Reviewerly/exHarmony huggingface dataset:
DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions

<ns4:p><ns4:bold>Background:</ns4:bold> Augmented reality (AR) and artificial intelligence (AI) are highly disruptive technologies that have revolutionised practices in a wide range of domains. Their potential has not gone unnoticed in the security sector with several law enforcement agencies (LEAs) employing AI applications in their daily operations for forensics and surveillance. In this paper, we present the DARLENE ecosystem, which aims to bridge existing gaps in applying AR and AI technologies for rapid tactical decision-making in situ with minimal error margin, thus enhancing LEAs’ efficiency and Situational Awareness (SA).</ns4:p><ns4:p> <ns4:bold>Methods:</ns4:bold><ns4:bold> </ns4:bold>DARLENE incorporates novel AI techniques for computer vision tasks such as activity recognition and pose estimation, while also building an AR framework for visualization of the inferenced results via dynamic content adaptation according to each individual officer’s stress level and current context. The concept has been validated with end-users through co-creation workshops, while the decision-making mechanism for enhancing LEAs’ SA has been assessed with experts. Regarding computer vision components, preliminary tests of the instance segmentation method for humans’ and objects’ detection have been conducted on a subset of videos from the RWF-2000 dataset for violence detection, which have also been used to test a human pose estimation method that has so far exhibited impressive results and will constitute the basis of further developments in DARLENE.</ns4:p><ns4:p> <ns4:bold>Results: </ns4:bold>Evaluation results highlight that target users are positive towards the adoption of the proposed solution in field operations, and that the SA decision-making mechanism produces highly acceptable outcomes. Evaluation of the computer vision components yielded promising results and identified opportunities for improvement.</ns4:p><ns4:p> <ns4:bold>Conclusions:</ns4:bold> This work provides the context of the DARLENE ecosystem and presents the DARLENE architecture, analyses its individual technologies, and demonstrates preliminary results, which are positive both in terms of technological achievements and user acceptance of the proposed solution.</ns4:p>
A Deep Neural Network for Estimating Low-Density Lipoprotein Cholesterol From Electronic Health Records: Real-Time Routine Clinical Application

Background Previously, we constructed a deep neural network (DNN) model to estimate low-density lipoprotein cholesterol (LDL-C). Objective To routinely provide estimated LDL-C levels, we applied the aforementioned DNN model to an electronic health record (EHR) system in real time (deep LDL-EHR). Methods The Korea National Health and Nutrition Examination Survey and the Wonju Severance Christian Hospital (WSCH) datasets were used as training and testing datasets, respectively. We measured our proposed model’s performance by using 5 indices, including bias, root mean-square error, P10-P30, concordance, and correlation coefficient. For transfer learning (TL), we pretrained the DNN model using a training dataset and fine-tuned it using 30% of the testing dataset. Results Based on 5 accuracy criteria, deep LDL-EHR generated inaccurate results compared with other methods for LDL-C estimation. By comparing the training and testing datasets, we found an overfitting problem. We then revised the DNN model using the TL algorithms and randomly selected subdata from the WSCH dataset. Therefore, the revised model (DNN+TL) exhibited the best performance among all methods. Conclusions Our DNN+TL is expected to be suitable for routine real-time clinical application for LDL-C estimation in a clinical laboratory.
MOON: Multi-hash codes joint learning for cross-media retrieval

In recent years, cross-media hashing technique has attracted increasing attention for its high computation efficiency and low storage cost. However, the existing approaches still have some limitations, which need to be explored. (1) A fixed hash length (e.g., 16bits or 32bits) is predefined before learning the binary codes. Therefore, these models need to be retrained when the hash length changes, that consumes additional computation power, reducing the scalability in practical applications. (2) Existing cross-modal approaches only explore the information in the original multimedia data to perform the hash learning, without exploiting the semantic information contained in the learned hash codes. To this end, we develop a novel Multiple hash cOdes jOint learNing method (MOON) for cross-media retrieval. Specifically, the developed MOON simultaneously learns the hash codes with multiple lengths in a unified framework. Besides, to enhance the underlying discrimination, we combine the clues from the multimodal data, semantic label and learned hash codes for hash learning. As far as we know, the proposed MOON is the first attempt to simultaneously learn different length hash codes without retraining in cross-media retrieval. Experiments on several databases show that our MOON can achieve promising performance, outperforming some recent competitive shallow and deep methods.
Understand Me, if You Refer to Aspect Knowledge: Knowledge-Aware Gated Recurrent Memory Network

