Reviewerly/exHarmony huggingface dataset:
Penetration Depth Between Two Convex Polyhedra: An Efficient Stochastic Global Optimization Approach

During the detailed design phase of an aerospace program, one of the most important consistency checks is to ensure that no two distinct objects occupy the same physical space. Since exact geometrical modeling is usually intractable, geometry models are discretized, which often introduces small interferences not present in the fully detailed model. In this paper, we focus on computing the depth of the interference, so that these false positive interferences can be removed, and attention can be properly focused on the actual design. Specifically, we focus on efficiently computing the penetration depth between two polyhedra, which is a well-studied problem in the computer graphics community. We formulate the problem as a constrained five-variable global optimization problem, and then derive an equivalent unconstrained, two-variable nonsmooth problem. To solve the optimization problem, we apply a popular stochastic multistart optimization algorithm in a novel way, which exploits the advantages of each problem formulation simultaneously. Numerical results for the algorithm, applied to 14 randomly generated pairs of penetrating polytopes, illustrate both the effectiveness and efficiency of the method.
Decomposition Principle in Control of Socio-Technical Systems

Abstract A general approach to decompose the interaction of agents in social, organizational and technical systems is proposed. Within this approach, the problems of reachability, sustainability and searching for an optimal trajectory can be solved independently for each agent.
Enhancing the E-Content Skills of Instructors Through the Use of Cloud-Based Mobile Learning Environment: A Case of a Saudi Arabian University

The research aims to design a mobile learning environment based on cloud computing applications to enhance the designing and publishing e-content skills of the faculty members of Northern Borders University, Saudi Arabia. The researcher used the semi- experimental approach to measure the impact of the mobile learning environment on the cognitive and performance aspects of the faculties. Two research tools were used in the study: an electronic achievement test to measure the cognitive aspect and an observation sheet to measure the performance aspects of designing and publishing e- content skills of faculty members (N=53). A training programme on mobile learning environment based on cloud computing was conducted to enhance the faculty members’ e-content designing skills. There was a statistically significant difference between the average scores of the members in their pre-and post-study assessment of the cognitive and performance aspects of designing and publishing e-content skills. Following the training programme, the faculty’s achievement scores increased to 84% from 42% in the pre-test; their performance to apply the learned skills in various activities improved to 91% from 43%. Based on the results of the study, it can be recommended that the mobile learning environment based on cloud computing applications is quite helpful in developing the skills of using technology by the faculty members.
SCIENTIFIC AND TECHNICAL ACTIVITY OF STUDENTS OF PEDAGOGICAL HIGHER EDUCATIONAL INSTITUTION: MODERN ASPECT

The article reveals the specifics of the organization of scientific and technical activities of students of technological and pedagogical specialties. The factors of improving the quality of students' knowledge in the process of scientific circles, the problems of implementation and use of educational and control computer programs in the organization of scientific and technical activities of students and possible ways to solve them are considered. An analysis of existing control test programs was performed in order to select one that would provide maximum functionality, breadth of operating modes, ease of use and test creation at a minimum cost and minimum disk space in the computer's permanent memory. The content of scientific and technical work of students in the direction of training "Technological education" is substantiated, namely: development of computer training and control programs, electronic textbooks, laboratory equipment. The results of scientific and technical activity of students in scientific circles are given: electronic textbooks on courses "Machine parts", "Fundamentals of interchangeability and standardization", "Technical mechanics", a large number of laboratory equipment: installation for testing thermocouples, hydrostatic paradox demonstration device, car battery charger, installation for determining the movement and viscosity of liquids, stands for liquid and gas flow meters, telpher installation, worm gearbox, hydraulic jack, rolling bearings, device for washing school boards. Key words: scientific and technical activity, scientific circle, computer textbooks, computer testing, laboratory equipment, educational control programs, organization of educational activity.
Relatively-Integrated Ship Navigation by H¥ Fusion Filters

For the system with unknown statistical property noises, the property that the energies of the system noise and the observation noise are limited is utilized in this paper. On this basis, two novel fusion algorithms are proposed for ship integrated navigation with the relative navigation information, broadcasted by the Automatic Identification Systems (AISs) in the adjacent ships. Firstly, an H∞ fusion filtering algorithm is given to deal with the navigation observation messages, under the centralized fusion framework. The integrated navigation method based on this algorithm cannot deal with the asynchronous navigation messages in real time. Therefore, a sequential H∞ fusion filtering algorithm is also given to sequentially deal with the asynchronous navigation messages, secondly. Finally, a computer simulation is employed to illustrate the validity and feasibility of the sequential method.
Modified Multipath Routing Protocol Applied On Ns3 Dcell Network Simulation System

