Reviewerly/exHarmony huggingface dataset:
How to do things with words: A Bayesian approach

Communication changes the beliefs of the listener and of the speaker. The value of a communicative act stems from the valuable belief states which result from this act. To model this we build on the Interactive POMDP (IPOMDP) framework, which extends POMDPs to allow agents to model others in multi-agent settings, and we include communication that can take place between the agents to formulate Communicative IPOMDPs (CIPOMDPs). We treat communication as a type of action and therefore, decisions regarding communicative acts are based on decision-theoretic planning using the Bellman optimality principle and value iteration, just as they are for all other rational actions. As in any form of planning, the results of actions need to be precisely specified. We use the Bayes’ theorem to derive how agents update their beliefs in CIPOMDPs; updates are due to agents’ actions, observations, messages they send to other agents, and messages they receive from others. The Bayesian decision-theoretic approach frees us from the commonly made assumption of cooperative discourse – we consider agents which are free to be dishonest while communicating and are guided only by their selfish rationality. We use a simple Tiger game to illustrate the belief update, and to show that the ability to rationally communicate allows agents to improve efficiency of their interactions.
Case Study of Difficulty Index and Discrimination Index Item of Year 5 English Language Summative Test (Comprehension)/ Kajian Kes Indeks Kesukaran Dan Diskriminasi Item Ujian Sumatif Bahasa Inggeris (Pemahaman) Tahun 5

Summative test is an evaluation process on students which is conducted comprehensively and administered at the end of a teaching and learning process. Evaluation is the benchmark for the success of a teaching and learning process which aims to determine the level of student achievement. The overall teaching objectives serve as the basis in the construction and administration of this summative test. Information from the summative tests conducted will be evaluated and interpreted about students’ mastery level. The summative test conducted should also provide an overview to what extent the teaching objectives that have been set are achieved. The results of these summative test scores are then used in various evaluation and taken as Take Off Value (TOV) of students for the following year. Therefore, a study on a summative test instrument was conducted in identifying the difficulty index and item discrimination for the items contained in the summative test paper. A case study on this English Summative Test (Comprehension) was conducted towards 27 Year 5 students from the same class. The results of the study found that the items of summative test need to be modified. Besides, the students' failure to get high marks and good grades in the summative test is due to the low item quality and low validity of the question. The results of this study have implications for subject teachers to improve the items in this summative test instrument in order to get accurate information on students’ ability. The index of difficulty and discrimination of items provides information on students’ strengths and weaknesses in a particular topic and also can provide an overview of the skills that need to be designed in the reinforcement and enrichment activities.
A Hierarchical Static-Dynamic Encoder-Decoder Structure for 3D Human Motion Prediction with Residual CNNs

Human motion prediction aims at predicting the future poses according to the motion dynamics given by the sequence of history poses. We present a new hierarchical static-dynamic encoder-decoder structure to predict the human motion with residual CNNs. Specifically, to better mine the law of the motion, a new residual CNN-based structure, v-CMU, is proposed to encode not only the static information but also the dynamic information. Based on v-CMU, a hierarchical structure is proposed to model different correlations between the different given poses and the predicted pose. Moreover, a new loss function combining the static and dynamic information is introduced in the decoder to guide the prediction of the future poses. Our framework features two-folds: (1) more effective dynamics mined due to the fusion of information of the poses and the dynamic information between poses and the hierarchical structure; (2) better decoding or prediction performance, thanks to the mid-level supervision introduced by the new loss function considering both the static and dynamic losses. Extensive experiments show that our algorithm can achieve state-of-the-art performance on the challenging G3D and FNTU datasets. The code is available at https://github.com/liujin0/SDnet .
The Effect of Mobile-Assisted Teaching of Collocations on Reading Ability of Iranian EFL Learners

This study aimed to discover the effect of mobile-assisted teaching of collocations on Iranian EFL learners’ reading achievement. For this purpose, a PET test was given to 85 intermediate EFL learners as the proficiency test. After homogenization, 30 female and male students within the age range of 16 to 30 years old from an institute in Alborz Province were selected as the participants in the experimental group and 30 female and male students within the age range of 14 to 25 years old from another institute in Alborz province were selected as the participants in the control group, as well. A reading test was utilized as the pre-test to measure the participants’ reading achievement. The participants in the experimental group received mobile-assisted teaching of collocations through Flax Completing Collocation game while the participants in the control group received teacher instruction of the collocations existing in the game inside the classroom. To discover the effect of mobile-assisted teaching of collocations, a post-test, the same as the pre-test was given to the participants after the treatment. An independent t-test was run to compare the experimental and control groups’ means on the pre and posttest of reading achievement. The results indicated that the experimental group, after receiving mobile-assisted teaching of collocations, significantly outperformed the control group on the posttest of reading achievement. Regarding the findings of the study, the achieved results would help EFL teachers, learners, and material developers to be informed about the effect of mobile-assisted teaching of collocations on reading ability. Furthermore, this study presents some recommendations in future studies.
Calibration of a three-dimensional profilometry system by temporal phase unwrapped algorithms

