Reviewerly/exHarmony huggingface dataset:
Design of Cloud Native Application Architecture Based on Kubernetes

With the development of software architecture and the concept of cloud computing, cloud Nativity has gradually become one of the most concerned concepts in the software industry. In cloud-native platform, Kubernetes is a micro-service style application architecture and choreography tool. It solves the complexity of software system through business logic modularization, and provides many primitives and abstract concepts of distributed application, and it provides an object-oriented distributed application construction idea. This paper designs and studies the cloud native application architecture based on Kubernetes to solve the performance monitoring and security problems of micro-service. The architecture has the following characteristics: decouples the environment related functions such as database deployment, performance detection deployment, service request security verification function and the main application, so that the micro-service applications pay more attention to the implementation of business logic; At the same time, based on the second level deployment of container, the above functional modules can be highly reused and the development cost can be reduced.
A Hybrid Character Segmentation Approach for Cursive Unconstrained Handwritten Historical Modi Script Documents

Segmentation of archaic handwritten degraded Modi script text lines in the isolated characters is the key steps of the offline automated Modi script document recognition system. The improved segmentation of characters gravitated towards the higher accuracy of the recognition. The intricacy of archaic handwritten degraded Modi script documents put forwards the number of challenges in character segmentation. This paper presents a multistep technique for Modi script character segmentation based on the analysis of background pixel density of global and local horizontal zones of the text line. At the initial isolation stage two types of fragments are segmented as isolated individual characters and cluster of overlapping/ touching characters. Further, the cluster of overlapping or touching characters are segmented in isolated individual characters. The isolated, overlapping, and touching characters are segmented efficiently using the proposed Modi character segmentation technique.
Proposal of Expert System to Predict Financial Frauds Using Data Mining

The phrase `Money Laundering' is a deplorable activity to mask the black money as white money. This criminal activity poses a severe threat to the monetary institution and also to the nation. There are three phases in Money Laundering Placement, Layering, and Integration. The advancement of competent mechanism to detect suspicious transaction is a critical problem because of financial transaction made in global market. The system we are developing targets on how monetary institutions can obtain better results in anti-money laundering initiatives. To overcome this criticality we propose an efficient approach to find out if the transactions are legal or illegal by adopting Hash-based technique and Graph Theoretic Approach.
Automatic IQ Estimation Using Stylometric Methods

Stylometry is a study of text linguistic properties that brings together various fields of research such as statistics, linguistics, computer science and more. Stylometry methods have been used for historic investigation, as forensic evidence and an educational tool. This chapter presents a method to automatically estimate individual's IQ based on quality of writing and discusses challenges associated with it. The method utilizes various text features and NLP techniques to calculate indexes which are used to estimate individual's IQ. The results show a high degree of correlation between expected and estimated IQs in cases when IQ is within the average range. Obtaining good estimation for IQs on the high and low ends of the spectrum proves to be more challenging and this work offers several reasons for that. Over the years stylometry benefitted from wide exposure and interest among researches, however it appears that there aren't studies that focus on using stylometry methods to estimate individual's intelligence. Perhaps this work presents the first in-depth attempt to do so.
Pandemic data challenges

The worldwide outbreak of COVID-19 has led to great tragedy and poses unprecedented challenges for countries' healthcare systems. Data has become an important instrument in the global fight against the unprecedented spread of the virus. But how will we ensure a return to previous forms of data privacy once the pandemic subsides?
A Robust Text Coverless Information Hiding Based on Multi-Index Method

Recently, researchers have shown that coverless information hiding technology can effectively resist the existing steganalysis tools.However, the robustness of existing coverless text information hiding methods is generally poor.To solve this problem, we propose a robust text coverless information hiding method based on multi-index.Firstly, the sender segment the secret information into several keywords.Secondly, we transform keywords into keyword IDs by the word index table and introduce a random increment factor to control.Then, search all texts containing the keyword ID in the big data text, and use the robust text search algorithm to find multiple texts.Finally, these texts are converted into mixed indexes sent to the receiver.The receiver disassembles received indexes through the index construction protocol and uses the random increment factor to extract the secret information.Experimental results show that this method improves the concealment and security of secret information and has strong robustness compared with the state-of-the-art methods.
A Deep Learning Model for Threat Hunting in Ethereum Blockchain

