Reviewerly/exHarmony huggingface dataset:
Accelerating the Training of Convolutional Neural Networks for Image Segmentation with Deep Active Learning

Semantic segmentation is an important perception function for automated driving (AD), but training a deep neural network for the task using supervised learning requires expensive manual labelling. Active learning (AL) addresses this challenge by automatically querying and selecting a subset of the dataset to label with the aim to iteratively improve the model performance while minimizing labelling costs. This paper presents a systematic study of deep AL for semantic segmentation and offers three contributions. First, we compare six different state-of-the-art querying methods, including uncertainty-estimate, Bayesian, and out-of-distribution methods. Our comparison uses the state-of-the-art image segmentation architecture DeepLab on the Cityscapes dataset. Our results demonstrate subtle differences between the querying methods, which we analyze and explain. We show that the differences are nevertheless robust by reproducing them on architecture-independent randomly generated data. Second, we propose a novel way to aggregate the output of a query, by counting the number of pixels having acquisition values above a certain threshold. Our method outperforms the standard averaging approach. Finally, we demonstrate that our findings remain consistent for whole images and image crops.
Subspace Structure-Aware Spectral Clustering for Robust Subspace Clustering

Subspace clustering is the problem of partitioning data drawn from a union of multiple subspaces. The most popular subspace clustering framework in recent years is the graph clustering-based approach, which performs subspace clustering in two steps: graph construction and graph clustering. Although both steps are equally important for accurate clustering, the vast majority of work has focused on improving the graph construction step rather than the graph clustering step. In this paper, we propose a novel graph clustering framework for robust subspace clustering. By incorporating a geometry-aware term with the spectral clustering objective, we encourage our framework to be robust to noise and outliers in given affinity matrices. We also develop an efficient expectation-maximization-based algorithm for optimization. Through extensive experiments on four real-world datasets, we demonstrate that the proposed method outperforms existing methods.
A Portuguese Dataset for Evaluation of Semantic Question Answering

Research on question answering tools over open linked data is increasing, and that brings the necessity of resources to allow for the evaluation and comparison of such systems. Question Answering over Linked Data (QALD) is a traditional benchmark event that occurs annually since 2011. However, although the multilingual task is available since its third edition, there is a necessity to foster the actual Portuguese Language resources present in this event benchmark. In this paper, we describe the development of the Portuguese language as a QALD corpus complement. The corpus is based on an existing QALD multilingual corpus and comprises 258 sentences used for the event challenge in 2017. We constructed a second corpus to allow direct comparison with the DBPedia Portuguese content. The main topics to highlight are the adopted methodology, which results in corpus related to frequent Brazilian Portuguese use of the language, and the work on adapting the answers to the DBPedia PT knowledge base, providing a corpus to evaluate Portuguese QA systems accurately.
The Design of Collaborative Surveillance System Based on Blockchain Technology

Traditional surveillance system is a centralized architecture and have some drawbacks including single point of failure and is a private environment. Surveillance videos or images in such environment could not be shared with others so that illegal behavior or people could not be discovered in other surveillance system. Therefore, in this paper we proposed the design of distributed collaborative surveillance system based on blockchain technology. The human face recognition is the basic function in the surveillance system, we use CNN which is a deep leaning technology as the recognition engine and distribute the recognition results to distributed ledger with a secure way in blockchain network. We also reveal the possibility to apply blockchain technology to surveillance system besides the popular usage in financial domain.
Learning Between Different Teacher and Student Models in ASR

Teacher-student learning can be applied in automatic speech recognition for model compression and domain adaptation. This trains a student model to emulate the behaviour of a teacher model, and only the student is used to perform recognition. Depending on the application, the teacher and student may differ in their model types, complexities, input contexts, and input features. In previous works, it is often shown that learning from a strong teacher allows the student to perform better than an equivalent model trained with only the reference transcriptions. However, there has not been much investigation into whether a particular form of teacher is appropriate for the student to learn from. This paper aims to study how effectively the student is able to learn from the teacher, when differences exist between their designs. The Augmented Multi-party Interaction (AMI) meeting transcription and Multi-Genre Broadcast (MGB-3) television broadcast audio tasks are used in this analysis. Experimental results suggest that a student can effectively learn from a more complex teacher, but may struggle when it lacks input information. It is therefore important to carefully consider the design of the student for each application.
Information Recommendation Model Based on Knowledge Graph in Personalized Context

