Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Hair Capture

The realistic reconstruction of hair motion is challenging because of hair’s complex occlusion, lack of a well-deﬁned surface, and non-Lambertian material. We present a system for passive capture of dynamic hair performances using a set of high-speed video cameras. Our key insight is that, while hair color is unlikely to match across multiple views, the response to oriented ﬁlters will. We combine a multi-scale version of this orientation-based matching metric with bilateral aggregation, a MRF-based stereo reconstruction technique, and algorithms for temporal tracking and de-noising. Our ﬁnal output is a set of hair strands for each frame, grown according to the per-frame reconstructed rough geometry and orientation ﬁeld. We demonstrate results for a number of hair styles ranging from smooth and ordered to curly and messy.
Antioxidant Activity of Various Parts of Cinnamomum cassia Extracted with Different Extraction Methods

The aim of this study was to investigate the antioxidant activities of various parts (barks, buds, and leaves) of Cinnamomum cassia extracted with ethanol and supercritical fluid extraction (SFE). For the antioxidant activity comparison, IC50 values ofthe SFE and ethanol extracts in the DPPH scavenging assay were 0.562–10.090 mg/mL and 0.072–0.208 mg/mL, and the Trolox equivalent antioxidant capacity (TEAC) values were 6.789–58.335 mmole Trolox/g and 133.039–335.779 mmole Trolox/g, respectively. In addition, the total flavonoid contents were 0.031–1.916 g/ 100 g dry weight of materials (DW) and 2.030–3.348 g/ 100 g DW, and the total phenolic contents were 0.151–2.018 g/ 100 g DW and 6.313–9.534 g/ 100 g DW in the SFE and ethanol extracts, respectively. Based on the results, the ethanol extracts of Cinnamon barks have potential value as an antioxidant substitute and this study also provide a better technique to extract the natural antioxidant substances from C. cassia.
An E cient k-Means Clustering Algorithm : Analysis and Implementation Tapas

K-means clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points R d , called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's algorithm. In this paper we present a simple and eecient implementation of Lloyd's k-means clustering algorithm, which we call the ltering algorithm. This algorithm is very easy to implement. It diiers from most other approaches in that it precomputes a kd-tree data structure for the data points rather than the center points. We establish the practical eeciency of the ltering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time. Second, we have implemented the algorithm and performed a number of empirical studies, both on synthetically generated data and on real data from applications in color quantization, compression, and segmentation.
A Novel Solution Technique for the Expansion Planning of Modern Distribution Systems

This paper addresses the expansion planning problem of modern distribution systems using a novel solution methodology based on matheuristics. The problem is formulated as a mixed-integer quadratic programming model. The proposed technique constitutes a novel local search algorithm that, using linearized and relaxed versions of the original problem, finds successive solutions, improving at each iteration the feasibility of the original problem until attaining a high-quality near-optimal solution. Planning actions include line reconductoring and installation of distributed generation (DG) units, electrical energy storage (EES) systems, and fixed and switchable capacitor banks (CBs). Operation system variability in demand and energy resources is modeled through representative days in order to preserve EES temporal transition. Furthermore, the model guarantees CO2 reduction in order to be on track to limit global warming. The effective performance of the proposed approach in terms of solution quality and computational time has been illustrated with a case study based on a 33-node distribution system.
Optimizing Distributed Generation Sizing and Siting using Particle Swarm Optimization: A Comparative Study

In recent years, distributed generation (DG) has become increasingly popular as a means of mitigating the impact of climate change and ensuring a reliable and resilient power supply. To optimize the use of DG, it is essential to determine the most effective DG sizing and siting for a given power system. This problem has been researched, and several heuristic algorithms have been proposed and tested. This paper focuses on applying particle swarm optimization (PSO) to this problem to minimize real power loss. Five different PSO variants in single and three DG unit scenarios using the IEEE 33 bus system are tested to reveal the efficiency of the variants and their potential for improving the solution's performance. By comparing the performance of the different PSO variants with the ones in the literature, we found that PSO outperformed other heuristic algorithms for this problem. Our findings highlight the importance of choosing the suitable optimization algorithm for DG sizing and siting to achieve the best possible outcomes for power systems operations.
Long distance quantum communication over noisy networks

