Reviewerly/exHarmony huggingface dataset:
Development of Information Retrieval Method and Haversine Formula to Determine Clinic Recommendation in Jember

Clinic is a facility where people seek treatment and obtain medical advice and a place where medical students observe cases of illness suffered by patients. Currently, the presence of the Corona Virus (COVID-19) has made many clinics accommodate patients who have been exposed to the virus. From these cases, recommendations for determining the clinic are needed because the conditions are very emergency and positive cases are increasing every day. In this paper, information retrieval methods are added, namely the TF-IDF and BM25 methods to determine clinical recommendations in Jember based on keyword from users and sorted by similarity from the largest to the smallest. Meanwhile, the Haversine Formula method is used to select a clinic with a distance.
Prototype Development of Onboard Training Monitoring System for Merchant Marine Polytechnic Students

Indonesia has many huge challenges in maritime sector as it rank second after Canada as a country that has the longest coastline in the world. The challenges are not only in terms of economic and socio-culture, development and implementation of related technology, or the development of human resources itself but also include all community aspect in country. Merchant Marine Polytechnic Malahayati Aceh is a an educational institution under the Ministry of Transportation of Indonesia which is tasked with educating and training of maritime affairs. Onboard Training is as an educational activity outside the campus by participating in national and international sailing. The role of educational institutions in monitoring the activities of cadets when practicing outside the campus environment is currently being neglected by many factors. This Research are intending to explore the problems appear during training process, from the preparation and registration section, onboard section and after training. The purpose of the research is providing collage the information of cadets during onboard training and reducing knowledge gap between collage and maritime industry with monitoring system application.
DensEMANN: Building A DenseNet From Scratch, Layer by Layer and Kernel by Kernel

This paper presents a novel and unconventional NAS method called DensEMANN, which can automatically build DenseNet and DenseNet-BC architectures layer by layer and kernel by kernel while they are being trained. DensEMANN is a complex method, divided into a macro-algorithm (which adds dense layers one by one to the network) and a micro-algorithm (which builds new layers by adding and/or pruning convolution kernels from them). Using the micro-algorithm alone, it is possible to build one-layer DenseNet trained on CIFAR-10 and SVHN with similar or greater accuracy levels than those obtained with equivalent prebuilt architectures. As for the macro-algorithm, its results are still unpredictable, and further research is needed before its automatically-generated DenseNet can compete with neural networks designed by humans.
Reducing the Volume of Broadband Signal Recordings Using a Digital Filter Bank

The use of digital receivers for processing broadband recordings involves processing and storing large amounts of data. The use of compression of the dynamic range of this data with loss of information and compression of data without loss of information will significantly reduce the amount of information storage, or increase the storage period of information before it is rewritten with a fixed volume of the storage device, and will also allow using lower-speed communication channels to transfer processed information to storage devices or post-processing. When using the dynamic range compression algorithm, a digital filter bank plays an important role, which allows you to divide the spectrum of a wideband signal into subchannels with a corresponding decrease in the rate of each subchannel. The article discusses the effect of bit width truncation on error-rate performance of processed records, and also includes the results of computer simulation.
TSS: A two‐party secure server‐aid chunking algorithm

Abstract Chunking is one of the most important processes in a secure deduplication system. It determines the deduplication ratio, metadata size, and the encryption key number. Existing chunking algorithms can be categorized as client‐side chunking algorithm and the server‐aid chunking algorithm. Although the former one can directly be applied into the secure deduplication, it ignores the management overhead caused by the metadata size and encryption key number. Thus, the conventional server‐aid chunking algorithm is proposed. However, it is not secure because the interaction between the client and the server is based on the plaintext. Thus, we propose a two‐party secure server‐aid chunking algorithm, TSS. It supports the secure server‐aid interaction and reduces the metadata size and the encryption key number in the secure deduplication with a comparable deduplication ratio. The theoretical proof and experimental results show that our method outperforms the state‐of‐the‐art chunking algorithm, elastic chunking algorithm.
Security-as-a-Service with Cyberspace Mimic Defense Technologies in Cloud

Users usually focus on the application-level requirements which are quite friendly and direct to them. However, there are no existing tools automating the application-level requirements to infrastructure provisioning and application deployment. Although some security issues have been solved during the development phase, the undiscovered vulnerabilities remain hidden threats to the application's security. Cyberspace mimic defense (CMD) technologies can help to enhance the application's security despite the existence of the vulnerability. In this paper, the concept of SECurity-as-a-Service (SECaaS) is proposed with CMD technologies in cloud environments. The experiment on it was implemented. It is found that the application's security is greatly improved to meet the user's security and performance requirements within budgets through SECaaS. The experimental results show that SECaaS can help the users to focus on application-level requirements (monetary costs, required security level, etc.) and automate the process of application orchestration.
Towards a Method to Classify Language Style for Enhancing Conversational Systems

