Miracle-dz/UniFAR-FTUs huggingface dataset:
A systematic review of density grid-based clustering for data streams

Various applications, such as electronic business, satellite remote sensing, intrusion discovery, and network traffic monitoring, generate large unbounded data stream sequences at a rapid pace. The clustering of data streams has attracted considerable interest due to the increasing usage of evolving data streams. In particular, evolving data streams affect clustering because they introduce numerous challenges, such as time and memory limits and one-pass clustering. Furthermore, researchers need to be able to determine arbitrarily shaped clusters present in evolving data streams from applications. Due to these characteristics, conventional density grid-based clustering techniques cannot be used. Moreover, the existing density grid-based clustering algorithms have low cluster quality for clustering evolving data streams. This study conducted a systematic literature review (SLR) and noted numerous research-related issues encountered in solving the aforementioned problems. We summarized numerous grid-based clustering algorithms that have been used and determined their distinctive and limited features. We also observed how these algorithms address the challenges affecting the clustering of evolving data streams and studied their advantages and disadvantages. SLR was based on 104 articles published between 2010 and 2021. Numerous challenges remain for grid-based clustering algorithms, particularly in terms of time-limited and high-dimensional data handling. Last, our findings indicated a variety of active studies on density grid-based clustering.
Critical Review of Data, Models and Performance Metrics for Wind and Solar Power Forecast

Global climatic changes and increased carbon footprints provided the main impetus for the decrease in the use of fossil fuels for electricity generation and transportation. Matured manufacturing technologies of solar PV panels and on-shore and off-shore windmills have brought down the cost of generation of electricity using solar energy on par with conventional fossil fuel. Initially, solar and wind power generation was envisioned for microgrids, serving small local communities. However, advancements in power electronics have now facilitated large solar and wind farms to be integrated with main power grids. In this context, hosting capacity, which is the amount of distributed energy resources a grid can accommodate, without significant infrastructure up-gradation, has gained importance. In determining the hosting capacity at a particular location, the uncertainties of wind and solar power generation play a role. Effective forecasting models using time-series weather data can be built to predict wind and solar power generation. This forecast is essential to ensure proper grid operation and control when renewable energy sources are already installed. The forecast is also useful in the planning stages for investment decisions and distribution system planning. While long-term forecasts are rarely needed for the operation of integrated grids, accurate short-term predictive models are necessary for scheduling. This paper presents an extensive review of various forecast models available in the literature. The study mainly focuses on the short-term forecast, providing a critical review of the duration of data used in each model and a synoptic comparison of their performance indices.
Surgical Skill Assessment System Using Fuzzy Logic in a Multi-Class Detection of Laparoscopic Box-Trainer Instruments

The development of automated systems to evaluate the trainees’ laparoscopic surgery skills is a challenging problem. Box trainers use actual laparoscopic instruments and a video system that allows the monitoring of the execution of the test procedures. In this paper, we propose a fuzzy logic-based performance assessment system that takes inputs from a multi-class laparoscopic instrument detection system and a measurement algorithm. The laparoscopic instruments are tracked during the test by a deep learning algorithm using the SSD-MobileNet architecture. Based on the experimental result, the trained model could identify each instrument at a score of 70% fidelity while providing input information for the performance assessment system. The experimental results demonstrate that our proposed approach is a promising component for the development of a comprehensive laparoscopic surgery skill assessment system. This project is a collaborative research effort between the Department of Electrical and Computer Engineering and the Department of Surgery, of the Homer Stryker M.D. School of Medicine, at Western Michigan University.
Bayesian-based Absolute Positions Estimation for the Nearby Vehicles through Vehicle-to-Vehicle Communications

