Reviewerly/exHarmony huggingface dataset:
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk

Abstract To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretability of attention-based models is leveraged to describe patients-at-risk. Several deep learning architectures making use of attention mechanisms, recurrent layers, neural ordinary differential equations (ODEs), and medical concept embeddings with time-aware attention were trained using publicly available electronic medical record data (MIMIC-III) associated with 45,298 ICU stays for 33,150 patients. Bayesian inference was used to compute the posterior over weights of an attention-based model. Odds ratios associated with an increased risk of readmission were computed for static variables. Diagnoses, procedures, medications, and vital signs were ranked according to the associated risk of readmission. A recurrent neural network, with time dynamics of code embeddings computed by neural ODEs, achieved the highest average precision of 0.331 (AUROC: 0.739, F 1 -Score: 0.372). Predictive accuracy was comparable across neural network architectures. Groups of patients at risk included those suffering from infectious complications, with chronic or progressive conditions, and for whom standard medical care was not suitable. Attention-based networks may be preferable to recurrent networks if an interpretable model is required, at only marginal cost in predictive accuracy.
A journey of WEB and Blockchain towards the Industry 4.0: An Overview

Today the WEB is growing and expanding with the unexpected high speed and recently it gained extensive attention. The world is focusing and working on the web so that it could make more ease in our life. The web start from static pages and goes towards the smart industries and factories. All the industrial machines are augmented and directly connected to the web which leads to connect with different systems. This type of systems visualizes the whole production process and these systems are autonomous to take decisions on its own. Forth industrial revolution is the second name of industry 4.0 which includes the Internet of Things (IoT), Cognitive Computing (CC), Big Data, AI and it is fostering Industrial Internet of Things (IIoT). Advanced web is covering the maximum aspects to encourage the businessman community to make trading using technology. The blockchain version 4.0 is helping the industry to ensure the transparency of supply, immutability of records and it ensures the trust of end to end trading organizations. Supply chain is now using the blockchain technology.
Nature-inspired algorithms for feed-forward neural network classifiers: A survey of one decade of research

Recently, an explosive growth in the potential use of natural metaphors in modelling and solving large-scale non-linear optimization problems. Artificial neural network (ANN) is a potent gadget broadly utilized in many data classification tasks. Fundamentally, nature-inspired algorithms have demonstrated their effectiveness and ability over traditional algorithms for generating the optimal ANN parameters, rules and topology that provide the best classification performance with regarding to the quality of the solution, computational cost and avoiding local minima. The literature is vast and growing. This study provides a review on the basic theories and main recent algorithms for optimizing the ANN. Different types of nature-inspired meta-heuristic algorithms are presented; outlining the concepts and components that are used in order to give a summary and ease of the state-of-the-arts to find suitable methods in real world applications for the readers. Additionally, this survey covers the most used type of neural networks, feed-forward neural network (FFNN) in several optimized applications. The performances of FFNNs designed by nature-inspired algorithms have been explored in single and multi-dimensional optimization space; highlighting their models, features, objectives, constraints, etc. to analyse their differences and similarities. A comprehensive survey of the earliest works and recent modified in the last decade in addition to expect approaches has been investigated in details.
Quantum Optimization and Quantum Learning: A Survey

Quantum mechanism, which has received widespread attention, is in continuous evolution rapidly. The powerful computing power and high parallel ability of quantum mechanism equip the quantum field with broad application scenarios and brand-new vitality. Inspired by nature, intelligent algorithm has always been one of the research hotspots. It is a frontier interdisciplinary subject with a perfect integration of biology, mathematics and other disciplines. Naturally, the idea of combining quantum mechanism with intelligent algorithms will inject new vitality into artificial intelligence system. This paper lists major breakthroughs in the development of quantum domain firstly, then summarizes the existing quantum algorithms from two aspects: quantum optimization and quantum learning. After that, related concepts, main contents and research progresses of quantum optimization and quantum learning are introduced respectively. At last, experiments are conducted to prove that quantum intelligent algorithms have strong competitiveness compared with traditional intelligent algorithms and possess great potential by simulating quantum computing.
Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors

Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation. We use reinforcement learning to train policies in simulation that transfer remarkably well to multiple different physical quadrotors. Our policies are low-level, i.e., we map the rotorcrafts' state directly to the motor outputs. The trained control policies are very robust to external disturbances and can withstand harsh initial conditions such as throws. We show how different training methodologies (change of the cost function, modeling of noise, use of domain randomization) might affect flight performance. To the best of our knowledge, this is the first work that demonstrates that a simple neural network can learn a robust stabilizing low-level quadrotor controller (without the use of a stabilizing PD controller) that is shown to generalize to multiple quadrotors. The video of our experiments can be found at https://sites.google.com/view/sim-to-multi-quad.
An Integrated Approach of Fuzzy Logic, AHP and TOPSIS for Estimating Usable-Security of Web Applications

Security and usability are often thought of as being contradictive in nature.One affects the other negatively.The relation and trade-offs between usability and security must be detected during developing web application to satisfy the user's requirements with security perspective.Current approaches of usable-security emphasizes on building systems that are easy to use and secure as well.Hence, this paper is recognizing usability-security as a problem with different attributes contributing towards it.Further, there is a need to assess this problem for the satisfaction of the end user.In this context, this study proposes the track of Fuzzy AHP-TOPSIS (Analytic Hierarchy Process-Technique for Order of Preference by Similarity to Ideal Solution) technique to assess the usable-security of web application and also identifies the most prioritized attribute contributing towards building usable-security of web application.Moreover, to corroborate the efficacy of the proposed technique, the authors have tested the results on the institutional web applications.The results of the assessment undertaken in this study and the findings tabulated thereafter will be a helpful reckoner for the developers while designing web applications that afford optimum usable-security.
An assessment study of evaporation rate models on a water basin with floating photovoltaic plants

Under the general topic of the impact of floating photovoltaics (FPVs) systems on water basins, the present study aims to model and analyze the effect of FPVs on the evaporation rate of water surfaces. The estimation of the evaporation of the water surface of a basin is usually calculated using mathematical evaporation models that require knowledge of some parameters (ie, solar radiation, humidity, air temperature, water temperature, and wind velocity). Thus, in the first section of this study, some evaporative models (EVM) for free water basin have been examined to evaluate which are the environmental variables used. On the basis of this analysis, new numerical models for the calculation of the daily evaporation rate have been developed using the design of experiments (DoE) method (three models) and the linear regression method (two models). The results of the developed models have been compared with the experimental measurements carried out by an evaporimeter; such comparison has highlighted the robustness of the proposed numerical models. Moreover, for estimating the evaporation rate in water basins partially covered by FPVs, further three numerical methods are proposed. Finally, the evaporation rates, arising by the installation of different typology of FPVs on water basins, have been evaluated as function of the energy balance on the water surface. It is possible to highlight that the amount of evaporated water depends not only on the percentage of surface covered but also on the characteristics of floating systems. Covering only 30% of the surface of a basin, it is possible to obtain up to 49% reduction in evaporation.
Designing Trans Technology

Transgender and non-binary people face substantial challenges in the world, ranging from social inequities and discrimination to lack of access to resources. Though technology cannot fully solve these problems, technological solutions may help to address some of the challenges trans people and communities face. We conducted a series of participatory design sessions (total N = 21 participants) to understand trans people's most pressing challenges and to involve this population in the design process. We detail four types of technologies trans people envision: technologies for changing bodies, technologies for changing appearances / gender expressions, technologies for safety, and technologies for finding resources. We found that centering trans people in the design process enabled inclusive technology design that primarily focused on sharing community resources and prioritized connection between community members.
Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation

Deciphering user purchase preferences, their likes and dislikes is a very tricky task even for humans, making its automation a very complex job. This research work augments heuristic-driven user interest profiling with reviewer credibility analysis and fine-grained feature sentiment analysis to devise a robust recommendation methodology. The proposed credibility, interest and sentiment enhanced recommendation (CISER) model has five modules namely candidate feature extraction, reviewer credibility analysis, user interest mining, candidate feature sentiment assignment and recommendation module. Review corpus is given as an input to the CISER model. Candidate feature extraction module uses context and sentiment confidence to extract features of importance. To make our model robust to fake and unworthy reviews and reviewers, reviewer credibility analysis proffers an approach of associating expertise, trust and influence scores with reviewers to weigh their opinion according to their credibility. The user interest mining module uses aesthetics of review writing as heuristics for interest-pattern mining. The candidate feature sentiment assignment module scores candidate features present in review based on their fastText sentiment polarity. Finally, the recommendation module uses credibility weighted sentiment scoring of user preferred features for purchase recommendations. The proposed recommendation methodology harnesses not only numeric ratings, but also sentiment expressions associated with features, customer preference profile and reviewer credibility for quantitative analysis of various alternative products. The mean average precision (MAP@1) for CISER is 93% and MAP@3 is 49%, which is better than current state-of-the-art systems.
Cryptanalysis and constructing S-Box based on chaotic map and backtracking

