Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating Adaptation and Transfer Learning for End-to-End Spoken Language Understanding from Speech

This work investigates speaker adaptation and transfer learning for spoken language understanding (SLU). We focus on the direct extraction of semantic tags from the audio signal using an end-to-end neural network approach. We demonstrate that the learning performance of the target predictive function for the semantic slot filling task can be substantially improved by speaker adaptation and by various knowledge transfer approaches. First, we explore speaker adaptive training (SAT) for end-to-end SLU models and propose to use zero pseudo i-vectors for more efficient model initialization and pretraining in SAT. Second, in order to improve the learning convergence for the target semantic slot filling (SF) task, models trained for different tasks, such as automatic speech recognition and named entity extraction are used to initialize neural end-to-end models trained for the target task. In addition, we explore the impact of the knowledge transfer for SLU from a speech recognition task trained in a different language. These approaches allow to develop end-to-end SLU systems in low-resource data scenarios when there is no enough in-domain semantically labeled data, but other resources, such as word transcriptions for the same or another language or named entity annotation, are available.
Multiple Intelligences (MI) Representation in Prospect Series and the MI Profile of Iranian Junior High School Students: A Compatibility Perspective

Textbooks play a central role in teaching and learning processes, and textbook evaluation helps teachers to be familiar with merits and demerits of a particular textbook, to adapt and supplement textbooks, and to develop new materials. This study aims to evaluate Prospect series, the junior high school English textbooks in Iran, based on Bothelho‟s (2003) checklist on the representation of Gardner‟s (1983) multiple intelligences (MI) theory and to analyze their compatibility with Iranian junior high school students‟ (IJHSS) MI profiles based on Multiple Intelligences Developmental Assessment Scale )MIDAS-kids( questionnaire. To this end, 68 English teachers and 300 high school students completed Bothelho‟s checklist and MIDAS questionnaire, respectively. Descriptive statistics and chi square goodness-of-fit analyses were applied. The results revealed that visual/spatial, verbal/linguistic, and intrapersonal intelligences were the most frequent types of intelligences represented in Prospect series, while interpersonal, linguistic, and intrapersonal intelligences were dominant in IJHSS‟ MI profiles. The paper concludes that student‟s books and workbooks of Prospect series did not cater for the interpersonal, kinesthetic, musical, and naturalistic intelligences of IJHSS. The findings of the study may have some implications for textbook designers to take into account all types of intelligences to satisfy teachers and learners, and thus to make textbooks more interesting and more effective.
Better User Personalization : A Sequential Pattern Matching Approach

The richness of web content has also made it progressively more difficult to leverage the value of information. Identifying users’ topic of interest, recommending content to a user based on past behavior without major restructuring of the site is a major challenge. Mining knowledge about the usage of a website can be used effectively for user personalization and that can facilitate search information very fast and efficiently. This paper proposes a novel approach to facilitate user navigation without restructuring the site by mining knowledge and by a probabilistic classification. Based on the cluster information a new approach for on ranking the web pages resulting in users’ possible link prediction is done. Segmentation of the log file of groups of users having similar navigation and similar pattern over time is studied. For pattern matching sequential patterns spanning over sessions is selected. In the Prefix Span algorithm which comes in sequential pattern matching a pattern growth method is employed. For better personalization in addition to prefix, a user based scan is performed in our new USP (user span pattern) algorithm. Results from extensive tests conducted on a real data set indicate that our model effectively improves the user navigation with minimal changes. The proposed model is more suitable for websites whose content remain stable overtime such as educational sites and is also suited for artistic, medical and military applications.
The effect of manual action on vowel production in nouns and verbs

