Reviewerly/exHarmony huggingface dataset:
HADIoT: A Hierarchical Anomaly Detection Framework for IoT

The Internet of Things establishes the intimacy between the Internet and the physical world.Due to portable size, most IoT devices have limited computing and storage capabilities and are vulnerable to various malicious intrusions.Therefore, it is vital to have efficient approaches to distinguish the true IoT data from fake one, we term such methods as anomaly detection (AD).To detect anomalies accurately and efficiently, in this article a 3-hierarchy joint local and global anomaly detection framework, HADIoT, is proposed, in which IoT devices generate and transmit sensory data to their local edge servers for local AD after data refinement which includes re-framing, normalization, complexity reduction via Principal Component Analysis, and symbol mapping.High detection accuracy is achieved by jointly local and global ADs.The local AD focuses on the data pattern consistency of individual devices via the Gated Recurrent Unit, and the processed data is then forwarded from edge servers to the cloud server for global AD.The global AD focuses on the analysis of the data pattern correlations between different IoT devices, using the Conditional Random Fields.For the maintenance of cyber-security, the proposed anomaly detection framework HADIoT enables to provide an accurate and faster anomaly detection for IoT applications, compared with existing anomaly detection methods.The performance of the proposed method is also empirically evaluated through simulations, using a real dataset -the Information Security Center of Excellence (ISCX) 2012 dataset.Simulation results demonstrate the effectiveness of the proposed framework in terms of True Positive Rate, False Positive Rate, Precision, Accuracy and F_score, compared with three benchmark schemes.
Improving Motion Safety and Efficiency of Intelligent Autonomous Swarm of Drones

Interest is growing in the use of autonomous swarms of drones in various mission-physical applications such as surveillance, intelligent monitoring, and rescue operations. Swarm systems should fulfill safety and efficiency constraints in order to guarantee dependable operations. To maximize motion safety, we should design the swarm system in such a way that drones do not collide with each other and/or other objects in the operating environment. On other hand, to ensure that the drones have sufficient resources to complete the required task reliably, we should also achieve efficiency while implementing the mission, by minimizing the travelling distance of the drones. In this paper, we propose a novel integrated approach that maximizes motion safety and efficiency while planning and controlling the operation of the swarm of drones. To achieve this goal, we propose a novel parallel evolutionary-based swarm mission planning algorithm. The evolutionary computing allows us to plan and optimize the routes of the drones at the run-time to maximize safety while minimizing travelling distance as the efficiency objective. In order to fulfill the defined constraints efficiently, our solution promotes a holistic approach that considers the whole design process from the definition of formal requirements through the software development. The results of benchmarking demonstrate that our approach improves the route efficiency by up to 10% route efficiency without any crashes in controlling swarms compared to state-of-the-art solutions.
Inpainting of Irregular Holes in a Manuscript using UNet and Partial Convolution

Image inpainting of irregular holes in the old handwritten manuscripts is a challenging task. It requires finding the missing part in the manuscript and then filling it with an appropriate pixel. It needs to fill it in such a way that words or letters in that manuscript can be recovered. There are many degraded manuscripts available in museums and libraries all across the globe. This degradation can be due to human mishandling or certain environmental factors (fire, heat, water, microorganisms, pests, and other vermin). Even if it is tired very hard to protect our written heritage from all this, since it is written on natural materials (wood, paper), it is prone to inherent vice. A novel method is presented to inpaint the irregular holes in manuscripts with appropriate pixels. Deep learning, which tends to solve many complex problems like image classification, object detection, image segmentation, helps in achieving image inpainting as well. UNet is proposed to generate the mask of an irregular hole part in a manuscript image and partial convolution, which takes two input masks and the image for inpainting.
Spatially Adaptive Regularization in Image Segmentation

We present a total-variation-regularized image segmentation model that uses local regularization parameters to take into account spatial image information. We propose some techniques for defining those parameters, based on the cartoon-texture decomposition of the given image, on the mean and median filters, and on a thresholding technique, with the aim of preventing excessive regularization in piecewise-constant or smooth regions and preserving spatial features in nonsmooth regions. Our model is obtained by modifying a well-known image segmentation model that was developed by T. Chan, S. Esedoḡlu, and M. Nikolova. We solve the modified model by an alternating minimization method using split Bregman iterations. Numerical experiments show the effectiveness of our approach.
Likes, Comments, Views

