Attack Phase Research Articles

Analysis of student’s mathematical thinking in solving HOTs problems viewed from mathematics anxiety

This study aims to describe students' mathematical thinking processes in terms of math anxiety. The method used in this research is descriptive qualitative. Data collection techniques were carried out through tests, documentation and interviews. The determination of the subject used a purposive sampling technique by taking 5 student level subjects who had very high, high, medium, low, and very low levels of math anxiety. The results showed that in the process of working on the test instrument in the form of HOTS questions, subjects with very low levels of anxiety were able to solve the first and second problems correctly. In the entry phase, the subject fulfills the I know, I want, and introduce rubik. And in the attack phase it fulfills the rubik's try, may be, and why. In the review phase it only fulfills the Rubik's check, and does not meet the rubik reflect and extend. This can be seen from the results of work that does not use a general formula.

Towards maximizing expected possession outcome in soccer

Soccer players need to make many decisions throughout a match in order to maximize their team’s chances of winning. Unfortunately, these decisions are challenging to measure and evaluate due to the low-scoring, complex, and highly dynamic nature of soccer. This article proposes an end-to-end deep reinforcement learning framework that receives raw tracking data for each situation in a game, and yields optimal ball destination location on the full surface of the pitch. Using the proposed approach, soccer players and coaches are able to analyze the actual behavior in their historical games, obtain the optimal behavior and plan for future games, and evaluate the outcome of the optimal decisions prior to deployment in a match. Concisely, the results of our optimization model propose more short passes (Tiki-Taka playing style) in all phases of a ball possession, and higher propensity of low distance shots (i.e. shots in attack phase). Such a modification will let the typical teams to increase their likelihood of possession ending in a goal by 0.025.

A Guessing Entropy-Based Framework for Deep Learning-Assisted Side-Channel Analysis

Recently deep-learning (DL) techniques have been widely adopted in side-channel power analysis. A DL-assisted SCA generally consists of two phases: a deep neural network (DNN) training phase and a follow-on attack phase using the trained DNN. However, currently the two phases are not well aligned, as there is no conclusion on what metric used in the training can result in the most effective attack in the second phase. When traditional loss functions such as negative log-likelihood (NLL) are used in training a DNN, the trained model does not yield optimal follow-on attack. Recently some information theoretical SCA leakage metrics are proposed, either as the validation metric to stop the DNN training with traditional loss functions, or as both the validation metric and the training loss function. None of those proposed metrics, however, directly measures the SCA effectiveness. We propose to conduct DNN training directly with a common SCA effectiveness metric, Guessing Entropy (GE). We overcome the prior practical difficulty of using GE in DNN training by utilizing the GEEA estimation algorithm introduced in CHES 2020. We show that using GEEA as either the validation metric or the loss function produces DNN models that lead to much more effective follow-on attacks. Our work consolidates the DL-assisted SCA framework with a consistent metric, which shows great potential to be adopted as the universal SCA-oriented DNN training framework.

Toward Effective Evaluation of Cyber Defense: Threat Based Adversary Emulation Approach

Attackers compromise organizations with increasingly sophisticated ways, such as Advanced Persistent Threat (APT) attackers. Usually, such attacks have the intention to exploit endpoints to gain access to critical data. For security controls and defense evaluation, organizations may employ offensive security activities. The most important one is penetration testing and red teaming, but such operations are usually resource exhaustive and extend over a longer period of time. Furthermore, traditional Venerability Assessment and Penetration Testing (VAPT) works effectively in the mitigation of known attacks but did not prove to be effective against stealthy attacks. VAPT considers the whole offsec as an acting problem but in reality, an attacker has to deal with uncertainty while conducting real-world attacks. In this paper, we are presenting an adversary emulation approach based on MITRE ATT&CK adversary emulation plan with consideration of planning as a major part of each attack phase. The approach utilizes stealthy attack vectors and paths to emulate adversary for defense evaluation. For effective defense evaluation, we picked more than 40 techniques from ATT&CK, deployed their mitigation on target machines, and then launched attacks against all those techniques. We show that attack paths and payloads generated using our approach are strong enough to evade security controls at endpoints. This approach provides a special environment for cyber defenders to think like adversary, and create new attack vectors and paths to evaluate organizational security preparedness. This process constructs a special environment to expand the attack landscape view and defense evaluation with minimal resources for the organization.

