Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Genetic Programming Variants

Genetic programming tends to optimize complicated structures producing human-competitive results; therefore, it is applied to a wide range of problems such as classification and regression. This work experimentally performs a comparative study of Genetic programming variants, namely gene expression, grammatical evolution, Cartesian, multi-expression programming, and stacked-based as general regression and classification solvers. The analyses will help to understand the strengths of each variant and identify the relative performance of variants that stand relative to each other for the given problem domains. To determine the performance difference between selected GP variants, hyper-parameter tuning was performed on each GP variant for each dataset to minimize the performance difference due to implementation. A total of 11 datasets were used in the experiments, seven from the regression benchmark suite, and four from the classification. The obtained results indicate that the choice of Genetic programming variant has an impact on the performance of regression and classification problems. Multi-expression programming exhibits outstanding performance as a regression and classification solver which scales graciously with problem size and complexity whereas other variants were problem-dependent. Future work could consider implementing a multi-expression paradigm with other Genetic programming variants such as grammatical evolution and gene expression programming.
Optimization of the 24-Bit Fixed-Point Format for the Laplacian Source

The 32-bit floating-point (FP32) binary format, commonly used for data representation in computers, introduces high complexity, requiring powerful and expensive hardware for data processing and high energy consumption, hence being unsuitable for implementation on sensor nodes, edge devices, and other devices with limited hardware resources. Therefore, it is often necessary to use binary formats of lower complexity than FP32. This paper proposes the usage of the 24-bit fixed-point format that will reduce the complexity in two ways, by decreasing the number of bits and by the fact that the fixed-point format has significantly less complexity than the floating-point format. The paper optimizes the 24-bit fixed-point format and examines its performance for data with the Laplacian distribution, exploiting the analogy between fixed-point binary representation and uniform quantization. Firstly, the optimization of the 24-bit uniform quantizer is performed by deriving two new closed-form formulas for a very accurate calculation of its maximal amplitude. Then, the 24-bit fixed-point format is optimized by optimization of its key parameter and by proposing two adaptation procedures, with the aim to obtain the same performance as of the optimal uniform quantizer in a wide range of variance of input data. It is shown that the proposed 24-bit fixed-point format achieves for 18.425 dB higher performance than the floating-point format with the same number of bits while being less complex.
A Federated Learning Framework against Data Poisoning Attacks on the Basis of the Genetic Algorithm

Obtaining the balance between information loss and training accuracy is crucial in federated learning. Nevertheless, inadequate data quality will affect training accuracy. Here, to improve the training accuracy without affecting information loss, we propose a malicious data detection model using the genetic algorithm to resist model poisoning attack. Specifically, the model consists of three modules: (1) Participants conduct single point training on data and upload accuracy to the third-party server; (2) Formulate data scoring formula based on data quantity and quality; (3) Use the genetic algorithm to obtain the threshold which makes the score highest. Data with accuracy which exceeds this threshold can participate in cooperative training of federated learning. Before participating in training, participants’ data is optimized to oppose data poisoning attacks. Experiments on two datasets validated the effectiveness of the proposed model. It was also verified in the fashion-MNIST data set and cifar10 data set that the training accuracy of GAFL is 7.45% higher than that of the federated learning model in the fashion-MNIST data set and 8.18% in the cifar10 data set.
A Wireless Electrooculogram (EOG) Wearable Using Conductive Fiber Electrode

Electrooculography (EOG) is a technique for detecting electrical signals from the extra-ocular muscles. The EOG is a precise method for quantifying eye movements, including drowsiness-induced eye closure, and is also a promising technology for its potential use as a contributing mechanism for brain–computer interface applications. Despite the fact that EOG signals change as humans move their eyes, it is still difficult to monitor eye movement patterns in natural behaviors, such as everyday activity. Wearable convenience is essential for obtaining EOG signals while moving freely. This paper proposes the development and use of semi-dry electrodes with low impedance and excellent wearability, as well as a small, portable device with wireless communication capabilities, to increase the likelihood of use in real-life scenarios. The semi-dry electrode produced by the electrospinning technique had an impedance that was 3.5 times lower than that of the existing dry electrode and demonstrated low impedance drift even after long-term use. Furthermore, three steps of eye motion separation were performed using a signal obtained from the wearable device. It was confirmed that the classification of eye movements was at a meaningful level.
A survey of feature detection methods for localisation of plain sections of axial brain magnetic resonance imaging

