High Information Gain Research Articles

LAEA: A 2D LiDAR-Assisted UAV Exploration Algorithm for Unknown Environments

In UAV autonomous exploration, large frontier clusters are commonly associated with high information gain and are visited first. In contrast, small and isolated frontier clusters with fewer frontiers are associated with smaller information gain and are thus explored with low priority. However, these small and isolated frontier clusters are often in close proximity to UAVs and surrounded by explored areas, which could result in back-and-forth flights that lower exploration efficiency. This paper proposes LAEA, a LiDAR-assisted and depth camera-dominated UAV exploration algorithm that aims to improve UAV autonomous exploration efficiency. A hybrid map is obtained that characterizes rich environmental profile information in real time, enabling us to detect small and isolated frontier clusters that can lead to repeated visits to explored areas. An environmental information gain optimization strategy is incorporated such that frontier clusters with larger unexplored areas behind them, as well as small and isolated frontier clusters close to the UAV, are assigned higher weights to prioritize their visit order. An optimized flight trajectory is generated to cover unexplored frontier clusters in the immediate vicinity of the UAV while flying to the next target. A comprehensive comparison between the proposed algorithm and state-of-the-art algorithms was conducted via a simulation study, which showed that our algorithm exhibits superior exploration efficiency in various environments. Experiments were also carried out to verify the feasibility of the proposed approach in real-world scenarios.

Anomaly Detection in Multi-Wavelength Photoplethysmography Using Lightweight Machine Learning Algorithms.

Over the past few years, there has been increased interest in photoplethysmography (PPG) technology, which has revealed that, in addition to heart rate and oxygen saturation, the pulse shape of the PPG signal contains much more valuable information. Lately, the wearable market has shifted towards a multi-wavelength and multichannel approach to increase signal robustness and facilitate the extraction of other intrinsic information from the signal. This transition presents several challenges related to complexity, accuracy, and reliability of algorithms. To address these challenges, anomaly detection stages can be employed to increase the accuracy and reliability of estimated parameters. Powerful algorithms, such as lightweight machine learning (ML) algorithms, can be used for anomaly detection in multi-wavelength PPG (MW-PPG). The main contributions of this paper are (a) proposing a set of features with high information gain for anomaly detection in MW-PPG signals in the classification context, (b) assessing the impact of window size and evaluating various lightweight ML models to achieve highly accurate anomaly detection, and (c) examining the effectiveness of MW-PPG signals in detecting artifacts.

Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method.

Currently, sentiment analysis is a research hotspot in many fields such as computer science and statistical science. Topic discovery of the literature in the field of text sentiment analysis aims to provide scholars with a quick and effective understanding of its research trends. In this paper, we propose a new model for the topic discovery analysis of literature. Firstly, the FastText model is applied to calculate the word vector of literature keywords, based on which cosine similarity is applied to calculate keyword similarity, to carry out the merging of synonymous keywords. Secondly, the hierarchical clustering method based on the Jaccard coefficient is used to cluster the domain literature and count the literature volume of each topic. Thirdly, the information gain method is applied to extract the high information gain characteristic words of various topics, based on which the connotation of each topic is condensed. Finally, by conducting a time series analysis of the literature, a four-quadrant matrix of topic distribution is constructed to compare the research trends of each topic within different stages. The 1186 articles in the field of text sentiment analysis from 2012 to 2022 can be divided into 12 categories. By comparing and analyzing the topic distribution matrices of the two phases of 2012 to 2016 and 2017 to 2022, it is found that the various categories of topics have obvious research development changes in different phases. The results show that: ① Among the 12 categories, online opinion analysis of social media comments represented by microblogs is one of the current hot topics. ② The integration and application of methods such as sentiment lexicon, traditional machine learning and deep learning should be enhanced. ③ Semantic disambiguation of aspect-level sentiment analysis is one of the current difficult problems this field faces. ④ Research on multimodal sentiment analysis and cross-modal sentiment analysis should be promoted.

