Reviewerly/exHarmony huggingface dataset:
An Integrated Disease Recommendation Framework for Multi-label Unstructured Data Sources

Usage of strong historical data in electronic health record (EHR), it is an Integrated Disease Recommendation Framework for Multi-label Unstructured Data Sources, a generalized model that covers the conditions that have been observed and medication uses. An Integrated Disease Recommendation Framework is a period model that uses Recurrent Neural Network (RNN) was developed and stamped at longitudinal time to EHR data from 260K patients over 8 years. The patient history to make multi - label forecasts The Framework assesses the patient history yet another label for each diagnosis or prescription type. Based on an assessment of the entire lab test collection, the system will conduct diagnostic differentials with up to 79% recall, substantially above several baselines. They also demonstrate great generalizability of Framework by acclimating the resulting models from one organization to another without losing significant accuracy.
A Deep Learning Technique for Web Phishing Detection Combined URL Features and Visual Similarity

The most popular way to deceive online users nowadays is phishing. Consequently, to increase cybersecurity, more efficient web page phishing detection mechanisms are needed. In this paper, we propose an approach that rely on websites image and URL to deals with the issue of phishing website recognition as a classification challenge. Our model uses webpage URLs and images to detect a phishing attack using convolution neural networks (CNNs) to extract the most important features of website images and URLs and then classifies them into benign and phishing pages. The accuracy rate of the results of the experiment was 99.67%, proving the effectiveness of the proposed model in detecting a web phishing attack.
Visual Secret Sharing Scheme for Digital Image Watermarking

Traditional visual secret sharing (VSS) strategieshide secret images in shares that are either imprinted on transparenciesor are encoded and put away in a digital form. Theshares can appear as noise-like pixels or as significant pictures;but it will arouse suspicion and increase interception risk duringtransmission of the shares. Subsequently, VSS schema experiencefrom a transmission risk problem for the secret itself and for themembers who are associated with the VSS conspire. To addressthis issue, proposed a advanced technique for advanced digitalwatermarking using a texture and also a natural-image-basedVSS scheme (VSS scheme) that shares secret images via variouscarrier media to protect the secret and the participants duringthe transmission phase. Devise the texture synthesis processinto digital image to hide secret messages. In comparison tousing an existing cover image to hide messages, our algorithmhides the source texture image and embeds secret messagesthrough the process of watermarking. The regular offers can bephotographs or hand-painted pictures in computerized structureor in printed structure. We likewise propose potential approachesto conceal the key to diminish the transmission chance issue forthe offer. Test results demonstrate that the proposed approach isan amazing answer for taking care of the transmission chanceissue for the VSS technique.
Personal Development - A Driving Force Of Changing The Educational Organization

This paper presents the role and importance of personal development of human resources in the context of organizational change in the pre-university education system. Personal development is essential to all actors involved in the educational act and causes changes from the individual level to the organizational level. The teacher has to cope with all the confrontations that take place in the process of change at the organizational and educational level, but also in the relationships with others as well as with oneself.
*WINNER* Optimal Fredkin Gate Designs for Logical Operations with Two and Three Inputs

Conservative and reversible logic gates are widely known to be compatible with and exhibit great efficiency on revolutionary computing paradigms such as low power, optical, and quantum computing. The fundamental conservative reversible logic (CRL) gate is the Fredkin gate. Functionally complete, Fredkin gates can implement any digital logic function, including those commonly used in classical computing. While Fredkin gates have been used by some researchers to build assorted logic functions, a comprehensive study of Fredkin gate implementations for logical operations has never been undertaken. This work describes a systematic study to discover efficient implementations of traditional computing functions using the Fredkin gate. The result of the study has yielded the optimal implementations of all two-input and the most common three-input logic operators.
Pengembangan Media Pembelajaran E-Modul Bank Dan Sistem Pembayaran Berbasis Android Untuk Peserta Didik Kelas X

This development research carried out against the lack of understanding of the material in students in economics. The purpose of this development research is to increase students' understanding through the use of e-module learning media based on android. This research uses research and development (R&D) with ADDIE models. The research design used is the One Group Pre-test-Post-test Design with research subjects in class X IPS students for testing android based e-module learning media products. The results of limited trials as part of the development phase have received excellent results. Students' understanding was measured using gain score analysis which obtained a value of 0.57 with moderate criteria. There are significant differences in the test results between before and after using e-modules in student learning. DOI: https://dx.doi.org/10.17977/UM014v14i12021p001
Research on Translation Processing of Complex Long Sentences Based on Multi-strategy Analysis Algorithm