The problem of sharing entanglement over large distances is crucial for implementations of quantum cryptography. A possible scheme for long distance entanglement sharing and communication exploits networks, whose nodes share EPR pairs. In [Perseguers et al., Phys. Rev. A 78,062324 (2008)] an important isomorphism between storing quantum information in dimension D and transmission of quantum information in D + 1 network has been put forward. It implies that fault tolerant quantum computing allows in principle for long distance quantum communication. However, in fault-tolerant schemes, one usually considers creating known encoded states. However the process of encoding and decoding is exposed to error. We show that during this stage fidelity drops by a constant factor related to the volume of circuit that replaces a gate, while going to an upper level of concatenation. This shows explicitly, that it is possible to obtain long distance entanglement in 2D network. For 3D networks, much simpler schemes are possible, e.g. due to existence of Kitaev topological quantum memory, shich uses 2D lattice. Again, we consider explicitly the encoding and decoding stages. In [Dennis et al. J. Math. Phys. 43, 4452 (2002)] such scheme was provided in terms of gates. Here we propose a very simple scheme, based solely on syndrome measurements. It is then showed that the scheme is equivalent to teleporting the state to be encoded through some virtual EPR pair existing within the rest of qubits. We then present numerical simulation of performance of such encoding/decoding scheme.
Generalised additive mixed models analysis via gammSlice

We demonstrate the use of our R package, gammSlice, for Bayesian fitting and inference in generalised additive mixed model analysis. This class of models includes generalised linear mixed models and generalised additive models as special cases. Accurate Bayesian inference is achievable via sufficiently large Markov chain Monte Carlo (MCMC) samples. Slice sampling is a key component of the MCMC scheme. Comparisons with existing generalised additive mixed model software shows that gammSlice offers improved inferential accuracy, albeit at the cost of longer computational time.
Diagnostic Test classification of cervical cancer abnormalities using Bagging Classifier

Regardless of the illness, a patient has a higher chance of recovery if the illness is caught early rather than later. It seems to reason that if we do not know how to treat patients, whatever treatment we can supply will be helpful and lead to a more pleasant existence for the patient. One such illness is cervical cancer, which ranks number four on the list of the most prevalent cancers among females worldwide. Age and the use of hormonal contraceptives are only two of the numerous risk factors that might lead to cervical cancer. Cervical cancer survival rates can be improved and mortality rates reduced with early identification. The goal of this work is to use machine learning methods towards the creation of a model that can detect cervical cancer with a high degree of specificity and accuracy. With the help of Bagging Classifier, we were able to develop a diagnostic test for cervical cancer. Lastly, we compared our results to those of other research and discovered that, according to several assessment metrics, our models improved upon their predecessors' performance in diagnosing cervical cancer.
OCR Based Image Text to Speech Conversion using K-Nearest Neighbors and Comparing with Fuzzy K-Means Clustering Algorithm

Using a variety of various techniques, this research is to transform OCR-based visual text to spoken language. The image dataset was used for novel K-Nearest Neighbours (KNN) and fuzzy k-means clustering (FKM). The novel k-nearest neighbours (KNN) (N = 10) and the fuzzy k-means clustering (FKM) (N = 10) groups are compared to one another in this study. according to the results of a calculation made with the g power software, the total sample size was determined to be 80% powerful, with a confidence interval of 95%, an enrolment ratio of 0.1, and an alpha value of 0.05. The SPSS software as well as an independent sample t-test were used to conduct the comparison of the accuracy and specificity rates. The results of a MATLAB simulation indicate that fuzzy k-means clustering, also known as FKM, obtains an accuracy of 93 percent when applied to text to vocal translation. The statistical analysis done with SPSS managed to reach a significant level of accuracy (0.02). The newly proposed revolutionary k-nearest neighbours method performs better than the FKM method for voice converting using the provided pictures.
An Effective Security Protocol Design for IRIS based Credential Evaluation using Intensive Deep Learning Scheme

The component of a system charged with ensuring the security of its users is among its most crucial parts. It has been demonstrated that using a password or login that is too basic makes you vulnerable to hackers and does not ensure a high level of security. Authentication using biometric methods can be achieved in several ways. Some of the most advanced and reliable are facial recognition technology and iris recognition. Because it relies heavily on detection of patterns, it is able to reliably identify the rightful owner of an Iris scan. Accuracy as well as effectiveness have both been greatly enhanced in the resultant recognition system. Security breaches and other identification scams are on the rise, making it all the more crucial to implement a robust biometric system. The option that has gained a lot of attention is biometric identification. The iris's potential as a biometric has gained traction in recent years. This quantifiable quality ensures great productivity and precision, which is what triggered the phenomenon. We present a complete ResNet50-based deep learning system for iris identification in this study. Utilizing only a small number of training photos from every class, we train our algorithm on a popular identification of iris dataset, achieving significant gains over prior methods. In addition, we introduce a visualization method that may identify key features of iris pictures that have a significant bearing on the precision of recognition. We anticipate that this approach will be utilized extensively in the future to improve the scalability and precision of various biometrics identification jobs
A Methodology to Optimize Laser Dicing Parameters to Maximize Dicing Quality Through Machine Learning