Solvent Accessibility of Coronaviridae Spike Proteins through the Lens of Information Gain

The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has generated a renewed interest in the larger family of Coronaviridae, which causes a variety of different respiratory infections in a variety of different hosts. Understanding the mechanisms behind the ability of a family of viruses to spill over into different hosts is an ongoing study. In this work, we studied the relationship between specific amino acid sites and the solvent accessibility of the surface (or spike) protein of different Coronaviridae. Since host specificity hinges on the portion(s) of the protein that interfaces with the host cell membrane, there could be a relationship between information gain in specific amino acid sites and solvent accessibility. We found a connection between sites with high information gain and solvent accessibility within several major subgenera of Coronaviridae. Such a connection could be used to study other lesser-known families of viruses, which is desirable because information gain is much easier to compute when the number of sequences is large, as we show. Finally, we produced a visualization of the sequences within each major subgenus and discussed several regions of interest, as well as focused on some pairs of Coronaviridae hosts of interest.

Query Join Order Optimization Method Based on Dynamic Double Deep Q-Network

A join order directly affects database query performance and computational overhead. Deep reinforcement learning (DRL) can explore efficient query plans while not exhausting the search space. However, the deep Q network (DQN) suffers from the overestimation of action values in query optimization, which can lead to limited query performance. In addition, ε-greedy exploration is not efficient enough and does not enable deep exploration. Accordingly, in this paper, we propose a dynamic double DQN (DDQN) order selection method(DDOS) for join order optimization. First, the method models the join query as a Markov decision process (MDP), then solves the DRL model by integrating the network model DQN and DDQN weighting into the DRL model’s estimation error problem in query joining, and finally improves the quality of developing query plans. And actions are selected using a dynamic progressive search strategy to improve the randomness and depth of exploration and accumulate a high information gain of exploration. The performance of the proposed method is compared with those of dynamic programming, heuristic algorithms, and DRL optimization methods based on the query set Join Order Benchmark (JOB). The experimental results show that the proposed method can effectively improve the query performance with a favorable generalization ability and robustness, and outperforms other baselines in multi-join query applications.

The amalgamation of wavelet packet information gain entropy tuned source and system parameters for improved speech emotion recognition

This paper proposes a three-stage feature selection algorithm by exploring the wavelet packet (WP) decomposition, statistical, and Information Gain (IG) feature ranking algorithm based on high IG entropy for the automatic classification of speech emotions (SE). Effective frame-level vocal tract systems and the excitation source parameters are initially extracted from a few significant WP sub-bands containing emotionally relevant information based on Eigenvalue decomposition. Further, Several intelligent amalgamations of the derived optimal feature vectors are formed for improved performance in a low-dimensional feature space. The fundamental argument is that in case the identification errors of any system subjected to individual feature streams transpire at separate points, there exists a probability that the inclusion of complementary information can nullify a few of these inaccuracies by increasing the available information. The models of the Cost-Sensitive-Decision Tree (CS-DT), Support Vector Machine (SVM), and Decision Tree (DT) have been validated and tested with the proposed setup for their efficacy. Results indicate the superiority of the proposed algorithms compared to other published articles cited in the literature with the CS-DT outperforming others. The proposed low-dimensional amalgamation vectors have witnessed more than 20% improvement in recognition performance with greater speed of response, savings in cost, and lower F- rank hence is a significant achievement in this direction.The link to compute the Hurst parameter for the feature amalgamation is available at (https://www.mathworks.com/matlabcentral/fileexchange/9842-hurst-exponent).

Possibilities of Real Time Monitoring of Micropollutants in Wastewater Using Laser-Induced Raman & Fluorescence Spectroscopy (LIRFS) and Artificial Intelligence (AI).

