Reviewerly/exHarmony huggingface dataset:
Chance-Constrained Multilayered Sampling-Based Path Planning for Temporal Logic-Based Missions

This article introduces a robust and safe path planning algorithm in order to satisfy mission requirements specified in linear temporal logic (LTL). When a path is planned to accomplish a mission, it is possible for a robot to fail to complete the mission or collide with obstacles due to noises and disturbances in the system. Hence, we need to find a robust path against possible disturbances. We introduce a robust path planning algorithm, which maximizes the probability of success in accomplishing a given mission by considering disturbances, while minimizing the moving distance of a robot. The proposed method can guarantee the safety of the planned trajectory by incorporating an LTL formula and chance constraints in a hierarchical manner. A high-level planner generates a discrete plan satisfying the mission requirements specified in LTL. A low-level planner builds a sampling-based rapidly exploring random tree search tree to minimize both the mission failure probability and the moving distance while guaranteeing the probability of collision with obstacles to be below a specified threshold. We have analyzed properties of the proposed algorithm theoretically and validated the robustness and safety of paths generated by the algorithm in simulation and experiments using a quadrotor.
Learning to combine classifiers outputs with the transformer for text classification

Text classification is a fairly explored task that has allowed dealing with a considerable amount of problems. However, one of its main difficulties is to conduct a learning process in data with class imbalance, i.e., datasets with only a few examples in some classes, which often represent the most interesting cases for the task. In this context, text classifiers overfit some particular classes, showing poor performance. To address this problem, we propose a scheme that combines the outputs of different classifiers, coding them in the encoder of a transformer. Feeding also a BERT encoding of each example, the encoder learns a joint representation of the text and the outputs of the classifiers. These encodings are used to train a new text classifier. Since the transformer is a highly complex model, we introduce a data augmentation technique, which allows the representation learning task to be driven without over-fitting the encoding to a particular class. The data augmentation technique also allows for producing a balanced dataset. The combination of both methods, representation learning, and data augmentation, allows improving the performance of trained classifiers. Results in benchmark data for two text classification tasks (stance classification and online harassment detection) show that the proposed scheme outperforms all of its direct competitors.
Toward Design Archetypes for Conversational Agent Personality

Conversational agents (CAs), often referred to as chatbots, are being widely deployed within existing commercial frameworks and online service websites. As society moves further into incorporating data rich systems, like the internet of things (IoT), into daily life, it is expected that conversational agents will take on an increasingly important role to help users manage these complex systems. In this, the concept of personality is becoming increasingly important, as we seek for more human-friendly ways to interact with these CAs. In this work a conceptual framework is proposed that considers how existing standard psychological and persona models could be mapped to different kinds of CA functionality outside of strictly dialogue. As CAs become more diverse in their abilities, and more integrated with different kinds of systems, it is important to consider how function can be impacted by the design of agent personality, whether intentionally designed or not. Based on this framework, derived archetype classes of CAs are presented as starting points that can hopefully aid designers, developers, and the curious, into thinking about how to work toward better CA personality development.
Smart Scribbles for Image Matting

Image matting is an ill-posed problem that usually requires additional user input, such as trimaps or scribbles. Drawing a fne trimap requires a large amount of user effort, while using scribbles can hardly obtain satisfactory alpha mattes for non-professional users. Some recent deep learning-based matting networks rely on large-scale composite datasets for training to improve performance, resulting in the occasional appearance of obvious artifacts when processing natural images. In this article, we explore the intrinsic relationship between user input and alpha mattes and strike a balance between user effort and the quality of alpha mattes. In particular, we propose an interactive framework, referred to as smart scribbles, to guide users to draw few scribbles on the input images to produce high-quality alpha mattes. It frst infers the most informative regions of an image for drawing scribbles to indicate different categories (foreground, background, or unknown) and then spreads these scribbles (i.e., the category labels) to the rest of the image via our well-designed two-phase propagation. Both neighboring low-level afnities and high-level semantic features are considered during the propagation process. Our method can be optimized without large-scale matting datasets and exhibits more universality in real situations. Extensive experiments demonstrate that smart scribbles can produce more accurate alpha mattes with reduced additional input, compared to the state-of-the-art matting methods.
Dominating OP Returns: The Impact of Omni and Veriblock on Bitcoin

