Reviewerly/exHarmony huggingface dataset:
Teaching the Prosody of Emotive Communication in a Second Language

The expression and the perception of emotional states in a foreign language represent a difficult task for the learners. One of the reasons is the fact that, more than other aspects related to speech, the expression of emotional states in second language requires full control of the prosodic resources that contribute to their realization. The aim of this chapter is to give an overview of the main tenets of the interface between prosody and pragmatic competence in L2 and in particular the expression and perception of emotions. The chapter will also outline some of the outcomes of the research in the field, focusing on experimental studies that have been conducted with learners of Italian as L2. The second part of the chapter will be devoted on the instructional practice aimed at developing the awareness of pragmatic-prosodic aspects of emotive communication in speech. Teaching practices such as a training focused on the expression of emotions (anger, joy, sadness, disgust, fear, and surprise) and video dubbing projects have proven to be useful tools to improve the performance of learners both in production and in perception of prosodic patterns of emotional communication.
Local Rank Modulation for Flash Memories

Local rank modulation (LRM) scheme was suggested for representing the information in flash memories in order to overcome the drawbacks of rank modulation. For 0 <; s ≤ t ≤ n with s dividing n, an (s, t, n)-LRM scheme is a LRM scheme where the n cells are locally viewed cyclically through a sliding window of size t resulting in a sequence of small permutations, which requires less comparisons and less distinct values. The gap between two such windows equals to s. In this paper, encoding, decoding, and asymptotic enumeration of the (1, t, n)-LRM scheme are studied.
Relative <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e19" altimg="si20.gif"><mml:mi>m</mml:mi></mml:math>-ovoids of elliptic quadrics

Let Q−(2n+1,q) be an elliptic quadric of PG(2n+1,q). A relative m-ovoid of Q−(2n+1,q) (with respect to a parabolic section Q≔Q(2n,q)⊂Q−(2n+1,q)) is a subset R of points of Q−(2n+1,q)∖Q such that every generator of Q−(2n+1,q) not contained in Q meets R in precisely m points. A relative m-ovoid having the same size as its complement (in Q−(2n+1,q)∖Q) is called a relative hemisystem. We show that a nontrivial relative m-ovoid of Q−(2n+1,q) is necessarily a relative hemisystem, forcing q to be even. Also, we construct an infinite family of relative hemisystems of Q−(4n+1,q), n≥2, admitting PSp(2n,q2) as an automorphism group. Finally, some applications are given.
Advanced Encryption Standard With Randomized Round Keys for Communication Security in IoT Networks

Internet of things (IoT) is a rapidly emerging architecture connecting smart devices all across the world in various fields like smart homes, smart cities, health sector, security, etc. Security is a very important aspect of IoT. As more and more devices are connecting to the Internet, it becomes a lucrative target for hackers. The communication between the various devices, nodes, and between nodes and the cloud, needs to be secured. A combination of public and private key cryptography systems is used to secure the IoT networks. The Advanced Encryption Standard (AES) is used for encrypting the data in transit. However, the AES is known to be prone to brute force attacks, side channel attacks, and other forms of cryptanalysis. This chapter proposes a more secure AES algorithm with randomised round keys, which provides better security with negligible overheads, and is ideal for use in IoT networks.
Proposed framework for planning software releases using fuzzy rule‐based system

Software release planning is one of the complicated areas of software engineering. Planning a software release includes assigning requirements to sequence of releases in the most beneficial way within the limited effort, budget, and time available. The complexity of the software release planning is due to the incompleteness and the uncertainty challenges that characterise the software release planning problem. In order to handle efficiently the incompleteness and uncertainty challenges, the computer intelligent fuzzy logic approach can be utilised. Although several methods have been proposed to handle the incompleteness and uncertainty challenges, they are all based on optimisation methods. The objective of this study is to propose a novel framework to handle the uncertainty and incomplete challenges of the software release planning based on intelligent approaches such as fuzzy rule-based system rather than optimisation method. Also, some predefined thresholds are determined to support the time flexible horizon challenge of the software release planning process and to increase the flexibility of the proposed framework. For validating the proposed framework, it is applied to plan a new release for updating a faculty website project.
3D Human Pose Lifting: From Joint Position to Joint Rotation

