Reviewerly/exHarmony huggingface dataset:
Deep-learning approaches for health care: Patients in intensive care

Providing the appropriate care for patients admitted to intensive care units (ICUs) is becoming increasingly complex and difficult, thus creating a need for the use of deep learning models. Real-time predictions provided by such models can aid physicians in interpreting the vast amount of patient data generated in the ICU. A huge percentage of electronic health record (EHR) data is generated through ICUs. Data for this study were extracted from MIMIC-III (Medical Information Mart for Intensive Care), a time-series database of intensive care unit (ICU) patient data. Applications of deep learning and nature-inspired algorithms for health care have evolved over the last few years. In this research, different deep-learning algorithms, such as long short-term memory (LSTM) and long short-term memory-attention (LSTM-AT), are investigated, using discharge notes and clinical and laboratory recordings, for hospital readmission and mortality prediction tasks. We have used explicit approaches for both the applications. An AUC (area under curve) of 0.72 for the hospital readmission task utilizing the discharge notes and an AUC of 0.87 for mortality prediction utilizing the bedside recordings were achieved.
Recovery algorithm to correct silent data corruption of synaptic storage in convolutional neural networks

With the surge of computational power and efficient energy consumption management on embedded devices, embedded processing has grown exponentially during the last decade. In particular, computer vision has become prevalent in real-time embedded systems, which have always been a victim of transient fault due to its pervasive presence in harsh environments. Convolutional Neural Networks (CNN) are popular in the domain of embedded vision (computer vision in embedded systems) given the success they have shown. One problem encountered is that a pre-trained CNN on embedded devices is vastly affected by Silent Data Corruption (SDC). SDC refers to undetected data corruption that causes errors in data without any indication that the data is incorrect, and thus goes undetected. In this paper, we propose a software-based approach to recover the corrupted bits of a pre-trained CNN due to SDC. Our approach uses a rule-mining algorithm and we conduct experiments on the propagation of error through the topology of the CNN in order to detect the association of the bits for the weights of the pre-trained CNN. This approach increases the robustness of safety-critical embedded vision applications in volatile conditions. A proof of concept has been conducted for a combination of a CNN and a vision data set. We have successfully established the effectiveness of this approach for a very high level of SDC. The proposed approach can further be extended to other networks and data sets.
A secured signcryption scheme for e-payment system using hyper elliptic curve

With the growing trend of Communication Technologies (ICT), buying goods through online has been increased drastically. Besides, e-payment makes online purchase easier and made our daily life more convenient. However, there exists a user privacy and data security issue in conventional e-payment systems. Thus, a hyper elliptic curve based signcryption scheme is proposed to achieve reduced computational cost. Because, most of the payment is processed on resource constraint devices like smart phone, hence an energy efficient e-payment system is in requisite. In order to ensure user authenticity, an aadhaar number (unique identity) is used to generate signcryption key. The proposed signcryption scheme can be implemented in real-time applications like e-payment system to ensure confidentiality, privacy, authenticity and integrity. The security of the system is validated through a simulation tool – AVISPA (Automated Validation of Internet Security Protocols and Applications). Further, the resistivity against various cryptographic attacks was analyzed informally and also the computational cost is estimated and compared with other related schemes.
Generating 2D Lego Compatible Puzzles Using Reinforcement Learning

We present a framework that generates a 2D Lego-compatible puzzle layout of greater than thousands pieces of bricks using a reinforcement learning technique. Many existing 2D legorization strategies have limitations in producing a Lego layout, which is composed of more than thousands of pieces. We attack this problem by employing a reinforcement learning technique, which accelerates the progress of various game strategies. We represent the legorization process as a game tree search problem, where each leaf node of the tree corresponds to a Lego layout. The goal of legorization is to find an optimal Lego layout that achieves maximum reward. To efficiently find a leaf node for the maximum reward layout, we reduce the search space using a dueling deep Q-Network (DQN), which is a widely used reinforcement learning model. Our framework is composed of a learning stage and a legorization stage. In the learning stage, we design a dueling DQN model and train this model using three heuristics for legorization strategies. In the legorization stage, we efficiently generate a large-scaled 2D Lego-compatible puzzle layout by reducing the search space using the trained dueling DQN. This approach enables us to produce a puzzle layout of more than a thousand of pieces, which has not been feasible for existing legorization schemes.
Research on High Precision Lithium Battery Management System

