Reviewerly/exHarmony huggingface dataset:
Comparing supervised learning algorithms for Spatial Nominal Entity recognition

Abstract. Discourse may contain both named and nominal entities. Most common nouns or nominal mentions in natural language do not have a single, simple meaning but rather a number of related meanings. This form of ambiguity led to the development of a task in natural language processing known as Word Sense Disambiguation. Recognition and categorisation of named and nominal entities is an essential step for Word Sense Disambiguation methods. Up to now, named entity recognition and categorisation systems mainly focused on the annotation, categorisation and identification of named entities. This paper focuses on the annotation and the identification of spatial nominal entities. We explore the combination of Transfer Learning principle and supervised learning algorithms, in order to build a system to detect spatial nominal entities. For this purpose, different supervised learning algorithms are evaluated with three different context sizes on two manually annotated datasets built from Wikipedia articles and hiking description texts. The studied algorithms have been selected for one or more of their specific properties potentially useful in solving our problem. The results of the first phase of experiments reveal that the selected algorithms have similar performances in terms of ability to detect spatial nominal entities. The study also confirms the importance of the size of the window to describe the context, when word-embedding principle is used to represent the semantics of each word.
A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.
Local Sensitive Dual Concept Factorization for Unsupervised Feature Selection

In this paper, we present a novel Local Sensitive Dual Concept Learning (LSDCL) method for the task of unsupervised feature selection. We first reconstruct the original data matrix by the proposed dual concept learning model, which inherits the merit of co-clustering based dual learning mechanism for more interpretable and compact data reconstruction. We then adopt the local sensitive loss function, which emphasizes more on most similar pairs with small errors to better characterize the local structure of data. In this way, our method can select features with better clustering results by more compact data reconstruction and more faithful local structure preserving. An iterative algorithm with convergence guarantee is also developed to find the optimal solution. We fully investigate the performance improvement by the newly developed terms, individually and simultaneously. Extensive experiments on benchmark datasets further show that LSDCL outperforms many state-of-the-art unsupervised feature selection algorithms.
Towards the entropy-limit conjecture

The maximum entropy principle is widely used to determine non-committal probabilities on a finite domain, subject to a set of constraints, but its application to continuous domains is notoriously problematic. This paper concerns an intermediate case, where the domain is a first-order predicate language. Two strategies have been put forward for applying the maximum entropy principle on such a domain: (i) applying it to finite sublanguages and taking the pointwise limit of the resulting probabilities as the size n of the sublanguage increases; (ii) selecting a probability function on the language as a whole whose entropy on finite sublanguages of size n is not dominated by that of any other probability function for sufficiently large n. The entropy-limit conjecture says that, where these two approaches yield determinate probabilities, the two methods yield the same probabilities. If this conjecture is found to be true, it would provide a boost to the project of seeking a single canonical inductive logic—a project which faltered when Carnap's attempts in this direction succeeded only in determining a continuum of inductive methods. The truth of the conjecture would also boost the project of providing a canonical characterisation of normal or default models of first-order theories. Hitherto, the entropy-limit conjecture has been verified for languages which contain only unary predicate symbols and also for the case in which the constraints can be captured by a categorical statement of Σ1 quantifier complexity. This paper shows that the entropy-limit conjecture also holds for categorical statements of Π1 complexity, for various non-categorical constraints, and in certain other general situations.
Reopening libraries in COVID 19 pandemic: challenges and recommendations

The COVID 19 has forced to 150 countries to close the school libraries, according to UNECSO 84 countries have closed their public libraries and 98 countries have closed their national libraries.The college and the university libraries are also compelled to close libraries.India has ranking second largest county in term of population in the World after China.As a precautionary measure, The Union Government of India has declared the national level closed down after 22 nd March, 2020 to break the chain of the COVID 19 pandemic.But the day is not far away to start the process of removing the restrictions step by step in phased manner.Hence, we the library professionals should also be prepare to reopen the libraries along with the COVID 19.We have a huge responsibility to protect the clienteles, staff and the library resources in such a pandemic situation.While reopening the library after the lock down we need to take precautionary measures to slow down and prevent the COVID 19.The author has mentioned the challenges and suggested recommendations of the libraries during post lock down period.The author has also suggesting to revise the library rules based on the recommendations and the level of the intensity of the pandemic.
A CENTER MANIFOLD THEORY-BASED APPROACH TO THE STABILITY ANALYSIS OF STATE FEEDBACK TAKAGI-SUGENO-KANG FUZZY CONTROL SYSTEMS

