Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning based identification of DDoS attacks in industrial application

Denial of Service (DoS) attacks are very common type of computer attack in the world of internet today. Automatically detecting such type of DDoS attack packets & dropping them before passing through is the best prevention method. Conventional solution only monitors and provide the feedforward solution instead of the feedback machine-based learning. A Design of Deep neural network has been suggested in this paper. In this approach, high level features are extracted for representation and inference of the dataset. Experiment has been conducted based on the ISCX dataset for year 2017, 2018 and CICDDoS2019 and program has been developed in Matlab R17b using Wireshark.
Assessing Qualification of Crypto Currency as A Financial Assets: A Case Study on Bitcoin

We assess the qualification of Crypto Currency as a new emerging financial asset class using Bitcoin as a sample study. As a financial asset class, its value should be derived from business prospects, uncertainty, and opportunity cost of money (riskless rate). We model the asset value relationship in form of an error correction model in regard of possible nonstationary data properties. We use GSCI commodity index, S&P 500 Index, Economic Policy Uncertainty Index and Yield of 5-year US Treasury Bonds as proxies of explanatory variables. Our findings show that the notion of crypto currency as a financial asset might be spurious. Common stochastic trend is the source of apparent correlation between Bitcoin and the regressors. This lack of fundamental linkage opens a way to improve cryptocurrency business model for greater global acceptance.
Alterations in the Cell Wall of Rhodococcus biphenylivorans Under Norfloxacin Stress

Many microorganisms can enter a viable but non-culturable (VBNC) state under various environmental stresses, while they can also resuscitate when the surroundings turn to suitable conditions. Cell walls play a vital role in maintaining cellular integrity and protecting cells from ambient threats. Here, we investigated the alterations in the cell wall of Rhodococcus biphenylivorans TG9 at VBNC state under norfloxacin stress and then at resuscitated state in fresh lysogeny broth medium. Electron microscopy analyses presented that TG9 in the VBNC state had a thicker and rougher cell wall than that in exponential phase or resuscitated state. Meanwhile, the results from infrared spectroscopy also showed that its VBNC state has different peptidoglycan structures in the cell wall. Moreover, in the VBNC cells the gene expressions related to cell wall synthesis and remodeling maintain a relatively high level. It indicates that the morphological variations of TG9 at the VBNC state might result from kinetic changes in the cell wall synthesis and remodeling. As a consequence, the alterations in the cell wall of VBNC TG9 may somewhat account for its tolerance mechanisms to antibiotic treatment.
Transcriptome Profiling-Based Analysis of Carbohydrate-Active Enzymes in Aspergillus terreus Involved in Plant Biomass Degradation

Given the global abundance of plant biomass residues, potential exists in biorefinery-based applications with lignocellulolytic fungi. Frequently isolated from agricultural cellulosic materials, Aspergillus terreus is a fungus efficient in secretion of commercial enzymes such as cellulases, xylanases and phytases. In the context of biomass saccharification, lignocellulolytic enzyme secretion was analyzed in a strain of A. terreus following liquid culture with sugarcane bagasse (SB) (1% w/v) and soybean hulls (SH) (1% w/v) as sole carbon source, in comparison to glucose (G) (1% w/v). Analysis of the fungal secretome revealed a maximum of 1.017 UI.mL–1 xylanases after growth in minimal medium with SB, and 1.019 UI.mL–1 after incubation with SH as carbon source. The fungal transcriptome was characterized on SB and SH, with gene expression examined in comparison to equivalent growth on G as carbon source. Over 8000 genes were identified, including numerous encoding enzymes and transcription factors involved in the degradation of the plant cell wall, with significant expression modulation according to carbon source. Eighty-nine carbohydrate-active enzyme (CAZyme)-encoding genes were identified following growth on SB, of which 77 were differentially expressed. These comprised 78% glycoside hydrolases, 8% carbohydrate esterases, 2.5% polysaccharide lyases, and 11.5% auxiliary activities. Analysis of the glycoside hydrolase family revealed significant up-regulation for genes encoding 25 different GH family proteins, with predominance for families GH3, 5, 7, 10, and 43. For SH, from a total of 91 CAZyme-encoding genes, 83 were also significantly up-regulated in comparison to G. These comprised 80% glycoside hydrolases, 7% carbohydrate esterases, 5% polysaccharide lyases, 7% auxiliary activities (AA), and 1% glycosyltransferases. Similarly, within the glycoside hydrolases, significant up-regulation was observed for genes encoding 26 different GH family proteins, with predominance again for families GH3, 5, 10, 31, and 43. A. terreus is a promising species for production of enzymes involved in the degradation of plant biomass. Given that this fungus is also able to produce thermophilic enzymes, this first global analysis of the transcriptome following cultivation on lignocellulosic carbon sources offers considerable potential for the application of candidate genes in biorefinery applications.
The Impact of Urbanization and LID Technology on Hydrological Effect

