Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure of the Ice–Clathrate Interface

Clathrate hydrates are crystals in which water molecules form hydrogen-bonded cages that enclose small nonpolar molecules, such as methane. In the laboratory, clathrates are customarily synthesized from ice and gas guest under conditions for which homogeneous nucleation of hydrates is not possible. It is not known how ice assists in the nucleation of clathrate hydrates or how ice forms on clathrate hydrate in the case of self-preservation. There is no lattice matching between any plane of ice and clathrate hydrates; therefore, an interfacial transition layer has to form to connect the two crystals. Here, we use molecular dynamic simulations to study the structure of ice–clathrate interfaces produced by alignment and equilibration of the crystals, competitive growth of ice and clathrate from a common solution, nucleation of hydrate in the presence of a growing ice front, and nucleation of ice in the presence of clathrate hydrates. We find that the interfacial transition layer between ice and clathrate is a.
Computer Vision Based Skin Disorder Recognition using EfficientNet: A Transfer Learning Approach

Skin disorders have a vital impact on people's health and quality of life, it is essential to develop a reliable and accurate computer vision approach to recognize multiple skin disorders. In this paper, a new rapid recognition approach using EfficientNet has been introduced to diagnose twenty types of skin disorders. Initially, image augmentation techniques have employed, and then eight architectures of EfficientNet between B0 and B7 have trained using the transfer learning approach. To evaluate the performance of models, different experimental studies have employed using a test set of 6300 images of skin disorders dataset that makes the proposed approach more reliable and accurate. EfficientNet-B7 has achieved the highest accuracy 97.10% among all architectures but has taken longer training time. EfficientNet-B0 has taken the lowest training time and has achieved 93.35% accuracy. EfficientNet-B7 has also taken the lowest time in recognizing unseen new images of skin disorders accurately than others.
Model-Based Approach for Anomaly Detection in Smart Home Inhabitant Daily Life

Due to population ageing, the number of people requiring monitoring and specific health care increased. Nevertheless, due to social or financial reasons, most of this population prefers to stay at home. Thanks to recent improvement in Ambient Assisted Living (AAL) and smart home technologies, new opportunities emerge to develop Health at Home (HaH) solutions. One of the main objectives of HaH is to offer to smart home inhabitants a life and health quality similar to patients in specialized medical institutions. To achieve this task, it is proposed in this paper an approach to monitor frail people at home to detect behavioral changes which can be symptoms of health problems. The method is model-based, working with Stochastic Timed Automata as it is an efficient tool to model activity duration and ordering habits, which are key features of human behavior. Once a model depicting life habits is built, it is proposed to use normalized likelihood and probabilistic distribution to evaluate the consistency between the regular life habits and newly observed behavior. Appearance of a inconsistent behavior may testify the evolution or apparition of a medical symptom. A case study demonstrates the relevancy of the proposed approach, and several scenarios highlight the possibility to have clear enough information on inhabitant behavior to identify a limited number of responsible medical disorder.
Digital Compensation of Residual Pump Dithering in Optical Phase Conjugation of High-Order QAM

We develop a new two-stage digital scheme to suppress the phase distortion due to residual pump dithering in the dual-pump fibre-based optical phase conjugation of high-order (64/256) QAM signals. We show more than 5-dB SNR improvement relative to conventional phase-noise compensation at high pump-phase mismatch levels.
A Low Profile, Dual-Band, Dual-Polarized Patch Antenna With Antenna-Filter Functions and Its Application in MIMO Systems

In this paper, a low profile, dual-band dual-polarized patch antenna is proposed for antenna-filter function with a relatively low profile of 0.032 wavelength. A simple symmetric open/short-circuited stub (O/SCS) is proposed to excite a square radiating patch, and an additional resonant mode is generated except for the patch-mode. At the lower band, the proposed antenna works as a dual-polarized patch antenna. At the upper band, transmission characteristic is obtained between the two ports, and the antenna structure can be used as a bandpass filter. A <inline-formula> <tex-math notation="LaTeX">$1\times 2$ </tex-math></inline-formula> array is also designed to validate its application potential in MIMO system. The two antennas can operate as radiators with high port isolation levels at the lower band. Meanwhile, at the upper band, the two antenna elements can act as one bandpass filter as the good transmission characteristic. In addition, a <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> array is also developed meet dual-function demands in dual-polarized MIMO systems. The low profile, simple structures and excellent performances indicate that the proposed antenna can be a good candidate in future wireless communication applications with shared functions.
Condition Monitoring with LoRaWAN: Preliminary Tests on Gas Turbine Exciters

