Reviewerly/exHarmony huggingface dataset:
Low Altitude and Small Target Tracking Based on IMM L-M Cubature Kalman Filter

At present, low waltitude, and small target (LST) tracking is an essential part of air defense. The essence of LST tracking is using a filtering algorithm to estimate the target’s trace. As most LST systems are non-linear, non-linear filtering is essential those systems. As a result, we choose Cubature Kalman Filter (CKF) as our filter due to CKF’s good performance in nonlinear systems. For LST tracking, there are two main problems. Firstly, a low altitude leads to more ground obstructions which can induce a more significant initial state error in the filtering process. Secondly, LST tends to have a maneuvering system model, which can induce a greater difficulty in matching the actual LST model with the filtering model. To tackle the first problem, we transfer the original measurement update process in CKF to an iterative version using Levenberg-Marquardt (L-M) method and thus reduce the influence of the large initial filtering state error. Here, we call the new filtering algorithm L-MCKF. To tackle the second problem, we use Interacting Multiple Model (IMM) to generate a hybrid system to better match the actual LST trace. Finally, we use a simulation to test the availability of our proposed IMML-MCKF algorithm.
Opposition-based multi-objective whale optimization algorithm with multi-leader guiding

During recent decades, evolutionary algorithms have been widely studied in optimization problems. The multi-objective whale optimization algorithm based on multi-leader guiding is proposed in this paper, which attempts to offer a proper framework to apply whale optimization algorithm and other swarm intelligence algorithms to solving multi-objective optimization problems. The proposed algorithm adopts several improvements to enhance optimization performance. First, search agents are classified into leadership set and ordinary set by grid mechanism, and multiple leadership solutions guide the population to search the sparse spaces to achieve more homogeneous exploration in per iteration. Second, the differential evolution and whale optimization algorithm are employed to generate the offspring for the leadership and ordinary solutions, respectively. In addition, a novel opposition-based learning strategy is developed to improve the distribution of the initial population. The performance of the proposed algorithm is verified in contrast to 10 classic or state-of-the-arts algorithms on 20 bi-objective and tri-objective unconstrained problems, and experimental results demonstrate the competitive advantages in optimization quality and convergence speed. Moreover, it is tested on load distribution of hot rolling, and the result proves its good performance in real-world applications. Thus, all of the aforementioned experiments have indicated that the proposed algorithm is comparatively effective and efficient.
Solving complex problems with coevolutionary algorithms

tutorial Share on Solving complex problems with coevolutionary algorithms Authors: Krzysztof Krawiec Poznan University of Technology, Poland Poznan University of Technology, PolandView Profile , Malcolm Heywood Dalhousie University, Canada Dalhousie University, CanadaView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 975–1001https://doi.org/10.1145/3319619.3323384Published:13 July 2019Publication History 3citation194DownloadsMetricsTotal Citations3Total Downloads194Last 12 Months14Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Ubiquitous LibGuides: Variations in Presence, Production, Application, and Convention

The LibGuides platform, a content management system (CMS) from Springshare, has become an integral part of the online presence for many academic libraries. Neither Springshare nor other recent studies have provided an in-depth look at the evolving nature of LibGuides adoption, production and application across university and college categories. This study compared the prevalence and production of LibGuides to other forms of library guides at 799 academic libraries throughout the United States. LibGuides naming conventions were recorded and compared. The use of LibGuides CMS software to create library websites was also documented. This study found that there are clear differences in LibGuides adoption and production across various types of institutions. LibGuides naming conventions were fairly consistent across all types of institutions. Institutions using LibGuides software produced substantially more library guides than institutions without this software. A small but significant portion of all non-R1 libraries in this study are now utilizing LibGuides software to create the majority of their library websites.
Building Access Control Policy Model for Privacy Preserving and Testing Policy Conflicting Problems

This chapter proposes a purpose-based access control model in distributed computing environment for privacy preserving policies and mechanisms, and describes algorithms for policy conflicting problems. The mechanism enforces access policy to data containing personally identifiable information. The key component is purpose involved access control models (PAC) that provide full support for expressing highly complex privacy-related policies, taking into account features like purposes, conditions and obligations. A policy refers to an access right that a subject can have on an object, based on attribute predicates, obligation actions, and system conditions. Policy conflicting problems may arise when new access policies are generated that are possible to be conflicted to existing policies. As a result of the policy conflicts, private information cannot be well protected. The structure of purpose involved access control policy is studied, and efficient conflict-checking algorithms are developed and implemented. Finally a discussion of our work in comparison with other access control and frameworks such as EPAL is presented.
Sentiment Analysis of Tweets for Estimating Criticality and Security of Events

