Reviewerly/exHarmony huggingface dataset:
Trajectory Analysis Method Based on Video Surveillance Anomaly Detection

Video surveillance system monitors potential malicious activities through video images, which is also an important part of city security prevention. In order to improve the application value of video surveillance system, how to change the mode of video surveillance system from post-forensics to active detection and prevention has become the focus of research. Because the suspect often paces back and forth and stays in the crime area abnormally, a large number of potential criminal behaviors can be excavated by extracting the track information of the target. However, video surveillance networks are widely deployed at present and massive data processing needs to consume a lot of manpower and material resources. In order to reduce the power consumption and resource consumption of the monitoring system, a hierarchical scheme based on cascaded RCNN multi-level head and shoulder Detection network (CRDNet) and trajectory analysis method is proposed to solve the abnormal event detection problem. This scheme only needs to analyze the trajectory information of pedestrians without establishing a sample data. Based on CityPersons, a mainstream pedestrian detection dataset, CRDNet achieved an accuracy of 0.73 and a recall rate of 0.65. Finally, this experiment is conducted on the UCF-CRIME dataset for theft events, and the identification accuracy is 81.9%. Experimental results show that the proposed scheme can effectively and timely detect the anomaly we are concerned about.
Online Trajectory Planning Strategy for UAV in Dynamic Threats Based on Bug-Rapidly-exploring Random Tree Algorithm

In the process of UAV mission execution, trajectory planning algorithm must have the ability of real-time response to the dynamic environment. However, traditional UAV planning algorithms need to make a trade-off between the execution speed and the path quality. In this paper, a dynamic obstacle avoidance oriented online trajectory planning strategy (bug-rapidly-exploring random tree, Bug-RRT) is proposed. It combines global offline planning and local online planning through the event trigger strategy. The decision-making mode is changed with the urgency level. The level is determined by the -weight of operation efficiency and path quality in evaluation, which are dynamically allocated under different environmental scenarios. In a known environment, the improved RRT and pruning strategy are used to get a global optimal trajectory. When UAV needs to avoid new obstacles, bug algorithm is used for online local planning. Simulation results show that this strategy can effectively improve the operation speed and stability.
Metacognitive Training

Metacognition is usually defined as “thinking about thinking,” and it refers to knowledge about factors that influence task performance and knowledge about strategies. Moreover, it includes metacognitive regulation processes such as planning and monitoring task performance as well as evaluating the efficiency of these planning and monitoring processes. Good metacognitive abilities are essential for academic success, and good metacognitive skills support a number of other cognitive processes that are necessary to perform a specific task. Thus, training of metacognitive skills has become an important element of different training programs in various domains. In the present chapter, we will give an overview of recent advancements in the knowledge about metacognitive training in the context of mathematical skills, reading abilities, and regarding executive function training. Research from all three domains reveals promising results, indicating that the integration of metacognitive training into more conventional training programs leads to greater improvements than conventional training alone. Metacognitive training is effective for many different age groups, via different methods, and in different contexts. At the same time, however, there are still a number of open questions like the question of interindividual differences or the question of long-term effects, indicating that the field of metacognitive training research is likely to keep in the future.
Three-Dimensional Printing in a Non-Gravitational Field

This paper discusses a smart and innovative approach towards 3D printing in space. The material used here is a base fluid mixed with magneto rheological (MR) or electro rheological (ER) fluids. This would allow any object to be made inside or outside of the spacecraft on a temporary or permanent basis. This is designed specifically for the tools and parts which would be required during a long duration manned space mission. This printer is much more compact and has lower weight as compared to present solutions, and the fluid used here is reusable. Moreover, this technology can bring a revolution in long-distance space travel by radically decreasing the material requirement and redundancies which are needed during a space voyage.
Advantages of Blockchain in Digital Forensic Evidence Management

The importance of digital evidence has been accepted by the judiciary over the globe. The digital evidence plays a very crucial role in the judgment of the crime. The protection of integrity, authenticity, admissibility of such evidence should always be given highest priority. A log of digital evidence handling, which can be verified for any tampering, but does not allow any of the stakeholders to erase such traces of tampering is the need of today. This can guarantee a full proof maintenance of chain of custody for the digital evidence. Blockchain technology and using its core feature can make the process of a chain of custody(CoC) more efficient and reliable. This work proposes a hybrid blockchain solution for maintaining CoC( Chain of Custody), making the evidence and the transaction of the evidence transparent. The proposed system is designed to underline the advantages and allow the stakeholders of the system to view the evidence transaction during the complete legal process, but does not allow any sort of tampering of the same.
Remote Sensing Image Zero-Watermark Algorithm Based on Bemd

