Reviewerly/exHarmony huggingface dataset:
Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression

Learning-enabled components (LECs) are widely used in cyber-physical systems (CPS) since they can handle the uncertainty and variability of the environment and increase the level of autonomy.However, it has been shown that LECs such as deep neural networks (DNN) are not robust and adversarial examples can cause the model to make a false prediction.The paper considers the problem of efficiently detecting adversarial examples in LECs used for regression in CPS.The proposed approach is based on inductive conformal prediction and uses a regression model based on variational autoencoder.The architecture allows to take into consideration both the input and the neural network prediction for detecting adversarial, and more generally, out-of-distribution examples.We demonstrate the method using an advanced emergency braking system implemented in an open source simulator for self-driving cars where a DNN is used to estimate the distance to an obstacle.The simulation results show that the method can effectively detect adversarial examples with a short detection delay.
A set of practical experiments to validate the fuzzy Kalman filter

The Fuzzy Kalman Filter (FKF) was recently reformulated, with a number of basic examples included in order to demonstrate its applicability. We now look ahead to its practical implementation, introducing deep analysis which includes very detailed simulations and complete real applications. This will allow us to not only validate the FKF but also to draw important conclusions which will help users in the adjustment of the fuzzy filter parameters and the choosing of possibilistic distributions.
Rational Graph Filter Design Using Spectral Transformation and IIR Digital Filter

In this paper, rational graph filters are designed by using conventional IIR digital filters and spectral transformation. First, the specification of the rational graph filter design is converted to that of IIR digital filter by using a spectral transformation. Then, conventional IIR digital filter design methods are directly employed to design converted digital filter without needing to develop new design algorithms. Next, the coefficients of rational graph filter are obtained from those of the designed digital filter by using Chebyshev polynomials and spectral transformation. Finally, one design example is demonstrated to show the effectiveness of the proposed method, and the application to temperature data denoising illustrates the usefulness of graph filter to practical problem.
Theoretical Understandings of Product Embedding for E-commerce Machine Learning

Product embeddings have been heavily investigated in the past few years, serving as the cornerstone for a broad range of machine learning applications in e-commerce. Despite the empirical success of product embeddings, little is known on how and why they work from the theoretical standpoint. Analogous results from the natural language processing (NLP) often rely on domain-specific properties that are not transferable to the e-commerce setting, and the downstream tasks often focus on different aspects of the embeddings. We take an e-commerce-oriented view of the product embeddings and reveal a complete theoretical view from both the representation learning and the learning theory perspective. We prove that product embeddings trained by the widely-adopted skip-gram negative sampling algorithm and its variants are sufficient dimension reduction regarding a critical product relatedness measure. The generalization performance in the downstream machine learning task is controlled by the alignment between the embeddings and the product relatedness measure. Following the theoretical discoveries, we conduct exploratory experiments that supports our theoretical insights for the product embeddings.
Socio-Economic Perspective: Mitigate Covid-19 Impact on Education

The Covid-19 pandemic, which is felt today, has a severe impact on the socio-economy in Indonesia, making education in Indonesia look very sad.The big challenge now is how Indonesia can balance education with the socio-economic community.The presence of the Merdeka Campus, which was pioneered by the Ministry of Education and Culture of the Republic of Indonesia (Kemendikbud), can be an element of supporting the freedom of the education system for students and lecturers.A quality learning system will be achieved so that it also has a positive impact on the socio-economic level.However, this is not functioning correctly due to the Covid-19 outbreak.The REP (Raharja Enrichment Program) system is a digital transformation that supports this study's objectives so that the application of higher education can provide significant social and economic benefits.The presence of the concept of gamification on the REP Apps website makes learning at Merdeka Campus much more enjoyable.Analysis of the REP program's quality is calculated to produce an Alpha Cronbach score of 0.81> 0.6, and student motivation produces an Alpha Cronbach score of 0.86> 0.6.The REP System provides an Alpha Cronbach score of 0.90> 0.6, which means the instrument is Reliability.Therefore, Covid-19 is not an obstacle in socio-economic or education, and this is a bold way to balance between socio-economics and education in the Covid-19 era.
Effects of hidden layer sizing on CNN fine-tuning

