Miracle-dz/UniFAR-FTUs huggingface dataset:
Undetectable Finite-Time Covert Attack on Constrained Cyber-Physical Systems

In the last few decades, several cyber-threats against cyber-physical systems (CPSs) have been reported. Of particular interest are the classes of network attacks capable of affecting the control systems’ performance while remaining undetectable. In this article, under some conditions, we show the existence of a novel class of finite-time undetectable attacks against constrained CPSs. The proposed finite-time attack has the peculiar capability of not producing anomalies after its termination. This is particularly dangerous because it enables a malicious entity to repeatedly or intermittently affect the CPS without raising alarms. Such an attack is here designed by resorting to a set-theoretic approach that leverages robust reachability arguments. Moreover, given the desired attack duration, the set of initial states from which the attack is feasible is characterized. A numerical simulation example involving an industrial continuous-stirred tank reactor system is presented to support the theoretical results.
K-Pop Marketing Tactics That Build Fanatical Behavior

Kpop fans are known for their loyalty, they support their idols by buying albums, streaming music videos (MV), following concerts, fan meetings, fanservice, and many more. K-Pop fans are synonymous with fanaticism where K-pop fans are never separated from goods or merchandise related to their idols such as albums, lightsticks, photo cards, and other merchandise or by coming to every fan meeting, fan sign, Showcase, and idol concert. K-Pop fans are always considered excessive and always consumptive, and fan fanaticism is shown by joining the K-Pop fan community as their existence in providing information about K-Pop idols. This study aims to collect and analyze articles related to K-Pop marketing that causes fanaticism towards K-pop fans. The design used is a literature review, a critical analysis of the research being carried out on a specific topic of a scientific section. articles are collected using search engines such as Scopus and Google Scholar. Based on the articles collected, it is found that an interesting album and a concert that makes a happy experience can build a sense of love that leads to fanatical behavior. The album marketing strategy carried out by the South Korean entertainment agency makes K-Pop fans consume which is not only driven by efforts to meet their needs, but they have a sense of satisfaction from buying the K-Pop album
The new emission standard for light-duty motor vehicle in Beijing

It is an urgent task to effectively control the air pollution caused by emissions from motor vehicle in Chinesemegacihes. The investigation and analysis on vehicular pollution in Beijing show that the emission contribution rate ofmotor vehicle rises up to 41% and 82% for NOx and CO respectively. Through calculation of emission reduction effectiveness and cost of 4 different control scenarios, rigid emission standards for new vehicles are found to have greaterreduction potential especially for NOx,as well as lower cost due to advanced technologies. Therefore, the most stringentemission standard should be adopted in Beijing as long as the feasibility allows, so as to combat vehicular pollution and toimprove air quality in the earliest possibility.
Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network (STAG-GCN) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network (TCN) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.
Invasive intraguild predators: Evidence of their effects, not assumptions

Applied Ecology Research Group, School of Life Sciences, Anglia Ruskin University, Cambridge, UK Facultad de Agronomia e Ingenieria Forestal, Pontificia Universidad Catolica de Chile, Santiago, Chile Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile Research Institute for Nature and Forest (INBO), Brussels, Belgium Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium UK Centre for Ecology and Hydrology, Wallingford, UK Centre for Ecology, Evolution and Environmental Changes and Azorean Biodiversity Group, Faculty of Sciences and Technology, University of the Azores, Ponta Delgada, Portugal
Strategy for Creating AR Applications in Static and Dynamic Environments Using SLAM- and Marker Detector-Based Tracking

Recently, simultaneous localization and mapping (SLAM) has received considerable attention in augmented reality (AR) libraries and applications. Although the assumption of scene rigidity is common in most visual SLAMs, this assumption limits the possibilities of AR applications in various real-world environments. In this paper, we propose a new tracking system that integrates SLAM with a marker detection module for real-time AR applications in static and dynamic environments. Because the proposed system assumes that the marker is movable, SLAM performs tracking and mapping of the static scene except for the marker, and the marker detector estimates the 3-dimensional pose of the marker attached to the dynamic object. We can place SLAM maps, cameras, and dynamic objects (markers) on the same coordinate system, and we can also accurately augment AR resources in real-time on both the static scene and dynamic objects simultaneously. Additionally, having a static map of the scene in advance has the advantage of being able to perform tracking of static and dynamic environments with mapping disabled. In this paper, we evaluate whether the proposed tracking system is suitable for AR applications and describe a strategy for creating AR applications in static and dynamic environments with a demonstration.
Comparison of a computer-based audiometer with a conventional audiometer for diagnostic accuracy

