Reviewerly/exHarmony huggingface dataset:
Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.
Blockchains for the Governance of Common Goods

This paper analyses the use of blockchain technology to support the governance of commons-pool resources, as studied by Elinor Ostrom. It argues that the technological guarantees of blockchain technology---in terms of ex-ante automation and ex-post verification---can replace the traditional requirements of monitoring and sanctioning. Despite its own limitations and challenges, this novel approach to governance could provide new opportunities for experimentation in the context of commons-pool resources.
A Survey on Emerging Software-Defined Networking and Blockchain in Smart Health Care

Abstract With assistance from Software Defined Networking (SDN) and Blockchain, networks have become more creative to build and maintain over the last few years. The inflexibility of modern network architecture is presenting researchers with a tough achievement. SDN replaces existing inescapable and complicated networks with a creative way of separating the control plane from the data plane and fixing those constraints, namely configurations done manually, monitoring, protection, usability, and functionality. This paper provides an overview of blockchain and SDN in healthcare. Various existing projects of their integration have been studied the benefits of the Blockchain provides security, privacy, integrity to the health care data has been discussed. The decentralization feature of blockchain provides access to data within and across the nation. Different use cases are discussed in the paper that provides the acceptability of blockchain and SDN in healthcare. This paper provides the overview and results that integration of blockchain and SDN in healthcare is a good research area and various initiatives have been taken to explore its integration.
Artificial intelligence against hate: Intervention reducing verbal aggression in the social network environment

Abstract This article presents a quasi‐experimental intervention study designed to reduce the level of verbal aggression on a social networking service (Reddit). The interventions were based on three psychological mechanisms: induction of a descriptive norm, induction of a prescriptive norm, and empathy induction. Each intervention was generated using a communicating bot. Participants exposed to these interventions were compared with a control group that received no intervention. The bot‐generated normative communications (both the ones priming descriptive and the ones priming prescriptive norms), as well as the empathizing intervention, reduced the proportion of verbal aggression posted by Reddit accounts. All three interventions proved effective in reducing verbal violence when compared with the control condition.
Assessment of predicting hourly global solar radiation in Jordan based on Rules, Trees, Meta, Lazy and Function prediction methods

Nowadays, predicting solar radiation is widely increased to maximize the efficiency of solar systems globally. Meteorological data from metrological stations is used to implement the intelligent prediction systems. Unfortunately, uncertainty in the used solar variables and the selected prediction models would increase the difficulties in using intelligent models to predict solar radiation. Several studies perfectly estimated solar radiation using only time and date variables. The main objective of this study is to review different prediction methods in predicting the solar radiation of Jordan. To achieve this target, five main methods including Rules, Trees, Meta, Lazy and Function Methods are selected, and then the most important and used algorithms in each method are selected to build a prediction model. The study shows that M5Rule, Random forest, Random committee, Instance Based Learning with Parameter K and multi-layer perceptron are the best algorithms in Rules, Trees, Meta, Lazy, and Function Methods respectively. Random forest algorithm performed better than other algorithms in predicting global solar radiation. The results of the analysis found that the accuracy of prediction depends on the used category, training algorithm and variables combinations.
Fusing attributed and topological global-relations for network embedding

Network embedding aims to learn a vector for each node while preserves inherent properties of the network. Topological structure and node attributes are both critical for understanding the network formulation. This paper focuses on making the topological and attributed properties complement each other. We propose FATNet for integrating the global relations of the topology and attributes into robust representations. Specifically, non-local attribute relations are proposed to capture long-distance dependencies for enriching the topological structure. Meanwhile, we design attributes smoothing filter to preserve the critical attribute values, while interpolating global topology relations via high-order proximity. These relations provide reasonable principles to fuse the structure and attribute for network embedding. Extensive experiments are carried out with five real-world datasets on four downstream tasks, including node classification, link prediction, node clustering and graph visualization. Experiments have shown that the FATNet can achieve superior performance in most cases.
The Acceptance and Use of Mobile Learning Applications in Higher Education

