Miracle-dz/UniFAR-FTUs huggingface dataset:
Battery-less Place Recognition System using Multiple Energy Harvesting Elements

We propose a room-level place recognition system which uses energy harvesting elements as both sensor and power source. As energy harvesting elements, we focus on solar cells. In our previous work, by combining multiple types of cells and learning the difference of the generated power, we succeeded to distinguish ten places thanks to different characteristics of solar cells according to their materials. However, since the previous research focused on investigating the feasibility of our fundamental idea, it used a microcomputer board that requires an additional battery to measure the amount of generated power. Hence, it did not reach the level to utilize the energy harvesting elements as a power supply required for running the whole system. In this paper, we re-design our previously proposed mechanism to measure the amount of generated power of the energy harvesting elements and propose the novel mechanism to measure the amount of generated power by using the generated power without any additional battery. In stead of measuring the amount of generated power, our system has the circuit that counts how many times the electrolytic capacitor has been fully charged. The capacitor used is the minimum size required for reading the time stamp and write it to the memory. Finally, we can measure the amount of generated power by counting the time stamps recorded per unit of time. We designed and implemented the new circuit on PCB, and applied our system to room-level place recognition. As a result, we confirmed that our system can accurately distinguish the eight places.
OSTNet: Calibration Method for Optical See-Through Head-Mounted Displays via Non-Parametric Distortion Map Generation

We propose a spatial calibration method for Optical See-Through Head-Mounted Displays (OST-HMDs) having complex optical distortion such as wide field-of-view (FoV) designs. Viewpoint-dependent non-linear optical distortion makes existing spatial calibration methods either impossible to handle or difficult to compensate without intensive computation. To overcome this issue, we propose OSTNet, a non-parametric data-driven calibration method that creates a generative 2D distortion model for a given six-degree-of-freedom viewpoint pose.
A Real-Time Cloud-Based Intelligent Car Parking System for Smart Cities

Nowadays, parking space shortages and traffic congestion are two issues that all drivers are facing in large cities around the world. Due to the growing number of vehicles and the mismanagement of available parking spaces, heavy traffic in crowded cities are severe. Therefore, there is a compelling urge for an intelligent and efficient car parking system which can be used for managing the limited parking spaces, thereby saving on time, cost, and fuel. In this paper, a secure and reliable cloud-based intelligent car parking system based on Internet-of-Things (IoT) technology for smart cities is presented. Integrated on-site data collection using wireless sensors along with real-time and streaming data analytics on data from IoT are investigated to dynamically check the availability of parking spaces in different parking areas and address the above-mentioned challenges.
Optical Pumping of Graphene-Based Heterostructures with Black-Arsenic-Phosphorus Absorbing-Cooling Layer for Terahertz Lasing

We evaluate the optical pumping efficiency of the graphene-layer (GL) heterostructures intended for the terahertz (THz) lasing using the interband transitions in the GL. The pumping of such by near- or mid-infrared (NIR or MIR) radiation leads to the creation of a substantially hot two-dimensional electron-hole plasma (2D-EHP) in the GL. This hampers the interband population inversion in the 2D-EHP and can suppress the THz lasing. To prevent the 2D-EHP overheating, we propose to use the NIR/MIR radiation pumping of the GL through a sufficiently thick layer absorbing this radiation. This layer with sufficiently small energy gap enables an increase in the quantum efficiency of the pumping accompanied by strong cooling of the electron-hole pairs injected into the GL. As shown, the absorbing-cooling layers made of black-arsenic-phosphorus can be fairly efficient if their energy gap is smaller than the optical phonon energy in the GL.
Human Prediction for the Natural Instruction of Handovers in Human Robot Collaboration

Human robot collaboration is aspiring to establish hybrid work environments in accordance with specific strengths of humans and robots. We present an approach of flexibly integrating robotic handover assistance into collaborative assembly tasks through the use of natural communication. For flexibly instructed handovers, we implement recent Convolutional Neural Networks in terms of object detection and grasping of arbitrary objects based on an RGB-D camera equipped to a robot following the eye-in-hand principle. In order to increase fluency and efficiency of the overall assembly process, we investigate the human ability to instruct the robot predictively with voice commands. We conduct a user study quantitatively and qualitatively evaluating the predictive instruction in order to achieve just-in-time handovers of tools needed for following subtasks. We compare our predictive strategy with a pure manual assembly having all tools in direct reach and a stepby-step reactive handover. The results reveal that the human is able to predict the handover comparable to algorithmbased predictors. Nevertheless, human prediction does not rely on extensive prior knowledge and is thus suitable for more flexible usage. However, the cognitive workload for the worker is increased compared to manual or reactive assembly.
Analysis of Hub Motor Configuration and Battery Placement on Ride Comfort of Electric Trucks

