Reviewerly/exHarmony huggingface dataset:
Nonparametric Bayesian Modeling of Multimodal Time Series

In this chapter, we take a Bayesian nonparametric approach in defining a prior on the hidden Markov model that allows for flexibility in addressing the problem of modeling the complex dynamics during robot manipulation task. At first, considering the underlying dynamics that can be well-modeled as a hidden discrete Markov process, but in which there is uncertainty about the cardinality of the state space. Through the use of the hierarchical Dirichlet process (HDP), one can examine an HMM with an unbounded number of possible states. Subsequently, the sticky HDP-HMM is investigated for allowing more robust learning of the complex dynamics through a learned bias by increasing the probability of self-transitions. Additionally, although the HDP-HMM and its sticky extension are very flexible time series models, they make a strong Markovian assumption that observations are conditionally independent given the discrete HMM state. This assumption is often insufficient for capturing the temporal dependencies of the observations in real data. To address this issue, we consider extensions of the sticky HDP-HMM for learning the switching dynamical processes with switching linear dynamical system. In the later chapters of this book, we will verify the performances in modeling mulitmodal time series and present the results of robot movement identification, anomaly monitoring, and anomaly diagnose.
Application of Apriori Algorithm on Examination Scores

As an interdisciplinary discipline, data mining is popular in education area especially when evaluating students’ learning performance. It focuses on analyzing educational assessment data to develop models for improving students’ learning experience and enhancing the institutional effectiveness. Therefore, data mining is helpful to academic institutions imparting high-quality education for their learners. Applying data mining in education enables a better understanding of how a student is progressing. This could also be used to identify ways in which one can improve educational outcomes. This paper discussed a specific example of Apriori algorithm (a popularly used in data mining) in generating association rules among scores of two exam sessions taken in succession. It was revealed that there was some strong association laying in the questions of the examination which were not that obvious in first place.
VisSnippets: A Web-Based System for Impromptu Collaborative Data Exploration on Large Displays

The VisSnippets system is designed to facilitate effective collaborative data exploration. VisSnippets leverages SAGE2 middleware that enables users to manage the display of digital media content on large displays, thereby providing collaborators with a high-resolution common workspace. Based in JavaScript, VisSnippets provides users with the flexibility to implement and/or select visualization packages and to quickly access data in the cloud. By simplifying the development process, VisSnippets removes the need to scaffold and integrate interactive visualization applications by hand. Users write reusable blocks of code called “snippets” for data retrieval, transformation, and visualization. By composing dataflows from the group’s collective snippet pool, users can quickly execute and explore complementary or contrasting analyses. By giving users the ability to explore alternative scenarios, VisSnippets facilitates parallel work for collaborative data exploration leveraging large-scale displays. We describe the system, its design and implementation, and showcase its flexibility through two example applications.
Mutual conditional independence and its applications to model selection in Markov networks

Abstract The fundamental concepts underlying Markov networks are the conditional independence and the set of rules called Markov properties that translate conditional independence constraints into graphs. We introduce the concept of mutual conditional independence in an independent set of a Markov network, and we prove its equivalence to the Markov properties under certain regularity conditions. This extends the notion of similarity between separation in graph and conditional independence in probability to similarity between the mutual separation in graph and the mutual conditional independence in probability. Model selection in graphical models remains a challenging task due to the large search space. We show that mutual conditional independence property can be exploited to reduce the search space. We present a new forward model selection algorithm for graphical log-linear models using mutual conditional independence. We illustrate our algorithm with a real data set example. We show that for sparse models the size of the search space can be reduced from $\mathcal {O} (n^{3})$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:math> to $\mathcal {O}(n^{2})$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:math> using our proposed forward selection method rather than the classical forward selection method. We also envision that this property can be leveraged for model selection and inference in different types of graphical models.
Disc Brake Type of Braking System on Rear Shaft of Go-kart Daiho 7.5 HP

