Reviewerly/exHarmony huggingface dataset:
Comorbidities and medical history essential for mortality prediction in critically ill patients

Critically ill patients are a highly heterogenous population who tend to have many comorbidities. Often, patients admitted to intensive-care units (ICUs) with the same diagnosis and similar risk profiles according to available risk prediction scores have completely different clinical trajectories and outcomes. Even with increasingly large amounts of electronic health record data available, including clinical notes, vital sign measurements, laboratory data, and imaging data, the goal of unravelling complex disease mechanisms to better forecast patient outcomes remains largely unattained in critical care.
Learning Ensembles of Anomaly Detectors on Synthetic Data

The main aim of this work is to develop and implement an automatic anomaly detection algorithm for meteorological time-series. To achieve this goal we develop an approach to constructing an ensemble of anomaly detectors in combination with adaptive threshold selection based on artificially generated anomalies. We demonstrate the efficiency of the proposed method by integrating the corresponding implementation into "Minimax-94" road weather information system.
A New security Approach to Support the operations of ECC and AES Algorithms on FPGA

The growth of embedded systems makes it deemed needful to furnish security services for such applications. High safety and high speed of data transmission with less hardware resources become much more significant. In this paper, we propound an efficient hybrid cryptosystem that mixes benefits of the symmetric algorithms to speed up data encryption and the asymmetric encryption scheme to secure the exchange of symmetric session key. The aim of this work is to extend an asymmetric cryptosystem to support also a symmetric algorithm, and we assess our proposed design on DE2-115 board. We suggested a novel functional unit which extends the polynomial modular multiplication of Elliptic Curve cryptography (ECC) to calculate also the polynomial arithmetic operations of Advanced Encryption Standard (AES). Results show that the proposed design allows a trade-of between speed, area and power dissipation.
ICON: Incast Congestion Control using Packet Pacing in Datacenter Networks

Datacenters host a mix of applications which generate qualitatively distinct traffic patterns and impose varying network objectives. Online, user-facing applications generate many-to-one, incast traffic of mostly short flows, which are sensitive to tail of Flow Completion Times (FCT). Data analytics applications generate all-to-all traffic (e.g., Web search) of mostly short flows that saturate network bisection and the job completions require all flows to complete. Background applications (e.g., MapReduce) generate large flows and are throughput sensitive due to the sheer amount of data that they transfer over the network. While datacenter fabric provides good bisection bandwidth to handle all-to-all traffic, incast traffic is bottle-necked at edge switches and causes queue buildup at the port connected to the receiver. Because datacenter switches use shallow buffers to reduce cost and latency, the queue buildup problem is further exacerbated as the shallow buffers easily overflow causing packet drops and expensive TCP timeouts. To address these issues we propose ICON, a novel scheme which reduces incast-induced packet loss by setting a fine-grained control over sending rate by pacing traffic. We propose two variants: an application agnostic version that simply paces packet smoothly over round trip time (RTT) and an application aware version that paces packets based on application knowledge (e.g., incast degree). Compared to existing state-of-the-art congestion control schemes, ICON achieves 77% lower 99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile flow completion times for short flows and 18% higher throughput for long flows on average. Further, ICON drastically reduces 99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile packet drops by a factor of about 3 on average.
Hardware Implementation of AES with S-Box Using Composite-Field for WLAN Systems

The Advanced Encryption Standard (AES) algorithm is the best choice for security services in many applications, such as WLAN systems. This paper presents an AES hardware using composite-field by decomposing the complex Galois-field GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ) to the lower order field. Conventional designs are based on the Lookup table Sbox (LUT-Sbox)[1] to implement the S-box and InvS-box that cost much memory and area. Our proposed design using combinational logic is based on composite-field in finite fields GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ). The area cost is reduced because most of the functionalities are shared for both S-box and InvS-box, they are implemented on the same hardware. The proposed implementation occupies 4.1 kGEs and the maximum frequency is 180 MHz. Our implementation uses Verilog HDL on Virtex7 FPGA board.
A Survey of Feature Selection for Vulnerability Prediction Using Feature-based Machine Learning

