Reviewerly/exHarmony huggingface dataset:
Stability analysis of time-varying delay neural network for convex quadratic programming with equality constraints and inequality constraints

&lt;p style='text-indent:20px;'&gt;This paper presented a class of neural networks with time-varying delays to solve quadratic programming problems. Compared with previous papers, the neural networks proposed in this paper replaced the constant time delays &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ \tau $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; with variable time delays &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ \tau(t) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and had a more concise structure. There was an improvement of previous method in proving the existence and uniqueness of solutions of the neural networks in this paper. Further, this paper gave the conditions to be satisfied for the global exponential stability of the proposed neural networks. Through numerical examples, this paper verified that the proposed neural networks were accurate and efficient in solving the quadratic programming problems.&lt;/p&gt;
Situation-Aware Model Refinement for Semantic Image Segmentation

The quality of semantic image segmentation models can be affected by external factors such as weather or daytime. Those factors can lead to safety-critical mistakes. In this work, we propose a systematic approach to detect and alleviate such weaknesses of semantic segmentation models. We systematically evaluate a semantic segmentation model under different external factors and analyze which factors have the largest impact on the performance. Then, we collect new training data under the most harmful external factors and fine-tune the model. We use the CARLA simulator to obtain driving data under various environment settings. We deploy a state-of-the-art semantic segmentation model in two distinct driving environments. Then, we use the proposed process to detect which external factors affect model performance the most. We collect new training data under those factors and fine-tune the model. The proposed approach outperforms collecting the same amount of random additional data by up to 10.6%. Our results show the benefit of using an iterative refinement approach as opposed to merely collecting larger data sets. Finally, we use the knowledge about which factors affect performance the most to train a simple decision tree classifier to predict the model's performance given the current external factors. Problematic environments can be detected at an average accuracy of 87.5%.
A Survey on Multi-Factor Authentication Methods for Mobile Devices

The use of mobile devices worldwide has been on the increase. More and more people are using mobile devices to carry out activities on the Internet. The activities include checking emails, online banking, school, and work activities. However, mobile devices are susceptible to security risks and attacks. In this study, a survey was administered to 74 participants. The participants were asked whether they have multi-factor authentication on their mobile devices and if this feature is configured. Participants were also asked about how they use their mobile devices, how often they change their passwords, as well as their perception about the importance of mobile device security. The data collected from the participants was analyzed and results indicated that many participants strongly believe that mobile device security is important. Most of the participants use their mobile devices to check emails, conduct work activities, and online banking among other tasks. The results also indicate that nearly half of those who have devices that support multi-factor authentication do not use it. Furthermore, the results also show that nearly half of the participants have never changed the passwords on their mobile devices. The results may help organizations to educate people and raise the awareness of mobile device security. The results may also help with policy making.
Expected 10-anonymity of HyperLogLog sketches for federated queries of clinical data repositories

The rapid growth in of electronic medical records provide immense potential to researchers, but are often silo-ed at separate hospitals. As a result, federated networks have arisen, which allow simultaneously querying medical databases at a group of connected institutions. The most basic such query is the aggregate count-e.g. How many patients have diabetes? However, depending on the protocol used to estimate that total, there is always a tradeoff in the accuracy of the estimate against the risk of leaking confidential data. Prior work has shown that it is possible to empirically control that tradeoff by using the HyperLogLog (HLL) probabilistic sketch.In this article, we prove complementary theoretical bounds on the k-anonymity privacy risk of using HLL sketches, as well as exhibit code to efficiently compute those bounds.https://github.com/tzyRachel/K-anonymity-Expectation.
Mapping Trusted Paths to VGI

We propose a novel method of assessing OpenStreetMap data using the concept of Data Maturity. Based on research into data quality and trust in user generated content, this is a set of measurements that can be derived from provenance data extracted from OpenStreetMap edit history.
Target-Tailored Source-Transformation for Scene Graph Generation

