Reviewerly/exHarmony huggingface dataset:
Mixed Reality and Volumetric Video in Cultural Heritage: Expert Opinions on Augmented and Virtual Reality

Mixed reality (MR) technology is currently growing in popularity for applications in the cultural heritage domain. Furthermore, with the ability to be viewed with six degrees of freedom, volumetric video (VV) is presently being explored as a viable approach to content creation within this area. When combined, MR technology and VV present both practitioners and audiences with innovative approaches to the creation and consumption of both tangible and intangible representations of cultural significance. While there are some existing quantitative studies appraising these new technologies, the precise effects of MR in a cultural heritage context have yet to be fully explored. Here we show the results of a systematic evaluation of MR technology as applied in a cultural heritage context, where subject matter expert interviews were conducted to identify how virtual reality and augmented reality technologies are influencing the creative practices of domain experts and audience engagements with modern dramatic literature. Gathered from high-level stakeholders within the cultural heritage domain, our results highlighted the problems, concerns, and desires of users who must consider this technology in practice. We found that MR and VV content were considered by many to be disruptive technologies for the future of film, theater, and performance practice from the perspectives of both practitioners and audiences. We anticipate that these results will help future MR and VV projects to create meaningful content that is sympathetic to the needs and requirements of creators and audiences.
Baybáyin Character Recognition Using Convolutional Neural Network

With the Philippine Congress approving the House Bill 1022 which states that the Baybáyin will be used as the national writing system of the country, the Department of Education and National Commission for Culture and Arts has vowed to reintroduce this old writing system back to the country.In order to hasten up the learning process, a convolutional neural network is designed to check the classification of hand-drawn characters.Nine convolutional neural network models were designed to check which is the best for this type of character recognition.From the 7000 hand-drawn Baybáyin characters used for training, it has found out that the best neural network for this type of classification is composed of three convolutional layers with 32 channels, 64 channels, and 128 channels respectively using 3x3 filters.The final model has also three max pooling layers right after each convolution layer with 2x2 size and two fully connected layers at the end.The number of output of this neural network model is 63 which is the same as the total number of Baybáyin characters.The model yields a 94% accuracy rate using the validation data.The other 8 CNN models also did well with accuracy rates ranging from 57% to 92%.
Focus of negation: Its identification in Spanish

Abstract This article describes the criteria for identifying the focus of negation in Spanish. This work involved an in-depth linguistic analysis of the focus of negation through which we identified some 10 different types of criteria that account for a wide variety of constructions containing negation. These criteria account for all the cases that appear in the NewsCom corpus and were assessed in the annotation of this corpus. The NewsCom corpus consists of 2955 comments posted in response to 18 different news articles from online newspapers. The NewsCom corpus contains 2965 negative structures with their corresponding negation marker, scope, and focus. This is the first corpus annotated with focus in Spanish and it is freely available. It is a valuable resource that can be used both for the training and evaluation of systems that aim to automatically detect the scope and focus of negation and for the linguistic analysis of negation grounded in real data.
EDSSA: An Encoder-Decoder Semantic Segmentation Networks Accelerator on OpenCL-Based FPGA Platform

Visual semantic segmentation, which is represented by the semantic segmentation network, has been widely used in many fields, such as intelligent robots, security, and autonomous driving. However, these Convolutional Neural Network (CNN)-based networks have high requirements for computing resources and programmability for hardware platforms. For embedded platforms and terminal devices in particular, Graphics Processing Unit (GPU)-based computing platforms cannot meet these requirements in terms of size and power consumption. In contrast, the Field Programmable Gate Array (FPGA)-based hardware system not only has flexible programmability and high embeddability, but can also meet lower power consumption requirements, which make it an appropriate solution for semantic segmentation on terminal devices. In this paper, we demonstrate EDSSA—an Encoder-Decoder semantic segmentation networks accelerator architecture which can be implemented with flexible parameter configurations and hardware resources on the FPGA platforms that support Open Computing Language (OpenCL) development. We introduce the related technologies, architecture design, algorithm optimization, and hardware implementation of the Encoder-Decoder semantic segmentation network SegNet as an example, and undertake a performance evaluation. Using an Intel Arria-10 GX1150 platform for evaluation, our work achieves a throughput higher than 432.8 GOP/s with power consumption of about 20 W, which is a 1.2× times improvement the energy-efficiency ratio compared to a high-performance GPU.
Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images