With the rapid development of the Internet, information overload makes it impossible for users to quickly find interesting content in front of massive information. Therefore, personalized recommendation method has wide prospect. In recent decades, collaborative filtering has become a mainstream recommendation technology. In view of the existing research on information recommendation and the lack of in-depth analysis of user personalized scenarios, this paper proposes a personalized scenario recommendation model in mobile e-commerce environment based on knowledge graph. Its main framework and specific content are given in detail. An information recommendation algorithm is proposed based on knowledge graph and collaborative filtering. This algorithm combines the recommendation information in knowledge graph with collaborative filtering method. It adopts alternate learning mode and use the semantic information extracted from knowledge map to make up for the defect of item-based collaborative filtering method. Simulation results demonstrate that the proposed algorithm can improve the quality of personalized recommendation.
New properties of a pure bipartite system in presence of dissipative environments

We investigate some new properties of the model of a pure bipartite system in dissipative environments. The mathematical model of bipartite system with the interaction cavity field in the presence of dissipative environment is designed. The systems is assumed to be initially in the Warner state. The system is analytically solved using master equation method. We quantified the dynamics of quantum correlation between the system, under the influence of the mean photon numbers, type of the initial state and spontaneous emission rate of atom using several measures are, measurement-induced disturbance (MID) and Geometric measure of quantum discord (GQD), negativity (N) and concurrence (C). The effect of the system parameters on dynamics of entanglement is investigated, it is found that the spontaneous emission rate of atom number have much effect on all measures. Moreover the Geometric measure of quantum discord (GQD) have different behavior rather than the others and more influenced by the mean photon number and type of the initial state.
Quo Vadis? Inakeyaa! Inferring Flow Directions Using a Bayesian Approach

Before we build a predictive groundwater flow model, we first identify candidate conceptual models. To cull our collection of candidate models, we ask the seemingly simple but key question: Where is the water coming from? We argue for the use of a Bayesian framework to address this question. We start with an uninformed prior distribution that says all directions are equally likely. We then incorporate the available information (usually error-prone, noisy information of varied quality) and appropriately update the characterization of the uncertain direction. When the added information is extensive and internally consistent, a clear flow direction emerges. On the other hand, if the added information is minimal or internally inconsistent, the uninformed prior is only slightly modified.
Data-Driven Modeling and Simulation of Daily Activity Patterns of Elderlies Living in a Smart Home

Considering the globally aging population, one of the main challenges the healthcare system would have to face is to help elderly people stay at home in good health conditions and as long as possible. Recent advances in technologies answer this need with Smart Homes and Ambient Assisted Living programs. Data collected by the sensors are labeled and used to monitor the inhabitant Activities of Daily Living (ADLs). This paper presents a new modeling framework of the smart home resident's behavior that mimics his behavior on multiple aspects and is able to simulate the resident daily behavior. Our approach is illustrated by a real-life case study application. Results show that the presented framework enables the modeling of human behavior living alone in a smart home without prior knowledge on the inhabitant. Such results enable further research on frailty prediction through simulation.
Improving the Performance of Inter-node Data Exchange in Cloud-based Distributed Ray Tracing by Compression

To improve the performance of cloud-based rendering systems, it not only requires a good load balancing algorithm, but also needs less cost for data transfer. This paper proposes an internal data compression method in the cloud, which reduces the cost of data transfer between worker nodes on the basis of Hadoop distributed framework. In this method, the coordinate system transformation and simple vector addition are used to reduce the storage space of each pixel's primary ray-object intersection point and unit normal vector. We implemented the experiment of data transfer in a Hadoop ray tracing cluster with gigabit switch. First, we compared the cost under different types of data transfer. Secondly, we measured the execution time of the compression method. Finally, we analyze the reason for the error between the decompressed data and the origin data. The results show that the cost of data transfer in our method is approximately 66% of that of the three-dimensional coordinate representation method, and the calculation error is less than 10-6. Thus, our proposed method can effectively improve the transfer efficiency of cloud-based rendering system.
Speeding up of the Nelder-Mead Method by Data-Driven Speculative Execution

