Reviewerly/exHarmony huggingface dataset:
Rich Screen Reader Experiences for Accessible Data Visualization

Abstract Current web accessibility guidelines ask visualization designers to support screen readers via basic non‐visual alternatives like textual descriptions and access to raw data tables. But charts do more than summarize data or reproduce tables; they afford interactive data exploration at varying levels of granularity—from fine‐grained datum‐by‐datum reading to skimming and surfacing high‐level trends. In response to the lack of comparable non‐visual affordances, we present a set of rich screen reader experiences for accessible data visualization and exploration. Through an iterative co‐design process, we identify three key design dimensions for expressive screen reader accessibility: structure , or how chart entities should be organized for a screen reader to traverse; navigation , or the structural, spatial, and targeted operations a user might perform to step through the structure; and, description , or the semantic content, composition, and verbosity of the screen reader's narration. We operationalize these dimensions to prototype screen‐reader‐accessible visualizations that cover a diverse range of chart types and combinations of our design dimensions. We evaluate a subset of these prototypes in a mixed‐methods study with 13 blind and visually impaired readers. Our findings demonstrate that these designs help users conceptualize data spatially, selectively attend to data of interest at different levels of granularity, and experience control and agency over their data analysis process. An accessible HTML version of this paper is available at: http://vis.csail.mit.edu/pubs/rich-screen-reader-vis-experiences .
A virtual sample generation algorithm supporting machine learning with a small-sample dataset: A case study for rubber materials

Machine learning (ML) is widely used in the field of material informatics. However, limitations on the size of available datasets are a key bottleneck in the use of machine learning methods to predict material properties or reverse-design high-performance materials. To solve this problem, we propose a virtual sample generation algorithm based on a Gaussian mixture model (GMM-VSG) to address the lack of training samples in machine learning. The core idea of the algorithm is to generate virtual samples by fitting the distribution of the original samples. We used an open rubber composite dataset (24 samples) to establish a machine learning model to predict the wear resistance of rubber materials through mechanical properties to verify the performance of the GMM-VSG algorithm. The results show that after using our algorithm, the R2 of the prediction model reached 0.95, and the prediction accuracy increased by 41%. This shows that the proposed algorithm can effectively promote the prediction accuracy of data model with small sample size.
Transfer learning based on sparse Gaussian process for regression

Transfer learning is to use the knowledge obtained from the source domain to improve the learning efficiency when the target domain has insufficient labeled data. For regression problems, when the conditional distribution function and the marginal distribution function of the source domain and the target domain are different, how to effectively extract similar knowledge for transfer learning is still a problem. In this paper, we propose a transfer learning method for regression problem based on the sparse Gaussian process (GP). GP models are very popular in regression modeling, as they have the capability to produce uncertainty estimation, however, they cannot be used directly for transfer learning. We propose an adaptive neural kernel network (ANKN) to ensure that the GP model can effectively transfer knowledge. Additionally, although many sparse GP methods are proposed to solve the time consumption problem of the GP models in large datasets, they cannot maintain the transfer performance. We propose a transfer inducing point (TIP) algorithm for data selection in large datasets to maintain the transfer performance. The experiments with transfer regression problems on both real-world small datasets and large datasets indicate that the our method significantly increases prediction accuracy and effectiveness.
Price DOES Matter!

Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based recommendation only focus on modeling users' interest preferences, while they all ignore a key attribute of an item, i.e., the price. Many marketing studies have shown that the price factor significantly influences users' behaviors and the purchase decisions of users are determined by both price and interest preferences simultaneously. However, it is nontrivial to incorporate price preferences for session-based recommendation. Firstly, it is hard to handle heterogeneous information from various features of items to capture users' price preferences. Secondly, it is difficult to model the complex relations between price and interest preferences in determining user choices. To address the above challenges, we propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation. Towards the first challenge, we devise a heterogeneous hypergraph to represent heterogeneous information and rich relations among them. A dual-channel aggregating mechanism is then designed to aggregate various information in the heterogeneous hypergraph. After that, we extract users' price preferences and interest preferences via attention layers. As to the second challenge, a co-guided learning scheme is designed to model the relations between price and interest preferences and enhance the learning of each other. Finally, we predict user actions based on item features and users' price and interest preferences. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoHHN. Further analysis reveals the significance of price for session-based recommendation.
An Integration of feature extraction and Guided Regularized Random Forest feature selection for Smartphone based Human Activity Recognition

