Reviewerly/exHarmony huggingface dataset:
Load Balancing Mechanism Based on Linear Regression Partition Prediction in Spark

Abstract With the rise of cloud computing, the internet of things, social networks and other technologies, information data is expanding rapidly, and traditional processing and storage systems have been difficult to deal with massive data. Spark is a fast and efficient MapReduce implementation developed after Hadoop. However, the shuffle operation in spark will cause the data set on some working nodes to be too large, while other nodes may be idle, which will affect the performance of spark jobs. This phenomenon is called data skew. Aiming at the problem of data skew in spark platform, this paper proposes a load balancing mechanism based on linear regression partition prediction: SP-LRP (Spark load balancing mechanism based on Linear Regression Partition), SP-LRP predicts the partition size of the reduce tasks at run-time, leverages the skew detection algorithm to identify the skew partition and adjusts the task resource allocation according to the fine-grained resource allocation algorithm. We use the benchmark dataset to evaluate SP-LRP, and compare the average execution time of two algorithms and the load balancing degree of reducers in different situations. The experimental confirmed the efficiency of SP-LRP in their respective usage scenarios.
Using Monte Carlo Particle Methods to Estimate and Quantify Uncertainty in Periodic Parameters (Research)

Estimating and quantifying uncertainty in system parameters remains a big challenge in applied and computational mathematics. A subset of these problems includes estimating periodic parameters that have unknown dynamics. Along with their time series, the period of these parameters may also be unknown and need to be estimated. The aim of this paper is to address the periodic parameter estimation problem, with particular focus on exploring the associated uncertainty, using Monte Carlo particle methods, such as the ensemble Kalman filter. Both parameter tracking and piecewise function approximations of periodic parameters are considered, highlighting aspects of parameter uncertainty in each approach when considering factors such as the frequency of available data and the number of piecewise segments used in the approximation. Estimation of the period of the periodic parameters and related uncertainty is also analyzed in the piecewise formulation. The pros and cons of each approach are discussed relative to a numerical example estimating the external voltage parameter in the FitzHugh–Nagumo system for modeling the spiking dynamics of neurons.
Detail-preserving single nighttime image dehazing

Most existing dehazing approaches employ image priors such as a dark channel prior, which may be violated in the nighttime. In addition, these approaches perform dehazing directly on the original hazy image, which usually results in detail blurring and noise amplifying. To address both issues, an effective single nighttime image haze removal approach is presented. We first decompose the hazy image into a structure layer and a texture layer. Specifically, the dehazing operation is performed only on the structure layer to avoid detail loss and noise amplification. Due to the low frequency characteristics of the haze and the glow of artificial lights, the atmospheric veil is estimated on structure layer dehazing. Furthermore, to eliminate the nonuniform illumination effects, we present an ambient light estimation approach based on multiscale fusion. At the same time, the denoising is performed in the texture layer. Extensive experiments demonstrate that the proposed approach recovers a high-quality, haze-free, and noise-free image with vivid details.
Pragmatic Distance in IoT Devices

Stand-alone devices perform their functions without human intervention. These types of devices would benefit from capabilities that allow collaboration among the devices in order to maximize service availability, as well as dynamic adaptation of the devices to user requirements in order to minimize resource usage. For example, when detecting a malfunction in one of the devices, the system should find devices with similar functionality to recover the overall service by functionality substitution. Additionally, for example, in the context of a user with hearing loss, the system should utilize devices with visual feedback functionality and disable audio feedback functionality to conserve resources. In this paper, we describe the implementation of a method to detect similar functionalities between heterogeneous devices, denoted Lightweight Machine to Machine Resource Pragmatic Distance (LwRPD). We define that devices are pragmatically close if they can be substituted with each other in order to perform a specific function in a particular context. We implement the method for enabling the aforementioned capabilities in specific use cases, using resources from real-life devices, to demonstrate the benefits. We also compare LwRPD with a multi-agent pragmatic similarity metric, Fuzzy logic and Levenshtein distance techniques, demonstrating LwRPD superiority over those techniques.
The Effect of Narration on User Comprehension and Recall of Information Visualisations

Information visualisation researchers have posited that author-driven narratives will allow information to be conveyed efficiently and argue for the adoption of storytelling techniques in information visualisation. However, there is limited work describing the effects of author-driven narratives in users' comprehension and memorability of visualisations in relation to interactive visualisations. Recommendations for author-driven visualisation stories are largely based on anecdotal reports or research from the arts, and not on studies in information visualisation. To investigate these issues, we carried out a study that compared purely author-driven narratives with interactive visualisations devoid of author narratives, in terms of comprehension and short-term and long-term memorability. We found that the presence of narration in author-driven stories significantly aided the understanding of information but had no significant effect on the long-term recall of information from visualisations.
PAMELA-CL: Partition Membership Based on Lazy Classifier for Neuromarketing

