Reviewerly/exHarmony huggingface dataset:
FSTR: Funds Skewness Aware Transaction Routing for Payment Channel Networks

Payment channel is an effective and popular technique to improve the scalability and throughput of blockchains by transferring transactions from on-chain to off-chain. Multiple payment channels can constitute a payment network and realize transaction execution via multi-hop paths. How to find a feasible and efficient transaction path, i.e., transaction routing, is a key issue in payment channel networks, and different solutions have been proposed. However, the problem of funds skewness, which may cause routing failures, has been largely ignored in existing routing algorithms. In this work, we design FSTR, a routing algorithm that attempts to route transactions using a funds skewness based path selection scheme so as to reduce funds skewness and increase transaction success probability. To evaluate the performance of FSTR, we conduct experiments using the real-world dataset of Ripple. The experiment results show that FSTR outperforms existing routing algorithms, in terms of success ratio, delay, and overhead.
Viva la Revolucion?

Despite a decades-long steady flow of technology innovations, most higher education instructors have consistently resisted moving their face-to-face lectures online. Examines the extent to which the COVID-19 pandemic will finally bring about the revolution/evolution to online teaching that educational technologists have promoted for many years.
The role of augmented reality in air accident investigation and practitioner training

The aim of this paper is to describe the application of digital visualization tools to assist air accident investigators, including both their investigation activities and as a training resource. Augmented Reality technology is used to re-create a real aircraft crash scene, both in terms of wreckage distribution and features of the surroundings, in a full-scale 3D representation. A case study shows both potentials and limitations of the approach, and recommendations on how to improve the methodology are also proposed. Overall, it is concluded that Augmented Reality has achieved a maturity stage sufficient to consider it as an effective tool for training of air accident investigators and, to some extent, to support the investigation process itself, although more developments are required to address some current limitations and fully exploit the capabilities of this technology.
Interpretable Personalized Knowledge Tracing and Next Learning Activity Recommendation

Online learning systems that provide actionable and personalized guidance can help learners make better decisions during learning. Bayesian Knowledge Tracing (BKT) extensions and deep learning based approaches have demonstrated improved mastery prediction accuracy compared to the basic BKT model; however, neither set of models provides actionable guidance on learning activities beyond mastery prediction. We propose a novel framework for personalized knowledge tracing with attention mechanism. Our proposed framework incorporates auxiliary learner attributes into knowledge tracing and interprets mastery prediction with the learning attributes. The proposed approach can also provide personalized next best learning activity recommendations. We demonstrate that the accuracy of the proposed approach in mastery prediction is slightly higher compared to deep learning based approaches and that the proposed approach can provide personalized next best learning activity recommendation.
A Taxonomy of ML for Systems Problems

Machine learning has the potential to significantly improve systems, but only under certain conditions. We describe a taxonomy to help identify whether or not machine learning should be applied to particular systems problems, and which approaches are most promising. We believe that this taxonomy can help practitioners and researchers decide how to most effectively use machine learning in their systems, and provide the community with a framework and vocabulary to discuss different approaches for applying machine learning in systems.
AutoML for Predictive Maintenance: One Tool to RUL Them All

Automated machine learning (AutoML) deals with the automatic composition and configuration of machine learning pipelines, including the selection and parametrization of preprocessors and learning algorithms. While recent work in this area has shown impressive results, existing approaches are essentially limited to standard problem classes such as classification and regression. In parallel, research in the field of predictive maintenance, particularly remaining useful lifetime (RUL) estimation, has received increasing attention, due to its practical relevance and potential to reduce unplanned downtime in industrial plants. However, applying existing AutoML methods to RUL estimation is non-trivial, as in this domain, one has to deal with varying-length multivariate time series data. Furthermore, the data often directly originates from real-world scenarios or simulations, and hence requires extensive preprocessing. In this work, we present ML-Plan-RUL, an adaptation of the AutoML tool ML-Plan to the problem of RUL estimation. To the best of our knowledge, it is the first tool specifically tailored towards automated RUL estimation, combining feature engineering, algorithm selection, and hyperparameter optimization into an end-to-end approach. First promising experimental results demonstrate the efficacy of ML-Plan-RUL.
Fuzzy logic web services for real-time fall detection using wearable accelerometer and gyroscope sensors