225. Efficacy and safety of sulbactam-durlobactam are consistent across regions in the global ATTACK phase 3 trial in the treatment of carbapenem-resistant Acinetobacter baumannii-calcoaceticus complex (CRABC) infections

Abstract Background ATTACK is a randomized, active-controlled, noninferiority trial that evaluated the efficacy and safety of sulbactam-durlobactam (SUL-DUR) compared to colistin for CRABC infections. The primary efficacy endpoint of all-cause mortality (ACM) at day 28 and the primary safety objective of incidence of nephrotoxicity based on RIFLE criteria were met. Due to the global variation of carbapenem and multi-drug resistance, geographic subgroups were evaluated to explore consistency of efficacy and safety across regions. Methods Patients were randomized to SUL-DUR or colistin treatment for 7-14 days and all patients received imipenem/cilastatin as background therapy. The primary efficacy analysis in the CRABC population consisted of 125 patients: SUL-DUR (N=63) and colistin (N=62). The primary safety population included 177 patients who received any amount of drug. Secondary efficacy endpoints included ACM at day 14, clinical cure and microbiological favorable assessment at end-of treatment (EOT), test-of-cure (TOC) and late follow-up (LFU). Results are described geographically by the following regions: the Americas, Asia-Pacific and Europe. Results Overall, ACM at day 28 in the SUL-DUR arm and colistin arm was 12/63 (19.0%) vs. 20/62 (32.3%), respectively; ACM at day 14 overall in the SUL-DUR arm and colistin arm was 4/63 (6.3%) vs. 12/62 (19.4%), respectively (Table 1). Day 28 ACM, and secondary endpoints were similar across the geographic regions and consistent with overall findings (Table 1). Criteria for RIFLE nephrotoxicity were met more often in the colistin arm than the SUL-DUR arm in all regions (Table 1). Conclusion ACM, clinical outcomes, microbiological favorable assessment, and RIFLE-assessed nephrotoxicity were consistently favorable in patients treated with SUL-DUR versus colistin across regions. These findings suggest, that if approved, SUL-DUR could be an important global therapeutic option for Acinetobacter infections including carbapenem-resistant and multidrug-resistant strains. Disclosures Alita Miller, PhD, Entasis Therapeutics: Ownership Interest|Entasis Therapeutics: Stocks/Bonds Adam B. Shapiro, Ph.D, Entasis Therapeutics: Employee|Entasis Therapeutics: Stocks/Bonds Sarah McLeod, PhD, Entasis Therapeutics: Employee|Entasis Therapeutics: Stocks/Bonds Khurram Rana, PharmD, Entasis Therapeutics: Employee|Entasis Therapeutics: Ownership Interest|Entasis Therapeutics: Stocks/Bonds Drew Lewis, MD, MTM&H, Entasis Therapeutics: employee|Entasis Therapeutics: employee|Entasis Therapeutics: Stocks/Bonds|Entasis Therapeutics: Stocks/Bonds David Altarac, MD, MPA, Entasis Therapeutics: Ownership Interest|Entasis Therapeutics: Stocks/Bonds.

