Reviewerly/exHarmony huggingface dataset:
On the Distribution of Deep Clausal Embeddings: A Large Cross-linguistic Study

Embedding a clause inside another ("the girl [who likes cars [that run fast]] has arrived") is a fundamental resource that has been argued to be a key driver of linguistic expressiveness.As such, it plays a central role in fundamental debates on what makes human language unique, and how they might have evolved.Empirical evidence on the prevalence and the limits of embeddings has however been based on either laboratory setups or corpus data of relatively limited size.We introduce here a collection of large, dependencyparsed written corpora in 17 languages, that allow us, for the first time, to capture clausal embedding through dependency graphs and assess their distribution.Our results indicate that there is no evidence for hard constraints on embedding depth: the tail of depth distributions is heavy.Moreover, although deeply embedded clauses tend to be shorter, suggesting processing load issues, complex sentences with many embeddings do not display a bias towards less deep embeddings.Taken together, the results suggest that deep embeddings are not disfavored in written language.More generally, our study illustrates how resources and methods from latest-generation big-data NLP can provide new perspectives on fundamental questions in theoretical linguistics.
Leader-follower formation control of multiple unmanned aerial vehicles for omnidirectional patrolling

Unmanned aerial vehicles (UAVs) are applied to various applications due to its maneuverable flight. Formation flight composed of multiple UAVs has obvious advantages in accomplishing effectively and speedily a task. We propose a formation control of multiple UAVs for omnidirectional patrolling. The formation flight adopts a leader-follower structure. Assumed that motion capture system detects the 3D configuration of formation flight, our method stably and safely controls the formation based on geometric constraint in 3D space. When we fly the leader UAV by remote control according to given flight trajectory, the positions and orientations of follower UAVs are automatically adjusted by our control system so that the leader-follower structure retains the geometric formation. We demonstrate that our proposed method offers excellent formation flight to provide practical omnidirectional patrolling.
Sparse Representation and Collaborative Representation? Both Help Image Classification

Image classification has attracted more and more attention. During the past decades, image classification has shown growing interest in representation-based classification methods, such as sparse representation-based classification and collaborative representation-based classification. However, the available representation-based methods still suffer from some problems. Especially, most methods only consider the shared representation of a test image. In this paper, we propose an elastic-net regularized regression algorithm (ENRR) for image classification. Specifically, our proposed method combines shared sparse representation with class specific collaborative representation when representing the test sample. Moreover, we extend the proposed ENRR to arbitrary kernel space to achieve better classification performance due to specificities and complexities of original images. The extensive experiments on face recognition datasets, handwritten recognition datasets, and remote sensing image datasets clearly demonstrate that the proposed ENRR outperforms several conventional methods in classification accuracy.
Efficient Hardware/Software Co-design for NTRU

The fast development of quantum computers represents a risk for secure communications. Current traditional public-key cryptography will not withstand attacks performed on quantum computers. In order to prepare for such a quantum threat, electronic systems must integrate efficient and secure post-quantum cryptography which is able to meet the different application requirements and to resist implementation attacks. The NTRU cryptosystem is one of the main candidates for practical implementations of post-quantum public-key cryptography. The standardized version of NTRU (IEEE-1363.1) provides security against a large range of attacks through a special padding scheme. So far, NTRU hardware and software solutions have been proposed. However, the hardware solutions do not include the padding scheme or they use optimized architectures that lead to a degradation of the security level. In addition, NTRU software implementations are flexible but most of the time present a low performance when compared to hardware solutions. In this work, for the first time, we present a hardware/software co-design approach compliant with the IEEE-1363.1 standard. Our solution takes advantage of the flexibility of the software NTRU implementation and the speedup due to the hardware accelerator specially designed in this work. Furthermore, we provide a refined security reduction analysis of an optimized NTRU hardware implementation presented in a previous work.
Hy-SAIL: Hyper-Scalability, Availability and Integrity Layer for Cloud Storage Systems

