Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling Ambiguous Assignments for Multi-Person Tracking in Crowds

Multi-person tracking is often solved with a tracking-by-detection approach that matches all tracks and detections simultaneously based on a distance matrix. In crowded scenes, ambiguous situations with similar track-detection distances occur, which leads to wrong assignments. To mitigate this problem, we propose a new association method that separately treats such difficult situations by modelling ambiguous assignments based on the differences in the distance matrix. Depending on the numbers of tracks and detections, for which the assignment task is determined ambiguous, different strategies to resolve these ambiguous situations are proposed. To further enhance the performance of our tracking framework, we introduce a camera motion-aware interpolation technique and make an adaptation to the motion model, which improves identity preservation. The effectiveness of our approach is demonstrated through extensive ablative experiments with different detection models. Moreover, the superiority w.r. t. other trackers is shown on the challenging MOT17 and MOT20 datasets, where state-of-the-art results are obtained.
Comparison of electromagnetic force densities evaluated in the electrodynamic lattice-Boltzmann method and finite-difference time-domain method

Centered-difference electromagnetic force densities are challenging to implement in the finite-difference time-domain method. Since the Yee-lattice electric and magnetic field components are offset by half unit-cells and time-steps, local averaging is required which can introduce numerical error. The alternative electrodynamic lattice-Boltzmann method can solve time-domain electric and magnetic fields of greater accuracy on a single lattice, which also simplifies its implementation. This letter extends the electrodynamic lattice-Boltzmann method to the evaluation of centered-difference electromagnetic force densities, and compares these time-averaged force densities with those evaluated by the finite-difference-time-domain method.
An Optical Electric Field Sensor for AC and DC Electric Field Measuring

The technology of electric field measurement is an important method to obtain the transient process of power system, such as the overvoltage caused by switching operation. The optical electric field sensor based on Pockels effect is non-contact measurement, which has better performance and more flexible application than the traditional methods. The electro-optic crystal is the key part of the sensor, and different kinds of crystal has different charge relaxation time constant, which causes problem when measures DC electric field. In this paper, an optical electric field sensor in type of common path interferometer with LiNbO3 crystal is proposed. The response characteristics of the sensor is verified by AC electric field under different waveforms and frequencies, and DC electric field. Experimental results show that the proposed sensor can be used to measure both AC and DC electric field. The waveform of transient overvoltage recorded in UHV AC and DC substation are employed to test the performance of the sensor to real application scenario, and the results indicate good performance of the sensor.
The Performance Argument for Blockchain-based Edge DNS Caching

The Domain Name System (DNS,) a standard way of looking up IP addresses of Internet services, has served the Internet ecosystem well. However with the advent of edge computing, it falls short of many desirable properties. These include accurate fine-grained geographic localization of edge services, fast look-ups, and ensuring record freshness and cache integrity for end users. To satisfy these properties we consider blockchain-based solutions, a counter-intuitive approach as blockchain is not often associated with performance or the latency requirements of the edge. Despite this, we argue blockchain can address the shortcomings we've identified in DNS specifically in the edge context. We've found blockchain-based solutions are not sufficient as is, thus we present GeoENS - a prototype based on the Ethereum blockchain suitable for, and enabled by, the edge. It achieves these goals via novel record organization for smart contract, push-based record invalidation, and a look through cache. Given the skepticism of blockchain to out-perform DNS, we provide preliminary results which show GeoENS achieves its goals of fine-grained geo-localization accurate to ±20 meters, fast query latency for non-cached records under 50 ms, and cache freshness at edge scale of 10 minutes (vs. 3 hour DNS TTLs.) GeoENS does this with negligible bandwidth and CPU load overhead and reasonable storage requirements in the proposed deployment scenario.
Nonnegative Low-Rank Tensor Completion via Dual Formulation with Applications to Image and Video Completion

Recent approaches to the tensor completion problem have often overlooked the nonnegative structure of the data. We consider the problem of learning a nonnegative low-rank tensor, and using duality theory, we propose a novel factorization of such tensors. The factorization decouples the nonnegative constraints from the low-rank constraints. The resulting problem is an optimization problem on manifolds, and we propose a variant of Riemannian conjugate gradients to solve it. We test the proposed algorithm across various tasks such as colour image inpainting, video completion, and hyperspectral image completion. Experimental results show that the proposed method outperforms many state-of-the-art tensor completion algorithms.
Detecting Lung Cancer from Histopathological Images using Convolution Neural Network

