Reviewerly/exHarmony huggingface dataset:
Nagios Core Optimization By Utilizing Telegram as Notification of Disturbance

Network Monitoring System (NMS) is a system that is highly demanded internet service provider industry in this fast-developing information technology era. The availability of NMS is the best option to restore the service level agreement as a means to compete with other internet service providers’ competitors. The occurrence of disturbance in the network is often unnoticed by the network administrator. This may lead to a crucial problem in decreasing network quality as the impact of time-consuming in solving the problem. Through the explanation, the writer tried to anticipate by classifying problems using Pareto and integrated Nagios with Telegram Messenger as a notification of disturbance. Nagios has many features such as reports, event handlers, monitoring resources (CPU load, memory usage, status up / down, uptime, data traffic, bandwidth), etc. One notable feature owned by Nagios is blast notification of disturbance. It is a feature that will function when one of the devices is in trouble. This feature will inform the network administrator or authorized person in certain divisions as regards the error network. In this case, the problematic device can be categorized according to the parameters made by the network administrator.
A Novel Approach of Lung Tumor Detection Using Deep Neural Network

Object detection is an important and complex task in computer vision because detection requires precise location of objects. This creates two problems: the first is that many locations (“proposals”) must be processed. The second is that the approximate location of the proposals must be specified to obtain a specific location. These articles present an approach to detecting a lung tumor. Our goal is to use the ASM (Active Form Model) and try to correct its defects related to the learning phase. To do this, we proposed the development of automated markers in which we integrate the DNN (deep neural network) learning method and automated storage at the detection stage. Finally, we evaluated the performance of the classifier according to certain criteria such as RMS (mean square error). Experimental results show that the deep neural network has archived the best performances with precision and sensitivity, specificity. These recent developments not only allow a better diagnosis but also offer new hopes of treatment for many diseases (cancer, epilepsy... etc.).
Analysis of Segmentation Algorithms for Detection of Anomalies in MR Brain Images

The role of segmentation is vital in image processing for the extraction of region of interest. In the perspective of medical images, the region of interest corresponds to anatomical organs or anomalies such as tumor, cyst. This work analyses various algorithms for the analysis of MR brain images. The clustering-based segmentation technique was found to be efficient and for the validation of results, performance metrics like Jaccard Index, rand index, false positive, and false negative are used. The segmentation algorithms are tested on real-time MR brain and brain web database images. The algorithms are developed in Matlab 2010a and the goal of this research work is to guide the researchers for choosing appropriate algorithm for the analysis of MR brain images.
Having Your Cake and Eating it Too: Training Neural Retrieval for Language Inference without Losing Lexical Match

We present a study on the importance of information retrieval (IR) techniques for both the interpretability and the performance of neural question answering (QA) methods. We show that the current state-of-the-art transformer methods (like RoBERTa) encode poorly simple information retrieval (IR) concepts such as lexical overlap between query and the document. To mitigate this limitation, we introduce a supervised RoBERTa QA method that is trained to mimic the behavior of BM25 and the soft-matching idea behind embedding-based alignment methods. We show that fusing the simple lexical-matching IR concepts in transformer techniques results in improvement a) of their (lexical-matching) interpretability, b) retrieval performance, and c) the QA performance on two multi-hop QA datasets. We further highlight the lexical-chasm gap bridging capabilities of transformer methods by analyzing the attention distributions of the supervised RoBERTa classifier over the context versus lexically-matched token pairs.
Robustness Analysis Framework for High-Performance Data Centers based on Immune Genetic Algorithm

Robustness analysis framework for the high-performance data centers based on immune genetic algorithm is implemented in this paper. The server equipment of the data center mainly includes the general computing server and the storage server. At present, the most widely used core energy consumption model of server equipment in data center is mainly additive model and system utilization model. We propose the 2 primary novelties. (1) Extend the constant number of data block copies of the quantum-aware storage strategy to a flexible and variable data block storage strategy under the premise of ensuring the availability of data blocks. (2) For files with high access frequency, the frequent reading of multiple demand ends of a single active data block will cause congestion of transmission channels and reduce the accuracy of basic data reading. The simulation has proven the model’s robustness.
Seamless Payment System Using Face And Low-Energy Bluetooth