Human Activity Recognition (HAR) is an eminent area of research due to its extensive scope of applications in remote health monitoring, sports, smart home, and many more. Smartphone-based HAR systems use sensor data to infer human physical activities. Extraction of pertinent and essential features to identify human activities is a crucial but challenging task. Researchers continuously endeavor to extract pertinent and non-redundant features without compromising the classification accuracy. Smartphone sensor data generates high dimensional feature sets to recognize human physical activities. This work aims to build an efficient HAR model that extracts the most relevant features from the 3-axial accelerometer and gyroscope signal data and enhances the HAR system’s classification accuracy without data loss using time–frequency domain features. After feature extraction, we apply the Guided Regularized Random Forest (GRRF) feature selection method to determine the most pertinent and non-redundant features to reduce the time to recognize the human activities efficiently. After selecting the most relevant features, Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), and Decision Tree (DT) are used to identify various human physical activities. Using two different datasets, we investigate that GRRF selects relevant feature sets compared to two benchmark feature selection methods such as Relief-F and GRF, without compromising the recognition accuracy. This integration model with GRRF shows improved performance using all methods mentioned above. Our proposed strategy achieves higher accuracy values of 99.10% and 99.30% for SVM using two different datasets.
Instantaneous tracking of earthquake growth with elastogravity signals

Rapid and reliable estimation of large earthquake magnitude (above 8) is key to mitigating the risks associated with strong shaking and tsunamis1. Standard early warning systems based on seismic waves fail to rapidly estimate the size of such large earthquakes2-5. Geodesy-based approaches provide better estimations, but are also subject to large uncertainties and latency associated with the slowness of seismic waves. Recently discovered speed-of-light prompt elastogravity signals (PEGS) have raised hopes that these limitations may be overcome6,7, but have not been tested for operational early warning. Here we show that PEGS can be used in real time to track earthquake growth instantaneously after the event reaches a certain magnitude. We develop a deep learning model that leverages the information carried by PEGS recorded by regional broadband seismometers in Japan before the arrival of seismic waves. After training on a database of synthetic waveforms augmented with empirical noise, we show that the algorithm can instantaneously track an earthquake source time function on real data. Our model unlocks 'true real-time' access to the rupture evolution of large earthquakes using a portion of seismograms that is routinely treated as noise, and can be immediately transformative for tsunami early warning.
Blockchain Enabled Intelligent Digital Forensics System for Autonomous Connected Vehicles

Autonomous connected vehicles are revolutionizing the automotive industry vision and impacting their business model. This trend will likely impact other related industries and their business model such as automotive insurance companies, tier-II automotive manufactures, and automotive maintenance service companies. The growing number of connected vehicles will require new policy formulation by the government about privacy and security issues in the era of massive digitalization and automation. Organizations such as 3GPP and IEEE are evolving standards that address security issues for connected vehicles. However, since Artificial Intelligence (AI) and Machine Learning (ML) will play a significant role in 5G networks and beyond, we address the privacy and security issues with a blockchain-based intelligent digital forensics system for autonomous connected vehicles (ACVs) in a connected smart world incorporating artificial intelligence. The proposed system includes a novel algorithm for the autonomous connected vehicles and the users' security and privacy. We utilize short randomizable signatures to anonymously authenticate witness's identities and to protect the witnesses' privacy. Then, we leverage fine-grained access control based on ciphertext-policy attribute-based encryption for evidence access and evaluate the feasibility of the proposed system by simulating the proposed model, its computational costs and communication overhead by implementing a prototype on a local Ethereum blockchain network platform. The simulation results show the proof-of-concept of the proposed system. We envision the application of the proposed system to ensure security and privacy of ACVs and their security applications for use with 5G-V2X and future XG-V2X networks.
Deep CNN hyperparameter optimization algorithms for sensor-based human activity recognition

Human activity recognition (HAR) is an active field of research for the classification of human movements and applications in a wide variety of areas such as medical diagnosis, health care systems, elderly care, rehabilitation, surveillance in a smart home, and so on. HAR data are collected from wearable devices which include different types of sensors and/or with the smartphone sensor's aid. In recent years, deep learning algorithms have been showed a significant robustness for classifying human activities on HAR data. In the architecture of such deep learning networks, there are several hyperparameters to control the model efficiency which are mainly set by experiment. In this paper, firstly, we introduced one dimensional Convolutional neural network (CNN) as a model among supervised deep learning for an online HAR data classification. In order to automatically choose the optimum hyperparameters of the CNN model, seven approaches based on metaheuristic algorithms were investigated. The optimization algorithms were evaluated on the HAR dataset from the UCI Machine Learning repository. Furthermore, the performance of the proposed method was compared with several state-of-the-art evolutionary algorithms and other deep learning models. The experimental results showed the robustness of using metaheuristic algorithms to optimize the hyperparameters in CNN.
On computing the hyperparameter of extreme learning machines: Algorithm and application to computational PDEs, and comparison with classical and high-order finite elements