Blockchain technology has found extensive applications in recent years, especially in financial and currency exchange applications, due to improved trustworthiness and security. Although blockchain technology improves security by design, it is not immune to security threats and vulnerabilities. Ethereum, as a decentralized, open-source blockchain, has shown high growth and widespread adoption in recent years, however, there is a wide range of vulnerability, security risks, and also attacks around it. To tackle such issues, machine learning could be a viable solution for threat hunting in the Ethereum blockchain. Machine learning algorithms, by analyzing the behav-ioral patterns, can achieve an insight for threat hunting. In this paper, we proposed a deep learning-based model for Ethereum threat hunting. The model applies a deep neural network for attack detection and uses a combination of machine learning algorithms (unsupervised with supervised algorithms) for attack classification. The performance evolution of the proposed model in terms of accuracy presents 97.72 % in Ethereum attack detection and 99.4% in attack classification.
Applying System Dynamics to Model Advanced Persistent Threats

System dynamics (SD) concept has been successfully applied to analyze issues that are non-linear, complex, and dynamic in disciplines namely social sciences and technology. However, its application to cyber security issues especially threats that involves multiple variables interacting with the technical as well as the organizational domain is lacking. In this respect, Advanced Persistent Threat (APT) is regarded as a highly targeted and sophisticated attack using zero-day malware, stealth, and multiple advanced techniques to gain entry and maintain its presence inside organizational network unnoticed. Being a threat that exploits technical as well as organizational vulnerabilities, preventing it at the security perimeter and, detecting it once it enters the system is a challenge till date. To demonstrate the application of SD in identifying and analyzing the effect of each of the variables, we took the Equinox data breach as a case study. The variables leading to the breach were identified, entered into Vensim software and simulated to get the results. Through this exercise, we could identify seven key independent management variables for the technical security and three key independent variables for records breach. This research being the foremost study to apply SD to APT, we presume that by modelling APT attacks using SD through a case study this paper, thus provides insights into the dynamics of the threat. Furthermore, it suggests 'what if' strategies to minimize APT risks thereby reduce the extent of damages should an APT attack occur.
Seamless manga inpainting with semantics awareness

Manga inpainting fills up the disoccluded pixels due to the removal of dialogue balloons or "sound effect" text. This process is long needed by the industry for the language localization and the conversion to animated manga. It is mostly done manually, as existing methods (mostly for natural image inpainting) cannot produce satisfying results. Manga inpainting is more tricky than natural image inpainting because its highly abstract illustration using structural lines and screentone patterns, which confuses the semantic interpretation and visual content synthesis. In this paper, we present the first manga inpainting method, a deep learning model, that generates high-quality results. Instead of direct inpainting, we propose to separate the complicated inpainting into two major phases, semantic inpainting and appearance synthesis. This separation eases both the feature understanding and hence the training of the learning model. A key idea is to disentangle the structural line and screentone, that helps the network to better distinguish the structural line and the screentone features for semantic interpretation. Both the visual comparison and the quantitative experiments evidence the effectiveness of our method and justify its superiority over existing state-of-the-art methods in the application of manga inpainting.
Privacy preserving solution to prevent classification inference attacks in online social networks

In order to improve their business solutions the data holders often release the social network data and its structure to the third party. This data undergo node and attribute anonymisation before its release. This however does not prevent the users from inference attacks which an un-trusted third party or an adversary would carry out at their end by analysing the structure of the graph. Therefore, there is an utmost necessity to not only anonymise the nodes and their attributes but also to anonymise the edge sets in the released social network graph. Anonymising involves perturbing the actual data which results in utility loss. Ensuring utility and preserving privacy are inversely proportional to each other and is a challenging task. In this work we have proposed, implemented and verified an efficient utility based privacy preserving solution to prevent the third party inference attacks for an online social network graph.
Side-Channel Attacks in the Internet of Things

The Internet of Things (IoT) rapidly closes the gap between the virtual and the physical world. As more and more information is processed through this expanding network, the security of IoT devices and backend services is increasingly important. Yet, side-channel attacks pose a significant threat to systems in practice, as the microarchitectures of processors, their power consumption, and electromagnetic emanation reveal sensitive information to adversaries. This chapter provides an extensive overview of previous attack literature. It illustrates that microarchitectural attacks can compromise the entire IoT ecosystem: from devices in the field to servers in the backend. A subsequent discussion illustrates that many of today's security mechanisms integrated in modern processors are in fact vulnerable to the previously outlined attacks. In conclusion to these observations, new countermeasures are needed that effectively defend against both microarchitectural and power/EM based side-channel attacks.
One-Stop Efficient PKI Authentication Service Model Based on Blockchain

