Reviewerly/exHarmony huggingface dataset:
Contrasting News Values in Newspaper Articles and Social Media: A Discursive Approach to the US Ruling on Same-Sex Marriage

The US Supreme Court ruling on the Obergefell v. Hodges case on June 26, 2015 led to the recognition of same-sex marriage in all fifty States, declaring it a constitutional right under the Fourteenth Amendment. The event received huge media coverage and gave rise to animated discussions on digital media platforms. In this context, if the fear of isolation due to spirals of silence (Noelle-Neumann 1974) is increasingly subdued thanks to the anonymity and ephemeral nature of the online environment, studying how users communicate on social media platforms can help researchers highlight how discourses on given minorities are linguistically conveyed in this fluid environment (Zappavigna 2012, 2013). Drawing on these observations, our investigation will focus on how the US Supreme Court ruling was framed by social media users. A corpus was collected of all Facebook comments related to the first news story reporting the event published online by leading US, UK and Italian newspapers. This Facebook user-generated corpus on marriage equality (FugMar) was then uploaded to the corpus analysis platform Sketch Engine (Kilgarriff et al. 2004, 2014) to analyse the discourses surrounding the event in a fluid and boundary-free environment such as that provided by new digital media in relation to news framing. The analysis of the news values (Fruttaldo and Venuti 2017), (Venuti and Fruttaldo 2017), which discursively construe the event in the newspapers, and the way they have been, or have not been, picked up by social media users when they discursively respond to and construe the event, enable us to reflect on the relationship between an event, its news framing, and the echo the framing has on audiences through social media. Ultimately, given the cross-cultural nature of the corpus under investigation, the following contribution sets out to compare and contrast how users discursively construct the US Supreme Court ruling on same-sex marriage in the US, UK and Italy.
Graph Algorithms

In this article, we describe the main graph algorithms that are applied in several fields, from transport network to computational biology. First, we will review two-well known traversal algorithms (depth-first search and breadth-first search). Then we consider the problem of computing a shortest path between two vertices and we review the Dijkstra’s algorithm, the Bellman-Ford algorithm and Floyd-Warshall algorithm. Another fundamental graph problem we will deal with is that of computing a maximum flow in graph and we will review the Ford-Fulkerson algorithm for the computation of a maximum flow. Next, we will consider consider the computation of a minimum spanning tree of a given graph, and we will review two well-know algorithms for solving this problem: Kruskal’s algorithm and Prim’s algorithm. Finally, we will consider the traveling salesman problem and the nearest neighbour algorithm, which is a heuristic for this problem.
Performance Assessment of the CCSDS-123 Standard for Panchromatic Video Compression on Space Missions

The use of next-generation and high-resolution imaging sensors is gaining interest for space missions, because of their properties for identification and exploration purposes. It is expected that the demand for video sensors in the space industry will increase during the next years, mainly for monitoring and exploration missions. In this context, onboard video compression techniques emerge as mandatory, because memory resources are available on space missions and the downlink bandwidth with ground prevents for the transmission of raw video, especially when near real-time capabilities are required. The complexity of the current commercial video encoders supposes a challenge for their implementation on the hardware used on space missions, because of their high architectural complexity and computational requirements, deriving in an area overhead and an unacceptable power consumption. In this context, we propose the use of the CCSDS-123.0-B-2 standard, originally focused on the near-lossless on-board compression of multispectral and hyperspectral images, for compressing panchromatic video. The proposed solution presents low complexity, because it is specifically designed for working on space missions and also has the capability of compressing both 3-D images and panchromatic video by using a single processing core. Results demonstrate that this solution achieves high compression ratios, maintaining a considerable video quality after reconstruction on ground and achieving a fine-grain control of the losses introduced in the compression chain.
Latency and Consistent Flow Migration: Relax for Lossless Updates

Consistency in network updates is a nascent research area, especially in the context of traffic engineering or Software Defined Networks. Various approaches have been proposed and implemented in the problem space of flow migration and congestion, primarily focusing on different flows not breaking the bandwidth capacities of the used links during updates. However, current network update techniques overlook the effect of flows congesting their own path during a network update due to latency on the links. Furthermore, while congestion will be resolved eventually after the network update, the buffers of the affected routers can be filled for a long time period, leading to the following paradox: a flow is moved to a path with less latency, but the latency stays the same! As flows are often migrated because of latency concerns, this is highly undesirable. We show that these effects occur already in a small topology in practice, causing packet loss due to overfull buffers. Furthermore, we prove that finding a lossless flow migration is NP-hard, already for a single (splittable) flow on directed acyclic graphs. Nonetheless, we can relax latency requirements to still obtain lossless flow migration. To this end, we show how to adapt current systems such as SWAN or Dionysus [SIGCOMM'13/'14], also developing our own polynomial time schedule algorithm, and discussing future consistent flow migration technique adaptations.
On a user authentication method to realise an authentication system using s-EMG