Studies show that word pronunciation alters grasping based either on facilitation between mouth and hand shape, or facilitation between speech meaning and hand movement. Less understood is the opposite of this relation, i.e., how speech is altered when meaningful words are pronounced during manual actions. To assess this, participants pronounced nouns and verbs that are homophones while performing different actions, including observing a block (look, served as baseline), placing fingers around a block (grasp), and reaching and grasping a block (reach-to-grasp). We found that manual actions influenced vowel production, significantly lowering the first formant during both the grasp and reach-to-grasp conditions. However, vowel production was not affected by words of different part of speech and no difference was observed between the verb/noun pairs. Our findings have implications for theoretical modelling of speech production processes to incorporate a linkage to the general kinaesthetic system.
16 0 v 2 6 S ep 2 01 9 Discrete-query quantum algorithm for NAND trees

Recently, Farhi, Goldstone, and Gutmann gave a quantum algorithm for evaluating nand trees that runs in time O( √ N logN) in the Hamiltonian query model. In this note, we point out that their algorithm can be converted into an algorithm using O(N) queries in the conventional quantum query model, for any fixed ǫ > 0. A nand tree of depth n is a balanced binary tree whose internal vertices represent nand gates. Placing bits x1, . , x2n at the leaves, the root of the nand tree evaluates to the function fn(x1, . . . , x2n), where fn : {0, 1}2 n → {0, 1} is defined recursively as follows. For n = 0, f0(x) = x, and for n > 0, fn(x1, . . . , x2n) = ¬ ( fn−1(x1, . . . , x2n−1) ∧ fn−1(x2n−1+1, . . . , x2n) )
Developing an Online Horseless Horse Learning Lesson: An Action Research Study

In today’s society it is becoming ever so important to find creative and influential outlets to keep young people occupied in positive ways. High quality youth programs are shown to make a positive difference in the lives of many young people; therefore, it is important to continue the use of established youth programs as well as develop new programs. Equine science programs have proven to be a popular and beneficial outlet for many youth due to the ability to learn a variety of life skills, …
A Deep-Learning Approach to Simple-Claims Classification 3 tries to define the problem of fact-checking as a one-toone automation mapping of the human fact-checking process

The information on the internet suffers from noise and corrupt knowledge that may arise due to human and mechanical errors. To further exacerbate this problem, an ever-increasing amount of fake news on social media, or internet in general, has created another challenge to drawing correct information from the web. This huge sea of data makes it difficult for human fact checkers and journalists to assess all the information manually. In recent years Automated Fact-Checking has emerged as a branch of natural language processing devoted to achieving this feat. In this work, we give an overview of recent approaches, emphasizing on the key challenges faced during the development of such frameworks. We test existing solutions to perform claim classification on simple-claims and introduce a new model dubbed SimpleLSTM, which outperforms baselines by 11%, 10.2% and 18.7% on FEVER-Support, FEVER-Reject and 3-Class datasets respectively. The data, metadata and code are released as open-source and will be available at https:// github.com/DeFacto/SimpleLSTM.
OPTICAL FLOW AS A TOOL FOR CT SEGMENTATION

Optical flow methods (OFM) are widely used for deformable registration of images acquired at different times. OFM can also be used to establish correspondences between adjacent slices in a CT image data set. The pixel-to-pixel displacement matrices generated from the registration of adjacent CT slices offer precise mappings for contours, which in turn allows automated segmentation of anatomic structures. Generation of contours of lung, heart, esophagus, and spinal cord has been studied. Examples are given in this paper. OFM-based segmentation method has the potential to supplement the manual delineation approach that is currently used in radiation oncology clinics for radiation treatment planning.
5-15-2019 TOWARDS PROCESS-ORIENTED EMPLOYEE APPRAISALS AND REWARDS : AN EXPLORATORY CASE STUDY

Although Business Process Management (BPM) scholars frequently refer to the importance of aligning employee appraisals and rewards with processes, little is known about how the BPM discipline relates to these human resource aspects. We therefore conducted an exploratory case study in an internationally oriented organization with high BPM maturity to acquire a deeper understanding with the first-hand practices. We first elaborate on how the case organization includes behavioural and results-related components into its appraisals and performance management approach to improve employee alignment and process performance. Afterwards, we explain how and for which processrelated elements employees get rewarded and recognized. As academic implications, we build two preliminary frameworks that lay the foundation for further research. We also provide managers with best practices and lessons learned, serving as a practical guidance.
A classification of real time analytics methods. An outlook for the use within the smart factory

