Reviewerly/exHarmony huggingface dataset:
Package and Classify Wireless Product Features to Their Sales Items and Categories Automatically

Aiming at automated decision making, this paper defines and analyzes two machine learning use cases for the product process in wireless infrastructure business. The first use case assigns a product to a product packet according to the functionality of the product. The second use case determines the category of the product so that it can be priced. Then, the product is ready for sale. This paper also provides solutions to these machine learning use cases. The solutions are examined with real data from the processes. The credibility of the solutions is also evaluated by comparing the machine learning decisions with the decisions of human users. These human users know the actual assignment and classification of those products. The results show that the solutions work well as they expected. These solutions assign and classify a part of the given products fully automatically with a high confidence and accuracy. Due to insufficient prediction confidences for the rest of the given products, the rest part of products needs to be escalated for the further decision by the human users. With an escalation, a set of assignment and classification options for a given product is also recommended by the solutions. Often, the correct assignment and classification exist in the set of options already. The human users can easily identify and select the correct assignment and classification from the recommended options. Significant costs and processing time can thus be prevented.
Stabilization of atmospheric turbulence-distorted video using complex steerable pyramid

Stabilization of atmospheric turbulence-distorted video not only needs to remove turbulence-induced spatiotemporal distortions but also needs to improve the sharpness. Using complex steerable pyramid (CSP), we propose a method to stabilize the turbulence-distorted video. First, each frame is decomposed into multiscale and multidirectional phases and amplitudes using CSP. Second, a lowpass filter is used to suppress the local phase variations to stabilize the spatiotemporal distorted. Next, we improve the sharpness by selecting and fusing the optimal amplitude. Experimental results demonstrate the proposed method can stabilize the video and improve the sharpness simultaneously.
EXPOSING JMS-BASED TRADING SOA MESSAGING API TO JS WEB CLIENTS

One of our ongoing research paths upon the academic trading system ASETS has been focused on how to expose, in a coherent yet flexible manner, the service oriented architecture functionality of the server side to a broader range of front-end client applications.This paper introduces our development regarding the communication mechanism between JavaScript web client, specifically the map of the Bucharest Stock Exchange (BSE), and the JMS-linked services responsible for creating the market map allocation and feeding the web application with updates, whenever they occur on the server side.ASETS trading architecture consists in a comprehensive collection of services interconnected through a custom-designed message oriented API built on top of JMS.Online financial trading systems require data push for near real-time stock price delivery.The data flow generated asynchronously on the server side has to be pushed to the front end without relying on explicit request from the client.Our solution proposes a gateway component that communicates via websockets with the JavaScript web application, along with an API extension that facilitates in particular the exposure of JMS's pub-sub communication model to JavaScript web clients.
A new method to detect the adversarial attack based on the residual image

Nowadays, with the development of artificial intelligence, deep learning has attracted more and more attention. Whereas deep neural network has made incredible progress in many domains including Computer Vision, Nature Language Processing, etc, recent studies show that they are vulnerable to the adversarial attacks which takes legitimate images with undetected perturbation as input and can mislead the model to predict incorrect outputs. We consider that the key point of the adversarial attack is the undetected perturbation added to the input. It will be of great significance to eliminate the effect of the added noise. Thus, we design a new, efficient model based on residual image which can detect this potential adversarial attack. We design a method to get the residual image which can capture these possible perturbations. Based on the residual image we got, the detection mechanism can help us detect whether it is an adversarial image or not. A serial of experiments has also been carried out. Subsequent experiments prove that the new detection method can detect the adversarial attack with high effectivity.
A Study on Educational Data Mining

Educational data mining is an emerging field that focuses on analysing educational data to develop models to improve learning experiences and improve institutional effectiveness. It provides inherent knowledge about the delivery of education, which is used to improve the quality of teaching and learning. Effective planning can provide personalized education. The mining of educational data develops new methods to discover the knowledge of the educational database and is used for decision making in the education system. This document presents a study on various components of educational data mining along with its objectives. The objective of this document is to present a brief general description of EDM and observe the development in the field of EDM.
Face Recognition using Artificial Neural Networks

