Reviewerly/exHarmony huggingface dataset:
A multi-objective memetic algorithm for query-oriented text summarization: Medicine texts as a case study

Automatic text summarization is a topic of great interest in many fields of knowledge.Particularly, queryoriented extractive multi-document text summarization methods have increased their importance recently, since they can automatically generate a summary according to a query given by the user.One way to address this problem is by multi-objective optimization approaches.In this paper, a memetic algorithm, specifically a Multi-Objective Shuffled Frog-Leaping Algorithm (MOSFLA) has been developed, implemented, and applied to solve the query-oriented extractive multi-document text summarization problem.Experiments have been conducted with datasets from Text Analysis Conference (TAC), and the obtained results have been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics.The results have shown that the proposed approach has achieved important improvements with respect to the works of scientific literature.Specifically, 25.41%, 7.13%, and 30.22% of percentage improvements in ROUGE-1, ROUGE-2, and ROUGE-SU4 scores have been respectively reached.In addition, MOSFLA has been applied to medicine texts from the Topically Diverse Query Focus Summarization (TD-QFS) dataset as a case study.
Improved fuzzy <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si103.svg"><mml:mi>c</mml:mi></mml:math>-means clustering by varying the fuzziness parameter

Fuzzy c-means (FCM) is one of the most frequently used methods for clustering, where the fuzziness weighting exponent m is a key hyper-parameter that directly affects the clustering performance. However, FCM requires careful tuning the fuzziness parameter which results in significant time costs. In this research, an improved FCM clustering by varying the fuzziness parameter, called vFCM, is proposed to overcome this issue, based on the facts that the FCM objective is easy to optimize when m is large, while more local valleys appear as m decreases, hence the optimization problem presents a search process from simple to complex when m varies from a large value to a small value approaching 1. Here, the nature of m is similar to the temperature parameter in the deterministic annealing, and moving along a sequence of the FCM objectives by a linear method that proposes to update m automatically provides a form of annealing. Extensive experiments on simulated and real-world data sets show that vFCM is not only more robust to initialization but also improves the clustering performance in high dimensions. Furthermore, the clustering results of vFCM have a low fluctuation according to different m, so it does not require careful tuning the fuzziness parameter. The time that vFCM takes is greatly reduced.
Wireless IoT and Cyber-Physical System for Health Monitoring Using Honey Badger Optimized Least-Squares Support-Vector Machine

Health monitoring is a prominent factor in a person's daily life. Healthcare for the elderly is becoming increasingly important as the population ages and grows. The health of an Elderly patient needs frequent examination because the health deteriorates with an increasing age profile. IoT is utilized everywhere in the health industry to identify and communicate with the patients by the professional. A cyber-physical system (CPS) is used to combine physical processes with communication and computation. CPS and IoT are both wirelessly connected via information and communication technologies. The novelty of the research lies in the Honey Badger (HB) algorithm optimized Least-squares Support-Vector Machine (LS-SVM) architecture proposed in this paper for monitoring multi parameters to categorize and determine the abnormal patient details present in the dataset. Since the performance of the LS-SVM is highly dependent on the width coefficient and regularization factor, the HB algorithm is employed in this study to optimize both parameters. The HB algorithm is capable of solving the medical problem that has a complex search space and it also improves the convergence performance of the LS-SVM classifier by achieving a tradeoff between the exploration and exploitation phases. The HB optimized LS-SVM classifier predicts the patients with deteriorating health conditions and evaluates the accuracy of the results obtained. In the end, the statistical data is provided to the caretaker via a smartphone application as a monthly statistical report. The proposed model offers a Positive Predictive Value (PPV), Negative Predictive Value (NPV), and an Area Under the Curve (AUC) score of 0.9478, 0.9587, and 0.9617 respectively which is relatively higher than the conventional techniques such as Decision tree, Random Forest, and Support Vector Machine (SVM) classifier. The simulation results demonstrate that the proposed model efficiently models the sensor parameters and offers timely support to elderly patients.
Multi-Contrast MRI Image Synthesis Using Switchable Cycle-Consistent Generative Adversarial Networks

