Reviewerly/exHarmony huggingface dataset:
Perceptive Factors That Affect Household Food Consumption Among Different Grassland Areas: A Case Study Based on Fuzzy Cognitive Map

Food consumption acts as an intermediary between ecosystems and human systems in grassland areas. In this study, we generated a fuzzy cognitive map (FCM) to obtain a semi-quantitative assessment of impact factors on food consumption in the Xilin Gol Grassland, China, from the perception of local rural households. Based on it, the food consumption impact factors networks of households in Xilin Gol Grassland were created. Results showed that household recognition of food consumption impact factors differed among grassland transects in both amount and category, and the results of principal component analysis (PCA) ascertain such differences and transitions. Livestock breeding, income, local economic development, consumption habit, age, food price, and cost were the most important impact factors mentioned by both farmers and herdsmen. From south to north in the grassland transects, households' cognition of ecological and economic factors increased, and the cognition of personal and social factors decreased. Differences in supply, culture, customs, and socioeconomic characteristics affected the cognition of households and their livelihoods, which influenced impact factors as well as associated connections. The role of livestock breeding also validated the connection between human actives and the grassland ecosystem. Results from this study can be used as a reference for policymakers during decision-making processes respective of regional sustainable development.
Interactive Analog Layout Editing with Instant Placement Legalization

Analog layout design still relies heavily on manual efforts. Current fully automated flows are not yet able to satisfy the demands of versatile customization and not compatible to the existing manual flows. Interactive layout editing has the potential to bridge the gap between the manual flows and fully automated flows shooting for both performance and productivity. In this paper, we propose an interactive editing framework with instructions for both topological editing and detailed customization. We also propose an effective instant legalization algorithm for fast layout update during the real-time interaction with users.
T2FA: A Heuristic Algorithm for Deadline-Constrained Workflow Scheduling in Cloud with Multicore Resource

Workflow scheduling is one of the most challenging problems in cloud computing. This paper proposes a heuristic algorithm task type first algorithm (T2FA) for solving deadline-constrained workflow scheduling in cloud with multicore resource (DWS_CMR). The objectives to be minimized are the maximal completion time (i.e., makespan) and the total costs. Firstly, resource model and workflow application model are introduced. Resource model has the configurations of multicore, processing capacity, bandwidth and leasing price, and workflow application model is described by directed acyclic graph (DAG). Based on above models, the mathematical model of DWS_CMR is established, which allows multiple tasks to run concurrently on multicore resources. Secondly, to exploit the characteristics of the problem, the structures of DAG are decomposed and formulated. Merging tasks conforming to the first structure into task blocks can simplify DAG. Four special types of tasks are extracted from the second and third structures, and are preferentially scheduled in task scheduling stage. Then, a new interrelated calculation method of estimated start time and actual start time of tasks is proposed, which can complete the task-to-resource mapping. Finally, T2FA is devised, which incorporates two important phases, including pre-processing and task scheduling. Experimental results show that T2FA can achieve significantly better schedules in most test cases compared to several existing algorithms.
An experimental study on convolutional neural network-based pooling techniques for the classification of HEp-2 cell images

Convolutional Neural Networks (CNNs) became the de-facto standard for medical image analysis. In CNN, pooling layers are used for downsampling feature maps by aggregating features from local regions, and hence help to learn invariant features and reduce computational complexity. Various pooling techniques have been proposed, among which average and max pooling are the widely used ones because of their simplicity. Since average pooling aggregates all the information of each region in each feature map, background features may dominate in the pooled representation. On the other hand, max pooling can capture noisy features as it focuses on the most activated features. To overcome this, researchers have proposed soft pooling techniques as intermediate to max and average pooling. In addition, various other pooling techniques also have been introduced for different purposes, which include techniques to reduce overfitting, to capture higher-order information such as the correlation between features, to capture spatial/structural information, etc. In this work, we investigate different pooling mechanisms with ResNet, a widely used CNN architecture, for the classification of HEp-2 cell images. We found that average pooling generally performs better than other pooling techniques such as max pooling, soft pooling, and bilinear pooling. By just changing the local pooling operation of the ResNet architecture from max to average we show over 2% of improvement in mean class accuracy (MCA). Overall, our approach gives an MCA of 88% for the classification of HEp-2 cell images.
A Conceptual Consumer Data Protection Maturity Model for Government Adoption: South African Context

