Reviewerly/exHarmony huggingface dataset:
Path Planning for On-road Autonomous Driving with Concentrated Iterative Search

This work proposes a novel method of generating an on-road path comprising simple, short line segments, guaranteeing both dynamic and obstacle constraints with enough smoothness. Instead of certain forms of spline, simple line segments are directly used to form the path. Initially, a very raw path is generated consisting of several rough line segments in sequence. The initial path represents a rough guess that lies close to the optimal solution with relatively high possibility. Therefore, subsequent search shrinks its sample space to the neighborhood of the generated path to produce a finer path comprising shorter line segments. The same process is repeated until the line segment is shorter enough and becomes negligible to be followed by the ego vehicle. The proposed method only uses simple line segments, but the result naturally forms a very smooth path that meets the constraints needed in the end.
Innovations in the Management of Marketing Activities of the Higher Education System

Modern educational organization is actually a full member of market relations. Universities now have the right to make money and manage it. On the education market there are many factors that affect the stability of the educational organization. The authors made an attempt to analyze the factors and risks in the education market. Analyzing the set of factors operating in the education market, the authors identify the most dangerous of them, which currently affect the sustainable functioning of universities. It is proposed to solve the problem of sustainable functioning of the University with the use of marketing management as one of the components of management studying market processes.
Detection of Timestamps Tampering in NTFS using Machine Learning

During a digital investigation, the recorded time of activity on the system is crucial for solving the case. But file times may be subject to user manipulation for deceptive reasons. Detecting such timestamps change, in a none-automatic way, will come to finding a needle in a haystack. Many ways can lead to timestamps manipulation: the presence of anti-forensics tools, unusual timestamp differences in the volume shadow copies, the system restore points and the filesystem metadata, inconsistencies in the filesystem timestamps or with the established rules of normal time behavior, timeline analysis, etc. However, while reviewing the literature, we found little use of the capabilities of machine learning algorithms in such detection. In this paper, a machine learning approach for the automatic detection of timestamps tampering is proposed to reduce the required manual search for such manipulation. Put differently, the approach predicts a classification of input files in whether they have been timestamp tampered or not. Furthermore, the process of a synthetic dataset collection, features engineering and extraction, dataset manipulation, training, and model evaluation is presented. To recapitulate, the held experiment generates the synthetic dataset from a virtual controlled environment, apply a machine learning algorithm on a subset of the dataset, predict on the other subset of the dataset and present the results using confusion matrix, receiver operating characteristic curves, precision-recall curves, accuracy, and log_loss.
Dynamic Tuning of the Optimal Fuzzy LQR Controller

The paper is dedicated to the problems of control of non-linear continuous systems. The nonlinear fuzzy model of Takagi-Sugeno type utilizes method of local approximation in fuzzy partition spaces. Model-based fuzzy controller synthesis is based on the concept of parallel distributed compensation PDC. LQR fuzzy controller is designed using linear matrix inequalities LMIs method. In additional, GA-based tuning of LQR weighting matrices is proposed to achieve better performance and high robustness of close-loop LQR fuzzy controller. Described method is illustrated to the problem of balancing an inverted pendulum on the cart in upright position.
Patient Oriented Readability Assessment for Heart Disease Healthcare Documents

Personal health literacy is an important indicator for a national health status. Providing citizens with sufficient medical knowledge can help them understand their own health conditions. To achieve this goal, an integrated system is developed for evaluating the readability of healthcare documents by taking heart disease as a specific topic. The mechanism can be extended to other target diseases and languages by changing the corresponding word databank. The assessment system for examining document readability is based on patient-oriented aspects rather than professional aspects. Commonly used terms and professional medical terms extracted from a query document were utilized as fundamental elements for readability analysis, and the derived features included term frequency of professional medical terms, proportion of professional medical terms, and diversity indicator of medical terms. A five-fold cross validation is applied to measure the robustness of the proposed approach. The experimental results achieved a recall rate of 0.93, a precision rate of 0.97, and an accuracy rate of 0.95.
Creditworthiness system with k-means model based z-score

Abstract Evaluation of the creditor’s ability needs to be done before the debtor provides the loan. The purpose of system design in this study is to establish a creditworthiness system by implementing K-Mean based Z-Score. The method of system design that used in this research is Rational Unified Process (RUP). The case study of those research is in the Savings and Loans Cooperative (KSP) at Junior High School 1st Cileunyi, Bandung. K-Means method-based Z-Score is used by the author as a model for solving problems, wherein the author uses three clusters with four parameters that applied to the method. Based on the results of the experiments, the application can classify prospective credits into three groups based on the four parameters. Those three groups among others: “OK” as many as 37 members, “Warning” as many as 28 members and “Bad” as many as 5 members. The results of those groups are used as a credit recommendation to the KSP.
Performance Improvement for Two-Lens Panoramic Endoscopic System during Minimally Invasive Surgery

