Reviewerly/exHarmony huggingface dataset:
A Rejection-Based Approach for Detecting SQL Injection Vulnerabilities in Web Applications

According to OWASP top10 Application Security Risks [8, 9] SQL injection (SQLi) remains the most dangerous and most commonly exploited vulnerability in web applications. Thus, a lot of attentions are devoted by the scientific community for the development of SQLi verification tools. In this paper we focus on the development of an efficient, black box, SQLi vulnerability scanner to achieve an accurate detection. Our new approach is based on the use of structural similarity between rejection pages and their corresponding injection pages. A software prototype has been implemented and showed promising results as compared to well-known web application scanners.
Empirical greedy machine‐based automatic liver segmentation in CT images

Segmentation of the liver from 3D computed tomography volumes plays a significant role in trajectory development for computer-assisted interventional surgery for the liver disease. Despite a lot of studies, liver segmentation remains a challenging task due to the lack of clear edges on most liver boundaries coupled with high variability of both anatomical and intensity patterns. In addition, there is a problem with the segmentation of the left portal vein, in which the size of this vein prominently estimates the liver tumour area. The empirical greedy machine is proposed to make the precise, automated segmentation of the liver as well as the left portal vein. In which the empirical robust nature trains the features of the liver proficiently thereby segmenting the liver from other organs without the omission of adjacent organs and liver lobe region. Hence this proposed method can achieve one of the highest accuracies compared to other segmentation methods and the performance is calculated using several parameters such as volumetric overlap error, relative absolute volume difference (RVD), average symmetric absolute surface distance (ASD), root mean square surface distance, maximum symmetric ASD.
Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-ranking Results

In this paper we look beyond metrics-based evaluation of Information Retrieval systems, to explore the reasons behind ranking results. We present the content-focused Neural-IR-Explorer, which empowers users to browse through retrieval results and inspect the inner workings and fine-grained results of neural re-ranking models. The explorer includes a categorized overview of the available queries, as well as an individual query result view with various options to highlight semantic connections between query-document pairs. The Neural-IR-Explorer is available at: https://neural-ir-explorer.ec.tuwien.ac.at/ .
Feature Selection Optimization of HealthCare Software Product Line using BBO

The Software Product Line (SPL) is the mandatory requirement of software industry as SPL minimizes the use of resources, reduces the cost of the system and maximizes the possibility of achieving the objective. The challenge with SPL is that it is exponential to number of features, hence there arises need to optimize the system. The metaheuristic algorithm provides this opportunity of optimize the SPL system. This paper presents BBO algorithm which is used for optimizing feature selection problem in SPL. Further, an empirical study against 10 features model has been done and compare results with Genetic Algorithm (GA).It is noticed that BBO provides best feature subsets for SPL.
Finding the optimal number of low dimension with locally linear embedding algorithm

1) The problem this paper is going to solve is how to determine the optimal number of dimension when using dimensionality reduction methods, and in this paper, we mainly use local linear embedding (LLE) method as example. 2) The solution proposed is on the condition of the parameter k in LLE is set in advance. Firstly, we select the parameter k, and compute the distance matrix of each feature in the source data and in the data after dimensionality reduction. Then, we use the Log-Euclidean metric to compute the divergence of the distance matrix between the features in the original data and in the low-dimensional data. Finally, the optimal low dimension is determined by the minimum Log-Euclidean metric. 3) The performances are verified by a public dataset and a handwritten digit dataset experiments and the results show that the dimension found by the method is better than other dimension number when classifying the dataset.
Cybersecurity Laws in Malaysia

The progression of information and communication technologies (ICT) use have been matched by the rise in corruption and abuse of technology for criminal activities. In 2018, The Malaysia Computer Emergency Response Team reported 10,699 incidents, of which “fraud” had the highest reported incidents (5,123) and the second highest “intrusion attempt” (1,805) of the total incidents. Malaysia cyber laws have existed since 1997 and are still used today to prosecute cybercrimes. Most recent cases were charged under Malaysian laws—Computer Crimes Act 1997, Copyright (Amendment) Act 1997, Communications and Multimedia Act 1998, Personal Data Protection Act 2010, and Malaysian Penal Code—to combat cybercrimes. This chapter discusses Malaysia's cyber laws, cases charged under these laws, and their relevance to combating cybercrimes in Malaysia.
Container-Based Performance Isolation for Multi-Tenant SaaS Applications in Micro-Service Architecture