The accuracy of a low cost, computer-based audiometer was assessed by comparing adult hearing thresholds from 57 subjects, obtained at 6 frequencies (250 Hz, 500 Hz, 1 kHz, 2 kHz, 4 kHz and 8 kHz), with those measured with a conventional audiometer, in sound-treated rooms. A repeated-measure ANOVA was performed to investigate whether there was any significant difference between hearing thresholds identified using the two audiometers. Correlations of hearing thresholds measured by the two audiometers were calculated using Pearson’s coefficient correlation and an evaluation of threshold difference values against average values. Significantly different hearing thresholds obtained between the two audiometers suggested that further improvement is needed before a computer-based audiometer can be used in routine clinical practice.
Toward Real-time Microseismic Event Detection using the YOLOv3 Algorithm

Real-time microseismic monitoring is essential for understanding fractures associated with underground fluid injection in unconventional reservoirs. However, microseismic events recorded on monitoring arrays are usually contaminated with strong noise. With a low signal-to-noise ratio (S/R), the detection of microseismic events is challenging using conventional detection methods such as the short-term average/long-term average (STA/LTA) technique. Common machine learning methods, e.g., feature extraction plus support vector machine (SVM) and convolutional neural networks (CNNs), can achieve higher accuracy with strong noise, but they are usually time-consuming and memory-intensive to run. We propose the use of YOLOv3, a state-of-art real-time object detection system in microseismic event detection. YOLOv3 is a one-stage deep CNN detector that predicts class confidence and bounding boxes for images at high speed and with great precision. With pre-trained weights from the ImageNet 1000-class competition dataset, physics-based training of the YOLOv3 algorithm is performed on a group of forward modeled synthetic microseismic data with varying S/R. We also add randomized forward-modeled surface seismic events and Gaussian white noise to generate ``semi-realistic'' training and testing datasets. YOLOv3 is able to detect weaker microseismic event signals with low signal-to-noise ratios (e.g., S/N=0.1) and achieves a mean average precision of 88.71\% in near real time. Further work is required to test YOLOv3 in field production settings.
A Study on the National Standard of Quantum Hall Resistance

A Quantum Hall Resistance (QHR) standard was established at NIM in 2003. The QHR samples used are designed and fabricated successfully. A Cryogenic Current Comparator (CCC) is developed to disseminate the QHR value to the decimal resistances. It is found that the main source of uncertainty in the system is the additional noise from the movement of the frozen flux by the pressure fluctuation in the cryostat, which disturbs the system during the dynamic process. Thus a new type of helium gas filter is designed to solve this problem. In addition, a series of measures are taken to improve the dynamic characteristic and feed-forward unit. Then a ratio with higher turn numbers about 8 times more than the former is attend and the S/N is improved correspondingly too. The total uncertainty of the QHR standard established at NIM is only several parts in 10~(-10)(k=1).
THEORY OF ROCKBURST START-UP DURING COAL MINING

By summing up the results of case analysis and microseismic monitoring,the three periods during rockburst occurred are pointed out.The types of rockburst are newly divided into centralized static load type and centralized dynamic load type,and engineering structure models of two kinds of rockbursts are established;then,energy criteria of rockburst start-up are analyzed,at last,theory of rockburst start-up during coal mining is put forward.The results show that,the intrinsic factor of rockburst stat-up is the excessive accumulation of static load in near-field system of mining rock;the possible areas of rockburst start-up are the maximum peak areas of stress in the region of limit equilibrium;the process of rockburst of two typical cases of rockburst can be analyzed by using the theory of rockburst start-up;and the time series and spatial series of rockburst are corresponded;so a guidance is provided for detection and prevention of rockburst.
Lightweight multiple scale-patch dehazing network for real-world hazy image

