Reviewerly/exHarmony huggingface dataset:
Brief Review of Short Utterance Speaker Verification Systems

Due to technological improvements many methods have been proposed for speaker verification.While performance is satisfactory with large amounts of speech data, there is significant degradation in performance with short utterances.Many research works have been carried out to handle short utterance issue of the speaker verification systems used in real-world scenario.In this paper we primarily emphasis on the survey of different feature extraction methods for textindependent speaker verification.We first briefly review conventional systems to show its progress.In this work, we present a brief review of features that are used to capture speaker information at different analysis lengths of speech utterance.We also put the major findings and challenges of this research feview in a nutshell.
Natural disasters warning for enterprises through fuzzy keywords search

With the ever-increasing number of natural disasters warning documents in document databases, the document database is becoming an economic and efficient way for enterprise staffs to learn and understand the contents of the natural disasters warning through searching for necessary text documents. Generally, the document database can recommend a mass of documents to the enterprise staffs through analyzing the enterprise staff's precisely typed keywords. In fact, these recommended documents place a heavy burden on the enterprise staffs to learn and select as the enterprise staffs have little background knowledge about the contents of the natural disasters warning. Thus, the enterprise staffs fail to retrieve and select appropriate documents to achieve their desired goals. Considering the above drawbacks, in this paper, we propose a fuzzy keywords-driven Natural Disasters Warning Documents retrieval approach (named NDWD keyword). Through the text description mining of documents and the fuzzy keywords searching technology, the retrieval approach can precisely capture the enterprise staffs' target requirements and then return necessary documents to the enterprise staffs. Finally, a case study is run to explain our retrieval approach step by step and demonstrate the effectiveness and feasibility of our proposal.
Amending Mistakes Post-hoc in Deep Networks by Leveraging Class Hierarchies

There has been increasing interest in building deep hierarchy-aware classifiers, aiming to quantify and reduce the severity of mistakes and not just count the number of errors. The idea is to exploit the label hierarchy (e.g., WordNet ontology) and consider graph distances as a proxy for mistake severity. Surprisingly, on examining mistake-severity distributions of the top-1 prediction, we find that current state-of-the-art hierarchy-aware deep classifiers do not show practical improvement in making better mistakes than the standard cross-entropy baseline. In fact, they reduce the average mistake-severity metric by largely making additional low-severity or easily avoidable mistakes. This might explain the noticeable accuracy drop. To this end, we resort to the classical Conditional Risk Minimization (CRM) framework for hierarchy aware classification. Given a cost matrix and a reliable estimate of likelihoods (obtained from a trained network), CRM simply amends mistakes at inference time; it needs no extra parameters; it requires adding just one line of code to the standard cross-entropy baseline. It significantly outperforms the state-of-the-art and consistently obtains large reductions in the average hierarchical distance of top-k predictions across datasets, with very little loss in accuracy. Since CRM does not require retraining or fine-tuning of any hyperparameter, it can be used with any off-the-shelf cross-entropy trained model.
An Introduction to Dance Technology

This chapter presents a research project on Laban Motion Analysis (LMA) in Taiwan. Motion capture has collected an extensive profile of human movements with LMA methodology during the three years of research. In total, over 1500 data sets were recorded and stored in MotionDB. The Digital Motion Capture and Data Mining results show that the recognition and reconstruction of LMA properties is feasible. From Data Mining results and data mapping technology, the sensors have been integrated into a 3-in-1 sensor technology, the LMA sensors. From the analysis of various kinematical quantities in MotionDB, the mapping function between properties and the related quantities of each body parts can be identified by computer techniques like Data Mining Process. The practical implementation applying LMA sensor is the interactive Rotation Chair. In response to the increasing need in the healthcare sector, other new exercise machines are being designed.
Knowledge of Software Design Patterns in Educational Institutes of Pakistan

Software design patterns are considered necessary for the development and maintenance of software programs, as they provide the best solution to problems faced during the development of the software, and thus learning them is necessary for the students who are going to graduate and work in the industry. In this paper, the results are reported from a questionnaire-based survey, involving computer science students of various universities in Pakistan to know about the standard of their education and their knowledge related to software design patterns. The results show that these universities need to further improve their syllabus and include design patterns in the curriculum at the bachelor level and also conduct workshops on this topic.
LibGuides: Library Information for History Graduate Students: Getting Started