3D human pose estimation is a fundamental task in computer vision. However, most of the related works focus on recovering human joint positions, which provides sparse and insufficient pose information for many applications like 3D avatar animation. Therefore, this paper presents a deep network for recovering joint angles from 3D joint positions, which learns the prior dependence between them. We test the validity and robustness of our method. We also discuss some details in designing and training the network. Our method is simple, effective and extensive. It can be combined with work of 3D human pose estimation that predict 3D joint positions from image or depth data to produce more detailed and natural poses. It builds a map between two joint sets with different numbers of joints, which provides a framework to unify multiple datasets for human pose estimation with different annotation formats.
Safe reinforcement learning using risk mapping by similarity

Reinforcement learning (RL) has been used to successfully solve sequential decision problem. However, considering risk at the same time as the learning process is an open research problem. In this work, we are interested in the type of risk that can lead to a catastrophic state. Related works that aim to deal with risk propose complex models. In contrast, we follow a simple, yet effective, idea: similar states might lead to similar risk. Using this idea, we propose risk mapping by similarity (RMS), an algorithm for discrete scenarios which infers the risk of newly discovered states by analyzing how similar they are to previously known risky states. In general terms, the RMS algorithm transfers the knowledge gathered by the agent regarding the risk to newly discovered states. We contribute with a new approach to consider risk based on similarity and with RMS, which is simple and generalizable as long as the premise similar states yield similar risk holds. RMS is not an RL algorithm, but a method to generate a risk-aware reward shaping signal that can be used with a RL algorithm to generate risk-aware policies.
Resource Sharing

The chapter examined resource sharing as vehicle for effective library information dissemination and services in the digital age. In relation to this purpose, studies have shown that the global information explosion, the cut in library budgetary allocations, the rising costs and complexities of information resources, as well as the need to provide the user community with optimal access to needed information make resource sharing initiatives indispensable particularly among academic libraries for effective information dissemination and service delivery in the digital age. However, certain questions remain unanswered in the literature regarding resource sharing. They include: How can libraries engage in effective dissemination of information to the clientele through resource sharing? How can resource sharing result to effective service delivery in academic library? Is there a reflection of resource sharing in Nigeria? What is the nature of resource sharing in this digital age? What does the future hold regarding resource sharing in academic libraries and what are the issues/challenges associated with resource sharing by academic libraries in the digital age? It is expected that this chapter will provide answers to all these pertinent questions.
On the complexity of exact counting of dynamically irreducible polynomials

We give an efficient algorithm to enumerate all sets of r⩾1 quadratic polynomials over a finite field which remain irreducible under iterations and compositions.
A Practical Approach to Integrating Live 2D Web Content with the Immersive Web

As 3D on the web gains momentum via standards such as WebGL and WebXR, a reoccurring frustration among web developers is inability to leverage 2D web technologies within immersive presentation contexts. The use-cases for blending 2D and 3D web content include progressive enhancement of 2D web pages, re-use of existing 2D web content, and layout/design of complex interactive user interfaces for 3D environments. We introduce WebLayer3D, a JavaScript plugin for three.js (a popular 3D scene-graph library) that makes it easy for web developers to integrate live interactive 2D web content (built using standard web technologies) into a 3D scene (rendered using WebGL). In this paper, we demonstrate that existing DOM-to-Texture techniques (when used thoughtfully) are sufficient for enabling a performant, flexible, and simple approach to building interactive 3D user interfaces for the immersive web.
Applying Neural Network Techniques for Topic Change Detection in the HuComTech Corpus

In the age of The Internet we are generating documents (both written and spoken) at an unprecedented rate. This rate of document creation—as well as the number of already existing documents—makes manual processing time-consuming and costly to the point of infeasibility. This is the reason why we are in need of automatic methods that are suitable for the processing of written as well as spoken documents. One crucial part of processing documents is partitioning said documents into different segments based on the topic being discussed. A self-evident application of this would be for example partitioning a news broadcast into different news stories. One of the first steps of doing so would be identifying the shifts in the topic framework, or in other words, finding the time-interval where the announcer is changing from one news story to the next. Naturally, as the transition between news stories are often accompanied by easily identifiable audio—(e.g. signal) and visual (e.g. change in graphics) cues, this would not be a particularly different task. However, in other cases the solution to this problem would be far less obvious. Here, we approach this task for the case of spoken dialogues (interviews). One particular difficulty of these dialogues is that the interlocutors often switch between languages. Because of this (and in the hope of contributing to the generality of our method) we carried out topic change detection in a content-free manner, focusing on speaker roles, and prosodic features. For the processing of said features we will employ neural networks, and will demonstrate that using the proper classifier combination methods this can lead to a detection performance that is competitive with that of the state-of-the-art.
A Novel Foreground Segmentation Method Using Convolutional Neural Network

