Reviewerly/exHarmony huggingface dataset:
Improving the Cybersecurity Framework for Future Consumer Networks

The framework Information Technology professionals and Network Organizations use is often seen as open and dynamic.This can create many different pathways for cybercriminals to launch an attack on an enterprise network to cause panic, this situation could be prevented.Using the proposed framework, network administrators and networked organizations can improve their cybersecurity framework for future consumer networks.Implementing a network security plan that is up to date and outlines responsibilities of team members, creating a government subsidy to implement and increase safeguards on US based networks, and the analyzing of past cyber-attacks metadata to further understand the attacks that are causing problems for consumer networks can improve the cybersecurity framework for consumer networks and increase potential security on US based networks.Research found that the implementation of security plans, creating a government subsidy, and analyzing past metadata all show signs of improving the framework of cybersecurity in consumer based networks.
Application of VR Technology in Museum Narrative Design with Computer Vision Models

The establishment of a virtual museum is an epoch-making thing. Its conceptual development has gone through four basic core stages: exhibition, reconstruction, replacement and reproduction, hence, pplication of modern VR technology in museum narrative design with computer vision models together with the scenario test is provided in this study. Virtual reality technology is a high-tech computer science technology that combines multiple technologies, including computer three-dimensional graphics technology, computer vision and data analysis. This designed freamework applied the technology and the vision minotirng to the systematci design. The hardware implementations are also validated through the platform constructions. The test, comparison analysis and the efficiency testing are provided.
Why 'Permissioned' and 'Private' are not Blockchains

This analysis shows that permissioned and private ledgers form a subclass of Distributed Ledger Technologies (DLT), which are distinguished from the original blockchain. DLTs operate with databases known as "ledgers," copies of which are distributed among multiple nodes, but not anyone can create and validate new blocks of data in such a ledger; they are not decentralized. On the contrary, blockchain was designed as a decentralized, open, and competitive peer-to-peer system. The widespread misconception is to call any technology "blockchain" when it has some chunks of data connected through cryptographic hashes. The technology of creating a sequence of timestamped records connected with hashes is not new, it was designed in the early 90s, but nobody has ever called it "blockchain," as it has never dealt with decentralized consensus protocols and did not operate in a distributed network. Hence, not every chain of blocks is the blockchain. The paper dives into the details of various of DLTs and blockchains. It aims to bring rigor into terminology and academic discourse. It also raises the alarm on false expectations that may appear across industries and the public about a decentralization effect that some DLTs might not bring.
PicPop: A pop-up picture book comprising mid-air images

In this study, we propose PicPop, a pop-up picture book system that uses mid-air images to make digital images pop out from a flat picture book. The user does not need to wear a special device to have an interactive experience with pop-up picture books that employ the proposed system. A reflective type of paper is used to turn the book into a display surface for projecting mid-air images onto. Furthermore, we realized a reduction in the overall volume of the system as compared to the conventional reflective mid-air image-display method, while retaining the desired luminance of the mid-air images.
Quick, Print This Page! The Value of Analogue Media in a Digital World

Quick, print this page!1 Otherwise, the information in this document might be digitally manipulated, and initial creative intention and contribution will be changed. You will lose your access to this document when the database containing this paper is decommissioned or compromised. This paper highlights the relevance of features from analogue media in an increasingly fragile digital world. Digital content can be altered to change its meaning or be deleted altogether. In analogue media, this is much more difficult without anyone noticing. Just imagine the impact if YouTube was decommissioned and all its content was rendered unavailable. In this paper, we outline concrete commercial examples and insight from the research of issues with distribution, storage, and manipulation of digital media and its impact on users. We conclude with strategies to preserve content, access, and artistic freedom in an increasingly digital future.
Sentence Dependent-Aware Network for Aspect-Category Sentiment Analysis

The purpose of Aspect-Category Sentiment Analysis is to predict sentiment polarities of given aspect categories in sentences. Most previous methods used attention-based neural network models to Establish connections between aspect categories and sentiment words and generate aspect-specific sentence representations. However, these models may mismatch sentiment words with aspect categories due to the complexity of sentence structures. To solve this problem, we reconstruct the dependency tree into an ACSA-oriented dependency tree, which builds a direct or indirect semantic connection between sentiment words and corresponding aspect categories, and avoid introducing redundant information from the original dependency tree. On this basis, we propose a Sentence Dependent-Aware Network (SDAN) to encode the tree effectively. The experimental results of applying SDAN to three public datasets demonstrate its effectiveness.
Better sensitivity to linear and nonlinear trends with position than with color