The entire water cycle is contaminated with largely undetected micropollutants, thus jeopardizing wastewater treatment. Currently, monitoring methods that are used by wastewater treatment plants (WWTP) are not able to detect these micropollutants, causing negative effects on aquatic ecosystems and human health. In our case study, we took collective samples around different treatment stages (aeration tank, membrane bioreactor, ozonation) of a WWTP and analyzed them via Deep-UV laser-induced Raman and fluorescence spectroscopy (LIRFS) in combination with a CNN-based AI support. This process allowed us to perform the spectra recognition of selected micropollutants and thus analyze their reliability. The results indicated that the combination of sensitive fluorescence measurements with very specific Raman measurements, supplemented with an artificial intelligence, lead to a high information gain for utilizing it as a monitoring purpose. Laser-induced Raman spectroscopy reaches detections limits of alert pharmaceuticals (carbamazepine, naproxen, tryptophan) in the range of a few µg/L; naproxen is detectable down to 1 × 10−4 mg/g. Furthermore, the monitoring of nitrate after biological treatment using Raman measurements and AI support showed a reliable assignment rate of over 95%. Applying the fluorescence technique seems to be a promising method in observing DOC changes in wastewater, leading to a correlation coefficient of R2 = 0.74 for all samples throughout the purification processes. The results also showed the influence of different extraction points in a cleaning stage; therefore, it would not be sensible to investigate them separately. Nevertheless, the interpretation suffers when many substances interact with one another and influence their optical behavior. In conclusion, the results that are presented in our paper elucidate the use of LIRFS in combination with AI support for online monitoring.

Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks

Internet of Things (IoT) devices work mainly in wireless mediums; requiring different Intrusion Detection System (IDS) kind of solutions to leverage 802.11 header information for intrusion detection. Wireless-specific traffic features with high information gain are primarily found in data link layers rather than application layers in wired networks. This survey investigates some of the complexities and challenges in deploying wireless IDS in terms of data collection methods, IDS techniques, IDS placement strategies, and traffic data analysis techniques. This paper’s main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific intrusions. Specifically, the Knowledge Discovery in Databases (KDD) Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network (WN). The paper starts with a review of various intrusion detection techniques, data collection methods and placement methods. The main goal of this paper is to study the design challenges of deploying intrusion detection system in a wireless environment. Intrusion detection system deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural complexities. So this paper reviews the traditional wired intrusion detection deployment methods and discusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless environment. The main wireless environments to look into would be Wireless Sensor Networks (WSN), Mobile Ad Hoc Networks (MANET) and IoT as this are the future trends and a lot of attacks have been targeted into these networks. So it is very crucial to design an IDS specifically to target on the wireless networks.

IoT Wireless Intrusion Detection and Network Traffic Analysis

Enhancement in wireless networks had given users the ability to use the Internet without a physical connection to the router. Almost every Internet of Things (IoT) devices such as smartphones, drones, and cameras use wireless technology (Infrared, Bluetooth, IrDA, IEEE 802.11, etc.) to establish multiple inter-device connections simultaneously. With the flexibility of the wireless network, one can set up numerous ad-hoc networks on-demand, connecting hundreds to thousands of users, increasing productivity and profitability significantly. However, the number of network attacks in wireless networks that exploit such flexibilities in setting and tearing down networks has become very alarming. Perpetrators can launch attacks since there is no first line of defense in an ad hoc network setup besides the standard IEEE802.11 WPA2 authentication. One feasible countermeasure is to deploy intrusion detection systems at the edge of these ad hoc networks (Network-based IDS) or at the node level (Host-based IDS). The challenge here is that there is no readily available benchmark data available for IoT network traffic. Creating this benchmark data is very tedious as IoT can work on multiple platforms and networks, and crafting and labelling such dataset is very labor-intensive. This research aims to study the characteristics of existing datasets available such as KDD-Cup and NSL-KDD, and their suitability for wireless IDS implementation. We hypothesize that network features are parametrically different depending on the types of network and assigning weight dynamically to these features can potentially improve the subsequent threat classifications. This paper analyses packet and flow features for the data packet captured on a wireless network rather than a wired network. Combining domain heuristcs and early classification results, the paper had identified 19 header fields exclusive to wireless network that contain high information gain to be used as ML features in Wireless IDS.

