Reviewerly/exHarmony huggingface dataset:
Decision Making of Seismic Performance Management Using Seismic Risk Assessment

The strategy for the management of earthquakes is shifting from post recovery to prevention; therefore, seismic performance management requires quantitative predictions of damage and the establishment of strategies for initial responses to earthquakes. Currently, seismic performance evaluation for seismic management in Korea consists of two stages: preliminary evaluation and detailed evaluation. Also, the priority of seismic performance management is determined in accordance with the preliminary evaluation. As a deterministic method, preliminary evaluation quantifies the physical condition and socio-economic importance of a facility by various predetermined indices, and the priority is decided by the relative value of the indices; however, with the deterministic method it is difficult to consider any uncertainty related to the return-year, epicenter, and propagation of seismic energy. Also this method cannot support tasks such as quantitative socio-economic damage and the provision of data for initial responses to earthquakes. Moreover, indirect damage is often greater than direct damage; therefore, a method to quantify damage is needed to enhance accuracy. In this paper, a Seismic Risk Assessment is used to quantify the cost of damage of road facilities in Pohang city and to support decision making.
Quantification in the Ontology Room

There is a growing movement towards construing some classic debates in ontology as meaningless, either because the answers seem obvious or the debates seem intractable. In this paper, I respond to this movement. The response has three components: First, the members of the two sides of the ontological debates that dismissivists have targeted are using different quantifiers. Second, the austere ontologist is using a more fundamental quantifier than her opponent. Third, the austere ontologist's more fundamental quantifier is a restriction of her opponent's quantifier. This response takes seriously the intuition that there is something wrong with the ontological disputes, but does not entail dismissivism.
Robustly Predicting Pedestrian Destinations Using Pre-trained Machine Learning Model for a Voice Guidance Robot

In this paper, we propose a method robustly predicting the destination of a pedestrian heading toward a robot in order to provide suitable voice guidance to him/her by communication robots installed at the reception desks of public facilities. For this purpose, we measure a pedestrian trajectory with a laser range scanner attached to the robot, and predict the destination among more than three branches by cascading multiple predictor models for two branches pre-trained by a machine learning algorithm. In order to verify the effectiveness of the proposed method, we conduct experiments using a dataset of tracking pedestrians at a shopping mall, and data observed in the real environment. The result shows that our method can predict three branch destinations with an accuracy of about 80%.
Closing Thoughts, Insights and Resources for Digital Touch Communication Research and Design

This chapter closes the book with a note on thematic directions, in response to the speculative and emergent character of digital touch communication, signalling our desire and need to keep the conversation open. We point to the significance of a social take on digital touch, particularly with reference to the types of questions this perspective raises and the way it positions technology in relation to people and society more generally. We draw attention to the research insights on digital touch communication discussed throughout the book that may inform design. Finally, we comment on the theoretical and methodological routes that we have taken to research digital touch communication, and draw on the ideas and research presented in this book to sketch an emergent research and design framework for digital touch communication.
LabelFool: A Trick In The Label Space

Adversarial attack methods can induce machine learning classifiers to mislabel errors. Current methods pay much attention to errors in the image space, i.e. the imperceptibility of adversarial perturbations, to avoid attacks being detected by humans. However, they overlook errors in the label space, i.e. the similarity between the wrong label and the true label. It is easy for humans to detect attacks if the wrong label has a big difference with the true label, for example, a dog is mislabeled as a cat. In this paper, we propose a novel attack method called LabelFool which attacks images with undetectable errors in both label space and image space. Given a classifier, for each input image, LabelFool first predicts the true label by estimating its probability distribution, then selects one label perceptually nearest to the predicted true label as the target label. Then LabelFool generates the adversarial sample by moving the input image towards the classification boundary between the predicted true label and the target label. The subjective experiments on ImageNet and visual results on CASIA-WebFace show that LabelFool is less detectable in the label space than other attack methods. Moreover, LabelFool has low perceptibility in the image space together with a high attack rate.
Case Based Reasoning: Recommendations of High School Student Program