Some applications have the property of being resilient, meaning that they are robust to noise (e.g. due to error) in the data. This characteristic is very useful in situations where an approximate computation allows to perform the task in less time or to deploy the algorithm on embedded hardware. Deep learning is one of the fields that can benefit from approximate computing to reduce the high number of involved parameters thanks to its impressive generalization ability. A common approach is to prune some neurons and perform an iterative re-training with the aim of both reducing the required memory and to speed-up the inference stage. In this work we propose to face CNN size reduction from a different perspective: instead of reducing the network weights or look for an approximated network very close to the Pareto frontier, we investigate whether it is possible to remove some neurons only from the fully connected layers before the network training without substantially affecting the network performance. As a case study, we will focus on "fine-tuning", a branch of transfer learning that has shown its effectiveness especially in domains lacking effective expert-designed features. To further compact the network, we apply weight quantization to the convolutional kernels. Results show that it is possible to tailor some layers to reduce the network size, both in terms of the number of parameters to learn and required memory, without statistically affecting the performance and without the need for any additional training. Finally, we investigate to what extent the sizing operation affects the network robustness against adversarial perturbations, a set of approaches aimed at misleading deep neural networks.
Control-over-the-cloud: A performance study for cloud-native, critical control systems

In the Industry 4.0 era, time-sensitive and mission-critical control applications still have a long way to go, from being tied down and co-located with the systems they control, to taking full advantage of the cloud. Conservatively keeping applications local will deprive these complex applications of abundant compute capacity, wider system integration, and the potential for collaborative control efforts. Feedback control systems are unlike other cloud applications - their performance and objectives can be formally defined, they require timely feedback, and they are sensitive to variations in system performance and noise. Although resources are plentiful, the cloud is a noisy and latency prone execution environment, detrimental to feedback control system. In this paper, we evaluate a set of cloud platforms and infrastructures with the intention of hosting feedback control systems. In lower levels of the software stack, we observe differences between clouds. Further up in the stack we see the disadvantages of applying cloud native platforms. With an understanding of expected performance we proceed to evaluate a simple control strategy and show how the sensitive nature of control can cause a seemingly adequate cloud platform to pose a high risk, while a seemingly inadequate platform can positively affect the performance of our proposed controller.
Recursive Secure Filtering Over Gilbert-Elliott Channels in Sensor Networks: The Distributed Case

This paper is concerned with the recursive secure filtering problem for a class of discrete-time systems subject to unreliable communication due to the security vulnerability of sensor networks.The unreliable communication, caused probably by denial-of-service cyber-attacks, is described by the well-known Gilbert-Elliott model.The addressed nonlinearities are applicable for some of the most investigated stochastic nonlinear models, including the well-known state-dependent multiplicative noises as special cases.The aim of this paper is to design a novel distributed filter that uses the information not only from the individual node itself but also from its neighboring nodes according to the given topology.In order to improve the security of designed filter, a χ 2 detector is utilized to detect abnormal innovations.By means of the failure and recovery rates of the Gilbert-Elliott channels, sufficient conditions are established to ensure the existence of an upper bound on the estimation error covariance, and then the desired filter parameters are designed by minimizing the trace of such an upper bound.The asymptotic boundedness of the estimation error covariance is subsequently investigated.Finally, a simulation example on the target tracking problem is employed to verify the effectiveness and the security of the proposed filtering scheme.
Fall Detection System for Elderly based on 2D LiDAR: A Preliminary Study of Fall Incident and Activities of Daily Living (ADL) Detection

FDS (Fall Detection System) is a technology that is very essential for the elderly, in order to immediately get help when the fall incident happens. This paper aims to build a FDS dedicated to the elderly. We propose 2D LiDAR as the main sensor in FDS. In this case, 2D LiDAR has the duty to obtain information data in a room. FDS is demanded to be able to distinguish between fall incidents and ADL (Activities of Daily Living). This paper presents trials on various positions of the human body that can be detected as fall incidents or ADL. The trials aim to produce a dataset that will later be processed using K-NN and RF as a fall detection algorithm. From the results of the trials, 2D LiDAR sensor data can describe two information as detection points. RF produces accuracy up to 94% and K-NN produces maximum accuracy of 100%.
ARsinoë - Learning Egyptian Hieroglyphs with Augmented Reality and Machine Learning

The "ARsinoë" project combines gamification, Augmented Reality (AR) and machine learning to discover Egyptian hieroglyphs in a single application for the smartphone or tablet. It targets K12 students in two ways. On the one hand, it offers new approaches to the study of hieroglyphs and thus Egyptian culture and history, on the other hand, the newly gained concepts can be transferred to the learning of languages with non-Latin writing systems.
PubChain: A Decentralized Open-Access Publication Platform with Participants Incentivized by Blockchain Technology

