Reviewerly/exHarmony huggingface dataset:
Image Sharpening by Grid Warping with Curvature Analysis

The paper proposes an improvement of the grid warping algorithm for solving the edge sharpening problem. The idea of the grid warping is to transform the neighborhood of the edges in order to make the edge transient area thinner. This approach does not amplify the noise and does not introduce ringing artifact. The idea of the improvement is to analyze the curvature of the gradient field at edge point and adjust warping vectors.
Information Extraction from WWW using Structural Approach

The expeditiously growing World Wide Web (WWW) consists of a large number of data sources from diverse organizations all over the world. The heterogeneous nature of these data poses challenges to researchers to extract specific information. In this regard, the area of finding answers of a specific question from available web contents is an emerging area of research. Questions are normally expressed in natural language and for finding answers to natural language questions from web contents; Question Answering (QA) is the most promising framework, which can be implemented on either closed domain or open domain. In this paper, we propose an automated QA system which can answer binary and wh-interrogated questions about closed domain using wikipedia articles as its knowledge source. The system allows us to generate questions from wikipedia pages and then to extract answers to questions from wikipedia pages in real time.
Application of 3D Digital Technology in the substation design

Abstract During the operation of a substation, the requirements for the engineering design are very strict, requiring the cooperation of multiple professions, and the demand for engineering digitalization is also increasing. The three-dimensional digital technology is to effectively solve the problems encountered in the substation operation in the form of data representation and data storage, and provides reliable technical support and efficient concepts of technology for substation operation. This time, the main analysis of the specific application of 3D digital technology in the substation design, and combined with the actual situation of China’s current substation, the development prospects of 3D digital design are analyzed and prospected.
Data Avalanche

Globalizing businesses from developing countries require a thoughtful strategy and adoption of state-of-the-art technologies to meet up with the rapidly changing society. Mobile money payment service is a growing service that provides opportunities for both the formal and informal sectors in Ghana. Despite its importance, fraudsters have capitalized on the vulnerabilities of users to defraud them. In this chapter, the authors have reviewed existing data mining techniques for exploring the detection of mobile payment fraud. With this technique, a hybrid-based machine learning framework for mobile money fraud detection is proposed. With the use of the machine learning technique, an avalanche of fraud-related cases is leveraged, as a corpus, for fraud detection. The implementation of the framework hinges on the formulation of policies and regulations that will guide the adoption and enforcement by Telcos and governmental agencies with oversight responsibilities in the telecommunication space. The authors, therefore, envision the implementation of the proposed framework by practitioners.
Stairway to Abstraction: an Iterative Algorithm for Whisker Detection in Video Frames

Automated whisker tracking is important for researching active touch in rodents. Earlier efforts to detect whiskers and represent them in a small set of parameters were either not accurate enough to enable tracking over time, or computationally expensive. In this article we propose an algorithm to cluster whisker centerline points, detected through a curvilinear structure algorithm, using the shape of smaller clusters to form bigger clusters of centerline points. After that, a least-squares approach is used to define each whisker by a set of four parameters. We implemented the algorithm in MATLAB in a parallelized fashion, and found that the processing time per frame is reasonable in MATLAB, and is likely to be short when ported to a lower-level language. When tested on a 33,634-frame segment, 89.2% of the whiskers could be represented in an abstract fashion by four parameters with a mean-squares fitting error of lower than 10 pixels, and visual inspection shows that crossing whiskers are detected and parameterized in an accurate way.
Authentication of Secret Message using Rabin-Karp in Image Steganography

This paper explicitly gives an inventive way to transfer the data without getting disrupted by using the methodologies of Steganography and Cryptography. Applying the strategies of Rabin-Karp, which is used to find the hash code for the message to be encrypted and Rotor Machine, which encrypts the message helps to protect the data from attackers. The coding has been implemented in MATLAB. After the implementation of the code, it is verified using the parameters called PSNR and MSE and obtained the clear results.
Approaches from cognitive neuroscience and comparative cognition

Cognitive science is basically the study of the psychological behavior of the tasks and the processes that human mind usually performs. It can be used to develop the computational models that are used to study the next sequential step, taken with the help of intelligence (artificial intelligence) which is derived mathematically. Python is a powerful tool that can be used for this cognitive science research where it is used for modeling the intelligence either mathematically or analytically. The tool can help increase the understanding of the developers of data by using a tree-like structure. The tree-like structure can be empathized as a neuron that can form clusters and have complex activities, which can be used for implementation. There are many other methods to represent data such as matrices and graphs, used in order to represent neural network. The neurons can be considered transistors or silicon chips that can be used to process data on the basis of programing, and similar results can be achieved what a normal human brain does. A Bayesian framework has been used to compare between the users and the data that can be processed cognitively by neurons.
High Performance Personal Adaptation Speech Recognition Framework by Incremental Learning with Plural Language Models

