Reviewerly/exHarmony huggingface dataset:
A Study of Compositional Generalization in Neural Models

Compositional and relational learning is a hallmark of human intelligence, but one which presents challenges for neural models. One difficulty in the development of such models is the lack of benchmarks with clear compositional and relational task structure on which to systematically evaluate them. In this paper, we introduce an environment called ConceptWorld, which enables the generation of images from compositional and relational concepts, defined using a logical domain specific language. We use it to generate images for a variety of compositional structures: 2x2 squares, pentominoes, sequences, scenes involving these objects, and other more complex concepts. We perform experiments to test the ability of standard neural architectures to generalize on relations with compositional arguments as the compositional depth of those arguments increases and under substitution. We compare standard neural networks such as MLP, CNN and ResNet, as well as state-of-the-art relational networks including WReN and PrediNet in a multi-class image classification setting. For simple problems, all models generalize well to close concepts but struggle with longer compositional chains. For more complex tests involving substitutivity, all models struggle, even with short chains. In highlighting these difficulties and providing an environment for further experimentation, we hope to encourage the development of models which are able to generalize effectively in compositional, relational domains.
Contemporaneous theory in education

The research contextually supports content focused on contemporary theories in the educational field and in which it presents the path of evolution in humanity, based on the learnings that have had to undergo it since the human being in its adaptation has allowed there to be a prospect to the educational advancement. The goal of research is to analyze the changes necessary for the creation and adaptation of learning to understand the development that learning has had in modern school. The bibliographic review and synthetic analytical&nbsp; method&nbsp;&nbsp; were used as a technique, to know contemporary theories and the influence they have on the learning-teaching process in education, it was obtained as a result that learning contemporary theories, fused to the modern school allow the expansion of knowledge by adapting techniques and strategies in students, in Ecuador has been involved the application of these, to develop new and diverse pedagogical processes, proposes training to teachers in new techniques to improve cognitive abilities.
The Influence of Computer Application Technology on Enterprise Computer Technology

Abstract This paper theoretically analyzes the mechanism and influence of computer technology on enterprise informatization, and makes up for the lack of empirical research. In practice, it can provide theoretical and practical basis for enterprises to better promote the informatization construction, and help to measure, plan and guide the informatization construction of enterprises. The value of information technology will be further realized to provide basic help.
Uncertainty in Gradient Boosting via Ensembles

For many practical, high-risk applications, it is essential to quantify uncertainty in a model's predictions to avoid costly mistakes. While predictive uncertainty is widely studied for neural networks, the topic seems to be under-explored for models based on gradient boosting. However, gradient boosting often achieves state-of-the-art results on tabular data. This work examines a probabilistic ensemble-based framework for deriving uncertainty estimates in the predictions of gradient boosting classification and regression models. We conducted experiments on a range of synthetic and real datasets and investigated the applicability of ensemble approaches to gradient boosting models that are themselves ensembles of decision trees. Our analysis shows that ensembles of gradient boosting models successfully detect anomalous inputs while having limited ability to improve the predicted total uncertainty. Importantly, we also propose a concept of a virtual ensemble to get the benefits of an ensemble via only one gradient boosting model, which significantly reduces complexity.
Research on Intelligent Security Video Image Analysis Based on Deep Learning

Abstract For video surveillance analysis, target detection algorithms are particularly important. Aiming at the problem of target detection in road traffic scenes, this paper proposes a non-motor vehicle target detection method that uses EdgeBoxes algorithm and deep learning fusion. It is inspired by deep learning target classification algorithm Fast R-CNN, and combines non-motor vehicle data samples in VOC format to integrate road traffic scenes. We use the EdgeBoxes algorithm to extract the target of the sample. It is recommended to construct an appropriate amount of region of interest, and inputs the network for iterative training with the sample. In the target detection of road traffic scenes, compared with the traditional method based on the fusion of the basic EdgeBoxes algorithm and the optimized Fast R-CNN method, the detection accuracy is slightly improved, the calculation is significantly reduced, and the algorithm has advantages in time complexity.
Higher-order nonclassical and entanglement properties in photon-added trio coherent state