In recent years, a large number of S-Box design schemes have been proposed. However, after detection we found that most of them contain fixed point or reverse fixed point, which may be an exploitable weakness in cryptography. In order to design a strong S-Box construction algorithm, and eliminate fixed point and reverse fixed point intelligently, we first designed an improved coupling quadratic map (ICQM), which is surjective and has good ergodicity and randomness, verified by bifurcation diagram, Lyapunov exponent and strict randomness testing tool of TestU01. Then we reconstructed a keyed strong S-Box scheme without weakness based on ICQM and backtracking. Experiments results indicated the effectiveness of the proposed S-Box construction scheme.
PROTECT: Efficient Password-Based Threshold Single-Sign-On Authentication for Mobile Users against Perpetual Leakage

Password-based single-sign-on authentication has been widely applied in mobile environments. It enables an identity server to issue authentication tokens to mobile users holding correct passwords. With an authentication token, one can request mobile services from related service providers without multiple registrations. However, if an adversary compromises the identity server, he can retrieve users' passwords by performing dictionary guessing attacks (DGA) and can overissue authentication tokens to break the security. In this paper, we propose a password-based threshold single-sign-on authentication scheme dubbed PROTECT that thwarts adversaries who can compromise identity server(s), where multiple identity servers are introduced to authenticate mobile users and issue authentication tokens in a threshold way. PROTECT supports key renewal that periodically updates the secret on each identity server to resist perpetual leakage of the secret. Furthermore, PROTECT is secure against off-line DGA: a credential used to authenticate a user is computed from the password and a server-side key. PROTECT is also resistant to online DGA and password testing attacks in an efficient way. We conduct a comprehensive performance evaluation of PROTECT, which demonstrates the high efficiency on the user side in terms of computation and communication and proves that it can be easily deployed on mobile devices.
Deep Representation Learning With Full Center Loss for Credit Card Fraud Detection

Credit card fraud detection is an important study in the current era of mobile payment. Improving the performance of a fraud detection model and keeping its stability are very challenging because users' payment behaviors and criminals' fraud behaviors are often changing. In this article, we focus on obtaining deep feature representations of legal and fraud transactions from the aspect of the loss function of a deep neural network. Our purpose is to obtain better separability and discrimination of features so that it can improve the performance of our fraud detection model and keep its stability. We propose a new kind of loss function, full center loss (FCL), which considers both distances and angles among features and, thus, can comprehensively supervise the deep representation learning. We conduct lots of experiments on two big data sets of credit card transactions, one is private and another is public, to demonstrate the detection performance of our model by comparing FCL with other state-of-the-art loss functions. The results illustrate that FCL outperforms others. We also conduct experiments to show that FCL can ensure a more stable model than others.
Seismological Observatory Software: 30 Yr of SEISAN

Abstract The SEISAN software package for processing of earthquake data has been in use for 30 yr. SEISAN is a collection of programs that help to carry out tasks from the basic processing at a seismological observatory to more advanced seismological research. During its history, the software has been adopted to different hardware and operating systems. However, the core of the software with a folder- and files-based database and event-based processing has remained stable. The main focus in the design and development of the software has been the efficiency in data processing for the user. The software comes with manual, tutorial, and training exercises. This together with regular training activities has made SEISAN a useful tool for many observatories around the world.
An Enhanced Neural Network Approach to Person-Job Fit in Talent Recruitment

