Reviewerly/exHarmony huggingface dataset:
An entropy empowered hybridized aggregation technique for group recommender systems

Group recommender systems aim to suggest appropriate products/services to a group of users rather than individuals. These recommendations rely solely on determining group preferences, which is accomplished by an aggregation technique that combines individuals’ preferences. A plethora of aggregation techniques of various types have been developed so far. However, they consider only one particular aspect of the provided ratings in aggregating (e.g., counts, rankings, high averages), which imposes some limitations in capturing group members’ propensities. Besides, maximizing the number of satisfied members with the recommended items is as significant as producing items tailored to the individual users. Therefore, the ratings’ distribution is an essential element for aggregation techniques to discover items on which the majority of the members provided a consensus. This study proposes two novel aggregation techniques by hybridizing additive utilitarian and approval voting methods to feature popular items on which group members provided a consensus. Experiments conducted on three real-world benchmark datasets demonstrate that the proposed hybridized techniques significantly outperform all traditional methods. For the first time in the literature, we offer to use entropy to analyze rating distributions and detect items on which group members have reached no or little consensus. Equipping the proposed hybridized type aggregation techniques with the entropy calculation, we end up with an ultimate enhanced aggregation technique, Agreement without Uncertainty, which was proven to be even better than the hybridized techniques and outperform two recent state-of-the-art techniques.
Physics successfully implements Lagrange multiplier optimization

Optimization is a major part of human effort. While being mathematical, optimization is also built into physics. For example, physics has the Principle of Least Action; the Principle of Minimum Power Dissipation, also called Minimum Entropy Generation; and the Variational Principle. Physics also has Physical Annealing, which, of course, preceded computational Simulated Annealing. Physics has the Adiabatic Principle, which, in its quantum form, is called Quantum Annealing. Thus, physical machines can solve the mathematical problem of optimization, including constraints. Binary constraints can be built into the physical optimization. In that case, the machines are digital in the same sense that a flip-flop is digital. A wide variety of machines have had recent success at optimizing the Ising magnetic energy. We demonstrate in this paper that almost all those machines perform optimization according to the Principle of Minimum Power Dissipation as put forth by Onsager. Further, we show that this optimization is in fact equivalent to Lagrange multiplier optimization for constrained problems. We find that the physical gain coefficients that drive those systems actually play the role of the corresponding Lagrange multipliers.
Towards online prediction of safety-critical landing metrics in aviation using supervised machine learning

In recent years, due to the increased availability of data and improvements in computing power, application of machine learning techniques to various aviation safety problems for identifying, isolating, and reducing risk has gained momentum. Data collected from on-board recorders in commercial aircraft contain thousands of parameters in the form of multivariate time-series (continuous, discrete, categorical, etc.) which are used to train the machine learning models. Among the phases of flight, approach and landing phases result in the most accidents and incidents. The performance and trajectory of the aircraft during the approach phase is an indicator of its landing performance which, in turn, affects incident or accident probability such as runway excursions. Landing performance is commonly measured using metrics such as landing airspeed, vertical speed, location of touchdown point on runway, etc. While current applications of machine learning to aviation focus on retrospective insights to implement corrective measures, they offer limited value for real-time risk identification or decision-making as they are inherently reactive in nature. In this work, a novel offline-online framework is developed for building a global predictive model offline to predict landing performance metrics online. The framework leverages flight data from the approach phase between certain approach altitudes (also called gates) in order to train the offline model to predict the landing true airspeed and ground speed using a Random Forest regression algorithm. Permutation importance is used to identify and retain important features among those available and the training data is balanced with respect to flight safety events to ensure a good performing model. The developed global model is robust and can predict landing true airspeed and ground speed with root mean square errors of 2.62 and 2.98 knots respectively over six different airframes operating at over seventy airports. The model operates fast in prediction mode which, coupled with the ability to provide the prediction at an altitude where a go-around decision may be made, makes it particularly suitable for online application. The framework is demonstrated using data obtained from commercial airline operations that contains thousands of flight records and performs better than existing techniques in literature at predicting true airspeed and ground speed at touchdown.
Richpedia: A Large-Scale, Comprehensive Multi-Modal Knowledge Graph