EFEKTIVITAS BIOPESTISIDA BERBAHAN AKTIF TRICHODERMA HARZIANUM PADA BERBAGAI FORMULASI DAN LAMA PENYIMPANAN DALAM MENGENDALIKAN PENYAKIT DAMPING-OFF TANAMAN KEDELAI (GLYCINE MAX (L.) MERILL)

Rhizoctonia solani is a soil-borne pathogen found in soybean plants. The attack can cause the soybean plants to collapse. The attack phase of Rhizoctonia solani occurs in the seedling phase of soybean plants. One of the control techniques for suppressing the attack of the fungus can be done with preventive measures using biopesticides. This study aims to determine spore viability, spore density, incubation period, disease incidence, disease severity, and effectiveness of control after the application of Trichoderma harzianum active ingredient biopesticide with different storage times. The research was conducted at the HPT Green House, Faculty of Agriculture, University of Jember. The experimental design used a factorial design and was repeated 3 times. The results showed that the effect of giving fish meal formulations on the germination ability of Trichoderma harzianum, incubation period, disease incidence, disease attack, and control effectiveness at 30 days of storage was able to suppress the attack of the fungus Rhizoctonia solani. The results of the analysis test were significantly different when compared to the control treatmen.

Biological control of soft rot in potato by κ-carrageenan carriers encapsulated microbial predators.

The Pectobacterium and Dickeya pectinolytic bacteria are phytopathogens responsible for several macerating diseases on a wide range of crops and ornamental plants. Recently, bacterial predators belonging to the Bdellovibrio and like organisms (BALOs) were shown to efficiently prey on these rot-causing bacteria and reduce soft rot-induced potato slice maceration. In the current research, our novel approach aimed at developing and studying a κ-carrageenan-based encapsulation system for fast-release of entrapped B. bacteriovorus HD100 in high numbers to prevent bacterial soft-rot infections. κ-carrageenan-dried carriers swelled and dissolved upon immersion in water due to a loss of potassium ions which are the main cross-linking agents. Survival rates of the predators after drying were higher for immobilized bdelloplasts (e.g., predator inside the host) compared to attack phase (host-searching, AP) cells, and with the addition of the osmoprotectant trehalose to the carriers. Released encapsulated predators preyed efficiently on soft rot bacteria, with bdelloplasts performing better as compared to AP cells. However, predation dynamics were influenced by the type of added osmoprotectant. Carrageenan-trehalose carriers encapsulating predators were able to reduce soft-rot disease in situ using a potato slice assay. To our knowledge, this research is the first to explore the potential of encapsulated BALOs against phytopathogens. KEY POINTS: • Dissolution of the carriers was affected by potassium concentration in the system. • Encapsulation of bdelloplasts with trehalose best maintained the predator viability. • The encapsulated predators efficiently controlled soft rot in vitro and in situ.

Genetic variants IL1B +3954C>T and -511C>T associated with incapacity and allodynia in migraine

IntroductionThe main mechanisms in the physiopathology of migraine are cortical spreading depression and trigeminal activation with the release of CGRP. Neurogenic inflammation and neuroinflammation can exert an influence over both these mechanisms, but there are still a number of gaps in our understanding. Interleukin (IL)- 1β is a pro-inflammatory cytokine whose levels of plasma increase during the attack phase of migraine. Thus far, its genetic variants have not been well studied. ObjectiveTo investigate the association between the genetic variants IL1B +3954C>T (rs1143634) and -511C>T (rs16944) and susceptibility to migraine and its clinical characteristics. Subjects and MethodsCase control study comprising 307 participants, of whom 152 had a diagnosis of migraine and 155 were healthy controls, paired by sex, age, ethnicity and BMI. The clinical and demographic data were evaluated. The patients with migraine were interviewed using a structured form containing information about the type of migraine (with or without aura, episodic or chronic), age at onset of the disease, frequency of attacks, accompanying symptoms that triggered headaches. The patients also answered validated questionnaires to evaluate incapacity (Migraine Disability Assessment - MIDAS) and impact (Headache Impact Test - HIT-6) for migraine, the presence of allodynia (ASC-12), as well as symptoms of anxiety (State Anxiety Inventory - STAI 1 and 2), depression (Beck Depression Inventory) and a hyperacusis scale.The genetic variants IL1B +3954C>T (rs1143634) and -511C>T (rs16944) were identified using polymerase chain reaction (PCR) and the fluorescence levels of PCR products were evaluated using a Step One thermocycler (Applied Biosystems). The analyses were conducted using the dominant, codominant, recessive and over- dominant genetic models. Categorical data were evaluated via the chi-squared test or Fisher’s exact test, and continuous data were evaluated using the Mann-Whitney test. (To see the complete abstract, please, check out the PDF).