This paper presents an edge-based color image segmentation approach, derived from the method of particle motion in a vector image field, which could previously be applied only to monochrome images. Rather than using an edge vector field derived from a gradient vector field and a normal compressive vector field derived from a Laplacian-gradient vector field, two novel orthogonal vector fields were directly computed from a color image, one parallel and another orthogonal to the edges. These were then used in the model to force a particle to move along the object edges. The normal compressive vector field is created from the collection of the center-to-centroid vectors of local color distance images. The edge vector field is later derived from the normal compressive vector field so as to obtain a vector field analogous to a Hamiltonian gradient vector field. Using the PASCAL Visual Object Classes Challenge 2012 (VOC2012), the Berkeley Segmentation Data Set, and Benchmarks 500 (BSDS500), the benchmark score of the proposed method is provided in comparison to those of the traditional particle motion in a vector image field (PMVIF), Watershed, simple linear iterative clustering (SLIC), K-means, mean shift, and J-value segmentation (JSEG). The proposed method yields better Rand index (RI), global consistency error (GCE), normalized variation of information (NVI), boundary displacement error (BDE), Dice coefficients, faster computation time, and noise resistance.
A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection

In Beijing, Shanghai, Hangzhou, and other cities in China, traffic congestion caused by traffic incidents also accounts for 50% to 75% of the total traffic congestion on expressways. Therefore, it is of great significance to study an accurate and timely automatic traffic incident detection algorithm for ensuring the operation efficiency of expressways and improving the level of road safety. At present, many effective automatic event detection algorithms have been proposed, but the existing algorithms usually take the original traffic flow parameters as input variables, ignoring the construction of feature variable sets and the screening of important feature variables. This paper presents an automatic event detection algorithm based on deep cycle limit learning machine. The traffic flow, speed, and occupancy of downstream urban expressway are extracted as input values of the deep-loop neural network. The initial connection weights and output thresholds of the deep-loop neural network are optimized by using the improved particle swarm optimization (PSO) algorithm for global search. The higher classification accuracy of the extreme learning machine is trained, and the generalization performance of the extreme learning machine is improved. In addition, the extreme learning machine is used as a learning unit for unsupervised learning layer by layer. Finally, the microwave detector data of Tangqiao viaduct in Hangzhou are used to verify the experiment and compared with LSTM, CNN, gradient-enhanced regression tree, SVM, BPNN, and other methods. The results show that the algorithm can transfer low-level features layer by layer to form a more complete feature representation, retaining more original input information. It can save expensive computing resources and reduce the complexity of the model. Moreover, the detection accuracy of the algorithm is high, the detection rate is higher than 98%, and the false alarm rate is lower than 3%. It is better than LSTM, CNN, gradient-enhanced regression tree, and other algorithms. It is suitable for urban expressway traffic incident detection.
Optimizing Complexity of Quick Sort

Quick Sort is considered as the fastest sorting algorithm among all the sorting algorithms. The idea of selecting a pivot was introduced in classical Quick Sort in 1962. This sorting algorithm takes favorably less time compared to other methods. It needs a complex time O( $$ nlogn $$ ) for the best case and O( $$ n^{2} $$ ) for worst-case which occurs when the input array is already sorted or reversely sorted. To reduce the worst-case complexity we provide a strong algorithm where it makes fewer comparisons and the time complexity after using this algorithm becomes a function of logarithm O( $$ nlogn $$ ) for worst-case complexity. We experimentally evaluate our algorithms and compare them with classical algorithms and with other papers. The algorithm presented here has profound implications for future studies of handling worst-case complexity and may one day help to solve this occurrence of the fastest sorting method.
Novel Swarm Intelligence Algorithm for Global Optimization and Multi-UAVs Cooperative Path Planning: Anas Platyrhynchos Optimizer

In this study, a novel type of swarm intelligence algorithm referred as the anas platyrhynchos optimizer is proposed by simulating the cluster action of the anas platyrhynchos. Starting from the core of swarm intelligence algorithm, on the premise of the use of few parameters and ease in implementation, the mathematical model and algorithm flow of the anas platyrhynchos optimizer are given, and the balance between global search and local development in the algorithm is ensured. The algorithm was applied to a benchmark function and a cooperative path planning solution for multi-UAVs as a means of testing the performance of the algorithm. The optimization results showed that the anas platyrhynchos optimizer is more superior in solving optimization problems compared with the mainstream intelligent algorithm. This study provides a new idea for solving more engineering problems.
A NOVEL PROBABILISTIC GENERATION MODEL FOR GRID CONNECTED PV BASED DISTRIBUTED GENERATION