In South Africa (SA), being a developing country, there is a significant gap between the level of trust consumers extend towards organizations they share personal information with whilst conducting everyday transactions and the degree to which that trust is justified by the Information Security (IS) compliance efforts of organizations. Consumers are lacking IS compliance awareness which influences their ability to make informed decisions on when to share or not to share their personal information with organizations. There is currently no government-led/sponsored IS compliance awareness training initiatives in SA and therefore no way to measure the progress/maturity of such framework implementation. This research problem called for the development of a Consumer Data Protection Framework (CDPF) in a prior study to assist the South African government with improving the IS compliance awareness of consumers through government-led awareness training initiatives []. This paper extends that work by proposing a Consumer Data Protection Maturity Model (CDPMM) to assist the South African government with evaluating the maturity of the aforementioned CDPF implementation. The evaluation results can be used to create an improvement plan which will guide the South African government to reach its target maturity level. The primary research objective of this paper is therefore to propose a CDPMM that can be used to measure the progress of the CDPF implementation in a South African government setting. The CDPMM is developed based on key components derived from literature and evaluated through expert reviews for improvement.
Promotion of VR Technology in Taijiquan Multimedia Edge Computing Technology

Taijiquan is a representative sport in my country’s traditional sports. It occupies an important position in China’s traditional sports. It has been rated as my country’s intangible cultural heritage. Widespread and promotion of Taijiquan can promote my country’s traditional culture. Using the unique advantages of Tai Chi and better development prospects, the method and path of VR technology are combined to create a new cultural atmosphere, and the active spread and promotion of Tai Chi culture are gradually carried out. VR technology is an augmented reality technology based on sensors and computers that can provide a strong visual experience. Playing the role of multimedia edge computing technology and combining VR technology with the advantages of Tai Chi itself are beneficial to change the traditional teaching methods and the spread of the Tai Chi movement. The purpose of this article is to solve some of the problems China faces in the promotion of Taijiquan and explore the role of VR technology in the spread and promotion of Taijiquan. This article uses methods such as searching literature, questionnaires, and experimental comparisons. The students in the 24-style Taijiquan elective course are the research objects, using VR technology as the teaching method of Taijiquan, scoring teaching efficiency, learning enthusiasm, students’ sensory effects, the attractiveness of Taijiquan, and statistics and analysis of relevant data. The research results show that the combination of VR technology and Taijiquan teaching can increase the visual and auditory effects of students in the learning process by 28.4%, the overall improvement of students’ learning enthusiasm by 25.3%, and the overall increase of learning efficiency by 34.8%, while greatly improving Taijiquan’s performance attractive. Therefore, it can be seen that the application of VR technology to Tai Chi is feasible.
Photovoltaic Energy All-Day and Intra-Day Forecasting Using Node by Node Developed Polynomial Networks Forming PDE Models Based on the L-Transformation

Forecasting Photovoltaic (PV) energy production, based on the last weather and power data only, can obtain acceptable prediction accuracy in short-time horizons. Numerical Weather Prediction (NWP) systems usually produce free forecasts of the local cloud amount each 6 h. These are considerably delayed by several hours and do not provide sufficient quality. A Differential Polynomial Neural Network (D-PNN) is a recent unconventional soft-computing technique that can model complex weather patterns. D-PNN expands the n-variable kth order Partial Differential Equation (PDE) into selected two-variable node PDEs of the first or second order. Their derivatives are easy to convert into the Laplace transforms and substitute using Operator Calculus (OC). D-PNN proves two-input nodes to insert their PDE components into its gradually expanded sum model. Its PDE representation allows for the variability and uncertainty of specific patterns in the surface layer. The proposed all-day single-model and intra-day several-step PV prediction schemes are compared and interpreted with differential and stochastic machine learning. The statistical models are evolved for the specific data time delay to predict the PV output in complete day sequences or specific hours. Spatial data from a larger territory and the initially recognized daily periods enable models to compute accurate predictions each day and compensate for unexpected pattern variations and different initial conditions. The optimal data samples, determined by the particular time shifts between the model inputs and output, are trained to predict the Clear Sky Index in the defined horizon.
A Reinforcement Learning based Eye-Gaze Behavior Tracking