Abstract Bitcoin has always been used to store arbitrary data, particularly since Bitcoin Core developers added a dedicated method for data storage in 2014: the OP Return operator. This paper provides an in-depth analysis of all OP Return transactions published on Bitcoin between September 14, 2018, and December 31, 2019. The 32.4 million OP Return transactions (22% of all Bitcoin transactions) published during this period added 10 GB to the blockchain’s size. Almost all OP Return transactions can be attributed to one of 37 blockchain services. The two dominant services are Veriblock (58% of OP Return transactions) and Omni/Tether (40%). Veriblock transactions pay only 14% of the average transaction fee, partly because most of them are submitted during times when overall activity on Bitcoin is low. Omni transactions, on the other hand, pay more than twice the average transaction fee and therefore compete with regular Bitcoin transactions for inclusion in new blocks.
Deep Learning in Age-invariant Face Recognition: A Comparative Study

Abstract This paper presents comparative evaluation of an application of deep convolutional neural networks (dCNNs) to age invariant face recognition. To this end, we use four distinct dCNN models, the AlexNet, VGGNet, GoogLeNet and ResNet. We assess their performance to recognize face images across aging variations, firstly by fine-tuning the models and secondly using them as face feature extractor. We also suggest a novel synthesized aging augmentation technique suitable for age-invariant face recognition using dCNNs. The face recognition experiments are conducted on three challenging FG-NET, MORPH and LAG aging datasets, and results are benchmarked with a simple CNN. The comparative study allows us to answer (i) when and why transfer learning or feature extraction strategies are useful in age-invariant face recognition scenarios, (ii) the potential of aging synthesized augmentation to increase accuracy and (iii) the choice of appropriate feature normalization and distance metrics to be used with deeply learned features. The extensive experiments, and valuable insights presented in this study can be extended to the design of effective age-invariant face recognition algorithms.
Post-hoc Interpretation of Clinical Pathways Clustering using Bayesian Inference

This study is dedicated to the domain of explainable artificial intelligence. We propose an approach to machine learning clustering results interpretation and apply it to clinical clusters interpretation. We use Bayesian inference to the post-hoc interpretation of clustering provided by K-means algorithm. We investigate what are the differences and similarities between clusters comparing posterior distributions of features. The proposed approach is not model-specific and may be used for interpretation of any clustering algorithm. Finally, we compare the results with a medical expert interpretation and analyze the differences and similarities.
The Design and Implementation of a Computerized Adaptive Testing System for School Mathematics Based on Item Response Theory

On the contrary to classical school test, the computerized adaptive test (CAT) based on IRT (Item Response Theory) stepwise estimates every examinee’ response to every question item and provides the examinee with the next corresponding and appropriate question item. The online intelligent tutoring system “Lexue 100” has been used by more than 100,000 junior high school students in China in the past 7 years. It has more than 70,000 sets of mathematics quizzes designed for different units in the various textbooks. Each of about 68,600 quizzes among the 70,000 sets has been used by more than 100 students. We analyzed those quizzes, calibrated the original 740,910 IRT parameters for the questions composing the quizzes, recalculated the parameters by using linking algorithms for 194,205 question items. Based on this large question bank with pre-calibrated parameters for every question, we designed and implemented a computerized adaptive test system for school mathematics.
CAPTCHAs Are Still in Danger: An Efficient Scheme to Bypass Adversarial CAPTCHAs

Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a challenge that is used to distinguish between humans and robots. However, attackers bypass the CAPTCHA schemes using deep learning (DL) based solvers. To defeat the attackers, CAPTCHA defense methods utilizing adversarial examples that are known for fooling deep learning models have been proposed. In this paper, we propose an efficient CAPTCHA solver that periodically retrain the solver model when its accuracy drops. The proposed method uses incremental learning that requires a small amount of data while achieving high accuracy. We demonstrate that the proposed solver bypasses the existing defense methods based on a text-based CAPTCHA scheme and an image-based CAPTCHA scheme.
Deep Siamese Cross-Residual Learning for Robust Visual Tracking

