Reviewerly/exHarmony huggingface dataset:
Ransomware Economics

This chapter explores the underlying economics of cybercrime. The chapter begins by examining how cybercrime syndicates adapt to changing market conditions. This following section of the chapter explores how ransomware demonstrates the ability to monetise both valuable and innocuous data. The chapter then moves forward to discuss how Internet marketplaces have changed the dynamic for criminal activities and why some cybercriminals are shifting their focus to ransomware. It explores why cybercriminals have become more focused on holding files hostage for money than on unleashing stolen data to the black market (Parrish 2018). The final section of the chapter examines what are cryptocurrencies and what impact they have in ransomware attacks.
Cognitive Advantage

We are moving into a new paradigm of hyper-accelerated decision making, where artificial intelligence and machine learning facilitate automated action at a pace and scale that human minds alone cannot keep up. We need to respond to this challenge by combining artificial and human intellect to build a cognitive advantage, which is the demonstrable superiority gained through comprehending and acting to shape a competitive environment at such a pace that an adversary’s ability to comprehend and act is compromised. In the longer term, cognitive advantage provides the means to cultivate the broader sustainability of the environment on which the organization depends. This white paper provides context for this trend, summarizes relevant scientific understanding and codifies the distinguishing features of the type of organization that will succeed and lead in this new competitive landscape – the cognitive enterprise.
Evaluation of clustering approach with euclidean and Manhattan distance for outlier detection

This paper presents the results of the seaweed drying data. We compared the two main approaches to clustering analysis between K-Means and (Partitioning Around Medoid) PAM. In agriculture, Clustering application is the method of finding the new insights in different kind of problems. For enhancement of agriculture, machine learning always seems to be helpful in conversion of raw data into useful information. But in case of outlier presence, machine learning algorithms and techniques could not work well. In this paper, the dataset consisted of 1924 observations are taken for the analysis purpose. The dataset included one dependent variable with 29 different predictors. The variable includes the hourly solar radiation, temperature, humidity, and moisture content. The outliers were found in the dataset. The comparison between PAM and K-Means using both Manhattan and Euclidean distance for internal validity results was evaluated on the performance and evaluation of algorithms in case of outliers. The purpose was to evaluate which used the internal clustering validation on the experimental drying process of seaweed data. The results are beneficial to minimize the outliers data and forecasting.
Formal Theories of Engineering Management

What is the key underpinning factor that determines the efficiency of group work? How does human group provide a mechanism for enabling the exchange between labor and time? This paper presents a coordinative work organization theory for explaining the fundamental mechanisms of engineering organization and management. The coordinative work organization theory is derived based on analyzing the nature of group workload and the exchange mechanisms between labor and time in team work. The human error mechanisms in task performance in groups are explored in order to develop a formal quality assurance theory in engineering management. Applications of the formal engineering organization theory have been demonstrated in efficient engineering project organization and optimization in supplement to traditional empirical practices in engineering management.
Ontology Driven Cross-Linked Domain Data Integration and Spatial Semantic Multi Criteria Query System for Geospatial Public Health

This article describes how public health information management is an interdisciplinary application which deals with cross linked application domains. Geospatial environment, place and meteorology parameters effect public health. Effective decision making plays a vital role and requires disease data analysis which in turn requires effective Public Health Knowledge Base (PHKB) and a strong efficient query engine. Ontologies enhance the performance of the retrieval system and achieve application interoperability. The current research aims at building PHKB through ontology based cross linked domain integration. It designs a dynamic GeoSPARQL query building from simple form based query composition. The spatial semantic multi criteria query engine is developed by identifying all possible query patterns considering the ontology elements and multi criteria from cross linked application domains. The research has adopted OGC, W3C, WHO and mHealth standards.
Path planning for ground target reconnaissance based on improved Astar algorithm

Aiming at solving the problems of complex and huge calculation and poor threats avoidance capabilities of UAVs carrying airborne reconnaissance equipment in ground targets tracking path planning, an improved Astar algorithm is proposed. Firstly, the improved Astar algorithm is combined with the track point extension method, and its evaluation function is improved based on traditional algorithms, so as to improve the real-time performance of the algorithm and reduce the amount of information stored in the algorithm. Secondly, add the threat cost to the heuristic function and assign weights. The simulation results show that the calculation of improved Astar algorithm is reduced and the planned path can avoid the threats well.
Research on Face Recognition Technology Fusion Deep Learning Under Different Light Intensity Changes