This paper studies the higher-order nonclassical and entanglement properties in the photon-added trio coherent state (PATCS). We use the criterion of higher-order single-mode antibunching to evaluate the role of the photon addition operation. Furthermore, the general criteria for detection of higher-order three-mode sum squeezing and entanglement features in the PATCS are also investigated. The results show that the photon addition operation to a trio coherent state can enhance the degree of both the higher-order single-mode antibunching and the higher-order three-mode sum squeezing and enlarge the value of the higher-order three-mode entanglement factor in the photon-added trio coherent state. In addition, the manifestation of the single-mode antibunching and the entanglement properties are more obvious with increasing the higher values of orders.
High security mechanism: fragmentation and replication in the cloud with auto update in the system

Cloud Computing makes immense use of internet to store a huge amount of data. Cloud computing provides high quality service with low cost and scalability with less requirement of hardware and software management. Security plays a vital role in cloud as data is handled by third party hence security is the biggest concern to matter. This proposed mechanism focuses on the security issues on the cloud. As the file is stored at a particular location which might get affected due to attack and will lost the data. So, in this proposed work instead of storing a complete file at a particular location, the file is divided into fragments and each fragment is stored at various locations. Fragments are more secured by providing the hash key to each fragment. This mechanism will not reveal all the information regarding a particular file even after successful attack. Here, the replication of fragments is also generated with strong authentication process using key generation. The auto update of a fragment or any file is also done here. The concept of auto update of filles is done where a file or a fragment can be updated online. Instead of downloading the whole file, a fragment can be downloaded to update. More time is saved using this methodology.
ICT literacy with google suite for education (GSFE) in junior high school with different academic abilities

Abstract The ICT Literacy skills of the Student are very important, but in fact, the ICT Literacy skills of the student have not appropriate with the expectation. In this research, researchers tried to use the Google Suite for Education in learning to improve ICT Literacy skills of the students with different academic abilities. This research was using quantitative methods and the type of the research was quasi experimental. The research respondents consisted of two classes, namely a control class as many as 32 students and the experimental class as many as 32 students. The results showed that, after using Google Suite for Education in learning, it was found that the percentage of knowledge value of ICT Literacy ability for the high control class category is 59% for Access, 69% for Manage, 41% for Integrate, 56% for Evaluate and 44% for Create. The medium categories is 31% for Access, 19% for Manage, 38% for Integrate, 25% for Evaluate and 56% for Create. The Low categories is 9% for Access, 13% for Manage, 22% for Integrate, 19% for Evaluate and 44% for Create. The value of ICT Literacy ability skills for high control class is 53% for Access, 69% for Manage, 66% for Integrate, 75% for Evaluate and 34% for Create. The medium categories is 44% for Access, 25% for Manage, 0% for Integrate, 0% for Evaluate and 50% for Create. The Low categories is 3% for Access, 6% for Manage, 34% for Integrate, 25% for Evaluate and 16% for Create. While the experimental class it was found that the percentage of knowledge value of ICT Literacy ability for the high control class category is 72% for Access, 78% for Manage, 53% for Integrate, 84% for Evaluate and 59% for Create. The medium categories is 25% for Access, 13% for Manage, 38% for Integrate, 9% for Evaluate and 34% for Create. The Low categories is 3% for Access, 9% for Manage, 9% for Integrate, 6% for Evaluate and 6% for Create. The value of ICT Literacy ability skills for high experimental class category is 63% for Access, 84% for Manage, 78% for Integrate, 78% for Evaluate and 41% for Create. The medium categories is 38 % for Access, 13% for Manage, 0% for Integrate, 0% for Evaluate and 47% for Create. The Low categories is 0% for Access, 3% for Manage, 22% for Integrate, 22% for Evaluate and 13% for Create. The score of the independent sample t-test from the ICT Literacy knowledge post-test shows that there is a significant difference between the control class and the experiment with the sig (2-tailed) value is 0.046 &lt; 0.05 (p = &lt; 0.05). Then the score of the independent sample t-test from the ICT Literacy skills post-test shows that there is a significant difference between the control class and the experiment with the sig (2-tailed) value is 0.045 &lt; 0.05 (p = &lt; 0.05). (RESULT) It can be concluded that the use of Google Suite For Education can improve ICT literacy knowledge dan skills of Junior High School students.
GPU-Accelerated RDP Algorithm for Data Segmentation

The Ramer-Douglas-Peucker (RDP) algorithm applies a recursive split-and-merge strategy, which can generate fast, compact and precise data compression for time-critical systems. The use of GPU parallelism accelerates the execution of RDP, but the recursive behavior and the dynamic size of the generated sub-tasks, requires adapting the algorithm to use the GPU resources efficiently. While previous research approaches propose the exploitation of task-based parallelism, our research advocates a general fine-grained solution, which avoids the dynamic and recursive execution of kernels. The segmentation of depth images, a typical application used on autonomous driving, reaches speeds of almost 1000 frames per second for typical workloads using our massively parallel proposal on low-consumption, embedded GPUs. The GPU-accelerated solution is at least an order of magnitude faster than the execution of the same program on multiple CPU cores with similar energy consumption.
Int-FLBCC: Model for Load Balancing in Cloud Computing using Fuzzy Logic Type-2 and Admissible Orders.

Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing, directly affecting Quality of Service aspects. This paper presents Int-FLBCC, a new proposal with exploring a Type-2 Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. Validation results in a simulated cloud computing environment showed improvements in energy efficiency of 8.83% with IQR_XY and 22.43% with MAD_XY. For fulfillment of Service Level Agreements (SLA), the best values achieved were 9.06% with MAD_XY and 25% of THR_Lex1.
Semantics and pragmatics of programming language ASAMPL

This paper presents semantics and practical implementation of the domain-specific programming language ASAMPL. This programming language has been developed to support the efficient processing of multimodal data processing, in particular, the processing of multimedia content which components are evidently defined in terms of time. The data processing concept employed in ASAMPL is based on the data structures, operations, and relations defined in the algebraic system of aggregates. The paper explains the compilation approach used for this programming language as well as it presents the test results and their discussion. Problems in programming 2020; 1: 74-83
GrabTutor: A web and mobile application for tutor appointment system with GPS security feature

Background/Objectives: Academic tutoring is sought to aid students academically, keep them in school, and eventually earn a degree aside from the learning they get from a typical classroom setting.Academic institutions somehow start programs for peer tutorial sessions among students, but matching of tutees with tutors takes time especially for complex and complicated topics.It even brings a security concern about where students are conducting tutorials.Hence, this study aims to design a tutor appointment system with location tracking feature to give students a more efficient and safer tutorial experience.Methods: GrabTutor is composed of a mobile and web application that communicates through a cloud server.The tutees find tutors through the mobile application.The system generates a unique barcode for each tutorial appointment.A barcode scanner is embedded in the mobile application accessible to the tutor.Once the tutor scans, the system will trigger the timer and sends GPS coordinates to the server to track the location.The location will be represented by a marker on the map of the moderator's web application.Findings: Based on the results of the usability tests, the system easily matched tutees to the right tutors.The GPS monitoring system was proven to be a helpful tool to ease the risk of students having to go somewhere else to meet up with a tutor.Novelty/Applications: The proposed system enables quick and efficient matching of tutors to tutees and integrates GPS information of students within the campus during the conduct of the tutorial to track their whereabouts for a more secure tutorial experience.
Jointly Linking Visual and Textual Entity Mentions with Background Knowledge

“A picture is worth a thousand words”, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed, reveal different and complementary information that, if combined, result in more information than the sum of that contained in the single media. The combination of visual and textual information can be obtained through linking the entities mentioned in the text with those shown in the pictures. To further integrate this with agent background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this paper, after providing a precise definition of the VTKEL task, we present a dataset composed of about 30K commented pictures, annotated with visual and textual entities, and linked to the YAGO ontology. Successively, we develop a purely unsupervised algorithm for the solution of the VTKEL tasks. The evaluation on the VTKEL dataset shows promising results.
Quality and Relevance Metrics for Selection of Multimodal Pretraining Data

Self-supervised pretraining has become a strong force in both language and vision tasks. Current efforts to improve the effects of pretraining focus on improving network architecture or defining new tasks to extract representations from the data. We focus on a third axis, the data itself, to quantify and measure how different sources and quality of data can affect the learned representations. As pretraining datasets grow larger and larger, the cost of pretraining will continue to increase. This issue is especially acute for visuolingusitic data, as the cost of storage and processing for image and video data will rise quickly. We therefore examine four vi- suolinguistic datasets (three preexisting datasets and one collected by us) for their utility as pretraining datasets. We define metrics for dataset quality and relevance, propose a method for subsampling large corpuses for the data most relevant to a set of downstream multimodal vision and language tasks of interest, and show that this method increases performance across the board for all downstream tasks.
On Alignment in Deep Linear Neural Networks