Many individuals are increasingly using ubiquitous technologies, including interactive applications (apps) that are widely available on our mobile devices, including the smart phones and tablets. Therefore, in the last few years, educators and policy makers have introduced mobile learning (m-learning) technologies in order to support their students during their learning journey. A thorough literature review suggests that there are several contributions in academia that have investigated the students' acceptance and use of technology, in different contexts. In this light, this research has integrated valid and reliable measures from the Technology Acceptance Model and the Theory of Planned Behavior to better understand the university students' readiness to engage with mobile technologies for educational purposes. Specifically, this study explores the perceived usefulness and ease of use of m-learning technologies. Moreover, it investigated whether the research participants were influenced by their friends, acquaintances and educators to engage with these technologies and / or by the facilitating conditions at their university. The findings revealed that students held positive attitudes toward the m-learning technologies as they perceived them as useful and easy to use. Moreover, the university's facilitating conditions had a significant effect on the students' usage of these technologies. This study also reported that the students' social influences did not have an effect on their intention to use these devices.
A robust tracking algorithm for a human‐following mobile robot

The capability of recognizing and tracking a specific human is considered a key technique for serving mobile robots. This paper proposes a real-time tracker that uses a stereo camera to track specific people in different environments. A tracker has been designed to detect the selected target from multiple people by use of block matching algorithm, human detection, and colour histogram comparison. Considering the similar colour of the objects, the predictor of the Kalman filter determines the position of the object. Finally, the mobile robot is moved according to the tracking results with a relative distance between the robot and the target. The effectiveness of the proposed method is demonstrated by experiments on many video sequences and real environments.
Capitalism after Satoshi

Purpose The purpose of this paper is to explore the long-run economic structure and economic policy consequences of wide-spread blockchain adoption. Design/methodology/approach The approach uses institutional, organisational and evolutionary economic theory to predict consequences of blockchain innovation for economic structure (dehierarchicalisation) and then to further predict the effect of that structural change on the demand for economic policy. Findings The paper makes two key predictions. First, that blockchain adoption will cause both market disintermediation and organisational dehierarchicalisation. And second, that these structural changes will unwind some of the rationale for economic policy developed through the twentieth century that sought to control the effects of market power and organisational hierarchy. Research limitations/implications The core implication that the theoretical prediction made in this paper is that wide-spread blockchain technology adoption could reduce the need for counter-veiling economic policy, and therefore limiting the role of government. Originality/value The paper takes a standard prediction made about blockchain adoption, namely disintermediation (or growth of markets), and extends it to point out that the same effect will occur to organisations. It then notes that much of the rationale for economic policy, and especially industry and regulatory policy through the twentieth century was justified in order to control economic power created by hierarchical organisations. The surprising implication, then, is that blockchain adoption weakens the rationale for such economic policy. This reveals the long-run relationship between digital technological innovation and the regulatory state.
Machine Learning as a Service

This study aims to evaluate the current state of research with regards to Machine Learning as a Service (MLaaS) and to identify challenges and research fields of this novel topic. First, a literature review on a basket of eight leading journals was performed. We motivate this study by identifying a lack of studies in the field of MLaaS. The structured literature review was further extended to established scientific databases relevant in this field. We found 30 contributions on MLaaS. As a result of the analysis we grouped them into four key concepts: Platform, Applications; Performance Enhancements and Challenges. Three of the derived concepts are discussed in detail to identify future research areas and to reveal challenges in research as well as in applications.
Design and Implementation of Fast Spoken Foul Language Recognition with Different End-to-End Deep Neural Network Architectures