Abstract Neuromarketing is one of the business strategies that has developed lately. The strategy studies the effect of product promotion on the brain. If the impact analysis on the brain is successfully carried out, the company can find a good and effective marketing strategy for potential customers. This study used electroencephalography (EEG) as data. 30 respondents were involved in data recording. The final goal in this study was to classify the emotions of respondents to the video simulations that were displayed. The video contains a number of products. There were 14 electrodes used for the recording process. Then the EEG data were preprocessed, and its characteristics were extracted before being classified. This study proposed PAMELA-CL for the classification. The classifier was compared with lazy classifier. The result was obtained that this new classifier has higher accuracy than the lazy classifier. The difference in accuracy between the two was above 25%. All experiments involving PAMELA-CL had accuracy above 85%. It showed that this new classifier could be recommended in solving neuromarketing problems, especially for the dataset used in this study.
Marabunta: Continuous Distributed Processing of Skewed Streams

Current stream processing systems (SPSs) suffer from the imbalanced load and limited parallelism due to skewed data distributions and imbalanced computational resources. We observed that the cause of these problems is current SPSs partition their workloads statically. To address this problem, we design a distributed stream processing system, Marabunta, for skewed stream processing. Marabunta performs dynamic scaling and load balancing automatically at runtime. Large partitions in a skewed data distribution can be processed in parallel or migrated to idle machines to achieve load balancing. Moreover, Marabunta uses a new execution model to accelerate the execution by increases the parallelism and the computational resources utilization. We implemented Marabunta in C++ and optimized it for modern hardware. Our evaluations on typical streaming workloads show that Marabunta achieves higher throughputs and better elasticity with both uniform and skewed datasets compared to the state-of-the-art SPSs, e.g., Flink and Heron.
Adaptive path planning of novel complex parts for industrial spraying operations

Industrial spraying operations in high-mix, low-volume manufacturing are usually not automated due to variety and variability among workpieces. Automated path planning for industrial spraying operations offers the potential to automate surface preparation and coating in such environments. Autonomous spray path planners in the literature have been limited to generally continuous and convex surfaces, which is not true of most real parts. There is a need for planners that consistently handle concavities and discontinuities, such as sharp corners, holes, protrusions, and other surface abnormalities when building a path. This research uses slicing-based adaptive planning methods to generate path trajectories. It identifies and quantifies the importance of concavities and surface abnormalities and whether they should be considered in the path plan by comparing the true part geometry to the convex hull path. Based on these considerations, the path is adapted accordingly. Results show that the path planning method generates smooth trajectories and indicate that the adaptive methods developed substantially increase both mean impingement and uniformity of impingement in a pressure washing application. From this, it is concluded that the method represents a substantial step toward one-off robotic path planning for complex geometries.
Collaborative Filtering Recommendation Algorithm based on Improved Similarity

The traditional collaborative filtering recommendation algorithm can't measure well the similarity among users under the condition of data sparsity, which leads to a decrease in the accuracy of the recommendation system. Therefore, we propose a collaborative filtering algorithm to improve similarity. We weight the number of common scoring items on the similarity calculation to improve the shortcomings of the usual Pearson correlation coefficient. Then, we use it to find the user's nearest neighbor set and predict the score of the item to generate a recommendation list. Experiments show that our algorithm can improve the accuracy of system recommendation effectively.
Clifford’s Identity and Generalized Cayley-Menger Determinants

Distance geometry is usually defined as the characterization and study of point sets in $${\mathbb R}^k$$ , the k-dimensional Euclidean space, based on the pairwise distances between their points. In this paper, we use Clifford’s identity to extend this kind of characterization to sets of n hyperspheres embedded in $${\mathbb S}^{n-3}$$ or $${\mathbb R}^{n-3}$$ where the role of the Euclidean distance between two points is replaced by the so-called power between two hyperspheres. By properly choosing the value of n and the radii of these hyperspheres, Clifford’s identity reduces to conditions in terms of generalized Cayley-Menger determinants which has been previously obtained on the basis of a case-by-case analysis.
Unsupervised Hashing with Gradient Attention

