Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Traffic Surveillance and Detection using Deep Learning and Computer Vision Techniques

This research paper presents a web app-based ML/DL model that performs traffic surveillance and detection with high accuracy and efficiency. The paper aims to improve the existing traffic system in India by proposing a Deep Learning/Computer Vision based solution for traffic surveillance and detection. The system includes four models: helmet detection, license plate detection, vehicle classification, and speed detection, implemented on a web app with three detection options. YOLOv5 and YOLOv7 are employed, achieving state-of-the-art performance in detecting objects, and evaluation on a large dataset shows accurate and efficient real-time detection, making it a valuable tool for traffic flow analysis. The proposed system demonstrates the potential of deep learning in developing advanced traffic surveillance and detection systems, enhancing the overall traffic management system in India. In conclusion, the proposed Deep Learning/Computer Vision based solution offers an efficient and accurate approach to traffic surveillance and detection, addressing the challenges faced by the current traffic system in India. The use of YOLOv5 and YOLOv7 demonstrates the potential of deep learning techniques in developing advanced traffic surveillance and detection systems, which can enhance the overall traffic management system in India.
Uniform Electron Emission from $\text{SiO}_{\mathrm{X}}$ Tunneling Diode Arrays under Rough Vacuum

Uniform electron emission (EE) under rough vacuum of $\sim 10^{-2}$ Pa from on-chip micro-emitter arrays based on $\text{SiO}_{\mathrm{x}}$ tunneling diodes (STDs) in electroformed $\text{SiO}_{\mathrm{x}}$ is reported. Here, we provide a unique means to visually resolve the spatial distribution of EE from STD arrays and emitter-to-emitter uniformity at the microscale resolution via accompanied light emission. Benefiting from good emitter-to-emitter uniformity and low pressure sensitivity of EE from STD arrays, improved emission performances is obtained.
Automation of the System of Efficient Packaging of Cargoes by Containers Using Heuristic Algorithms

The article discusses the concept of an web aggregator for automated calculation of the cost of cargo delivery. Within the framework of optimizing the operation of the aggregator, the problem of efficient packaging of cargoes by consignments is relevant, which is reduced to the problem of discrete optimization. The automatic cargo packaging system is a software module that approximates the minimum number of containers needed to pack all cargo. The module is based on heuristic algorithms capable of calculating solutions close to optimal in a short period of time. The article conducted a comparative analysis of heuristic algorithms for solving the problem of packaging, taking into account the weight and volume of cargoes.
An Investigation on the Damping Ratio of Marine Oil Slicks in Synthetic Aperture Radar Imagery

The damping ratio has recently been used to indicate the relative internal oil thickness within oil slicks observed in synthetic aperture radar (SAR) imagery. However, there exists no well-defined and evaluated methodology for calculating the damping ratio. In this study, we review prior work regarding the damping ratio and outline its theoretical and practical aspects. We show that the most often used methodology yields damping ratio values that differ, in some cases significantly, for the same scene. Three alternative methods are tested on multifrequency datasets of verified oil slicks acquired from DLR's F-SAR instrument, NASA's unmanned aerial vehicle synthetic aperture radar, and Sentinel-1. All methods yielded similar results regarding relative thickness variations within slick. The proposed damping ratio derivation methods were found to be sensitive to the proportion of oil covered pixels versus open water pixels in the azimuth direction, as well as to the scene size in question. We show that the fully automatable histogram method provides the most consistent results even under challenging conditions. Comparisons between optical imagery and derived damping ratio values using F-SAR data show good agreement between the relatively thicker oil slick areas for the two different types of sensors.
Low Complexity Construction of Polar Codes with BP Decoder over Medium to High SNR Region

In this paper, we focus on the low-complexity construction method of polar codes with belief propagation (BP) decoder over additive white Gaussian noise (AWGN) channels. Firstly, the zero-noise BP decoding analysis method is proposed to determine the reliability of each bit channel via recursively calculating log-likelihood ratios (LLRs) of nodes from right to left in the factor graph. The final LLR can comprehensively reflect the structural characteristics of each bit channel. Then, the zero-noise LLR criterion based construction is introduced to select the frozen bit indices. Specifically, the lower the LLR value, the higher the priority of the corresponding bit selected as the frozen bit. Due to the assumed noise-free environment, the proposed zero-noise LLR criterion based construction is channel-independent. Finally, the simulation results show that, the zero-noise LLR criterion based construction can sort out the critical bit channels to load the frozen bits. When compared with other construction methods for BP decoder, such as the LLR-based bit-swapping construction and the simulation-based bit-swapping construction, the proposed zero-noise LLR criterion based construction not only has the best error correction ability over medium to high signal-to-noise ratio (SNR) region, but also exhibits the lowest construction complexity.
The effects of scalings on the performance of a sparse symmetric indefinite solver

