Reviewerly/exHarmony huggingface dataset:
Online Product Review Monitoring System Using Machine Learning

In this fast moving world, having major proportion of people depending on online Web sites for purchase of their day-to-day necessities, their only scope of trust is the reviews available in the about the particular product. These reviews can either be authentic made by loyal buyers or even it may be forged for marketing purposes. According to survey, almost sixty percent of the reviews present in Amazon are fake and nearly fifteen percent of companies as sellers, wage individuals for creating fake reviews. In order to settle the imbalance and forgery, a study has been made to identify the fake reviews and filter it out from the user’s sight, such that they cannot be manipulated as well as the particular company’s reputation will not be at stake. This system majorly works on the basis of identifying fake reviews by user extracting the multiple review of single user from reviewers
Human Resources as a Stakeholder in Cyber Risk Management

The regulatory and cyber risk landscapes are reshaping the role of human resources. As a result, information security/information technology (InfoSec/IT) professionals are increasingly being asked to assist organisations in determining and enforcing employee data permissions, training employees on cybersecurity policies, and assisting with employee-related cyber incidents in order to be prepared for any kind of cyber attack. This chapter delves into the role of HR in managing cyber risk and provides recommendations on how organisations can manage cyber risk effectively to support their HR departments.
A Novel Family of Edge Preserving Anisotropic Filters

In this article, new anisotropic image filters are introduced and their performances are compared with existing isotropic and anisotropic filters. The developed filters were examined according to their image noise removing performances in terms of standard image quality metrics, as well as the edge preserving properties of the filtered images. Mathematical inferences of anisotropic filters are made based on the minimization of the Polyakov action energy integral. New anisotropic metrics are found by means of Finsler metrics that minimize the corresponding integral. The new metrics perform filtering by updating the image with anisotropic Laplace-Beltrami flow. After filtering, it was observed that the introduced metrics perform well against other anisotropic metrics. It is also observed that the developed New Randers, New Normalized Miron, and New Metric filters preserve edges better than other filters, making it a plausible noise removal tool prior to edge detection in image processing. The source codes of proposed filters are publicly available at https://github.com/HAYDARKILIC.
Stance Detection on Vietnamese Social Media

This paper studies a new emerging NLP topic, stance detection on social media. The goal is to automatically elicit stance information towards a specific claim made by a primary author which is explicitly or implicitly stated in texts. While most studies have been done for high-resource languages, this work is dedicated to a low-resource language. To stimulate this research direction, we introduce the first Vietnamese corpus (Contact the first author) annotated with stance information on social issues. This corpus consists of 11,253 pairs of claims-comments labeled with one of four stances (i.e. agree, disagree, discuss, and unrelated). Based on this corpus, a wide variety of advanced classification methods have been carefully investigated. Experimental results show that the deep learning model - attentive biLSTM outperforms the traditional methods by a large margin. This model is further enhanced when it is enriched with character embeddings, and stance knowledge. Using this best model, we achieve an $$F_1$$ -macro score of 66.71%, an accuracy score of 66.32% in detecting stances. To our knowledge, this is the first work that provides a baseline result for future research on this interesting yet unexplored problem in Vietnamese.
Minimum-time Path Convergence for UAVs in Wind Using Vector Field Guidance

We present a novel guidance algorithm for unmanned aircraft to converge to a smooth path optimising time in the presence of wind. A Lyapunov vector field based method is used to achieve convergence and tracking. The proposed framework performs better in comparison with the similar work existing in literature. The presented approach modifies the vector field according to the velocity of the wind, facilitating faster convergence. A customised optimisation algorithm is then used to find a suitable design parameter for vector fields. To demonstrate the efficacy of the proposed approach, numerical simulations are shown and the results are compared with the optimal time paths obtained using optimal control theory.
Extraction of Secret Message Based on Uniformly Most Powerful Test