Appeal to the head and heart: The persuasive effects of medical crowdfunding charitable appeals on willingness to donate

Medical crowdfunding helps low-income patients raise money for medical treatment and has grown tremendously in recent years. The most appropriate messaging strategy for writing charitable appeals to attract donations remains unclear. This study fills this gap by drawing on Aristotle's three modes of persuasion to explore factors affecting willingness to donate to medical crowdfunding projects from three aspects: logos, pathos, and ethos. This study adopted a multi-method approach by conducting two laboratory experiments (N = 125 and N = 123) and a field study (N = 1645). Analysis of variance (ANOVA) in Study 1 showed that high information quality (F = 9.774, p = 0.002) and gain frame (F = 8.620, p = 0.004) have positive effects on the trustworthiness of the project initiator (ethos), which in turn promoting potential donors’ willingness to donate (β = 0.339, p = 0.001). Study 2 confirmed the findings about information quality in Study 1, and further show that there was no significant difference between gain-first and gain-last frame on trustworthiness and willingness to donate (p > 0.05). Then, information quality is further detailed into three sub-dimensions in Study 3: text length, number of images, and number of health-related words. The results of ordinary least squares (OLS) regression with robust standard error indicate that the text length (β = 0.350, p < 0.001) and number of images (β = 0.048, p < 0.001) positively influence donation behavior, but the opposite conclusion yields health-related words (β = -0.027, p < 0.01). This study provides theoretical insights into the role of medical crowdfunding charitable appeals by verifying the persuasion effects of rational, emotional, and credibility appeals. This study also contributes to persuasion theory by highlighting the role of emotional appeals and identifying the mediating impact of credibility appeals in the context of medical crowdfunding. This study also has important practical implications by guiding funders to write persuasive charity appeals that will attract the attention of potential donors.

Informed Sampling Exploration Path Planner for 3D Reconstruction of Large Scenes

As vision-based navigation of small aircraft has been demonstrated to reach relative maturity, research into effective path-planning algorithms to complete the loop of autonomous navigation has been booming. Although the literature has seen some impressive works in this area, efficient path-planning that can be used in tasks such as inspection and coverage is still an open problem. In this spirit, this letter presents an online path-planning algorithm for fast exploration and 3D reconstruction of a previously unknown area of interest. Micro Aerial Vehicles (MAVs) are an ideal candidate for this task due to their maneuverability, but their limited computational power and endurance require efficient planning strategies. Popular sampling-based methods randomly sample the MAV's configuration space and evaluate viewpoints according to their expected information gain. Most often, however, valuable resources are spent on information gain calculations of unpromising viewpoints. This letter proposes a novel informed sampling approach that leverages surface frontiers to sample viewpoints only where high information gain is expected, leading to faster exploration. We study the impact of informed sampling in a wide range of photo-realistic scenes, and we show that our approach outperforms state-of-the-art exploration path planners in terms of both speed and reconstruction quality.

Information gain modulates brain activity evoked by reading

The human brain processes language to optimise efficient communication. Studies have shown extensive evidence that the brain’s response to language is affected both by lower-level features, such as word-length and frequency, and syntactic and semantic violations within sentences. However, our understanding on cognitive processes at discourse level remains limited: How does the relationship between words and the wider topic one is reading about affect language processing? We propose an information theoretic model to explain cognitive resourcing. In a study in which participants read sentences from Wikipedia entries, we show information gain, an information theoretic measure that quantifies the specificity of a word given its topic context, modulates word-synchronised brain activity in the EEG. Words with high information gain amplified a slow positive shift in the event related potential. To show that the effect persists for individual and unseen brain responses, we furthermore show that a classifier trained on EEG data can successfully predict information gain from previously unseen EEG. The findings suggest that biological information processing seeks to maximise performance subject to constraints on information capacity.

Unveiling the Merger Dynamics of the Most Massive MaDCoWS Cluster at z = 1.2 from a Multiwavelength Mapping of Its Intracluster Medium Properties

