Reviewerly/exHarmony huggingface dataset:
A reconstruction method for graph signals based on the power spectral density estimation

Graph signal reconstruction is a classic problem of graph signal processing. The ultimate goal of signal reconstruction is to obtain an estimate as close as possible to the original signals. Most of existing reconstruction models are based on assumption of signal smoothness. This paper proposes a new reconstruction model based on signal power spectral density estimation. The basic idea is to suppress those graph frequency components with low power spectral density when reconstructing a signal, while preserving those graph frequency contents with high power spectral density. Therefore, the reconstructed signals can well maintain the frequency structures of the original signals. Under the stationarity assumption of stochastic graph signals, we further propose a method to estimate the power spectral density directly from the sampled signals. Experimental results show that the proposed power spectral density estimation algorithm works well even when the proportion of labeled vertices is extremely low, and is robust to the PSD of signals itself and the various structures of graphs. Compared with traditional models based on smoothness assumption, the proposed reconstruction model can significantly improve the reconstruction performance.
A Shared Memory Approach for Function Chaining in Serverless Platforms

Serverless platforms are increasingly gaining importance in the cloud computing landscape due to their benefit of shortening the time to market of solutions and capability of automatic, fast, fine-grained scaling of resources. In this context, function chaining represents an appealing feature, allowing the composition of two or more functions to create a complex computation from simpler units while incentivizing modularity and reusability of functions. In this paper, we propose a portable and transparent, container-based serverless architecture that introduces an innovative infrastructural support, enabling an efficient composition of functions co-located on the same host. The proposal relies on a shared-memory approach and a message-oriented middleware serving as a communication medium among components. The experimental assessment shows the approach comes with the benefit of optimized resource usage and performance benefits measured in terms of request completion rate and a decrease in response latency.
Topic Modeling and Sentiment Analysis using Online Reviews for Bangladesh Airlines

Air travel is one of the most used ways of transit in our daily lives. So it's no wonder that more and more people are sharing their experience with airlines and airports using web-based online surveys. This study aims to do topic modeling and sentiment analysis on Skytrax (airlinequality.com) and Tripadvisor (tripadvisor.com) postings where there is a lot of interest and engagement from people who have used it or want to use it for airlines. The goal of individuals gathering at Skytrax and Tripadvisor is to make better decisions based on the actual experiences of other customers who have flown with airlines. We gathered online reviews submitted by consumers who have flown with Bangladesh airlines in the past. The data was collected from internet reviews from 4 Bangladesh airlines, totaling 1095 reviews. With the obtained data, topic modeling and sentiment analysis were utilized to determine key phrases in the online reviews. Through frequency analysis, the topic modeling revealed that ‘airline,’ ‘service,’ ‘crew,’ and ‘food’ were essential concerns in the trip. Furthermore, the results indicated that the primary issue, which may impact customer discontent, was delay, but ‘food’ can make customers satisfied through sentiment analysis, as the result indicates the ‘Food and beverage’ topic with no. of highest word count ‘good’, ‘biman’ and ‘service’ keyword in the topic modeling.
Privacy-preserving indoor localization based on inner product encryption in a cloud environment

How to utilize the resource-rich cloud to provide scalable indoor localization service to solve the problem of limited user equipment resources while preventing privacy leakage has become a hot topic. A novel privacy-preserving indoor localization scheme based on Inner Product Encryption in a cloud environment is proposed to address this issue. By using the computability of Inner Product Encryption on ciphertext without exposing plaintext information, an honest-but-curious cloud server estimates the users location without obtaining any private information. The least-squares estimation algorithm of the Time Difference location system is decomposed into the basic form of the inner product of two sets of vectors. One is the anchor information encrypted with Inner Product Encryption during the offline phase, the other is the users distance difference information encrypted and sent to the cloud server during the online phase. To hide the users real location, the users real distance difference information and several decoy information are encrypted and included in a request to achieve k-Anonymity. The location estimation algorithm operates on the cloud to locate the user from the ciphertext. In addition, three optimization methods applied to cloud server location estimation algorithm were designed to further improve the performance. The experimental results and theoretical analysis show that the proposed scheme consumes less time, and has a lower communication overhead, while maintaining positioning accuracy. At the same time, the scheme protects the users locational and data privacy of the indoor positioning service provider when migrating the indoor positioning services to the cloud environment.
Environmental Pollution Indices and Multivariate Modeling Approaches for Assessing the Potentially Harmful Elements in Bottom Sediments of Qaroun Lake, Egypt

