Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection and tracking of honeybees using YOLO and StrongSORT

Understanding the behavior of honey bees is essential for maintaining a healthy bee colony. Hive monitoring systems are crucial for this purpose. In the last few years, computer vision and deep learning have become widely used in such systems. This paper uses a deep learning approach for detecting and tracking honey bees. Firstly, for detection, we employed the YOLO model using a data set of 1000 ground truth images. Secondly, for tracking, we used the StrongSORT approach. Results show that the detector performs well in both classes of honey bees (with or without pollen). The models based on this approach provide considerably good average tracking accuracy with low latency. Thus, this procedure is reliable and can be used in the future for real-time monitoring systems.
Karhunen-Loeve transform in optical microscopy: application to linewidth measurement

The linewidth measurement on wafers depends on the small local irregularities in the region being tested. Such defects must be first of all detected and then canceled in order to give an estimation of the average linewidth. Image processing based on the Karhunen-Loeve transform of multiple acquisitions in different experimental conditions is proposed. Using statistical information compression, irregularities are easily detected and efficient noise filtering is performed.
Data-Driven Force Control of an Automated Scratch Test

The development of chemicals, like coatings, is increasingly automated in high-throughput plants. The assessment of the quality of the coating formulation is also automated to achieve precise and reproducible results. Usually, a cross-cut test is performed to characterize the adhesion of coatings. To obtain comparable conditions and comply with standards, the force during scratching has to be controlled. In this paper, the force control of an automated testing equipment is optimized. There are different substrates, coating formulations and process parameters influencing the quality of the adhesion of the coatings. Because of this, the force controller has to cover a variety of dynamics. The different coatings and nonlinear behavior of the scratching makes modeling the system extremely difficult. Therefore, a data-driven approach is used. Two data-driven methods, the intelligent PID (iPID) and Virtual Reference Feedback Tuning (VRFT), are applied to the field of force control. The results for both control methods are compared. Additionally, both methods are combined with an Iterative Learning Control (ILC) to optimize the control performance over multiple iterations. The qualities of the data-driven controllers are evaluated. All results are compared to the currently implemented controller.
Anomalous diffusion on a selfsimilar hierarchical structure

The temporal increase of the moments in diffusion on a fractal with variable hopping range and lower cut-off is given. The essential parameters are the growth ratio, the length scaling and, as a new feature, the time scaling along the hierarchy. We find algebraical or exponential increase, logarithmic corrections, or trapping if the cut-off is removed. For the first time anomalous enhancement of the variance increase σ'0 tθ, θ larger than 2, is obtained as observed in turbulence On etudie la croissance temporelle des moments de la distribution de particules diffusant sur un fractal a portee de saut variable avec une coupure inferieure. Les parametres essentiels sont: le taux de croissance, le facteur d'echelle de la longueur et celui du temps le long de la hierarchie, ce dernier critere est nouveau. On trouve des lois de croissance algebriques et exponentielles et des corrections logarithmiques, ou un piegeage si la coupure est eliminee. Une augmentation anormale du taux de croissance de la variance σαtθ, θ>2, comme cela a deja ete observe pour la turbulence, est obtenue pour la 1ere fois
A Novel Cloud Security Enhancement Scheme to Defend against DDoS Attacks by using Deep Learning Strategy

