Reviewerly/exHarmony huggingface dataset:
MUSE-RNN: A Multilayer Self-Evolving Recurrent Neural Network for Data Stream Classification

In this paper, we propose MUSE-RNN, a multilayer self-evolving recurrent neural network model for real-time classification of streaming data. Unlike the existing approaches, MUSE-RNN offers special treatment towards capturing temporal aspects of data stream through its novel recurrent learning approach based on the teacher forcing policy. Novelties here are twofold. First, in contrast to the traditional RNN models, MUSE-RNN has intrinsic ability to self-adjust its capacity by growing and pruning hidden nodes as well as layers, to handle the ever-changing characteristics of data stream. Second, MUSERNN adopts a unique scoring-based layer adaptation mechanism, which makes it capable of recalling prior tasks, with minimum exploitation of network parameters. The performance of MUSERNN is evaluated in comparison with a number of state-of-theart techniques, using seven popular data streams and continual learning problems under prequential test-then-train protocol. Experimental results demonstrate the effectiveness of MUSERNN in stream classification scenario.
Hierarchical Granular Computing-Based Model and Its Reinforcement Structural Learning for Construction of Long-Term Prediction Intervals

As one of the most essential sources of energy, byproduct gas plays a pivotal role in the steel industry, for which the flow tendency is generally regarded as the guidance for planning and scheduling in real production. In order to obtain the numeric estimation along with its reliability, the construction of prediction intervals (PIs) is highly demanded by any practical applications as well as being long term for providing more information on future trends. Bearing this in mind, in this article, a hierarchical granular computing (HGrC)-based model is established for constructing long-term PIs, in which probabilistic modeling gives rise to a long horizon of numeric prediction, and the deployment of information granularities hierarchically extends the result to be interval-valued format. Considering that the structure of this model has a direct impact on its performance, Monte-Carlo search with a policy gradient technique is then applied for reinforcement structure learning. Compared with the existing methods, the size (length) of the granules in the proposed approach is unequal so that it becomes effective for not only periodic but also nonperiodic data. Furthermore, with the use of parallel strategy, the efficiency can be also guaranteed for real-world applications. The experimental results demonstrate that the proposed method is superior to other commonly encountered techniques, and the stability of the structure learning process behaves better when compared with other reinforcement learning approaches.
Individualized AI Tutor Based on Developmental Learning Networks

In recent years, in the field of education technology, artificial intelligence tutors have come to be expected to provide individualized educational services to help learners achieve high levels of academic success. To this end, AI tutors need to be able to understand the current status and preferences of a learner and then suggest appropriate learning contents accordingly. However, it is challenging to monitor learner status and preferences continually and to recommend appropriate educational services. In this paper, we propose an individualized AI tutor as an integrated system of three developmental learning networks (DLNs) by extending a deep adaptive resonance theory (Deep ART) network, a neural network capable of incremental learning. Specifically, the learner status DLN is able to easily add new input channels about learner status without disrupting existing classifiers. The learner preference DLN is to categorize learner preferences based on frequency as well as sequence of events. The learner experience DLN is updated to immediately reflect alteration of the educational effectiveness in the current classification. Our AI tutor is currently embedded in a commercialized mobile application for teaching the Korean language to children. Experimental results show that the AI tutor application efficiently helps children learn the Korean language.
A Hybrid Formal Verification System in Coq for Ensuring the Reliability and Security of Ethereum-Based Service Smart Contracts

This paper reports a formal symbolic process virtual machine (FSPVM) denoted as FSPVM-E for verifying the reliability and security of Ethereum-based services at the source code level of smart contracts.A Coq proof assistant is employed for programming the system and for proving its correctness.The current version of FSPVM-E adopts execution-verification isomorphism, which is an application extension of Curry-Howard isomorphism, as its fundamental theoretical framework to combine symbolic execution and higher-order logic theorem proving.The four primary components of FSPVM-E include a general, extensible, and reusable formal memory framework, an extensible and universal formal intermediate programming language denoted as Lolisa, which is a large subset of the Solidity programming language using generalized algebraic datatypes, the corresponding formally verified interpreter of Lolisa, denoted as FEther, and assistant tools and libraries.The self-correctness of all components is certified in Coq.FSPVM-E supports the ERC20 token standard, and can automatically and symbolically execute Ethereum-based smart contracts, scan their standard vulnerabilities, and verify their reliability and security properties with Hoare-style logic in Coq.
Accountability of NLP Tools in Text Summarization for Indian Languages