Condition monitoring of huge mechanical plants has recently encountered a broad attention due to several possible implications linked to reliability, availability and system safety. Indeed, it is nowadays crucial to provide electromechanical systems to the market with the outmost and highest confidence degrees not only in terms of performance but also in terms of endurance capabilities. Additionally, together with conventional measurement and sensing systems, novel wireless technologies have been deployed in the operating field providing new challenges and opportunities. If on one hand it is possible to get signals which were not available before, on the other hand, a question on the new transmission systems reliability has arisen. This unresolved clue is moreover stressed by the fact that such systems are often exploited in heavy duty balance of plants and in harsh environments. Such conditions require a deep analysis of the sensing and transmission systems in order to let the signal gathered be highly trustable: as an example the Gas Turbine activation scenario is presented. Under these assumptions, in this paper the authors provide an analysis about the possibility to exploit LoRaWAN transmission system in a gas turbine exciter. The challenge is to investigate how the exciter sparking, as well as the transmission system, performances are affected by environmental operating conditions.
Asynchronous Deep Reinforcement Learning for Data-Driven Task Offloading in MEC-Empowered Vehicular Networks

Mobile edge computing (MEC) has been an effective paradigm to support real-time computation-intensive vehicular applications. However, due to highly dynamic vehicular topology, these existing centralized-based or distributed-based scheduling algorithms requiring high communication overhead, are not suitable for task offloading in vehicular networks. Therefore, we investigate a novel service scenario of MEC-based vehicular crowdsourcing, where each MEC server is an independent agent and responsible for making scheduling of processing traffic data sensed by crowdsourcing vehicles. On this basis, we formulate a data-driven task offloading problem by jointly optimizing offloading decision and bandwidth/computation resource allocation, and renting cost of heterogeneous servers, such as powerful vehicles, MEC servers and cloud, which is a mixed-integer programming problem and NP-hard. To reduce high time-complexity, we propose the solution in two stages. First, we design an asynchronous deep Q-learning to determine offloading decision, which achieves fast convergence by training the local DQN model at each agent in parallel and uploading for global model update asynchronously. Second, we decompose the remaining resource allocation problem into several independent subproblems and derive optimal analytic formula based on convex theory. Lastly, we build a simulation model and conduct comprehensive simulation, which demonstrates the superiority of the proposed algorithm.
Maximum hands-off control with time-space sparsity

This paper treats a novel maximum hands-off control problem in which two types of sparsity are considered. Our optimization problem is mathematically formulated as an $L^{0}$ optimal control problem with an ℓ0constraint. This optimal control has the minimum length of support and a small number of activated components among all control inputs that steer the system to a target state within a fixed time duration. Since the formulated problem is combinatorial, we introduce a convex relaxation problem for its computational tractability. We show a sufficient condition under which our sparse optimization problem boils down to the convex optimization problem and give an existence theorem of the optimal controls. The proposed control is illustrated through a numerical example.
A General Framework for Intersection Traffic Control With Backpressure Routing

Traffic congestion has long been a worldwide difficult problem in metropolitan transportation networks, incurring tremendous time waste and exhaust pollution. Extensive efforts have been made to address this problem, among which traffic control at intersections is known to be especially crucial while challenging. Also, it is helpful to the recent advent of autopilot technologies by enhancing the schedule accuracy and reducing the infrastructure cost. In practice, however, existing researches can hardly work well in a pervasive manner since they are essentially limited to two ideal assumptions: 1) each intersection comprises four ways; 2) each way is homogeneously composed of two lanes. Through an in-depth examination of their basic models, we find that the two-fold ideal assumptions are largely compelled by the surprisingly high complexity of converting a practical intersection topology into a theoretical conflict graph. Moreover, existing works seldom consider the modeling of complex intersections in the scene of cooperative control of multiply intersections. Driven by the above understandings, our first effort towards a general framework (for handling multiple-way heterogeneous intersections) is to carefully transform a practical intersection topology into a homomorphic, regular conflict graph which is suited to theoretical modeling and further processing with an affordable complexity. Besides, a maximum weight independent set (MWIS) based approach is proposed to minimize the average waiting time of vehicles at an isolated intersection. In addition, we apply a backpressure-based algorithm to our framework to further optimize the global average waiting time of vehicles in a whole road network. Simulation results have demonstrated that the average waiting time achieved by our approach is merely 80 seconds while traditional traffic light control reaches 370 seconds.
Takagi-Sugeno Multimodeling-Based Large Signal Stability Analysis of DC Microgrid Clusters