We design and implement Publication Chain (PubChain), a decentralized open-access publication platform built on decentralized and distributed technologies of blockchain and IPFS peer-to-peer file sharing systems. The existing publication platforms have some severe drawbacks. First, instead of promoting widespread knowledge sharing, access to publications on the platforms owned by publishers is often on a fee basis. This drawback of pay wall prevents researchers from "standing on the shoulders of giants". Moreover, the peer review process on most all existing publication platforms (including both openaccess and publisher platforms) is prone to be ineffective, since there is no proper incentive to reviewers for performing high-qualified reviews. PubChain is an alternative platform to the existing publication venues aiming to address their drawbacks. No central third-party owns the contents (i.e., papers and reviews) of PubChain. Exploiting blockchain technology, we devise an elaborate incentive scheme on PubChain to incentivize key stakeholders (i.e., authors, readers and reviewers) to participate publication activities on PubChain in a substantive manner by earning credits and rewards through self-motivated interactions. We have performed simulations to investigate the robustness of our proposed incentive scheme against fraudulent publications and reviews. We also have implemented a prototype of PubChain to demonstrate its key concepts.
Network traffic prediction method based on echo state network with adaptive reservoir

Abstract Network traffic prediction is of great significance to resource management in cyber‐physical systems (CPSs). In particular, network traffic is a nonlinear time series. Echo state network (ESN) is a new neural network with strong nonlinear processing capacity and short‐term memory capacity, and thus can achieve good performance in predicting nonlinear time series. However, network traffic has various characteristics such as self‐similarity, chaos, mutability. As the core of ESN, the reservoir will be fixed rather than adjustable once it is generated, which limits the prediction performance of ESN in different network traffic. To achieve universal excellent prediction performance, this paper proposes a new network traffic prediction method based on ESN with adaptive reservoir (ESN‐AR). First, the framework of ESN‐AR is constructed for network traffic prediction, in which the idea of generative adversarial network (GAN) is incorporated into ESN to adaptively adjust the reservoir. Specifically, ESN is used as the generative model to predict network traffic and feedforward neural network (FNN) is used as the discriminative model to distinguish between the real network traffic and the predicted network traffic. Second, the adversarial training algorithm of ESN‐AR is proposed to obtain the appropriate reservoir depending on the network traffic characteristics. Finally, ESN‐AR is applied to the prediction of three actual network traffic with different characteristics. Simulation results show that compared with the state‐of‐the‐art models, the proposed method achieves more accurate and stable prediction performance.
Hybrid IG and GA based Feature Selection Approach for Text Categorization

Feature selection is considered as the most important research area due to its accuracy and time considerations in the field of text classification. If the initial feature set is large, it becomes very important to select the necessary features. Text classification remains as one of the examples that one can see when hundreds or even thousands of records can be included in the size of the feature set. Many research studies are carried out on feature selection by proposing different feature selection approaches for text classification. Although several numbers of studies are done on feature selection, but there is no substantial work to prove the combination of features. The aim of the analysis is to evaluate the redundancy of textual properties selected using a different method such as data set features, algorithms, metrics, a hybrid feature selection method. The test results show that the combination of characteristics chosen by different methods is precise over those selected by each selection process. In any case, the proposed selection of hybrid features depends on the data set characteristics, classification algorithm selection and assessment metrics.
An Event-Driven Serverless ETL Pipeline on AWS

This work presents an event-driven Extract, Transform, and Load (ETL) pipeline serverless architecture and provides an evaluation of its performance over a range of dataflow tasks of varying frequency, velocity, and payload size. We design an experiment while using generated tabular data throughout varying data volumes, event frequencies, and processing power in order to measure: (i) the consistency of pipeline executions; (ii) reliability on data delivery; (iii) maximum payload size per pipeline; and, (iv) economic scalability (cost of chargeable tasks). We run 92 parameterised experiments on a simple AWS architecture, thus avoiding any AWS-enhanced platform features, in order to allow for unbiased assessment of our model’s performance. Our results indicate that our reference architecture can achieve time-consistent data processing of event payloads of more than 100 MB, with a throughput of 750 KB/s across four event frequencies. It is also observed that, although the utilisation of an SQS queue for data transfer enables easy concurrency control and data slicing, it becomes a bottleneck on large sized event payloads. Finally, we develop and discuss a candidate pricing model for our reference architecture usage.
A recursive ensemble-based feature selection for multi-output models to discover patterns among the soil nutrients

