Reviewerly/exHarmony huggingface dataset:
Challenges in Determining the Depth in 2-D Images

When an object is pictured, it’s projection is changed from 3-D scene to 2-D plane. In these cases, the depth information of the object is lost. Depth allows the calculation of an object’s distance and its three-dimensional perception. Using two or even more 2-D pictures from various perspectives of same scene aids in estimating relative depth information. However, if only one 2-D image is used, then the depth estimation problem becomes very hard to solve. In this study, we present the theoretical basis of this problem, summarize the proposed solution approaches, and examines the challenges.
A General Purpose Turkish CLIP Model (TrCLIP) for Image&amp;Text Retrieval and its Application to E-Commerce

In this paper, we introduce a Turkish adaption of CLIP (Contrastive Language-Image Pre-Training). Our approach is to train a model with the same output space as the Text encoder of the CLIP model while processing Turkish input. For this, we collected 2.5M unique English-Turkish data. The model we named TrCLIP performed 71% in CIFAR100, 86% in VOC2007, and 47% in FER2013 as zero-shot accuracy. We have examined its performance on e-commerce data and a vast domain-independent dataset in image and text retrieval tasks. The model can work in Turkish without any extra fine-tuning. Models and dataset can be reachable from https://github.com/yusufani/TrCLIP.
A Fully Convolutional Encoder-Decoder Network for Moving Object Segmentation

Moving object segmentation (MOS) is one of the important and well-studied computer vision problems. It is used in applications such as video surveillance systems, human tracking, self-driving cars, and video compression. Traditional approaches solve this problem by using hand-crafted features and then modeling the background by using these features. Convolutional Neural Networks (CNNs), on the other hand, have proven to be more powerful than traditional methods in extracting features. In this work, a hybrid system is presented that contains flux tensors together with 3D CNN, enhancing the performance of the algorithm on the unseen videos. 3D CNN can extract spatial and temporal features, thus exploiting motion information between adjacent frames. Motion entropy feature maps extracted by 3D CNN and the output of the flux tensor are jointly fed into an encoder-decoder network. ChangeDetection 2014 dataset is used for both training and test stages. Training and test videos are selected separately, and the networks are tested on unseen videos. Our proposed network gives promising segmentation results, which are competitive with existing methods.
Modulation Classification on Radio Communication Using Convolutional Neural Network

Modulation type classification for radio communications signals is purposed and examined the effect of deep learning on the classification performance in this paper. In this work, search how utilize a convolutional neural network (CNN) for classification of modulation is searched. Firstly, artificial, impaired channel waveforms are created. A CNN is trained that used the generated waveforms as data, then, modulation classification is realized. In the end, Software-defined radio (SDR) hardware and over-the-air signals are utilized to test the CNN. Different effects of offset of carrier frequency, rate of symbol, multipath fading, delay spread, and thermal noise in the simulation are considered. All of these impacts can be fully modeled decently and also, there are many additional dispersion influences that can be shaped artificially except within the scope of our work here. The CNN that is trained in this paper identifies such as; Binary phase-shift keying (BPSK), Quadrature phase-shift keying (QPSK), (8-PSK), 16-quadrature amplitude modulation (QAM), 64 QAM, PAM4 (4-pulse amplitude modulation), GFSK (Gaussian frequency-shift keying), CPFSK (Continuous phase frequency shift keying), B-FM (Broadcast FM), DSB-AM (Double sideband amplitude modulation), SSB-AM (Single sideband amplitude modulation). The trained CNN predicts and makes comparisons about the modulation sort of each frame for 1024 and 512 channel-impaired samples in this paper. Trade-offs of the performance shown in this paper consider principal parameters in data symbols and training symbols generation, enhance sensing and focus future workings on the optimization of such systems.
Interactive Visualization of Ant Colony Optimization - Pathfinding on a Dynamic Grid

Ant colony optimization (ACO) algorithms often prove effective and widely applicable for various problems, even in grid-based environments. However, from an outside perspective, the field of ACO and other swarm intelligence approaches may appear needlessly complex.This work should make the topic more approachable. It aims to enable newcomers and experienced researchers to quickly grasp and further develop solutions for ACO in grid-based environments. An open-source, Java-based simulation framework is presented, that allows other research or leisure-time projects to be implemented and executed more efficiently. Visualizing the resulting simulations can aid with understanding the basic principles and tweaking the underlying algorithms or parameters. Experiments have proven that the solution could produce pathfinding solutions in a grid environment using an example ACO-based algorithm. The corresponding algorithms are easy to implement using an abstract Java class that can be extended and used as a template. Furthermore, the visualization of the simulation is realized based on general principles that should make it intuitive and easy to understand. Future work based on this work should have all the necessary tools to create extensions or modifications as needed.
Development of Decision Support System for Prevention Measures and Fire Accident Handling on Container Ship

