Reviewerly/exHarmony huggingface dataset:
Android Real Time Earthquake &amp; Tsunami Warning Alert System Based on Open Data of Indonesia Government Agency of Geophysics

Abstract There are approximately 4000 earthquakes happened in Indonesia per year, whereas earthquakes of a magnitude greater than 5.5, or earthquakes that can be physically felt on land, average around 70-100 per year, and destructive earthquakes around one to two times a year. Not all earthquakes cause tsunamis, but the majority of tsunamis are caused by earthquakes. Since the earth was formed, a number of tsunamis have been happening all over the world. The Aceh tsunami is one of the biggest tsunami history over past 2 decades which occurred on 26 December 2004, claimed the lives of around a quarter of a million people in areas surrounding the Indian Ocean. The very short tsunami arrival time of 10 – 60 minutes is a challenge for tsunami early warning. It also has a direct impact on evacuation procedures and means that evacuation times are very short as well. The objective of this study is to develop android real time earthquake &amp; tsunami alarm as early warning system, evacuation directions and earthquake survivor detection for rescuer to reducing disaster risk. This study used the Research and Development method with the ADDIE (Analyze, Design, Development, Implementation and Evaluate) procedure. The result of this study the apps obtained less than 5 seconds respond time to proceed the earthquake &amp; tsunami information from BMKG website to trigger early alarm in users android mobile phone. Android alert system more effective because it has a lower response time (&lt; 5 seconds) than previous early warning system with a longer response time (13.48 seconds). In additions, developers improve the system not only distribute earthquake and tsunami information, but also provide evacuation route direction &amp; survivor detection as a disaster risk reduction. Although in actual, this feature has the potential to be disrupted due to the large amount of user traffic. So it requires a large number of server resources if there are many active users.
Addressing Task Prioritization in Model-based Reinforcement Learning

World models facilitate sample-efficient reinforcement learning (RL) and, by design, can benefit from the multitask information. However, it is not used by typical model-based RL (MBRL) agents. We propose a data-centric approach to this problem. We build a controllable optimization process for MBRL agents that selectively prioritizes the data used by the model-based agent to improve its performance. We show how this can favor implicit task generalization in a custom environment based on MetaWorld with a parametric task variability. Furthermore, by bootstrapping the agent’s data, our method can boost the performance on unstable environments from DeepMind Control Suite. This is done without any additional data and architectural changes outperforming state-of-the-art visual model-based RL algorithms. Additionally, we frame the approach within the scope of methods that have unintentionally followed the controllable optimization process paradigm, filling the gap of the data-centric task-bootstrapping methods.
Unmanned Aerial Vehicle Information Collection Missions with Uncertain Characteristics

We study the unmanned aerial vehicle (UAV) route planning problem for information collection missions performed in terrains with stochastic attributes. Uncertainty is associated with the availability of information, the effectiveness of the search and collection sensors the UAV carries, and the flight time required to travel between target regions in the mission terrain. Additionally, uncertainties in flight duration vary the detection threat exposed in missions performed in nonfriendly terrains. We develop a mixed integer programming model to maximize the expected information collection while limiting the risks of not completing the mission on time and of being detected and restricting the variance imposed on flight duration. The model allows multiple path alternatives between target pairs and revisits to the same target regions. Computational experiments are performed on randomly generated instances to investigate the impact of problem parameters and mission restrictions. We also develop a case study with a military and a civilian application, each of which with different specifications. In addition, we validate that the actual performance of the optimal solution of the model is close to the result reported by the solver via a simulation study. We conclude that the developed model is robust and can be used for practical-sized missions, and the failure rate of its optimal routes in actual missions is negligibly small. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods &amp; Analysis. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplementary Information [ https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1245 ] or is available from the IJOC GitHub software repository ( https://github.com/INFORMSJoC ) at [ http://dx.doi.org/10.5281/zenodo.7055494 .
PISDGAN: Perceive image structure and details for laryngeal image enhancement

The introduction of optical techniques such as narrow-band imaging (NBI) during laryngeal endoscopic diagnosis can emphasize the superficial mucosal veins and increase the identification rate of lesions. To eliminate the limitation of endoscopic devices, in this paper, an image enhancement method that can perceive the information and details of the image structure (PISDGAN) is proposed. The method is based on the classical cycle consistency image translation architecture, and its generator is adjusted to a U-shaped residual structure. At the same time, a multiscale cross-layer adaptation module (CLA) is designed, which integrates features of different depths through 'feature transfer' to obtain abundant image information. A non-local feature denoising module (FD) is also introduced to enhance the quality of the translated image. In addition, the structure difference loss is proposed to constrain the image structure to ensure the integrity of the translated image. Based on the self-built white light and narrow band laryngeal image datasets, the experimental comparison of PISDGAN and other similar image translation methods shows that the proposed method not only effectively preserves the image structure, but also maintains good image quality while highlighting the details.
A Novel Machine Language-Driven Data Aggregation Approach to Predict Data Redundancy in IoT-Connected Wireless Sensor Networks

