Reviewerly/exHarmony huggingface dataset:
A Data-Driven Approach for Assembling Intertrochanteric Fractures by Axis-Position Alignment

In clinics, the reduction of femoral intertrochanteric fractures should meet the medical demands of both axis alignment and position alignment.State-of-the-art approaches are designed for merely position alignment, not allowing for axis alignment.The axis-position alignment can be formulated as a least square optimization problem with the inequality constraints.The main challenges include how to solve this constrained optimization problem and effectively extract the semantic of the randomly fractured bone pieces.To address these problems, a semi-automatic data-driven method is introduced.First, the medical semantic parameters are computed, at the beginning of when the 3D input pieces' anatomical areas are labeled by using the deep neural network.A statistical shape model is leveraged to generate the synthetic training data so as to learn the anatomical landmarks of the pieces, greatly reducing the labeling costs for training.The final reduction position of the pieces is obtained through iterative axis alignment and position alignment.Our method is evaluated by three baselines, i.e., the manual assembly of the orthopaedic specialists and two typical bone assembling methods.The presented method solves an optimization problem for assembling intertrochanteric fracture by axis-position alignment.All cases can be successfully assembled with the developed algorithm which is proved to be capable of reaching the clinical demand.
Blockchain technology based e-voting system

Election could be a important event during a trendy democracy however massive sections of society round the world don’t trust their election system that is major concern for the democracy. Even the world’s largest democracies like Republic of India, us, and Japan still suffer from a blemished legal system. Vote rigging, hacking of the EVM (Electronic vote machine), election manipulation, and booth capturing square measure the key problems within the current electoral system. during this system, we tend to square measure work the problems|the problems within the election vote systems and attempting to propose the E-voting model which might resolve these issues. The system can highlight a number of the popular blockchain frameworks that provide blockchain as a service and associated electronic E-voting system that is predicated on blockchain that addresses all limitations severally, it additionally preserve participant’s obscurity whereas still being hospitable public examination. Building Associate in Nursing electronic electoral system that satisfies the legal necessities of legislators has been a challenge for an extended time. Distributed ledger technologies is Associate in Nursing exciting technological advancement within the info technology world. Blockchain technologies supply Associate in Nursing infinite vary of applications cashing in on sharing economies. Blockchain could be a unquiet technology of current era and guarantees to enhance the resilience of e-voting systems. this technique presents a shot to leverage edges of blockchain like cryptological foundations and transparency to attain an efficient theme for e-voting. The projected theme conforms to the elemental necessities for e-voting schemes and achieves end-to-end verifiability. The system presents in-depth analysis of the theme that with success demonstrates its effectiveness to attain Associate in Nursing end-to-end verifiable e-voting theme.
Quantifying resources for the Page-Wootters mechanism: Shared asymmetry as relative entropy of entanglement

Recently, some attention has been given to the so-called Page-Wootters mechanism of quantum clocks. Among the various proposals to explore the mechanism using more modern techniques, some have chosen to use a quantum information perspective, defining and using informational measures to quantify how well a quantum system can stand as a reference frame for other quantum system. In this work, we explore the proposal based on resource theory of asymmetry, known as mutual or shared asymmetry, which actually is equivalent to the approach from coherence theory in the case of interest here: quantum reference frames described by the $U(1)$ compact group. We extend some previous results in literature about shared asymmetry and Page-Wootters mechanism to more general cases, culminating in the enunciation of a theorem relating shared asymmetry of a bipartite state $\rho_{SR}$ with the relative entropy of entanglement of \textit{internal states} $\rho_M$ on the charge sectors of the Hilbert space $\mathcal{H}_S\otimes\mathcal{H}_R$. Using this result we reinterpret the relation between Page-Wootters mechanism and entanglement and also open some paths to further studies.
Filter-Based Landweber Iterative Method for Reconstructing the Light Field