Multi-contrast MRI images use different echo and repetition times to highlight different tissues. However, not all desired image contrasts may be available due to scan-time limitations, suboptimal signal-to-noise ratio, and/or image artifacts. Deep learning approaches have brought revolutionary advances in medical image synthesis, enabling the generation of unacquired image contrasts (e.g., T1-weighted MRI images) from available image contrasts (e.g., T2-weighted images). Particularly, CycleGAN is an advanced technique for image synthesis using unpaired images. However, it requires two separate image generators, demanding more training resources and computations. Recently, a switchable CycleGAN has been proposed to address this limitation and successfully implemented using CT images. However, it remains unclear if switchable CycleGAN can be applied to cross-contrast MRI synthesis. In addition, whether switchable CycleGAN is able to outperform original CycleGAN on cross-contrast MRI image synthesis is still an open question. In this paper, we developed a switchable CycleGAN model for image synthesis between multi-contrast brain MRI images using a large set of publicly accessible pediatric structural brain MRI images. We conducted extensive experiments to compare switchable CycleGAN with original CycleGAN both quantitatively and qualitatively. Experimental results demonstrate that switchable CycleGAN is able to outperform CycleGAN model on pediatric MRI brain image synthesis.
Cross-Task Knowledge Distillation in Multi-Task Recommendation

Multi-task learning (MTL) has been widely used in recommender systems, wherein predicting each type of user feedback on items (e.g, click, purchase) are treated as individual tasks and jointly trained with a unified model. Our key observation is that the prediction results of each task may contain task-specific knowledge about user’s fine-grained preference towards items. While such knowledge could be transferred to benefit other tasks, it is being overlooked under the current MTL paradigm. This paper, instead, proposes a Cross-Task Knowledge Distillation framework that attempts to leverage prediction results of one task as supervised signals to teach another task. However, integrating MTL and KD in a proper manner is non-trivial due to several challenges including task conflicts, inconsistent magnitude and requirement of synchronous optimization. As countermeasures, we 1) introduce auxiliary tasks with quadruplet loss functions to capture cross-task fine-grained ranking information and avoid task conflicts, 2) design a calibrated distillation approach to align and distill knowledge from auxiliary tasks, and 3) propose a novel error correction mechanism to enable and facilitate synchronous training of teacher and student models. Comprehensive experiments are conducted to verify the effectiveness of our framework in real-world datasets.
DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization

Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research. However, existing survey articles on AI+VIS focus on visual analytics and information visualization, not scientific visualization (SciVis). In this article, we survey related deep learning (DL) works in SciVis, specifically in the direction of DL4SciVis: designing DL solutions for solving SciVis problems. To stay focused, we primarily consider works that handle scalar and vector field data but exclude mesh data. We classify and discuss these works along six dimensions: domain setting, research task, learning type, network architecture, loss function, and evaluation metric. The article concludes with a discussion of the remaining gaps to fill along the discussed dimensions and the grand challenges we need to tackle as a community. This state-of-the-art survey guides SciVis researchers in gaining an overview of this emerging topic and points out future directions to grow this research.
TrailChain: Traceability of data ownership across blockchain-enabled multiple marketplaces

Today massive amounts of data are generated from Internet-of-Things (IoT) sensors that can be streamed in real-time and utilized for building valuable services. As the demand for data sharing has increased, a new business model of data marketplace has emerged that allows individuals to sell their data to buyers for monetary gain. However, these data marketplaces are prone to various threats such as unauthorized data redistribution/reselling, tampering of data, dishonest data ownership claims, and trade of bogus data. The existing solutions related to data ownership traceability are unable to address the above issues due to ambiguous data ownership, undisclosed data reselling, and dispersal of data ownership across multiple marketplaces. In order to solve the above problems, we propose a novel blockchain framework, TrailChain, that uses watermarking to generate a trusted trade trail for tracking the data ownership spanning across multiple decentralized marketplaces. Our solution includes mechanisms for detecting any unauthorized data reselling within and across marketplaces. We also propose a fair resell payment sharing scheme that ensures the resell revenue is shared with the data owners over authorized reselling. We present a prototype implementation of the system using Ethereum. We perform extensive simulations to demonstrate TrailChain's feasibility by benchmarking performance metrics including execution gas costs, execution time, latency and throughput.
ASTEROIDS: Exploring Swarms of Mini-Telepresence Robots for Physical Skill Demonstration

Online synchronous tutoring allows for immediate engagement between instructors and audiences over distance. However, tutoring physical skills remains challenging because current telepresence approaches may not allow for adequate spatial awareness, viewpoint control of the demonstration activities scattered across an entire work area, and the instructor's sufficient awareness of the audience. We present Asteroids, a novel approach for tangible robotic telepresence, to enable workbench-scale physical embodiments of remote people and tangible interactions by the instructor. With Asteroids, the audience can actively control a swarm of mini-telepresence robots, change camera positions, and switch to other robots' viewpoints. Demonstrators can perceive the audiences' physical presence while using tangible manipulations to control the audience's viewpoints and presentation flow. We conducted an exploratory evaluation for Asteroids with 12 remote participants in a model-making tutorial scenario with an architectural expert demonstrator. Results suggest our unique features benefitted participants' engagement, sense of presence, and understanding.
Elements of XR Prototyping: Characterizing the Role and Use of Prototypes in Augmented and Virtual Reality Design

