Reviewerly/exHarmony huggingface dataset:
Whale Optimization Algorithm Improved Effectiveness Analysis Based on Compound Chaos Optimization Strategy and Dynamic Optimization Parameters

Basing on the fact that the basic whale optimization algorithm has the defects including low convergence precision and it is easy to fall into the local optimal solution when solving the objective function whose optimal solution is not near the origin. A new whale optimization algorithm (Dio-WOA) based on compound chaos optimization strategy and dynamic improved parameters is proposed. In the algorithm, the chaos optimization strategy set is first introduced for multiple solution links. At the same time, the convergence factor of WOA a is improved and the inertia weight w is introduced, so the algorithm can slow down the convergence speed in the early stage for each generation to fully explore the overall algorithm and accelerate in the later iteration to improve the accuracy of the algorithm. At the same time, chaos strategy concentrates on the chaos optimization strategy for the optimal solution can help the algorithm effectively choose the solution out of the local optimal solution. This paper verifies the comprehensive performance of the Dio-WOA algorithm and the effectiveness of various improvement measures. Several undetermined dimension single-peak and multi-peak test functions are introduced to verify the performance of the overall algorithm and the local improvement algorithm. The results show that the single improvement measures can effectively improve the performance of the algorithm and each measure has different performance directions. The comprehensive performance of Dio-WOA is better than that of single improvement measures, which proves the effective compatibility between the improvement measures.
Analysis of the Utilization of Scrum Framework Effort Estimation Metrics in Federal Public Administration

The usage of agile software development methods is a reality in private and public organizations. However, the use of story points, a Scrum's typical metric for effort estimation has encountered resistance to its adoption in public administration, especially by regulatory authorities, once it varies according to the team's expertise. Thus, this paper aims to identify alternative metrics used by public administration to measure the effort in agile software development. From a documental research with qualitative approach, we analyzed the legal and normative framework of the federal sphere and we identified 57 software development contracts, whose metrics were analyzed. Hence, the main results showed that function points analysis remains being the most used metric, even in agile software development projects, due to their objectivity regarding functional requirements. However, the Technical Service Unit (UST) or related metrics has gained strength in agile software development projects, considering that it is indispensable to use a service catalog prepared at the contracting planning stage, as preconized by Brazilian Federal Court of Accounts (TCU) in its Judgment 2.362/2015.
Towards Meta-Learning for Multi-Target Regression Problems

Several multi-target regression methods were developed in the last years aiming at improving predictive performance by exploring inter-target correlation within the problem. However, none of these methods outperforms the others for all problems. This motivates the development of automatic approaches to recommend the most suitable multi-target regression method. In this paper, we propose a meta-learning system to recommend the best predictive method for a given multi-target regression problem. We performed experiments with a meta-dataset generated by a total of 648 synthetic datasets. These datasets were created to explore distinct inter-targets characteristics toward recommending the most promising method. In experiments, we evaluated four different algorithms with different biases as meta-learners. Our meta-dataset is composed of 58 meta-features, based on: statistical information, correlation characteristics, linear landmarking, from the distribution and smoothness of the data, and has four different meta-labels. Results showed that induced meta-models were able to recommend the best method for different base level datasets with a balanced accuracy superior to 70% using a Random Forest meta-model, which statistically outperformed the meta-learning baselines.
Open-Mindedness of Gradual Argumentation Semantics

Gradual argumentation frameworks allow modeling arguments and their relationships and have been applied to problems like decision support and social media analysis. Semantics assign strength values to arguments based on an initial belief and their relationships. The final assignment should usually satisfy some common-sense properties. One property that may currently be missing in the literature is Open-Mindedness. Intuitively, Open-Mindedness is the ability to move away from the initial belief in an argument if sufficient evidence against this belief is given by other arguments. We generalize and refine a previously introduced notion of Open-Mindedness and use this definition to analyze nine gradual argumentation approaches from the literature.
Model Predictive Obstacle Avoidance Control Suppressing Expectation of Relative Velocity against Obstacles

The following topics are dealt with: control system synthesis; predictive control; nonlinear control systems; feedback; linear systems; optimal control; optimisation; stability; closed loop systems; robust control.
Image Encryption Using Permutation–Diffusion Approach