This research intends to offer a scientific foundation for environmental monitoring and early warning which will aid in the environmental protection management of Qaroun Lake. Qaroun Lake is increasingly influenced by untreated wastewater discharge from many anthropogenic activities, making it vulnerable to pollution. For that, six environmental pollution indices, namely contamination factor (Cf), enrichment factor (EF), geo-accumulation index (Igeo), degree of contamination (Dc), pollution load index (PLI), and potential ecological risk index (RI), were utilized to assess the bottom sediment and to determine the different geo-environmental variables affecting the lake system. Cluster analysis (CA), and principal component analysis (PCA) were used to explore the potential pollution sources of heavy metal. Moreover, the efficiency of partial least-square regression (PLSR) and multiple linear regression (MLR) were tested to assess the Dc, PLI, and RI depending on the selected elements. The sediment samples were carefully collected from 16 locations of Qaroun Lake in two investigated years in 2018 and 2019. Total concentrations of Al, As, Ba, Cd, Co, Cr, Cu, Fe, Ga, Hf, Li, Mg, Mn, Mo, Ni, P, Pb, Sb, Se, Zn, and Zr were quantified using inductively coupled plasma mass spectra (ICP-MS). According to the Cf, EF, and Igeo results, As, Cd, Ga, Hf, P, Sb, Se, and Zr demonstrated significant enrichment in sediment and were derived from anthropogenic sources. According to Dc results, all collected samples were categorized under a very high degree of contamination. Further, the results of RI showed that the lake is at very high ecological risk. Meanwhile, the PLI data indicated 59% of lake was polluted and 41% had PLI &lt; 1. The PLSR and MLR models based on studied elements presented the highest efficiency as alternative approaches to assess the Dc, PLI, and RI of sediments. For examples, the validation (Val.) models presented the best performance of these indices, with R2val = 0.948–0.989 and with model accuracy ACCv = 0.984–0.999 for PLSR, and with R2val = 0.760–0.979 and with ACCv = 0.867–0.984 for MLR. Both models for Dc, PLI, and RI showed that there was no clear overfitting or underfitting between measuring, calibrating, and validating datasets. Finally, the combinations of Cf, EF, Igeo, PLI, Dc, RI, CA, PCA, PLSR, and MLR approaches represent valuable and applicable methods for assessing the risk of potentially harmful elemental contamination in the sediment of Qaroun Lake.
Smart-VPoseNet: 3D human pose estimation models and methods based on multi-view discriminant network

In the multi-view 3D human pose estimation, when there are some poor-quality views in multi-view data, these views will cause serious errors to multi-view 3D pose estimation. We also found many similar problems in the single-view model. For example, in a motion sequence of the current view, there are some frames in which the human body are occluded or the body joints exceed the camera’s field of view. Therefore, we hope to implement an intelligent method to make the model jump in multiple views during 3D pose modeling, so as to avoid the model using the views that have serious problems and pay more attention to higher quality views. We call this method Smart-VPoseNet. We defined three view selection rules to explain the quality of each view in the dataset, and designed a novel view discriminant network. According to the three rules, the data distribution and characteristics in the dataset are visualized for analyzing and designing the details of the model. In addition, we also introduced a hybrid view selection method based on these rules. In the experiment, we used a single-view pretrained model and Smart-VPoseNet to complete a series of comprehensive comparison experiments. These experiments illustrate that the work of this article has a novel and feasible optimization theory and show the application effect of our work in 3D pose estimation. We believe that our work has opened up a new optimization model based on multiple views.
A Survey on TLS-Encrypted Malware Network Traffic Analysis Applicable to Security Operations Centers

Recently, a majority of security operations centers (SOCs) have been facing a critical issue of increased adoption of transport layer security (TLS) encryption on the Internet, in network traffic analysis (NTA). To this end, in this survey article, we present existing research on NTA and related areas, primarily focusing on TLS-encrypted traffic to detect and classify malicious traffic with deployment scenarios for SOCs. Security experts in SOCs and researchers in academia can obtain useful information from our survey, as the main focus of our survey is NTA methods applicable to malware detection and family classification. Especially, we have discussed pros and cons of three main deployment models for encrypted NTA: TLS interception, inspection using cryptographic functions, and passive inspection without decryption. In addition, we have discussed the state-of-the-art methods in TLS-encrypted NTA for each component of a machine learning pipeline, typically used in the state-of-the-art methods.
Convolutional Neural Networks for Breast Density Classification: Performance and Explanation Insights