We consider the use of extreme learning machines (ELM) for computational partial differential equations (PDE). In ELM the hidden-layer coefficients in the neural network are assigned to random values generated on [−Rm,Rm] and fixed, where Rm is a user-provided constant, and the output-layer coefficients are trained by a linear or nonlinear least squares computation. We present a method for computing the optimal or near-optimal value of Rm based on the differential evolution algorithm. The presented method enables us to illuminate the characteristics of the optimal Rm for two types of ELM configurations: (i) Single-Rm-ELM, corresponding to the conventional ELM method in which a single Rm is used for generating the random coefficients in all the hidden layers, and (ii) Multi-Rm-ELM, corresponding to a modified ELM method in which multiple Rm constants are involved with each used for generating the random coefficients of a different hidden layer. We adopt the optimal Rm from this method and also incorporate other improvements into the ELM implementation. In particular, here we compute all the differential operators involving the output fields of the last hidden layer by a forward-mode auto-differentiation, as opposed to the reverse-mode auto-differentiation in a previous work. These improvements significantly reduce the network training time and enhance the ELM performance. We systematically compare the computational performance of the current improved ELM with that of the finite element method (FEM), both the classical second-order FEM and the high-order FEM with Lagrange elements of higher degrees, for solving a number of linear and nonlinear PDEs. It is shown that the current improved ELM far outperforms the classical FEM. Its computational performance is comparable to that of the high-order FEM for smaller problem sizes, and for larger problem sizes the ELM markedly outperforms the high-order FEM.
A comprehensive survey of authentication methods in Internet-of-Things and its conjunctions

Internet of Thing (IoT) is one of the most influential technologies in the present time. People, processes, and things are connected with the Internet through IoT. With the increasing demands of user applications, the number of connections is also increasing exponentially. Therefore, security becomes a critical issue in IoTs. Confidentiality, Integrity, and Availability (CIA) services are important for IoT applications. IoTs must also ensure proper authentication mechanisms to ensure CIA in the second stage. Various researches in this direction address the authentication issues in IoTs. In this paper, we survey the authentication aspects in IoTs and their allied domains. We analyze the potentialities of the existing state-of-the art-approaches and also identify their limitations. We discuss the basics of authentication and its related attacks for the ease of interpretability of the readers. We show a taxonomical understanding of the approaches and try to connect the evolution of the solution strategies. These connections, to the best of our knowledge, are novel as compared to the existing authentication surveys. Besides, the multidimensional vision of this survey for IoT extensions is an add-on to the benefits. We also provide a discussion on the future direction of research in this domain. In a nutshell, this survey is a one-stop solution for academia and industry to understand the status quo of IoT authentication schemes/protocols.
BiCHAT: BiLSTM with deep CNN and hierarchical attention for hate speech detection

Online social networks(OSNs) face the challenging problem of hate speech, which should be moderated for the growth of OSNs. The machine learning approaches dominate the existing set of approaches for hate speech detection. In this study, we introduce BiCHAT: a novel BiLSTM with deep CNN and Hierarchical ATtention-based deep learning model for tweet representation learning toward hate speech detection. The proposed model takes the tweets as input and passes through a BERT layer followed by an attention-aware deep convolutional layer. The convolutional encoded representation further passes through an attention-aware Bidirectional LSTM network. Finally, the model labels the tweet as hateful or normal through a softmax layer. The proposed model is trained and evaluated over the three benchmark datasets extracted from Twitter and outperforms the state-of-the-art (SOTA) (Khan et al., 2022; Roy et al., 2020; Ding et al., 2019) and baseline methods with an improvement of 8%,7% and 8% in terms of precision, recall, and f-score, respectively. BiCHAT also demonstrates good performance considering training and validation accuracy with an improvement of 5% and 9%, respectively. We also examined the impact of different constituting neural network components on the model. On analysis, we observed that the exclusion of the deep convolutional layer has the highest impact on the performance of the proposed model. We also investigated the efficacy of different embedding techniques, activation function, batch size, and optimization algorithms on the performance of the BiCHAT model.
A novel Random Forest integrated model for imbalanced data classification problem

