Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Haptic Rendering of Double-Points Interaction

Force generation will improve the immersion and authenticity of the virtual environment with human computer interaction. Considering the limitations of single-point interaction with force generation, this paper proposes real-time force generation methods based on double-points interaction. Analyzing the double-points interaction behaviors, we propose different equations to calculate the interactive force under four different interaction states. We establish an experimental environment to test the efficiency of double-points haptic interaction. The evaluation results show that the method can generate interactive force in time, enhance the immersion and authenticity of the virtual environment, and improve the naturalness of human computer interaction.
Sparse Bayesian Harmonic State Estimation

This paper presents a novel iterative method for harmonic state estimation based on the sparse Bayesian learning framework. The proposed method can locate harmonic sources and estimate the distribution of harmonic voltages using fewer harmonic meters than buses, despite the strong correlation between the columns of the system matrix. Extensive simulations are performed on a benchmark transmission system to corroborate the efficacy of this method when measurements are noise free. Our results show that the proposed state estimator achieves an identification error of less than $1.6\times 10^{-6}$ and can locate harmonic sources with an average success rate of 97.92%, outperforming state-of-the-art harmonic state estimators.
Human Activity and Heart Rate Monitoring System in a Mobile Platform

This paper describes a real-time human activity recognition and heart rate tracking system using ultra-low power wearables in a mobile platform. The mobile application shows both real-time and historical data of activities performed by the user along with the average heart rate for each activity. Random Forest and k-Nearest Neighbors algorithms were used to classify, showing general accuracies of 97.3% and 98.6%, respectively, with only eight features. The presented heart rate monitor is compared with an Apple Watch series 3 and they were demonstrated to display statistically equal values, confirming the reliability of the built monitor. Both wearables have a diameter of 4.5cm and consume currents below 10mA on transmission events and are powered with coin cell batteries, setting feasible characteristics for the need of modern wearable systems.
Tracing MIRAI Malware in Networked System

In 2021, it is anticipated that there will be approximately 30 billion Internet of Things (IoT) devices. The tremendous aggregate value of the IoT makes it a tempting and lucrative target for cyber criminals. The breakout of Mirai malware, which compromises poorly secured IoT devices with factory-default username and passphrase to launch Distributed Denial of Service (DDoS) attacks, has raised broad awareness towards the need for increased IoT security. To better defend against Mirai infection and spread, it is critical to know how the malware operates as the first step. In this paper, we give a combined static and dynamic analysis of Mirai, basing on the results of which, we introduce the application of Threat Tracer. Threat tracer is an information system simulator initially developed to help design a system robust against Advanced Persistent Attacks(APT). It offers an intuitive track on how a cyber threat behaves in a complicated networked system. The feedback simultaneously contributes to revealing vulnerabilities of a system. Our work focuses on the replication of Mirai Malware's operating processes in Threat Tracer simulation. By achieving doing so, we believe it could offer a comprehensible description of how Mirai acts. Also, considering the continuous emergence of Mirai variants, the simulation serves as a predictor on upcoming threats' behavior patterns.
High Performance Biosensor Based on RGO/ZnO Thin Film Transistor

This paper demonstrates the biosensing performance of graphene (RGO)/zinc oxide (ZnO) hybrid thin film transistor (TFT). The high mobility coupled with appreciable on/off ratio enables the detection of Hep-B surface antigen(HBsAg) down to 0.1 fM in high ionic strength buffer, not only with a sensitivity of 60% but also with a high signal to noise ratio(SNR) of 21.5 dB.
DAG Scheduling Algorithm for a Cluster-Based Many-Core Architecture

This paper proposes a directed acyclic graph (DAG) scheduling algorithm for cluster-based many-core architecture. Most of DAG scheduling methods that consider multiple processors and communication delays use a heuristic approach because it is difficult to shorten a schedule length (i.e.,makespan). Unfortunately, existing heuristic algorithms do not consider tasks that require a large number of computational resources. Such tasks typically need to be offloaded to a large-scale computational resource such as a many-core system or graphics processing unit (GPU). Therefore, we propose a DAG scheduling algorithm that uses a cluster-based many-core architecture to offload such computation; here, we use Kalray MPPA-256 as the many-core architecture. Our algorithm divides many-core computational resources for such tasks. Comparing our results with existing algorithms, we succeeded in shortening makespan with many types of DAGs while also considering Amdahl's law. We also compared the deadline miss ratio and execution time of our algorithm to existing algorithms, and show that the proposed algorithm is superior to the existing algorithms for any evaluation metrics.
Using Blog Articles in Software Engineering Research: Benefits, Challenges and Case–Survey Method

