Miracle-dz/UniFAR-FTUs huggingface dataset:
MagicTCAM: A Multiple-TCAM Scheme for Fast TCAM Update

Ternary Content-Addressable Memory (TCAM) is a popular solution for high-speed flow table lookup in Software-Defined Networking (SDN). Rule insertion in TCAM is a time-consuming operation. To ensure semantic correctness, rules overlapped must be stored in TCAM with decreasing priority order and many rule movements may be needed to make space for a single inserted rule. When a rule insertion is in progress, the regular flow table lookup will be suspended, which could lead to a degraded user experience for SDN applications. In this paper, we propose a multiple-TCAM framework named MagicTCAM to reduce the rule movements during a rule insertion. The core of MagicTCAM lies in three operations: layering, partitioning and rotating. By layering, rules with the least overlapping will be grouped (i.e., layered) into a sub-ruleset. The number of rule movements is therefore greatly reduced as most of rules in a sub-ruleset are non-overlapped. To achieve balanced load in TCAMs, rules in each sub-ruleset are further partitioned and dispatched into different TCAMs in a rotating manner. In addition, an inter-TCAM movement algorithm is proposed to allow rules to be moved between TCAMs for reduced rule movement. Experiment results show that with two half-sized TCAMs, MagicTCAM reduces the rule movements by 39% on average compared with the state-of-the-art work while the computation time is shortened by half as well.
TROD: Evolving From Electrical Data Center to Optical Data Center

Despite the bandwidth scaling limit of electrical switching and the high cost of building Clos data center networks (DCNs), the adoption of optical DCNs is still limited. There are two reasons. First, existing optical DCN designs usually face tremendous deployment complexity. Second, these designs are not full-optical and the performance benefit against the non-blocking Clos DCN is not clear.After exploring the design tradeoffs of the existing optical DCN designs, we propose TROD (Threshold Routing based Optical Datacenter), a low-complexity optical DCN with superior performance than other optical DCNs. There are two novel designs in TROD that contribute to its success. First, TROD performs robust topology optimization based on the recurring traffic patterns and thus does not need to react to every traffic change, which lowers deployment and management complexity. Second, TROD introduces tVLB (threshold-based VLB), which can avoid network congestion as much as possible even under unexpected traffic bursts. We conduct simulation based on both Facebook’s real DCN traces and our synthesized highly bursty DCN traces. TROD reduces flow completion time (FCT) by at least 2× compared with the existing optical DCN designs, and by approximately 2.4-3.2× compared with expander graph DCN. Compared with the non-blocking Clos, TROD reduces the hop count of the majority packets by one, and could even outperform the non-blocking Clos with proper bandwidth over-provision at the optical layer. Note that TROD can be built with commercially available hardware and does not require host modifications.
Spontaneous Gaze-Based Application Using 2D Correlation and Velocity Threshold Identification

Demand for touchless technology has grown with the surge of Covid-19 pandemic. Spontaneous gaze-based application is one of several promising technologies for touchless interaction. Despite of this potential, little attention has been paid to the performance of traditional eye movements classification methods on improving accuracy of gaze-based object selection. To handle this research gap, we proposed a novel workflow of spontaneous gaze-based object selection using 2D Correlation as similarity measure and Velocity Threshold Identification (I-VT) as a method for eye movements classification. We compared our method with Pearson Product-Moment Correlation (PPMC) as similarity measure and Dispersion Threshold Identification (I-DT) for eye movements classification. Our experimental results showed that the proposed method yielded object selection accuracy up to $95.62 \%\pm 3.48\%$. In future, our proposed method can be implemented in the development of touchless interactive technologies that adhere to the World Health Organization guidelines, especially during the Covid-19 pandemic.
Deep Learning with Noise Data Augmentation and Detrended Fluctuation Analysis for Physical Action Classification by Brain-Computer Interface