The fall of the elderly population is a significant source of serious illness. Various wearable fall warning devices have been created recently to ensure older people's health. However, most of these devices are dependent on local data processing. This paper presents a new algorithm used in wearable sensors to track a real-time fall effectively and focuses on fall detection via fuzzy-as-a-service based on IEEE 1855-2016, Java fuzzy markup language and service-oriented architecture. Fuzzy logic systems (FLSs) have revealed their capability in ambient intelligence (AmI) applications. However, FLS deployment requires committed and quasi-scalable hardware/software systems. Sharing FLSs capability as web services allows flexibility, transparency, load balancing, efficient allocation of resources and ultimately cost-effectiveness. In this study, wearable sensors (i.e., accelerometer and gyroscope) that stimulate human activity monitoring using a rule-dependent FLS are demonstrated. Research findings exhibit that the proposed algorithm could easily differentiate between fall and non-fall occurrences with an accuracy, sensitivity and specificity of 90%, 88.89% and 91.67%, respectively.
A Work-Time Optimal Parallel Exhaustive Search Algorithm for the QUBO and the Ising model, with GPU implementation

The main contribution of this paper is to present a simple exhaustive search algorithm for the quadratic unconstraint binary optimization (QUBO) problem. It computes the values of the objective function E(X) for all n-bit input vector X in O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ) time. Since Ω(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ) time is necessary to output E(X) for all 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> vectors X, this sequential algorithm is optimal. We also present a work-time optimal parallel algorithm running O(log n) time using 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> / log n processors on the CREW-PRAM. This parallel algorithm is work optimal, because the total number of computational operations is equal to the running time of the optimal sequential algorithm. Also, it is time optimal because any parallel algorithm using any large number of processors takes at least Ω(log n) time for evaluating E(X). Further, we have implemented this parallel algorithm to run on the GPU. The experimental results on NVIDIA GeForce RTX 2080Ti GPU show that our GPU implementation runs more than 1000 times faster than the sequential algorithm running on Intel Corei7-8700K CPU(3.70GHz) for the QUBO with nbit vector whenever n ≥ 33. We also compare our exhaustive search parallel algorithm with several non-exhaustive search approaches for solving the QUBO including D-Wave 2000Q quantum annealer, simulated annealing algorithm, and Gurobi optimizer.
Substring-searchable attribute-based encryption and its application for IoT devices

With the development of big data and cloud computing technology, more and more users choose to store data on cloud servers, which brings much convenience to their management and use of data, and also the risk of data leakage. A common method to prevent data leakage is to encrypt the data before uploading it, but the traditional encryption method is often not conducive to data sharing and querying. In this paper, a new kind of Attribute-Based Encryption (ABE) scheme, which is called the Sub-String Searchable ABE (SSS-ABE) scheme, is proposed for the sharing and querying of the encrypted data. In the SSS-ABE scheme, the data owner encrypts the data under an access structure, and only the data user who satisfies the access structure can query and decrypt it. The data user can make a substring query on the whole ciphertext without setting keywords in advance. In addition, the outsourcing method is also introduced to reduce the local computation of the decryption process so that the outsourcing SSS-ABE scheme can be applied to IoT devices.
Discrete-Time Recurrent Neural Network for Solving Bound-Constrained Time-Varying Underdetermined Linear System

A typical recurrent neural network (RNN) has been developed for the online solution of a time-varying underdetermined linear system (TVULS) with bound constraint. This article provides a complete investigation by proposing a new discrete-time RNN (DTRNN) model to solve the bound-constrained TVULS. In particular, the continuous-time RNN (CTRNN) model in the existing literature for solving the bound-constrained TVULS is presented. The new DTRNN model is established and investigated by utilizing the Taylor difference formula to discretize the CTRNN model for determining the solution of the TVULS with bound constraint. Theoretical analysis and numerical results are provided to validate the effectiveness and superiority of the proposed DTRNN model. The model applicability is substantiated through the simulations on a PUMA560 robotic manipulator using the proposed DTRNN model.
Unified Hardware for High-Throughput AES-Based Authenticated Encryptions

This brief presents an efficient unified hardware for up-to-date authenticated encryptions with associated data (AEADs). Although some major AEADs share several fundamental components (e.g., advanced encryption standard (AES), block chaining, and XOR-Encryption-XOR (XEX) scheme), each AEAD is equipped with a unique mode of operation and/or sub-functions, which makes it difficult to integrate various AEADs in a hardware efficiently. The proposed hardware in this brief efficiently unifies the fundamental components to perform a set of AEADs with minimal area and power overheads. The proposed configurable datapath is adapted to a set of peripheral operations (e.g., block chaining and XEX), dictated by the given AEAD algorithm. In this brief, we also demonstrate the validity of the proposed hardware through an experimental design adapted to four AES-based AEADs. Consequently, we confirm that the proposed hardware can perform the four AEADs with quite smaller area than the sum of the each dedicated AEAD hardware, comparable throughput and power consumption. In addition, we confirmed that the proposed hardware is superior to software implementation on general-purpose processor in terms of both throughput and power consumption.
Modeling and Simulation of Entrepreneur Individual Based on Dynamic and Complex System Computing