ABSTRACT Wang, J.; Zhou, L.L. ; Han, P., and Li, G.X. , 2020. The impact of urbanization and LID technology on hydrological effect. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 14–22. Coconut Creek (Florida), ISSN 0749-0208. The rapid development of urbanization aggravates the problems of urban rain flood disaster and water environment pollution. In order to effectively solve the above problems, the concept of LID (Low Impact Development) were proposed, studied and applied. This research took Baoshan garden sub-watershed, with significant urbanization in Jinan, China, as the study objective. Firstly, in order to clarify the impact of urbanization on hydrological effect, it was simulated and analyzed with SWMM (Storm Water Management Model) at the time node 1985, 1995, 2005 and 2015, respectively. Secondly, in order to restore the hydrological characteristics to the state undevelopment and improve the water environment, a series of LID technical measures were set in the investigated area. Lastly, in order to test the effect of LID technical measures, SWMM was adopted again to simulate the hydrological effect with the setting of LID parameters. And the simulation results after LID application were contrastively analyzed with the hydrological characteristics before and after urbanization. In conclusion, with the development of urbanization, the investigated area showed growing trend in impervious surface coverage, runoff coefficient, the total runoff volume, peak flow and runoff TSS (Total Suspended Solids) pollutant emission, declined in infiltration capacity, and appeared runoff peak ahead of time. Besides, rational utilization of LID technology could not only restore hydrological characteristics, such as the total runoff volume and peak appearance time, to the level of undevelopment, but also achieve remarkably effective control on runoff TSS pollutant emission. The research results can provide theoretical guidance for the prevention and control of rainfall flood, the utilization of rainwater resources, the treatment of water environment and the restoration of water ecology in the urbanized areas.
Introducing a Model of Automated Brand-Generated Content in an Era of Computational Advertising

Abstract Advancements in computing, technology, and their applications to advertising enable marketers to deliver brand messages tailored to individuals and consumer segments. The growth of computational advertising (CA) has created new opportunities but also poses risks in the use of algorithms to generate and optimize the impact of such messages. This article addresses a particular domain influenced by these advancements, namely, automated brand-generated content. We offer an automated brand-generated content (ABC) model that posits two advances. First, rather than solely optimizing consumer data for enhanced impact of automated content, we submit, and provide extra key variables to further illustrate, that there is a desirable balance of both consumer and brand data as inputs to algorithms to serve short- and long-term impact goals. Second, this article guides research by addressing tensions between understanding the relationship between inputs and desired impacts (both short and long term) and proposing a research agenda for future work.
I came, I saw, I hacked: Automated Generation of Process-independent Attacks for Industrial Control Systems

Malicious manipulations on Industrial Control Systems (ICSs) endanger critical infrastructures, causing unprecedented losses. State-of-the-art research in the discovery and exploitation of vulnerability typically assumes full visibility and control of the industrial process, which in real-world scenarios is unrealistic. In this work, we investigate the possibility of an automated end-to-end attack for an unknown control process in the constrained scenario of infecting just one industrial computer. We create databases of human-machine interface images, and Programmable Logic Controller (PLC) binaries using publicly available resources to train machine-learning models for modular and granular fingerprinting of the ICS sectors and the processes, respectively. We then explore control-theoretic attacks on the process leveraging common/ubiquitous control algorithm modules like Proportional Integral Derivative blocks using a PLC binary reverse-engineering tool, causing stable or oscillatory deviations within the operational limits of the plant. We package the automated attack and evaluate it against a benchmark chemical process, demonstrating the feasibility of advanced attacks even in constrained scenarios.
The Impact of Adipose Tissue–Derived miRNAs in Metabolic Syndrome, Obesity, and Cancer

