Reviewerly/exHarmony huggingface dataset:
Real Time Data Science Decision Tree Approach to Approve Bank Loan from Lawyer’s Perspective

Information technology, Digital Services using API, Artificial Intelligence, Data Science, Mobile banking, Automation spawn tremendous contribution in the direction of growing the efficiency, increasing huge number of customers, advance eminence customer services to most banking business application processes. With the enhancement in the banking sector lots of people are applying for bank loans Individual, Mortgage Loans, House loans, Apartment loans, Agriculture loans. These days bank managers and lawyers are facing abounding complications to sanction a loan by perceiving previous 30 years of link documents for specific site for house loan. For the most part from lawyer's perspective, to sanction legal approval, he must have to verify all bulk bundles of Registration documents, link connectivity, Identifying miss matches of sale deeds, owner relationships, family member certificates, death certificates, Re-registrations on same land, verifying any Hostage lands previously. In order to do the total procedure, lawyers may grasp three months span of time to extract conclusions by studying all documents based on project cost and litigations based on lands and it may vary according to project capital of Investment. In this project, we are trying to turn down the risk factor, computational time, ill- assorted Registration documents and Cross verification of all edge communications to make decisions to sanction the loan from lawyers, Bank Mangers, legal perspective and approve loan efficiently within stipulated time using decision tree approach of " Real Time Data Science Decision Tree Approach To Approve Bank Loan From Lawyer Perspective". we incorporated PHP, Python, XAMPP Web server, HTML, Data Science Decision tree approach to make lawyers decisions for legal approval within fraction of two minutes by entering all the document details into a database. we also incorporated execution results and discussed efficiency of proposed system with multiple scenarios.
Muzzling Anti-Vaxxer FEAR* Speech: Overcoming Free Speech Obstacles with Compelled Speech

As the anti-vax industry continues to stoke fear and incite vaccine resistance, some means must be found to detoxify their false messages. Counterspeech, the preferred mode to deal with unfortunate rhetoric, I demonstrate, is both ineffective and counter-effective when it comes to scientific speech addressing health. I therefore investigate other free speech protections available to shield false anti-vax speech, concluding that while complete protection may exist in the context of political speech without proof of fraud, it is more limited in the context of commercial speech. I then investigate the commercial ties of anti-vax mechanisms used in the strikingly effective outreach targeting insular audiences: the conference and pamphlet vehicles. Research indicates that these vehicles incorporate fingerprints of commercial enterprise, thereby making them eligible for regulation under the doctrine of compelled speech. This approach allows for the requirement of imposing warning labels on pamphlets as well as conference advertising and marketing. This novel approach may provide the salutary benefit not obtainable by counterspeech.
Comparison of NB and NB-PSO to determine level of vehicles sales

Abstract Nowadays, there are various types and brands of vehicles in Indonesia, especially kinds of motor vehicles. In this case, motor vehicles are cars, trucks, and buses (exclude the motorcycles). This research classified the various brands of motor vehicles in the form of classes that are Well Selling (Laris) and Not Selling (Tidak Laris), so that consumers and producers can find out which motor vehicles brands are well selling based on their category and output. This study analyzed 3908 data into 3126 training data and 782 testing data. The data was obtained from the GAIKINDO (Indonesian Automotive Industries Association) site. There were 19 attributes but to ease the research, the attributes used are 8 (including 1 Class attribute to facilitate the search for the best-selling motor vehicles). This research compared the accuracy value among Naive Bayes method and NB-PSO (Naive Bayes- Particle Swarm Optimization) by using such dataset. NB-PSO is adjusted parameters of 1 inertia weight and 5 population size. The results of classification accuracy with the Naive Bayes method produces accuracy values of 92.11%, Precision values: 86.57% and Recall values: 97.12%. Meanwhile, the solutions of NB-PSO have accuracy values of 92.44%, Precision values: 87.07% and Recall values: 97.18%, so PSO method was able to improve the accuracy of classification of NB as many as 0.33%.
Service Interoperability System Architecture and Service Extension Method

Abstract With the development of power grid, the amount of data and calculation generated by network edge devices is increasing rapidly, which brings the pressure of data analysis and storage, service interoperability and model expansion. Traditional edge cloud collaboration mode uses user-defined model manually, and then downloads the model from the cloud to the edge for execution to expand the model. This method relies heavily on human experience and is difficult to ensure the efficiency of model expansion when the amount of data is large. Based on the analysis of three basic capabilities of service interoperability system, this paper innovatively proposes an automatic construction method of service extension. Firstly, the model is described as a tree structure, and then the model is automatically added from the edge to the cloud as a branch. Finally, a complete industry interoperability model library is formed in the cloud.
K-modes Based Categorical Data Clustering Algorithms Satisfying Differential Privacy