In the era of digital world, online information is growing exponentially.It leads to emergence of inconvenient searching of relevant information in relevant time.In this regard, automatic text summarizer proves to be a good tool.It helps in creating a brief and meaningful form of the given text using natural language tool kit so that users can access the information in quick manner.Today, a lot of summarization tools are available for rich resource languages such as English.But, it seems difficult to summarize the text for Indian languages (low resource languages) due to limited availability of NLP tools and techniques for Indian languages.In this paper, we present a survey on existing text summarization methods and NLP tools for Indian languages.We also discuss about the issues associated with the Indian languages that are the bottlenecks for summarizing Indian language text.
Efficient and Secure Ciphertext-Policy Attribute-Based Encryption Without Pairing for Cloud-Assisted Smart Grid

Cloud-assisted smart grid has been broadly deployed to improve the economics, efficiency, sustainability, and reliability of electricity services.The new revolution of technology will also bring new challenges to data security, particularly data confidentiality.As a promising encryption method for supporting fine-grained access control, attribute-based encryption (ABE) is widely given attention by the academia and industry.However, most existing schemes suffer from the efficiency problem limiting its deployment for the cloud-assisted smart grid, especially for resource-limited users.To address this problem, this paper proposes two efficient pairing-free ciphertext-policy attribute-based schemes that eliminate the computation-intensive bilinear pairing operation.The basic scheme only uses some simple scalar multiplications based on elliptic curves, and the enhanced scheme provides an important usability feature, namely the users and attributes revocation.The security of the proposed schemes is reduced to the elliptic curve decisional Diffie-Hellman problem.Finally, the performance analysis and extensive experiments demonstrate that our proposed schemes are suitable for the cloud-assisted smart grid.
Studying the Association Between Bountysource Bounties and the Issue-Addressing Likelihood of GitHub Issue Reports

Due to the voluntary nature of open source software, it can be hard to find a developer to work on a particular task. For example, some issue reports may be too cumbersome and unexciting for someone to volunteer to do them, yet these issue reports may be of high priority to the success of a project. To provide an incentive for implementing such issue reports, one can propose a monetary reward, i.e., a bounty, to the developer who completes that particular task. In this paper, we study bounties in open source projects on GitHub to better understand how bounties can be leveraged to evolve such projects in terms of addressing issue reports. We investigated 5,445 bounties for GitHub projects. These bounties were proposed through the Bountysource platform with a total bounty value of $406,425. We find that 1) in general, the timing of proposing bounties is the most important factor that is associated with the likelihood of an issue being addressed. More specifically, issue reports are more likely to be addressed if they are for projects in which bounties are used more frequently and if they are proposed earlier. 2) The bounty value of an issue report is the most important factor that is associated with the issue-addressing likelihood in the projects in which no bounties were used before. 3) There is a risk of wasting money for backers who invest money on long-standing issue reports.
An Effective Model for SMS Spam Detection Using Content-based Features and Averaged Neural Network

In recent years, there has been considerable interest among people to use short message service (SMS) as one of the essential and straightforward communications services on mobile devices. The increased popularity of this service also increased the number of mobile devices attacks such as SMS spam messages. SMS spam messages constitute a real problem to mobile subscribers; this worries telecommunication service providers as it disturbs their customers and causes them to lose business. Therefore, in this paper, we proposed a novel machine learning method for detection of SMS spam messages. The proposed model contains two main stages: feature extraction and decision making. In the first stage, we have extracted relevant features from the dataset based on the characteristics of spam and legitimate messages to reduce the complexity and improve performance of the model. Then, an averaged neural network model was applied on extracted features to classify messages into either spam or legitimate classes. The method is evaluated in terms of accuracy and F-measure metrics on a real-world SMS dataset with over 5000 messages. Moreover, the achieved results were compared against three recently published works. Our results show that the proposed approach achieved successfully high detection rates in terms of F-measure and classification accuracy, compared with other considered researches.Moreover, the achieved results were compared against three recently published works. The results show that the proposed approach achieved high detection rate, which was successful in terms of the F-measure and classification accuracy compared with other considered researches.
A break-glass protocol based on ciphertext-policy attribute-based encryption to access medical records in the cloud