Public Key Infrastructure (PKI) technology is a widely used identity authentication technology. This paper uses blockchain technology to improve it and implements decentralized PKI authentication, which resolves the issues in the traditional PKI such as single point of failure and certificate transparency. However, most of the current research uses the method of traversing the blockchain to query the certificate (identity, public key) to realize identity authentication, which is inefficient. And as the size of blockchain continues to grow, storage overhead is growing. In this paper, we combine the blockchain and the dynamic accumulator to construct a blockchain PKI model that can batch update certificates, which improves the efficiency of identity authentication. The model can effectively add, revoke and update user certificates. Meanwhile, this paper builds a one-stop PKI authentication service model based on blockchain, Through the certificate blockchain, we can provide one-stop user authentication service to third-party service providers. Finally, we verify the security and effectiveness of the scheme.
A Survey on Static and Dynamic Load Balancing Algorithms in Cloud Computing

Cloud computing is the most reliable, proficient and useful in today’s world of technology. Cloud computing is a technique which consists of various software and web-enabled services. The resource scheduling and resource allocation in cloud computing is the vast area of research now a days. Thus the efficient algorithm for cloud resource scheduling and allocation will result in more elastic and efficient cloud computing services. Implementation of cloud uses various steps from maintenance to implementation and so on. In this paper, we had conducted a survey on different load balancing algorithms and categorized them on the basis of their dynamicity and found the challenges faced by those algorithms. Then, we compared these algorithms with an approach of Hidden Markov Model technique and by this technique, we will try to clarify the load balancing scenario of the cloud service during resource scheduling. The Hidden Markov Model in cloud resource scheduling can help to predict the next work load coming on the server, according to which the system will allocate the resources to a particular work. This will help in better CPU utilization as well as faster execution environment in cloud computing.
Data Reduction

Thanks to the technological advancements of high throughput technologies, biological data can be massive. Thus, a process to reduce unnecessary data is needed in order to save time and/or resource consumption in data analysis. Data reduction techniques can be useful to obtain a smaller dataset in volume, avoiding to lose, in the reduced representation, significant information of the original data. The aim is to generate and process subsequently a more efficient dataset. Data reduction methods include dimensionality reduction, numerosity reduction, and data compression. In this contribution, the main methodologies are presented and discussed.
E-Discovery Components of E-Teaching And M-Learning

Electronic discovery is the electronic aspect of identifying, collecting and producing electronically stored information (ESI) in response to a request for production in a law suit or investigation.ESI includes, but is not limited to, emails, documents, presentations, databases, voicemail, audio and video files, social media, and web sites. E-Teaching is the teaching students using electronic equipment either directly or indirectly. As well as M-Learning also anytime and anywhere for fixing timings for learning process. M-learning or ‘m'obile learning is defined as “learning across multiple contexts, through social and content interactions, using personal electronic devices”.A form ofdistance education, m-learners use mobile device educational technology at their time convenience.M-learning technologies include handheld computers, MP3 players, notebooks, mobile phones and tablets. M-learning focuses on the mobility of the learner, interacting with portable technologies. This paper deals with some important things about E-Teaching and M-Learning with advantages of both.
Exploiting the Homomorphic Property of Visual Cryptography

In this paper, homomorphic visual cryptographic scheme (HVCS) is proposed. The proposed HVCS inherits the good features of traditional VCS, such as, loss-tolerant (e.g., (k, n) threshold) and simply reconstructed method, where simply reconstructed method means that the decryption of the secret image is based on human visual system (HVS) without any cryptographic computation. In addition, the proposed HVCS can support signal processing in the encrypted domain (SPED), e.g., homomorphic operations and authentication, which can protect the user's privacy as well as improve the security in some applications, such as, cloud computing and so on. Both the theoretical analysis and simulation results demonstrate the effectiveness and security of the proposed HVCS.
Short-term Solar Irradiation Prediction Model Based on WCNN_ALSTM