The creation of value in a factory is transforming. The spread of sensors, embedded systems, and the development of the Internet of Things (IoT) creates a multitude of possibilities relating to upcoming Real Time Analytics (RTA) application. However, already the topic of big data had rendered the use of analytical solutions related to a processing in real time. Now, the introduced methods and concepts can be transferred into the industrial area. This paper deals with the topic of the current state of RTA having the objective to identify applied methods. In addition, the paper also includes a classification of these methods and contains an outlook for the use of them within the area of the smart factory.
Learning Rich Event Representations and Interactions for Temporal Relation Classification

Most existing systems for identifying temporal relations between events heavily rely on hand-crafted features derived from event words and explicit temporal markers. Besides, less attention has been given to automatically learning con-textualized event representations or to finding complex interactions between events. This paper fills this gap in showing that a combination of rich event representations and interaction learning is essential to more accurate temporal relation classification. Specifically, we propose a method in which i) Recurrent Neural Networks (RNN) extract contextual information ii) character embeddings capture morpho-semantic features (e.g. tense, mood, aspect), and iii) a deep Convolutional Neu-ral Network (CNN) finds out intricate interactions between events. We show that the proposed approach outperforms most existing systems on the commonly used dataset while using fully automatic feature extraction and simple local inference.
A Normalized Trace Geometry Modeling Method with Bulge-Free Analysis for Direct Ink Writing Process Planning

Direct ink writing (DIW) is an additive manufacturing technology that has been widely used in many fields, including soft electronics, ceramic structures, and flexible biomedical applications. In DIW process, writing a trace with desired geometry is of fundamental importance to fabricate a three-dimensional model. In this article, a normalized geometry modeling method with bulge-free analysis for process planning in DIW is presented. The geometry prediction model is developed by converting conventional dispensing parameters to a dimensionless variable, speed ratio v*. The developed model is independent of tip gauge and feeding mechanism. To address the common bulge issue in DIW of low-viscosity fluids, the critical bulge-free printing range has been identified by characterizing the bulge formation process. The developed geometry prediction model and the identified bulge-free printing range are validated by experimental data of several different tips and inks. Finally, a DIW process planning method based on the developed geometry prediction model is demonstrated.
BlueMUSE Project Overview and Science Cases June 4 th , 2019

Johan Richard1, Roland Bacon1, Jérémy Blaizot1, Samuel Boissier2, Alessandro Boselli2, Nicolas Bouché1, Jarle Brinchmann3,4, Norberto Castro5, Laure Ciesla2, Paul Crowther6, Emanuele Daddi7, Stefan Dreizler8, Pierre-Alain Duc9, David Elbaz7, Benoit Épinat2, Chris Evans10, Matteo Fossati11, Michele Fumagalli11, Miriam Garcia12, Thibault Garel1,13, Matthew Hayes14, Artemio Herrero15,16, Emmanuel Hugot2, Andrew Humphrey3, Pascale Jablonka17, Sebastian Kamann18, Lex Kaper19, Andreas Kelz5, Jean-Paul Kneib17, Alex de Koter19,20, Davor Krajnović5, Rolf-Peter Kudritzki21, Norbert Langer22, Carmela Lardo17, Floriane Leclercq13, Danny Lennon15, Guillaume Mahler23, Fabrice Martins24, Richard Massey11, Peter Mitchell4, Ana Monreal-Ibero15,16, Paco Najarro12, Cyrielle Opitom25, Polychronis Papaderos3,26, Céline Péroux28,2, Yves Revaz17, Martin M. Roth5, Philippe Rousselot29, Andreas Sander30, Charlotte Simmonds Wagemann13, Ian Smail11, Anthony Mark Swinbank11, Frank Tramper31, Tanya Urrutia5, Anne Verhamme13, Jorick Vink30, Jeremy Walsh28, Peter Weilbacher5, Martin Wendt32, Lutz Wisotzki5, Bin Yang25. Abstract
Malware Detection Techniques

