Miracle-dz/UniFAR-FTUs huggingface dataset:
PRIDE: A Private and Decentralized Usage-Based Insurance Using Blockchain

Usage-based Insurance (UBI) for vehicles determines the insurance premiums according to actual usage and driving pattern. It can significantly reduce insurance costs for safe drivers, however, UBI schemes require detailed driving data to determine the insurance premiums, which may lead to serious privacy breach for drivers. Moreover, most existing UBI schemes require a centralized insurance company as the intermediary to manage insurances. Such a centralized solution incurs too much monetary costs as well as time cost. In this paper, we propose PRIDE, a privacy-preserving and decentralized UBI scheme using the blockchain to record encrypted driving data, and the smart contract running on the blockchain to calculate insurance premiums. Different from existing UBI schemes, PRIDE achieves security and privacy without relying on any centralized party or any trusted/tamper-proof hardware. We have analyzed security of PRIDE and evaluated its performance. The results show that PRIDE is very efficient in processing UBI insurances-each insurance request can be processed in about 898ms.
An Efficient and Privacy-Preserving $k$-NN Query Scheme for eHealthcare Data

As eHealthcare data is very sensitive yet cloud servers are not fully trustable today, many security, privacy and efficiency challenges will arise when cloud meets eHealthcare data. In this paper, aiming at addressing the privacy and efficiency challenges, we present an efficient and privacy-preserving $k$ Nearest Neighbour ($k$-NN) query scheme for encrypted eHealthcare data in cloud. The proposed scheme is characterized by integrating kd-tree and homomorphic encryption techniques for efficient storing encrypted data in cloud and privacy-preserving $k$-NN query over encrypted data. Detailed security analysis shows that our proposed scheme is really privacy preserving under our security model. In addition, performance evaluation also indicates that our proposed scheme is efficient in terms of computational complexity.
Towards Improved Generalization in Financial Markets with Synthetic Data Generation

Training deep learning models that generalize well to live deployment is a challenging problem in the financial markets. The challenge arises because of high dimensionality, limited observations, changing data distributions, and a low signal-to-noise ratio. High dimensionality can be dealt with using robust feature selection or dimensionality reduction, but limited observations often result in a model that overfits due to the large parameter space of most deep neural networks. We propose a generative model for financial time series, which allows us to train deep learning models on millions of simulated paths. We show that our generative model is able to create realistic paths that embed the underlying structure of the markets in a way stochastic processes cannot.
RF-Energy Harvesting from Moving Vehicles: Mathematical Modeling and Selection Protocol

Wireless energy transfer can extend the lifetime of wireless sensor networks deployed in hard-to-reach and/or harsh environments. A number of works have proposed using dedicated mobile chargers (e.g., robots, drones, or vehicles) to transmit Radio Frequency (RF) energy to sensor nodes, which, despite optimal route selection, remains an expensive approach. In this article, we consider a scenario where sensor nodes could be recharged by passing vehicles. This could be the case of a wireless sensor network deployed to monitor the health of a bridge or a dam. We provide exact mathematical modeling of the harvested energy from a moving vehicle over $T$ seconds and over a trip time. This model captures the effect of the vehicle's speed and location. Then, we extend the discussion to the case of multiple vehicles and propose a multi vehicle harvesting protocol. This protocol allows the sensor node to maximize its energy intake by proactively selecting the source vehicle. This work paves the ground for accurate designs of energy harvesting protocols that leverage surrounding sources of RF energy. The accuracy of the derived results is verified using computer simulations.
Blockchain Use Cases in Digital Sectors: A Review of the Literature

Blockchain technology is a ledger system that is popularly known as the backbone of the Bitcoin cryptocur-rency. Since its conception, the potential beneficial applications of blockchain in other digital sectors have been lauded in the literature, and related challenges have been disputed. In this study, the literature is reviewed for frameworks and use cases that fully realize the applicability of blockchain beyond financial applications and cryptocurrencies. A network analysis of the literature was performed to identify the most popularly documented digital sectors in this context, which include the Internet of Things (IoT), healthcare, supply chain management, and government sectors. For each sector, this review documents use cases in which an attempt is made to implement blockchain solutions. The main purpose of this paper is to probe each sector for the growing maturity of blockchain technology and to document the unique benefits and challenges arising from the use of this technology. The findings show that despite the growing reputation of blockchain technology, its implementation within these four sectors remains in infancy because the use cases lack concrete evaluations of its effectiveness and plausibility. Nevertheless, the categorization of current blockchain use cases demonstrates current applications and sector-specific concerns that suggest future directions for further research.
AI Enhanced Alarm Presentation for Quality Monitoring

