Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic Literature Review on IoT-Based Botnet Attack

The adoption of the Internet of Things (IoT) technology is expanding exponentially because of its capability to provide a better service. This technology has been successfully implemented on various devices. The growth of IoT devices is massive at present. However, security is becoming a major challenge with this growth. Attacks, such as IoT-based botnet attacks, are becoming frequent and have become popular amongst attackers.IoT has a resource constraint and heterogeneous environments, such as low computational power and memory. Hence, these constraints create problems in implementing a security solution in IoT devices. Therefore, various kind of attacks are possible due to this vulnerability, with IoT-based botnet attack being one of the most popular.In this study, we conducted a comprehensive systematic literature review on IoT-based botnet attacks. Existing state of the art in the area of study was presented and discussed in detail. A systematic methodology was adopted to ensure the coverage of all important studies. This methodology was detailed and repeatable. The review outlined the existing proposed contributions, datasets utilised, network forensic methods utilised and research focus of the primary selected studies. The demographic characteristics of primary studies were also outlined.The result of this review revealed that research in this domain is gaining momentum, particularly in the last 3 years (2018-2020). Nine key contributions were also identified, with Evaluation, System, and Model being the most conducted.
Localization and Mapping for Robots in Agriculture and Forestry: A Survey

Research and development of autonomous mobile robotic solutions that can perform several active agricultural tasks (pruning, harvesting, mowing) have been growing. Robots are now used for a variety of tasks such as planting, harvesting, environmental monitoring, supply of water and nutrients, and others. To do so, robots need to be able to perform online localization and, if desired, mapping. The most used approach for localization in agricultural applications is based in standalone Global Navigation Satellite System-based systems. However, in many agricultural and forest environments, satellite signals are unavailable or inaccurate, which leads to the need of advanced solutions independent from these signals. Approaches like simultaneous localization and mapping and visual odometry are the most promising solutions to increase localization reliability and availability. This work leads to the main conclusion that, few methods can achieve simultaneously the desired goals of scalability, availability, and accuracy, due to the challenges imposed by these harsh environments. In the near future, novel contributions to this field are expected that will help one to achieve the desired goals, with the development of more advanced techniques, based on 3D localization, and semantic and topological mapping. In this context, this work proposes an analysis of the current state-of-the-art of localization and mapping approaches in agriculture and forest environments. Additionally, an overview about the available datasets to develop and test these approaches is performed. Finally, a critical analysis of this research field is done, with the characterization of the literature using a variety of metrics.
Towards Annotation-free Instance Segmentation and Tracking with Adversarial Simulations

—The quantitative analysis of microscope videos often requires instance segmentation and tracking of cellular and subcellular objects. The traditional method is composed of two stages: (1) performing instance object segmentation of each frame, and (2) associating objects frame-by-frame. Recently, pixel-embedding-based deep learning approaches provide single stage holistic solutions to tackle instance segmentation and tracking simultaneously. However, such deep learning methods require consistent annotations not only spatially (for segmentation), but also temporally (for tracking). In computer vision, annotated training data with consistent segmentation and tracking is resource intensive, the severity of which can be multiplied in microscopy imaging due to (1) dense objects (e.g., overlapping or touching), and (2) high dynamics (e.g., irregular motion and mitosis). To alleviate the lack of such annotations in dynamics scenes, adversarial simulations have provided successful solutions in computer vision, such as using simulated environments (e.g., computer games) to train real-world self-driving systems. In this paper, we propose an annotation-free synthetic instance segmentation and tracking (ASIST) method with adversarial simulation and single-stage pixel-embedding based learning. The contribution of this paper is three-fold: (1) the proposed method aggregates adversarial simulations and single-stage pixel-embedding based deep learning; (2) the method is assessed with both the cellular (i.e., HeLa cells) and subcellular (i.e., microvilli) objects; and (3) to the best of our knowledge, this is the ﬁrst study to explore annotation-free instance segmentation and tracking study for microscope videos. This ASIST method achieved an important step forward, when compared with fully supervised approaches.
Thermal ablation in non-small cell lung cancer: a review of treatment modalities and the evidence for combination with immune checkpoint inhibitors