There are disadvantages such as lack of resources and experience in college students’ entrepreneurship and the current research belong to the investigation and research, lacking the prediction simulation model research. Based on the theory of individual learning and the theory of complex systems, this study analyzes the mechanism of college students’ entrepreneurial process through dynamic learning theory, establishes the model of college students’ entrepreneurial subject, studies the different learning styles of college students, and discusses the influence of environmental dynamics on college students’ chance recognition. Through simulation and practice analysis, it is concluded that college students’ entrepreneurship is the process of learning and development of an individual and enterprise subject. Simultaneously, this study finds that the improvement of learning efficiency, learning channels and vision can effectively promote the development of enterprises, and can provide new ideas for the theoretical research of college students’ entrepreneurship.
A Tutorial Introduction to Quantum Circuit Programming in Dependently Typed Proto-Quipper

We introduce dependently typed Proto-Quipper, or Proto-Quipper-D for short, an experimental quantum circuit programming language with linear dependent types. We give several examples to illustrate how linear dependent types can help in the construction of correct quantum circuits. Specifically, we show how dependent types enable programming families of circuits, and how dependent types solve the problem of type-safe uncomputation of garbage qubits. We also discuss other language features along the way.
Intelligent Data Compression Policy for Hadoop Performance Optimization

Hadoop can deal with Zeta-level data, but the huge request for Disk I/O and Network utilization often appears as the limitations in Hadoop. During different job execution phases of Hadoop, the production of intermediate data is enormous, and transferring the same data over the network to the “reduce” process becomes an overload. In this paper, we discuss an intelligent data compression policy to overcome these limitations and to improve the performance of Hadoop. An intelligent compression policy is devised that starts compression at an apt time when all the map tasks are not completed in the job. This policy reduces the data transfer time in a network. The results are evaluated by running several benchmarks, which shows an improvement of about 8–15% during job execution and depicts the merits of the proposed compression policy.
Method for camera motion parameter estimation from a small number of corresponding points using quaternions

In this paper, we study methods for determining parameters of camera movement from a set of corresponding points. Unlike the traditional approach, the corresponding points in this paper are not used to determine the fundamental matrix, but directly to determine motion parameters. In addition, in this work, we use a multi-angle image formation model based on the representation of three-dimensional images and motion parameters in the form of quaternions. We propose method for determining motion parameters, including the selection of the most noise-free matches using the RANSAC method. The study presents results of an experiment on the “Middlebury” and “ETH3D” test kits, which contains a set of images with known values of the motion parameters. Using a program written in Python, a comparative experiment was conducted to evaluate the accuracy and reliability of the estimates obtained using the proposed method under conditions of a small number of corresponding points and a shallow depth of the scene. In the course of experimental studies, it was shown that under the above-described conditions, the reliability of parameter determination using the proposed method significantly exceeds the reliability of traditional methods for estimating motion parameters based on the calculation of the fundamental matrix.
Csrgan: Medical Image Super-Resolution Using A Generative Adversarial Network

Super-resolution medical image is vital for doctor’s diagnosis and quantitative analysis. In this work we propose a novel super-resolution generative adversarial network which combine conditional GAN (CGAN) and SRGAN, refer to it as CSRGAN to generate super-resolution (SR) images. We use differential geometric information including Jacobian determinant (JD) and curl vector (CV) as conditional inputs of both the discriminator and generator of SRGAN, which make full use of the idea of using GANs to learn a mapping from one manifold to another. In addition, we proposed a content loss motivated by CV feature information instead of VGG loss in SRGAN. We trained our model on a large-scale dataset CelebFaces Attributes, tested it on medical ultrasound image dataset. The experimental results show the method can achieve better performance in SR image generation with higher average peak signal-tonoise ratio (PSNR), Structural Similarity (SSIM) and Mean Opinion Score (MOS) compared with SRGAN.
Applications of Blockchain and Smart Contract for Sustainable Tourism Ecosystems