Image dehazing is an ill-posed problem which is far from being solved. Traditional image dehazing methods often yield mediocre effects and possess substandard processing speed, while modern deep learning methods perform best only in certain datasets. The haze removal effect when processed by said methods is unsatisfactory, meaning the generalization performance fails to meet the requirements. Concurrently, due to the limited processing speed, most dehazing algorithms cannot be employed in the industry. To alleviate said problems, a light-weight fast dehazing network based on a multiple scale-patch framework (MSP) is proposed in the present paper. Firstly, the multi-scale structure is employed as the backbone network and the multi-patch structure as the supplementary network. Dehazing through a single network causes problems, such as loss of object details and color in some image areas, the multi-patch structure was employed for MSP as an information supplement. In the algorithm image processing module, the image is segmented up and down for processed separately. Secondly, MSP generates a clear dehazing effect and significant robustness when targeting real-world homogeneous and nonhomogeneous hazy maps and different datasets. Compared with existing dehazing methods, MSP demonstrated a fast inference speed and the feasibility of real-time processing. The overall size and model parameters of the entire dehazing model are 20.75M and 6.8M, and the processing time for the single image is 0.026s. Experiments on NTIRE 2018 and NTIRE 2020 demonstrate that MSP can achieve superior performance among the state-of-the-art methods, such as PSNR, SSIM, LPIPS, and individual subjective evaluation.
An Evolutionary Multi-objective Optimization Technique to Deploy the IoT Services in Fog-enabled Networks: An Autonomous Approach

ABSTRACT The Internet of Things (IoT) generates countless amounts of data, much of which is processed in cloud data centers. When data is transferred to the cloud over longer distances, there is a long latency in IoT services. Therefore, in order to increase the speed of service provision, resources should be placed close to the user (i.e., at the edge of the network). To address this challenge, a new paradigm called Fog Computing was introduced and added as a layer in the IoT architecture. Fog computing is a decentralized computing infrastructure in which provides storage and computing in the vicinity of IoT devices instead of sending to the cloud. Hence, fog computing can provide less latency and better Quality of Service (QoS) for real-time applications than cloud computing. In general, the theoretical foundations of fog computing have already been presented, but the problem of IoT services placement to fog nodes is still challenging and has attracted much attention from researchers. In this paper, a conceptual computing framework based on fog-cloud control middleware is proposed to optimally IoT services placement. Here, this problem is formulated as an automated planning model for managing service requests due to some limitations that take into account the heterogeneity of applications and resources. To solve the problem of IoT services placement, an automated evolutionary approach based on Particle Swarm Optimization (PSO) has been proposed with the aim of making maximize the utilization of fog resources and improving QoS. Experimental studies on a synthetic environment have been evaluated based on various metrics including services performed, waiting time, failed services, services cost, services remaining, and runtime. The results of the comparisons showed that the proposed framework based on PSO performs better than the state-of-the-art methods.
Phonon-Enhanced Mid-Infrared CO2 Gas Sensing Using Boron Nitride Nanoresonators

: Hexagonal boron nitride (hBN) hosts long-lived phonon polaritons, yielding a strong mid-infrared (mid-IR) electric ﬁ eld enhancement and concentration on the nanometer scale. It is thus a promising material for highly sensitive mid-IR sensing and spectroscopy. In addition, hBN possesses high chemical and thermal stability as well as mechanical durability, making it suitable for operation in demanding environments. In this work, we demonstrate a mid-IR CO 2 gas sensor exploiting phonon polariton (PhP) modes in hBN nanoresonators functionalized by a thin CO 2 - adsorbing polyethylenimine (PEI) layer. We ﬁ nd that the PhP resonance shifts to lower frequency, weakens, and broadens for increasing CO 2 concentrations, which are related to the change of the permittivity of PEI upon CO 2 adsorption. Moreover, the PhP resonance exhibits a high signal-to-noise ratio even for small ribbon arrays of 30 × 30 μ m 2 . Our results show the potential of hBN nanoresonators to become a novel platform for miniaturized phonon-enhanced SEIRA gas sensors.
Female directors, audit effort and financial reporting quality

ABSTRACT This paper analyses the influence of female directors on the demand for audit effort and on financial reporting quality for a sample of Australian non-financial listed firms over the period 2004–2018. Our results suggest that gender diversity at the Board and the audit committee have a beneficial impact both on the demand of audit effort and on the outcomes of the audit process. This beneficial effect of female directors is attributable to non-executive directors. Long tenured and busy female directors and women chairing the board or the audit committee are revealed as especially influential at improving audit efforts and outcomes. We obtain further indication that audit effort and reporting quality improve from the first women added to the board not being necessary to attain a certain critical mass to generate these effects. Also, gender diversity improves the external auditor independence though the presence of lower of non-audit fees. The results are robust to endogeneity corrections. Evidence from the Australian market provides a positive argument for small and mid-cap Australian listed firms to follow the path of large Australian listed firms towards gender diversity.
Face Recognition System Using Deep Belief Network and Particle Swarm Optimization