In this work, we proposed a procedure for the calibration of 3D surface shape measurement system, which is based on fringe projection and phase shifting algorithms. Our approach consists in the use of temporal phase unwrapping methods to determine the phase-to-height mapping relationship. In particular, we propose the use of the two-step temporal phase-unwrapping algorithm. For that, two sequences of fringe patterns (low and high sensitivity) are projected onto the reference plane, which is shifted perpendicularly to the camera-projector plane. Then, the phase maps at each shifting step are retrieved from acquired sequences of sinusoidal intensity patterns using the two-step temporal unwrapping formula. Finally, using the phase maps at well-known in a least-squares scheme, the system parameters, nonlinear model of calibration, are estimated, i.e. the phase-to-height mapping relationship. Validation experiments are presented.
Innovative Approaches To Teaching Composition In Art And Pedagogical Education

The article discusses the issues of improving the quality of teaching composition in art and pedagogical education related to the need to develop the creative potential of a future painter-teacher in the context of humanization of education. The author notes that the artistic composition, uniting all the disciplines of the art block in art and pedagogical education, is not sufficiently studied from the point of view of the conceptual apparatus, categories, and terminology. The article suggests ways of productive organization of teaching composition for the formation of professional competencies of university students in art, depictive and pedagogical activities. The article presents the results of surveys of students, graduates, employers and teachers of art disciplines of the specialty "Visual Arts and Continuing Education: Decorative and Applied Arts" at Yaroslav the Wise Novgorod State University. Questionnaires were conducted from 2012 to 2018 and were related to the process of studying the “Composition” training module. The questionnaires contained questions that would reveal students' understanding of the basic principles of the theory of artistic composition. Based on the results of the surveys, the most effective approaches to the design of educational and methodological support for the educational process were identified. The author of the article offers a number of recommendations on the creation of study guides and teaching packages that can be useful for university teachers, teachers of secondary vocational and primary art education.
Coarse-To-Fine Framework For Music Generation via Generative Adversarial Networks

Automatic music generation is highly related to Natural Language Processing (NLP). A current note in melody always depends on its context, just like a word in NLP. Yet the difference is that music is built upon a set of special chords that formulates the skeleton of the melody. To enhance automatic music generation, we propose a two-step adversarial procedure: Step 1 learns to generate chords via a chord generative adversarial networks (GANs); and step 2 trains a melody GAN to generate music for which the input is conditioned on the chords produced through the first step. Under such a two-step procedure, the chords generated in the first step formulate a basic framework of the music, which can theoretically and practically improve the performance of melody generation in the second step. Experiments demonstrate that such a cascading process is able to generate high-quality music samples with both acoustical and music theoretical guarantees.
Making robots useful

Artificial IntelligenceThe goal of creating generally intelligent robots that can carry out diverse tasks is a huge challenge. In particular, programming how robots learn and adapt to their surroundings quickly and nondestructively is proving especially difficult. In a Perspective, Pack Kaelbling discusses advances in machine learning based on ideas from cognitive neuroscience. In particular, the applications of reinforcement learning, where a valued outcome is reinforced, and how robots can be taught general lessons to apply to numerous situations are discussed.Science , this issue p. [915][1] [1]: /lookup/doi/10.1126/science.aaz7597
Relationship between boreholes productivity and major fractures extracted from satellite images of katiola region

The hard rock systems of crystalline basement are now much more under consideration as they may actually constitute viable groundwater resources.The area of katiola is located in the North-Center part of Côte d'Ivoire and belongs to Paleoproterozoic domain, where fractures constitute preferential of flow and accumulation groundwater.This study aims to establish the relationship between the wells efficiency and the structural lineaments detected.The map of major fractures was carried using a 50 x 50 cm radar image processing.A total of 323 major fractures were carried out with a dominant direction N90-100.The flow map was surimposed to those of major fractures to show the relationship between the wells efficiency and the structural lineaments detected.Several boreholes with low flows (0 < Q < 2.5 m 3 /h) are not superimposed on the major structural lineaments detected and seem have to be implanted using simple geomorphological characteristics.Whoever, more of wells with high flow (Q ≥ 5 m 3 /h) are match and overlap with open and interconnected fractures.There is an influence of distance between boreholes and major accidents on the productivity of the works.This result show that fractures arrangement and interconnection degree take an important role in storage and conduct properties of hard rock systems in Katiola area.This approach may be used as an indirect mean of fracture network validation which can be used during the future drilling campaigns.
Aberration-induced errors in deflectometric measurement