Malware is code designed for a malicious purpose, such as to disrupt computer operation, gather sensitive information, or gain access to private computer systems. A malware detector identifies malware and thus prevents it from from adversely affecting a host. Detectors which use pattern matching to identify malware, can easily be evaded by simple code transformations and various obfuscations. To overcome this shortcoming various malware detectors have been proposed which rely on semantic signatures and employ static analysis techniques such as model checking and theorem proving to perform detection. While these techniques are quite successful in identifying malwares, there are certain obfuscations which make the malwares undetectable. In this paper, we try to study the design of a semantic based malware detector which undoes the obfuscations performed by a malware writer. We also study dynamic approaches which are resistant to most obfuscations but are resource intensive. We try to improve on the current techniques and propose a hybrid system which will hopefully improve the quality of malware detector.
Economics and Politics of the Public-Private Wage Gap ( The Case of Russia ) MARCH 2019

IZA DP No. 12247 MARCH 2019 Economics and Politics of the Public-Private Wage Gap (The Case of Russia)* The paper explores the public-private wage gap in the Russian economy over time and along the whole wage distribution. Using the RLMS-HSE panel data set, we examine how gaps at various points of wage distribution changed from 2005 to 2015 and present decompositions of the gaps into components explained by differences in characteristics and differences in returns. The results suggest that the gap persists over time and varies along the wage distribution. During the 2000’s, low-skilled public sector workers had smaller pay gaps than higher-skilled workers had. Multiple governmental policy interventions and the economic crisis of 2008-2009 contributed to the narrowing of the gap and its partial equalization along the distribution. A new set of policy changes associated with the May 2012 Presidential Decrees strengthened these tendencies but failed to eliminate the gaps. JEL Classification: J31, J45
Sentential negation at the syntax-prosody interface

Some authors (Selkirk, 1984; Féry and Ishihara, 2010) have argued that prosodic constituents exhibit a relation to syntactic constituents, which is observed in some constraints relating syntactic and prosodic structures. Taking into account that non-canonical sentential negation constructions in Brazilian Portuguese (BP) – [VP Neg3] and [Neg VP Neg2] – seem to exhibit some syntactic constraints if compared to standard negation [Neg1 VP], this paper aims at investigating the interaction between syntax and prosody in these structures. To do so, I have compared two varieties of BP, from the Southeast and Northeast regions, which differ in that only in the latter one prosodic phrasing is used as a strategy to indicate different syntactic structures.
A study of root causes of delays in the public-sector construction projects in Kuwait

After the Kuwait Government launched the 2035 vision of Kuwait, development of the construction industry became one of the priority themes of the country. However, the construction industry faces many problems such as delays, defects, cost overruns and poor productivity and efficiency. Nevertheless, delays in projects are one of the most significant issues in this industry globally and in particular, in Kuwait. To solve this problem, it is important to understand the main root causes of delays by examining and identifying the causes. This paper aims to examine the key root causes of delays in public construction projects in Kuwait. This study provides a comprehensive insight into the issues causing delays in construction projects in Kuwait from reviewing the literature and by reviewing the results of a distributed online questionnaire. This online survey targets the experts in the AEC industry who are involved in these governmental projects from different public construction authorities in Kuwait. These government agencies are the Ministry of Public Works (MPW) and the Public Authority of Housing Welfare (PAHW) which are responsible for the major public facilities projects and residential projects in Kuwait. The findings of this study provide an in-depth understanding of this problem and possible solutions will be recommended. The survey results proved that group 1 which is the client-related factors group has the most significant factors for the delays in these construction projects in Kuwait. Therefore, the client in these projects was the main responsible part of these delays. However, one of the reasons of delays in public-sector construction projects is the government did not apply new construction management methods for this sector. Yet, lack of collaboration and coordination between stakeholders, traditional procurement system, and long approval processes are the main reason behind this issue.
Diversification benefits of investments in emerging markets – A Swedish perspective

