Reviewerly/exHarmony huggingface dataset:
Using graph databases to detect financial fraud

Online fraud will cost businesses more than $200bn between 2020 and 2024, according to Juniper Research. 1 This stunning amount is driven by the increased sophistication of fraud attempts and the rising number of attack vectors. And, while banks are fighting back harder than ever, fraudsters have adjusted their techniques to remain below the radar. Online fraud will cost businesses more than $200bn between 2020 and 2024. This stunning amount is driven by the increased sophistication of fraud attempts and the rising number of attack vectors. Banks, however, have a new weapon in the war against fraud – graph analytics. These techniques can be used for fighting financial fraud by analysing the links between people, phones and bank accounts to reveal indicators of fraudulent behaviour, helping banks pinpoint suspicious activity in a sea of data, as Richard Henderson of TigerGraph explains.
Mining frequent itemsets from streaming transaction data using genetic algorithms

Abstract This paper presents a study of mining frequent itemsets from streaming data in the presence of concept drift. Streaming data, being volatile in nature, is particularly challenging to mine. An approach using genetic algorithms is presented, and various relationships between concept drift, sliding window size, and genetic algorithm constraints are explored. Concept drift is identified by changes in frequent itemsets. The novelty of this work lies in determining concept drift using frequent itemsets for mining streaming data, using the genetic algorithm framework. Formulas have been presented for calculating minimum support counts in streaming data using sliding windows. Testing highlighted that the ratio of the window size to transactions per drift was a key to good performance. Getting good results when the sliding window size was too small was a challenge since normal fluctuations in the data could appear to be a concept drift. Window size must be managed in conjunction with support and confidence values in order to achieve reasonable results. This method of detecting concept drift performed well when larger window sizes were used.
Block Mining or Service Providing: A Profit Optimizing Game of the PoW-Based Miners

The Proof-of-Work (PoW) consensus is widely used to maintain the security and consistency of the blockchain systems. In PoW, the miners have to consume tremendous computing resources to compete for generating a valid block and win the reward. However, in recent years, the revenue of the miners face a large oscillation, due to economic policy changes, virtual currency price fluctuations, etc. Some miners have to stop mining and exit the market, leaving large number of computing resources vacant. Whereas, vast computing resources are required outside the blockchain ecosystem, such as cloud/edge computing and artificial intelligence. In this paper, the traditional miners (T-miner) which concentrate on block mining are promoted to the evolutionary miners (E-miner) with additional ability to provide computing services. We propose a scheme for the E-miners to connect the blockchain ecosystem with external computation requirements, releasing the redundant computing power of the E-miners to provide computing services for extra profits. A two-stage Stackelberg game is presented to model the interaction between the computing service pricing and each E-miner's computing power allocation. The details of the game are separately elaborated for both flexible and specified external computing requirements, in pure environment with only E-miners and hybrid environment with both E-miners and T-miners, respectively. We utilize two distributed iteration algorithms to find the equilibrium point of the Stackelberg game, where both sides can achieve optimal utility. Extensive simulation results demonstrate the effect of our proposed schemes, and show that the profits of the E-miners are considerably increased compared with the T-miners that devote all computing power to block mining.
Study on Inside Threats Based on Analytic Hierarchy Process

Insider threats that occur within organizations cause more serious damage than external threats. However, there are many factors that are difficult to determine, such as the definition, classification, and severity of security breaches; hence, it is necessary to analyze system logs and user behavior-based scenarios within organizations. The reality is that qualitative judgment criteria are different for everyone to apply, and there is no detailed verification procedure to compare them objectively. In this study, realistic insider threats were examined through the definition, classification, and correlation/association analysis of various human–machine logs of acts associated with security breaches that occur in an organization. In addition, a quantitative process and decision-making tool were developed for insider threats by establishing various internal information leakage scenarios. As a result, insider threats were assessed quantitatively and a decision-making process was completed that enabled case analysis based on several insider threat scenarios. This study will enable precise modeling of insider threats that occur in real organizations and will support an objective process and a decision-making system to establish a range of required information for security protection measures.
Hardware Implementation of Sobel Edge Detection Algorithm