Chatbots have received significant attention in the last years. These systems have improved operational efficiency by reducing the cost of customer service and more and more are used in customer service channels. More recently, with the addition of speech capabilities added to those systems, bot language modifications have to be done in order to improve user experience. In this paper, we analyze the language used in those systems using datasets collected from real chatbots. For that, we first propose models that are able to identify the language style (writing, speech, and computer-mediated) using as linguist features syntactic (Biber's dimensions) and sentence embeddings as well as state-of-art datasets. Our results show that our models were able to distinguish among the three classes with an accuracy of up to 66%. Finally, we evaluate real chatbots systems, either speech- and text-based ones, using our proposed models. We found that these real chatbots are generally using a computer-mediated style, but there is indication that their developers tend to adapt the language style to be more speech-like when text-to-speech is used.
Interlayer Augmentation in a Classification Task

In deep learning, it is necessary to train on huge datasets to obtain accurate models, but in certain domains, such as medical imaging, it is difficult to develop large datasets. Therefore, research is being conducted to realize good accuracy, even with small datasets. One strategy to achieve good accuracy with small datasets is input data augmentation. However, input data augmentation needs to be carefully prepared according to the domain. In this article, we propose an interlayer augmentation method that produces new data between layers. Then, we propose batch generalization (BG) and random BG (RBG) as specific methods. We applied BG and RBG to VGG, ResNet, and ViT, evaluated each using CIFAR10 and CIFAR100 classification tasks, and compared them with scratch learning. We obtained an average improvement of 0.39% and 0.27% for RBG and BG, respectively, in CIFAR10 and an average improvement of 1.07% and 0.30% for RBG and BG, respectively, in CIFAR100. In particular, in all cases, RBG showed better results than scratch learning.
An Efficient Video Desnowing and Deraining Method with a Novel Variant Dataset

Video desnowing/deraining plays a vital role in outdoor vision systems, such as autonomous driving and surveillance systems, since the weather conditions significantly degrade their performance. Although numerous approaches have been reported for video snow/rain removal, they are limited to a few videos and did not consider the variations that occurred for the camera and background in real applications. We build a complete snow and rain dataset to overcome this limitation, consisting of 577 videos with synthetic snow and rain, quasi-snow, and real snow and rain. All possible variations of the background and the camera are considered in the dataset. Then, an efficient pixel-wise video desnowing/deraining method is proposed based on the color and temporal information in consecutive video frames. It is highly likely for a single pixel to be a background pixel rather than a snowy pixel at least once in the consecutive frames. Inspiring from this fact along with the color information of the snow pixels, we extract the background pixels from different consecutive frames by searching for the minimum gray-scale intensity. Experimental results demonstrate and validate the proposed method’s robustness to illumination and high-performance static background and camera.
Co-Operative E-Learning for Multilingual and Multicultural Education

The Classroom 2.0 initiative is one of the most fundamental reforms to the way education is performed across the European Union. Starting its life at the ‘Digital Classroom of Tomorrow' (DCOT) Project in Wales, the initiative has shown that concepts like electronic individual education programmes (eIEPs) and the electronic twinning of schools (eTwinning) can play an important role in enhancing learning outcomes for school age learners. This chapter presents a review of the impact of the original Classroom 2.0 Project – DCOT – and explores some of the technical issues essential to the project's success across Europe and how this has led to Technologies 4.0, which has taken the form of ‘Supporting Information and Education with Multimedia and E-Learning for Networked Societies' (SIEMENS) in Wales.
An Improved Ant Colony Algorithm for UAV Path Planning in Uncertain Environment

Track planning for drones has been a common problem. In order to ensure that the UAV can fly long distance in accordance with the predetermined path, it is necessary to set calibration points on the flight path to correct the sensor errors of the UAV. This problem can be abstracted into a path planning problem and solved by ant colony algorithm. Considering the uncertainty of correction failure in these calibration points. This paper presents an improved ant colony algorithm and set up the “Enhanced pheromone volatilization strategy“ to ensure that the UAV could reach the destination with the greatest possibility in this uncertain situation. We verify our algorithm on public data sets**. On data set 1, our algorithm has a 100% probability of reaching the destination, while the traditional ant colony algorithm has only a 61% probability of reaching the destination. On data set 2, our algorithm has a 56% probability of reaching the destination, while the traditional ant colony algorithm cannot find a path can reach the destination. The algorithm code*** in this paper is simple to implement, strong robustness, and can be extended to other scenarios.
A Hybrid Segmentation Technique for Brain Tumor Detection in MRI Images