One of the major challenges for Minimally Invasive Surgery (MIS) is the limited field of vision (FOV) of the endoscope. A previous study by the authors designed a MIS Panoramic Endoscope (MISPE) that gives the physician a broad field of view, but this approach is still limited, in terms of performance and quality because it encounters difficulty when there is smoke, specular reflections, or a change in viewpoint. This study proposes a novel algorithm that increases the MISPE’s performance. The method calculates the disparity for the region that is overlapped by the two cameras to allow image stitching. An improved evaluation of the homography matrix uses a frame-by-frame calculation, so the stitched videos are more stable for MIS. The experimental results show that the revised MISPE has a FOV that is 55% greater, and the system operates stably in real time. The proposed system allows a frame rate of 26.7 fps on a single CPU computer. The proposed stitching method is 1.55 times faster than the previous method. The stitched image that is obtained using the proposed method is as similar as the ground truth as the SURF-based stitching method that was used in the previous study.
System development for different information systems

The main goal of this chapter is learning how to apply the concept of this book, which is related to Mobile Medical Information System (mMIS), to different types of information systems. Different types of information systems are discussed in this chapter using O'Brien and Marakas (2010) classification. Furthermore, O'Brien and Marakas's information system classification is enhanced in this chapter by adding Multifunction Support Systems (MfSSs). Finally, Development Life Cycle of three case studies is included in this chapter to find the similarities between the concepts of this book and development processes of different information systems.
Detection of Splicing Forgery Using CNN-Extracted Camera-Specific Features

This paper proposes a convolutional neural network (CNN)-based method to detect forgeries in images, leveraging camera-specific features. The underlying principle is that all the pixels in a pristine image are captured by a single camera. On the other hand, the forged regions present in a spliced image are likely to be captured by cameras other than the one used to capture the pristine regions. The proposed method divides the test image into a number of non-overlapping patches, and extracts the camera-specific features from each patch using a CNN, which is trained for camera model identification. Once the features are obtained, they are clustered using the hierarchical agglomerative clustering technique for localizing the forgery. The test image is decided to be forged on the basis of the percentage of forged patches. The experimental results show the relative merits of the method over the state-of-the-art.
Sequential Naval Target Range Profile Classification Using Special Fusion Method

This study proposes a novel classification method for sequential range profile data using naval target range profile data. Classification marine targets using radar outputs has become an important research area in modern society. Several algorithms developed for sequential data classification. The Hidden Markov Model (HMM) is one of the most useful methods which uses such specific data. It is difficult to analyze sequential marine target range profile data because data itself contains many environmental effects. Data changes due to target orientation, weather condition, clutter effect and so on. These external effects change range profile data condition. We propose a classification method using fusion of sequential range profile data set to achieve better classification results.
The strength of the Shepard illusion in children coincides with age and cognitive skills but not perceptual abilities

We examined how the strength of the Shepard illusion develops with age in typically developing children. To this end, we recruited children between the ages of 6 and 14 years and quantified the degree to which they experienced the illusion. The strength of the illusion increased with age – reaching adult levels by 11.5 years. We also measured abilities in size and shape discrimination, receptive language, and abstract reasoning to determine if changes in illusion strength were also associated with these factors. Abilities in size and shape matching increased with age but did not correlate with the strength of the Shepard illusion. Receptive language and abstract reasoning increased with age and correlated with the strength of the Shepard illusion. However, a multiple regression analysis revealed that they did not contribute beyond their shared variance with age. Based on these findings, we propose that the illusion has a strong acquired component to it and requires the maturation of high-level processes before it is experienced to adult levels at preadolescence.
The Implementation of Naïve Bayes Algorithm for Classifying Tweets Containing Hate Speech with Political Motive

The mid-of 2018 until the mid-of 2019 has been densely marked with political agendas in Indonesia. This moment has created vulnerability to spread hate speech with political motive which is one of the most commonly encountered as cyber-crime on Indonesia's social media. Twitter, as one of the most popular social medias in Indonesia becomes the target of spreading hate speech. Tweets that are suspected to contain hate speech can be drawn automatically using twitter scraper. Filtering and labeling stage before being classified using Naïve Bayes Algorithm. The classification process is done by using WEKA, so finally the accuracy of Naïve Bayes Algorithm for tweet containing hate speech with political motive can be identified. By using that method, the classification can be started and the particular tweets that include non-political hate speech, hate speech with political motive, or non-hate speech can be identified. Data tweet that has been drawn, should then be processed through the average value from the accuracy is 93.4%.
Indoor Object Classification Using Higher Dimensional MPEG Features

