Reviewerly/exHarmony huggingface dataset:
The Advent of the Voice Moment of Truth: The Case of Amazon’s Alexa

Interactive Voice Assistants (IVAs), such as Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa and Google’s Assistant, are increasingly being integrated into consumers’ daily lives. Indeed, IVAs are unfolding new opportunities for retailers to capitalize on Internet of Things (IoT) technologies and generate incremental value. This is affecting the consumer purchasing cycle through the advent of what this study coins as the Voice Moment of Truth (VMOT). This paper introduces a conceptual framework discussing this new moment of truth, which is expected to alter shoppers’ behavior, as well as brands’ and retailers’ strategies. The VMOT is presented to be segmented into five key components: activation, conversation, perception formation, duration, and relationship formation. In addition, the introduction of the VMOT sets forth a new relation between shoppers, brands and online retailers that is crucial for its success. This new voice-based moment of truth introduces new challenges that are yet to be defined or analyzed by any pre-existing literature. The importance of defining the interaction between these IVA devices and users is key to understand the implications that these interactions will have on shoppers.
Improving K Nearest Neighbor into String Vector Version for Text Categorization

This research is concerned with the string vector based version of the KNN which is the approach to the text categorization. Traditionally, texts have been encoded into numerical vectors for using the traditional version of KNN, and encoding so leads to the three main problems: huge dimensionality, sparse distribution, and poor transparency. In order to solve the problems, this research propose that texts should be encoded into string vectors the similarity measure between string vectors is defined, and the KNN is modified into the version where string vector is given its input. The proposed KNN version is validated empirically by comparing it with the traditional KNN version on the three collections: NewsPage.com, Opiniopsis, and 20NewsGroups. The goal of this research is to improve the text categorization performance by solving them.
Trail of Hacks

This paper describes a process of collaborative sense-making through co-designing a reflective poster. We used this method in the 'Empowering Hacks' project which gathered two non-academic individuals with disabilities (authors 2 & 3) and a non-disabled HCI researcher (author 1) around DIY/Making by, for and with people with disabilities. To collectively review the achievements and challenges we experienced in this project, we designed a timeline which allowed us to equally engage in reflective thinking and curatorial discussions on how to present and explain identified key moments. We see the instance of this co-created poster as an opportunity to discuss with the CHI-community the potential and relevance of including research participants in analysis processes.
The Hubness Phenomenon in High-Dimensional Spaces

High-dimensional data analysis is often negatively affected by the curse of dimensionality. In high-dimensional spaces, data becomes extremely sparse and distances between points become indistinguishable. As a consequence, reliable estimations of density, or meaningful distance-based similarity measures, cannot be obtained. This issue is particularly prevalent in clustering, which is commonly employed in exploratory data analysis. Another challenge for clustering high-dimensional data is that data often exist in subspaces consisting of combinations of dimensions, with different subspaces being relevant for different clusters. The hubness phenomenon is a recently discovered aspect of high-dimensional spaces. It is observed that the distribution of neighbor occurrences becomes skewed in intrinsically high-dimensional data, with few points, the hubs, having high occurrence counts. Hubness is observed to be more pronounced with increasing dimensionality. Hubs are also known to exhibit useful clustering properties and could be leveraged to mitigate the challenges in high-dimensional data analysis. In this chapter, we identify new geometric relationships between hubness, data density, and data distance distribution, as well as between hubness, subspaces, and intrinsic dimensionality of data. In addition, we formulate various potential research directions to leverage hubness for clustering and for subspace estimation.
Memory Saving Feature Descriptor Using Scale and Rotation Invariant Patches around the Feature Ppoints

To expand the use of systems using a camera on portable devices such as tablets and smartphones, we have developed and propose a memory saving feature descriptor, the use of which is one of the essential techniques in computer vision. The proposed descriptor compares pixel values of pre-fixed positions in the small patch around the feature point and stores binary values. Like the conventional descriptors, it extracts the patch on the basis of the scale and orientation of the feature point. For memories of the same size, it achieves higher accuracy than ORB and BRISK in all cases and AKAZE for the images with textured regions.
Using a Novel Image Analysis Metric to Calculate Similarity of Input Image and Images Generated by WAE