Perceived Information Revisited

In this study, we present new analytical metrics for evaluating the performance of side-channel attacks (SCAs) by revisiting the perceived information (PI), which is defined using cross-entropy (CE). PI represents the amount of information utilized by a probability distribution that determines a distinguishing rule in SCA. Our analysis partially solves an important open problem in the performance evaluation of deep-learning based SCAs (DL-SCAs) that the relationship between neural network (NN) model evaluation metrics (such as accuracy, loss, and recall) and guessing entropy (GE)/success rate (SR) is unclear. We first theoretically show that the conventional CE/PI is non-calibrated and insufficient for evaluating the SCA performance, as it contains uncertainty in terms of SR. More precisely, we show that an infinite number of probability distributions with different CE/PI can achieve an identical SR. With the above analysis result, we present a modification of CE/PI, named effective CE/PI (ECE/EPI), to eliminate the above uncertainty. The ECE/EPI can be easily calculated for a given probability distribution and dataset, which would be suitable for DL-SCA. Using the ECE/EPI, we can accurately evaluate the SR hrough the validation loss in the training phase, and can measure the generalization of the NN model in terms of SR in the attack phase. We then analyze and discuss the proposed metrics regarding their relationship to SR, conditions of successful attacks for a distinguishing rule with a probability distribution, a statistic/asymptotic aspect, and the order of key ranks in SCA. Finally, we validate the proposed metrics through experimental attacks on masked AES implementations using DL-SCA.

Migraine: An Underestimated Neurological Condition Affecting Billions.

The second most prevalent cause of years of disability worldwide is migraine, a neurological condition that causes a persistent headache that is lifelong. This condition affects more than a billion people globally. Its widespread prevalence and concomitant impairment have several adverse effects. Migraines are brought on by numerous behavioural, ecological, and genetic factors. There are various types of migraines, of which migraine without aura is the most common. The objective of this article is to determine the causes of migraine headaches, review the appropriate diagnosis of migraine, and describe the migraine headache management alternatives that are available. Various treatments for migraine are available on the market. Among the many types of headaches, migraines are neurological in nature and are inherited in some people. It has four phases: prodromal phase, aura, attack phase, and postdrome phase. Stress, anxiety, changes in the female endocrine system, bright lights, loud noises, foul odour, excessive or insufficient sleep, changes in the environment or the climate, flashes of light or intense lighting, overexertion, missing meals, drinking alcohol and smoking, quitting caffeine, and taking too many migraine drugs are some of the triggering factors for migraine. Diagnosis of migraine mainly relies on a good history. Appropriate prevention can be done for specific indications. Treatment mainly revolves around medications like analgesics, triptans, ergot derivatives, and newly derived biologics. Lifestyle modifications are also essential as many daily life factors contribute to migraine. Migraine can be well-managed if sufficient attention and care are given along with proper medications and guidance. The patient should not ignore symptoms and report to the physician at the earliest so that correct management can be done.