In recent years, most researchers focused on the classification problems of imbalanced data sets, and these problems are widely distributed in industrial production and medical research fields. For these highly imbalanced data sets, the ensemble method based on over-sampling is one of the most competitive techniques in the present research. However, the incorrect sampling strategy easily affected the model performance, which increased the training complexity and caused an over-fitting problem. This article proposed an equilibrium ensemble method (DCI-ISSA) with two novel techniques to conquer these shortcomings. Firstly, this paper raised an over-sampling approach (Data Center Interpolation DCI) to offer a counterbalanced data set for the single learner, which can prevent the base learners from the impact of class imbalance. Additionally, we provided a parameter optimization method for Random Forest (RF), which used the Improved Sparrow Search Algorithm (ISSA) to find the optimal parameters for different imbalanced data sets dynamically. These parameters can improve the classification performance of base classifiers and adjust to all kinds of lopsided data sets with distinct sizes. Experimental results showed that the DCI-ISSA-RF model outperforms other famous approaches for the imbalanced data sets with various dimensions.
Effects of hyperspectral data with different spectral resolutions on the estimation of soil heavy metal content: From ground-based and airborne data to satellite-simulated data

Soil heavy metal distribution maps can provide decision-making information for pollution control and agricultural management. However, the estimation of heavy metals is sensitive to the resolution of the soil spectra due to their sparse content in soils. The purposes of this study were to test the sensitivity of Ni, Zn and Pb prediction results to variations in spectral resolution, then to map their spatial distributions over a large area. In addition, the effectiveness of spectral feature extraction was investigated. In total, 92 soil samples and corresponding field soil spectra were obtained from the Tongwei-Zhuanglang area in Gansu Province, China. Airborne HyMap hyperspectral image of this area was acquired simultaneously. Three satellite image spectra (AHSIGF-5, Hyperion, AHSIZY-1 02D) were simulated using the field spectra which were measured under real environmental conditions rather than laboratory conditions. The combination of genetic algorithm and partial least squares regression (GA-PLSR) was used as prediction algorithm. The models calibrated by HyMap image full spectral bands had the highest accuracies (RP2 = 0.8558, 0.8002, and 0.8592 for Ni, Zn, and Pb, respectively) because of high consistency. For field spectra and three simulated satellite spectra, models calibrated by simulated AHSIGF-5 spectra performed best because of appropriate resolution (5 nm in the visible near-infrared [VNIR] and 10 nm in the short-wave infrared [SWIR]). The spectral feature extraction method only improved prediction accuracy of the field spectra, indicating that this method benefited from higher spectral resolution. The mapping of the spatial distribution of soil heavy metals over a large area was realized based on HyMap image. According to the results of the satellite simulation spectra, this study proposes to use GF-5 hyperspectral image to estimate heavy metals content. The outcomes provide a reference for the utilization of aerial and satellite hyperspectral images in prediction of soil heavy metal concentrations.
Advanced digital forensics and anti-digital forensics for IoT systems: Techniques, limitations and recommendations

Recently, the number of cyber attacks against IoT domains has increased tremendously. This resulted into both human and financial losses at all IoT levels especially individual and organization levels. Recently, cyber-criminals have kept on leveraging new skills and capabilities by conducting anti-forensics activities and employing techniques and tools to cover their tracks to evade any possible detection of the attack’s events, which has targeted either the IoT system or/and its component(s). Consequently, IoT cyber-attacks are becoming more efficient and more sophisticated with higher risks and threat levels based on their more frequent likelihood to occur and their impact. However, traditional security and forensics solutions are no longer enough to prevent nor investigate such cyber attacks, especially in terms of acquiring evidence for attack investigation. Hence, the need for well-defined, sophisticated, and advanced forensics investigation techniques is highly required to prevent anti-forensics techniques and track down cyber criminals. This paper reviews the different forensics and anti-forensics methods that can be applied in the IoT domain including tools, techniques, types, and challenges, while also discussing the rise of the anti-anti-forensics as a new forensics protection mechanism against anti-forensics activities. This would help forensics investigators to better understand the different anti-forensics tools, methods and techniques that cyber criminals employ while launching their attacks. Moreover, the limitations of the current forensics techniques are discussed, especially in terms of issues and challenges. Finally, this paper presents a holistic view from a literature point of view over the forensics domain in general and for IoT in particular.