Image similarity is a core concept in Image analysis due to its extensive application in computer vision. Our study of Quasi Euclidean distance metric displayed a potential in the metric viz-a-viz image similarity computation. Here, it has been used to calculate similarity between input images and images generated by the generative model, Wasserstein Auto-Encoder. Standard GANs are great generative models, but has the concerned training instability. Therefore, we have used Wasserstein Auto-Encoder, which also has proven to generate more accurate images, to produce images on which the performance of the metric has been observed.
Threshold Ring Signature with Message Block Sharing

Lattices have attracted a great attention during recent years and many signature schemes have emerged based on lattices. In this paper we have proposed Threshold-Ring Signature K–out–of–Nscheme, here signature is generated by a subset of K signers from the N signers while maintaining the anonymity of these K signers using the Ring Signature by Wang et al. The scheme uses message block sharing between members and is proved to be unforgeable as well as anonymous.
Dialogue analysis: a case study on the New Testament

There has been much research on the nature of dialogues in the Bible. While the research literature abounds with qualitative analyses on these dialogues, they are rarely corroborated on statistics from the entire text. In this article, we leverage a corpus of annotated direct speech in the New Testament, as well as recent advances in automatic speaker and listener identification, to present a quantitative study on dialogue structure in the Gospels. The contributions of this article are three-fold. First, we quantify a variety of features that are widely used in characterizing dialogue structure—including dialogue length, turn length, and the initiation and conclusion of a dialogue—and show how they distinguish between different Gospels. Second, we compare our statistics with qualitative comments in the New Testament research literature, and extend them to cover the entirety of the Gospels. Most significantly, we gauge the feasibility of applying our approach to other literary works, by measuring the amount of errors that would be introduced by automatically identified dialogues, speakers and listeners.
L<sub>2</sub>-Sensitivity Analysis and Minimization for Generalized Direct-Form II Realization of 2-D Separable-Denominator Digital Filters

In this paper, l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -sensitivity for the generalized direct-form II state-space realization of two-dimensional (2-D) separable-denominator digital filters is analyzed and minimized with respect to free parameters subject to l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -scaling constraints. Improved l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -sensitivity for 2-D separable-denominator digital filters is investigated by taking 0 and ±1 elements into account. An l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -sensitivity measure for the generalized direct-form II state-space realization of 2-D separable-denominator digital filters is deduced by applying improved l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -sensitivity. The l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -sensitivity measure is then minimized with respect to free parameters subject to l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -scaling constraints by employing an exhaustive search over a set of discrete values of finite cardinality. A numerical example is included to illustrate the validity and effectiveness of the proposed technique.
Semi-automatic ontology-driven development documentation

For a data-driven economy, digitization of product information throughout the entire product lifecycle is key to agility and efficiency of product-related processes. Documenting products and their development, e.g., creating requirement specifications, is an indispensable, time-consuming and resource-intensive activity in large organizations. A vast amount of related information often emerges across several siloing lifecycle tools, and only a portion of it is available in the post-hoc documentation. Additionally, numerous product lines and versions additionally increase the documentation effort. To tackle these issues in a research project, we developed a semi-automatic end-to-end documentation system, able to generate documents based on templates and structured data. As a use case for document generation, we employ the RDF-based lifecycle tool integration standard OSLC and add extended publishing information. In order to generate target documents, we leverage DITA, an established digital publishing standard. A pilot implementation demonstrates that the approach is able to extract distributed lifecycle data and to generate several types of documents in multiple formats. Since the method can also be used to generate documents from arbitrary RDF graphs, the results can be generalized to other domains beyond software development. We believe that the results support the change from a document-driven to a data-driven documentation paradigm in large organizations.
Static security evaluation of an industrial web application

JavaScript is the most popular programming language for web applications. Static analysis of JavaScript applications is highly challenging due to its dynamic language constructs and event-driven asynchronous executions, which also give rise to many security-related bugs. Several static analysis tools to detect such bugs exist, however, research has not yet reported much on the precision and scalability trade-off of these analyzers. As a further obstacle, JavaScript programs structured in Node.js modules need to be collected for analysis, but existing bundlers are either specific to their respective analysis tools or not particularly suitable for static analysis.
A High Contrast Video Inverse Tone Mapping Operator for High Dynamic Range Applications