Increasing intensity of deimatic behaviour in response to repeated simulated attacks: a case study on the mountain katydid (Acripeza reticulata)

How and when deimatic behaviours are performed can change during encounters between predators and prey. Some predators attack repeatedly, investigating and manipulating prey, and in response, an individual’s deimatic behaviour may intensify or may diminish in favour of escaping. The presence of a resource can further force a trade-off between displaying and escaping. Here, we examined the intensity of the katydid’s deimatic behaviour, a visual display, the propensity of their escape response under repeated simulated attacks, and how these responses change in the presence of foraging resources. We found that display intensity increased with repeated simulated attacks and that females displayed at a greater intensity than males. The presence of their preferred food plant had no significant effect on display intensity, but reduced escape probability in both sexes. Some katydids were predictable in their display intensity and at the population level we found that strong display intensity is moderately repeatable. Overall, our results suggest that 1) display intensity increases with repeated attacks and might indicate a cost in performing at maximum intensity upon first attack, 2) deploying a deimatic display while feeding can reduce the need to flee a rich foraging patch and 3) some individuals are consistent in their display intensities. Future experiments that aim to determine causal mechanisms such as limitations to perception of predators, sensitisation to stimuli and physiological constraints to display intensity will provide necessary insight into how deimatic displays function.Significance statementThough often regarded as success or failure, interactions between predators and prey during the attack phase of a predation event are complex, especially when predators make repeated investigative attacks in quick succession. Our study shows that in mountain katydids, intensity of deimatic behaviour increases with repeated attacks, perhaps indicating that prey sensitise or that maximal displays during initial attacks carry high costs such as conspicuousness. The intensity of the display does not change with the introduction of a valuable food resource, but the probability of fleeing decreased, suggesting that displaying may reduce the opportunity costs of leaving a patch. We also show that individuals vary in the repeatability of their display, suggesting that deimatic display may be highly adaptable, nuanced and targeted.

Response to Visceral Leishmaniasis Cases through Active Case Detection and Vector Control in Low-Endemic Hilly Districts of Nepal.

ABSTRACT.The visceral leishmaniasis (VL) elimination program in Nepal has largely completed the attack phase and is moving toward consolidation and maintenance phases. New VL foci are, however, appearing in Nepal, and therefore new innovative community-centered strategies need to be developed and tested. We conducted early case detection by an index case–based approach and assessed the feasibility, efficacy, and cost of an intervention for sandfly control through indoor residual spraying (IRS) or insecticidal wall painting (IWP) in new and low-endemic districts Palpa and Surkhet. IRS was performed in 236 households and IWP in 178 households. We screened 1,239 and 596 persons in Palpa and Surkhet, respectively, resulting in the detection of one VL case in Palpa. Both IWP and IRS were well accepted, and the percentage reductions in sandfly density after 1, 9, and 12 months of intervention were 90%, 81%, and 75%, respectively, for IWP and 81%, 59%, and 63% respectively for IRS. The cost per household protected per year was USD 10.3 for IRS and 32.8 for IWP, although over a 2-year period, IWP was more cost-effective than IRS. Active case detection combined with sandfly control through IWP or IRS can support to VL elimination in the consolidation and maintenance phase.

ICSA: Intelligent chatbot security assistant using Text-CNN and multi-phase real-time defense against SNS phishing attacks

SNS phishing attack is one of the representative social engineering attacks exploiting humans’ emotions and trust and thus usually proceeds in multi-phases such that the attacker first forms an intimate bond with victims emotionally and then forces victims to conduct serious actions according to the attacker’s malicious intents. Meanwhile, according to our extensive survey, we observed that existing works on defending against SNS phishing attacks are inefficient in that they detect the attack mostly in the final stage of the attack or after the incident is reported. This is mainly because they neglect the characteristics of SNS phishing attacks following social engineering attack cycles. By this motivation, we first formalize SNS phishing attack phases based on the existing social engineering attack cycles and propose an Intelligent Chatbot Security Assistant (ICSA) that detects the progress phase of an SNS phishing attack by using Text-CNN-based attack phase classifiers and AI Chatbot technology. ICSA provides appropriate suggestions to victims and conducts necessary actions according to pre-defined defense produces designed by security experts. In addition, we implement ICSA as Telegram Chatbot by using Google Dialogflow and AWS (Amazon Web Services) server in the Telegram messenger. To validate our idea, we conduct extensive experiments to show our Telegram Chatbot works properly in real-time according to its design purpose and also we compare two representative ML models (Text-CNN and LSTM) in terms of the training and test accuracy to show why we choose the Text-CNN model to generate attack phase classifiers.