Deflectometry is a powerful measuring technique of complex optical surfaces. Usually a series of binary patterns or sinusoidal fringes are displayed on a screen, and correspondences are established between the screen and camera points according to their gray levels or phases. The image associated with a screen pixel is blurred due to the defocus and aberrations of the off-axis imaging system, and the calculated location of the correspondence point will in turn be biased. The space variant point spread function associated with the catadioptric system is analyzed based on the light field method, and the resulting blurring effect is then addressed using Wiener deconvolution algorithm. Henceforth the phase errors in the captured images can be compensated effectively. Experimental results are presented to demonstrate the feasibility and effectiveness of the proposed method.
Fringe Order Correction for Number-Theoretical Phase Unwrapping Method via Maximum Likelihood Principle

The number-theoretical phase unwrapping method has recently been widely applied in fringe projection profilometry.But fringe order errors may occur due to noise or distortion, leading to errors in the unwrapped phase map, and eventually affecting the accuracy of the reconstructed object surface.In this paper, we propose a novel fringe order correction method based on the maximum likelihood principle.The direct cause of fringe order error is the deviation of an intermediate variable which in theory should be an integer, and the ground truth of the integer stays unchanged within a valid neighborhood.By modeling the calculated intermediate variable as an observed sample from the normal distribution of the unknown ground truth integer, we can determine a valid neighborhood relative to the observed pixel.Then the ground truth integer can be calculated by maximizing the likelihood function and then the fringe order error is corrected.The simulation results and experimental comparisons have verified the feasibility, robustness, and superiority of the proposed method in contrast with other fringe order correction methods.
A Model for Mammogram Image Segmentation based on Hybrid Enhancement

Introduction: Detection of breast cancer at early stages is very important and it helps in saving the life of the patient.The mammography is one of the techniques which is used worldwide by radiologists to detect breast cancer.It is very important to detect breast cancer correctly as human life is associated with correct detection.So, nowadays computer-aided diagnose (CAD) systems are used to detect breast cancer from a mammography image.In this work, a transfer learning-based method is used to train the system for the detection of breast cancer.The proposed model is based on a hybrid technique of image enhancement and segmentation for the pre-processing.The pre-processing is done to improve the peak signal to noise ratio (PSNR) value of originally acquired images.The Mammography Image Analysis Society (MIAS) dataset is used in this work along with other clinical images which result in the creation of around 928 images in the dataset.Aim: This work aims to segment the affected area image from the original image.Then enhanced the segmented image by using PSNR so that better result is helpful to identify the affected area.The correct detection is associated with a life decision. Result:Mammogram image has been segmented from the affected area.Various type of noises may be induced in the black and white image.The image represents the original, enhanced and segmented image.The PSNR value for an image varies from 33 to 36.The segmented image is representing the affected regions to limit the image and to eliminate useless details. Conclusion:This paper put forward the methodology which will segment the affected area from the image.The image has been enhanced based on LCM and CLAHE.Morphology technique has been used along with the Otsu threshold for accurate segmentation.The proposed network is promising as it is trained for mammogram dataset for higher accuracy.
ON METHODS FOR DETECTING INTERFERENCE TRACES IN ACCEPTED M-SEQUENCES

Modern systems of information transmitting give attention of correctness received sequences, but they are interested ordinally only current moment of receiving. Nevertheless, taking into account the prehistory in the process of receiving may also be useful. In this case, the communication system, in addition to its main function of transmitting information, can acquire a new quality - the ability to detect interference and identify their type. Information obtained this way about the prehistory of the origin of errors can used for correcting parameters of transmission. Using the collected prehistory data and using a neural network, it possible also to determine the type of interference that is in effect.
Augmentasi Data Pengenalan Citra Mobil Menggunakan Pendekatan Random Crop, Rotate, dan Mixup

Deep convolutional neural networks (CNNs) have achieved remarkable results in two-dimensional (2D) image detection tasks. However, their high expression ability risks overfitting. Consequently, data augmentation techniques have been proposed to prevent overfitting while enriching datasets. In this paper, a Deep Learning system for accurate car model detection is proposed using the ResNet-152 network with a fully convolutional architecture. It is demonstrated that significant generalization gains in the learning process are attained by randomly generating augmented training data using several geometric transformations and pixel-wise changes, such as image cropping and image rotation. We evaluated data augmentation techniques by comparison with competitive data augmentation techniques such as mixup. Data augmented ResNet models achieve better results for accuracy metrics than baseline ResNet models with accuracy 82.6714% on Stanford Cars Dataset.
Rochester: The eRMS 2020!

