Reviewerly/exHarmony huggingface dataset:
Supporting information use and task accomplishment: What system features do users like and expect?

Information systems have been improving in helping users find information. However, they have been less attended to regarding helping searchers in using located information. This research attempts to address the issue of information use by investigating what information systems and features searchers think are helpful in using located information to accomplish information tasks. In all, 32 college students were invited to an information interaction lab, first being interviewed on a recently completed task and then working on a to-be-finished task, both being their real-life tasks of their own choices. Through questionnaires, the study discovered the most favoured existing and expected features helpful for users’ task completion. Users expected convenient citations, note taking in search result pages and being kept on task. Findings in this study have implications on designing search systems that can better support task accomplishment, in addition to returning search results.
Independent distributions on a multi-branching AND–OR tree of height 2

We investigate an AND–OR tree T and a probability distribution d on the truth assignments to the leaves. Tarsi (1983) inquired into an independent and identical distribution (IID) d such that at each leaf, the probability of having value 0 is neither 0 nor 1. Under a reasonable assumption on the shape of a tree, he showed the existence of an optimal algorithm that is depth-first. We investigate the case where d is an independent distribution (ID) and probability may depend on each leaf. It is known that in this general case, if the height is greater than or equal to 3, Tarsi-type result does not hold. It is also known that for a complete binary tree of height 2, Tarsi-type result certainly holds. In this paper, we ask whether Tarsi-type result holds for an AND–OR tree of height 2. Here, a child node of the root is either an OR-gate or a leaf: The number of child nodes of an internal node is arbitrary, and depends on an internal node. We give an affirmative answer. Our strategy of the proof is to reduce the problem to the case of directional algorithms. We perform induction on the number of leaves, and modify Tarsi's method to suite height 2 trees. We discuss why our proof does not apply to height 3 trees. This research sheds fresh light on the difference of algorithmic nature between shallow trees and deep trees.
An Industry-Ready Single PTZ-Camera Based Attendance Management System

In this paper, we present a smart management system for automatic headcount and attendance monitoring in the field of distance education and online training. Our system exploits the state-of-the-art object detection and face recognition models with customized modifications to achieve real-time performance under limited computation resources. Moreover, we present a novel route planning model for a pan-tilt-zoom camera (PTZ-camera) which significantly reduces the amount of time required to scan the whole environment. Experimental results demonstrate our system is able to deliver stable performance in terms of head detection and face recognition accuracy and system reliability. We believe our work showcases a successful integration of cutting edge academic approaches and affordable hardware resources, presenting an industry solution for the low-resolution face recognition task.
High Resolution Image Reconstruction via Dictionary Learning in Sparse Environment

In this paper, single- image super-resolution framework is presented by means of sparse representation. Patch-based super resolution is a technique where spatial features from a low-resolution (LR) patches are used as references for the reconstruction of high-resolution (HR) image patches. Sparse representation for each patch is extracted and these coefficients are used to recover super-resolution patch. Two dictionaries are jointly trained for the LR and HR image patches. By this, similarity between sparse representation between the LR and HR patch pair is established. Hence, the sparse representation of a LR image patch can be applied with the HR image patch dictionary to obtain a HR image patch. The dictionary thus learnt is a compact one. The experimental results demonstrate the effectiveness of the proposed algorithm.
BVMSOD: Bionic Vision Mechanism based Salient Object Detection

Salient object detection is proved to be important in vision tasks, and recent advances in this task is substantial, mostly benefiting from the explosive development of convolutional neural networks (CNNs). However, most of the existing methods are exploited to boost the metrics, while whether the proposed models are consistent with the human visual mechanism is rarely considered. In terms of this issue, we proposed the bionic vision mechanism based salient object detection framework, named BVMSOD. Our framework contains two branches, which have cross connection with each other to simulate the binocular vision pathway. In each single branch, there are hierarchical short connections between the features of different level layers, which is consistent with the mechanism that the human vision contains the primary vision and the advanced vision. Extensive experiments demonstrate that our method is effective and robust in various scenes. This reveals that there are numerous essential mechanisms in the bionic and brain-inspired intelligence, which could be combined with deep learning method in various vision tasks.
Phrase-Level Global-Local Hybrid Model For Sentence Embedding