Abstract Syntax analysis depends on a certain grammatical theory. In formal grammar system, dependency grammar has gradually become the focus of researchers. In this paper, an automatic translation method of English-Chinese terms based on multi-strategy fusion is proposed, which uses the term case base to optimize the translation of terms with low evaluation scores obtained from the translation quality estimation method. Through the levenshtein algorithm, we find a number of terms translation examples with high similarity with the source language of the terms to be optimized, including grammatical semantic features, sentence length, punctuation marks, functional words and context conditions, etc., to segment and simplify complex long sentences and compound generate translations. By designing the same knowledge representation for different languages, the algorithm can be universally used for translation systems of different languages.
Optimasi Business Process Improvement Berbantuan Metode FLASH dengan Integrasi API Trello

The emergence of the COVID-19 case has a major impact on all sectors. At this critical time, customer satisfaction can be done by optimizing the use of resources by implementing BPI. The BPI method will conduct a review regarding the resources owned and will be adjusted to the current conditions. BPI is closely related to changes in project management. One of the optimal methods used in project management is FLASH, where the project duration will be in the form of a more flexible time interval. In this research, the system also uses Trello as a project management application. The purpose of this research is to design a scheme for calculating the duration of the project, as well as mapping project management from the data tasks that are owned using the FLASH method. System testing on the fuzzy system algorithm is implemented on the AOA network. Based on the calculation on the test object carried out, the probability of completing the project on time is 76%. This amount is obtained from the average delay factor for each task. With these values, scheduling using the FLASH method obtained the fastest duration is 19 days and the latest is 30 days.
PENYUSUNAN ENSIKLOPEDIA BERBASIS RISET PENGARUH LIMBAH ORGANIK TERHADAP PERTUMBUHAN JAMUR TIRAM (Pleurotus ostreatus) PADA MATERI BIOTEKNOLOGI KELAS XII SMA

This research aims to determine the research-based preparation of the influence of organic waste on Oyster mushroom growth (Pleurotus ostreatus). The research methods for data collection used was a completely randomized design and used are Research and Development (R&D). The Model used is 4-D modification with 3 stages: (1) definition , (2) Design, and (3) Develop. The results showed the encyclopedia of research-based organic waste influences on oyster mushroom growth (Pleurotus ostreatus) categorized as valid or excellent with a value of 90% by the expert validator. Keywords : Organic waste, lignocellulose, oyster mushrooms .
Gradient-domain image reconstruction framework using chroma-preserving intensity-range and base-structure constraints

We present a gradient-domain image reconstruction framework with the chroma-preserving pixel-wise intensity-range constraint and the base-structure constraint. The existing methods for manipulating base structures and detailed textures are classifiable into two major streams: gradient-domain and layer-decomposition. To generate detail-preserving and artifact-free output images, we combine the two approaches’ benefits into the proposed framework by introducing the chroma-preserving intensity-range constraint and the base-structure constraint. To preserve details of the input image, the proposed method takes advantage of reconstructing the output image in the gradient domain, whereas the output intensity is guaranteed to lie within the specified intensity range by the intensity-range constraint. The reconstructed image lies close to the base structure by the base-structure constraint, which is effective for restraining artifacts. Using this chroma-preserving pixel-wise luminance constraint, the proposed algorithm does not require post-processing such as intensity clipping or rescaling. The proposed framework directly generates the output luminance, which guarantees that the output RGB intensities are within the target intensity range, preserving the chromatic component. Experiments demonstrated that (1) the proposed framework is effective for various applications such as tone mapping, seamless image cloning, detail enhancement, and image restoration, and (2) the proposed framework can preserve chroma components compared with the existing methods.
PERENCANAAN INSTALASI AIR BERSIH DAN AIR LIMBAH PADA GEDUNG CONTINUING PROGRAM DEVELOPMENT UNIVERSITAS NEGERI SURABAYA

Human activity in a building is closely related to the use of clean water. Planning for clean water and wastewater installations at the Continuing Program Development Program Building State University of Surabaya is carried out in order to meet water needs and the disposal process, as well as to estimate the budget. The method used is hydraulic analysis with SNI 03-7065-2005 as standard. The data required are technical specifications, design drawings, designed planning facilities and the Surabaya City HSPK in 2019. The need for clean water is determined using the number of users on each floor of the building. The planning of clean water pipe dimensions uses the Hazen-William method, while the dimensional planning of wastewater pipes and ventilation pipes uses the unit load plumbing method. The results of the calculation of large clean water needs 222.64 m3 / hour; clean water pipe dimensions range ¾ "- 5"; black water pipe diameter 4 "and grey water pipe diameter range 1" - 2 "; vent pipe diameter 2 "and 4"; Plumbing installation planning costs Rp. 2,481,776,832.00.
IMPLEMENTATION OF ANDROID BASED PHOTOGRAPHIC BASIC LEARNING MEDIA