This paper introduces a speech recognition framework for high performance personalized adaption. It is based on plural language models and personalized incremental learning interface for error correction. If an error in a recognition result is detected by a bidirectional neural language model, it generates a corrected sentence by a majority decision among multiple n-gram language models considering several aspects. Moreover, we introduce a speaker adaptation by updating language models through incremental learning, which can adjust the parameter from training data. The experiments show that our framework improves word-error rate to 78% compared with Google Chrome's Speech Recognition API. Our framework can be used for improving one-to-one human-machine dialogue systems such as intelligent (counseling) agents.
Multi-attribute Recognition, the Key to Universal Neural Network

To achieve the recognition of multi-attribute of object, I redesign the mnist dataset, change the color, size, location of the number. Meanwhile, I change the label accordingly. The deep neural network I use is the most common convolution neural network. Through test, we can conclude that we can use one neural network to recognize multi-attribute so long as the attribute difference of objects can be represented by functions. The Concrete network (generation network) can generate the output which the input rarely contained from the attributes the network learned. Its generalization ability is good because the network is a continuous function. Through one more test, We can conclude that one neural network can do image recognition, speech recognition, nature language processing and other things so long as the output node and the input node and more parameters add into the network. The network is universal so long as the network can process different inputs. The phenomenon of synesthesia is the result of multi-input and multi-output. Connection in mind can realize through the universal network and sending the output into input. Connection in mind is the key of creativity, synesthesia is the assistant.
The line segmentation algorithm of Indonesian electronic identity card (e-KTP) for data digitization

The Indonesian Electronic Identity Card (e-KTP) become a source of information for its owner identity which has a lot of use in administrative purpose. The biodata segment of e-KTP consisted of multiple lines, each of the lines is unique in terms of length and wide which is become a problem in digitizing data using Optical Character Recognition (OCR). Therefore, line segmentation algorithm must be applied, this research proposed the line segmentation algorithm using rectangular cropping and Tesseract OCR. First, the algorithm cropped the owner biodata and the line indicator. There are three line indicators, which are below the ‘alamat’,’tempat/tanggal lahir’ and ‘nama’ area. Then, OCR reads all of the cropped area. If the line indicator value is blank, then those segment known has two lines. The OCR result is converted into an array which is separated by lines. The algorithm exercised into four different conditions which are, e-KTP with two lines of address; two lines of date and place of birth; two lines of name; and one lines for every segment. Result of the applied algorithm manage to reach 85% from 30 samples. Failure in line segmentation is caused by a threshold value that is not optimal.
Two-Stages Occupancy Number Detection Based on Indoor Environment Attributes By Utilizing Machine Learning Algorithm

As the building energy conservation awareness increased, many sensors and methods utilized as an effort to achieve energy saving. There are many sensors already utilized. However, each sensor has its disadvantages. Therefore, the indoor environment sensor seems to be suitable because it is a non-intrusive method, non-terminal based method, and the variables have a high correlation with the presence of the occupant. Time-series prediction of the indoor environment attributes conducted using LSTM. LSTM has proven to give high-accuracy prediction result for the three different testing datasets the accuracy as follows: 89.3%, 95.1%, 93.6%. Hence, after prediction conducted to obtain occupant number from the indoor environment features the comparison of six machine learning classification methods which are: SVM, RF, DTC, MLP, k-NN, ANN also conducted. Three datasets used to test the model, both original data and oversampling data used. Thus, to address the real-world problem condition where imbalanced class occurred due to uncertainty. Overall, SMOTE oversampling boost all of the machine learning performance while obtaining occupant number. SMOTE Algorithm contributes significantly to all of the machine learning while predicting multiclass imbalanced dataset (Dataset-2 and Dataset-3). For the binary problem (Dataset-1), SMOTE's contribution to the machine learning model performance is not significant. Three of the machine learning algorithms which are RF, DTC, and k-NN produce good accuracy consistently. Thus, those three algorithms utilized to obtain occupant numbers after Indoor Environment attributes which predicted using LSTM. From the three algorithms utilized RF, and KNN outperforms DTC in terms of accuracy consistency for three oversampled datasets. For three dataset RF with SMOTE obtain accuracy as follows: 97.8%, 63.2%, and 79.1%. On the other hand, KNN with SMOTE produces accuracy as follows: 96.8%, 61.4%, and 81.5%.
Intelligent Diagnosis of Rolling Bearing Fault Based on QFMAM

