Reviewerly/exHarmony huggingface dataset:
An efficient algorithm based on the multi-wavelet Galerkin method for telegraph equation

We employ the multi-wavelet Galerkin method for the numerical solution of the telegraph equation with initial and boundary conditions. The problem becomes a sparse system of linear equations and the GMRES method is used to solve this system. The advantages of this scheme are complexity reduction, simplicity, and less grid selection. The convergence analysis is investigated and numerical experiments guarantee it. To show the ability of the method, we compare it with other methods and it can be confirmed that our results are better than others.
Distributional Robustness with IPMs and links to Regularization and GANs

Robustness to adversarial attacks is an important concern due to the fragility of deep neural networks to small perturbations and has received an abundance of attention in recent years. Distributionally Robust Optimization (DRO), a particularly promising way of addressing this challenge, studies robustness via divergence-based uncertainty sets and has provided valuable insights into robustification strategies such as regularization. In the context of machine learning, the majority of existing results have chosen $f$-divergences, Wasserstein distances and more recently, the Maximum Mean Discrepancy (MMD) to construct uncertainty sets. We extend this line of work for the purposes of understanding robustness via regularization by studying uncertainty sets constructed with Integral Probability Metrics (IPMs) - a large family of divergences including the MMD, Total Variation and Wasserstein distances. Our main result shows that DRO under \textit{any} choice of IPM corresponds to a family of regularization penalties, which recover and improve upon existing results in the setting of MMD and Wasserstein distances. Due to the generality of our result, we show that other choices of IPMs correspond to other commonly used penalties in machine learning. Furthermore, we extend our results to shed light on adversarial generative modelling via $f$-GANs, constituting the first study of distributional robustness for the $f$-GAN objective. Our results unveil the inductive properties of the discriminator set with regards to robustness, allowing us to give positive comments for several penalty-based GAN methods such as Wasserstein-, MMD- and Sobolev-GANs. In summary, our results intimately link GANs to distributional robustness, extend previous results on DRO and contribute to our understanding of the link between regularization and robustness at large.
Probably Approximately Correct Constrained Learning

As learning solutions reach critical applications in social, industrial, and medical domains, the need to curtail their behavior has become paramount. There is now ample evidence that without explicit tailoring, learning can lead to biased, unsafe, and prejudiced solutions. To tackle these problems, we develop a generalization theory of constrained learning based on the probably approximately correct (PAC) learning framework. In particular, we show that imposing requirements does not make a learning problem harder in the sense that any PAC learnable class is also PAC constrained learnable using a constrained counterpart of the empirical risk minimization (ERM) rule. For typical parametrized models, however, this learner involves solving a constrained non-convex optimization program for which even obtaining a feasible solution is challenging. To overcome this issue, we prove that under mild conditions the empirical dual problem of constrained learning is also a PAC constrained learner that now leads to a practical constrained learning algorithm based solely on solving unconstrained problems. We analyze the generalization properties of this solution and use it to illustrate how constrained learning can address problems in fair and robust classification.
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity

As a popular approach to modeling the dynamics of training overparametrized neural networks (NNs), the neural tangent kernels (NTK) are known to fall behind real-world NNs in generalization ability. This performance gap is in part due to the \textit{label agnostic} nature of the NTK, which renders the resulting kernel not as \textit{locally elastic} as NNs~\citep{he2019local}. In this paper, we introduce a novel approach from the perspective of \emph{label-awareness} to reduce this gap for the NTK. Specifically, we propose two label-aware kernels that are each a superimposition of a label-agnostic part and a hierarchy of label-aware parts with increasing complexity of label dependence, using the Hoeffding decomposition. Through both theoretical and empirical evidence, we show that the models trained with the proposed kernels better simulate NNs in terms of generalization ability and local elasticity.
SGD with shuffling: optimal rates without component convexity and large epoch requirements

We study without-replacement SGD for solving finite-sum optimization problems. Specifically, depending on how the indices of the finite-sum are shuffled, we consider the RandomShuffle (shuffle at the beginning of each epoch) and SingleShuffle (shuffle only once) algorithms. First, we establish minimax optimal convergence rates of these algorithms up to poly-log factors. Notably, our analysis is general enough to cover gradient dominated nonconvex costs, and does not rely on the convexity of individual component functions unlike existing optimal convergence results. Secondly, assuming convexity of the individual components, we further sharpen the tight convergence results for RandomShuffle by removing the drawbacks common to all prior arts: large number of epochs required for the results to hold, and extra poly-log factor gaps to the lower bound.
Interactive Learning Environment in the English Language Teaching with the help of Computer Technology