Laser dicing has been shown to improve the die strength for thin dies compared to standard machine dicing processes. But like blade dicing, or any separation process, the laser dicing process needs to be optimized for a given substrate to obtain the best dicing quality. Optimization of laser dicing process parameters is challenging because the dicing quality can be influenced by multiple parameters, which can either be independent or co-dependent. Practically, in the absence of structured studies, each laser parameter needs to be varied and the output dicing quality is required to be tested to obtain an acceptable dicing process window for any substrate. Such a method needs to be repeated to create new recipes for any change in die thicknesses, back-end-of-line (BEOL) configurations, etc. This approach is ineffective, sub-optimal and time-consuming. In this study, a methodology is developed to systematically study the effect of key laser dicing process parameters on dicing quality using a machine learning algorithm. First, a design of experiments (DoE) is modeled to study six input parameters with ten levels. The levels are carefully designed based on experience, past data, and tool limitations in an effort to further push the boundaries of the current process window. The design space for training and test data is generated using space filling algorithm such as Latin Hypercube. The dicing quality measures such as dicing width and die strength are measured for each iteration and treated as output. Each experiment is repeated 15 times and the mean value of the output parameter is used for training a machine learning model. A random forest-based machine learning algorithm is used to create a surrogate model relating the input and output parameters. Such a model is then used to understand the interactions between input parameters and identify the optimal process window maximizing dicing quality for various sets of input parameter settings. The optimized process parameters are then validated experimentally. The optimized process parameters resulted in increasing the dicing quality compared to the baseline appreciably. This methodology can be extended to any complex, multi-factor dependent separation process to find the optimized process window.
Multimodal backside imaging of a microcontroller using confocal laser scanning and optical-beam-induced current imaging

Microscopy imaging with a single technology is usually restricted to a single contrast mechanism. Multimodal imaging is a promising technique to improve the structural information that could be obtained about a device under test (DUT). Due to the different contrast mechanisms of laser scanning microscopy (LSM), confocal laser scanning microscopy (CLSM) and optical beam induced current microscopy (OBICM), a combination could improve the detection of structures in integrated circuits (ICs) and helps to reveal their layout. While OBIC imaging is sensitive to the changes between differently doped areas and to semiconductor-metal transitions, CLSM imaging is mostly sensitive to changes in absorption and reflection. In this work we present the implementation of OBIC imaging into a CLSM. We show first results using industry standard Atmel microcontrollers (MCUs) with a feature size of about 250nm as DUTs. Analyzing these types of microcontrollers helps to improve in the field of side-channel attacks to find hardware Trojans, possible spots for laser fault attacks and for reverse engineering. For the experimental results the DUT is placed on a custom circuit board that allows us to measure the current while imaging it in our in-house built stage scanning microscope using a near infrared (NIR) laser diode as light source. The DUT is thinned and polished, allowing backside imaging through the Si-substrate. We demonstrate the possibilities using this optical setup by evaluating OBIC, LSM and CLSM images above and below the threshold of the laser source.
Identification of Cardiovascular Disease using ECG Images based on Deep Learning Procedure

When it comes to finding out what's wrong with your heart, cardiac imaging is crucial (CVD). Its previous purpose was restricted to qualitative and quantitative evaluations of the heart. The advent of big data and machine learning, however, has opened up new possibilities for developing AI tools that can aid the physician in the diagnosis of CVDs. Our study offers a unique approach to ECG illness prediction by combining cardiology, signal processing techniques, and a deep learning model. Then, using wavelet transformation and cardiology, we convert the ECG data into a time-frequency representation. Next, a deep convolutional network is used to categories the results of the time-frequency analysis. We find that by combining the benefits of signal processing with those of deep learning, we can significantly enhance the precision of categorization using our approach. The suggested methodology has the potential to enhance performance and reduces staffing costs for major general hospitals, all while lowering the rates at which primary care facilities make diagnostic errors.
Algorithms Synthesis of the Adaptive Logical Network on Basis of Universal Logical Elements

An approach to the synthesis of adaptive structures represented by multi-level logic, Boolean network described as an acyclic graph, whose vertices are universal logical elements, is proposed. As a result of the synthesis of such structures are determined the types of logic functions of the vertices of a given learning sample of binary vectors, which allows using of this structure for the problem of classification of input vectors. Unlike known methods for the synthesis of multilevel logic, two approaches to the synthesis of such schemes proposed in this paper. The first way based on the description of a Boolean network represented by polynomials, the coefficients of the polynomial in this case are given by Hadamard matrix. The second way based on the description of Boolean network by Zhegalkin’s polynomials considered in the article.
Programming by Voice : A Hands-Free Approach for Motorically Challenged Children Proposal June

