Reviewerly/exHarmony huggingface dataset:
Hubungan Penguasaan Fungsi Kalimat Dengan Kemampuan Menulis Karangan Pada Siswa Kelas VIII SMP Negeri 2 Sosa

The purpose of this study is to find out whether there is a signcant relationship between the mastery of sentence functions with the ability to write essays in Class VIII students of SMP Negeri 2 Sosa. The method used by the author to carry out this research is descriptive method. The population of this research is all students of Class VIII of SMP Negeri 2 Sosa in the 2014/2015 Academic Year, amounting to 214, in which all students are divided into six classes. The sample of this study was taken using cluster sample techniques of 35 students. The instrument used by the authors to collect data is a test. To analyze the data, the authors used descriptive techniques and statistical analysis techniques, namely the Product Moment by Pearson approach. Descriptive analysis results showed the mean value of students of Class VIII of SMP Negeri 2 Sosa was 64.43. That is, the mastery of student sentence functions is still in sufficient criteria. Furthermore, the value of students in writing essays is 66.57. That is, the ability to write student essays is still in sufficient criteria. The results of statistical analysis show that the research hypothesis can be accepted, where the value rxy is greater than the value rt or 0.953 > 0.344. So, there is a signficant relationship between the mastery of sentence functions with the ability to write essays in Class VIII students of SMP Negeri 2 Sosa.
Recovery-oriented Big Data Computing for Exactly Once Message Processing

Big data computing is a process to handle large volumes of information, which typically crosses different functional units in a distributed system. Like any processes involving distributed systems, it has a concern of reliability problems, such as lossy communication links between functional units and crashed computation nodes inside functional units. The paper focuses on resolving this concern in a particular distributed system scenario where the cross-boundary network connections have a high rate of failure and the internal computation nodes are relatively reliable. We propose a pure client side protocol to achieve exactly once message processing which makes big data computing in the above scenario more reliable. Moreover, we optimize the protocol to be more efficient in resource consumption using methods such as machine learning.
Testing the Agreement of Trees with Internal Labels

The input to the agreement problem is a collection $$\mathcal {P}= \{\mathcal {T}_1, \mathcal {T}_2, \dots , \mathcal {T}_k\}$$ of phylogenetic trees, called input trees, over partially overlapping sets of taxa. The question is whether there exists a tree $$\mathcal {T}$$ , called an agreement tree, whose taxon set is the union of the taxon sets of the input trees, such that for each $$i \in \{1, 2, \dots , k\}$$ , the restriction of $$\mathcal {T}$$ to the taxon set of $$\mathcal {T}_i$$ is isomorphic to $$\mathcal {T}_i$$ . We give a $$\mathcal {O}(n k (\sum _{i \in [k]} d_i + \log ^2(nk)))$$ algorithm for a generalization of the agreement problem in which the input trees may have internal labels, where n is the total number of distinct taxa in $$\mathcal {P}$$ , k is the number of trees in $$\mathcal {P}$$ , and $$d_i$$ is the maximum number of children of a node in $$\mathcal {T}_i$$ .
Distributed Mean Estimation with Optimal Error Bounds.

We consider the problem of distributed variance reduction: $n$ machines each receive probabilistic estimates of an unknown true vector $\Delta$, and must cooperate to find a common estimate of $\Delta$ with lower variance, while minimizing communication. Variance reduction is closely related to the well-studied problem of distributed mean estimation, and is a key procedure in instances of distributed optimization, such as data-parallel stochastic gradient descent. Previous work typically assumes an upper bound on the norm of the input vectors, and achieves an output variance bound in terms of this norm. However, in real applications, the input vectors can be concentrated around the true vector $\Delta$, but $\Delta$ itself may have large norm. In this case, output variance bounds in terms of input norm perform poorly, and may even increase variance. In this paper, we show that output variance need not depend on input norm. We provide a method of quantization which allows variance reduction to be performed with solution quality dependent only on input variance, not on input norm, and show an analogous result for mean estimation. This method is effective over a wide range of communication regimes, from sublinear to superlinear in the dimension. We also provide lower bounds showing that in many cases the communication to output variance trade-off is asymptotically optimal. Further, we show experimentally that our method yields improvements for common optimization tasks, when compared to prior approaches to distributed mean estimation.
ConvCloud: An Adaptive Convolutional Neural Network Accelerator on Cloud FPGAs