Context: Social media has dramatically changed the landscape of software engineering, and there is increasing use of blogs in software development. Objective: To examine opportunities for using blogs as a source of evidence in software engineering research. Method: We informally review practitioners use of blogs, and review the research literature. Results: We identify benefits and challenges to the use of blogs, consider quality criteria, and describe an automated methodology, based on the case survey, to gather and analyse blog–based evidence. Conclusion: Further work is required on automating the case survey methodology to ensure that large volumes of higher–quality blog content can be used effectively in research.
Static Output Feedback Control with H∞ performance for 2-D Discrete Systems in Roesser Model

This paper revisits the problem of H∞ static output feedback control of two-dimensional (2-D) discrete systems described by the Roesser model. Based on a Bounded Real Lemma of 2-D Roesser Model and Finsler’s Lemma, a new sufficient condition for H∞ performance analysis of the closed-loop system is first derived and then the static output feedback controller is obtained such that the closed-loop system is asymptotically stable with a guaranteed H∞ disturbance attenuation level. All results are developed in terms of linear matrix inequalitiy (LMI). Finally a numerical example is given to show the effectiveness of the proposed method.
AmICare: Ambient Intelligent and Assistive System for Caregivers Support

Recent and continuous technological advances in sensors, wireless communications and computing regarding price, size and power consumption have allowed the seamless integration of sensors into everyday objects in our homes and offices making non-intrusive user monitoring a reality. Intelligent processing of sensor data can be used to extract valuable information for carers so that they can provide better assistance to people such as elderly or disabled. These systems are commonly referred to as AAL (Ambient Assisted Living). In this paper we present an example of this approach called "Ambient Intelligence for Caregivers" or AmICare for short. Nodes of a WSN (Wireless Sensor Network) are integrated into furniture for non-intrusively monitoring of users' behaviour, whereas a software layer with an HMI (Human Machine Interface), that runs in a central node and server, can be configured with certain rules in a per resident basis to generate alerts to caregivers. AmICare is especially useful where there is need to supervise a high number of people (nursing homes, elderly or disabled people residences). A complete set of experimental tests has been carried out at a residence for intellectually disabled which requires massively distributed sensor nodes around all residence's bedrooms. This deployment in this scenario, the system architecture and its HMI are described in the paper. Finally, the testing procedure with the main significant results and caregivers' point of view are detailed.
A Software Cache Mechanism for Reducing the OpenTSDB Query Time

As the volume of Internet of Things (IoT) data grows rapidly, a time series database is a major part of the system that helps in storing and retrieving the massive data for analysis and monitoring. Accord-ing to the user behaviors, the data queries often are similar or overlapped to the previous queries, especially when the user adjusts the query parameters on the same time series data for displaying on a visualization tool. Caching the previous data will help reduce the query response time, in case that the current query range is overlapping with the previous query ranges. This paper proposes a cache mechanism for an open source time series database, OpenTSDB. Exploiting our cache mechanism, the query response time can be 3-16 times faster than the original OpenTSDB depending on the user query behaviors and related parameters.
Experimental Evaluation of Information Dissemination Scheme Considering Mobile Network Base Station Load in Wireless Networks

Most of mobile terminals such as smart phones are equipped with cellular connections (4G/LTE) and short range connections (WiFi/Bluetooth). In case that these nodes/devices use cellular connections simultaneously to access to the mobile core network, there is the possibility that many of them cannot establish the cellular connections due to the limited wireless resources of the cellular base stations. In this paper, we propose an information dissemination scheme which considers the load of mobile core network base station based on wireless network environments where we can utilize Mobile Ad-hoc NETwork (MANET) and wireless infrastructure communication. We evaluated the information dissemination scheme to share the sensing information of the monitoring system among residents in an area through simulation experiments.
Visible Light Communication System with 815 MHz-Modulation Bandwidth Based on Micro-Size Light-Emitting Diode and Post-Equalization Circuit

In order to get high modulation bandwidth in the visible light communication (VLC) system, the micro-size light-emitting diode (LED) with the diameter of 60 μm and the equalization circuit (EQC) are used. The micro-size LED works under low current density of 707.4 A/cm2 to extend its lift time. Four key parameters, including the resistance and the capacitance, in the EQC that consists of a two-stage common-emitter transistor amplifier were modified to adjust the frequency response, and, thus, to tune the 3-dB modulation bandwidth of the VLC system. The experimental results show that the 3-dB modulation bandwidth of the VLC system can be expanded from 94.2 MHz to 815 MHz, if the EQC was applied and optimized.
Unsupervised Surface Defect Detection Using Deep Autoencoders and Data Augmentation