Abstract Go-kart is one of the four-wheeled racing vehicles with high speed at a speed of approximately 100 km/hour more depending on the type and specification of the machine. One of the types of kart machines uses 4-step and 2-step machines. It means that the 4-step machine works by rotating two rounds of the crank shaft and 4 times the downward piston. The result of the calculation of the The weight of the kart taken at the central point is 678.8 mm and the rear kart is 273.7 mm. Torque obtained from the disk brake system is 377.5 Nm. As well as from torque obtained retarding 0.102 m/s. Braking force Result is 895.965 N. As well as force press on the brake canvas is 3.318 N. As well as the flow rate of the brake system is 1.078 kg/cm. From this study, we can draw the conclusion that the braking disc brake is influenced by the flow rate, the radius inside and outside the wheels and center of gravity Central point karts at the time of installation of the disc brake system so that the braking system can be maximal. Vital braking has a function and purpose that can be used as the main equipment on the kart or other vehicles. In addition, the use of a disc brake system is mostly used both on two wheels and four wheels because of its ease to treat and use and replacement of the spare parts.
A Modified Round Robin Method to Enhance the Performance in Cloud Computing

Cloud computing is a new age technology. The quality of services in the cloud depends on priority of the job, time taken to transfer the specific task, length of the task, number of CPUs available for every virtual machine and capacity of virtual machine. Previously, there were many researches done on the issue of task scheduling, about workflow and job scheduling in cloud computing. Scheduling in cloud has to cope with many issues like allocation of small request, workload characterization, different types of request, real-time allocation and provision for queuing. Throughput of the system depends on resources allocation to process which is performed by the CPU scheduling algorithms. The objective of scheduling algorithm is to minimize the waiting time, turnaround time, response time and context switching and highest degree of utilization of CPU. Round Robin is a pre-emptive algorithm which works on fixed time quantum and used in time sharing system. Round Robin is also known for its fairness task distribution and optimum utilization of CPU. In this paper, we have tried to introduce an improved Round Robin algorithm by calculating the quantum number by taking the average of burst time of all tasks which arrive at the same time and finish the execution of the tasks which have less quantum number and the task which has greater quantum number will be executed as per the existing Round Robin method. By simulating the proposed algorithm, we have observed that the waiting time and turnaround time decrease considerably.
Trait Extraction Method for Digital Equipment Using Clock Drifts Based on Comparison of Criterion Server

Open connectivity is required to expand the IoT services, but such environments are prone to the risk of information leakage and illegal operation by spoofing. In this paper, we present clock fingerprints concept based on clock frequency trait, and propose a method for extracting trait using clock drifts and criterion server. This trait can be available in identifying digital equipment to increase security in the future open IoT services. In our two experiments, we demonstrate the extraction of traits from some computers. First one show that unique characteristics can be extracted from two kinds of clock signals. Second show that different characteristics can be extracted from the same model number and the same software settings. We conclude with the challenges that we plan to address in our future work to improve this method.
A Hybrid Approach Based on Lp1 Norm-Based Filters and Normalized Cut Segmentation for Salient Object Detection

The graph theory-based graph cut algorithm is successfully applied in a wide range of problems in computer vision. The normalized graph cut algorithm is one of the efficient algorithms used for the detection of salient objects in one image. The efficiency of normalized graph cut algorithm has been improved by applying Lp1 norm-based Gaussian filter and median filter, with normalized cut algorithm, on individual color channels rather than the whole image. Different experiments are conducted to prove the efficiency of these applications by considering images from different standard image segmentation datasets like Berkley image segmentation dataset and COREL image segmentation dataset. The results of the proposed methods are compared with Otsu thresholding and C-means clustering algorithms. Three validity indices are used to quantify the results.
NetKernel: Making Network Stack Part of the Virtualized Infrastructure

This paper presents a system called NetKernel that decouples the network stack from the guest virtual machine and offers it as an independent module. NetKernel represents a new paradigm where network stack can be managed as part of the virtualized infrastructure. It provides important efficiency benefits: By gaining control and visibility of the network stack, operators can perform network management more directly and flexibly, such as multiplexing VMs running different applications to the same network stack module to save CPU cores, and enforcing fair bandwidth sharing. Users also benefit from the simplified stack deployment and better performance: For example mTCP can be deployed without API change to support nginx natively, and shared memory networking can be readily enabled to improve performance of colocated VMs. Testbed evaluation using 100G NICs shows that NetKernel preserves the performance and scalability of both kernel and userspace network stacks, and provides the same isolation as the current architecture.
Rapid Training Data Creation by Synthesizing Medical Images for Classification and Localization