Aiming at the problem of face recognition under different illumination intensities combined with deep learning algorithms, this research designed a new type of loss function, the I-center loss function. Use face image data set LFW with different light intensity to train and test LeNets++ deep learning network based on softmax, center, I-center loss function, and a variety of common image recognition networks. The calculation results show that although the LeNets++ deep learning network training requires much more data than other networks selected in the study, when the loss function is changed to I-center, the network has a significant improvement in the accuracy of face image recognition under different light intensities, reaching 99.65%. Therefore, experiments have proved that the use of an improved deep learning neural network based on the I-center loss function can improve the face recognition effect under different light intensities.
Technical Normativity

Despite their increasingly expanding empire, technical norms have scarcely attracted the attention of legal scholars. This article tries to understand the working of such norms by approaching the issue from the standpoint of the notion of “artefactual normativity”—i.e. normativity concerning artefacts and their characteristics—and then by analysing a strikingly growing class of technical norms more or less explicitly directed at influencing behaviour. What emerges from this analysis is a non-legal form of normativity which involves the subject in a process of continuous observation, surveillance and direction of his/her conduct whose final aim is his/her self-identification with certain imposed standards. The paper ends with a section on the standardisation of the language of regulation.
Synthesize Missing Modality Based on Latent Space Model

Multi-modal learning is currently a research hotspot in the field of artificial intelligence. Multi-modal learning effectively improves learning performance since it comprehensively utilizes information from multiple modalities. However, in real-world application scenarios, it is often impossible to collect complete multi-modal data, which limits the wide application of multi-modal learning. High-quality missing modality synthesis is still a challenge. In this work, we propose a novel method to synthesize the missing modality. Specifically, we utilize the common latent representation space model to adaptively fuse the consistent and complementary information in existing modalities, and then the synthesis network with 1d-CNN layers and MLP is employed to synthesize the missing modality. In addition, “threshold-loss” is proposed to tackle the over-optimizing phenomenon during the testing stage. Experiments demonstrate the proposed method outperforms other existing methods.
An Enhanced Lévy-Based Whale Optimization Algorithm for Global Optimization

Whale optimization algorithm(WOA) is often used to solve engineering optimization and real parameter optimization problems. However, the original WOA algorithm has some problems. First, The position iteration mechanism based on the optimal whale makes the algorithm lack of exploration ability. In addition, due to the lack of mechanisms to guide the algorithm to escape from the local optimum, the algorithm is prone to stall prematurely. To solve these problems, an Enhanced Lévy-Based whale Optimization Algorithm(ELWOA) is proposed in this paper, which uses the Lévy flight mechanism to enhance the in-depth exploration capability of the algorithm and introduces a population diversity maintenance strategy to guide the population to escape from the local optimum. Finally, its performance is verified by the CEC2017 test suite, and the results show that it has a strong advantage in the convergence of the error and convergence speed than the other three algorithms.
A Cancer Cell Image Classification Program : Based on CNN Model

Breast Cancer, as a deadly existence, has negatively influenced women’s health. To determine whether a cell is benign or malignant is critical for doctors to diagnose breast cancer. However, simply judging whether the cells are malignant by the appearance of the X-ray’s outcome greatly reduces the efficiency of diagnosis and the probability of misdiagnosis. This paper proposed a simulation model to tackle this issue, which can be utilized to analyze and determine whether the cell is benign or malignant. With the help of CNN and Text-to-speech, I have researched a solution for doctors to identify Breast Cancer with the help of machine learning. The experiment consists of Image classification built upon CNN and a user interface for upload functionality by Streamlit framework, combined with an NLP speech synthesis interface to communicate the result done with gTTS. The result of the experiment has brought up a fast, efficient, accurate result after the model has been trained measured by sensitivity and specificitysignflcantly reduced the amount of error rate. Building an interface that interacts with the result allows the result to be more visualized and straightforward.
Graph matching

Abstract This chapter explores parallel algorithms for graph matching. Here, a graph is the mathematical representation of a network, with vertices representing the nodes of the network and edges representing their connections. The edges have positive weights, and the aim is to find a matching with maximum total weight. The chapter first presents a sequential, parallelizable approximation algorithm based on local dominance that guarantees attaining at least half the optimal weight in near-linear time. This algorithm, coupled with a vertex partitioning, is the basis for developing a parallel algorithm. The BSP approach is shown to be especially advantageous for graph problems, both in developing a parallel algorithm and in proving it correct. The basic parallel algorithm is enhanced by giving preference to local matches when breaking ties and by adding a load-balancing mechanism. The scalability of the parallel algorithm is put to the test using graphs of up to 150 million edges.
A Data Augmentation Strategy for Skeleton-Based Fall Detection