To prevent shoulder-surfing attacks, we proposed a user authentication method using surface electromyogram (s-EMG) signals, which can be used to identify who generated the signals and which gestures were made. Our method uses a technique called 'pass-gesture', which refers to a series of hand gestures, to achieve s-EMG-based authentication. However, it is necessary to introduce computer programs that can recognise gestures from the s-EMG signals. In this paper, we propose two methods that can be used to compare s-EMG signals and determine whether they were made by the same gesture. One uses support vector machines (SVMs), and the other uses dynamic time warping. We also introduced an appropriate method for selecting the validation data used to train SVMs using correlation coefficients and cross-correlation functions. A series of experiments was carried out to confirm the performance of those proposed methods, and the effectiveness of the two methods was confirmed.
High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays: erratum

An erratum to correct a typo in the author list in [ Opt. Express 27 ( 14 ), 19778 ( 2019 ) ].
Wearable, untethered hands tracking with passive magnets

Accurate tracking of the hands and fingers allows users to employ natural gestures in various interactive applications, e.g., controller-free interaction in augmented reality. Hand tracking also supports health applications, such as monitoring face-touching, a common vector for infectious disease. However, for both types of applications, the utility of hand tracking is often limited by the impracticality of bulky tethered systems (e.g., instrumented gloves) or inherent limitations (e.g., Line of Sight or privacy concerns with vision-based systems). These limitations have severely restricted the adoption of hand tracking in real-world applications. We demonstrate MagX, a fully untethered on-body hand tracking system utilizing passive magnets and a novel magnetic sensing platform. Since passive magnets require no maintenance, they can be worn on the hands indefinitely, and only the sensor board needs recharging, akin to a smartwatch.
A Randomized Switched-Mode Voltage Regulation System for IoT Edge Devices to Defend Against Power Analysis based Side Channel Attacks

The prevalence of Internet of Things (IoT) allows heterogeneous and lightweight smart devices to collaboratively provide services with or without human intervention. With an ever-increasing presence of IoT-based smart applications and their ubiquitous visibility from the Internet, user data generated by highly connected smart IoT devices also incur more concerns on security and privacy. While a lot of efforts are reported to develop lightweight information assurance approaches that are affordable to resource-constrained IoT devices, there is not sufficient attention paid from the aspect of security solutions against hardware-oriented attacks, i.e. side channel attacks. In this paper, a COTS (commercial off-the-shelf) based Randomized Switched-Mode Voltage Regulation System (RSMVRS) is proposed to prevent power analysis based side channel attacks (P-SCA) on bare metal IoT edge device. The RSMVRS is implemented to direct power to IoT edge devices. The power is supplied to the target device by randomly activating power stages with random time delays. Therefore, the cryptography algorithm executing on the IoT device will not correlate to a predictable power profile, if an adversary performs a SCA by measuring the power traces. The RSMVRS leverages COTS components and experimental study has verified the correctness and effectiveness of the proposed solution.
Who Trades Bitcoin Futures and Why?

Using regulatory data with identifiers, we analyze the traders active in the Bitcoin futures (BTC) contracts traded on the Chicago Mercantile Exchange (CME). We find two primary trader types, those who hold almost exclusively BTC (concentrated traders) and those who hold BTC to diversify a broader futures portfolio (diversified traders). The prevalence of these two types changes over time. We also study how BTC markets are connected to other futures markets through common holdings of BTC traders. Finally, we analyze the micro BTC contract and find that the trader composition is different than that of the full-size contract.
Real-time Transparent Object Segmentation Based on Improved DeepLabv3+

To solve the problem of inaccurate segmentation caused by the similarity of adjacent pixels between the transparent object and its background, we propose a real-time segmentation model for transparent object based on DeepLabV3+. We design a new feature extraction network for semantic segmentation on the basis of Darknet53 structure, and refactor the Atrous Spatial Pyramid Pooling module by using dense connection between different atrous convolution blocks to restore more detailed information. Compared with Deeplabv3+, our method improves accuracy in transparent object segmentation, effectively reducing the boundary pixel classification error, and our segmentation speed is 1.6 times that of the original DeepLabv3+, indicating that our method can be faster and more effective on the segmentation of transparent object in the context of natural experiments.
Sentiment Analysis