The development of vehicle-to-vehicle (V2V) communication techniques have proliferated collaborative vehicle localization (CVL) approaches to estimate the absolute positions of the nearby vehicles. In general, the information of the nearby vehicles in terms of geometry information and historical states are utilized to increase localization accuracy. However, the geometry information can be unknown and the historical states of the nearby vehicles cannot be obtained under certain circumstances. In this paper, a Bayesian-based localization approach, named as BayesNVL is proposed to estimate the absolute positions of the nearby vehicles from an ego vehicle through incorporating the GPS measurements and measured relative positions. Different from the existing work, our pro-posed approach can be applied to the real driving scenarios without the geometry information of road network and the historical states of the nearby vehicles. We evaluate the proposed approach in terms of localization accuracy and efficiency by using a real dataset containing the trajectories of vehicles. The experimental results show that our proposed approach is able to achieve higher localization accuracy compared with the existing approaches and acceptable localization efficiency.
DA2-Net : Diverse & Adaptive Attention Convolutional Neural Network

Standard Convolutional Neural Network (CNN) designs rarely focus on the importance of explicitly capturing diverse features to enhance the network’s performance. Instead, most existing methods follow an indirect approach of increasing or tuning the networks’ depth and width, which in many cases significantly increase the computational cost. Inspired by biological visual system, we proposes a Diverse and Adaptive Attention Convolutional Network (DA2-Net), which enables any feed-forward CNNs to explicitly capture diverse features and adaptively select and emphasize the most informative features to efficiently boost the network’s performance. DA2-Net incurs negligible computational overhead and it is designed to be easily integrated with any CNN architecture. We extensively evaluated DA2-Net on benchmark datasets, including CIFAR100, SVHN, and ImageNet, with various CNN architectures. The experimental results show DA2-Net provides a significant performance improvement with very minimal computational overhead.
Neural Network Assisted Branch-and-Bound Method for Dynamic Berth Allocation Problems

The berth allocation problem is one of the important problems in the maritime ship operations. Efficient sea transportation can be realized by optimizing the berth operation schedule. The dynamic berth allocation problem asks to determine the allocation and berthing time of each vessel to the berth that minimizes the total service time given a set of vessels and a set of berths. The branch and bound algorithms have been used to solve the problem as an exact solution algorithm. However, it becomes intractable to solve the problem by the conventional branch and bound algorithms when the number of ships and berths is increased. In this paper, we propose a new branch-and-bound algorithm called Neural Network Assisted Branch-and-Bound (NN-B&B) to determine the search priority of each node to reduce the total computation time of the branch-and-bound method. By determining the search priority of the node from the result of the neural network, the optimal solution is quickly searched for the learned problem. We compare the performance of the proposed method with the conventional branch and bound method from computational experiments.
Customer Support Chat Intent Classification using Weak Supervision and Data Augmentation

Understanding the actual intent of customers is an essential step in automating the conversational experience on a chat platform. Typically, chatbots are powered by machine learning algorithms that rely on the acquisition of a large amount of high quality labeled training data which can be prohibitively expensive. To overcome this dependence on labeled training data, weaker forms of supervision have been recently exploited to generate samples in a more cost effective manner though the samples may be noisy. In this paper, we analyse a use-case specific to food delivery services where customer-agent conversations in incoherent English and code-mixed language (Hindi mixed with English, commonly referred to as Hinglish) are associated with a single customer chosen noisy label (referred to as “Conversation Level Intent” in the current paper) for the entire conversation. However, in reality, a conversation can have several messages and can be comprised of multiple labels. Moreover, each label may be associated with one or more messages in the conversation. In this paper, we demonstrate how simple light-weight word embeddings based weak supervision techniques can be used to tag individual customer messages with the most relevant label. We also show that simple augmentation techniques can significantly improve performance on code-mixed messages. On an internal benchmark dataset, we show that our sampling approach achieves an absolute performance gain of 33% in F1 score on random sampling strategy, 19% in F1 score over an approach using entire raw samples and 4% over a Snorkel (a state-of-the-art weak supervision framework) based approach.
Indian Classical Music Synthesis

