Reviewerly/exHarmony huggingface dataset:
A Flexible Phishing Detection Approach Based on Software-Defined Networking Using Ensemble Learning Method

Phishing is a plague in cyberspace, in which threat actors try to lure unsuspecting victims to disclose their sensitive information like bank account or password. Existing detection techniques of phishing attacks in traditional networks generally use a proxy server to determine whether a website is a phishing website lacking flexibility. The flexibility of software-defined networks solves the limitation of traditional networks. Based on this, this paper proposes a new scalable phishing detection approach based on Software-Defined Networking (SDN) to identify phishing activities through web-based communications, which separates detection mechanism from end users with good accuracy and flexibility with ensemble learning method.
Selective Coflow Completion for Time-sensitive Distributed Applications with Poco

Recently, the abstraction of coflow is introduced to capture the collective data transmission patterns among modern distributed data-parallel application. During processing, coflows generally act as barriers; accordingly, time-sensitive applications prefer their coflows to complete within deadlines and deadline-aware coflow scheduling becomes very crucial.
Clustering, multicollinearity, and singular vectors

Let A be a matrix with its Moore-Penrose pseudo-inverse A†. It is proved that, after re-ordering the columns of A, the projector P=I−A†A has a block-diagonal form, that is there is a permutation matrix Π such that ΠPΠT=diag(S1,S2,…,Sk). It is further proved that each block Si corresponds to a cluster of columns of A that are linearly dependent with each other. A clustering algorithm is provided that allows to partition the columns of A into clusters where columns in a cluster correlate only with columns within the same cluster. Some applications in supervised and unsupervised learning, specially feature selection, clustering, and sensitivity of solutions of least squares solutions are discussed.
Entrainment of Weakly Coupled Canonical Oscillators with Applications in Gradient Frequency Neural Networks Using Approximating Analytical Methods

Solving phase equations for systems with high degrees of nonlinearities is cumbersome. However, in the case of two coupled canonical oscillators, that is, a reduced model of translated Wilson–Cowan neuronal dynamics, under slowly varying amplitude and rotating wave approximations, we suggested a convenient way to find their average relative phase evolution. This approach enabled us to find an explicit solution for the average relative phase of the two coupled canonical oscillators based on the original neuronal model parameters, and importantly, to find their phase-locking constraint. This methodology is straightforward to implement in any Wilson–Cowan-type coupled oscillators with applications in gradient frequency neural networks (GFNNs).
JTaint: Finding Privacy-Leakage in Chrome Extensions

Extensions are used by many Chrome browser users to enhance browser functions and users' online experience. These extensions run with special permissions, they can read and modify the element of DOM (Document Object Model) in users' web pages. But, excessive permissions and operation behaviors have brought users heavy risks such as the privacy leakage caused by extensions. Dynamic taint analysis techniques are often exploited to discover the privacy leakage, it monitors code execution by modifying the JavaScript interpreter or rewriting the JavaScript source code. However, interpreter-level taint technique needs to overcome the complexity of the interpreter, and there are also many difficulties in designing taint propagation rules for bytecode. And source-level taint technique is undertainted like Jalangi2, which will trigger some exceptions in practice. To this end, we design JalangiEX based on Jalangi2. JalangiEX fixes problems in Jalangi2 and strips its redundant codes. Besides, JalangiEX also monitors two types of initialization actions and provides taint propagation support for message passing between different pages, which further solves the undertaint problem of Jalangi2. Moreover we implement JTaint, a dynamic taint analysis system that uses JalangiEX to rewrite the extension and monitors the process of taint propagation to discover potential privacy leaks in Chrome extensions. Finally, we use JTaint to analyze 20,000 extensions from Chrome Web Store and observe the data flow of extensions on a special honey page. Fifty-seven malicious extensions are recognized to leak sensitive-privacy information and are still active in the Chrome Web Store.
Protein Structured Reservoir Computing for Spike-Based Pattern Recognition