Finding the reduced and the relevant subsets of the predictors is inevitable when it comes to predictive modelling. If the datasets involved are heterogeneous and heteroscedastic in nature such as the soil samples, the task turns out to be trickier and such scenarios demand ensemble-based feature selection approach. The proposed algorithm uses an ensemble of filter (RReliefF), wrapper (Adaptive Plus –l and Minus –r), and embedded (Neighbourhood Component Analysis) approaches and applies it to the datasets in a heterogeneous and homogeneous manner. The Adaptive Plus –l and Minus –r is an experiment done on the Plus –l and Minus –r wrapper method to enhance the performance of the algorithm. The proposed combination rule of the ensemble filters out the irrelevant predictors for each response variable. Further, this ensemble is recursively implemented using the floating set of predictors to estimate the optimal subsets for multiple response variables at one go. Each step in recursion ensures that only the best subset of features (in terms of length and weights) among the current and previous iterations is retained. The Akaikes Information Criteria and the predictors weights calculated, assessed the efficiency of the resultant predictor sets of the recursive ensemble. The adjusted R2, Median Absolute Deviation and Root Mean Square Error on the unseen datasets confirmed the suitability of the same for predictive modelling in ecological domains.
Research on the improvement of teachers’ teaching ability based on machine learning and digital twin technology

The qualitative analysis results of teachers’ abilities are difficult to quantify, and ability problems in the teaching process are difficult to be effectively measured. In order to study methods to improve teachers’ teaching abilities, this paper builds a corresponding teacher competence evaluation model based on machine learning and digital twin technology, establishes a data collection model for teachers’ professional competence, and establishes a data fusion model. It includes data cleaning model based on XML information template, data integration model, multi-index screening mechanism and clustering strategy based on perturbation attributes. On this basis, this paper uses decision tree algorithm, random forest algorithm and neural network algorithm to construct three scheduling rule mining models aiming at teachers’ professional ability. In addition, this paper establishes a digital twin-driven multi-knowledge model scheduling optimization architecture that uses the three scheduling rules mined. The research results show that the model constructed in this paper has good performance.
Embeddings in Natural Language Processing

Embeddings have been one of the most important topics of interest in Natural Language Processing (NLP) for the past decade.Representing knowledge through a low-dimensional vector which is easily integrable in modern machine learning models has played a central role in the development of the field.Embedding techniques initially focused on words but the attention soon started to shift to other forms.This tutorial will provide a high-level synthesis of the main embedding techniques in NLP, in the broad sense.We will start by conventional word embeddings (e.g., Word2Vec and GloVe) and then move to other types of embeddings, such as sense-specific and graph alternatives.We will finalize with an overview of the trending contextualized representations (e.g., ELMo and BERT) and explain their potential and impact in NLP.
Adaptive Spatial Location With Balanced Loss for Video Captioning

Many pioneering approaches have verified the effectiveness of utilizing the global temporal and local object information for video understanding tasks and have achieved significant progress. However, existing methods utilize object detectors to extract all objects overall video frames. This may bring performance degradation due to the information redundancy both spatially and temporally. To address this problem, we propose an adaptive spatial location module for the video captioning task which dynamically predicts an important position of each video frame in the procedure of generating the description sentence. The proposed adaptive spatial location method not only makes our model focus on local object information, but also reduces time and memory consumption brought by the temporal redundancy in extensive video frames and improves the accuracy of generated description. Besides, we propose a balanced loss function to address the class imbalance problem existing in training data. The proposed balanced loss assigns different weight to each word of ground-truth sentence in the training process which can generate more diversified description sentences. Extensive experimental results on the MSVD and MSR-VTT dataset show that the proposed method achieves competitive performance compared to state-of-the-art methods.
The use of virtual reality in the training of professionals: with the example of firefighters

Abstract The article presents a brief history of the development of virtual reality (VR), followed by a comprehensive literature overview on the matter as well as an example of the application of VR in the training of professionals on the example of firefighters. The history, starting in 1965 with the ideas, concepts, and solutions presented by the VR's “forefather”—Ivan Sutherland, delves into multiple projects. The literature review presents various examples of the use of VR in the training of professionals coming from multiple fields, such as medicine, psychology, forestry, iron casting, or construction. The case study shows it can be used to recreate even the most complex scenarios and surroundings, even those of the firefight in confined spaces where enclosed fires tend to appear and have been considered vastly hazardous for not only civilians or equipment but also to firefighters dispatched to contain them.