Abstract The characterization of the Intracluster Medium (ICM) properties of high-redshift galaxy clusters is fundamental to our understanding of large-scale structure formation processes. We present the results of a multiwavelength analysis of the very massive cluster MOO J1142+1527 at a redshift z = 1.2 discovered as part of the Massive and Distant Clusters of WISE Survey. This analysis is based on high angular resolution Chandra X-ray and NIKA2 Sunyaev–Zel’dovich (SZ) data. The cluster thermodynamic radial profiles have been obtained with unprecedented precision at this redshift and up to 0.7R 500, thanks to the combination of high-resolution X-ray and SZ data. The comparison between the galaxy distribution mapped in infrared by Spitzer and the morphological properties of the ICM derived from the combined analysis of the Chandra and NIKA2 data leads us to the conclusion that the cluster is an ongoing merger. We have estimated a systematic uncertainty on the cluster total mass that characterizes both the impact of the observed deviations from spherical symmetry and of the core dynamics on the mass profile. We further combine the X-ray and SZ data at the pixel level to obtain maps of the temperature and entropy distributions. We find a relatively low-entropy core at the position of the X-ray peak and high-temperature regions located on its south and west sides. This work demonstrates that the addition of spatially resolved SZ observations to low signal-to-noise X-ray data brings a high information gain on the characterization of the evolution of ICM thermodynamic properties at z &gt; 1.

Automatic Detection of Sleep Stages based on Accelerometer Signals from a Wristband

In this paper, we suggest an automated sleep scoring method using machine learning algorithms on accelerometer data from a wristband device. For an experiment, 36 subjects slept for about eight hours while polysomnography (PSG) data and accelerometer data were simultaneously recorded. After the experiments, the recorded signals from the subjects were preprocessed, and significant features for sleep stages were extracted. The extracted features were classified into each sleep stage using five machine learning algorithms. For validation of our approach, the obtained results were compared with PSG scoring results evaluated by sleep clinicians. Both accuracy and specificity yielded over 90 percent, and sensitivity was between 50 and 80 percent. In order to investigate the relevance between features and PSG scoring results, information gains were calculated. As a result, the features that had the lowest and highest information gain were skewness and band energy, respectively. In conclusion, the sleep stages were classified using the top 10 significant features with high information gain.

Fast Bayesian hyperparameter optimization on large datasets

Bayesian optimization has become a successful tool for optimizing the hyperparameters of machine learning algorithms, such as support vector machines or deep neural networks. Despite its success, for large datasets, training and validating a single configuration often takes hours, days, or even weeks, which limits the achievable performance. To accelerate hyperparameter optimization, we propose a generative model for the validation error as a function of training set size, which is learned during the optimization process and allows exploration of preliminary configurations on small subsets, by extrapolating to the full dataset. We construct a Bayesian optimization procedure, dubbed FABOLAS, which models loss and training time as a function of dataset size and automatically trades off high information gain about the global optimum against computational cost. Experiments optimizing support vector machines and deep neural networks show that FABOLAS often finds high-quality solutions 10 to 100 times faster than other state-of-the-art Bayesian optimization methods or the recently proposed bandit strategy Hyperband.

Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation.

Grouping and clustering alerts for intrusion detection based on the similarity of features is referred to as structurally base alert correlation and can discover a list of attack steps. Previous researchers selected different features and data sources manually based on their knowledge and experience, which lead to the less accurate identification of attack steps and inconsistent performance of clustering accuracy. Furthermore, the existing alert correlation systems deal with a huge amount of data that contains null values, incomplete information, and irrelevant features causing the analysis of the alerts to be tedious, time-consuming and error-prone. Therefore, this paper focuses on selecting accurate and significant features of alerts that are appropriate to represent the attack steps, thus, enhancing the structural-based alert correlation model. A two-tier feature selection method is proposed to obtain the significant features. The first tier aims at ranking the subset of features based on high information gain entropy in decreasing order. The‏ second tier extends additional features with a better discriminative ability than the initially ranked features. Performance analysis results show the significance of the selected features in terms of the clustering accuracy using 2000 DARPA intrusion detection scenario-specific dataset.

The electron localization as the information content of the conditional pair density.

In the present work, the information gained by an electron for "knowing" about the position of another electron with the same spin is calculated using the Kullback-Leibler divergence (DKL) between the same-spin conditional pair probability density and the marginal probability. DKL is proposed as an electron localization measurement, based on the observation that regions of the space with high information gain can be associated with strong correlated localized electrons. Taking into consideration the scaling of DKL with the number of σ-spin electrons of a system (N(σ)), the quantity χ = (N(σ) - 1) DKLfcut is introduced as a general descriptor that allows the quantification of the electron localization in the space. fcut is defined such that it goes smoothly to zero for negligible densities. χ is computed for a selection of atomic and molecular systems in order to test its capability to determine the region in space where electrons are localized. As a general conclusion, χ is able to explain the electron structure of molecules on the basis of chemical grounds with a high degree of success and to produce a clear differentiation of the localization of electrons that can be traced to the fluctuation in the average number of electrons in these regions.