Real world data aggregation and delivery in Internet of Things (IoT) technology are essential to predict and retrieve target data in short time so that the end user feels no delay but ensures a high quality of information. In addition to habitat monitoring and disaster management, these networks have a wide range of other uses, including security and military operations. The processing capabilities of sensor nodes are restricted due to the fact that they have a limited battery life and hence a modest size and processing capacity. WSNs are also susceptible to failure as a result of the limited battery power available. In WSNs, data aggregation is practiced as an energy efficient strategy to reduce computing and transmission latency. It is because of sensor node distribution density that shares the same data at a time data redundancy comes to exist. It is possible to reduce redundancy by adopting a suitable machine learning algorithm while executing the data aggregation process. Researchers are still chasing behind algorithms and modeling strategies effectively to ease the process of developing an effective and acceptable data aggregation strategy from existing wireless sensor network (WSN) models. A three stage framework is proposed for an efficient data aggregation mechanism, and the stages are Modified LEACH, extreme learning machines (ELM), adaptive Kalman filter, and Bi-LSTM. This experiment result shows better performance than the existing methods.
FYU-Net: A Cascading Segmentation Network for Kidney Tumor Medical Imaging

Automated segmentation of renal tumors is essential for the diagnostic evaluation of kidney cancer. However, renal tumor volume is generally small compared with the volume of the kidney and is irregularly distributed; moreover, the location and shape of renal tumors are highly variable, making the segmentation task extremely challenging. To solve the aforementioned problems, a cascaded segmentation model (FYU-Net) for computed tomography (CT) images is proposed in this paper to achieve automatic kidney tumor segmentation. The proposed model involves two main steps. In the first step, a fast scan of the kidney CT data is performed using a localization network to find slices containing tumors, and coarse segmentation is performed simultaneously. In the second step, a segmentation framework embedded with the feature pyramid network module is employed to finely segment kidney tumors. By building a feature pyramid structure, targets of different sizes are distributed to be detected on different feature layers to extract richer feature information. In addition, the top-down structure allows the information of the higher-level feature maps to be transferred to the lower-level feature maps, enhancing the semantic information of the lower-level feature maps. Comparative experiments were conducted on the Kidney PArsing Challenge 2022 public dataset; the average Jaccard coefficient and average Dice coefficient of tumor structure segmentation were more than 70.73% and more than 82.85%, respectively. The results demonstrate the effectiveness of the proposed model for kidney tumor segmentation.
SceneViewer: Automating Residential Photography in Virtual Environments

Selecting views is one of the most common but overlooked procedures in topics related to 3D scenes. Typically, existing applications and researchers manually select views through a trial-and-error process or "preset" a direction, such as the top-down views. For example, literature for scene synthesis requires views for visualizing scenes. Research on panorama and VR also require initial placements for cameras, etc. This article presents SceneViewer, an integrated system for automatic view selections. Our system is achieved by applying rules of interior photography, which guides potential views and seeks better views. Through experiments and applications, we show the potentiality and novelty of the proposed method.
Responsible model deployment via model-agnostic uncertainty learning

Reliably predicting potential failure risks of machine learning (ML) systems when deployed with production data is a crucial aspect of trustworthy AI. This paper introduces the Risk Advisor, a novel post-hoc meta-learner for estimating failure risks and predictive uncertainties of any already-trained black-box classification model. In addition to providing a risk score, the Risk Advisor decomposes the uncertainty estimates into aleatoric and epistemic uncertainty components, thus giving informative insights into the sources of uncertainty inducing the failures. Consequently, Risk Advisor can distinguish between failures caused by data variability, data shifts and model limitations and provide useful guidance on appropriate risk mitigation actions (e.g., collecting more data to counter data shift). Extensive experiments on various families of black-box classification models and on real-world and synthetic datasets covering common ML failure scenarios show that the Risk Advisor reliably predicts deployment-time failure risks in all the scenarios, and outperforms strong baselines.
A novel context-aware system to improve driver’s field of view in urban traffic networks