Deep neural networks (DNNs) demonstrate their high potential for investigation of time sequences in many domains, including analysis of electroencephalography (EEG) signals gathered by brain-computer interface (BCI). This work is targeted on physical actions classification problem based on analysis of EEG data collected by BCI. The relatively small DNN with components of fully-connected networks (FCN) and convolutional neural network (CNN) was considered to classify physical activities (namely, hand manipulations) from the grasp-and-lift (GAL) dataset. The noise data augmentation (NDA) allowed us to estimate reliability of the results obtained in the view of the possible influence from environment. The detrended fluctuation analysis (DFA) was applied to investigate the fluctuation properties and calculate the correspondent Hurst exponents. The results of DNN training, validation and testing with and without NDA along with DFA demonstrated that some physical actions can be divided in separate groups of actions that can be characterized by complexity and the feasibility of their classification: the easiest (HandStart), medium (LiftOff, Replace, and BothReleased), and hardest (BothStartLoadPhase and FirstDigitTouch) classification. As far as the statistically reliable results were obtained by the relatively small DNN with the low resource requirements, this approach can be used for porting such models to Edge Computing infrastructures on devices with the very limited computational resources.
Analysis of doubly fed slip-ring machines

It is shown that a broad class of machines exists whose members may be regarded as doubly fed slip-ring machines. The paper presents a simple approach to the analysis of the steady-state performance of such machines, and indicates the interrelation between a number of conventional machines and other less orthodox machines. In particular, the double-speed doubly fed machine is examined in some detail, and it is shown theoretically that the peak-power capability of this machine when acting as a motor is greater than that of any comparable form of machine.
Automatic Segmentation of Finger Bone Regions from CR Images Using Improved DeepLabv3+

The number of hospitalized patients and the number of people requiring nursing care are serious social problems in Japan due to the increasing elderly population. The major causes of bedridden patients are bone and joint disorders caused by rheumatoid arthritis and osteoporosis. Early detection and treatment of these bone diseases are important because they significantly interfere with the quality of life (QOL) as the symptoms progress. Visual screening based on CR is used as a diagnosing tool for bone diseases. However, imaging diagnosis is subjective and lacks objectivity, and there is a possibility that lesions may be overlooked. In addition, it is difficult to find out subtle changes from images, increasing the workload for doctors. To solve these problems, there is a need to develop a computer aided diagnosis (CAD) system that can quantitatively diagnose bone diseases. We propose a method for automatic extraction of phalange regions for the CAD system to diagnose these diseases. The proposed method can extract the phalanges with high accuracy by using the improved DeepLabv3+. In this paper, we apply the proposed method to 101 cases of CR images and mIoU of 0.949 was obtained.
Evolution of road traffic congestion control: A survey from perspective of sensing, communication, and computation

Road traffic congestion can inevitably degrade road infrastructure and decrease travel efficiency in urban traffic networks, which can be relieved by employing appropriate congestion control. According to different developmental driving forces, in this paper, the evolution of road traffic congestion control is divided into two stages. The ever-growing number of advanced sensing techniques can be seen as the key driving force of the first stage, called the sensing stage, in which congestion control strategies experienced rapid growth owing to the accessibility of traffic data. At the second stage, i.e., the communication stage, communication and computation capability can be regarded as the identifying symbols for this stage, where the ability of collecting finer-grained insight into transportation and mobility reality improves dramatically with advances in vehicular networks, Big Data, and artificial intelligence. Specifically, as the pre-requisite for congestion control, in this paper, existing congestion detection techniques are first elaborated and classified. Then, a comprehensive survey of the recent advances for current congestion control strategies with a focus on traffic signal control, vehicle route guidance, and their combined techniques is provided. In this regard, the evolution of these strategies with continuous development of sensing, communication, and computation capability are also introduced. Finally, the paper concludes with several research challenges and trends to fully promote the integration of advanced techniques for traffic congestion mitigation in transportation systems.
Determination methodology for optimising the energy storage size for power system