Latent structure models have drawn much attention due to the ability to learn an optimal latent hierarchical structures without explicit structure annotations. However, most existing models suffer from high computation complexity and hard training. To this end, this paper proposes a novel phrase-level global-local hybrid model, which inherits the advantages of existing latent structure models while requires less time complexity. Our model splits a sentence into multiple phrases by a category-selection module. Then, it encodes the context dependency by a phrase-level global encoding module, and encodes the task-specific information by a phrase-level local encoding module. Finally, sentence embedding is obtained by integrating the global encoding and task-specific encoding. Experiments on public benchmarks show that, our model achieves state-of-the-art performance on the tasks of sentence classification and natural language inference. Meanwhile, our model is at least 10 times faster than existing state-of-the-art method at the training stage.
VR FOR ASSEMBLY TASKS IN THE MANUFACTURING INDUSTRY – INTERACTION AND BEHAVIOUR

Abstract A testbed was developed aiming to contribute to further knowledge on what is required from a VR application in order to be useful for planning of assembly tasks. In a pilot study the testbed was tested on students. The focus of the study was to explore the users’ behaviour, and to gain a better understanding of their experience using VR. The students experienced a gap between the real world and VR, which confirms theories that VR is not a copy or twin of an object or environment.
Research on an Improved CNN Speech Recognition System Based on Hidden Markov Model

In order to improve the accuracy of speech recognition and speaker identification in the field of production and life, an improved convolutional neural network algorithm based on hidden markov model is proposed, which includes four parts: feature extraction, segmentation, training and clustering. Quantization and clustering were carried out in the recorded sound set, the feature vectors of the obtained sound signal were segmented, and further training was conducted by deep convolutional network. Finally, after the state segmentation of HMM was completed, the recognition was completed by CNN as the input part of CNN. The experimental results show that the improved CNN based on hidden markov model can complete the recognition function and verify the correctness of the algorithm.
Secure Communication for Intelligent Enterprises

The emerging rapid increase in the connectivity of computer networks and their usage has drawn the attention of organizations and data science researchers towards a secure data storage architecture, where it can be protected from unauthorized entities. In this paper a novel methodology "Image Encryption with Modified Diffie Hellman" (IEMDH) is designed and developed to address the suitability of encryption algorithms for low powered de vices and to offer a better security than existing approaches to contribute towards the Intelligent Enterprise security and sustainability. Here, the symmetric AES algorithm and Modified Diffie Hellman (MDH) algorithms are combined to enhance the overall security and it also eliminates the key exchange drawback in symmetric key techniques. Later, a performance evaluation is carried out between the proposed IEMDH approach and IERSA (Image Encryption with RSA algorithm) to check the suitability on low power devices.
Query/Task Satisfaction and Grid-based Evaluation Metrics Under Different Image Search Intents

People use web image search with various search intents: from serious demands for work to just passing time by browsing images of a favorite actor. Such a diversity of intents can influence user satisfaction and evaluation metrics, both of which are important factors for providing a better image search environment. In this paper, we investigate this influence by using a publicly available one-month field study dataset. With respect to satisfaction, we take into consideration both query-level and task-level satisfaction provided by search users. Regarding the evaluation metrics, we use grid-based evaluation metrics that incorporate user behavior specific to image search. The results of our analysis indicate that both query/task satisfaction and grid-based evaluation metrics are influenced by the image search intent. Based on the results, we show possibilities to support users' search processes according to their search intents. We also discuss that there is still room for improvement in evaluation metrics through the development of intent-aware evaluation metrics in image search.
Twinvo: Unsupervised Learning of Monocular Visual Odometry Using Bi-Direction Twin Network

In recent years, more attention has been paid to the use of unsupervised deep learning approaches in Visual Odometry (VO). In this paper, we present a novel unsupervised learning framework called TwinVO for the estimation of 6-DoF camera poses and monocular depths. Taking account of the extreme imbalance between forward and backward camera motions in datasets, we provide an innovative twin module to predict bi-direction ego-motions simultaneously. Meanwhile, motivated by the cooperative game theory, an Inversion Consistency Constraint is suggested to supervise the bi-direction motions so that a final win-win state is achieved. Furthermore, more delicate structures are adopted in depth estimation network to gain about 37% the number of parameters reduction as well as achieve better performance. Extensive experiments on the KITTI dataset reveal that our scheme achieves superior performance and provides better results for both pose and depth estimation.
Peanet: The Products of Experts Autoencoder for Abnormal Detection

