Reviewerly/exHarmony huggingface dataset:
Mortality Prediction of Patients With Cardiovascular Disease Using Medical Claims Data Under Artificial Intelligence Architectures: Validation Study

Background Cardiovascular disease (CVD) is the greatest health problem in Australia, which kills more people than any other disease and incurs enormous costs for the health care system. In this study, we present a benchmark comparison of various artificial intelligence (AI) architectures for predicting the mortality rate of patients with CVD using structured medical claims data. Compared with other research in the clinical literature, our models are more efficient because we use a smaller number of features, and this study could help health professionals accurately choose AI models to predict mortality among patients with CVD using only claims data before a clinic visit. Objective This study aims to support health clinicians in accurately predicting mortality among patients with CVD using only claims data before a clinic visit. Methods The data set was obtained from the Medicare Benefits Scheme and Pharmaceutical Benefits Scheme service information in the period between 2004 and 2014, released by the Department of Health Australia in 2016. It included 346,201 records, corresponding to 346,201 patients. A total of five AI algorithms, including four classical machine learning algorithms (logistic regression [LR], random forest [RF], extra trees [ET], and gradient boosting trees [GBT]) and a deep learning algorithm, which is a densely connected neural network (DNN), were developed and compared in this study. In addition, because of the minority of deceased patients in the data set, a separate experiment using the Synthetic Minority Oversampling Technique (SMOTE) was conducted to enrich the data. Results Regarding model performance, in terms of discrimination, GBT and RF were the models with the highest area under the receiver operating characteristic curve (97.8% and 97.7%, respectively), followed by ET (96.8%) and LR (96.4%), whereas DNN was the least discriminative (95.3%). In terms of reliability, LR predictions were the least calibrated compared with the other four algorithms. In this study, despite increasing the training time, SMOTE was proven to further improve the model performance of LR, whereas other algorithms, especially GBT and DNN, worked well with class imbalanced data. Conclusions Compared with other research in the clinical literature involving AI models using claims data to predict patient health outcomes, our models are more efficient because we use a smaller number of features but still achieve high performance. This study could help health professionals accurately choose AI models to predict mortality among patients with CVD using only claims data before a clinic visit.
Comparison of Different Bat Initialization Techniques for Global Optimization Problems

Bat algorithm (BA) is a population-based stochastic search technique that has been widely used to solve the diverse kind of optimization problems. Population initialization is the current ongoing research problem in evolutionary computing algorithms. Appropriate population initialization assists the algorithm to investigate the swarm search space effectively. BA faces premature convergence problem to find actual global optimization value. Low discrepancy sequences are slightly lesser random number than pseudo-random; however, they are more powerful for computational approaches. In this work, new population initialization approach Halton (BA-HA), Sobol (BA-SO), and Torus (BA-TO) are proposed, which helps bats to avoid from the premature convergence. The proposed approaches are examined on standard benchmark functions, and simulation results are compared with standard BA initialized with uniform distribution. The results depict that substantial enhancement can be attained in the performance of standard BA while varying the random numbers sequences to low discrepancy sequences.
Safety Wing for Industry (SWI 2020) – An Advanced Unmanned Aerial Vehicle Design for Safety and Security Facility Management in Industries

An increase in the use of innovative technology brings a security nightmare. The Quadcopters are usually loaded with multiple sensors and miniature processors. Industrial drones are known by multiple terms like Unmanned Aircraft System (UAV); Remotely Piloted Aircraft is a standard vehicle which is used to monitor the terrains in and out of the Industrial Plant. All commercial and military drones have a point-to-point connection with the wireless network at one end and the control system at the other end. Automatons, authoritatively named as UAV, are pilotless airplanes whose fundamental utility is to accumulate data on a domain and transfer it back to its controller. In the early 2020s, a few companies such as Amazon and Facebook started utilizing the UAV, improving Internet access and delivery of products to the door; it is in the testing phase in some countries.
Sensor Technology for Smart Homes

As advances in technology continue relentlessly, intriguing possibilities for smart home services have emerged [...]
Low-Cost Optical Tracking of Soccer Players

Sports analytics are on the rise in European football, however, due to the high cost so far only the top tier leagues and championships have had the privilege of collecting high precision data to build upon. We believe that this opportunity should be available for everyone especially for youth teams, to develop and recognize talent earlier. We therefore set the goal of creating a low-cost player tracking system that could be applied in a wide base of football clubs and pitches, which in turn would widen the reach for sports analytics, ultimately assisting the work of scouts and coaches in general. In this paper, we present a low-cost optical tracking solution based on cheap action cameras and cloud-deployed data processing. As we build on existing research results in terms of methods for player detection, i.e., background-foreground separation, and for tracking, i.e., Kalman filter, we adapt those algorithms with the aim of sacrificing as least as possible on accuracy while keeping costs low. The results are promising: our system yields significantly better accuracy than a standard deep learning based tracking model at the fraction of its cost. In fact, at a cost of $2.4 per match spent on cloud processing of videos for real-time results, all players can be tracked with a 11-meter precision on average.
A Deep Learning Approach with Line Drawing for Isolated Online Bangla Character Recognition