The performance of machine learning algorithms considerably depends on the hyperparameter configurations. Previous studies reported that the Nelder-Mead (NM) method known as a local search method requires a small number of evaluations to converge and that these properties enable achieving considerable success in the hyperparameter optimization (HPO) of machine learning algorithms for image recognition. However, most evaluations using the NM method need to be implemented sequentially, which requires a large amount of time. To alleviate the problem that the NM method cannot be computed in parallel, we propose a data-driven speculative execution method based on the statistical features of the NM method. We analyze the behaviors of the NM method on several benchmark functions and experimentally demonstrated that the NM method tends to take certain specific operations. The experimental results show that the proposed method reduced the elapsed time by approximately 50% and the number of evaluations by approximately 60% compared to the naïve speculative execution.
The dynamics of prosodic adaptation between Italian conversational partners

This paper explores the dynamics of prosodic adaptation between speakers of Bari (BI) and Lecce (BI) varieties of Italian, in which polar question intonation contour is different as to the assignment of the rise for marking questioning: on the nuclear syllable in BI, on the phrase boundary in LI.Intonation analysis and distribution of contour types produced by BI-LI speaker pairs in a game-based interaction revealed that only BI speakers tended to accommodate their question intonation to their LI partners'.Dynamics of adaptation varied across BI speakers, hinting to different accommodation strategies which not necessarily entail overriding the rule of their native variety intonational grammar in spontaneous imitation.
Embedded Hardware Implementation for Image Security Using Chaotic Maps

In this chapter, an embedded hardware implementation of efficient image security using a chaotic map has been proposed. The proposed system consists three security levels for image transmission based on chaotic system (Nahrain hyperchaotic system (NHS)). In the first security level, NHS is used as an encryption key for color image encryption system. The proposed color image encryption system contains two stage. The first stage is scrambling pixel position and the second stage is diffusing bit value. A color image encryption is simulated in software via Matlab, and implemented via Cyclone V GX Starter Kit FPGA platform. The security analysis and simulation/hardware results have shown that, the color image encryption system that presented is resilient and robust. As example, entropy measurement of the suggested encryption system is 7.9964, that is highly approximately to its typical value (8), and the Number of Pixel change Rate (NPCR) is 99.76%, that is the exceptional value to obtain. At the second security level, the carrier signal based on NHS is used to modulate binary cipher data. Two type of chaotic modulation techniques are proposed. The technique number one contain seven forms which is based on one bit modulation. The technique number two contain three forms which is based on two bit modulation. The simulation results show that, NHS gained Bit Error Rate (BER) of 7.42 × 10−6 at Signal to Noise Ratio (SNR) of 21 dB, whilst the Logistic and Henon realized 0.0316, and 0.0136 correspondingly. Finally, the third security level is the synchronization controller. The synchronization test of the proposed system showed that the perfect synchronization occurs in 0.136 m s, which is very short time. The good randomness properties and fast synchronization capability offered by NHS qualify NHS for the use in designing robust encryption algorithms and real time secure transmission systems based on chaos synchronization. Therefore, NHS offers the possibility of realizing multi-level security system efficiently. The implementation of NHS and chaotic modulation is prepared using FPAA board.
AB005. The effect of audio quality on eye movements in a video chat

Background: Difficulty in hearing can occur for numerous reasons across a variety of ages in humans. To overcome this, humans can employ a number of techniques to help improve their understanding of sound in other ways. One is to use vision, and attempt to lip-read in order to understand someone else in a face-to-face conversation. Audio-visual integration has a long history in perception (e.g., the McGurk Effect), and researchers have shown that older adults will look at the mouth region for additional information in noisy situations. However, this concept has not been explored in the context of social media. A common way to communicate virtually that simulates a live conversation is the concept of video chatting or conferencing. It is used for a variety of reasons including work, maintaining social interactions, and has started to be used in clinical settings. However, video chat session quality is often sub-optimal, and may contain degraded audio and/or decoupled audio and video. The goal of this study is to determine whether humans use the same visual compensation mechanism, lip reading, in a digital setting as they would in a face-to-face conversation.
Ajouter des champs à une table à partir d'une autre base – PatCatNat's

Access 97 | Compatible | Access 2003 | Non teste | Par exemple pour creer un patch.... Private Sub BtnPatch_Click() Dim dbsNorthwind As Database Dim tdfAvisB As TableDef Dim tdfAvisN As TableDef…
Reverse-Transliteration of Hebrew script for Entity Disambiguation