Abstract In order to solve the problem of information security when acquiring and disseminating images and improve the performance of digital watermarking, this paper proposes a remote sensing image zero-watermarking algorithm based on Bidimensional Empirical Mode Decomposition (BEMD). Initially, to perform this algorithm the DCT transform is used to extract the characteristic image of the host image. In the next step, BEMD decomposition on the grayscaled image need to be performed. The number of watermarks embedding layers is determined according to the NC value of the original image without the i-th IMF layer and the original image. The information matrix of the watermark embedding layer is XORed with the characteristic image to obtain the image embedded with the watermark. The extraction of the watermark image is the XOR result of the first XOR and the attacked image. After the watermark is embedded, the image is processed with noise, enhanced processing attacks, etc., and the NC value of the extracted watermark and embedded watermark is mostly above 0.9. The experimental results show that the watermarking algorithm proposed in this paper has better anti-attack performance, stronger invisibility and robustness.
Implementation of Algorithms for Identity Based Encryption and Decryption

Identity-based cryptosystems were introduced to overcome one of the main problems in public key encryption, the generation of public and private keys. In the identity-based cryptosystem, an identifier such as an e-mail address of a user can be used to generate public and private keys by a trusted third party. The trusted third party uses a system-wide master secret to provide private keys to a user. Identity-based cryptosystems can be constructed using the idea of pairings. This article discusses four different identity-based cryptosystems: the Boneh-Franklin scheme, the Cock's scheme, the Authenticated IBE scheme and the Hierarchical IBE scheme. This article also discusses the security notions considered for the identity-based cryptosystem. The security notions considered are: one-wayness, indistinguishability, semantic security and non-malleability. An architecture consisting of a public parameter server and private key generator for the implementation of the identity-based cryptosystems is also discussed.
Novel Payment Wallet Management with Blockchain Based Cryptocurrency

Blockchain for business is a new concept which enables many industries and organizations to implement even the basic of systems on foundation of blockchain technology. Using this technology, our goal is to develop a payments system that enables transfer of funds for a monetary transaction between two parties. Hyperledger is an open source community oriented effort which was made to propel cross-industry blockchain advances that were available. The Linux Foundation has it. It has partners from everywhere throughout the world , at a worldwide dimension and incorporates ventures like funding, banking, Internet of Things, supply chains, assembling and Technology. Using Blockchain for Enterprise technology, we are going to develop a new payments system that makes use of regulated cryptocurrency. Using this system, we want to create a new cryptocurrency specific to the payment portal for people to buy, sell and pay or earn rewards using this cryptocurrency. This system will majorly consist of participants and admins that will be divided based on the certificates assigned to every participant. Our implementation involves. using the fabric for creating a payment system run on the backend of blockchain technology. This will involve having a regulatory authority to maintain the cryptocurrency, ledger and authenticity of the users. Theoretically, the blockchain technology maintains anonymity for transactions. It uses a distributed ledger to record transactions for people to be able to make secure transactions without any repercussions. Blockchain for Enterprise implements Blockchain technology by using concepts like Trust, Privacy and Smart contracts in addition to the distributed ledger to create an industry friendly Blockchain business application. Blockchain is a rapidly growing field with multiple implementations which can be explored not just on anonymity but also on actual life implementations. Distributed ledger technology is applied to the payment systems. Cryptocurrency would now not only be used for anonymous transactions but also for regular day to day transactions.
Analyzing the findings

This chapter presents a plan for analyzing usability testing results. It focuses on gathering input from everyone and organizing the findings using a top-down approach, based on assigned codes or categories of issues, or a bottom-up approach (also called affinity diagramming), which organizes the findings into groups that are then assigned category labels. Next, it covers the methodology for analyzing quantitative data, questionnaire responses, and qualitative data. It also reviews the ways to handle outliers. Assigning scope and severity ratings is also discussed, along with approaches to making recommendations.
Standards for Cloud Risk Assessments—What's Missing?

