Reviewerly/exHarmony huggingface dataset:
Evaluation Study of Unsupervised Face-to-Face Translation using Generative Adversarial Networks

Cross-domain image-to-image translation provides mechanism to capture special characteristics of one image collection and convert into other image collection with different representations. Recent research on generative learning have produced powerful image-to-image translation methods in supervised setting, where paired training datasets are available. However, collecting paired training data is difficult, expensive and required manual authoring. We present an evaluation study of recent unsupervised Generative Adversarial Network (GAN) models that can learn to translate a facial image from a source domain X to a target domain Y without paired labeled training dataset. Each GAN model is trained on the same facial image dataset and comparable hyperparameters. We report a comparison result using same GAN model evaluation metrics.
HelloBox

Children's ability to walk to school or play around their neighborhoods without parental supervision has severely declined in the past decade. Loss of local activities and mobility may leave children prepared for transitions to adulthood and make them less independent and may have negative health effects. In this paper, we discuss the findings of our research on children's unsupervised play in their neighborhoods. We propose HelloBox, a system including an app, wearable RFID tags, and check-in stations to keep track of children when they are outside without an adult. HelloBox lets parents know where their children are without restricting their independence and builds community between local families. The project focuses on improving children's independent mobility and encouraging social interactions between families within neighborhoods.
Basic Study on Cybernic Interface for Neuromuscular Intractable Rare Diseases Patients Operating an Electric Wheelchair

Patients suffering from neuromuscular intractable rare diseases gradually lose muscle strength; this muscle weakness can cause various motor dysfunctions, such as gait disturbance. In order to maintain their ability to move independently, these patients must use electric wheelchairs. Eventually, however, they will lose the muscle strength required to operate the joystick of their electric wheelchair as the disease progresses. In this study, we develop a cybernic interface that supports neuromuscular intractable rare disease patients in controlling their electric wheelchairs, and to establish the basic performance of this interface through experiments. The cybernic interface consists of a unit that can operate the joystick and an input interface that can detect small displacements. The joystick operation unit is equipped with two high-resolution linear stepping motors; it operates the electric wheelchair joystick with a high degree of sensitivity. Additionally, the small displacement of a fingertip, which is the input for the joystick operation unit, is measured by the input interface, which consists of a touch panel sensor. We conducted repetitive accuracy verification of the joystick operation unit and performed driving experiments using an electric wheelchair equipped with our system. Our results show that the system exhibited sufficient repeatability and controllability. In conclusion, we confirm that our developed system can aid neuromuscular intractable rare disease patients in controlling an electric wheelchair.
Privacy preserving 2-party queries on bipartite graphs with private set intersection

In this paper, we study the problem of privately querying a directed bipartite graph. We motivate the problem by a real life scenario where the graph is constructed from a trust relation database, that can be illustrated as a directed graph. We assume that the graph of trust relations has two parts with two owners, A and B. We present a novel protocol that utilizes Private Set Intersection protocol, to privately query such a graph. The goal of our protocol is to determine, whether there is a path from node a (belonging to A) to node b (belonging to B), in a privacy preserving manner. We also present a private set intersection protocol to determine whether the set intersection is non-empty.
Augmenting Circle Dance with Physical Computing

Pusing Tiang (Malay; loosely translated as Round-a-Pole) is an installation that augments circle dance by turning it into a game. It was pilot tested in a school among 16 primary schoolchildren. The results show that the installation encourages schoolchildren to synchronise their movement through play. The installation matches CHI 2019 theme of weaving the threads of CHI and playing the game at CHI symbolises the Celtic knot logo of strength and friendship.
Design and Analysis of an Enhanced Multifactor Authentication Through a Covert Approach

Most of the network service providers currently use two-factor authentication for their wireless networks. This exposes the network subscribers to identify theft and user’s data to security threats like snooping, sniffing, spoofing and phishing. There is need to control these problems with the use of an enhanced multi factor authentication approach. The objective of this work is to create a multi-factor authentication software for a wireless network. Multi factor authentication involves user’s knowledge factor, user’s possession factor and user’s inherence factor; that is who the user is to be presented before system access can be granted. Multi factor authentication depends mainly on three factors: (1) Something a user knows, such as a password or PIN (2) Something a user has, such as a key, a card, or another kind of token (3) Something a user is, such as a retina scan, or fingerprint. We may enhance the reliability and security of the authentication mechanism by combining multiple authentication factors into a single model. Multi factor authentication is far better if we use this schema both statically and dynamically. The three factors together provide a much higher confidence in the all prerequisite parameters of cryptography.
Concept of sediment filtration intake design for raw water drinking water