A methodology for determining the optimal size of energy storage system (ESS) integrated with thermal power system is presented in this study. The optimal size is characterised by the rated stored energy and the maximum power rating of installed ESS for which the power system can achieve maximum revenue. Therefore the ESS cost formulation is conducted by analysing economic cost benefit measures considering life cycle of ESS. Since unit commitment (UC) scheduling is an important and integral part of power system cost optimisation, this study considers the operating schedule of thermal units (TU) while resolving ESS schedule. This proposed method uses tabu search (TS)-based evolutionary technique for solving this optimisation problem. TS is included in this algorithm to avoid re-evaluation of already evaluated ESS size which is powered by max priority heap and hash table data structure. The weekly schedule period is considered instead of daily to achieve more precise results. The proposed method is applied in two different power systems to determine the appropriate size of to be installed ESS. Experimental results reported that establishing the proposed method is an effective one to compute the optimal size of ESS for different sized power systems.
Machine Learning Model for Electric and Magnetic Fields Estimation in the Proximity of Overhead Transmission Lines

This paper considers the application of machine learning models to electric field intensity and magnetic flux density estimation in the proximity of the overhead transmission lines. The machine learning models are applied on two horizontal overhead transmission line configurations at different rated voltages, at height 1 m above ground surface. The obtained results are compared with the results obtained by charge simulation method and Biot-Savart law based method as well as with the field measurement results.
Synchronization Strategies on Many-Core SMT Systems

The complexity of efficient synchronization design increases with the continuous growth in the number of physical and logical cores on today's machines. Opinion is divided on which synchronization strategy is more powerful, opposing typical mechanisms, such as locks and atomic primitives, to emergent technologies, like transactional memory. We perform an extensive scalability study on many-core systems, evaluating most widely-used synchronization mechanisms in terms of application throughput and operation latency. We show that, from a performance perspective, current best-effort implementations of hardware transactional memory (HTM) are comparable to well-established locking or lock-free mechanisms. We also find that they scale better with the number of threads. We then showcase the ease-of-use of HTM in real-life applications. Finally, we analyze the impact of simultaneous multithreading (SMT) technologies on HTM performance. We propose a new cache replacement strategy that takes into account the transactional state of each cache line and aims to mitigate SMT-induced transactional overflow aborts.
Deployment control of wireless multihop‐relay mobile robots based on voronoi partition

This paper describes a new method for the deployment of wireless relay nodes. When using rescue robots in a building or underground city, radio signals are attenuated significantly, and therefore, multihop extension involving wireless communication relays is required. The goal of this research is to deploy wireless relay nodes to maintain connectivity between the base station and the leader robot that explores around the front line. To move the relay robot autonomously, a distributed algorithm is required.
Sub 5 Å-EOT HfₓZr1–xO₂ for Next-Generation DRAM Capacitors Using Morphotropic Phase Boundary and High-Pressure (200 atm) Annealing With Rapid Cooling Process

Achieving low equivalent oxide thickness (EOT) with CMOS-compatible materials is of prime importance for further miniaturization of dynamic random access memory (DRAM) technology. Despite several efforts made in this regard, especially by using high dielectric constant (<inline-formula> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula>) materials, utilization of novel electrodes to counterfeit the high leakage current limits its immediate implementation. Herein, we experimentally demonstrate CMOS-compatible ~4.8 Å-EOT ferroelectric (FE) Zr-rich Hf<sub><italic>x</italic></sub>Zr<sub>1–<italic>x</italic></sub>O<sub>2</sub> (HZO) with morphotropic phase boundary (MPB) formed by high-pressure annealing (HPA) and rapid cooling (RC) processes for next-generation DRAM device applications. Such low EOT was achieved by precise process optimization such as HZO composition, annealing condition, and thickness in the vicinity of MPB. The utilization of HPA reduces the physical thickness for emergence of MPB in Zr-rich HZO films, whereas the RC of the HPA samples reduces the leakage current considerably. A maximum dielectric constant (<inline-formula> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula>) of ~49 was achieved for 6.0-nm HZO [1:2] films for 500 °C HPA-RC resulting in an EOT as low as ~4.8 Å. This is the lowest among all reported EOT value using CMOS-compatible HZO films with leakage current <10<sup>−7</sup> A/cm<sup>2</sup> using TiN as top and bottom electrodes and can be of significant importance for the future DRAM technology.
MPLibra: Complementing the Benefits of Classic and Learning-based Multipath Congestion Control

