Reviewerly/exHarmony huggingface dataset:
Delta Encoding Overhead Analysis of Cloud Storage Systems Using Client-Side Encryption

With client-side encryption (CSE), a user's data is encrypted before being transferred to a cloud provider. This ensures that only the intended user has access to the information, but complicates effective file synchronization (between different devices and the cloud). Motivated by prior findings that empirically show that the largest performance differences between popular CSE services (CSEs) and non-CSEs typically are related to the implementation of delta encoding solutions to reduce bandwidth usage, in this paper, we evaluate and provide insights into the practical CSE-related delta encoding overheads. First, we use targeted experiments to demonstrate the delta encoding problem associated with CSE and to compare the practical overhead differences associated with three example services implementing delta encoding. Second, we develop an analytic cost model and use it to show that a simple threshold-based CSE policy can reduce the bandwidth and storage usage seen by the best CSE considered here, that such a policy has a provable worst-case overhead within a factor two of the best non-CSE, and typically performs much better. The results are highly encouraging, and show that it is possible to provide CSE at limited additional overhead compared to non-CSE services.
A Nonlinear Multivariate Approach to Identify Differentially Coexpressed Pathways

Research on Gene Regulatory Networks (GRN) is primarily guided by differential co-expression analysis. Through this approach, we can observe a biologically significant difference in gene regulatory control under varied metabolic conditions. Although quite successful, the traditional analysis gets restricted either from the point of view of a single gene-specific topology where only pairwise co-expression plays a major role or in the type of gene coexpression. In the latter context, state of the art examples mostly considers linear correlative regulatory patterns in a network. In the current work, we overcome these limitations using an established gene regulatory framework which accounts for the complete co-expression structure. An overall improvement on the regulatory functions could be observed incorporating nonlinear gene to gene regulation. In this regard, the nonlinear differential orchestrated action of genes in different conditions present in certain unreported gene regulatory pathways of p-53 dataset have been found to be significant, where linear measures of differential regulations reflected meager importance.
Improving Action Recognition Using Sequence Prediction Learning

Skeleton-based action recognition distinguishes human actions using the trajectories of skeleton joints, which can be a good representation of human behaviors. Conventional methods usually construct classifiers with hand-crafted or the learned features to recognize human actions. Different from constructing a direct action classifier for action recognition task, this paper attempts to identify human actions based on the development trends of behavior sequences. Specifically, we first utilize the memory neural network to construct action predictors for each kind of activity. These action predictors can then output the action trends at the next time step. According to the predictions of these action predictors at each time step and the removal rule, the poor predictors can be eliminated step by step, and the IDentity(ID) number of the last predictor left is considered as the label of the action sequence to be categorized. We compare the proposed action recognition algorithm using sequence prediction learning with other methods on two publicly available datasets. Our experimental results consistently demonstrate the feasibility and effectiveness of the suggested method. It also proves the importance of prediction learning for action recognition.
e-Dokuw is A Smart Wallet : Identifying Nominal Money Using Homography Estimation Methodes

Many limitations of a conventional wallet, one of them is unable to detect the nominal value of the money inside it. e-Dokuw is a smart wallet perfecting from a conventional wallet. These improvements are in the form features, that based on the Internet of Things (IoT). e-Dokuw which has reliable features such as GPS, RFID Reader and equipped with Mini Camera, will be further enhanced with additional performance: “Reading Nominal Money”, using homography estimation. Under homography, can be write the transformation of points in 3D from camera 1 to others camera. Using this estimation method in reading the money characteristics by processing the image the nominal value can be detected. With the successful implementation of this method, certainly helps the disabled people to detect the nominal amount of money to be used.
Novel orchestration architecture for Fog computing

Cloud computing technologies such as virtualization and orchestration have revolutionized a great number of different domains, from the industry to healthcare. However, there are still certain applications that can not take advantage of this paradigm due to stricter requirements such as end-to-end response times. A new paradigm called Fog computing is arising to answer the needs of these applications. Fog nodes have lower hardware resources that their Cloud counterparts and are distributed over a wider geographical area. Porting technologies designed for the Cloud, specifically orchestration, to this new paradigm requires to handle these peculiarities. A novel orchestration architecture is proposed evolving on current ones, directly focusing Fog characteristics. Multiple schedulers are dynamically deployed in order to achieve a better scheduling throughput and spread the resources required by the scheduler among multiple lower-resource nodes. It uses an application-centric approach at the scheduler level in order to enable new scheduling parameters. The scheduling throughput of this architecture is compared to the most common Cloud orchestration architecture.
Visibility Driven Contrast Enhanced Surgical Defogging