&lt;p class="0abstract"&gt;&lt;strong&gt;—&lt;/strong&gt;In the communication networks, guidance has become an important factor, with a significant impact on network performance, where the network orientation area has been and continues to be an ongoing development, intensive research for many years aimed at optimizing the network. This paper performs three modifications for a multipath routing protocol to solve the problem of routing in a DCell network simulation and apply online solutions on the network, the goal is to improve the transition efficiency of data. The modifications used to avoid data transmission failures which are delay problem&lt;strong&gt;, &lt;/strong&gt;link failure problem, and power off (rack problem). The implementation of multipath routing protocol on the DCell network in actual simulation using the NS-3 program, which represents the rule that the DCell network was built and simulated. Finally, the modifications succeeded and return good results decreasing the delay time and solving the data transaction problems.&lt;/p&gt;
Quantitative Software Change Prediction in Open Source Web Projects Using Time Series Forecasting

Software change prediction (SCP) is used for the prediction of changes earlier in the software development life cycle. It identifies the files that are change prone. Software maintenance costs can be reduced with the help of accurate prediction of change-prone files. Most of the literature of SCP deals with the identification of a class as change prone or not change prone. In the present work, the amount of change in a web project in terms of line of code added (loc_added), line of code deleted (loc_deleted), and lines of code (LOC) are predicted using time series forecasting method of machine learning. Data of web projects is obtained from GIT repository using Pydriller Python package extractor. The obtained result showed that support vector machine (SVM) is good for prediction of loc_added and loc_removed while the random forest is good for the prediction of LOC. Results advocate the use machine learning techniques for forecasting changes amount in web projects.
Semantic Matching Based on Semantic Segmentation and Neighborhood Consensus

Establishing dense correspondences across semantically similar images is a challenging task, due to the large intra-class variation caused by the unconstrained setting of images, which is prone to cause matching errors. To suppress potential matching ambiguity, NCNet explores the neighborhood consensus pattern in the 4D space of all possible correspondences, which is based on the assumption that the correspondence is continuous in space. We retain the neighborhood consensus constraint, while introducing semantic segmentation information into the features, which makes them more distinguishable and reduces matching ambiguity from a feature perspective. Specifically, we combine the semantic segmentation network to extract semantic features and the 4D convolution to explore 4D-space context consistency. Experiments demonstrate that our algorithm has good semantic matching performances and semantic segmentation information can improve semantic matching accuracy.
Natural Language Processing 4 All (NLP4All): A New Online Platform for Teaching and Learning NLP Concepts

Natural Language Processing offers new insights into language data across almost all disciplines and domains, and allows us to corroborate and/or challenge existing knowledge. The primary hurdles to widening participation in and use of these new research tools are, first, a lack of coding skills in students across K-16, and in the population at large, and second, a lack of knowledge of how NLP-methods can be used to answer questions of disciplinary interest outside of linguistics and/or computer science. To broaden participation in NLP and improve NLP-literacy, we introduced a new tool web-based tool called Natural Language Processing 4 All (NLP4All). The intended purpose of NLP4All is to help teachers facilitate learning with and about NLP, by providing easy-to-use interfaces to NLP-methods, data, and analyses, making it possible for non- and novice-programmers to learn NLP concepts interactively.
Research on Design of Intelligent Background Differential Model for Training Target Monitoring

The detection of moving targets is to detect the change area in a sequence of images and extract the moving targets from the background image. It is the basis. Whether the moving targets can be correctly detected and segmented has a huge impact on the subsequent work. Aiming at the problem of high failure rate in the detection of sports targets under complex backgrounds, this paper proposes a research on the design of an intelligent background differential model for training target monitoring. This paper proposes a background difference method based on RGB colour separation. The colour image is separated into independent RGB three-channel images, and the corresponding channels are subjected to the background difference operation to obtain the foreground image of each channel. In order to retain the difference of each channel, the information of the foreground images of the three channels is fused to obtain a complete foreground image. The feature of the edge detection is not affected by light; the foreground image is corrected. From the experimental results, the ordinary background difference method uses grey value processing, and some parts of the target with different colours but similar grey levels to the background cannot be extracted. However, the method in this paper can better solve the defect of misdetection. At the same time, compared with traditional methods, it also has a higher detection efficiency.
An Improved Impulse Noise Removal VLSI Architecture Using DTBDM Method

