Reviewerly/exHarmony huggingface dataset:
Determination of the Children Classification with Special Needs in Extraordinary High School in Banjarmasin Using the Naive Bayes

Children with special needs with different physical characteristics are very easy to distinguish. However, in children with special needs with different psychological characteristics, there are difficulties in determining the classification of the child. Children with mental disorders are children who have deviations in the ability to think critically, logically in responding to the world around them. This study determined the classification of children with special needs, the decision support system can analyze using the naive bayes classification (NBC) method. The result showed that Naïve Bayes Classifier algorithm can be used as the basis for the decision-making process by the Special High School Special Education Foundation (SMALB YPLB) Banjarmasin. In this study, it is hoped that using an application based on the Naive Bayesian Classification method can be used as an alternative calculation that is more effective than calculations using the manual method, with the achievement of conformity of the results reaching 80%.
An oversampling method that fuses boundary factor and data density

Whereas standard classifiers fail to achieve substantial results in dealing with under-represented instances. In this paper, a method of improving SMOTE, BDSMOTE(Fusion of boundary factors and data density for improved SMOTE) is proposed. In this method, the concepts of data density and boundary factor are introduced. Firstly, minority samples are divided into boundary samples and safety samples based on data density, and each minority sample is given initial weight, the safety sample region is combined with more samples, and the boundary sample region is relatively less. Secondly, the boundary factor is introduced to update the sample weight, so that the number of synthetic instances of each sample is different. Then compose a new instance with the SMOTE method. Finally, six pretreatment methods are compared on random forest classifier. Experiments on KEEL and UCI data sets show that the classification model using the proposed method and random forest has better classification performance than the other five classification models.
Space Station Power Forecasting with LSTMs for an Embedded Platform

Accurate prediction of space station telemetry data can improve system monitoring. This paper presents a methodology for the acceleration of short-term forecasting of power data on an embedded platform designed for space. Initially, a long short-term memory (LSTM) network is trained to forecast voltage and current values from the ISS. This LSTM forecasts voltage and current minutes into the future while maintaining a low error rate. This LSTM network is then accelerated using the FPGA from a Zynq-7045 system on a chip (SoC). The Zynq-7045 was selected because it is the same SoC used on the SHREC Space Processor. Networks with differing parameters and dimensions were realized and evaluated withing the resource constraints of the Zynq-7045. The best performing LSTM networks were able to achieve over 3 × speedup against a software baseline with minimal increase in forecasting error.
Machine Learning Based Solution for the Detection of Malicious JPEG Image and QR Code

The biggest invention of 21st century is the social media. It is biggest platform which is using to share data, files and documents. Even it is using to share thoughts, ideas and feelings using different tools and techniques. People are hyper connected with each other and they are continuously sharing the information. For criminals, deploying malware in such scenario is very easy and propagating malware through JPEG images and QR Code is one of the best and most advanced method. Using steganography techniques, criminals embedded the malicious codes with legitimate or innocent looking images. This malicious content is just few line of codes which exploit the vulnerability of application. It give remote access of this system to the attacker which can do criminal act. In this framework, our primary purpose is to find the presence of any code or data in image. After it, the major section of this framework based upon the finding of code and its adverse effects. This framework shows the corresponding solution to the malicious code presence in JPEG images and QR code which are spreading through online social networking sites.
Sentimental Analysis on Multi-domain Sentiment Dataset Using SVM and Naive Bayes Algorithm

With the advent of the significant data era, people are confronted with the vast amount of information they receive each day. The quantity of information accrued and processed by Facebook, Twitter, and other significant social networks (such as Instagram) is vast. The Twitter platform encourages users to use 280 characters each to tweet their thoughts. Because tweets can use a limited number of characters, sentiment analysis becomes more accurate. Sentiment analysis is a technique for determining whether a text is positively, negatively, or neutral. Some experiments are conducted using Natural Language Processing Toolkit (NLTK) to determine whether a tweet has a neutral, positive, or negative polarity with accuracy. Moreover, by using Naïve Bayes and SVM, the accuracy of the tweets is compared. Finally, the ROC curve will decide the efficiency of both algorithms.
Patron Drivers, Patron Impacts: Investigating Potential Patron Impacts of Moving to a Patron Driven Acquisition Model for Print Books

