Reviewerly/exHarmony huggingface dataset:
Refining Micro Services Placement over Multiple Kubernetes-orchestrated Clusters employing Resource Monitoring

In the cloud field, there is an increasing demand for globalized services and corresponding execution environments that overcome local limitations and selectively utilize optimal resources. Utilizing multi-cloud deployments and operations rather than using a single cloud is an effective way to satisfy the increasing demand. In particular, we need to provide cloud-native environment to organically support services based on a microservices architecture. In this paper, we propose a cloud-native workload profiling system with Kubernetes-orchestrated multi-cluster configuration. The contributions of this paper are as follows. (i) We design the operating software over multiple cloud-native cluster to select optimal resources by monitoring. (ii) For operating the multiple clusters through the design, we define and design specific general service workloads. Also, we implement the workloads in application software (iii) To seek optimal resources, we deployed the general workloads and monitored resource usage repeatedly in detail. We calculate resource variation in comparison with initial resource usage and average resource usage after deploying the service workloads. Also, we analyze the resource monitoring result. We expect this methodology can find proper resources for service workload types.
A Methodology for Estimating Hospital Intensive Care Unit Length of Stay Using Novel Machine Learning Tools

In this work we investigate the use of electronic health records (EHR) to explore length of stay (LOS) prediction models using machine learning methods. We use the Medical Information Mart for Intensive Care, version 3 (MIMIC-III) dataset, developed by the Massachusetts Institute of Technology (MIT) Lab for Computational Physiology. In this study we focus on the implementation of supervised learning algorithms that classify the patients in three different LOS ranges considering the underlying relationship between the patient hospital stay and several other attributes available in the MIMIC-III dataset. The use of a modern, consistent, and unifying framework for machine learning modeling allows for fair comparison and stable implementations of the different supervised learning techniques considered for LOS estimation. Tree-based models and maximum margin classifiers show reasonable accuracy when predicting patient LOS in a subset of observations for a group of patients with a respiratory disease.
WITHDRAWN: Light weight encryption model for map reduce layer to preserve security in the big data and cloud

The storage and distribution of the raw data are major components in the medical, commercial and academic environments. With the advancements in the open applications like mobiles and social networking sites the huge data is published and the volume of the data is called as Big Data. Existing mechanism of Big Data is not sufficient to differentiate the structured and unstructured data. In addition to this, the privacy and security is major issue in Big Data. This paper focused on implementing the secure Map Reduce layer in the Hadoop platform. This new layer concentrated on the data individuality and the output of the map reduce phase for providing the security. The light weight security algorithm is proposed for reducing the overhead on the Big Data functionality. Hence the data can be protected once it reaches to the security Map Reduce layer. In the proposed model, the information loss is minimized and the running time is recorded less.
Design of a single-electron neural network circuit controlling weights for reservoir computing

Abstract We propose a weight function controllable circuit for single-electron (SE) reservoir computing (RC). While an SE circuit has advantages, e.g., low power consumption and non-linear operation, it also has the disadvantage of being sensitive to fluctuation. Therefore, we focus on RC, as weights do not need to be adjusted in the reservoir layer and there is redundancy for noise. Our idea is that introducing RC can overcome the disadvantages of an SE circuit. Our designed circuit expresses and adjusts the weights by determining how many element signals can pass through it. The result of an operation test indicates that our circuit could accurately express the weights and change them correctly. We also evaluated the discrimination of input signals and found that our circuit could accurately do this. Therefore, our SE RC circuit has the potential to become a new information processing circuit that overcomes the problems of the SE circuit.
Marketing decision making using sentiment score with co-extracting opinion target words from reviews

Abstract This paper proposes a broad-spectrum outline that utilizes natural language processing approaches counting sentiment analysis, web mining that uses clustering schemes to acquire fresh outcome elicited from reviews in support of diverse features of products. An ultimate part in the scheme is finding positive and negative sentiment scope for all features of a product in the given reviews. Massive figures of product reviews are crawling on the Web with the rapid development of Internet. Commencing these reviews, customers can get hold ofactualexamination of product rank and unswerving supervision of their acquisition actions. Withdrawal feelings from web reviews have developed into a gradually more fiery activity and haveengrossed a vastpact of considerationcommencingthe researchers. The key task for opinion mining is obtaining opinion emotions from reviews in online, the novelfactor involves in identifying relations between words. The project proposes a new scheme that contains graph based co-ranking scheme that is employed in finding poise of every word. Then, words with superiorpoise are withdrawn as opinion objects. Based on the nearest neighbor rule we compare with previous schemes, the scheme more preciselycaptures opinion relations, particularly for elongated cover relations. The investigationaloutcome shows that the schemesuccessfully outperforms all other methods and techniques.
Secure Coded Matrix Multiplication Against Cooperative Attack in Edge Computing