While doing an endoscopic surgery, surgical fog is a common problem which can lead to improper device use and inaccurately targeted tissue. Defogging is the process to remove the effects of fog in captured images and reconstruct the original colour of natural scene. To address the colour infidelity problem introduced by the visibility recovery and contrast enhancement, the luminances of the recovered and enhanced images are fused in the gradient domain, and the fused luminance is reconstructed by solving the Poisson equation in the frequency domain. Contrast enhancement after fusion of images gives a result named as bilateral of bilateral filtering partially repeated (BBFPR). The fused image is given as input once again to the visibility recovery section and contrast enhancement section and fusion of images is carried once more. This gives a different output bilateral of bilateral filtering repeated (BBFR). Bilateral filtering is used to smoothen the image in visibility restoration stage. This has the advantage that it retains the edges and hence edge preservation is achieved. Moreover this method improves the speed of processing compared to median filtering. The experimental results demonstrate that the BBFR method has better contrast to noise ratio whereas BBFPR method has better naturalness and sharpness.
Parameters Learning of BPS M7 Banknote Processing Machine for Banknote Fitness Classification

This paper presents a method to adjust banknote processing machine parameters so that it performance is comparable to the banknote specialist. Using Support Vector Machine to find important factors and select those factors to be adjusted and then find each threshold from individual histogram of each parameters. The result shows that the proposed method can boost classification accuracy to the banknote processing machine.
A Deep Neural Network based Approach to Energy Efficiency Analysis for Cloud Data Center

The energy consumption growth of the Information and Communication Technology (ICT) sector contributes to almost 2% of the global carbon footprint with an estimated trend of 3-3.6% by 2020. Most of this growth (45%) can be attributed to data centers (DC) which now represent the core infrastructure for different industries. Furthermore, cloud DCs are complex systems composed of several ICT and non-ICT (i.e. mechanical and electrical) sub-systems. The variety of configurations and the inter-dependencies of the different DC sub-systems leads to enormous challenges in understanding and optimizing DC energy efficiency based on the Power Usage Effectiveness (PUE) metric. Within this context, we focus in this work on analyzing the behavior of Deep Neural Network (DNN)-based model to predict the DC energy efficiency metric (PUE). In fact, the proposed model is used to evaluate the impact of various DC sub-systems on energy efficiency. Through an experimentation with real datasets from a real DC, we observed that DNN-based model achieves a good Root Mean Square Error (RMSE). The obtained results of this experimentation indicate that our proposed DNN-based model improves the PUE optimization, and consequently, shows its promise for a practical implementation.
Crowdsourcing Data Analysis for Crowd Systems

Crowdsourcing permits huge scale and adaptable summon of human contribution for information social affair and examination, which presents another world view of information mining process. Customary information mining techniques frequently require specialists in investigative areas to comment on the information. In any case, it is costly and ordinarily takes a long time. Crowdsourcing empowers the utilization of heterogeneous foundation information from volunteers and circulates the explanation procedure to little segments of endeavors from various commitments. The data analytics process is done via logistic regression.
Sorting Matrix Architecture for Continuous Data Sequences

Sorting is one of the most fundamental problems in mathematics and computer science. Because high-throughput and flexible sorting is a key requirement in modern databases, this paper presents efficient techniques for designing a high-throughput sorting matrix that supports continuous data sequences. There have been numerous studies on the optimization of sorting circuits on FPGA (field-programmable gate array) platforms. These studies focused on attaining high throughput for a single command with fixed data width. However, the architectures proposed do not meet the requirement of diversity for database data types. A sorting matrix architecture is thus proposed to overcome this problem. Our design consists of a matrix of identical basic sorting cells. The sorting cells work in a pipeline and in parallel, and the matrix can simultaneously process multiple data streams, which can be combined into a high-width single-channel data stream or low-width multiple-channel data streams. It can handle continuous sequences and allows for sorting variable-length data sequences. Its maximum throughput is approximately 1.4 GB/s for 32-bit sequences and approximately 2.5 GB/s for 64-bit sequences on our platform.
Open Source Implementation for Fall Classification and Fall Detection Systems