Multi-Step Attack Detection Based on Pre-Trained Hidden Markov Models.

Currently, hidden Markov-based multi-step attack detection models are mainly trained using the unsupervised Baum–Welch algorithm. The Baum–Welch algorithm is sensitive to the initial values of model parameters. However, its training uses random or average parameter initialization methods, which frequently results in the model training into a local optimum, thus, making the model unable to fit the alert logs well and thereby reducing the detection effectiveness of the model. To solve this issue, we propose a pre-training method for multi-step attack detection models based on the high semantic similarity of alerts in the same attack phase. The method first clusters the alerts based on their semantic information and pre-classifies the attack phase to which each alert belongs. Then, the distance of the alert vector to each attack stage is converted into the probability of generating alerts in each attack stage, replacing the initial value of Baum–Welch. The effectiveness of the proposed method is evaluated using the DARPA 2000 dataset, DEFCON21 CTF dataset, and ISCXIDS 2012 dataset. The experimental results show that the hidden Markov multi-step attack detection method based on pre-training of the proposed model parameters had higher detection accuracy than the Baum–Welch-based, K-means-based, and transfer learning differential evolution-based hidden Markov multi-step attack detection methods.

The Statistical Analysis of the Game Actions of the Middle-Blocker Based on the Application of the “Data Volley” Software

The purpose of the present study is to increase the action efficiency of the center players in the Arcada Galaţi Club by using the ”Data Volley” Software, analysing the attack and defense of our own players, as well as the analysis of the opponent centre players participating in the national senior voleyball championships. As coaches of the Arcada Galaţi team it was possible for us to access the tracking statistics of 24 centre players participating in the 2018 National Senior Volleyball Championships and evaluate our own centre players in relation to the national level statistics, as well as the actions within the team. In the ample statistics for each play action, rotation, upon analysing 18 matches (stage I-III) we observed the efficiency of the serve and attack at national level as 43% and 36% respectively, as compared to 48% and 43% as it was recorded for the average centre in Arcada Galaţi.The differences of 5% for the serve and 7% for the attack over the national level point to an advantage in the attack phase. The centre play’s low blocking efficiency of 10% as compared to the 40% national average was the point of interest in the technico-tactical training for a three-month period, so that based on the distribution of the opponents’ passes/attacks an improvement of 8% was achieved in comparison to the initial analysis of the defense phase.

Cyberattack Models for Ship Equipment Based on the MITRE ATT&CK Framework.

Cybersecurity is important on ships that use information and communication technology. On such ships, the work, control, and sensor systems are connected for steering, navigation, and cargo management inside the hull, and a cyberattack can have physical consequences such as sinking and crashing. Research on ship cybersecurity is a new challenge, and related studies are lacking. Cyberattack models can provide better insight. With this study, we aim to introduce a cyberattack analysis method based on the MITRE ATT&CK framework so that a cyberattack model for ships can be established. In addition, we identify the characteristics of the attack phase by analyzing cases of hacking and vulnerability research for ship systems using tactics, techniques, and procedures, and suggest the minimum measures essential for defense. Using the ship cyberattack model, we aim to identify the characteristics of the systems used for ship navigation, communication, and control; provide an understanding of the threats and vulnerabilities; and suggest mitigation measures through the proposed model. We believe the results of this study could guide future research.

