Reviewerly/exHarmony huggingface dataset:
Mask and respirator detection: analysis and potential solutions for a frequently ill-conditioned problem

During the coronavirus pandemic, the mask detection problem has become of particular interest. Usually, the goal is to create a system that can detect whether or not a person is wearing a mask or respirator. However, this tends to trivialize a problem that hides a greater complexity. In fact, people wear masks or respirators in various ways, many of which are incorrect. This makes the problem ill-conditioned and creates a bias compared to training cases, with the consequence that these systems have a considerably lower accuracy when used in practice. We claim that focusing on the ways in which a mask can be worn and classifying the problem not as binary but at least as ternary, thus adding an intermediate class containing all those ways in which a mask or respirator can be worn incorrectly, could help address this problem. For this reason, this paper describes and puts to the proof the Ways to Wear a Mask or a Respirator Database (WWMR-DB). It has a fine classification of the most common ways in which a mask or respirator is worn, which can be used to test how mask detection systems work in cases that resemble the real ones more. It was used to test a neural network, the ResNet-152, which was trained on less fine databases, like the Face-Mask Label Dataset and the MaskedFace-Net. The mixed results denote the shortcomings of these databases and the need to enhance them or resort to finer databases.
Social Networking in an Information-Centric System with Blockchain

for many users nowadays social media plays a big role in their world, as it helps to connect with new people while keeping the old bonds intact. The drawback is that we can&#x2019;t able to see that, the data is being generated every minute of a second and that data is kept secure in the hands of a few tech giants, who are using it and selling it as they see fit and using it in a manner as they see profitable and with this paper I&#x2019;d like to propose that shifting social media from host-centric to the information-centric network on the blockchain would not only be safer yet convenient and secured.In peer-to-peer system, cryptocurrency is used to pay for and secure content acquisition.The blockchain lets individuals acquire ownership over their Data, Property and Assets. In social networking, a decentralised approach to blockchain technology protects privacy and allows for e-commerce, ledger based crowd funding, smart apps, and smart contracts. Platforms like Facebook, LinkedIn, Instagram, Twitter, Reddit, and other digital social network media platforms offer free for all access, which is convenient in the name of connecting and socialising but they are collecting are data and using it to sell it to other third party buyers for the price of our privacySome of the most important difficulties confronting social media network business period, profit is focused on advertising and analytics. False propaganda based information on (Facebook), rampant bullying and harassment (Twitter), single party controlled censorship, and demonetization are some of the most serious issues facing social networking (YouTube).
Learning-Relevant Concept Extraction By Utilizing Automatically Generated Textbook Corpora

Learners comprehend complex texts only if they understand the concepts involved, and assessing their comprehension works best if the assessment targets those concepts. Hence, assessment systems, such as question generators, often profit from learning-relevant concepts as input to solve their tasks. However, automatically detecting a given text’s relevant concepts is non-trivial. If a concept is deemed relevant heavily depends on the context and thus on automatic text understanding. Recent advancements in supervised machine learning improved state-of-the-art automatic text understanding. However, those methods need training corpora to learn the association between the relevant concepts and their text’s context. This work shows that textbooks comprise all the necessary information to construct such corpora. We introduce and evaluate an open-source automatic back-of-the-book index extractor and a corpus construction algorithm to derive training corpora from a set of high-quality PDF textbooks. We furthermore investigate to what extent state-of-the-art machine learning models can learn to extract concepts from constructed corpora. The results show that the models have decent performance and provide evidence generalization to unseen concepts.
Hubungan Perhatian Orang Tua dengan Motivasi dan Kesiapan Belajar Siswa Kelas V SDN 2 Surabaya Kec. Limbangan Kab. Garut

This study uses the correlational method to determine the relationship between parental attention and learning motivation and learning readiness of fifth grade students at SDN 2 Surabaya. Based on the results of the study showed that: (1) there is a relationship between parental attention and student learning motivation with a significance value for the effect of parental attention on motivation is 0.013 &lt; 0.05 and for the tcount value is 2.681 &gt; 2.055, (2) there is a relationship between people's attention parents with student learning readiness, this is evidenced by the significance value for the effect of parental attention on learning readiness is 0.000 &lt; 0.05 and for the t count value is 4.684 &gt; 2.055, (3) parental attention has an influence on learning motivation and student readiness to learn by see a percentage of 22.3% parental attention has an influence on student motivation. Meanwhile, 46.78% of parents' attention has an influence on the learning readiness of fifth grade students at SDN 2 Surabaya.
Analytics for early detection of insider threat