Large-scale knowledge graphs such as Wikidata and DBpedia have become a powerful asset for semantic search and question answering. However, most of the knowledge graph construction works focus on organizing and discovering textual knowledge in a structured representation, while paying little attention to the proliferation of visual resources on the Web. To consolidate this recent trend, in this paper, we present Richpedia, aiming to provide a comprehensive multi-modal knowledge graph by distributing sufficient and diverse images to textual entities in Wikidata. We also set Resource Description Framework links (visual semantic relations) between image entities based on the hyperlinks and descriptions in Wikipedia. The Richpedia resource is accessible on the Web via a faceted query endpoint, which provides a pathway for knowledge graph and computer vision tasks, such as link prediction and visual relation detection.
Sequential Attention GAN for Interactive Image Editing

Most existing text-to-image synthesis tasks are static single-turn generation, based on pre-defined textual descriptions of images. To explore more practical and interactive real-life applications, we introduce a new task - Interactive Image Editing, where users can guide an agent to edit images via multi-turn textual commands on-the-fly. In each session, the agent takes a natural language description from the user as the input, and modifies the image generated in previous turn to a new design, following the user description. The main challenges in this sequential and interactive image generation task are two-fold: 1) contextual consistency between a generated image and the provided textual description; 2) step-by-step region-level modification to maintain visual consistency across the generated image sequence in each session. To address these challenges, we propose a novel Sequential Attention Generative Adversarial Network (SeqAttnGAN), which applies a neural state tracker to encode the previous image and the textual description in each turn of the sequence, and uses a GAN framework to generate a modified version of the image that is consistent with the preceding images and coherent with the description. To achieve better region-specific refinement, we also introduce a sequential attention mechanism into the model. To benchmark on the new task, we introduce two new datasets, Zap-Seq and DeepFashion-Seq, which contain multi-turn sessions with image-description sequences in the fashion domain. Experiments on both datasets show that the proposed SeqAttnGAN model outperforms state-of-the-art approaches on the interactive image editing task across all evaluation metrics including visual quality, image sequence coherence and text-image consistency.
MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos

Visual data collected from Unmanned Aerial Vehicles (UAVs) has opened a new frontier of computer vision that requires automated analysis of aerial images/videos. However, the existing UAV datasets primarily focus on object detection. An object detector does not differentiate between the moving and non-moving objects. Given a real-time UAV video stream, how can we both localize and classify the moving objects, i.e. perform moving object recognition (MOR) The MOR is one of the essential tasks to support various UAV vision-based applications including aerial surveillance, search and rescue, event recognition, urban and rural scene understanding.To the best of our knowledge, no labeled dataset is available for MOR evaluation in UAV videos. Therefore, in this paper, we introduce MOR-UAV, a large-scale video dataset for MOR in aerial videos. We achieve this by labeling axis-aligned bounding boxes for moving objects which requires less computational resources than producing pixel-level estimates. We annotate 89,783 moving object instances collected from 30 UAV videos, consisting of 10,948 frames in various scenarios such as weather conditions, occlusion, changing flying altitude and multiple camera views. We assigned the labels for two categories of vehicles (car and heavy vehicle). Furthermore, we propose a deep unified framework MOR-UAVNet for MOR in UAV videos. Since, this is a first attempt for MOR in UAV videos, we present 16 baseline results based on the proposed framework over the MOR-UAV dataset through quantitative and qualitative experiments. We also analyze the motion-salient regions in the network through multiple layer visualizations. The MOR-UAVNet works online at inference as it requires only few past frames. Moreover, it doesn't require predefined target initialization from user. Experiments also demonstrate that the MOR-UAV dataset is quite challenging.
DIMC-net: Deep Incomplete Multi-view Clustering Network

In this paper, a new deep incomplete multi-view clustering network, called DIMC-net, is proposed to address the challenge of multi-view clustering on missing views. In particular, DIMC-net designs several view-specific encoders to extract the high-level information of multiple views and introduces a fusion graph based constraint to explore the local geometric information of data. To reduce the negative influence of missing views, a weighted fusion layer is introduced to obtain the consensus representation shared by all views. Moreover, a clustering layer is introduced to guarantee that the obtained consensus representation is the best one for the clustering task. Compared with the existing deep learning based approaches, DIMC-net is more flexible and efficient since it can handle all kinds of incomplete cases and directly produce the clustering results. Experimental results show that DIMC-net achieves significant improvement over state-of-the-art incomplete multi-view clustering methods.
Skeleton edge motion networks for human action recognition