Among all the neural network specialized hardware accelerators like the Application-Specific-Integrate-Circuit(ASIC), an FPGA accelerator stands out for its flexibility, short time-to-market, and energy efficiency. However, when it comes to multitasking and high-speed requirements or realtime and power-efficient scenarios (e.g., UAVs, self-driving cars, and IoT devices), a single-board FPGA accelerator has difficulties in achieving excellent performance. Therefore, Cloud FPGAs(Multi-FPGAs) will be a significant role in high-performance and energy-efficient computation of CNNs for both mobile and cloud computing domains. In this work, we propose an adaptive neural network accelerator on Cloud FPGAs, using multi-FPGA design to satisfy multitasking and high-speed requirements or realtime and power-efficient scenarios. We adopt the roofline model to figure out the optimal configuration of each CNN layer. And a layer clustering algorithm and a layer sequence detection method are proposed to transform CNN models into layer sequences for mapping the CNN model layers efficiently to different FPGA boards. Then, we built an adaptive CNN mapping method of Multi-FPGA chips for CNN models. Preliminary results on the Multi-FPGAs platform demonstrate that our accelerator can improve the performance significantly due to the adaptive mapping method.
Construction of an Intelligent Tennis Coach Based on Kinect and a Sensor-Based Tennis Racket

The purpose of this paper is to develop an artificial intelligence coach system which can be used to provide beginners a more economical way to learn tennis and to achieve as similar as the coach to guide the training. To reach the goal, artificial intelligence coach system is composed of three phases that are (1) Tennis swing motion data collection, (2) CAST-based swing motion group construction, and (3) Online tennis swing motion analysis. In the first phase, the tennis motions are collected using the Kinect and the built sensor-based tennis racket. Then, the cluster affinity search technique (CAST) and the dynamic time warping (DTW) are utilized to divide the collected swing motion series data into groups to form swing motion groups. In the third phase, using the swing motion groups, the system can provide possible tennis motion improvement suggestions. At last, experiments were also conducted on the real dataset to show the effectiveness of the proposed system.
A Subscription Service for Automated Communication and Fair Cost Distribution in Collaborative Blockchainbased Business Processes

Blockchain capabilities like Ethereum Smart Contracts offer great opportunities to manage cross-organizational business processes due to their trustless and tamperproof nature. However, communication in such processes poses a major issue since there is no direct option for one participating organization to inform other collaborators about their individual progress in their impersonation as a Smart Contract. As that knowledge is vital to execute a cross-organizational process, we design a Smart Contract architecture in which participants express their progress through Blockchain events. Furthermore, we implement a prototype that subscribes to the relevant events of one or more participants and reacts to their occurrence by triggering the subsequent step(s) of the process. Evaluation of the prototype and architecture shows that this does not only avoid unnecessary latency in process communication but also results in a fair cost distribution as each participant is only charged for the expenses of its individual actions.
Domain adaptation of HDR training data for semantic road scene segmentation by deep learning

Training and validating systems which involve the use of artificial intelligence (AI) is an importanttask for many applications. Especially automotive applications require to be safe and reliable. Inthis thesis it is aimed to improve the safety of such systems demonstrated by the performance ofa semantic segmentation network. This allows a computer to gain a comprehensive scene under-standing by classifying and localizing important semantic classes like pedestrians or road in animage. High dynamic range (HDR) images contain more brightness information than regular im-ages, which are considered as low dynamic range (LDR) images. They are able to display sceneswith a much higher contrast. In this thesis it is hypothesized that HDR images are a remarkablecomponent to achieve an increased semantic segmentation performance. This will be demon-strated by three approaches. The first one allows to generate a set of LDR from HDR imageswith different tone mapping operators. Additionally, there is proposed a ranking method to esti-mate the semantic segmentation performance for each of them. The second approach enables toevaluate semantic segmentation networks trained on LDR and HDR datasets. The third attemptaims to improve the performance on a real-world test set, as the training set consists of solelysynthetic images. This is approached with a domain adaptation. In this thesis it is shown thatcomponents of the proposed ranking method are reasonably correlated with the semantic seg-mentation performances. Furthermore, experiments demonstrate that HDR images outperformLDR images for a semantic segmentation of real-world scenes. In addition, it is demonstratedthat HDR images are less sensitive to different domains.
A Study on Digital Image Restoration Filters

In this paper, at first a color image is taken Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3.the blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via MATLAB programming. Many methods can restore the noisy and motion blurred image: particularly in this paper inverse filtering as well as wiener filtering are implemented for the restoration purpose consequently, both motion blurred and noisy motion blurred image are restored via inverse filtering as well as wiener filtering techniques and the comparison is made among them.
Robust Real-time Detection of Abandoned Objects using a Dual Background Model