This paper presents a methodology for sizing hub motors and battery pack in electric trucks and analysing the influence of the hub motor drive configuration and battery placement on driver ride comfort. The hub motors were sized based on the vehicle performance requirements and the battery pack was sized accordingly using AVL CRUISETM simulations, to meet the power and energy demand requirements of the vehicle. Two different hub motor configurations - front and rear drive were considered and the battery pack longitudinal placement was varied to realise the variation in vehicle sprung mass distribution. These configurations were analysed and evaluated for driver ride comfort as per ISO 2631-1 standard, using IPG TruckMaker for different road profiles, cruising speeds and loading conditions. It was observed that the driver ride comfort levels were better for the front wheel hub motor drive as compared to rear wheel drive and improved as the battery pack was placed longitudinally closer to the front axle.
An Intra-Affine Current Picture Referencing Mode for Screen Content Coding in VVC

With the rapid development of emerging applications, screen content coding (SCC) is playing a more and more important role. Intra block copy (IBC), as a new tool for SCC, is proved to be efficient when there are many repeating or similar areas within the same picture. However, IBC is based on translational motion model, which may not work well for some blocks with complicated movement content, such as rotation and zoom. In this paper, a new intra-affine current picture referencing mode is proposed. In this new mode, non-translational motion model (affine model) is introduced to intra prediction for SCC to improve coding efficiency. First, candidate set of initial block-affine vectors (BVAffis) is established by a newly designed method. Then, those initial BVAffis are updated through iterative searching algorithm. Moreover, compatibility checking is applied. Compared to VTM3.0, the proposed new mode can achieve 2.53%, 2.47%, and 2.48% BD-rate saving on average for Y, U and V respectively for SCC test sequences. The draft about the new mode (JVET-O0682) was submitted in the 15th JVET meeting.
ATuMm: Auto-tuning Memory Manager in Apache Spark

Apache Spark is an in-memory analytic framework that has been adopted in the industry and research fields. Two memory managers, Static and Unified, are available in Spark to allocate memory for caching Resilient Distributed Datasets (RDDs) and executing tasks. However, we found that the static memory manager (SMM) lacks flexibility, while the unified memory manager (UMM) puts heavy pressure on the garbage collection of JVM on which Spark resides. To address these issues, we design an auto-tuning memory manager (ATuMm) to support dynamic memory allocation with the consideration of both memory demands and latency introduced by garbage collection. We implement our new memory manager in Spark 2.2. 0 and evaluate it by conducting experiments in a real Spark cluster. Our experimental results show that our auto-tuning memory manager can reduce the total garbage collection time and thus further improve the performance (i.e., reduced latency) of Spark applications, compared to the existing Spark memory management solutions.
Typification of Impersonated Accounts on Instagram

Fake accounts and Impersonators on Online Social Networks such as Instagram are turning difficulties for society. This has attended to an increasing interest in detecting fake profiles and investigating their behaviours. Questions like who are impersonators? what are their characteristics? and are they bots? will arise. To answer, we begin this research by collecting data from three important communities on Instagram including “Politician”, “News agency”, and “Sports star”. Inside each community, four verified top accounts are picked. Based on the users who reacted to their published posts, we detect 4K impersonators [1]. Then we employed well-known clustering methods to distribute impersonators into separated clusters to observe obscure behaviours and unusual profile characteristics. We also studied the cross-group analysis of clusters inside each community to explore engagements. Finally, we conclude the study by providing a complete investigation of the bot-like cluster.
Expedited Design Closure of Antenna Input Characteristics by Trust Region Gradient Search and Principal Component Analysis

Optimization-based parameter tuning has become an inherent part of contemporary antenna design process. For the sake of reliability, it is typically conducted at the level of full-wave electromagnetic (EM) simulation models. This may incur considerable computational expenses depending on the cost of an individual EM analysis, the number of adjustable variables, the type of task (local, global, single-/multi-objective optimization), and the constraints involved. For these reasons, utilization of conventional algorithms is often impractical. This paper proposes a novel gradient-based algorithm with numerical derivatives for expedited antenna optimization. The improvement of computational efficiency is obtained by employing a rank-one Broyden formula and restricting finite differentiation sensitivity updates to the principal directions of the Jacobian matrix, i.e., those corresponding to the most significant changes of the antenna responses. Comprehensive numerical validation carried out using three wideband antennas indicates that the presented methodology offers considerable savings of sixty percent with respect to the reference trust-region algorithm. At the same time, virtually no degradation of the design quality is observed. Furthermore, algorithm reliability is greatly improved (while offering comparable computational efficiency) over the recent state-of-the-art accelerated gradient-based procedures.
Analysis of the Efficiency of Two Different Electric-continuously Variable Transmission for Hybrid Electric Bus