While the use of artificial intelligence (AI) for medical image analysis is gaining wide acceptance, the expertise, time and cost required to generate annotated data in the medical field are significantly high, due to limited availability of both data and expert annotation. Strongly supervised object localization models require data that is exhaustively annotated, meaning all objects of interest in an image are identified. This is difficult to achieve and verify for medical images. We present a method for the transformation of real data to train any Deep Neural Network to solve the above problems. We show the efficacy of this approach on both a weakly supervised localization model and a strongly supervised localization model. For the weakly supervised model, we show that the localization accuracy increases significantly using the generated data. For the strongly supervised model, this approach overcomes the need for exhaustive annotation on real images. In the latter model, we show that the accuracy, when trained with generated images, closely parallels the accuracy when trained with exhaustively annotated real images. The results are demonstrated on images of human urine samples obtained using microscopy.
Disaster Preparedness Backend Database to Read and Write Separation Technology Research

GoldenGate and Active DataGuard are both products of Oracle. GoldenGate can realize data synchronization with production database at the second level. And the same time it can realize writing data on both ends at the same time. Active DataGuard not only can realize the data real-time synchronization. Therefore, to combine GoldenGate and Active DataGuard technology not only can realize the disaster of the production data, but also can realize separation of disaster preparedness back-end database, reading and writing, and improve the business processing ability of the system as a whole. At the same time it can make full use of the computing power of the backend resources to reduce the cost.
Plug-And-Pipeline: Efficient Regularization for Single-Step Adversarial Training

Adversarial Training (AT) is a straight forward solution to learn robust models by augmenting the training mini-batches with adversarial samples. Adversarial attack methods range from simple non-iterative (single-step) methods to computationally complex iterative (multi-step) methods. Although the single-step methods are efficient, the models trained using these methods merely appear to be robust, due to the masked gradients. In this work, we propose a novel regularizer named Plug-And-Pipeline (PAP) for single-step AT. The proposed regularizer attenuates the gradient masking effect by promoting the model to learn similar representations for both single-step and multi-step adversaries. Further, we present a novel pipelined approach that allows an efficient implementation of the proposed regularizer. Plug-And-Pipeline yields robustness comparable to multi-step AT methods, while requiring a low computational overhead, similar to that of single-step AT methods.
Enhancement of Hazy Images Using Atmospheric Light Estimation Technique

Images captured in degraded weather conditions often suffer from bad visibility. Pre-existing haze removal methods, the ones that are effective are computationally complex too. In common de-hazing approaches, estimation of atmospheric light is not achieved properly as a consequence, haze is not removed significantly from the sky region. In this paper, an efficient method of haze removal from a single image is introduced. To restore haze-free images comprising of both sky as well as nonsky regions, we developed a linear model to predict atmospheric light and estimated the transmission map using the dark channel prior followed by an application of a guided filter for quick refinement. Several experiments were conducted on a large variety of images, both reference and nonreference, where the proposed image de-hazing algorithm outperforms most of the prevalent algorithms in terms of perceptual visibility of the scene and computational efficiency. The proposed method has been empirically measured through quantitative and qualitative evaluations while retaining structure, edges, and improved color.
Analysis of the Evolution of Eight VSEs Using the ISO/IEC 29110 to Reinforce Their Agile Approaches

Most very small entities (VSEs) develop software for medium and large companies and organizations. This situation creates an opportunity for them to become key players in the production chain by providing quality software within schedule and budget. A feature of most VSEs is that they do not have experience in the implementation of engineering standards due to specific features such as lack of support, lack of resources, time-consuming, and the use of agile approaches. This chapter presents an analysis of a set of eight VSEs that used agile approaches to develop software and that have implemented the software Basic profile of the ISO/IEC 29110 to reinforce their agile approach. The results show that ISO/IEC 29110 were easily implemented and helped VSEs to improve their agile approaches while helping them to understand the importance of formalizing some key artifacts produced during the development of a software product.
Analysing the Performance of Mobile Cross-platform Development Approaches Using UI Interaction Scenarios

For developing mobile apps, developers can choose between a native development approach, in which a unique code base needs to be maintained for each supported mobile platform, and mobile cross-platform development (MCPD) approaches. MCPD approaches allow building and deploying mobile apps for several mobile platforms from one single code base. As MCPD approaches build on top of different technologies, in this paper, we analyze the performance of MCPD approaches based on UI interactions. We developed one app natively, and two using MCPD approaches. Using automated tests, we measured CPU usage, memory consumption and rendered frames of these apps when executing UI interaction scenarios consisting of three selected UI interactions (i.e., opening/closing a navigation drawer, screen transition, and virtual scrolling). The study confirms results of previous studies showing that, compared to natively developed apps, apps developed using MCPD approaches put a higher load on mobile devices regarding CPU usage, main memory and GPU memory consumption.
Evaluating the impact of polygonal representations on mass classification