This study investigates the use of print titles purchased on approval and how quickly they become available as ebooks. Using this information, we explore the potential impact on patrons if the library was to move to a demand driven acquisitions process for print books. We found that at our library, the low overall usage of print titles received on approval indicates that patrons would be minimally inconvenienced by switching to this method in which records would be loaded into the catalog and titles would only be purchased when requested. Additionally, we found that the impact on patrons decreases over time as more titles become available as ebooks. Although differences exist between disciplines, overall findings show that a demand driven acquisitions process for print books would result in substantial budgetary savings without sacrificing patron service at our library. In addition to the description of our program, we describe some data analysis methods that can be used at other libraries in order to estimate potential impact on patron service and satisfaction.
Client layer becomes bottleneck

Recent years have witnessed the fast development of file and storage systems. Many improvements of file and storage systems are inspired by Workload analysis, which reveals the characteristics of I/O behavior. Although cloud storage systems are becoming increasingly prominent, few real-world and large-scale cloud storage workload studies are presented. Alibaba Cloud is one of the world's largest cloud providers, and we have collected and analyzed workloads from Alibaba for an extended period. We observe that modern cloud network architecture can easily handle the peak load during busy festivals. However, the client layer is the system bottleneck during the peak period, which calls for further optimization. We also find that the workload is heavily skewed toward a small percentage of virtual disks, and its distribution conforms 80/20 rule. In summary, the characteristics of such a large-scale cloud storage system in production environments are important for future cloud storage system modifications.
Remote-control Real-time Video Interactive Robot

This paper introduces a remote-control real-time video interactive robot. To realize the Internet access, we use an ARM Cortex-A53 processor as the main controller, embedded with the Linux operating system. An improved PID closed-loop speed algorithm is developed to control the Mecanum wheels more intelligently and accurately, so that the robot has the ability of plane omnidirectional motion. We use a FOC algorithm controlled brushless motor to achieve camera pan-tilt 360&#x00B0; rotation in the horizontal direction and realize Wi-Fi and Bluetooth communication based on the Intel 9260 wireless module. The camera real-time data is encoded through the FFmpeg technology, streamed by RTMP protocol, and transmitted by Nginx server. The robotic arm can be replaced with alternative modules to realize remote environmental control and interaction. The average delay of the video data stream is about 150ms, and the robot responds well within a range of about 10m around the Wi-Fi node.
Resolving battery status and customer matching to create 24/7 drones based advertisement system

We consider the problem of devising an advertising system that uses drones that carry advertising signs. The system should satisfy the following constraints:•There are several types of customers the system should identify and classify the current customers.•Each drone can carry a one/double-sided advertisement sign suitable to only one or two types of customers.•The system must use ultra-light drowns whose battery is limited to few flight minutes after which a hovering drone must land for recharge.•The system must use the minimal number of drones that are needed to maintain one drone hovering 24/7.This leads to an algorithmic problem wherein the system must decide A) when to send the hovering drone to recharge and B) which of the recharging drones should replace it. This should be done such that: 1) a maximal number of customers see a sign matching to their type, and 2) maintaining the battery power for the 24/7 mode. The proposed solution is based on a way to classify the discharging curve of the hovering-drone and the charging curve of the currently charging drones. Based on these battery statuses and the current two largest sets of customers, the system maximizes the number of customers that are matched with a suitable sign yet maintains the battery power suffices for the 24/7 hovering mode. The results, obtained by a system of three ultralight drones in a supermarket, show that this algorithm works with high efficiency. We describe how the system was implemented and how the results were obtained.
An integrated approach for cloud computing service selection and cost estimation

Selecting suitable cloud services and estimating future cloud costs for organizations with a large set of software services is a challenging task. To achieve this, one needs to understand all software applications' cloud service configuration requirements. Further, one needs to estimate how the applications' requirements change over time, e.g., due to scaling with a growing number of application users. Then, one needs to compare for different cloud providers the costs over time of setups matching the requirements. This task, if done manually, is laborious, error prone, and has to be repeated when the organization's business or application requirements change.
Machine Learning-based web security intrusion detection system

In recent years, the rise of technologies such as dynamic web pages have led to the emergence of many web applications. In addition, the complexity and variety of attacks against web applications make it difficult for intrusion detection systems based on misuse detection to use constructed attack signatures for detection effectively. This paper first presents two common web attacks, SQL injection and XSS cross-site scripting attacks, their specific classification, followed by a simple optimization of the collected SQL and XSS datasets, the optimization of the model parameters using machine learning algorithms, and a comparison of the performance of the different algorithms on GPU/CPU. In addition, they were deployed in a production environment for practical testing. After experimental results and error analysis, the best algorithmic model was determined. Based on these experiments, a machine learning-based network intrusion detection system was designed without compromising the quality of the webserver.
T*$\boldsymbol{\varepsilon}$—Bounded-Suboptimal Efficient Motion Planning for Minimum-Time Planar Curvature-Constrained Systems