Useful data visualizations have the potential to leverage the visual system's natural abilities to process and summarize simple and complex information. Here, we tested whether the design recommendations made for pairwise comparisons generalize to the detection of trends. We created two different types of graphs: line graphs and stripplots. These graphs were created from identical datasets that simulated temperature changes across time. These datasets varied in the type of trend (linear and exponential). Human observers performed a trend detection task for which they judged whether the trend in temperature over time was increasing or decreasing. Participants were more sensitive to trend direction with line graphs compared to stripplots. Participants also demonstrated a systematic bias to respond that the trend was increasing for line graphs. However, this bias decreased with increasing sensitivity. Despite the better sensitivity to line graphs, more than half of the participants found the stripplots more appealing and liked them more than the line graphs. In conclusion, our results indicate that, for trend detection, depicting data with position (line graphs) leads to better performance compared to depicting graphs with color (stripplots). Yet, graphs with color (stripplots) were preferred over the line graphs, suggesting that there may be a tradeoff between the aesthetic design of the graphs and the precision in communicating the information.
A Survey on Advanced Schemes applied within Trusted Platform modules (TPM) and IaaS in cloud computing

Trusted Platform Modules (TPM) have grown to be crucial safeguards from the number of software-based strikes. By giving a restricted range of cryptographic providers by way of a well-defined user interface, divided as a result of the program itself, TPM and Infrastructure as a service (IaaS) can function as a root of loyalty so when a foundation aimed at advanced equal protection methods. This information studies the works aimed at uses on TPM within the cloud computing atmosphere, by journal times composed somewhere among 2013 as well as 2020. It identifies the present fashion as well as goals from these technologies within the cloud, as well as the kind of risks that it mitigates. The primary investigation is being focused on the TPM's association to the IaaS security based on the authorization and the enabling schema for integrity. Since integrity measurement is among the key uses of TPM and IaaS, particular focus is given towards the evaluation of operating period phases as well as S/W levels it's put on to. Finally, the deep survey on recent schemes can be applied on Cloud Environment.
Data mining system for predicting quality of polymeric films

Abstract A computer data mining system for predicting the quality of polymer films in international, large-scale and multi-assortment industries is described. This article presents a library of statistical and data mining methods that allows, using statistical tests, to test data for distribution normality, predict the quality of polymer film materials for various line configurations and different types of film and includes the following methods: recurrent neural networks, neural network with long short-term memory and convolutional neural network. Analysis of methods of predicting the quality of polymer films was carried out and an algorithm was developed that allows selecting the most appropriate method for predicting the quality of polymer films based on the type of film, line configuration and requirements for film quality. The system includes interfaces that display trends in the process characteristics of the process class. The system was tested using the example of industrial data of the corporation on production of polymer film in plants of Russia and Germany.
Counterexample-Guided Repair for Symbolic-Geometric Action Abstractions

Integrated task and motion planning (TMP) offers a promising class of approaches for solving robot planning problems with intricate symbolic and geometric constraints. However, TMP planners rely on difficult-to-construct abstract models of robot actions. In this article, we propose a method for automatically constructing and continuously improving an abstraction of robot actions via observations of the robot performing the actions. This method, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">automatic abstraction repair</i> , allows action abstractions to be initially incorrect or incomplete and converge toward a correct model over time. Here, we demonstrate abstraction repair using constrained polynomial zonotopes (CPZs), an expressive nonconvex set representation for modeling predicates over joint symbolic and geometric state. The repair process performs a hybrid optimizing search over symbolic edit operations to predicate formulae and continuous predicate parameters to improve the grounding of the abstraction to the behavior of a physical robot. In this work, we describe the predicate model, introduce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">symbolic-geometric abstraction repair</i> problem, and present an anytime algorithm for automatic abstraction repair. We demonstrate that abstraction repair can improve realistic action abstractions for common mobile manipulation actions from a handful of observations and discuss the tradeoffs of the CPZ model for predicate representation.
Electronic learning at a university as a new educational paradigm