Handwriting-based technologiesMukherjee, Himadri haveMajumder, Chandrima evolvedDhar, Ankita tremendouslySen, Shibaprasad with time for multitudinous languages but theObaidullah, Sk Md developmentRoy, Kaushik has been limited for Indic languages. One of the driving factors behind this is the complexity of the Indic scripts. Development in the field of Bangla online handwriting has been even slower, one of the reasons being the wide varieties of writing characters. The availability of datasets is also an issue especially for Bangla which is the seventh most spoken language in the World. It is thus very much necessary to develop online recognizers for Bangla. One of the primal tasks for recognizing Bangla handwriting is the identification of characters in isolation. In this paper, different line drawing techniques are used to reconstruct the isolated characters from their online information which were then fed to a deep learning-based framework for segregation. Experiments were performed with 15000 characters which produced the highest accuracy of 99.89%.
Fusion of Mathematical Morphology with Adaptive Gamma Correction for Dehazing and Visibility Enhancement of Images

Images contaminated with haze generally have faded colors and low contrast, and thus affect object tracking, object recognition, intelligent surveillance, etc. Therefore, dehazing becomes necessary and is aimed to recover the image without color distortion. This paper presents a dehazing approach combining dark channel prior (DCP) with mathematical morphology and a visibility enhancement algorithm. Adaptive gamma correction based weighted distribution (AGCWD) is employed for visibility restoration with a fast processing time. The proposed method is able to eliminate halo artifacts in the restored images. Experimental results obtained are compared with the state- of- the- art dehazing algorithms using some standard metrics.
Return on Investment in Machine Learning: Crossing the Chasm between Academia and Business

Abstract Academia remains the central place of machine learning education. While academic culture is the predominant factor influencing the way we teach machine learning to students, many practitioners question this culture, claiming the lack of alignment between academic and business environments. Drawing on professional experiences from both sides of the chasm, we describe the main points of contention, in the hope that it will help better align academic syllabi with the expectations towards future machine learning practitioners. We also provide recommendations for teaching of the applied aspects of machine learning.
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data

Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White. Proceedings of the 28th International Conference on Computational Linguistics: Industry Track. 2020.
Feel Vibration: Challenge-Response Mobile Authentication with Covert Channel

PIN is widely used as an authentication method on the mobile phone in various public places. However a malicious person nearby may peek into the screen and remember the password. This kind of shoulder surfing attack is more difficult to prevent in modern society. A stronger adversary may use his camera or video surveillance system to record the entry process. In this paper, we present three approaches to solve this problem. Those approaches utilize the phone's vibration as the covert channel to deliver challenge- response secretly from camera. The experimental results show that those approaches can effectively prevent the shoulder surfing attack, even if the password entry process is completely exposed to the adversary's camera.
Digital Holographic Watermarking Algorithm Based on DWT-DCT

Abstract In order to improve the robustness and invisibility of watermarking, a digital holographic watermarking algorithm based on DWT-DCT is proposed. First, the watermark image is generated into a digital hologram to improve the security and non-tearability of the watermark, and the I1 image is selected as the watermark image to be embedded. Perform discrete wavelet transform on the original carrier image to extract its low-frequency coefficients; secondly, the low-frequency coefficients are subjected to DCT transformation, and finally the watermark information is embedded in the high-frequency coefficient matrix after DCT, thereby completing the watermark information embedding. This algorithm combines the transformation characteristics of DWT and DCT, makes up for each other’s shortcomings, and makes digital watermarking more robust and invisible. In order to verify the anti-attack ability of this algorithm, different attack tests were performed on the watermark image. The results show that this algorithm is robust against simple linear attacks and noise attacks.
A Strategic Vision to Reduce Cybercrime to Enhance Cyber Security