This paper proposes two different electric-continuously variable transmission (EVT) configurations for a typical hybrid electric bus (HEB). The two proposed configurations are the Output-split Configuration with Dual Planetary gearsets (OCDP) and the Input-split Configuration with Single Planetary gearset equipped with a Clutch (C-ICSP), respectively. Then the power performance and efficiency of the two configurations are analyzed and compared based on the lever analogy. The analysis indicates the OCDP cannot apply for the HEB because the motor speed exceeds the reasonable range, because the efficiency in high-speed area is low. Only the C-ICSP has good applicability to the HEB.
Performance Analysis of Tile Low-Rank Cholesky Factorization Using PaRSEC Instrumentation Tools

This paper highlights the necessary development of new instrumentation tools within the PaRSE task-based runtime system to leverage the performance of low-rank matrix computations. In particular, the tile low-rank (TLR) Cholesky factorization represents one of the most critical matrix operations toward solving challenging large-scale scientific applications. The challenge resides in the heterogeneous arithmetic intensity of the various computational kernels, which stresses PaRSE's dynamic engine when orchestrating the task executions at runtime. Such irregular workload imposes the deployment of new scheduling heuristics to privilege the critical path, while exposing task parallelism to maximize hardware occupancy. To measure the effectiveness of PaRSE's engine and its various scheduling strategies for tackling such workloads, it becomes paramount to implement adequate performance analysis and profiling tools tailored to fine-grained and heterogeneous task execution. This permits us not only to provide insights from PaRSE, but also to identify potential applications' performance bottlenecks. These instrumentation tools may actually foster synergism between applications and PaRSE developers for productivity as well as high-performance computing purposes. We demonstrate the benefits of these amenable tools, while assessing the performance of TLR Cholesky factorization from data distribution, communication-reducing and synchronization-reducing perspectives. This tool-assisted performance analysis results in three major contributions: a new hybrid data distribution, a new hierarchical TLR Cholesky algorithm, and a new performance model for tuning the tile size. The new TLR Cholesky factorization achieves an 8X performance speedup over existing implementations on massively parallel supercomputers, toward solving large-scale 3D climate and weather prediction applications.
Unsupervised Learning to Analysis of Population of Models in Computational Electrophysiological Studies

Unsupervised learning is a helpful tool for processing experimental data. These types of machine learning algorithms can address problems of dimensionality reduction, clusterization, anomaly detection, visualization, and many others. We propose that unsupervised learning may also be useful for the analysis of the results of computational experiments. This proof-of-concept paper is about the application of several unsupervised learning algorithms for computational studies of the electrical activity in human atrial cardiomyocytes. A dataset for analysis was generated with a population modeling approach, which is widely used in cardiac modeling. At first, the signals generated by a model population were processed with the manifold learning and clusterization methods. Then, the dataset was processed with the deep autoencoder neural networks, and the latent space of the autoencoder was used as a tool for comparison of the two ionic models of the human atrial cardiomyocyte. Applied methods allowed us to classify the model solutions according to their physiological meaning, to visualize the model behavior, and to find special types of model solutions which would be almost impossible to notice via manual analysis.
Design of an AR-Based System for Group Piano Learning

Based on the fact that most systems that support piano learning only focus on self-study and ignore the piano class learning situation, we propose an AR-based system to support live group piano learning. The system has two modes: formal learning mode (FLM) and group competition mode (GCM). The first mode, FLM, is designed to allow students to observe teacher's key pressing and finger movement directly from their HMDs so as to enhance mutual understanding between the two parties during the learning process. The second mode, GCM, is developed to further ignite students' passion on learning piano via competition among students. Our goal is to exploit AR technologies for enhancing the learning fun and enthusiasm of beginner-level students on a group piano learning situation, and thus ultimately provide students with a great learning experience.
A Model for Quantifying Pilot's Situation Perception Based on Attention Allocation

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, the tasks that pilots need to accomplish in human-machine collaborative operations are more and more complex; the cognitive load is getting heavier and the situation awareness status is seriously affected. In order to assess the pilot's situation awareness status, it is necessary to quantify its first level, known as situation perception. Therefore, in this paper, the SEEV attention allocation algorithm is improved based on the background of human-machine collaboration and a new model for quantifying pilot's situation perception is established based on attention allocation. The eye movement data of subjects is also collected through specific flight simulation experiments and the situation perception of the subjects is quantified in this paper. The final experimental results show that the main information that consumes most of the subjects' attention is the roll and attack angle information, the route map information and the aircraft attitude information which is much easier to be perceived by the subjects. The results are in compliance with the flight experimental settings and can provide data support to assess the pilot's situation awareness status in the future work.
Topic Judgment Helps Question Similarity Prediction in Medical FAQ Dialogue Systems