In today's networked world, there is need to maintain the security of information or physical property . From time to time we hear about the crimes of credit card fraud, computer breaking's by hackers, or security breaches . This security can be achieved by using passcode , thumb recognition etc . The above techniques are weak in providing security . So we are going for face recognition. In present era of computation face detection is useful in performing face recognition for security purpose . Human Brain can easily analyses vast array of faces from the images formed on the eyes, at the same time it is very difficult for a computer to locate the faces in a digital image . Face detection is one of the basic fundamental pillars of face recognition system, which is very fast growing and challenging in real world applications such as security systems . In this project we are proposing a Face Recognition system using Artificial Neural Networks , where we use Principal Component Analysis for dimensionality reduction of the image and can be represented as the Eigen faces coordinate space , i.e., face image can be divided into a number of pixels and plot them according to eigenvector coordinates. Then the extracted law dimensional feature vectors are considered as the input patterns in Back Propagation Neural Networks to classify the extracted features into one of the possible classes. This project is done in Matlab Face recognition using artificial neural network.In this paper we are designing a Face Recognition system using Artificial Neural Networks.
MACHINE LEARNING CONTENT ADAPTIVE FILTERS FOR IMAGE DE-BLURRING

This paper proposes a novel algorithm to recover blindly a sharp image from its degraded form by pixel patternclassification and filtering by using the deconvolution filters trained for the corresponding pixel pattern. Noiseamplification is a
Video Face Recognition using Deep Learning based Representation

Smart phones and surveillance cameras have initiated research in video face identification. Face recognition in video is extremely significant in the area of law enforcement purposes, the recent advances have reported high accuracies at equal error rates, performance at lower false accept rates require significant enhancement. The decision of verification can be obtained by using a classifier called multilayer neural network. The proposed algorithm yields accuracy of verification about 97% at equal error rate on the database of YouTube faces. We proposed to design a novel video face verification algorithm that uses discrete wavelet transform and entropy computation to select feature-rich frames from a video sequence. This proposed algorithm is implemented and simulated using MATLAB tool.
SISTEM INFORMASI REKAM MEDIS PASIEN RAWAT JALAN DI UPTD PUSKESMAS KUNINGAN BERBASIS WEB

Puskesmas is a level 1 health facility. More than 40% of Indonesia's population uses health services at the Puskesmas. It is interesting that the Puskesmas is the health care provider that is closest to the community. Recording medical records of patients at the Kuningan Health Center is still done manually. Data search has time constraints. This happens because the same data is often found. Ineffective management of medical records will become a major problem in health services at the Puskesmas. This problem must be overcome so that the puskesmas has good data and information. One way to overcome this problem is to build a computerized medical record information system. Medical Record is a compilation of facts about the health and illness of a patient. Medical Records become a very important thing in the delivery of health services. Because the importance of a medical record, the author is interested in conducting research with the title "Information Systems for Outpatient Medical Records in UPTD Puskesmas Kuningan Web-Based". The medical record information system is expected to help improve the function of the Puskesmas as a place of health care. With the existence of a medical record system, each patient visit can be taken in a database making it easier for officers in the process of finding medical record data when needed. With the database, the compilation of patients forgetting to bring a treatment card can be done by searching the patient's data by the electronic officer. Making a report will be easier because it retrieves data that is done through the request system so as to facilitate the process and minimize errors in data management.Keywords: php, mysql, medical record, outpatient
Raw Data Truthfulness and Privacy Preservation in a Practical Data Market

In the era of block chain, ensuring data truthfulness and protecting the privacies of data contributors are both important to the long term healthy development of data markets. On one hand, the ultimate goal of the service provider in a data market is to maximize their profit. Yet, to reduce operation cost, a strategic service provider may provide data services based on the whole raw data set, or even return a fake result without processing the data from designated data sources. The content of raw data should not be disclosed to data consumers to guarantee data confidentiality, even if the real identities of the data contributors are hidden. TPDM is structured internally in associate degree Encrypt-then-Sign fashion, using partially homomorphic encryption and identity-based signature. It at the same time facilitates batch verification, data processing, and outcome verification, while maintaining identity preservation and data confidentiality. We additionally instantiate TPDM with a profile matching service and an information distribution service, and extensively evaluate their performances
Types of IP Pledges