The sixth-generation (6G) wireless technology contributes to the establishment of the Internet of Things (IoT). Recently, the IoT has become popular because of its smart architectures and various applications. Among these applications, intelligent urban surveillance systems for smart cities are becoming more and more important. Therefore, designing a robust visual tracking method has become an urgent task. Deep Siamese convolutional neural networks have been applied to visual tracking recently because of their advantageous abilities to learn a matching function between the template and the target candidate. Unlike traditional Siamese networks, which separately treat the two branches, we propose deep Siamese cross-residual learning to entangle the two branches from the beginning to the end of the Siamese network. This strategy can make the two branches exchange instance-specific information at different nodes of the network and learn a more compact representation of the target. In addition, we propose a combined loss function, which consists of two complementary tasks. One task is to learn a matching function directly and the other one is to learn a classification function. Moreover, our model does not need to load any pretrained weights and is trained with limited sequences from scratch. Plenty of experiments show that our tracker performs favorably against many state-of-the-art tracking methods.
Distance learning during coronavirus: problems and solutions

This article concerns distance learning in modern conditions, its principles of operation, advantages and disadvantages, problems of students learning remotely, and also tells about our own experience of overcoming problems associated with the distance learning organization. In this article the difference between external programs and distance learning is being given, with the following describing features of the process. The authors cover the issues of organizing a proper distance learning process and analyze the experience of telecommuting and problems revealed by coronavirus restrictions. Results of the research suggest the structure of distant courses and discussion of distance learning strengths and weaknesses that teachers all around Russia faced to. Also attention is paid to analyses of the best suitable tasks that perform all goals of educational process and the problems that were discovered are being suggested solutions and ways to eliminate them.
Medical Image Segmentation Based on 3D U-net

For medical image processing, as the target area of the tumor lesions is small, and the boundaries of the organs are blurred, so the segmentation of the medical images is difficult. In the original 3D U-net model, feature extraction is performed on the interest image region by increasing the channel attention mechanism, so that the model keep a watchful eye on key region before segmentation. Test results indicate that the improved model has significantly improved segmentation accuracy relative to the original 3D U-net model and is a valid image segmentation model.
Healthcare Data Management by Using Blockchain Technology

Electronic healthcare solutions permit interconnecting hospitals and clinics to enable sharing of electronic medical records according to interoperability and legal standards. However, healthcare record data is siloed across hospitals and data sharing processes are unsuccessful in providing accountable audit of the data. Blockchain technology has been successfully applied to support the management of distributed data. Its decentralisation and immutability features can underpin the development of next-generation services for health data sharing. This chapter positions recent blockchain research outcomes within the healthcare legal and technical standard frameworks (e.g. IHE and FHIR). It then presents how blockchain can be applied to healthcare data sharing practices in order to enhance trust with automated provenance tracking and accountable credential verification. The data sharing practices related to international organ transplant processes are used as motivating and application case study.
Blockchain: A Solution for Improved Traceability with Reduced Counterfeits in Supply Chain of Drugs

The Pharmaceutical supply chain needs a huge attention while developing technology based solutions as there is a great need of surveillance in production and distribution of fake, substandard, counterfeit and grey market medicines which account for hundreds of billions per year across the globe. Counterfeit drugs have captured the global markets over the period and affecting and risking many lives in a large [7], solutions to address these have become inevitable. Studies have uncovered a lot of pharmaceutical products, medical devices and biologics have been subject to counterfeiting the entire drug supply chain which is susceptible to lots of pharmaceutical crime. The international growth of the pharmaceutical market and a rise in global drug sales leading to emergence of various forms of technology and digital health platforms has given rise to many supply chain solutions. In addition to pharmaceutical frauds, improving security and addressing vulnerabilities of the medical commodities is a priority area. This leads to the formation of many technology solutions aimed at making supply chains secure, trustable and removing counterfeit drugs from it by applying various different methods and approaches. Our system proposes to leverage blockchain technology to make the supply chain of drugs transparent to increase trust among actors and provide security, authenticity and traceability with the help of IoT.
Assessing GAN-based approaches for generative modeling of crime text reports

Analysis and modeling of crime text report data has important applications, including refinement of crime classifications, clustering of documents, and feature extraction for spatio-temporal forecasts. Having better neural network representations of crime text data may facilitate all of these tasks. This paper evaluates the ability of generative adversarial network models to represent crime text data and generate realistic crime reports. We compare four state of the art GAN algorithms in terms of quantitative metrics such as coherence, embedding similarity, negative log-likelihood, and qualitatively based on inspection of generated text. We discuss current challenges with crime text representation and directions for future research.
Practical Over-Threshold Multi-Party Private Set Intersection