We propose and evaluate a procedure for the explainability of a breast density deep learning based classifier. A total of 1662 mammography exams labeled according to the BI-RADS categories of breast density was used. We built a residual Convolutional Neural Network, trained it and studied the responses of the model to input changes, such as different distributions of class labels in training and test sets and suitable image pre-processing. The aim was to identify the steps of the analysis with a relevant impact on the classifier performance and on the model explainability. We used the grad-CAM algorithm for CNN to produce saliency maps and computed the Spearman’s rank correlation between input images and saliency maps as a measure of explanation accuracy. We found that pre-processing is critical not only for accuracy, precision and recall of a model but also to have a reasonable explanation of the model itself. Our CNN reaches good performances compared to the state-of-art and it considers the dense pattern to make the classification. Saliency maps strongly correlate with the dense pattern. This work is a starting point towards the implementation of a standard framework to evaluate both CNN performances and the explainability of their predictions in medical image classification problems.
Construction of Multimedia Assisted Legal Classroom Teaching Model Based on Data Mining Algorithm

In order to quickly and accurately retrieve a required part from massive multimedia educational resources and improve the utilization of educational resources, a multimedia assisted legal classroom teaching model based on data mining algorithm is designed. Firstly, the attributes of multimedia assisted legal classroom teaching resources are judged, and the numerical resources are standardized and discretized. Then, the B+ tree is used to establish the model’s indexes and index library, and the corresponding retrieval algorithm is designed to complete the resource search, establish the data distribution structure model of the multimedia assisted legal classroom teaching system, mine the data, reconstruct the phase space of the fused data information flow, extract the high-order moment features of the specific data in the multimedia assisted legal classroom teaching system in the reconstructed high-dimensional phase space, and realize the accurate mining of the feature data. The experimental results show that the teaching effect of the designed model has more advantages and can promote the improvement of students’ performance.
Agricultural development in the presence of foreign divestment: Policy options

Following the implications of the loss of employment, revenue, foreign exchange, and the negative impact on overall agricultural development resulting from foreign divestment, estimates of the extent of foreign divestment on agricultural development and policy options for ameliorating this effect were obtained. A cross-sectional data of 159 observations was fitted to a derived model and estimated by OLS. One US$ increase in foreign divestment reduces agricultural development by US$ 0.04. Policy mixes involving the combination of domestic investment, aid, and trade in response to foreign divestment in agriculture have been proposed. The best policy mix is the combination of domestic investment, aid, and trade to respond to foreign divestment in agriculture. Aid to agriculture independently was ineffective in promoting agricultural development, however, other variables produced a synergistic effect. Despite efforts to attract foreign direct investment in agriculture, foreign divestment does occur with the associated disbenefits to the sector which is detrimental to agricultural development. It is important to identify the policy options to address this in the study.
The influence of organizational commitment on the perceived effectiveness of virtual meetings by Filipino professionals during the COVID-19 pandemic: A structural equation modeling approach

Virtual meetings have been widely utilized during the COVID-19 pandemic.The purpose of this study was to explore the influence of organizational commitment on the perceived effectiveness of virtual meeting by Filipino professionals during the COVID-19 pandemic.A total of 513 Filipino professionals answered an online questionnaire which covered four latent variables: organizational commitment to virtual meetings, attitude toward virtual meetings, perceived effectiveness of virtual meeting as collaboration tool, and perceived effectiveness of virtual meeting as a social tool. Structural Equation Modeling (SEM) was utilized to analyze the causal relationships between the latent variables construct.SEM showed that organizational commitment to virtual meeting during the COVID-19 pandemic influenced the positive attitude of the employees which subsequently led to the perceived effectiveness of virtual meeting as a collaboration and social tool.This study is the first study that analyzed the influence of organizational commitment on the perceived effectiveness of virtual meeting during the COVID-19 pandemic in the Philippines. Our SEM construct can be applied and extended further, particularly in analyzing factors influencing the perceived effectiveness of virtual meeting during the COVID-19 pandemic.
Dilated-CBAM: An Efficient Attention Network with Dilated Convolution

In computer vision field, image classification as a basic image processing task has been widely concerned. This paper puts forward an efficient attention network with dilated convolution named Dilated-CBAM for image classification. The dilated convolution is applied in the backbone of the residual network to extract the residual edge path features and integrate the global information of the processed image. The amount of network parameters is greatly reduced while the receptive field is expanded, and the network parameters are learnable. By embedding our spatial attention mechanism and channel attention mechanism, the model uses FCN to strengthen the effective information in the image, weaken the invalid information, and summarize the local features of the processed image. Combining the global information and local information, the time and space for network training are saved, while the effective image features can be extracted better. In the design of attention module, this work innovatively applies residual path in attention module for combining context information inside attention mechanism without adding parameters, and helps attention module extract features of interest in image classification task more accurately. In image classification, experiment, we verify the classification effect of the Dilated-CBAM model on Cifar-10 dataset, which is 2.5% higher than ResNet-18, and reaches the classification accuracy of 93.5%. In terms of the efficiency of network training, the Dilated-CBAM reduces the number of training epochs to about 10 on the basis of CBAM model, shortens the training time to about half of the original, which greatly testifies the training efficiency.
Inference of faults through symbolic regression of system data