Background subtraction is a commonly used approach for foreground segmentation (moving object detection). Different methods have been proposed based on this background subtraction technique. However, the algorithms give the false alarm in case of complex scenarios such as dynamic background, camera motion, shadow, illumination variation, camouflage, etc. A foreground segmentation system using convolutional neural network framework is proposed in this paper to handle these complex scenarios. In this approach, the non-handcrafted features learned from the deep neural network are used for the detection of moving objects. These non-handcrafted features are robust and efficient compared to the handcrafted features. The presented method is learned using spatial and temporal information. Additionally, a new background model is proposed to estimate the temporal information. We train the model end-to-end using input images, background images, and optical flow images. For the training purpose, we have randomly selected few images and its ground truth images from CDnet 2014. The proposed method is evaluated with benchmark datasets, and it outperforms the state-of-the-art methods in terms of qualitative and quantitative analyzes. The proposed model is capable of real-time processing because of its network architecture. Hence the model can be used in real-surveillance applications.
Facing parallel market and counterfeit issues by the combined use of blockchain and CP-ABE encryption technologies

Blockchains are emerging technologies that propose new business models and value propositions. Besides their application for cryptocurrency purposes, as distributed ledgers of transactions, they enable new ways to provision trusted information in a distributed fashion. In this paper, we present our product tagging solution designed to help Small & Medium Enterprises (SMEs) protect their brands against counterfeit products and parallel markets, as well as to enhance UX (User Experience) and promote the brand and product.Our solution combines the use of DLT to assure, in a verifiable and permanent way, the trustworthiness and confidentiality of the information associated to the goods and the innovative CP-ABE encryption technique to differentiate accessibility to the product's information.
Mining Actionable Information from Security Forums: The Case of Malicious IP Addresses

The goal of this work is to systematically extract information from hacker forums, whose information would be in general described as unstructured: the text of a post is not necessarily following any writing rules. By contrast, many security initiatives and commercial entities are harnessing the readily public information, but they seem to focus on structured sources of information. Here, we focus on the problem of identifying malicious IP addresses, among the IP addresses which are reported in the forums. We develop a method to automate the identification of malicious IP addresses with the design goal of being independent of external sources. A key novelty is that we use a matrix decomposition method to extract latent features of the behavioral information of the users, which we combine with textual information from the related posts. A key design feature of our technique is that it can be readily applied to different language forums, since it does not require a sophisticated natural language processing approach. In particular, our solution only needs a small number of keywords in the new language plus the user's behavior captured by specific features. We also develop a tool to automate the data collection from security forums. Using our tool, we collect approximately 600K posts from three different forums. Our method exhibits high classification accuracy, while the precision of identifying malicious IP in post is greater than 88% in all three forums. We argue that our method can provide significantly more information: we find up to three times more potentially malicious IP address compared to the reference blacklist VirusTotal. As the cyber-wars are becoming more intense, having early accesses to useful information becomes more imperative to remove the hackers first-move advantage, and our work is a solid step towards this direction.
Mitigating Tail Response Time of n-Tier Applications

Consistent low response time is essential for e-commerce due to intense competitive pressure. However, practitioners of web applications have often encountered the long-tail response time problem in cloud data centers as the system utilization reaches moderate levels (e.g., 50%). Our fine-grained measurements of an open source n-tier benchmark application (RUBBoS) show such long response times are often caused by Cross-tier Queue Overflow (CTQO). Our experiments reveal the CTQO is primarily created by the synchronous nature of RPC-style call/response inter-tier communications, which create strong inter-tier dependencies due to the request processing chain of classic n-tier applications composed of synchronous RPC/thread-based servers. We remove gradually the dependencies in n-tier applications by replacing the classic synchronous servers (e.g., Apache, Tomcat, and MySQL) with their corresponding event-driven asynchronous version (e.g., Nginx, XTomcat, and XMySQL) one-by-one. Our measurements with two application scenarios (virtual machine co-location and background monitoring interference) show that replacing a subset of asynchronous servers will shift the CTQO, without significant improvements in long-tail response time. Only when all the servers become asynchronous the CTQO is resolved. In synchronous n-tier applications, long-tail response times resulting from CTQO arise at utilization as low as 43%. On the other hand, the completely asynchronous n-tier system can disrupt CTQO and remove the long tail latency at utilization as high as 83%.
Modeling User Exposure with Explicit and Implicit Social Relations for Recommendation

Social recommender systems have been well studied in both academia and industry. Social information helps to solve the data sparsity and cold start problems in traditional recommender systems, while most existing works in social recommendation assume that social friends have similar preferences. This assumption is too strict and not accord with real world situations, because of the diversity of social relations. We tend to share item information with our socially connected friends. We don't know whether they will like the items, while we help them be exposed to the items. So we model the social information for exposure rather than preferences. In this paper, we propose a novel social exposure-based recommendation model by integrating social information into the basic ExpoMF model [5]. In order to address the sparse issue in social network, we exploit implicit social relations. To the author's knowledge, the work reported is the first to extend exposure model with explicit and implicit social relations for recommendation. Experimental results on the two public datasets demonstrate that our approach SoEx++ performs the best comparing to other three models.
What sets Verified Users apart?