Abstract Study Program Recommendation System is a computer-based information system that aims to provide an overview or recommendation of study program based on students’ interests or character from testing through Case Based Reasoning method. The main purpose of this article is to develop of expert system as study program recommendation for High School student through Case Based Reasoning. This system is made because many parents tend to force their health when choosing a college for their children. It can be impact on the children’s developing. Determining the recommendations of a student‘s study program with a certain character, often not enough for the expert‘s knowledge, but need to pay attention to cases or events similar to those of the student. A concept held in the field of artificial intelligence that uses case-based or CBR reasons for analysis and decision making. Decisions are determined by calculating the similarities between the new cases and the expert case that has occurred. This study is observation and literature study. The results of this study showed that an engineering inference expertise with the aim of obtaining a decision of study program recommendations can be done using the CBR method based on the characteristics of students.
Belief Merging for Possibilistic Belief Bases

Belief merging has received much attention from the research community with a large range of applications in Computer Science and Artificial Inteligence. In this paper, we represent a new belief merging approach for prioritized belief bases. The main idea of this method is to use two operators, namely connective strong operator and averagely increasing operator to merge possibilistic belief bases. By this way, the proposed method allows to keep more useful beliefs, which may be lost in other methods because of drowning effect. The logical properties of merging result are also analyzed and discussed.
The Study of the Object Depth Estimation Based on Point Pair Features on Images

Using dual cameras to grab the object's image and then estimating the distance of this object is a useful technique. However, the feature of the object is the key role, while identifying the conjugate pair pixels of this object between the left and right images. In this study, we projected laser light on the object to create obvious features, and then apply the oFAST algorithm to extract feature points. Furthermore, ORB algorithm was applied to identify the conjugate pair pixels from all feature points on the left and right images, respectively. Finally, an epipolar plane was applied to estimate the distance between the laser point and camera lens. According to the experimental results, the error of height of the object is 2.5%. Consequently, the advance study to extract more valuable feature pixels or conjugate pairs pixel is an interesting topic.
Exploring Network Vulnerabilities for Corporate Security Operations

User credentials often serve as an entry point into an organization’s network and its information assets. Well-managed identities mean greater control of user access, that would lead to a reduced risk of internal and external breaches. This paper has studied how to apply the Elastic stack for security monitoring and analytics. The Elastic stack, formerly known as ELK, is an open-source product well known as a powerful platform of log management and analytics solutions. Several preliminary experiments were carried out by means of log analysis procedure and malicious traffics monitoring to explore the vulnerabilities or weakness that will cause network failures or illegitimate access to information assets. The experimental results and findings would be beneficial as feedbacks for further establishing corporate policies and regulations, as well as implementing in-house preventive security operations.
A Balcony Dangerous Behavior Detection Algorithm Based on Video Image

Target detection, recognition and analysis in video images are the core content of pattern recognition research. And it is also very important to perform rapid detection, accurate behavior recognition and dangerous behavior analysis in video images. In daily life, if burglaries from balcony is not discovered by the householder in time, and the property safety of the householder will be threatened. In addition, if the dangerous behavior of children and the elderly with diseases on the balcony are not discovered by the guardian in time, they will be in danger. For the problems, a dangerous behavior detection algorithm based on video images was proposed. It mainly includes human body detection, zone division, and dangerous behavior recognition. The background subtraction and the threshold segmentation method were combined to detect the foreground target of the video image. And the background was divided into some key zones according to the application. Finally, a logical judgment method was proposed according to background zones divided and foreground target, and the dangerous behaviour was identified by it. The experimental results showed that the algorithm can effectively identify the dangerous behavior of climbing the railing on the balcony in time.
Grayscale Image Quality Analysis Result of Noises Reduction using Adaptive Fuzzy Filter (AFF) and Spatial Median Filter (SMF) Against Image Depth Variations

Abstract Removing salt and pepper noise in the image will have an impact on information in the image, with a greater percentage of noise, the changes in the results image will also be large, this is very likely different from the types of grayscale images with different depths such as 8, 16 and 24 bit. The percentage of noise that will be used starts from 45%, this is based on previous studies which reveal the ability of noise reduction algorithms to be able to work a maximum of below 20%, so that it needs to be studied more deeply the performance of the algorithm and its impact on images with greater noise. From the results of previous research studies, the Spatial Median Filter algorithm and Adaptive Fuzzy Filter algorithm can reduce noise with the maximum percentage of noise below 45% in the 8 Bit image, but leave some noise after being reduced. The test results on grayscale image obtained the average overall results quality Adaptive Fuzzy Filter algorithm is better than Spatial Median Filter in terms of image quality using PSNR but the diversity of information is even more reduced compared to the SMF algorithm referring to the entropy shannon value.
Use of ontologies for search and navigation improvement in the online version of the "Great ukrainian encyclopedia"