Images are vulnerable to anomalies called as distortion or corruption during the transmission of signals. When the impulse noise encounters the image quality gets disturbed, to reconstruct the pixel information with reduced complexity, we hypothesize an efficacious VLSI architecture to drastically reduce impulse noise in pictures leveraging edge preserving filter. The method is compared with two mask sizes in terms of performance evaluation. To take advantage of detailed identification including certain PSNR generalizations and picture flexibility, the proposed framework will prove to be more effective than the conventional poor complexity paradigms. Throughout the low noise concentrations, the edge conserving median filter spotlights stellar performance and indeed in high noise concentrations through high window sizes. These effects are demonstrated through the simulation results. The comparisons are framed between area and power consumptions of 3*3 and 5*5 window sizes concluding which window size gives better performance in terms of those constraints.
Writing for the web

How to write for online, understand analytics and SEO and make social media work for you Writing for online news platforms builds on all the skills we have learned up to this point, but it also requires an appreciation and understanding of the way people use digital devices to access news and features. So here readers will learn how to format stories on the page to make them more engaging and they will be introduced to the research that supports this approach. In addition, they will be introduced to Search Engine Optimisation (SEO), learn how to build that into a headline and sell line and given an exercise of their own to tackle. In addition, they will learn how to get the best from social media – as a research tool as well as a way to market their own news stories. They will learn how to make Tweets and Facebook posts cut through the noise and will be given strategies to get people clicking and liking their posts.
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation

In this paper, we propose a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks. The proposed approach consists in minimizing pairwise ranking loss over blocks of consecutive items constituted by a sequence of non-clicked items followed by a clicked one for each user. We present two variants of this strategy where model parameters are updated using either the momentum method or a gradient-based approach. To prevent updating the parameters for an abnormally high number of clicks over some targeted items (mainly due to bots), we introduce an upper and a lower threshold on the number of updates for each user. These thresholds are estimated over the distribution of the number of blocks in the training set. They affect the decision of RS by shifting the distribution of items that are shown to the users. Furthermore, we provide a convergence analysis of both algorithms and demonstrate their practical efficiency over six large-scale collections with respect to various ranking measures and computational time.
Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision

Sorting and ranking supervision is a method for training neural networks end-to-end based on ordering constraints. That is, the ground truth order of sets of samples is known, while their absolute values remain unsupervised. For that, we propose differentiable sorting networks by relaxing their pairwise conditional swap operations. To address the problems of vanishing gradients and extensive blurring that arise with larger numbers of layers, we propose mapping activations to regions with moderate gradients. We consider odd-even as well as bitonic sorting networks, which outperform existing relaxations of the sorting operation. We show that bitonic sorting networks can achieve stable training on large input sets of up to 1024 elements.
Bayesian Attention Belief Networks

Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks. Most such models use deterministic attention while stochastic attention is less explored due to the optimization difficulties or complicated model design. This paper introduces Bayesian attention belief networks, which construct a decoder network by modeling unnormalized attention weights with a hierarchy of gamma distributions, and an encoder network by stacking Weibull distributions with a deterministic-upward-stochastic-downward structure to approximate the posterior. The resulting auto-encoding networks can be optimized in a differentiable way with a variational lower bound. It is simple to convert any models with deterministic attention, including pretrained ones, to the proposed Bayesian attention belief networks. On a variety of language understanding tasks, we show that our method outperforms deterministic attention and state-of-the-art stochastic attention in accuracy, uncertainty estimation, generalization across domains, and robustness to adversarial attacks. We further demonstrate the general applicability of our method on neural machine translation and visual question answering, showing great potential of incorporating our method into various attention-related tasks.
The Role of Strategic Leadership in Activating Time Management Strategies to Enhance Administrative Creativity Skills