Social Media has become one of the major industries in the world. It has been noted that almost three fourth of the world's population use social media. This has instigated many researches towards social media. One such useful application is the sentimental analysis of real time social media data for security purposes. The insights that are generated can be used by law enforcement agencies and for intelligence purposes. There are many types of analyses that have been done for security purposes. Here, the authors propose a comprehensive software application which will meticulously scrape data from Twitter and analyse them using the lexicon based analysis to look for possible threats. They propose a methodology to obtain a quantitative result called criticality to assess the level of threat for a public event. The results can be used to understand people's opinions and comments with regard to specific events. The proposed system combines this lexicon based sentimental analysis along with deep data collection and segregates the emotions into different levels to analyse the threat for an event.
Intuitive Semantics for First-Degree Entailment and ‘Coupled Trees’

Classically, an argument A therefore B is ‘valid’ (or A is said to ‘entail’ B) if and only if (iff) each situation (model) is such that either A is false or B is true. This fits well with so-called ‘tableau’ methods for showing that A entails B by working out the mutual inconsistency of A and ~B. But both the classical notion of validity and the corresponding tableau methods allow that A may entail B because of some feature of A alone, irrespective of B, and vice versa. Thus if A is a contradiction, then each situation is such that A is false, and so a fortiori is such that A is false or B is true. And if A is a contradiction, then a tableau construction will show that A is inconsistent, and so a fortiori that A and ~B are inconsistent. Of course, the same points can be made dually when B is a logical truth.
Cyber-Security for ICS/SCADA

Industrial control systems (ICS) or supervisory, control, and data acquisition (SCADA) systems drive many key components of the national infrastructure. It makes these control systems targets for cyber-attacks by terrorists and nation-states who wish to damage their target economically and socially, and cyber-criminals who blackmail the companies operating the infrastructure. Despite the high risk of leaving these systems exposed, providing adequate cyber-security is often challenging. The Stuxnet worm illustrated how vulnerable control systems potentially are when it bypassed a number of security mechanisms to cause physical damage to an Iranian nuclear facility. The article focuses on ICS/SCADA in South Africa discussing the unique challenges and legislation relate to securing control system in the South Africa. A governance and security framework for overcoming these challenges are proposed.
MoLaBSS: Server-Specific Add-On Biometric Security Layer Model to Enhance the Usage of Biometrics

With high-paced growth in biometrics, and its easy availability to capture various biometric features, it is emerging as one of the most valuable technologies for multifactor authentication to verify a user’s identity, for data security. Organizations encourage their members to use biometrics, but they are hesitant to use them due to perceived security risks. Because of its low usage rate, many medium and small segment organizations find it unfeasible to deploy robust biometric systems. We propose a server-specific add-on biometric security layer model (MoLaBSS) to enhance confidence in the usage of biometrics. We tested this model via a biometric mobile app, and the survey showed a favorable response of 80%. The innovative mobile app was tested for its usability and got a score of more than 71%. For test tool reliability, we examined the equal error rate (EER) of the app and got a reasonably low score of 6%. The results show good potential of this framework to enhance users’ confidence level in the usage of biometrics. Higher usage rates may make deployment of biometrics more cost-effective for many organizations to decrease their information security risk.
Deep Learning Framework Study for Twitter Sentiment Analysis

Nowadays social media shows surging significance in our lives. Sentiment analysis emerged to automatically predict the sentiment expressed by the comments on platforms such as twitter and Weibo. In this paper, a number of classical machine-learning-based methods are introduced, discussed, and evaluated at first. Then, a deep-learning-based model for sentiment analysis is proposed. The main contribution consists of avoiding underfitting and overfitting, dropout, different layers exploration, and LSTM. In addition, a publicly available benchmark dataset is utilized for evaluation. Finally, variety of evaluation methods are used for the proposed model. The results show promising results against other classical machine learning models.
Decentralized Personal Cloud Data Model and its Application in Campus Health Information System