This paper summarized the basic process of software vulnerability prediction using feature-based machine learning for the first time. In addition to sorting out the related types and basis of vulnerability features definition, the advantages and disadvantages of different methods are compared. Finally, this paper analyzed the difficulties and challenges in this research field, and put forward some suggestions for future work.
Identification of Financial Statement Fraud in Greece by Using Computational Intelligence Techniques

The consequences of financial fraud are an issue with far-reaching for investors, lenders, regulators, corporate sectors and consumers. The range of development of new technologies such as cloud and mobile computing in recent years has compounded the problem. Manual detection which is a traditional method is not only inaccurate, expensive and time-consuming but also they are impractical for the management of big data. Auditors, financial institutions and regulators have tried to automated processes using statistical and computational methods. This paper presents comprehensive research in financial statement fraud detection by using machine learning techniques with a particular focus on computational intelligence (CI) techniques. We have collected a sample of 2469 observations since 2002 to 2015. Research gap was identified as none of the existing researchers address the association between financial statement fraud and CI-based detection algorithms and their performance, as reported in the literature. Also, the innovation of this research is that the selection of data sample is aimed to create models which will be capable of detecting the falsification in financial statements.
Clinical virtual reality in mental health and rehabilitation: a brief review of the future!

The paper details some of the history of Clinical Virtual Reality (VR) as it has evolved over the last 25 years and provides a brief overview of the key scientific findings for making a judgment regarding its value in the areas of mental health and rehabilitation. This write-up is designed be a companion piece to my SPIE keynote on the topic of, “Is Clinical Virtual Reality Ready for Primetime?” As such, the paper is packed with citations to key scientific research in this area that should provide readers who are interested in this topic with a roadmap for further exploration of the literature. After presenting a brief history of the area, a discussion follows as to the theory, research, and pragmatic issues that support the view that this VR use case is theoretically informed, has a large and convincing scientific literature to support its clinical application, and that recent technology advances and concomitant cost reductions have made clinical implementation feasible and pragmatically supported. The paper concludes with the perspective that Clinical VR applications will soon become indispensable tools in the toolbox of psychological researchers and practitioners and will only grow in relevance and popularity in the future.
The Role of Structure and Complexity on Reservoir Computing Quality

We explore the effect of structure and connection complexity on the dynamical behaviour of Reservoir Computers (RC). At present, considerable effort is taken to design and hand-craft physical reservoir computers. Both structure and physical complexity are often pivotal to task performance, however, assessing their overall importance is challenging. Using a recently proposed framework, we evaluate and compare the dynamical freedom (referring to quality) of neural network structures, as an analogy for physical systems. The results quantify how structure affects the range of behaviours exhibited by these networks. It highlights that high quality reached by more complex structures is often also achievable in simpler structures with greater network size. Alternatively, quality is often improved in smaller networks by adding greater connection complexity. This work demonstrates the benefits of using abstract behaviour representation, rather than evaluation through benchmark tasks, to assess the quality of computing substrates, as the latter typically has biases, and often provides little insight into the complete computing quality of physical systems.
Quantum Ternary Multiplication Gate (QTMG): Toward Quantum Ternary Multiplier and a New Realization for Ternary Toffoli Gate

The designs using ternary logic exploit its logarithmic reduction in the number of qudits compared with the binary circuits. In this paper, we propose quantum ternary multiplication gate. We term it as QTMG. Then we present the symbol and the realization of QTMG. Researchers will be able to use this gate as well as its symbol and realizations in their future studies. We also present a new realization of ternary Toffoli gate in specific state. Moreover, in this paper, we propose 1-qutrit multiplier circuit. The symbol and the realization of the proposed 1-qutrit multiplier circuit are also provided. Afterward, we proposed ternary partial products generation circuit (TPPG) and summation network circuit in order to design quantum ternary 2-qutrit multiplier circuit. Overall, the proposed design of QTMG in this paper is suggested for the first time. In addition, the proposed realization of ternary Toffoli gate, TPPG, summation network and 2-qutrit multiplier circuits are compared with the existing designs and the improvements are reported. The proposed gate and circuits are realized using macro-level ternary gates which are built on the top of the ion-trap realizable 1-qutrit gates and 2-qutrit Muthukrishnan–Stroud gates.
Multi-Objective Genetic Algorithm for Tasks Allocation in Cloud Computing