Incidents related to insider threats are steadily increasing, especially technology thefts.<sup>1</sup> Dr. Larry Ponemon wrote, “insider threats are not viewed as seriously as external threats, like a cyber-attack. But when companies had an insider threat, in general, they were much more costly than external incidents. This was largely because the insider that is smart has the skills to hide the crime, for months, for years, sometimes forever.”<sup>2</sup> Insider threat is a relatively rare occurrence, often perpetuated by revenge seeking employees with a grievance against their employer. The capitol assault was an eye-opening event in that it clearly demonstrated that insiders, in this case military and ex-military, were willing to engage in violence against the government. Detection of insider threat is a difficult problem as data is limited and the factors surrounding insider threat are highly contextual. Previous research tends to focus on theoretical perspectives and threat mitigation, frequently emphasizing cyber-technical indicators of insider threat versus focusing on the human behind the screen.<sup>3</sup> More behavior focused research has identified several psychosocial, individual-level risk factors for insider threat (e.g., disgruntlement, poor work performance, etc.) or has conducted personality assessments on known insiders post-hoc. <sup>4,5</sup> However, future directions in this line of research need to address early detection of mobilization as part of the defensive strategy against these threats. Subsequently, this paper will summarize the state-of-the-knowledge in terms of research on behavioral factors and approaches for insider threat detection, highlighting methods for assessing social-cyber information to enable early detection of insider threat.
Resilient Machine Learning in Space Systems: Pose Estimation as a Case Study

The space industry is rapidly growing at present and is not limited to the traditional players like The National Aeronautics and Space Administration (NASA) and European Space Agency (ESA), and it has spread to medium and small commercial organisations as well. The advancement in both hardware and software technologies is leading to the industry players&#x0027; expansion. In parallel, the adoption of Artificial Intelligence (AI) and Machine Learning (ML) have been surging in the space industry. There are diverse applications in the space sectors that ML may be applied, such as assisting astronauts, debris removal in the orbit etc. However, several studies have shown that ML specifically deep learning methods are vulnerable to adversarial attacks. However, vulnerabilities are studied mainly on the classification tasks, only a few studies have been carried out on identifying the adversarial attacks on the regression models such as pose estimation. This paper, undertaken as part of the UK FAIR-SPACE Hub, aims to identify adversarial actions against learning methods and their impact in the space domain where pose estimation of a space object is taken as an exemplar. The importance of pose estimation and the consequences of undesired activity while computing pose estimation can be expensive. For example, estimating a wrong pose during the docking of a spacecraft can result in a collision and damage the assets. In this work, we first analyse the impact of adversarial attacks in the space for estimating pose using various adversarial machine learning techniques. We then present the possible implications of existing and emerging defensive strategies for building resilient machine learning for pose estimation. The results show that the optimised based attack method performs well compared to the Iterative Fast Gradient Simple Method (IT-FGSM) and Generative Adversarial Network (GAN) based AdvGAN methods to generate adversarial examples. In terms of defensive strategies, ML model is vulnerable and still work needs to be done to make them robust against adversarial attacks. The results of this work showcase potential attacks on current and future ML based space missions and the necessity to make them resilient. We believe incorporating resilient methods in the design phase may save time, economy, and potential embarrassment caused by mission failure.
A Computationally Efficient UAV Exploration Strategy for Uncertain Complex Environments

The paper proposes a frontier based strategy for exploration. Unlike traditional frontier based approaches, a computationally efficient approach is proposed, which does not require to process all the frontiers in a map frequently, and is suitable for on-board computing. To achieve this, the global set of frontiers is divided into three subsets. Each subset is associated with a distinct planning strategy, which allows exploration in a computationally efficient way. Complexity analysis conveys the superiority of the proposed approach in terms of computational load. Performance of the proposed approach is validated in simulation by employing a quadrotor in large-scale and complex problem setups, e.g., powerplant, bridge structure, and typical office like scenario. Simulation results confirm the efficacy of the proposed approach over state-of-the-art (SoA) algorithms.
Why Bother Enabling Biomedical Literature Analysis with Semantics?