The aim of this paper is to propose a stability analysis approach based on the application of the center manifold theory and applied to state feedback Takagi-Sugeno-Kang fuzzy control systems. The approach is built upon a similar approach developed for Mamdani fuzzy controllers. It starts with a linearized mathematical model of the process that is accepted to belong to the family of single input second-order nonlinear systems which are linear with respect to the control signal. In addition, smooth right-hand terms of the state-space equations that model the processes are assumed. The paper includes the validation of the approach by application to stable state feedback Takagi-Sugeno-Kang fuzzy control system for the position control of an electro-hydraulic servo-system.
Study on Feature Complementarity of Statistics, Energy, and Principal Information for Spoofing Detection

Conventional speaker verification systems become frail or incompetent while facing attack from spoofed speech.Presently many anti-spoofing countermeasures have been studied for automatic speaker verification.It has been known that the salient feature is of a more important role rather than the selection of classifiers in the current research field of spoofing detection.The effectiveness of constant-Q transform (CQT) has been demonstrated for anti-spoofing feature analysis in many research literatures on automatic speaker verification.On the basis of CQT-based information sub-features, i.e. octave-band principal information (OPI), full-band principal information (FPI), short-term spectral statistics information (STSSI) and magnitude-phase energy information (MPEI), three concatenated features are proposed by investigating their information complementarity in this paper, the first one is constant-Q statisticsplus-principal information coefficients (CQSPIC) by combining OPI, FPI and STSSI; the second one is constant-Q energy-plus-principal information coefficients (CQEPIC) by combining OPI, FPI and MPEI and the third one is constant-Q energy-statistics-principal information coefficients (CESPIC) by combining OPI, FPI, MPEI and STSSI.In this paper, we set up deep neural network (DNN) classifiers for evaluation of the proposed features.Experiments show that the proposed features can outperform some commonly used features meanwhile the proposed systems give better or comparable performance comparing with state-of-the-art performance on ASVspoof 2019 logical access and physical access corpus.
Background Subtraction Based on Principal Motion for a Freely Moving Camera

As a fundamental research topic of computer vision, background subtraction technology can be implemented in many applications. This problem becomes even more challenging once the videos are obtained with a moving camera. To solve the challenging task, we analyze the principal motion of pixels for subtracting background in videos obtained from freely moving cameras. In particular, the optical flow is captured for the representation of motion for pixels. Moreover, assuming that the background motion should be the principal part, Robust Principal Components Analysis (RPCA) is utilized for subtracting the background of videos obtained from freely moving cameras, in which both angle and magnitude of the optical flow are utilized for the analysis of motion. Finally, super-pixels are utilized to compensate for the defects produced by inaccuracies in optical flow, where a double threshold strategy is proposed to integrate the foreground results captured from the angle as well as the magnitude. Experiments based on several videos from standard benchmark datasets illustrate that the proposed method achieves promising performance compared to the state-of-the-art.
The Method for Extracting New Login Sentiment Words from Chinese Micro-Blog Basedf on Improved Mutual Information

The current method of extracting new login sentiment words not only ignores the diversity of patterns constituted by new multi-character words (the number of words is greater than two), but also disregards the influence of o... | Find, read and cite all the research you need on Tech Science Press
Analysis of 3D segmented anatomical districts through grey-levels mapping

Nowadays, image processing and 3D shape analysis are an integral part of clinical practice and have the potentiality to support clinicians with advanced analysis and visualization techniques. The main contribution of the paper is the integration of these two approaches in order to increase the amount of information available and, thus, allow a more accurate analysis of each patient. Given a segmented anatomical district, we propose a novel mapping of volumetric data onto the segmented surface. The grey levels of the image voxels are mapped through a volume-surface correspondence map, which defines a grey level texture on the segmented surface. The resulting texture mapping is coherent to the local morphology of the segmented anatomical structure and provides an enhanced visual representation of the anatomical district. The integration of volume-based and surface-based information in a unique 3D representation also supports the identification and characterization of morphological landmarks and pathology evaluations.
A Secure DICOM Image Encryption Scheme Based on ECC, Linear Cryptography and Chaos