We consider the problem of finding collision-free paths for curvature-constrained systems in the presence of obstacles while minimizing execution time. Specifically, we focus on the setting where a planar system can travel at some range of speeds with unbounded acceleration. This setting can model many systems, such as fixed-wing drones. Unfortunately, planning for such systems might require evaluating many (local) time-optimal transitions connecting two close-by configurations, which is computationally expensive. Existing methods either pre-compute all such transitions in a preprocessing stage or use heuristics to speed up the search, thus foregoing any guarantees on solution quality. Our key insight is that computing all the time-optimal transitions is both (i) computationally expensive and (ii) unnecessary for many problem instances. We show that by finding bounded-suboptimal solutions (solutions whose cost is bounded by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{1+\varepsilon}$</tex-math></inline-formula> times the cost of the optimal solution for any user-provided <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\varepsilon}$</tex-math></inline-formula> ) and not time-optimal solutions, one can dramatically reduce the number of time-optimal transitions used. We demonstrate using empirical evaluation that our planning framework can reduce the runtime by several orders of magnitude compared to the state-of-the-art while still providing guarantees on the quality of the solution.
GANAN: GAN-based for Anomaly Detection on Attributed Networks

Anomaly detection on attributed networks is crucial for many applications such as social networks security and fraud detection. Existing models utilize deep autoencoder to reconstruct structure and attribute and measure reconstruction errors to obtain anomaly nodes. However, there are problems such as over-smoothing and node&#x2019;s representation learning. In order to solve these problems, in this paper, we propose a GAN-based for Anomaly Detection on Attributed Networks (GANAN). The fake graph network is generated by autoencoder. We use two encoders map both fake and real graph to latent space. The structure and attribute are used as the input of the discriminator. The game method can alleviate the over-smoothing and help encoder learn node representation. With the decoder output, the measurement considers the reconstruction error of nodes from both structure and attribute perspectives. Extensive experiments on real-world datasets demonstrate that our proposed model has better performance than various existing mothed.
Alternative Difficulty Adjustment Algorithms for Preventing Selfish Mining Attack

Selfish mining is one of the most famous attacks on the mining process of Bitcoin and other Proof-of-Work blockchains in general. So, preventing selfish mining is an important objective. There have been a few attempts to prevent selfish mining. In this work, we focus on alternative difficulty adjustment algorithms that try to discourage selfish miners by extending their waiting time for selfish mining to become profitable, Zeno's DAA in particular. We propose two extensions on Zeno's DAA: 1) Zeno's Max DAA, which increase the difficulty in one step but decreases it in multiple steps, and 2) Zeno's Parametric DAA that has a parameter that can be tuned to control the rate of the decrement (or increment) in the value of difficulty. We evaluate these two alternatives DAAs and show that Zeno's Max performs better than Zeno's for scalability of the network and scales the network faster. Also, Zeno's Parametric is a family of DAAs that not only has the default DAA and Zeno's DAA as its members but also can include anything in between or even DAAs more conservative than Zeno's, which decrease the difficulty even slower than Zeno's.
External Factors and Their Impact on Satisfaction with Virtual Education in Peruvian University Students

Satisfaction with virtual education is susceptible to several factors; therefore, the present research was aimed at determining the impact of external factors on satisfaction with virtual education provided by a private university in Lima, Peru. The sample consisted of 43 students, 2 males (4.7%) and 41 females (95.3%) between 20 and 53 years of age (Mage = 28.84, SD = 7.24), who completed in-surveys showing the following results: it was demonstrated that students who used only mobile data had a higher degree of satisfaction with virtual education. Finally, the student’s experience in virtual education was positively related to satisfaction with virtual education and that both the difficulties presented by the student and the number of suggestions for improvements in virtual education were negatively related to student satisfaction.
Exponential Separations in Local Privacy

We prove a general connection between the communication complexity of two-player games and the sample complexity of their multi-player locally private analogues. We use this connection to prove sample complexity lower bounds for locally differentially private protocols as straightforward corollaries of results from communication complexity. In particular, we (1) use a communication lower bound for the hidden layers problem to prove an exponential sample complexity separation between sequentially and fully interactive locally private protocols, and (2) use a communication lower bound for the pointer chasing problem to prove an exponential sample complexity separation between k -round and ( k+1 )-round sequentially interactive locally private protocols, for every k .
Fusion Recommendation System Based on Collaborative Filtering and Knowledge Graph