As one of the important algorithms in data mining, clustering algorithm has a wide range of applications in the real world. However, clustering algorithms have the risk of privacy leakage, such as the k-modes algorithm for clustering categorical data. Therefore, this paper proposes k-Modes based clustering algorithms for categorical data satisfying differential privacy. In the process of clustering iteration, the Laplace mechanism, Gaussian mechanism or Exponential mechanism is used to interfere the clustering algorithm to protect the privacy of data. And, in order to improve the accuracy of the clustering results, we changed the selection method of the initial center points. At the end of the paper, we compare the influence of different differential privacy mechanisms on the accuracy of clustering algorithm through experiments.
An Improved Adversarial Example Generating Method With Optimized Spatial Transfrom

Deep learning model has good performance in image classification, target detection, face recognition and other tasks, but it is prone to misjudgment in the face of adversarial attack. Research on adversarial sample generation method can improve the security of deep learning model. Compared with the algorithm based on spatial transform, the existing generation methods of adversarial samples based on pixel value perturbation of the original image can effectively reduce the construction time of the adversarial samples, but the generated adversarial samples are significantly different from the original image in perception, which can be easily find by human eyes. The method proposed in this paper aims to reduce the attack time and ensure the similarity of visual perception and attack success rate between the adversarial sample and the original image. The simulation results show that compared with traditional adversarial sample generation method based on spatial transform, this method can reduce time about 30%, at the same time, this method can effectively guarantee the human eye observation under the adversarial samples and the similarity of the original image.
WITHDRAWN: Network Promotion of Rural Tourist Attractions Based on Visual Hardware System and Internet of Things Technology

As we all know, the Internet of things system is not only a concept, but also a paradigm. It can not only consider the existence of things in different environments, but also generate personal addresses through wireless network systems. Finally realize the common goal of its service system. At present, tourism industry is the largest and one of the fastest growing industries in the world. The tourism industry is an effective means to promote its regional economic growth, both in developed and developing countries. In addition, there are other development tasks in the region, such as quickly solving the problem of retention of local enterprises, further developing the regional tourism sector, and promoting micro-movies related to local tourism projects. At present, the new communication technology is developing continuously. The application of this technology can not only carry out logical regression analysis on the related industry terms of intelligent tourism and tourism, but also carry out data conversion on this basis. Turn a lot of tourism data into valuable goods. Although the concept involved is clear, it still hinders its own development to some extent. Intelligent tourism not only reveals the development trend of tourism, but also further plans the technology and industrial foundation related to tourism. The opportunities and challenges brought by the development of intelligent tourism are briefly discussed. The paper can solve the development and management needs of intelligent tourism to a great extent.
Measuring Image Blooming using Blurriness Metrics

Having areas in an image or a video frame with abnormally high light intensity such as including the sun in the frame is oftentimes unwanted. This artefact, however, is sometimes artificially included in an image. Blooming is the computer graphic artefact deployed on bright areas of an image to provide the illusion of extremely bright light. This artefact is often used in cinema post-processing, computer-generated imagery, and video games to produce attraction and real-world sense in the image. Nevertheless, too much blooming is always unwanted. Because of the leakage of light from these abnormally bright areas to neighboring image regions, the edges in these regions get partly faded. Therefore, it is important to measure the amount of blooming in an image or a video stream. In this paper, we propose a method to detect and measure the amount of blooming in an image or a video frame. This technique is based on measuring the sharpness (or conversely, the blurriness) of an image along the edges. The experiments show that the proposed metric can evaluate variations of blooming artifact in an image. Therefore, this measure could be used as an image quality metric.
Optimizing Blockchains Structures Based on Entropy and TOPSIS Model

In this study, a new and applicable model is proposed which represents the opportunities of implementing blockchains into IoT framework in the sensor data acquisition.We aim to propose a simple perspective of blockchain in IoT which is flexible with high throughput in sensor networks.This method is based on probabilities that use the combination of the Entropy and TOPSIS model and the blockchain parameter configuration used in the proposed model in this study includes: Parameters, Block, Epoch Time, Consensus Protocol, Throughput Queuing, Network Topology and Containerization.We selected Hyperledger Fabric as our blockchain solution to deploy across our network with the use of Docker Swarm.Our framework leverages the containerization of Fabric so that the network can operate between the edge devices and the cloud in a single, private system.This model contains some areas in security, such as privacy policy and delay versus the value of the task to be processed.
From technologies to algorithms through the prism of algorithm analysis