Given the excessive foul language identified in audio and video files and the detrimental consequences to an individual’s character and behaviour, content censorship is crucial to filter profanities from young viewers with higher exposure to uncensored content. Although manual detection and censorship were implemented, the methods proved tedious. Inevitably, misidentifications involving foul language owing to human weariness and the low performance in human visual systems concerning long screening time occurred. As such, this paper proposed an intelligent system for foul language censorship through a mechanized and strong detection method using advanced deep Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) through Long Short-Term Memory (LSTM) cells. Data on foul language were collected, annotated, augmented, and analysed for the development and evaluation of both CNN and RNN configurations. Hence, the results indicated the feasibility of the suggested systems by reporting a high volume of curse word identifications with only 2.53% to 5.92% of False Negative Rate (FNR). The proposed system outperformed state-of-the-art pre-trained neural networks on the novel foul language dataset and proved to reduce the computational cost with minimal trainable parameters.
Multi-element analysis of minerals using laser ablation inductively coupled plasma time of flight mass spectrometry and geochemical data visualization using t-distributed stochastic neighbor embedding: case study on emeralds

A concept of “<italic>first measure, then determine</italic>” which elements are of interest is introduced for multi-element analysis using LA-ICP-TOF-MS. Geochemical data visualization using t-SNE is advantageous over PCA for determining the provenances of emerald.
How Virtual Personal Assistants Influence Children’s Communication

Virtual Personal Assistants (VPA) are becoming an integral part of the lives of modern children. Its role extends far beyond performing the utilitarian functions of searching for information and working with applications. A child is interested in the VPA personality and the ability to conduct a small talk with it. To achieve an adequate communicative response of VPA children are ready to change their speech behavior. Using content analysis method, the article presents the analysis of video conversations of children with a VPA known as Alice. The authors identified a “corridor” of adequate communication that lies between “adult” responses, especially humorous ones that are unclear to the child most often due to different cultural backgrounds, and children’s fantasies and realities that are poorly handled by the VPA. It was found that the VPA’s responses are particularly inconsistent when it is a question of whether to maintain faith in magic, or strictly adhere to the facts. In particular, the problem appears to be caused by the “personality” of the VPA, which is interpreted by children anthropomorphically. A separate problem is the child’s assimilation of patterns of command communication with the VPA, disregard for polite words and an easy transition to scolding.
Improved Phishing Detection Algorithms using Adversarial Autoencoder Synthesized Data

Malicious actors often use phishing attacks to compromise legitimate users' credentials. Machine learning is a promising approach for phishing detection. While the accuracy of machine learning algorithms is often dependent on the training data, very little attack data for training is available. We propose an approach for augmenting existing datasets that can be used by machine learning algorithms. We use an Adversarial Autoencoder (AAE) to generate samples that mimic the phishing websites and provide metrics to assess the quality of the generated samples. We test these samples against models trained with real-world data. Some of generated samples are able to evade existing detection model. We then use a portion of these samples in training. The new machine learning models are more robust and have higher accuracy. In other words, real-world phishing site data augmented with AAE synthesized data used for training the model is more effective for phishing detection.
Digital forensic readiness in operational cloud leveraging <scp>ISO</scp>/<scp>IEC</scp> 27043 guidelines on security monitoring

Abstract An increase in the use of cloud computing technologies by organizations has led to cybercriminals targeting cloud environments to orchestrate malicious attacks. Conversely, this has led to the need for proactive approaches through the use of digital forensic readiness (DFR). Existing studies have attempted to develop proactive prototypes using diverse agent‐based solutions that are capable of extracting a forensically sound potential digital evidence. As a way to address this limitation and further evaluate the degree of PDE relevance in an operational platform, this study sought to develop a prototype in an operational cloud environment to achieve DFR in the cloud. The prototype is deployed and executed in cloud instances hosted on OpenStack: the operational cloud environment. The experiments performed in this study show that it is viable to attain DFR in an operational cloud platform. Further observations show that the prototype is capable of harvesting digital data from cloud instances and store the data in a forensic sound database. The prototype also prepares the operational cloud environment to be forensically ready for digital forensic investigations without alternating the functionality of the OpenStack cloud architecture by leveraging the ISO/IEC 27043 guidelines on security monitoring.
A Novel Bat Algorithm using Sobol Sequence for the Initialization of Population