This paper presents an AI based method for improving poorly performing quality prediction models. The method improves automatically the usability of the low quality alarm predictions in the web based quality monitoring tool that provides decision support for users. The tool enables the utilization of the models that suffer from the lack of information because of a long time gap to the predicted future. The reliability of the presented alarms in a monitoring tool will be improved by reducing the amount of false alarms.
Representation and Computation Method for Bumblebee's Abdomen Movements

The research of insect robots requires accurate behavioral characterization and quantitative methods to model the mapping relationship between control instructions and induced behaviors. We define a parametric bumblebee somatic body geometry model, using a Gaussian mixture model and the Mean-shift algorithm to calculate the motion parameters, establishing a computational framework for calculating the fine motion characterization of the main moving parts of the body. Experiments show that our method has achieved high accuracy. The method has low calculation overhead and can satisfy the behavioral experiment requirements.
Anomalous cell detection with kernel density-based local outlier factor

Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor (KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOF is applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators (KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.
A simulation of basic logic circuit based on ternary quantum-dot cellular automata

For the purpose of developing the feature of ternary quantum-dot cellular automata (QCA), the simulation algorithm which is directed to its semiclassical model was compiled, then the simulations about three simple ternary QCA devices have been achieved by virtue of Matlab, the results confirm that these structure's behavior and physical truth accord perfectly. The simulations show that the behavior of structures which are composed of ternary Quantum-dot Cell agree with the ternary truth table raised by ÃLukasiewicz except the majority gate shaped in cross. In order to implement the logic function AND and OR we have to modify this kind of majority gate and develop some novel QCA devices using this simulation algorithm.
Signal Based Device-Free Tracking

In recent years, device-free tracking has been studied for convenience and needs in a variety of areas including the AR, VR, and entertainment industries. Motion tracking with the camera provides good performance, however there are some restrictions caused by lighting. In addition, there is a drawback that the amount of calculation is also high as much data is input. Many approaches have been proposed to overcome these shortcomings. Acoustic signal-based tracking is a good alternative because it consumes relatively low energy and does not require additional equipment. Signal-based tracking measures the relative distance of an object using the phase difference of the received signals. There are still several challenges when estimating the absolute distance to an object, and hence the accuracy is not guaranteed. We propose a peak signal-based estimation method that is very suitable for mobile devices with very accurate and low computational complexity. Experiment results show high accuracy and feasibility.
A long short-term memory stochastic volatility model

Stochastic Volatility (SV) models are widely used in the financial sector while Long Short-Term Memory (LSTM) models are successfully used in many large-scale industrial applications of Deep Learning. Our article combines these two methods in a non-trivial way and proposes a model, which we call the LSTM-SV model, to capture the dynamics of stochastic volatility. The proposed model overcomes the short-term memory problem in conventional SV models, is able to capture non-linear dependence in the latent volatility process, and often has a better out-of-sample forecast performance than SV models. These properties are illustrated through simulation study and applications to three financial time series datasets: The US stock market weekly index SP500, the Australian stock weekly index ASX200 and the Australian-US dollar daily exchange rates. Based on our analysis, we argue that there are significant differences in the underlying dynamics between the volatility process of the SP500 and ASX200 datasets and that of the exchange rate dataset. For the stock index data, there is strong evidence of long-term memory and non-linear dependence in the volatility process, while this is not the case for the exchange rates. An user-friendly software package together with the examples reported in the paper are available at https://github.com/vbayeslab.
Within‐Day Consistency in Cyclosporine Pharmacokinetics from a Microemulsion Formulation in Renal Transplant Patients

