Reviewerly/exHarmony huggingface dataset:
Underwater Structure from Motion for Cameras Under Refractive Surfaces

Structure from Motion (SfM), as a three-dimensional (3D) reconstruction technique, can estimate the structure of an object by using a single moving camera. Cameras deployed in underwater environments are generally confined to waterproof housings. Thus, the light rays entering the camera are refracted twice; once at the interface between the water and the camera housing, and again at the interface between the camera housing and air. Images captured from scenes in underwater environments are prone to, and deteriorate, from distortion caused by this refraction. Severe distortions in geometric reconstruction would be caused if the refractive distortion is not properly addressed. Here, we propose a SfM approach to deal with the refraction in a camera system including a refractive surface. The impact of light refraction is precisely modeled in the refractive model. Based on the model, a new calibration and camera pose estimation method is proposed. This proposed method assists in accurate 3D reconstruction using the refractive camera system. Experiments, including simulations and real images, show that the proposed method can achieve accurate reconstruction, and effectively reduce the refractive distortion compared to conventional SfM.
COMPUTER-AIDED TESTING AS A METHOD OF ASSESSING THE LEVEL OF STUDENTS’ COMPETENCES FORMATION ON THE BASIS OF THE «ACTION RESEARCH» PRINCIPLE

Problem and purpose. The article proposes a model of computer-aided testing on the level of students’ competencies formation. The model is based on non-standard logic and is aimed at solving the problem of systemic teaching results monitoring. The aim of the study is to identify the possibilities of computer-aided testing of students’ competences as an effective way to assessing the results of teaching in the competences paradigm. The research methodology is based on systemic, activity-related and competence-building approaches as well as on the major provisions of pedagogical testing and modeling. The results of the study. Computer-aided testing based on the principle of “action research” can be effectively used as a method and tool that helps the student in mastering the discipline. In order to assess the level of students’ competencies formation making use of computer-aided testing in teaching students a certain subject it is suggested using assignments of three types: (1) for assessing the levels of knowledge acquisition; (2) assessing skills mastering; (3) assessing habits perfection. Conclusion The method of computer testing developed by the authors is an effective way for identifying the goal-targeted dynamics of the competencies formation through assessing the levels of their development at each stage of teaching.
Um nýyrði sem tengjast tölvum og tækni

English is increasingly influencing the Icelandic language community, raising concerns about the state and prospects of the Icelandic language. Recent studies indicate that such concerns are probably justified. The viability of the language depends on it being used in all areas of daily communication and the attitudes of speakers toward the contact between Icelandic and English is important in this respect. This paper describes the results of a recent study of the attitudes of about 350 Icelandic speakers toward neologisms and their use of a few technology-related neologisms. The paper discusses possible reasons for why some neologisms are received more positively than others and the relationship between neologisms and language viability in the context of Icelandic. The general discussion is then related to the findings of the study which indicate that there is a general consensus among all age groups that Icelandic neologisms should be created and adopted instead of foreign loanwords, although the proportion of such responses is lower in the younger age groups. Icelandic neologisms were viewed more favorably than loanwords in most cases but it is nevertheless clear that not all neologisms are equally well received. We consider some possible explanations for such differences.
Contour detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random Field

Abstract In Cassini ISS (Imaging Science Subsystem) images, contour detection is often performed on disk-resolved objects to accurately locate their center. Thus, contour detection is a key problem. Traditional edge detection methods, such as Canny and Roberts, often extract the contour with too much interior details and noise. Although the deep convolutional neural network has been applied successfully in many image tasks, such as classification and object detection, it needs more time and computer resources. In this paper, a contour detection algorithm based on H-ELM (Hierarchical Extreme Learning Machine) and DenseCRF (Dense Conditional Random Field) is proposed for Cassini ISS images. The experimental results show that this algorithm’s performance is better than both traditional machine learning methods, such as Support Vector Machine, Extreme Learning Machine and even deep Convolutional Neural Network. The extracted contour is closer to the actual contour. Moreover, it can be trained and tested quickly on the general configuration of PC, and thus can be applied to contour detection for Cassini ISS images.
The Strategies to Increase Customer Value in Agile: A Survey of Brazilian Software Industry