An improved hybrid approach to detect brain tumor is implemented by combing watershed segmentation, k means clustering and level set segmentation sequentially. The image is preprocessed by removing the skull and later Watershed segmentation is applied. K-means clustering is extended to the past step. Finally, Level set segmentation is applied as the last step to detect tumor. This Hybrid approach is compared with other five techniques like Threshold segmentation, K-means clustering, Watershed segmentation, Level set segmentation, and a Hybrid approach that sequentially combines Watershed, Level set, and K means. Statistical and Visual research is performed. The proposed hybrid approach has better specificity, accuracy, and precision among all five techniques and detects tumors more precisely. This study could aid clinicians in surgical preparation, drug planning, and precisely segmenting tumor part with the most exact technique.
Density-Fixing: Simple yet Effective Regularization Method based on the Class Priors

Machine learning models suffer from overfitting, which is caused by a lack of labeled data. We proposed a framework of regularization methods, called density-fixing, that can be used commonly for supervised and semi-supervised learning to tackle this problem. Our proposed regularization method improves the generalization performance by forcing the model to approximate the class prior distribution or occurrence frequency. This regularization term is naturally derived from the formula of maximum likelihood estimation and is theoretically justified. We further investigated the asymptotic behavior of the proposed method and how the regularization term behaves when assuming a prior distribution of several classes in practice. We provide several theoretical analyses of the proposed method including asymptotic behavior. Our experimental results on multiple benchmark datasets are sufficient to support our argument, and we suggest that this simple and effective regularization method is useful in real-world machine learning problems.
A Moving Object Detection Method Based on 3D Convolution Neural Network

Moving object detection technology, which is always used in many aspects such as video classification, animal motion capture and human-computer interaction, is now the most common technology in computer vision and video information processing. Although the current moving object detection technology has been quite mature, due to environmental factors and a series of reasons, it still faces many challenges such as the impact of noise, camera shake and automatic adjustment, natural light changes, moving objects stagnation, background object movement, dynamic background and so on. In this paper, we use the convolution neural network to solve the problem of moving object detection, the proposed method will extract the feature of moving object through a lot of training. The model proposed in this paper is called 3D-CNN, it combines information of both time and space to extract feature which makes the model more rational. According to the experiment results, our 3D-CNN model achieves a higher accuracy than the most traditional methods.
Diffusion SLAM: Localizing Diffusion Sources From Samples Taken by Location-Unaware Mobile Sensors

We consider diffusion fields induced by multiple localised and instantaneous sources. We assume a mobile sensor samples the field, uniformly along a piecewise linear trajectory, which is unknown. The problem we address is the estimation of the amplitudes and locations of the diffusion sources, as well as of the trajectory of the sensor. We first propose a method for diffusion source localisation and trajectory mapping (D-SLAM) in 2D, where we assume the activation times of the sources are known and the evolution of the diffusion field over time is negligible. The reconstruction method we propose maps the measurements obtained using the mobile sensor to a sequence of generalised field samples. From these generalised samples, we can then retrieve the locations of the sources as well as the trajectory of the sensor (up to a 2D orthogonal geometric transformation). We then relax these assumptions and show that we can perform D-SLAM also in the case of unknown activation times, from samples of a time-varying field, as well as in 3D spaces. Finally, simulation results on both synthetic and real data further validate the proposed framework.
A cry for Palestine: vocal practice and imaginaries of Palestinian-ness among Chilean football supporters of Club Deportivo Palestino

Palestino is a football team founded by Palestinian immigrants in Chile in 1916. This article examines how a group of Palestino supporters with no Palestinian heritage understand their sonic practices of football fandom. I argue that they conceptualise their vocalisations as indexing an imaginary wherein the Palestinian experience in the Middle East is marked by struggle and resilience. Disjunctive pro-Palestine images have led them to imagine the feeling of resistance that the Israeli-Palestinian conflict must stir up among Palestinians abroad – an experience they seek to ground in their vocalisations in order to sonically dominate rivals, cheer for Palestino, and express solidarity with and raise awareness about Palestine. In addition to foregrounding the potentials and constraints of sounds that are not overtly political, the article demonstrates the affordances of vocal expressions to bundle transnational elements with local practices and inhabit the resulting imaginative space via performance.
Model-Based Offline Policy Optimization with Distribution Correcting Regularization