The drinking water intake capacity of drinking water is very susceptible to sedimentation, and is one of the main obstacles in drinking water services. The existing intake building is not yet equipped with a filtration system that takes into account turbidity. The level of turbidity of water entering the intake and flowing to the water treatment plant (WTP) through the transmission pipe will greatly affect the workload of the PAPs. Sedimentation problems and turbidity of raw water need special handling, starting from the design of the intake building that has the ability to dodge sediment so that the level of turbidity of the water can be minimized. Sediment evasion filtration intake designed with a 0.2 mm strainer nozzle filter system so that the sediment with a size above 0.2 mm will not enter the intake. In the cleaning process the filters installed at the intake door can be done by replacing with a backup filter, so that filter cleaning can be done easily and does not stop the intake operation for a long time. With sediment evasion filtration intakes, the raw water entering the water treatment plant (WTP) will also have a lower turbidity level so that the WTP workload is lower
Comparison between Sunhour Software Results and Autodesk FormIt360 as a Solar Based Application Analysis

The complexity of design process and climate issues for architecture students often leaves behind design issues that are less responsive to the local environment. The design recommendations somehow lack of quantitative approaches, which are unable to achieve optimal building performance. Such as the solar analysis on buildings, especially in buildings with locations in tropical climates, can be both advantages and disadvantages. Therefore, the urgency to do simulations to solar analysis buildings becomes necessary. There are many software about solar analysis and many studies have examined it. In this study will be discussed further about the comparison between the results of the Sunhour (Sketchup Plugin) and Autodesk FormIt 360 solar analysis software, where both software are widely used by architectural students and can be obtained free of charge. The building that became the case study in this study is the right-wing facade building of the FPTK UPI (Universitas Pendidikan Indonesia) located in Bandung City, analyzed with both software with the exact same location and time. The results from Sunhour and Autodesk FormIt360 compared were found to have correlated results.
A fast and efficient correction technique for slant images

In many image processing applications, such as OCR and object analysis, the input image is often inclined. For the subsequent processing and analysis, geometric correction is the most part in pre-processing phase. For the image or scanned document which is rectangular or near-rectangular, if its corner(s) is/are missing or folding, to find the real inclination angle of image or document is time consuming by previous techniques. In this paper, we proposed a fast and efficient algorithm to find the inclination angle of such an image or document. Experiments show that the correction results for the inclined image or scanned document by proposed technique are perfect. Compared with the previous algorithms, the amount of calculation has been greatly reduced, so it is suitable for real-time correction of slant images, such as scanned financial note, vehicle license plate, and text document.
Detection of Static Objects in an Image Based on Texture Analysis

The article deals with the design of a method for the automatic detection of static objects in the image captured by an optical microscope. The search algorithm for static objects in the image - non-moving cilia is based on texture description methods. The texture of the image is described by statistical values, where it can be noticed that background texture, cells and cilia have different mathematical statistical parameters. Just based on the different statistical parameters of the textures, the classification for each texture parameter was done separately. As a result, the resulting classification considers the most predominant group to which the pixel has been assigned. In the end, the obtained mask was adjusted by morphological operations to obtain the boundary of the area, where the algorithm automatically evaluated that one was about Cilia. This work is supported by medical specialists from Jessenius Faculty of Medicine in Martin (Slovakia) and proposed tools would fill the gap in the diagnostics in the field of respirology in Slovakia.
Managing the Learner Model With Multi-Entity Bayesian Networks in Adaptive Hypermedia Systems

Modeling the learner in adaptive systems involves different information. There are several methods to manage the learner model. They do not handle the uncertainty in the dynamic modeling of the learner. The main hypothesis of this chapter is the management of the learner model based on multi-entity Bayesian networks. This chapter focuses on modeling the learner model in a dynamic and probabilistic way. The authors propose in this work the use of the notion of fragments and m-theory to lead to a Bayesian multi-entity network. The use of this Bayesian method can handle the whole course of a learner as well as all of its shares in an adaptive educational hypermedia.
Speech recognition and Filipino sign language E-tutor system