Lung cancer is the leading cause of cancer death worldwide, with approximately 1.6 million cancer related deaths each year. Prognosis is best in patients with early stage disease, though even then five-year survival is only 55% in some groups. Median survival for advanced non-small cell lung cancer (NSCLC) is 8–12 months with conventional treatment. Immune checkpoint inhibitor (ICI) therapy has revolutionised the treatment of NSCLC with significant long-term improvements in survival demonstrated in some patients with advanced NSCLC. However, only a small proportion of patients respond to ICI, suggesting the need for further techniques to harness the potential of ICI therapy. Thermal ablation utilizes the extremes of temperature to cause tumour destruction. Commonly used modalities are radiofrequency ablation (RFA), cryoablation and microwave ablation (MWA). At present thermal ablation is reserved for curative-intent therapy in patients with localized NSCLC who are unable to undergo surgical resection or stereotactic ablative body radiotherapy (SABR). Limited evidence suggests that thermal ablative modalities can upregulate an anticancer immune response in NSCLC. It is postulated that thermal ablation can increase tumour antigen release, which would initiate and upregulated steps in the cancer immunity cycle required to elicit an anticancer immune response. This article will review the current thermal ablative techniques and their ability to modulate an anti-cancer immune response with a view of using thermal ablation in conjunction with ICI therapy.
The Optimal Capital Structure under Risks of Illiquidity and Over-indebtedness in a Double Barrier Option Framework

Existing dynamic capital structure models are based on single triggers determining bankruptcy, mainly over-indebtedness or illiquidity. The latter one tends to underestimate optimal capital structures by ignoring capital providers' flexibility to inject fresh money. The former one leans towards overestimation as it neglects agency conflicts between equity investors and debt holders while implying infinitely "deep pockets" of equity investors. This article incorporates both constraints, over-indebtedness and illiquidity, examining corporate debt value and optimal capital structure in a double barrier world with knock-in and knock-out options. We are first to derive closed-form solutions for all value components of a levered firm and for the optimal capital structure in such a setting. By testing our model for firms publicly listed in the US, we gain evidence that incorporating both triggers allows for capital structure estimations that are in accordance with empirical findings.
Learning-Based Stock Market Trending Analysis by Incorporating Social Media Sentiment Analysis

Stock market trending analysis is one of the key research topics in financial analysis. Various theories once highlighted the non-viability of stock market prediction. With the advent of machine learning and Artificial Intelligence (AI), more and more efforts have been devoted to this research area, and predicting the stock market has been demonstrated to be possible. Learning-based methods have been popularly studied for stock price prediction. However, due to the dynamic nature of the stock market and its non-linearity, stock market prediction is still one of the most dificult tasks. With the rise of social networks, huge amount of data is being generated every day and there is a gaining in popularity of incorporating these data into prediction model in the effort to enhance the prediction performance. Therefore, this paper explores the possibilities of the viability of learning-based stock market trending prediction by incorporating social media sentiment analysis. Six machine learning methods including Multi-Layer Perception, Support Vector Machine, Naïve Bayes, Random Forest, Logistic Regression and Extreme Gradient Boosting are selected as the baseline model. The result indicates the possibilities of successful stock market trending prediction and the performance of different learning-based methods is discussed. It is discovered that the distribution of the value of stocks may affect the prediction performance of the methods involved. This research not only demonstrates the merits and weaknesses of different learning-based methods, but also points out that incorporating social opinion is a right direction for improving the performance of stock market trending prediction.
Experimental Study on Partial Replacement of Msand with Ground Granulated Blast Slag