This study aimed to identify the role of strategic leadership in activating time management strategies to enhance administrative creativity skills at Al-Azhar University. The sample of the study was random stratified sample from (245) administrative employees using questionnaire as a main tool for primary data collection. Total respondent were (112) questioners. Descriptive and quantitative approach used in this study. The general results of the study showed high levels of strategic leadership at Al-Azhar University, average level of application of time management strategies and administrative creativity. This means there is acceptance approval that there is a statistically significant relationship between strategic leadership and time management strategies in Palestinian universities in Gaza Strip with a correlation level (0.724). Also, there is a statistically significant relationship between time management strategies and administrative creativity in Palestinian universities in Gaza Strip under study with a correlation level (0.848). According to static analysis, there is a statistically significant relationship between the dimensions of strategic leadership and administrative creativity in Palestinian universities in Gaza Strip under study, with a level of correlation (0.767%). This leases to high investment process for all available factors and elements in order to maximize and increase production and eliminate waste, wasted and unexploited employees' time and capabilities, which leads to achieving more effectiveness, positivity, productivity and increased organized effort. The study recommended necessity of having the elements of strategic academic leadership and activating time management strategies towards enhancing administrative creativity among workers in Palestinian universities in Gaza Strip. Also study recommended to encourage, support, and reward personal initiatives and creative ideas. Also study recommended available of wise and conscious strategic leadership to rationalize the behavior of individuals, mobilize their energies, mobilize their capabilities, coordinate their efforts, organize their affairs and direct them in the right direction towards achieving the desired goals and objectives.
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks

Recent work has shown great promise in explaining neural network behavior. In particular, feature attribution methods explain which features were most important to a model's prediction on a given input. However, for many tasks, simply knowing which features were important to a model's prediction may not provide enough insight to understand model behavior. The interactions between features within the model may better help us understand not only the model, but also why certain features are more important than others. In this work, we present Integrated Hessians, an extension of Integrated Gradients that explains pairwise feature interactions in neural networks. Integrated Hessians overcomes several theoretical limitations of previous methods to explain interactions, and unlike such previous methods is not limited to a specific architecture or class of neural network. Additionally, we find that our method is faster than existing methods when the number of features is large, and outperforms previous methods on existing quantitative benchmarks. Code available at this https URL
Multi-task Learning Based Online Dialogic Instruction Detection with Pre-trained Language Models

In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits. This task is rather challenging due to the widely-varying quality and pedagogical styles of dialogic instructions. To address these challenges, we utilize pre-trained language models, and propose a multi-task paradigm which enhances the ability to distinguish instances of different classes by enlarging the margin between categories via contrastive loss. Furthermore, we design a strategy to fully exploit the misclassified examples during the training stage. Extensive experiments on a real-world online educational data set demonstrate that our approach achieves superior performance compared to representative baselines. To encourage reproducible results, we make our implementation online available at https://github.com/AIED2021/multitask-dialogic-instruction .
Language Scaling for Universal Suggested Replies Model

We consider the problem of scaling automated suggested replies for Outlook email system to multiple languages.Faced with increased compute requirements and low resources for language expansion, we build a single universal model for improving the quality and reducing run-time costs of our production system.However, restricted data movement across regional centers prevents joint training across languages.To this end, we propose a multitask continual learning framework, with auxiliary tasks and language adapters to learn universal language representation across regions.The experimental results show positive crosslingual transfer across languages while reducing catastrophic forgetting across regions.Our online results on real user traffic show significant gains in CTR and characters saved, as well as 65% training cost reduction compared with per-language models.As a consequence, we have scaled the feature in multiple languages including low-resource markets.
Generative Video Transformer: Can Objects be the Words?

Transformers have been successful for many natural language processing tasks. However, applying transformers to the video domain for tasks such as long-term video generation and scene understanding has remained elusive due to the high computational complexity and the lack of natural tokenization. In this paper, we propose the Object-Centric Video Transformer (OCVT) which utilizes an object-centric approach for decomposing scenes into tokens suitable for use in a generative video transformer. By factoring the video into objects, our fully unsupervised model is able to learn complex spatio-temporal dynamics of multiple interacting objects in a scene and generate future frames of the video. Our model is also significantly more memory-efficient than pixel-based models and thus able to train on videos of length up to 70 frames with a single 48GB GPU. We compare our model with previous RNN-based approaches as well as other possible video transformer baselines. We demonstrate OCVT performs well when compared to baselines in generating future frames. OCVT also develops useful representations for video reasoning, achieving start-of-the-art performance on the CATER task.