We study the properties of alignment, a form of implicit regularization, in linear neural networks under gradient descent. We define alignment for fully connected networks with multidimensional outputs and show that it is a natural extension of alignment in networks with 1-dimensional outputs as defined by Ji and Telgarsky, 2018. While in fully connected networks, there always exists a global minimum corresponding to an aligned solution, we analyze alignment as it relates to the training process. Namely, we characterize when alignment is an invariant of training under gradient descent by providing necessary and sufficient conditions for this invariant to hold. In such settings, the dynamics of gradient descent simplify, thereby allowing us to provide an explicit learning rate under which the network converges linearly to a global minimum. We then analyze networks with layer constraints such as convolutional networks. In this setting, we prove that gradient descent is equivalent to projected gradient descent, and that alignment is impossible with sufficiently large datasets.
Bayesian Analysis for Random Effects Models

Random effects models have been widely used to analyze correlated data sets, and Bayesian techniques have emerged as a powerful tool to fit the models. However, there has been scarce literature that systematically reviews and summarizes the recent advances of Bayesian analyses of random effects models. This chapter reviews the use of the Dirichlet process mixture (DPM) prior to approximate the distribution of random errors within the general semiparametric random effects models with parametric random effects for longitudinal data setting and failure time setting separately. In a survival setting with clusters, we propose a new class of nonparametric random effects models which is motivated from the accelerated failure models. We employ a beta process prior to tact clustering and estimation simultaneously. We analyze a new data set integrated from Alzheimer’s disease (AD) study to illustrate the presented model and methods.
Efficient and scalable multitenant placement approach for in-memory database over supple architecture

Of late Multitenant model with In-Memory database has become prominent area for research. The paper has used advantages of multitenancy to reduce the cost for hardware, labor and make availability of storage by sharing database memory and file execution. The purpose of this paper is to give overview of proposed Supple architecture for implementing in-memory database backend and multitenancy, applicable in public and private cloud settings. Backend in-memory database uses column-oriented approach with dictionary based compression technique. We used dedicated sample benchmark for the workload processing and also adopt the SLA penalty model. In particular, we present two approximation algorithms, Multi-tenant placement (MTP) and Best-fit Greedy to show the quality of tenant placement. The experimental results show that Multi-tenant placement (MTP) algorithm is scalable and efficient in comparison with Best-fit Greedy Algorithm over proposed architecture.
TRADITIONAL EDUCATION AND DIGITALIZATION OF LIFE - POINTS OF CONTACT AND PROBLEMS

At present, modern information technologies are persistently being introduced into the traditional education system, the manifestation of which is the digitalization of the thinking of modern man. The article shows the need to take into account the psychological characteristics of students in the study of mathematics
Specificity of Domestic and Foreign Online Courses from the Viewpoint of a Teacher-User (by the Material of Online Courses of the Russian and French Authors)

The research objective is to identify reasons for negative evaluation of online courses on the part of the users. Scientific originality of the paper involves a new approach to evaluating an online course quality from the viewpoint of a teacher participating in the course. The analysis of strong and weak points of the online courses under study shows that the authors of domestic online courses need to solve the following problems: to improve e-learning methodology, to develop assessment means fund, to raise teachers’ digital and information competence, which includes skills of working with video camera, competence in working with virtual audience.
NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks

Deciding the amount of neurons during the design of a deep neural network to maximize performance is not intuitive. In this work, we attempt to search for the neuron (filter) configuration of a fixed network architecture that maximizes accuracy. Using iterative pruning methods as a proxy, we parameterize the change of the neuron (filter) number of each layer with respect to the change in parameters, allowing us to efficiently scale an architecture across arbitrary sizes. We also introduce architecture descent which iteratively refines the parameterized function used for model scaling. The combination of both proposed methods is coined as NeuralScale. To prove the efficiency of NeuralScale in terms of parameters, we show empirical simulations on VGG11, MobileNetV2 and ResNet18 using CIFAR10, CIFAR100 and TinyImageNet as benchmark datasets. Our results show an increase in accuracy of 3.04%, 8.56% and 3.41% for VGG11, MobileNetV2 and ResNet18 on CIFAR10, CIFAR100 and TinyImageNet respectively under a parameter-constrained setting (output neurons (filters) of default configuration with scaling factor of 0.25).
Representation Mode for Automobile-Oriented Customer Knowledge about After-sale Service

Abstract From the view of automobile after-sale service industry, this paper analyzes the connotation and characteristics of customer knowledge of this field, discusses on the method for acquiring customer knowledge of automobile after-sale service, and presents the ontology-based customer knowledge representation mode, thus ensuring the re-usability and consensus of automobile after-sales service knowledge.