Principal objectives of the Intelligent Transportation Systems (ITS) are to improve traffic safety, facilitate informed traffic decision making, and enhance quality of life and services in a smart traffic environment. Vehicle crashes at urban traffic intersections are among the rudimentary sources of injuries and fatalities in the cities. According to the report of the World Health Organization (WHO), in every 25 seconds, one vulnerable road-user is being killed by a vehicle crash. Therefore, it is necessary to take a novel and smart approach for improving the safety and reducing vehicle crashes. This leads to a contextual perception and spatial awareness of driver to increase security and safety for the driver, vehicle, and road users. Autonomous vehicles collects the information from the environment through equipped sensors on the vehicle such as camera, laser, radar, and Global Navigation Satellite Systems (GNSS). The main challenge arises when the person or objects are located beyond the driver’s Field of View (FOV) and cannot be detected by embedded sensors on the vehicles. This paper proposes an Advanced Driver Assistance System (ADAS) to increase the safety on road intersections by taking advantage of existing infrastructures (e.g road camera) being used for traffic control. The aim of this research is improving the driver’s FOV using a computer vision approach (e.g background subtraction algorithm) and Location Based Service (LBS). The case study results at Tehran metropolitan demonstrate the reduction in traffic collision risk and improvement of pedestrian safety using the proposed system.
A new authentication scheme based on monogenic semigroup

The reduction of constrained mathematical structures leads us to generalize any abstract structures. Using minimum conditions to construct a secure and robust component of the modern encryption algorithm is one crucial problem in multimedia security. With this understanding, we have proposed a new algebraic structure, namely monogenic semigroup, to construct a digital information authentication scheme. Authentication is always completed at the beginning of the application, before any throttling or approval checks are performed, and before any other code is allowed to begin running in the background. Many authentication schemes offer a complex structure for implementation in cryptosystems and applications. The anticipated mechanism uses a simple mathematical structure having the least conditions as compared to other mathematical structures. The suggested scheme provides structures for the authentication of text messages and images.
A simple Bayesian state-space approach to the collective risk models

The collective risk model (CRM) for frequency and severity is an important tool for retail insurance ratemaking, natural disaster forecasting, as well as operational risk in banking regulation. This model, initially designed for cross-sectional data, has recently been adapted to a longitudinal context for both a priori and a posteriori ratemaking, through random effects specifications. However, the random effects are usually assumed to be static due to computational concerns, leading to predictive premiums that omit the seniority of the claims. In this paper, we propose a new CRM model with bivariate dynamic random effects processes. The model is based on Bayesian state-space models. It is associated with a simple predictive mean and closed form expression for the likelihood function, while also allowing for the dependence between the frequency and severity components. A real data application for auto insurance is proposed to show the performance of our method.
Continuous speech segmentation by L1 and L2 speakers of English: the role of syntactic and prosodic cues

We investigated how native and fluent users of English segment continuous speech and to what extent they use sound-related and structure-related cues. As suggested by the notion of multi-competence, L1 users are not seen as ideal models with perfect command of English, and L2 users not as lacking in competence. We wanted to see how language experience affects speech segmentation. We had participants listen to extracts of spontaneous spoken English and asked them to mark boundaries between speech segments. We found that in chunking authentic speech, both groups made the most use of prosody, with L1 users relying slightly more on it. However, the groups did not differ in segmentation strategies and performed alike in efficiency and agreement. Results show that in line with multi-competence, native speakers do not have an advantage over fluent speakers in higher-level speech processes, and that the outcome of natural speech comprehension does not significantly depend on the different language experiences at high levels of fluency. We suggest that research on speech segmentation should take natural continuous speech on board and investigate fluent users independently of their L1s to gain a more holistic view of processes and consequences of speech segmentation.
Multiple-symbol noncoherent learning detection of coded QAM signals in IEEE 802.15.3 Wireless Multi-media Networks

We consider the noncoherent deep learning problem for coded signal detection under the phase noncoherent channels for remote home healthcare applications with high data rate. In particular, a multiple-symbol noncoherent learning detection (MNLD) scheme based on neural networks is proposed for low-density parity-check (LDPC) coded noncoherent quadrature amplitude modulation (QAM) signals in IEEE 802.15.3 Wireless Multi-media Networks. Our derivation shows that extensive operations for the first kind zero-order modified Bessel function is unavoidable for the implementation of the optimal bit log-likelihood ratio (LLR) for decoding in traditional multiple-symbol detection (MSD) scheme. The perfect estimation of the channel state information (CSI), i.e., a priori information about the variance of the additive white Gaussian noise (AWGN), is also required for the receiver. This is clearly not computationally practical for Wireless Multi-media Networks. Consequently, we developed an improved approach based on feed-forward neural networks to accurately calculate the bit LLR. Furthermore, to decrease the generation size of training set and thus increase the training speed of the proposed neural networks, we uniformly quantize the continuous carrier phase offset (CPO), which is random and unknown, into discrete status. Our simulation results verify the learning efficiency of this simplified training-set generation configuration. The decoding convergence is successfully accelerated and much performance gain is finally achieved when compared with traditional decoding using the perfect bit LLR. This is clearly critical for high reliable transmission of home healthcare information.
DeepPavlov Topics: Topic Classification Dataset for Conversational Domain in English