Conducting a risk assessment (RA) for cloud computing platforms presents new challenges in the space of Information Security Management Systems (ISMS). ISO 27001 RA methods have been used for Alcohol Monitoring Systems (AMS) across a portfolio of products and services. Scaling these localized techniques to national and international cloud security standards shows that a `one size fits all' RA approach does not exist in the industry today. The context and methods for conducting cloud RA are examined across representative national and international standards and guidelines.
A Recapitulation of Imbalanced Data

In today’s authentic universe almost all applications are imbalanced. Data imbalance is growing faster than ever before as many systems are interested in extracting knowledge from lake of data. Imbalance issue occurs because required data is very rare and using that rare data if classification is done we may lead to inaccurate result. In few sensitive imbalance cases like medical and finance if classification is done health and wealth both will get a huge lost. It is observed that big data and imbalance issue are having hand in hand relationship. So, imbalance data is gaining much importance in data science. It is predicted that by the year 2020, about 1.7MB of lake of information will be created every second by each device due to development in science and technology. Almost this lake of information generated will be imbalanced. So, in this paper we will define big data and imbalanced data, how there are related to each other, some of the reasons why imbalance data problems are occurring, various areas where imbalance issues is been effecting, current four machine learning methods for imbalanced data (Data based method, Algorithm based method, Cost sensitive method and ensemble methods), overall performance evaluation of imbalance methods are done using a comparison chart and interpreting achievements of imbalanced data using confusion matrix, Combined evaluation measures (G-means, F-Measure, Balanced Accuracy, Youden Index and Matthews’s correlation coefficient) and Graphical performance evaluation using Receiver operating characteristic (ROC) curve and Area under the curve (AUC) and lastly, considering of result of various imbalance methods.
Enhanced Image Steganography with PVD and Edge Detection

Steganography is the concept to conceal information and the data by embedding it as secret data into various digital medium in order to achieve higher security. To achieve this, many steganographic algorithms are already proposed. The ability of human eyes as well as invisibility remain the most important and prominent factor for the security and protection. The most commonly used security measure of data hiding within imagesYet it is ineffective against Steganalysis and lacks proper verifications. Thus the proposed system of Image Steganography using PVD (Pixel Value Differentiating) proves to be a better choice. It compresses and embeds data in images at the pixel value difference calculated between two consecutive pixels. To increase the security, another technique called Edge Detection is used along with PVD to embed data at the edges. Edge Detection techniques like Canny algorithm are used to find the edges in an image horizontally as well as vertically. The edge pixels in an image can be used to handle more bits of messages, because more pixel value shifts can be handled by the image edge area.
Relations Between Explainability, Evaluation and Trust in AI-Based Information Fusion Systems

Explainability is generally considered an important means to gain trust in complex automated decision support systems. Different types of explainability of processes and models used in a complex information fusion solution based on Artificial Intelligence (AI) are relevant throughout its life-cycle, i.e. during the system development as well as its deployment. However, it is often difficult to understand the real value of explainability in specific cases. To study the impact of explainability on trust, there is a need to emphasize the trust building processes, especially various types of evaluations supporting trust assessment. The paper emphasizes that the value of explainability is as an enabler of certain types of evaluations leading to improved trust in automated solutions. A conceptual model brings together different types of explainability, evaluations, and operational conditions along with human factors influencing the trust in automated systems. The introduced model describes the types of possible evaluations and related explainability at different stages of life cycles of AI-based information fusion solutions. This enables adaptation of life cycles, such that the trust assessment is facilitated. The concepts are illustrated with the help of examples using different modelling and processing techniques.
Internal Marketing Cybersecurity-Conscious Culture

The chapter is putting forward the idea that internal marketing is a tool of which there are many to embed a culture to combat cybersecurity threats. This conceptual paper is suggesting that cybersecurity threats are multi-facet and although internal marketing is a major contributing factor in reducing the threats, other factors are in play. The shape of the organisation (i.e., bureaucratic or organic) has an important bearing on the implementation of a marketing-oriented culture, including that of internal marketing and, thus, the success of a cybersecurity-conscious organisational culture. Another significant factor in creating a cybersecurity-conscious organisational culture is the management willingness to empower and employees and their willingness to accept the responsibility to make decisions and be accountable, which requires acceptance of the authority.
A Social Media Mining and Analysis Approach for Supporting Cyber Youth Work