Abstract According to data 2018-2021 on the status of the National Transportation Safety Committee, the largest percentage of ship accidents (29%) in Indonesia are caused by fire or exploded. The main purpose of this research is to design a website-based application that can be accessed by phone to support ship crews in preventing and handling fires on board using a decision support system method, where the website-based application itself is an application that can be run using web and browser technology bases of internet or intranet. In the preparation of the research, first, observations were made on the cargo of dangerous goods transported by the container ship (MV. Sinar Penida). A website-based application for fire prevention and handling system is designed after making a mock-up. Application design is done by making mock-ups, designing interfaces, creating databases, and coding the application system. The features have been equipped with an emergency muster list, fire handling based on the class of cause, and a cargo separation feature based on the ship’s bay plan using the Decision Support System (DSS) method or decision support system that can increase the effectiveness in decision making.
Barrel Distortion Correction for Digital Optic-Based Motion Tracking in Ship Hydrodynamic Model Experiments

Abstract In hydrodynamic ship-model experiments, motion tracking of ship model trajectory is employed to predict the maneuverability of the ship when operating in its actual environment. There are some systems for motion tracking, such as inertial measurement unit, radar, laser, and infrared sensors. The system has proven reliable, however, the disadvantage is that they are all relatively expensive. This paper proposes a low-cost digital optic-based motion tracking system using consumer-grade video camera. In order to examine the accuracy of the system, some experiments to apply the method were carried out in maneuvering offshore engineering basin at the Indonesian Hydrodynamics Laboratory, The National Agency for Research and Innovation. To cover a broader area of the trajectory, a wide-angle camera lens was utilized, consequently, the image recorded by the camera experienced a barrel distortion. As a reference for the measurements, gridlines spaced 2 meters apart were drawn on the floor of the basin. Using the grid pattern, the distortion was corrected by finding the relationship between the grid pattern in the object space and in the distorted image. A fourth-order polynomial regression was applied to solve the problem. The results showed that the method was effective for correcting distorted images with a relative standard error of 0.42%.
Treatment Discontinuation Prediction in Patients With Diabetes Using a Ranking Model: Machine Learning Model Development

Background Treatment discontinuation (TD) is one of the major prognostic issues in diabetes care, and several models have been proposed to predict a missed appointment that may lead to TD in patients with diabetes by using binary classification models for the early detection of TD and for providing intervention support for patients. However, as binary classification models output the probability of a missed appointment occurring within a predetermined period, they are limited in their ability to estimate the magnitude of TD risk in patients with inconsistent intervals between appointments, making it difficult to prioritize patients for whom intervention support should be provided. Objective This study aimed to develop a machine-learned prediction model that can output a TD risk score defined by the length of time until TD and prioritize patients for intervention according to their TD risk. Methods This model included patients with diagnostic codes indicative of diabetes at the University of Tokyo Hospital between September 3, 2012, and May 17, 2014. The model was internally validated with patients from the same hospital from May 18, 2014, to January 29, 2016. The data used in this study included 7551 patients who visited the hospital after January 1, 2004, and had diagnostic codes indicative of diabetes. In particular, data that were recorded in the electronic medical records between September 3, 2012, and January 29, 2016, were used. The main outcome was the TD of a patient, which was defined as missing a scheduled clinical appointment and having no hospital visits within 3 times the average number of days between the visits of the patient and within 60 days. The TD risk score was calculated by using the parameters derived from the machine-learned ranking model. The prediction capacity was evaluated by using test data with the C-index for the performance of ranking patients, area under the receiver operating characteristic curve, and area under the precision-recall curve for discrimination, in addition to a calibration plot. Results The means (95% confidence limits) of the C-index, area under the receiver operating characteristic curve, and area under the precision-recall curve for the TD risk score were 0.749 (0.655, 0.823), 0.758 (0.649, 0.857), and 0.713 (0.554, 0.841), respectively. The observed and predicted probabilities were correlated with the calibration plots. Conclusions A TD risk score was developed for patients with diabetes by combining a machine-learned method with electronic medical records. The score calculation can be integrated into medical records to identify patients at high risk of TD, which would be useful in supporting diabetes care and preventing TD.
Cognitive and structural predictors of novel task learning, and contextual predictors of time series of daily task performance during the learning period