Cloud computing is a recent technology that allows users to create services on-demand. Cloud computing has achieved benefits as a result of its self-service capability as well as on demand services. This offers significant adaptability to its users, as they simply pay for the services they require, rather than worrying about the expense of equipment or software support. The major benefit of utilizing the cloud -based environment in organization is to enhance the data maintenance scheme in an easy way as well as improve the integrity of service to avoid manual flaws over maintenance. However, the remote cloud based data maintenance and evaluation leads certain security related threats, especially with Distributed Denial of Service (DDoS) Attacks. These attacks are caused by attempts of intruders or hackers to hack the data present in the server end or traverse between client and server end. The attacker obtains the data and modifies it according to their convenience without the knowledge of the data owner. These kinds of attacks are most dangerous, and the confidentiality of the data is totally disturbed due to such threats. This paper is intended to design a novel deep learning strategy called Modified Learning based Cloud Attack Detection (MLCAD), in which it adapts the features from the conventional security handling scheme called Intelligent Attack Identification Strategy (IAIS). This proposed MLCAD approach identifies the DDoS attacks over cloud environment by means of analyzing the authorization and authentication logics of the respective user, examining the Internet Protocol (IP) Address mentioned in the relevant request as well as the metadata acquired from the user end. These provisions have made the proposed approach MLCAD to act better to identify the DDoS attack in an efficient manner with full significance. The paper provides the proper graphical proofs to prove the integrity and performance of the proposed approach in a clear manner.
A Run-time Tapered Floating-Point Adder/Subtractor Supporting Vectorization

In this era of widespread embedded computing, energy efficiency has become the new performance criterion; as a result, accelerator-rich multi-processor system-on-chips are widely utilized in embedded computing hardware. Due to abun-dant and inexpensive computational capacity, computationally intensive machine learning applications have gained a lot of traction and are currently being used in a wide range of application domains. Furthermore, there is an increasing trend toward developing hardware accelerators for machine learning applications for embedded edge devices where performance and energy efficiency are critical. Although floating-point operations are frequently used for accuracy in these hardware accelerators, reduced width floating point formats are also used to reduce hardware complexity and thus power consumption while pre-serving accuracy. Mixed-precision DNN, vectorization techniques, and any-precision DNN concepts have also proven to boost performance, energy efficiency, and memory bandwidth. In this paper, we propose the design of a vectorized floating-point adder/subtractor that can handle arbitrary length floating-point formats with varying exponent and mantissa widths. The whole idea of this paper is to bring flexibility to each layer in a DNN model for arithmetic operations; depending on the requirement of computation of each layer, exponent width and the floating-point format are chosen dynamically. In comparison to existing designs in the literature, the proposed design is $1.69\times$ area and $1.61\times$ power-efficient, and it supports true vectorization with no restrictions on exponent and mantissa widths.
Comparing of CNN Algorithms for Feature Extraction and Classification on Cardiac MRI

Deep Learning (DL) has been incorporated into image processing, including cardiac MRI images to address Machine Learning (ML) issues, the best model based on research evaluation is still up for debate among experts in the field. The diversity of datasets has characteristics that are not always in accordance with the objectives of the research to be carried out. Each of the existing models each has a different performance achievement. This review contributes to presenting various datasets and DL models related to their application to cardiac MRI images. The proposed classification method goes through several main stages, including object detection to obtain the region of interest (RoI), segmentation to obtain feature maps, classification with a fully connected layer (FCN).
Efficient Implementation of Tam and Auriault's Time-Domain Impedance Boundary Condition

A new and efficient time-domain implementation is proposed for the impedance model originally introduced by Tam and Auriault (Tam, C. K. W., and Auriault, L., "Time-Domain Impedance Boundary Conditions for Computational Aeroacoustics," AIAA Journal, Vol. 34, No. 5, May 1996, pp. 917-923). In the frequency domain, the pressure can be calculated from the product of the impedance with the normal velocity. In the time domain, a convolution is needed, which may become a costly operation. Present solutions for this problem involve either a full convolution and storage of a time history of data or a translation of the frequency-domain models to time-domain formulations involving time derivatives. The former approach is very costly in terms of memory and CPU time; the latter makes the implementation discretization specific and vulnerable to stability issues. A new formulation, based on recursive convolution, results in an efficient implementation that stores only one accumulator, which can be updated at each time step with minimal addition and multiplication. It allows for the exact representation of the impedance at a given frequency. The formulation is validated by a comparison with the measurement data from the NASA Langley Grazing Flow Impedance Tube. The simulations are performed by introducing a plane wave at a single frequency in the tube, and this is done for six frequencies and five different mean flow speeds.
NMT for English-Marathi using RNNs & Attention