Matching MRI brain images between patients or mapping patients' MRI slices to the simulated atlas of a brain is key to the automatic registration of MRI of a brain. The ability to match MRI images would also enable such applications as indexing and searching MRI images among multiple patients or selecting images from the region of interest. In this work, we have introduced robustness, accuracy and cumulative distance metrics and methodology that allows us to compare different techniques and approaches in matching brain MRI of different patients or matching MRI brain slice to a position in the brain atlas. To that end, we have used feature detection methods AGAST, AKAZE, BRISK, GFTT, HardNet, and ORB, which are established methods in image processing, and compared them on their resistance to image degradation and their ability to match the same brain MRI slice of different patients. We have demonstrated that some of these techniques can correctly match most of the brain MRI slices of different patients. When matching is performed with the atlas of the human brain, their performance is significantly lower. The best performing feature detection method was a combination of SIFT detector and HardNet descriptor that achieved 93% accuracy in matching images with other patients and only 52% accurately matched images when compared to atlas.
The Theory of Corporate Information Systems

The paper introduces a new discipline that combines the design of information systems, databases, programming, as well as project and change management, called the theory of corporate information systems. The content of each area correlates with the levels of software implementation: processes, data, application, technology as well as the project and changes. The theory assumes the formalization of strategies for analysis, design, roles and authorization, technical and business cutover, change management, build, data migration, training, testing and post go-live support, each of which is decomposed into subtasks, consistent with other activities, and then planned for execution. Planning plays a key role during the implementation of corporate information systems, since most of the project tasks involve receiving feedback from future users of the system. Thus, the proposed theory combines the existing technical disciplines and expands the scope of their application to larger-scale IT projects.
The Impact of Artificial Intelligence on Intergenerational Diversity

The internet has become one of the most important commodity products in society. Artificial intelligence (AI) is one of the most discussed topics in human resources. Nowadays, four or five generations in the workplace are working together. The young generation is born in a digital era, but the elderly are in an analogic era, so their skills and decision-making behaviors are different. The working values for intergenerational workers are very different. Younger generations value their flexibility to fit their lives, once they believe that working careers are no longer their main goal in life, but the elderly have other values such as family time and flexible work provided by AI development. COVID-19 brings a new mainstream concept to the workplace, only possible through AI development. The effects of artificial intelligence and intergenerational diversity are fundamental topics that need to be discussed at a high level in institutions.
Sequential exploratory design by performing correspondence analysis to investigate procedural fluency of undergraduate student

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Karunia Eka Lestari, Marsah Rahmawati Utami, Mokhammad Ridwan Yudhanegara; Sequential exploratory design by performing correspondence analysis to investigate procedural fluency of undergraduate student. AIP Conference Proceedings 23 January 2023; 2588 (1): 050004. https://doi.org/10.1063/5.0111974 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Semantic scene upgrades for trajectory prediction

Understanding pedestrian motion is critical for many real-world applications, e.g., autonomous driving and social robot navigation. It is a challenging problem since autonomous agents require complete understanding of its surroundings including complex spatial, social and scene dependencies. In trajectory prediction research, spatial and social interactions are widely studied while scene understanding has received less attention. In this paper, we study the effectiveness of different encoding mechanisms to understand the influence of the scene on pedestrian trajectories. We leverage a recurrent Variational Autoencoder to encode pedestrian motion history, its social interaction with other pedestrians and semantic scene information. We then evaluate the performance on various public datasets, such as ETH-UCY, Stanford Drone and Grand Central Station. Experimental results show that utilizing a fully segmented map, for explicit scene semantics, out performs other variants of scene representations (semantic and CNN embedding) for trajectory prediction tasks.
Learning successive weak synchronization transitions and coupling directions by reservoir computing

Synchronization prediction from oscillatory time series is one of traditional topics in nonlinear dynamics. This becomes more challenging when coupled systems show a series of different synchronization transitions when the coupling strength is progressively increased. In this work, we generalize the control parameter-aware reservoir computing to predict transitions to phase synchronization which is a rather weak form of interactions between two processes requiring long-term phase dynamics prediction. We demonstrate that a reliable long prediction for the phase variables can be achieved by considering proper bias terms and one intermittent driving variable of the target system. In addition, the reservoir computing successfully predict different transitions from phase synchronization to lag synchronization. In even weaker coupling regimes with signatures of partial synchronization, the reservoir computing predicts the coupling directions which are promising for link predictions in networks.
LFR-Net: Local feature residual network for single image dehazing