To be precise, the details don’t matter: On predictive processing, precision, and level of detail of predictions

Many theoretical and empirical contributions to the Predictive Processing account emphasize the important role of precision modulation of prediction errors. Recently it has been proposed that the causal models used in human predictive processing are best formally modeled by categorical probability distributions. Crucially, such distributions assume a well-defined, discrete state space. In this paper we explore the consequences of this formalization. In particular we argue that the level of detail of generative models and predictions modulates prediction error. We show that both increasing the level of detail of the generative models and decreasing the level of detail of the predictions can be suitable mechanisms for lowering prediction errors. Both increase precision, yet come at the price of lowering the amount of information that can be gained by correct predictions. Our theoretical result establishes a key open empirical question to address: How does the brain optimize the trade-off between high precision and information gain when making its predictions?

Incorporating known malware signatures to classify new malware variants in network traffic

SummaryContent‐based malware classification technique using n‐gram features required high computational overhead because of the size of feature space. This paper proposes the augmentation of domain knowledge in the form of known Snort malware signatures to machine learning techniques to reduce resources (in terms of the time to generate machine learning model and the memory usage to store generative model). Although current malware can be encrypted or mutated, these malware still exhibit prevalent contents or payloads as their predecessors. Using a dataset of traffic captured from a campus network, our approach is able to reduce initial generated million n‐gram features to only around 90000 features, which significantly reduces processing time to generate naive Bayes model by 95%. The generated model that has been trained by the most descriptive features (4‐gram Snort signatures with high information gain) produces lower false negative, about 2% compared with other models. Moreover, the proposed method is capable of detecting 10 new malware variants with 0% false negative. The findings from this paper can be the basis for improving malware classification based on content classification to detect known and new malware. Copyright © 2015 John Wiley &amp; Sons, Ltd.

속성 변동 최소화에 의한 러프집합 누락 패턴 부합

러프집합에서 누락된 속성 값들은 Reduct와 Core 계산, 더 나아가서 결정 트리 구축에 있어서 식별 불능의 패턴 부합 문제를 가진다. 현재 누락된 속성 값들의 추정과 관련하여 보편적인 속성 값으로의 대체, 속성들의 모든 가능한 값 할당, 이벤트 포장 방법, C4.5, 특수한 LEM2 알고리즘과 같은 접근방식들이 적용되고 있다. 그렇지만, 이들 접근방식은 결국 전형적으로 자주 등장하는 속성 값 혹은 가장 보편적인 속성 값으로의 단순 대체를 나타내기 때문에, 주요 속성 값들이 누락된 경우에 정보 손실이 큰 의사 결정 규칙들이 유도되기 때문에 의사결정 규칙들의 교차 검증에서 문제가 된다. 본 연구에서는 이러한 문제점을 개선시키기 위해 속성들간에 엔트로피 변동을 활용하여 정보 이득이 높은 방향으로 누락된 속성 값들을 대체하는 방식을 제안한다. 제안된 접근방식에 관한 타당성 검토는 비교적 가까운 유사 관계에 의해 누락 값 대체 방식을 적용하는 ROSE 프로그램과의 비교를 나타낸다. In Rough set, attribute missing values have several problems such as reduct and core estimation. Further, they do not give some discernable pattern for decision tree construction. Now, there are several methods such as substitutions of typical attribute values, assignment of every possible value, event covering, C4.5 and special LEMS algorithm. However, they are mainly substitutions into frequently appearing values or common attribute ones. Thus, decision rules with high information loss are derived in case that important attribute values are missing in pattern matching. In particular, there is difficult to implement cross validation of the decision rules. In this paper we suggest new method for substituting the missing attribute values into high information gain by using entropy variation among given attributes, and thereby completing the information table. The suggested method is validated by conducting the same rough set analysis on the incomplete information system using the software ROSE.