The main task of the state social policy is formation of a digital society, the development of the digital economy as an urgent sphere of people's life. This has led to the transition of many areas to digital technologies: education, healthcare, services, etc. Objective circumstances of the global scale of spring 2019, associated with the widespread spread of coronavirus infection has caused optimization of distant educational technologies. The implementation of the new idea of e-education is inextricably linked with the process of its digitalization. The research problem: what are the factors of e-learning as a new educational paradigm in a university? The purpose of the research is to reveal the essence of e-learning at a university, its goals, components and functions. The research methodology is composed of a set of methods of scientific knowledge: generalization, analysis, synthesis. The research results. Theoretical and methodological prerequisites for the digitalization of education as a new educational paradigm have been revealed; the functions of digital education have been defined; technology brands of e-education have been identified; the concept of «digital literacy» has been disclosed. Key findings: e-learning is an innovative educational paradigm based on the transformation of the educational process (change in guidelines, organizational and managerial forms, technologies, teaching aids) and the use of a digital educational environment; e-education serves as a tool, an environment that opens up new learning opportunities through the use of technology brands (cloud technologies, online courses, «mobile» learning, adaptive learning, artificial intelligence, behavioral analytics, gamification). The research results can be used for further theoretical and methodological substantiation of the problem of e-learning in higher education, and in activities of modern scientific and pedagogical workers in the context of digitalization of the educational space.
Layer-Wise Interpretation of Deep Neural Networks using Identity Initialization

The interpretability of neural networks (NNs) is a challenging but essential topic for transparency in the decision-making process using machine learning. One of the reasons for the lack of interpretability is random weight initialization, where the input is randomly embedded into a different feature space in each layer. In this paper, we propose an interpretation method for a deep multilayer perceptron, which is the most general architecture of NNs, based on identity initialization (namely, initialization using identity matrices). The proposed method allows us to analyze the contribution of each neuron to classification and class likelihood in each hidden layer. As a property of the identity-initialized perceptron, the weight matrices remain near the identity matrices even after learning. This property enables us to treat the change of features from the input to each hidden layer as the contribution to classification. Furthermore, we can separate the output of each hidden layer into a contribution map that depicts the contribution to classification and class likelihood, by adding extra dimensions to each layer according to the number of classes, thereby allowing the calculation of the recognition accuracy in each layer and thus revealing the roles of independent layers, such as feature extraction and classification.
Small Object Detection Leveraging on Simultaneous Super-resolution

Despite the impressive advancement achieved in object detection, the detection performance of small object is still far from satisfactory due to the lack of sufficient detailed appearance to distinguish it from similar objects. Inspired by the positive effects of super-resolution for object detection, we propose a framework that can be incorporated with detector networks to improve the performance of small object detection, in which the low-resolution image is super-resolved via generative adversarial network (GAN) in an unsupervised manner. In our method, the super-resolution network and the detection network are trained jointly. In particular, the detection loss is back-propagated into the super-resolution network during training to facilitate detection. Compared with available simultaneous super-resolution and detection methods which heavily rely on low-/high-resolution image pairs, our work breaks through such restriction via applying the CycleGAN strategy, achieving increased generality and applicability, while remaining an elegant structure. Extensive experiments on datasets from both computer vision and remote sensing communities demonstrate that our method obtains competitive performance on a wide range of complex scenarios.
DRILL: Dynamic Representations for Imbalanced Lifelong Learning

Continual or lifelong learning has been a long-standing challenge in machine learning to date, especially in natural language processing (NLP). Although state-of-the-art language models such as BERT have ushered in a new era in this field due to their outstanding performance in multitask learning scenarios, they suffer from forgetting when being exposed to a continuous stream of data with shifting data distributions. In this paper, we introduce DRILL, a novel continual learning architecture for open-domain text classification. DRILL leverages a biologically inspired self-organizing neural architecture to selectively gate latent language representations from BERT in a task-incremental manner. We demonstrate in our experiments that DRILL outperforms current methods in a realistic scenario of imbalanced, non-stationary data without prior knowledge about task boundaries. To the best of our knowledge, DRILL is the first of its kind to use a self-organizing neural architecture for open-domain lifelong learning in NLP.
Optimal sensor channel selection for resource-efficient deep activity recognition