Recent researches have shown great progress in abnormal detection with the application of deep neural network. However, those works tend to solve the task concentrating on homogeneous features or with a decoupled model that combines features inefficiently. In this paper, we propose a method for abnormal detection that learns different features' distributions in low-dimensionalities and combines them in an efficient way. The main architecture of our work consists of a two-stream AutoEncoder and LSTM architecture model to get the compressed low-dimensional spatial and temporal features respectively. Instead of standard Expectation-Maximization algorithm, we further design two estimation network to estimate probability densities and combine them with the Products of Experts. In addition, the experiments of our method on different dataset deliver on-par or superior performance compared to state-of-the-art methods in one-class and abnormal detection settings.
Automatic Modulation Classification and SNR Estimation Based on CNN in Physical-layer Network Coding

In this paper, we first propose the Automatic Modulation Classification (AMC) problem based on the Physical layer Network Coding (PNC) system and elaborate in detail. We use Convolutional Neural Networks (CNN) to identify nine cases including three modulation formats with three phase shifts respectively, and estimate the Signal-to-Noise Ratio (SNR) simultaneously. As the result, we correctly identify several modulation formats and typical phase offsets with a 100% recognition rate, and estimate the received signal-to-noise ratio effectively with recognition rate above 98%.
A Fast Optimization Method for Additive Model via Partial Generalized Ridge Regression

Although many statistical toolsFukui, Keisuke for analyzing the additive model apply the back-fitting algorithm,Ohishi, Mineaki it is well-known that it is not guaranteed that the algorithm will converge or obtain a unique solution. Furthermore,Yamamura, Mariko running the algorithm on large datasets is computationally challenging. In order to address these issues, we propose a new optimization method for the additive model via partial generalized ridge regression.Yanagihara, Hirokazu Using the proposed method, all trends of the additive model are estimated simultaneously, and closed-form expressions for the smoothing parameters that minimize the GCV criterion are derived. From a numerical study, compared to the back-fitting algorithm, our new method achieved higher performance with respect to both predictive accuracy and computational efficiency.
El aula invertida como estrategia para el aprendizaje de las Matemáticas en segundo año de E.G.B. (Revisión)

espanolEl modelo de aula invertida, Invertedo o Classroom Model (ICM/FCM, por sus siglas en ingles) es util porque permite el desarrollo del pensamiento en el nivel de razonamiento logico para potenciar lo aprendido en forma independiente, el cual se ha extendido de manera favorable en las escuelas, y ha ganado aceptacion en la docencia, pues ha llegado a ser una de las propuestas de ensenanza – aprendizaje mediana por tecnologia , que se esta promoviendo en los centros de educacion media superior en America, asi lo demuestran diversos autores en sus investigaciones publicadas. Este trabajo tiene como objetivo fundamentar la importancia del metodo “Flipped Classroom o aula invertida” para el aprendizaje de las matematicas. A fin de dar congruencia y logica del modelo de estudio, se abordan datos como sustento legal, origen, elementos teoricos de reconocer queel aula invertida que se desarrolla en la practica de la pedagogia constituye una reconstruccion epistemica a medida que se aplican los conceptos, las teorias que lo apoyan y la conceptualizacion del objeto y campo de estudio. EnglishThe inverted model, Inverted or Classroom Model (ICM / FCM) is useful because it allows the development of thinking at the level of logical reasoning to enhance what has been learned independently, which has been extended from favorable way in schools, and has gained acceptance in teaching, because it has become one of the proposals of teaching - medium learning by technology, which is being promoted in the centers of higher secondary education in America, as demonstrated by various authors in their published research. The purpose of this work is to support the importance of the Flipped Classroom or inverted classroom method for learning mathematics. In order to give congruence and logic of the study model, data are addressed as legal support, origin, theoretical elements to recognize that the inverted that develops in the practice of pedagogy constitutes an epistemic reconstruction as the concepts are applied, the theories that support it and the conceptualization of the object and field of study.
Demo: Execution and Access Control for Restricted Application Containers on Managed Hosts (xRAC)

Restricted application containers (RACs) encapsulate applications with their dependencies and configuration for execution on a hypervisor host. xRAC [1] is a novel approach for execution control and network access control (NAC). That is, a RAC can be executed only after successful authentication and authorization (AA) and obtain limited access to network resources. A RAC has a unique IPv6 address so that its traffic is identifiable and controllable by network components. For AA, xRAC adopts and extends components and procedures of 802.1X. We publish its source code and a testbed setup guide on GitHub [2]. In this paper, we give a brief overview on the architecture and functionality of xRAC, describe the prototypical implementation, a testbed, and four demo scenarios.
Practical Aspects of Creating a Teacher's Information Space

