Miracle-dz/UniFAR-FTUs huggingface dataset:
MapReuse: Recycling Routing API Queries

Commercial maps often offer traffic awareness which is critical for many location based services. On the other hand free and open map services (such as government maps or OSM) are traffic oblivious and hence are of limited value for such services. In this paper we show that coarse information available from a commercial map routing API, can be dissected into fine-grained per-road-segment traffic information which can be reused in any application requiring traffic-awareness. Our system MapReuse queries a commercial map for a (relatively small) number of routes, and uses the returned routes and expected travel times, to infer travel time on each individual edge of the road network. Such fine-grained travel time information can be used not only to infer travel time on any given route but also to compute complex spatial queries (such as traffic-aware isochrone map) for free. We test our system on four representative metropolitan areas: Bogota, Doha, NYC and Rome, and report very encouraging results. Namely, we observe the median and mean percentage errors of MapReuse, measured against the travel times reported by the commercial map, to be in the range of 4% to 8%, implying that MapReuse is capable to accurately reconstruct the traffic conditions in all four studied cities.
5G-Based Smart Healthcare Network: Architecture, Taxonomy, Challenges and Future Research Directions

Healthcare is undergoing a rapid transformation from traditional hospital and specialist focused approach to a distributed patient-centric approach. Advances in several technologies fuel this rapid transformation of healthcare vertical. Among various technologies, communication technologies have enabled to deliver personalized and remote healthcare services. At present, healthcare widely uses the existing 4G network and other communication technologies for smart healthcare applications and are continually evolving to accommodate the needs of future intelligent healthcare applications. As the smart healthcare market expands the number of applications connecting to the network will generate data that will vary in size and formats. This will place complex demands on the network in terms of bandwidth, data rate, and latency, among other factors. As this smart healthcare market matures, the connectivity needs for a large number of devices and machines with sensor-based applications in hospitals will necessitate the need to implement Massive-Machine Type Communication. Further use cases such as remote surgeries and Tactile Internet will spur the need for Ultra Reliability and Low Latency Communications or Critical Machine Type Communication. The existing communication technologies are unable to fulfill the complex and dynamic need that is put on the communication networks by the diverse smart healthcare applications. Therefore, the emerging 5G network is expected to support smart healthcare applications, which can fulfill most of the requirements such as ultra-low latency, high bandwidth, ultra-high reliability, high density, and high energy efficiency. The future smart healthcare networks are expected to be a combination of the 5G and IoT devices which are expected to increase cellular coverage, network performance and address security-related concerns. This paper provides a state-of-the-art review of the 5G and IoT enabled smart healthcare, Taxonomy, research trends, challenges, and future research directions.
Topological Transient Models of Three-Phase, Three-Legged Transformer

The paper further develops a modeling concept of three-phase, three-legged transformer with tank walls represented as a distributed parameter structure. The circuital models proposed replicate accurately all possible zero-sequence impedances and losses of a three-winding transformer. Several model versions are compared to each other and to a conventional topological model with respect to their transient behavior during inrush and short circuit events, and in the presence of geomagnetically induced current (GIC). It was found that in all the cases considered, except that with a large GIC, all the models yield similar results. The reliability of the models is due to the representation of the tank walls behavior in a physical way, allowing one to observe the field distribution over the wall thickness as a function of transformer excitation. The modeled results are in a close agreement with positive and zero sequence data measured on a 25 MVA transformer as well as with inrush current test on a 300 kVA unit.
Influencing Factors of Driving Decision-Making Under the Moral Dilemma

Autonomous vehicles (AVs) are supposed to make appropriate strategies to ensure driving safety and improve traffic efficiency. However, not all collisions will be avoidable. In a typical dilemma scenario of this paper, we have to make hard decisions from two evils, sparing a child with red-light running behavior or crashing into a well-equipped motorcycle driver without violation of the traffic law. Combined with the existing traffic laws and the accident liability judgment cases in China, 12 typical dilemma scenarios are established. In order to acquire data to analyze the driving decision-making factors involving ethics and legal under the moral dilemma of AVs, we conduct a series of experiments in virtual reality environments. Furthermore, key factors are extracted to characterize the driving decisions under different scenarios and quantified by the gray relation entropy analysis method. Particularly, the ethical factor is represented by the quantity and type of collision targets, the legal factor is qualified by rights of way. The results showed that priority levels of the influencing factors in each moral dilemma were considerably different. Nevertheless, when the quantity of collision targets on two sides is equal, more participants prefer to protect the ones that comply with traffic rules. The research results can provide the basis for designing moral algorithms for the AVs.
Genetic Algorithm for Optimizing Global Alignment of Protein-Protein Interaction Network

