditions based on 2D LiDAR

Automated monitoring of pedestrians on non-motorized facilities with high pedestrian flows is challenging. Several automated sensor solutions are commercially available that have been evaluated in the literature including traditional point-based sensors, such as inductive loop detectors for bicycles and infrared sensors for pedestrians. More recently, image-based systems, based on video cameras or thermal video cameras, have been developed. Despite the various options, some key limitations of existing solutions exist, in particular, the lack of low-cost solutions using embedded systems capable of performing in real-time under high volume (flow) conditions. This work aims at developing and evaluating the performance of a novel, real-time counting system, developed for environments with high pedestrian flows. The proposed system is based on emerging LiDAR (Light Detection and Ranging) technology. As an input, the system uses the distance measurements from a two-dimensional LiDAR sensor with a set of distinct laser channels and a given angular resolution between each channel. The developed system processes those measurements using a clustering algorithm to detect, count, and identify the direction of travel of each pedestrian. The system’s performance is evaluated by comparing its directional counting outputs with manual counts (ground truth) using disaggregate and aggregate (15-minutes interval) counts at two different monitoring locations. The results demonstrate that the system accurately counts more than 97% of the pedestrians at the disaggregate level, with a false direction detection rate of 1.1%. The over-counting error is 0.7% and the under-counting errors are 1.3% and 2.7% for the two selected sites. At the aggregate level (15-minutes interval), the average absolute percentage deviations (AAPDs) are 1.6% and 4.3% while the weighted AAPDs are 1.5% and 3.5% for the first and second sites, respectively. The accuracy of the proposed system is higher than the traditional technologies used for the same purpose.