Reviewerly/exHarmony huggingface dataset:
An assessment of the impact of IQ imbalances on the physical layer authentication of IoT wireless devices

Physical Layer Authentication of wireless devices using their intrinsic physical features has been investigated in recent years by the research community. The concept is that small differences in the material and the composition of the electronic circuits of the wireless devices produce specific features in the Radio Frequency (RF) signal transmitted over the air. While these differences are usually not relevant to obstacle the correct functioning of wireless services, they are significant enough to uniquely identify the model or the electronic device itself once they are collected and processed by a RF receiver. Researchers have applied a variety of techniques to extract the features from the signal in space including statistical analysis and machine learning algorithms. In ideal conditions, the classification accuracy presented in the research literature is often higher than 95% but it can degrade significantly in the presence of non Line of Sight conditions. The research community has investigated the impact of low Signal to Noise (SNR) ratios or fading effects on the classification performance, but the disturbances introduced by the RF receiver itself have received little attention. In this paper, we investigate the impact of IQ imbalances of the RF receiver on the classification performance, which has not been attempted in the literature, yet. This impact is evaluated by means of the signals collected from 11 IoT wireless devices, using different representations of the signal for different values of the IQ imbalances.
Using learner corpus methods in L2 acquisition research

Abstract Since the 1970s, findings from Morpheme Order Studies (MOS) have suggested that the emergence of morphemes follows a predictable order in L2 English. In this paper we show how the tools and practices in Learner Corpus Research (LCR) offer a richer descriptive basis, which is achieved with Interlanguage Annotation (ILA), a manual, fined-grained, purpose-oriented type of annotation. Additionally, we use a standardised placement test, since proficiency level has been overlooked in most previous MOS. Both of these practices provide a more detailed description of morpheme accuracy order across different levels. We analyse four proficiency levels (A1-B2) in a subcorpus of L1 Spanish-L2 English secondary-school learners from the CORpus of English as a Foreign Language (COREFL). Our results partially confirm findings from previous MOS, but also reveal key findings that had gone previously unnoticed regarding the role of proficiency level and the subtype of errors, which are relevant factors for SLA research.
Reading Analysis for Barcode Scanner With Interference from LED-Based Lighting

This paper addresses reading failures of a barcode scanner interfered by light emitting diode (LED) lamps. It quantifies the reading performance in terms of timing signal-to-interference ratio (TSIR), in particular, as a function of modulation depth and frequency of the interference from modulated LED lighting. At the decision variable, the interference typically generated in the LED driver by a switched mode power supply (SMPS) is neither additive nor Gaussian. It has frequencies up to several MHz that can seriously affect the barcode reading performance. To calculate the signal and interference power for TSIR, the laser scanner physical channels are analyzed, including the laser beam path and the LED interference path. Since barcode scanners usually use peak or edge detection, the reading reliability is subject to the first and second derivatives of the LED inference, respectively. We validate the proposed TSIR for predicting scanner reading performance by experiments. For instance, we found that typical scanners reach a specified sensitivity at a TSIR value of around 50. We further investigate the reading performance under multi-frequency interference, e.g., interference from the harmonics of the SMPS control signal. In general, this paper presents a simple, yet realistic model to quantify the reading performance in terms of TSIR for barcode scanner subject to single-frequency LED interference, and it proposes an empirically verified flicker interference metric (FIM) for multi-frequency interference.
Samawa Language Part of Speech Tagging with Probabilistic Approach: Comparison of Unigram, HMM and TnT Models

Abstract Samawa language is one of living languages with more than 500K native speakers in Sumbawa island, Indonesia. There are, however, extremely small amounts of available resources and efforts to develop tools in the Natural Language Processing (NLP) discipline area. In this paper, we observe and evaluate three models of probabilistic approach which are Unigram, Hidden Markov Model (HMM) and Trigram’n’Tags (TnT) models for part of speech tagging problem, which is a process to label either word or punctuation in a sentence. We used k-fold cross-validation (with k = 5 and 10) and tagged corpus around 20K tokens with 24 tags. TnT model gives the best performance reached 96.18% compared with the other models. This result shows that TnT model could be considered and used to extend Samawa corpora and help some NLP tasks in the future.
Tightly secure signature schemes from the LWE and subset sum assumptions

In this paper, we study how to construct tightly secure signature scheme against adaptive chosen message attacks in the multi-user setting (i.e., tightly euf-m-cma secure signature) from the learning with errors (LWE) assumptions. More precisely, we propose a modular framework of euf-m-cma secure signature from a weak partial one-time signature (POS) scheme that is secure only against random message attacks in the multi-user setting (i.e., euf-m-rma secure) and possesses imperfect correctness. By instantiating the weak POS with the LWE assumption, we obtain the first LWE-based tightly euf-m-cma secure signature scheme in the multi-user setting. Moreover, we also present an instantiation of the weak POS based on the Subset Sum (SS) assumption, and again we obtain the first almost tightly euf-cma secure signature scheme from the SS assumption in the single-user setting. All our security reductions are tight and without random oracles.
Hierarchical Deep Cosegmentation of Primary Objects in Aerial Videos