Lung cancer is one of the leading causes of mortality in both men and women throughout the world. That is why early identification and treatment of lung cancer patients bear a huge significance in the recovery procedure of such patients. A lot of time, pathologists use histopathological pictures of tissue biopsy from possibly diseased regions of the lungs to detect the probability and type of cancer. However, this procedure is both tedious and sometimes fallible too. Machine learning based solutions for medical image analysis can help a lot in this regard. The aim of this work is to provide a convolution neural network (CNN) model that can accurately recognize and categorize lung cancer types with superior accuracy which is very important for treatment. We propose a CNN model with 15000 images split into 3 categories: Training, validation, and testing. Three different types of lung tissues (Benign tissue, Adenocarcinoma, and squamous cell carcinoma) have been examined. 50 instances from every class were kept for testing procedure. The rest of the data was split as: about 80% and 20% for training and validation respectively. Eventually, our model obtained 98.15% training accuracy and 98.07% validation accuracy.
MoESR: Blind Super-Resolution using Kernel-Aware Mixture of Experts

Modern deep learning super-resolution approaches have achieved remarkable performance where the low-resolution (LR) input is a degraded high-resolution (HR) image by a fixed known kernel i.e. kernel-specific super-resolution (SR). However, real images often vary in their degradation kernels, thus a single kernel-specific SR approach does not often produce accurate HR results. Recently, degradation-aware networks are introduced to generate blind SR results for unknown kernel conditions. They can restore images for multiple blur kernels. However, they have to compromise in quality compared to their kernel-specific counterparts. To address this issue, we propose a novel blind SR method called Mixture of Experts Super-Resolution (MoESR), which uses different experts for different degradation kernels. A broad space of degradation kernels is covered by kernel-specific SR networks (experts). We present an accurate kernel prediction method (gating mechanism) by evaluating the sharpness of images generated by experts. Based on the predicted kernel, our most suited expert network is selected for the input image. Finally, we fine-tune the selected network on the test image itself to leverage the advantage of internal learning. Our experimental results on standard synthetic datasets and real images demonstrate that MoESR outperforms state-of-the-art methods both quantitatively and qualitatively. Especially for the challenging ×4 SR task, our PSNR improvement of 0.93 dB on the DIV2KRK dataset is substantial1.
Real-time traffic monitoring and traffic offense detection using YOLOv4 and OpenCV DNN

This paper presents a computer vision-based system for traffic offense detection. The system detects traffic offenses such as speed limit violations, unauthorized vehicles, traffic signal violations, unauthorized parking, wrong-way driving, and motorbike riders without helmets. The traffic offense detection system consists of a pipeline of four different modules. These are a vehicle detection module, a vehicle classification module, a vehicle tracking module, and a traffic offense detection module. Vehicles on the roads are detected in the vehicle detection module using visual data such as live camera feed. Next, after the vehicles are detected, they are classified into different classes using a vehicle classification module. A vehicle tracking module is developed to track the vehicle as it moves through the traffic. Lastly, we have implemented a traffic offense detection module that analyzes traffic patterns and detects different types of traffic violations in real-time. The entire system is implemented using OpenCV Deep Neural Network (DNN) module. We have used YOLOv4 to detect vehicles on the roads with high accuracy. For motorbike riders without helmets, we have used a fast YOLOv4-tiny model. The DeepSORT algorithm is used to track vehicles in real-time. Obtained accuracies are 86% in YOLOv4 for vehicle detection and 92% in YOLOv4-tiny for helmet detection.
Programmable Metasurface-Based DOA Estimation Using Atomic Norm Minimization

This paper presents a atomic norm minimization (ANM)-based direction of arrival (DOA) estimation method using programmable metasurface sensor (PMS). Different from the traditional phased-array, PMS utilizes the low-cost, passive, reflective elements to independently reflect the incident signal. The ANM-based DOA estimation method overcomes the drawback of grid effect by working directly in continuous parameter domain. Simulation results verify that the proposed method based on PMS can achieve better DOA estimation performance with low hardware complexity and low-cost.
Design of Series- fed Patch Array with Modified Binomial Coefficients for MIMO Radar Application

In this paper, we propose a collocated antenna configuration having three transmitter and five receiver arrays for automotive MIMO Radar application. The individual antenna arrays consist of a seven-element series fed array with the feed provided at the edge. We introduce a modified-binomial coefficient-based excitation scheme to achieve improved side-lobe level (SLL) in series-fed patch arrays with edge feeding. The proposed MIMO configuration exhibits low SLL less than 16 dB and a wide field-of-view (FoV) of about 80° in the azimuth plane, along with satisfactory angular resolution (5.82°) and low cross-polar level (30 dB), rendering it extremely suitable for automotive medium-range radar (MRR) application.
High Temperature Reliability Investigations of EEPROM Memory Cells Realised in Silicon-on-Insulator (SOI) Technology