Previous empirical studies have demonstrated the relevance of the concept of customer value to the software industry and the state-of-the-art of strategies to increase customer value in Agile Software Development (ASD) has been recently documented in a Systematic Literature Review (SLR). However, to date, there are no studies on the state of practice for this area focusing on issues similar to those investigated in the SLRs mentioned above. The objective of this article was to report on the state of the practice of adopting strategies to increase customer value in the ASD and to detail these strategies concerning goals pursued, customer value metrics, challenges and usage impacts, and the reasons for non-adoption. A survey was performed using a web questionnaire answered by software professionals who are members of agile teams. Data were collected from 378 respondents from 123 Brazilian software intensive organizations. Prioritizing the development of high-value customer features and validating them frequently (92.3%), as well as focusing on value aspects of the client’s market segment and applying the most relevant agile practices to make them viable (64.8%), appeared to be the strategies most frequently practiced by agile teams. Generally, the experiences of using these strategies appear to be very positive, although challenges have also been identified, such as defining what customer value and customer collaboration is.
Study on the Smart Speaker Security Evaluations and Countermeasures

The smart speaker provides users with useful functions such as music playback and online search with simple operation. However, since smart speakers always wait for the user’s voice, if they are exposed to security threats, serious problems can occur such as eavesdropping and privacy disclosure. Therefore, in order to provide improved security for of all smart speakers, it is necessary to identify potential security threats and systematically investigate vulnerabilities. In this paper, we perform security threat modeling for four products with high market share. STRIDE threat modeling was used to make a checklist for systematic vulnerability checks and the checklist was used to check vulnerabilities of commercial devices. Here, we propose a new method to improve the security of smart speaker through the analysis of the vulnerability check result and the vulnerability of each product.
A novel Hexa data encoding method for 2D image crypto-compression

Abstract We proposed a novel method for 2D image compression-encryption whose quality is demonstrated through accurate 2D image reconstruction at higher compression ratios. The method is based on the DWT-Discrete Wavelet Transform where high frequency sub-bands are connected with a novel Hexadata crypto-compression algorithm at compression stage and a new fast matching search algorithm at decoding stage. The novel crypto-compression method consists of four main steps: 1) A five-level DWT is applied to an image to zoom out the low frequency sub-band and increase the number of high frequency sub-bands to facilitate the compression process; 2) The Hexa data compression algorithm is applied to each high frequency sub-band independently by using five different keys to reduce each sub-band to1/6of its original size; 3) Build a look up table of probability data to enable decoding of the original high frequency sub-bands, and 4) Apply arithmetic coding to the outputs of steps (2) and (3). At decompression stage a fast matching search algorithm is used to reconstruct all high frequency sub-bands. We have tested the technique on 2D images including streaming from videos (YouTube). Results show that the proposed crypto-compression method yields high compression ratios up to 99% with high perceptual quality images.
Complete factorization for poly-phase matrix with linear phase based on semi-rank orthogonal projection matrix

This paper puts forward a new factorization, named the semi-rank factorization, for para-unitary poly-phase matrix with linear phase, and reveals the equivalent relationship between the orthogonal projection matrix and the linear phase, and characterizes the orthogonal projection matrix. The semi-rank factorization has the completeness property no matter for even or odd number of channels. In addition, a decomposition technique for the starting block matrix is developed using the reflection matrix. The semi-rank factorization provides an efficient and compact representation for multi-band filter banks with linear phase. In particular, the complete representation for multi-band symmetry filter banks with odd number of channels is brand new. As a result, the programmable algorithms are obtained for designs of optimal filter banks based on the complete representation. The optimal examples are presented to illustrate the excellent performance on image compression.
Automatic Stop Word Generation for Mining Software Artifact Using Topic Model with Pointwise Mutual Information

Mining software artifacts is a useful way to understand the source code of software projects. Topic modeling in particular has been widely used to discover meaningful information from software artifacts. However, software artifacts are unstructured and contain a mix of textual types within the natural text. These software artifact characteristics worsen the performance of topic modeling. Among several natural language pre-processing tasks, removing stop words to reduce meaningless and uninteresting terms is an efficient way to improve the quality of topic models. Although many approaches are used to generate effective stop words, the lists are outdated or too general to apply to mining software artifacts. In addition, the performance of the topic model is sensitive to the datasets used in the training for each approach. To resolve these problems, we propose an automatic stop word generation approach for topic models of software artifacts. By measuring topic coherence among words in the topic using Pointwise Mutual Information (PMI), we added words with a low PMI score to our stop words list for every topic modeling loop. Through our experiment, we proved that our stop words list results in a higher performance of the topic model than lists from other approaches.
IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering

This paper presents the experiments accomplished as a part of our participation in the MEDIQA challenge, an (Abacha et al., 2019) shared task. We participated in all the three tasks defined in this particular shared task. The tasks are viz. i. Natural Language Inference (NLI) ii. Recognizing Question Entailment(RQE) and their application in medical Question Answering (QA). We submitted runs using multiple deep learning based systems (runs) for each of these three tasks. We submitted five system results in each of the NLI and RQE tasks, and four system results for the QA task. The systems yield encouraging results in all three tasks. The highest performance obtained in NLI, RQE and QA tasks are 81.8%, 53.2%, and 71.7%, respectively.
Innovative Methods in Medical Education in Bulgaria: Video Materials and Serious Games