Nowadays we witness a miniaturisation trend in the semiconductor industry backed up by groundbreaking discoveries and designs in nanoscale characterisation and fabrication.To facilitate the trend and produce ever smaller, faster and cheaper computing devices, the size of nanoelectronic devices is now reaching the scale of atoms or molecules -a technical goal undoubtedly demanding for novel devices.Following the trend, we explore an unconventional route of implementing reservoir computing on a single protein molecule and introduce neuromorphic connectivity with a small-world networking property.We have chosen Izhikevich spiking neurons as elementary processors, corresponding to the atoms of verotoxin protein, and its molecule as a 'hardware' architecture of the communication networks connecting the processors.We apply on a single readout layer, various training methods in a supervised fashion to investigate whether the molecular structured Reservoir Computing (RC) system is capable to deal with machine learning benchmarks.We start with the Remote Supervised Method, based on Spike-Timing-Dependent-Plasticity, and carry on with linear regression and scaled conjugate gradient back-propagation training methods.The RC network is evaluated as a proof-of-concept on the handwritten digit images from the standard MNIST and the extended MNIST datasets and demonstrates acceptable classification accuracies in comparison with other similar approaches.
Web Application Vulnerability Detection Using Taint Analysis and Black-box Testing

Abstract Web applications continue to grow however web attacks are also increasing, this shows an increase in web application vulnerabilities. Several methods have been used to detect vulnerabilities in web applications such as black-box testing, dynamic analysis, and static analysis. In this article, we propose a framework for detecting web application vulnerabilities by combining all three methods. The strengths of one method are used to overcome the weaknesses of another method. This framework is believed to provide better results than if each method was used separately.
Fast online computation of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e515" altimg="si11.svg"><mml:msub><mml:mrow><mml:mi>Q</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:math> estimator with applications to the detection of outliers in data streams

We present fqn (Fast Qn), a novel algorithm for online computation of the Qn scale estimator. The algorithm works in the sliding window model, cleverly computing the Qn scale estimator in the current window. We thoroughly compare our algorithm for online Qn with the state of the art competing algorithm by Nunkesser et al., and show that fqn (i) is faster, requiring only O(s) time in the worst case where s is the length of the window (ii) its computational complexity does not depend on the input distribution and (iii) it requires less space. To the best of our knowledge, our algorithm is the first that allows online computation of the Qn scale estimator in worst case time linear in the size of the window. As an example of a possible application, besides its use as a robust measure of statistical dispersion, we show how to use the Qn estimator for fast detection of outliers in data streams. Extensive experimental results on both synthetic and real datasets confirm the validity of our approach.
Groupchain

Blockchain technology is a type of decentralized technology that enables secure and trusted operations to be performed in untrusted environments. Privacy preservation and supervision are contradictions in decentralized environments. First, to reach a consensus in a decentralized environment, each node of the blockchain holds all of the ledger data, but this brings a huge risk of privacy leakage; alternately, the ledger data is encrypted with a hash to ensure security, making it difficult for regulatory authorities to regulate the blockchain through legal methods. This paper proposes a blockchain model based on a Group signature idea with the permission blockchain as the underlying architecture, aiming to neutralize the contradiction between privacy preservation and supervision in the blockchain technology. This paper designs three schemes entitled GroupSig, GroupCT, and Semi-Stealthaddress to achieve the goals we set. Finally, we analyze the security of Groupchain and evaluate the efficiency of the corresponding scheme.
Sentiment Analysis of Ayodhya Verdict using Twitter

Sentiment Analysis is quickly emerging on top in research field of Computer Science. There are several forms of determining the sentiment of people's opinions, either through reviews, feedback, blogs etc. However, in order to know opinions of different people a platform is needed. Twitter is most desired one as it has user-friendly interface that is focused for engagement of audience online which include vast variety of people as students, professionals, celebrities, corporations and politicians. The proposed work aims to get tweets of various political leaders on Ayodhya Verdict using Python tools and libraries, and after fetching, analyze them as Positive, Negative or Neutral and plotting the results in graphical representation.
On the application of convex transforms to metric search

Scalable similarity search in metric spaces relies on using the mathematical properties of the space in order to allow efficient querying. Most important in this context is the triangle inequality property, which can allow the majority of individual similarity comparisons to be avoided for a given query. However many important metric spaces, typically those with high dimensionality, are not amenable to such techniques. In the past convex transforms have been studied as a pragmatic mechanism which can overcome this effect; however the problem with this approach is that the metric properties may be lost, leading to loss of accuracy. Here, we study the underlying properties of such transforms and their effect on metric indexing mechanisms. We show there are some spaces where certain transforms may be applied without loss of accuracy, and further spaces where we can understand the engineering tradeoffs between accuracy and efficiency. We back these observations with experimental analysis. To highlight the value of the approach, we show three large spaces deriving from practical domains whose dimensionality prevents normal indexing techniques, but where the transforms applied give scalable access with a relatively small loss of accuracy.
Cross Fertilization in Software Engineering