Abstract Lithium battery has been used more and more in recent years due to its excellent performance such as high energy, high voltage, long service life, low self-discharge rate, environmental protection and so on. However, lithium battery also has the disadvantages of poor safety performance, complex protection circuit, high cost, etc. The high-precision lithium ion battery management system (BMS) is proposed to solve the charging and discharging protection problem of multiple lithium batteries. In order to solve this problem, we have designed a set of high-precision lithium battery management system by pulse width modulation based on previous studies.
Costs and Benefits of Authentication Advice

Authentication security advice is given with the goal of guiding users and organisations towards secure actions and practices. In this article, a taxonomy of 270 pieces of authentication advice is created, and a survey is conducted to gather information on the costs associated with following or enforcing the advice. Our findings indicate that security advice can be ambiguous and contradictory, with 41% of the advice collected being contradicted by another source. Additionally, users reported high levels of frustration with the advice and identified high usability costs. The study also found that end-users disagreed with each other 71% of the time about whether a piece of advice was valuable or not. We define a formal approach to identifying security benefits of advice. Our research suggests that cost-benefit analysis is essential in understanding the value of enforcing security policies. Furthermore, we find that organisation investment in security seems to have better payoffs than mechanisms with high costs to users.
Ecological education in the libraries of the world

In the latter 50—30 years of the 20 th century the excessive load on the environment became obvious, and through the efforts of many natural scientists the young independent discipline of ecology came into being and became popular research and discussion vector in economics, sociology, and politics. For today, the international library community has accumulated solid and diverse experience related to ecology and sustainable development. However, these efforts are insufficiently presented in Russian professional publications. The role of libraries in building ecological outlook has become more evident in the circumstances of ecological crisis and intensifying focus on the problem. The libraries’ efforts reflect the global trends and approaches taken by scientists and researchers, politicians, industrial and agricultural producers, educationalists and cultural figures, and the wide public as ecological information users. The libraries’ experience and efforts in ecological education and information awareness evidence that educa­ting ecological and information culture has been growing into a new relevant function of the libraries in the modern world.
Angle-Based Hierarchical Classification Using Exact Label Embedding

Hierarchical classification problems are commonly seen in practice. However, most existing methods do not fully use the hierarchical information among class labels. In this article, a novel label embedding approach is proposed, which keeps the hierarchy of labels exactly, and reduces the complexity of the hypothesis space significantly. Based on the newly proposed label embedding approach, a new angle-based classifier is developed for hierarchical classification. Moreover, to handle massive data, a new (weighted) linear loss is designed, which has a closed form solution and is computationally efficient. Theoretical properties of the new method are established and intensive numerical comparisons with other methods are conducted. Both simulations and applications in document categorization demonstrate the advantages of the proposed method. Supplementary materials for this article are available online.
Digitally Modulated Signal Recognition Based on Feature Extraction Optimization and Random Forest Classifier

In the past decades, there was a growing need for the automatic classification that is related to digital signal formats, that also appears to be on going tendency in the future. Automatic modulation recognition (AMR) is considered to be of high importance in military and civil applications and communication systems. The recognition regarding the received signal modulation can be defined as a transitional stage between detection and demodulation of signals. in this paper, several features which are associated with the received signal will be extracted and used. Which is of high importance in increasing the AMR's effectiveness. Algorithms from Chicken Swarm optimization and Bat Swarm optimization were used to improve the features of modulated signals and thus increase the accuracy of the classification. It then classifies the features of the modified signals resulting from the optimization algorithms by a random forest. The results showed that swarm Chickens algorithm performs better than the Bat swarm algorithm, even at level SNR low, Results From chicken Algorithm & Random forest classifier were Accuracy of classification 95% while Accuracy of classification From Bat Algorithm & Random forest 91%.
A vision of facility siting possibilities