Modern trends in teaching and learning aim to use effective methods by which students better understand the learning material and are more involved in the learning process. These methods should take into account the ability of today's students to work multi-functional and quickly process large amounts of information. The introduction of modern technology in the classroom is the most important change in education now. This article examines ways to provide specialized medical information in an intriguing interactive way, geared towards effective support for medical students. The paper presents new methods included in the training of medical students in Bulgaria: Video algorithms that show didactically in detail the necessary sequence of actions to perform a particular medical manipulation, including everything they need to know and can make the student achieve this skill. In order to better master the series of actions involved in the manipulation and the acquisition of preliminary experience, it is proposed to include simulation games in the medical training. The use of simulation games in medical training helps to improve the skills of future medics and to build self-confident specialists.
Order Optimal One-Shot Distributed Learning

We consider distributed statistical optimization in one-shot setting, where there are $m$ machines each observing $n$ i.i.d samples. Based on its observed samples, each machine then sends an $O(\log(mn))$-length message to a server, at which a parameter minimizing an expected loss is to be estimated. We propose an algorithm called Multi-Resolution Estimator (MRE) whose expected error is no larger than $\tilde{O}( m^{-1/\max(d,2)} n^{-1/2})$, where $d$ is the dimension of the parameter space. This error bound meets existing lower bounds up to poly-logarithmic factors, and is thereby order optimal. The expected error of MRE, unlike existing algorithms, tends to zero as the number of machines ($m$) goes to infinity, even when the number of samples per machine ($n$) remains upper bounded by a constant. This property of the MRE algorithm makes it applicable in new machine learning paradigms where $m$ is much larger than $n$.
Tackling the Challenge of Computational Identification of Characters in Fictional Narratives

This paper focuses on the computational identification of characters in fictional narratives, regardless of their nature, i.e., either humans, animals or other type of beings. We approach this problem as a supervised binary classification task, whether or not a noun in a narrative -specifically in a fairy taleis classified as a character. A wide range of Machine Learning algorithms and configurations were tested in order to come up with the most appropriate model (or set of models) to successfully fulfil this task. Despite the challenges associated with the character identification in the domain of children stories, the best models obtain an F-Measure above 0.80, proving a good performance and broadly outperforming the baselines.
Feature Selection for Classification with Artificial Bee Colony Programming

Feature selection and classification are the most applied machine learning processes.In the feature selection, it is aimed to find useful properties containing class information by eliminating noisy and unnecessary features in the data sets and facilitating the classifiers.Classification is used to distribute data among the various classes defined on the resulting feature set.In this chapter, artificial bee colony programming (ABCP) is proposed and applied to feature selection for classification problems on four different data sets.The best models are obtained by using the sensitivity fitness function defined according to the total number of classes in the data sets and are compared with the models obtained by genetic programming (GP).The results of the experiments show that the proposed technique is accurate and efficient when compared with GP in terms of critical features selection and classification accuracy on well-known benchmark problems.
Localization of Copy-Move Forgery in Speech Signals Through Watermarking Using DCT-QIM

Digital speech copyright protection and forgery identification are the prevalent issues in our advancing digital world. In speech forgery, voiced part of the speech signal is copied and pasted to a specific location which alters the meaning of the speech signal. Watermarking can be used to safe guard the copyrights of the owner. To detect copy-move forgeries a transform domain watermarking method is proposed. In the proposed method, watermarking is achieved through Discrete Cosine Transform (DCT) and Quantization Index Modulation (QIM) rule. Hash bits are also inserted in watermarked voice segments to detect Copy-Move Forgery (CMF) in speech signals. Proposed method is evaluated on two databases and achieved good imperceptibility. It exhibits robustness in detecting the watermark and forgeries against signal processing attacks such as resample, low-pass filtering, jittering, compression and cropping. The proposed work contributes for forensics analysis in speech signals. This proposed work also compared with the some of the state-of-art methods.
COMPARISON OF THE PRINTING AREAS FOR COMMONLY USED FONT TYPES: EXAMPLE OF GREEN INFORMATION

Some of the fonts used in computer systems are designed to facilitate readability on the computer screen while others are designed to use both readability and print space economically. However, computer users do not have any idea which of the most commonly used fonts are used more economically. In the present study, the printing area of the 10 commonly used fonts were examined with image processing methods and compared among each other. It is seen that the font which uses the least print area from Century Schoolbook, Courier New, Georgia, Times New Roman, Arial, Comic Sans MS, Tahoma, Verdana, Cambria, Calibri fonts is Courier New and then the type that uses the least print area is Times New Roman. It was found that Comic Sans MS and Verdana used almost two times the printing area according to the font Courier New font. With this study, it is thought that computer users will be more careful and more conscious about the font they use for printing.
UWB-GCN: Hardware Acceleration of Graph-Convolution-Network through Runtime Workload Rebalancing.