Protein-protein interactions can be modeled as networks. The huge amount of produced Protein-Protein Interaction (PPI)data has many applications in biological studies. These studies improved human knowledge in the life process and diseases. One of these studies is PPI network alignment, which finds the similarity between PPI networks as a biological similarity. Aligning these networks is important to investigate evolutionary pathways or protein complexes. The main challenge of all global PPI network alignment is to improve both accuracy and efficiency. In this research, the accuracy and efficiency of global PPI network alignment are improved by using the genetic algorithm instead of the greedy algorithm and applied in HubAlign framework. The performance of the proposed method is compared with HubAlign based on the total execution time for the alignment process and the correctness of the results of the aligned networks. The new approach enhanced the overall accuracy and reduce execution time compared to the HubAlign approach.
Optimal Energy Management and Control of an Industrial Microgrid With Plug-in Electric Vehicles

An industrial microgrid (IMG) consists in a microgrid involving manufacturer plants that are usually equipped with distributed generation facilities, industrial electric vehicles, energy storage systems, and so on. In this paper, the problem of IMG-efficient operation in the presence of plug-in electric vehicles is addressed. To this purpose, the schedule of the different device operations of IMGs has to be optimally computed, minimizing the operation cost while guaranteeing the electrical network stability and the production constraints. Such a problem is formulated in a receding horizon framework involving dynamic optimal power flow equations. Uncertainty affecting plug-in electric vehicles is handled by means of a chance constraint approach. The obtained nonconvex problem is then approximately solved by exploiting suitable convex relaxation techniques. The numerical simulations have been performed showing computational feasibility and robustness of the proposed approach against increased penetration of the electric vehicles.
Interface and Doping Engineering of HfO2 Based Multi-Level RRAM: Towards Synaptic Simulation for Neuromorphic Computation

An ability to simulate synaptic behavior by interface and doping engineering has been reported. A significant improvement in on/off ratio can be obtained by ozone oxidation pre-treatment on a RRAM device. The improved resistive switching behavior with high memory windows can be obtained by Al3+ doping and post deposition annealing helps the device to better show synaptic characteristics.
Next Generation Blockchain Network (NGBN)

A disruptive revolution is forming for the blockchain based communication network framework with end-to-end P2P based architecture of distributed computation, storage, and networking paradigm - the Next Generation Blockchain Network (NGBN). The core theme of NGBN is to push up communication PHY layer and push down application layer (e.g. storage and computation) to converge on a single networking layer - called Blockchain Network Layer (BNL) - such that blockchain token economy can be efficiently implemented to support an end-to-end P2P mesh network with unlimitedly scalable, available to everyone, and expandable by peer nodes' joining openly, robust and secure network environment for all the IoT, big data, AI applications to be coming in the next 10 years. To achieve this goal, a joint effort is needed to pull together various resources, including network resources, communication resources, application and chip level support, software and system, and initial proof of concept trials for the end-to-end deployment.
Antenna Gain Enhancement by Using Low-Infill 3D-Printed Dielectric Lens Antennas

This paper reports on the design and three-dimensional (3D) integration of low-cost, low-loss and easy-to-fabricate 3D-printed integrated lens antennas (ILAs) for 5G broadband wireless communications in the 28 GHz frequency range. The ILA designs consist of an extended hemispherical lens, fed by two different types of source antennas, a substrate integrated waveguide (SIW) slot antenna array and a microstrip patch antenna (MPA) array. Results from comprehensive parametric analyses of the infill pattern and density of the 3D printed dielectric lenses are also intensively investigated and characterized for their electromagnetic properties, e.g., electric-field distribution. The ILAs are fabricated using polylactic acid (PLA) as the fused deposition modelling (FDM) polymer with an optimized infill density of 50%, which speeds up prototyping time and decreases the relative permittivity, dielectric loss, manufacturing cost, and overall mass of the lens. These features are illustrated through experimental verification and characterization of at least three samples. From the measurement results, the ILAs achieve a fractional bandwidth of 10.7%, ranging from 26.5 to 29.4 GHz with a maximum gain of 15.6 dBi at boresight and half power beam-width of approximately 58° and 75° in the $E $ and $H$ planes, respectively.
Attention Based Stack ResNet for Citywide Traffic Accident Prediction