This article presents a content analysis of academic library Instagram accounts at eleven land-grant universities. Previous research has examined personal, corporate, and university use of Instagram, but fewer studies have used this methodology to examine how academic libraries share content on this platform and the engagement generated by different categories of posts. Findings indicate that showcasing posts (highlighting library or campus resources) accounted for more than 50 percent of posts shared, while a much smaller percentage of posts reflected humanizing content (emphasizing warmth or humor) or crowdsourcing content (encouraging user feedback). Crowdsourcing posts generated the most likes on average, followed closely by orienting posts (situating the library within the campus community), while a larger proportion of crowdsourcing posts, compared to other post categories, included comments. The results of this study indicate that libraries should seek to create Instagram posts that include various types of content while also ensuring that the content shared reflects their unique campus contexts. By sharing a framework for analyzing library Instagram content, this article will provide libraries with the tools they need to more effectively identify the types of content their users respond to and enjoy as well as make their social media marketing on Instagram more impactful.
Thermal infrared pedestrian tracking using joint siamese network and exemplar prediction model

Tracking pedestrian targets over a thermal infrared (TIR) image sequence is a hot topic in visual tracking. The imagery characteristics of TIR targets such as low target-background contrast and far imaging distance make TIR object tracking very difficult. In this paper, based on a convolutional neural network (CNN) and the siamese region proposal network (SiamRPN), we design an improved TIR pedestrian tracker. By fully considering the temporal and spatial information around an object, we firstly construct a CNN-based prediction model to produce the exemplar of a pedestrian target. Then the predicted exemplar is combined with SiamRPN to form an improved real-time TIR pedestrian tracker. The proposed tracker is evaluated on the TIR pedestrian tracking benchmark dataset PTB-TIR. Our experimental results demonstrate that the proposed tracker achieves promising tracking performance. In terms of tracking success rate and precision, our tracker outperforms traditional trackers such as KCF, and state-of-the-art trackers such as SiamRPN, SRDCF, and DSST. Moreover, similar to other siamese-network-based trackers, our tracker runs in real-time.
DSQA: A Domain Specific QA System for Smart Health Based on Knowledge Graph

In recent years, medical Question Answering (QA) systems have gained a lot of attentions, since they can not only help professionals make quick decisions, but also give ordinary people advice when they seek for helpful information. However, the interpretability and accuracy problems have plagued QA systems for a long time. In this paper, we propose a QA system, DSQA, based on knowledge graph for answering domain-specific medical questions. The data we use are from Electronic Medical Records (EMRs) that are complex and heterogeneous with varied qualities. We introduce doctor-in-the-loop mechanism to design the knowledge graph, which improves the interpretability and the accuracy of our QA system. For every natural-language question, we generate the answer with a hybrid of the traditional deep learning model and the reasoning on the knowledge graph. Finally, the answer is returned to the user in the format of a subgraph with corresponding natural-language sentences. Our system shows high interpretability and gives excellent performance according to the experiment results.
PMSM speed control based on intelligent sliding mode technique

Purpose The purpose of this paper is to present a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor. Design/methodology/approach A novel recurrent radial basis function network (RBFN) is used to is used to approximate unknown nonlinear functions in permanent magnet synchronous motor (PMSM) dynamics. Then, using the functions obtained from the neural network, it is possible to design a model-based and precise controller for PMSM using the immersive modeling method. Findings Experimental results indicate the appropriate performance of the proposed method. Originality/value This paper presents a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor. A novel recurrent RBFN is used to is used to approximate unknown nonlinear functions in PMSM dynamics.
Aspect-Based Sentiment Analysis and Emotion Detection for Code-Mixed Review

Review can affect customer decision making because by reading it, people manage to know whether the review is positive, or negative.However, positive, negative, and neutral, without considering the emotion will be not enough because emotion can strengthen the sentiment result.This study explains about the comparison of machine learning and deep learning in sentiment as well as emotion classification with multilabel classification.In machine learning comparison, the problem transformation that we used are Binary Relevance (BR), Classifier Chain (CC), and Label Powerset (LP), with Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extra Tree Classifier (ET) as algorithms of machine learning.The features we compared are n-gram language model (unigram, bigram, unigram-bigram).For deep learning, algorithms that we applied are Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM), using selfdeveloped word embedding.The comparison results show RF dominates with 88.4% and 89.54% F1 scores with CC method for food aspect, and LP for price, respectively.For service and ambience aspects, ET leads with 92.65% and 87.1% with LP and CC methods, respectively.On the other hand, in deep learning comparison, GRU and BiLSTM obtained similar F1-score for food aspect, 88.16%.On price aspect, GRU leads with 83.01%.However, for service and ambience, BiLSTM achieved higher F1score, 89.03% and 84.78%.
Analysis of triple random phase encoding cryptosystem in Fresnel domain