DC microgrid (DCMG) clusters represent interconnections of multiple DCMGs to enable flexible power flow, and hence advantages of high resilience, economic dispatch, loss minimization, and optimal load response with microgrid-based distributed generations can fully be taken. However, small-scale DCMGs are known to be weak in nature due to low inertia and high grid impedance. Meanwhile, dynamic analyses of DCMG clusters have been plagued by their high order dynamic nonlinear system models since numerous state variables are involved. This article proposes large signal stability analysis of DCMG clusters based on Takagi-Sugeno multimodeling approach. With the proposed method, the large signal Lyapunov stability of the DCMG cluster is reduced to the computation of a series of linear matrix inequalities, which will significantly simplify the analysis. The influences of circuit parameters, power flows, and topological change on large signal stability of the DCMG cluster are revealed, and asymptotic stability regions of the network are estimated as well. In addition to simulation verification, a laboratory prototype of a ring topology DCMG cluster with three 48 V DCMGs interconnected is also built to validate the analysis by experimental results.
Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques With a Novelty Feature Engineering Scheme

Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, complex and chaotic nature of the stock price time series. With a simple eight-trigram feature engineering scheme of the inter-day candlestick patterns, we construct a novel ensemble machine learning framework for daily stock pattern prediction, combining traditional candlestick charting with the latest artificial intelligence methods. Several machine learning techniques, including deep learning methods, are applied to stock data to predict the direction of the closing price. This framework can give a suitable machine learning prediction method for each pattern based on the trained results. The investment strategy is constructed according to the ensemble machine learning techniques. Empirical results from 2000 to 2017 of China’s stock market confirm that our feature engineering has effective predictive power, with a prediction accuracy of more than 60% for some trend patterns. Various measures such as big data, feature standardization, and elimination of abnormal data can effectively solve data noise. An investment strategy based on our forecasting framework excels in both individual stock and portfolio performance theoretically. However, transaction costs have a significant impact on investment. Additional technical indicators can improve the forecast accuracy to varying degrees. Technical indicators, especially momentum indicators, can improve forecasting accuracy in most cases.
Repurposing GPU Microarchitectures with Light-Weight Out-Of-Order Execution

GPU is the dominant platform for accelerating general-purpose workloads due to its computing capacity and cost-efficiency. GPU applications cover an ever-growing range of domains. To achieve high throughput, GPUs rely on massive multi-threading and fast context switching to overlap computations with memory operations. We observe that among the diverse GPU workloads, there exists a significant class of kernels that fail to maintain a sufficient number of active warps to hide the latency of memory operations, and thus suffer from frequent stalling. We argue that the dominant Thread-Level Parallelism model is not enough to efficiently accommodate the variability of modern GPU applications. To address this inherent inefficiency, we propose a novel micro-architecture with lightweight Out-Of-Order execution capability enabling Instruction-Level Parallelism to complement the conventional Thread-Level Parallelism model. To minimize the hardware overhead, we carefully design our extension to highly re-use the existing micro-architectural structures and study various design trade-offs to contain the overall area and power overhead, while providing improved performance. We show that the proposed architecture outperforms traditional platforms by 23 percent on average for low-occupancy kernels, with an area and power overhead of 1.29 and 10.05 percent, respectively. Finally, we establish the potential of our proposal as a micro-architecture alternative by providing 16 percent speedup over a wide collection of 60 general-purpose kernels.
Argument Extraction for Key Point Generation Using MMR-Based Methods

When people debate, they want to familiarize themselves with a whole range of arguments about a given topic in order to deepen their knowledge and inspire new claims. However, the amount of differently phrased arguments is humongous, making the process of processing them time-consuming. In spite of many works on using arguments (e.g. counter-argument generation), there is only a few studies on argument aggregation. To address this problem, we propose a new task in argument mining – Argument Extraction, which gathers similar arguments into key points, usually single sentences describing a set of arguments for a given debate topic. Such a short summary of related arguments has been manually created in previous research, while in our research key point generation becomes fully automatic, saving time and cost. As the first step of key point generation we explore existing similarity calculation methods, i.e. Sentence-BERT and MoverScore to investigate their performance. Next, we propose a combination of argument similarity and Maximal Marginal Relevance (MMR) for extracting key phrases to be utilized in our novel task of Argument Extraction. Experimental results show that MoverScore-based MMR outperforms strong baselines covering 72.5% of arguments when eleven or more arguments are extracted. This percentage is almost identical with the cover rate of human-made key points.
Leader-Following Coordination of Heterogeneous Multi-Agent Systems via Displacement Feedback

This paper deals with heterogeneous second-order multi-agent consensus, for attitude coordinated control of spacecraft. A robust consensus for undirected and connected graph is shown, with dynamic weight interaction and only displacement measurements available. Leader-following coordination is proposed, with proof of internal stability. Flexible appendages, external disturbances and uncertainties are included in the model. A space maneuver is considered to show the achievement of the consensus.
RFace: Anti-Spoofing Facial Authentication Using COTS RFID