Primary object segmentation plays an important role in understanding videos generated by unmanned aerial vehicles. In this paper, we propose a large-scale dataset with 500 aerial videos and manually annotated primary objects. To the best of our knowledge, it is the largest dataset to date for primary object segmentation in aerial videos. From this dataset, we find most aerial videos contain large-scale scenes, small primary objects as well as consistently varying scales and viewpoints. Inspired by that, we propose a hierarchical deep cosegmentation approach that repeatedly divides a video into two sub-videos formed by the odd and even frames, respectively. In this manner, the primary objects shared by sub-videos can be cosegmented by training two-stream CNNs and finally refined within the neighborhood reversible flows. Experimental results show that our approach remarkably outperforms 17 state-of-the-art methods in segmenting primary objects in various types of aerial videos.
Device-independent dimension test in a multiparty Bell experiment

A device-independent dimension test for a Bell experiment aims to estimate the underlying Hilbert space dimension that is required to produce given measurement statistical data without any other assumptions concerning the quantum apparatus. Previous work mostly deals with the two-party version of this problem. In this paper, we propose a very general and robust approach to test the dimension of any subsystem in a multiparty Bell experiment. Our dimension test stems from the study of a new multiparty scenario which we call prepare-and-distribute. This is like the prepare-and-measure scenario, but the quantum state is sent to multiple, noncommunicating parties. Through specific examples, we show that our test results can be tight. Furthermore, we compare the performance of our test to results based on known bipartite tests, and witness remarkable advantages, which indicates that our test is of a true multiparty nature. We conclude by pointing out that with some partial information about the quantum states involved in the experiment, it is possible to learn other interesting properties beyond dimension.
A cognitive diagnosis framework based on peer assessment

Given examinees' performance (i. e., scores) on each problem, cognitive diagnosis models can discover the latent characteristics of examinees. Traditional cognitive diagnosis models require teachers to provide scores in time. Thus we can hardly apply traditional models in large-scale scenarios, such as Massive Open Online Courses (MOOC). Peer assessment refers to a teaching activity in which students evaluate each other's assignments. The scores given by students could replace the teacher's assessments to a certain extent. In this paper, we propose a novel cognitive diagnosis model named Peer-Assessment Cognitive Diagnosis Framework (PACDF). This model combines peer assessments with cognitive diagnosis, aiming at reduce the burden of teachers. Specifically, we propose a novel probabilistic graphic model at first. This model characterizes not only the relationships between real scores and scores given by peer assessment, but also the relationship between examinees' skill proficiency and problem mastery. Then we adopt Monte Carol Markov Chain (MCMC) sampling algorithm to estimate the parameters of the model. Lastly, we use the model to predict examinees' performance. The experimental results show that PACDF could quantitatively explain and analyze skill proficiencies of examinees, thus perform better in predicting examinees' performances.
Online Job Scheduling with Redundancy and Opportunistic Checkpointing: A Speedup-Function-Based Analysis

In a large-scale computing cluster, the job completions can be substantially delayed due to two sources of variability, namely, variability in the job size and that in the machine service capacity. To tackle this issue, existing works have proposed various scheduling algorithms which exploit redundancy wherein a job runs on multiple servers until the first completes. In this paper, we explore the impact of variability in the machine service capacity and adopt a rigorous analytical approach to design scheduling algorithms using redundancy and checkpointing. We design several online algorithms which can dynamically vary the number of redundant copies for jobs. We also provide new theoretical performance bounds for these algorithms in terms of the overall job flowtime by introducing the notion of a speedup function, based on which a novel potential function can be defined to enable the corresponding competitive ratio analysis. In particular, by adopting the online primal-dual fitting approach, we prove that our SRPT+R Algorithm in a non-multitasking cluster is <inline-formula><tex-math notation="LaTeX">$(1+\epsilon)$</tex-math></inline-formula> -speed, <inline-formula><tex-math notation="LaTeX">$\ O(\frac{1}{\epsilon })$</tex-math></inline-formula> -competitive. We also show that our proposed Fair+R and LAPS+R( <inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> ) Algorithms for a multitasking cluster are <inline-formula><tex-math notation="LaTeX">$(4+\epsilon)$</tex-math></inline-formula> -speed, <inline-formula><tex-math notation="LaTeX">$\ O(\frac{1}{\epsilon })$</tex-math></inline-formula> -competitive and ( <inline-formula><tex-math notation="LaTeX">$2 + 2\beta + 2\epsilon)$</tex-math></inline-formula> -speed <inline-formula><tex-math notation="LaTeX">$O(\frac{1}{\beta \epsilon })$</tex-math></inline-formula> -competitive respectively. We demonstrate via extensive simulations that our proposed algorithms can significantly reduce job flowtime under both the non-multitasking and multitasking modes.
An Efficient Density-Based Clustering Algorithm Using Reverse Nearest Neighbour