Several studies in the fields of Artificial Intelligence and Natural Language Processing have been conducted on Music Synthesis. However, due to the limited availability of structured datasets, the field of Indian Classical Music remains unexplored. Additionally, the considerable influence of western music in the past decade has adversely affected the market and demand for Indian Classical Music. In this work, we propose a model to generate music for Indian Classical Music, specifically Carnatic Music, by leveraging the structured nature of Indian Carnatic Music. We build a dataset of Classical Indian Music with paired lyrics and melody to map melody with notes to extract features. Generative Adversarial Networks (GANs) are proven to be very effective for music generation in several research works. We experiment with GANs and Auto Encoder (Variational AE and Conditional VAE) on classical lyrics. The curated dataset shall also be helpful for further research in the domain of Indian Classical Music.
Image Caption Generator using Siamese Graph Convolutional Networks and LSTM

Image captions are those crisp descriptions that you see under images. They generally provide the viewer with a brief idea about the image context. To generate an accurate description of the scene, the model requires a semantic and spatial understanding of the contents in the scene. This paper proposes a novel approach using Siamese Graph Convolutional Network (S-GCN), making use of a non-parametric Kernel Activation function (KAF) followed by an LSTM with attention to generate natural language captions for the input image. Siamese-GCN captures deep semantic relations and makes the model more robust to class imbalances. We use an extended kernel activation function and regularize with standard lp-norm techniques, improving the overall model performance by a significant margin. The model is trained and tested on the Flickr30K data set and evaluated on BLEU-4 scores.
The Dancer–Drummer–Drum Body: Expanding corporeal experiences through improvisation in black dance

This article examines how the dancer–drummer–drum body can expand corporeal experiences through improvisation in black dance. Since the late 1990s, black Brazilian dancer-choreographer Edileusa Santos has argued for a multisensorial engagement in her Dance of Black Expression methodology, as a way to deepen the relationship between drums and bodies. According to Santos, as in the Afro-Brazilian Candomblé, the drum is fundamental in African-rooted dance classes; once ‘the drum’s sound emerges in the [dancer’s] body, encouraging the vibrations and sensations; it suggests new possibilities, corporeal experiences, and new attitudes’ (Santos 2015: 53; my translation). Concomitant to Santos’s emphasis on drums’ power to enhance dancers towards new corporeal experiences, organization and identity, Santos invites drummers to perceive dancers’ bodies as musical scores for their compositions. This full way of perceiving others’ expressions and sensations allows dancers and drummers to access personal and collective memories in the context of their ancestry. In this article, I draw from authors in dance and music studies, black performance theories, phenomenology and philosophy of sound and perception to argue that by working with improvisation and advocating for multisensory engagement in a black dance class that is not exclusive to black bodies, Santos expands corporeal experiences by encouraging the exploration of dancers’ and drummers’ body-movements as extensions of drums’ sounds and vibrations. Furthermore, I contend that she decentres notions of the ‘black dancing body’ (Gottschild 2003) by suggesting the emergence of a drum/body and body/drum identity, which is anchored in the notion that dancer, drummer and drum are complementary. Moreover, I borrow from my own bodied experience acquired in Santos’s workshops and conducted interviews to posit the Dance of Black Expression classroom as a space for the creation of self-referenced movements—a space for expressions of blackness that are anchored in black bodies’ experiences but not restricted to them.
Supervised domain adaptation approach on heterogenous, multi-center MR imaging datasets