Firms dealing with Intellectual Property (IP) have always challenged theories that aim to explain why and how companies use Intellectual Property Rights (IPRs). Existing licensing-theories cannot explain why firms like Tesla Motors give away their patents for free (i. e. without demanding royalty payments). Especially the rise of open innovation and open-source software caused some rethinking of long-established aspects within an organization, including their attitude towards IP strategies. The announcement made by Tesla in 2014 triggered an ongoing debate inside and outside the academic world and has often been coined an Open IP Strategy or IP Pledge . While Tesla’s IP Pledge might be the most popular one, it is not the only one. A less known example for freely available IPRs is the bundle of patents and trademarks relating to the QR-Code technology. Even though such IP Pledges become increasingly frequent, they are not new. The rise of IP Pledges, along with the increase of other IP licensing approaches such as patent pools, requires some rethinking of current IP licensing theories and definitions. This demand for more research has been recognized by many scholars, some of which have produced seminal works in the area of IP Pledges. The rapid and simultaneous research, however, is followed by ambiguity: Academic scholars and industry professionals utilize terms, such as IP Pledges and Open IP, inconsistently. For some, only IPRs that are free of charge and available to the unrestricted public constitute an IP Pledge. Others include the access to IPRs on reasonable licensing-fees available only to a certain community in their definition. This inconsistency hinders the comparison of academic work and impedes communication between IP professionals.
On the Design and Architecture of Deployment Pipelines in Cloud- and Service-Based Computing - A Model-Based Qualitative Study

DevOps and Continuous Delivery (CD) are becoming the de-facto standard for software deployment in the cloud. Deployment pipelines are a core artefact in such practices, but so far their design is largely discussed informally, and existing sources on DevOps and CD practices are often inconsistent or incomplete. The lack of a generic, complete, tool-agnostic, and application-independent treatment of deployment pipeline design and architecture impedes their understanding by human designers and the creation of generic tools that work across different technologies and applications. To alleviate this problem, we have performed a qualitative, in-depth study of 25 deployment practice descriptions by practitioners containing informal deployment pipeline models. From our study we derived a precisely specified model of deployment pipeline architectures. We can show that the formal model substantially increases the precision of the modelling compared to the informally modelled pipelines in the original sources.
Competent QR Code Scanning and Finger Print Matching-Strategy to Overcome IRCTC Reservation Incommodity

Indian railroad is the world's biggest human transport framework which is as of now managing a ton of issues. For instance, unlimited lines, wastage of papers, loads of assets and staff usage. Furthermore there is no such structure in Indian railroad that instantly offers accommodation for holding up once-over voyager in the midst of their voyage. This framework proposes a modern ticket reservation and reallocation considering the benefit of QR code and finger print. . In this framework, passenger holds the ticket, utilizing his/her AADHAR number and the ticket is created alongside the QR code. The QR code contains the data about the ticket and furthermore the seat number of the passenger. In addition, the correct seating position of the passenger is confirmed with the help of Fingerprint technology. RSA-AES calculation is utilized for encryption process. MYSQL Database is used for securing the passenger's information. Our structure is dexterous for diminishing the manual checking and affirmation process done by Traveling Ticket Examiner (TTE).
Some Useful Results Related with Sampling Theorem and Reconstruction Formula

In this paper, we present some useful results related with the sampling theorem and the reconstruction formula. The first of them regards a relation existing between bandwidths of interpolating functions different from a perfect-reconstruction one and the bandwidth of the latter. Furthermore, we prove here that two non-identical interpolating functions can have the same  bandwidths if and only if their (same) bandwidth is a multiple of the bandwidth of an original unsampled signal. The next result shows that sets of sampling points of two non-identical (but not necessarily interpolating) functions possessing different bandwidths are unique for all sampling periods smaller or equal to a given period (calculated in a theorem provided). These results are completed by the following one: in case of two different signals possessing the same bandwidth but different spectra shapes, their sets of sampling points must differ from each other.
Compact Catadioptric Wide Imaging with Secondary Planar Mirror

Wide FOV imaging systems are important for acquiring rich visual information. A conventional catadioptric imaging system deploys a camera in front of a curved mirror to acquire a wide FOV image. This is a cumbersome setup and causes unnecessary occlusions in the acquired image. In order to reduce both the burden of the camera deployment and the occlusions in the images, this study uses a secondary planar mirror in the catadioptric imaging system. A compact design of the catadioptric imaging system and a condition for the position of the secondary planar mirror to satisfy the central imaging are presented. The image acquisition model of the catadioptric imaging system with a secondary planar mirror is discussed based on the principles of geometric optics in this study. As a backward mapping, the acquired image is restored to a distortion-free image in the experiments.
A Multiple Mitosis Genetic Algorithm

