Reviewerly/exHarmony huggingface dataset:
A Combined Security Ontology based on the Unified Foundational Ontology

While ontology comparison and alignment have been extensively researched in the last decade, there are still some challenges to these disciplines, such as incomplete ontologies, those that cover only a portion of a domain, and differences in domain modeling due to varying viewpoints. Although the literature has compared ontological concepts from the same domain, comparisons of concepts from different domains (e.g., security and safety) remain unexplored. To compare the concepts of security and safety domains, a security ontology must first be created to bridge the gap between these domains. Therefore, this paper presents a Combined Security Ontology (CSO) based on the Unified Foundational Ontology (UFO) that could be compared to or aligned with other ontologies. This CSO includes the core ontological concepts and their respective relationships that had been extracted through a previous systematic literature review. The CSO concepts and their relationships were mapped to the UFO to get a common terminology that facilitates to bridge the gap between the security and safety domains. Since the proposed CSO is based on the UFO, it could be compared to or aligned with other ontologies from different domains.
SUnet++:Joint Demosaicing and Denoising of Extreme Low-Light Raw Image

Despite the rapid development of photography equipment, shooting high-definition RAW images in extreme low-light environments has always been a difficult problem to solve. Existing methods use neural networks to automatically learn the mapping from extreme low-light noise RAW images to long-exposure RGB images for jointly denoising and demosaicing of extreme low-light images, but the performance on other datasets is unpleasant. In order to address this problem, we present a separable Unet++ (SUnet++) network structure to improve the generalization ability of the joint denoising and demosaicing method for extreme low-light images. We introduce Unet++ to adapt the model to other datasets, and then replace the conventional convolutions of Unet++ with M sets of depthwise separable convolutions, which greatly reduced the number of parameters without losing performance. Experimental results on SID and ELD dataset demonstrate our proposed SUnet++ outperform the state-of-the-arts methods in term of subjective and objective results, which further validates the robust generalization of our proposed method.
Weakness Evaluation on In-Vehicle Violence Detection: An Assessment of X3D, C2D and I3D against FGSM and PGD

When constructing a deep learning model for recognizing violence inside a vehicle, it is crucial to consider several aspects. One aspect is the computational limitations, and the other is the deep learning model architecture chosen. Nevertheless, to choose the best deep learning model, it is necessary to test and evaluate the model against adversarial attacks. This paper presented three different architecture models for violence recognition inside a vehicle. These model architectures were evaluated based on adversarial attacks and interpretability methods. An analysis of the model’s convergence was conducted, followed by adversarial robustness for each model and a sanity-check based on interpretability analysis. It compared a standard evaluation for training and testing data samples with the adversarial attacks techniques. These two levels of analysis are essential to verify model weakness and sensibility regarding the complete video and in a frame-by-frame way.
Complex-valued Neural Network-based Quantum Language Models

Language modeling is essential in Natural Language Processing and Information Retrieval related tasks. After the statistical language models, Quantum Language Model (QLM) has been proposed to unify both single words and compound terms in the same probability space without extending term space exponentially. Although QLM achieved good performance in ad hoc retrieval, it still has two major limitations: (1) QLM cannot make use of supervised information, mainly due to the iterative and non-differentiable estimation of the density matrix, which represents both queries and documents in QLM. (2) QLM assumes the exchangeability of words or word dependencies, neglecting the order or position information of words. This article aims to generalize QLM and make it applicable to more complicated matching tasks (e.g., Question Answering) beyond ad hoc retrieval. We propose a complex-valued neural network-based QLM solution called C-NNQLM to employ an end-to-end approach to build and train density matrices in a light-weight and differentiable manner, and it can therefore make use of external well-trained word vectors and supervised labels. Furthermore, C-NNQLM adopts complex-valued word vectors whose phase vectors can directly encode the order (or position) information of words. Note that complex numbers are also essential in the quantum theory. We show that the real-valued NNQLM (R-NNQLM) is a special case of C-NNQLM. The experimental results on the QA task show that both R-NNQLM and C-NNQLM achieve much better performance than the vanilla QLM, and C-NNQLM’s performance is on par with state-of-the-art neural network models. We also evaluate the proposed C-NNQLM on text classification and document retrieval tasks. The results on most datasets show that the C-NNQLM can outperform R-NNQLM, which demonstrates the usefulness of the complex representation for words and sentences in C-NNQLM.
Task-Aware Feature Composition for Few-Shot Relation Classification