Solid waste management is the major threat in these days. There are various sources in which the wastes are generated, among those the major role is played by the industries. The industries manufacture a product by giving so many by-products. These by-products are simply dumped in the land which causes pollution. So, it should be treated as well as reused in one form or another. One such by-product is the Ground granulated blast slag which is obtained from the blast furnaces used to make iron. The demand for the construction materials leads to the replacement of new materials in the concrete. Proposed idea is to use the ground granulated furnace slag in the concrete. An experimental investigation is conducted to study the properties of concrete containing the ground granulated blast slag as the replacement of the fine aggregate. The fine aggregate is the manufactured sand. The manufactured sand is widely used nowadays due to the demand and the cost of the river sand. The GGBS is replaced for the manufactured sand by 30%,35%,40% and 45%. Then the strength is obtained by carrying out compression, split tensile and the flexural tests. Then the optimization value of the GGBS is found as 30% and the effect of size of coarse aggregate is found out with the optimized value of GGBS. Then the durability tests are carried out for the acid action due to sulphate, chloride, etc., Finally, the results are discussed and the conclusions are obtained.
DYNAMIC DENSITY: AN AIR TRAFFIC MANAGEMENT METRIC

Summary The definition of a metric of air traffic controller workloadbased on air traffic characteristics is essentiai to the devel-opment of both air traffic management automation and airtraffic procedures. Dynamic density is a proposed conceptfor a metric that includes both traffic density (a count ofaircraft in a volume of airspace) and traffic complexity (ameasure of the complexity of the air traffic in a volume ofairspace). It was hypothesized that a metric that includesterms that capture air traffic complexity will be a bettermeasure of air traffic controller workload than currentmeasures based only on traffic density.A weighted linear dyn_wnic density function was developedand validated operationally. The proposed dynamic densityfunction includes a traffic density term and eight trafficcomplexity terms. A unit-weighted dynamic density func-tion was able to account for an average of 22% of thevariance in observed controller activity not accounted forby traffic density atone.A comparative analysis of unit weights, subjectiveweights, and regression weights for the terms in thedynamic density equation was conducted. The bestpredictor of controller activity was the dynamic densityequation with regression-weighted complexity terms.IntroductionProviding system flexibility to the user is an importantgoal in advanced air traffic operations (RTCA, 1995). It isexpected that automation to support air traffic manage-ment, accompanied by changes in air traffic procedures,will result in increased system flexibility. The develop-ment of a metric that predicts controller workload as afunction of air tr_fic characteristics in a volume ofairspace is essential to the development of both air trafficmanagement automation and air traffic procedures.The dynamic density metric proposed here includes bothtraffic density (a count of aircraft in a volume of airspace)and traffic complexity (a quantitative description of the air
Development of Interactive Multimedia-Based Science Teaching Materials on Vibration and Wave Materials

This study aims to describe the validity, practicality and effectiveness of interactive multimedia-based science teaching materials. This study can be classified in the type of research and development using the APPED development model with the following stages: analysis and initial research, design, production, evaluation and dissemination. The subjects in this study are the VIII grade students of SMP Negeri 4 Mandrehe. There are three instruments used to collect data, they are: questionnaires used to validate teaching materials by material and media experts team, science teacher assessments and student responses to interactive multimedia-based teaching materials, and student activity observation sheets used in each learning process. The results showed that interactive multimediabased science teaching materials have met the valid criteria with the achievement of 86.5% in the "very valid" category. The teaching materials are practical with the achievement of 83.8% in “good” category and have been effective with activities development has reached 85.51% in the "very active" category and the student response to the teaching materials has reached 87.17% in the "very high" category”.
Integration of Waste to Bioenergy Conversion Systems: A Critical Review