Human skeleton is receiving increasing attention from the community of human action recognition due to its robustness to complex image backgrounds. Previous methods usually utilize body joint-based representation, i.e., joint locations, while leaving edge-based movement poorly investigated. In this paper, we propose a new human action recognition method, skeleton edge motion networks (SEMN), to further explore the motion information of human body parts. Specifically, we address the movement of skeleton edge by using the angle changes of skeleton edge and the movement of the corresponding body joints. We then devise the proposed skeleton edge motion networks by stacking multiple spatial-temporal blocks to learn a robust deep representation from skeleton sequences. Furthermore, we propose a new progressive ranking loss to help the proposed skeleton edge motion networks maintain temporal order information in a self-supervised manner. Experimental results on five popular human action recognition datasets, PennAction, UTD-MHAD, NTU RGB+D, NTU RGB+D 120, and CSL, demonstrate the effectiveness of the proposed method.
Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning

This paper focuses on communication-efficient federated learning problem, and develops a novel distributed quantized gradient approach, which is characterized by adaptive communications of the quantized gradients. Specifically, the federated learning builds upon the server-worker infrastructure, where the workers calculate local gradients and upload them to the server; then the server obtain the global gradient by aggregating all the local gradients and utilizes it to update the model parameter. The key idea to save communications from the worker to the server is to quantize gradients as well as skip less informative quantized gradient communications by reusing previous gradients. Quantizing and skipping result in 'lazy' worker-server communications, which justifies the term Lazily Aggregated Quantized (LAQ) gradient. Theoretically, the LAQ algorithm achieves the same linear convergence as the gradient descent in the strongly convex case, while effecting major savings in the communication in terms of transmitted bits and communication rounds. Empirically, extensive experiments using realistic data corroborate a significant communication reduction compared with state-of-the-art gradient- and stochastic gradient-based algorithms.
Sentiment analysis of tweets using a unified convolutional neural network‐long short‐term memory network model

Abstract Sentiment analysis focuses on identifying and classifying the sentiments expressed in text messages and reviews. Social networks like Twitter, Facebook, and Instagram generate heaps of data filled with sentiments, and the analysis of such data is very fruitful when trying to improve the quality of both products and services alike. Classic machine learning techniques have a limited capability to efficiently analyze such large amounts of data and produce precise results; they are thus supported by deep learning models to achieve higher accuracy. This study proposes a combination of convolutional neural network and long short‐term memory (CNN‐LSTM) deep network for performing sentiment analysis on Twitter datasets. The performance of the proposed model is analyzed with machine learning classifiers, including the support vector classifier, random forest (RF), stochastic gradient descent (SGD), logistic regression, a voting classifier (VC) of RF and SGD, and state‐of‐the‐art classifier models. Furthermore, two feature extraction methods (term frequency‐inverse document frequency and word2vec) are also investigated to determine their impact on prediction accuracy. Three datasets (US airline sentiments, women's e‐commerce clothing reviews, and hate speech) are utilized to evaluate the performance of the proposed model. Experiment results demonstrate that the CNN‐LSTM achieves higher accuracy than those of other classifiers.
Improved clustering algorithms for image segmentation based on non-local information and back projection

Accurate image segmentation is a prerequisite to conducting an image analysis task, and the complexity stemming from the semantic diversity plays a pivotal role in image segmentation. Existing algorithms employed different types of information in the process of segmentation to improve the robustness. However, these algorithms were characterized by a tradeoff between noise removal and detail retention; this is because it is difficult to distinguish image artifacts from details. This paper proposes an improved image segmentation schema and presents two improved clustering algorithms, in which self-similarity and back projection are considered simultaneously to enhance the robustness. With the aid of self-similarity, non-local information is fully exploited, while the original information can be retained by back projection. Extensive experiments on various types of images demonstrate that our algorithms can balance noise restraining and detail retention to improve the adaptation of complex images in segmentation.
On the implementation of a novel data-intelligence model based on extreme learning machine optimized by bat algorithm for estimating daily chlorophyll-a concentration: Case studies of river and lake in USA