Detection of abandoned objects for smart video surveillance should be robust and accurate in various situations with low computational costs.This paper presents a new algorithm for abandoned object detection based on the dual background model.Through the template registration of a candidate stationary object and presence authentication methods presented in this paper, we can handle some complex cases such as occlusions, illumination changes, long-term abandonment, and owner's re-attendance as well as general detection of abandoned objects.The proposed algorithm also analyzes video frames at specific intervals rather than consecutive video frames to reduce the computational overhead.For performance evaluation, we experimented with the algorithm using the well-known PETS2006, ABODA datasets, and our video dataset in a live streaming environment, which shows that the proposed algorithm works well in various situations
Face Recognition Systems in Math Classroom Through Computer Vision Traditional Techniques

The methods and techniques of detection of the human face and facial recognition have presented a great impulse in recent years, thanks to the advance in areas such as artificial vision and machine learning. Although deep neural network techniques are in vogue, traditional techniques allow you to create applications that do not consume many resources from computing devices. In this research, we present a facial recognition system that implements the Eigenfaces method, developed in C # of Microsoft Visual Studio and open-source video processing libraries such as OpenCV as EmguCV. The application is divided into two sections: the first called register where, through an integrated camera, images of the user’s face or other means such as video and stored images are captured, and the second section is known as recognition where the user is compared with all the records of the data set, indicating whether this is registered and the recognition percentage. The project was implemented with a universe of the size of twenty-five users, of which six are men (24%) and nineteen are women (76%), developing tests for five weeks.
ANALYSIS AND DISTANCE ACCESS DESIGN FAR WITH VPN TECHNOLOGY IN BMT OFFICE. MENTARI EAST LAMPUNG

Along with the widespread use of the internet, many companies then switch to using the internet as part of their network to save costs. However, security issues are still a major factor in the reliability of a network. Virtual Private Network (VPN) is one way that can be used to create a private network and long-distance connections (remote access) with a high level of security over public networks or the Internet. The conclusions of this study are a) analysis and design of remote access with VPN technology in the Mentari East Lampung BMT Office, b) the resulting management of IP Address distribution for connections between branches using VPN, c) the creation of a VPN account management function for connection between branch BMT office. Mentari East Lampung, d) has successfully tested the VPN account login from the Client side.
Autopia: An AI Collaborator for Live Coding Music Performances

Live coding is “the activity of writing (parts of) a program while it runs” (Ward et al., 2004). One significant application of live coding is in algorithmic music, where the performer modifies the code generating the music in a live context. Utopia is a software tool for collaborative live coding performances, allowing several performers (each with their own laptop producing its own sound) to communicate and share code during a performance. We have made an AI bot, Autopia, which can participate in such performances, communicating with human performers through Utopia. This form of human-AI collaboration allows us to explore the implications of computational creativity from the perspective of live coding.
Improved Correlation Identification of Subsurface Using All Phase FFT Algorithm

The correlation identification of the subsurface is a novel electrical prospecting method which could suppress stochastic noise.This method is increasingly being utilized by geophysicists.It achieves the frequency response of the underground media through division of the cross spectrum of the input & output signal and the auto spectrum of the input signal.This is subject to the spectral leakage when the cross spectrum and the auto spectrum are computed from cross correlation and autocorrelation function by Discrete Fourier Transformation (DFT, "To obtain an accurate frequency response of the earth system, we propose an improved correlation identification method which uses all phase Fast Fourier Transform (APFFT) to acquire the cross spectrum and the auto spectrum.Simulation and engineering application results show that compared to existing correlation identification algorithm the new approach demonstrates more precise frequency response, especially the phase response of the system under identification
A Nature-inspired Multiple Kernel Extreme Learning Machine Model for Intrusion Detection

The application of machine learning (ML) in intrusion detection has attracted much attention with the rapid growth of information security threat.As an efficient multi-label classifier, kernel extreme learning machine (KELM) has been gradually used in intrusion detection system.However, the performance of KELM heavily relies on the kernel selection.In this paper, a novel multiple kernel extreme learning machine (MKELM) model combining the ReliefF with nature-inspired methods is proposed for intrusion detection.The MKELM is designed to estimate whether the attack is carried out and the ReliefF is used as a preprocessor of MKELM to select appropriate features.In addition, the nature-inspired methods whose fitness functions are defined based on the kernel alignment are employed to build the optimal composite kernel in the MKELM.The KDD99, NSL and Kyoto datasets are used to evaluate the performance of the model.The experimental results indicate that the optimal composite kernel function can be determined by using any heuristic optimization method, including PSO, GA, GWO, BA and DE.Since the filter-based feature selection method is combined with the multiple kernel learning approach independent of the classifier, the proposed model can have a good performance while saving a lot of training time.
Supreme or Unproven?