Speech recognition technology facilitates student learning. It has potential benefits for students with physical disabilities and the technology has been implemented in the classroom over the years in order to learn in a more efficient way. This study provides deaf students with various methods of studying, learning, and remembering new information. Aside from speech-to-text, the developed system is also provided with speech-to-visual approach, which represents information associated with objects. Also, Filipino Sign Language was used utilize as an alternative way of presenting Statistics lessons included in K-12 curriculum. Practical real world approach in presenting Statistics lessons are used to enhance delivery in face to face class se-up or in self-phasing learning. These multiple learning strategies were combined together to have a balance approach for a greater practice and recall will be more successful, especially for the target users. From the initial results, this research showed a significant advantage of using speech recognition and Filipino Sign Language in learning basic Statistics lessons compared from the traditional method.
Socially-Enriched Multimedia Data Co-clustering

Heterogeneous data co-clustering is a commonly used technique for tapping the rich meta-information of multimedia web documents, including category, annotation, and description, for associative discovery. However, most co-clustering methods proposed for heterogeneous data do not consider the representation problem of short and noisy text and their performance is limited by the empirical weighting of the multimodal features. This chapter explains how to use the Generalized Heterogeneous Fusion Adaptive Resonance Theory (GHF-ART) Generalized heterogeneous fusion ART (GHF-ART) generalized heterogeneous fusion adaptive resonance theory for clustering large-scale web multimedia documents. Specifically, GHF-ART is designed to handle multimedia data with an arbitrarily rich level of meta-information. For handling short and noisy text, GHF-ART employs the representation and learning methods of PF-ART as described in Sect. 3.5 , which identify key tags for cluster prototype modelingPrototype modeling by learning the probabilistic distribution of tagTag occurrencesOccurrence of clusters. More importantly, GHF-ART incorporates an adaptive method for effective fusion of the multimodal features, which weights the features of multiple data sources by incrementally measuring the importance of feature modalities through the intra-cluster scatters. Extensive experiments on two web image datasets and one text document set have shown that GHF-ART achieves significantly better clustering performance and is much faster than many existing state-of-the-art algorithms. The content of this chapter is summarized and extended from [12] (©2014 IEEE. Reprinted, with permission, from [12]), and the Python codes of GHF-ART are available at https://github.com/Lei-Meng/GHF-ART .
Towards information extraction from ISR reports for decision support using a two-stage learning-based approach

The main challenge of computer linguistics is to represent the meaning of text in a computer model. Statistics based methods with manually created features have been used for more than 30 years with a divide and conquer approach to mark interesting features in free text. Around 2010, deep learning concepts found their way into the text-understanding research community. Deep learning is very attractive and easy to apply but needs massive pools of annotated and high quality data from every target domain, which is generally not available especially for the military domain. When changing the application domain one needs additional or new data to adopt the language models to the new domain. To overcome the everlasting “data problem” we chose a novel two-step approach by first using formal representations of the meaning and then applying a rule-based mapping to the target domain. As an intermediate language representation, we used abstract meaning representation (AMR) and trained a general base model. This base model was then trained with additional data from the intended domains (transfer learning) evaluating the quality of the parser with a stepwise approach in which we measured the parser performance against the amount of training data. This approach answered the question of how much data we need to get the required quality when changing an application domain. The mapping of the meaning representation to the target domain model gave us more control over specifics of the domain, which are not generally representable by a machine learning approach with self-learned feature vectors.
A formulation of noise operators and their applications

In previous work, we have discussed the use of operators to solve some signal analysis problems that are at the heart of some signal processing and analysis problems. In this paper, we extend the formalism to include noise by the introduction of a noise operator as an alternative way to think about the various signal in noise problems in radar and sonar. In addition, we show how non-linear combinations of signal with noise can be analyzed as noise operators acting on signals can expressed as combinations of operators with random parameters with a given distribution acting on the signal. Then, we show that the noise reverberation or clutter problem can be thought of as a parameter randomized operator acting on signals in either the radar and sonar domain. Then, we note that this formulation lends itself to finding unexpected consequences of non-linear transformations of random variables. Finally, we note a radar problem first addressed by Cartwright and Littlewood in WWII of identifying a spectrum being produced by a non-linearity in a circuit versus noise can be attacked by these methods.
Forecasting of Ramp-Up of Spatial Average Irradiance by Formulation of Cloudy to Fine State Transition