Scene graph generation aims to provide a semantic and structural description of an image, denoting the objects (with nodes) and their relationships (with edges). The best performing works to date are based on exploiting the context surrounding objects or relations, e.g., by passing information among objects. In these approaches, to transform the representation of source objects is a critical process for extracting information for the use by target objects. In this paper, we argue that a source object should give what target object needs and give different objects different information rather than contributing common information to all targets.To achieve this goal, we propose a Target-Tailored Source-Transformation (TTST) method to propagate information among object proposals and relations.Particularly, for a source object proposal which will contribute information to other target objects, we transform the source object feature to the target object feature domain by simultaneously taking both the source and target into account.We further explore more powerful representation by integrating language prior with visual context in the transformation for scene graph generation.By doing so the target object is able to extract target-specific information from source object and source relation accordingly to refine its representation.Our framework is validated on the Visual Genome benchmark and demonstrated its state-of-the-art performance for the scene graph generation.The experimental results show that the performance of object detection and visual relationship detection are promoted mutually by our method.The code will be released upon acceptance.
To Keystone or Not to Keystone, that is the Correction

To Keystone or not to Keystone, that is the correction... and indeed the question! Outside of highly constrained conditions, the vast majority of photographed imagery of the natural environment is taken non-square to the objects that they represent Consequently, those objects appearing at a distorted perspective may be computationally corrected via Keystone Correction. This disparity is frequently observed when considering imagery sourced from vehicle-mounted cameras, such as those levied in autonomous vehicle infrastructure or by streetscape collection initiatives such as Google Street View. As visual creatures, the lived environment proximal to roadways is filled with text- and numeric-based advertisements vying for our attention and, conveniently, this signage isn't placed perpendicular to a vehicle's forward-facing camera. Given the perspective distortion of the text and/or values contained therein, their automated detection and reading may benefit from Keystone correction. In this work, we address the yet-unanswered question: what benefit might we expect from Keystone correction preprocessing of images? We do not explicitly promote the use of Keystone correction but rather, evaluate its utility within a prediction pipeline. To this end, we leverage the Gas Prices of America (GPA) dataset containing multi-digit, multi-price values and the French Street Sign Names (FSNS) multi-word text dataset given their known geometry enabling the automation of image Keystone correction. We compare the outcomes of Keystoned imagery versus non - Keystoned imagery along five axes: 1) predictive performance, 2) annotation correctness, 3) algorithmic computational complexity and empirical time estimation, 4) image scaling, and 5) degree of perspective transform. From our findings, we arrive at several recommendations on both the benefit & burden of Keystone correction to inform future research on extracting information in the wild.
New trends and competition in the market of higher education services in the context of digitalization

The aim of the study was to analyze new trends, competition and structural changes in higher education in the context of digitalization. The main directions of higher education development in the XXI century are identified. It is shown that by 2020, new trends in the market of higher education services have appeared and fully manifested themselves, due to three key factors: 1) increased global competition; 2) the active introduction of digital technologies in higher education; 3) the impact of the global COVID pandemic on educational technologies. It is shown that the coronavirus pandemic was a powerful incentive for the transition to digital technologies in higher education. Thanks to digitalization, the structure of the market has changed, and the number of players has increased significantly, and the market consists of private and public institutions, ministries of education and government agencies, educational, consulting and test (rating) companies, etc. Moreover, the global competition of universities for quality is becoming more organized and technically equipped; it increasingly relies on impersonal methods of assessing quality using digital technologies. At the same time, the authors draw a number of conclusions about the negative consequences of distance education. It is shown that competitiveness and accessibility of educational services for different categories of citizens in the difficult conditions of global challenges and threats, the wave pandemic, is ensured through high-quality interaction of all participants in the process, the activity of scientists and practitioners around the world, and state support for teaching initiatives in all regions of the country.
Multiagent Pathfinding Under Rigid, Optimization, and Uncertainty Constraints

Many contemporary navigation applications rely on the well-known A* algorithm for pathfinding. We present a pathfinding technique utilizing the cross-entropy method and Bayesian networks that is capable of finding paths for multiple agents while satisfying rigid, optimization, and uncertainty constraints.
The impact of different tasks on evolved robot morphologies

A well-established fact in biology is that the environmental conditions have a paramount impact on the evolved life forms. In this paper we investigate this in an evolutionary robot system where morphologies and controllers evolve together. We evolve robots for two tasks independently and simultaneously and compare the outcomes. The results show that the robots evolved for multiple tasks simultaneously developed new morphologies that were not present in the robots evolved for single tasks independently.
From critical pedagogy to critical digital pedagogy: a prospective model for the EFL classrooms