Neural Machine Translation (NMT) has led to significant changes in the world of machine translation. It is a huge improvement over Statistical Machine Translation (SMT). With the advent of the transformer model introduced in the paper “Attention is all you need” [1], (which introduces mechanism of self attention) the industry has seen significant improvements in translation, but the transformer model is resource & data intensive hence RNNs seem a better option in terms of data and computing constraints. While there have been numerous implementations of NMT for western to western language translations, there have been comparatively fewer implementations for Western to other languages especially Indic languages apart from Hindi. Indian languages also have various features like the use of honorifics, gendered objects, differences in usage like formal/informal setting, etc thus differing from most of the Western languages in a fundamental way where a lot of mentioned features are unavailable & language structure is rigid in nature. Our paper aims to find the best as well as give a comparative analysis of various NMT models for English to Marathi translations. Our models include - Plain LSTM, Plain GRU, Plain Bidirectional LSTM & GRU, LSTM with Attention, GRU with Attention, Bidirectional LSTM with Attention & Bidirectional GRU with Attention.
Temporal Graph based Overcommitted Routing for Deterministic Networking

Deterministic networking technologies play significant roles in Internet of Things, Augmented Reality and so on. In fact, the resource reservation policy is employed to guarantee the time-deterministic transmission of flows in deterministic networks. However, for flows with dynamic bandwidth demands, this policy will allocate bandwidths according to the peak volume in rush hours, and the link bandwidths may be just authorized but not fully utilized, which results in bandwidth overprovision and low resource utilization. Besides, due to the time-varying characteristics of both the network topology and link bandwidths, traditional static graph based routing schemes are inefficient for time-varying networks. Therefore, it is important to find a feasible way to improve the bandwidth utilization while guaranteeing the deterministic performance of flows. In this paper, we explore the deterministic routing problem for flows with dynamic bandwidth demands in stochastic time-varying networks (STN). First, we adopt the stochastic time-varying graph to characterize the dynamic attributes of networks, including the link bandwidth and topology, where random variables are utilized to depict the dynamicness of both dynamic bandwidth demands of flows and time-varying bandwidth resources of links. Then, we design an overcommitment rule, which allows us to design a bandwidth resource overcommitment shortest path algorithm in STN (ROSP-STN) to guarantee the delay of flows. Finally, we run numerical experiments to evaluate performance of the ROSP schme. Simulation shows that the proposed ROSP scheme can increase the bandwidth utilization by 24% and the transmission delay of flows can also be guaranteed into a deterministic range.
Microgrid Demand Response: A Comparison of Simulated and Real Results

Microgrids can be used in demand response (DR) and islanding operations. This paper explores a real-world microgrid implementation performed at the University of California, Riverside. The intended purpose is to see the feasibility of demand response and islanding in a real microgrid. An algorithm is used to generate “target data” for the Battery Energy Storage (BES) Inverter to follow. The other purpose is to see the accuracy when transitioning from a simulated model to a real-life implementation. The results from this implementation show it is feasible to respond to demand response scenarios with the current setup. However, more research is needed to successfully implement islanding responses due to infrequent power spikes that briefly draw more power than the microgrid can produce.
Energy Efficiency Policy Evaluation Model using Hybrid Evolutionary Programming (EP) - Artificial Neural Network (ANN) for Measurement and Verification Baseline Development