The feasibility of renewable energy resources such as solar energy is marked by uncertainties that make it an unpredictable mean of power generation. To guarantee an uninterrupted power supply, solar irradiance modelling can be taken as a useful step towards meeting the operational challenges of electric power grid. This paper proposes a dynamic Probabilistic generation model to estimate and generate the time-coupled solar irradiance patterns. Initially, clustering of yearly solar irradiance measurements is performed to obtain a meaningful grouping of similar days. One-hour time step is considered to construct a time-coupled probabilistic model of solar irradiance data based on a Beta distribution. The parameters of beta distribution are found by considering the variations of irradiance patterns at two successive time steps. The probabilistic model is then used to generate number of aggregate solar irradiance generation scenarios. The effectiveness of proposed scenario generation approach is evaluated through Average Mean Absolute Percentage Error (AMAPE) and comparison with the probabilistic model already available in the literature.
Binary and Multiclass Classification of Histopathological Images Using Machine Learning Techniques

Background and Objective : Breast cancer is fairly common and widespread form of cancer among women. Digital mammogram, thermal images of breast and digital histopathological images serve as a major tool for the diagnosis and grading of cancer. In this paper, a novel attempt has been proposed using image analysis and machine learning algorithm to develop an automated system for the diagnosis and grading of cancer. Methods : BreaKHis dataset is employed for the present work where images are available with different magnification factor namely 40×, 100×, 200×, 400× and 200× magnification factor is utilized for the present work. Accurate preprocessing steps and precise segmentation of nuclei in histopathology image is a necessary prerequisite for building an automated system. In this work, 103 images from benign and 103 malignant images are used. Initially color image is reshaped to gray scale format by applying Otsu thresholding, followed by top hat, bottom hat transform in preprocessing stage. The threshold value selected based on Ridler and calvard algorithm, extended minima transform and median filtering is applied for doing further steps in preprocessing. For segmentation of nuclei distance transform and watershed are used. Finally, for feature extraction, two different methods are explored. Result : In binary classification benign and malignant classification is done with the highest accuracy rate of 89.7% using ensemble bagged tree classifier. In case of multiclass classification 5-class are taken which are adenosis, fibro adenoma, tubular adenoma, mucinous carcinoma and papillary carcinoma the combination of multiclass classification gives the accuracy of 88.1% using ensemble subspace discriminant classifier. To the best of author’s knowledge, it is the first made in a novel attempt made for binary and multiclass classification of histopathology images. Conclusion : By using ensemble bagged tree and ensemble subspace discriminant classifiers the proposed method is efficient and outperform the state of art method in the literature.
MulticlusterKDE: a new algorithm for clustering based on multivariate kernel density estimation

In this paper, we propose the MulticlusterKDE algorithm applied to classify elements of a database into categories based on their similarity. MulticlusterKDE is centered on the multiple optimization of the kernel density estimator function with multivariate Gaussian kernel. One of the main features of the proposed algorithm is that the number of clusters is an optional input parameter. Furthermore, it is very simple, easy to implement, well defined and stops at a finite number of steps and it always converges regardless of the data set. We illustrate our findings by implementing the algorithm in R software. The results indicate that the MulticlusterKDE algorithm is competitive when compared to K-means, K-medoids, CLARA, DBSCAN and PdfCluster algorithms. Features such as simplicity and efficiency make the proposed algorithm an attractive and promising research field that can be used as basis for its improvement and also for the development of new density-based clustering algorithms.
Near-optimal erasure list-decodable codes

A code C ⊆ (0, 1}n is (s, L) erasure list-decodable if for every word w, after erasing any s symbols of w, the remaining n - s symbols have at most L possible completions into a codeword of C. Non-explicitly, there exist binary [MATH HERE] erasure list-decodable codes with rate approaching τ and tiny list-size L = [MATH HERE]. Achieving either of these parameters explicitly is a natural open problem (see, e.g., [26, 24, 25]). While partial progress on the problem has been achieved, no prior nontrivial explicit construction achieved rate better than Ω(τ2) or list-size smaller than Ω(1/τ). Furthermore, Guruswami showed no linear code can have list-size smaller than Ω(1/τ) [24]. We construct an explicit binary ((1 - τ)n, L) erasure list-decodable code having rate τ1+γ (for any constant γ > 0 and small τ) and list-size poly(log [MATH HERE]), answering simultaneously both questions, and exhibiting an explicit non-linear code that provably beats the best possible linear code.The binary erasure list-decoding problem is equivalent to the construction of explicit, low-error, strong dispersers outputting one bit with minimal entropy-loss and seed-length. For error e, no prior explicit construction achieved seed-length better than 2log(1/e) or entropy-loss smaller than 2log(1/e), which are the best possible parameters for extractors. We explicitly construct an e-error one-bit strong disperser with near-optimal seed-length (1 + γ) log(1/e) and entropy-loss O(log log 1/e).The main ingredient in our construction is a new (and almost-optimal) unbalanced two-source extractor. The extractor extracts one bit with constant error from two independent sources, where one source has length n and tiny min-entropy O(log log n) and the other source has length O(log n) and arbitrarily small constant min-entropy rate. When instantiated as a balanced two-source extractor, it improves upon Raz's extractor [39] in the constant error regime. The construction incorporates recent components and ideas from extractor theory with a delicate and novel analysis needed in order to solve dependency and error issues that prevented previous papers (such as [27, 9, 13]) from achieving the above results.
Performance Evaluation of Container’s Shared Volumes