In recent years, the computation security of edge computing has been raised as a major concern since the edge devices are often distributed on the edge of the network, less trustworthy than cloud servers and have limited storage/ computation/ communication resources. Recently, coded computing has been proposed to protect the confidentiality of computing data under edge device's independent attack and minimize the total cost (resource consumption) of edge system. In this paper, for the cooperative attack, we design an efficient scheme to ensure the information-theory security (ITS) of user's data and further reduce the total cost of edge system. Specifically, we take matrix multiplication as an example, which is an important module appeared in many application operations. Moreover, we theoretically analyze the necessary and sufficient conditions for the existence of feasible scheme, prove the security and decodeability of the proposed scheme. We also prove the effectiveness of the proposed scheme through considerable simulation experiments. Compared with the existing schemes, the proposed scheme further reduces the total cost of edge system. The experiments also show a trade-off between storage and communication.
Explainable classification by learning human-readable sentences in feature subsets

We propose a new methodology (Sentences in Feature Subsets, i.e., SiFS) to mine human-readable decision rules from empirical data sets. Unlike opaque classifiers obtained using deep learning, the proposed methodology derives decision rules that are compact and comprised of Boolean logic sentences involving subsets of features in the input data. For this purpose, we develop a new classifier model defined in terms of sets of inequalities among selected features in the input data. To empirically derive suitable inequalities from training data, our approach combines a differentiable representation of sets of Boolean logic sentences, gradient-based optimization of coefficients in the inequalities, a genetic-based algorithm for selection of the subsets of features, and a “goodness” model of sentences to prune and down-select sentences. We present results on synthetic and real-world benchmark datasets to demonstrate efficacy of SiFS in deriving human-readable decision rules. It is seen that SiFS achieves comparable accuracies to the best among various other classification algorithms (accuracies of 95% to 100% on several datasets, F1 scores between 0.95 and 1.0), reasonable computation times (training times of a few seconds for considered datasets), and compact human-readable decision rules (between 1 to 10 sentences of 3 words or less for considered datasets).
Comparison of Malaria Parasite Image Segmentation Algorithm Using Thresholding and Watershed Method

Abstract Malaria is an infectious disease caused by plasmodium that lives and breeds in the red blood cells, transmitted by the Anopheles mosquito. During this time, the paramedics to diagnose symptoms use any imagery that is done manually. In the identification analysis of the malaria parasite cell infection, there is a possibility of human error factor done by paramedics because of the number of samples analyzed. This case is because the human eye tends to be tired while working continuously, leading to misclassification and treatment that is not right. Therefore, it takes a computer-based system that facilitates image processing to paramedics or laboratory technicians to identify the parasite cells and reduce human error instances. This research conducted on identification of the thresholding and watershed of segmentation method for three types of plasmodium parasite, namely Plasmodium falciparum, Plasmodium malaria, and Plasmodium vivax. This study offered modifications thresholding and watershed algorithm. The results showed the success of the technique that can effectively segment on the three types of Plasmodium malaria, which has an accuracy rate above 90% as well as the results of the computation time between the thresholding method could segment imagery for 1-2 seconds and the watershed method intelligent segmented representation for 3-4 seconds.
A semantics and a logic for Fuzzy Arden Syntax

Fuzzy programming languages, such as the Fuzzy Arden Syntax (FAS), are used to describe behaviours which evolve in a fuzzy way and thus cannot be characterized neither by a Boolean outcome nor by a probability distribution. This paper introduces a semantics for FAS, focusing on the weighted parallel interpretation of its conditional statement. The proposed construction is based on the notion of a fuzzy multirelation which associates with each state in a program a fuzzy set of weighted possible evolutions. The latter is parametric on a residuated lattice which models the underlying semantic ‘truth space’. Finally, a family of dynamic logics, equally parametric on the residuated lattice, is introduced to reason about FAS programs.
Optimization of the Online Teaching System Based on Streaming Media