Nowadays, medical image information as a part of Telecare Medical Information Systems (TMIS) plays an important role in medicine by helping healthcare actors to deliver efficient diagnosis. Therefore, the security and privacy of the broadcasted medical images must be addressed seriously, regarding the number of malicious attacks launched by cybercriminals. In this paper, a new medical image encryption scheme that combines elliptic curve cryptography (ECC), the classical Hill cipher, the Arnold's Cat map (ACM) and linear congruence generator (LCG) is proposed. Confusion and diffusion properties are introduced on the encrypted medical images through Arnold's Cat map and LCG. Compared with the state-of-the art techniques, the suggested encryption scheme is found to be robust against various attacks and provides better security features.
Deep Learning Side-Channel Analysis on Large-Scale Traces

Code polymorphism is an approach to efficiently address the challenge of automatically applying the hiding of sensitive information leakage, as a way to protect cryptographic primitives against side-channel attacks (SCA) involving layman adversaries. Yet, recent improvements in SCA, involving more powerful threat models, e.g., using deep learning, emphasized the weaknesses of some hiding counter-measures. This raises two questions. On the one hand, the security of code polymorphism against more powerful attackers, which has never been addressed so far, might be affected. On the other hand, using deep learning SCA on code polymorphism would require to scale the state-of-the-art models to much larger traces than considered so far in the literature. Such a case typically occurs with code polymorphism due to the unknown precise location of the leakage from one execution to another. We tackle those questions through the evaluation of two polymorphic implementations of AES, similar to the ones used in a recent paper published in TACO 2019 [6]. We show on our analysis how to efficiently adapt deep learning models used in SCA to scale on traces $$32$$ folds larger than what has been done so far in the literature. Our results show that the targeted polymorphic implementations are broken within $$20$$ queries with the most powerful threat models involving deep learning, whereas $$100,000$$ queries would not be sufficient to succeed the attacks previously investigated against code polymorphism. As a consequence, this paper pushes towards the search of new polymorphic implementations secured against state-of-the-art attacks, which currently remains to be found.
Fast and efficient reconstruction of digitized frescoes

Virtually recomposing destroyed frescoes is of great importance for heritage conservation. Given a digitized fresco image and a digitized set of fragments, such a problem is challenging due to the potentially large number of fragments, their irregular shape, uniqueness and non-overlapping constraints, the possible absence of fragments and the possible presence of small, homogeneous, eroded and/or spurious fragments. To cope with these specific features, we propose in this paper a fast and efficient non-dense approach benefiting from previous developments in pattern matching. Preliminary experiments led on simulations exhibit a mean accuracy above 90% with a mean translation error of less than 4 pixels and a mean orientation error of about 1 degree. An analysis of fresco and fragment features impacting the algorithm is also provided. Compared to a dense approach and the recent DeepMatch approach, the proposed one remains competitive both in running time and accuracy.
Work-in-progress—ARETE - An Interactive Educational System using Augmented Reality

ARETE (Augmented Reality Interactive Educational System) is a European project, which aims to develop an interactive toolkit for Augmented Reality (AR) content. In this work in progress paper, we describe the system which is currently being developed. The ARETE system follows human-centered interaction design practices and has a strong focus in interactive, multi-user and multi-lingual technologies. On completion, the system will be evaluated across three pilot studies. The three pilots will involve more than 3000 students across ten European countries and they will assess the impact of usage of the ARETE system and the educational value of AR for English literacy skills, STEM (Science, Technology, Engineering and Math) skills and in the implementation of Positive Behaviour Support in Schools (PBIS). ARETE will also be involved in the implementation of new standards for the creation of cross platform content and services as well as the creation of learning experience data repositories.
A person living with dementia learning to navigate an iPad: a case study

This study challenges the notion that people living with dementia are unable to achieve novel learning without focussed intervention techniques. The purpose of this study is to explore how a woman living with dementia (Alzheimer's disease) learns to use a tablet computer with support from communicative partners.The study is based on video recordings and the theoretical framework of learning as changing participation in joint activities. Quantitative and qualitative focus is on changes in the interactional organization over the course of six weeks in the activity of using an augmentative and alternative communication application.Over time, the participant living with dementia, relies less on the expertise and explicit instructions of her communicative partners when navigating the application, and more on the immediate feedback provided by the tablet computer.The findings suggest that novel learning still is possible for people living with dementia, even without the implementation of focussed interventions. This study further emphasizes the procedural nature of learning for people living with dementia as the woman's embodied actions were carried out in an increasingly more direct fashion.Implications for rehabilitationFor people living with dementia, learning in everyday activities is facilitated by repeated exposure to the activity and the scaffolding practices of a more experienced communicative partner.In instances of novel learning, one should not underestimate the importance of embodied engagement from people living dementia.Care professionals need not to worry about exposing people living with dementia to unfamiliar activities.
VECTORIZED NEURAL KEY EXCHANGE USING TREE PARITY MACHINE