Scaling is an important part of solving large sparse symmetric linear systems. In a direct method where the analysis is based only on strucuture it can help by reducing the number of delayed pivots and hence the memory required, the size of the computed factors, and total solution time. In this paper, we examine the effects of scaling on the performance of a sparse symmetric indefinite solver than implements a multifrontal algorithm. We compare several scalings from the mathematical software library HSL, using a large test set of 367 problems from a wide range of practical applications.
Enhancing Security and Privacy Preservation of Sensitive Information in e-Health Datasets Using FCA Approach

Advances in data collection, storage, and processing in e-Health systems have recently increased the importance and popularity of data mining in the health care field. However, the high sensitivity of the handled and shared data, brings a high risk of information disclosure and exposure. It is therefore important to hide sensitive relationships by modifying the shared data. This major information security threat has, therefore, mandated the requirement of hiding/securing sensitive relationships of shared data. As a large number of data mining activities that attempt to identify interesting patterns from databases depend on locating frequent item sets, further investigation of frequent item sets requires privacy-preserving techniques. To solve many difficult combinatorial problems, such as data distribution problem, exact and heuristic algorithms have been used. Exact algorithms are studied and considered optimal for such problems, however they suffer scalability bottleneck, as they are limited to medium-sized instances only. Heuristic algorithms, on the other hand, are scalable, however, they perform poor on security and privacy preservation. This paper proposes a novel heuristic approach based on Formal Concept Analysis (FCA) for enhancing security and privacy preservation of sensitive e-Health information using itemset hiding techniques. Our approach, named FACHS (FCA Hiding Sensitive-itemsets) uses constraints to minimise side effects and asymmetry between the original database and the clean database (minimal distortion on the database). Moreover, our approach does not require frequent itemset extraction before the masking process. This gives the proposed approach an advantage in terms of total availability. We tested our FCAHS heuristic on various reference datasets. Extensive experimental results showed the effectiveness of the proposed masking approach and the time efficiency of itemset extraction, making it very promising for e-Health sensitive data security and privacy.
Anisotropic Ballistic Transport through a Potential Barrier on Monolayer Phosphorene

We demonstrate theoretically the anisotropic quantum transport of electrons through a single barrier on monolayer phosphorene. Using an effective kp Hamiltonian, we find that the transmission probability for transport through n–n–n (or n–p–n) junction is an oscillating function of the incident angle, the barrier height, as well as the incident energy of electrons. The conductance in such systems depends sensitively on the transport direction due to the anisotropic effective mass. By tuning the Fermi energy and gate voltage, the channels can be transited from opaque to transparent, which provides us with an efficient way to control the transport of monolayer phosphorene-based microstructures.
Spatial–Spectral ConvNeXt for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a difficult task due to the heterogeneous spatial–spectral information, high-dimensionality, and noise effect in the HSI. Lately, an enhanced convolutional approach, i.e., ConvNeXt, has demonstrated a stronger feature representation capability than the popular vision transformer approaches. This article presents a spatial–spectral ConvNeXt approach, called SS-ConvNeXt, for hyperspectral classification. To better learn the spatial and spectral information in the HSI, the Spatial-ConvNeXt block, Spectral-ConvNeXt block, and spectral projection module are, respectively, designed. The depthwise and pointwise convolutions are adopted to reduce the model size and prevent vanishing gradient. The proposed model is evaluated against 14 other state-of-the-art methods on four different HSI datasets. Moreover, extensive ablation studies are conducted to investigate the roles of building blocks in the proposed model. The results demonstrate that the proposed method not only can achieve a high classification accuracy but also can better preserve class boundaries and reduce within-class noise.
Extreme Weather Effects on Transmission Grids and the Potential of the Utilization of Real Time Monitoring for Smart Grids Applications

Extreme weather events are increasing and becoming more frequent because of global warming and hence the exposure of transmission lines can have detrimental effects on our modern power systems. The power systems and especially transmission lines were designed for expected weather conditions and the rapid change in weather is putting a real strain on the transmission line grid. The transmission lines have not changed fundamentally in the last 100 years, and they have not been considered when it comes to advancements in technology over the last few decades. There are no common practices in monitoring transmission lines, but recent advancement has finally made real time monitoring a financially reasonable decision. This paper will review different extreme weather events and how they can affect transmission lines and how the real time monitoring of these effects could be utilized for smart grid applications in transmission grids.
A Multifloor Path-Loss Model for 433 MHz LoRa-WAN in Concrete Buildings