Abstract In order to solve the problem that the accuracy is not high when the traditional fuzzy morphological associative memories network (FMAM) is used to classify samples, based on quantum superposition and collapse, the quantum fuzzy morphological associative memories network (QFMAM) is proposed. In QFMAM, the structural element is constructed by the qubit system and the qubit probability represents corresponding membership degree to obtain adaptive structural elements. The samples are preprocessed first and then classified to ensure high classification accuracy. QFMAM, FMAM, support vector machine (SVM) and naive Bayes classifier (NBC) are used to classify simulation data and experimental signals of rolling bearings respectively. Comparing the performance of the four classifiers, it is obvious that the training time of QFMAM is far less than SVM and NBC, and the classification accuracy of the test samples by QFMAM is higher than that by the other three classifiers. QFMAM is very suitable for intelligent diagnosis of rolling bearing fault.
Framework Design of Information Retrieval System for Official Letter Using Extraction of Geometry Feature Method

Abstract In a government institution many official letters are made daily, the letters have an official format in the Tanggal, No. Surat, Lampiran dan Hal. area, with an OCR (Optical Character Recognition) scanner an official letter can be digitized and processed to obtain characters that will be used as searches, but in fact in certain desired parts there are often handwriting, a method of pattern recognition is needed to recognize the handwriting. The process in this study including of normalization, segmentation and pattern recognition from labels in the Tanggal, No. Surat, Lampiran dan Hal. area,this system will generate output in text form, the output text is result that will use as a term in the process of the Information Retrieval System, so the search for an official letter will be easy to do. This study is only makes the framework design for the next research authors will explain the detail of process and results obtained.
Optimization of Daily Physical Activity Recognition with Feature Selection

Daily physical activity monitoring and recognition have become a big health caring challenge in modern times. Fast recognition of physical activity from wearable sensors dataset with acceptable accuracy has got great research attention. In this paper, we have presented an optimization framework with feature selection techniques. The Bayesian Optimization algorithm has been employed to optimize hyper-parameters of Support Vector Machine(SVM), Random Forest (RF), Extreme Gradient Boosting(XGBoost). Two feature selection algorithms like Fast Correlation Based Filter (FCBF) and Maximum Redundancy Maximum Relevance(mRMR) feature selection have been applied to reduce the size of the extracted feature vector. Classification performances of the two feature selection techniques are compared in terms of accuracy and F1 score. For reduced feature vector the highest acceptable accuracy and F1 score of 95.4% and 94.76% respectively have been achieved by optimized XGBoost classifier with mRMR feature selection. In addition to this, it is shown that reduced computation time with considerable classification performance can be achieved by an efficient feature selection algorithm which is useful for designing a simplified system.
Approaching data visualizations as interfaces : An empirical demonstration of how data are imag(in)ed

This chapter points out data visualization’s double role as explorative and communicative means in humanities research. We draw from science and technology studies looking at the mediation process at stake: the interaction between visualization tool and researcher. To emphasize this mediation process and expose the various decisions at its heart we introduce the term ‘data interface’. We highlight how visualizations function as data interfaces and visualization practices allow for interfacing with data biographing a network graph’s ‘life’. Using the lens of the ‘data interface’ underscores that a particular (network) visualization provides just one perspective on the data. Moreover, we examine if and how the used data interfaces encourage scholars to critically position their investigative work, during research processes and communication.
Sentiment Analysis for Decision Support Systems of Employee

Abstract This paper presents sentiment analysis that will be used as Decision Support in employee recruitment. Sentiment analysis used Term Frekuensi.Index Document Frekuensi (TF.IDF) weight calculations. Weighting results were classified using the Support Vector Machine (SVM) method into several categories, namely negative sentiment, positive sentiment and neutral. the results of this study showed an accuracy value of 0.65 which was the best accuracy for text classification.
Classification and segmentation approach for detecting moving object in road applications

In this paper, we bring up the problem of moving object detection which is very essential for many applications in computer vision. In fact, so many algorithms are proposed in the research field these last years. The basic idea of our paper is to propose an efficient segmentation and classification approach based on a discriminating classifier capable of distinguishing between moving objects and static objects in real time. Thus, static objects are considered to belong to the background and moving objects will be surrounded by a bounding box facilitating their tracking. In fact our proposed algorithm proves itself by the experimental results which show its strength by having the highest rate of recognition and localization precision comparing to other classical methods.
The fourfold relation between the essence of money, inflation, bubbles and debt—A theoretical macrofounded analysis