In this paper, a triple random phase encryption scheme in the Fresnel domain is presented in a 4f system. The scheme is an extension of the double random phase encoding scheme in the Fresnel domain. Firstly, the image is subjected to double random phase encoding in the Fresnel domain and thereafter, another random phase mask is bonded with it. The scheme is validated for grayscale and binary images. Cryptosystem is exposed to statistical attack in the form of histogram, 3-D plots and information entropy, and results reveal that scheme endures these statistical attacks. The sensitivity of the Fresnel parameters to their original values is also shown in the figures. A comparative analysis also performed to demonstrate that the proposed scheme is effective and provides enhanced security against known-plaintext and chosen-ciphertext attack as compared to double random phase encoding method in Fresnel domain.
Risk Diagnosis and Mitigation System of COVID-19 Using Expert System and Web Scraping

The Novel Coronavirus that just appeared at the end of 2019 was named SARS-COV-2 which caused a pandemic of a respiratory disease known as COVID-19.In Indonesia itself, there was a case of COVID-19 first announced on March 2, 2020.The spread of COVID-19 in Indonesia is so fast because of one factor namely the lack of knowledge about COVID-19 prevention and early detection.This study will discuss the system to provide the latest information about the development of the COVID-19 case in Indonesia and help the community to conduct an independent detection of COVID-19 using an expert system.Information that will later be displayed on this application is obtained by web scraping techniques from the official website of the task force for the acceleration of handling COVID-19 in Indonesia.This system also has an early detection feature using the rule base method expert system.The results of this system are obtained from the responses of respondents and the results of respondents interested in this application with a percentage of 95.12%, and testing about the validation of the results of the expert system is the same as expected.
g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a New Open Benchmark Dataset

Conversion of Chinese graphemes to phonemes (G2P) is an essential component in Mandarin Chinese Text-To-Speech (TTS) systems. One of the biggest challenges in Chinese G2P conversion is how to disambiguate the pronunciation of polyphones - characters having multiple pronunciations. Although many academic efforts have been made to address it, there has been no open dataset that can serve as a standard benchmark for fair comparison to date. In addition, most of the reported systems are hard to employ for researchers or practitioners who want to convert Chinese text into pinyin at their convenience. Motivated by these, in this work, we introduce a new benchmark dataset that consists of 99,000+ sentences for Chinese polyphone disambiguation. We train a simple neural network model on it, and find that it outperforms other preexisting G2P systems. Finally, we package our project and share it on PyPi.
Open-Set Short Utterance Forensic Speaker Verification Using Teacher-Student Network with Explicit Inductive Bias

In forensic applications, it is very common that only small naturalistic datasets consisting of short utterances in complex or unknown acoustic environments are available. In this study, we propose a pipeline solution to improve speaker verification on a small actual forensic field dataset. By leveraging large-scale out-of-domain datasets, a knowledge distillation based objective function is proposed for teacher-student learning, which is applied for short utterance forensic speaker verification. The objective function collectively considers speaker classification loss, Kullback-Leibler divergence, and similarity of embeddings. In order to advance the trained deep speaker embedding network to be robust for a small target dataset, we introduce a novel strategy to fine-tune the pre-trained student model towards a forensic target domain by utilizing the model as a finetuning start point and a reference in regularization. The proposed approaches are evaluated on the 1st48-UTD forensic corpus, a newly established naturalistic dataset of actual homicide investigations consisting of short utterances recorded in uncontrolled conditions. We show that the proposed objective function can efficiently improve the performance of teacher-student learning on short utterances and that our fine-tuning strategy outperforms the commonly used weight decay method by providing an explicit inductive bias towards the pre-trained model.
Task-Robust Model-Agnostic Meta-Learning

Meta-learning methods have shown an impressive ability to train models that rapidly learn new tasks. However, these methods only aim to perform well in expectation over tasks coming from some particular distribution that is typically equivalent across meta-training and meta-testing, rather than considering worst-case task performance. In this work we introduce the notion of task-robustness by reformulating the popular Model-Agnostic Meta-Learning (MAML) objective [Finn et al. 2017] such that the goal is to minimize the maximum loss over the observed meta-training tasks. The solution to this novel formulation is task-robust in the sense that it places equal importance on even the most difficult and/or rare tasks. This also means that it performs well over all distributions of the observed tasks, making it robust to shifts in the task distribution between meta-training and meta-testing. We present an algorithm to solve the proposed min-max problem, and show that it converges to an $\epsilon$-accurate point at the optimal rate of $\mathcal{O}(1/\epsilon^2)$ in the convex setting and to an $(\epsilon, \delta)$-stationary point at the rate of $\mathcal{O}(\max\{1/\epsilon^5, 1/\delta^5\})$ in nonconvex settings. We also provide an upper bound on the new task generalization error that captures the advantage of minimizing the worst-case task loss, and demonstrate this advantage in sinusoid regression and image classification experiments.
A little goes a long way: Improving toxic language classification despite data scarcity