Abstract In emergency care, fast and efficient treatment is vital. The availability of Electronic Medical Records (EMR) allows healthcare professionals to access a patient’s data promptly, which facilitates the decision-making process and saves time by not repeating medical procedures. Unfortunately, the complete EMR of a patient is often not available during an emergency situation to all treatment teams. Cloud services emerge as a promising solution to this problem by allowing ubiquitous access to information. However, EMR storage and sharing through clouds raise several concerns about security and privacy. To this end, we propose a protocol through which all treatment teams involved in the emergency care can securely decrypt relevant data from the patient’s EMR and add new information about the patient’s status. Furthermore, our protocol ensures that treatment teams will only access the patient’s EMR for the period during which the patient is under their care. Finally, we present a formal security analysis of our protocol and some initial experimental results.
An End-to-End Task-Simplified and Anchor-Guided Deep Learning Framework for Image-Based Head Pose Estimation

Image-based Head Pose Estimation (HPE) from an arbitrary view is still challenging due to the complex imaging conditions as well as the intrinsic and extrinsic property of the faces.Different from existing HPE methods combining additional cues or tasks, this paper solves the HPE problem by relieving problem complexity.Our method integrates the deep Task-Simplification oriented Image Regularization (TSIR) module with the Anchor-Guided Pose Estimation (AGPE) module, and formulate the HPE problem into a unified end-to-end learning framework.In this paper, we define anchors as images that strictly obey the ''gravity rule in camera'', which follows the assumption that camera coordinate of the vertical axis should always be consistent with that of the local head coordinate.We formulate image pair as the regularized image produced by TSIR along with its anchor counterpart, both of which are fed into the AGPE module for estimating fine-grained head poses.This paper also proposes an Anchor-Guided Pairwise Loss (AGPL), which describes the interdependent relevance of poses between each pair of images.The proposed method is evaluated and validated with sufficient experiments which show its effectiveness.Comprehensive experiments show that our approach outperforms the state-of-the-art image-based methods on both indoor and outdoor datasets.
Predicting Inpatient Medication Orders From Electronic Health Record Data

In a general inpatient population, we predicted patient‐specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train two machine‐learning models: A deep learning sequence model and a logistic regression model. Both were compared with a baseline that ranked the most frequently ordered medications based on a patient’s discharge hospital service and amount of time since admission. Models were trained to predict from 990 possible medications at the time of order entry. Fifty‐five percent of medications ordered by physicians were ranked in the sequence model’s top‐10 predictions (logistic model: 49%) and 75% ranked in the top‐25 (logistic model: 69%). Ninety‐three percent of the sequence model’s top‐10 prediction sets contained at least one medication that physicians ordered within the next day. These findings demonstrate that medication orders can be predicted from information present in the EHR.
Cross Reality Spaces in Engineering Education – Online Laboratories for Supporting International Student Collaboration in Merging Realities

&lt;span&gt;This paper discusses online laboratories as cross reality spaces in education. Cross Reality (also XR) as a term is borrowed from the field of gaming and broadly describes the integration of immersive, augmented, mixed, and virtual reality technology within physical reality. Online laboratories form an ideal example, as different realities (the real hands-on world in real labs, the virtual world in simulated labs or even a mixture of both in context with remote labs) are merged. The connection between different realities in laboratory education and their relation to engineering education contexts are discussed by describing a three-dimensional matrix for categorizing (online) laboratories in teaching and displaying current research results in this area. On basis of this, a scholarly research study is discussed, which is making use of a remote lab in mechanical engineering education for an international student body. In this study, the lab and its application are evaluated from the learner, the system, and the course perspective. These three perspectives offer a holistic view of the lab and the students’ learning. The study proofs a positive effect on the students learning experiences. The results also show which current needs and future potentials lie on the intersection of engineering education, internationalization, and digitalization in terms of collaborating and learning in Cross Reality Spaces.&lt;/span&gt;
Image restoration for synthetic aperture systems with a non-blind deconvolution algorithm via a deep convolutional neural network

Optical synthetic aperture imaging systems, which consist of in-phase circular sub-mirrors, can greatly improve the spatial resolution of a space telescope. Due to the sub-mirrors’ dispersion and sparsity, the modulation transfer function is decreased significantly compared to a fully filled aperture system, which causes obvious blurring and loss of contrast in the collected image. Image restoration is the key to get the ideal clear image. In this paper, an appropriative non-blind deconvolution algorithm for image restoration of optical synthetic aperture systems is proposed. A synthetic aperture convolutional neural network (CNN) is trained as a denoiser prior to restoring the image. By improving the half-quadratic splitting algorithm, the image restoration process is divided into two subproblems: deconvolution and denoising. The CNN is able to remove noise in the gradient domain and the learned gradients are then used to guide the image deconvolution step. Compared with several conventional algorithms, scores of evaluation indexes of the proposed method are the highest. When the signal to noise ratio is 40 dB, the average peak signal to noise ratio is raised from 23.7 dB of the degraded images to 30.8 dB of the restored images. The structural similarity index of the results is increased from 0.78 to 0.93. Both quantitative and qualitative evaluations demonstrate that the proposed method is effective.
Combinations of Reusable Abstract Domains for a Multilingual Static Analyzer