Sustainable biofuel production is the most effective way to mitigate greenhouse gas emissions associated with fossil fuels while preserving food security and land use. In addition to producing bioenergy, waste biorefineries can be incorporated into the waste management system to solve the future challenges of waste disposal. Biomass waste, on the other hand, is regarded as a low-quality biorefinery feedstock with a wide range of compositions and seasonal variability. In light of these factors, biomass waste presents limitations on the conversion technologies available for value addition, and therefore more research is needed to enhance the profitability of waste biorefineries. Perhaps, to keep waste biorefineries economically and environmentally sustainable, bioprocesses need to be integrated to process a wide range of biomass resources and yield a diverse range of bioenergy products. To achieve optimal integration, the classification of biomass wastes to match the available bioprocesses is vital, as it minimizes unnecessary processes that may increase the production costs of the biorefinery. Based on biomass classification, this study discusses the suitability of the commonly used waste-to-energy conversion methods and the creation of integrated biorefineries. In this study, the integration of waste biorefineries is discussed through the integration of feedstocks, processes, platforms, and the symbiosis of wastes and byproducts. This review seeks to conceptualize a framework for identifying and integrating waste-to-energy technologies for the varioussets of biomass wastes.
Jena microbial soil organic carbon model integrated with nitrogen and phosphorus processes

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‘Transposition’ of EU regulations: the politics of supplementing EU regulations with national rules

ABSTRACT This article argues for the inclusion of regulations in studies of EU policy implementation and compliance. Although both EU directives and EU regulations are to be implemented by the member states, the literature has devoted almost exclusive attention to directives. This is because, first, directives are considered more important than regulations. Second, the notification of the transposition of directives provides a convenient way to compare implementation records. Third, the transposition of directives allegedly introduces an extra implementation step. However, upon closer inspection all three reasons are flawed. Regulations – like directives – often require national rules to enter into effect, a fact long accepted by the European Court of Justice. This creates a situation remarkably similar to the transposition of directives. By analysing two case studies, we contribute by detailing this politico-legal situation, demonstrating its empirical relevance and sketching a new research agenda on the supplementation of EU regulations with national rules.
Review of energy performance gap and solutions in residential buildings

The energy consumption in the world due to economic development, rising population, and technological developments is growing, which has led to an increase in global warming. Therefore, there is a strong need to develop new strategies to reduce energy consumption. Buildings account for about 40% of global energy consumption. Since occupancy time in residential buildings is longer than in commercial and office buildings, residential buildings have a more significant impact on energy consumption. Due to this issue, efforts are being made to optimize energy consumption in residential buildings. Evaluating the performance of a building through building certificates that include as-designed and in-operation is essential for improving energy efficiency. Building energy simulations must be performed before construction to ensure that energy consumption in buildings is acceptable. However, it is observed that the energy consumption of the building after construction is higher than what was designed. This difference is defined as the energy performance gap, which indicates extra energy consumption or failure to comply with energy standards in the operation of the building. There are several reasons for the increased energy consumption of an operating building compared to the designed model. So far, studies have been conducted to determine the factors affecting the energy performance gap, but it is necessary to analyze these studies comprehensively. This article investigates the causes of energy performance gaps and ways to reduce this difference through a comprehensive literature review study. In this way, it is possible to achieve solutions in the building certification criteria that minimize the difference in energy consumption between the as-designed model and the in-operation mode of the building. This article can help decision-makers select the certificate that best fits their purposes.
Introduction to the special section on the interaction between formal and computational linguistics

While computational linguistics is historically rooted in formal linguistics, it might seem that the distance between these two fields has only grown larger as each field evolved. Still, whether this impression is correct or not, not all links have been cut, and new ones have appeared. Indeed, while we are currently witnessing a growing interest within formal linguistics in both explaining the remarkable successes of neural-based language models and uncovering their limitations, one should not forget the contribution to theoretical linguistics provided, for example, by the computational implementation of grammatical formalisms. And while neural-based methods have recently received the lion’s share of the public attention, interpretable models based on symbolic methods are still relevant and widely used in the natural language processing industry. The links that exist between formal and computational linguistics have been the subject of discussion for a long time. At the 2009 European Meeting of the Association for Computational Linguistics, a workshop entitled “Interaction between Linguistics and Computational Linguistics: Virtuous, Vicious or Vacuous? ” was organised. This workshop led to the publication a couple of years later of the sixth volume of Linguistic Issues in Language Technology (Baldwin and Kordoni 2011). At the centre of this publication were discussions about