Network and related network technology limit the traditional online teaching activities, making teaching activities only limited to asynchronous teaching, limiting the advantages of real-time, interactive, and vivid online teaching. As a new online teaching network technology, streaming media technology can realize flexible and efficient two-way communication between teachers and students, simulate virtual face-to-face teaching environment, and produce enough emotional resonance for both sides in the corresponding time and space. In view of the poor communication quality and flexibility of current streaming media technology, this paper will build a selective streaming media online teaching architecture based on animation media service platform. The system innovatively uses DXSDK filtering technology to realize real-time audio acquisition and filtering processing and solves the communication quality problem of streaming media online teaching. Aiming at the flexibility of streaming media online teaching, the system adds online auxiliary teaching function, which can realize online text communication, file information management, and learning tracking interaction between teaching and learning. The experimental part is applied to the online teaching activities of a training institution. From the experimental results, the stability and flexibility of the system are greatly improved within a certain range of users, which has obvious practical value. It has high stability and application value.
TempoWatch: a Wearable Music Control Interface for Dance Instructors

We present TempoWatch, a wearable smartwatch-based interface designed to allow dance instructors to control music playback and tempo directly on their wrist via touch gestures using a circular watch display. Dance instructors have unique requirements with respect to music playback in their classes, in particular the ability to stay in position while controlling the playback, and to change speed via time-stretching. However, common stereo decks and mobile music player apps do not support these requirements well. We present the design and architecture of our system, and a qualitative evaluation performed with 9 semi-professional instructors in their own dance classes. Dance instructors were involved in this project from the very beginning to match the system and interface design to its prospective use cases. Results show that instructors are able to use TempoWatch productively after only a short learning phase.
Sensor Data Integrity Verification for Autonomous Vehicles Using Spread 3D Dither QIM

An autonomous vehicle is a modern-day cyber-physical system with multiple sensors connected over its internal network. Many crucial features of the autonomy depend on the data from these sensors that get transmitted over the vehicle network to reach the data processing central modules. Protecting and verifying the sensor data integrity as it traverses the vehicle network is essential for the proper functioning of the autonomous vehicle. We propose a novel approach called spread dither 3D quantization index modulation (QIM) to verify the integrity of LiDAR sensor data used in autonomous vehicle applications. We propose a framework to verify the sensor data integrity in time-critical autonomous vehicle applications based on a data hiding technique called dither modulation. The sensor data is embedded with a watermark using the proposed spread 3D dither modulation at the sensor domain. The embedded sensor data can be directly used by the data processing algorithms due to very low embedded induced distortion. The sensor data integrity can be verified by a parallel process that can decode the embedded sensor data to detect and localize the tampering. Our proposed countermeasure framework is verified with real-life LiDAR data frames against the simulated transmission layer insider attacks on sensor data such as object insertion and deletion. The dither QIM method's optimum parameter values are also deduced through multiple experiments.
Remote cytological diagnosis of salivary gland lesions by means of precaptured videos

The objective of this study was to investigate the feasibility of implementing videos captured by static telecytological applications for remote cytological diagnosis of fine needle aspiration (FNA) specimens from salivary gland lesions.The current study was performed on 102 specimens from patients referred to the Alpha Prolipsis Cytopathology Department for preoperative evaluation of salivary gland lesions. In all cases, surgical excision followed the initial cytological diagnosis. (benign lesions, 11; benign neoplasms, 68; malignant neoplasms, 23). Videos were transferred via file transfer protocol to password-protected accounts for remote review by three independent cytopathologists. In addition to diagnosis, reviewers commented on overall digital video quality. Contributor's and reviewer's diagnoses were collected, recorded and statistically evaluated.Statistical evaluation of cytological diagnoses detected no significant difference in diagnostic accuracy between the diagnoses proffered on the basis of precaptured videos and conventional slides. The overall interobserver agreement was ranging from substantial to almost perfect with κ values of 0.71-0.89.Videos production by static telecytology applications can be used as an alternative method for telecytological diagnosis of salivary glands FNAs. Videos of salivary glands FNAs can be used for accurate diagnosis, educational and second opinion purposes,. They can also be used for archiving, teleconsultation and educational purposes, improving the performance of the already existing static telecytology stations and small cytology departments' quality indices.
Knowledge Graph for the Visualisation of CRM Objects in a Social Network of Business Objects (SoNBO): Development of the SoNBO Visualiser

This paper investigates possibilities to visually support the collaborative design process of Enterprise Knowledge Graphs for Business Application Systems. Starting with a description of the concept of the Social Network of Business Objects (SoNBO), we show how the SoNBO Visualiser supports the visualisation of a company-specific ontology and the resulting knowledge graph. Our previous research showed that existing visualisation tools from the Semantic Web are unsuited for the human involvement that is required in the design of an ontology for a SoNBO. In order to address the lack of a tool, the SoNBO Visualiser was developed using a Design Science Research approach. This paper aims to provide a methodological as well as a technical contribution: The method provides support for the visualisation of the ontology (on two levels) and facilitates the involvement of domain experts (employees) without technical knowledge in the design process. The complementary technical solution connects heterogeneous source systems in a simple configuration process and allows to visualise the ontology and the resulting knowledge graph.
Cyber Insurance Rate Making Based on Markov Model for Regular Networks Topology