As a prominent application of artificial intelligence in healthcare, medical dialogue systems have a promising future. In this paper, we built a medical Frequently Asked Questions (FAQ) dialogue system using an XGBoost classifier based on handcrafted features. Moreover, we propose to improve accuracy in domain-specific information retrieval by adding a topic judgment module to general sentence similarity prediction. As the size of the labeled medical corpus in Chinese is very limited, we built a corpus of Chinese medical FAQ from mvyxws https://www.mvyxws. com/ and annotated a small part of it. We applied our topic judgment module to the XGBoost model, a general sentence similarity model, and Baidu short text similarity API. Testing on two datasets, the models can be improved by adding the topic judgment in most cases.
Coordinated Zero-Forcing precoder with Leakage Minimization

This paper considers multiuser precoder design approach for distributive multiple-input multiple-output (D-MIMO) wireless network. Possibly large amount of radio-remote units (RRU) connected to one cell might be distributed over a wide area which leads to high computational requirements. For the complexity bottleneck overcoming the splitting network into fixed cluster-based topology with individual per-cluster processing assumed as one of the possible solution for D-MIMO system design. However such cluster-centric mode (CCM) yields cluster-edge users suffering from inter-cluster interference. In order to suppress the inter-cluster interference the Zero-Forcing (ZF) precoder modification for leakage minimization in CCM is proposing in the paper. The main idea is based on the condition of number of Users Equipments (UE) served by a cluster is less than cluster's antenna number which is provide opportunity to utilize free antennas space for inter-cluster leakage minimization. The high precision of leakage minimization is getting by the coordination between clusters for sharing list of scheduled UEs.
Target localization based on weighted total least squares in underwater acoustic networks

Most existed target localization methods in underwater sensor networks assume that the locations of sensor nodes are accurate as references to infer target location. However, in practice especially in underwater acoustic networks, the self-localization errors are inevitable. In this paper, considering the presence of both self-localization errors and measurements noises, a target localization model using time difference of arrival is constructed and then the localization problem is formulated. In order to solve the problem, we propose a TDOA target localization algorithm based on weighted total least squares in an iterative fashion. The proposed model takes into account the prior information of measurement noise variance and self-localization error variance which improve the performance. Simulations are conducted to validate the effectiveness of the proposed method. The results show that the proposed WTLS-based target localization method has superior performance compared with the existed method.
Machine Learning Based DDoS Attack Detection

Distributed denial of service attack has more risk especially in the field of cyber security. The DDoS attack usually arises from the application layer or the network layer where the victims system and the attackers system are interconnected in a network. The effects of these attacks may vary from causing significant failures at the aimed servers to causing inconvenience for users to use a particular service. The DDoS attack brings reputation damage, productivity loss, revenue loss, and even theft for huge business firms and also for banking sectors. Hence there is a need for a good distributed denial detection and prevention technique. The major goal is to deliver optimum solution for these problems using feature analysis. When a heavy traffic flow is experienced at the targeted server, it is important to classify them as an attack or legitimate access. Therefore a novel method has been proposed for the detection of Distributed denial of service attacks through the traces in the traffic flow. A confusion matrix has been generated from these traces. Two classifiers namely Naive Bayes and Random Forest are used to classify the traffic as abnormal or normal, using the normal and attack profile obtained from existing datasets. Naive Bayes algorithm gives better results than Random Forest algorithm.
A Coordinated Peak Shaving Strategy Using Neural Network for Discretely Adjustable Energy-Intensive Load and Battery Energy Storage

The large-scale wind power introduces the challenge of the power demand and generation balancing. Energy-intensive load (EIL) is a promising option for peak shaving since it can change its production time and power demand without affecting its overall production. However, EIL which is discretely adjustable is unable to track the net load in real time. A two-stage complementary peak shaving strategy of EILs with the aid of battery energy storage systems (BESSs) is proposed to address this issue. This paper establishes an optimization model with the minimum system operation costs and wind curtailment costs as the objective function, in which EIL operation constraints and BESS power and energy balance constraints are added to the unit commitment model. And the neural network algorithm is used to solve this optimization problem. Finally, a system with a high proportion of wind power is adopted to analyze the functions of EIL and BESS in the method. It is verified that the proposed strategy can effectively reduce the amount of wind curtailment and the operation costs of the system.