Current research in augmented, virtual, and mixed reality (XR) reveals a lack of tool support for designing and, in particular, prototyping XR applications. While recent tools research is often motivated by studying the requirements of non-technical designers and end-user developers, the perspective of industry practitioners is less well understood. In an interview study with 17 practitioners from different industry sectors working on professional XR projects, we establish the design practices in industry, from early project stages to the final product. To better understand XR design challenges, we characterize the different methods and tools used for prototyping and describe the role and use of key prototypes in the different projects. We extract common elements of XR prototyping, elaborating on the tools and materials used for prototyping and establishing different views on the notion of fidelity. Finally, we highlight key issues for future XR tools research.
Opportunistic Interfaces for Augmented Reality: Transforming Everyday Objects into Tangible 6DoF Interfaces Using Ad hoc UI

Real-time environmental tracking has become a fundamental capability in modern mobile phones and AR/VR devices. However, it only allows user interfaces to be anchored at a static location. Although fiducial and natural-feature tracking overlays interfaces with specific visual features, they typically require developers to define the pattern before deployment. In this paper, we introduce opportunistic interfaces to grant users complete freedom to summon virtual interfaces on everyday objects via voice commands or tapping gestures. We present the workflow and technical details of Ad hoc UI (AhUI), a prototyping toolkit to empower users to turn everyday objects into opportunistic interfaces on the fly. We showcase a set of demos with real-time tracking, voice activation, 6DoF interactions, and mid-air gestures and prospect the future of opportunistic interfaces.
Educational Opportunities and Challenges in Augmented Reality: Featuring Implementations in Physics Education

This review paper provides the conceptualization and development of augmented reality (AR) environment for education by featuring
Forecasts on Future Evolution of Artificial Intelligence and Intelligent Systems

The field of artificial intelligence has gained a significant attention in the media.Some counties claim to be the leaders in the field, other countries claim to be winning in the race for leadership in artificial intelligence.This article conducts a statistical (i.e., bibliometric) analysis of research data records on artificial intelligence by year, country, language, and organisation.The results are clearly in favour of the USA on a national level, and English is clearly the dominant language for disseminating results.But in terms of leading organisation in the field of artificial intelligence creates more confusing result -e.g., between the Chinese Academy of Sciences and the University of California -in the leadership race.The forecasts from this study on future evolution of artificial intelligence is that it is unlikely that (in the next 60 years) AI 'superintelligence' would trigger a catastrophic event for humanity.
Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion Cause Pair Extraction

The Emotion-Cause Pair Extraction (ECPE) task aims to extract emotions and causes as pairs from documents.We observe that the relative distance distribution of emotions and causes is extremely imbalanced in the typical ECPE dataset.Existing methods have set a fixed size window to capture relations between neighboring clauses.However, they neglect the effective semantic connections between distant clauses, leading to poor generalization ability towards position-insensitive data.To alleviate the problem, we propose a novel Multi-Granularity Semantic Aware Graph model (MGSAG) to incorporate finegrained and coarse-grained semantic features jointly, without regard to distance limitation.In particular, we first explore semantic dependencies between clauses and keywords extracted from the document that convey finegrained semantic features, obtaining keywords enhanced clause representations.Besides, a clause graph is also established to model coarsegrained semantic relations between clauses.Experimental results indicate that MGSAG surpasses the existing state-of-the-art ECPE models.Especially, MGSAG outperforms other models significantly in the condition of position-insensitive data.
Mutual prediction learning and mixed viewpoints for unsupervised-domain adaptation person re-identification on blockchain

In addition to the domain shift between different datasets, the diversity of pedestrian appearance (physical appearance and postures) caused by different camera views also affects the performance of person re-identification (re-ID). Since existing methods tend to extract the shared information of the same pedestrian across multiple images, the above diversity issue has not been effectively alleviated. In addition, while making full use of pedestrian image data and realizing its value, there are also risks of privacy leakage and data loss. Therefore, this paper proposes the mutual prediction learning (MPL) and mixed viewpoints for unsupervised domain adaptation (UDA) person re-ID on blockchain. This method enables the network to first obtain the ability of MPL on multi-view polymorphic features and further acquire the reasoning imagination to alleviate the ambiguity caused by morphological differences. In the process of MPL, the training samples are first divided into different groups and each group has two sets. Then the corresponding identity classifiers of every two sets are integrated and applied to the cross-prediction of polymorphic features. Finally, the joint distribution alignment of domain- and identity-level features is achieved. Furthermore, an adversarial mechanism of mixed viewpoints is proposed to improve the accuracy of identity matching. The domain-invariant salient features are extracted and fused with the polymorphic features obtained by global average pooling (GAP) after domain alignment. Thanks to blockchain technology, the pedestrian image data of the data owner is also protected. Comparative experimental results confirm the effectiveness of the proposed solution in person re-ID. The related source codes will be available at: https://github.com/lhf12278/MPL-MV.
Robotic pharmacies potential and limitations of artificial intelligence: a case study

