Reviewerly/exHarmony huggingface dataset:
CLARIFICATION OF THE ALGORITHM FOR ESTIMATING THE FREQUENCY OF THE SIGNAL RECEIVED BY THE SATELLITE COMMUNICATION SYSTEM IN A CONTINUOUS MODE UNDER THE INFLUENCE OF «NEIGHBORING CHANNELS»

Satellite communication systems that use phase modulation of a signal designed to transmit useful information in a continuous mode face the problem of frequency uncertainty of the signal when used as intended. For demodulators of satellite modems of such systems operating with a continuous input signal, the most significant is the problem of synchronization in the frequency of carrier oscillations in the conditions of frequency uncertainty of the signal. This synchronization task is actually reduced to estimating the true parameters of the signal, namely the estimation of the carrier frequency. The complexity of the task of estimating the carrier frequency in a satellite channel with phase modulation is exacerbated by the presence of additional interfering actions of neighboring channels - signals with the same type of modulation and the same data rate. The paper specifies the algorithm for estimating the carrier frequency of the signal received by the satellite communication system in a continuous mode, taking into account the influence of neighboring channels of information transmission. This algorithm allows to estimate the carrier frequency according to the rule of maximum likelihood, taking into account the condition of uncertainty of all parameters of the signal received by the satellite communication system in continuous mode, taking into account the influence of neighboring channels of information with a minimum observation interval. It includes the steps of: calculating the readings of the amplitude spectrum of the received signal and counting the convolution of the obtained amplitude spectrum with frequency response UV; calculating the count of the derivative convolution and finding the initial estimate of the frequency; based on the obtained evaluation of the evaluation procedure based on the multiplication of the phase of the received signal and the calculation of the estimated carrier frequency of the received signal.In order to assess the effectiveness of this algorithm in the comparison of the effectiveness of the estimates provided by the proposed procedure and the estimates made on the basis of subtraction of the global maximum convolution of the amplitude spectrum of the received signal. The results of this assessment presented in the paper showed that the dependences of the normalized variance on practically do not differ. That confirms the efficiency and feasibility and practical value of the algorithm for estimating the carrier frequency given in the work, taking into account the influence of neighboring channels of useful signal transmission. A promising area for further study of the issues raised in the work is the adaptation of this algorithm to the carrier frequency estimation in combined phase synchronization systems that have the ability to increase the order of astatism, monitoring the carrier frequency (pilot signal), the phase of which is modulated by a deterministic Doppler signal.
Cloud Computing in Agricultural Development of Rural India

Now India lives in its villages and smaller towns and in that respect lies the future of India.Indian villages have been ignored for more than 72 years.Now the cloud computing technology will bring the changes that were required to bridge the rural India and Urban India.This will enhance the Indian rural economy as well as country's economy.The paramount sources of income of Indian people are agriculture.Therefore, the development of the ICT is basically focused on the Indian agriculture field.Cloud computing technology is a general term which is used to describe a new class of network based computing that take place via the internet.These platforms hide the complication and details of elemental infrastructure from users and applications by providing very simple graphical interface.Latest Cloud computing technology impacted positively on agriculture field and related services they provide for users.
Detecção de Roubo de Computadores em Laboratório Usando Visão Computacional

Computer theft in computer labs causes academic damage to coursesthat require this resource and ends up directly harming students. Inthis context, this paper describes a methodology applied to detectcomputer removal through video analysis in real-time. For eachframe, image processing and computer vision techniques were used,subtracting background information, binarization, segmentationof the region of interest and definition of contours. The case studywas developed at a Brazilian university. For theft detection, it wasconsidered a black computer tower case carried by people leavingthe laboratory. Monitoring is carried out by a camera positioned infront of the lab exit door. The software developed alerts a suspiciousactivity that may indicate a possible computer theft.
Structural Knowledge Distillation

Knowledge distillation is a critical technique to transfer knowledge between models, typically from a large model (the teacher) to a smaller one (the student). The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student's output distributions. However, for structured prediction problems, the output space is exponential in size; therefore, the cross-entropy objective becomes intractable to compute and optimize directly. In this paper, we derive a factorized form of the knowledge distillation objective for structured prediction, which is tractable for many typical choices of the teacher and student models. In particular, we show the tractability and empirical effectiveness of structural knowledge distillation between sequence labeling and dependency parsing models under four different scenarios: 1) the teacher and student share the same factorization form of the output structure scoring function; 2) the student factorization produces smaller substructures than the teacher factorization; 3) the teacher factorization produces smaller substructures than the student factorization; 4) the factorization forms from the teacher and the student are incompatible.
A template-free object motion estimation method for industrial vision system in aligning machine