Investigation into methods of addressing cognitive loss exhibited later in life is of paramount importance to the field of cognitive aging. The field continues to make significant strides in designing efficacious cognitive interventions to mitigate cognitive decline, and the very act of learning a demanding task has been implicated as a potential mechanism of augmenting cognition in both the field of cognitive intervention and studies of cognitive reserve. The present study examines individual-level predictors of complex skill learning and day-to-day performance on a gamified working memory updating task, the BirdWatch Game, intended for use as a cognitive intervention tool in older adults. A measure of verbal episodic memory and the volume of a brain region involved in verbal working memory and cognitive control (the left inferior frontal gyrus) were identified as predictors of learning rates on the BirdWatch Game. These two neuro-cognitive measures were more predictive of learning when considered in conjunction than when considered separately, indicating a complementary effect. Additionally, auto-regressive time series forecasting analyses were able to identify meaningful daily predictors (that is, mood, stress, busyness, and hours of sleep) of performance-over-time on the BirdWatch Game in 50% of cases, with the specific pattern of contextual influences on performance being highly idiosyncratic between participants. These results highlight the specific contribution of language processing and cognitive control abilities to the learning of the novel task examined in this study, as well as the variability of subject-level influences on task performance during task learning.
A simple mechanical model of the drumroll

Abstract We propose an experiment in which the sound of a drumroll is recorded and analysed according to a simple mechanical model in which the inelastic restitution coefficient of the collision between the stick and drum surface, as well as the ongoing kinematics of the drumstick, are considered. The agreement shows that this model is well suited for describing the most relevant features of drumstick behaviour during this type of motion.
Pilot testing of experimental procedures to measure user's judgment errors in simulated social engineering attacks

Distracted users appear to have difficulties correctly distinguishing between legitimate and malicious emails or search engine results. Additionally, mobile phone users appear to have a more challenging time identifying malicious content due to the smaller screen size and the limited security features in mobile phone applications. Thus, the goal of this research study was to conduct a pilot test and validate a set of field experiments based on Subject Matter Experts (SMEs) feedback to assess users’ judgment when exposed to two types of simulated social engineering attacks: phishing and Potentially Malicious Search Engine Results (PMSER), based on the interaction of the environment (distracting vs. non-distracting) and type of device used (mobile vs. computer). This paper provides the results from the pilot test we conducted using recruited volunteers consisting of 10 participants out of 20 volunteers invited. Due to COVID-19 restrictions, all interactions in this pilot testing were conducted remotely. These restrictions somewhat limited our ability to control the testing environment to ensure a completely non-distractive environment during these parts of the study; however, a significant attempt was made to ensure such a non-distractive environment was genuinely adhered to during that part of the study. Our initial pilot testing results indicate that the findings were counterintuitive for the Phishing Intelligence Quotient (IQ) tests. In contrast, results of the PMSER were intuitive with improved detection on a computer compared to mobile. We conclude with a discussion on the study limitations and further research.
Application Design for a Virtual Reality Therapy Game for Patients with Behavioral and Psychological Symptoms of Dementia

Behavioral and psychological symptoms of dementia (BPSD) is a complex myriad of symptoms associated with the dementing condition. They are costly to treat and cause poor health outcomes both for the patient and their caregivers. This paper is part of an on-going research on developing an effective immersive technology for the management of patients with BPSD. Undertaken in collaboration with healthcare professionals and scientists from the Philippines, a virtual reality (VR) game prototype is designed, curated and set within the Philippine culture. For this paper, the goals are to gather necessary factors needed to administer a therapy anchored on VR to address BPSD; and to acquire insights, comments, and recommendations on the design and architecture for further development of a VR game prototype. To achieve these goals, two focus group discussions (FGD) were conducted and a VR game prototype was enhanced based on the recommendations generated from the first FGD. Findings of this study may be used as foundations or template for future studies on VR applications for other diseases and their eventual adoption into the Philippine healthcare system.
On Hyperbolic Embeddings in Object Detection

Object detection, for the most part, has been formulated in the euclidean space, where euclidean or spherical geodesic distances measure the similarity of an image region to an object class prototype. In this work, we study whether a hyperbolic geometry better matches the underlying structure of the object classification space. We incorporate a hyperbolic classifier in two-stage, keypoint-based, and transformer-based object detection architectures and evaluate them on large-scale, long-tailed, and zero-shot object detection benchmarks. In our extensive experimental evaluations, we observe categorical class hierarchies emerging in the structure of the classification space, resulting in lower classification errors and boosting the overall object detection performance.
Blind Single Image Super-Resolution via Iterated Shared Prior Learning