Performance is one example of the Extra-Functional Properties (EFPs), an essential part of software development with limited resources. In such a context of software development, containers represent a widely used abstraction technology that requires fewer resources, when compared to hypervisor-based virtualization. Containers provide access to environments and speed up development by abstracting the underlying infrastructure layer for different application domains. Volume sharing is a usual abstraction provided by containers for data persistence, which can be accomplished, especially with Network File System (NFS) or Docker's sharing method. Evaluate the performance of these volumes in containers is critical to software development; however, we did not find available in the literature such evaluation. This paper describes the volume sharing performance for containers using NFS and Docker, which is essential for data persistence. We evaluated the performance of these shared volumes by simulating three application domains: database accesses from a benchmark, direct reads and writes in the file systems and concurrent accesses from an MPI parallel program. Our results show significant differences among the distinct volume sharing methods analyzed for Docker and NFS. While asynchronous NFS has better performances for the set composed of heavy workloads, bursting read and write sequences, the Docker volumes, by the other hand, outperformed for processes with concurrent accesses when using a parallel program.
Bus Width Aware Off-Chip Memory Access Minimization for CNN Accelerators

Convolutional Neural Network (CNN) accelerators have gained popularity due to their ability to speed up the CNN based applications. However, the energy efficiency of these accelerators is limiting their ubiquitous usage in energy-constrained devices. A significant fraction of their energy consumption results from off-chip memory accesses. In order to get high throughput, these accelerators connect to off-chip memory by a wide data bus. However, accessing the data of size, not a multiple of the bus width, results in wastage of energy. We observed that off-chip memory accesses could be reduced significantly by partitioning the data that optimally utilizes bus width and increases the number of aligned accesses. In this work, we propose a bus width aware approach to determine the optimal partition of the convolution layers to reduce the off-chip memory accesses. Our tool evaluates the off-chip memory accesses for different data partitions, and data reuse schemes to find the optimal partition. We have experimented with two popular CNNs, VGG16 and AlexNet. Our approach reduces off-chip memory accesses of VGG16 by 16% and 29% and of AlexNet by 9% and 16% on 64 and 128 bits data bus, respectively, compared to the state of the art approach.
wKSR-NLM: An Ultrasound Despeckling Filter Based on Patch Ratio and Statistical Similarity

Ultrasound images are affected by the well known speckle phenomenon, that degrades their perceived quality.In recent years, several denoising approaches have been proposed.Among all, those belonging to the non-local (NL) family have shown interesting performance.The main difference among the proposed NL filters is the metric adopted for measuring the similarity between patches.Within this manuscript, a statistical metric based on the ratio between two patches is presented.Compared to other statistical measurements, the proposed one is able to take into account the texture of the patch, to consider a weighting kernel and to limit the computational burden.A comparative analysis with other despeckling filters is presented.The method provided good balance between noise reduction and details preserving both in case of simulated (by means of Field II software) and real (breast tumor) datasets.
Cloud Computing Services: Taxonomy of Discovery Approaches and Extraction Solutions

Cloud computing offers new features of sharing resources and applications to meet users’ computing requirements. It is a model by which the users can access computing resources as services offered on the Internet (cloud services). Cloud service providers offer a highly diverse range of asymmetric cloud services with heterogeneous features, which makes it difficult for the users to find the best service that fits his needs. Many research studies have been done on cloud service discovery, and several models and solutions that applied different techniques have been proposed. This paper aims at presenting the state of the art in the area of cloud services discovery by exploring the current approaches, techniques, and models. Furthermore, it proposes a taxonomy of cloud service discovery approaches. An integrative review approach was used to explore the related literature. Then, by analyzing the existing cloud service discovery solutions, a taxonomy of discovery approaches was suggested based on several perspectives including the discovery environment and the discovery process methods. The proposed taxonomy allows easily classifying and comparing cloud services discovery solutions. Moreover, it may reveal issues and gaps for further research and expose new insights for more innovative and effective cloud services discovery solutions.
Token- or Account-Based? A Digital Currency Can Be Both

Digital currencies, including potential central bank digital currencies (CBDC), have generated a lot of interest over the past decade, since the emergence of Bitcoin. The interest has only grown in recent months because of a desire for contactless payment methods, stemming from the coronavirus pandemic. In this post, we discuss a common distinction made between “token-based” and “account-based” digital currencies. We show that this distinction is problematic because Bitcoin and many other digital currencies satisfy both definitions.