Blockchain is one of the most promising technologies for innovating business ecosystems in tourism. Blockchain enables secure, reliable, and efficient decentralized management systems without trusted third parties which are a core part of centralized management systems. Many tourism business activities have been doing under globalized and decentralized environments. Thus, It is necessary to do research regarding applications of blockchain technology to the tourism industry for building sustainable tourism business ecosystems. The purpose of the present paper is to examine application cases of blockchain and smart contract to the tourism industry and to identify the opportunity to innovate existing tourism business ecosystems. Stakeholders of tourism business ecosystems are able to innovate their ecosystems being conducive to their business using blockchain technology. Application cases of blockchain to tourism such as TripEcosys and TravelChain can be helpful to tourism business managers who seek new opportunities for innovative businesses.
Relationship Matters: Relation Guided Knowledge Transfer for Incremental Learning of Object Detectors

Standard deep learning based object detectors suffer from catastrophic forgetting, which results in performance degradation on old classes as new classes are incrementally added. There has been a few recent methods that attempt to address this problem by minimizing the discrepancy between individual object proposal responses for old classes from the original and the updated networks. Different from these methods, we introduce a novel approach that not only focuses on what knowledge to transfer but also how to effectively transfer for minimizing the effect of catastrophic forgetting in incremental learning of object detectors. Towards this, we first propose a proposal selection mechanism using ground truth objects from the new classes and then a relation guided transfer loss function that aims to preserve the relations of selected proposals between the base network and the new network trained on additional classes. Experiments on three standard datasets demonstrate the efficacy of our proposed approach over state-of-the-art methods.
Study of Block Chain and its Impact on Internet of Health Things (IoHT):Challenges and Opportunities

Blockchain has been making some vital progress in these days because of its secure and amiable structure. It is a distributed and decentralized database that deals with transactions. The blockchain is also emerging in the healthcare sector rapidly. Healthcare has now become a prestigious part of our life as it is making life-changing innovations day by day whether it is about artificial intelligence influence in the diagnostic system, expert system, patient's data storage, retrieval, security, and any other healthcare smart services. Smart health data is the most important ingredient to help us in solving our healthcare needs. Many researchers are now emerging blockchain in smart health because of its secure, flexible, and reliable architecture. In this paper, the major progress made in implementing the blockchain and its vulnerabilities are discussed in the area of smart health. The major focus is on the issues of patient's electronic health record, retrieval, security, and interoperability in Internet of Health Things (IoHT).
Interactive system design of entrepreneurship education based on internet of things and machine learning

The rapid development of cloud computing, internet of things and entrepreneurship education has enabled more young educators to choose an online education platform for education and teaching. Moreover, applying cloud computing technology with more advantages such as large storage, strong computing power and high stability to the construction of online education platform is an inevitable result. This paper applied the advantages of cloud computing to the development and implementation of entrepreneurship education online education platforms. In terms of curriculum setting, colleges and universities should add and improve sustainable entrepreneurship courses. Education departments should optimize the curriculum system of entrepreneurship education, improve the structure of students’ entrepreneurship knowledge, and take corresponding measures to achieve the positive development of sustainable entrepreneurship education.At the same time, the overall architecture, functional modules and details of the cloud-based online education platform were further explored, which provides a more complete theoretical basis for the research and design of the platform. Through comparative experiments, the research shows that the proposed algorithm has certain effects.
Monocular Depth Estimation Using Information Exchange Network

Depth estimation from single monocular image attracts increasing attention in autonomous driving and computer vision. While most existing approaches regress depth values or classify depth labels based on features extracted from limited image area, the resulting depth maps are still perceptually unsatisfying. Neither local context nor low-level semantic information is sufficient to predict depth. Learning based approaches suffer from inherent defects of supervision signals. This paper addresses monocular depth estimation with a general information exchange convolutional neural network. We maintain a high-resolution prediction throughout the network. Meanwhile, both low-resolution features capturing long-range context and fine-grained features describing local context can be refined with information exchange path stage by stage. Mutual channel attention mechanism is applied to emphasize interdependent feature maps and improve the feature representation of specific semantics. The network is trained under the supervision of improved log-cosh and gradient constraints so that the abnormal predictions have less impacts and the estimation can be consistent in high order. The results of ablation studies verify the efficiency of every proposed components. Experiments on the popular indoor and street-view datasets show competitive results compared with the recent state-of-the-art approaches.
Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN

Finding a template in a search image is an important task underlying many computer vision applications. Recent approaches perform template matching in a deep feature-space, produced by a convolutional neural network (CNN), which is found to provide more tolerance to changes in appearance. In this article, we investigate whether enhancing the CNN's encoding of shape information can produce more distinguishable features, so as to improve the performance of template matching. This investigation results in a new template matching method that produces state-of-the-art results in a standard benchmark. To confirm these results, we also create a new benchmark and show that the proposed method also outperforms existing techniques on this new dataset. Our code and dataset is available at: https://github.com/iminfine/Deep-DIM .