In this paper, we adapt shared prior learning for blind single image super-resolution (SISR). From a variational perspective, we are aiming at minimizing an energy functional consisting of a learned data fidelity term and a data-driven prior, where the learnable parameters are computed in a mean-field optimal control problem. In the associated loss functional, we combine a supervised loss evaluated on synthesized observations and an unsupervised Wasserstein loss for real observations, in which local statistics of images with different resolutions are compared. In shared prior learning, only the parameters of the prior are shared among both loss functions. The kernel estimate is updated iteratively after each step of shared prior learning. In numerous numerical experiments, we achieve state-of-the-art results for blind SISR with a low number of learnable parameters and small training sets to account for real applications.
GazeTransformer: Gaze Forecasting for Virtual Reality Using Transformer Networks

In this paper, we propose GazeTransformer, a transformer architecture for forecasting egocentric gaze points in virtual environments (VEs) presented on immersive head-mounted displays (HMDs). In contrast to previous architectures, we do not rely on information that depends on the application state, but rather focus on data modalities provided by the eye-tracker or sent to the HMD. GazeTransformer allows to forecast multiple types of eye-movements, including saccades and fixations, by creating two unfiltered datasets, using the raw gaze from the eye-tracker for forecasting. Moreover, we analyze six different image encoding backends in their quality to forecast gaze positions. To evaluate the performance of our model, we compared all architectures on the generated datasets. The results show that our architecture with all chosen backends outperforms the current state-of-the-art approaches in forecasting egocentric gaze points in VEs.
From Threads to Textiles: Building an Ontology for the Indigenous Fabrics of the Ifugao

Indigenous weaving in Ifugao is becoming less popular because of constant recurring threats to indigenous communities. This paper focuses on the development of a controlled vocabulary and an ontology to aid different weaving communities, advocacy groups, and research and government institutions in safeguarding indigenous weaving. The Ifugao Weaving Ontology, or IWO, describes different concepts in a formal, machine-readable way by using semantic technologies. The Simple Knowledge Organization System (SKOS) is used as the backbone of this ontology, and semantic relationships within the vocabulary are further enriched by overlaying domain-specific ontology structures written in the Web Ontology Language (OWL). Activities and techniques concerning domain definition, information elicitation, knowledge modelling, and formalization are laid out as an ontology development blueprint for the development of any future applications in related cultural heritage domains. Ongoing and future work on how this ontology could be used as the foundation of a knowledgebase is also discussed.
Evaluating the Feasibility of Fast Game Development Using Open Source Tools and AI Algorithms

Video games designer production is boosted, in recent years, by the evolution in hardware and software domains. Today is easier than ever to use multiple free and open-source softwares, libraries and frameworks for the creation of a game. In this work we describe a simple pipeline for the creation of a hand game emulator, using exclusively free and open-source tools. This is achieved by combining different visualization and rendering tools, such as Blender and Gimp to create simple graphics, as well as python libraries, such as PyQt5, OpenGL and Tensorflow to create the game’s interface and AI decision mechanisms. In less than 48 h, a full featured Rock-Paper-Scissors game has been created. The game supports multiple ways for input, ranging from point and click in the UI, to hand-gestured recognition from RGB cameras.
Disk Space Consumption by Triple Storage Systems

Resource Description Framework (RDF) is a widespread standard and flexible method of data representation. RDF storage systems are actively used for storing, sharing, and publishing RDF data on the Internet. RDF models are used in business applications and by research teams who wish to share their data with the community. Generally, most RDF stores are optimized for queries, but usually, this is achieved at the cost of increased disk space consumption. Renting a dedicated server with a large volume of local storage is quite expensive, especially for small research teams and business startups which makes it an important factor in choosing the data storage. In this study we compared disk space usage of four popular triple storage which can serve as SPARQL (an SQL-like query language) endpoints, depending on the amount and structure of the loaded RDF data. To the best of our knowledge, no previous work has compared the disk space occupied by triple stores. We found that all of the compared open-source solutions, namely Apache Jena Fuseki, consume large amounts of hard disk space and should be used with caution in resource-limited environments. The data structure – one large graph or many smaller named graphs – strongly affected Parliament’s disk space usage so it also should be taken into account when selecting an RDF storage. Free versions of commercial systems show adequate disk consumption and appear to be weakly dependent on data structure, but Ontotext GraphDb is deliberately limited in performance, and Stardog is limited in license term and may need additional manual maintenance.