This guide provides an overview of the library resources and remote services available for our graduate students.
Quantum-synergetic ontology of generalized corporeality (Part III): psychosemantic language of theater, anthropological jazz

The paper continues the topic of applications of the methodology of generalized physicality, which goes back to the concept of quantum synergetic anthropology, elaborated in detail in the previous parts of the triptych. Ontologies of states (functional bodies and subteles of generalized corporeality) and temporal ontologies are considered on the material of the theatrical play of actors. An isomorphism of a well-known musical notation and representation of a changing anthropological profile in the form of a chord of simultaneously activated "sounding" states of the actor is constructed. The relevance of such a musical metaphor, an analogy of the freedom of acting improvisation, as an anthropological jazz inherent in any performer, albeit to varying degrees, is discussed. The classical manner has less variability than, in fact, jazz improvisation. В Parallels of such performances are drawn with the works of classics of theater directing, philosophy of play and communication of musicologists: K. S. Stanislavsky, A. A. Vasiliev, B. Latour, J. Huizinga, I. E. Berendt.In fact, a certain language of interpretation of acting is proposed with the help of dynamic schemes for describing what is happening during the game. If these schemes are adequate to the stage reality, which we tried to prove, then they may well be used in the preparation of actors, staging performances, etc. In fact, a new psychotechnics is being created that allows changing the available capabilities of acting technique, replenishing its tools. In addition, this kind of notational representation of actors' parts allows you to archive and decipher the director's idea in a fairly universal way.
Generating Reasoning Plan of SWRL Rule with Spark

With the advent of the big data era, large-scale semantic data has emerged. Semantic data contains some important but usually implicit information, which can be derived through reasoning. The conventional single-machine reasoners inevitably have problems such as insufficient computing performance and scalability. And the existing largescale reasoners have limited functions, they cannot fully and effectively support Semantic Web Rule Language (SWRL). In this regard, we propose a scalable distributed reasoning method for SWRL using Spark SQL. This paper shows how to use Spark to generate reasoning plan of SWRL rules, which are used in subsequent reasoning stages. We define a hierarchical data structure to represent the reasoning plan, and designed some variant data structures to facilitate generating reasoning plan. We also use Spark to implement distributed generating the reasoning plan of rule base which composed of rules.
A Novel Approach to Optimize Deep Neural Network Architectures

Deep Neural Networks have the generic layers and have appeared as a strong Machine Learning model as they use a different approach for classification of objects and can learn very complex models also. This paper tries to provide analysis of various related approaches (model optimization techniques such as MobileNets, MorphNet, SqueezNet, and so on) by which existing deep neural network architectures can be optimized. In order to aid the proposed approach of optimization, we built a tool on CO-LAB in order to deeply understand each and every model layer structure as well as visualized them closely using feature maps, which highlights each and every feature clearly and visibly. Keras and TensorFlow APIs are also used to understand the model building.
Hybrid Deep Neural Model for Duplicate Question Detection in Trans-Literated Bi-Lingual Data

Background: Duplicate content often corrupts the filtering mechanism in online question answering. Moreover, as users are usually more comfortable conversing in their native language questions, transliteration adds to the challenges in detecting duplicate questions. This compromises with the response time and increases the answer overload. Thus, it has now become crucial to build clever, intelligent and semantic filters which semantically match linguistically disparate questions. Objective: Most of the research on duplicate question detection has been done on mono-lingual, majorly English Q&amp;A platforms. The aim is to build a model which extends the cognitive capabilities of machines to interpret, comprehend and learn features for semantic matching in transliterated bi-lingual Hinglish (Hindi + English) data acquired from different Q&amp;A platforms. Method: In the proposed DQD Hinglish (Duplicate Question Detection) Model, firstly language transformation (transliteration &amp; translation) is done to convert the bi-lingual transliterated question into a monolingual English only text. Next, a hybrid of Siamese neural network containing two identical Long-Term- Short-Memory (LSTM) models and Multi-layer perceptron network is proposed to detect semantically similar question pairs. Manhattan distance function is used as the similarity measure. Result: A dataset was prepared by scrapping 100 question pairs from various social media platforms, such as Quora and TripAdvisor. The performance of the proposed model on the basis of accuracy and Fscore. The proposed DQD Hinglish achieves a validation accuracy of 82.40%. Conclusion: A deep neural model was introduced to find semantic match between English question and a Hinglish (Hindi + English) question such that similar intent questions can be combined to enable fast and efficient information processing and delivery. A dataset was created and the proposed model was evaluated on the basis of performance accuracy. To the best of our knowledge, this work is the first reported study on transliterated Hinglish semantic question matching.
Social-cultural aspect of the development of ideal in the Russian theatre