Distributed social coding has created many benefits for software developers. Open source code and publicly available datasets can leverage the development of fall detection and fall classification systems. These systems can help to improve the time in which a person receives help after a fall occurs. Many of the simulated falls datasets consider different types of fall however, very few fall detection systems actually identify and discriminate between each category of falls. In this chapter, we present an open source implementation for fall classification and detection systems using the public UP-Fall Detection dataset. This implementation comprises a set of open codes stored in a GitHub repository for full access and provides a tutorial for using the codes and a concise example for their application.
NOVEL PAJANG KARYA WAWAN SUSETYA SEBAGAI MATERI PEMBELAJARAN SASTRA: SOSIOLOGI SASTRA DAN NILAI PENDIDIKAN KARAKTER

This research  aims to explain: (1) social background of Pajang  novel writer; (2) the sociological background of Pajang  novel by Wawan Susetya; (3) readers' responses to Pajang's novel by Wawan Susetya; (4) the value of character education contained in Pajang's novel by Wawan Susetya; and (5) the relevance of Pajang's  novel by Wawan Susetya as a literary learning material in high school. This research uses descriptive qualitative method and using a study of sociology of literature. The result of this research is description about: (1) social background of Pajang novel writer covering aspect of race, moment (moment), and environment (milieu); (2) Sociological background of Pajang's work by Wawan Susetya includes social  group,  culture,  social  institution, stratification, and  power;  (3) readers' responses  to  Pajang's  novel by  Wawan Susetya;  (4)  the  value  of  character education contained in the Pajang  novel by Wawan Susetya include religious, tolerance, hard work, democratic, curiosity, nationalism, achievement, friendly / communicative, social, and responsibility; (5) the relevance of Pajang's  novel by Wawan Susetya as a literary learning material in Senior High School which is adapted to the content aspect, presentation aspect, language aspect, and graphictions aspect.
Cognitive Visualization of Multistructured and Multimodal Data

A problem of Big Data cognitive visualization in interactive user interfaces is addressed. A requirement to develop a new approach of data visualization using several dimensions is identified on the basis of analysis of existing approaches. It is necessary to develop new principles for choosing the dimension to display data and place the elements of the user interface in space. To solve this problem, a methodology that describes the existing data display dimension is proposed. The description of each of the dimensions is given. And a decomposition matrix for selecting the main dimension is introduced. Practical use of the solution is illustrated by the introduction of several industrial applications. Basic features of cognitive visualization are described considering the application requirements and particular properties of different problem domains. Main attention is given to the development of efficient and convenient user interfaces, capable of representing the results of Big Data processing and analysis.
A New Distributed Ensemble Method with Applications to Machine Learning

The main objective of this paper is to introduce a new ensemble learning model which takes advantage of the data which is originally distributed among a group of local centers. In this model, we first train a group of client nodes which have access only to their own local data sets. High classification rate is not required in this phase. In the second phase, the master node learns which client nodes are more likely to classify correctly a given data instance. Therefore, only the responses of these effective nodes will be used in the classification step. A major advantage of our algorithm, as the experimental results confirm, is that the network can obtain high classification rates by using a relatively small fraction of the whole data set. Moreover, this learning scheme is fairly general and can be employed in other contexts as well.
A Bandwidth and Latency Based Replica Selection Mechanism for Data-Intensive Workflow Applications in the Multi-Cloud Environment

Data-intensive workflow applications need effective data management strategies to handle large amount of data generated. To fit this goal, data replication is one of the most significant strategies to balance the data access load and improve the quality of services. The replica selection strategy can help users decide which replica is the most suitable to access. This paper proposes a bandwidth and latency based replica selection mechanism (namely, BLRS) to avoid potential network overloading issues and increase the number of concurrent-running instances in the multi-cloud environment. The simulation results show that our BLRS mechanism can significantly increase the number of concurrent-running workflow instances, in comparison with the least response time replica selection, on the traditional three-replica placement basis.
Analyzing Different Multiparty Computation Techniques

Strong encryption provides support to data privacy. Although encryption can make the data secure in data transfer and at rest, at some point this encrypted data definitely needs to be decrypted. Now at this very point, it so happens that the data becomes susceptible to attacks ultimately resulting in compromised data privacy. This is where secure multiparty computation (MPC) comes into picture; thereby, it provides ability to calculate required values from numerous encrypted data sources without any party compromising on their secret data. At ground level, MPC is a very general concept that can be realized using different protocols, such as secret sharing, in which secret data from each party is divided and then distributed randomly, encrypted “shares” among the parties. This distributed data when eventually aggregated would provide the final desired result. If anyone happens to intersect the data at hand of any of the parties, it would prove futile. With this paper, we focus on different algorithms or techniques that work behind the scenes in implementing MPC. These techniques include homomorphic encryption, followed by RSA combined with Paillier’s algorithm and lastly the concept of garbled circuits.
Randomness Analysis of YUGAM-128 Using Diehard Test Suite