Abstract With the development of cloud computing technology, SaaS has become a popular delivery mode in enterprise applications. Since multi-tenant can share a set of customized software applications deployed on the cloud, it significantly reduces the economic cost of providing software services of the cloud service provider. However, the sharing of cloud computing resources leads to service performance competition among tenants. Therefore, how to ensure that all tenants in the cloud have high-quality services becomes the most concerned issue for cloud service provider. To solve the above problem, our research aims at exploring an effective performance isolation mechanism based on container technology and micro-service architecture in multi-tenant scenario. This mechanism includes an SLA-oriented multi-tenant and multi-instance hybrid deployment scheme and a tenant performance isolation algorithm for microservice clusters, which can effectively reduce the performance competition among tenants, guarantee the SLA of tenants, and improve the cost-efficiency of cloud services. Experiments show that the request latency of the tenant working within their assigned quota can be reduced by 88.1% by applying the proposed performance isolation mechanism.
A Survey on Privacy-Preserving Data Mining Methods

Abstract In recent years, the increase in massive data in various fields has promoted the development of data mining. Still, the storage and mining of user data bring about the threat of privacy leakage. Researches on privacy-preserving data mining have become an increasingly significant research area. Focusing on the hidden dangers of privacy leakage in data mining methods, three leading privacy-preserving technologies came into being, which are data distortion technology, data encryption technology, and restricted publication technology. In this paper, we introduce and summarize the latest researches based on these privacy-preserving technologies. Besides, we describe the state-of-art research trends on privacy-preserving data mining methods in image processing and natural language processing.
Feature Vector Creation Using Hierarchical Data Structure for Spatial Domain Image Retrieval

Building efficient Image Features with optimal dimension and high discriminative power is important in processing the digital information hidden inside the images. Image Retrieval systems use the feature vectors to retrieve similar images based on feature vector similarity. Image Feature Vector generation is important step in CBIR. Most of the CBIR use Linear Data structure for Feature Vector. In this paper the Hierarchical data Structure: Binary Tree is used for Feature vector creation in Image Retrieval Systems. Images in Spatial Domain are considered for Feature Vector Generation. Low-level Feature: Color Descriptors are used as Image Contents, to represents the image in feature vector form. Performance of the image retrieval is tested for Color Images. It has been observed that, along with the Dimensionally reduction, the proposed approach of Feature Vector generation, improves the discriminative power of Feature Vector. For RGB colorspaces the cascaded statistical features, when stores in hierarchical data structure, this provides the facility to perform the calculations in parallel manner also capable to keep discriminative power comparable w.r.t size of feature vector.
An Integration of Blockchain and Artificial intelligence: A Concept

Blockchain is a distributed, reliable storage and data transmission technology. On the other hand AI is self learning process with help of self learning, which is based on analyzing and findings of patterns form big data. it is also evaluate and making decision based on big data analysis by supervised or unsupervised learning. This paper is based on a conceptual view on integration of Blockchain and AI or looking opportunity of Blockchain for AI. It means that in future how both can be combined with each other for the benefits of people and businesses.
Comparing Hypervisor Virtualization Performance with the Example of Citrix Hypervisor (XenServer) and Microsoft Hyper-V

This paper presents the comparison of two representatives of type 1 hypervisors: XenServer and Microsoft Hyper-V. With the help of HD Tune Pro, we compare the performance of a hypervisor. Both hypervisors were tested in the same conditions with the MS Windows Server as the guest operating system. The obtained results are in accordance with the defined hypotheses for the expected behavior of the hypervisors.
Fuzzy Softset based VM Selection in Cloud Datacenter

The data is increasing day to day exponentially, which increases demand for cloud storage that leads to the establishment of an enormous number of cloud data centers. But usage of cloud data center employs huge amounts of electrical power and end up costly. Virtual machine consolidation decreases Service Level Agreement (SLA) Violations and energy consumption. In order to achieve that optimal selection of VM for migration is needed. Existing approaches concentrate on one selection factor at a time for VM selection. But the proposed algorithm considers all four selection parameters at a time using fuzzy soft set theory. The experimental results show that the proposed FSS-RCMCor algorithm efficiently selects VM for migration than the existing algorithms with minimum SLA violation rate and less number of Virtual machine migrations, and maximum energy efficiency.
Customized Alumni Portal implementing Natural Language Processing Techniques

The communication gap between students and alumni is a hindrance for students to exploit opportunities in the field of their study, and alumni to help students since they do not have a platform to connect with them. Thus it is essential to create an interactive collaboration and communication platform that can effectively and easily connect students, faculty, alumni and industry. The key modules of the proposed portal include an events and calendar module, opportunities module, mentoring and collaboration module, and a discussion forum module. All content posted in the portal is summarized and tagged as per the preference of the user, enabling easy search and recommendation systems. The portal also obstructs obscenity in textual content, and keeps the portal professional. The portal would also be a platform that uses natural language processing and machine learning to build a completely intelligent human-like social media platform, focusing on customization.
Cryptanalysis on the Head and Tail Technique for Hashing Passwords