This paper introduces a multi-modal authentication approach for payments using a face image and the low energy Bluetooth (BLE) signal of a user's device. Devices of registered users transmit temporally changing one-time identifiers. During a payment request, the query face image is only matched with database entries corresponding to nearby users, thereby significantly reducing the complexity of the task. For cases in which a user does not carry their device, the system includes a fallback mechanism to PIN-based two-factor authentication. A classifier on depth data input is used to reduce vulnerability to presentation attacks. We conducted a user study of different payment methods and demonstrated our system at a public event with 951 users.
Context Intelligent Crypto-Switching Algorithm for Document Security in Cloud Environment

Data privacy and security is a primary concern for any government/non-government organizations. This organizational data primarily exists in the form of textual documents. These documents aids an organization for executing their day-to-day operations. Further these documents may contain some sensitive information which needs additional security. For the purpose of storing the documents, local file systems pose a restriction in terms of storage capacity moreover they are vulnerable to physical damage. Large organizations need hefty amount of data to be stored hence cloud storage is preferred. The proposed methodology incorporates the use of finding the the context of the given document using a Recurrent Neural Network algorithm. Depending on the context a severity score is obtained which acts as a deciding factor for the selection of an appropriate encryption algorithm. A suite of high level encryption algorithm such as AES is used depending on the sensitivity of the document.
Developing An Expert System Of Palm Oil Plant Disease

Oil palm cultivation requires the right fertilizer for maximum production. Expert system with Forward Chaining method is applied in determining the type of disease for palm oil. Expert systems are housed in a knowledge base and processed to obtain information to help humans solve problems. This expert system is used to detect types of diseases in increasing the productivity of palm oil. Expert system designed to provide accuracy and speed in finding solutions to oil palm disease.
Providing Digital Infrastructure for Audio-Visual Linguistic Research Data with Diverse Usage Scenarios: Lessons Learnt

This article describes the development of the digital infrastructure at a research data centre for audio-visual linguistic research data, the Hamburg Centre for Language Corpora (HZSK) at the University of Hamburg in Germany, over the past ten years. The typical resource hosted in the HZSK Repository, the core component of the infrastructure, is a collection of recordings with time-aligned transcripts and additional contextual data, a spoken language corpus. Since the centre has a thematic focus on multilingualism and linguistic diversity and provides its service to researchers within linguistics and other disciplines, the development of the infrastructure was driven by diverse usage scenarios and user needs on the one hand, and by the common technical requirements for certified service centres of the CLARIN infrastructure on the other. Beyond the technical details, the article also aims to be a contribution to the discussion on responsibilities and services within emerging digital research data infrastructures and the fundamental issues in sustainability of research software engineering, concluding that in order to truly cater to user needs across the research data lifecycle, we still need to bridge the gap between discipline-specific research methods in the process of digitalisation and generic digital research data management approaches.
Event-Triggered Particle Filter for State Estimation of Non-linear Stochastics Hybrid System with Missing Measurements

The precision of state estimation is subjected to correct state prediction for the multi-model system. Our proposed algorithm is able to detect the State transition govern by event detection for Non-linear Stochastics Hybrid system. In this paper, a novel algorithm based on particle filter for the non-linear stochastic hybrid system called Event Trigger Particle filter based on reallocation resample method (RRPF), which detect the state transition of Hybrid System to adapt itself for accurate state estimation is presented. Target Tracking benchmark test model of the non-linear system example is used to check the performance of the proposed algorithm. Simulated results show the competence of the proposed algorithm.
A Probabilistic Model for User Interest Propagation in Recommender Systems

User interests modeling has been exploited as a critical component to improve the predictive performance of recommender systems. However, with the absence of explicit information to model user interests, most approaches to recommender systems exploit users activities (user generated contents or user ratings) to inference the interest of users. In reality, the relationship among users also serves as a rich source of information of shared interest. To this end, we propose a framework which avoids the sole dependence of user activities to infer user interests and allows the exploitation of the direct relationship between users to propagate user interests to improve system's performance. In this paper, we advocate a novel modeling framework. We construct a probabilistic user interests model and propose a user interests propagation algorithm (UIP), which applies a factor graph based approach to estimate the distribution of the interests of users. Moreover, we incorporate our UIP algorithm with conventional matrix factorization (MF) for recommender systems. Experimental results demonstrate that our proposed approach outperforms previous methods used for recommender systems.
Quality and Its Assurance

Shortly after the end of World War II, Kiichiro Toyoda claimed that if Japanese automobiles could not compete with foreign counterparts in terms of cost and quality, this meant “the death of the management of the [Japanese] automobile industry” (Wada 1999, p. 522). Once Toyota somehow managed to produce the automobile, the issue of automobile quality was recognized as an important management issue.
Modular panel house design with prefabricated production technology