JudaicaLink is a novel domain-specific knowledge base for Jewish culture, history, and studies. JudaicaLink is built by extracting structured, multilingual knowledge from different sources and it is mainly used for contextualization and entity linking. One of the main challenges in the process of aggregating Jewish digital resources is the use of the Hebrew script. The proof of materials in German central cataloging systems is based on the conversion of the original script of the publication into the Latin script, known as Romanization. Many of our datasets, especially those from library catalogs, contain Hebrew authors' names and titles which are only in Latin script without their Hebrew script. Therefore, it is not possible to identify them in and link them to other corresponding Hebrew resources. To overcome this problem, we designed a reverse-transliteration model which reconstructs the Hebrew script from the Romanization and consequently makes the entities more accessible.
Building a Large-Scale Knowledge Graph for Elementary Education in China

With the penetration of information technology into all areas of society, Internet-assisted education has become an important opportunity for current educational reform. In order to better assist in teaching and learning, help students deepen their understanding and absorption of knowledge. We build a knowledge graph for elementary education, firstly, we define elementary education ontology, divide the knowledge graph into three sub-graphs. Then extracting concept instance and relation instance form textbook and existing knowledge base based on unsupervised method. In addition, we have acquired four different learning resources to assist in learning. At last, the results show that the procedure we proposed is scientific and efficient.
THEORETICAL AND PRACTICAL ASPECTS OF MEASUREMENT AND EVALUATIONOF INTEGRATED CHARACTERISTICS OF PROFESSIONAL COMPETENCY OF THE PEDAGOGUE

The article highlights the actual aspects of the interpretation of the notion of computation, competence, their use and priority directions of diagnostics of the components of professional competence of the teacher. The analysis of modern works on the competencies and competence as a multidirectional and variety of their characteristics is proved, which testifies to the very large complexity of their measurement and evaluation; an attempt was made to find ways to solve this urgent problem, which is connected with the improvement of the quality of education in the general context of its humanization. The aim is to highlight the individual professional qualities of the teacher: the general orientation of the motivational sphere, the degree of manifestation of a personal sense of professional freedom and abilities. these problems are coverage from the position of J. Raven and his emphasis on motivation and values of the individual. The role of the acmeological orientation of the individual is emphasized as an important factor in achieving pedagogical success. The acmeological orientation acts as a personal potential, which is a component of the individual psychological characteristics of the individual. The concept of personal potential has been developed in his works by D.O. Leontev and is interpreted as the ability of the individual to maintain the stability of activity under the influence of external conditions. An attempt has been made to gauge their specificity more deeply with the help of the Rorschach method. The general tendency is highlighted concerning the peculiarities of the study of the general orientation of the motivational sphere, the degree of manifestation of the personal feeling of professional freedom and abilities, which, in our opinion, is that it is relatively autonomous and its meaning lies in the field of determining the state and trends of the functioning of professional freedom, in general. Further searches are seen in solving the problem of developing effective methods for the professional diagnosis of the competence of educators in education and the creation of a continuous experimental system, which learns on the basis of own samples and errors, corrects its actions and initiates the forms of activity.
Electronic Music Arrangement Based on the Computer Platform

Since the rise in the twentieth century, electronic music has also fluctuated along with the social changes and development. With the rapid development of science and technology, people pay more and more attention to the electronic music. With the combination of the electronic music and the modern information technology, with the rapid development of the electronic information age, its creative techniques and ideas are quite different from those of the traditional composers, showing a vigorous and dynamic trend. As a result, more composers have gained unprecedented new materials and creative inspirations, and become an inexhaustible motive to promote composers to create more excellent music while expanding their creative space. Therefore, starting from the deduction of the computer electronic music, and starting from the development of the electronic music, this paper studies the innovation of the electronic music and the terminology and translation of the electronic music in the comparison between the traditional composing and the electronic music composing, so as to discuss and analyze the strategy of the electronic music arrangement on the computer platform.
Analysis of High-value Reviews based on Sentiment

When people use online shopping, they often refer reviews which are written about the products. They can understand the product more deeply by reading the reviews. The review has a star rating that shows what other people think about the product. The star rating is not always appropriate for the evaluation of the product. There are so many reviews and it is difficult to find the review that affects the users' willingness to buy. We call such review "high-value review". Our proposed high-value review does not depend on the number of star ratings. The high-value review is that people find useful information when they read the review and they think it is a good review. In this paper, we investigate the relation between high-value reviews and their sentiment of clause based on four hypotheses. Our analyzing sentiment is three-axis which are positive/negative/neutral. Finally, we extract the characteristics of high-value reviews from the results of our investigate.