With the popularity of varied sensors as well as the development of communication technologies. Industrial vision systems have been deployed in many manufacture applications. Particularly, industrial vision systems coped with air nozzle are often adopted in aligning machines for object aligning. Since alignment efficiency depends on propriety of blown timing/pressure which is casually manifested in the motion of the observed blown objects, motion estimation method is critical to adopt. Unlike conventional methods typically estimate objects' motions using prior prepared templates, this paper proposed a template-free object motion estimation method for industrial vision system in aligning machine. By virtue of the properties of industrial images, an observation area with bounding box are initialized for each object, then motion estimation is achieved by updating them following expectation-maximization principle. Experiments revealed that the proposed method is able to achieve continuous motion estimation in less processing time.
Conformal retrofitting via Riemannian manifolds: distilling task-specific graphs into pretrained embeddings

Pretrained (language) embeddings are versatile, task-agnostic feature representations of entities, like words, that are central to many machine learning applications. These representations can be enriched through retrofitting, a class of methods that incorporate task-specific domain knowledge encoded as a graph over a subset of these entities. However, existing retrofitting algorithms face two limitations: they overfit the observed graph by failing to represent relationships with missing entities; and they underfit the observed graph by only learning embeddings in Euclidean manifolds, which cannot faithfully represent even simple tree-structured or cyclic graphs. We address these problems with two key contributions: (i) we propose a novel regularizer, a conformality regularizer, that preserves local geometry from the pretrained embeddings---enabling generalization to missing entities and (ii) a new Riemannian feedforward layer that learns to map pre-trained embeddings onto a non-Euclidean manifold that can better represent the entire graph. Through experiments on WordNet, we demonstrate that the conformality regularizer prevents even existing (Euclidean-only) methods from overfitting on link prediction for missing entities, and---together with the Riemannian feedforward layer---learns non-Euclidean embeddings that outperform them.
Subject Guides: Welcome to Special Collections: Use What You Request

A guide to visiting the Special Collections department at the University of Iowa Libraries.
Memory and Computation-Efficient Kernel SVM via Binary Embedding and Ternary Model Coefficients

Kernel approximation is widely used to scale up kernel SVM training and prediction. However, the memory and computation costs of kernel approximation models are still too high if we want to deploy them on memory-limited devices such as mobile phones, smartwatches, and IoT devices. To address this challenge, we propose a novel memory and computation-efficient kernel SVM model by using both binary embedding and binary model coefficients. First, we propose an efficient way to generate compact binary embedding of the data, preserving the kernel similarity. Second, we propose a simple but effective algorithm to learn a linear classification model with ternary coefficients that can support different types of loss function and regularizer. Our algorithm can achieve better generalization accuracy than existing works on learning binary coefficients since we allow coefficient to be $-1$, $0$, or $1$ during the training stage, and coefficient $0$ can be removed during model inference for binary classification. Moreover, we provide a detailed analysis of the convergence of our algorithm and the inference complexity of our model. The analysis shows that the convergence to a local optimum is guaranteed, and the inference complexity of our model is much lower than other competing methods. Our experimental results on five large real-world datasets have demonstrated that our proposed method can build accurate nonlinear SVM models with memory costs less than 30KB.
Developing Technical Teacher Evaluation Model Based on Cloud Computing

Teacher evaluation plays a crucial role in enhancing the learning process by evaluating the actual scene of learning and teaching in a certain institution. Moreover, it provides the educational field with the needed data that helps in achieving high levels of learning outcomes. It also focusses on the various factors that are involved in developing the learning process positively such as teachers, learners, administration, curriculum, etc. The main aim of this study is to develop, design a digital teacher evaluation cloud-system based on education ministry policy, and rules in Oman. In order to satisfy the main aim of this study; researchers reviews the current used methods for teacher evaluation process in Sultanate schools, and analyses and compares the current evaluation model with related works models. A conceptual model to clarify all related indicators and parameters that affect teacher evaluation process in Omani Schools was proposed, and then a cloud teacher evaluation system based on the proposed conceptual model was developed, implements and deployed by using a web-based system. Educational stockholder focusses to improve teacher’s performance to satisfy the education learning goals. The proposed web based system provides decision makers and stockholders with accurate teacher evaluation processes that help them to share the good practices, recover any weaknesses, and target a specific training and workshops. Educational supervisors in the department of applied sciences in the North Batinah Governorate evaluated the proposed system using technology acceptance model (TAM). All users declares that the system is useful, easy to use and they intend to use and recommend it.
Distilling a Deep Neural Network into a Takagi-Sugeno-Kang Fuzzy Inference System