Microelectronic manufacturing progresses not only towards further miniaturisation, but also application fields tend to become more and more diverse. Recently there has been an increasing demand for electronic devices and circuits that function in harsh environments such as high temperatures. Under these conditions, reliability aspects are highly critical and testing remains a great challenge. A versatile CMOS process based on 200 mm thin film Silicon-on-Insulator (SOI) wafers is in production at Fraunhofer IMS. It features three layers of tungsten metallisation for optimum electromigration reliability, voltage independent capacitors, high resistance resistors and single-poly-EEPROM cells. Non-volatile memories such as EEPROMs are a key technology that enables flexible data storage, for example of calibration and measurement information. The reliability of these devices is especially crucial in high temperature applications since charge loss is drastically increased in this case. The behaviour of single-po.
Application of Fuzzy Matter-Element Model in DSM Evaluation Based on AHP-Entropy

DSM multi-agent comprehensive effect evaluation system is established from the macroscopic perspective.Then based on fuzzy matter-element model,index weight is calculated through AHP-entropy,the subjective and objective weight combination evaluation method.Based on the listed data,the DSM implementation effects in a provincial region are comprehensively evaluated and ranked.This research aims to provide the reference for the evaluation and decision-making in power demand side management.
Sustainability: Opportunities and Challenges from a Construction Contractor's Perspective

Sustainability in the traditional delivery of the built environment is typically targeted towards the facility owners and designers. The contractor’s input in such delivery models is limited to value engineering exercises during late design or early construction phases. Project delivery models that increase the contractor’s stake in a project beyond the construction phase are increasingly becoming commonplace and present opportunities for contractors to play a bigger role in the sustainability of the built environment. This paper explores the challenges and opportunities for contractors in the traditional as well as ownership based delivery models. A review of the state of the practice in the Middle East region from the perspective of a multinational contracting firm is presented to highlight the sustainability impact of the contractor and the challenges contractors associate with sustainable construction.
ANALYSIS OF ARCH DAM-ABUTMENT STABILITY BY 3D DEFORMABLE DISTINCT ELEMENTS

3D deformable distinct element method(3D DEM) is an efficient numerical method for simulation of mechanical behaviors of discontinuous media.It is especially suitable for analyzing problems of nonlinear,large deformation and dynamic systems due to the explicit finite-difference scheme and the automatic contact detection skill.Hereint,he principles and features of the 3D deformable distinct element method are first summarized.Then,an analysis of stability against sliding of an arch dam-abutment system is performed by 3D DEM.Two methods,i.e.overloading method and strength reduction method are used to determine the safety factor against sliding of the system.Some conclusions are drawn as follows:(1) the rock abutment will not slide under even large overloads due to self-locking effect for this specific case,but the dam concrete will yield to compression failure at the overload factor of 4.0;(2) the strength reduction method shows that the left abutment rock reaches failure,which causes the dam collapse under the designed upstream water and seepage pressure when the coefficient of reduction arrives at 3.5. Based on the explicit finite-difference scheme of the 3DEC,the dam collapse process is emulated very well.
An investigation on the use of SNR distributions for the optimisation of coarse-fine spectrum sensing for cognitive radio

This thesis investigates the optimisation of Coarse-Fine (CF) spectrum sensing architectures under a distribution of SNRs for Dynamic Spectrum Access (DSA). Three different detector architectures are investigated: the Coarse-Sorting Fine Detector (CSFD), the Coarse-Deciding Fine Detector (CDFD) and the Hybrid Coarse-Fine Detector (HCFD). To date, the majority of the work on coarse-fine spectrum sensing for cognitive radio has focused on a single value for the SNR. This approach overlooks the key advantage that CF sensing has to offer, namely that high powered signals can be easily detected without extra signal processing. By considering a range of SNR values, the detector can be optimised more effectively and greater performance gains realised. This work considers the optimisation of CF spectrum sensing schemes where the security and performance are treated separately. Instead of optimising system performance at a single, constant, low SNR value, the system instead is optimised for the average operating conditions. The security is still provided such that at the low SNR values the safety specifications are met. By decoupling the security and performance, the system’s average performance increases whilst maintaining the protection of licensed users from harmful interference. The different architectures considered in this thesis are investigated in theory, simulation and physical implementation to provide a complete overview of the performance of each system. This thesis provides a method for estimating SNR distributions which is quick, accurate and relatively low cost. The CSFD is modelled and the characteristic equations are found for the CDFD scheme. The HCFD is introduced and optimisation schemes for all three architectures are proposed. Finally, using the Implementing Radio In Software (IRIS) test-bed to confirm simulation results, CF spectrum sensing is shown to be significantly quicker than naive methods, whilst still meeting the required interference probability rates and not requiring substantial receiver complexity increases.
Analysis on factors leading to human fault in marine accidents based on attribute reduction