Abstract Facility siting methods to optimize the layout of industrial facilities for risk reduction have been evolving for decades from subjective views, standards, and guidelines to quantitative numerical analysis. The authors of this paper have tossed out the past, moved beyond the present, and taken out their crystal balls to provide a discussion around the future of facility siting by focusing on technology driven enhancements associated with three main themes: mainstreaming of current advanced analysis techniques into the base case methodology, incorporating company and/or site specific data trending and analytics to operationalize the studies, and the potential transformational change to machine learning‐based predictive risk management. With technological advancements touching nearly every area of business, it is no surprise that it is also changing the landscape of consequence and risk‐based facility siting approaches. As with all markets, the customer will be a key driver for the advancements of technical safety studies to suit their adapting needs. However, as this article will show, personnel conducting facility siting studies are also using technological advancements to challenge the status quo by improving data fidelity, increasing the robustness and depth of analysis, and providing improved insights to aid decision making.
Literature Review of Recommendation Systems

Artificial intelligence technologies are currently at the core of many sectors and industries—from cyber security to healthcare—and also have the power to influence the governance of domestic industry, the security and privacy citizens. In particular, the rise of new machine learning methods, such as those used in recommendation systems, provides many opportunities in terms of personalization. Big players like YouTube, Amazon, Netflix, Spotify, and many others are currently using recommendation systems to improve their business. Recommender systems are critical in some industries as they can generate income and provide a way to stand out from competitors. In this chapter, a literature review of recommendation systems is presented, as well as the application of recommendation systems in industry.
Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.
Concentration in the Generalized Chinese Restaurant Process

The Generalized Chinese Restaurant Process (GCRP) describes a sequence of exchangeable random partitions of the numbers $\{1,\dots ,n\}$ . This process is related to the Ewens sampling model in Genetics and to Bayesian nonparametric methods such as topic models. In this paper, we study the GCRP in a regime where the number of parts grows like nα with α > 0. We prove a non-asymptotic concentration result for the number of parts of size $k=o(n^{\alpha /(2\alpha +4)}/(\log n)^{1/(2+\alpha )})$ . In particular, we show that these random variables concentrate around ckV∗nα where V∗nα is the asymptotic number of parts and ck ≈ k−(1+α) is a positive value depending on k. We also obtain finite-n bounds for the total number of parts. Our theorems complement asymptotic statements by Pitman and more recent results on large and moderate deviations by Favaro, Feng and Gao.
Recommender System for Retail Domain

Recommender system has been developed as a useful tool especially when we reached the era of big data and in the meanwhile the internet has been overwhelming with lots of choices. There is a need for people to filter the information to search for their needs and wants efficiently. E-commerce website such as Amazon and Netflix have been using recommender system to build and boost their sales through the personalization recommendation. With the success in the e-commerce area, researchers are keen on finding a method to boost traditional offline retailer sales thru the recommender system. Therefore, in this paper, we introduced the existing recommender system and discuss the method of filtering of each method. Then, we provide the overview of the recent paper in retailer and e-commerce domain to provide the insight and trends such as the filtering techniques and evaluation metric used. Several possible research direction has been discussed based on the current trends and problems.
On Construction of Financial Management Ecosystem Model Based on Blockchain Technology

Blockchain technology has the characteristics of decentralization, trustless, verifiability and programmability, which can be used in many fields and industries. A system model, touches the internal and external environment of the enterprise and various departments and organizations together to make contributions to the financial management of the enterprise and promote the improvement of the financial management standard of the enterprise, derived from the natural ecosystem, is the financial management ecosystem. Based on blockchain technology, this text constructs an ecosystem model of enterprise financial management and analyzes the challenging of blockchain technology in the modeling process. Finally, the paper puts corresponding suggestions.
User-Oriented Elicitation of Membership Functions

The issue of elicitation and interpretation of fuzzy sets (their membership functions) is of significant relevance from the conceptual, algorithmic, and application-oriented standpoints (Dubois and Prade in Theory and Applications. Academic Press, New York, 1980 []; Klir and Yuan in Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice-Hall, Upper Saddle River, 1995 []; Nguyen and Walker in A First Course in Fuzzy Logic, CRC Press, Boca Raton, 1999 []). In the literature we can encounter a great deal of methods that support the construction of membership functions. In general, we distinguish a variety of methods positioned in-between user-driven and data-driven approaches with a number of techniques that share some features specific to both data- and user-driven techniques and hence being located somewhere in-between. The determination of membership functions has been a debatable issue for a long time almost since the very inception of fuzzy sets. In contrast to interval analysis and set theory where the estimation of bounds of the interval constructs has not attracted a great deal of attention and seemed to be somewhat taken for granted, an estimation of membership degrees (and membership functions, in general) became essential and over time has led us to sound, well justified and algorithmically appealing estimation techniques (Civanlar and Trussell in Fuzzy Sets Syst 18:1–13, 1986 []; Dishkant in Fuzzy Sets Syst 5:141–147, 1981 []; Dombi in Fuzzy Sets Syst 35:1–21, 1990 []; Hong and Lee in Fuzzy Sets Syst 84:389–404, 1996 []; Medaglia et al. in Eur J Oper Res 139:84–95, 2002 []; Turksen in Fuzzy Sets Syst 40:5–38, 1991 []).
Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams

Nutrition and social interactions are both key aspects of the daily lives of humans. In this work, we propose a system to evaluate the influence of social interaction in the nutritional habits of a person from a first-person perspective. In order to detect the routine of an individual, we construct a nutritional behaviour pattern discovery model, which outputs routines over a number of days. Our method evaluates similarity of routines with respect to visited food-related scenes over the collected days, making use of Dynamic Time Warping, as well as considering social engagement and its correlation with food-related activities. The nutritional and social descriptors of the collected days are evaluated and encoded using an LSTM Autoencoder. Later, the obtained latent space is clustered to find similar days unaffected by outliers using the Isolation Forest method. Moreover, we introduce a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100 k egocentric images gathered by 7 users. Several different visualizations are evaluated for the understanding of the findings. Our results demonstrate good performance and applicability of our proposed model for social-related nutritional behaviour understanding. At the end, relevant applications of the model are discussed by analysing the discovered routine of particular individuals.
High Empirical Study of Edge Detection-Based Image Denoising Corrupted by the Additive White Gaussian Noise (WGN)

Abstract Denoising of images is one of the Sparky subjects in image manipulating. The goal behind new design approaches to the denoising of image chains is to alleviation the superinduced noise into minimal rate after adopting spatial and temporal areas. However, eliciting edges from denoising images consider the largest trouble that facing many of researchers. Many wavelet-based images denoising methods been proposed to elicit edges from the corrupted images. In this paper, denoising images can be actualized by thresholding the wavelets coefficients at the low — low — subband s. In addition, a new technique approach to the edge detection of images corrupted by the “White-Gaussian Noise” been proposed. This technique comprises two treads: First, all likely edge points elicited with the applying of the first and second partial derivatives. Second, edge detection based-gradients which, relying on the two-dimensional convolution-based on the theory of the finite impulse response (FIR) filter been attained. Here, the histograms of the V/H image gradients can be exploited to create the essential threshold. This will facilitate the access to the convincing simulations in the process of image gradients detection. Experimental results show that the performance efficiency of our proposed technique was best comparing with the classical detection method in terms of blurriness and artifacts specifically, with areas that contain the edges.
Adaptive passive sensor selection for maneuvering target localization and tracking using a multisensor surveillance system

This paper investigates maneuvering-target tracking problem based on a multisensor system and interacting multiple model (IMM). The estimation is performed by a novel particle filter (PF) with a capability to deal with the state-dependent noises and interference of the sensors’ coverage environment. An adaptive sensor selection algorithm, where some sensors are selected in each stage based on the signal-to-interference pulse noise ratio (SINR) and participate in the state estimation, is proposed. To deal with the effect of interference, we focus on designing and implementing the sensor selection algorithm, where a multisensor system with nonuniform arrays is derived by solving a convex optimization problem. On this basis, a nonuniform array of sensors is selected in each time interval aiming at maximizing the SINR of the received information from the undercoverage area. This would allow tracking in practical environments experiences interference. This method also is able to reduce the tracking error rate.
Scalable Multi-agent Reinforcement Learning Architecture for Semi-MDP Real-Time Strategy Games

Action-value has been widely used in multi-agent reinforcement learning. However, action-value is hard to be adapted to scenarios such as real-time strategy games where the number of agents can vary from time to time. In this paper, we explore approaches of avoiding the action-value in systems in order to make multi-agent architectures more scalable. We present a general architecture for real-time strategy games and design the global reward function which can fit into it. In addition, in our architecture, we also propose the algorithm without human knowledge which can work for Semi Markov Decision Processes where rewards cannot be received until actions last for a while. To evaluate the performance of our approach, experiments with respect to micromanagement are carried out on a simplified real-time strategy game called MicroRTS. The result shows that the trained artificial intelligence is highly competitive against strong baseline robots.