As the ultimate goal of steganalysis, secret message extraction is a bottleneck and difficulty that has long plagued the development of steganalysis technology. Existing pioneering work on secret message extraction for adaptive steganography based on STC (Syndrome-Trellis Codes): the method based on randomness test under plaintext embedding may misjudge incorrect steganographic key as correct steganographic key, resulting in the failure of extraction. For this reason, a method for extracting message with 100% accuracy for plaintext embedding is proposed in this manuscript. First, by analyzing the probability distribution of stego images, a theorem is given: For STC-based adaptive steganography algorithm, the sequences extracted by correct and incorrect steganographic keys are statistically different. Then based on this theorem, a uniformly most powerful test model is designed to recover the correct steganographic key. Finally, given the probability of type I and II errors, the sample size and threshold in the hypothesis test are derived. Classic adaptive steganography such as HUGO (Highly Undetectable Steganography) and J-UNIWARD (JPEG Universal Wavelet Relative Distortion) have been conducted experiment, showing that the proposed method can extract message with 100% accuracy and 44 bits sample size, which verifies the correctness of the theorem and the effectiveness of the method.
Enhancing Performance of Occlusion-Based Explanation Methods by a Hierarchical Search Method on Input Images

In this work, we address some drawbacks of back-propagation-based and perturbation-based visualization methods by proposing an explanation method called Fast Multi-resolution Occlusion (FMO). FMO, opposite to the back-propagation-based methods that cannot be applied on all types of Convolutional Neural Networks (CNNs), can highlight the important input features independent of the architecture. Also, FMO introduces a novel fast occlusion strategy called multi-resolution occlusion which not only efficiently addresses the time-consumption issue of the traditional Occlusion Test method but also outperforms the well-known perturbation-based methods. We assess the methods on CNNs DenseNet121, InceptionV3, InceptionResnetV2, MobileNet, and ResNet50 using three datasets ILSVRC2012, PASCAL VOC07, and COCO14.
Refactoring Product Lines by Replaying Version Histories

When evolving software product lines, new features are added over time and existing features are revised. Engineers also decide to merge different features or split features in other cases. Such refactoring tasks are difficult when using manually maintained feature-to-code mappings. Intensional version control systems such as ECCO overcome this issue with automatically computed feature-to-code mappings. Furthermore, they allow creating variants that have not been explicitly committed before. However, such systems are still rarely used compared to extensional version control systems like Git, which keep track of the evolution history by assigning revisions to states of a system. This paper presents an approach combining both extensional and intensional version control systems, which relies on the extensional version control system Git to store versions. Developers selectively tag existing versions to describe the evolution at the level of features. Our approach then automatically replays the evolution history to create a repository of the intensional variation control system ECCO. The approach contributes to research on refactoring features of existing product lines and migrating existing systems to product lines. We provide an initial evaluation of the approach regarding correctness and performance based on an existing system.
The complexity of online bribery in sequential elections

Prior work on the complexity of bribery assumes that the bribery happens simultaneously, and that the briber has full knowledge of all votes. However, in many real-world settings votes come in sequentially, and the briber may have a use-it-or-lose-it moment to decide whether to alter a given vote, and when making that decision the briber may not know what votes remaining voters will cast. We introduce a model for, and initiate the study of, bribery in such an online, sequential setting. We show that even for election systems whose winner-determination problem is polynomial-time computable, an online, sequential setting may vastly increase the complexity of bribery, jumping the problem up to completeness for high levels of the polynomial hierarchy or even PSPACE. But we also show that for some natural, important election systems, such a dramatic complexity increase does not occur, and we pinpoint the complexity of their bribery problems.
Die Analyse des CIRSmedical.de mittels Natural Language Processing

CIRSmedical.de is a publicly accessible, cross-institutional reporting and learning system, which is organized by the German Agency for Quality in Medicine (ÄZQ). CIRSmedical.de has existed since 2005 and has published more than 6,000 event reports. Up to now it has been common practice to analyse these reports in detail or carry out systematic evaluations focusing on specific topics. A systematic evaluation of all case reports has not yet been conducted. Natural Language Processing (NLP) is an analysis strategy from the field of Artificial Intelligence for indexing texts. The examination of case reports using NLP was carried out to describe the characteristics of event reports and comments.For this analysis 6,480 case reports from CIRSmedical.de (as of December 10, 2019) were provided by the ÄZQ as Excel files. Several free text fields were included in the analysis as well as the feedback of the CIRS team (expert commentary). Text lengths, reporting behaviour, sentiment values and keywords were examined. The algorithms for the analysis were developed with the programming language Python and the corresponding libraries NLTK and SpaCy.The comparison of report lengths depending on the different subject groups presented a heterogeneous picture, in terms of both the number of reports and the number of words. There are more than 4,000 reports from the field of anaesthesiology, whereby text lengths vary particularly strongly with a right-skewed distribution. There are only a few reports from the field of psychotherapy, and these are also very short. The different professional groups (nurses, doctors, other staff) write reports of about the same length. Reports and expert commentaries also differ in terms of sentiment values. Due to the length of the comments, they are more negative in terms of sentiment. Keywords can be identified but show a high heterogeneity.Systematic analysis using NLP allows for the description of text properties in event reports and comments. It is now possible to draw a conclusion about the reporters' intention, focus and mood when they report in CIRS. The sentiment analysis is an indication of the mood which the texts convey, both as a report and as a commentary. Text length analysis draws attention to different problems and tendencies: event reports are usually much shorter. Texts that are too short, however, run the risk that the information will not be readily usable for analysis. Comments are often longer, but here one faces the opposite problem: texts that are too long may not be read. The examination of texts by means of NLP helps to rethink the reason for and the form of input, both when reporting and when commenting. It is a first step in the automatic, supportive classification of texts and an improvement of the interaction between reporters and the system.
Evaluation and Comparison of Various Static and Dynamic Load Balancing Strategies Used in Cloud Computing