Breast cancer is one of the most severe types of cancer and responsible for about 15% of all women death related to cancer worldwide. Mammography is the most reliable and recommended approach to help early detection of breast cancer, since this technique can detect the disease in its asymptomatic phase. Different Computer-Aided Diagnosis (CAD) systems have been used to aid in the diagnosis serving a second opinion to the physicians. These systems extract features from masses found in mammograms, which compose inputs to the classifiers used in CAD systems. The main contribution of this study is the evaluation of different polygonal representations on the phase of feature extraction. In this context, two different polygonal models were tested with different parameters to represent boundaries of masses. Artificial neural networks, support vector machines and k-nearest neighbors were used to discriminate masses as benign and malignant achieving accuracy of 85%, 84% and 85%, respectively.
Cloud computing model in higher education

Abstract Currently, higher education is institutions that should be managed as a global business institution. Higher education should improve continually its business processes according to the demands of increasing its stakeholders. of the problems faced in the development of educational, activities if related to the use of information technology for each service in higher education for stakeholders is the issue of available funds. Cloud computing also has advantages related to infrastructure and maintenance and increases operating efficiency. Most institutions are currently moving towards cloud computing technology to reduce operational costs. For achieving these objectives, the institutions need to be supported by reliable technology. This study aim to propose cloud model for education and IT Governance conceptual framework for Cloud Computing model adoption, Tarumanagara University as a study case.
Mining Temporal Sequence Patterns Using Association Rule Mining Algorithms for Prediction of Human Activity from Surveillance Videos

One of the most interesting and important open issues in the automated video surveillance community is the analysis of human activities which requires a higher level of understanding. A framework was introduced for the prediction of human activity using temporal sequence patterns. In this framework, Sequential Pattern Mining (SPM) converted the complex symbolic sequence in the video into frequent itemsets using the Apriori algorithm. Apriori algorithm has to scan the video multiple times to find the frequent itemset that results in high execution time and memory consumption problems. To solve these problems, various association mining algorithms such as Rapid Association Rule Mining (RARM), Equivalence class clustering, and bottom-up lattice traversal (Eclat), diffset Eclat (dEclat) and Frequent Pattern-growth (FP-growth) algorithms are introduced for mining frequent itemsets. RARM speeds up the mining process at the minimum support threshold by using the Support-Ordered Trie Itemset (SOTrieIT) tree structure. The performance of frequent itemset mining is enhanced by using Eclat where depth-first approach is accommodated in it to find the frequent itemsets. However, the pruning technique in Eclat is more difficult. So, a dEclat algorithm is used where a depth-first search is performed to find frequent itemsets. But, dEclat is not more suitable for the sparse database. So, an FP-growth algorithm is introduced where a compressed data structure called FP-tree is used that solves the time and memory problem of the Apriori algorithm. The mined frequent itemsets are normal human behavior and the remaining activities are abnormal human activity. The experiments are carried out to prove the effectiveness of the RARM, Eclat, dEclat, and FP-growth for mining frequent activities in the video.
Wearable sensor networks: A measurement study

Abstract Wearable technology is no longer science fiction. Thanks to the growing capability in the production chain to miniaturize complex electronics, a wide variety of gadgets that can be worn or included in dresses and accessories have emerged. These smart gadgets can collect data about the physical condition of the user and/or the environment providing the basis for innovative and valuable services. The main goal of this paper is to assess this context through field experiments undertaken in a testbed comprised of sensing hardware deployed on open source boards such as Arduino. Moreover, coupled with the sensing tier, we propose a proof‐of‐concept deployment architecture enabling a wide range of wearable sensors to collect and transmit data to a logically centralized unit.
MEMBANGUN KARAKTER SISWA MELALUI PENGGUNAAN MEDIA PEMBELAJARAN SCRABBLE GAMES

Character education is very important in instilling positive behavior in students. Teachers as educators are expected to be able to internalize the values ​​of character in students through innovative learning in the classroom. One of the method used is to optimize the use of media through game-based learning that are able to change classroom conditions into fun by not eliminating the essence of the learning objectives themselves. The purpose of this study is to describe in depth about the use of learning media as a means of character building through descriptive qualitative approach. The selection of scrabble media is based on the development of learning models applied at SMAN 5 Cimahi through games on the one day one juz program which uses a series of words (verses) to instill the religious character of students. Based on the results of the study concluded that the use of creative learning media can shape student character.