Cyber youth work is a pioneering and proactive approach used by non-government organizations to address the changing needs of the youth groups, particularly those at-risk and “hidden” young people. This paper describes the development process of a social media mining and analysis method, which is built to facilitate services for cyber youth work. The method incorporates an iterative method for collecting, selecting and extracting domain keywords in selected social media. A hybrid approach which combines heuristic rules and n-gram analysis for bilingual word segmentation has been developed. Then, social network analysis is used to analyze the extracted results. A pilot study has been done by using drug abuse as the study topic. It demonstrated high potential of the method to enhance cyber youth work. The weights deduced by the method have found to have a positive correlation with the benchmarked scores. It helps to understand the characteristic of the network, identify target clients, and provide data support for marking decisions.
Classifiers for Arabic NLP: survey

In this paper, we reviewed most common-used models and classifiers that used for the Arabic language to classify texts into categories, classes, or topics in tasks of opinion mining, sentence categorisation, part of speech tagging, language identification, name entity recognition, authorship attribution, word sense disambiguation, and text classification. Comparisons between classification tasks conducted in terms of models' performances and accuracies. Classification approaches are three types: lexicon-based, machine and deep learning, or hybrid ones. Research sample is 34 articles in the classification domain. Challenges facing the Arabic language discussed with further solutions: 1) solid research training on both approaches: lexicon-based and corpus-based (machine and deep learning); 2) research contribution mainly corpus, approach technique, and free accessibility; 3) fund increase to the research development in the Arab world.
A Differential Evolution Based Self-Adaptive Multi-Task Evolutionary Algorithm

This paper proposes a novel self-adaptive evolutionary multi-task optimization algorithm based on differential evolution (SMTDE) for solving multiple different optimization problems or tasks simultaneously. The algorithm arranges a specific population and three differential strategies for each task. Among the three strategies, one is the transfer strategy and the others are non-transfer strategies. The transfer strategy is mainly responsible for utilizing the information of other tasks, and the two non-transfer strategies are responsible for accelerating convergence and improving the diversity of intra-task, respectively. Based on strategies, a self-adaptive mechanism is proposed to adjust the selection probabilities of the three strategies to reduce the harm of negative transfer and balance the diversity and convergence within the population. The experiment is conducted on a single-objective multi-task test suite. The experiment results show that SMTDE can find better solutions with a higher convergence rate in comparison with several competitive evolutionary multi-task optimization algorithms.
Universal Website Fingerprinting Defense Based on Adversarial Examples

Website fingerprinting (WF) attacks pose a threat to privacy of web activity, especially on anonymity networks such as Tor. Recent studies show that the deep neural network (DNN) significantly improves the impact of website fingerprinting attacks. Especially, DNN-based attack undermines the existing defense methods which are mainly rely on the manually designed rule. In this paper, we present a novel defense that generates universal perturbation that can transform original examples to adversarial examples which is effectively defending against a specific WF model. The proposed defense is evaluated on state-of-the-art DNN attack over a public Tor traffic dataset. The experimental results show our adversarial example generation method performs better than the baseline methods. The proposed defense defeats all existing WF attacks based on deep neural networks with a low overhead. Comparing with state-of-the-art defenses such as Walkie-Talkie and WTF-PAD with a lower bound of 31% and 64% overheads, the proposed defense achieves identical defense performance with at least 50% bandwidth overhead saving.
Heuristic methods in a nutshell

Heuristics are a powerful class of algorithms for numerical optimization. We explain the principles by which these methods operate and detail several specific techniques like Simulated Annealing or Differential Evolution; the construction of hybrids is briefly described, too. We provide sample code for various algorithms. Almost all heuristics are stochastic algorithms, hence we explain how the solutions obtained from heuristics can be evaluated, and what factors (e.g., computational resources) influence a solution's quality. We also discuss several practical issues: how constraints can be included in algorithms, and why we should care about efficient implementations. All discussion is kept informal, and examples and sample code are provided.
Mean-Square Error Linear Estimation

In this chapter, mean-square linear estimation is discussed and the normal equations are derived. The orthogonality theorem concerning random variables is introduced as an alternative for their derivation. Issues concerning complex random variables, such as widely linear estimation and Wirtinger's calculus, are presented. Some typical applications of MSE, such as image deblurring, interference cancelation, system identification, and channel equalization, are defined. Issues related to the efficient solution of the normal equations in the context of linear filtering are discussed and the Levinson and lattice-ladder algorithms are derived. The Gauss–Markov theorem for MSE of linear models is provided and the case of constrained estimation in the context of beamforming is introduced. The chapter concludes with the introduction and derivation of Kalman filtering.