The problem of allocating real-time tasks to cloud computing resources minimizing the makespan is defined as a NP-hard problem. This work studies the same problem with two realistic multi-objective criteria; the makespan and the total cost of execution and communication between tasks. A mathematical model including objective functions and constraints is proposed. In addition, a theoretical lower bound for the makespan which served later as a baseline to benchmark the experimental results is theoretically determined and proven. To solve the studied problem, a multi-objective genetic algorithm is proposed in which new crossover and mutation operators are proposed. Pareto-optimal solutions are retrieved using the genetic algorithm. The experimental results show that genetic algorithm provides efficient solutions in term of makespan for different-size problem instances with reference to the lower bound. Moreover, the proposed genetic algorithm produces many Pareto optimal solutions that dominate the solution given by greedy algorithm for both criteria.
Feasibility analysis of unsupervised industrial activity recognition based on a frequent micro action

The ubiquity of wearable sensors has contributed a lot in human activity recognition. Although activities of daily living have been studied over the past decades, there is a lack of such efforts on workers' activities in manufacturing industry. In this paper, we look at a simple yet frequent task for manufacturing, screwing, and investigate the ability to recognize this repetitive task through wearable sensors and unsupervised learning, by describing an experiment for industrial activity recognition and showing how effective clustering analysis is in detecting such a frequent micro action, by comparing different techniques throughout a machine learning pipeline. The achieved results demonstrated that unsupervised learning is a good solution to deal with large amount of unlabelled sensory data. We also show the different stages of pre-preprocessing, data normalization, segmentation, dimensional reduction and algorithm selection and how this affects the overall outcome by provide an in depth view, on state of the art techniques, by applying it on a relevant industrial problem.
An architecture for digital hate content reduction with mobile edge computing

Mobile devices with social media applications are the prevalent user equipment to generate and consume digital hate content. The objective of this paper is to propose a mobile edge computing architecture for regulating and reducing hate content at the user's level. In this regard, the profiling of hate content is obtained from the results of multiple studies by quantitative and qualitative analyses. Profiling resulted in different categories of hate content caused by gender, religion, race, and disability. Based on this information, an architectural framework is developed to regulate and reduce hate content at the user's level in the mobile computing environment. The proposed architecture will be a novel idea to reduce hate content generation and its impact.
Temporal convolution neural network for food and drink intake recognition

Eating difficulties are a prevalent issue within the elderly population, leading to weight loss and malnutrition. Likewise a poor diet is considered a confounding factor for developing chronic diseases and functional limitations. Given the above issues, alongside the current advances in computational intelligence achieved with the use Convolutional Neural Networks (CNNs), this paper proposes a wrist-worn tri-axial accelerometer-based food and drink intake monitoring system by combining an adaptive segmentation technique and a CNN model using 1-dimensional (1D) temporal convolutions. First, potential eating or drinking gestures are identified by the use of the adaptive segmentation technique. Once identified, the resultant gesture set is used to train the network for the recognition of four commonly occurring dietary gestures (drinking, using a spoon, using a fork and using the hand to take a bite). The problem is tackled as a 5-class classification model where the remaining class is composed by all the irrelevant gestures. The results reported, with an average per-class classification accuracy of 97.15%, suggest the system proposed is a viable solution for food and drink intake monitoring as well as a great contribution to the field of pervasive computing in support of independent living.
New Technologies in Education for Security and Safety

The paper is a qualitative study on the use of new technologies as didactic tools in education for security and safety. The aim of the research was to explore their potential in two aspects: key competencies acquired and developed during technology enhanced training, as well as the most desirable tools used in education for security and safety. The respondents were 32 international experts who teach security and safety topics in civil and military institutions. The research results indicated that reactions on emerging threats, crisis response situations, decision making and managerial skills were the key competencies. The most desirable tools were characterized by two features: instant access for learners and teachers, and the possibility of painless learning on mistakes. The study also reflected the teachers' awareness on the use of digital tools in education for security and safety.
A Scalable Operational Framework for Requirements Validation Using Semantic and Functional Models