This paper presents the development of Energy Efficiency (EE) Policy evaluation model using Hybrid Evolutionary Programming (EP) - Artificial Neural Network (ANN). There are various of EE policies and programmes imposed by governments to promote EE implementation among the industries and societies. However, very little effort was found to evaluate the effectiveness of EE policy for reducing energy consumption. This has created the need for a model that can be used to assess the performance of EE policy and programmes. In this paper, measurement and verification (M&V) approach is proposed for evaluating the performance of Minimum Energy Performance Standards (MEPS) in Malaysia. Artificial Neural Network (ANN) is used for developing a baseline energy model in the M&V framework. Evolutionary Programming (EP) is then integrated with the ANN baseline energy model to improve the performance of ANN. Results show that hybrid EP-ANN provides the highest regression value followed by multi-regression ANOVA model and ANN. With its highest regression value, the EP-ANN provides more accurate and reliable energy savings of 20.03% for the period between 2013 – 2016 from the implementation of MEPS policy in Malaysia.
A Preliminary Conceptualization and Analysis on Automated Static Analysis Tools for Vulnerability Detection in Android Apps

The availability of dependable mobile apps is a crucial need for over three billion people who use apps daily for any social and emergency connectivity. A key challenge for mobile developers concerns the detection of security-related issues. While a number of tools have been proposed over the years—especially for the ANDROID operating system—we point out a lack of empirical investigations on the actual support provided by these tools; these might guide developers in selecting the most appropriate instruments to improve their apps. In this paper, we propose a preliminary conceptualization of the vulnerabilities detected by three automated static analysis tools such as ANDROBUGS2, TRUESEEING, and INSIDER. We first derive a taxonomy of the issues detectable by the tools. Then, we run the tools against a dataset composed of 6,500 ANDROID apps to investigate their detection capabilities in terms of frequency of detection of vulnerabilities and complementarity among tools. Key findings of the study show that current tools identify similar concerns, but they use different naming conventions. Perhaps more importantly, the tools only partially cover the most common vulnerabilities classified by the Open Web Application Security Project (OWASP) Foundation.
Performance Analysis of Sparse Vector Coding over $\kappa-\mu$ Fading Channel

In this paper, the symbol error rate (SER) per-formance of sparse vector coding (SVC) for the short packet transmission over independent and identically distributed (i. i. d.) k - u shadowed fading channels is studied. We firstly analyze the signal to interference plus noise ratio (SINR) of the SVC scheme and show that the corresponding signal to noise ratio (SNR) performance can be improved with the increasing number of resource blocks compared to the conventional baseline, which indicates that the reliability of the system is improved. Then, we derive the simple SER analytical expression over the $\kappa-\mu$ fading scenario with low computational complexity. The versatility of the $\kappa-\mu$ fading model determines that the other well-known fading distributions and their inclusive ones can be derived as special cases. Simulation results indicate the validness of our derivations, and that the SVC scheme can greatly improve the system reliability with longer spreading sequences.
A 28Gb/s NRZ and 56Gb/s PAM4 SerDes Dual-Mode Transmitters Based on 28nm CMOS

This paper designs a dual-mode transmitter that supports non-return to-zero modulation (NRZ) at a rate of 28 Gb/s and four-level pulse amplitude modulation (PAM4) at a rate of 56Gb/s, using CMOS 28nm process. The transmitter uses a 4:1MUX with a 1/2 speed architecture to reduce the transmitter area. CML2CMOS circuit is used to convert the signal into a full swing signal. The design of the clock buffer circuit improves the driving ability. The output 56Gb/s PAM4 call consumes 265mW and the energy efficiency is 4.7pJ/bit. The 28GB /s NRZ signal consumes 215mW and the energy efficiency is 7.6pJ/bit.
A Lightweight Tire Tread Image Classification Network

VCIP 2022 “Tire pattern image classification based on lightweight network challenge” aims to design lightweight networks that correctly classify tire surface tread patterns and indentation images using less overhead. To this end, we present a novel lightweight tire tread classification network. Concretely, we adopt the ShuffleNet-V2-x0.5 network as our backbone. To reduce the computation complexity, we introduce the Space-To-Depth and Anti-Alias Downsampling modules to pre-process the input image. Moreover, to enhance the classification ability of our model, we adopt the knowledge distillation strategy by considering Vision Transformer as the teacher network. To ensure the robustness of our model, we pre-train it on ImageNet and fine-tune the training set of the challenge. Experiments on the challenge dataset demonstrate that our model achieves supe-rior performance, with 99.00% classification accuracy, 25.51M FLOPs, and 0.20M parameters.
Estimation of Breast Density from Human Mammograms using U-Nets