Chlorophyll-a is one of the main indicators for water quality (WQ) analysis in environmental monitoring of aquatic ecosystems. WQ degradation is mostly a result of the increase of the concentration of chlorophyll-a in a waterbody, however, proper estimation of daily chlorophyll-a concentration is a complex problem. In this study, the classic extreme learning machine (ELM), group method of data handling (GMDH), random forest (RF), classification and regression tree (CART), and a novel integrated Bat-ELM model (with the bat optimization algorithm) were developed and applied to predict daily chlorophyll-a (Chl-a) concentration in river and lake ecosystems. Input parameters including turbidity (TU), pH, specific conductance (SC), water temperature (TE), and periodicity were applied as the influential elements for estimating daily Chl-a concentration for two different USGS stations. General results based on RMSE values indicated that the Bat-ELM as the most robust model improved the performance of standard ELM, GMDH, RF, and CART models during the testing procedure by 20.7%, 23.9%, 18.3%, and 27.4% in USGS no. 05543010 and 13.8%, 16.8%, 17.5%, and 52.0% in USGS no. 09014050 in terms of the 7th input-combination, respectively. Moreover, the results revealed that periodicity is the most effective input parameter that considered as the last scenario (input combination) on daily chlorophyll-a (Chl-a) concentration.
Realistic Adversarial Data Augmentation for MR Image Segmentation

Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learning. Acquiring and manually labelling a large medical dataset is expensive and sometimes impractical due to data sharing and privacy issues. In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation. Instead of generating pixel-wise adversarial attacks, our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common type of artefacts in MR imaging: bias field. The proposed method does not rely on generative networks, and can be used as a plug-in module for general segmentation networks in both supervised and semi-supervised learning. Using cardiac MR imaging we show that such an approach can improve the generalization ability and robustness of models as well as provide significant improvements in low-data scenarios.
Cryptocurrencies as a Financial Tool: Acceptance Factors

Cryptocurrencies are a new form of digital asset that operate through blockchain technology and whose purpose is to be used as a means of exchange. Some, such as bitcoin, have become globally recognized in recent years, but the uncertainty surrounding cryptocurrencies raises questions about their intended use. This study has the task of investigating the different factors that affect the intention behind the use of cryptocurrencies by developing a new research model and using Partial Least Squares (PLS) to assess it. The results show that all the constructs proposed have significative influence, either directly or indirectly, on the intention behind the use of cryptocurrencies. The findings provide value and utility for companies’ and cryptocurrencies’ intermediaries to formulate their business strategies.
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning

Machine learning (ML), and deep learning (DL) in particular, play a vital role in providing smart services to the industry. These techniques, however, suffer from privacy and security concerns since data are collected from clients and then stored and processed at a central location. Federated learning (FL), an architecture in which model parameters are exchanged instead of client data, has been proposed as a solution to these concerns. Nevertheless, FL trains a global model by communicating with clients over communication rounds, which introduces more traffic on the network and increases the convergence time to the target accuracy. In this work, we solve the problem of optimizing accuracy in stateful FL with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process. Next, we propose an online stateful FL heuristic to find the best candidate clients. Additionally, we propose an IoT client alarm application that utilizes the proposed heuristic in training a stateful FL global model based on IoT device-type classification to alert clients about unauthorized IoT devices in their environment. To test the efficiency of the proposed online heuristic, we conduct several experiments using a real data set and compare the results against state-of-the-art algorithms. Our results indicate that the proposed heuristic outperforms the online random algorithm with up to 27% gain in accuracy. Additionally, the performance of the proposed online heuristic is comparable to the performance of the best offline algorithm.
Incorporation of multimodal multiobjective optimization in designing a filter based feature selection technique

Current work reports about the development of a new feature selection technique fusing two concepts, multimodal multiobjective optimization and filter-based feature selection. Use of the concept of multimodality in multiobjective based feature selection helps in generating diverse set of feature subsets on final Pareto front. This approach evaluates the quality of the reduced feature set by utilizing different quality measures. This process of feature selection focuses on achieving two discrete objectives: (1) identification of a large number of Pareto-optimal solutions along with achieving a good distribution in both objective and decision spaces; (2) making a selection of feature subset with minimal redundancy and high correlation with classes. To achieve the second objective, a variety of objective functions based on information-theoretic measures like normalized mutual information and correlation with class attributes are utilized. A multiobjective ring-based Particle swarm Optimization (PSO) and non-dominated sorting with special crowding distance are employed to cover the aspects corresponding to the first objective. An evaluation is carried out on seven publicly available datasets concerning different classifiers. The results of these experiments illustrate that the multimodal PSO based feature selection approach finds more feature subsets than its simple PSO counterpart in multiobjective environment. And, the results are also compared with those of existing wrapper based multimodal multiobjective feature selection methods.