Deep learning has permitted unprecedented performance in sensor-based human activity recognition (HAR). However, deep learning models often present high computational overheads, which poses challenges to their implementation on resource-constraint devices such as microcontrollers. Usually, the computational overhead increases with the input size. One way to reduce the input size is by constraining the number of sensor channels. We refer to sensor channel as a specific data modality (e.g. accelerometer) placed on a specific body location (e.g. chest). Identifying and removing irrelevant and redundant sensor channels is feasible via exhaustive search only in cases where few candidates exist. In this paper, we propose a smarter and more efficient way to optimize the sensor channel selection during the training of deep neural networks for HAR. Firstly, we propose a light-weight deep neural network architecture that learns to minimize the use of redundant and irrelevant information in the classification task, while achieving high performance. Secondly, we propose a sensor channel selection algorithm that utilizes the knowledge learned by the neural network to rank the sensor channels by their contribution to the classification task. The neural network is then trimmed by removing the sensor channels with the least contribution from the input and pruning the corresponding weights involved in processing them. The pipeline that consists of the above two steps iterates until the optimal set of sensor channels has been found to balance the trade-off between resource consumption and classification performance. Compared with other selection methods in the literature, experiments on 5 public datasets showed that our proposal achieved significantly higher F1-scores at the same time as utilizing from 76% to 93% less memory, with up to 75% faster inference time and as far as 76% lower energy consumption.
Locally Optimal Detection of Stochastic Targeted Universal Adversarial Perturbations

Deep learning image classifiers are known to be vulnerable to small adversarial perturbations of input images. In this paper, we derive the locally optimal generalized likelihood ratio test based detector for detecting stochastic targeted universal adversarial perturbations to a classifier’s input. We employ a two-stage process to learn the detector’s parameters, which involves unsupervised maximum likelihood estimation followed by supervised training and demonstrates better performance of the detector compared to other detection methods on several popular image classification datasets.
A Transfer Learning Framework for IoT-enabled Environments

The major enabling technologies for creating smart building services are IoT and cognitive computing. While deploying IoT infrastructure during the construction and renovation processes is rather a straightforward practice, the implementation of cognitive components that benefit from generated data of IoT infrastructure is a complicated task that should be elaborated based on the specific attributes of target services and smart buildings. This research investigates the potential of communication and reuse of cognitive knowledge between smart environments for a seamless and automatic transfer of services and machine learning models.
Precise Temporal Localization for Complete Actions with Quantified Temporal Structure

Existing temporal action detection algorithms cannot distinguish complete and incomplete actions while this property is essential in many applications. To tackle this challenge, we proposed the action progression networks (APN), a novel model that predicts action progression of video frames with continuous numbers. Using the progression sequence of test video, on the top of the APN, a complete action searching algorithm (CAS) was designed to detect complete actions only. With the usage of frame-level fine-grained temporal structure modeling and detecting actions according to their whole temporal context, our framework can locate actions precisely and is good at avoiding incomplete action detection. We evaluated our framework on a new dataset (DFMAD-70) collected by ourselves which contains both complete and incomplete actions. Our framework got good temporal localization results with 95.77% average precision when the IoU threshold is 0.5. On the benchmark THUMOS14, an incomplete-ignostic dataset, our framework still obtain competitive performance. The code is available online at https://github.com/MakeCent/Action-Progression-Network.
The Bayesian Causal Inference of Body Ownership Model: Use in VR and Plausible Parameter Choices

Experiencing virtual body ownership is an important component of user experience in virtual reality applications with embodied avatars. A functioning model of body ownership could allow designers of such applications to predict the occurrence of body ownership illusions in users. One attempt at such a model, the Bayesian Causal Inference of Body Ownership (BCIBO) model, explains body ownership as inference about the causes of sensory signals. When sensory signals under consideration (e.g. tactile and visual signals) are attributed to a single object (e.g. a rubber hand), then this object is incorporated into the body. We investigate an unrealistic choice of parameter values in the original specification of the BCIBO model and make some suggestions for improvements.
Burst Detection-Based Selective Classifier Resetting

Concept drift detection algorithms have historically been faithful to the aged architecture of forcefully resetting the base classifiers for each detected drift. This approach prevents underlying classifiers becoming outdated as the distribution of a data stream shifts from one concept to another. In situations where both concept drift and temporal dependence are present within a data stream, forced resetting can cause complications in classifier evaluation. Resetting the base classifier too frequently when temporal dependence is present can cause classifier performance to appear successful, when in fact this is misleading. In this research, a novel architectural method for determining base classifier resets, Burst Detection-Based Selective Classifier Resetting (BD-SCR), is presented. BD-SCR statistically monitors changes in the temporal dependence of a data stream to determine if a base classifier should be reset for detected drifts. The experimental process compares the predictive performance of state-of-the-art drift detectors in comparison to the “No-Change” detector using BD-SCR to inform and control the resetting decision. Results show that BD-SCR effectively reduces the negative impact of temporal dependence during concept drift detection through a clear negation in the performance of the “No-Change” detector, but is capable of maintaining the predictive performance of state-of-the-art drift detection methods.