In the fields of image processing, feature detection, the edge detection is an important aspect. For detection of sharp changes in the properties of an image, edges are recognized as important factors which provides more information or data regarding the analysis of an image. In this work coding of various edge detection algorithms such as Sobel, Canny, etc. have been done on the MATLAB software, also this work is implemented on the FPGA Nexys 4 DDR board. The results are then displayed on a VGA screen. The implementation of this work using Verilog language of FPGA has been executed on Vivado 18.2 software tool.
FFR v1.1: Fon-French Neural Machine Translation

All over the world and especially in Africa, researchers are putting efforts into building Neural Machine Translation (NMT) systems to help tackle the language barriers in Africa, a continent of over 2000 different languages. However, the low-resourceness, diacritical, and tonal complexities of African languages are major issues being faced. The FFR project is a major step towards creating a robust translation model from Fon, a very low-resource and tonal language, to French, for research and public use. In this paper, we introduce FFR Dataset, a corpus of Fon-to-French translations, describe the diacritical encoding process, and introduce our FFR v1.1 model, trained on the dataset. The dataset and model are made publicly available, to promote collaboration and reproducibility.
Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture

Single image super-resolution using deep learning techniques has shown very high reconstruction performance over the last few years. We propose a novel three-dimensional convolutional neural network called 3D FSRCNN based on FSRCNN, which reinstates the high-resolution quality of structural MRI. The 3D neural network generates output brain images of high-resolution (HR) from a low-resolution (LR) input image. A simple design ensures less time complexity and high reconstruction quality. The network is trained using T1-weighted structural MRI images from the human connectome project dataset which is a large publicly available brain MRI database.
Immersive and participatory serious games for heritage education, applied to the cultural heritage of South Tyrol

Heritage education is an activity that is increasingly present in the educational curricula of schools and museums. Cognitive mechanisms and Representation devices must be thoroughly analysed and designed in order to promote the creation of didactic paths to foster effective learning experiences. Immersive visualization technologies are well suited for gamification applications and the technological and economic accessibility of VR HMD (head-mounted display) viewers makes these technologies particularly attractive for the development of potentially more widespread methodologies. This article will describe an educational path, and the relative experimentation, on the cultural heritage focused on the production of the typical bread of the Val Pusteria area - and the rural life around it. The project was aimed at primary school children and was based on a serious game in Virtual Immersive Reality.
Linear-Complexity Private Function Evaluation is Practical

Private function evaluation (PFE) allows to obliviously evaluate a private function on private inputs. PFE has several applications such as privacy-preserving credit checking and user-specific insurance tariffs. Recently, PFE protocols based on universal circuits (UCs), that have an inevitable superlinear overhead, have been investigated thoroughly. Specialized public key-based protocols with linear complexity were believed to be less efficient than UC-based approaches.
Rapid Penetration Path Planning Method for Stealth UAV in Complex Environment with BB Threats

This paper presents the flight penetration path planning algorithm in a complex environment with Bogie or Bandit (BB) threats for stealth unmanned aerial vehicle (UAV). The emergence of rigorous air defense radar net necessitates efficient flight path planning and replanning for stealth UAV concerning survivability and penetration ability. We propose the improved A-Star algorithm based on the multiple step search approach to deal with this uprising problem. The objective is to achieve rapid penetration path planning for stealth UAV in a complex environment. Firstly, the combination of single-base radar, dual-base radar, and BB threats is adopted to different threat scenarios which are closer to the real combat environment. Besides, the multistep search strategy, the prediction technique, and path planning algorithm are developed for stealth UAV to deal with BB threats and achieve the penetration path replanning in complex scenarios. Moreover, the attitude angle information is integrated into the flight path which can meet real flight requirements for stealth UAV. The theoretical analysis and numerical results prove the validity of our method.
Generating highly entangled states via discrete-time quantum walks with Parrondo sequences

Quantum entanglement has multiple applications in quantum information processing. Developing methods to generate highly entangled states independent of initial conditions is an essential task. Herein we aim to generate highly entangled states via discrete-time quantum walks. We propose deterministic Parrondo sequences that generate states that are generally much more entangled than states produced by sequences using only one of the two coins. We show that some Parrondo sequences generate highly entangled states, which are independent of the phase of the initial state used and further lead to maximally entangled states in some cases. We study Parrondo sequences for a small number of time steps and the asymptotic limit of a large number of time steps.
Human Feedback as Action Assignment in Interactive Reinforcement Learning