Software engineering is related to a set of disciplines both inside and outside computing. One of the aspects to consider in the development of a discipline is cross fertilization. In this paper, author reviews the cross fertilization produced by related disciplines in Software Engineering. The influences come from Computer Engineering and Computer Science inside computing outside this field quality and project management, naming just a few of them. More in particular by focusing on specific technologies, author will overview bidirectional relationships between two of the most promising technologies nowadays namely: blockchain and machine learning.
PVRS: Publication Venue Recommendation System A Systematic Literature Review

With the passage of time, academic researchers are increasing as well as a number of publication venues are also grown immensely. Finding an appropriate publication venue for submitting the research article is the main objective of the researcher. An appropriate publication venue refers to an academic forum that is according to the domain of the publisher. Finding a suitable publication venue can result in great chances of acceptance of the paper. When a publisher sends its paper for publishing in a venue which does not accommodate its scope will result in time wastage and editorial rejection. To handle the issue of finding an appropriate publication venue several recommendations and expert system solutions are devised. Moreover, there are online available tools as well which aid the user to select the publication venue for the submission of their article. This article performed a systematic literature review analysis on existing publication venue recommendation systems. In this paper, a total of 20 studies are analyzed in detail. The article discusses existing schemes and also provide some future directions in this domain.
A solution for annotating sensor data streams - An industrial use case in building management system

Smart buildings equipped with various building management systems and digital control systems produce enormous amounts of sensor data that can be used to investigate and diagnose operational issues such as unsatisfactory thermal comfort outcomes, excessive energy consumption and/or predicting failures before they occur. However, current building management systems often face the issues with incomplete or unstructured metadata associated with sensor data which prevent such pro-active, predictive and prescriptive analysis. Currently, building service engineers manually map the sensor data streams to aid their diagnostic process. This process is expensive, ineffective and is also prone to human errors. This paper proposes a novel semi-automated approach that annotates incoming sensor data streams. We also propose extensions to Project Haystack, a well-known ontology used for naming conventions and taxonomies for building equipment and operational data. We have developed a tool that is currently used by our industry partner and incorporates the proposed automatic annotation approach and maps the data streams to our Haystack-extended ontology. The tool includes an easy to use interface for engineers to easily diagnose issues in mechanical building services. The proposed approach has been validated via both usability and technical evaluation.
Design and Development of Coffee Production Information System to Support Coffee Production Productivity in Farmers Group

Abstract The purpose of this study is to create an information system that processes the resulting data of coffee plantations produced in Laksana Village Farmers Group (Kelompok Tani), Majalaya, Bandung district with the type of coffee produced are Arabica coffee. The main problem that exists at this time is the lack of information about raw materials in the warehouse, so that sometimes the production process is constrained. Entities involved in this system are the production department, the warehouse section, farmers, and farmer group officers. The system approach method used is prototype and system development method using Object Oriented, with a tool for depicting system design using the Unified Modeling Language (UML). While the application program of the information system is made on a desktop-based. With this information system, the stock of raw materials will be accessible to all entities involved and the information produced will be more accurate. The implementation of the information system produced in Laksana Village Farmers Group applied to solve the problem of lack of information availability in raw material of production that slow production activities down. The inner entities involve production entities, warehouse and farmer group officers on this information systems are connected in one integrated database, which facilitates information accurately is acceptable.
Workload Analysis of Cloud Resources using Time Series and Machine Learning Prediction