Deep learning systems have been successfully applied to Euclidean data such as images, video, and audio. In many applications, however, information and their relationships are better expressed with graphs. Graph Convolutional Networks (GCNs) appear to be a promising approach to efficiently learn from graph data structures, having shown advantages in many critical applications. As with other deep learning modalities, hardware acceleration is critical. The challenge is that real-world graphs are often extremely large and unbalanced; this poses significant performance demands and design challenges. In this paper, we propose Autotuning-Workload-Balancing GCN (AWB-GCN) to accelerate GCN inference. To address the issue of workload imbalance in processing real-world graphs, three hardware-based autotuning techniques are proposed: dynamic distribution smoothing, remote switching, and row remapping. In particular, AWB-GCN continuously monitors the sparse graph pattern, dynamically adjusts the workload distribution among a large number of processing elements (up to 4K PEs), and, after converging, reuses the ideal configuration. Evaluation is performed using an Intel D5005 FPGA with five commonly-used datasets. Results show that 4K-PE AWB-GCN can significantly elevate PE utilization by 7.7x on average and demonstrate considerable performance speedups over CPUs (3255x), GPUs (80.3x), and a prior GCN accelerator (5.1x).
A DBSCAN based Algorithm for Ship Spot Area Detection in AIS Trajectory Data

The big data acquired by AIS system contains abundant maritime traffic information. With the wide application of data mining in various fields in recent years, the mining on AIS data has draw attention of related researchers. Based on the ship AIS location data, this paper studies the relevant spot area detection algorithm. Firstly, the sample data are pre-processed from the original data, and the residence point of each ship is identified according to the ship speed and course change. Then a DBSCAN based clustering algorithm is used to cluster several latitude and longitude lattice, that is spot areas. The experiments on real AIS data sets shows that the algorithm is efficient and correct.
Cross-VM Cache Timing Attacks on Virtualized Network Functions

Network function virtualization (NFV) achieves the flexibility of network service provisioning by using virtualization technology. However, NFV is exposed to a serious security threat known as cross-VM cache timing attacks. In this letter, we look into real security impacts on network virtualization. Specifically, we present two kinds of practical cache timing attacks on virtualized firewalls and routers. We also propose some countermeasures to mitigate such attacks on virtualized network functions.
$X$-secure $T$-private Information Retrieval from MDS Coded Storage with Byzantine and Unresponsive Servers

The problem of $X$-secure $T$-private information retrieval from MDS coded storage is studied in this paper, where the user wishes to privately retrieve one out of $K$ independent messages that are distributed over $N$ servers according to an MDS code. It is guaranteed that any group of up to $X$ colluding servers learn nothing about the messages and that any group of up to $T$ colluding servers learn nothing about the identity of desired message. A lower bound of achievable rates is proved by presenting a novel scheme based on \emph{cross-subspace alignment} and a successive decoding with interference cancellation strategy. For large number of messages $(K\rightarrow\infty)$ the achieved rate, which we conjecture to be optimal, improves upon the best known rates previously reported in the literature by Raviv and Karpuk, and generalizes an achievable rate for MDS-TPIR previously found by Freij-Hollanti et al. that is also conjectured to be asymptotically optimal. The setting is then expanded to allow unresponsive and Byzantine servers. Finally, the scheme is applied to find a new lower convex hull of (download, upload) pairs of secure and private distributed matrix multiplication that generalizes, and in certain asymptotic settings strictly improves upon the best known previous results.
ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge

We report on our system for textual inference and question entailment in the medical domain for the ACL BioNLP 2019 Shared Task, MEDIQA.Textual inference is the task of finding the semantic relationships between pairs of text.Question entailment involves identifying pairs of questions which have similar semantic content.To improve upon medical natural language inference and question entailment approaches to further medical question answering, we propose a system that incorporates open-domain and biomedical domain approaches to improve semantic understanding and ambiguity resolution.Our models achieve 80% accuracy on medical natural language inference (6.5% absolute improvement over the original baseline), 48.9% accuracy on recognising medical question entailment, 0.248 Spearman's rho for question answering ranking and 68.6% accuracy for question answering classification.