Abstract Computers and Internet play a key role in the processes of data transaction, data exchange and data storage in a business. Malicious software or computer viruses is one of the biggest threats that can attack computer networks and cause huge losses due to loss of data and information. As a way to transfer risk, cyber insurance requires precise and appropriate calculations even though many challenges are faced including the effects of differences in network structure. Standards of cyber insurance that have not been established as in the mortality table for life insurance open the possibility to set a standard calculation based on network structure by determining cyber insurance rates. This study uses a general susceptible-infectious-susceptible model with Markovian property to simulate the process of spreading computer virus and calculate the total loss for each computer. Rate making will consider the number of infected neighbours on a node as an exposure to set insurance rates on regular network topology. The simulation process shows that the rates at each node are affected by the probability of initial infection, the degree of each node on the network, and the parameters of infection or recovery.
Development of Smart Card Media for Elementary Students

Abstract Learning medium is a means of communication to ease the transfer of messages during the learning process. Continuous innovation in the learning process shall be conducted to increase the quality of education. This research and development form of research aims to develop the Smart Card media on conservation of living beings subject and know the smart card media qualification on conservation of living beings subject for 4 th -grade elementary students. The development of the smart card uses the ADDIE development model, which involves five stages that includes Analysis, Design, Development, Implementation, and Evaluation. The data is collected by distributing questionnaires of the Likert scale with the range from 1 to 5. The analysis technique of this research uses quantitative data analysis and qualitative data analysis. The result of validating the smart card qualifications by material experts, media experts, teachers, and participants shows that the smart card’s use in the conservation of living beings subject is qualified to be used for 4 th -grade students of elementary school as an alternative learning media.
Deep Neural Networks for Real-Time Remote Fall Detection

We present a pose estimation, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based fall detection method. Our RNN takes time series of 2D body poses as inputs. Each pose is made of 34 numerical values which represent the 2D coordinates of 17 body keypoints and is obtained using a combination of PoseNet [] and a CNN on RGB videos. Each series is classified as containing a fall or not. The proposed method can be configured to be suitable for real-time usage even on low-end machines. Furthermore, the proposed architecture can run completely inside the web browser, making it possible to use it without a dedicated software or hardware. As no data is exchanged with external servers, the proposed system preserves user privacy. Our implementation focuses on the detection of a single individual's fall in a controlled environment, such as an elderly person who lives alone or does not have continuous assistance.
SHELTER – Structural Hyper-resisting Element for Life Threatening Earthquake Risk. An innovative approach for seismic protection

This paper presents an innovative approach to save human lives in the event of an earthquake, even in the case of building collapse, when overall structural strengthening, the ideal solution, is not viable. The proposed solution consists of a seismic shelter for individual apartments, with reasonable costs and fast installation. This approach takes advantage of recent developments in Earthquake Early Warning Systems (EEWS), which may allow for a few tens of seconds of anticipation. Such amount of time is normally insufficient for a safe building evacuation but long enough to get protection within the shelter. The shelter unit mainly consists of a protecting “capsule”, integrated in the architectural layout. The life protection is ensured during the possible building collapse (or just falling debris) by the “capsule” mechanical strength and stiffness together with specific safety systems, namely against severe shock. Besides, the shelter is provided with technical systems and goods to ensure survival with reasonable comfort during the enclosure period, between the earthquake occurrence and the rescue arrival.
Innovation on Machine Learning in Healthcare Services—An Introduction

The healthcare offerings in evolved and developing international locations are seriously important. The use of machine gaining knowledge of strategies in healthcare enterprise has a crucial significance and increases swiftly. In the past few years, there have been widespread traits in how system gaining knowledge can be utilized in diverse industries and research. The organizations in healthcare quarter need to take benefit of the system studying techniques to gain valuable statistics that could later be used to diagnose illnesses at a great deal in advance ranges. Present-day electronic wellness records (EHRs) offer realities to answer clinically noteworthy inquiries. The developing measurements in EHRs make human services ready for the use of gadget acquisition. In any case, acquisition in a logical putting offers exact requesting circumstances that confound the use of ordinary machine becoming acquainted with methodologies. This liquidation fills in as groundwork to enlighten these difficulties and features possibilities for contributors of the device learning network and also discusses the potential of utilizing gadget mastering technology in healthcare and descriptions numerous industry projects the usage of system studying initiatives to make contributions to healthcare area.