Image encryption is a critical area of cyber security that has emerged as a topic of interest for recent researches. This paper proposes a novel method for image encryption using chaos theory. Logistic maps are used to generate a set of 24 keys that are used to encrypt the image. The complete design is simple and efficient based on permutation, diffusion and optimization. The results obtained showed increased randomness in the encrypted images which is a primary parameter for quality of cipher. We have performed analysis on the proposed approach using histogram analysis, entropy and correlation coefficient.
Simple wedge plate based lateral shearing interferometry technique for coherent alignment of tiled grating assembly

A simple, sensitive and robust technique is proposed for coherent alignment of tiled grating assembly using wedge plate lateral shearing interferometry. In this technique, two thick glass wedge plates are used to generate interferograms from reflected and diffracted laser beams, from the tiled grating assembly under alignment. This single optical element based interferometric technique is easy to set up, insensitive to the vibrations and provides simultaneous information of both reference as well as error interference patterns, which are used to estimate the alignment errors. Performance of this technique was tested through alignment of two reflective gratings (size 140 mm X 120 mm), with an angular and translation sensitivities of less than 1 μrad and 50 nm respectively.
Dual-entropy-source quantum random number generation based on spontaneous emission

We describe a dual-entropy-source quantum random number generator based on atomic spontaneous emission. Using the intensity fluctuations of a coherent beam to modulate the detuning frequency of the driving field, the atomic ensemble exhibits larger fluctuations of the spontaneous emission noise. The quantum-to-classical-noise ratio of the measured noise is increased to 10.14 dB. After post-processing, a string of random numbers at rates up to 5.12 Mb/s is extracted, which passes all the NIST tests. The presented scheme shows the feasibility to combine multiple independent randomness sources to produce secure quantum random bits.
SegGuard: Segmentation-based Anonymization of Network Data in Clouds for Privacy-Preserving Security Auditing

Security auditing allows cloud tenants to verify the compliance of cloud infrastructure with respect to desirable security properties, e.g., whether a tenant’s virtual network is properly isolated from other tenants’ networks. However, the input to the auditing task, such as the detailed topology of the underlying cloud infrastructure, typically contains sensitive information which a cloud provider may be reluctant to hand over to a third party auditor. Additionally, auditing results intended for one tenant may inadvertently reveal private information about other tenants, e.g., another tenant’s VM is reachable due to a misconfiguration. How to anonymize both the input data and the auditing results in order to prevent such information leakage is a novel challenge that has received little attention. Directly applying most of the existing anonymization techniques to such a context would either lead to insufficient protection or render the data unsuitable for auditing. In this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SegGuard</i> , a novel anonymization approach that prevents cross-tenant information leakage through per-tenant encryption, and prevents information leakage to auditors through hiding real input segments among fake ones; in addition, applying property-preserving encryption in an innovative way enables <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SegGuard</i> to preserve the data utility for auditing while mitigating semantic attacks. We implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SegGuard</i> based on OpenStack, and evaluate its effectiveness and overhead using both synthetic and real data. Our experimental results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SegGuard</i> can reduce the information leakage to a negligible level (e.g., less than 1 percent for an adversary with 50 percent pre-knowledge) with a practical response time (e.g., 62 seconds to anonymize a cloud infrastructure with 25,000 virtual machines).
Analysis of the PSO Parameters for a Robots Positioning System in SSL

The changes in the Small Size League rules have brought greater possibilities of playing. With the increased complexity of soccer matches, the positioning of the robots has become important as a defense and attack mechanism. The learning of opposing team game playing has been shown to be effective, but an SSL soccer match indicates the need for solutions that analyze the strategy of the opposing team during the game and make any necessary adaptations. This paper proposes the use of the Particle Swarm Optimization (PSO) algorithm as an option to determine the positioning during the match. A prototype has been developed to validate the configuration parameters. Experiments in a simulator, analysis of game logs and results in a real matches have demonstrated the feasibility of applying the PSO algorithm to find the robots positions.
An analytic study of architecture, security, privacy, query processing, and performance evaluation of database‐as‐a‐service

Abstract In the present era of technology, the pace of data generation is very high, and the biggest challenge is the manageability of data with efficient and secure storage options as well as at lower cost. Therefore, in this article, we have elaborated the database‐as‐a‐service (DaaS) concept having its introduction, architecture, security and privacy, query processing, and an analytic performance evaluation relating to query performance on encrypted data of DaaS. We have conducted an analytic study of the state‐of‐the‐art by analyzing CPU and query execution time. According to this evaluation, it is concluded that data encryption and decryption need heavy processing power and are time‐consuming. Moreover, there is several query execution problems that exist on encrypted data. A major improvement in query performance may be achieved through parallel query execution after the deployment of multithreading having maximum possible threads.
Analysis of validity, reliability, discrimination, difficulty and distraction effectiveness in learning assessment