The cyber-security development for future generations is at stake as a global concern. The existing strategic and policy structures on cyber security and awareness-raising at many levels needs more investigation in order to formulate workable and efficient strategic vision that addresses actual needs and challenges. The justification for this work is therefore to test the robustness, in contrast with some of the most technologically advanced countries on the Asian Continent and others like the USA, Japan, of Saudi Arabia's current cyber security strategy in order to keep the NCSS up-to-date. This research aimed to develop a strategic vision to combat cybercrime to enhance cyber security. The research results confirmed the approval of the members of the study community to a medium degree on the reality of digital extremism and cyber terrorism as seen by the researcher. Moreover, the approval of the members of the study community was to a high degree on the role of combating cybercrime in promoting human security as seen by the researcher. The members of the study community agreed with a high degree on the obstacles identified by the researcher to combat cyber-crimes to enhance human security in Saudi Arabia. There was a high approval of the study community members on the strategic vision developed by the researcher to combat cyber-crimes to enhance human security in Saudi Arabia.
Evaluating FAIRness of Genomic Databases

Several studies show the difficulty experienced for the reuse of the ever increasing amount of genomic data. Initiatives are being created to mitigate this concern; one of the most well-known is the FAIR Data Principles. Nonetheless, the related works are too generic and do not describe simultaneously and properly the human and machine perspectives of the FAIRness of databases. Hence, in order to bridge this gap, our paper introduces an approach named the Bio FAIR Evaluator Framework, a semiautomated tool aimed to analyze the FAIRness of genomic databases. Furthermore, we performed experiments that analyzed selected genomic databases according to two orthogonal and complementary perspectives (human and machine). The approach uses standardized FAIR metrics and generates recommendation reports to researchers indicating how to enhance the FAIRness of databases. Our findings, when compared with related works, show the feasibility of the approach, indicating that the current genomic databases are poorly compliant with FAIR Principles.
Identifying Implicit Quotes for Unsupervised Extractive Summarization of Conversations

We propose Implicit Quote Extractor, an end-to-end unsupervised extractive neural summarization model for conversational texts. When we reply to posts, quotes are used to highlight important part of texts. We aim to extract quoted sentences as summaries. Most replies do not explicitly include quotes, so it is difficult to use quotes as supervision. However, even if it is not explicitly shown, replies always refer to certain parts of texts; we call them implicit quotes. Implicit Quote Extractor aims to extract implicit quotes as summaries. The training task of the model is to predict whether a reply candidate is a true reply to a post. For prediction, the model has to choose a few sentences from the post. To predict accurately, the model learns to extract sentences that replies frequently refer to. We evaluate our model on two email datasets and one social media dataset, and confirm that our model is useful for extractive summarization. We further discuss two topics; one is whether quote extraction is an important factor for summarization, and the other is whether our model can capture salient sentences that conventional methods cannot.
Bibliometric Analysis of Water Resource Management

Li, Q.; Guo, X., and Zhang, L., 2020. Bibliometric analysis of water resource management. In: Hu, C. and Cai, M. (eds.), Geo-informatics and Oceanography. Journal of Coastal Research, Special Issue No. 105, pp. 210–214. Coconut Creek (Florida), ISSN 0749-0208.Based on the Web of Science database, this paper systematically reveals a general trend of publication, country or region, and organization distribution; source publication distribution; frequently cited papers; co-occurring keywords; and time zone analysis of keywords in the field of water resource management (WRM) from multiple perspectives by using bibliometric analysis and visualization software. A corresponding visualization knowledge graph is obtained that clearly and intuitively shows the current situation and research hot spots of WRM research from a specific perspective. The results show that in the past 21 years, research in WRM has been increasing continuously. China's research on WRM is in the leading position, and “water resource management,” “management,” and “integrated water resource management” are the top 3 high-frequency keywords. These findings provide a knowledge basis for further research on WRM.
Blockchain technology and its relevance in healthcare

Blockchain technology is extensively used in the recent years to maintain data security for the digital asset transformation without any third-party intervention. Blockchain is a rapidly evolving and emerging technology that has gained more attention from industry, academia and government sectors because of its latent relevance and offering potential solutions. The characteristics of blockchain made it as a cutting-edge technology and can be leveraged to solve significant issues in many promising applications. A plethora of blockchain application leverages and revolutionizes several disciplines with the potential of large-scale exploration. There is a promising future for the blockchain in the healthcare domain because of scalability, traceability, fault tolerance and high security. The benefits, challenges and relevant use cases for incorporating blockchain in the healthcare domain are presented by performing studies with in-depth analysis. The chapter provided a detailed study on the fundamentals of blockchain technology, types of blockchain technology and the relevance of blockchain in the healthcare industry along with several use cases. The research challenges and opportunities are also discussed in detail.
Anomaly Detection in Crowded Scenes Using Motion Influence Map and Convolutional Autoencoder