Deep learning neural networks are a common tool in medical imaging and frequently used to solve a variety of complex problems. Magnetic resonance (MR) images are frequently employed to develop these networks because of their high spatial resolution and user selectable image contrast between tissues. More advanced deep learning models are being developed, which when combined with improvements in MR image acquisition techniques, will allow for image analysis techniques that are more efficient yet able to solve increasingly challenging problems. A current significant disadvantage of deep learning networks is that they are extremely sensitive to the distribution of the data used for training, therefore, network implementation can be challenging in clinical applications with heterogeneous images. The main problem is that, in a clinical environment, data distributions of target datasets can vary from subject-to-subject due to differences in scanner vendor, magnetic field strength, and the setting of specific MR acquisition parameters. These variations create inherent scan variability that diversifies the data distributions of different datasets. This effect can result in the model becoming inaccurate and producing undesirable outcomes. Thus, to improve model generalizability, we explored a supervised domain-adaptation approach. To test this method, we created a convolutional neural network model that performed a classification task and was composed of three components: (1) a feature extractor, (2) a pathology classifier, and (3) a domain classifier. In a single, unified training process, the pathology classifier was trained by minimizing the pathology loss function and the domain classifier was trained by maximizing the domain loss function. This procedure allows the model to penalize learning of features specific to the domain, and thus attempts to produce a domaininvariant feature vector. The performance of this domain-adapted model was compared to the same model but without domain classification (i.e., a baseline traditional model consisting of a feature extractor and a pathology classifier). We found that the domain-adapted model achieved a higher accuracy rate in predicting images from both source and target datasets.
Simulation and analysis of passengers' evacuation ability in subway under urgent conditions

Through analysis of a kind of subway vehicle's space feature and using of the technique that passengers adjust their speed and move direction dynamically,a cellular automata model was built which took the restriction of vehicle and dynamical feature of pedestrians into account on the base of cellular automata models and pedestrian flow models.This model has features of the dynamical speed for people on the basis of pedestrian density and using self-organization phenomena to modify pedestrians' behaviors.A kind of subway vehicle was studied and the effect of different vehicle space distribution on the safety of passengers' evacuation was simulated with the improvement advice for vehicle.
“LIQUIDITY IN THE GLOBAL FOREIGN EXCHANGE MARKET”

We study the global market liquidity of foreign exchange rates by analysing the price impact of trading. We propose a simple theoretical framework that renes the popular Amihud (2002) illiquidity measure and term our new estimator realized Amihud. We highlight our measure’s consistency and higher accuracy, and these properties are validated numerically and empirically using CLS data representative of global FX trading. e price impact of FX trading increases with transaction costs, money market stress, uncertainty, and risk aversion. Furthermore, currencies with smaller price impact are less prone to deviations from triangular no-arbitrage parity. Our results are corroborated by an instrumental variables approach and a quasi-natural experiment using the shock of the Swiss franc’s uncapping in January 2015.
Novel Insight to Neuroprotective Potential of silver nanoparticles using flavonoid: diosmin and their antibacterial effects in SH-SY5Y Neuroblastoma Cells

The present work focuses on the development and evaluation of the silvernanoparticles using diosmin with the goal of improving its therapeutic efficacy in SH-SY5Y Neuroblastoma Cells (Parkinson disease). Nanotechnology and nanoparticles based methodology medical transport systems could reverse the treatment of Parkinson's disease. In the present investigation diosmin (constituents of citrus plants) were used for the synthesis of silver nanoparticles (AgNPs). Aqueous solutions of pure flavonoids 100nM mixed with 1 mM AgNO3 solution were exposed to bright sunlight to prepare the nanoparticles. Characterization of the synthesized nanoparticles by UV–Visible spectrophotometer, X-ray diffraction, Fourier transform infrared spectroscopy, and transmission electron microscopy revealed that the synthesized silver nanoparticles were 10– 85 nm in size and polydispersed in nature. Bactericidal effect against common pathogens and cytotoxicity of the synthesized silver nanoparticles was investigated on SH-SY5Y Neuroblastoma Cells. It is concluded that AgNPs synthesized using diosmin as reducing agent showed higher stability and better antibacterial and cytotoxic activities.
Mathematical Modelling and Computational Simulation of Mammalian Cell Cycle Progression in Batch Systems