Multipath TCP (MPTCP) is a burgeoning transport protocol which enables the server to split the traffic across multiple network interfaces. Classic MPTCPs have good friendliness and practicality such as relatively low overhead, but are hard to achieve consistent high-throughput and adaptability, especially for the ability of flexibly balancing congestion among different paths. In contrast, learning-based MPTCPs can essentially achieve consistent high-throughput and adaptability, but have poor friendliness and practicality. In this paper, we proposed MPLibra, a combined multipath congestion control framework that can complement the advantages of classic MPTCPs and learning-based MPTCPs. Extensive simulations on NS3 show that MPLibra can achieve good performance and outperform state-of-the-art MPTCPs under different network conditions. MPLibra improves the throughput by 40.5% and reduces the file download time by 47.7% compared with LIA, achieves good friendliness and balances congestion timely.
Autonomous Temperature and Humidity Wireless Sensor Node for Indoor Air Quality Monitoring Application

Air quality of any indoor area has been a major issue to be maintained since nowadays a lot of indoor activity occurs more often. The existence of a system to measure, analyze, and present the data of Indoor Air Quality (IAQ) is needed on most modern buildings. This paper presents a preliminary sensor node design to measure temperature and relative humidity for indoor IAQ monitoring applications. Compact and low-power features were considered in the design. The sensor node was designed to serve as a base operating device that has built-in wireless transfer data to be monitored using the Internet of Things (IoT). A temperature and relative humidity sensor from MikroElektronika (SHT click) is used as a primary sensor that detects both the temperature and humidity simultaneously. The AVR board has been chosen because of its compatibility with an ATMega4808 and WINC1510 are the main components for an IoT board. For standalone operation, a rechargeable battery was applied to be the main energy source for the sensor node placed in any location within a room. The designed sensor node was verified with an actual parameter for measurements under a real-life situation. The experiment results reveal that the designed wireless sensor node can run autonomously and monitor the indoor room temperature at an average 25°C and relative humidity at 52–54 % in real-time sampling that can be shown from the IoT Cloud Interface.
An Indigenous Application for Estimating Carbon footprint of academia library systems based on life cycle assessment

Global Warming is one of the pressing problems of the current century and can have disastrous effects in disturbing the ecological balance and climate stability on the planet Earth, if not addressed pro-actively by all nations across the world. A carbon footprint is a measure of the impact human activities have on the environment in terms of the amount of green house gases produced, measured in units of carbon dioxide. It is meant to be useful for individuals and organizations to conceptualize their personal (or organizational) impact in contributing to global warming. The current work highlights a cradle-to-grave approach based on a thorough Life Cycle Assessment (LCA) of all the components of a library system that involve energy usage, varying from the energy required to sustain labor, transportation of that labor, electrical energy consumption involved with the library hall/building, embodied energy of both books and supplies and all the infrastructure associated with the premises. A web-based user-friendly application that can calculate the carbon footprint of a library system in California during the 2007‐2008 year to a sufficient degree of accuracy based on user inputs has been developed and its electronic version is now available online at the following URL http://librarylca.berkeley. edu
Analytical dynamic tire model