Deep neural networks (DNNs) demonstrate great success in classification tasks. However, they act as black boxes and we don't know how they make decisions in a particular classification task. To this end, we propose to distill the knowledge from a DNN into a fuzzy inference system (FIS), which is Takagi-Sugeno-Kang (TSK)-type in this paper. The model has the capability to express the knowledge acquired by a DNN based on fuzzy rules, thus explaining a particular decision much easier. Knowledge distillation (KD) is applied to create a TSK-type FIS that generalizes better than one directly from the training data, which is guaranteed through experiments in this paper. To further improve the performances, we modify the baseline method of KD and obtain good results.
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems

Neural network-based models augmented with unsupervised pre-trained knowledge have achieved impressive performance on text summarization. However, most existing evaluation methods are limited to an in-domain setting, where summarizers are trained and evaluated on the same dataset. We argue that this approach can narrow our understanding of the generalization ability for different summarization systems. In this paper, we perform an in-depth analysis of characteristics of different datasets and investigate the performance of different summarization models under a cross-dataset setting, in which a summarizer trained on one corpus will be evaluated on a range of out-of-domain corpora. A comprehensive study of 11 representative summarization systems on 5 datasets from different domains reveals the effect of model architectures and generation ways (i.e. abstractive and extractive) on model generalization ability. Further, experimental results shed light on the limitations of existing summarizers. Brief introduction and supplementary code can be found in https://github.com/zide05/CDEvalSumm.
Design Of Automatic Response System For Handheld Mobile Learning Equipment Based On Augmented Reality

In order to overcome the problems of inadequate reality and long response time in traditional learning equipment response system, this paper proposes an automatic response system for handheld mobile learning equipment based on augmented reality. The hardware of the system is composed of keys, LED indicator, wireless communication transmitting module, wireless communication receiving module and microcontroller module. The system software is introduced into augmented reality. The technology is implemented and applied by AR display, and the design of automatic response system for handheld mobile learning equipment is completed. The experimental results show that the response time of the system is shorter than that of the traditional automatic response system for mobile learning equipment, which proves that the system has improved the performance of the automatic response system for handheld mobile learning equipment.
Learning Half-Spaces and other Concept Classes in the Limit with Iterative Learners

In order to model an efficient learning paradigm, iterative learning algorithms access data one by one, updating the current hypothesis without regress to past data. Past research on iterative learning analyzed for example many important additional requirements and their impact on iterative learners. In this paper, our results are twofold. First, we analyze the relative learning power of various settings of iterative learning, including learning from text and from informant, as well as various further restrictions, for example we show that strongly non-U-shaped learning is restrictive for iterative learning from informant. Second, we investigate the learnability of the concept class of half-spaces and provide a constructive iterative algorithm to learn the set of half-spaces from informant.
The Differences of Elementary School Students' Interest on Traditional Games and Online Games

Children's interest is one indicator that can illustrate the popularity of the game.Nowdays, online games are developing rapidly and traditional games are getting forgotten.This study aims to find differences in elementary school students' interest in traditional games and online games.The method used is quantitative descriptive with a sample of 80 elementary school students in Semarang City.Data collection techniques using a questionnaire.Data were analyzed using t-test with SPSS 21 series.There are significant differences between elementary school students' interest towards traditional games and online games with significant value 0.000 < 0.05.Elementary school students' interest towards traditional games is higher than online games with an average score of traditional games as much as 21.025 and an average score of online games as much as 14.663.Elementary school students' interest towards traditional games is higher than online games.
Regularizing Dialogue Generation by Imitating Implicit Scenarios

Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario. To enable responses that are more meaningful and context-specific, we propose to improve generative dialogue systems from the scenario perspective, where both dialogue history and future conversation are taken into account to implicitly reconstruct the scenario knowledge. More importantly, the conversation scenarios are further internalized using imitation learning framework, where the conventional dialogue model that has no access to future conversations is effectively regularized by transferring the scenario knowledge contained in hierarchical supervising signals from the scenario-based dialogue model, so that the future conversation is not required in actual inference. Extensive evaluations show that our approach significantly outperforms state-of-the-art baselines on diversity and relevance, and expresses scenario-specific knowledge.
Comparison of Persian Handwritten Digit Recognition in Three Color Modalities Using Deep Neural Networks

Most of the methods on handwritten digit recognition in the literature are focused and evaluated on black and white image databases. In this paper we try to answer a fundamental question in document recognition. Using Convolutional Neural Networks (CNNs), we investigate to see whether color modalities of handwritten digits affect their recognition rate? To the best of our knowledge, so far this question has not been answered due to the lack of handwritten digit databases that have all three color modalities of handwritten digits. To answer this question, we select 13,330 isolated digits from novel Persian handwritten database, which have three different color modalities and are unique in term of size and variety. Our selected dataset are divided into training, validation, and testing sets. Afterward, similar conventional CNN models are trained with the samples of our training set. While the experimental results on the testing set show that CNN on the black and white digit images has a better performance compared to the other two color modalities, in general there are no significant differences for network accuracy in different color modality. Also, comparisons of training times in three color modalities show that recognition of handwritten digits in black and white using CNN is much more efficient.
MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education

During their formative years, radiology trainees are required to interpret hundreds of mammograms per month, with the objective of becoming apt at discerning the subtle patterns differentiating benign from malignant lesions. Unfortunately, medico-legal and technical hurdles make it difficult to access and query medical images for training. In this paper we train a generative adversarial network (GAN) to synthesize 512 x 512 high-resolution mammograms. The resulting model leads to the unsupervised separation of high-level features (e.g. the standard mammography views and the nature of the breast lesions), with stochastic variation in the generated images (e.g. breast adipose tissue, calcification), enabling user-controlled global and local attribute-editing of the synthesized images. We demonstrate the model's ability to generate anatomically and medically relevant mammograms by achieving an average AUC of 0.54 in a double-blind study on four expert mammography radiologists to distinguish between generated and real images, ascribing to the high visual quality of the synthesized and edited mammograms, and to their potential use in advancing and facilitating medical education.
Proposal of Stacked Modeling Method of Colored Silicone Sheets for Anatomical Manikin

An anatomical model (manikin) that can be used for studying anatomy and training for surgery is required in the medical field. By stacking particles that are the same size as the cell, an anatomically accurate manikin would be produced. However, it is difficult to handle cell-sized particles, so we make a manikin by stacking particles with a diameter of 0.33 mm with controlled color and hardness. Manikin is made from two types of silicone oil, a pigment and a photocrosslinker. By irradiating ultraviolet light to silicone oil, silicone gel of any color and hardness would be made. First, a 0.33 mm thickness of transparent silicone gel sheet with dents arranged at intervals of 0.33 mm is produced. Next, a colored silicone oil is dropped into these dents and cured by ultraviolet light to produce a silicone gel sheet with colored particles. Finally, this silicone gel sheet is stacked to make a manikin. Since the hardness and color can be changed for each particle, the manikin is more accurately to the color and hardness of the human body. In this paper, the manikin material and modeling technique are proposed and confirmed by experiments.
NEAT Snake: a both evolutionary and neural network adaptation approach

There are several challenges when modeling artificial intelligencemethods for autonomous players on games (bots). NEAT is one ofthe models that, combining genetic algorithms and neural networks,seek to describe a bot behavior more intelligently. In NEAT, a neuralnetwork is used for decision making, taking relevant inputs fromthe environment and giving real-time decisions. In a more abstractway, a genetic algorithm is applied for the learning step of the neuralnetworks’ weights, layers, and parameters. This paper proposes theuse of relative position as the input of the neural network, basedon the hypothesis that the bot profit will be improved.
PSYCHOLOGICAL AND COGNITIVE REACTIONS TO COVID-19

This work deals with the psychological and cognitive reactions that are established in individuals experiencing the COVID-19 pandemic in four perspectives, namely: social isolation, psychological factors, personality factors and behavioral methods, established interdisciplinarily within the scope of Social and Human Sciences and Public / Collective Health. It also presents partial results obtained with the development of bibliographic research aimed at studying the impact of COVID-19 in environments of communication, journalism and / or individual and collective social education