Emotion is a fundamental part of the human experience. New data sources and methods are enabling geographers to study how people experience emotions in place. Emotions are challenging to study as they are composed of both internal processes in the mind and are sometimes externally expressed in the body. No single method captures the full human experience of an emotion. Two methods of characterizing emotion that are becoming frequently used are facial emotion recognition and sentiment analysis. In particular, sentiment analysis has been applied to location-based social media data in an attempt to capture the public mood in different locations at specific points in time. These data sources and methods help geographers to understand how people feel as well to compare the emotions of different people at a particular place and time.
Kalman Filter

Kalman filter can be considered as an extension of Gauss’ original development of least squares to estimate unknown parameters of a dynamic model. Currently, Kalman filter is the most suitable algorithm to apply to the estimation process by linking real-time measurements and estimating the state of a system parameters through time epochs. Two illustrative examples are given with full descriptions and supported with MATLAB codes.
Intellectualization Methods of Population Algorithms of Global Optimization

We consider constrained global optimization algorithms that are adaptive (self-adaptive) to the optimization problem being solved. We set tasks of parametric, structural and structural-parametric adaptation of these algorithms. We present the following methods for synthesis of adaptive algorithms for global optimization: tuning methods; control methods; self-control methods. We give some examples of adaptive algorithms and the results of research of their efficiency.
ICurry

FlatCurry is a well-established intermediate representation of Curry programs used in compilers that translate Curry code into Prolog or Haskell code. Some FlatCurry constructs have no direct translation into imperative code. These constructs must be each handled differently when translating Curry code into, e.g., C, Open image in new window or Python code. We introduce a new representation of Curry programs, called ICurry, and derive a translation from all FlatCurry constructs into ICurry. We present the syntax and semantics of ICurry and the translation from FlatCurry to ICurry. We present a model of functional logic computations as graph rewriting and show how this model can be implemented with ICurry in a low-level imperative language.
Distributional Word Representations for Code-mixed Text in Moroccan Darija

In this paper, we build different word embedding representations for code-mixed Moroccan Darija-French-English text. We begin by collecting a large corpus based on social media posts and song lyrics. We then experiment with four algorithms: namely, Word2Vec using both Continuous Bag of Words (CBOW) and SkipGram flavors, FastText-CBOW and FastText-SkipGram. Finally, we evaluate these models on two tasks: Word Analogies and Word-Level Language Detection. The use of character n-grams in the FastText models allowed them to perform much better than their word2vec counterparts on the word analogy task, while Word2Vec-SkipGram performed best on the language identification. To the knowledge of the authors, our embeddings are the first set of distributed representations for Arabizi Moroccan Darija text. In the context of this work, we also managed to build the first ever code-switched English – French - Moroccan Darija word analogy dataset and the largest Arabizi code-switched English – French - Moroccan Darija.
PDAAA: Progressive Defense Against Adversarial Attacks for Deep Learning-as-a-Service in Internet of Things

Nowadays, Deep Learning-as-a-Service can be de-ployed in the Internet of Things (IoT) to provide smart services and sensor data processing. However, recent research has re-vealed that some Deep Neural Networks (DNN) can be easily misled by adding relatively small but adversarial perturbations to the input (e.g., pixel mutation in input images). One challenge in defending DNN against these attacks is to efficiently identify and filtering out the adversarial pixels. The state-of-the-art defense strategies with good robustness often require additional model training for specific attacks. To reduce the computational cost without loss of generality, we present a defense strategy called a progressive defense against adversarial attacks (PDAAA) for efficiently and effectively filtering out the adversarial pixel mutations, which could mislead the neural network towards erro-neous outputs, without a-priori knowledge about the attack type. We evaluated our progressive defense strategy against various attack methods on two well-known datasets. Experimental result shows it outperforms the state-of-the-art methods(Adversarial-PGD, Adversarial-Network, and Adversarial-Dual-Network) with dramatically reduced computation cost.
ARMed Frodo

FrodoKEM is one of Post-quantum Cryptography, which is selected Round 3 alternate candidates of Public-key Encryption and Key-establishment Algorithms at NIST Post-quantum Cryptography Standardization. FrodoKEM uses AES algorithm for generate pseudo-random matrix, and also uses matrix-multiplication. At that time, a huge computational load occurred to the pseudo-random number generation and matrix-multiplication operation, reducing the overall performance of the FrodoKEM scheme. In this paper, we propose the parallel matrix-multiplication and built-in AES accelerator for AES encryption on ARMv8 processors, and applied these techniques to the FrodoKEM-640 scheme. To implement the parallel matrix-multiplication, vector registers (i.e. 128-bit wise) and vector instructions (i.e. NEON) are used. The proposed parallel matrix-multiplication can be generated 80 element of output matrix at once. As a result, the matrix-multiplication has 43.8\(\times \) faster than the normal matrix-multiplication in the best-case, the implementation FrodoKEM-640 with all of proposed techniques has 10.22\(\times \) better performance in maximum than previous C only implementation.
Belief Shadowing