The article explores the formation and development of the problem of aesthetic ideal in Russian theatrical art, analyzes the socio-cultural evolution and historical periodization of Russian theater, its distinctive features in the context of the development of society. The main areas that form the activities of theatrical currents and schools, their philosophical and social importance and influence on historical formations and consciousness of society are defined, the criteria for expressing the aesthetic ideal in the art of theatre from the point of view of educational and educational potential are investigated.
Multi-Scale Hybrid Attention Cascade Network for Pancreas Segmentation

The shape of pancreas in different patients is very different and the boundary is fuzzy, so the reliable automatic segmentation of pancreas is an important and difficult task. In this paper, we propose a multi-scale hybrid attention cascade network for challenging pancreas segmentation. First, the original CT images are coarsely segmented through the first-level network, then, the coarse segmentation results are clipped and sent to the second-level network for further training to obtain the fine segmentation results. The network adopts full convolutional network (FCN) integrating multi-scale mixed attention. The cut CT image eliminates the irrelevant background interference and reduces the input size of the secondary network, thus improving the segmentation accuracy. An extensive evaluation of 82 open data sets was performed by quadruple cross validation. The experimental results showed that the Dice coefficient was 84.62±4.20% compared with several advanced methods. In addition, this method has excellent performance in three measurement indexes of precision, Jaccard and recall.
Online Writing Effectiveness Under the Blended Teaching Mode of Moscotech APP

The rapid development of modern information technology has brought great changes to People’s Daily work and life, and the extensive application of network information technology in modern English teaching will bring about a series of innovations to the traditional English teaching mode. Based on the mixed English teaching mode promoted by the network environment, this paper constructs the mixed learning mode of Moscotech APP and applies it to English writing teaching. The results show that blended learning is more effective than traditional teaching. There is a significant positive correlation between the learning experience value and the final assessment result. This comparative study on the effectiveness of the large-scale online automatic correction software will be helpful to improve the teaching of college English writing.
ASSOCIATION RULE MINING AND INTERESTINGNESS MEASURES: A CASE STUDY

Association Rule Mining is a method which used in data mining.In this method, the objects that are seen together in the data sets are identified and interesting patterns that will help the decision maker are revealed.Revealed associations are expressed in the form of rules.Interestingness measures are used to evaluate the rules.The decisionmaker pursues strong and interesting patterns and uses this analysis in the most efficient and correct manner on behalf of the firm.In this study, the interestingness measures which are used in the evaluation of association rules are discussed extensively.The main purpose of the study is to contribute to the Turkish literature by presenting a large number of interesting measures used in the evaluation of association rules.In addition, some association rules have been obtained according to some criteria on the data of various stores of a firm and these rules have been evaluated by the interestingness measures.It can be stated that the findings are useful for the firm's decision makers. ÖZETBirliktelik Kural Madenciliği, veri madenciliğinde kullanılan yöntemlerden biridir.Bu yöntemde, veri kümelerinde birlikte görülen nesneler belirlenerek, karar vericiye yardımcı olacak ilginç örüntüleri ortaya çıkartılmaktadır.Ortaya konulan birliktelikler, kurallar biçiminde ifade edilir
Enhancing grammar and valence resources for Akan and Ga