Conversion of existing image and video content to High Dynamic Range (HDR) using inverse Tone Mapping Operators (iTMOs) is expected to enable the HDR market and open new market opportunities for studios and content owners. In this paper, we propose a high contrast video iTMO that addresses shortcomings of existing approaches, yielding HDR video quality worth the expectations of the emerging HDR technology. Our approach is content adaptive and is able to convert SDR videos to HDR videos of any target dynamic range. Our approach follows the Human Visual System (HVS) characteristic of being more sensitive to luminance changes in dark areas than bright and normal ones, mapping each of these areas accordingly. Our subjective evaluations demonstrate that our proposed method on average outperforms existing iTMOs in terms of overall HDR visual quality.
Economic Impact of Cyber Attacks on Critical Infrastructures

Critical infrastructures ensure that activities that are vital and important for individuals can be safely delivered to the society uninterruptedly. The damage on these critical infrastructures caused by cyber-attacks whose control is carried out through computers and network systems is very large. Cyber-attacks directly or indirectly affect companies, institutions, and organizations economically and cause great financial losses. In this chapter, two different categories, energy and finance sector, which are described as critical infrastructure, are discussed; cyber-attacks carried out on these sectors, cyber-attack weapons, and economic losses caused by these attacks are examined.
Cloud-Based Secured QR Code for Self-service Access Control System at Resort and Hotels

Self-service technologies (SSTs) are increasingly becoming popular with the advancement of mobile technologies, cheaper Internet prices and the growing popularity of mobile devices in daily life. Popular hotels/resorts tend to adopt SSTs to enhance their service quality standards, consumer experience, loyalty and most importantly overall customer satisfaction. The long queue in hotels and resorts has been the major problem for causing dissatisfaction for customers, especially for minors and senior citizen. Customer satisfaction has been the main criteria for the business to grow. With this goal, we propose a cloud-based secure access control for hotel and resort rooms using the QR code technique. To gain access to the hotel room, the customer needs just to scan the QR code sent from the secure cloud. The user details are encrypted using MD5 algorithm and stored as checksum in database to avoid various attacks such as phishing, malware, code injections and others possible in Web services. When the user scans the QR and decodes, it compares the checksum stored in database. In case of any discrepancies, further process is stopped and hence makes our approach more secure from other techniques. Also, cloud provides additional security for the Web services.
Cloud‐based simulation studies in <b>R</b> ‐ A tutorial on using doRedis with Amazon spot fleets

Simulation studies are helpful in testing novel statistical methods. From a computational perspective, they constitute embarrassingly parallel tasks. We describe parallelization techniques in the programming language R that can be used on Amazon's cloud‐based infrastructure. After a short conceptual overview of the parallelization techniques in R, we provide a hands‐on tutorial on how the doRedis package in conjunction with the Redis server can be used on Amazon Web Services, specifically running spot fleets. The tutorial proceeds in seven steps, ie, (1) starting up an EC2 instance, (2) installing a Redis server, (3) using doRedis with a local worker, (4) using doRedis with a remote worker, (5) setting up instances that automatically fetch tasks from a specific master, (6) using spot‐fleets, and (7) shutting down the instances. As a basic example, we show how these techniques can be used to assess the effects of heteroscedasticity on the equal‐variance t‐test. Furthermore, we address several advanced issues, such as multiple conditions, cost‐management, and chunking.
Movie+

Collaborative movie viewing with the loved ones increases connectedness and social bonds within family members and friends. Furthermore, with the rapid adoption of personal mobile devices, people often engage in this activity being geographically separated. However, conveying our feelings and emotions about a recently watched movie or a video clip is often limited to a post on social media or a short blurb on an instant messaging app. Drawing on the popular interest in quantified-self, which envisioned one collecting and sharing biophysical information from everyday routines (e.g., workouts), we have designed and developed Movie+, a mobile application, which utilizes personal biophysical data to construct an individual's "emotional fingerprint" while viewing a video clip. Movie+ allows the selective sharing of this information through different visualization options, as well as rendering others' emotional fingerprints over the same clip. In this submission, we outline the design rationale and briefly describe our application prototype.
Fake Twitter Message Identification Using ECDSA Message Validation

Social media like Twitter is often used for spreading fake messages. Fake messages are made as if from an official account even though the sender does not have access to the account. Identification of fake twitter messages in this paper designed using ECDSA's digital signature algorithm. Each created message will be signed using the account owner's private key and any messages read will be validated using the author's public key. The proposed schemes implemented in Android-based applications and through testing of account accounts accessible to the Twitter API, this application successfully signed and validated 30 messages.
VeriCCM