While the digital information age has brought us convenience in recent years. It has also brought many security risks. The centralized data storage poses the risk of data leakage. With the breakout of the Novel Coronavirus, it is common for schools to import the health management tools such as the body temperature monitoring system. If these health data are integrated together, it can facilitate school authorities to track students' health status, but centralized management has the risk of widespread data leakage. We propose a decentralized, personal cloud data model. The history of a student's health data will be stored only in each student's personal online datastore managed by Edu Pod, which is a student health information data management application that provides various services and mechanisms to secure the personal data in the Pod. By decentralizing the data to the students' individual Pods, it prevents the occurrence of large-scale data leaks by centralized management. We apply this model to the Campus Health Information System (CHIS) which we are studying in. The student's temperature data collected by face recognition will eventually be stored only in his or her personal Pod, which is a portable educational information storage unit, and the student can share the data with the university and any third-party health management software. The application of a centralized personal cloud data model can help students' personal data not be limited to a centralized server farm and can control their personal data more freely. It can also avoid information leakage and help to protect personal privacy at the same time.
Optimization algorithms

This chapter introduces some commonly used optimization techniques, including classical gradient-based methods, gradient-free methods, and the new optimizers for deep learning. It also introduces evolutionary algorithms and nature-inspired algorithms for optimization and computational intelligence.
RNN

Calculations performed in the CNN are independent, meaning that there is no relationship between the previous and current inputs.
Multi-Robot Coverage Path Planning based on Deep Reinforcement Learning

The multi-robot coverage path planning (CPP) is the design of optimal motion sequence of robots, which can make robots execute the task covering all positions of the work area except the obstacles. In this article, the communication capability of the multi-robot system is applied, and a multi-robot CPP mechanism is proposed to control the robots to perform CPP tasks in an unknown environment. In this mechanism, an algorithm based on deep reinforcement learning is proposed, which can generate the next action for robots in real-time according to the current state of the robots. In addition, a real-time obstacle avoidance scheme for multi-robot is proposed based on the information interaction capability of multi-robot. Experiment results show that the method can plan the optimal path for multi-robot to complete the covering task in an unknown environment. Moreover, compared with other reinforcement learning methods, the algorithm proposed can efficiently learning with fast convergence speed and good stability.
Safer museum guide interaction during a pandemic and further Using NLP in human interactive museum visits : Museum guide Chatbot

This paper introduces an NLP based solution for conversational agents in art exhibits. The conversational techniques (text-to-speech and speech-to-text) applied on chatbots are an important issue according to the user needs in different environments, art domains, and regions. The NLP agent is trained on relevant data by matching questions that are partially similar or contain only keywords from the knowledge base, assuring an understanding of a broader spectrum of questions. This paper proposes the theoretical background, the solution implementation, running scenarios, and experimental results as well. The application can be implemented in any kind of exhibition, as long as the knowledge base is adapted to the exhibits in focus.
Real-Time Hardware-Based Malware and Micro-Architectural Attack Detection Utilizing CMOS Reservoir Computing

In this work we demonstrate a novel CMOS reservoir computer (RC) prototype in 65nm CMOS that is capable of detecting Malware and micro-architectural attacks in real-time utilizing hardware performance counter (HPC) traces. A 65nm test chip achieves 96.8% and 96.5% accuracy when classifying Malware and micro-architectural attacks respectively, while achieving better classification performance than digital machine learning models and with lower energy consumption. The on-chip classifier consumes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$38.2~\mu \text{W}$ </tex-math></inline-formula> from a 1.2V supply while running at 40kHz.
The Blockchain Technology

This article describes how Blockchain is a technology that has a great potential to change the way business is done in the future, exactly like the internet did in the early nineties. Blockchain offers new opportunities to develop new types of digital services to overcome business problems, and improve business practices by making transaction information a public resource. While research on the topic is still emerging, it has mostly focused on crypto-currencies instead of taking advantage of this novel concept to create new advanced services. This article discusses blockchain and the technology behind it, some of its possible applications, as well as threats targeting the new poorly understood technology.
Bayesian Causality

Although no universally accepted definition of causality exists, in practice one is often faced with the question of statistically assessing causal relationships in different settings. We present a uniform general approach to causality problems derived from the axiomatic foundations of the Bayesian statistical framework. In this approach, causality statements are viewed as hypotheses, or models, about the world and the fundamental object to be computed is the posterior distribution of the causal hypotheses, given the data and the background knowledge. Computation of the posterior, illustrated here in simple examples, may involve complex probabilistic modeling but this is no different than in any other Bayesian modeling situation. The main advantage of the approach is its connection to the axiomatic foundations of the Bayesian framework, and the general uniformity with which it can be applied to a variety of causality settings, ranging from specific to general cases, or from causes of effects to effects of causes.
Homomorphic encryption