Previous learning-based methods only employ clear images to train the dehazing network, but some useful information such as hazy images, media transmission maps and atmospheric light values in datasets were ignored. Here, we propose a local feature residual network (LFR-Net) for single image dehazing, which is aimed at improving the quality of dehazed images by fully utilizing the information in the training dataset. The backbone of LFR-Net is structured by feature residual block and adaptive feature fusion model. Furthermore, to preserve more details for the recovered clear images, we design an adaptive feature fusion model that adaptively fuses shallow and deep features at each scale of the encoder and decoder. Extended experiments show that the performance of our LFR-Net outperforms the state-of-the-art methods.
Super-Resolution Reconstruction of Reservoir Saturation Map with Physical Constraints Using Generative Adversarial Network

Abstract Complete physics-based numerical simulations currently provide the most accurate approach for predicting fluid flow behavior in geological reservoirs. However, the amount of computational resources required to perform these simulations increase exponentially with the increase in resolution to the point that they are infeasible. Therefore, a common practice is to upscale the reservoir model to reduce the resolution such that numerous simulations, as required, can be performed within a reasonable time. The problem we are trying to solve here is that the simulation results from these upscaled models, although they provide a zoomed-out and global view of the reservoir dynamics, however, they lack a detailed zoomed-in view of a local region in the reservoir, which is required to take actionable decisions. This work proposes using super-resolution techniques, recently developed using machine learning methods, to obtain fine-scale flow behavior given flow behavior from a low-resolution simulation of an upscaled-reservoir model. We demonstrate our model on a two-phase, deal-oil, and heterogenous oil reservoir, and we reconstruct the oil saturation map of the reservoir. We also demonstrate how the network can be trained using dynamic coarse geological properties at various resolutions. The findings imply that even when coarse geological features and with limited resolution, the super-resolution reconstructions are able to recreate missing information that is close to the ground facts.
A Comparative Study of Interactive Environments for Investigative Interview of A Virtual Child Avatar

In cases of suspected child abuse, police and child protection services (CPS) personnel often conduct investigative interviews. These interviews are intended to obtain a reliable account from the child about the alleged crime so that proper legal and clinical interventions can be implemented, with the prosecution presenting the case in court. Studies show that the quality of these interviews is often poor, and interviewers’ current training programs are not that efficient. Training must be spaced and repeated over time. In this paper, we present a system that simulates the scenario of interviewing a victim of child sexual abuse in four different interactive environments. We conducted a user study in which participating experts interviewed a simulated child to determine which interactive environment would provide the highest quality of experience (QoE) and motivates experts for higher practice time with the system. The experts include CPS workers and child welfare students. This study measures each user interaction’s overall QoE, realism, responsiveness, presence, and flow as well as the learning effects, engagement in learning, and self-efficacy. The study shows that 66% of the participants would prefer to use virtual reality (VR) for interactive training over other environments. VR had the highest rate in most of the assessment metrics.
Robust Depth Estimation in Foggy Environments Combining RGB Images and mmWave Radar

In this paper, we propose a robust depth estimation strategy that uses RGB images and mmWave radar data to deal with limited visibility in foggy environments. While the state-of-the-art RGB or LiDAR-based depth estimation works well in scenarios with good visibility, their performance dramatically degrades in the presence of fog. In contrast, mmWave radar sensors are not affected by fog and hence are a promising complement. To leverage this property of mmWave radar, we combine RGB image-based depth estimation with radar information. The proposed combination is an extension of the Sparse-to-Dense (S2D) model. Moreover, a weight-based sensor fusion strategy is presented to improve system performance. Our experiments show that a fog density of meteorological optical range (MOR) less than 50m leads to strongly degraded performance for RGB image-based and LiDAR-based depth estimation. For a MOR of 30m in our dataset, the experiments show an improvement of 26% in mean square error for our proposed approach compared to the combination of RGB images and LiDAR data.
Effects of Color Stain Normalization in Histopathology Image Retrieval using Deep Learning

In the last decade, many digital slides have been available in the pathological field thanks to the spreading of new technologies for computerized acquisition. Often hardware and software tools and devices are different among biomedical analysis centers; consequently, the digital slides do not have the same representation using different colorization, exposition, contrast, brightness, and other distortions. Many computer vision algorithms are sensitive to these differences, and, in specific tasks such as image retrieval, color stain normalization can be a helpful technique to mitigate this misunderstanding. In this paper, we explored the effects of color stain normalization in the patches based on Hematoxylin and Eosin (H&E) image retrieval to measure how and how much it impacts the accuracy of this task providing an exhaustive analysis employing a standard dataset.
Attention-driven RetinaNet for Parasitic Egg Detection