This study evaluates the robotic pharmacies as one of the latest smart technologies adopted in the hospitals of the United Arab Emirates, an evolving global healthcare hub. Three government hospitals were investigated with the 'makes' of same robot pharmacy Omnicell German Co. and compared with the robot pharmacy named UCSF Medical Center pharmacy being used in San Francisco to investigate the differences if any between the two robots. The findings reveal robotic pharmacy results in the cost and error reduction, decrease in patient waiting time and increase in service delivery efficiency. Artificial intelligence (AI) can provide an opportunity for more significant benefits to the robotic pharmacy. The study suggests that it is essential to take substantive steps towards the enhancement of AI in robotic pharmacy, which the authorities in the UAE, known for its technological dynamism, may take due cognizance in the course of time.
How Effective is Spotted Hyena Optimizer for Training Multilayer Perceptrons

This paper focuses on training multilayer perceptron (MLP) using a recently proposed meta-heuristic algorithm termed as Spotted Hyena Optimizer (SHO). To test the efficacy of the said algorithm fifteen standard datasets are used. At the same time the result of the proposed method is examined by some popular heuristic training algorithms such as Differential Evolution (DE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA) and Grey Wolf Optimization algorithm (GWO). Final result shows that SHO successfully avoids the local minima trap problem, simultaneously showing higher accuracy in classification as compared to other meta-heuristic methods. The statistical significance of the proposed SHO-MLP has been verified by deploying the Friedman &amp; Holm’s test. It has been observed that the SHO-MLP is giving promisingly better result than other compared method for training MLP.
LQ45 Stock Index Prediction using k-Nearest Neighbors Regression

The capital market is an organized financial system consisting of commercial banks, intermediary institutions in the financial sector and all outstanding securities. One of the benefits of the capital market is creating opportunities for the community to participate in economic activities, especially in investing. In daily stock trading activities, stock prices tend to have fluctuated. Therefore, stock price prediction is needed to help investors make decisions when they want to buy or sell their shares. One asset for investment is shares. One of the stock price indices that attracts many investors is the LQ45 stock index on the Indonesian stock exchange. One of the algorithms that can be used to predict is the k-Nearest Neighbors (kNN) algorithm. In the previous study, kNN had a higher accuracy than the moving average method of 14.7%. This study uses kNN regression method because it predicts numerical data. The results of the research in making the LQ45 stock index prediction application have been successfully built. The highest accuracy achieved reaches 91.81% by WSKT share.
Zero-Shot Learning to Detect Object Instances from Unknown Image Sources

Inspired by the human capability, zero-shot learning research has been approaches to detect object instances from unknown sources. Human brains are capable of making decisions for any unknown object from a given attributes. They can make relation between the unknown and unseen object just by having the description of them. If human brain is given enough attributes, they can assess about the object. Zero-shot learning aims to reach this capability of human brain. First, we consider a machine to detect unknown object with training examples. Zero-shot learning approaches to do this type of object detection where there are no training examples. Through the process, a machine can detect object instances from images without any training examples. In this paper, we develop a dynamic system which will be able to detect object instances from an image that it never seen before. Which means during the testing process the test image will completely unknown from trained images. The system will be able to detect completely unseen objects from some bounded region of given images using zero shot learning approach. We approach to detect object instances from unknown class, because there are lots of growing category in the world and the new categories are always emerging. It is not possible to limit objects in this fast-forwarding world. Again, collecting, annotating and training each category is impossible. So, zero-shot learning will reduce the complexity to detect unknown objects.
Advanced Security Framework for Internet of Things (IoT)

The stimulus to carry out this research was to identify and propose a secure framework for the Internet of Things (IoT). Due to the massive accessibility and interconnection of IoT devices, systems are at risk of being exploited by hackers. Therefore, there is a need to find an advanced security framework that covers data security, data confidentiality, and data integrity issues. The study uses a systematic literature review (SLR) technique and complete substantive literature is reviewed to find out the constructs and themes in the existing literature. We performed it in four steps, which were inclusion, eligibility, screening, and identification. We reviewed around 568 articles from well-reputable journals, and after exclusion, 260 articles and 54 reports were analyzed. We performed an analysis using MAXQDA in which the nodes and themes were first identified. After the classification, a qualitative model was generated using MAXQDA. The proposed model is supported by the literature so it will be useful for the IT managers, developers, and the users of IoT.