There are many research carried on cloud computing, but key factor is load balancing. Load balancing plays an important role to organized work allocation on server which consists of cost, material, time duration, etc. There are few issues which are faced in load balancing are resource utilization, security reason, tolerance, and many more. Many researchers have done work on different parameters of load balancing, and similar results are found in different articles. This paper consists of simulation of three different loads balancing which include static as well as dynamic strategies. Three algorithms are taken each from static and dynamic. Static algorithm include are round robin, threshold, and randomized. Dynamic algorithm include are active clustering, honey bee and Join-Idle-Queue where simulation is performed in cloud simulator and parameter considered is resource utilization, response period, and processing time with overall data transfer cost. This paper shows study regarding comparison between three static algorithm and dynamic algorithm with their parameter and result outcome. This work is still in progress where it aims to do analysis and evaluation for success of load balancing. In cloud computing to improve quality approaches.
Comparing two probabilities: an essay in honour of Herman Chernoff

Abstract This is a story of a lawsuit in Japan, about an alleged incident in America thirty years before. The focus of the analysis is comparing the rates of skips in ballpoint pen writing in a diary. Chernoff proposed several methods to address the comparison between the skips observed in different passages in the diary. I also give my own alternative analysis of the data.
The Effects of Use of Restaurant Management Systems Perceived by the Personnel According to Their Demographic Characteristics

Restaurant management systems (RMSs) developed to increase business efficiency and service quality in operation and management processes in restaurants were discussed in this study. This research reveals the current impact of RMSs in restaurants processes from the perspectives of staff working in restaurants. The purpose of this study was to assess the restaurant staff's perspective on restaurant management systems (RMS) based on their demographic characteristics. Research data were collected by conducting face-to-face and online surveys to a total of 385 staff working in restaurants in different cities of Turkey. ANOVA test and independent-sample t-test were used in the analysis of research data. A significant difference was found between the education level of the restaurant staff and the components of operation management, sales increase, and production/service standard of the RMSs perspective scale (p&lt;0.05). Sector employees with higher general education and/or vocational training have a more positive view of RMSs' contributions to the business.
Jónsson-style canonicity in distributive modal <i>µ</i>-calculus

Abstract In the present paper, we obtain a Jónsson-style canonicity proof for the Sahlqvist fragment of distributive modal $\mu $-calculus in clopen semantics, alternative to the canonicity-via-correspondence argument in Conradie and Craig (2017, Journal of Logic and Computation, 27, 705–748), generalizing the results in Bezhanishvili and Hodkinson (2012, Theoretical Computer Science, 424, 1–19). The key ingredient of this proof is a characterization result about $\sigma $-contracting maps.
A Privacy-preserving Outsourcing Scheme for Zero-knowledge Proof Generation

We propose a privacy-preserving scheme to outsource zero-knowledge proof generation to a party that we call a worker. Our scheme can be applied to zk-SNARKs with a trusted setup, zero-knowledge proofs deployed in many applications. Compared to known privacy-preserving outsourcing schemes, our scheme is more practical in the sense that the computational and memory load on the worker is almost the same as that on the prover in cases where the provers generate proofs on their own.
Perceptions of Cloud Computing Risks in the Public Sector