These days, ELMo [3], BERT [1], BART [2] and other similarly cutely-named models appear to have dramatically advanced the state of the art in basically every problem in natural language processing and information retrieval. It can leave a researcher wondering whether there is more to language processing than deploying or fine-tuning contextual word embeddings. What of formal semantics and knowledge representation? What value do these bring to text analysis, either in modelling or in task definitions? In this talk, I will try to explore these questions, from the perspective of my long-running experiences in biomedical information extraction and literature exploration. Perhaps we can shift the academic conversation from a one-model-fits-all solution for individual tasks to a more nuanced consideration of complex, multi-faceted problems in which such models certainly can play a critical role but aren’t necessarily “all you need” [4].
New Student typology: Studentbots?: Architecture students facing disruptive technologies in the pandemic era

The coronavirus pandemic and the obligation to be confined to small spaces for the majority of the population left us questioning how to teach the new design rules and communicate spaces without our physical presence. The 21st century student was already heading towards having a "bionic personality" due to new technologies. Simultaneously the classic project design outputs like paper presentations were gaining a high level of abstraction due to the massive data overlap and were demanding new formats for better public interaction. In such a context as confined designers and educators, we have seen a clear opportunity to boost all new digital formats, allowing design decision-making, new interaction platforms, and disruptive visual technologies such as virtual reality (VR) and augmented reality (AR). By applying new teaching tools using VR/AR in different contexts and projects the concept of 'studentbot' will be narrowed down. Through different teaching experiences and project examples, we will evaluate the successes, failures, fields of expansion, and controversies of this new student typology.
The Virtual Reality Workplace

This study is centred around social research that aims to reconstruct relevant knowledge on the contemporary VR work environment. More particularly, we seek to answer three research questions. First, we seek an understanding of elements and factors within the VR work environment that influence an individual’s productivity. Second, we explore how a VR work environment can impact individuals’ well-being. Finally, we investigate what can strengthen the formal and informal interactions between individuals and subsequently knit collaborative ties.
Abstractions from Aristotle’s Polis and Plato’s Republic for Education, Self-Actualization and Sustainability in Nigeria

The paper adumbrated on prevalent circumstances surrounding the delivery of the type of education that may not correspond to sustainability in Nigeria. This is against the backdrop of a plethora of negative imperatives in the motive, mode and usages for educational certificates instead of knowledge and merit. Abstracting from the philosophical postulations embedded in Aristotle’s Polis and Plato’s Republic as its theoretical foundation, the paper argued that education for self-actualization, if not skewed to align with the goal needs of the larger society cannot confer sustainability in the long run. Mainly descriptive in approach, the paper inferred that apart from the fact of poor funding and outmoded curriculum contents of Nigerian educational programme, which result in the churning out of graduates adjudged to be largely unemployable, a great number of education seekers still hold unto the misconception that an educational certificate, no matter how acquired and what nature it is, provides, not just jobs, but white collar jobs, whereas this had long been supplanted by pragmatism, smartness, innovation and creativity. The paper therefore recommended, among others, improved funding, the re-appraisal of educational curriculum, improvement in schools’ infrastructure, training of teachers etc.; community involvement in platforms for counselling, support and skill acquisition centres for alternative development; and the acquisition of skill sets in computer/ICT related programmes, arts and crafts, as complements to educational certificates that are expected to train the head and equip the hands at the same time.
Graph-level Semantic Matching model for Knowledge base Aggregate Question Answering

In knowledge base question answering, complex question always has long-distance dependencies, especially aggregate question, which affects query graph matching. Many previous approaches have made conspicuous progress in complex question answering. However, they mostly only compare based on the textual similarity of the predicate sequences, ignoring the degree of semantic information either questions or query graphs. In this paper, we propose a Graph-level Semantic Matching (GSM) model to obtain the global semantics representation. Due to the structural complexity of query graphs, we propose a global semantic model to explicitly encode the structural and relational semantics of query graphs. Then, a question-guiding mechanism is applied to enhance the understanding of question semantics in query graph representation. Finally, GSM outperforms existing question answering models, and exhibits capabilities to deal with aggregate questions, e.g., correctly handling counting and comparison in questions.
A recommendation model based on Stepped Six Channel CNN feature extraction

Abstract In a recommendation system, users and projects often do not exist independently. There are a lot of potential factors. Although the existing mainstream models can extract the potential features between users and projects, the extracted potential features are mainly high-order features and original features. We believe that there are many features at the intermediate level and the interaction between many features. Therefore, we proposed a Stepped Six Channel CNN network model (SSCC). By using different network depths to process the interaction information of user information and project attributes, the potential characteristics of multiple layers can be extracted, and feature layers of different channels are combined to form new feature channels. In addition, the network model based on DNN is used to predict the interaction between the user and the project, and the two models are combined in parallel to form a new model which name is Stepped Six Channel CNN&amp;DNN(SSCCD). Our proposed model is clearly superior to some of today’s mainstream models through experimental tests on publicly available standard data sets.
Discrimination Correction and Balance for Class-Incremental Learning