In video established eye tracking methods, there are both mechanical and electrical based approaches existing. With the emerging spread of gaze tracking technology in the recent years and its significance in daily life routine, the data content acquired from the eye behavior tracing turn into important. Several research works were proposed to track the behavior of gaze while playing videos. Tracking an eye gaze while playing a dynamic videos consisting of numerous frames is a complex problem which needs excessive computational efforts. To handle such a complex task, this research proposes Reinforcement Learning (RL) based gaze behavior prediction model. These techniques are found to be invasive in nature and for visual attention behavior analysis applications, these invasive eye tracking system is not applicable. Hence the non-invasive eye tracking could be developed by determining the point of gaze based on observed image processing techniques. Some of the prevailing techniques include artificial intelligence, deep learning, and reinforcement learning and so on. Though quite a few research works has been admitted in this research area, there are several challenges existing so far. The suggested learning techniques are found to be computationally complex and time consuming. This current research work intends to propose a deep convolutional reinforcement learning (DC-RL) model for predicting the visual attention behavior of a person over dynamic scenes.
Invited: Trainable Discrete Feature Embeddings for Quantum Machine Learning

Quantum classifiers provide sophisticated embeddings of input data in Hilbert space promising quantum advantage. The advantage stems from quantum feature maps encoding the inputs into quantum states with variational quantum circuits. A recent work shows how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC) to encode binary strings into quantum states. We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning. The proposed trainable embedding requires not only as few qubits as QRAC but also overcomes the limitations of QRAC to classify inputs whose classes are based on hard Boolean functions. We numerically demonstrate its use in variational quantum classifiers to achieve better performances to classify real-world datasets, and thus its possibility to use near-term quantum computers for machine learning.
DCAB: An Effective Data Collection and Analysis Service for Blockchain

The characteristics of blockchain technology such as tampering resistance, non-repudiation, and traceability have built a foundation of trust and security for data sharing and exchange. However, the performance of the blockchain always encounters the bottleneck in scenarios where high-frequency data is generated such as IoT edge devices, data exchange, and bulk commodity trade. In this paper, we propose DCAB, a service that is effectively applied to blockchain data collection and analysis, to speed up the integration process of data written into the blockchain, and to improve the performance of the entire blockchain system. We also design an index database to improve the efficiency of data analysis. In order to make it easier for users to join different blockchain systems and simplify the operation and modification of the blockchain as much as possible, we design a modular data integration policy and a general smart contract template. Finally, we conduct a series of experiments to prove that the DCAB service can bring significant performance improvements to the blockchain system.
Blood Cells Classification for Identification of Acute Lymphoblastic Leukemia on Microscopic Images Using Image Processing

Acute lymphoblastic leukemia (ALL) is a type of leukemia (cancer of the white blood cells) that generally occurs in children. ALL have 3 sub-types, namely Ll, L2, and L3. Microscopic examination to classify ALL subtypes are still done manually by hematologists through visual identification under a microscope, it is difficult to classify ALL subtypes because the characteristics of each subtype are almost the same. This paper proposes a system that is able to detect and classify subtypes of Acute Lymphoblastic Leukemia blood cells using Image Processing. The classification method using K-Nearest Neighbor (K-NN) algorithm based on geometrical and statistical features. In cell object detection, the pre-processing step is used to improve the image quality before going further to the segmentation step using threshold and watershed algorithms. 73 K-NN Dataset from all subtypes of ALL image features were generated to calculate the similarity between new unseen data. In testing results, our proposed classification system achieves 80 % overall accuracy. Each subtype’s accuracy was 75 %, 73.33 %, and 93.33 % for the L1 subtype, L2 subtype, and L3 subtype.
Blind Quantum Computation Using a Circuit-Based Quantum Computer

When a universal quantum computer is used by the public, it is assumed that it will be in the form of a quantum cloud server that exists in a few bases due to its cost. In this cloud server, privacy will be a crucial issue, and a blind quantum computation protocol will be necessary so that each user can use the server without the details of the calculations being revealed. It is also important to be able to verify that the server is performing calculations as instructed by the user, since quantum calculations cannot be verified by classical computation. In this paper, we put forward a protocol that achieves blindness using the quantum one-time pad for encryption and a T-like gate, and while verifying computation using trap qubits.
Grey wolf optimizer with global search strategy

A grey wolf optimization algorithm with global search strategy is proposed to address the issue that the standard grey wolf optimizer (GWO) has the disadvantage of global search ability. Adaptive weight and search strategy randomly are added to strengthen the global search ability. 13 test function are used to test the performance of the proposed algorithm compared with other well-known meta-heuristics, and the results show that the proposed algorithm is feasible and effective.
UAV Path Planning Based on Improved Genetic Algorithm