Surface level defect detection, such as detecting missing components, misalignments and physical damages, is an important step in any manufacturing process. In this paper, similarity matching techniques for manufacturing defect detection are discussed. We are proposing an algorithm which detects surface level defects without relying on the availability of defect samples for training. Furthermore, we are also proposing a method which works when only one or a few reference images are available. It implements a deep autoencoder network and trains input reference image(s) along with various copies automatically generated by data augmentation. The trained network is then able to generate a descriptor—a unique signature of the reference image. After training, a test image of the same product is sent to the trained network to generate a test image descriptor. By matching the reference and test descriptors, a similarity score is generated which indicates if a defect is found. Our experiments show that this approach is more generic than traditional hand-engineered feature extraction methods and it can be applied to detect multiple type of defects.
Deep Q-Learning for Low-Latency Tactile Applications: Microgrid Communications

Ultra low-latency is one of the key requirements of tactile internet applications such as microgrid communication, where low end-to-end delay of control messages is essential. In addition, small cell wireless networks emerge as the enabler of networked microgrids, given their coverage, capacity and flexibility. Such densification can help in addressing the stringent QoS requirements of microgrid communications. In dense networks, besides the traditional resource allocation problem, user association can help in reducing communication latency, where users can associate with the base stations that can serve best. In this paper, we jointly address resource allocation and user/device association when Critical User Devices (CUDs), i.e. microgrid controllers, and non-critical users, i.e. User Equipments (UEs), co-exist in a small cell network. We formulate our optimization problem as a resource allocation as well as user association problem. We propose a deep Q-Network based algorithm, namely Delay Minimizing Deep Q-Network (DM-DQN) to address the low-latency requirement. DM-DQN aims at reducing the delay of CUDs by balancing the trade-off between allocating more RBs and associating devices to base stations with high channel quality. We compare the performance of DM-DQN to a tabular Q-learning algorithm. Our results show that the proposed scheme achieves 41% delay reduction for CUDs and it converges faster than Q-learning based scheme algorithm.
Sensing Room Occupancy Levels with Signal-to-Noise Ratio and Signal Phase and Multiple Antenna Configurations

We propose a novel machine learning based method for estimating the number of people present in a room (e.g, in a shared office space) based on WiFi signal- to- noise ratio and signal phase data provided by WiFi Channel State Information compatible hardware. We apply random decision forests machine learning and show that the precise number of people can be estimated with a score of 0.66 and the occupancy levels (empty, low, high) with a score of 0.87 at an affordable cost. We evaluate our approach in two settings: one small room with 0–2 and in a medium-sized office space with 0–8 people performing their usual office desk work. Beyond determining maximum recognition rates we systematically investigate the impact of different design choices (antennas, training data) on system performance. The proposed method outperforms a statistical baseline method significantly.
Line Formation Using Multi-layer Control Scheme

This paper presents a strategy to guide multiple robots in a virtual adaptable structure. The Multi-Layer Control Scheme (MLCS) is here implemented in order to guide various robots during trajectory tracking and positioning tasks. In this work, the robots are split in sets of two agents to establish several line-structure formation, which are individually controlled. The proposal is validated numerically. First, two robots in line formation perform positioning task and then they follow a circular trajectory. In the sequel, five robots generate a regular polygon, which should follow a straight-line reference. The concept of line-formation can be expanded for n-robots, only establishing and rearranging the new regular polygon, in a closed rigid structure.
A Method of Semantic Redundant Information Filtering for Abstract Meaning Representation Graph

Aiming at the problem of semantic redundancy in the existing text summarization, this paper proposes a method of semantic redundant information filtering based on Abstract Meaning Representation Graph. First, it transforms the original text into the corresponding AMR graph, then filters the semantic similar redundant information based on the WordNet semantic dictionary, and then extracts the important parts of AMR graphs to generate a de-redundant summarization. The experimental results show that the semantic redundancy removal method proposed in this paper has significantly improved the ROUGE and Smatch values compared to other word-aligned eliminate redundant methods. The ROUGE-1 increased by 7.2 percentage points and The Smatch increased by 13.2 percentage points. The novelty and readability of the summarization are greatly improved.
Dual D-q Current Control of FPMSM Based on Six Vector DVSVPWM