This paper presents “DeepPavlov Topics", a new dataset for topic classification in conversational domain. The dataset was collected and filtered automatically from web-sites and open datasets. We identify 33 topics, and present full (4.2M samples) and down-sampled (2.2M samples) versions of the “DeepPavlov Topics". The proposed topics are aimed to cover conversational domain in details but maintain interpretability. We report baseline classification results trained in multi-label setup to allow multiple classes per text during inference. We also release pre-trained models for topic classification including distilled and multi-lingual versions.
Optimized scheduling of multi‐user Map‐Reduce jobs in heterogeneous environment

Summary Map‐Reduce is a programming paradigm widely used for data intensive applications in distributed computing. Divisible load theory (DLT) is another model to partition divisible load for parallel processing. In this work, a new job scheduler named virtual job scheduler (VJS) is developed to schedule the MapReduce jobs based on DLT. VJS constructs a virtual job set from the queue of jobs awaiting execution by considering the CPU and IO resource utilization levels of each job. The core of VJS is in its partitioning algorithm. Two novel partitioning algorithms, namely, two level successive partitioning (TLSP) and predictive partitioning (PRED) have been proposed. TLSP applies DLT to both map and reduce phase in succession. This second load partitioning performed during the comparatively longer reduce phase, exploits the advantage of DLT better. PRED is a modification of TLSP aimed at optimizing the overall schedule length, by taking the idle time of the reducers into consideration. Evaluation of these two models across various execution environments has indicated a profound decrease in the wait time of reducers and as a result has shown a significant reduction in the makespan of the whole job as such. VJS altered to incorporate PRED partitioning algorithm has proved to be suitable for heterogeneous environment, with high resource utilization compared with the default Hadoop MapReduce scheduler.
Multilingual Case-Insensitive Named Entity Recognition

Although capitalisation is an important feature for the Named Entity Recognition (NER) task, the NER input data is not always cased. Recent studies suggest two main methods of dealing with such inconsistency: truecasing and training a model on a modified dataset. Furthermore, while developing virtual assistants there is often a need to support interaction in several languages. It has been shown that multilingual BERT can be successfully used for cross-lingual transfer, performing on datasets in various languages with scores comparable to those obtained with language-specific models. In this paper, we address the task of Named Entity Recognition on inconsistently capitalised data in English and Russian. We demonstrate that using multilingual BERT trained on a concatenation of original and lowered datasets is the most effective way to solve the task. Our model achieves the highest average result on CoNLL-2003 and Collection 3 datasets while being robust to missing casing.
Toward masked face recognition: An effective facial feature extraction and refinement model in multiple scenes

Abstract With the impact of the COVID‐19 epidemic, the demand for masked face recognition technology has increased. In the process of masked face recognition, some problems such as less feature information and poor robustness to the environment are obvious. The current masked face recognition model is not quantified enough for feature extraction, there are large errors for faces with high similarity, and the categories cannot be clustered during the detection process, resulting in poor classification of masks, which cannot be well adapted to changes in multiple environments. To solve current problems, this paper designs a new masked face recognition model, taking improved Single Shot Multibox Detector (SSD) model as a face detector, and replaces the input layer VGG16 of SSD with Deep Residual Network (ResNet) to increase the receptive field. In order to better adapt to the network, we adjust the convolution kernel size of ResNet. In addition, we fine‐tune the Xception network by designing a new fully connected layer, and reduce the training cycle. The weights of the three input samples including anchor, positive and negative are shared and clustered together with triplet network to improve recognition accuracy. Meanwhile, this paper adjusts alpha parameter in triplet loss. A higher value of alpha can improve the accuracy of model recognition. We further adopt a small trick to classify and predict face feature vectors using multi‐layer perceptron (MLP), and a total of 60 neural nodes are set in the three neural layers of MLP to get higher classification accuracy. Moreover, three datasets of MFDD, RMFRD and SMFRD are fused to obtain high‐quality images in different scenes, and we also add data augmentation and face alignment methods for processing, effectively reducing the interference of the external environment in the process of model recognition. According to the experimental results, the accuracy of masked face recognition reaches 98.3%, it achieves better results compared with other mainstream models. In addition, the hyper‐parameters tuning experiment is carried out to improve the utilization of computing resources, which shows better results than the indicators of different networks.