The light field reconstruction aims to recover the 4D light field from the 3D focal stack, so it is a severe incomplete data reconstructing problem.The Landweber iteration is a general method to find the least-square solution of the discretized linear system, which is widely used for solving such problem.The relaxation strategies of the Landweber iteration are essential for both accelerating iterative convergence and decreasing errors.As a fast and non-approximate linear-time method, the fast guided filtering has good edge-preserving smoothing properties.It can be incorporated into the light field reconstruction process to improve the precision of the reconstructed results.In this paper, we propose a filter-based Landweber iterative method by introducing the optimized relaxation strategy and the fast guided filter.Specifically, the updated image in the iteration step contains the guidance image and the image obtained by the Landweber iteration, which makes the proposed method flexible.The experimental results show that the proposed method is more practical and effective compared with the relevant reconstruction methods.
Residual-adaptive Key Provisioning in Quantum-Key-Distribution Enhanced Internet of Things (Q-IoT)

With the advent of smart homes, smart cities, and smart everything, the Internet of Things (IoT) has emerged as an area of incredible impact, potential, and growth. Internet of Things date security remain a major challenge, in the current Internet of Things systems, a relatively easy method of data encryption is used to ensure the security of data transmission, which is commonly called lightweight cryptography. However, such method is at the risk of being cracked by quantum computers, which will contribute to many challenges specially related to privacy and security in IoT. As a result, the architecture of IoT needs to be re-designed considering the security challenges brought by quantum computers. On the other hand, Quantum Key Distribution (QKD) allows two users to share unconditionally secure keys. Unlike classical cryptosystems, the security of QKD is based on the fundamentals of quantum mechanics. This security of QKD is independent of computational complexity and will not be affected, no matter how much computing power the adversary has. This paper introduces a quantum key distribution enhanced Internet of Things architecture and proposes a residual-adaptive key provisioning scheme, which is evaluated in terms of key distribution success rate in the simulation.
Modelling generalisation gradients as augmented Gaussian functions

Studying generalisation of associative learning requires analysis of response gradients measured over a continuous stimulus dimension. In human studies, there is often a high degree of individual variation in the gradients, making it difficult to draw conclusions about group-level trends with traditional statistical methods. Here, we demonstrate a novel method of analysing generalisation gradients based on hierarchical Bayesian curve-fitting. This method involves fitting an augmented (asymmetrical) Gaussian function to individual gradients and estimating its parameters in a hierarchical Bayesian framework. We show how the posteriors can be used to characterise group differences in generalisation and how classic generalisation phenomena such as peak shift and area shift can be measured and inferred. Estimation of descriptive parameters can provide a detailed and informative way of analysing human generalisation gradients.
Accuracy vs. Cost Trade-off for Machine Learning Based QoE Estimation in 5G Networks

Since their first release, 5G systems have been enhanced with Network Data Analytics Functionalities (NWDAF) as well as with the ability to interact with 3rd parties' Application Functions (AFs). Such capabilities enable a variety of potentials, unimaginable for earlier generation networks, notable examples being 5G built-in Machine Learning (ML) mechanisms for QoE estimation, subject of this paper. In this work, an ML-based mechanism for video streaming QoE estimation in 5G networks is presented and evaluated. The mechanism relies on an ML algorithm embedded in NWDAF, the collection of 5G network KPIs, and the collection of QoE information from video streaming service provider, i.e., the 3rd party AF. The mechanism has been evaluated in terms of QoE estimation accuracy against the cost in terms of required input sources and data for the estimation, and its performance has been compared to alternative methodologies not making use of ML. The evaluation, via simulation activity, clearly highlights the benefits of the proposed mechanism. Based on the derived results, the required input sources are ranked with respect to their importance.
News keywords extraction algorithm based on TextRank and classified TF-IDF

TextRank algorithm tends to extract words with frequent occurrence as keywords, while TF-IDF only considers the word frequency relation in the text library to extract keywords. In order to combine the advantages of the two algorithms, this paper proposes a Weighted TF-IDF of the Same Category Historical News Library and TextRank (TFSL-TR). This method first uses the classification model based on LSTM to classify the news, and calculates the TF-IDF value as the first weight of the word by using the news library of the target news category. Then use the TextRank algorithm to calculate the second weight of words. Finally, sum the two weights by weight and take the TopK words as keywords in order of size. The experiment was carried out in the Chinese news library, and the results showed that, compared with the traditional method, TFSL-TR could effectively improve the accuracy of keyword extraction.
Skeleton Based Abnormal Behavior Recognition Using Spatio-Temporal Convolution and Attention-Based LSTM