Over-Threshold Multi-Party Private Set Intersection (OT-MP-PSI) is the problem where several parties, each holding a set of elements, want to know which elements appear in at least t sets, for a certain threshold t, without revealing any information about elements that do not meet this threshold. This problem has many practical applications, but current solutions require a number of expensive operations exponential in t and thus are impractical.
Development and initial validation of Digital Age Teaching Scale (DATS) to assess application of ISTE Standards for Educators in K–12 education classrooms

(2020). Development and initial validation of Digital Age Teaching Scale (DATS) to assess application of ISTE Standards for Educators in K–12 education classrooms. Journal of Research on Technology in Education. Ahead of Print.
ACS-FIT: A Secure and Efficient Access Control Scheme for Fog-enabled IoT

The explosion of data generated by IoT devices encouraged the introduction of paradigm fog computing, which facilitates computation and analysis at the edge. Alongside fog, cloud computing co-exists for facilities such as massive storage, large processing capability, etc. However, storage and computation of data at different levels increase the risk of data security, which persuades the need for a proper access control scheme. Ciphertext-policy attribute-based encryption (CP-ABE) is a well-known cryptographic mechanism that provides data confidentiality and fine-grained access control. Unfortunately, the existing CP-ABE schemes are not well suited for the cloud-fog-IoT environment as they do not provide functionalities like key-escrow resistance, attribute update, attribute revocation, user revocation, and outsourcing of expensive operations with verifiable outsourced decryption, simultaneously in a single scheme. Therefore, this paper proposes a CP-ABE scheme named ACS-FIT, which supports key-escrow resistance, attribute update, user revocation, attribute revocation, and outsourcing of expensive operations with verifiable outsourced decryption functionalities altogether. The scheme is efficient as the expensive encryption and decryption operations are outsourced to fog nodes leaving only a small and constant amount of computation for the IoT devices. Additionally, the task of attribute update and revocation is also outsourced to a third party. The cost incurred during attribute update and revocation are also efficient as only those components are updated which are associated with the affected attributes. Meanwhile, the user holds a constant size key which remains unchanged during any update. The security analysis proves that the proposed scheme is secure against Chosen-Plaintext Attack under Decisional Bilinear Diffie-Hellman assumption. The performance analysis shows that the proposed scheme is efficient and suitable for IoT devices.
Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process

A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy saving and its prerequisite is a time-series prediction of carbon efficiency. The existing carbon efficiency models usually have a complex structure, leading to a time-consuming training process. In addition, a complete retraining process will be encountered if the models are inaccurate or data change. Analyzing the complex characteristics of the sintering process, we develop an original prediction framework, that is, a weighted kernel-based fuzzy C-means (WKFCM)-based broad learning model (BLM), to achieve fast and effective carbon efficiency modeling. First, sintering parameters affecting carbon efficiency are determined, following the sintering process mechanism. Next, WKFCM clustering is first presented for the identification of multiple operating conditions to better reflect the system dynamics of this process. Then, the BLM is built under each operating condition. Finally, a nearest neighbor criterion is used to determine which BLM is invoked for the time-series prediction of carbon efficiency. Experimental results using actual run data exhibit that, compared with other prediction models, the developed model can more accurately and efficiently achieve the time-series prediction of carbon efficiency. Furthermore, the developed model can also be used for the efficient and effective modeling of other industrial processes due to its flexible structure.
Multimodal In-bed Pose and Shape Estimation under the Blankets

Advancing technology to monitor our bodies and behavior while sleeping and resting are essential for healthcare. However, keen challenges arise from our tendency to rest under blankets. We present a multimodal approach to uncover the subjects and view bodies at rest without the blankets obscuring the view. For this, we introduce a channel-based fusion scheme to effectively fuse different modalities in a way that best leverages the knowledge captured by the multimodal sensors, including visual- and non-visual-based. The channel-based fusion scheme enhances the model's flexibility in the input at inference: one-to-many input modalities required at test time. Nonetheless, multimodal data or not, detecting humans at rest in bed is still a challenge due to the extreme occlusion when covered by a blanket. To mitigate the negative effects of blanket occlusion, we use an attention-based reconstruction module to explicitly reduce the uncertainty of occluded parts by generating uncovered modalities, which further update the current estimation via a cyclic fashion. Extensive experiments validate the proposed model's superiority over others.