The aim of the work is to analyze the means of semantic search in Wiki resources and to develop directions for expanding its capabilities through the use of modern tools of knowledge management. The article analyzes the benefits that the use of semantic markup of the Web information resources provides for users by more convenient and quick access to content. The techniques of application of ontological analysis for search and navigation in semantized Wiki resources are considered. The definition of Wiki ontology that formalizes the information about the structure of the knowledge base of information resource (both ordinary and semantized) based on Wiki technology is proposed. The theoretical foundations of determining the measure of semantic similarity between domain concepts based on ontologies and taxonomies are analyzed. Methods of design of navigation tools oriented on Wiki resources are proposed. These methods are based on retrieval of pages that use semantically related concepts. The implementation of the proposed approach in the online version of the Great Ukrainian Encyclopedia (e-VUE) is described. Such approach supports an extension of the functionality of the encyclopedic resource by providing means of access to content on a semantic level. The methodological principles of the organization of the e-VUE knowledge base and the categorization of its articles that ensure the use of semantic proximity estimations between domain concepts for discovery of such concepts are substanti­ated. Problems in programming 2019; 4: 11-27
Distributed Generation with Photovoltaic Power Prediction in Remote Microgrid Application

Distributed generation with Solar photovoltaic power integration is gaining wide acceptance and popularity into grid electricity networks and remote microgrids are better positioned to benefit from this integration. The cost of fossil fuel consumption has a huge negative impact on our environment, most especially with greenhouse gas emission of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . Most of the remote microgrids for electrical power supply are largely powered by diesel fuel and thus increases the amount of environmental pollution around us today. It is possible to reduce drastically the use of fossil fuel by remote microgrids, if solar photovoltaic (SPV) power is considered as an alternative power source to supplement microgrid power generation. It is a known knowledge that SPV is unpredictable due to the variability in its power generation caused by intermittent solar irradiance. It becomes inevitable that SPV power be predicted to allow for efficient energy management in microgrid application. The accurate prediction of SPV electrical power generation, will assist in the accurate estimation of SPV electrical power supply required to support microgrid load and provides for an optimal scheduling of the limited SPV power. By extension, this will reduce the operational cost of diesel fuel consumption in remote microgrids and allow for cleaner energy supply to our environment. In this research paper, the focus lies in the use of artificial neural network as a technique to predict SPV power supply for remote microgrid power management.
Character Profiling in Low-Resource Language Documents

This paper focuses on automatic character profiling --- connecting "who", "what" and "when" --- in literary documents. This task is especially challenging for low-resource languages, since off-the-shelf tools for named entity recognition, syntactic parsing and other natural language processing tasks are rarely available. We investigate the impact of human annotation on automatic profiling. Based on a Medieval Chinese corpus, experimental results show that even a relatively small amount of word segmentation, part-of-speech and dependency annotation can improve accuracy in named entity recognition and in identifying character-verb associations, but not character-toponym associations.
Subjective Evaluation of High Dynamic Range Imaging for Face Matching

Human facial recognition in the context of surveillance, forensics and photo-ID verification is a task for which accuracy is critical. Quite often limitations in the overall quality of facial images reduces individuals’ ability in taking decisions regarding a person’s identity. To verify the suitability of advanced imaging techniques to improve individuals’ performance in face matching we investigate how High Dynamic Range (HDR) imaging compares with traditional low (or standard) dynamic range (LDR) imaging in a facial recognition task. An HDR face dataset with five different lighting conditions is created. Subsequently, this dataset is used in a controlled experiment ( <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> = 40) to measure performance and accuracy of human participants when identifying faces in HDR versus LDR. Results demonstrate that face matching accuracy and reaction time are improved significantly by HDR imaging. This work demonstrates scope for realistic image reproduction and delivery in face matching tasks and suggests that security systems could benefit from the adoption of HDR imaging techniques.
USE OF INTERNET SERVICES IN THE TEACHER'S ACTIVITY AS AN ALTERNATIVE TO CONSOLE APPLICATIONS

The article is devoted to the current topic: the use of Internet services in the activities of a teacher. In a modern school, the use of information technologies in a lesson is very common. Their use allows the educational process to be carried out in new conditions, when the teacher ceases to be the only source of information for students. The article reveals the possibility of replacing console applications in the teacher's activities with Internet services with similar functions.
Cloud Data Sharing and Device-Loss Recovery with Hardware-Bound Keys