Purpose Critical digital pedagogy (CDP) is an emerging field in education. The basic tenet of CDP involves taking learners' experiences into account and engaging them in critical thinking about social oppression. With the outbreak of the unprecedented COVID-19 pandemic, CDP has got more currency and appropriacy in the current paradigm shift in learning and teaching. Design/methodology/approach This paper scrutinizes different aspects of CDP including its origins, theoretical underpinnings and its implementation in different contexts. It also critically reviews Freire's (1972) problem-posing education and Morris and Stommel's (2017) model of CDP. Findings The article proposes a CDP model based on the previous ones, which includes the core concepts and criteria of CDP and focuses on EFL classrooms. Research limitations/implications One of the limitations of CDP is gaining the learners' approval in creating an environment of co-constructing knowledge moving away from traditional practices. In addition to that, the use of new media in the classroom can be intimidating for students and stakeholders alike. The lack of logistic support in many rural, remote and underdeveloped contexts cannot be ignored either Practical implications The paper provides recommendations for future research in CDP. Originality/value Critical pedagogy (CP) is a teaching approach in which the oppressed are basically focused and teachers and learners construct knowledge together. Recently, with the outbreak of the COVID-19 pandemic, global education had to go online. Consequently, traditional teaching and learning had to undergo a paradigm shift. Along with other changes in traditional teaching and learning practices, there has been a significant change in teaching philosophy. This is how the CDP finds its currency in this emerging unprecedented teaching and learning situation.
Adversarial training regularization for negative sampling based network embedding

The aim of network embedding is to learn compact node representations. This has been shown to be effective in various downstream learning tasks, such as link prediction and node classification. Most methods focus on preserving different network structures and properties, ignoring the fact that networks are usually noisy and incomplete, thus such methods potentially lack robustness and suffer from the overfitting issue. Recently, generative adversarial networks based methods have been exploited to impose a prior distribution on node embeddings to encourage a global smoothness, but their model architecture is very complicated and they suffer from the non-convergence problem. Here, we propose adversarial training (AdvT), a more succinct and effective local regularization method, for negative-sampling-based network embedding to improve model robustness and generalization ability. Specifically, we first define the adversarial perturbations in the embedding space instead of in the discrete graph domain to circumvent the challenge of generating discrete adversarial examples. Then, to enable more effective regularization, we design the adaptive l2 norm constraints on adversarial perturbations that depend upon the connectivity pattern of node pairs. We integrate AdvT into several famous models including DeepWalk, LINE and node2vec, and conduct extensive experiments on benchmark datasets to verify its effectiveness.
Efficacy of Romantic Poetry: Chinese Classical Poetry Education Project Based on Augmented Reality Technology for Elementary School Students

Ancient poetry is an important carrier of Chinese traditional culture. At present, there are some problems in ancient poetry teaching, such as mechanical learning, utilitarian education, neglect of background interpretation and emotional teaching. In recent years, researchers have reported positive outcomes and effects from applying Augmented Reality to the educational process. Towards this direction, we present “Romantic Poetry”, a custom-designed augmented reality system that harnesses the motivating power of interest and the affordances of augmented reality to engage children in learning ancient poetry. The review of academic performance tests and a course experience questionnaire were completed by fifth-grade students. A pre-test, a post-test and a questionnaire were designed to respectively assess students' learning efficiency and perceived of the system. The results showed that the system presented excellent performance in terms of learning effectiveness and stimulating leaning motivation. Also, students are highly receptive to the use of content enriched with AR using their mobile devices.
Adversaries Strike Hard: Adversarial Attacks Against Malware Classifiers Using Dynamic API Calls as Features

Malware designers have become increasingly sophisticated over time, crafting polymorphic and metamorphic malware employing obfuscation tricks such as packing and encryption to evade signature-based malware detection systems. Therefore, security professionals use machine learning-based systems to toughen their defenses – based on malware’s dynamic behavioral features. However, these systems are susceptible to adversarial inputs. Some malware designers exploit this vulnerability to bypass detection. In this work, we develop two approaches to evade machine learning-based classifiers. First, we create a Generative Adversarial Networks (GAN) based method, which we call ‘Malware Evasion using GAN’ (MEGAN) and the extended version ‘Malware Evasion using GAN with Reduced Perturbation (MEGAN-RP).’ Second, we develop a novel reinforcement learning-based approach called ‘Malware Evasion using Reinforcement Agent (MERA).’ We generate adversarial malware that simultaneously minimizes the recall of a target classifier and the amount of perturbation needed in the actual malware to evade detection. We evaluate our work against 13 different BlackBox detection models – all of which use dynamic presence-absence of API calls as features. We observe that our approaches reduce the recall of almost all BlackBox models to zero. Further, MERA outperforms all the other models and reduces True Positive Rate (TPR) to zero against all target models except the Decision Tree (DT) – with minimum perturbation in 6 out of 13 target models. We also present experimental results on adversarial retraining defense and its evasion for GAN based strategies.
Fuzzy Clustering Algorithm Based on Improved Lion Swarm Optimization Algorithm