West Africa as an area of linguistic diversity and unification processesWest Africa as a sub-region of the African continent is defi ned mostly on geographic and political criteria which exclude Northern Africa and the Maghreb covering the Sub-Saharan countries from Senegal to Nigeria.As a region of linguistic studies, West Africa adheres to these limits, though genetic relationship and historical contacts between languages make these conventional boundaries vague in a number of respects.The region is characterized by linguistic diversity which determines the prominence of research oriented at multilingualism and language contact.The works conducted so far has focused on identifying convergence zones rather than providing the proof of the linguistic coherence in the entire region.The term convergence zone refers to a region where many linguistic features are shared across the language boundaries.The two largest units, i.e.Macro-Sudan Belt extending from Senegal to Ethiopia (Güldemann 2008) and Wider Lake Chad Region overlap to some extent, especially in Nigeria where genetically distinct and structurally diff erent languages meet (Ziegelmeyer 2015;Wolff & Löhr 2005;Zima 2009; Cyff er & Ziegelmeyer 2009).West Africa is characterized by extensive societal multilingualism (Lüpke & Chambers 2010).Along with indigenous languages superimposed foreign languages such as French, English or Portuguese are used.The region has always been an area where languages brought by scholars, traders or travelers were in constant confrontation with those used locally.Muslim teachers and traders moving along West Africa brought Arabic to this region.The emergence of political centers such as Ghana in 12 th century, Mali in 14 th century, Songhay in 15 th century or the Sokoto Caliphate in 19 th century strengthened the position of Arabic as the language of courts, written correspondence, religious and legal teaching.Arabic also became an important contact language among educated and infl uential people living in towns and it had an impact on the major languages of the Sahel and northern savannah.The historical empires and city-states also promoted languages spoken by the ruling class such as Hausa, Fulfulde or Mande languages, pushing many other local languages aside.Due to globalization, urbanization and economic development, the number of languages
An Adaptive Non Local Means Image Denoising Algorithm

The image will be contaminated by noise during the propagation process and will adversely affect the subsequent processing. It is necessary to denoise the image. The Asymptotic filtering algorithm based on non-local means reduces the calculation time, but it cannot obtain adaptive weights for different noise levels. This paper improves the asymptotic filter weights under different noise levels and does not change the calculation time. The optimal weight can be obtained through adaptation. Experimental results show that better Peak Signal to Noise Ratio(PSNR) can be obtained for different noise levels.
Petrography, Provenance And Diagenesis Of Murree Group Exposed along Basohli- Bani Road, Kathua District, Jammu and Kashmir

Sandstones of Murree Group of rocks exposed along Bani- Basohli road, Kathua District in Jammu were analyzed for petrography, petrofacies and provenance. These sandstones are classified as sublithic arenites and have been derived from mixed provenance including plutonic basement, sedimentary and metasedimentary rocks. Different types of quartz grains and other constituent minerals suggest the source from lower and middle and upper rank metamorphic terrains of the continental block-recycled orogen and subduction zone complex. The imprints of shallow burial diagenesis suggest low mechanical compaction probably just before cementation leading to moderate packing and reduction of porosity.
Edwards Curve Points Counting Method and Supersingular Edwards and Montgomery Curves. (Cryptosystems, Cryptology and Theoretical Computer Science)

In this paper, an algebraic affine and projective curves of Edwards [3, 9] over the finite field Fpn . In the theory of Cryptosystems, Cryptology and Theoretical Computer Science it is well known that many modern cryptosystems [11] can be naturally transformed into elliptic curves [5]. Here we study Edwards algebraic curves over a finite field, which are one of the most promising supports of sets of points which are used for fast group operations [1]. We construct a new method for counting the order of an Edwards curve over a finite field. It should be noted that this method can be applied to the order of elliptic curves due to the birational equivalence between elliptic curves and Edwards curves. We not only find a specific set of coefficients with corresponding field characteristics for which these curves are supersingular, but we additionally find a general formula by which one can determine whether a curve [ ] d p E F is supersingular over this field or not. The embedding degree of the supersingular curve of Edwards over pn F in a finite field is investigated and the field characteristic, where this degree is minimal, is found. A birational isomorphism between the Montgomery curve and the Edwards curve is also constructed. A one-to-one correspondence between the Edwards supersingular curves and Montgomery supersingular curves is established. The criterion of supersingularity for Edwards curves is found over pn F .
Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge.

Multi-label zero-shot classification aims to predict multiple unseen class labels for an input image. It is more challenging than its single-label counterpart. On one hand, the unconstrained number of labels assigned to each image makes the model more easily overfit to those seen classes. On the other hand, there is a large semantic gap between seen and unseen classes in the existing multi-label classification datasets. To address these difficult issues, this paper introduces a novel multi-label zero-shot classification framework by learning to transfer from external knowledge. We observe that ImageNet is commonly used to pretrain the feature extractor and has a large and fine-grained label space. This motivates us to exploit it as external knowledge to bridge the seen and unseen classes and promote generalization. Specifically, we construct a knowledge graph including not only classes from the target dataset but also those from ImageNet. Since ImageNet labels are not available in the target dataset, we propose a novel PosVAE module to infer their initial states in the extended knowledge graph. Then we design a relational graph convolutional network (RGCN) to propagate information among classes and achieve knowledge transfer. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed approach.