The recommendation algorithm based on collaborative filtering is currently the most successful recommendation method. It recommends items to the user based on the known historical interaction data of the target user. Furthermore, the combination of the recommended algorithm based on collaborative filtration and other auxiliary knowledge base is an effective way to improve the performance of the recommended system, of which the Co-Factorization Model (<i>CoFM</i>) is one representative research. <i>CoFM</i>, a fusion recommendation model combining the collaborative filtering model <i>FM</i> and the graph embedding model <i>TransE</i>, introduces the information of many entities and their relations in the knowledge graph into the recommendation system as effective auxiliary information. It can effectively improve the accuracy of recommendations and alleviate the problem of sparse user historical interaction data. Unfortunately, the graph-embedded model <i>TransE</i> used in the <i>CoFM</i> model cannot solve the 1-N, N-1, and N-N problems well. To tackle this problem, a novel fusion recommendation model Joint Factorization Machines and <i>TransH</i> Model (<i>JFMH</i>) is proposed, which improves <i>CoFM</i> by replacing the <i>TransE</i> model with <i>TransH</i> model. A large number of experiments on two widely used benchmark data sets show that compared with <i>CoFM</i>, <i>JFMH</i> has improved performance in terms of item recommendation and knowledge graph completion, and is more competitive than multiple baseline methods.
Multi-objective prediction-based optimization of power consumption for cloud data centers

The overall development of the cloud paradigm is dominating omnipresence in the industry 4.0 business world. Over the last decade, the control measures for power utilization among the proliferative Hyper-Scale Data Centers (HSDCs) have been elucidated. However, the lack of attention to regulating power in Small and Medium-Scale Data Centers (SMSDCs) has ensued in excessive power drainage in small and medium-scale cloud data centers. The crucial factor for excessive power utilization of SMSDCs encompasses providing excessive resources, high certainty tasks. Majority of the previously reported studies zeroed-in on problems associated with hyper-scale data centers, excluding probes of the issues prevalent in small and medium-scale cloud data centers. This paper proffers a framework for a predictive optimization approach for delivering the data center services to end-users. In the first phase, the Multi-Output (MO) Random Forest Regressor (RFR) (MO-RFR) concurrently predicts the multiple-resource utilization of Virtual Machines (VMs). The predictive framework outcome was utilized by the Multi-Objective Particle Swarm Optimization (MO-PSO) framework in the second phase to resolve the issue in virtual machine placement and to accomplish better physical machine consolidation. The proposed multi-prediction-based MO-PSO to escalate the resource usage, minimizes the power utilization, and curtail the carbon footprint. The efficacy of the proposed approach was appraised via performance metrics and actual workload traces. The acquired result from the proposed method outperforms the baseline approaches.
Opinion Mining in Sociopolitical Research

The objective of this study was to explore the potential of the opinion mining methodology in sociopolitical research, its techniques, and applications from the field of reflexivity. It is a non-experimental and exploratory study. It concludes that advances in the field of artificial intelligence have provided the sociopolitical sciences with tools that make it possible to approach the dominant trends of opinion in society during specific junctures where decision-making and/or the positioning of an idea, public policy, or social project can be measured in real-time, with broad demographic scopes that can be segmented, bringing the researcher closer to the subject of study with minimum levels of bias. Opinion mining research continues to be dominated by electoral and marketing topics; however, there are potentialities in the research of public policies, social programs, democracy, and governance that are still waiting for the application of opinion mining as a sociopolitical research methodology.
Human Behavior Recognition Based on Partition Strategy and MS-G3D

Graph convolutional neural networks have been widely used in skeletal behavior recognition. In recent years, there have been many improved algorithms for basic graph convolutional neural networks, all this in order to improve the performance of the model. Long-distance and multi-scale feature extraction is the focus of these models. Among these models, Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition (MS-G3D) is currently the best performing model. However, there is still the problem that the disentangling is excessively eliminated, and some behavior characteristics cannot be obtained. Therefore, a disentangling and unifying graph convolutional neural network based on a partition strategy is proposed. This model effectively connects the joint information associated with behavioral features to increase the range of feature extraction. Experiments on NTU RGB+D 60 and Kinetics Skeleton 400 datasets show that the algorithm effectively improves the accuracy of recognition.