Homomorphic encryption is a powerful encryption technology that allows computations to be performed directly on encrypted data, without requiring access to a secret key. Since its inception in 2009, the technology has seen a huge number of changes, improving its performance by multiple orders of magnitude compared to the first implementations. Today's cloud computing economies make homomorphic encryption more relevant than ever, as it can be used to build completely private outsourced storage and computation services. In this chapter we discuss many fundamental aspects of homomorphic encryption: schemes and constructions, security, implementations, performance, and applications, specifically emphasizing applications in the biomedical field.
Random Forest Algorithm Based on Differential Privacy Protection

Random Forest classification algorithm is a data mining algorithm with high classification accuracy. During the construction of the random forest, personal privacy may be leaked. Aiming at the problems of low prediction accuracy and unsatisfactory classification results in existing random forest classification algorithms with privacy protection, a high-accuracy differential privacy protection random forest classification algorithm (DPFMaxTree) is proposed. First, based on the existing Max attribute measurement, a new attribute measurement method F _Max is designed to improve the classification accuracy of a single decision tree; Secondly, combined with a new privacy budget allocation mechanism, the datasets with continuous attributes are discretized by the exponential mechanism using CART algorithm, then Laplace mechanism is used to add noise, and the algorithm uses the F_Max attribute measurement to construct the DPFMaxTree classification algorithm. Experimental result on real datasets shows that the new algorithm can perform classification predictions well under a reasonable privacy budget.
How Do Learners Perceive and Evaluate Their Digital Skills?

Self-assessment is commonly used in educational research (PISA, ICILS, etc.) and in real-life situations (advertisement for a post on labour market, etc.). Research studies demonstrate that in mathematics some people either over- or underestimate their competence. A similar situation can be expected in self-assessment of ICT skills. The authors of this article introduce pilot research carried out among Bachelors student teachers aimed at identifying key factors (economic, cultural, social, and personal) that may influence how young people perceive their digital literacy and knowledge.
Model checking boot code from AWS data centers

Abstract This paper describes our experience with symbolic model checking in an industrial setting. We have proved that the initial boot code running in data centers at Amazon Web Services is memory safe, an essential step in establishing the security of any data center. Standard static analysis tools cannot be easily used on boot code without modification owing to issues not commonly found in higher-level code, including memory-mapped device interfaces, byte-level memory access, and linker scripts. This paper describes automated solutions to these issues and their implementation in the C Bounded Model Checker (CBMC). CBMC is now the first source-level static analysis tool to extract the memory layout described in a linker script for use in its analysis.
Deep Metric Learning With Manifold Class Variability Analysis

In deep metric learning (DML) techniques, understanding both the local and global characteristics of embedding space is essential. However, conventional DML techniques have two limitations as follows: First, Euclidean distance-based metrics never imply global information such as class variability because they only depend on the physical distance of samples. Second, they assume that the embedding space is simply a vector space which cannot represent complex data features. Therefore, we propose a novel loss function which can fully utilize characteristics of embedding space by using discriminant analysis and nonlinear mapping. With theoretical analysis, the superior performance of the proposed method is verified for the fine-grained retrieval datasets such as Cars196, CUB200-2011, Stanford online products, and In-shop clothes. Source code is available at <uri>https://github.com/kdhht2334/MCVA</uri>.
Arduino Based Smart Gloves for Blind, Deaf and Dumb Using Sign and Home Automation Mode

Clever Hand Mitten assist debilitated people to live with able people and make their existence comfort and efficient. As we all know that a dumb person are unable to speak then these intelligent hand mitten aid him/her to convert his finger indication into words and speaker voice. This also helps the able people to acknowledge what s/he is trying to say and reply accordingly. This Intelligent mitten has a provision of Home Automation control from which a physically challenged people become independent to live their life. The physical intent of the this IOT based project is to progress a comfort and efficient, easy to use, superficial smart hand gloves system which will minimize the obstacles for physically challenged people where they can stand with the race. An amalgamation of fingers will be used for demonstrating multiple gestures.
Electronic Training Technology on Mathematics of Students Oriented to Work in Nuclear Industry

The article presents the developed and tested technology of e-learning for students at the university. It gives practical examples on the organization of the educational process in accordance with this technology in the mathematical preparation of students. The significance of this approach for the mathematical preparation of students oriented to work in the nuclear industry in connection with the digitalization of all processes in the industry is shown. The effectiveness of the use of this technology in the educational process is confirmed by studies on the level of preparation of students in mathematics, their satisfaction with the organization of the educational process and the dynamics of the students' mathematical intelligence.