The existing learning-based unsupervised hashing method usually uses a pre-trained network to extract features, and then uses the extracted feature vectors to construct a similarity matrix which guides the generation of hash codes through gradient descent. Existing research shows that the algorithm based on gradient descent will cause the hash codes of the paired images to be updated toward each other’s position during the training process. For unsupervised training, this situation will cause large fluctuations in the hash code during training and limit the learning efficiency of the hash code. In this paper, we propose a method named Deep Unsupervised Hashing with Gradient Attention (UHGA) to solve this problem. UHGA mainly includes the following contents: (1) use pre-trained network models to extract image features; (2) calculate the cosine distance of the corresponding features of the pair of images, and construct a similarity matrix through the cosine distance to guide the generation of hash codes; (3) a gradient attention mechanism is added during the training of the hash code to pay attention to the gradient. Experiments on two existing public datasets show that our proposed method can obtain more discriminating hash codes.
Multi-Band Contourlet Transform For Adaptive Remote Sensing Image Denoising

Abstract The ability to remove noise from remote sensing images, while retaining the important features of the images, is becoming increasingly important. In this paper, we introduce the multi-band contourlet transform, a new method for adaptively denoising remote sensing images. We describe existing methods that use multi-resolution analysis transforms for denoising images and discuss their respective advantages and disadvantages. We then introduce our novel denoising method, which exploits the advantages of existing methods. We summarize the results of a comprehensive set of experiments designed to evaluate the performance of our method and compare it with the performance of existing methods. The results demonstrate that our method is superior to existing methods, both in terms of its ability to denoise images and to retain salient features of those images following denoising.
Boolean functions with multiplicative complexity 3 and 4

Multiplicative complexity (MC) is defined as the minimum number of AND gates required to implement a function with a circuit over the basis (AND, XOR, NOT). Boolean functions with MC 1 and 2 have been characterized in Fisher and Peralta (2002), and Find et al. (IJICoT 4(4), 222–236, 2017), respectively. In this work, we identify the affine equivalence classes for functions with MC 3 and 4. In order to achieve this, we utilize the notion of the dimension dim(f) of a Boolean function in relation to its linearity dimension, and provide a new lower bound suggesting that the multiplicative complexity of f is at least ⌈dim(f)/2⌉. For MC 3, this implies that there are no equivalence classes other than those 24 identified in Çalık et al. (2018). Using the techniques from Çalık et al. and the new relation between the dimension and MC, we identify all 1277 equivalence classes having MC 4. We also provide a closed formula for the number of n-variable functions with MC 3 and 4. These results allow us to construct AND-optimal circuits for Boolean functions that have MC 4 or less, independent of the number of variables they are defined on.
Online Course of Reading-Writing and Multimedia Application with Virtual Assistants for Schools

Reading and writing are important parts of everyone's life as it develops the capacity for reflection, analysis, and synthesis. This paper presents the results of a research project applied to the area of e-learning and multimedia for education. The main goal is to implement an online course for teachers and a multimedia application with virtual assistants focused on strengthening the teaching-learning process of children through reading and writing. We applied the CROA and the OOHDM Methodologies for the design and development of these applications. The results show the satisfaction of students and teachers with regard to the organization and structuring of educational software applications, which significantly improve the reading and writing skills of children between 9 and 12 years of Christian schools that learn principles and human values.
Implementation of a Privacy Preserving and Loss Data Retrieval System for Cloud Environment

The cloud supplier doesn't have recommendations concerning the data and the cloud information put away and kept up internationally anyplace in the cloud. The security assurance plans are normally founded on encryption innovation. There are numerous security saving techniques on the side to forestall information in the cloud. A three-layer stockpiling structure dependent on mist registering is proposed. The proposed structure can both exploit distributed storage and secure the protection of information. Here Hash-Solomon code calculation is intended to isolate information into various parts. If one information part missing, the information data is tend to loss. Right now is utilizing pail idea based calculations and secures the information data and afterward, it can show the security and proficiency in our plan. Also, in light of computational knowledge, this calculation can figure the conveyance extent put away in a cloud, mist, and neighborhood machine, individually Software as a Service (SaaS) client discharges their application on a facilitating situation which can be gotten to through system from different customers by application clients. The customer doesn't oversee or control the basic cloud foundation with the conceivable exemption of constrained client explicit application arrangement settings. Google Apps and Microsoft Office 365 are the models for S aaS.
A Case Study of Software Security Red Teams at Microsoft