Cell cycle and its progression play a crucial role in the life of all living organisms, in tissues and organs of animals and humans, and therefore are the subject of intense study by scientists in various fields of biomedicine, bioengineering and biotechnology. Effective and predictive simulation models can offer new development opportunities in such fields. In the present paper a comprehensive mathematical model for simulating the cell cycle progression in batch systems is proposed. The model includes a structured population balance with two internal variables (i.e., cell volume and age) that properly describes cell cycle evolution through the various stages that a cell of an entire population undergoes as it grows and divides. The rate of transitions between two subsequent phases of the cell cycle are obtained by considering a detailed biochemical model which simulates the series of complex events that take place during cell growth and its division. The model capability for simulating the effect of various seeding conditions and the adding of few substances during in vitro tests, is discussed by considering specific cases of interest in tissue engineering and biomedicine.
Monitoring of Eccentric Hamstring Strength and Eccentric Derived Strength Ratios in Judokas from a Single Weight Category

Background: This study was designed to perform isokinetic knee testing of male judokas competing in the under 73 kg category. The main aims were: to establish the concentric (CON) and eccentric (ECC) strength profile of hamstrings (H) and CON profile of quadriceps (Q) muscles; to evaluate the differences in CON and ECC peak torques (PT) with various strength ratios and their bilateral asymmetries; the calculation of the dynamic control ratio (DCR) and H ECC to CON ratio (HEC); Methods: 12 judokas competing on a national and international levels with a mean age of 19 ± 4 years, a weight of 75 ± 2 kg and with a height of 176 ± 5 cm were tested. All the subjects were right-hand dominant. Isokinetic testing was performed on iMOMENT, SMM isokinetic machine (SMM, Maribor, Slovenia). The paired t-test was used to determine the difference between paired variables. The level of significance was set at p ≤ 0.05; Results: Statistical differences between left (L) and right (R) Q PT (L 266; R 241 Nm), H ECC PT (L 145; R 169 Nm), HQR (L 0.54; R 0.63), DCR (L 0.55; R 0.70), HEC (L 1.02; R 1.14) and PTQ/BW (L 3.57; R 3.23 Nm/kg) were shown. Bilateral strength asymmetries in CON contraction of 13.52% ± 10.04 % for Q, 10.86% ± 7.67 % for H and 22.04% ± 12.13% for H ECC contraction were shown. Conclusions: This study reports the isokinetic strength values of judokas in the under 73 kg category, emphasising eccentric hamstring strength and eccentric derived strength ratios DCR and HEC. It was shown that asymmetries are better detected using eccentric testing and that the dominant leg in judokas had stronger eccentric hamstring strength resulting in higher DCR and HEC.
Nutritional and dietetic quality of milk and traditional cheese made from the milk of native breeds of sheep and goats

ABSTRACT Sheep and goats of native breeds provide milk rich in nutrients as a raw material for traditional products, especially cheeses available only in specific regions. The aim of this study was to evaluate the quality of the raw material for production of traditional fresh cheese (bundz) and the product itself made of milk from the Podhale Zackel sheep and Carpathian goat – both Polish breeds. The milk from the Podhale Zackels contained more than twice as much fat (7.30%, P ≤ 0.01) as milk from the Carpathian goats (2.82%) and it was richer in protein and lactose. The goat milk had twice as high a content (1.10%, P ≤ 0.01) of immunoglobulins compared to the sheep milk (0.58%). Higher levels of serum albumin (3.69%, P ≤ 0.05) and β-casein (58.89%, P ≤ 0.01) were determined in the Zackel milk. Podhale Zackel bundz was characterized by higher fat (24.92%, P ≤ 0.01) and protein (18.06%, P ≤ 0.01) content than the goat bundz (21.33%-fat; 16.74%-protein). In the sheep bundz, the content of CLA acid was significantly higher (2.23%, P ≤ 0.01), than in the goat bundz (0.28%). In sensory evaluation both cheeses received high marks (≥4.50) but the panellists preferred the aroma of the sheep bundz.