Cloud storage has gained increasing attention from the industry and research communities with concerns about data stored in cloud computing environments, despite its many advantages. The reasons come from economic viability to the latency along with behavioral changes that may affect the applications that use data stored remotely. Among those challenges, there is the problem of ensuring the integrity and retrievability of users' data in the cloud. Several schemas have been proposed: proof of data possession (PDP), proof of retrievability (PoR), and proof of ownership (PoW) that differ on the approach to guarantee the effective retrieval of data stored remotely. In this paper, a novel PoR protocol is proposed: hyper-scalability, availability, and integrity layer (Hy-SAIL), where a new PoR scheme adds higher availability to data stored and a flexible manner to perform integrity checks. It is demonstrated that Hy-SAIL leads to an efficient and scalable cryptographic system that meets near-optimal bounds in terms of communication and storage complexities. It is also proposed a new adversarial model that aggregates the main functionalities of a realistic adversary in cloud computing environments. Hy-SAIL operates on data that are not affected by any kind of incremental change or update, which is the characteristic of various file types stored in clouds, such as stored video and audio streams. When compared to other systems, such as HAIL, Hy-SAIL is more scalable and efficient. The results collected with an unoptimized implementation of Hy-SAIL point to a better perspective than other approaches.
A Pilot Study on Architecture and Vulnerabilities: Lessons Learned

Finding and preventing software vulnerabilities re-quires analyzing their root causes. Numerous cases of vulnerabilities are caused by architectural decisions, or lack thereof. Solutions for issues such as user authentication are crafted at the architectural level, and they are linked to many instances of vulnerabilities. Despite this, there are few studies conducted that try to explore the connection between the architectures of systems, their evolution and vulnerabilities. Moreover, there is a gap in empirical knowledge on how to conduct such studies. In this paper, we aim to close that gap by providing guidelines on how to conduct studies that focus on architecture and vulnerabilities by proposing architecture recovery techniques, metrics to represent architecture change, generating representative datasets and ways to interpret results. To do this, we conducted a pilot study with Tomcat and several of its vulnerabilities.
Saagie at Semeval-2019 Task 5: From Universal Text Embeddings and Classical Features to Domain-specific Text Classification

This paper describes our contribution to Se-mEval 2019 Task 5: Hateval.We propose to investigate how domain-specific text classification task can benefit from pretrained state of the art language models and how they can be combined with classical handcrafted features.For this purpose, we propose an approach based on a feature-level Meta-Embedding to let the model choose which features to keep and how to use them.
A Secure Data Sharing Scheme with Designated Server

The cloud-assisted Internet of Things (CIoT) is booming, which utilizes powerful data processing capabilities of the cloud platform to solve massive Internet of Things (IoT) data. However, the CIoT faces new security challenges, such as the confidentiality of the outsourced data. Data encryption is a fundamental technique that can guarantee the confidentiality of outsourced data, but it limits target encrypted data retrieval from cloud platform. Public key encryption with keyword search (PEKS) provides a promising solution to address this problem. In PEKS, a cloud server can be authorized to search the keyword in encrypted documents and retrieve associated encrypted documents for the receiver. However, most existing PEKS schemes merely focus on keyword search function while ignoring the associated documents encryption/decryption function. Thus, in practice, a PEKS scheme must cooperate with another separated public key encryption (PKE) scheme to fulfill a completely secure data sharing scheme. To address this problem, in this paper, we propose a secure data sharing scheme with designated server that combines PKE scheme with PEKS scheme, which provides both keyword search and documents encryption/decryption functions. Furthermore, only the designated server can search the keyword via encrypted documents for enhanced security in our work. Moreover, our scheme also satisfies the public verifiability of search results, which includes both keywords and documents ciphertexts’ correctness and integrity. As to the security, our scheme provides stronger indistinguishability security of document and keyword in the proposed security model.
Number systems over general orders