This paper evaluates the possibilities for Swedish investors to diversify their portfolios through investments in emerging market equities. Two different investor profiles are considered, where one seeks to minimize the risk in her portfolio and the other seeks to maximize her risk-adjusted return. We start off by analyzing the return characteristics of a Swedish stock index and an emerging markets index. A set of portfolios are then created based on, and evaluated with, the volatility estimated by the DCC MGARCH model and the Conditional Value-at-Risk. Our findings suggest that the investor seeking to minimize her portfolio risk can enjoy diversification benefits from investments in emerging markets. Conversely, the investor seeking to maximize her risk-adjusted return cannot improve her base portfolio by investing in emerging markets. This implies that investors should consider their ultimate purpose before investing in emerging market equities.
Identifying Grammar Rules for Language Education with Dependency Parsing in German

We propose a method of determining the syntactic difﬁculty of a sentence, using syntactic patterns that identify grammatical rules on dependency parses. We have constructed a novel query language based on constraint-based dependency grammars and a grammar of German rules (relevant to primary school education) with patterns in our language. We annotated these rules with a difﬁculty score and grammatical prerequisites and built a matching algorithm that matches the dependency parse of a sentence in CoNLL-U format with its relevant syntactic patterns. We achieved 96% precision and 95% recall on a manually annotated set of sentences, and our best results on using parses from four parsers are 88% and 84% respectively
Multi-Task CTC Training with Auxiliary Feature Reconstruction for End-to-End Speech Recognition

We present a multi-task Connectionist Temporal Classification (CTC) training for end-to-end (E2E) automatic speech recognition with input feature reconstruction as an auxiliary task. Whereas the main task of E2E CTC training and the auxiliary reconstruction task share the encoder network, the auxiliary task tries to reconstruct the input feature from the encoded information. In addition to standard feature reconstruction, we distort the input feature only in the auxiliary reconstruction task, such as (1) swapping the former and latter parts of an utterance, or (2) using a part of an utterance by stripping the beginning or end parts. These distortions intentionally suppress long-span dependencies in the time domain, which avoids overfitting to the training data. We trained phone-based CTC and word-based CTC models with the proposed multi-task learning and demonstrated that it improves ASR accuracy on various test sets that are matched and unmatched with the training data.
Supplementary data Co-suppression of NbClpC 1 and NbClpC 2 in Nicotiana benthamiana lowers photosynthetic capacity via altered leaf structure

The Clp protease composed of more than 15 proteins, is the central protein degradation machinery in the plastids of a plant cell which plays a similar role as proteasomes in cytoplasm and nucleus. ClpC1 and ClpC2 are two chaperonic proteins for the Clp protease and share more than 90% similarities in the nucleotide and amino acid sequences. In this study, we investigated the functions of ClpC1 and ClpC2 in photosynthesis by co-suppression of both genes (NbClpC1 and NbClpC2, NbClpC1/C2) in Nicotiana benthamiana using virus-induced gene silencing (VIGS) technique. Co-suppression of NbClpC1/C2 in N. benthamiana resulted in aberrant structure with severe chlorotic leaves, stunted growth and reduced numbers of stomata and in lowered photosynthetic capacity. Leaf CO2 assimilation rate, the maximum quantum yield of photosystem II (Fv/Fm), chlorophyll and carotenoid contents were also significantly lower in the leaves of NbClpC1/C2 co-suppressed N. benthamiana than those in the control. Microscopic analysis revealed that NbClpC1/C2 co-suppressed leaves had coarsely packed mesophyll cells. Our findings strongly suggest that ClpC1 and ClpC2 play a pivotal role in photosynthetic competence by affecting the proper structure of leaves and numbers of stomata.