In current education sphere it is easy to develop interactive learning environment to encourageour students to learn something with great interest as advanced information and communication technologyenables to create a number of new ways to teach foreign languages. As Wasson says interactive learningenvironment has an important role in teaching and learning and will also have in the future. Specifically,interactive learning environment always supports to discover, create, think critically, convey something indifferent way than it is expected to be or used to be. In this article we focus on the ways of how to createinteractive learning environment and supportive learning conditions in language teaching using computertechnology. We argue that as pedagogical institute students our learners can use the same way when theyface in real teaching process in the future where they go to teach
It matters what designs design designs: speculations on multispecies worlding

Critical contemporary discourses on extinction, climate change and planetary boundaries are needed to counter and reject our current ways of living on this planet. But they often end badly. Therefore, we also need to tell the stories that create openings and generate more desirable alternatives. This paper contributes to the effort of resituating design as less anthropocentric and much more of a multispecies affair. Following scholars such as Donna Haraway, Timothy Morton, Anna Tsing and John Law, this text does so by unpacking the notion of ‘multispecies worlding’ for speculative design practices that involve other living entities. By carrying multiplicities into design processes and rethinking how other species can become a more deliberate part of our (re)worlding efforts, this text articulates the importance of advancing decolonial design aims to generate interspecies harmonies rather than reinforcing oppressive relations. The annotated illustrations and examples of multispecies design projects that appear in this paper involve an additional effort in identifying ‘big-enough’ stories and already existing multispecies design speculations. As such, this work offers merely one collection of enactments that can allow further worlding and further design work. Such a repertoire of speculative multispecies design work can thereby knot together different realities, from different actors, that can propose and embody other kinds of worlding relations between species. They thereby slowly but steadily break down existing grand narratives that seem all-explanatory to speculate about different ways in which humans and other species already make worlds together.
Mapping of Conceptual Framework for Augmented Reality Application in Logistics

Logistics services include a variety of operations, from freight transportation and warehousing, to order placement in distribution channels and delivery to customers. In order to increase the efficiency and responsiveness of the supply chain, a digital transformation is being carried out, which includes the business model changes and the introduction of various digital technologies in the planning, management and administration of logistics operations. One of the disruptive technologies is Augmented Reality (AR). The aim of the article is to provide a conceptual view of the use of AR in the logistics and supply chain.
BluBLE, space-time social distancing to monitor the spread of COVID-19

Social distancing has been the key factor which has helped control the COVID-19 pandemic spread. We present BluBLE, which utilizes Bluetooth Low Energy (BLE) based mobile sensing to help monitor these social distancing protocols. Specifically, we formulate the problem in two parts - spatial and temporal social distancing. The spatial distancing formulation aims to enforce the 6 feet distance recommended by various public health organization around the world. The temporal distancing formulation aims to inform and prevent users from entering high-occupancy regions (hotspots) in buildings. BluBLE achieved more than 80 % classification accuracy in both the tasks, that is, predicting if a user is within '6' feet of another user as well as characterizing the user's location within a particular hotspot.
Streamlining XR Technology Into Industrial Training and Maintenance Processes

The nature of industrial manufacturing processes and the need to learn and adapt production systems to new demands require new tools to cope with the vast amount of information being generated. This research aims to design, implement, and evaluate a process to streamline industrial assets to immersive environments and harness the interconnectivity of machines, processes, and products for more informed decision making. To evaluate the effectiveness of our solution, we compare it against existing solutions. A statistical test is used to corroborate our hypotheses, and the results of the usability test indicate well-perceived learnability.
Performance Evaluation of Data Center Network with Network Micro-segmentation