Abstract Catastrophic forgetting is the key role in incremental learning, since the model gains poor quality for old classes than new classes with continuously incoming tasks and less storage. In order to solve the balance between model plasticity and stability, this paper proposes a novel class incremental learning method DCBIL, which adopts discrimination correction and balance to avoid model bias. First, three components Conservative, Balancer and Opener with identical structure are designed. Then, Conservative and Opener have a bias to old and new classes respectively. And Balancer modifies the fully connected layer and replaces the base model for the next training. Moreover, a stochastic perturbation probability synergy is incorporated for final output fusion. Experimental evaluations on Mnist, Cifar-10, Cifar-100 and TinyImageNet demonstrate the effectiveness of DCBIL. DCBIL is portable for it can be used as a plug-in to strengthen the performance of existing incremental learning model.
A Novel Perspective towards SVM Combined with Autoencoder

Abstract In this paper, we propose a novel perspective towards the hybrid algorithm about support vector machine combined with neural network. We suggest that the depth of convolution neural network is supposed to insight the view of machines to acquiring an equal level of features as human do. The kernel function of support vector machine can be grasped flexibly where the neural network makes an efficient cross calculation for features exactly instead of the kernel function but more adjustable. To develop such a coincident format, we build a hybrid model with the half former part of autoencoder working as the kernel function and support vector machine working as the core classifier, with certain ways to train the hybrid model: discrete, continuous and prejudice. The hybrid model inherits asset of each algorithm, and that process is generally subject to the objective perspective. We take the hybrid model to Covid 19 detection compared with other well-performed models, and experimental results illustrate that our perspective is advisable which achieves a state-of-the-art performance in medical scheme.
Multi-Grained Ranking Network for Event Extraction

Abstract Event Extraction is a complicated task in Information Extraction including three subtasks: Entity Mention Detection, Event Detection, and Argument Role Prediction. Previous work for Event Extraction is usually done in the form of pipelined approach. Recently, end-to-end models for Event Extraction are getting more attention, but two problems still exist. First, different subtasks focus on different granularities of context information, but in the existing methods, all three subtasks share a unified representation, thus the information interested by a specific task may be underestimated or even ignored. Second, the interrelationship among events and entities has a strong influence in figuring out the meaning of a specific trigger or entity, but the existing methods are suffering in issues like time-dependency that a decision made later can only affect the rest of the sentence but has no way to update the previous one. Such limitations lead to insufficient use of the correlation between event elements in the sentence. In this paper, we propose a Multi-Grained Ranking Network for Event Extraction, which performs the three subtasks simultaneously in a single model. To handle the first problem, we design a dynamic Span Generator to construct multi-grained span representations from three granularities: sentence, neighbors, and content. Task-specific BERT-Rescale Gate units are applied in the Generator to capture the features of interest for each subtask respectively. As for the second challenge, we implement two attention mechanisms to explore the association of event-event and event-entity. The Events-Aware Attention is assigned to capture the association between events. While the Arguments-Aware Co-Attention is designed to model the relationship among entities in a specific event. The experiments on ACE-2005 prove that the proposed method outperforms the state-of-the-art methods in all subtasks of end-to-end Event Extraction.
Efficient Adaptive Upsampling Module for Real-Time Semantic Segmentation

Upsampling operation is necessary for semantic segmentation and other pixel-level prediction tasks. Among the commonly used upsampling operations, some are too simple to effectively recover the spatial details lost during downsampling process, and some are too complex and have high computation complexity. In real-world applications, it is critical to achieve high accuracy and maintain real-time inference speed. Therefore, an efficient upsampling operation is essential for these tasks. In this paper, we introduce efficient adaptive upsampling module (EAUM) for real-time semantic segmentation. Inspired by dynamic filter networks, EAUM adaptively predicts the kernel weight of each point in the upsampled feature map according to the corresponding points in the input feature map. To reduce computational cost, EAUM decomposes the spatial information and channel information required for upsampling. The proposed EAUM shows impressive performance on Cityscapes and CamVid benchmarks. Specifically, DenseENet with EAUM outperforms the baseline by 1.4% [Formula: see text] and 1.6% [Formula: see text] in accuracy with a slight drop in inference speed on Cityscapes test dataset.
An unfeasibility view of neural network learning

We define the notion of a continuously differentiable perfect learning algorithm for multilayer neural network architectures and show that such algorithms do not exist provided that the length of the data set exceeds the number of involved parameters and the activation functions are logistic, tanh or sin.