Offline Reinforcement Learning (RL) aims at learning effective policies by leveraging previously collected datasets without further exploration in environments. Model-based algorithms, which first learn a dynamics model using the offline dataset and then conservatively learn a policy under the model, have demonstrated great potential in offline RL. Previous model-based algorithms typically penalize the rewards with the uncertainty of the dynamics model, which, however, is not necessarily consistent with the model error. Inspired by the lower bound on the return in the real dynamics, in this paper we present a model-based alternative called DROP for offline RL. In particular, DROP estimates the density ratio between model-rollouts distribution and offline data distribution via the DICE framework [], and then regularizes the model-predicted rewards with the ratio for pessimistic policy learning. Extensive experiments show our DROP can achieve comparable or better performance compared to baselines on widely studied offline RL benchmarks.
Auroral Image Super-Resolution via Dynamic Nonlocal Intensity Fusion and Low-rank Regularization

Analyzing auroral images is particularly significant for studying the relationship between auroral phenomena and magnetospheric dynamics. Using artificial intelligence methods, we have implemented auroral classification and event detection based on auroral images. However, in previous studies, only images with a single resolution were considered. In this paper, facing the natural image dataset with time continuity such as auroral image, we propose a novel super-resolution method of dynamic nonlocal intensity fusion and low-rank regularization. First, based on the fact that similar patterns are more likely to appear in adjacent frames than in the current image, we fuse the adjacent frames information of the target image, and dynamically divide the image into overlapping image patches along the time dimension. Then, an asymptotic nonlocal algorithm is adopted to assign different weights to image patches, which is used to distinguish the representation contribution to local image patches. Finally, the alternating direction method of multipliers (ADMM) and the augmented lagrangian method (ALM) are used to solve the convex optimization problem of the low-rank super-resolution algorithm. Through comparative experiments on the all-sky auroral image dataset, it is shown from both subjective and objective aspects that this method can retain details and boundary information, and is superior to the current mainstream super-resolution models.
Analysis of Associative Protected Spatio-Temporal Data

Associative steganography is the method for protecting information using associative masking, which should be considered as a special case of the so-called screening method of historical steganography, when the hidden message is written on a stencil on a clean sheet, after which a meaningful text is formed with such an insert. This work is devoted to the analysis of associative protected spatio-temporal data. A software package was developed for the analysis of associative protected spatiotemporal data. Describes functions for the analysis of associative protected spatio-temporal data. Describes the software products that were used in this work. Describes the architecture and components of the software product that was developed in this work. The results of the developed software are presented. The estimation of the performance and scalability of the developed program product is presented.
Optimizing Space Utilization for More Effective Multi-Robot Path Planning

We perform a systematic exploration of the principle of Space Utilization Optimization (SUO) as a heuristic for planning better individual paths in a decoupled multi-robot path planner, with applications to both one-shot and life-long multi-robot path planning problems. We show that the heuristic set, SU - I, preserves single path optimality and significantly reduces congestion that naturally happens when many paths are planned without coordination. Integration of SU - I into complete planners brings dramatic reductions in computation time due to the significantly reduced number of conflicts and leads to sizable solution optimality gains in diverse evaluation scenarios over medium and large maps, for both one-shot and life-long problem settings.
Towards a Template Matching Approach for Human Fall Detection

The technology and products related to fall detection has always been in high demand within the security and healthcare industries. A fall detection system must provide a reliable notification mechanism to significantly reduce the risk and medical care costs associated with falls. The rapid developments in Smart Environments and the Internet of Things paradigms, together with the increasing number of low-cost cameras, form a favorable setup for vision-based fall detection systems. In this paper, we propose a novel vision-based fall detection technique that uses fall templates. Fall templates represent human shape variations in case of a fall. We implemented different template matching techniques to evaluate the performance of our proposed template-based approach. Performance evaluation shows encouraging results for single camera-based live deployments.
Don’t Catch It: An Interactive Virtual-Reality Environment to Learn About COVID-19 Measures Using Gamification Elements

The world is still under the influence of the COVID-19 pandemic. Even though vaccines are deployed as rapidly as possible, it is still necessary to use other measures to reduce the spread of the virus. Measures such as social distancing or wearing a mask receive a lot of criticism. Therefore, we want to demonstrate a serious game to help the players understand these measures better and show them why they are still necessary. The player of the game has to avoid other agents to keep their risk of a COVID-19 infection low. The game uses Virtual Reality through a Head-Mounted-Display to deliver an immersive and enjoyable experience. Gamification elements are used to engage the user with the game while they explore various environments. We also implemented visualizations that help the user with social distancing.