We present the development of inferential sensors that use system input and output measurements to improve the accuracy and robustness of fault detection and isolation. These inferential sensors transform and augment the sensed information of a system to: (i) minimize the evidence of uncertainty in the inferred variables, decreasing the rates of false alarms and nondetections; and (ii) provide distinguishable estimates of the existence and/or severity of faults, decreasing the rate of misdiagnoses. The proposed method symbolically regresses the noisy and uncertain system measurements, using genetic programming, to evolve uniquely explainable mathematical functions that minimize a least-squares objective of the fault inference. A standard workflow utilizing the proposed algorithm for fault diagnostics is presented and illustrated in the classification of the severity of fouling in a cross-flow plate-fin heat exchanger. The effectiveness and robustness of the method are explored at different test designs, assessed using k-nearest neighbors classification, and compared to other traditional fault classification methods. The extension of the inferential sensors to information theoretic metrics, where the system model is augmented to improve the evidence of fault(s) is also discussed.
Application of 3D face recognition in the access control system

Abstract Over the years, face recognition has been the research topic that has attracted many researchers around the world. One of the most significant applications of face recognition is the access control system. The access control system allows authorized persons to enter or exit certain or restricted areas. As a result, it will increase the security situation without over-investment in staff security. The access information can be the identification, time, and location, etc. It can be used to carry out human resource management tasks such as attendance and inspection of employees in a more fair and transparent manner. Although face recognition has been widely used in access control systems because of its better accuracy and convenience without requiring too much user cooperation, the 2D-based face recognition systems also retain many limitations due to the variations in pose and illumination. By analyzing facial geometries, 3D facial recognition systems can theoretically overcome the disadvantages of prior 2D methods and improve robustness in different working conditions. In this paper, we propose the 3D facial recognition algorithm for use in an access control system. The proposed algorithm includes the preprocessing, feature extraction, and classification stages. The application of the proposed access control system is the automatic sliding door, the controller of the system, the web-based monitoring, control, and storage of data.
Inferring Goals with Gaze during Teleoperated Manipulation

Assistive robot manipulators help people with upper motor impairments perform tasks by themselves. However, teleoperating a robot to perform complex tasks is difficult. Shared control algorithms make this easier: these algorithms predict the user’s goal, autonomously generate a plan to accomplish the goal, and fuse that plan with the user’s input. To accurately predict the user’s goal, these algorithms typically use the user’s input command (e.g., joystick input) directly. We use another sensing modality: the user’s natural eye gaze behavior, which is highly task-relevant and informative early in the task. We develop an algorithm using hidden Markov models to infer goals from natural eye gaze behavior that appears while users are teleoperating a robot. We show that gaze-based predictions outperform goal prediction based on the control input and that our sequence model improves the prediction quality relative to gaze-based aggregate models.
A novel binary classification approach based on geometric semantic genetic programming

Geometric semantic genetic programming (GSGP) is a recent variant of genetic programming. GSGP allows the landscape of any supervised regression problem to be transformed into a unimodal error surface, thus it has been applied only to this kind of problem. In a previous paper, we presented a novel variant of GSGP for binary classification problems that, taking inspiration from perceptron neural networks, uses a logistic-based activation function to constrain the output value of a GSGP tree in the interval [0,1]. This simple approach allowed us to use the standard RMSE function to evaluate the train classification error on binary classification problems and, consequently, to preserve the intrinsic properties of the geometric semantic operators. The results encouraged us to investigate this approach further. To this aim, in this paper, we present the results from 18 test problems, which we compared with those achieved by eleven well-known and widely classification schemes. We also studied how the parameter settings affect the classification performance and the use of the F-score function to deal with imbalanced data. The results confirmed the effectiveness of the proposed approach.
Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model

Magnetic anomaly detection (MAD) is used for detecting moving ferromagnetic targets. In this study, we present an end-to-end deep-learning model for magnetic anomaly detection on data recorded by a single static three-axis magnetometer. We incorporate an attention mechanism into our network to improve the detection capability of long time-series signals. Our model has good performance under the Gaussian colored noise with the power spectral density of 1/fα, which is similar to the field magnetic noise. Our method does not require another magnetometer to eliminate the effects of the Earth’s magnetic field or external interferences. We evaluate the network’s performance through computer simulations and real-world experiments. The high detection performance and the single magnetometer implementation show great potential for real-time detection and edge computing.