We propose a generic model to classify a given image by detecting a specific image patch. Employing MPEG-7 features along with feature selection populates the feature space which is used to train using SVM classier. Our work target toward classifying objects in an unclassified image. We propose a model that can detect objects through generic framework for larger classes. Our model gives an overall accuracy of over 97%.
Energy Industry Perspective on the Definition of Autonomy for Mobile Robots

Autonomy refers to a system that decides and performs actions motivated by some intended objectives, and those actions are justifiable by sound reasoning with respect to these objectives. Artificial intelligence (AI) is here intended as the technology that enables autonomy. Artificially intelligent autonomous robots are predicted to play an increasingly important role in the energy industry capability to address the society demand for energy. The development of such advanced systems needs to start with defining AI and autonomy for asset owners in the energy industry. In general, different applications will require different engineering definitions of AI and different levels of autonomy.
Generating mind map from an article using machine learning

Abstract Sequence teaching materials is very important in learning because it will improve the effectiveness of learning. But not all teachers can make the sequences in detail and accurate. The purpose of this research generates sequences of teaching materials automatically by using the concept map. The approach used is the Information Retrieval Approach and the use of the CoreNLP library. Concept map is generated from articles or other teaching materials. Testing is done by comparing the concept map of automatic generation with concept map made by experts. The results showed 52.5% accuracy.
A Future Prototype: 3D Models for Veterinary Education

Life structures is a basic part of therapeutic training as it is basic for the exact analysis in organs and human frameworks. The psychological portrayal of the shape and connection of various anatomical structures is a critical advance in the learning procedure. The motive behind this pilot study is to show the attainability and advantages of creating imaginative showing modules for life systems training of first-year restorative understudies dependent on 3D recreations from genuine patient information. The investigation on 3D model is creating and ended up being increasingly reasonable to the extent three dimensional data. The ascent of laser checks and impelled 3D instruments presented advance towards the quick improvement of any capricious model.
Multiple-Antenna Emitters Identification Based on a Memoryless Power Amplifier Model

Power amplifier (PA) nonlinearity is typically unique at the radio frequency (RF) front-end for particular emitters. It can play a crucial role in the application of specific emitter identification (SEI). In this paper, under the Multi-Input Multi-Output (MIMO) multipath communication scenario, two data-aided approaches are proposed to identify multi-antenna emitters using PA nonlinearity. Built upon a memoryless polynomial model, the first approach formulates a linear least square (LLS) problem and presents the closed-form solution of nonlinear coefficients in a MIMO system by means of singular value decomposition (SVD) operation. Another alternative approach estimates nonlinear coefficients of each individual PA through nonlinear least square (NLS) solved by the regularized Gauss-Newton iterative scheme. Moreover, there are some practical discussions of our proposed approaches about the mismatch of the order of PA model and the rank-deficient condition. Finally, the average misclassification rate is derived based on the minimum error probability (MEP) criterion, and the proposed approaches are validated to be effective through extensively numerical simulations.
Scaffolding Open Text Input in a Scripted Communication Skills Learning Environment

Serious games, as well as entertainment games, often employ a scripted dialogue for player interaction with a virtual character. In our serious game Communicate, a domain expert develops a structured, scripted scenario as a sequence of potential interactions in an authoring tool. Communicate is widely used and several domain experts have already developed over a thousand scenarios. In the original version of Communicate, a student ‘navigates’ a dialogue with a virtual character by clicking one of the multiple statement options at a step of a scenario. Open text response often requires more complex thinking from a student. In this paper we explore ways to handle open text input from a student at a step of a scenario. Our goal is to match open text to scripted statements using a Natural Language Processing (NLP) method and explore mechanisms to handle matched and unmatched input.
Ciphertext-Police Attribute Based Encryption Performance on Notebook Device