Behavior recognition is a well-known computer version technology, it has been used in many applications such as video surveillance, robotics, human-computer interaction and sports video, etc. However, most of existing works ignored the depth information so that they resulted in over-fitting and the inferior performance. Consequently, a novel framework for behavior recognition is proposed which is based on spatio-temporal convolution and attention-based LSTM (ST-CNN & ATT-LSTM). In this framework, deep spatial information is merged into each segment, meanwhile we focus on the method of key information extraction, which is essential for improving behavior recognition performance. Finally, the proposed framework is evaluated with real world surveillance video data, and the results indicate that our framework is superior to existing methods.
Comparing View Factor modeling frameworks for the estimation of incident solar energy

View Factors are instrumental in using widely available horizontal solar radiation data for calculating the incident radiation on harvesting surfaces with arbitrary positions. This capability is notably useful for the design, optimization, and performance forecasting of solar devices. There are several main View Factor models (Liu-Jordan’s, Tian’s, and Badescu’s), which can lead to different theoretical implications and energy estimates. However, the assessments about the validity and underlying assumptions of these models are sometimes contradictory. Resolving this is important for utilizing the most appropriate framework given specific schemes and modeling goals. This work presents a comparative systematic analysis of a wide range of View Factor modeling frameworks with the purpose to gain a deeper understanding of the theoretical consistency and implications of the main View Factor models. The different sets of assumptions are evaluated through stochastic rays simulations and verified against integral models. Five frameworks for the Isotropic and Albedo View Factors were found to be consistent with Liu-Jordan’s model, two with Tian’s, and two with Badescu’s (partially); all with RMSE ⩽0.0014. Considering the most common ways to conceptualize the other components of the radiation, there was consistency with the Perez sky models (RMSE ⩽0.0055) for the View Factor of the Circumsolar radiation as a 25° cone and Horizon Brightening as a flat ring. For the View Factor of the Horizon Brightening as a 6.5° band, two regression models are introduced. By enabling a deeper insight into the sets of assumptions that are consistent with the main View Factor models, this work is valuable for the convergence and best implementation of the various theories in the modeling of incident solar radiation.
Cooperative Convolutional Neural Network Deployment over Mobile Networks

Inference acceleration has drawn much attention to cope with the real-time requirement of artificial intelligence (AI) applications. To this end, model partition for Deep Neural Networks (DNN) has been proposed to utilize the parallel and distributed computing units. However, the previous works focus on the load balancing among servers but may overlook the interplay between the computing and communication. This issue makes the existing approaches less efficient especially in mobile edge networks at which smart devices usually with limited computing capacity have to offload the tasks via limited bandwidth capacity to nearby servers. In this paper, therefore, we innovate a new system and formulate a new optimization problem, CONVENE, to minimize the completion time of inference for the smart devices with one or more antennas. To explore the intrinsic properties, we first study CONVENE with Single Antenna and derive an algorithm termed THREAD-SA to foster the optimum solution. Then, an extension, THREAD, is proposed to subtly utilize multiple antennas to further reduce completion time. Simulation results manifest that our algorithm outperforms others by 100%.
Spark implementation of the enhanced Scatter Search metaheuristic: Methodology and assessment

Optimization problems arise nowadays in all disciplines, not only in the scientific area but also in the field of engineering or economics, and in many others. Currently, challenging optimization problems require solution methods that consume a significant amount of computational resources. The application of High-Performance Computing techniques is a common approach to obtain efficient implementations in traditional parallel computing systems. However, more recent approaches are exploring distributed programming frameworks developed in recent years to achieve efficient computations on clusters and cloud systems. In this paper we present a parallel implementation of the enhanced Scatter Search metaheuristic using Spark. The parallel program was obtained as a particularization of a general software framework we developed to support different realisations of the Scatter Search metaheuristic. The aim of this paper is to provide helpful guidance to readers interested in applying, or developing their own, parallel metaheuristics to solve challenging problems in the Cloud. With the twofold objective of demonstrating the potential of the parallelization with Spark and also of studying the factors that influence the performance of the solution, the proposal has been thoroughly evaluated on two different platforms, a cluster and a cloud platform, using a representative set of parameter estimation problems in the field of Computational Systems Biology.
RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness.
Medical IoT: A Comprehensive Survey of Different Encryption and Security Techniques