Evolutionary and Swarm intelligence algorithms are widely used for global optimization problems. Bat Algorithm is one such algorithm based on a population-based metaheuristic algorithm. Bat Algorithm faces the premature convergence problem in which bats get stuck in local optimum and cannot reach a global optimum safely. The initial population generation of the bats plays an important role to move efficiently in a d-dimensional search space. The Standard Bat Algorithm (StdBA) uses random distribution for the initialization of bats. In this paper, we propose a novel variant of the Bat algorithm, namely, the Bat Algorithm using the Sobol sequence (BASobol). In this variant, the exploration abilities of StdBA are enhanced using Sobol distribution instead of random distribution. Furthermore, the velocity update equation is also modified using Inertia Weight. Ten different standard benchmark functions are compared with the proposed technique to test its validity. Statistical results of BASobol are compared with StdBA and Modified Bat Algorithm (MBA). Superior results are seen by BASobol over seven benchmark functions.
Design of a Dynamic Parameter-Controlled Chaotic-PRNG in a 65 nm CMOS process

In this paper, we present the design of a new chaotic map circuit with a 65 nm CMOS process. This chaotic map circuit uses a dynamic parameter-control topology and generates a wide chaotic range. We propose two designs of dynamic parameter-controlled chaotic map (DPCCM)-based pseudo-random number generators (PRNG). The randomness of the generated sequence is verified using three different statistical tests, namely, NIST SP 800-22 test, FIPS PUB 140-2 test, and Diehard test. Our first design offers a throughput of 200 MS/s with an on-chip area of 0.024 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and a power consumption of 2.33 mW. The throughput of our second design is 300 MS/s with an area consumption of 0.132 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and power consumption of 2.14 mW. The wider chaotic range and lower-overhead, offered by our designs, can be highly suitable for various applications such as, logic obfuscation, chaos-based cryptography, re-configurable random number generation, and hard-ware security for resource-constrained edge devices like IoT.
Reliability Analysis of Internal Power Supply Architecture of Data Centers in Terms of Power Losses

The number of data centers and the energy demand are increasing globally with the development of information and communication technology (ICT). The data center operators are facing challenges to limit the internal power losses and the unexpected outages of the computational resources or servers. The power losses of the internal power supply system (IPSS) increase with the increasing number of servers that causes power supply capacity shortage for the devices in IPSS. The aim of this paper is to address the outage probability of the computational resources or servers due to the power supply capacity shortage of the power distribution units (PDUs) in the IPSS. The servers outage probability at rack-level defines the service availability of the data center since the servers are the main computational resource of it. The overall availability of the IPSS and the power consumption models of the IPSS devices are also presented in this paper. Quantitative studies are performed to show the impacts of the power losses on the service availability and the overall availability of the IPSS for two different IPSS architectures, which are equivalent to the Tier I and Tier IV models of the data center.
Generalization-Based Acquisition of Training Data for Motor Primitive Learning by Neural Networks

Autonomous robot learning in unstructured environments often faces the problem that the dimensionality of the search space is too large for practical applications. Dimensionality reduction techniques have been developed to address this problem and describe motor skills in low-dimensional latent spaces. Most of these techniques require the availability of a sufficiently large database of example task executions to compute the latent space. However, the generation of many example task executions on a real robot is tedious, and prone to errors and equipment failures. The main result of this paper is a new approach for efficient database gathering by performing a small number of task executions with a real robot and applying statistical generalization, e.g., Gaussian process regression, to generate more data. We have shown in our experiments that the data generated this way can be used for dimensionality reduction with autoencoder neural networks. The resulting latent spaces can be exploited to implement robot learning more efficiently. The proposed approach has been evaluated on the problem of robotic throwing at a target. Simulation and real-world results with a humanoid robot TALOS are provided. They confirm the effectiveness of generalization-based database acquisition and the efficiency of learning in a low-dimensional latent space.