Aspect-level sentiment classification (ASC) aims to predict the fine-grained sentiment polarity towards a given aspect mentioned in a review. Despite recent advances in ASC, enabling machines to preciously infer aspect sentiments is still challenging. This paper tackles two challenges in ASC: (1) due to lack of aspect knowledge, aspect representation derived in prior works is inadequate to represent aspect’s exact meaning and property information; (2) prior works only capture either local syntactic information or global relational information, thus missing either one of them leads to insufficient syntactic information. To tackle these challenges, we propose a novel ASC model which not only end-to-end embeds and leverages aspect knowledge but also marries the two kinds of syntactic information and lets them compensate for each other. Our model includes four key components: (1) a knowledge-aware gated recurrent memory network recurrently integrates dynamically summarized aspect knowledge; (2) a dual syntax graph network combines both kinds of syntactic information to comprehensively capture sufficient syntactic information; (3) a knowledge integrating gate re-enhances the final representation with further needed aspect knowledge; (4) an aspect-to-context attention mechanism aggregates the aspect-related semantics from all hidden states into the final representation. Experimental results on several benchmark datasets demonstrate the effectiveness of our model, which overpass previous state-of-the-art models by large margins in terms of both Accuracy and Macro-F1. To facilitate further research in the community, we have released our source code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XingBowen714/KaGRMN-DSG_ABSA</uri> .
Various Approaches of Machine Translation for Marathi to English Language

Machine Translation (MT) is a generic term for computerised systems that generate translations from one natural language to another, with or without human intervention. Text may be used to examine knowledge, and turning that information into pictures helps people to communicate and acquire information.There seems to be a lot of work conducted on translating English to Hindi, Tamil, Bangla and other languages. The important parts of translation are to provide translated sentences with correct words and proper grammar. There has been a comprehensive review of 10 primary publications used in research. Two separate approaches are proposed, one uses rule based approach and other uses neural-machine translation approach to translate basic Marathi phrases to English. While designed primarily for Marathi-English language pairs, the design can be applied to other language pairs with a similar structure.
Families in Wild Multimedia: A Multimodal Database for Recognizing Kinship

Kinship, a soft biometric detectable in media, is fundamental for a myriad of use-cases. Despite the difficulty of detecting kinship, annual data challenges using still-images have consistently improved performances and attracted new researchers. Now, systems reach performance levels unforeseeable a decade ago, closing in on performances acceptable to deploy in practice. Like other biometric tasks, we expect systems can receive help from other modalities. We hypothesize that adding modalities to FIW, which has only still-images, will improve performance. Thus, to narrow the gap between research and reality and enhance the power of kinship recognition systems, we extend FIW with multimedia (MM) data (i.e., video, audio, and text captions). Specifically, we introduce the first publicly available multi-task MM kinship dataset. To build FIW MM, we developed machinery to automatically collect, annotate, and prepare the data, requiring minimal human input and no financial cost. The proposed MM corpus allows the problem statements to be more realistic template-based protocols. We show significant improvements in all benchmarks with the added modalities. The results highlight edge cases to inspire future research with different areas of improvement. FIW MM supplies the data needed to increase the potential of automated systems to detect kinship in MM. It also allows experts from diverse fields to collaborate in novel ways.
A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the domain of pseudo-Boolean functions in a black-box setting. Moreover, instead of using a surrogate model as an approximation of a fitness function, we propose to precisely learn the coefficients of the Walsh decomposition of a fitness function and use the Walsh decomposition as a surrogate. If the coefficients are learned correctly, then the Walsh decomposition values perfectly match with the fitness function, and, thus, the optimal solution to the problem can be found by optimizing the surrogate without any additional evaluations of the original fitness function. It is known that the Walsh coefficients can be efficiently learned for pseudo-Boolean functions with k -bounded epistasis and known problem structure. We propose to learn dependencies between variables first and, therefore, substantially reduce the number of Walsh coefficients to be calculated. After the accurate Walsh decomposition is obtained, the surrogate model is optimized using GOMEA, which is considered to be a state-of-the-art binary optimization algorithm. We compare the proposed approach with standard GOMEA and two other Walsh decomposition-based algorithms. The benchmark functions in the experiments are well-known trap functions, NK-landscapes, MaxCut, and MAX3SAT problems. The experimental results demonstrate that the proposed approach is scalable at the supposed complexity of O (ℓ log ℓ) function evaluations when the number of subfunctions is O (ℓ) and all subfunctions are k -bounded, outperforming all considered algorithms.
Copula-based software metrics aggregation