Automatic microorganism detection, segmentation, and identification are essential to speeding up research in parasitology, biological treatment processes, and environment quality evaluation. Traditional methods based on microscopic images aim to find the differences in morphological features among the target species, such as outer shape and local features. This research focuses on species that share similar round shapes and seriously affect human health. Previous works show that segmentation is an essential step in improving detection accuracy. However, preparing segmentation ground truth is labor-intensive, especially in large datasets. For the dataset with no segmentation ground truths, we first generate pseudo-segmentation ground truths for training by applying a pre-trained segmentation network on a smaller dataset. We propose Attention-driven RetinaNet to detect, segment, and identify microorganisms in the microscopic images even when the training datasets have no annotated segmentation. The attention mechanism is applied to refine the incorrect pseudo-segmentation ground truths via Guided-attention. Self-attention is applied to select the essential part of the microorganism to improve detection performance. Experiments on the IEEE Parasitic Egg Detection and Classification in Microscopic Images Competition dataset show that the proposed method achieves 0.82 in mAP and outperforms other object detection methods.
Hand Tracking vs Motion Controllers: The effects on Immersive Virtual Reality Game Experience

Immersive Virtual Reality (IVR) systems allow for high fidelity interaction techniques using Motion Controllers which resemble users’ hands inside virtual worlds. Recently, many IVR systems allow developers to user Hand Tracking instead of Motion Controllers via infrared depth cameras embedded in Head Mounted Displays (HMDs), which sparked a number of conversations on the effect each interaction method has on user experience in IVR applications. This work presents preliminarily evaluation results of Motion Tracking and Hand Tracking usage on game experience provided by an IVR game. Our results indicate lower usability and learnability levels for hand tracking, which not affect the overall game experience compared to motion controllers.
Low-precision post-filtering in video coding

Neural Networks (NNs) have demonstrated their effectiveness in tackling challenges involving multimedia content. In the video coding field, NNs are actively exploited as novel tools that complement conventional signal processing tools, as well as end-to-end coding solutions. Since NNs use commonly floating-point arithmetic, different results may be generated in different computing environments, leading to discrepancies and even corrupted reconstructions. Accordingly, this issue is solved by employing fixed-point arithmetic instead. This paper studies the quantisation of a 32-bit floating-point (float32) NN post-filter to 32-bit fixed-point (int32) and 16-bit fixed-point (int16). On top of the VVC Test Model (VTM) 11.0 NN-based Video Coding (NNVC) 1.0, the coding gains of the float32 post-filter are 5.01% (Y), 18.95% (Cb) and 17.33% Cr. Compared to the float32 inference, the quantised models produce coding losses: 0.01% (Y, Cb and Cr) for the int32 inference and 0.45% (Y), 1.97% (Cb) and 1.19% (Cr) for the int16 inference. Nevertheless, the fixed-point approaches achieve bit exact matches in different computing environments. Moreover, the decoding time with int16 is about half the decoding time of int32.
A Meta-Heuristic Solution Approach to Isolated Evacuation Problems

This paper provides an approximation method for the optimization of isolated evacuation operations, modeled through the recently introduced Isolated Community Evacuation Problem (ICEP). This routing model optimizes the planning for evacuations of isolated areas, such as islands, mountain valleys, or locations cut off through hostile military action or other hazards that are not accessible by road and require evacuation by a coordinated set of special equipment. Due to its routing structure, the ICEP is NP-complete and does not scale well. The urgent need for decisions during emergencies requires evacuation models to be solved quickly. Therefore, this paper investigates solving this problem using a Biased Random-Key Genetic Algorithm. The paper presents a new decoder specific to the ICEP, that allows to translate in between an instance of the S-ICEP and the BRKGA. This method approximates the global optimum and is suitable for parallel processing. The method is validated through computational experiments.
From Narratives to Conceptual Models via Natural Language Processing

This paper explores the use of natural language processing (NLP) towards the semi-automatic generation of conceptual models, and eventual simulation specifications, from descriptions of a phenomenon. Narratives describing the problem are transformed into a list of concepts and relationships and visualized using a network graph. The process relies on pattern-based grammatical rules and an NLP dependency parser identifying important concept types, namely actors, factors, and mechanisms. We use three conceptualizations, created by potential users, to understand how the NLP-generated model should and could be adjusted. The objective of the research is to develop potential standard approaches users can use to generate conceptual models; develop a conceptual modeling assistant that subject matter experts can use to make them participant in the simulation creation process; and to identify how narratives should be written so an NLP-based conceptual modeling assistant may provide a thorough description of a phenomenon.