In five phase permanent magnet synchronous motor(FPMSM) drive system, the nonlinearity of multiphase motor and the dead zone effect of the inverter can produce harmonic current in the harmonic subspace with the traditional single plane current control. In order to control the harmonic current of FPMSM better, the dual d-q current control of FPMSM is necessary. So the dual vector space SVPWM(DVSVPWM) is particularly important. In order to improve the dual d-q current control of FPMSM, this paper proposes a novel dual plane space voltage vector modulation method based on space six vector(SV-DVSVPWM). The method of PWM waveform centralization can effectively solve the problem that the asymmetry of the PWM waveform. And a harmonic compensation method is proposed furthermore, which can completely eliminate the harmonic voltage in the harmonic subspace. The SV-DVSVPWM with compensation is used in the dual d-q current control of FPMSM. Finally, the simulation verified the correctness of the proposed modulation method and the validity of harmonic compensation. The experiment with dual d-q current control of FPMSM based on SV-DVSVPWM is verified, and an excellent current control effect is achieved.
Research on Online Quiz Scheme Based on Double-Layer Consortium Blockchain

The students’ online quiz is a way to realize the informatization and modernization of higher education. To solve the problem that the scoring process is non-transparent, injustice and the final results are easy to be changed, an online quiz scheme based on Double-layer Consortium Blockchain is proposed. The scheme achieves the public verification of students’ answers and the answer records cannot be tampered with but can be traced. Besides, group signature is used to solve the problem of anonymous abuse in blockchain applications. In addition, the storage pressure of sub-chains’ nodes can be further reduced by adding the prime-chain’s index to the sub-chains.
Non-volatile Memory Driver for Applying Automated Tiered Storage with Fast Memory and Slow Flash Storage

Automated tiered storage with fast memory and slow flash storage (ATSMF) is a hybrid storage system located between non-volatile memories (NVMs) and solid state drives (SSDs). ATSMF aims to reduce average response time for inputoutput (IO) accesses by migrating concentrated IO access areas from SSD to NVM. However, the current ATSMF implementation cannot reduce average response time sufficiently because of the bottleneck caused by the Linux brd driver, which is used for the NVM access driver. The response time of the brd driver is more than ten times larger than memory access speed. To reduce the average response time sufficiently, we developed a block-level driver for NVM called a "two-mode (2M) memory driver. " The 2M memory driver has both the. map IO access mode and direct IO access mode to reduce the response time while maintaining compatibility with the Linux device-mapper framework. The direct IO access mode has a drastically lower response time than the Linux brd driver because the ATSMF driver can execute the IO access function of 2M memory driver directly. Experimental results also indicate that ATSMF using the 2M memory driver reduces the IO access response time to less than that of ATSMF using the Linux brd driver in most cases.
User Health Information Analysis With a Urine and Feces Separable Smart Toilet System

In this paper, the integration of Internet of Things (IoT) regarding recent technology is discussed in a healthcare perspective. Various healthcare devices have emerged in recent years, allowing individuals to diagnose and manage their health conditions easily at home. However, the diagnostic utilization of IoT healthcare services is not yet fully integrated with the Internet. For this reason, people still have to go, for example, through a hands-on sampling process to self-analyze their urine, which is the same for inspectors in hospital labs. Therefore, with the exception of people suffering from certain diseases (e.g., diabetes), there are only a few who check their own health status due to the inconvenience. In this paper, sensor-based urine and feces separating toilet proposes a categorizing method for the user’s toilet, which applies various scenarios and analyses of the urine components using long-range wireless communication technology to transmit data from the smart toilet to the user.
Real-time enforcement of local energy market transactions respecting distribution grid constraints

Future electricity distribution grids will host a considerable share of the renewable energy sources needed for enforcing the energy transition. Demand side management mechanisms play a key role in the integration of such renewable energy resources by exploiting the flexibility of elastic loads, generation or electricity storage technologies. In particular, local energy markets enable households to exchange energy with each other while increasing the amount of renewable energy that is consumed locally. Nevertheless, as most ex-ante mechanisms, local market schedules rely on hour-ahead forecasts whose accuracy may be low. In this paper we cope with forecast errors by proposing a game theory approach to model the interactions among prosumers and distribution system operators for the control of electricity flows in real-time. The presented game has an aggregative equilibrium which can be attained in a semi-distributed manner, driving prosumers towards a final exchange of energy with the grid that benefits both households and operators, favoring the enforcement of prosumers’ local market commitments while respecting the constraints defined by the operator. The proposed mechanism requires only one-to-all broadcast of price signals, which do not depend either on the amount of players or their local objective function and constraints, making the approach highly scalable. Its impact on distribution grid quality of supply was evaluated through load flow analysis and realistic load profiles, demonstrating the capacity of the mechanism ensure that voltage deviation and thermal limit constraints are respected.