Interactive Three-Phase Structure for Table Tennis Performance Analysis: Application to Elite Men’s Singles Matches

The aim of the current study was twofold: (a) to construct an Interactive Three-Phase Structure for table tennis performance analysis and (b) to apply the proposed structure to the performance analysis of elite men’s singles matches. The current structure makes improvements over the former ones in the following aspects: more comprehensive performance variables consisting of bilateral actions covering the whole rally competing process, a better phase division method fitting to the real match situation and more focused analysis achieved by the designed critical phase. The analysis of 56 elite men’s singles matches (5507 rallies) was conducted using the proposed structure. The results demonstrated that performance variables of the rally competing process ceasing in Phase 2 (initial attack and counterattack phase) were decisive for the rally outcome of elite men’s singles matches. The proposed structure provides practitioners with a better model to enhance the effectiveness of table tennis performance analysis.

Single-Trace Attack Using One-Shot Learning With Siamese Network in Non-Profiled Setting

Recently, many studies have shown that using deep learning for side-channel attacks offers several advantages, including simplification of the attack phase and target breaking, even in protected implementations, while presenting outstanding attack performance. Power and electromagnetic analysis, which is known as the most robust attack, can be classified into profiling and non-profiling attacks. In the real world, a non-profiling attack is more ideal than a profiling attack. In particular, studies on non-profiling attacks using deep learning for asymmetric cryptosystems are rare and have shortcomings, such as a long analysis time. In this paper, we propose a novel non-profiling attack method for asymmetric cryptosystems that requires only a single trace and a reasonably short attack time to recover a full private key, overcoming the limitations of previous studies. The proposed method applies one-shot learning with a convolutional Siamese network, which is used for the first time in side-channel attacks. Thus, our proposed method can leak private keys used in a protected public-key cryptosystem with up to 100% accuracy with only one single trace in a non-profiled setting.

Mapping European Spruce Bark Beetle Infestation at Its Early Phase Using Gyrocopter-Mounted Hyperspectral Data and Field Measurements

The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany’s spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected using indices based on airborne spatial high-resolution (0.3 m) hyperspectral data and field spectrometer measurements. In particular, a new hyperspectral index based on airborne data has been defined and compared with other common indices for bark beetle detection. It shows a very high overall accuracy (OAA = 98.84%) when validated with field data. Field measurements and a long-term validation in a second study area serve the validation of the robustness and transferability of the index to other areas. In comparison with commonly used indices, the defined index has the ability to detect a larger proportion of infested spruces in the green attack phase (60% against 20% for commonly used indices). This index confirms the high potential of the red-edge domain to distinguish infested spruces at an early stage. Overall, our index has great potential for forest preservation strategies aimed at the detection of infested spruces in order to mitigate the outbreaks.

Improved Harris hawks optimization algorithm based on random unscented sigma point mutation strategy

The Harris hawks optimization algorithm (HHO) is a new swarm intelligence algorithm for simulating the capture and attack of the hawks which has good global exploration and local exploitation capabilities. In order to further improve the optimization performance of the algorithm, quasi-opposite learning and quasi-reflection learning strategies performed according to probability are involved in the attack phase to enhance the diversity of the population and accelerate the convergence rate of HHO while a logarithmic nonlinear convergence factor is designed to balance the ability of global search and local optimization of the algorithm. Furthermore, in order to avoid the algorithm falling into a local optimum, using the characteristics of the unscented transform (UT) to estimate the mean and variance of a random variable function can achieve second-order accuracy, a new strategy for generating random symmetric sigma points is designed to mutate the current best individual in the visible range, at last, an improved Harris hawk algorithm (IHHO) based on random unscented sigma point mutation is proposed. The new stochastic UT ensures the random exploitation of the algorithm and has a certain theoretical support, which overcomes the theoretical deficiency of the stochastic optimization algorithm to some extent. The numerical optimization ability of IHHO is verified by CEC2017 benchmark functions, CEC2020 benchmark function and fifteen standard test functions. Finally, the practicality and effectiveness of the IHHO algorithm are verified by three engineering constraint optimization and the discounted {0-1} knapsack problem.