Fall is the main cause of injuries in elderly people. With the development of monitor techniques and deep-learning, vision-based behavior recognition algorithms have a promising future in the fall detection task. Recently, graph convolution net-works (GCN) for skeleton-based action recognition have achieved significant development, thus fall detection can be realized by training such networks in fall datasets. However, it is impossible for a network to learn enough features due to the less behavior categories and the small scale of existing fall datasets. In addition, although current skeleton-based algorithms are robust in the face of complex background, multi-scale human, multi-perspective and multi-motion speed, it is still difficult to solve the occlusion problem. In this paper, a novel real fall dataset is proposed by fusion strategy, and it contains a large number of fall and fall-like behaviors. Moreover, we propose a data augmentation mechanism to simulate the case of occlusion. We create an occlusion matrix by data-driven method to get the occlusion skeleton dataset. We experimentally show that the proposed methods effectively improve detecion accuracy and anti-occlusion ability of the fall detection system.
Artificial Intelligence Review

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.
Creating leadership minds for the digital VUCA world - sculpting functional perceptual architectures via cultural expertise

Few phenomena have incited as much passion as the unravelling of what 'effective leadership' means in constantly changing environments. It is suggested here that for leading successfully in the digital VUCA a fundamental transformation of individual minds is necessary, due to the novel bio-cultural paradoxes posed by it. Such 'VUCA resilient' minds are claimed to emerge from a specific perceptual architecture built on new, functional connections set up via the integration of cultural otherness into highly interlinked perception, cognition, affect and action systems. The relevant neurobiological underpinnings of this architecture biasing vision are outlined and an innovative perceptuo-cognitive experimental pilot (n = 34) is introduced to visualise the resultant changes in vision. Eyetracking (implicit) and questionnaire (explicit) data are analysed in tandem to improve the quality of data. Based on such promising initial tendencies it is suggested to add visual tools to leadership assessments.
Research and Practice on Smart Classroom Teaching Model of College English Based on Big Data Analysis

The research explores smart classroom in college English based on big data analysis and a MOOC resource, offers a new teaching mode which can efficiently enhance students both language skills and comprehensive quality by using modern network technology. The paper that adopts questionnaires and comparison test, analyses the effectiveness of a smart classroom in big data environment, and improves the "two skins" phenomenon of information technology and teaching.
Understanding Cooking Recipes’ Structure Using Grammars

The main objective of this research is to understand the structure of cooking recipes using context free grammars, and to build corresponding parse trees for them, labeled with semantic roles. Grammars are used as to provide a white box alternative in a world dominated by transformers. The data is gathered from two recipe websites, containing 45 000 steps, made of a variable number of sentences. The solution uses the tools Lex and Yacc, in combination with features of AllenNLP. Identification of the symbols of the context free grammars is done with Lex for which the words were extracted and tagged as parts of speech using AllenNLP’s constituency parsing. The context free grammar is processed by Yacc and the idea guiding its description is that a part of speech can only take specific positions in a sentence. Meanwhile, prepositions and keywords help identify the proper semantic role for a group of words. The construction of the tree was developed in a bottom-up approach, employing C functions in the Yacc file. Therefore, the result is a portable JSON file, equivalent to a parse tree for a cooking recipe, where each group of words is labeled with an appropriate semantic role. The trees can be further utilized in the comparison and evaluation of cooking recipes. Additionally, this methodology can be applied in processing several types of instruction manuals
Basic Probability Theory

Before entering the field of statistics, we warm up with basic probability theory. Without insights into the basics of probability it is difficult to interpret information as it is provided in science and everyday life. In particular, a lot of information provided in the media is essentially useless because it is based on partial information. In this chapter, we will explain what type of complete information is needed for proper conclusions and introduce Bayes’ theorem. We will present the ideas using simple equations and, for readers not comfortable with mathematics, we will provide the basic intuitions with simple examples and figures.
Digital Image Steganography