Abstract A quality model is a conceptual decomposition of an abstract notion of quality into relevant, possibly conflicting characteristics and further into measurable metrics. For quality assessment and decision making, metrics values are aggregated to characteristics and ultimately to quality scores. Aggregation has often been problematic as quality models do not provide the semantics of aggregation. This makes it hard to formally reason about metrics, characteristics, and quality. We argue that aggregation needs to be interpretable and mathematically well defined in order to assess, to compare, and to improve quality. To address this challenge, we propose a probabilistic approach to aggregation and define quality scores based on joint distributions of absolute metrics values. To evaluate the proposed approach and its implementation under realistic conditions, we conduct empirical studies on bug prediction of ca. 5000 software classes, maintainability of ca. 15000 open-source software systems, and on the information quality of ca. 100000 real-world technical documents. We found that our approach is feasible, accurate, and scalable in performance.
Multitarget Real-Time Tracking Algorithm for UAV IoT

Unmanned aerial vehicles (UAVs) have increased the convenience of urban life. Representing the recent rapid development of drone technology, UAVs have been widely used in fifth-generation (5G) cellular networks and the Internet of Things (IoT), such as drone aerial photography, express drone delivery, and drone traffic supervision. However, owing to low altitude and low speed, drones can only limitedly monitor and detect small target objects, resulting in frequent intrusion and collision. Traditional methods of monitoring the safety of drones are mostly expensive and difficult to implement. In smart city construction, a large number of smart IoT cameras connected to 5G networks are installed in the city. Captured drone images are transmitted to the cloud via a high-speed and low-latency 5G network, and machine learning algorithms are used for target detection and tracking. In this study, we propose a method for real-time tracking of drone targets by using the existing monitoring network to obtain drone images in real time and employing deep learning methods by which drones in urban environments can be guided. To achieve real-time tracking of UAV targets, we employed the tracking-by-detection mode in machine learning, with the network-modified YOLOv3 (you only look once v3) as the target detector and Deep SORT as the target tracking correlation algorithm. We established a drone tracking dataset that contains four types of drones and 2800 pictures in different environments. The tracking model we trained achieved 94.4% tracking accuracy in real-time UAV target tracking and a tracking speed of 54 FPS. These results comprehensively demonstrate that our tracking model achieves high-precision real-time UAV target tracking at a reduced cost.
STEM and STEAM Education in Russian Education: Conceptual Framework

The paper examines the STEM approach as one of the revolutionary tools for transforming education. This paper discloses the content of the concept of “STEAM-education”, substantiates its importance and prerequisites for its appearance. The author elucidates a comparative analytical review of trends and prospects for the development of STEAM education in the Russian Federation. In this paper, the authors want to touch upon a comprehensive theoretical and methodological study conducted on the declared problem to identify the problem of the formation and development of the STEAM approach in the education system in the Russian Federation at all levels: general education, vocational education, and additional education. The object of this study was STEM education. The subject of this research was the STEM approach and STEM technologies in the system of general education, vocational education, and additional education. The methodological foundation of the study was the general scientific propositions on the holistic, systemic, and dialectical approaches to research, which made it possible to assess the totality of interrelated and interacting elements that form a certain integrity. The use of these approaches provided a comprehensive analysis and formulation of the research problem and determined the strategy for its study. The analysis of the current state of STEM and STEAM education in higher education in the Russian Federation suggests that it is necessary to stimulate the development of information and communication technologies in the country’s education system in general, as well as to develop comprehensive programs for adapting STEM and STEAM approaches among the subjects of educational relations. In the context of modern global risks and challenges, the educational trajectory of development in Russia should be conceptually consistent with the new strategy in the field of natural science education adopted in the United States, “The Path to Success: American Strategy for STEM Education”. Experts believe that through the practical implementation of STEM and STEAM technologies, it is possible to improve the efficiency of the quality of training for the purposes of the modern digital economic sphere. In this regard, the primary competences obtained in the higher education system in the areas of STEM and STEAM training should be cognitive, personal, and interpersonal competences, expressed in digital literacy, computational thinking, creativity, open mind, soft skills, and others. The concept of STEAM education in the system of higher professional education in Russia will ensure the transformation of ideas about scientific knowledge and build an innovative development process through the formation of skills in the field of exact sciences, linguistics, engineering, art, etc.
Optimizing Convolutional Neural Network Performance by Mitigating Underfitting and Overfitting

With human society stepping into the data era, deep learning has been widely used in various industries. However, in the training process of deep learning, underfitting and overfitting are often encountered, leading to poor network generalization performance. Based on a Convolutional Neural network (CNN), this paper optimizes the model by mitigating underfitting and overfitting. Incorporating multiple approaches, the accuracy of the model is finally improved by 4 percentage points by adjusting the learning rate and adding regularization, etc.
Fast Hybrid Image Retargeting