Relation classification tends to struggle when training data are limited or when it needs to adapt to unseen categories. In such challenging scenarios, recent approaches employ the metric-learning framework to measure similarities between query and support examples and to determine relation labels of the query sentences based on the similarities. However, these approaches treat each support class independently from one another, never looking at the entire task as a whole. Because of this, they are constrained to using a shared set of features for all meta-tasks, which hinders the ability to compose discriminative features for the task at hand. For example, if two similar relation types occur in a meta-task, the model needs to construct more detailed, task-related features instead of common features shared by all tasks. In this paper, we propose a novel task-aware relation classification model to tackle this issue. We first build a task embedding component to capture task-specific information, after which two mechanisms, such as task-specific gate and gated feature combination methods, are proposed to utilize the task-specific information to guide feature composition dynamically for each meta-task. Experiment results show that our model improves performance considerably over high performing baseline systems on both FewRel 1.0 and FewRel 2.0 benchmarks. Moreover, our proposed methods can be incorporated into metric-learning-based methods and significantly improve their performance.
Entropy Minimizing Matrix Factorization

Nonnegative matrix factorization (NMF) is a widely used data analysis technique and has yielded impressive results in many real-world tasks. Generally, existing NMF methods represent each sample with several centroids and find the optimal centroids by minimizing the sum of the residual errors. However, outliers deviating from the normal data distribution may have large residues and then dominate the objective value. In this study, an entropy minimizing matrix factorization (EMMF) framework is developed to tackle the above problem. Considering that outliers are usually much less than the normal samples, a new entropy loss function is established for matrix factorization, which minimizes the entropy of the residue distribution and allows a few samples to have large errors. In this way, the outliers do not affect the approximation of normal samples. Multiplicative updating rules for EMMF are derived, and the convergence is proven theoretically. In addition, a Graph regularized version of EMMF (G-EMMF) is also presented, which uses a data graph to capture the data relationship. Clustering results on various synthetic and real-world datasets demonstrate the advantages of the proposed models, and the effectiveness is also verified through the comparison with state-of-the-art methods.
The Role of Covid-19 in Moving Towards a Paperless Campus: The Case of University of Sharjah

As our concern to the environment is progressively growing, the transition to a paperless environment has become a driving force behind sustainable development. Evidence suggests that the traditional use of paper-based processes in daily working practices results in numerous flaws, risks, and financial burdens. Many higher education institutions are currently adopting this initiative of going paperless to make their operations more cost-effective, seamless and efficient. Thus due to the high consumption and use of paper within universities, this initiative implements ways to reduce paper usage. The aim of this study is to investigate the impact of the COVID-19 pandemic on moving towards a paperless campus. We will take the University of Sharjah (UoS) as a case study. UoS attempted to implement many practices and techniques to match the paperless system, including blackboard system and e-services. Data on paper usage was collected for the previous three academic years, and it was analyzed. Our results revealed a huge drop in paper usage during September 2019 to August 2020 (covid-19 peak period) and cost percentage drop of 53% in September 2019 to August 2020 academic year. Followed by a 38% decrease from September 2020 to August 2021. Thus, a paperless campus ensures providing an eco-friendly environment that reduces deforestation along with providing quality academic and administrative services.
Feasibility Analysis of Wearables Guiding Scientific Movements and Promoting Health

Wearable devices have gradually integrated into people's healthy lives because of their mobility, portability, and other characteristics, and have shown their value and status in sports and health. Wearable devices can be used to capture a large amount of human body activity data, but how to effectively use these data to serve people and help people form a healthy lifestyle is a problem to be considered. In order to further study the feasibility of wearable devices to guide scientific movements and promote health, a new layered motion recognition algorithm is proposed in this study. In this study, a C4.5-based decision tree algorithm is used to identify the state layer, and only the mean and variance features are extracted from the acceleration sensor data. Three corresponding BP neural network classifiers are constructed and classified. Each classifier is responsible for identifying actions in the corresponding states and verifying the method in this study through experiments. The experimental results in this study show that the recognition rate of the mRMR feature selection recognition algorithm is 1.13% higher than the BE algorithm and 2.02% higher than the recognition method without any feature selection algorithm. In addition, the research in this article found that wearable devices can realize the real-time detection of the physiological indicators of the wearer throughout the day to evaluate the efficacy of the drug and apply it to the early detection and treatment of diseases, which may improve patient compliance and promote health to a certain extent.
Salary prediction using random forest with fundamental features