Researchers and experts had developed numerous hash-based password authentication schemes. Inappropriately, most of them are susceptible to different attacks. This study centers on the process of performing cryptanalysis on the developed Head and Tail (HT) technique for hashing passwords. The research tested the HT technique in terms of its capacity to resist a dictionary attack, rainbow tables attack, and brute-force attack. To test the strength of the HT technique, HashCat, John the Ripper, RainbowCrack, and online cracking systems from crackstation.net and hashkiller.co.uk were used as tools for cracking. After the experiment, the result shows that the cracking tools failed to crack the HT technique. Further tests showed that generating a password-hash value pair or lookup table for MD5-HT and SHA1-HT is 16 times slower than standard MD5 and SHA1. Thus, the Head and Tail (HT) technique is a secured method for hashing passwords.
Dynamic Third-Factor for Enhanced Authentication in Human Resource Information System

The Dynamic Third-Factor (DTF) is an enhanced authenticator that ensures integrity, confidentiality and security in information systems. DTF is designed to be one-time use alphanumeric key combination that is auto-generated upon registration to the system and re-generated when the user logs out from the system. It is used as one of the factors in a Multi-factor Authentication (MFA), which is widely known as an approach that enforces strong security. The proposed DTF has exhibited a significant degree of avalanche effect (75%), suggesting an increased degree of difficulty for cryptanalysts to make predictions of input based on the output. Accordingly, a DTF-enhanced authentication implemented in a human resource information system is assumed to fit in a good measure of security, as it provides an authenticator with enhanced user experience, a higher level of safety, and continuous protection against unauthorized access.
Research on interactive design of multilingual e-commerce platform based on eye tracker

Abstract In “interactive design”, designers usually rely on personal experience and understanding to work. The design interface has strong subjectivity, and it is difficult to match the intention of users. Based on the basic experiments of eye movements, this study collected and analyzed the pages of the search engine for a multilingual e-commerce platform through the indicators such as the duration of the objects being watched, the number of objects to be followed, and the sequence of objects to be looked at. After the experiment, we found that the first movement of the eyeball was still focused on the upper left corner of the page. During the browsing process, the subjects were used to the order from top to bottom and from left to right. So when we design the page, we should keep this style and respect the user’s habits. After analyzing the data, we found that the simple design made a good impression on the subjects, so we should keep the simple style in the page design, avoid filling every inch of space, otherwise it will cause the burden of visual memory. The data analysis also showed that the use of controls, prompt wording, color settings and page style are consistent, which can make users easily speculate about various functions and increase the user’s preference.
A Model for Corrective Software Maintenance Effort Estimation after Privacy Leak Detection in Social Network

The main aspect that users of social networks are concerned is the privacy of the data is being shared over the social network whose use has become ubiquitous. Most of the research work in this area is concentrated around either privacy preservation or privacy leak detection while the most essential feature of any resilient social network is the ability to handle a privacy breach if one occurs. This ability definitely requires some effort to be expended in order to ensure that the trust of the users is maintained intact. The present paper considers this effort in terms of the software maintenance effort which the software development team has to expend. There are various types of maintenance including adaptive, perfective, predictive and corrective maintenance. The present problem of software maintenance effort estimation after privacy leak detection falls under the category of corrective maintenance; hence, a model for corrective software maintenance effort estimation is presented that is based on architecture of a social network. The main aim of this paper is to ensure that the social network unlearns all the personally identifiable information it has learnt about a user by basing on the framework that can be overlaid on any existing Social Network so that the metrics used for Privacy leak detection and handling are also used for Corrective Software Maintenance Effort Estimation.
Review on Candidate Feature Extraction and Categorization for Unstructured Text Document

Word is a primary unit in the sentences, which contains some extra information. This extra information is crucial in the candidate feature categorization progression. To gain such information the established techniques mine the candidate feature via n gram and noun phrase based approaches, but such approaches ignore the grammatical structure, which laid in huge quantity of insubstantial features. This paper inspects various mechanisms for feature mining and various issues are explored. A system is propounded which is based on tree structure for the candidate feature mining and branches of the tree are extracted using part-of-speech (POS) labelling for candidate phrase. To avoided redundant phrases, filtering is recommended. Finally, machine learning is used for the progression of feature categorization.
AutoCouch