Facial expression for different emotional feelings makes it interesting for researchers to develop recognition techniques. Facial expression is the outcome of emotions they feel, behavioral acts, and the physiological condition of one’s mind. In the world of computer visions and algorithms, precise facial recognition is tough. In predicting the expression of a face, machine learning/artificial intelligence plays a significant role. The deep learning techniques are widely used in more challenging real-world problems which are highly encouraged in facial emotional analysis. In this article, we use three phases for facial expression recognition techniques. The principal component analysis-based dimensionality reduction techniques are used with Eigen face value for edge detection. Then the feature extraction is performed using swarm intelligence-based grey wolf with particle swarm optimization techniques. The neural network is highly used in deep learning techniques for classification. Here we use a deep belief network (DBN) for classifying the recognized image. The proposed method’s results are assessed using the most comprehensive facial expression datasets, including RAF-DB, AffecteNet, and Cohn-Kanade (CK+). This developed approach improves existing methods with the maximum accuracy of 94.82%, 95.34%, 98.82%, and 97.82% on the test RAF-DB, AFfectNet, CK+, and FED-RO datasets respectively.
Identification of modal parameters and cable tension of harbin sifangtai bridge

Finite element model of Harbin Sifangtai Bridge was established,eight accelerometers with force feedback were applied to measure acceleration of bridge deck by the way of group test;piezoelectric accelerometer was applied to measure acceleration of cable;and the combined method of NExT and ERA was applied to identify the cable-stayed bridge modal parameters.The identification result was compared with the calculated result by FEM.Frequency difference fitting method was introduced to identify frequency and tension of cable and the result was compared with the designed cable tension of real bridge.
The Analysis of Quick Charge for Battery of Electric Vehicle

To cope with climate change and protect environment,a lot of enterprises are in the study of electric vehicle,in which the battery's quick charging is one of the most significant.As for the features of lithium battery,the charging means of limitative voltage variational pulse were goven,it may shorten charging time,the control circuit of balanced charging was designed,solving the imbalance of battery was solved.
Root Architectural Characteristics and Phosphorus Acquisition Efficiency in Plants

Root architecture, or the spatial configuration and distribution of root systems in the growth medium, is closely related to P acquisition efficiency. Understanding of root architecture may provide scientific basis of genetic improvement of P efficiency in plants. For a long time, attempts have been made to quantify root architecture and to establish a comprehensive criterion which can quantitatively describe the three_dimentional root architecture. Results from experiments indicate that changes in root architecture are mainly accomplished through root gravitropic sensitivity and carbon allocation between shoot and root, hence affecting phosphorus acquisition efficiency. Root gravitropic responses are adaptive, inheritable traits induced by low phosphorus availability and other factors. Evidence exists for a genetic regulation of gravitropic responses to low phosphorus availability, with possible involvement of ethylene as a physiological regulator. To date quantitative trait loci (QTLs) conferring root architectural characteristics have been identified in some plants, offering hopes for molecular improvement of this trait. As modern technologies advance, great breakthrough is expected for root architectural research in the future.
Physical and Mental Health Differences Reported by Three Age Groups of Older Adults With Diabetes

Most studies have classified older adults with diabetes into one group despite substantial variation in health status across different stages of late adulthood. In this study, we examined difference in self-reported physical and mental health among three age groups of older adults with diabetes. Using data from the 2016 National Social Life, Health and Aging Project, Wave 3, we classified 424 individuals diagnosed with diabetes into three age groups, young-old (YO): 50–64 years; middle-old (MO): 65–74; and oldest old (OO): 75+ years. A one-way multivariate analysis of covariance was used to assess group differences, followed by univariate analyses. The results indicate that the YO group reported significantly lower physical health and higher depression than the MO group and higher levels of loneliness than the MO and OO groups. These findings indicate that physical and mental health may differ among different age groups of older adults with diabetes and suggest that the YO might be more vulnerable to diminished physical and mental health than the other age groups.
Designing of an all-optical time division multiplexing scheme with the help of nonlinear material based tree-net architecture

In the field of optical interconnecting network and in super fast photonic computing system, the tree architecture and optical nonlinear materials can play a significant role. Nonlinear optical material may find important uses in optical switching. Optical switch using nonlinear material makes it possible for one optical signal to control and switch another optical signal through nonlinear interaction in a material. In this communication such materials have been successfully exploited to design an all-optical tree-net architecture, which can be utilized for time division multiplexing scheme in all-optical domain.