Abstract The paper makes an unprecedented claim by identifying a significant relationship between money's (immaterial and intrinsic‐worth‐detached) essence and the measurement of inflation rates, on the one hand, and bubbles and private/public indebtedness, on the other hand. The inflationary potential of cryptocurrencies—among others: xenocurrencies and special drawing rights—is also analysed. Another added value is the consistently macroeconomic approach, which starts from the structural and interconnected mechanisms and then explains economic‐financial crises and their increasingly common features.
Protecting Critical Business Processes of Smart Hospitals from Cyber Attacks

The work presented in this paper aims to improve the security of Smart Hospitals, by proposing a practical approach and developing effective protection mechanisms and tools for cyber protection, with respect to the most critical asset categories in Smart Hospitals and focusing on the cyber-related issues most relevant for Smart Hospitals, and for the health care domain in general: system failures and human errors, insecurity of medical devices (including IoT) and their communications, employee-owned mobile devices (BYOD), and identity theft. The approach aims at mitigating threats against both active and passive medical devices, both large (e.g. radiotherapy and imagery) and small (wearable devices and sensors). The approach is presented with respect to a challenging use case, contributed by one of the major public hospitals of a European country. By relying on continuous monitoring of security relevant events, the proposed risk-containment approach supports Smart Hospitals in understanding the risks they are up against and in prioritizing them based on detailed context information. This results in an increased level of security across valuable assets as well as with respect to data exchange. It further supports Smart Hospitals in the management of the residual risk, by enabling them to estimate it and thus to negotiate with insurance companies additional coverage for it.
Expert knowledge for the recognition of leukemic cells

This work shows the advantage of expert knowledge for leukemic cell recognition. In the medical area, visual analysis of microscopic images has regularly used biological samples to recognize hematological disorders. Nowadays, techniques of image recognition are needed to achieve an adequate identification of blood tissues. This paper presents a procedure to acquire expert knowledge from blood cell images. We apply Gaussian mixtures, evolutionary computing, and standard techniques of image processing to extract knowledge. This information feeds a support vector machine or multilayer perceptron to classify healthy or leukemic cells. Additionally, convolutional neural networks are used as a benchmark to compare our proposed method with the state of the art. We use a public database of 260 healthy and leukemic cell images. Results show that our traditional pattern recognition methodology matches deep learning accuracy since the recognition of blood cells achieves 99.63%, whereas the convolutional neural networks reach 97.74% on average. Moreover, the computational effort of our approach is minimal, while meeting the requirement of being explainable.
Semantic Representation Based on Deep Learning for Spam Detection

This paper addresses the email spam filtering problem by proposing an approach based on two levels text semantic analysis. In the first level, a deep learning technique, based on Word2Vec is used to categorize emails by specific domains (e.g., health, education, finance, etc.). This enables a separate conceptual view for spams in each domain. In the second level, we extract a set of latent topics from email contents and represent them by rules to summarize the email content into compact topics discriminating spam from legitimate emails in an efficient way. The experimental study shows promising results in term of the precision of the spam detection.
Chinese Abrupt Event Recognition Based on CBiGRU-Att Model

Abstract Aiming at the problem of poor portability of traditional event recognition methods, the need for a large number of learning features, and the poor interpretability of recurrent neural networks in different information features about degrees of importance, this paper proposes a Chinese abrupt event recognition method based CBiGRU-Att model. Firstly, the text corpus was preprocessed.Word2vec was used to generate word vectors and the local features of the word vectors were extracted by using the convolutional layer. Then the extracted features were used as the input of the BiGRU to obtain higher-order context features, and introduced the attention mechanism to weight feature. Finally, softmax function was used to activate the learned features and output the recognition results. Simulation results show that this method is superior to other methods in the precision and recall rate for Chinese abrupt event recognition.
Adaptation Plan Policy in Traffic Routing for Priority Vehicle

The self-adaptive systems have been proposed by researchers as one of the approaches to manage the complexity of the software. The traffic routing is an example of complex software which can be modeled as a multi-agent system. The traffic routing often raises several problems that require a solution to tackle the uncertainties. Road closure, failures in systems, and destination changes are a few illustrations for problems in traffic routing. In this paper, we present the problems occurred in traffic routing related to priority vehicle. We define the problem with a number of scenarios which express the uncertainties during the run-time. Then, we propose our adaptation plan policy to address the issues occurred during the experiments. We show that the approach has a guarantee that the priority vehicle satisfied its primary goals while maintaining the other objectives of its traffic routing mechanisms.