Teaching by demonstrations and teaching by assigning rewards are two popular methods of knowledge transfer in humans. However, showing the right behaviour (by demonstration) may appear more natural to a human teacher than assessing the learner’s performance and assigning a reward or punishment to it. In the context of robot learning, the preference between these two approaches has not been studied extensively. In this article, we propose a method that replaces the traditional method of reward assignment with action assignment (which is similar to providing a demonstration) in interactive reinforcement learning. The main purpose of the suggested action is to compute a reward by seeing if the suggested action was followed by the self-acting agent or not. We compared action assignment with reward assignment via a user study conducted over the web using a two-dimensional maze game. The logs of interactions showed that action assignment significantly improved users’ ability to teach the right behaviour. The survey results showed that both action and reward assignment seemed highly natural and usable, reward assignment required more mental effort, repeatedly assigning rewards and seeing the agent disobey commands caused frustration in users, and many users desired to control the agent’s behaviour directly.
A Hybrid Document Features Extraction with Clustering based Classification Framework on Large Document Sets

As the size of the document collections are increasing day-by-day, finding an essential document clusters for classification problem is one of the major problem due to high inter and intra document variations.Also, most of the conventional classification models such as SVM, neural network and Bayesian models have high true negative rate and error rate for document classification process.In order to improve the computational efficacy of the traditional document classification models, a hybrid feature extraction-based document cluster approach and classification approaches are developed on the large document sets.In the proposed work, a hybrid glove feature selection model is proposed to improve the contextual similarity of the keywords in the large document corpus.In this work, a hybrid document clustering similarity index is optimized to find the essential key document clusters based on the contextual keywords.Finally, a hybrid document classification model is used to classify the clustered documents on large corpus.Experimental results are conducted on different datasets, it is noted that the proposed document clustering-based classification model has high true positive rate, accuracy and low error rate than the conventional models.
RaCon: A gesture recognition approach via Doppler radar for intelligent human-robot interaction

As an important entrance for human-robot interaction, the hand gesture recognition based on wireless sensor has received great attention in recent years. By recognizing fine-grained arm movements, remotely deployed collaborative robot could work more accurately to satisfy human demands. Existing approaches mostly use wearable sensors or wireless devices to recognize human movement, which is with strict position requirements. In this paper, we propose a robust gesture recognition method based on double Doppler radars. Specifically, we use two Doppler radars to collect two sources of Doppler signal of a gesture. Then 6 types of gestures with different angles between people and the radar were classified by employing an improved dynamic time warping (DTW) algorithm. Furthermore, we demonstrate the practicability of the proposed method by developing a cooperative robot control system and the average recognition accuracy is 96%.
Prioritizing Runtime Verification Violations

Runtime Verification (RV) can help find software bugs by monitoring formally specified properties during testing. A key problem when using RV during testing is how to reduce the manual inspection effort for checking whether property violations are true bugs. To date, there was no automated approach for determining the likelihood that property violations were true bugs to reduce tedious and time-consuming manual inspection.We present RVPRIO, the first automated approach for prioritizing RV violations in order of likelihood of being true bugs. RVPRIO uses machine learning classifiers to prioritize violations. For training, we used a labeled dataset of 1,170 violations from 110 projects. On that dataset, (1) RVPRIO reached 90% of the effectiveness of a theoretically optimal prioritizer that ranks all true bugs at the top of the ranked list, and (2) 88.1% of true bugs were in the top 25% of RVPRIO-ranked violations; 32.7% of true bugs were in the top 10%. RVPRIO was also effective when we applied it to new unlabeled violations, from which we found previously unknown bugs-29 bugs in 7 projects and two bugs in two properties. Our dataset is publicly available online.
A Weighted Evidence Combination Method Based on the Pignistic Probability Distance and Deng Entropy

The Dempster-Shafer (D-S) theory is widely applied in various fields involved with multi-sensor information fusion for radar target tracking, which offers a useful tool for decision-making. However, the application of D-S evidence theory has some limitations when evidences are conflicting. This paper proposed a new method combining the Pignistic probability distance and the Deng entropy to address the problem. First, the Pignistic probability distance is applied to measure the conflict degree of evidences. Then, the uncertain information is measured by introducing the Deng entropy. Finally, the evidence correction factor is calculated for modifying the bodies of evidence, and the Dempster’s combination rule is adopted for evidence fusion. Simulation experiments illustrate the effectiveness of the proposed method dealing with conflicting evidences.
Performance and Key Factors of Cloud Computing Implementation in the Public Sector