The fine-grained citywide traffic accident prediction is of great significance for urban traffic management. Existing approaches mainly apply classic machine learning methods based on historical accident records. Thus they failed to involve the cross-domain data, which contains spatial and temporal dependency. Recently, with more cross-domain urban data available, leveraging the cross-domain data by deep learning algorithms to predict fine-grained accidents becomes possible, we propose an attention based ResNet framework to model the sophisticated correlation between urban data.
Author Name Disambiguation Using Graph Node Embedding Method

In real-world, name ambiguity mainly arises when many people share the same name or express their names in the same way, which often causes erroneous aggregation of records of multiple persons with the same name. This name ambiguity problem deteriorates the performance of information retrieval in digital libraries, web search etc. It is nontrivial to distinguish those name references, especially when there is very limited information about them. Most existing studies uses features like email address, frequent words etc. However, the information is not always available because of privacy or too expensive to get. In this paper, we utilize a graph node embedding approach to solve author name disambiguation problem, where a graph is constructed only using the collaborator relationships. In the methodological aspect, the proposed method uses random walk and a graph node representation learning method to embed each node into a low dimensional vector space. Finally, we solve this problem by partitioning the records associated with a name reference such that each partition contains records pertaining to a unique real-world person. We evaluate our method on the real world CiteSeerX dataset, and the experimental results demonstrate that the proposed method is significantly better than most of the existing name disambiguation methods working in a similar setting.
Machine Learning for Position Detection in Football

In recent years, analytics became increasingly important in sports. Newly developed, wearable tracking devices allow football players to log position and motion data during a game. These data can be exploited for enhancing the performance of individual players and entire teams. We present different machine learning approaches to predict spatial football player positions, which serve for advanced tactical analyses.
Visualizing High-Dimensional Single-Cell RNA-seq Data via Random Projections and Geodesic Distances

The recent advent in Next Generation Sequencing has created a huge data source which offers a great potential for elucidating complex disease mechanisms and biological processes. A recent technology is the single-cell RNA sequencing, which allows transcriptomics measurements in individual cells, having promising results. However, such studies measure the entire genome for thousands of cells, creating datasets with extremely high dimensionality and complexity. Following this perspective, we propose a dimensionality reduction approach, called RGt-SNE, which visualizes single-cell RNA-seq data in two dimensions. Initially, RGt-SNE defines a cell-cell distance matrix based on Random Projections and Geodesic Distances. The first is used to define the pairwise cells distances in a low dimensional projected space avoiding the difficulties that exist in data with ultra-high dimensionality. The latter is used to better define the large pairwise cells distances. Subsequently, the t-SNE method is applied in the customized distance matrix for two dimensional visualization. RGt-SNE was evaluated in two real experimental single-cell RNA-seq data against three well-known methods, such as t-SNE, Multidimensional scaling, and ISOMAP. Outcomes provide the superiority of RGt-SNE suggesting it as a reliable tool for single-cell RNA-seq data analysis and visualization.
Direction Finding Using Coprime Array with Sensor Gain and Phase Errors

This paper investigates the issue of direction finding of multiple signals using coprime array with sensor gain and phase errors. As the sensor errors decrease the performance of direction estimation, it proposes a calibration method with one signal, where one accurate direction signal enables to obtain high precise result. By building the rough direction signal in real system as usual, it introduces the gain self-calibration and iterative self-calibration to enhance the performance of error estimation, which is based on the rough error estimate by one signal calibration. Considering the analyze of computational complexity and performance simulation, the gain selfcalibration improves the performance in low signal to noise ratio compared with no self-calibration and the iterative selfcalibration gets the best performance with the most computational complexity. With known error values, it can be found more signals than the sensor number with coprime array.
Multi-agent System Engineering for Emphatic Human-Robot Interaction

Human-robot interactions have to take into account the natural multi-modal bidirectional communication model that is common among humans. The model does not rely just on speech and verbal exchange, but it shall include emotional exchange through different channels: face muscles, body posture, voice modulation, skin responses, odors, etc. While some aspects are feasible yet far from being adopted by daily robotic interaction with humans, the other ones can exploit current level of technology so as to be included in common, although complex, human-robot communication use cases. In order to cope in synergic but efficient and modular way with the various emphatic communication aspects, we propose to employ intelligent agents and multi-agent system. Such multi-agent system comprises a controller sub-system aboard the robot, which is coordinated by logical agents that can incorporate perceptive modules which generates state predicates, reason about them, plan, and deliver emotionally intelligent action while interacting with human beings, emulating as much as possible human empathy.
Rolling Bearing Fault Diagnosis Algorithm Based on FMCNN-Sparse Representation