Detection of some types of toxic language is hampered by extreme scarcity of labeled training data.Data augmentation -generating new synthetic data from a labeled seed datasetcan help.The efficacy of data augmentation on toxic language classification has not been fully explored.We present the first systematic study on how data augmentation techniques impact performance across toxic language classifiers, ranging from shallow logistic regression architectures to BERT -a state-of-the-art pretrained Transformer network.We compare the performance of eight techniques on very scarce seed datasets.We show that while BERT performed the best, shallow classifiers performed comparably when trained on data augmented with a combination of three techniques, including GPT-2-generated sentences.We discuss the interplay of performance and computational overhead, which can inform the choice of techniques under different constraints.
Modular Ontology Modeling: A Tutorial

We provide an in-depth example of modular ontology engineering with ontology design patterns. The style and content of this chapter is adapted from previous work and tutorials on Modular Ontology Modeling. It offers expanded steps and updated tool information. The tutorial is largely self-contained, but assumes that the reader is familiar with the Web Ontology Language OWL; however, we do briefly review some foundational concepts. By the end of the tutorial, we expect the reader to have an understanding of the underlying motivation and methodology for producing a modular ontology.
Win–Win-Driven D2D Content Sharing

Win-win cooperation has been broadly treated as one of the most promising goals for device-to-device (D2D) content sharing, especially for the ultrareliable low-latency communications (URLLC) scenario. Unfortunately, the exiting solutions are built on several seemly unpractical conditions: 1) abundant prior information on network and user; 2) optimization variables limited to 1-D space; and 3) a preset order on users' decision making. In this work, we propose a win-win-driven D2D content-sharing scheme by exploring the blind matching theory. Specifically, we first derive the closed-form expressions of latency and reliability performance for D2D content-sharing scenarios. Accordingly, the URLLC-oriented joint optimization problem for provider-demander pairing and power control of potential providers is formulated as a two-sided one-to-one context-free matching game, which involves a collection of agreement functions of potential providers and demanders' aspiration levels and exploits a modified notion of pairwise stability as the solution concept. Then, we design a distributed algorithm by utilizing the market and information decentralization characteristics of the blind matching algorithm. Both theoretical analysis and numerical results validate the performance properties, including convergence, optimality, and complexity.
Systems Thinking in Software Projects-an Artificial Neural Network Approach

The authors discuss why the current conceptual base of project management research and practice continues to attract criticism since it does not adequately address the complexity that leads to software-project failure.To do so, the study explores systems thinking and artificial neural networks to shed light on complexity in software-project behavior using nonlinear functional relationships between critical success factors and project success to utilize their connectedness as an approach in order to create projectoutcome prediction models.The artificial neural networks were used to create two project-outcome prediction models: one for a binary classification task to discriminate failed from successful projects using a multi-input-single-output configuration and one for a multi-task binary classification to discriminate success from failure in multiple project-success dimensions using a multi-input multi-output configuration.The results yielded high-performance values for a binary classification task, performed to predict overall project success, and slightly lower performance values for the multi-task binary classification, which was also performed to predict success in project-success dimensions.It was found that the nonlinear behavior of critical success factors may be used to create prediction models, by embedding equifinality and connectedness constructs that prove to be useful to understand projects as complex, multi-loop, and nonlinear systems.Further research is needed to investigate the causality between critical success factors in order to explore the possible propagation of critical success factors within a project system network and its implications on project success.
Learning Abstracted Non-deterministic Finite State Machines

Active automata learning gains increasing interest since it gives an insight into the behavior of a black-box system. A crucial drawback of the frequently used learning algorithms based on Angluin's $$L^*$$ is that they become impractical if systems with a large input/output alphabet are learned. Previous work suggested to circumvent this problem by abstracting the input alphabet and the observed outputs. However, abstraction could introduce non-deterministic behavior. Already existing active automata learning algorithms for observable non-deterministic systems learn larger models if outputs are only observable after certain input/output sequences. In this paper, we introduce an abstraction scheme that merges akin states. Hence, we learn a more generic behavioral model of a black-box system. Furthermore, we evaluate our algorithm in a practical case study. In this case study, we learn the behavior of five different Message Queuing Telemetry Transport (mqtt) brokers interacting with multiple clients.