Computer Science (CS) educators frequently develop new methodologies, languages, and programming environments to teach novice programmers the fundamental concepts of CS. A recent trend has focused on new environments that reduce the initial challenges associated with the heavy syntax focus of traditional textual environments. There are numerous Initial Programming Environments (IPEs) available that have been created for student use that in some cases have fostered self-discovery and inquiry-based exploration. These three IPEs were selected due to the high number of students and teachers currently utilizing them, as well as my personal experience with these environments through past teaching activities. The creative and graphical nature of these three IPEs have been shown to increase student and teacher interest in CS through adoption in classrooms worldwide at the K-12 levels, as well as interest in introductory university courses. Although the graphical nature of these IPEs can be helpful for learning concepts in CS, a small group of students is being left out from learning experiences and engagement in CS (approximately 5% of students). Graphical environments often require the use of both a mouse and keyboard, which motorically challenged users often are unable to operate. Based on research performed and presented in this proposal, a Vocal User Interface (VUI) is a viable solution that offers a " Programming by Voice " (PBV) capability (i.e., a capability to describe a program without the use of a keyboard or mouse). There are two primary disadvantages with VUIs as a technology to address the limitations of motorically challenged users: 1) vocal strain can emerge for technical solutions that require a deep amount of vocal interactions, and 2) the process of integrating voice controls into a legacy application (e.g., an IPE) is a very time consuming process. There are existing vocal tools (e.g., the generic Vocal Joystick) that could be integrated into various applications; however, due to the needs of IPEs and the duration of IPE usage, the Vocal Joystick is not feasible due to the potential vocal strain, which is why a more command-driven approach may offer benefits for the PBV concept. The command-driven approach leads to a time-consuming process in terms of adapting legacy applications, particularly, if multiple applications (such as the three IPEs previously mentioned) require specialized VUIs. Each environment has its own layout and its own commands; therefore, each application requires a different VUI. In order to create a more generic solution, Model-Driven …
Power-aware Computing with Optane Persistent Memory Modules

The increasing popularity and interest in heterogeneous memory solutions has led to the emergence of new high-performance computing (HPC) memory technologies requiring in-depth analysis for efficient performance. On the other hand, energy consumption in modern HPC clusters has reached prohibitive constraints posing a major challenge to power-efficient computation. This paper explores the performance characteristics of Intel’s Optane Data Center Persistent Memory modules (PMM) and investigates power consumption optimization strategies. We evaluate the bandwidth and latency tradeoffs between DRAM and PMM memories and assess the power-saving potential with two approaches: power capping and frequency scaling using Intel’s SST-CP technology. Two different generation systems equipped with Optane PMMs were used: Intel’s Cascade Lake and Ice Lake CPUs. Our results indicate that up to 22% of power consumption can be saved at 4% increased time with memory-bound kernels accessing PMM memory.
Water as a pivotal resource for the environmentally conscious management of bus fleets in Europe

There is not much interest in the transport sector, specifically among transit operators, on the potential achievable from improving water management as consolidated garage practice. Bus washing operations are highly water and energy-demanding, but this seems to go unnoticed, being operators' primary concern on pollution and energy. However, proper water management can improve the sustainability of the transit fleets, as the European Commission's LIFEH2OBUS project is slated to demonstrate. The paper describes the water-saving technologies to be tested in the project, i.e., water reclamation; water reclamation and harvesting; waxing without water, and provides tangible, quantitative facts on reducing water consumption when washing buses in terms of water, energy and monetary savings, according to a cost-benefit analysis simulating the implementation of the three technologies in three different bus garages in Europe. All of the above, with the research goal to launch a new “Water Culture” among transit operators and advance scientific knowledge further afield.
Multiview registration-based handheld 3D profiling system using visual navigation and structured light

ABSTRACT This article describes the 3D handheld profiling system composed of a stereo camera and an illumination projector to collect high-resolution data for close range of applications. Visual navigation approach is either based on feature matching or on accurate target, and the target-based approach was found to be more accurate if the 3D object has less texture on its surface. Block matching algorithm was used to render the single-view 3D reconstruction. For multiview 3D modeling, coarse registration and final refinement of the point clouds using iterative closest point algorithm were utilized. The proposed approach yields good accuracy for multiview registration as demonstrated in the results of this research.