Assessment is not only to measure learning progress, but also allows educators use information of student condition to improve learning. Result of students final exam eight grade in science subjects at SMPN 2 Lembah Gumanti are low. Items of question that are not qualified can be a factor that makes the results of the assessment low. Quality of items is not yet known because the teacher has not carried out the analysis. Therefore, need to analyze the quality of items in several aspects. This type of research is descriptive research. Sample are items on biology topics. Data are item sheet, answer key and answer sheets which is taken through documentation. Analysis is carried out with a program was name Anates version 4.0.9. The results of data analysis were obtained: 1) validity, 65.38 % invalid, 2) reliability of 0.67 classified as sufficient, 3) level of difficulty, 11.53 % very easy, 7.69 % easy, 50% medium, 19.23% difficult, and 11.53 % very difficult, 4) discrimination, 15% very bad, 15 % bad, 30% enough, and 40% good, 5) distraction effectiveness, 24.36 % does not work. It can be concluded that the item does not match the criteria either.
Automatic Design of Convolutional Neural Networks using Grammatical Evolution

The use of Convolutional Neural Networks (CNNs) has been demonstrated to be a solid approach for solving many machine learning problems, such as image classification and natural language processing tasks. Usual CNN architectures are composed of many convolutions, pooling and fully connected layers, from which the networks also learn a suitable representation for the data being processed. The manual design of CNNs is a complex task due to the high number of possible parameter configurations. Recent studies about automatic design of CNNs have shown positive results. Since it can be expressed as a hyperparameter optimization problem, in this study we propose to explore the design of CNN architectures through the use of Grammatical Evolution (GE). GE is a grammar based approach where a grammar is used to define the CNN components and structural rules. We performed a set of experiments using two well-known image classification datasets, the MNIST and CIFAR-10. The obtained results show that the presented approach achieved competitive results, while maintaining relatively small architectures, when compared with similar state-of-the-art approaches.
Respecting low-level components of content with skip connections and semantic information in image style transfer

Style transfer represents the most creative task of deep learning, creating a virtual world in art. However, most of the current deep networks concentrate on style and ignore to exploit low-level components of content such as edges, shapes, which define objects in a virtualized image. In our study, we present a scheme to use skip connections and leverage semantic information as input, which effectively preserve low-level components in great detail. To understand the meaning of our added components, besides the ablation study to visualize the proficiency, we propose to use constrained hyper-parameters for all skip connections to find how each layer influence on stylized images. Our models are trained on images in COCO-stuff with their semantic maps and testing without them. We also compare our work to previous works. As a result, our method outperforms in retaining definable details of content with significant style using skip connections, especially semantic information.
Extractive Myanmar News Summarization Using Centroid Based Word Embedding

Nowadays, many researches are going on for text summarization because there are a lot of data on the internet and it is required to process, store and manage. Text summarization is a process of distilling important information from the original text and presents that information in the form of summary. The system is proposed to summarize Myanmar news with centroid based method. Centroid based method ranks the sentences based on their similarity to the centroid. Centroid based method uses the bags of words model to represent sentences. Bags of words representation does not capture the semantic relationship between words. To overcome this problem, centroid based method is combined with word embedding representation instead of bags of words in this paper. Experiments were done on Myanmar news dataset. Centroid based on word embedding method gets better performance than centroid based on bags of words method.
Annotation Intent Identification toward Enhancement of Marketing Campaign Performance

An annotation is a short free text associating with each financial transaction made on SCB Easy, one of the leading mobile banking applications in Thailand. Identifying the intent(s) hidden within this text is a challenging task in Natural Language Processing (NLP). On the one hand, the availability of labeled data is limited, and on the other hand, an annotation oftentimes carries multiple intents. This paper describes AI2, an NLP framework for identifying multiple intents from short annotations. The framework is based on long short-term memory architectures. We conduct two sets of empirical experiments: one helps identify the best architecture between MIBC and SMLC; and the other finds the optimal model parameters. Apart from assuring experimental results, AI2 predictions are used in marketing campaigns with promising performance uplifts.
LSTM-Based Consumption Type Prediction Model