In recent years, unmanned aerial vehicles (UAVs) have been widely used in autonomous cruise missions such as high-altitude aerial photography, power inspection, and geographic mapping with the advantages of flexible maneuvering and high stability. In order to ensure that UAVs can choose the most efficient and reliable flight route in complex environments, this paper designs a genetic algorithm-based path planning for UAVs in autonomous cruise operations. The main contents include: 1. Risk assessment of the route using the return value of the fitness function as the safety factor, and solving the optimal path between two points using an improved genetic algorithm; 2. The selection of strategies for multiple landing points under different conditions is proposed to form the optimal path for U AV cruising within range. Through the path planning verification experiments in Matlab, the results show that the improved genetic algorithm has a shorter total path distance compared with the traditional genetic algorithm, and the results have good feasibility and effectiveness.
Quality Prediction of Wearable Apps in the Google Play Store

Play Store reviews play an important role in demonstrating that decisions are made from the user's perspective, and contain a wealth of knowledge that can be used to understand quality issues and help developers build higher-quality mobile applications. Even for very important information, it can ensure the authenticity of user-generated content. In Play Store, wearable applications were recently launched, and are always open and easy to use, and are gradually being welcomed by users. Driven by popularity and self-interest, profit-incentive developers are developing low-quality applications and hiring robots to exaggerate ratings, reviews, or install counts. This is how spam in applications increases. Low-quality applications reduce the user's quality experience and trust, because after users download an application, they will know the irrelevant and annoying content of the application. As a result, the reputation of the Play Store is damaged. Therefore, we analyzed the review content of different wearable applications and proposed a regression model that has a wide range of recommended features, including sentiment, content similarity, language and time features, to detect wearable applications in the Play Store. We use advanced machine learning techniques to evaluate and verify the quality of the model. Compared with existing models, the performance of our proposed model is very good, with an error rate as low as 0.40 MSE. Therefore, our regression model is most suitable for deep neural network (DNN) training.
A systematic analysis of identity based encryption (IBE)

Light-weight cryptography is a major research area due to the minimization of the size of the devices utilized for such services. The associated security threats do increase as their applications are more now. Identity-Based Encryption (IBE) with its wide range of cryptographic schemes and protocols is specifically found suitable for low-end devices that have much resource constraint. This work describes various schemes and protocols in IBE. In this paper an analysis of IBE schemes and the various attacks they are prone to are discussed. The future trends are found to be very promising and challenging.
Max-Min Problems of Searching for Two Disjoint Subsets

The work considers three problems of searching for two disjoint subsets among a finite set of points in Euclidean space. In all three problems, it is required to maximize the minimal size of these subsets so that in each cluster, the total intra-cluster scatter of points relative to the cluster center does not exceed a predetermined threshold. In the first problem, the centers of the clusters are fixed points of Euclidean space and are given as input. In the second one, centers are unknown, but they belong to the initial set. In the last problem, the center of the cluster is the arithmetic mean of all its elements. Earlier works considered problems with constraints on the quadratic intra-cluster scatter.Quadratic analogs of the first two problems were proven to be NP-hard even in the one-dimensional case. For the third analog, the complexity remains unknown. The main result of the work are proofs of NP-hardness of all considered problems even in the one-dimensional case.
Approaches and Applications for Sentiment Analysis

With the increasing penetration of the internet, an ever-growing number of people are voicing their opinions in the numerous blogs, tweets, forums, social networking, and consumer review websites. Each such opinion has a sentiment (positive, negative, or neutral) associated with it. But the problem is that the amount of data is simply overwhelming. Methods like supervised machine learning and lexical-based approaches are available for measuring sentiments that have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services. This chapter presents sentiment analysis applications and challenges with their approaches and tools. The techniques and applications discussed in this chapter will provide a clear-cut idea to the sentiment analysis researchers to carry out their work in this field.
Large dimensional analysis of general margin based classification methods

Margin-based classifiers have been popular in both machine learning and statistics for classification problems. Since a large number of classifiers are available, one natural question is which type of classifiers should be used given a particular classification task. We answer this question by investigating the asymptotic performance of a family of large-margin classifiers under the two component mixture models in situations where the data dimension p and the sample n are both large. This family covers a broad range of classifiers including support vector machine, distance weighted discrimination, penalized logistic regression, and large-margin unified machine as special cases. The asymptotic results are described by a set of nonlinear equations and we observe a close match of them with Monte Carlo simulation on finite data samples. Our analytical studies shed new light on how to select the best classifier among various classification methods as well as on how to choose the optimal tuning parameters for a given method.