A new microemulsion formulation of cyclosporine (Sandimmune Neoral) was compared to the commercially available formulation (Sandimmune) in 11 stable renal transplant patients with regard to the consistency in cyclosporine pharmacokinetics between a daytime fasting, and a nighttime nonfasting administration. Daily cyclosporine doses were individualized and administered in equal, divided doses every 12 h as soft gelatin capsules; doses were kept constant throughout the study. Serial blood samples were obtained over a 24-h period (two consecutive dosing intervals) at steady-state for each formulation, and cyclosporine concentrations were determined in whole blood by a specific radioimmunoassay method. Within-formulation consistency in pharmacokinetic parameters between the daytime and nighttime administrations was assessed in terms of bioequivalence criteria. Following the mg-to-mg conversion from the commercial to the microemulsion formulation, area under the curve (AUC) was increased on average by 30% due to absorption-related pharmacokinetic differences, while trough concentrations remained in the therapeutic range. Within each formulation, AUC was bioequivalent when comparing the daytime fasting to the nighttime nonfasting administration. For the commercial formulation, however, there was considerable variation in absorption rate, dampening of peak-trough fluctuation, and elevation of trough concentration following the nighttime nonfasting dose. By contrast, the microemulsion exhibited a more stable concentration-time profile over the two dosing intervals, with bioequivalence in peak-trough fluctuation and trough concentrations. Hence, the steady-state pharmacokinetics of cyclosporine from the microemulsion formulation exhibit greater within-day consistency compared to the commercial formulation in stable renal allograft recipients.
A Novel Image Mosaic Method Based on Improved ORB and its Application in Police-UAV

Images acquired by police-UAV (Unmanned Aerial Vehicle) for reconnaissance and forensics need to be stitched in real-time. Image registration affects the quality of image mosaic. For the UAV aerial images with high resolution and rich information, the feature points detected by ORB (oriented FAST and rotated BRIEF) algorithm are unevenly distributed and easy to cluster, which will influence the matching rate and efficiency during image registration. In order to solve the problem, this paper proposes a novel image mosaic method based on improved ORB algorithm. First, a mask is constructed in the image to be registered, and an improved ORB algorithm is adopted to detect and describe feature points. Then, the feature points are matched by Hamming distance and the matched pairs are purified by the Progressive Sample Consensus (PROSAC) algorithm. Finally, the max-flow min-cut algorithm is utilized to determine the best seam-line and the improved Laplacian fusion algorithm is used to eliminate the ghosting, while realizing the seamless splicing of the UAV images. The experimental results show that the proposed method can obtain higher matching rate and efficiency for images with changes of scale, rotation, blur, viewpoint and illumination, and also have good performance in eliminating stitching seam and ghosting.
An Improved TFIDF Algorithm Based on Dual Parallel Adaptive Computing Model

The double parallel cloud computing framework based on GPU (Graphics Processing Unit) and MapReduce is proposed, which aims at the low efficiency for the large data sets on the stand-alone by text classification algorithm, constructs the adaptive computation process of double parallel computing and combines the advantage of improved TFIDF (term frequency-inverse document frequency) algorithm, and improves TFIDF text categorization algorithm with double parallel adaptive computing. In different operating environments, the efficiency of improved TFIDF algorithm will be compared with different computing nodes. The result shows that massive data can be processed effectively in high speed by improved TFIDF algorithm which adopts double parallel adaptive computing. With the number of nodes increasing, the algorithm execution efficiency with double parallel adaptive computing is getting more and more effective.
A genetic programming approach for searching on nearest neighbors graphs

Nearest neighbors graphs have gained a lot of attention from the information retrieval community since they were demonstrated to outperform classical approaches in the task of approximate nearest neighbor search. These approaches, firstly, index feature vectors by using a graph-based data structure. Then, for a given query, the search is performed by traversing the graph in a greedy-way, moving in each step towards the neighbor of the current vertex that is closer to the query (based on a distance function). However, local topological properties of vertices could be also considered at the moment of deciding the next vertex to be explored. In this work, we introduce a Genetic Programming framework that combines topological properties along with the distance to the query, aiming to improve the selection of the next vertex in each step of graph traversal and, therefore, reduce the number of vertices explored (scan rate) to find the true nearest neighbors. Experimental results, conducted over three large collections of feature vectors and four different graph-based techniques, show significant gains of the proposed approach over classic graph-based search algorithms.
Network Energy Efficiency Through Matching and Reconfiguration

Energy efficiency has become a major concern in the design and operation of fixed networks. Energy-Efficient Ethernet (EEE) came as a response to reduce the energy consumption of Ethernet technology. The performance of EEE depends on the link utilization and may degrade when the utilization increases. This is especially true when an energy-aware routing (EAR) protocol is deployed on top of EEE as it tends to aggregate traffic over a subset of links, thereby increasing their utilization. This behavior introduces a tradeoff between EEE and EAR. As network traffic patterns change frequently, it requires a significant effort to configure the EAR protocol on an EEE network in order to reduce the downside effects of the tradeoff, thus benefiting from both the EEE and EAR energy efficiency. In this work, we propose a network matching and reconfiguration algorithm (NetRec), which dynamically adapts the topology control (ESTOP) settings of an EEE network to increase energy savings. Our reasoning is that larger energy savings can be obtained if the network topology is dynamically reconfigured according to traffic demand. NetRec is designed in the context of Software-Defined Networking (SDN). We evaluate NetRec's energy performance in networks of different sizes under varying traffic conditions.
Expressiveness of Matrix and Tensor Query Languages in terms of ML Operators