In order to predict consumer behaviors, this paper proposes a consumption type prediction model using LSTM (Long Short Term Memory), a modification algorithm of RNN (Recurrent Neural Network). To do this, we derive and define age and gender consumption type patterns through Association Rule Analysis based on PrefixSpan algorithm using actual card consumption statistical data. Based on this, we used a pattern of daily consumption pattern as an input value, and constructed a model that predicts age and gender consumption patterns by learning the differences between the actual and forecast error rates in a week.
Experimental Study on Software Fault Prediction Using Machine Learning Model

Faults are the leading cause of time consuming and cost wasting during software life cycle. Predicting faults in early stage improves the quality and reliability of the system and also reduces cost for software development. Many researches proved that software metrics are effective elements for software fault prediction. In addition, many machine learning techniques have been developed for software fault prediction. It is important to determine which set of metrics are effective for predicting fault by using machine learning techniques. In this paper, we conduct an experimental study to evaluate the performance of seven popular techniques including Logistic Regression, K-nearest Neighbors, Decision Tree, Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron using software metrics from Promise repository dataset usage. Our experiment is performed on both method-level and class-level datasets. The experimental results show that Support Vector Machine archives a higher performance in class-level datasets and Multilayer Perception produces a better accuracy in method-level datasets among seven techniques above.
Real-Time Burst Photo Selection Using a Light-Head Adversarial Network

We present an automatic moment capture system that runs in real-time on mobile cameras. The system is designed to run in the viewfinder mode and capture a burst sequence of frames before and after the shutter is pressed. For each frame, the system predicts in real-time a goodness score, based on which the best moment in the burst can be selected immediately after the shutter is released. We develop a highly efficient deep neural network ranking model, which implicitly learns a latent relative attribute space to capture subtle visual differences within a sequence of burst images. The overall goodness is computed as a linear aggregation of the goodnesses of all the latent attributes. To obtain a compact model which can run on mobile devices in real-time, we have explored and evaluated a wide range of network design choices, taking into account the constraints of model size, computational cost, and accuracy. Extensive studies show that the best frame predicted by our model hit users’ top-1 (out of 11 on average) choice for 64.1% cases and top-3 choices for 86.2% cases. Moreover, the model (only 0.47M Bytes) can run in real time on mobile devices, e.g. 13ms on iPhone 7.
Hierarchical Path Planning for Urban On-Demand Air Mobility

On-demand air mobility has gained attention in the recent years as a promising future transportation mode for daily commutes. In this paper, we propose a hierarchical motion planning system for on-demand passenger-carrying air vehicles. Specifically, we consider a path planning problem with requirements for flight efficiency, system safety, and passenger comfort. Prior to the execution of the mission, we simplify a given city map into a grid world and estimate the transfer time among the cells in the presence of no-fly zones and air traffic congestion. Then, we consider system reachability and apply A* search to find a time-optimal path in the map. To ensure that the system follows a feasible trajectory consistent with the off-line plan after take-off, we use model predictive control to track the waypoints on the path and minimize the energy consumption during flight. In order to account for collisions unforeseen in off-line planning, we further adopt a collision avoidance module in the system, which optimizes a new waypoint near the predicted collision point and generates an avoidance trajectory through model predictive control. At last, we demonstrate the effectiveness of the hierarchical planning system under different flight scenarios in a simulated urban environment.
A Study on Real Estate Price by Using Probability Statistics Theory and Grey Theory

By using of probability statistics theory and grey theory, the real estate price is studied that data is come from China. According to the data, the influence indexes of real estate price model are obtained. the data analysis is conducted By using multiple linear regression model, and the calculation formula of the predicted real estate price.
Development of education game media for xii multimedia class students in vocational school

Abstract According to the results of interviews with teachers it is known that students experience difficulties in understanding action script material. This is because this material has a high level of complexity, it is proven that only 30% of students are able to achieve the learning objectives. This study aims to produce educational game media in terms of the feasibility and effectiveness of the media. The development model used is ADDIE (analyze, design, development, implementation, evaluation). Methods of data collection using interview techniques, questionnaires, and tests. From this study the results are based on validation with material experts and media experts by getting a very good category, so that the media can be said to be suitable for use in learning. The results of the post-test value of the experimental class and the control class were analyzed using the t-test obtained by t count = 4.584&gt; t table = 2.038 which is a significant difference, so it can be concluded that the media is effectively used in learning.
Construction of Cyber Range Network Security Indication System Based on Deep Learning

The main purpose of this paper is to solve the problem of quantitative and qualitative evaluation of network security. Referring to the relevant network security situation assessment algorithms, and by means of advanced artificial intelligence deep learning technology, to build a network security Indication System based on Cyber Range, and optimize the guidance model of deep learning technology.