The widespread use of online recruitment services has led to an information explosion in the job market. As a result, recruiters have to seek intelligent ways for Person-Job Fit, which is the bridge for adapting the right candidates to the right positions. Existing studies on Person-Job Fit usually focus on measuring the matching degree between talent qualification and job requirements mainly based on the manual inspection of human resource experts, which could be easily misguided by the subjective, incomplete, and inefficient nature of human judgment. To that end, in this article, we propose a novel end-to-end T opic-based A bility-aware P erson- J ob F it N eural N etwork (TAPJFNN) framework, which has a goal of reducing the dependence on manual labor and can provide better interpretability about the fitting results. The key idea is to exploit the rich information available in abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers’ experiences based on Recurrent Neural Network (RNN). Along this line, two hierarchical topic-based ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measure the different contribution of each job experience to a specific ability requirement. In addition, we design a refinement strategy for Person-Job Fit prediction based on historical recruitment records. Furthermore, we introduce how to exploit our TAPJFNN framework for enabling two specific applications in talent recruitment: talent sourcing and job recommendation. Particularly, in the application of job recommendation, a novel training mechanism is designed for addressing the challenge of biased negative labels. Finally, extensive experiments on a large-scale real-world dataset clearly validate the effectiveness and interpretability of the TAPJFNN and its variants compared with several baselines.
An Intelligent Optimization Method for Optimal Virtual Machine Allocation in Cloud Data Centers

A cloud computing paradigm has quickly developed and been applied widely for more than ten years. In a cloud data center, cloud service providers offer many kinds of cloud services, such as virtual machines (VMs), to users. How to achieve the optimized allocation of VMs for users to satisfy the requirements of both users and providers is an important problem. To make full use of VMs for providers and ensure low makespan of user tasks, we formulate an optimal allocation model of VMs and develop an improved differential evolution (IDE) method to solve this optimization problem, given a batch of user tasks. We compare the proposed method with several existing methods, such as round-robin (RR), min-min, and differential evolution. The experimental results show that it can more efficiently decrease the cost of cloud service providers while achieving lower makespan of user tasks than its three peers. Note to Practitioners-VM allocation is one of the challenging problems in cloud computing systems, especially when user task makespan and cost of cloud service providers have to be considered together. We propose an IDE approach to solve this problem. To show its performance, this article compares the commonly used methods, i.e., RR and min-min, as well as the classic differential evolution method. A cloud simulation platform called CloudSim is used to test these methods. The experimental results show that the proposed one can well outperform its compared ones, and its VM allocation results can achieve the highest satisfaction of both users and providers. The proposed method can be readily applicable to industrial cloud computing systems.
Deep Cross-Modal Image–Voice Retrieval in Remote Sensing

With the rapid progress of satellite and aircraft technologies, cross-modal remote sensing image-voice retrieval has been studied in geography recently. However, there still exist some bottlenecks: how to consider the characteristics of remote sensing data adequately and how to reduce the memory and improve the retrieval efficiency in large-scale remote sensing data. In this article, we propose a novel deep cross-modal remote sensing image-voice retrieval approach, namely, deep image-voice retrieval (DIVR), to capture more information of remote sensing data to generate hash codes with low memory and fast retrieval properties. Especially, the DIVR approach proposes inception dilated convolution module to capture multiscale contextual information of remote sensing images and voices. Moreover, in order to enhance cross-modal similarity, the deep features' similarity term is designed to make paired similar deep features as close as possible and paired dissimilar deep features as mutually far as possible. In addition, the quantization error term is designed to drive hash-like codes to approximate hash codes, which can effectively reduce the quantization error for hash codes' learning. Extensive experimental results on three remote sensing image-voice data sets show that the proposed DIVR approach can outperform other cross-modal retrieval approaches.
Identity-Based Encryption Transformation for Flexible Sharing of Encrypted Data in Public Cloud

With the rapid development of cloud computing, an increasing number of individuals and organizations are sharing data in the public cloud. To protect the privacy of data stored in the cloud, a data owner usually encrypts his data in such a way that certain designated data users can decrypt the data. This raises a serious problem when the encrypted data needs to be shared to more people beyond those initially designated by the data owner. To address this problem, we introduce and formalize an identity-based encryption transformation (IBET) model by seamlessly integrating two well-established encryption mechanisms, namely identity-based encryption (IBE) and identity-based broadcast encryption (IBBE). In IBET, data users are identified and authorized for data access based on their recognizable identities, which avoids complicated certificate management in usual secure distributed systems. More importantly, IBET provides a transformation mechanism that converts an IBE ciphertext into an IBBE ciphertext so that a new group of users not specified during the IBE encryption can access the underlying data. We design a concrete IBET scheme based on bilinear groups and prove its security against powerful attacks. Thorough theoretical and experimental analyses demonstrate the high efficiency and practicability of the proposed scheme.