In order to find a simple and effective method to analyze the factors leading to human fault in marine accidents,the data mining method based on attribute reduction of rough set theory is introduced.A data-set comprising 30 influencing factors which may lead to fault on look-out is analyzed by a rough set analysis tool RSES,and the main factors are obtained by the attribute reduction method based on genetic algorithm.The results show that the attribute reduction method based on rough set theory would simplify the analysis,and improve the accuracy and visulization in analyzing the factors leading to human fault in marine accidents.It provides a powerful foudation for the effective management of marine traffic.
STUDY ON EMERGING IMPORTANCE OF CLOUD BASED IOT SCHEME WITH WIRELESS SENSOR NETWORK IN PRECISION AGRICULTURE USING CRYPTOGRAPHIC ALGORITHM

For many mission-critical related wireless sensor network applications such as military and homeland security, user’s access restriction is necessary to be enforced by access control mechanisms for different access rights. Public key-based access control schemes are more attractive than symmetric-key based approaches due to high scalability, low memory requirement, easy key-addition/revocation for a new node, and no key pre-distribution requirement. Precision farming is an approach where inputs are utilised in precise amounts to get increased average yields, compared to traditional cultivation techniques. Precision agriculture refers to the precise application of agricultural inputs with respect to soil, weather and crop need in order to improve productivity, quality, and profitability in agriculture. It enables farmers to use crop inputs more efficiently including pesticides, fertilizers, tillage and irrigation water
Phlomis fruticosa L. exerts in vitro antineurodegenerative and antioxidant activities and induces prooxidant effect in glioblastoma cell line

Despite the significant advances in drug development we are witnessing the inability of health systems to combat both neurodegenerative diseases and cancers, especially glioblastoma. Hence, natural products are comprehensively studied in order to provide novel therapeutic options. This study aimed to explore anti-neurodegenerative and anti-glioblastoma potential of extract of Phlomis fruticosa L. using in vitro model systems. It was found that the methanol extract of P. fruticosa was able to efficiently reduce activities of enzymes linked to neurodegenerative disease including acetylcholinesterase, butyrylcholinesterase and tyrosinase. Furthermore, P. fruticosa extract has shown excellent antioxidant potential, as evidenced by six different methods. Analysis of cytotoxic effect of P. fruticosa extract on A172 glioblastoma cell line revealed that the concentration of the extract necessary for 50 % inhibition of A172 growth (IC50) was 710 μg/mL. The extract did not induce changes in proliferation and morphology of A172 glioblastoma cells. On the other side, production of ROS was increased in A172 cells treated with the extract. Observed cytotoxic effect of P. fruticosa extract might be based on increase in ROS generation upon treatment. Quantitative chemical analysis revealed the presence of twelve different polyphenols with the cis 3-O-caffeoylquinic acid being the most abundant. This study provided scientific evidence for further exploration of P. fruticosa as a promising natural anti-neurodegenerative therapeutic option.
Good Fences Make Good Neighbours? On the Impact of Cultural and Geographical Dispersion on Community Smells

Software development is de facto a social activity that often involves people from all places to join forces globally. In such common instances, project managers must face social challenges, e.g., personality conflicts and language barriers, which often amount literally to “culture shock”. In this paper, we seek to analyze and illustrate how cultural and geographical dispersion-that is, how much a com-munity is diverse in terms of its members‘ cultural attitudes and geographical collocation-influence the emergence of collaboration and communication problems in open-source communities, a.k. a. community smells, the socio-technical precursors of unforeseen, often nasty organizational conditions amounting collectively to the phenomenon called social debt. We perform an extensive empirical study on cultural characteristics of GITHUB developers, and build a regression model relating the two types of dispersion-cultural and geographical-with the emergence of four types of commu-nity smells, i.e., Organizational Silo, Lone Wolf, Radio Silence, and Black Cloud. Results indicate that cultural and geographical factors influence collaboration and communication within open-source communities, to an extent which incites-or even more interestingly mitigates, in some cases-community smells, e.g., Lone Wolf, in development teams. Managers can use these findings to address their own organizational structure and tentatively diagnose any nasty phenomena related to the conditions under study. To what extent does the global and multi-cultural nature of software engineering influence software processes welfare? More specifically, does an increase in “globalization” of software activities negatively or positively influence known nasty effects common in the pro-cess of software construction? Rotating around these questions, this research finds that there is in fact evidence of the aforementioned in-fluence but it does not provide for positive effects only. Specifically, a decrease of globalization does not necessarily bode positively on conditions such as lone developers working in an individualistic fashion-a phenomenon known as “lone wolf” effect-and other nasty organizational phenomena potentially slowing down or halting software construction and maintenance activities.