Figure 1. Screenshot during What’s New in Minerals, Part 1, by Jeff Scovil, and the lineup of talks for the day.Reproduced with permissionIn early March, we realized that the 47th Rochester Mineral...
Building Acoustic and Language Model for Continuous Speech Recognition in Bahasa Indonesia

Here a development of an Acoustic and Language Model is presented. Low Word Error Rate is an early good sign of a good Language and Acoustic Model. Although there are still parameters other than Words Error Rate, our work focused on building Bahasa Indonesia with approximately 2000 common words and achieved the minimum threshold of 25% Word Error Rate. There were several experiments consist of different cases, training data, and testing data with Word Error Rate and Testing Ratio as the main comparison. The language and acoustic model were built using Sphinx4 from Carnegie Mellon University using Hidden Markov Model for the acoustic model and ARPA Model for the language model. The models configurations, which are Beam Width and Force Alignment, directly correlates with Word Error Rate. The configurations were set to 1e-80 for Beam Width and 1e-60 for Force Alignment to prevent underfitting or overfitting of the acoustic model. The goals of this research are to build continuous speech recognition in Bahasa Indonesia which has low Word Error Rate and to determine the optimum numbers of training and testing data which minimize the Word Error Rate.&#x0D;
A TOOL TO ENHANCE THE CAPACITY FOR DEEP LEARNING BASED OBJECT DETECTION AND TRACKING WITH UAV DATA

Abstract. Currently, deployment of UAV has transformed from crucial to day-to-day scenarios for various purposes such as wastage collection, live entertainment, product delivery, town mapping, etc. Object tracking based UAV applications such as traffic monitoring, wildlife monitoring and surveillance have undergone phenomenal changeover due to deep learning based methodologies. With such transformation, there is also lack of resources to practically explore the UAV images and videos with deep learning methodologies. Hence, a deep learning-based object detection and tracking tool with UAV data (DL-ODT-UAV) is proposed to fill the learning gap, especially among students. DL-ODT-UAV is a resource to acquire basic knowledge about UAV and deep learning based object detection and tracking. It integrates various object annotators, object detectors and object tracker. Single object detection and tracking is performed with YOLO as object detector and LSTM as object tracker. Faster R-CNN is adopted in multiple object detection. With exploring the tool, the ability of students to approach problems related to deep learning methodologies will improve to a greater level.
Photomechanics evaluation combining fringe projection and digital height correlation

Fringe projection (FP) is one of the most widely used method to precisely measure the 3D geometry of complicated surfaces. However, when the object is in motion, it is difficult to estimate the motion or displacement that happens between the frames. The most commonly used method for estimating the in-plane displacement is Digital Image Correlation (DIC). It has been widely used in the area of experimental mechanics. As the traditional DIC method uses a single camera, it is difficult to recover the 3D shape of the object. In this paper, we propose a novel method called digital height correlation method for measuring surface profile deformation. The height data obtained from FP can be used to estimate the deformation. The proposed method involves measuring the 3D profile of an object, which has rich surface topographic variations using a high-accuracy fringe projection method. Then, the 3D profiles obtained from the before and after displacement state are correlated to extract the displacement map. Specifically, the out-of-plane displacement is extracted by applying the correlation technique to the height map obtained from the 3D profile of the object. The accuracy and efficacy of the digital height correlation method are validated using rigid-body translation tests and tensile tests of a rubber specimen. Experiments conducted will demonstrate the success of the proposed method.
An infrastructure for faithful execution of remote attestation protocols

Experience shows that even with a well-intentioned user at the keyboard, a motivated attacker can compromise a computer system at a layer below or adjacent to the shallow forms of authentication that are now accepted as commonplace[3]. Therefore, rather than asking "Can we trust the person behind the keyboard", a still better question might be: "Can we trust the computer system underneath?". An emerging technology for gaining trust in a remote computing system is remote attestation. Remote attestation is the activity of making a claim about properties of a target by supplying evidence to an appraiser over a network[2]. Although many existing approaches to remote attestation wisely adopt a layered architecture-where the bottom layers measure layers above-the dependencies between components remain static and measurement orderings fixed. For modern computing environments with diverse topologies, we can no longer fix a target architecture any more than we can fix a protocol to measure that architecture.