Salary prediction can get the income range during a certain period, which is widely used in a credit decision, career choice, and HR strategy. The decision tree is a common method for salary prediction, and it can sum up the experience of training data. However, the high dimension and high variance in splitting data remain a challenge of the decision tree in salary prediction. To overcome the above challenge, in this paper, we use Random Forest (RF) to predict salary and improve accuracy. Firstly, we preprocess the dataset by dealing with missing data, categorical data, and deleting the useless data. Then, we construct RF to eliminate the variance of a single decision tree by repeating grouping and splitting data. To decrease variance produced by multiple variables, we arbitrarily choose subsets of given variables to reduce factors. Besides, the repetition of tree model constructions in RF eliminates clashes caused by splitting data in each tree level. To verify the effectiveness of the proposed method, we compare it with other state-of-art baselines, including decision tree, logistic regression, naïve bayes, and k-nearest neighbor on the Adult dataset. The experimental results demonstrate that the proposed method outperforms related benchmarks in predicting salary.
Rule extraction from decision tree: Transparent expert system of rules

Abstract A system which is transparent and has less decision rules is an efficient, user‐convincing system and moreover convenient and manageable to fields like banking, business, and medical. Decision Tree (DT) is a data mining technique which is transparent and produces a set of production rules for decision‐making. However sometimes it creates some unnecessary and redundant rules which diminish its comprehensibility. Thus a system named Transparent Expert System of Rules (TESR) is proposed in this paper to efficiently improve comprehensibility of the DT by reducing the number of rules drastically without compromising accuracy. The proposed system adopts a Sequential Hill Climbing method with a flexible heuristic function to prune the insignificant rules from decision rules generated by DT. Finally, the proposed TESR system produces a transparent and comprehensible rule set for a decision. The proposed TESR performance is evaluated using 10 datasets and is compared with simple DT (ID3, C4.5, and Classification and Regression Trees) and also two of the existing transparent systems with respect to comprehensibility, accuracy, precision, recall, and F‐measures.
Stochastic Variational Optimization of a Hierarchical Dirichlet Process Latent Beta-Liouville Topic Model

In topic models, collections are organized as documents where they arise as mixtures over latent clusters called topics. A topic is a distribution over the vocabulary. In large-scale applications, parametric or finite topic mixture models such as LDA (latent Dirichlet allocation) and its variants are very restrictive in performance due to their reduced hypothesis space. In this article, we address the problem related to model selection and sharing ability of topics across multiple documents in standard parametric topic models. We propose as an alternative a BNP (Bayesian nonparametric) topic model where the HDP (hierarchical Dirichlet process) prior models documents topic mixtures through their multinomials on infinite simplex. We, therefore, propose asymmetric BL (Beta-Liouville) as a diffuse base measure at the corpus level DP (Dirichlet process) over a measurable space. This step illustrates the highly heterogeneous structure in the set of all topics that describes the corpus probability measure. For consistency in posterior inference and predictive distributions, we efficiently characterize random probability measures whose limits are the global and local DPs to approximate the HDP from the stick-breaking formulation with the GEM (Griffiths-Engen-McCloskey) random variables. Due to the diffuse measure with the BL prior as conjugate to the count data distribution, we obtain an improved version of the standard HDP that is usually based on symmetric Dirichlet (Dir). In addition, to improve coordinate ascent framework while taking advantage of its deterministic nature, our model implements an online optimization method based on stochastic, at document level, variational inference to accommodate fast topic learning when processing large collections of text documents with natural gradient. The high value in the predictive likelihood per document obtained when compared to the performance of its competitors is also consistent with the robustness of our fully asymmetric BL-based HDP. While insuring the predictive accuracy of the model using the probability of the held-out documents, we also added a combination of metrics such as the topic coherence and topic diversity to improve the quality and interpretability of the topics discovered. We also compared the performance of our model using these metrics against the standard symmetric LDA. We show that online HDP-LBLA (Latent BL Allocation)’s performance is the asymptote for parametric topic models. The accuracy in the results (improved predictive distributions of the held out) is a product of the model’s ability to efficiently characterize dependency between documents (topic correlation) as now they can easily share topics, resulting in a much robust and realistic compression algorithm for information modeling.
Machine learning based Predictive Modeling of Plasma Treatment in Biomedical Surfaces