SMK Cendekia Madiun is one of the multimedia-based schools, there is learning photography in it. The learning is not optimal, seen from the number of SMK Cendekia graduates who choose photography as a profession a little, for that researchers make an innovation and are classified as an effective learning model in the process. Namely, the Android-based Photography Basic Learning media at SMK Cendekia Madiun, in which there is a fairly complete explanation equipped with interactive videos and can be used on smartphone phones. This study uses the waterfall method, a sequential method where the previous steps are completed before moving on to the next steps starting from needs analysis, learning media design, design / implementation, evaluation / testing, and maintenance, and designed with Adobe Flash Cs6 software. The results of this study are in the form of learning media that have been designed and can be used. In addition, this research can be expected to provide better benefits at SMK Cendekia Madiun. This learning media serves to help the learning process, especially the Multimedia department, so that it can be maximized and effective in exploring the basic knowledge of photography in addition to being interesting and easy to use. So that it further increases and influences the number of SMK Cendekia Madiun graduates who choose photography as a profession for future provisions. Keywords: Learning Media, Android, Photography
Integrated Studies Intrigue to Mineral Prospectivity. Applicability to Kikombo, Dodoma, Tanzania.

Summary Detailed prospecting studies were carried out in Kikombo, Dodoma, Central Tanzania to examine the possible mineralization zones through delineating subsurface structures and characterizing geochemical distribution of various elements by integrating the results of the methods applied. The prospecting methods utilized include geological methods, regional aeromagnetic survey, local ground magnetic survey, airborne gravity survey, induced polarization survey and geochemical survey. Geological observation shows the area’s underneath geology to be composed of tonalitic — orthogneiss rock types whose regolith is composed of brown sand which is light as well as reddish. Gravity survey results suggest high gravity anomaly to most of the occupied area. Aeromagnetic surveys reveal high magnetic anomaly to most of the occupied area with NE-SW structural trending patterns. Local ground magnetic survey reveals to lack prominent structures (faults) that are deep and localized, however, the shallow structures were revealed showing similar trend of NE -SW. The depth to the magnetic anomaly of various methods employed goes up to 494.1 m. IP results indicate high resistive and chargeable bodies at the basement with some faults striking NE-SW. Geochemical survey shows the concentration of elements such as As, Au, Ca, Cr, Cu, Fe, K, Mg, Mn, P, S, Ti, V, Zn and Zr while suggesting a strong positive correlation of Mn and Cu.
SoWaF: Shuffling of Weights and Feature Maps: A Novel Hardware Intrinsic Attack (HIA) on Convolutional Neural Network (CNN)

Security of inference phase deployment of Convolutional neural network (CNN) into resource-constrained embedded systems (e.g., low-end FPGAs) is a growing research area. Using secure practices, third-party FPGA designers can be provided with no knowledge of initial and final classification layers. In this work, we demonstrate that hardware intrinsic attack (HIA) in such a “secure” design is still possible. Proposed HIA is inserted inside mathematical operations of individual layers of CNN, which propagates erroneous operations in all the subsequent CNN layers that lead to misclassification. The attack is non-periodic and completely random; hence it becomes difficult to detect. Five different attack scenarios with respect to each CNN layer are designed and evaluated based on the overhead resources and the rate of triggering in comparison to the original implementation. Our results for two CNN architectures show that in all the attack scenarios, additional latency is negligible (<0.61%), increment in DSP, LUT, FF is also less than2.36%. Three attack scenarios do not require any additional BRAM resources, while in two scenarios BRAM increases, which compensates with the corresponding decrease in FF and LUTs. To the authors’ best knowledge this work is the first to address the hardware intrinsic CNN attack where the attacker does not have knowledge of the full CNN.
handwritten character recognition using python

The communication of ideas in today’s world is necessary and there are various symbols and characters denoting a particular meaning being used, the digits being one of them. This paper discusses the digit recognition using machine learning classifiers and comparing their performances. The input given in this case is the set of digits (0-9). And further, the model is trained using Random Forest Classifier, Naives Bayes using the Jupyter notebook in the Python language. The classifiers have been compared on various parameters, which are discussed in the paper. Though, the classifier can work only on the digits (0-9) and cannot classify more than a single digit, when given as input. In addition to that, the prerequisite is that the input digits should only be black or white in colour.
Fusing Knowledge and Experience with Graph Convolutional Network for Cross-task Learning in Visual Cognitive Development