The modern software security adversary employs persistent and evasive attack techniques, for example-using zero-day exploits that have not been disclosed publicly-to target high-profile companies for political and economic espionage or to exfiltrate sensitive data or intellectual property. To combat these threats, large organizations are adopting an emerging practice of staffing full-time offensive security teams, or red teams. To understand the workflows, culture, and day-to-day practices of software security engineers in red teams, we conducted 17 interviews with informants across five red teams within Microsoft. We found that software security engineers have substantial impact in the organization as they harden security practices, drawing from their diverse backgrounds. Software security engineers are both agile yet specialized in their activities, and closely emulate malicious adversaries-subject to some reasonable constraints. Although software security engineers are in some respects software engineers, they also have several consequential differences in how they write, maintain, and distribute software. The results of this work are applicable to practitioners, researchers, and toolsmiths who wish to understand how offensive security teams operate, situate, and collaborate with partner teams in their organization.
Readability of Spanish e-government information

This paper proposes the automated evaluation of readability, through the analysis of different linguistic characteristics associated with a better understanding of the websites of the Spanish Government's administrative procedures. To fulfil this task, a corpus made by web documents with distinct difficulty levels has been gathered. Then, these documents' difficulty is assessed through different classic readability metrics. By the use of machine learning methods, different algorithms are analyzed to measure their capability to predict text difficulty. The results obtained show that the official Spanish Government websites have a high difficulty level. The main contribution of this work is the combined application of a wide number of linguistic attributes and the construction of a new corpus addressed to official government texts.
Reform in the Teaching Model of English Writing in the Big Data Era

The increasing application of the Internet and various mobile terminals has brought mankind into a new era of the big data. The era of big data has brought changes from theories to practices of English writing teaching in such areas as the teaching resources, purposes and contents of writing, tools and software programs assisting writing, the assessment of writing, and the construction of writing competence. Many scholars have studied the teaching of English writing from different perspectives and made great achievements in improving the effectiveness of English writing. But few studies have been done on the integration of big data with English writing teaching. Through literature review and empirical study, this study probed to the reconstruction of the teaching model of English writing in the big data era. In the empirical study, the final writing test show that the progress of students in writing is obvious after the application of new teaching model in classroom when compared to the traditional one. The result of questionnaire carried out among the experimental class show that the new model of writing teaching is quite effective in improving students' writing ability and they think highly that it is much better than the traditional one. Furthermore, the evaluation system of the new teaching model is highly appreciated by students, which may tell us that students are eager to get immediate assessment after they finished the writing.
Algorithmic Study on Facial Emotion Recognition model with Optimal Feature Selection via Firefly Plus Jaya Algorithm

Facial emotions are significant constraints that assist us to identify the intents of others. Generally, inhabitants understand the emotional condition of other people, like anger, sadness, and joy, using vocal tone and facial expressions. Here, a novel Facial Emotion Recognition system (FER) is developed that includes four major processes: (a) Face detection (b) Feature extraction (c) Optimal feature selection and (d) Classification. The input facial images are provided as input to a face detection model referred to as the viola-jones method. Then, from the detected facial images, the Local Binary Pattern (LBP), Discrete Wavelet Transform (DWT), and Gray Level Co-occurrence Matrix (GLCM) features are extracted. The length of the features is large, so there is a requirement to choose the optimal features from the image. After selecting the optimal features, it is subjected to the classification process via Neural Network (NN). As a novelty, the optimal feature selection and the weight optimization of NN are carried out via a new hybrid algorithm called Mean Fitness Oriented JA+FF position update (MF-JFF). Later, an algorithmic analysis is performed for validating the performance of the presented model. From the analysis, the accuracy obtained for the values γ attained at 0.6 was 2.2% better than the values attained when γ = 0.2, 0.4, 0.8, and 1 respectively.
An Analysis on Paths of Promoting the Construction of Modern Vocational Education System by Educational Informatization

The main characteristics of modern vocational education system are the coordinated development and close connection of secondary vocational education, higher vocational education and undergraduate education. At present, there are various modes of connection between Schools of different educational levels, but most of them stay at the formal level. The connotation of connection is insufficient and problematic. However, educational informatization plays an important role in promoting the construction of modern vocational education system. It has been proved by practice that, in view of the five outstanding problems, such as unclear target orientation, not closely connected courses, weak and unbalanced teachers, lack of standards and comparison in professional ability evaluation, lax process management and difficult monitoring of teaching quality, we can promote integration of “Secondary vocational schools - Higher Vocational schools - undergraduate schools - enterprise” from five paths: talent training plan, teaching resources, teaching staff, evaluation mode and teaching management, and promote the coordinated development of the four main bodies.