LPWAN (low power wide-area network) for smart building applications will benefit positioning, energy management system, parking and so on. This paper proposes a propagation model for LPWAN 433 MHz in two different concrete buildings, 5 story laboratory and parking buildings. The path loss model in terms of RSSI (received signal strength indicator) calculation uses empirical measurement data. The path-loss exponent for LoS propagation of the proposed prediction model is found to be 2.03 and 1.99. Again, for the NLoS propagation links, the floor attenuation factors (FAF) of 1 to 5 floors are found to be 6.0, 10.8, 13.9, 17.0 and 9.8, 14.0, 17.2, 21.5 for the first and the second buildings respectively
Characteristics of mean flow and turbulence statistics of depth-limited flows with submerged vegetation in a rectangular open-channel

ABSTRACT This paper reports a numerical investigation of the characteristics of mean flow and turbulence statistics for a depth-limited flow with submerged vegetation in a rectangular channel. To achieve this, the double-averaged Navier–Stokes equations were solved using the Reynolds stress closure model. A zone with increased velocity was observed at the corner between sidewall and free surface, thus forming two velocity maxima, one at the centre of the channel and one at the corner. This is consistent with previous experimental observations. The velocity maximum at the corner can be attributed to the enlarged bottom vortex caused by submerged vegetation, which transports high-momentum fluids from the centre to the corner of the channel. The velocity maximum at the corner was found to occur in the flow with submerged vegetation regardless of roughness density and relative submergence. The velocity maximum at the corner affects the mean flow, but has a negligible effect on turbulence statistics.
Determinants Of Retail Electronic Purchasing: A Multi-Period Investigation1

Abstract Academic research as well as business literature recognizes the importance of electronic commerce and retail electronic purchasing (REP), i.e., the use of the Internet by individuals to purchase goods and services. This paper draws upon established theoretical areas of marketing and innovation adoption to identify the factors expected to influence REP. It empirically examines the effects of these factors, as well as the changes in these factors and in their effects over time, using publicly available multi-period data. The results indicate that REP is facilitated by the individual’s household income, educational level, Internet use and Internet search, as well as by perceptions of Internet security and the perceived quality of web vendors’ sales processes. We did find some surprising results: most notably, there was no association between REP and the perceived quality of web vendors’ post-sales activities. Moreover, there was no significant change over time in the perceived quality of web vendors’ sales and post-sales activities. However, Internet use, Internet search, and perceptions of Internet security, show an increasing trend.
Unsupervised Sentence Embedding Model Based on Contrastive Learning

The unsupervised sentence embedding model of the contrastive learning framework SimCSE uses dropout noise as a data expansion method, which often defaults to having sentences of the same length to have more similar semantic information, and the random nature of dropout may lead to loss of semantic information or large differences due to sentence embedding. For this reason, we propose two agent tasks random deletion as well as R-Dropout to solve these problems. We conducted experiments on the text semantic similarity task on the publicly available datasets STS12-16, STS B, and SICK-R. The experimental results show that our proposed sentence embedding model improves the average Spearman correlation coefficient to 77.67 %, compared with the benchmark models IS-BERTbase, CT-BERTbase, and SimCSE- We also used the SenEval toolkit to evaluate the quality of sentence embed dings generated by the model, and used sentence embeddings as features of migration tasks MR, SUBJ, MPQA, TREC, and MRPC for classification tasks using SenEval, and the experimental results showed that our proposed sentence embedding model achieves better performance in the accuracy of classification in all cases.
Multi-reference Adaptive Gain FXLMS Algorithm for Active Noise Control

Multi-reference least mean square algorithm (MR-FXLMS) has been performed more efficiently than the traditional Least mean square algorithm (FXLMS). In this paper, a multi-reference adaptive gain (MRAG-FXLMS) algorithm which is designed to enhance the performance of the multi-reference FXLMS algorithm. The proposed method is derived by combining a novel multi-reference structure and adaptive gain. The adaptive gain is obtained by using the gradient descent method. To evaluate the effectiveness of the proposed algorithm, a computational complexity analysis is conducted. According to the simulation results, it can be concluded that the proposed MRAG-FXLMS algorithm achieves excellent performance in convergence rate and steady-state error compared with the conventional FXLMS algorithm and MR-FXLMS algorithm.1
Work function mapping of single-crystal graphene

Kelvin potential force microscopy (KPFM) was used to study the surface potential of single-crystal graphene transferred to silicon dioxide substrate and calculate the work function of single-crystal graphene. It was observed that when graphene on copper substrate was directly scanned, the surface potential distribution of normal graphene appeared to be more disordered compared to that of single-crystal graphene. This feature is used to determine the single-crystal property of graphene grown on copper foil by chemical vapor deposition without transfer. The surface potential of the multilayer graphene area at the nucleation point of single-crystal graphene and the surrounding single-layer area is analyzed. It is found that the work function of the multilayer graphene area is slightly lower than that of the single-layer graphene, which provides guidance for the application of single-crystal graphene and multilayer graphene in field emission.
A specific gene-microbe interaction drives the development of Crohn’s disease–like colitis in mice