The transformation of public services through the adoption of cloud computing has shown greater advancement with service delivery efficiencies and reduction of costs. Cloud computing has been in existence for some time. However, the adoption has been seemingly low and stagnant in the South African (SA) government context. This study investigates the stakeholders’ perceptions in the SA public sector regarding the risks associated with cloud computing adoption. The study was exploratory, qualitative and interpretative in nature. Data was collected via semi-structured interviews and a questionnaire with IT representatives within government departments. The findings highlight that the perceived risks include security breaches, lack of supporting network infrastructure and concerns on redundancy. Additionally, poor skills and contract management were noted. This was coupled with concerns about compliance with relevant legislation and risks associated with transborder information flows. The study contributes to the literature on cloud computing in government.
Obtaining Robust Models from Imbalanced Data

The vulnerability of deep neural network (DNN) models has been verified by the existence of adversarial examples. By exploiting slight changes to input examples, the generated adversarial examples can easily cause well trained DNN models make wrong predictions. Many defense methods have been proposed to improve the robustness of DNN models against adversarial examples. Among them, adversarial training has been empirically proven to be one of the most effective methods. Almost all existing studies about adversarial training are focused on balanced datasets, where each class has an equal amount of training examples. However, as datasets collected in real-world applications cannot guarantee all contained classes are uniformly distributed, it would be much challenging to obtain robust models in those real applications where the available training datasets are imbalanced. As the initial effort to study this problem, we first investigate the different behaviors between adversarially trained models and naturally trained models using imbalanced training datasets and then explore possible solutions to facilitate adversarial training under imbalanced settings.
Interaction Modelling for IoT

Informal design artefacts allow end-users and non-experts to contribute to software design ideas and development. In contrast, software engineering techniques such as model-driven development support experts in ensuring quality properties of the software they propose and build. Each of these approaches have benefits which contribute to the development of robust, reliable and usable software, however it is not always obvious how best to combine these two. In this paper we describe a novel technique which allows us to use informal design artefacts, in the form of ideation card designs, to generate formal models of IoT applications. To implement this technique, we created the Cards-to-Model (C2M) tool which allows us to automate the model generation process. We demonstrate this technique with a case study for a safety-critical IoT application called &#x201C;Medication Reminders&#x201D;. By generating formal models directly from the design we reduce the complexity of the modelling process. In addition, by incorporating easy-to-use informal design artefacts in the process we allow non-experts to engage in the design and modelling process of IoT applications.
Detecting Tear Gas Canisters With Limited Training Data

Human rights investigations often entail triaging large volumes of open source images and video in order to find moments that are relevant to a given investigation and warrant further inspection. Searching for instances of tear gas usage online manually is laborious and time-consuming. In this paper, we study various object detection models for their potential use in the discovery and identification of tear gas canisters for human rights monitors. CNN based object detection typically requires large volumes of training data, and prior to our work, an appropriate dataset of tear gas canisters did not exist. We benchmark methods for training object detectors using limited labelled data: we fine-tune different object detection models on the limited labelled data and compare performance to a few shot detector and augmentation strategies using synthetic data. We provide a dataset for evaluating and training tear gas canister detectors and indicate how such detectors can be deployed in real-world contexts for investigating human rights violations. Our experiments show that various techniques can improve results, including fine-tuning state of the art detectors, using few shot detectors, and including synthetic data as part of the training set.
Futuristic Cloud Market—Game Theoretic Equilibrium

Recent development has initiated the importance of cloud computing platforms. Cloud is not merely used for data storage any more but computation platform has been an integral constituent of cloud systems. The day is not far away when there will be a futuristic cloud market where customers will have the provision of choosing among cloud providers. But then the question arises whether CP’s will compromise on QoS for penetrating into the market. We try to answer this question with a double-bargaining game where cloud providers do not have to sacrifice their Cournot equilibrium and the incentives which are required for their survival. This is a new area which we are trying to put in the Cloudnomics domain. Here, one side the customer will be increasing the bidding value and CP’s will reduce their incentive to an extent such that the successful negotiation can take place. Broker has to play a key role in the negotiation mechanism.
Specializing PostgreSQL Expression Interpreter with Code Annotations

Vast majority of execution engines are first im-plemented as interpreters. Once they reach maturity and gain audience authors strive to improve performance by utilizing static or JIT compilation techniques. The latter helps to achieve the goal but at the expense of increased maintenance costs as source code splits into two loosely connected components. One solution to this problem is a program specialization method that utilizes existing interpreter&#x0027;s code and semi-automatically generates a compiler. In this paper we apply program specialization to the in-terpreter of the language of SQL expressions in PostgreSQL and show how the results compare against both the original interpreter and the built-in LLVM-based just-in-time compiler.