Abstract The purpose of this study is to get efficient home needs from modular panel houses at low cost and have a comfort factor and describe the design of the modular panel type house. This research method used the descriptive quantitative method, with research subjects describing the modular panel house design with its criteria. With the modular panel house building method would be easier with low costs compared to conventional because the industrial process was carried out with prefabrication, and pay attention to ecological factors in the building. Designed houses of modular panel house are built at low cost, taking into account thermal comfort factors and have eco-architectural values so that they can influence people’s interest in this type of house.
A Semantic Calculus

With the continuing goal of discovering a theoretical foundation to have information systems think more like humans, this paper shows that as information systems explode in size and complexity, the notion that technologies such as Big Data, Deep Neural Networks, Graphic Processing Units, Artificial Intelligence and Machine Learning can keep up to the pace that information systems are exploding, has failed. 80% of the trillions of petabytes being stored on information systems every day, are being stored by unsophisticated end users, and that's just the start of the chaos. In essence we are drowning in data and we need a new technology to take us the surface so we can breathe. In this paper, the author explores the viability of combining sentiment analysis, and semantic intelligence into the paradigm of a common-sense semantic calculus, specifically designed for Information Systems.
An Effective Feature Extraction Based Particle Swarm Optimization with Support Vector Machine for Biomedical Mammogram Image Diagnosis

Breast cancer is a second significant reason for the increased mortality rate of women in both developing and developed countries. When abnormalities in breast cancer are identified in the earlier stage, there is a greater chance to increase the survival rate. This paper presents a new breast cancer diagnosis model using feature extraction and classification process. The presented model involves preprocessing, Hough transforms based feature extraction, particle swarm optimization (PSO) with support vector machine (SVM) called PSO-SVM based classification. Initially, preprocessing takes place to remove the noise present in the image. Then, Hough transform based feature extraction process is carried out to extract the features exist in the image. Then, the PSO-SVM model is applied to classify breast cancer images into normal and abnormal. The validation of the presented PSO-SVM model takes place on MIAS dataset and the experimental outcome indicated that the presented model achieved a maximum accuracy of 94.61%.
Multiresolution Mixture Generative Adversarial Network For Image Super-Resolution

With regard to the problem of image super-resolution (SR), generative adversarial network (GAN) can make generated images have more details and better effect on perceptual quality than other methods. However, GAN-based methods may lose the contour of object in some texture-intensive areas. In order to recover contour better and further enhance perceptual quality, we propose a Multiresolution Mixture Generative Adversarial Network for Image Super-Resolution (MRMGAN), which employs a multiresolution mixture network (MRMNet) for image super-resolution. The MRMNet is able to have multiple resolution feature maps at the same time when training. Meanwhile, we propose a residual fluctuation loss, which aims to reduce the overall fluctuation of residual between SR image and high-resolution (HR) image. We evaluated the proposed method on benchmark datasets. Experimental results show that the proposed MRMGAN can get satisfactory performance.
BaKer-Nets: Bayesian Random Kernel Mapping Networks

Recently, deep spectral kernel networks (DSKNs) have attracted wide attention. They consist of periodic computational elements that can be activated across the whole feature spaces. In theory, DSKNs have the potential to reveal input-dependent and long-range characteristics, and thus are expected to perform more competitive than prevailing networks. But in practice, they are still unable to achieve the desired effects. The structural superiority of DSKNs comes at the cost of the difficult optimization. The periodicity of computational elements leads to many poor and dense local minima in loss landscapes. DSKNs are more likely stuck in these local minima, and perform worse than expected. Hence, in this paper, we propose the novel Bayesian random Kernel mapping Networks (BaKer-Nets) with preferable learning processes by escaping randomly from most local minima. Specifically, BaKer-Nets consist of two core components: 1) a prior-posterior bridge is derived to enable the uncertainty of computational elements reasonably; 2) a Bayesian learning paradigm is presented to optimize the prior-posterior bridge efficiently. With the well-tuned uncertainty, BaKer-Nets can not only explore more potential solutions to avoid local minima, but also exploit these ensemble solutions to strengthen their robustness. Systematical experiments demonstrate the significance of BaKer-Nets in improving learning processes on the premise of preserving the structural superiority.
Using directed graphs to define viewpoints to keep a metamodel, an architecture framework and views using different modeling languages consistent