Abstract In anticipation of the predicted transition from the technology economy to the algorithm economy the article is devoted to the analysis of algorithms as an object of intellectual activity of universities registered for the period from 2010 to the present. The selection of information about algorithms and their statistical processing is carried out automatically by the collector_stat program developed by the authors of the article. This article is based on the results of the National Supercomputer Forum (NSCF-2019) and the materials of the joint Fund for electronic resources “Science and education”.
Detection and Prevention of Fraud in the Digital Era

It is essential for businesses to keep up with the technological advances. Today nearly all the businesses depend on computer technologies and the Internet to operate as technological developments have introduced many practical methods for businesses. Yet, transformation of businesses technologically also presents new means for the criminals, which has led to new types of fraud. It is crucial for businesses to take measures to prevent fraud. Traditional methods to prevent or to detect fraud seems to be ineffective for new types of fraud in the digital era. Therefore, new methods have been used to prevent and detect fraud. This chapter reviews fraud as a form of cybercrime in the digital era and aims to introduce the methods that have been used to detect and prevent it.
Development and Evaluation of a Mouse Emulator Using Multi-modal Real-Time Head Tracking Systems with Facial Gesture Recognition as a Switching Mechanism

The objective of this study is to evaluate and compare the performance of a set of low-cost multi-modal head tracking systems incorporating facial gestures as a switching mechanism. The proposed systems are aimed to enable severely disabled patients to access a computer. In this paper, we are comparing RGB (2D) and RGB-D (3D) sensors for both head tracking and facial gesture recognition. System evaluations and usability assessment were carried out on 21 healthy individuals. Two types of head tracking systems were compared - a web camera-based and another using the Kinect sensor. The two facial switching mechanisms were eye blink and eyebrows movement. Fitts’ Test is used to evaluate the proposed systems. Movement Time (MT) was used to rank the performance of the proposed systems. The Kinect-Eyebrows system had the lowest MT, followed by the Kinect-Blink, Webcam-Blink and Webcam-Eyebrows systems. The 3D Kinect systems performed better than the 2D Vision systems for both gestures. Both Kinect systems have the lowest MT and best performance, thus showing the advantage of using depth.
An End-to-End Basis-Spline Based Text Spotting Network for Scene Text

Scene text spotting is considered as an essential step for many applications in text-based visual question answering (VQA), such as auto scoring system, image caption retrieval and keyword spotting (KWS). The performance of a text-based VQA system is largely based on its text detection accuracy. Since irregular text accounts for large proportion in outdoor text-based VQA system, traditional text spotting algorithms for handling regular text has been disappointing. The recent text detection algorithms are mainly divided into two categories. They are regression-based methods and segmentation-based methods. Regression-based methods cannot deal with perfectly with the text of various layout while segmentation-based methods require much computation cost to post-process the pixel-wise prediction result. Thus, we propose a basis-spline based text spotting model for robust irregular text spotting. We propose a basis-spline text representation method with few parameters to represent irregular text layout. With this novel basis spline curve representation, our model can precisely fit the text layout of various types. Finally, we propose a new text-based VQA dataset including images collected from a real-world computer operator qualification exam. Based on this dataset, we implement an auto scoring system and other state-of-the-art recognition algorithms that automatically provides a score corresponding to a student's feedback given a set of standard answers.
Multi-agent Planning with High-Level Human Guidance

Planning and coordination of multiple agents in the presence of uncertainty and noisy sensors is extremely hard. A human operator who observes a multi-agent team can provide valuable guidance to the team based on her superior ability to interpret observations and assess the overall situation. We propose an extension of decentralized POMDPs that allows such human guidance to be factored into the planning and execution processes. Human guidance in our framework consists of intuitive high-level commands that the agents must translate into a suitable joint plan that is sensitive to what they know from local observations. The result is a framework that allows multi-agent systems to benefit from the complex strategic thinking of a human supervising them. We evaluate this approach on several common benchmark problems and show that it can lead to dramatic improvement in performance.
Sentiment Analysis based on Hybrid Approach: A Survey

Sentiment Analysis is the most widely used text classification technique which is used to analyze any text and result in terms of whether the sentiment of particular text is positive, negative or neutral. Micro blogging is used widely to express opinions like on twitter or Face book with limited character space of 140, one liner is best way to express your opinion. These messages are usually short texts like single sentences and opinions with limited contextual information. Retrieving the sentiment of such short text is a challenging task. It requires a strategy and prior knowledge to extract the correct emotion of post. Like any identification task this will also need a good feature extractor and classification approach. In this paper we are focusing on comparing the feature extractors and also utility of hybrid approach in sentiment analysis.
Partitioning Wide Area Graphs Using a Space Filling Curve