Most web-based applications require an undisrupted connection to some kind of server for updating the current application state and propagating changes made locally at the clients. By their very nature, CRDTs provide the right semantics for these applications as they resolve conflicts after concurrent state changes from different users or when a user reconnects after some period of unavailability. Although the JSON format is often used to summarize and represent the complete state of web applications, there is currently - to our knowledge - no generic integrated implementation of a JSON CRDT framework that supports the standard clientserver setup for web apps.
Advances in Single Image Super-Resolution: A Deep Learning Perspective

With the increase in the resolution supported by displays and screens, the consumption of high-quality content such as 4 K resolution videos and images is increasing rapidly. To address the need for increasing fidelity, image super-resolution has been proposed to increase the resolution of images artificially. Image super-resolution produces a high-resolution image from a low-resolution image. The domain has seen outstanding growth in recent years due to the introduction of deep learning techniques. In this paper, we aim to give a quick review of the various approaches used, their merits and what inspired them. The highlight of this work is the inculcation of recently introduced layers and the review of non-traditional approaches such as the importance of the receptive field and perceptually oriented metrics. We also provide directions and recommendations to the community to extend the research done in this domain.
Multiple marine ships tracking from multistatic Doppler data with unknown clutter rate

The data collected by a multi static Doppler radar system includes information on both true targets and clutter. The estimation of clutter directly influences the effectiveness of the multitarget tracker in practical applications. This paper proposes a method for tracking multiple marine ships using multistatic Doppler radar system in an environment with unknown clutter rate. Specifically, the clutter rate is first estimated and then bootstrapped into the state-of-the-art Vo- Vo filter to track multiple targets on-the-fly. Numerical results demonstrate the validity of the proposed method provide that the clutter parameter vary slower than that of the data.
Electrical Impedance Mammography: Screening and Basic Principles

Nowadays, screening uses the method of X-ray mammography for the early diagnosis of breast cancer. However, as a screening method, X-ray mammography has its limitations, such as age, periodicity of screening, ‘dense’ mammary gland and dynamic survey (repeated radiation). In order to overpass these limitations, more advanced alternative methods of breast examination should be used, which would be as effective as the ‘golden standard.’ Characteristics of electroimpedance diagnostics are given. The aim of screening is to detect breast cancer including early breast cancer (tumor size below 1 cm) and to form a survey of high-risk group. In order to reach these aims, the following actions need to be undertaken: search for areas with anomalous conductivity, detection of distorted mammographic scheme and evaluation of age-related electrical conductivity. The application of a scale for age-related breast conductivity with defined percentile limits allows to organize a survey group. Electrical properties of a cancerous tumor differ significantly from those of the surrounding tissues. Statistics of anomalous conductivity in cases of breast cancer is given. The disease development connected with the destruction of epithelial basement membrane is linked with various phenomena occurring in the tumor and the surrounding tissues. Statistics of disrupted mammographic scheme in cases of cancer is given.
An Investigation of QoS Criteria for Optimal Services Selection in Composition

Web service plays a vital role in the service industry to improve the web service applications and it is service oriented architecture. Due to which formation of composite service leads to non-optimal. Service selection task in the composition process is to select the best service for each candidate services out of available services which are a functionally similar but non-functional measures of services are different. This paper, presents the investigation of quality of service parameters and optimality criteria for services selection. The work in the paper provides the analysis of quality of service parameters used in existing works for services selection/composition. Through the survey and analysis it has been revealed that the response time, availability, and reliability are most common used quality of service attributes with minimum/maximum as optimality criteria. However, after analysis of these parameters, the work suggests to use these attributes to solve optimal services selection problem in composition.
Multi-Resolution Siamese Networks for One-Shot Learning

Few-shot learning, a rapidly evolving theme in deep learning research, aims to endow artificial intelligence with the same ability of humans to assimilate new information very quickly. Siamese networks have been used in this context to learn similarity between image pairs and quickly classify novel objects. This work proposes an improved architecture and a novel training method that increases a 1-shot 5-way classification accuracy on 5 entirely novel classes by around 5%, 19%, 18% and 13% respectively compared to vanilla Siamese networks when tested on Omniglot, Tiny-Imagenet, CIFAR100 as well as a custom dataset recorded with an event-driven camera. These networks, when run on a Jetson TX2 GPU can be executed within 108 ms on average.
Gesture detection from RGB hand image using modified convolutional neural network

In this paper we study the RGB-only hand gesture recognition in human computer interaction. First, the hand region and landmarks are detected, in order to remove the complex background and convert the RGB image to iconic image. Active Shape Model is adopted for landmark detection and Markov Random Fields are used for error correction. Second, we propose to use a modified CNN model to recognize the visual hand gesture information. Two stages are tested in experiments and compared with conventional machine learning models. Experimental results show that our proposed method has an advantage in recognition accuracy.