The article discusses one of the aspects of digitalization of education, in terms of digitalization of the educational process itself. The methodology of creating a personal information space of a teacher is presented. The form of this space is a teaching site with teaching materials. A method for structuring methodological content is proposed. The methodology of posting structured content on a WordPress blog is described. A method for creating media content for special disciplines of technical areas of training is considered. The analysis of educational video content presented on the Internet is carried out. A step-by-step methodology for creating a video lesson in the Camtasia Studio program based on the Power Point presentation is described. There is an example of its application, successfully implemented in the educational process.
Symbolic Conflict In Political Discourse: Case Study In ‘People Power’ Discourse Post General Election 2019 In Indonesia

The aftermath of Indonesian general election 2019 generated the discourse on People power triggered by the dispute between Jokowi-Amin supporters and Prabowo-Sandi’s. This article describes (1) how symbolic conflict on people power produced by both competing parties is, and (2) why they fought on the discourse of people power, while the study also examined relationship amid discourse, supply chain system, symbolic conflict and people power with mediating effect of politics. This study is qualitatively approached by using pragmatics framework. The data were collected through documentation technique, derived from the broadcasted speech on YouTube found after general election 2019. The collected data were analyzed by the interactive model of Miles dan Huberman. Considering the fact that political discourse contains a variety of interests, the researchers applied discourse conflict analysis coined by Bourdieu, comprising two sorts of discourse, namely prior discourse and counter discourse. Based on data analysis, the study discovers some findings as follow; first, symbolic conflict on people power discourse takes place in two forms, i.e.: (1) introducing a single in-group’s meaning, perspective, and values, and (2) rejecting the meaning of the other group’s logic and perspective.  Second, the strategy of symbolic conflict is meconnaisance; i.e. euphemism and censorization. Third, simbolic conflict on people power discourse motivated by (1) the desire to win the election, (2) difference of habitus, and (3) difference of political goal.
The Effect Of Wind Turbine On Sea Flow

Abstract Indonesia is a maritime country that had quite extensive territorial waters, one of which was located in the East Kalimantan region. Despite having a large enough water area, only 30 percent of new marine products were used. Marine resources, especially fisheries in Indonesia, were very abundant but had not been utilized to the full, so it needs to be made offshore cages with the concept of aquaculture. Pembangkit Listrik Tenaga Bayu (PLTB) was renewable alternative energy for the coastal region. To support the development of this technological innovation, this study discussed the movement of ocean currents under the influence of wind turbines. The governing equation consists of the Shallow Water Equation and the wind turbine model. After the governing equation is formed, the model is solved numerically and the effect of wind turbines on sea flow can be observed. The influence of wind turbines on sea currents is large enough that it needs to be reviewed in the planning of developing wind turbines in the offshore sea area.
Analysis of Light Field Imaging and Segmentation on All-Focus Images

Image analysis is one of the fastest-growing research areas, and within it, light field or plenoptic image processing has gained great popularity in the recent years. Plenoptic images have got the inbuilt capability of imaging an object at slightly variant viewing angles which opens up possibilities for a deeper image analysis. A light field image is basically constituted with an array of sub-aperture images that have been captured using an array of lenses. Segmenting an image into its constituent parts of interest is very crucial in an image analysis point of view. Its efficiency influences the subsequent image processing stages. Various segmentation algorithms for conventional images have been developed over the recent years. This paper analyses the field of light field imaging in terms of its acquisition and discusses the various application areas and challenges pertaining to it. It also throws light on some of the research possibilities in this field. Finally, few segmentation algorithms are analysed on how it works when applied to light field all-focus images. The segmentation parameters like jaccard, dice, sensitivity, accuracy and specificity are being analysed here. From among the available sub-aperture images, the central view all-focus image has been chosen to evaluate the segmentation results. The segmentation is being evaluated on light field saliency dataset (LFSD) which has the ground truth data of segmentation corresponding to all-focus light field images.
A Fast Method of Function Approximation using Broad Learning System

In this paper, A new method is applied to approach function -The Broad Learning System (BLS). Due to the complexity of the system in actual engineering, when the traditional algorithm handles the approximation problem, it faces problems such as complicated calculations and severe time consumption. To solve the above problems, the BLS uses the way of network horizontal expansion, horizontal extension and vertical immobility. Input data is mapped to feature nodes as feature vectors of the network, and then connected enhancement nodes by random weights, the enhancement node and the function node are spliced together as an input layer, and then directly connected to the output of the system. Finally, compared with traditional algorithms: back propagation neural network (BPNN), Radial Basis Function neural network (RBFNN), the results of experiments show that the function approximation method based on the BLS in this paper can make the system use less time under the premise of ensuring the approximation accuracy. This result provides the possibility of real-time and high-precision approximation for other intelligent algorithms.