Cryptography is a technique to secure communication or exchange messages between one user with another user, by encrypting the message to be sent guaranteed to be safe from eavesdroppers because the message is equipped with a key that is not possessed by the eavesdropper. One encryption method that can guarantee the security of communication is Ciphertext-Police Attribute Based Encryption (CP-ABE). CP-ABE belongs to the type of public key encryption where the secret key of the user and the ciphertext depends on the user's attributes such as place of birth, position in the organization and other attributes. Ciphertext can only be described if the set of user key attributes matches the ciphertext attribute. This paper discusses CP-ABE performance on notebook devices. Evaluation is carried out on two notebook devices namely MacBook Air (Intel Core i5 1.8Ghz, 8GB RAM) with the MacOS Sierra operating system version 10.12.6. and MacBook Air (Intel Core i5 1.4Ghz, 4GB RAM) with the MacOS Mojave operating system version 10.14.6. Evaluation is carried out on the encryption process and description with the number of attributes from 1 to 30 attributes, with the calculation of the average number of executions, memory and notebook device power. The evaluation results show that the more antributes used as a data access policy, the longer the encryption and description execution time needed, and the more memory and power used. Effect of the largest number of attributes on the average execution time, and power consumption of notebook devices, but not significant on memory usage. Nevertheless CP-ABE is suitable for use in notebook devices.
Cross-Channel Similarity based Histograms of Oriented Gradients for Color Images

The local features have gained prestige as the powerful descriptors, however, when handling color images, most existing descriptors fail to find an efficient strategy to make full use of channel correlation. To tackle this problem, we propose the cross-channel similarity based histograms of oriented gradients (CCS-HOG) model for color images. Different from the existing methods, our model integrates the color correlation with the structure features together in a better way. To find out the inner connection between channels, the cross-channel similarity measure is developed as a suitable approach. Experimental results for face recognition and kinship verification illustrate the the performance of CCS-HOG superior to other state-of-the-arts descriptors.
People as products: Exploring replication and corroboration in the dimensions of theory, method and context

This paper corroborates Hirschman’s (1987) “People as Products” work by first replicating it in a different context, and then extending it by using new methodologies that examine partner-seeking through a different theoretical lens. The replication finds that some of the original hypotheses hold, some did not, and some, in fact, reversed. This extension shows significant differences between male and female partner seekers on important linguistic psychology dimensions, namely clout, authenticity and tone. Managerial implications relate to emerging trends in human brands, specifically how the words that people use to describe themselves may impact the success or otherwise of human branding and influencing efforts. Overall, the paper demonstrates how an original study can be corroborated and extended in meaningful and interesting ways by varying the context, methodology, or theoretical backdrop, while keeping the problem constant.
An Optimal Decision Tree Model for Diabetes Diagnosis

Diabetes is one of the fastest growing non-communicable diseases in the world. The reason is that the body's ability to produce or respond to insulin is impaired, leading to abnormal metabolism of carbohydrates and elevated levels of diabetes in the blood and urine. This study helped diagnose diabetes by selecting the optimal decision tree model. In order to prevent overfitting of the decision tree model, Expectation-maximization (EM) clustering algorithm is used for data reduction, and then the data is divided into three data sets. The decision tree model is established by different hyperparameters, then the model with the highest accuracy is selected as the optimal model. The model is efficient evaluated by confusion matrix, accuracy, sensitivity and specificity. Compared with other previous studies mentioned in the literature, the proposed model can achieve better accuracy.
Verifying the Relationship Among Three Descriptions in Problem Frames Using CSP

In requirements engineering (RE), there are three essential descriptions, i.e., requirements, specification and domain properties. Their relationship is proposed by Jackson et al, and verified in various requirements approaches. However, at present, there is no formal verification for the relationship in the Problem Frames (PF) which is a well known approach in the RE. Our previous work based on the PF explicitly captures the three descriptions. Based on that work, this paper further formalizes the three descriptions using Communicating Sequential Process (CSP), transforms the relationship into two "refines", and verifies them with Process Analysis Tool (PAT). The verification ensures that the machine which behaves as in the specification installed in a specific domain will satisfy the requirements.
Simulator Sickness Coping Strategies: Findings From Reddit

The purpose of this study was to examine trends in online postings about at-home VR users’ simulator sickness coping strategies. Virtual reality devices have gained in popularity, with an estimated 8% of people having a dedicated VR headset at home. These devices, referred to as head-mounted displays (HMDs), can be used for gaming, education, and social purposes at home. One of the challenges associated with these devices is the incidence of simulator sickness that people experience when wearing them, particularly for extended periods of time. 310 comments from Reddit forums specifically about simulator sickness in gamers were analyzed. A total of eight themes emerged in reporting coping strategies: behavioral methods, cognitive methods, schedule-based exposure, pharmacological/dietary methods, environmental factors, avoiding games that were sickening, and identifying computer system factors that contributed to sickness. This showed some divergence from the larger body of literature on motion sickness coping. Studying simulator sickness in VR users over time is a challenge for laboratory based work, but online communities can help bridge that gap. In the future, these known strategies could be used to inform VR users of coping methods and improve games that are particularly nauseating.