Many problems in software systems can ultimately be traced to problematic system requirements. To mitigate this problem, some systems builders have adopted a more systematic approach to requirements elicitation and analysis. However, many of these approaches treat requirements as isolated entities having no direct connection to the actual system implementation. We propose Veri-CCM, an approach that addresses potential problems both in the semantics and the syntax of requirements models. We also evaluate our approach with an empirical study.
SvgAI – Training Methods Analysis of Artificial Intelligent Agent to use SVG Editor

Deep reinforcement learning has been successfully used to train artificial intelligent (AI) agents, which outperforms humans in many tasks. The objective of this research is to train an AI agent to draw SVG images by using scalable vector graphic (SVG) editor with deep reinforcement learning, where the AI agent is to draw SVG images that are similar as much as possible to the given target raster images. In this paper, we propose framework to train the AI agent by value-function based Q-learning and policy-gradient based learning methods. With Q-learning based method, we find that it is crucial to distinguish the action space into two sets to apply a different exploration policy on each set during the training process. Evaluations show that our proposed dual ε-greedy exploration policy greatly stabilizes the training process and increases the accuracy of the AI agent. On the other hand, policy-gradient based training does not depend on external reward function. However, it is hard to implement especially in the environment with a large action space. To overcome this difficulty, we propose a strategy similar to the dynamic programming method to allow the agent to generate training samples by itself. In our evaluation, the highest score is archived by the agent trained by this proposed method. SVG images produced by the proposed AI agent have also superior quality compared to popular raster-to-SVG conversion softwares.
Building a National Network through peered community area networks: realising ICTs within developing countries

The purpose of the South African broadband policy, SA Connect, is to provide a long-term approach that will begin to develop broadband access in SA with immediate effect. The policy aims to achieve this by implementing a broadband model that incorporates private and public sectors, increasing monetary investments towards ICT, reducing the number of impediments in the broadband policy, removing bottlenecks that inhibit broadband expansion and identifying successful methods and applications that will enhance broadband access. The current approach has been through a centralized deployment and ownership approach, which is largely delayed. In this paper we propose an alternative, decentralized approach to deployment and ownership, in order to incrementally and in parallel build a national telecommunications network through the interconnection of community area networks.
Representing and comparing large sets of extensions of abstract argumentation frameworks

In argumentation theory, some reasoning problems involve the enumeration of all extensions of an abstract argumentation framework. Abstractly speaking, extensions are simply subsets of a given domain having some special properties. The result of the enumeration is usually presented as a single text file with elements and sets separated by designated delimiters. Neither the elements within each set (a single extension), nor the extensions themselves are presented in any pre-defined order. Events such as the International Competition of Computational Models of Argumentation require the comparison of a large number of enumerations and thus performing the comparisons very efficiently has become very desirable. This paper presents and compares three different alternative representations of extensions, one of which is novel for the argumentation domain, and provides an empirical evaluation of their effectiveness in the comparison of large enumerations. We found that the newly proposed representation can perform the comparisons in a much more memory and time efficient manner than existing solutions.
ATM Heist Threats: A Proposed ICT Governance Strategy

Cyber black markets for hacking tools or services are gaining widespread attention as more advanced persistent threat attacks are relevant to such markets. In July 2016, the ATM heist of Taiwan First bank is based on well-known Carberp malware family, which is available for sharing, sale or cooperation on such markets. This study reviews organizational cybercrime activities and ATM threats, and proposes an ICT governance framework in online ATM heist management. This framework further points out capable people, efficient process, effective technology, and data governance. This framework is potentially useful for cybercrime prevention.
Word Sense Disambiguation of Malayalam Nouns

The present study on word sense disambiguation of Malayalam aims at to understand the causes for lexical ambiguity and finding was to resolve the lexical ambiguity. It has been understood that homonymy and polysemy are the reason for creating ambiguity. Here we are concerned with ambiguity due to homonymy. To resolve the ambiguity we propose two approaches: cluster and deep learning approaches. Certain number of ambiguous words is collected with their occurrence in sentences. Cluster approach is a supervised approach involving POS tagging, lemmatization and sense annotation. The context words are identified for each sense of the experimental ambiguous words. A collocational dictionary is prepared based on this. WSD is implemented using the collocational dictionary. The neural network approach is based on deep learning. It is a corpus driven approach in which the necessary information for disambiguating homonymous words is extracted from the corpus itself. The quantity of the corpus used for WSD decides the accuracy of this approach.