Satellite imagery is useful data source for several hours-ahead forecasting of aggregated power output of high penetration photovoltaic power generation. In order to improve the forecast accuracy of ramp event caused by a rapid transition from cloudy state to very fine state (or vice versa), this study proposes a forecast method using a sigmoid function to express a rapid weather transition. The parameters of sigmoid function is calculated based on the cloud motion vector estimated by using satellite visible imagery and the forecasted wind speed of numerical weather prediction model. This study demonstrates that the proposed method improves the forecast accuracy compared with the forecast by a conventional method based on the cloud motion vector only.
Bayesian Networks Modeling Using Partial Least Squares Approach to Predict Stroke Disease

Stroke is one of the leading causes of death in Indonesia. However, the incidence of stroke continues to increase. It could affect not only old people but also young people. In Indonesia, it is estimated that 500,000 people suffer from stroke every year, and about 25% or 125,000 people die and the rest suffer minor or severe defects. This study aims to build a decision-making model to help diagnose the possibility of stroke attacks. The model combines Bayesian Networks (BNs) and Partial Least Square (PLS) methods for predicting the attack on suspected patients. The model have been tested using PLS-PM approach. Hospital medical record was used as the testing data with the help of expert verification. The results showed that the model structure of BNs built on expert assumptions can be tested using the PLS approach, and stroke disease can be predicted using interrelated indicators in the model structure of BNs.
AILocker

This paper proposes a new object-based authenticated encryption scheme with a constant sized signature. We combine the deep learning algorithm and some cryptographic techniques, such as symmetric key encryption and digital signatures, to satisfy the requirements mentioned above. Based on the deep learning algorithm, the objects are detected and each object unit is encrypted with a different key, thereby enabling access per object unit. The applied forward secure digital signature scheme guarantees not to forge a signature on the already captured image frames even if the device is hijacked.
Proposal of PingPong-128 Countermeasure against Power Analysis Attack

Typical power analysis attack countermeasures include the ability to inject defensive logic during the hardware gate design phase, or to add additional hardware logic or software coding depending on how the cryptographic module is configured. For example, the Hardware-based high speed cryptographic module via FPGA can be protected with a gate array logic, and the firmware cryptographic module via the microprocessor module will allow you to implement defence technology with software coding. In this paper, Using Scrambler to propose a secure PingPong-128 crypto algorithm for power analysis attacks.
Populating the knowledge base of a conversational agent

In this paper we study the task of creating questions to populate the knowledge base of a domain-oriented conversational agent, constituted of question-answer pairs. Considering the growing interest in Question Generation, a question arises: how good are current systems in the task of automatically populating an agent's knowledge base? In this study, three systems are evaluated in several dimensions specifically tailored for the task in hand. We evaluate not only the capacity of these systems to create questions close to the ones suggested by humans, but also the human effort in editing the set of generated questions to be ready to be added to the agent's knowledge base. This experiment leads us to a second question: does the set of automatically generated question complement/extend the questions generated by humans? Results show that these state-of-the-art Question Generation systems, even as a whole, are far from being able to generate the majority of the questions proposed by humans (up to 20% of coverage), and that, on average, several edits are needed (around 3) to correct the generated questions. However, on the other hand, these systems also contribute with questions that humans have not thought about, thus contributing to extend the pool of questions generated by humans.
The Effect of the Expertise of Disaster Management Officials on Collaborative Disaster Management: Focusing on Collaboration Capacity and Performance

To overcome the uncertainty and complexity of disasters, "collaborative disaster management," which is based on collaboration, communication, and collective decision-making among various actors, is being emphasized (Kapucu & Garayev, 2011).In particular, the collaborative capacity of disaster management officials, as actors who institutionalize and conduct collaborative disaster management, is very important for the successful implementation of collaborative disaster management.Therefore, disaster management officials should improve their collaboration capacity and performance by increasing their expertise.Thus, the research question of this study is as follows: "How does the expertise of disaster management officials affect collaboration capacity and performance?"In particular, the impact of the knowledge, experience, work ability, and attitudes of disaster management public servants on their collaborative capacity was analyzed.In addition, the impact of the expertise of disaster management officials on their performance was analyzed by mediating the capacity of collaboration between government organizations and public-private collaboration.The analysis results are as follows.First, scientific knowledge, work ability, and attitude have improved collaboration capacity among government organizations.Second, scientific knowledge, work ability, and attitude have improved the capacity of collaboration between public and private.Third, the collaboration capacity between government organizations and public-private collaboration capacity have improved performance.Fourth, scientific knowledge and work ability improved performance by mediating collaboration capacities among government organizations.Fifth, scientific knowledge and work ability have improved performance by mediating collaboration between government organization and collaboration between public and private.This study presents policy implications based on the above analysis results.