The communication boom in the past few decades has resulted in a large flow of data. This entails the need for having high security and privacy with regards to data confidentiality and authenticity. One method of doing so is by utilizing a synchronized key. In the domain of cryptography there exist several methods of key generation, one such method is the tree parity machine (TPM) involving neural cryptography. In our paper, we provide a novel vectorized TPM (vTPM) in order to develop a key. We have also provided a system to detect any unwanted listeners, as one of the weakness of the TPM algorithm is Man in the middle attacks. We have further utilized this key for authentication between a sender and a receiver. The authentication is carried out by means of H-MAC with the SHA-512 hashing mechanism. Finally, a comparison is drawn out between the serial and vector implementation of the Tree Parity Machine.
UX-Painter: An Approach to Explore Interaction Fixes in the Browser

Usability and user interaction improvement is a central task in web development to guarantee the success of a web application. However, designers are barely able to keep up with the current development cycle because their practices are too costly, while interaction issues accumulate in applications that end-users keep suffering. In this work, we propose a method for designers to rapidly explore solutions through visual programming to the interaction problems of an application under development, even when it has been already deployed. The method is realized by a tool called UX-Painter, an exploratory tool for designers to apply quick fixes to interaction issues at the client-side of a web application without the need of any script programming knowledge. The palette of available fixes in UX-Painter are client-side web refactorings, i.e., changes to web page elements that solve specific user interaction problems without changing the underlying functionality. UX-Painter allows designers to quickly set up new versions of a web application by combining refactorings to create alternative designs for user testing or an inspection review. UX-Painter also provides the means to communicate design improvements, as a sequence of refactorings with clear semantics. We show the feedback provided by interviews with designers about UX-Painter's functionality and the results of a user test about its usability.
Artificial classification of cervical squamous lesions in ThinPrep cytologic tests using a deep convolutional neural network

The diagnosis of squamous cell carcinoma requires the accurate classification of cervical squamous lesions in the ThinPrep cytologic test (TCT). It primarily relies on a pathologist's interpretation under a microscope. Deep convolutional neural networks (DCNN) have played an increasingly important role in digital pathology. However, they have not been applied to diverse datasets and externally validated. In the present study, a DCNN model based on VGG16 and an ensemble training strategy (ETS) based on 5-fold cross-validation was employed to automatically classify normal and abnormal cervical squamous cells from a multi-center dataset. First, we collected a dataset comprising 82 TCT samples from four hospitals and fine-tuned our model twice on the dataset with and without the ETS. Then, we compared the classifications obtained from the models with those provided by two skilled pathologists to discriminate the performance of the models in terms of classification accuracy and efficiency. Finally, paired sample t-tests were used to validate the consistency between the classification provided by the proposed methods and that of the pathologists. The results showed that ETS slightly, though not significantly, improved the classification accuracy compared with that of the pathologists: P0=0.387>0.05 (DCNN without ETS vs. DCNN with ETS), P1=0.771>0.05 (DCNN with ETS vs. pathologist 1), P2=0.489>0.05 (DCNN with ETS vs. pathologist 2). The DCNN model was almost 6-fold faster than that of the pathologists. The accuracy of our automated scheme was similar to that of the pathologists, but a higher efficiency in the accurate identification of cervical squamous lesions was provided by the scheme. This result allows for wider and more efficient screening and may provide a replacement for pathologists in the future. Future research should address the viability of the practical implementation of such DCNN models in the laboratory setting.
The use of <i>Whatsapp</i> in today’s mobile language teaching and learning

This paper discusses the advantages and disadvantages of ICTs in the teaching and learning of foreign languages, in particular the use of WhatsApp in the Certificate in Portuguese programme offered by the Institute of Distance Education of the University of Eswatini. Theoretical aspects will be combined with practical examples of what has been happening on the WhatsApp platform since 2013 with learners of Portuguese. The practical part comes from field research undertaken by the tutor as a direct observer of activities and feedback between lecturers, tutors and students. The impact of COVID19 on the use of WhatsApp will also be looked at briefly. In conclusion, it will be noted that WhatsApp is a valuable environment to ensure that teaching and learning continues beyond the classroom and can be an important motivator for lifelong learning.