Plasma medicine is considered as a future research field which mainly consists of science, physics and clinical medicine. For the therapeutic outcome research purpose, based on thermal, electrical and chemical effects, CAP is used. Plasma treatment in terms of biomedical surfaces is difficult as it creates more complexity, thus CAP is proposed. Systematic way of approving plasma treatment creates more inadequate prediction. CAP does give less treatment in plasma surface modeling based on. CAP is used by the human who attains less accuracy as the error rate of accuracy gets high due to handheld devices by the human. CAP is used to identify different parameter which is monitored and controlled for Plasma-based treatments and its various effects. CAP generally supports inbuilt UV radiation gas which determines biomedical surfaces of the plasma. In this novel, the machine learning is used to determine the data set with supervised learning based upon the classification and prediction of the data which are driven and analyzed for the Plasma surface using CAP. In this paper, it proposes the effective way of using CAP in plasma surface treatment in an automated way over the human body.
A Weakly-Supervised Anomaly Detection Method via Adversarial Training for Medical Images

Convolutional neural networks have been widely used for anomaly detection and one of their most common methods is autoencoder. The autoencoder is expected to produce lower reconstruction error for the normal data than the abnormal ones, and the reconstruction error is typically set as a measurement index for distinguishing anomalies. In practice, however, this notion is not always compatible. The autoencoder&#x0027;s reconstruction ability is sometimes so good that it can reconstruct anomalies with low error, resulting in the loss of anomaly detection. To address this limitation, we present a novel weakly-supervised learning method based on the generative adversarial network. The network learns the feature distribution of both normal and abnormal samples. The use of an autoencoder in the generator network allows the model to map the input image to a lower dimension vector and then remap it back to its reconstructions. The additional encoder discriminator network maps the real and generated images to their latent representations and determines whether the generated image is true or false. As a result, a higher error-index indicates that the sample is an anomaly. Experimentation on medical images from a publicly available liver dataset demonstrates the model&#x0027;s superiority over previous state-of-the-art approaches.
Application of neural network language models based on distributive semantics for ontological modeling of the domain

Abstract The article discusses the technology of automated formation of SKOS-ontologies for semantic modeling of the subject area, based on natural language texts analysis. The technology is based on neural network and distributive (vector) language models. A brief description of the content and formulation of the problem of extracting concepts and relations from natural language texts is given, the results of constructing a neural network classifier of SKOS relations using the Glove vector model, as well as an example of using the technology to construct a fragment of an applied SKOS ontology are given.
LifeRec: A Mobile App for Lifelog Recording and Ubiquitous Recommendation

In recent years, context information has played an increasingly significant role in recommendation systems. With the rapid growth of portable sensor devices, lifelog data, such as mood, location, and daily activity, has been recorded and used for ubiquitous recommendation tasks. However, since the multi-modal lifelog data contains objective context information and subjective user labeling, it is challenging to record the lifelog thoroughly and perform personalized recommendations in real-time. In this work, we design a mobile application (App), LifeRec, to record multi-modal lifelog data and perform personalized recommendations by communicating with the remote server. The App helps users collect various lifelog information (e.g., location, diet, activity, and mood) and receive real-time recommendation with privacy protection and little effort. It is useful for lifelog data collection, user status monitoring, and various ubiquitous recommendation tasks. We examine LifeRec in a one-week field study with seven subjects. The users’ experience feedback and recording results show great usability and task completeness with our App.
Security limitations of Shamir’s secret sharing

The security is so important for both storing and transmitting the digital data, the choice of parameters is critical for a security system, that is, a weak parameter will make the scheme very vulnerable to attacks, for example the use of supersingular curves or anomalous curves leads to weaknesses in elliptic curve cryptosystems, for RSA cryptosystem there are some attacks for low public exponent or small private exponent. In certain circumstances the secret sharing scheme is required to decentralize the risk. In the context of the security of secret sharing schemes, it is known that for the scheme of Shamir, an unqualified set of shares cannot leak any information about the secret. This paper aims to show that the well-known Shamir’s secret sharing is not always perfect and that the uniform randomization before sharing is insufficient to obtain a secure scheme. The second purpose of this paper is to give an explicit construction of weak polynomials for which the Shamir’s (k, n) threshold scheme is insecure in the sense that there exist a fewer than k shares which can reconstruct the secret. Particular attention is given to the scheme whose threshold is less than or equal to 6. It also showed that for certain threshold k, the secret can be calculated by a pair of shares with the probability of 1/2. Finally, in order to address the mentioned vulnerabilities, several classes of polynomials should be avoided.
Sharing is caring

Multi-Variant Execution Environments (MVEEs) are a powerful tool for protecting legacy software against memory corruption attacks. MVEEs employ software diversity to run multiple variants of the same program in lockstep, whilst providing them with the same inputs and comparing their behavior. Well-constructed variants will behave equivalently under normal operating conditions but diverge when under attack. The MVEE detects these divergences and takes action before compromised variants can damage the host system.