Graph structure is a very powerful tool to model system and represent their actual shape. For instance, modelling an infrastructure or social network naturally leads to graph. Yet, graphs can be very different from one another as they do not share the same properties (size, connectivity, communities, etc.) and building a system able to manage graphs should take into account this diversity. A big challenge concerning graph management is to design a system providing a scalable persistent storage and allowing efficient browsing. Mainly to study social graphs, the most recent developments in graph partitioning research often consider scale-free graphs. As we are interested in modelling connected objects and their context, we focus on partitioning geometric graphs. Consequently our strategy differs, we consider geometry as our main partitioning tool. In fact, we rely on Inverse Space-filling Partitioning, a technique which relies on a space filling curve to partition a graph and was previously applied to graphs essentially generated from Meshes. Furthermore, we extend Inverse Space-Filling Partitioning toward a new target we define as Wide Area Graphs. We provide an extended comparison with two state-of-the-art graph partitioning streaming strategies, namely LDG and FENNEL. We also propose customized metrics to better understand and identify the use cases for which the ISP partitioning solution is best suited. Experimentations show that in favourable contexts, edge-cuts can be drastically reduced, going from more 34% using FENNEL to less than 1% using ISP.
The Structure of the Music Process and the Work’s Thirteen ‘Forms of Existence’. Identity in and as a Process, Against and Through Differences

The music process is an overarching whole from its motivation to its effect. Here, we analyze a section of it, the work process in its metamorphoses from the composer's initial idea through the performances to the quotation of the work. This work process is structured into its thirteen ‘forms of existence’ (H.-W. Heister): Conceptual, Sketch, Thought, Score, Anticipation, Sound, Perception, Phonographic, Recollection, Word, Picture, Imagination, and Quotation Form. The music process and the work process are a simultaneously and successively fanned out ‘Segmented continuum’, vertically organized in five anthropological layers, and horizontally in different, in tendency all sensorial areas with the ‘Mimetic Ceremony’ as background. The work develops its identity in and through differences: a sublation as ‘processing identity’. Basic categories are thus provided for the following detailed analyses of all facets of the work relevant to Fuzzy Logic.
Side Channel Leakage Alignment Based on Longest Common Subsequence

Side channel attacks (SCA) are powerful tools to recover the secret keys of cryptographic devices by employing physical leakages of the devices. In practice, numerous countermeasures (e.g.random process interrupts) and unintentional factors (e.g.imprecise triggering) are able to significantly degrade the performance of side channel attacks (SCA) by producing misaligning leakage measurements. The main solution to over-coming influences of misalignment is to align these traces before mounting an attack. According to this view, this paper proposes a novel leakage alignment method based on the longest common subsequence (LCS) algorithm. Initially, the misaligned traces are preprocessed by non-uniform quantification to remove part of the noise and retain the key-dependent information. Subsequently, the quantified traces are converted into symbols for sequence comparison. Finally, the aligned traces are obtained by inserting and deleting operations. Experiments for different cryptographic implementations with different levels of noise are carried out on several different devices. The experimental results show that the proposed method is effective for countering leakage misalignment and robust under high noise. Especially, it is still effective for multi-channel leakage alignment.
Fundamental Fuzziness: The Imperfect Perfection

This chapter unfolds the central concept of blurs or fuzziness from the side of FL as well as from the side of art, especially music, and thus forms, continuing Chapter 2, a basis, also for the analyses in the following chapters. “Vagueness” is avoidable, fuzziness is principally not. But: In many processes, both in the compositional and the tonal realization of the idea of the work, fuzziness has a productive and formative effect, sometimes indirectly, sometimes purposefully. With reference to the ‘forms of existence’ it is shown precisely how the blurs unfold in different phases of the musical process and in different musical genres, and which mechanisms work and are used in order to master and make them productive for the musical composition and reception, such as ‘leitmotiv’, transcription, reinforcement of recollection by technical re-/production. Attention is also drawn to border experiences, such as ‘forgetting’, false memories, prejudices, hallucinations.
Short Term Performance Investigation of Solar PV Module: A Machine Learning Based Approach

This study presents a short-term performance analysis of the photovoltaic (PV) module considering weather impact in the context of Bangladesh by using machine learning. A Multilayer perceptron model is used to analyze the data and to predict the output. To collect the weather data and the output data, a weather station has been developed and deployed on the rooftop of a 7-story building in Gabtoli, Dhaka, Bangladesh. All the sensor data can be accessed remotely. In this study data from 1st November 2019 to 28 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> February 2020 are used in four separate data set for training purpose. It is observed that the output energy prediction improves with the increase in training data. The result shows that the temperature has the highest linear correlation with the module short circuit current among all the weather parameters i.e. humidity, wind speed, and air pressure.