Let $$\mathcal{O}$$ be an order, that is a commutative ring with 1 whose additive structure is a free $$\mathbb{Z}$$ -module of finite rank. A generalized number system (GNS for short) over $$\mathcal{O}$$ is a pair $$(p, \mathcal{D})$$ where $$p \in \mathcal{O}[x]$$ is monic with constant term p(0) not a zero divisor of $$\mathcal{O}$$ , and where $$\mathcal{D}$$ is a complete residue system modulo p(0) in $$\mathcal{O}$$ containing 0. We say that $$(p, \mathcal{D})$$ is a GNS over $$\mathcal{O}$$ with the finiteness property if all elements of $$\mathcal{O}[x]/(p)$$ have a representative in $$\mathcal{D}[x]$$ (the polynomials with coefficients in $$\mathcal{D}$$ ). Our purpose is to extend several of the results from a previous paper of Pethő and Thuswaldner, where GNS over orders of number fields were considered. We prove that it is algorithmically decidable whether or not for a given order $$\mathcal{O}$$ and GNS $$(p, \mathcal{D})$$ over $$\mathcal{O}$$ , the pair $$(p, \mathcal{D})$$ admits the finiteness property. This is closely related to work of Vince on matrix number systems. Let $$\mathcal{F}$$ be a fundamental domain for $$\mathcal{O}{\otimes}_\mathbb{Z} \mathbb{R}/\mathcal{O}\, {\rm and}\, p \in \mathcal{O}[X]$$ a monic polynomial. For $$\alpha \in \mathcal{O}$$ , define $$p_{\alpha}(x) := p(x+\alpha) {\rm and} \mathcal{D}_{\mathcal{F},p(\alpha)} := p(\alpha)\mathcal{F} \bigcap \mathcal{O}$$ . Under mild conditions we show that the pairs $$(p_{\alpha},\mathcal{D}_{\mathcal{F},p(\alpha)})$$ are GNS over $$\mathcal{O}$$ with finiteness property provided $$\alpha \in \mathcal{O}$$ in some sense approximates a sufficiently large positive rational integer. In the opposite direction we prove under different conditions that $$(p_{-m},\mathcal{D}_{\mathcal{F},p(-m)})$$ does not have the finiteness property for each large enough positive rational integer m. We obtain important relations between power integral bases of étale orders and GNS over $$\mathbb{Z}$$ . Their proofs depend on some general effective finiteness results of Evertse and Győry on monogenic étale orders.
Malay Named Entity Recognition Using Rule Based Approach

Named Entity Recognition (NER) research based on rule is widely investigated and is used in various languages mainly English. However, the English NER rules are different with Malay language due to different morphology. Some of challenging issue in Malay is cross reference between named entities, and entity repetition. This paper proposes to solve the issues in Malay NER. This study starts by providing Malay online news corpus, gazeteer development, rules development and evaluation. This study focus on nine name entities i.e person, organization, position, date, time, currency, measurement and percentage. Overall the experimental result shows 90.23% precision, 92.13% recall and 91.05% f-measure. The outcome from this research is expected to help other researchers in implementing the Malay NER using rule based approach through the addition of new rules to achieve higher accuracy.
An Evaluation Method of the Influence of Icon Shape Complexity on Visual Search Based on Eye Tracking

In order to evaluate the icon shape complexity from the aspects of human visual cognition, the present paper summarizes two factors of icon shape affecting visual cognition, and establishes a new formula which can quantify the complexity of two-dimensional icon shape. The outcome of test applying the formula indicates the method has ability to quantify the shape complexity of different icons. Meanwhile, the paper studies the influence of different levels of shape complexity of icon on visual search through eye tracking experiments. The results of the eye tracking experiment show that the level of icon shape complexity should be in a reasonable interval which measured by the formula provided in this paper. Because of the ability to quantify the complexity of the icon,the method can improve the design and the application of icon according to visual cognition.
Enhanced Bacterial Foraging Optimization Based on Progressive Exploitation Toward Local Optimum and Adaptive Raid