Visual cognitive ability is important for intelligent robots in unstructured and dynamic environments. The high reliance on large amounts of data prevents prior methods to handle this task. Therefore, we propose a model called knowledge-experience fusion graph (KEFG) network for novel inference. It exploits information from both knowledge and experience. With the employment of graph convolutional network (GCN), KEFG generates the predictive classifiers of the novel classes with few labeled samples. Experiments show that KEFG can decrease the training time by the fusion of the source information and also increase the classification accuracy in cross-task learning.
Campus Navigation APP With Augmented Reality Techniques

Abstract—For all the newcomers to the college campus it's difficult to find all the places, whether it's the cafeteria or the library. Therefore, in this research, a mobile application was built for campus navigation. The underlying technology upon which the appliance was based is augmented reality, which was wont to enhance the standard and simple use of the appliance, as users could get the information easily. Communication with teachers was abundantly quicker than in the ancient approach. In future work, we shall extend the system to incorporate all faculties and add more features, like 3D objects and live chat with the administration. Also want to involve the Computer department in developing the application so that any faults can be reported and data updated directly.
Insights from Implementing Click DCG in Adobe Analytics

Discounted Cumulative Gain (DCG) is a commonly used metric for evaluating the precision and ranking quality of search engines results. It provides a common baseline to compare and improve search algorithms in web applications, for example through A/B testing. While the metric is proven to be useful for product improvement of products with search functionality, it has not been implemented in common Web Analytics products, such as Adobe Analytics or Google Analytics.In this work, we have implemented DCG in Adobe Analytics. This implementation allows computation of DCG@3, DCG@5, and DCG@25 using the standard log2 model. The solution uses the click-through rates for search results, hence we coin this version of DCG: Click DCG (cDCG). We show which events need to be triggered on the website and how an Adobe Analytics Calculated Metric can be built to compute cDCG.Further to implementation, we demonstrate the value and rich insights we can gained from analyzing over 50 search pages at Elsevier, by breaking cDCG down by over 100 dimensions available in Adobe Analytics. For example, we show how cDCG can be used in query analysis to identify query terms that perform below average. And, we present how the users’ origin (i.e. country) affects cDCG. Lastly, we demonstrate how cDCG can be used in A/B experiments using Adobe Target, and how it was used in an actual A/B experiment to validate that cDCG values remained at high levels while migrating the search backend to a new technology.
Generation and Analysis of English Phonetic Stress Modeling Dependent on Auto-context Algorithm

Stress is not only a component of the English phonetic structure but also a distinguisher of word meaning and part of speech. Moreover, it is the foundation of intonation and speech rhythm structure. In this paper, an auto-context algorithm conversion method is proposed based on the features of English focus stress expression. Data analysis suggests that the change in acoustic features of each syllable from neutral to focal phonetics is related to the position of the syllable relative to the focal point. Hence, the conversion model is applied to neutral phonetics, and a subjective evaluation test is performed on the conversion results. The experimental results suggest that the model established based on the auto-context algorithm has expressed the focus stress properly and achieved the purpose of improving the English phonetic expressiveness.
Spammer Detection And Fake User Identification on Social Media

As e-commerce is growing and becoming popular day-by-day, the number of reviews received from customer about any product grows rapidly. People nowadays heavily rely on reviews before buying anything. Product reviews play an important role in deciding the sale of a particular product on the ecommerce websites or applications like Flipkart, Amazon, Snapdeal, etc. In this paper, we propose a framework to detect fake product reviews or spam reviews by using Opinion Mining. The Opinion mining is also known as Sentiment Analysis. In sentiment analysis, we try to figure out the opinion of a customer through a piece of text. The proposed method called VWNB-FIUT (Value Weighted Naive Bayes with Frequent Pattern Ultra Metric Tree) automatically classifies users' reviews into suspicious, clear and categories by phase-wise processing. The hazy category recursively eliminates elements into suspicious or clear. This results into richer detection and be useful to business organization as well as to customers. Business organization can monitor their product selling by analyzing and understanding what the customers are saying about products. This can help customers to purchase valuable product and spend their money on quality products. Finally end users see that each individual review with polarity scores and credibility score annotated on it. We first take the review and check if the review is related to the specific product with the help of VWNB. We use Spam dictionary to identify the spam words in the reviews by using FIUT. In Text Mining we apply several algorithms and on the basis of these algorithms we get the specific results.