Density-based clustering is the task of discovering high-density regions of entities (clusters) that are separated from each other by contiguous regions of low-density. DBSCAN is, arguably, the most popular density-based clustering algorithm. However, its cluster recovery capabilities depend on the combination of the two parameters. In this paper we present a new density-based clustering algorithm which uses reverse nearest neighbour (RNN) and has a single parameter. We also show that it is possible to estimate a good value for this parameter using a clustering validity index. The RNN queries enable our algorithm to estimate densities taking more than a single entity into account, and to recover clusters that are not well-separated or have different densities. Our experiments on synthetic and real-world data sets show our proposed algorithm outperforms DBSCAN and its recent variant ISDBSCAN.
Glove Based Sign Interpreter for Medical Emergencies

Deaf people experience a communication barrier with people who do not understand sign language. It is especially difficult for Deaf patients to explain their ailments to medical personnel without the aid of interpreters and as a result their treatment is either delayed or even neglected. This paper presents a glove based sign interpreter that enables a Deaf person to communicate with those who do not understand sign language. The glove based sign interpreter consists of a smart glove embedded with flex sensors and accelerometers that capture the hand gestures. The signals from the sensors are processed by a microprocessor and displayed on a computer monitor as words that can be read by medical personnel. The glove based sign interpreter can greatly improve the lives of Deaf persons and application can be extended beyond medical emergencies to other fields including business and education.
Aggregating Probabilistic Judgments

In this paper we explore the application of methods for classical judgment aggregation in pooling probabilistic opinions on logically related issues. For this reason, we first modify the Boolean judgment aggregation framework in the way that allows handling probabilistic judgments and then define probabilistic aggregation functions obtained by generalization of the classical ones. In addition, we discuss essential desirable properties for the aggregation functions and explore impossibility results.
CubeHarmonic

A contemporary challenge involves scientific education and the connection between new technologies and the heritage of the past. CubeHarmonic (CH) joins novelty and tradition, creativity and education, science and art. It takes shape as a novel musical instrument where magnetic 3D motion tracking technology meets musical performance and composition. CH is a Rubik's cube with a note on each facet, and a chord or chord sequence on each face. The position of each facet is detected through magnetic 3D motion tracking. While scrambling the cube, the performer gets new chords and new chord sequences. CH can be used to compose, improvise,1 and teach music and mathematics (group theory, permutations) with colors and physical manipulation supporting abstract thinking. Furthermore, CH allows visually impaired people to enjoy Rubik's cube manipulation by using sounds instead of colors.
Manual semantic annotations: User evaluation of interface and interaction designs

Semantic annotation is the process by which existing texts receive a mark-up that allows automatic identification of named entities (e.g., to distinguish between a turkey bird and the country Turkey). Manual annotation is useful both as stand-alone process in a domain-specific setting or as post-processing for automatic annotation algorithms. Development of most annotation tools strongly focuses on providing novel functionality — end-user evaluations of interface and user interaction are rare. This article reports on the results of a series of user studies executed to explore how non-expert users understand the process of enriching texts with semantic annotations. We find that these users can easily create simple semantic annotations (e.g., assigning concepts to text passages) but have difficulties understanding complex semantic annotations (e.g., assigning semantic identifiers to text passages). We present the results of three user studies on manual semantic annotation and discusses the lessons learned about both the semantic enrichment process and our methodology of exposing non-experts to semantic enrichment.
A New Cube Attack on MORUS by Using Division Property

MORUS is an authenticated encryption algorithm and one of the candidates in the CAESAR competition. Currently, the security of MORUS received extensive attention. In this paper, a new existence terms detection method in superpoly recovery phase in cube attack is proposed. More precisely, the upper bounding degree of superpoly is first estimated by using the cube attack based on the division property with Mixed Integer Linear Programming tool. Moreover, the t-degree monomials that may be involved in the superpoly are divided into two groups, where the elements of the first group can be directly determined without using the solver via the embedded property. Compared with previous methods, the time consumption by the solvers of our new method is reduced significantly. In particular, the truth table from only the existent terms can be used to recover the superpoly in the offline phase of the cube attack. Therefore, the time complexity of cube attack can be further reduced. As illustrative example, the security of the reduced-step variants of MORUS-640-128 against cube attack is evaluated by using this new method. It is demonstrated that the key recovery attacks can be applied to 6/7-step MORUS-640-128. Furthermore, some integral distinguishers of 7-step MORUS-640-128/MORUS-1280-256 are achieved.
Automating Legal Compliance Documentation for IoT Devices on the Network