With the rapid development of grid structure, the proportion of renewable energy generation increases gradually. Photovoltaic power has gradually become an important energy resource to the power grid. In this paper, a model based on wavelet convolutional neural network combining attention-LSTM model is proposed to solve the problem of solar irradiance forecasting. In the proposed hybrid model, the original solar radiation data is decomposed by wavelet transform to reduce noise. Features are extracted by temporal convolution neural network. Combining long short-term memory neural network (LSTM) and attention mechanism, the important features in prediction are focused, obtaining better prediction results. The data utilized in this article contains the solar irradiance data collected within one year time. Experiments were conducted at different time intervals in the four seasons. Comparison of multiple machine learning and deep learning models, the results show that the model proposed in this paper has a higher prediction accuracy.
Anomaly analyses for feature-model evolution

Software Product Lines (SPLs) are a common technique to capture families of software products in terms of commonalities and variabilities. On a conceptual level, functionality of an SPL is modeled in terms of features in Feature Models (FMs). As other software systems, SPLs and their FMs are subject to evolution that may lead to the introduction of anomalies (e.g., non-selectable features). To fix such anomalies, developers need to understand the cause for them. However, for large evolution histories and large SPLs, explanations may become very long and, as a consequence, hard to understand. In this paper, we present a method for anomaly detection and explanation that, by encoding the entire evolution history, identifies the evolution step of anomaly introduction and explains which of the performed evolution operations lead to it. In our evaluation, we show that our method significantly reduces the complexity of generated explanations.
ALCOVE: An exemplar-based connectionist model of category learning

This chapter describes a connectionist model of category learning called attention learning covering map (ALCOVE). It discusses variations, extensions, and limitations of ALCOVE. ALCOVE is a connectionist model of category learning that incorporates an exemplar-based representation with error-driven learning. ALCOVE is also closely related to standard back-propagation networks. Although ALCOVE is a feedforward network that learns by gradient descent on error, it is unlike standard back propagation in its architecture, its behavior, and its goals. ALCOVE is a feed-forward connectionist network with three layers of nodes. On presentation of a training exemplar to ALCOVE, the association strengths and dimensional attention strengths are changed by a small amount so that the error decreases. ALCOVE was applied to the six category types by using three input nodes, eight hidden nodes, and two output nodes. Several reasonable extensions of ALCOVE that might allow it to fit a wider array of category learning phenomena, without violating the motivating principles of the model, are possible.
A Report on the Third Workshop on Emerging Software Engineering Education (WESEE 2020)

Software engineering has to cater to the needs and challenges of fast-evolving technology innovations to produce high quality and reliable software. One of the key elements for a successful future of the software engineering industry is to have a well-trained and highly-skilled workforce. Therefore, academia has to play a prominent role in producing the workforce which is industry ready and ready to cope with the challenges and needs posed by the ever-increasing modern software revolution. Inculcating the art and science of software engineering in the young and old demands the utilization of effective teaching methods at all levels of education dissemination. With a unique theme of Software engineering Education and Training - Teaching Methodologies and Experiences, the third edition of the Workshop on Emerging Software Engineering Education (WESEE) aims to bring together software engineering educators from academia and industry for understanding the challenges and current and future needs of software engineering education and finding futuristic solutions. In this report, we present a pre-organization summary of the workshop to be held on February 27, 2020, at the Indian Institute of Information Technology, Design and Manufacturing, Jabalpur (India), co-located with the 13th Innovations in Software Engineering Conference (ISEC 2020).
Likeness-Based dissimilarity measures for Gaussian Mixture Reduction and Data Fusion

In many practical contexts, Gaussian Mixtures are used as density approximators due to their versatility and representation capabilities. In some scenarios, it might be convenient to approximate a set of Gaussian densities with a single one, according to criteria which aim to preserve information while reducing the model complexity. This task can be seen as a particular case of the Gaussian Mixture Reduction problem, where the goal is to find a mixture of reduced size yielding the least dissimilarity from the original mixture. From a different perspective, this can be interpreted as a data fusion process, where several Gaussian densities are fused into one. In this work, an information-theoretic class of measures will be explored in the analytical and numerical properties in order to provide insights on their nature when adopted in a Gaussian mixture reduction or data fusion process.