Current facial authentication (FA) systems are mostly based on the images of human faces, thus suffering from privacy leakage and spoofing attacks. Mainstream systems utilize facial geometry features for spoofing mitigation, which are still easy to deceive with the feature manipulation, e.g., 3D-printed human faces. In this paper, we propose a novel privacy-preserving anti-spoofing FA system, named RFace, which extracts both the 3D geometry and inner biomaterial features of faces using a COTS RFID tag array. These features are difficult to obtain and forge, hence are resistant to spoofing attacks. RFace only requires users to pose their faces in front of a tag array for a few seconds, without leaking their visual facial information. We build a theoretical model to rigorously prove the feasibility of feature acquisition and the correlation between the facial features and RF signals. For practicality, we design an effective algorithm to mitigate the impact of unstable distance and angle deflection from the face to the array. Extensive experiments with 30 participants and three types of spoofing attacks show that RFace achieves an average authentication success rate of over 95.7% and an EER of 4.4%. More importantly, no spoofing attack succeeds in deceiving RFace in the experiments.
Dual-Polarized Wide-Angle Energy Harvester for Self-Powered IoT Devices

In this work, a dual-polarized wide-angle energy harvester operating at 3.75 GHz is proposed for ambient RF energy harvesting applications. A $4\times 4$ array harvester can efficiently harvest incident RF waves from all polarizations, resulting in high RF-AC efficiency of 94% and final RF-DC efficiency of 34% at 0.02 W/m2 input power density, or −15 dBm input power. This performance is preserved under a wide incident angle up to 65 degrees in TE mode and 75 degrees in TM mode. Owing to its simple power combining configuration, the array can be easily expanded to incorporate more unit cells, increasing the amount of collected power. Not only having the RF-DC conversion function, it is also equipped with a power management circuit to create a usable output voltage level. With this advantage, the harvester is suitable to be employed in real-life applications in which the polarization and angle of incidence are unknown and the incident power is very low.
COFlood: Concurrent Opportunistic Flooding in Asynchronous Duty Cycle Networks

For energy constrained wireless IoT nodes, their radios usually operate in duty cycle mode. With low maintenance and negotiation cost, asynchronous duty cycle radio management is widely adopted. To achieve fast network flooding is challenging in asynchronous duty cycle networks. Recently, concurrent flooding is a promising approach to improve the performance of network flooding. In concurrent flooding, a key challenge is how to select a set of concurrent senders to improve both flooding speed and energy efficiency. We observe that selecting neither large nor small number of concurrent senders can achieve the optimal performance in different deployments. In this paper, we propose COFlood (Concurrent Opportunistic Flooding), a practical and effective concurrent flooding protocol in asynchronous duty cycle networks. The basic idea is based on an energy-efficient flooding tree, COFlood opportunistically selects extra concurrent senders that can speed up network flooding. First, COFlood constructs an energy-efficient flooding tree in distributed manner. The non-leaf nodes are selected as senders and they can cover the entire network with low energy consumption. Moreover, we find that exploiting both early wakeup nodes and long lossy links can speed up the flooding tree based network flooding. Then, COFlood develops a light-weight method to select the nodes that meet the conditions of these two opportunities as opportunistic senders. We implement COFlood in TinyOS and evaluate it on two real testbeds. In comparison with state-of-the-art concurrent flooding protocol, completion time and energy consumption can be reduced by up to 35.3% and 26.6%.
An Approach to Detecting Malicious Information Attacks for Platoon Safety

Malicious attacks reduce the benefits of cooperative adaptive cruise control (CACC) such as safety, driving convenience, traffic flow, and fuel efficiency, by destabilizing the stability. To reinforce the resiliency of a CACC based platoon of connected and automated vehicles (CAVs), this work investigates a detection method for malicious information attacks in the platoon. In this work, we propose an attack detection method, called LMID (long short-term memory (LSTM) based malicious information detection). We consider two attack models: correlated attacks and non-correlated attacks. In our attack scenarios, one of the platoon members attacks the platoon using the attack models. Using PLEXE, a well-known platoon simulator, we develop a simulation framework to implement attack scenarios and evaluate the proposed detection method. LMID is trained depending on the length of input data and analyzed under various scenarios regarding platoon trajectories, attack types, and an emergency brake case. We have shown that without fast detection of such attacks, crashes may happen within a platoon. The simulation results demonstrate that LMID detects the malicious information attacks with higher than 96% accuracy and the attacks are detected very quickly. The performance evaluation indicates the superiority of the proposed detection method under various circumstances.