We discuss the design of Mopsa, an ongoing effort to design a novel semantic static analyzer by abstract interpretation. Mopsa strives to achieve a high degree of modularity and extensibility by considering value abstractions for numeric, pointer, objects, arrays, etc. as well as syntax-driven iterators and control-flow abstractions uniformly as domain modules, which offer a unified signature and loose coupling, so that they can be combined and reused at will. Moreover, domains can dynamically rewrite expressions, which simplifies the design of relational abstractions, encourages a design based on layered semantics, and enables domain reuse across different analyses and different languages. We present preliminary applications of Mopsa analyzing simple programs in subsets of the C and Python programming languages, checking them for run-time errors and uncaught exceptions.
Quantum State Optimization and Computational Pathway Evaluation for Gate-Model Quantum Computers

A computational problem fed into a gate-model quantum computer identifies an objective function with a particular computational pathway (objective function connectivity). The solution of the computational problem involves identifying a target objective function value that is the subject to be reached. A bottleneck in a gate-model quantum computer is the requirement of several rounds of quantum state preparations, high-cost run sequences, and multiple rounds of measurements to determine a target (optimal) state of the quantum computer that achieves the target objective function value. Here, we define a method for optimal quantum state determination and computational path evaluation for gate-model quantum computers. We prove a state determination method that finds a target system state for a quantum computer at a given target objective function value. The computational pathway evaluation procedure sets the connectivity of the objective function in the target system state on a fixed hardware architecture of the quantum computer. The proposed solution evolves the target system state without requiring the preparation of intermediate states between the initial and target states of the quantum computer. Our method avoids high-cost system state preparations and expensive running procedures and measurement apparatuses in gate-model quantum computers. The results are convenient for gate-model quantum computations and the near-term quantum devices of the quantum Internet.
Profitability of technical trading rules among cryptocurrencies with privacy function

This paper studies simple moving average trading strategies employing daily price data on the ten most-traded cryptocurrencies that exhibit the ‘privacy function’. Investigating the 2016–2018 period, our results indicate a variable moving average strategy is successful only when applied to Dash generating returns of 14.6%−18.25% p.a. in excess of the simple buy-and-hold benchmark strategy. However, when applying our technical trading rules to the entire set of ten privacy coins shows that, on an aggregate level, simple technical trading rules do not generate positive returns in excess of a buy-and-hold strategy.
SERU: A cascaded SE‐ResNeXT U‐Net for kidney and tumor segmentation

Summary According to statistics, kidney cancer is one of the most deadly cancer. An early and accurate diagnosis can significantly increase the cure rate. Accurate segmentation of kidney tumors in CT images plays an important role in kidney cancer diagnosis. However, it is a challenging task due to many different aspects, such as low contrast, irregular motion, diverse shapes, and sizes. For solving this issue, we proposed a SE‐R esNeXT U ‐Net (SERU) model in this study, which takes the advantages of SE‐Net, ResNeXT and U‐Net. Besides, we implement our model in a coarse‐to‐fine manner to utilize the information of context and key slices from the left and right kidney. We train and test our method on the KiTS19 Challenge. Experimental results demonstrate that our model can achieve promising results.
A Bayesian perspective of statistical machine learning for big data

Statistical Machine Learning (SML) refers to a body of algorithms and methods by which computers are allowed to discover important features of input data sets which are often very large in size. The very task of feature discovery from data is essentially the meaning of the keyword 'learning' in SML. Theoretical justifications for the effectiveness of the SML algorithms are underpinned by sound principles from different disciplines, such as Computer Science and Statistics. The theoretical underpinnings particularly justified by statistical inference methods are together termed as statistical learning theory. This paper provides a review of SML from a Bayesian decision theoretic point of view—where we argue that many SML techniques are closely connected to making inference by using the so called Bayesian paradigm. We discuss many important SML techniques such as supervised and unsupervised learning, deep learning, online learning and Gaussian processes especially in the context of very large data sets where these are often employed. We present a dictionary which maps the key concepts of SML from Computer Science and Statistics. We illustrate the SML techniques with three moderately large data sets where we also discuss many practical implementation issues. Thus the review is especially targeted at statisticians and computer scientists who are aspiring to understand and apply SML for moderately large to big data sets.