Since the bacterial foraging optimization algorithm (BFO) was proposed, many variants about it have been designed in order to improve the performance and applied in different fields. Even so, people are constantly probing new methods designed to enhance the performance of BFO, so as to form new variants with superior performance. As a new variant of original BFO, bacterial foraging optimization using strategies of progressive exploitation approximating local optimum and adaptive raid (BFO-DX) was proposed. On the one hand, the strategy of progressive exploration approximating local optimum (PELO) was introduced into BFO to enhance its ability of exploitation in a local space, which enables the algorithm to find the global optima better possibly. On the other hand, the strategy of the adaptive raid for the leader (ARL) was adopted to boost the speed of convergence by strengthening its exploration capacity. The numerical experiments indicates that the BFO-DX possesses better ability of finding global optima, better stability and other acceptable terms such as iteration and running time compared with classical genetic algorithm (GA), particle swarm algorithm (PSO), and conventional bacterial foraging optimization (BFO).
Determinants of key facets of job satisfaction in the banking sector: applying SMART PLS and artificial neural networks

Job satisfaction is closely associated with life satisfaction and important workplace behaviours (Judge and Kammeyer-Mueller, 2012). Minnesota satisfaction questionnaire (MSQ) or job description index (JDI) (Castanheira, 2014; Saane et al., 2003) are widely used to measure job satisfaction. However, since these instruments cannot be generalised to all industrial sectors (Khalilzadeh et al., 2013), this two-part study attempted to manifest the facets of job satisfaction of public sector bank employees in India through factor identification using systematic literature review, exploratory factor analysis and statistical mode, followed by partial least squares (PLS) modelling of the problem using SmartPLS and comparing the outcome to artificial neural networks (ANN) output. The study has important implications for policy recommendations to banking sector to promote satisfaction among employees. Secondly, it is able to demonstrate which of the two techniques is better - PLS or ANN.
PyLZJD: An Easy to Use Tool for Machine Learning

As Machine Learning (ML) becomes more widely known and popular, so too does the desire for new users from other backgrounds to apply ML techniques to their own domains.A difficult prerequisite that often confounds new users is the feature creation and engineering process.This is especially true when users attempt to apply ML to domains that have not historically received attention from the ML community (e.g., outside of text, images, and audio).The Lempel Ziv Jaccard Distance (LZJD) is a compression based technique that can be used for many machine learning tasks.Because of its compression background, users do not need to specify any feature extraction, making it easy to apply to new domains.We introduce PyLZJD, a library that implements LZJD in a manner meant to be easy to use and apply for novice practitioners.We will discuss the intuition and high-level mechanics behind LZJD, followed by examples of how to use it on problems of disparate data types.
GazeLens: Guiding Attention to Improve Gaze Interpretation in Hub-Satellite Collaboration

In hub-satellite collaboration using video, interpreting gaze direction is critical for communication between hub coworkers sitting around a table and their remote satellite colleague. However, 2D video distorts images and makes this interpretation inaccurate. We present GazeLens, a video conferencing system that improves hub coworkers’ ability to interpret the satellite worker’s gaze. A $$360^{\circ }$$ camera captures the hub coworkers and a ceiling camera captures artifacts on the hub table. The system combines these two video feeds in an interface. Lens widgets strategically guide the satellite worker’s attention toward specific areas of her/his screen allow hub coworkers to clearly interpret her/his gaze direction. Our evaluation shows that GazeLens (1) increases hub coworkers’ overall gaze interpretation accuracy by $$25.8\%$$ in comparison to a conventional video conferencing system, (2) especially for physical artifacts on the hub table, and (3) improves hub coworkers’ ability to distinguish between gazes toward people and artifacts. We discuss how screen space can be leveraged to improve gaze interpretation.
Fixed-target runtime analysis of the (1 + 1) EA with resampling