The reason to explain the cloud computing assimilation, as well as the understanding of the operational performance, still remains unclear. To investigate this issue, this study has examined the assimilation of cloud computing by examining the organization’s implementation of cloud computing and performance in the Malaysian public sector. Grounded by the Diffusion of Innovation Theory (DOI), Technology-Organization-Environment Framework (TOE) and IS Success Model. This study proposes a framework by decomposing Technological characteristic, Organizational characteristics, Environmental characteristics and Human characteristics as factors. A total of 169 agencies from the Malaysian public sector have participated in this study. Data for all the study variables have been collected through self-administered survey questionnaires and analysed using SEM-PLS. The study has shown that the effect of the operational effectiveness of cloud implementation is at a lower level. The study also reveals that factors of relative advantage, compatibility, perceived risks, top management support, and organizational readiness are found to have a significant effect on cloud computing implementation. However, both external support and government regulations are not significant to the cloud computing implementation in the Malaysian public sector. The results indicate that the model provides a good understanding of the factors that influence the implementation of cloud computing as well as operational performance.
Text Summarization Using Natural Language Processing

With the advancements in the technology, most of the things in this world have become automated. The concept of text summarization came into limelight as summarization of text manually has become a tough and time-consuming task. So, the main purpose of text summarization is to overcome the difficulties faced during manual summarization of text documents or other information from various sources. Text summarization is the process of extracting the main idea of the context or the text and briefly explaining about the context. This process is not only to extract key idea and phrases from the text sources but also generating meaningful summary in a concise and crisp way. The demand for text summarization is raising nowadays because of the large amounts of data from multiple sources like Internet, Twitter, Facebook, Instagram, research papers, and other news articles. Text summarization can be efficiently implemented using NLP as it has many packages and methods in Python or R. Text summarization is also related to text mining as summary is generated based on classifying the given input text. There are different approaches for text summarization and some algorithms are identified to implement these approaches. In this paper, unsupervised learning approach is implemented and cosine similarity technique is used to find the similarity between sentences. To generate rank based on similarity, text rank algorithm is used and sentences with top rank are placed in summarized text.
Electronic education system as an instrument of increasing the specialist's competitiveness in digital economy

Introduction. The article describes the e-education system, modern e-learning tools and their role in enhancing the competitiveness of a graduate of a modern educational institution, in particular, concerns applying graduate’s competences in modern conditions of the territories of advanced social and economic development. Materials and methods. The author considers the competency-based characteristics of the involved teacher, diverse electronic educational resources and their types, their advantages and certain disadvantages, as well as their prospects in the perspective of the educational subject. Results. Great importance is given to the motivation of the specialist, including educational and cognitive aspects. Potential competitiveness directly depends not only on the educational and cognitive motivation that drives the learning process and allows the specialist to acquire the maximum amount of specialty-related knowledge and skills, but also on the professional one, which provides the professional orientation. Discussion and Conclusions. The social significance and the prospect of implementing electronic education system in Russian reality, especially in the context of economic, geographical and social inequality at the territories of advanced social and economic development, are revealed. Elements of e-education, adequately integrated into the vocational training system, are able to provide increased competitiveness and adaptability of a specialist in the digital economy.
Cathedral and Indian Mughal Monument Recognition Using Tensorflow

Image classification is the elementary and widely addressed task in computer vision. Classification of monument architecture is a real challenge due to its structural complexity. Analyzing architecture using the machine learning or data mining techniques can be useful. Various feature descriptors are used for large scale image classification. In this context, optimal feature descriptors as filters are used in Convolutional Neural Network. When analyzing architecture using the machine learning or data mining techniques, it can be useful to infer whether it is Cathedral or Indian Mughal Monuments in architecture involved. This paper focuses on architecture recognition, which has been studied extensively in the history literature, using Convolutional Neural Network (CNN) based on highly effective Tensorflow, an open source software library. The important task is to generalize the model to perform well on the new database. Ontology performs a significance role in learning to categorize given picture into metaphysical classes. A deep learning based approach is used to train the convolutional neural network. In the proposed work, images are classified into Cathedrals and Indian Mughal Monuments. Experimental results demonstrate that the method can successfully classify if the given image is Cathedral or Indian Mughal Monument which mainly includes the Taj Mahal and Char Minar. Proposed method substantially outperform state-of art approaches which is demonstrated by satisfactory result obtained on the dataset.