Most of the businesses now-a-days have started using cloud platforms to host their software applications. A Cloud platform is shared resource that provides various services like software as a service (SAAS), infrastructure as a service (IAAS) or anything as a service (XAAS) that is required to develop and deploy any business application. These cloud services are provided as virtual machines (VM) that can handle the end user's requirements. The cloud providers must ensure efficient resource handling mechanisms for different time intervals to avoid wastage of resources. Auto-scaling mechanisms would take care of using these resources appropriately along with providing an excellent quality of service. Auto-scaling supports the cloud service providers achieve the goal of supplying the required resources automatically. It use methods that will calculate the number of requests and decides the resources to release based on workload. The workload consists of some quantity of application program running on the machine and usually some number of users connected to and communicating with the computer's applications. The researchers have used various approaches to perform autoscaling which is a process to predict the workload that is required to handle the end users request and provide required resources as Virtual Machines (VM) disruptively. Along with providing uninterrupted service, the businesses also only pay for the service they use, thus increasing the popularity of Cloud computing. Based on the workload identified the resources are provisioned. The resource provisioning techniques is a model used for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, applications, and services) required resources are released. In this regard, the aim of this paper is to develop a framework to predict the workload using deep learning which would be able to handle provisioning of cloud resources dynamically. This framework would handle the user request efficiently and allocate the required virtual machines. As a result, an efficient dynamic method of provisioning of cloud services would be implemented supporting both the cloud providers and users.
Identifying Sentence Structure in Bahasa Indonesia by Using POS Tag and LALR Parser

The sentence structure can be used to obtain the meaning of the sentence. Bahasa Indonesia has several components of the sentence structure such as Subjek (Subject), Predikat (Predicate), Objek (Object), Keterangan (Adverb), and Pelengkap (Complement). This research aims to identify the sentence structure in Bahasa Indonesia. The pre-processing section includes text cleaning, tokenization, and POS tagging. Text cleaning removes all the punctuation that will not be used for the next process. Tokenization is used to split the sentences into tokens which are used as the input for the POS tagging process. Next, the processing section implements LALR parser to determine the sentence structure based on the label (the output of POS tagging) in each token. This research conducts two evaluation. The first evaluation compares the sentence structure generated by the expert with the LALR parser. The sentences provided by the expert is very standard and follow the Bahasa Indonesia grammar. According to the result, LALR parser successfully identifies all the sentence structure generated by the expert. The second evaluation is applying the LALR parser to 150 sentences from several categories of local online newspaper articles. The application successfully identifies 86.7% sentence structure and detect 3.3% incorrect sentence structure.
Performance of Shuffling: Taking It to the Limits

In this paper, we propose further performance improvements for Wikström’s shuffle proof. Compared to an implementation based on general-purpose exponentiation algorithms, we show that the overall shuffle operation can be accelerated by approximately one order of magnitude. The improvements result partly from applying special-purpose algorithms for fixed-based and product exponentiations in the right way and partly from other optimization techniques. Given that shuffling is often one of the most time-consuming tasks in cryptographic voting protocols, the achieved speed-up is significant for practical implementations of electronic voting systems.
Knowledge-aware Attentive Wasserstein Adversarial Dialogue Response Generation

Natural language generation has become a fundamental task in dialogue systems. RNN-based natural response generation methods encode the dialogue context and decode it into a response. However, they tend to generate dull and simple responses. In this article, we propose a novel framework, called KAWA-DRG (Knowledge-aware Attentive Wasserstein Adversarial Dialogue Response Generation) to model conversation-specific external knowledge and the importance variances of dialogue context in a unified adversarial encoder-decoder learning framework. In KAWA-DRG, a co-attention mechanism attends to important parts within and among context utterances with word-utterance-level attention. Prior knowledge is integrated into the conditional Wasserstein auto-encoder for learning the latent variable space. The posterior and prior distribution of latent variables are generated and trained through adversarial learning. We evaluate our model on Switchboard, DailyDialog, In-Car Assistant, and Ubuntu Dialogue Corpus. Experimental results show that KAWA-DRG outperforms the existing methods.
Determining Method-Call Sequences for Object Creation in C++

Unit tests in object-oriented programming languages must instantiate objects as an essential part of their set-up. Finding feasible method-call sequences for object creation and selecting a most desirable sequence can be a time-consuming challenge for developers in large C++ projects. This is caused by the intricacies of the C++ language, complexity of recursive object creation, and a large number of alternatives. We confirm the significance of the problem by analysis of 7 large C++ projects and a survey with 143 practitioners. We then design an approach for recommending method-call sequences for object creation that align with criteria gathered by the survey. Our approach exploits accurate and efficient compiler-based source code analysis to build an object dependency graph that is processed by a divide-and-conquer algorithm.An evaluation on a large industrial project shows that our tool finds solutions that require in 99% of 1104 cases identical or fewer objects compared to manually crafted solutions. Developer feedback and manual analysis confirm these results. Moreover, solutions found by our tool require up to 6 times fewer objects on average compared to approaches from prior work.