Modeling of tire friction is one of the central problems for vehicle control systems design. LuGre-type dynamic tire model has been proposed and well discussed in previous studies, because it offers a compact form of dynamic model that is convenient in advanced control studies. It has been successfully used in tire slip control design and vehicle state estimation problems. In this article, a concept of time-constrained Stribeck effect is introduced to interpret the mechanism of the LuGre friction model in predicting tire friction characteristics. A modified two-dimensional (2D) dynamic LuGre friction model is introduced to make it compatible with the governing theorem in the steady state. An analytical 2D modified LuGre-type dynamic tire model is developed, in which some fundamental limitations of classical LuGre models are eliminated. The main modifications involve a change in the structure of the 2D LuGre friction model, introduction of load-dependent parameters in 1D and 2D tire models, and a changed structure in the distributed parameter model. The proposed model is compared, in the steady state, to both the Magic Formula and the classical LuGre model. It improves model accuracy in the steady state and gives a physically reasonable distribution of the bristle deflection. A first-order lumped parameter (LP) nonlinear model, which has simpler structure than the distributed parameter model and the classical LP LuGre model, is then derived. Numerical simulations show that the proposed LP model has a good estimation for tire transient dynamics. Thus, the proposed model retains the merits of LuGre-type models and improves the agreement with observation and experimental data on friction force distribution along the patch and on the steady-state friction prediction.
Numerical simulation of gaseous fuel injection: A new methodology for multi‐dimensional modelling

The use of natural gas (instead of liquid or solid fuels) is nowadays drawing an increasing interest in many applications (gas turbines, boilers, internal combustion engines), because of the greater attention to environmental issues. To facilitate the development of these applications, computer models are being developed to simulate gaseous injection, air entrainment and the ensuing combustion.
Evaluation of Seismic Vulnerability of Santa Maria del Mar in Barcelona by an Integrated Approach Based on Terrestrial Laser Scanner and Finite Element Modeling

Structural assessment of historical constructions is a particular issue as these structures were built with technical knowledge and construction methods based on in-field experience and on the observation of previous failures and successes of similar buildings. Their structural behavior is influenced by numerous aspects, sometimes not completely known (geometry, structural schemes and details, material mechanical properties), which often results in historical structures inadequate to achieve the safety level required for new constructions. In the case of earthquakes, these structures can suffer heavy local or global damages that sometimes lead to collapse. Thus static and seismic vulnerability assessment of historical buildings should therefore be performed by proper methodologies that differ from those used for new constructions. In Italy, an official document (Direttiva del Presidente del Consiglio dei Ministri [DPCM] 2007, revised in 2011) was released, defining a step-by-step procedure for data acquisition and vulnerability assessment of Cultural Heritage buildings. Such procedures can be coupled with new survey technologies such as terrestrial laser scanning (TLS), which, for performing a high-detailed three-dimensional geometrical survey, can provide detailed structural models refining the assessment results. In this study, the DPCM procedure is applied to Santa Maria del Mar Church in Barcelona (Spain) for seismic vulnerability assessment, using TLS three-dimensional survey and finite element analysis.
AimeLaw at ALQAC 2021: Enriching Neural Network Models with Legal-Domain Knowledge

In this paper, we present our participated systems for three Vietnamese legal text processing tasks at Automated Legal Question Answering Competition (ALQAC 2021). In our systems, we leverage the strength of traditional information retrieval methods (BM25), pre-trained masked language models (BERT), and legal domain knowledge. Our proposed methods help to overcome the shortage of training data. Especially, in the legal textual entailment task, we propose a novel data augmentation method that is based on legal domain knowledge. Evaluation results show the effectiveness of our proposed methods. Our team (AimeLaw) obtained the first prize in Task 2 (legal textual entailment) with 69.89% of accuracy; ranked second in Task 1 (legal document retrieval) with 80.61% of F2 and in Task 3 (legal question answering) with 64.77% of accuracy. We even improved the result on Task 2 to 72.16% in an extra experiment.