Despite much recent fanfare, quantum computers still need to show that they can do something useful.
MÜŞTERİ ODAKLI WEB SİTELERİNİN TASARLANMASI VE BU SİTELERİN DEĞERLENDİRİLMESİNE İLİŞKİN BİR UYGULAMA

Internet and web sites which are considered to be among the most important inventions of the century provide 24 hours one to one communication with costumers. The data gathered from one to one communication have contributed both marketing decisions and company brand. However it is not sufficient to only design web sites. In designing web sites, companies should care about design and interactivity attributes providing long time visits. With this study, it has been aimed that whether companies have designed their web sites to provide interactiviyt or not.According to findings of the research, it has been found that companies should more care about interactivity in designing their web sites.
The Combination of Naive Bayes and Particle Swarm Optimization Methods of Student’s Graduation Prediction

This research conducted classification testing on the study case of student graduation prediction in a university. It aims to assist the university in maintaining academic development and in finding solutions for improving timely graduation. This study combined two methods, i.e., Naive Bayes and Particle Swarm Optimization, to produce a better level of accuracy. The Naive Bayes method is a statistical classification method used to predict a student's graduation in this study. That will be further enhanced using the Particle Swarm Optimization method to produce a better level of accuracy. There are 10 (ten) samples in this study randomly selected from the alumni data of UIGM students in 2011-2014. From the test results, this research resulted in an accuracy value of 90% from the Naive Bayes algorithm testing, after testing the Naive Bayes with Particle Swarm Optimization, which produced an accuracy value of 100%. The conclusion obtained from the results is the Naive Bayes method has a higher accuracy value if combined with Particle Swarm Optimization. Thus the university can more easily predict whether or not the students graduate on time for the upcoming graduation period. The results of this test prove that to predict student graduation using the Naive Bayes method with Particle Swarm Optimization is appropriate.
The Role of Knowledge Sharing in Organizational Performance

With the tremendous development in the current world, it has become necessary for organizations to convey with the rhythm of development in order to achieve the advantage of constructive competition with other organizations in their field.Where knowledge management is considered one of the methods used to raise the level of development, increase revenue and competitive advantage.The purpose of this paper is to discover the role of knowledge sharing in the organization in terms of increasing the effectiveness and efficiency, the significance of knowledge sharing, relationship between knowledge sharing and organizational leadership, the issues of knowledge sharing and how can be addressed.The applied method in this study is Qualitative Research which includes the use of non-statistical data to explain or explore a certain phenomenon.This paper has been failed to include the correspondence between knowledge sharing, employee motivation and performance, as this topic is also reflective of the extended role of knowledge sharing in the efficiency, satisfaction and retention of employees.
Research on Principle and Application of Convolutional Neural Networks

Abstract At present, in the field of image recognition, convolutional neural networks have become a research hotspot. Based on the neural cognitive machine, it further reduces the complexity of network models.Convolutional neural network (CNN) has the characteristics of simple structure, less training parameters and strong adaptability because it reduces the weight of nodes.In this paper, the network structure, neuron model and training algorithm of convolutional neural network are studied. On the basis of this, the application of CNN in shape recognition is taken as an example, and the related network structure and experimental results are given.
Implementation of Android-Based Vehicle Tracking System in Trac Astra Rent A Car Palembang

TRAC Astra Rent A Car is a leading car rental service provider in Indonesia, which also has a branch in the city of Palembang. Starting with only 5 units of vehicles, now PT. TRAC Astra Rent A Car rental car into a business unit that has more than 12,000 vehicles operating in more than 1,500 corporate customers segment. PT.TRAC help corporate customers improve efficiency in the transportation business and enable them to concentrate on their core business. Besides short-term car rental needs a safe and comfortable can be easily fulfilled because PT.TRAC location very easy to reach. With the development of information technology today, it would need to complete the management PT.TRAC which she rented vehicle with a tracking facility (tracking) the vehicle's location in real-time so that management can monitor the location where the vehicle which she rented was with the development of Android-Based Tracking system
Fast Ship Detection in Optical Remote Sensing Images Based on Sparse MobileNetV2 Network

Ship detection in optical remote sensing images (ORSIs) has drawn lots of attention because of its extensive potential in maritime applications. Although many methods have been proposed in recent years, there are still great challenges for improving the detection accuracy and detection speed. In this paper, a fast ship detection method in optical remote sensing images based on sparse MobileNetV2 network is proposed, which has high accuracy and fast detection speed. Ship detection problem is turned into a sub-image classification one, which successfully avoids the massive computation caused by the region proposal stage in previous methods. The sparse MobileNetV2 network has high detection accuracy and less computation benefited from the convolutional neural networks and the depth separable convolution. Furthermore, the pruning method is used to compress the network to decrease model complexity and prevent overfitting. Several experiments are conducted based on some optical remote sensing images from Google Earth. The results demonstrate that the proposed method achieves over 5x speed enhancement compared with several mainstream ship detection methods, while the accuracy is competitive.