A successful operational software depends on adequacy and degrees of freedom in requirements definitions. The software developer in conjunction with the customer validates requirements to ensure the completion of the intended use and the capability of the target application. Notwithstanding, requirements validation is time-consuming, effortless and expensive, and many times involves error-prone manual activities. The difficulty of the problem increases with an increase in the application size, the application domain, and inherit textual requirements constructs. Current approaches to the problem are considered as passive-defect aggregations, domain-specific, or rather fine-grained with formal specifications. We propose a scalable operational framework to learn, predict, and recognize requirements defects using semantic similarity models and the Integration Functional Definition methods. The proposed framework automates the validation process and increases the productivity of software engineers online with customer needs. A proof of concept shows the applicability of our solution to requirements inconsistency defects.
Toward a Blockchain Based Image Network Copyright Transaction Protection Approach

In existing image network copyright transaction, the original attribute of the transaction image content cannot be identified. In this paper, we propose an image network copyright transaction protection approach based on blockchain technology. In this approach the entire copyright transaction process is protected and the attribute identification of image content is identified. The advantages of blockchain applied to the copyright transaction of an image network are discussed. On this basis, a realization structure of copyright market transaction based on blockchain is described, and a specific business scheme of image copyright transaction is presented. Comparing and analyzing with the existing implementation schemes, the results show that the proposed scheme has great advantages in terms of reliability and practicality.
Multiplatform Application Development for Android and Java

Software developers of today are under increasing pressure to support multiple platforms, in particular mobile platforms. However, developing a multiplatform application is difficult and challenging due to a variety of platform differences. We propose a native approach for developing a multiplatform application running on two similar but different platforms, Java and Android. We address practical software engineering concerns attributed to native multiplatform application development, from configuration of tools to software design and development process. Our approach allows one to share 37%~40% of application code between the two platforms as well as improving the quality of the application. We believe our approach can also be adapted to transforming existing Java applications to Android applications.
Unsupervised Classifying of Software Source Code Using Graph Neural Networks

Usually automated programming systems consist of two parts: source code analysis and source code generation. This paper is focused on the first part. Automated source code analysis can be useful for errors and vulnerabilities searching and for representing source code snippets for further investigating. Also gotten representations can be used for synthesizing source code snippets of certain types. A machine learning approach is used in this work. The training set is formed by augmented abstract syntax trees of Java classes. A graph autoencoder is trained and a latent representation of Java classes graphs is inspected. Experiments showed that the proposed model can split Java classes graphs to common classes with some business logic implementation and interfaces and utility classes. The results are good enough be used for more accurate software source code generation.
An Intelligent Online Grooming Detection System Using AI Technologies

The rapid expansion of the Internet has experienced a significant increase in cases of child abuse, as more and more young children have greater access to the Internet.In particular, adults and minors are able to exchange sexually explicit messages and media via a variety of online platforms that are widely available, which leads to an increasing concern of child grooming.Traditionally, the identification of child grooming relies on the analysis and localisation of conversation texts, but this is usually time-consuming and associated with other implications such as psychological pressure on the investigators.Therefore, automatic methods to detect grooming conversations have attracted the attention of many researchers.This paper proposes such a system to identify child grooming in online chat conversations, where the training data of the system were harvested from publicly available information.The data processing is based on a group of AI technologies, including fuzzy-rough feature selection and fuzzy twin support vector machine.Evaluation shows the promise of the proposed approach in identifying online grooming conversations to be implemented in the future after further development to support real-world cases.
Simplifying the Algorithm Selection Using Reduction of Rankings of Classification Algorithms

The average ranking method (AR) is one of the simplest and effective algorithms selection methods. This method uses metadata in the form of test results of a given set of algorithms on a given set of datasets and calculates an average rank for each algorithm. The ranks are used to construct the average ranking. In this paper we investigate the problem of how the rankings can be reduced by removing non-competitive and redundant algorithms, thereby reducing the number of tests a user needs to conduct on a new dataset to identify the most suitable algorithm. The method proposed involves two phases. In the first one, the aim is to identify the most competitive algorithms for each dataset used in the past. This is done with the recourse to a statistical test. The second phase involves a covering method whose aim is to reduce the algorithms by eliminating redundant variants. The proposed method differs from one earlier proposal in various aspects. One important one is that it takes both accuracy and time into consideration. The proposed method was compared to the baseline strategy which consists of executing all algorithms from the ranking. It is shown that the proposed method leads to much better performance than the baseline.