Social network and publishing platforms, such as Twitter, support the concept of a secret proprietary verification process, for handles they deem worthy of platform-wide public interest. In line with significant prior work which suggests that possessing such a status symbolizes enhanced credibility in the eyes of the platform audience, a verified badge is clearly coveted among public figures and brands. What are less obvious are the inner workings of the verification process and what being verified represents. This lack of clarity, coupled with the flak that Twitter received by extending aforementioned status to political extremists in 2017, backed Twitter into publicly admitting that the process and what the status represented needed to be rethought. With this in mind, we seek to unravel the aspects of a user's profile which likely engender or preclude verification. The aim of the paper is two-fold: First, we test if discerning the verification status of a handle from profile metadata and content features is feasible. Second, we unravel the features which have the greatest bearing on a handle's verification status. We collected a dataset consisting of profile metadata of all 231,235 verified English-speaking users (as of July 2018), a control sample of 175,930 non-verified English-speaking users and all their 494 million tweets over a one year collection period. Our proposed models are able to reliably identify verification status (Area under curve AUC > 99%). We show that number of public list memberships, presence of neutral sentiment in tweets and an authoritative language style are the most pertinent predictors of verification status. To the best of our knowledge, this work represents the first attempt at discerning and classifying verification worthy users on Twitter.
Symplectic isometries of stabilizer codes

In this paper, we study the equivalence of quantum stabilizer codes via symplectic isometries of stabilizer codes. We define monomially and symplectically equivalent stabilizer codes and determine how different the two notions can be. Further, we show that monomial maps correspond to local Clifford operators. We relate the latter with the LU-LC Conjecture.
Cut to Fit

Graph analytics applications are very often built using off-the-shelf analytics frameworks, which are profiled and optimized for the general case and have to perform for all kind of graphs. As performance is affected by the selection of the partition strategy analytics frameworks often offer a selection of partitioning algorithms. In this paper we evaluate the impact of partitioning strategies on the performance of graph computations. We evaluate eight graph partitioning algorithms on a diverse set of graph datasets, using four standard graph algorithms by measuring a set of five partitioning metrics.
A Noticeboard Application Using Context Aware Services: Case of Strathmore University, Kenya

The dissemination of information in large organizations using noticeboards is both inefficient and ineffective due to the static, unreliable and limiting nature of the boards. Noticeboards are constrained in terms of reach, poor feedback mechanisms and a general lack of security. Posts can be tampered with by malicious people or even removed by competitors. Due to their fixed nature, noticeboards also do not cater for context, meaning that a lot of posts are consumed outside of their actionable spaces. This research proposes a context aware noticeboard in which subscribers are able to receive, store or share notices based on their context and be in a better position to react or act on them accordingly. Object Oriented Analysis and Agile development methodologies were applied to develop a robust and dynamic, context aware Android-based noticeboard application with an analytical backend for visualizations. The final prototype was tested for usability and functionality with usability and acceptance rates of more than 90%. Furthermore, we recommend that more services being hinged on BLE and context awareness should be explored due to the success of the proposed solution. Additionally, it is imperative that institutions migrate towards smart solutions for communication.
Augmented Reality-Based Simulation of Some Basic Electrical Circuits Which Requires Oscilloscope for Analysis without Hardware

This study aims to contribute to the electronics education through the use of Augmented Reality (AR) technology, and thus, limit the dependency on a physical environment and the equipment required for the experiments performed in electronics education. In this regard, an Augmented Reality-based mobile application (ARElectronicLab) has been designed to provide a technology–reality blended experience of electronic circuits in real physical life. This AR-based mobile application has been used to create simulations of diode clipper circuit and inverting operational amplifier circuit. The mobile application operates with a marker in real life and enables monitoring of 3D simulations of electronic components through a touch screen. Hence, the application offers a real-like experience and brings an innovative and enriching perspective into the electronics education.