Abstract Keeping an architecture description of a complex system consistent is difficult. View content is specified by viewpoints which draw upon an underlying metamodel. Viewpoints need to define consistency rules. This becomes increasingly hard as the metamodel size and the number of viewpoints increase. Use of an architecture description language (ADL) such as the Unified Modeling Language (UML) complicates this further because its metamodel may not be able to implement the triples in the AD metamodel and therefore one or more views requiring use of an additional ADL. Even for an architecture framework with a small footprint, such as TRAK, it is a time‐consuming and error‐prone process to keep the metamodel, viewpoint definitions, and implementation consistent. To tackle these TRAK has been modeled by creating views against five viewpoints. These viewpoints can be used to define a metamodel, define the viewpoints for an architecture framework, assess the implementation using one or more ADLs, and record model changes over time. The viewpoint definitions and views are wholly graph‐based and provide a mechanism to define allowed and minimum view content. Views and viewpoints can be read as simple assertions requiring no technical knowledge and support query and analysis. The viewpoint definitions have been validated against a model of TRAK held in a Neo4J graph database. This showed that the TRAK metamodel contains c 750 triples not 500 as had been previously thought. It also identified metamodel elements missing from one of the UML profiles. Example views are provided together with CYPHER queries.
Bayesian Learning for Neural Network Compression

Quantization on weight parameters in neural network training plays a key role for model compression in mobile devices. This paper presents a general M-ary adaptive quantization in construction of Bayesian neural networks. The trade-off between model capacity and memory cost is adjustable. The stochastic weight parameters are faithfully reflected. A compact model is trained to achieve robustness to model uncertainty due to heterogeneous data collection. To minimize the performance loss, the representation levels in quantized neural network are estimated by maximizing the variational lower bound of log likelihood conditioned on M-ary quantization. Bayesian learning is formulated by using the multi-spike-and- slab prior for quantization levels. An adaptive quantization is derived to implement a flexible parameter space for learning representation which is applied for object recognition. Experiments on image recognition show the merit of this Bayesian model compression for M-ary quantized neural networks.
Differential Privacy Optimal Consensus for Multiagent System by Using Functional Perturbation

Optimal consensus algorithm is affective algorithm using for distributed system, which makes each agent realize consensus and minimize the cost function. However, cooperative control requires that the agents exchange information in the public channel, which means that the important information disclosure may appear. To solve the problem, in this paper, we propose a differential privacy optimal algorithm by adding functional perturbation. By using the convex properties, the mean square consensus conditions are obtained. In addition, we analyze the privacy-preserving level and compute the sensitivity of the differential privacy at the same time. At last, the simulation is given to show the effectiveness of the theoretical results.
Training Capsule Networks with Various Parameters

Nowadays convolutional neural networks (CNNs) have produced the state-of-the-art performance in image classification and segmentation tasks. The efficiency of the neural networks is one of the bests when the testing samples are close to the training data. Nevertheless, if we make some transformation on the dataset, the performance of the convolutional neural network reduced. Recently, capsule networks (CapsNet) have been introduced to solve some of the problems of neural networks. In this paper we examine the effectiveness of three different capsule based neural networks, and compare the performance when the parameters of the dynamic routing algorithm and the squashing function are modified.
Optimizing Production Planning for Tempe Babe Within Goal Programming Method

Abstract Production planning is the most important part of the production process in Tempe Babe. The material consideration in the production planning in this research is the optimizing of the amount of production in fulfilling the number of tempe demand, optimizing of the amount of profit Tempe obtained and the minimization of the amount of raw materials used for Tempe production. The implementation of the goal programming method can provide solutions for optimizing production planning on Tempe Babe, where the deviational variables in goal programming serve to demonstrate the possibility of negative and positive deviations from right side value of the destination function. The results of calculation analysis with goal programming, indicating the excess amount of product production will be added to the product that has the biggest advantage and the excess amount of production will be added to the product that has the number of requests Optimal profit from each product resulting in the amount of prediction of Tempe production. The results of this research provide the optimum solution for production planning on Tempe Babe every month. In addition, this study also resulted in a new mindset to find production planning models in the IoT environment as a continuation of this research.
Neural Network adaptability from 2D to 3D Chess

The adaptability and competitiveness of neural networks has been the focus of much research. The most popular applications of neural networks are in classic games like 2D chess. The focus of this paper is to study the adaptability of a neural network that was trained on chess boards of different dimensions (2,3). We define and formalize chess in N-dimensions, including board, pieces and legal moves. The paper compares a classic chess algorithm as a benchmark for both training and checking the progress of the neural network. We provide an experimental comparison of the two algorithms playing N-dimensional chess.