Cloud-based storage services, such as Dropbox, Google Drive, or NextCloud, are broadly used to share data with others or between the individual devices of one user due to their convenience. Various end-to-end encryption mechanisms can be applied to protect the confidentiality of sensitive data in a not fully trusted cloud environment. As all such encryption mechanisms require to store keys on the client’s device, losing a device (and key) might lead to catastrophic consequences: Losing access to all outsourced data. Strategies to recover from key-loss have various trade-offs. For example, storing the key on a flash drive burdens the user to keep it secure and available, while encrypting the key with a password before uploading it to the cloud requires users to remember a complex password. These strategies also require that the key can be extracted from the device’s hardware, which risks the confidentiality of the key and data once a curious person finds a lost device or a thief steals it. In this paper, we propose and implement a cloud-based data sharing system that supports recovery after key-loss while binding the keys to the devices’ hardware. By using multi-use proxy re-encryption, we build a network of re-encryption keys that enables users to use any of their devices to access data or share it with other users. In case of device-loss, we amend this network of re-encryption keys – potentially with the help of one or more user-selected recovery users – to restore data access to the user’s new device. Our implementation highlights the system’s feasibility and underlines its practical performance.
Maximal Information Based Similarity Measure for Collaborative Filtering

The memory-based collaborative filtering (CF) algorithm has been widespread as it can help users discover their favorite items or products. Finding the similarities among user or items is the critical step for memory-based CF. But, conventional similarity measurement approaches significantly enhanced for collaborative filtering recommendation. We applied the maximal information coefficient (MIC) which based on mutual information as a measure of similarity to improve the accuracy of CF. We experimented with the benchmark MovieLens-100k dataset and captured item-based CF as an example to demonstrate the strength of our strategy. The empirical results show that our MIC technique accomplishes preferable prediction accuracy over the conventional strategy of measuring similarity.
Fuzzy Multi Attribute Decision Making (FMADM) Implementation for Classifying Student’s Single Tuition Fee (UKT) Based on Android Applications

Abstract Universitas Negeri Yogyakarta is one of the state universities in Indonesia that assigned Student’s Single Tuition Fee (UKT) policy. UKT is an Indonesian government policy of tuition fees charged to every student with nominal based on the student’s financial capabilities. Although the implementation of UKT nominal has been arranged in such ways, there are problems occurred, such as inconsistency and inaccuracy of UKT nominal classification. This causes so many dissatisfactions between students to the UKT nominal that did not meet the expectations and raises questions about the transparency of the determination of student’s UKT nominal. Therefore, the application of Fuzzy Multi Attribute Decision Making (FMADM) can improve the accuracy and the effectivity of the determination of student’s UKT nominal also answering the student’s questions and giving clarifications about student’s UKT nominal. The result of this research was an Android-based application that can be used for determining student’s UKT nominal and determining what group that nominal belongs to with ease for under 5 minutes fast. With this android application, determining and calculating student’s UKT nominal will be much faster and more accurate when groupping the UKT nominal than the conventional method used before.
Mapping of Flood-Prone Locations Impacted by Cilember River Overflow in Cimahi City Based on Geographic Information System

Abstract The high intensity of rainfall in Indonesia has a potential to cause catastrophic flooding in Cimahi City which is geographically located in the valley of basin and causes the overflow of water to the Cimahi City. There are nine flood points in Cimahi City and eight of them are located near Cilember River. This research creates a 2-dimensional map of flood-prone location impacted by Cilember Riveroverflow. The map is integrated by constructing a Geographic Information System (GIS) not only at flood point location, but also at point of the existing survey, point of the river crossing measurement, and river crossing point location at each STA with the information of location coordinates, flood depth, flood solutions, and images. Data processing begin with hydrological analysis that will be used for hydraulic analysis using HEC-RAS software and flood-prone map using ArcGIS software. The map is processed and become GIS that built in ArcGIS software. This research product is a flood-prone map impacted by Cilember River overflow in period of 2, 5, 10, 25, 50, and 100 years based on the GIS. The difference of flood distribution generated in each return period does not appear significantly, but it can be seen at the height of flood in every return period. The benefit of this research is to provide information on the vulnerability of flood area to Cimah iCity Government to solve flood problem that occur almost every year in Cimahi City.