In this paper, we presentAgrawal, Shilpi aDash, Ratnakar method to detect and localize unusual activity in crowded scenes. A large number of surveillance cameras are fixed at various places for security purposes. We propose an autoencoder-based deep learning framework to categorize abnormality. Optical flow is computed using motion influence map and fed to the convolutional autoencoder. Thus, the spatio-temporal features obtained from the output of the encoder are used for classification. K-means clustering has been utilized to classify the spatio-temporal features. Experiments were conducted on standard crowd datasets and it is observed that the proposed model achieves comparable accuracy measure with state-of-the-art techniques.
Energetic Sustainability of Systems

An important problem in our world is that humanity’s energy consumption is constantly rising. Therefore, nowadays there is an increasing emphasis on the problem of reusability and efficient energy management. The present paper studies the energy sustainability of systems by developing a unique test model. Using this test model, the theoretical problems of closed systems are investigated. With a theoretical experiment, the temporal motion of rigid systems is monitored and the behavior of flexible systems is analyzed. Finally, the study of the energy interaction of the general system and its environment shows the basic condition for the system’s overall sustainability.
Living Without Beth and Craig: Definitions and Interpolants in Description Logics with Nominals and Role Inclusions

The Craig interpolation property (CIP) states that an interpolant for an implication exists iff it is valid. The projective Beth definability property (PBDP) states that an explicit definition exists iff a formula stating implicit definability is valid. Thus, the CIP and PBDP transform potentially hard existence problems into deduction problems in the underlying logic. Description Logics with nominals and/or role inclusions do not enjoy the CIP nor PBDP, but interpolants and explicit definitions have many potential applications in ontology engineering and ontology-based data management. In this article we show the following: even without Craig and Beth, the existence of interpolants and explicit definitions is decidable in description logics with nominals and/or role inclusions such as ALCO, ALCH and ALCHIO. However, living without Craig and Beth makes this problem harder than deduction: we prove that the existence problems become 2EXPTIME-complete, thus one exponential harder than validity. The existence of explicit definitions is 2EXPTIME-hard even if one asks for a definition of a nominal using any symbol distinct from that nominal, but it becomes EXPTIME-complete if one asks for a definition of a concept name using any symbol distinct from that concept name.
Task Proposal: Abstractive Snippet Generation for Web Pages

We propose a shared task on abstractive snippet generation for web pages, a novel task of generating query-biased abstractive summaries for documents that are to be shown on a search results page. Conventional snippets are extractive in nature, which recently gave rise to copyright claims from news publishers as well as a new copyright legislation being passed in the European Union, limiting the fair use of web page contents for snippets. At the same time, abstractive summarization has matured considerably in recent years, potentially allowing for more personalization of snippets in the future. Taken together, these facts render further research into generating abstractive snippets both timely and promising.
Fault Diagnosis with Imbalanced Data Based on Auto-encoder

In class imbalance problem, researchers proposed data-level and algorithm-level methods successively. However, we found the imbalance is usually accompanied by the problem of small samples. For example, in the case of few fault samples and many normal samples, we need to deal with both imbalance problem and small sample problem. Based on the influence mechanism of the imbalance on the algorithm, this paper proposes a novel neural network model for solving the class imbalance problem with small samples. By changing the feature extraction process of auto-encoder, we can extract vector features, while the standard auto-encoder can only extract scalar features. By stacking the new auto-encoder model, we can deeply mine the data characteristics and extract the diversified vector features to complete the fault diagnosis task. Vector features effectively improve the feature extraction ability of the model and reduce the sensitivity of the model to class imbalance. The proposed deep model is verified using two types of fault diagnosis datasets and compared with other methods. The results indicate that the proposed method significantly improves the fault diagnosis accuracy in the task with class imbalance and small samples.
Development of a Prediction Model and an Adaptive Control Algorithm for the Data Center Thermal Environment

Purpose: As cooling energy accounts for 50.5% of the data center total energy use, it is necessary to reduce cooling energy with optimal control method. Thus, this study aimed at developing an adaptive control algorithm for data center thermal environment based on Artificial Neural Network (ANN). Method: A data center thermal environment model was built to obtain variable data which is used to train and evaluate performance of the prediction model and algorithm. The thermal environment prediction model was developed using ANN and optimization process was conducted by the Bayesian Optimization Algorithm. The adaptive control algorithm, which embedded the prediction model, adopted the Sliding Windows method and was optimized to maximize the control performance. Result: The performance evaluation of the developed algorithm was conducted compared with non-adaptive algorithm. As a result, the adaptive algorithm presented better performance than the non-adaptive with 0.45 of RMSE. Therefore, the developed algorithm secured the stability and accuracy and will be applied to supervisory control platforms.