&lt;p&gt;Genetic algorithm is a well-known metaheuristic method to solve optimization problem mimic the natural process of cell reproduction. Having great advantages on solving optimization problem makes this method popular among researchers to improve the performance of simple Genetic Algorithm and apply it in many areas. However, Genetic Algorithm has its own weakness of less diversity which cause premature convergence where the potential answer trapped in its local optimum. This paper proposed a method Multiple Mitosis Genetic Algorithm to improve the performance of simple Genetic Algorithm to promote high diversity of high-quality individuals by having 3 different steps which are set multiplying factor before the crossover process, conduct multiple mitosis crossover and introduce mini loop in each generation. Results shows that the percentage of great quality individuals improve until 90 percent of total population to find the global optimum.&lt;/p&gt;
AUTOMATING USABILITY EVALUATION OF VISUAL USER INTERFACES IN THE TILE LOGIC

This article presents the methodology of formal automated evaluation of user interface usability.The tiles logic was selected as the language for solving problems in formal usability verification.The paper contains an overview of the related works, explains the reasons for using the tile logic, and considers the principles of verification (proof) of usability as validation of agent's interaction.The differences and advantages of the developed approach from the well-known are shown.A description of the agents interaction in the tile logic is used to automate usability evaluation.The methodology of formal automated usability evaluation is demonstrated by describing the interaction of two simple agents in the tile logic.In conclusion, we present future of use and development of the proposed approach.
Design Azure Chatbots

In this chapter, we learn how to design Azure chatbots. We start with an introduction to the Microsoft Bot Framework and examine the start-to-finish workings of chatbots and the requirements necessary for creating Azure chatbots. Last, we look at a real implementation of an Azure chatbot.
Comparison of Different Image Enhancement Methods for Effective Whole-Body Bone Scan Image

Nuclear medicine is one of the most important diagnostic tools used for different types of investigations such as a thyroid scan, renal function and whole-body bone scan.The main problem in nuclear medicine imaging system is the resulting images degraded with large amounts of noise.In this work we want use four different enhancement methods to enhance wholebody bone scan image so as to reduce the noise from the image and improve the resolution to achieve a better image quality and maintain quality for accurate diagnosis.Histogram equalization, adaptive histogram equalization, log transformation and gamma correction algorithms were used to improve the image quality.Four pair of bone scan images from gamma camera were used to perform this work.Enhanced images were quantified and evaluated by calculating the Peak Signal Noise Ratio, Mean Square Error and entropy.The result shows that the gamma correction algorithm gives best result among the four algorithms used for enhancing the bone images.The gamma correction algorithm can assist the radiologist in diagnosis the patient and quantify any changes accurately and quickly.
Increasing Cryptographic Security Using Message Authentication Code (MAC)

The study dealt with efficiency and extensive use of the Hash-based Message Authentication Code (HMAC) function in well-known protocols like IP-Sec and SSL and its use in identifying the authenticity of the message using the studies conducted.With the spread of using HMAC, the security of this algorithm becomes important and can greatly help cryptographic community.In cryptography using HMAC, one can ensure the authenticity and validity of a message.HMAC creates the MAC of the message in question using a cryptographic key and a hash functions and adds it to the message intended to be sent to the receiver.The packet is verified if the message authenticity code is identical to the message on the receiver's side.Nowadays, HMAC is widely used in popular security protocols like Ip-Sec, Transport Layer Security (TLS), and Secure Sockets Layer (SSL).
Enhanced K-Classifier - A Framework to Measure the Reusability Metrics of Software

Reusability can be defined as a use of existing assets in some other form within the process of software product development. In other words, reusability of the component indicates the level to which the component can be reused in the other as well as same component based applications. On the basis of metrics which contribute towards the reusability factor, the training samples are prepared with the help of metric plug in. This research work is focused on building a framework which will help to measure the degree of reusability by using K means Classifier. The reusability of a component is calculated by using various samples and each of which falls in a category or a cluster like high to low reusability cluster. The proposed technique automatically calculates the reusability of the testing samples which are given as an input to the classifier with the help of enhanced K-means classifier.
Evaluation of automatic collocation extraction methods for language learning

A number of methods have been proposed to automatically extract collocations, i.e., conventionalized lexical combinations, from text corpora.However, the attempts to evaluate and compare them with a specific application in mind lag behind.This paper compares three end-to-end resources for collocation learning, all of which used the same corpus but different methods.Adopting a goldstandard evaluation method, the results show that the method of dependency parsing outperforms regex-over-pos in collocation identification.The lexical association measures (AMs) used for collocation ranking perform about the same overall but differently for individual collocation types.Further analysis has also revealed that there are considerable differences between other commonly used AMs.