Auditing of compliance with laws and regulations takes up much of security administrator's time. With the increasing number of IoT devices in a company network verification may go unchecked. This paper describes stage one of a research project to automate the generation of compliance documentation for Irish Laws in a large industry. Initial findings from the implementation and configuration of the tool indicate that the process is still significantly labor intensive.
Optimization of a shell-and-tube heat exchanger using the grey wolf algorithm

Most chemical processes require heat exchangers to modify the temperatures of the streams involved in the production steps. Due to their many applications, the rigorous design of such exchangers is of interest, aiming to determine the physical characteristics required to obtain the desired variation in temperature. The mathematical models representing such devices have several degrees of freedom and non-lineal equations, thus robust optimization algorithms are required to obtain the optimal solution in a short time. In this work, the use of the grey wolf algorithm is proposed for the optimization of a shell-and-tube heat exchanger, modeled by the Bell-Delaware equations. With the proposed method, a minimum total annual cost of 3,978.2 USD was obtained, in a mean time of 0.7976 seconds.
In the Eye of the Beholder

As the use of information and communication technology (ICT) solutions become more embedded in our everyday lives, ICT graduates are required to design and develop solutions that are not only easy to use, but evoke overall positive user experiences. The incorporation of human-computer interaction (HCI) principles, such as user-centered design (UCD), usability, and user experience (UX) into the design of ICT solutions can positively influence the success of deployed solutions. However, developers of ICT solutions, especially those from developing countries, have been slow to apply these principles in their development practices. Some of the reasons for this slow pace include lack of experienced practitioners due to limited number of universities offering HCI courses, especially in African countries, lack of consensus on the measures of UCD effectiveness, and little appreciation of the benefits of incorporating these design principles into development processes. This challenge is compounded by ineffective teaching strategies, in situations where HCI courses are taught. The application of an experiential learning strategy can go a long way in addressing the gap between the concepts of HCI, UX, and UCD that is taught in the classroom and their application by ICT graduates in the work environment. In this chapter, the authors describe how they incorporate eye tracking technology in an HCI course that forms part of a postgraduate informatics degree. The focus is on an eye tracking assignment that involves student groups performing usability evaluation studies for real-world clients. They posit that eye tracking is a powerful technology to convince students of the importance of user centered design. They conducted a survey amongst HCI students and analyzed student course evaluation results over a period of 3 years. The findings confirm that students regard the eye tracking assignment as a mind-altering experience and that it is potentially an effective technology for convincing future ICT professionals of the importance of usability, UX, and UCD.
Mining User Opinion Influences on Twitter Social Network: Find that Friend who Leads your Opinion Using Bayesian Method and a New Emotional PageRank Algorithm

With about 326 million[1] monthly active users and many millions of tweets sent per day, Twitter is undoubtedly one of the social networks most requested[2] by users sharing opinions, and feelings about trends, events... As a result, how users influence their opinions mutually constitute a hot issue for researchers. Indeed, the study and the estimation of the opinion influence observed between Twitter users constitutes a rich opportunity for the adjustment of services/products offered involved in the service discovery process. In this paper we propose an approach to determine the target user's Twitter friends from whom the target user opinion is influenced by. Our model is based on opinion mining of retweets and target user's Favorites markings from which we estimate the opinion influence using the Bayesian method combined with our EPR (Emotional PageRank) algorithm. The results obtained highlight our contribution compared to the standard PR (PageRank) algorithm.
Predicting Probing Rate Severity by Leveraging Twitter Sentiments

Probing is the first step to gain access to a network. Predicting the rate levels of probing against a network sufficiently ahead of time could be insightful to security analysts and practitioners. Indeed, an accurate prediction would help to understand the potential threats and attacks menacing an organization's network. However, this prediction problem is a challenging task; prior works make predictions over time horizons not exceeding a few hours. In this work, we propose a machine learning approach to predict the next day probing rate levels for a network telescope by leveraging Twitter users' sentiments toward the country hosting the network telescope. First, we investigate the relationship between probing rates and Twitter sentiments. Second, we cluster the probing rates to determine the probing severity levels. Finally, we predict future rate levels using several classifiers. We show that incorporating negative sentiments improves significantly the prediction performance. This demonstrates the importance of incorporating social signals as predictors when predicting future probing rates.