The random key generation for the encryption/decryption process prevails as the significant character of the stream ciphers. Here, we have introduced a novel idea to elucidate the results from the randomness test using the Diehard test suite. The proposed method is used to test the randomness of the YUGAM-128 stream cipher. The randomness tests resulted in p-values. In this paper, Diehard test results have been consolidated, and the p-values obtained for the algorithm are distributed accordingly, such that it indicates improved randomness of the security level for the Safe Arena, while the presence of many p-values in the Failure Arena reports that the produced text is less likely to be random. The outcome of the conducted tests showed that YUGAM-128 resulted in an 81.2% of the p-values distributed over the safe area.
A Performance Comparison of Clustering Algorithms for Big Data on DataMPI

Clustering algorithms for big data have important applications in finance. DataMPI is a communication library based on key-value pairs that extends MPI for Hadoop and Spark. We study the performance of K-means, fuzzy K-means and Canopy clustering algorithms on the DataMPI cluster by experiments. Firstly, we observe the influence of the number of nodes on the clustering time and scaleup; and then we observe the influence of the size of the memory of each node on the clustering time and memoryup; at the same time, we compare the performance of these three clustering algorithms on different text data set. From experimental results we can find that: (1) When the size of data set, the size of the memory, and the number of nodes keep the same, Canopy is the fastest, followed by K-means, and the fuzzy K-means is the slowest; (2) When the size of the memory of each node is fixed, these three algorithms have a good scaleup on all of text data set, which shows that the increase of the number of nodes can significantly improve the efficiency of these three algorithms; (3) When the number of nodes is fixed, and as the size of the memory is increased from 1 GB to 4 GB, the clustering time is significantly decreased, which shows that these three clustering algorithms have a good memoryup.
Multi-channels CNN temporal features for depth-based action recognition

In this paper, we investigate temporal features that are extracted by a multi-channel convolutional neural network in depth map-based human action recognition. At the beginning, for the non-zero pixels representing the person shape in each depth map we calculate handcrafted features. On multivariate time-series of such handcrafted features we train a multi-class, multi-channel CNN to model temporal features as well as we extract statistical features of time-series. The concatenated features are stored in a common feature vector. Afterwards, for each class we train a separate one-against-all convolutional neural network to extract class-specific features of depth maps. For each class-specific, multivariate time-series we calculate statistical features of time-series. Finally, each class-specific feature vector is concatenated with the common feature vector resulting in an action feature vector. For each action represented by action feature vectors we train a multi-class classifier with one-hot encoding of output labels. The recognition of the action is done by a voting-based ensemble operating on such one-hot encodings. We demonstrate experimentally that on UTD-MHAD dataset the proposed algorithm outperforms state-of-the-art depth-based algorithms and attains promising results on MSR-Action3D dataset.
Scene Text Magnifier

Scene text magnifier aims to magnify text in natural scene images without recognition. It could help the special groups, who have myopia or dyslexia to better understand the scene. In this paper, we design the scene text magnifier through interacted four CNN-based networks: character erasing, character extraction, character magnify, and image synthesis. The architecture of the networks are extended based on the hourglass encoder-decoders. It inputs the original scene text image and outputs the text magnified image while keeps the background unchange. Intermediately, we can get the side-output results of text erasing and text extraction. The four sub-networks are first trained independently and fine-tuned in end-to-end mode. The training samples for each stage are processed through a flow with original image and text annotation in ICDAR2013 and Flickr dataset as input, and corresponding text erased image, magnified text annotation, and text magnified scene image as output. To evaluate the performance of text magnifier, the Structural Similarity is used to measure the regional changes in each character region. The experimental results demonstrate our method can magnify scene text effectively without effecting the background.
Design and Implementation of Web-based Church Information Systems (Case Study: HKBP Kebon Jeruk)

HKBP Kebon Jeruk Church has a lot of data consisting of church data, Pastor data, Church server data, family data, marital data, baptismal data, and also about church agenda such as the schedule of activities Church, schedule of church service. The problem in HKBP Kebon Jeruk is that the Data is provided and managed manually, as well as difficulties in finding the necessary information. Therefore, the system needs to be built by the HKBP Kebon Jeruk Church to request church management data. The method used in the HKBP Kebon Jeruk system is the Extreme Programming method, and the analysis used is the PIECES analysis. The result of this research is to build the HKBP Kebon Jeruk system according to the needs of the user.