The time-frequency analysis of vibration signals is an effective means to analyze the fault characteristics of rolling bearings. The traditional pattern recognition method is difficult to adapt to the complex mapping relationship between the high-dimensional feature space and the state space. The deep learning method has high-dimensional feature adaptive analysis ability, which is suitable for the intelligent analysis of the high-dimensional feature space in fault states. The feedforward deep convolutional neural network (CNN) has achieved some success in mechanical fault diagnosis. However, the rolling bearing fault signal is complex, and there are many interference factors. The CNN relying on the simple feedforward method cannot effectively meet the actual needs in the field of fault diagnosis. Although there are some CNNs with feedback methods, the CNNs of these feedback methods cannot systematically obtain the characteristic information of rolling bearing faults. Therefore, they do not solve the feature extraction problem of rolling bearing faults well. In view of this, this paper provides a specific mathematical definition of the feedback mechanism for constructing the feedback mechanism in the deep CNN, models the feedback mechanism into an optimization problem, determines the basic framework of the feedback mechanism, and an effective feedback mechanism calculation model is proposed. Based on this, a solution algorithm based on the gradient descent method is proposed. Then, an effective supervised feature extraction method based on sparse expression is proposed. It maps the sample features to the feature domain through the effective transform method. In the process, the wavelet packet transform (WPT) transform is used as the basis function to construct a dictionary with structural effects, and mixed penalty terms are introduced to further optimize the performance of structural sparse expression. Finally, the sparse expression is combined with the feedback mechanism CNN (FCNN) to establish a sub-module fault diagnosis network so that a diagnosis can determine the fault severity while assessing the bearing fault location. The example shows that the method proposed in this paper has high accuracy in determining the state of rolling bearings and has great application potential in engineering.
The 100-Digit Challenge: Algorithm jDE100

Real parameter optimization problems are often very complex and computationally expensive. We can find such problems in engineering and scientific applications. In this paper, a new algorithm is proposed to tackle the 100-Digit Challenge. There are 10 functions representing 10 optimization problems, and the goal is to compute each function’s minimum value to 10 digits of accuracy. There is no limit on either time or the maximum number of function evaluations. The proposed algorithm is based on the self-adaptive differential evolution algorithm jDE. Our algorithm uses two populations and some other mechanisms when tackling the challenge. We provide the score for each function as required by the organizers of this challenge competition.
Investigation of Grid Benefits from a Solar-Powered Electric Vehicle Using Real-World Driving Data

Solar-powered electric vehicles (EVs) are often considered with small rooftop panels that help slightly extend the electric range. This study takes a fresh look at the potential of solar EVs with two new perspectives: firstly, the forward-looking vision of the architecture is to have high-efficiency thin-film solar cells integrated onto the steel of all upward-facing vehicle body panels, increasing the potential solar energy capture during driving and parking; secondly, this study investigates the potential grid-level benefits of solar EVs with a focus on how the reduction in required EV charging energy could extend the lifetime of distribution transformers. Logged driving data from ten people over one week is used to simulate realistic results. Overall, the use of solar EVs is shown to approximately halve the rate of transformer aging over one sunny summer week, compared to the use of non-solar EVs.
Energy Optimization of Online Tracker for Mobile Devices

Nowadays, it is common that both mobile applications and websites log users' visits and keep record of users browser path, even every time user click and every word user type. Each tracking information will be respectively sent to the third-party server via HTTP request. Previous studies have shown that combining multiple small HTTP requests can significantly decrease energy consumption, but not have considered a typical scenario where massive tracking records can be efficiently compressed and transferred with a delay. In this paper, we propose a comprehensive approach to reduce the energy consumption of online tracker by bundling multiple HTTP requests. Our approach first intercepts all tracking-related HTTP requests, logging and compressing the requests for bundling, and then uses a proxy-based technique to combine these requests at runtime. Through a set of real-world experiment, the evaluation demonstrates that our approach can achieve an average energy reduction of 10% for mobile devices and make apparent performance and security improvement.