Aiming at the shortcomings of fuzzy C-means (FCM) clustering algorithm that it is easy to fall into local minima and sensitive to initial values and noisy data, this paper proposes a fuzzy clustering algorithm based on improved lion swarm optimization algorithm. Aiming at the problem that lion swarm optimization (LSO) algorithm is easy to fall into the local optimum, this paper improves lion swarm optimization algorithm by introducing sin cos algorithm and elite opposition-based learning. In addition, the introduction of a supervision mechanism enhances the lions’ ability to jump out of local optimum and improves the local search ability of lion swarm optimization algorithm. The optimal solution obtained by improved lion swarm optimization algorithm is used as the initial clustering center of FCM algorithm, then FCM algorithm is run to obtain the global optimal solution, which effectively overcomes the shortcomings of FCM algorithm. The experimental results show that, compared with original FCM clustering algorithm, FCM clustering algorithm based on improved lion swarm optimization algorithm has improved the algorithm’s optimization ability and has better clustering results.
Trace elements in pyrite and pyrrhotite in the Pitangui Orogenic Au deposit, Pitangui greenstone belt, São Francisco Craton: Implications for the ore-forming fluids and metal sources

Orogenic Au deposits represent one of the largest sources of gold in the world and in Brazil, particularly at the Quadrilátero Ferrífero, southeastern portion of the São Francisco Craton. In the last decades, advances in in-situ analytical techniques allowed a detailed investigation of the chemistry of sulfides. This can reveal information about the origin and composition of the mineralizing fluids, potential sources of metals and sulfur and the fluid-rock interaction during the ore-forming processes. In this paper we combine Electron Microprobe Analysis and LA-ICP-MS analyses to put constraints on the nature and evolution of the Pitangui gold deposit. The Pitangui deposit represents an important Orogenic Au deposit hosted in clastic metasedimentary rocks of the Pitangui greenstone belt, northwestern portion of the Quadrilátero Ferrífero. Detailed drill hole, petrographic observations and in-situ chemical analyses of the hydrothermal alteration zones show that pyrite is the dominant ore mineral in the deposit and two possible origins are suggest including (i) syngenetic pyrite development at the early stages of the greenstone belt evolution and (ii) epigenetic pyrite, which is associated with hydrothermal processes during the Orogenic Au event. Syngenetic pyrite (PyI) has high Co, Bi, Pb, Sb, Te, Se, Ag and Au concentrations. The PyI could represent an important source of metals, a set of trace elements and likely sulfur for the later Orogenic Au mineralization. Epigenetic pyrites (PyII, PyIII and PyIV), conversely, are mainly found in association with hydrothermal minerals and within the mineralized carbonate-quartz-vein. The variations at Co, Ni, Pb, Te, Sn, Se, As, Bi, Sb, Ag, Mo and W in the epigenetic pyrite indicate that the dehydration PGB sequences during orogenesis constitutes the main fluids sources. The PyV, in turn, only appears at metamafic dyke, but its origin remains unclear.
Chatbot User Experience: Speed and Content Are King

Chatbots provide some key advantages for businesses such as increased cost savings and efficiencies, but often fail to meet customer expectations due to their unsuitable, inaccurate and difficult to understand responses to questions. This can often lead to customer skepticism and resistance to using this technology. There are, however, many best practices that can be taken when designing and implementing chatbots that may increase the chance of your customers being satisfied with their experience. In this paper, we will discuss the findings of a user study we conducted examining attitudes, preferences, and performance with using chatbots for customer technical support. We will discuss the research findings, review lessons learned and best design practices, as well as discuss which attributes are most predictive of a successful chatbot experience based on a regression model we conducted.