Digital data transmitted over the insecure communication can be prone to attacks. Intruders try various attacks to unauthorized access of the confidential information. The Steganography is such as security system that provide the protection to the images, text and other type of data digitally transferred through the data communication network. This chapter elaborates the basics of Digital Image Steganographic techniques from ancient era to digital edge, types of images used for the steganography, payload used for the steganography, various attacks and different algorithms that can provide the information security. The performance analysis of the various Digital Image Steganographic algorithms are discussed. The current applications and their necessities are discussed in this chapter.
LVRT: Low Variances of Solo Mining Reward &amp; Inter-block Time in Collaborative PoW

The paper goes into Bitcoin PoW (Proof-of-Work) consensus, especially the variances of inter-block time and of mining reward which may turn out to be the origin of Bitcoin's fundamental weakness (e.g., unexpected centralization and double-spend attack). We procure a contribution-and-gain solution - LVRT, a collaborative PoW consensus with low variances of mining reward and of inter-block time, to intensify mining transparency and incentivize collaborative effort. More precisely, LVRT looses the solutions of cryptographic puzzles by introducing tweakable factor to difficulty target and exploits all qualified computing powers (rather than throws them all away except the first one that wins the puzzle, as handled in Bitcoin) to generate the block (all qualified miners would thereby be rewarded). Low variance of inter-block time also stems from our strategy in packing the block that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> (a pre-defined system parameter to characterize collaborative effort) smallest out of all qualified hash values are determined and the corresponding average is bounded by difficulty target as well. We define particular block structure to fit our requirements. LVRT is supported by theoretical analysis on the relationship between collaborative effort and the variances of inter-block time and mining reward. Experiments further demonstrate LVRT's resistance against selfish-mining and double-spend attacks. For a double-spend attacker with 40% mining power, it will succeed in Bitcoin with a probability ≥ 35%, in contrast, his probability is less than 1% in LVRT (for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k\geq 50$</tex> ).
Incentives for Stable Mining in Pay Per Last N Shares Pools

A large number of blockchain consensus protocols 7 use the Proof of Work (PoW) principle, which relies on miners who exchange computation for newly minted currency. Their) task is to support consensus, safeguarding the immutability of the chain's history. For the sake of regular income, a vast majority of miners team-up in large independent pools. These pools distribute among all their members the rewards gathered from individual miners, thus guaranteeing a stable income for each miner in the pool. The monetary compensation follows a specific reward system enforced by the pool administrator. Pay Per Last N Shares (PPLNS) is one of the most popular reward systems in PoW pools. Despite many desirable properties, in this paper, we show that composition of PPLNS pools may be unstable. To better understand the incentives of miners, we explore the effect of time preferences in the mining decisions of miners. Using a) game-theoretical model we study conditions for equilibrium in a) game with two different PPLNS pools. We find that the range of parameters that support equilibria between the pools with large number of miners is minuscule. This implies that in many cases, miners may have incentives to migrate towards larger pools, harming decentralization in the process.
Privacy Preservation of Social Media Services

Social media has revolutionized the way of communication and interaction in daily life. It provides an effortless, expeditious and reliable approach for communicating with family, friends, and others. With the stupendous popularity of social media, users and their information over the social networking sites has also increased and accumulated the unprecedented amount of user's information. These tremendous data attract sniffer to perform attack and breach the privacy. Social networking sites provide their data for research and analysis purpose in anonymized format. But still with certain means, if a victim has an identical sub-network and the attacker has some background knowledge of the victim then the attacker can re-identify the victim by performing structural based attack such as degree based attack, neighborhood attack, sub graph attack, etc. This chapter provides a bird eye over Social Media, social media services, privacy preservation over social media, and social media attack with their graph prospective.
Software Defect Prediction Using Genetic Programming and Neural Networks

This article describes how classification methods on software defect prediction is widely researched due to the need to increase the software quality and decrease testing efforts. However, findings of past researches done on this issue has not shown any classifier which proves to be superior to the other. Additionally, there is a lack of research that studies the effects and accuracy of genetic programming on software defect prediction. To find solutions for this problem, a comparative software defect prediction experiment between genetic programming and neural networks are performed on four datasets from the NASA Metrics Data repository. Generally, an interesting degree of accuracy is detected, which shows how the metric-based classification is useful. Nevertheless, this article specifies that the application and usage of genetic programming is highly recommended due to the detailed analysis it provides, as well as an important feature in this classification method which allows the viewing of each attributes impact in the dataset.