We conduct a fixed-target runtime analysis of (1 + 1) EA with resampling on the OneMax and BinVal problems. For OneMax, our fixed-target upper bound refines the previously known bound. Our fixed-target lower bound for OneMax is the first of this kind. We also consider linear functions and show that the traditional approaches via drift analysis cannot easily be extended to yield fixed-target results. However, for the particular case of BinVal, a relatively precise fixed-target bound is obtained.
Quality-based Regularization for Iterative Deep Image Segmentation

Traditional image segmentation algorithms operate by iteratively working over an image, as if refining a segmentation until a stopping criterion is met. Deep learning has replaced traditional approaches, achieving state-of-the-art performance in many problems, one of them being image segmentation. However, the concept of segmentation refinement is not present anymore, since usually the images are segmented in a single step. This work focuses on the refinement of image segmentations using deep convolutional neural networks, with the addition of a quality prediction output. The output from a state-of-the-art base segmenter is refined, simultaneously improving it and trying to predict its quality. We show that the quality concept can be used as a regularizer while training a network for direct segmentation refinement.
Precise Geometric Correction and Robust Mosaicking for Airborne Lightweight Optical Butting Infrared Imaging System

In view of the urgent need for high-resolution and high-precision infrared image data for geological environmental monitoring, we have developed a lightweight optical butting infrared imaging system for unmanned aerial vehicles (UAVs) to obtain infrared image data. In this paper, we proposed a robust mosaicking method combination of as-projective-as-possible warps and similarity transformation, which uses multiple images to generate a wide viewing field panorama. It is developed based on an analysis of projective transformation and similarity transformation. The proposed method simultaneously takes into consideration the flexibility of alignment and the preservation of image shapes. On the overlapping regions, it performs as-projective-as-possible warps. On the non-overlapping regions, it gradually becomes a similarity warp. The results show that the proposed method weakens the “ghosting,” shape distortion, and area distortion.
The Impact of Targeted Data Management Training for Field Research Projects – A Case Study

We present a joint effort at Virginia Tech between a research group in the Department of Fish and Wildlife Conservation and Data Services in the University Libraries to improve data management for long-term ecological field research projects in the Florida Panhandle. Consultative research data management support from Data Services in the University Libraries played an integral role in the development of the training curriculum. Emphasizing the importance of data quality to the field workers at the beginning of this training curriculum was a vital part of its success. Also critical for success was the research group’s investment of time and effort to work with field workers and improve data management systems. We compare this case study to three others in the literature to compare and contrast data management processes and procedures. This case study serves as one example of how targeted training and efforts in data and project management for a research project can lead to substantial improvements in research data quality.
An overview on skew constacyclic codes and their subclass of LCD codes

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Inflection-Tolerant Ontology-Based Named Entity Recognition for Real-Time Applications

A growing number of applications users daily interact with have to operate in (near) real-time: chatbots, digital companions, knowledge work support systems - just to name a few. To perform the services desired by the user, these systems have to analyze user activity logs or explicit user input extremely fast. In particular, text content (e.g. in form of text snippets) needs to be processed in an information extraction task. Regarding the aforementioned temporal requirements, this has to be accomplished in just a few milliseconds, which limits the number of methods that can be applied. Practically, only very fast methods remain, which on the other hand deliver worse results than slower but more sophisticated Natural Language Processing (NLP) pipelines.In this paper, we investigate and propose methods for real-time capable Named Entity Recognition (NER). As a first improvement step, we address word variations induced by inflection, for example present in the German language. Our approach is ontology-based and makes use of several language information sources like Wiktionary. We evaluated it using the German Wikipedia (about 9.4B characters), for which the whole NER process took considerably less than an hour. Since precision and recall are higher than with comparably fast methods, we conclude that the quality gap between high speed methods and sophisticated NLP pipelines can be narrowed a bit more without losing real-time capable runtime performance.