Image retargeting changes the aspect ratio of images while aiming to preserve content and minimise noticeable distortion.Fast and high-quality methods are particularly relevant at present, due to the large variety of image and display aspect ratios.We propose a retargeting method that quantifies and limits warping distortions with the use of contentaware cropping.The pipeline of the proposed approach consists of the following steps.First, an importance map of a source image is generated using deep semantic segmentation and saliency detection models.Then, a preliminary warping mesh is computed using axis aligned deformations [1], enhanced with the use of a distortion measure to ensure low warping deformations.Finally, the retargeted image is produced using a content-aware cropping algorithm.In order to evaluate our method, we perform a user study based on the RetargetMe benchmark.Experimental analyses show that our method outperforms recent approaches, while running in a fraction of their execution time.
Job Recommendation System using Content and Collaborative Filtering based Techniques

Internet based recruiting platforms decrease advertisement cost, but they suffer from information overload problem. The job recommendation systems (JRS) have achieved success in e-recruitment process but still they are not able to capture the complexity of matching between candidates’ desires and organizations’ requirements. Thus, we propose a hybrid JRS which combines recommendations of content-based filtering (CBF) and collaborative filtering (CF) to overcome their individual major shortcomings namely overspecialization and over-fitting. In proposed system, CBF model makes recommendations based on candidates’ skills identified from past jobs in which they have applied and CF model makes recommendations based on jobs in which similar users have applied and also those jobs in which that user has applied frequently together in very similar contexts using Word2Vec’s skip-gram model. We used k-Nearest Neighbors technique and Pearson Correlation Coefficient. The recall of our proposed model is found to be 63.97% on a data set which had nearly 1900+ jobs and 23,000 job applicants.
Offloading load balancers onto SmartNICs

Load balancers are pervasively used inside today's clouds to distribute network requests across data center servers at scale. While load balancers were initially built using dedicated and custom hardware, most cloud providers now use software-based load balancers. This allows the implementations to be more agile and also enables on-demand provisioning of load balancing workload on generic servers, but it comes with increased provisioning and operating costs.
Risk Management and Standard Compliance for Cyber-Physical Systems of Systems

The Internet of Things (IoT) and cloud technologies are increasingly implemented in the form of Cyber-Physical Systems of Systems (CPSoS) for the railway sector. In order to satisfy the security requirements of Cyber-Physical Systems (CPS), domainspecific risk identification assessment procedures have been developed. Threat modelling is one of the most commonly used methods for threat identification for the security analysis of CPSoS and is capable of targeting various domains. This paper reports our experience of using a risk management framework identify the most critical security vulnerabilities in CPSoS in the domain and shows the broader impact this work can have on the domain of safety and security management. Moreover, we emphasize the application of common analytical methods for cyber-security based on international industry standards to identify the most vulnerable assets. These will be applied to a meta-model for automated railway systems in the concept phase to support the development and deployment of these systems. Furthermore, it is the first step to create a secure and standard complaint system by design.
Qualifying Certainty in Radiology Reports through Deep Learning–Based Natural Language Processing

Communication gaps exist between radiologists and referring physicians in conveying diagnostic certainty. We aimed to explore deep learning-based bidirectional contextual language models for automatically assessing diagnostic certainty expressed in the radiology reports to facilitate the precision of communication.We randomly sampled 594 head MR imaging reports from an academic medical center. We asked 3 board-certified radiologists to read sentences from the Impression section and assign each sentence 1 of the 4 certainty categories: "Non-Definitive," "Definitive-Mild," "Definitive-Strong," "Other." Using the annotated 2352 sentences, we developed and validated a natural language-processing system based on the start-of-the-art bidirectional encoder representations from transformers (BERT), which can capture contextual uncertainty semantics beyond the lexicon level. Finally, we evaluated 3 BERT variant models and reported standard metrics including sensitivity, specificity, and area under the curve.A κ score of 0.74 was achieved for interannotator agreement on uncertainty interpretations among 3 radiologists. For the 3 BERT variant models, the biomedical variant (BioBERT) achieved the best macro-average area under the curve of 0.931 (compared with 0.928 for the BERT-base and 0.925 for the clinical variant [ClinicalBERT]) on the validation data. All 3 models yielded high macro-average specificity (93.13%-93.65%), while the BERT-base obtained the highest macro-average sensitivity of 79.46% (compared with 79.08% for BioBERT and 78.52% for ClinicalBERT). The BioBERT model showed great generalizability on the heldout test data with a macro-average sensitivity of 77.29%, specificity of 92.89%, and area under the curve of 0.93.A deep transfer learning model can be developed to reliably assess the level of uncertainty communicated in a radiology report.