Recently, there is a revolution in internet of things (IoT) technologies. Research advancements in this field proved to be very useful to automate daily tasks, it quickly reached the medical field resulting in the creation of a new research term called Internet of Medical Things (IoMT). Medical IoT devices have many applications that adds accessibility and reach to the medical field. Such applications vary from remote patient monitoring, remote surgery, and many more health-related tasks. Medical IoT applications require precise readings of biometrics and real time haptic feedback to work as intended without putting any risk on the human life. With all of these IoT medical applications, securing the information becomes a priority. Any un-intentional modification in a biometric reading can prove to be fatal in most scenarios. In this paper, we try to survey the current state-of-the-art encryption techniques that provide different solutions with varying levels of security.
Multi-Task Offloading over Vehicular Clouds under Graph-based Representation

Vehicular cloud computing has emerged as a promising paradigm for fulfilling user requirements in computation-intensive tasks in modern driving environments. In this paper, a novel framework of multi-task offloading over vehicular clouds (VCs) is introduced where tasks and VCs along with their internal connections are modeled as undirected weighted graphs. Aiming to achieve a trade-off between minimizing task completion time and data exchange costs, task components are efficiently mapped to available virtual machines in the related VCs. The problem is formulated as a non-linear integer programming problem, mainly under constraints of limited contact between vehicles as well as available resources, and addressed considering different problem sizes. In small size scenarios with a couple of tasks and service providers in a VC, we determine optimal solutions; in larger size cases, a connection-restricted random-matching-based subgraph isomorphism algorithm is proposed that presents low computational complexity. Evaluation of the proposed algorithms against greedy-based baseline methods is conducted via extensive simulations.
WITHDRAWN: Residual Dense Convolutional Neural Network for Image Super-Resolution

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A MultiView Courses Recommendation System Based on Deep Learning

For a large number of courses, educators have always been very concerned about how to help students choose the right courses. This article attempts to describe a course recommendation model based on deep learning, which can generate views from a different perspective and make intelligent course recommendations for students. First, collect various types of data to generate student models and curriculum models. By studying the actual relationship between specific models, and then using deep learning technology to extract key features, select different recommended features from these relationships, and generate different views to further recommend for students.
Type-2 fuzzy control for line following using line detection images

This work presents a comparative analysis of Type-1 and Type-2 fuzzy controllers to drive an omnidirectional mobile robot in line-following tasks using line detection images. Image processing uses a Prewitt filter for edge detection and determines the error from the line location. The control systems are tested using four different paths from the Robotino® SIM simulator. Also, two different strategies in the design and implementation of the controllers are presented. In the first one, a PD controller scheme is extended by using a fuzzy system to have adaptive parameters P and D, additionally, Type-2 Fuzzy sets are used to give robustness to the controller. In the second case, a Fuzzy controller is designed to compute in a direct way the control variables and it is extended to Type-2 Fuzzy controller. Finally, experimental results and comparative analysis are presented for the five control schemes by comparing the running time and the standard deviation to measure the robustness of the control systems.
Breast Cancer Prognosis Using Machine Learning Techniques and Genetic Algorithm: Experiment on Six Different Datasets

The strategy used in this research is to select the best features using the genetic algorithm from various breast cancer dataset for getting better prediction results using machine learning algorithms. This research involves two main phases. One is feature selection using Genetic Algorithm (GA) and second is breast cancer prediction using Logistic Regression (LR) and k-Nearest Neighbor techniques (k-NN).
A Random Forest Classification Algorithm Based on Dichotomy Rule Fusion

The classical random forest algorithm has associated features and bias problems, which leads to a reduction in classification accuracy, in this paper we propose a random forest classification algorithm based on dichotomy rule fusion. The dichotomy rule fusion method is based on the idea of information gain and recursive feature elimination to select a better feature sequence, which improves the classification accuracy. Experimental results on international standard data sets show that the algorithm has better performance in classification than some commonly used algorithms.