Research on the design of data center infrastructure is increasing, both from academia and industry, due to the rapid development of cloud-based applications such as search engines, social networks, and large-scale computing. On a large scale, data centers can consist of hundreds to thousands of servers that require systems with high-performance requirements and low downtime. To meet the network's needs in a dynamic data center, infrastructure of applications and services are growing. It takes a process of designing a network topology so that it can guarantee availability and security. One way to surmount this is by implementing the zero trust security model based on micro-segmentation. Zero trust is a security idea based on the principle of "never trust, always verify" in which no concepts of trust and untrust in network traffic. The zero trust security model implemented network traffic in the form of untrust. Micro-segmentation is a way to achieve zero trust by dividing a network into smaller logical segments to restrict the traffic. In this research, data center network performance based on software-defined networking with zero trust security model using micro-segmentation has been evaluated using a testbed simulation of Cisco Application Centric Infrastructure by measuring the round trip time, jitter, and packet loss during experiments. Performance evaluation results show that micro-segmentation adds an average round trip time of 4 μs and jitter of 11 μs without packet loss so that the security can be improved without significantly affecting network performance on the data center.
Perfect Is the Enemy of Good: Best-Effort Program Synthesis

Program synthesis promises to help software developers with everyday tasks by generating code snippets automatically from input-output examples and other high-level specifications. The conventional wisdom is that a synthesizer must always satisfy the specification exactly. We conjecture that this all-or-nothing paradigm stands in the way of adopting program synthesis as a developer tool: in practice, the user-written specification often contains errors or is simply too hard for the synthesizer to solve within a reasonable time; in these cases, the user is left with a single over-fitted result or, more often than not, no result at all. In this paper we propose a new program synthesis paradigm we call best-effort program synthesis, where the synthesizer returns a ranked list of partially-valid results, i.e. programs that satisfy some part of the specification.To support this paradigm, we develop best-effort enumeration, a new synthesis algorithm that extends a popular program enumeration technique with the ability to accumulate and return multiple partially-valid results with minimal overhead. We implement this algorithm in a tool called BESTER, and evaluate it on 79 synthesis benchmarks from the literature. Contrary to the conventional wisdom, our evaluation shows that BESTER returns useful results even when the specification is flawed or too hard: i) for all benchmarks with an error in the specification, the top three BESTER results contain the correct solution, and ii) for most hard benchmarks, the top three results contain non-trivial fragments of the correct solution. We also performed an exploratory user study, which confirms our intuition that partially-valid results are useful: the study shows that programmers use the output of the synthesizer for comprehension and often incorporate it into their solutions.
Placepedia: Comprehensive Place Understanding with Multi-faceted Annotations

Place is an important element in visual understanding. Given a photo of a building, people can often tell its functionality, e.g. a restaurant or a shop, its cultural style, e.g. Asian or European, as well as its economic type, e.g. industry oriented or tourism oriented. While place recognition has been widely studied in previous work, there remains a long way towards comprehensive place understanding, which is far beyond categorizing a place with an image and requires information of multiple aspects. In this work, we contribute Placepedia $$^{1}$$ , a large-scale place dataset with more than 35M photos from 240K unique places. Besides the photos, each place also comes with massive multi-faceted information, e.g. GDP, population, etc., and labels at multiple levels, including function, city, country, etc. This dataset, with its large amount of data and rich annotations, allows various studies to be conducted. Particularly, in our studies, we develop 1) PlaceNet, a unified framework for multi-level place recognition, and 2) a method for city embedding, which can produce a vector representation for a city that captures both visual and multi-faceted side information. Such studies not only reveal key challenges in place understanding, but also establish connections between visual observations and underlying socioeconomic/cultural implications. ( $$^{1}$$ The dataset is available at: https://hahehi.github.io/placepedia.html ).
Deep learning of terrain morphology and pattern discovery via network-based representational similarity analysis for deep-sea mineral exploration

The race to explore valuable metals in the deep ocean recently emerged, and nations now seek to secure prospective areas for minerals that may support the low-carbon transition, from electric vehicles to wind turbines. Yet, the deep seafloor remains unexplored and vast, which asserts the need for technological advances in exploration. As key areas for new mineral discoveries often reside in vast zones of undersea eruptions, it becomes crucial to examine seafloor processes and spatial patterns to elucidate the nature of the geological phenomena and their complex interactions. Especially, seafloor mounds provide important information about surface changes, sometimes attributable to mineral accumulations at the seafloor. This study applies a 2-step method to investigate these mounds: (1) semantic segmentation with an encoder-decoder convolutional neural network, then (2) morphological similarity analysis and clustering of segmented features by exploiting convolution signals generated by the model with computer vision algorithms and data processing procedures. The study uses high-resolution bathymetric data of a mid-ocean ridge, which includes a known polymetallic mineral occurrence (case study). The model segmented 1,659 features and achieved accuracy up to 84% pixel-wise, and 80% object-wise, using data combination of bathymetry and terrain attributes as input. Clusters reveal morphological patterns that are immediate aftermaths of diverse eruption mechanisms. Eventually, some clusters may be targeted for undiscovered mineral occurrences.
Classification of Invasive Ductal Carcinoma from histopathology breast cancer images using Stacked Generalized Ensemble

Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. However, machine learning algorithms have given rise to progress across different domains. There are various diagnostic methods available for cancer detection. However, cancer detection through histopathological images is considered to be more accurate. In this research, we have proposed the Stacked Generalized Ensemble (SGE) approach for breast cancer classification into Invasive Ductal Carcinoma+ and Invasive Ductal Carcinoma-. SGE is inspired by the stacking model which utilizes output predictions. Here, SGE uses six deep learning models as level-0 learner models or sub-models and Logistic regression is used as Level – 1 learner or meta – learner model. Invasive Ductal Carcinoma dataset for histopathology images is used for experimentation. The results of the proposed methodology have been compared and analyzed with existing machine learning and deep learning methods. The results demonstrate that the proposed methodology performed exponentially good in image classification in terms of accuracy, precision, recall, and F1 measure.
Research on path planning of quadruped robot based on globally mapping localization

Perception and navigation control is core key technology for the legged robot to adapt to complex terrain and achieve autonomous walking. It is the key to distinguish the legged robot from manned equipment and become a ground unmanned system. In this paper, the SLAM globally localization system based on the lidar point cloud is established, and a localization framework based on the combination of topological measurements is proposed to realize the map reconstruction and localization of the wild environment. Path planning based on Dijkstra algorithm is proposed to implement the globally terrain autonomous navigation task of the quadruped robot, and autonomous obstacle avoidance strategy of the local map for quadruped robot based on the artificial potential field theory is applied considering the motion and vibration of legged robot. The experimental results show that the globally path navigation can accurately plan the optimal path and complete the autonomous obstacle avoidance of the local map. The results have demonstrated the effectiveness of the proposed method and realize the globally field autonomous walking of the quadruped robot.
Approaching Another Tourism Recommender

With the concurrent spread and development of Internet and a warm welcome to the Age of Big Data, Internet has become a main channel for travelers to obtain online information before traveling, but in the meantime they are often submerged in a large number of information by searching and for selecting. In this case, travel recommender system is created to solve the problem of information overload effectively. This article analyses the concept, application and development status of travel recommender systems through the collection and arrangement of relevant literature published in recent years. Also, it pays special attention to the analysis of key technologies in the system, pointing out its complexity and uniqueness an application. Besides, the limitations of recommendation methods based on collaborative filtering and content-based filtering are considered as well, then the using knowledge-based filtering or hybrid recommendation method is proposed. It also discusses the role and application of tourism decision-making theory in the recommender system, and finally puts forward a general model of travel recommender system and future research hot spots. It's wished that this research can expand the vision and serve as a reference in this field.
Toward Automated Quest Generation in Text-Adventure Games

Interactive fictions, or text-adventures, are games in which a player interacts with a world entirely through textual descriptions and text actions. Text-adventure games are typically structured as puzzles or quests wherein the player must execute certain actions in a certain order to succeed. In this paper, we consider the problem of procedurally generating a quest, defined as a series of actions required to progress towards a goal, in a text-adventure game. Quest generation in text environments is challenging because they must be semantically coherent. We present and evaluate two quest generation techniques: (1) a Markov model, and (2) a neural generative model. We specifically look at generating quests about cooking and train our models on recipe data. We evaluate our techniques with human participant studies looking at perceived creativity and coherence.
A MULTI-DIMENSIONAL ALGEBRA FOR DESIGNING AN IMPROVED NTRU CRYPTOSYSTEM

The NTRU public key cryptosystem is essentially based on the Nth degree trun- cated polynomial ring Z[X]/XN−1 . By altering the Z[X]/XN−1 or its mathematical con- struction, many researchers have worked to improve NTRU. In this paper, we present a new multi-dimensional public key cryptosystem which depends on a commutative quaternion al- gebra with a new structure. The new structure is an alternative to NTRU called NTRTE. Some data are used for comparing the key security and message security of NTRTE with the original NTRU and QTRU cryptosystems. Based on a security assessment, the comparison reveals that NTRTE is more resistant to them.