Combined deficiency of NOD2 and NAPDH oxidase triggers Crohn’s-like disease driven by a mouse pathobiont, Mucispirillum. A mouse model for Crohn’s disease Although several mouse models of inflammatory bowel disease exist, Crohn’s disease has been particularly difficult to model. Here, Caruso et al. report a mouse model where they can recapitulate multiple hallmarks of intestinal inflammation seen in patients with Crohn’s disease. Mice lacking nucleotide-binding oligomerization domain–containing protein 2 (NOD2) and the cytochrome b-245 beta chain (CYBB) subunit of phagocyte NADPH oxidase develop spontaneous colitis that is driven by a Gram-negative pathobiont, Mucispirillum schaedleri. NOD2/CYBB-deficient mice developed colitis only after weaning because maternal antibodies protected them from it before weaning. In addition to developing a model for studying Crohn’s disease, the study also highlights the importance of maternal antibodies in regulating immune homeostasis in early life. Bacterial dysbiosis is associated with Crohn’s disease (CD), a chronic intestinal inflammatory disorder thought to result from an abnormal immune response against intestinal bacteria in genetically susceptible individuals. However, it is unclear whether dysbiosis is a cause or consequence of intestinal inflammation and whether overall dysbiosis or specific bacteria trigger the disease. Here, we show that the combined deficiency of NOD2 and phagocyte NADPH oxidase, two CD susceptibility genes, triggers early-onset spontaneous TH1-type intestinal inflammation in mice with the pathological hallmarks of CD. Disease was induced by Mucispirillum schaedleri, a Gram-negative mucus-dwelling anaerobe. NOD2 and CYBB deficiencies led to marked accumulation of Mucispirillum, which was associated with impaired neutrophil recruitment and killing of the bacterium by luminal neutrophils. Maternal immunoglobulins against Mucispirillum protected mutant mice from disease during breastfeeding. Our results indicate that a specific intestinal microbe triggers CD-like disease in the presence of impaired clearance of the bacterium by innate immunity.
Channel Estimation and Data Detection in MIMO channels with 1-bit ADC using Probit Regression

We address in this article the uplink transmission in a multiple-input multiple-output channel employing 1-bit analog-to-digital converters at the base station, assuming no a priori channel state information. In particular, we investigate under the original "probit" statistical model, the problems of channel estimation and data detection by first formulating them as binary classification procedures based on the cross-entropy loss. Under perfect CSI, the proposed data detection scheme relaxes the exhaustive search requirement to a convex problem with a box boundary constraint that can be solved using gradient descent methods. Achievable mismatched rates of proposed metrics are evaluated with the generalized mutual information and symbol error rates are presented. Simulation results show that the proposed channel estimation scheme under the probit model does not exhibit any instability with imperfect CSI in comparison with the Bussgang linear minimum mean square error estimator.
Firm-Level Propagation of The Effect of Disruption of International Trade Through Domestic Supply Chains

Risks of disruption to global supply chains are rising and threatening the sustainability of global supply chains. This study simulates how the disruption of imports from various regions affect the total production of Japanese firms. We particularly incorporate the propagation of the economic effect through domestic supply chains, using data on more than one million firms and four million supply chain ties. We find that the negative effect of disruption of intermediate imports grows exponentially as its duration and level increase because of downstream propagation. In addition, the propagation of the economic effect is substantially affected by the network topology of importers, such as the number of importers (affected nodes) and their upstreamness in supply chains, but not necessarily by their centrality. Furthermore, the negative effect of import disruption can be mitigated by reorganisation of domestic supply chains, even when the reorganisation is conducted among only network neighbours.
Human Factors in the Age of Autonomous UAVs: Impact of Artificial Intelligence on Operator Performance and Safety

This research reviews the current literature on the impact of Artificial Intelligence (AI) in the operation of autonomous Unmanned Aerial Vehicles (UAVs). This paper examines three key aspects in developing the future of Unmanned Aircraft Systems (UAS) and UAV operations: (i) design, (ii) human factors, and (iii) operation process. The use of widely accepted frameworks such as the "Human Factors Analysis and Classification System (HFACS)" and "Observe– Orient–Decide–Act (OODA)" loops are discussed. The comprehensive review of this research found that as autonomy increases, operator cognitive workload decreases and situation awareness improves, but also found a corresponding decline in operator vigilance and an increase in trust in the AI system. These results provide valuable insights and opportunities for improving the safety and efficiency of autonomous UAVs in the future and suggest the need to include human factors in the development process.
Perceptions of MOOC Systems Among Domestic University Students at Different Levels

In this research we investigate the attitudes of domestic students studying at different university levels towards learning in Mooc systems. In the literature section, I review the development of online education in the 21st century. After that, in the primary research, a questionnaire survey was conducted with 792 people completing the questionnaire in which an Anova analysis was employed. Conclusions were then drawn from the results.