Tensors are one of the most widely used data structures in modern Machine Learning applications. Although they provide a flexible way of storing and accessing data, they often expose too many low-level details that may result in error prone code that is difficult to maintain and extend. Abstracting low-level functionalities into high-level operators in the form of a query language is a task in which the Data Management community has extensive experience. It is thus important to understand how such an experience can be applied in the design of useful languages for tensor manipulation. In this short paper we study a matrix and a tensor query language that have been recently proposed in the database literature. We show, by using examples, how these proposals are in line with the practical interest in rethinking tensor abstractions. On the technical side, we compare the two languages in terms of operators that naturally arise in Machine Learning pipelines, such as convolution, matrix-inverse, and Einstein summation. We hope our results to provide a theoretical kick-off for the discussion on the design of core declarative query languages for tensors.
Data Stream Analysis of Online Activities

Given a large volume of multiple data streams, such as online web-click logs and sensor data, how can we discover typical patterns and compress them into compact models? How can we incrementally distinguish multiple patterns while considering the information obtained from a pattern found in a streaming setting? In this paper, we propose a streaming algorithm, namely STREAMSCOPE, that can find intuitive patterns efficiently from event streams evolving over time. Our method has the following properties: (a) Effective: It operates on semi-infinite data streams and summarizes all the streams into a set of multiple discrete segments grouped by their similarities. (b) Automatic: It automatically and incrementally recognizes such patterns and generates models for each of them if necessary. (c) Scalable: The complexity of our method does not depend on the length of the input streams. Our experiments on real datasets demonstrate that StreamsCopecan find meaningful patterns and achieve great improvements in terms of computational time and memory space over its full batch method competitors.
Role-play in Virtual Reality Game for the Senior

The games market is growing, in the current generation, no matter what age or gender you are, everyone plays games. It can be seen that if the strong power of the game can be used well, it will improve the reality. Role-playing games(RPG) have always been the most popular and influential game in the game market. This study used Role-playing games as a game type, combined with VR, which provides excellent immersion experience, to design senior applied games. Before the design, we visited the elderly care center to understand the needs of the elderly. The completed game is tested by the elderly, interviewed after the test, and the game will be modified. The purpose of this study was to provide a good strategy for senior VRRPG applied game design.
Estimating Software Energy Consumption with Machine Learning Approach by Software Performance Feature

With the growing scale of the application and ability to compute, more and more people pay attention to software energy consumption. There is a huge potential for controlling energy consumption during the application's development phase. Although the energy consumption of software can be obtained by tools or models, the problem of how energy consumption is consumed is still not explained. To solve this problem, a model of energy consumption characterized by performance events is established with using the method of ridge regression machine learning, which can explain the origin of energy consumption, and the error rate is only 6.8%. Our model is based on performance events from perf tool and is independent of the application scenario. Using this model, it does not require programmers to measure and train their own applications, it can also decrypt the causes of energy consumption.
Optimal Beamforming for Enabling Self-Sustainable Secondary Tier in Heterogeneous Networks

Simultaneous wireless information and power transfer (SWIPT) is an attractive technique to provide wireless data services and energy charging at the same time. Conventionally, SWIPT is applied to one-tier networks where a central node is deployed to serve users in the coverage. As the notion of heterogeneous network becomes emerging, it is of paramount importance to investigate effective SWIPT methods in the context of heterogeneous networks. In this work, we consider a two-tier heterogeneous network where the primary tier performs SWIPT to feed energy to the secondary tier. As a result, the secondary tier can be self-sustainable yet the beamforming weights for data and energy flows need to be carefully designed. We explore the optimal beamforming design based on two optimization formulations including the transmission power minimization subject to the rate requirement and energy requirement, and the rate maximization subject to the maximal transmission power and the harvested energy target. Since both the formulated problems are nonconvex, we apply the semi-definite relaxation (SDR) technique to obtain the optimal solution of the convex formulation. Numerical results are presented to evaluate the performance of the proposed optimal beamforming design in comparison with some benchmark schemes.