Adapting beliefs to new circumstances, like belief change, update, revision or merging, typically requires deep and/or complex adjustments of belief bases even when adaptations happen to be transient. We present a novel, lightweight and tractable approach to a new kind of beliefs’ interference which we call belief shadowing. Put simply, it is a transient swap of beliefs when part of one belief base is to be shadowed by another belief base representing new observations and/or beliefs of superior agents/teams. In this case no changes to belief bases are needed. This substantially improves the performance of systems based on doxastic reasoning. We ensure tractability of our formal framework, what makes it suitable for real-world applications. The presented approach is based on a carefully chosen four-valued paraconsistent logic with truth values representing truth, falsity, incompleteness and inconsistency. Moreover, potentially undesired or forbidden conclusions are prevented by integrity constrains together with their shadowing machinery. As an implementation environment we use $$4\hbox {QL}^{\mathrm{Bel}}$$ , a recently developed four-valued query language based on the same underlying logic and providing necessary reasoning tools. Importantly, the shadowing techniques are general enough to be embedded in any reasoning environment addressing related phenomena.
Communicating pictures – the future

In this chapter we briefly review the demands of future content types in the context of an extended video parameter space, to examine how parameters such as spatial resolution, temporal resolution, dynamic range, and color gamut affect our viewing experience, both individually and collectively. The delivery requirements associated with this extended parameter space are considered, with a focus on how compression might affect and influence video quality. Emphasizing the impact of, and the interactions between, the video parameters and also their content dependence, the justification is made for an increased use of perceptual models in an analysis–synthesis framework. It is argued that perception-based compression techniques, particularly those exploiting deep learning methods, can improve video quality, providing the basis for a new rate-quality optimization (RQO) approach for effective coding of the immersive formats of the future.
Git Leaks: Boosting Detection Effectiveness Through Endpoint Visibility

Development platforms, such as GitHub, GitLab, and BitBucket, have become very popular among software developers. Unfortunately, it is not uncommon for developers to inadvertently leak secrets (e.g. API keys, credentials) and confidential source code on public Git repositories, facilitating numerous security breaches that impact reputation and result in losing revenue. Current Git leak detection strategies and tools often fall short due to a high false-positive rate, an ever-growing number of platforms and repositories to inspect, and a limited reach. This paper investigates the shortcomings of traditional Git leak detection methodologies and introduces an alternative approach to overcome those shortcomings. The approach consists of identifying all Git repositories associated with an organisation by collecting the URLs and the respective maintainer names from its endpoint fleet, empowering blue-teams to prioritise content inspection efforts against a well-defined set of targets and to boost incident response capabilities. The paper describes a prototype we implemented that serves as a proof of concept for the approach.
Benchmarking the Software Engineering Undergraduate Program Curriculum at Jordan University of Science and Technology with the IEEE Software Engineering Body of Knowledge (SWE Knowledge Areas #6–10)

This paper evaluates the compliance of the Software Engineering Program (SWE-Curriculum) at Jordan University of Science and Technology (JUST) with the first five of the fifteen Software Engineering Knowledge Areas (SWE-KAs #6–10) of the SWEBOK-V3.0 of the IEEE Computer Society. This research is the first to measure the coverage of the SWE-KAs in any SWE-Curriculum. It is essential to line up the said SWE-Curriculum with the IEEE view of Software Engineering (SWEBOK-V3.0), in addition to its IET accreditation.This research was divided into three parts. The first part (P#1) focused on SWE-KAs#1–5, the second part (P#2) focuses on SWE-KAs#6–10, and the third part (P#3) will focus on SWE-KA#11–15. This paper inspected SWE-KAs#6–10’s coverage across the said SWE-Curriculum courses. The results were identified as either Fully Compliant, Highly Compliant, Partially Compliant, or Poorly Compliant (e.g., the concerned SWE-KA is either fully, highly, partially, or poorly covered across one or more of the said SWE-Curriculum courses).This research found the compliance as Fully Compliant in the cases of the SWE-KAs of the SWE management and software quality but found Partially Compliant in the cases of the SWE-KAs of the software configuration management, SWE process, and SWE models and methods.