Breast cancer, a potentially fatal condition, affects one in twenty-eight women in India. Breast density, which is defined as the ratio of the volume of breast tissue to that of fatty tissue, is an important criterion in measuring the likelihood of a person developing breast cancer. In breast cancer, it is almost always the breast tissue that turns cancerous, not the fatty tissue. Women who have dense breast tissue, are four to six times more likely to develop breast cancer than women who do not have dense breast tissue. In our study, we examine breast mammograms and aim to develop a technique to automatically quantify breast density objectively, which is important in the prediction of the risk of breast cancer. While increased breast density greatly predisposes the risk of breast cancer, it is important to assess the risk quantitatively, which is difficult. In our technique, we develop a technique based on U-Net to segment breast tissues. We aim to classify the breasts based on the amount of breast tissue they have compared with the total volume of the breast. Therefore, we utilize a training database of 20 human-segmented images to train our algorithm and test it on a separate set of 15 human-segmented images. Examining the accuracy of the segmentation, we find that we obtain an accuracy of $0.79\pm 0.12$ in the Dice similarity coefficient. The qualitative images have also been examined and as may be observed from the qualitative results. A fully replicable, open-source code that everyone can use to segment breast tissue from mammogram images is the project's final goal.
Application of Information Technologies for Solving Economic Problems: In-depth Analysis of Okun’s Law for Azerbaijan

This article discusses the advantages of modern computer technologies, as well as their use in assessing the applicability of Okun's law to describe short-term and long-term relationships between macroeconomic indicators. The article presents a computer implementation of research on the relationship between employment, unemployment, and production. We provide a comprehensive overview of the main theoretical approaches to the study of this interdependence and analyze the practical experience of assessing it. With the help of the SPSS software package, based on the generalization of the results of theoretical analysis, a conclusion is made about the most adequate models in modern socio-economic conditions. Furthermore, we test these models based on official statistics. One consequence of Okun's law is that a rise in labor productivity and an increase in the labor force can cause in production growth without reducing employment. Our findings obtained in the course of an in-depth analysis using the statistical data of Azerbaijan and due to the results of using the software product fully confirm this statement.
An energy ratio approach to the analysis of forced oscillations in power grids

In this paper, a new framework based on non-modal stability theory to study the forced system response to harmonic excitation. Using this approach, the amplification of forced oscillation can be studied as a function of system location and excitation frequency. As a by-product, the technique enables comparing the output and the input sizes and is not limited to modal frequencies. In addition, the forcing and response modes give the dominant behavior of the forced oscillations. Applications to a two-area, four-machine test system are used to assess the applicability of the proposed framework.
A Step-Down Hybrid Converter With Unequal Inductor

A step-down topology derived from a hybrid converter is presented. The converter contains only two inductors, the output capacitor and one transistor. The operation is analyzed assuming unequal inductors and the main equations are provided. It is shown that the static conversion ratio it is not affected by the fact that the inductors have different values. The simulation results accompanied by the practical experiments they all confirmed the validity of the theoretical considerations.
SPECTROPHOTOMETRY OF THE ORION NEBULA

Observations of line and continuum strengths were made in six regions in the Orion nebula. From the observations, electron temperatures, electron densities, and chemical abundances are derived. A model for the Orion nebula is computed that agrees with the optical and radio observations of the nebula. Scattering and absorption by dust were added to the model and the predicted scattered continuum is compared to the observed continuum. Key words: H ii regions - Orion nebula - interstellar dust