Obesity is a multifactorial and complex condition that is characterized by abnormal and excessive white adipose tissue accumulation, which can lead to the development of metabolic diseases, such as type 2 diabetes mellitus, nonalcoholic fatty liver disease, cardiovascular diseases, and several types of cancer. Obesity is characterized by excessive adipose tissue accumulation and associated with alterations in immunity, displaying a chronic low-grade inflammation profile. Adipose tissue is a dynamic and complex endocrine organ composed not only by adipocytes, but several immunological cells, which can secrete hormones, cytokines and many other factors capable of regulating metabolic homeostasis and several critical biological pathways. Remarkably, adipose tissue is a major source of circulating microRNAs (miRNAs), recently described as a novel form of adipokines. Several adipose tissue–derived miRNAs are deeply associated with adipocytes differentiation and have been identified with an essential role in obesity-associated inflammation, insulin resistance, and tumor microenvironment. During obesity, adipose tissue can completely change the profile of the secreted miRNAs, influencing circulating miRNAs and impacting the development of different pathological conditions, such as obesity, metabolic syndrome, and cancer. In this review, we discuss how miRNAs can act as epigenetic regulators affecting adipogenesis, adipocyte differentiation, lipid metabolism, browning of the white adipose tissue, glucose homeostasis, and insulin resistance, impacting deeply obesity and metabolic diseases. Moreover, we characterize how miRNAs can often act as oncogenic and tumor suppressor molecules, significantly modulating cancer establishment and progression. Furthermore, we highlight in this manuscript how adipose tissue–derived miRNAs can function as important new therapeutic targets.
Human-Machine Collective Intelligence Environment for Decision Support: Conceptual and Technological Design

The paper describes a conceptual and technological design of a novel class of environments, providing means for leveraging collective intelligence of ad hoc human-machine teams for decision support. The paper describes theoretical background used for creating human-machine collective intelligence environment, principles guiding the design and foundational technologies. The core of the proposed environment is an ontology-based representation of the decision-relevant information that can be processed by both human and machine participants. The proposed environment can be used for decisionmaking support in a variety of domains characterized by high levels of uncertainty and dynamics (emergency, natural disaster, government and business scenarios).
Controlling the Interior of Greenhouses using Elman Recurrent Neural Network

This work proposed Elman recurrent neural network prediction model for predicting greenhouses' interior temperature and relative humidity using four input parameters, viz., wind velocity, carbon dioxide concentration, outside temperature and humidity. Implementing a data acquisition technique we have gathered real time data for each of the parameters. The data was harvestedfrom the greenhouse found inside the premises of the Ethiopian Environmental and Forest Research Institute which is used to develop the model. Generally, 18000 data was collected and processed to develop the model using the Elman Recurrent Neural Network (ERNN). Analysis of several selection criteria has been done to decide the number of neurons in the hidden layer. After evaluating the performance by fixing the hidden neuron using convergence theorem we got a better mean square error of 0.0218 in turn showing98% accuracy of the proposed prediction model.
UCIS-X: An Updatable Compact Indexing Scheme for Efficient Extensible Markup Language Document Updating and Query Evaluation

One of the difficulties faced when using XML as the data storage structure is query inefficiency. Therefore, various indexing methods have been proposed. When designing indexing methods, the first step is to choose the labeling method. Some labeling methods can work well; however, if they cannot effectively support update operations, their use is subject to considerable limitations. Most of the update-friendly labeling methods proposed in the literature assign a unique label to each node in XML and provide an expandable mechanism for future insertion. However, they encounter some difficulties, such as increasing the index space, more difficulty in evaluating the relationships between nodes, and increasing the complexity of labels. In this paper, we introduce a novel update-friendly labeling scheme called branch map, which records the correspondence between parent and child nodes instead of assigning a label to each node. The space required for the index is reduced considerably. More importantly, the branch map can maintain the profile as if it was encoded initially, even after being frequently updated. This paper also proposes a compact indexing scheme called UCIS-X. Experimental results indicate that UCIS-X performs well in terms of index size, query, and update efficiency.
Machine-Learning-Based Read Reference Voltage Estimation for NAND Flash Memory Systems Without Knowledge of Retention Time

To achieve a low error rate of NAND flash memory, reliable reference voltages should be updated based on the accurate knowledge of program/erase (P/E) cycles and retention time, because those severely distort the threshold voltage distribution of memory cell. Due to the sensitivity to the temperature, however, a flash memory controller is unable to acquire the exact knowledge of retention time, meaning that it is challenging to estimate accurate read reference voltages in practice. In this article, we propose a novel machine-learning-based read reference voltage estimation framework for the NAND flash memory systems without the knowledge of retention time. To establish an unknown input-output relation of the estimation model, we derive input features by sensing and decoding memory cells in the minimum read unit. In order to define the relation between unlabeled input features and a pre-assigned class label, namely label read reference voltages, we propose three mapping functions: 1) $k$ -nearest neighbors-based, 2) nearest-centroid-based, and 3) polynomial regression-based read reference voltage estimators. For the proposed estimation schemes, we analyze that the storage overhead and computational complexity are increasing function of the exploited feature dimension. Accordingly, we propose a feature selection (or dimension reduction) algorithm to select the minimum dimension and corresponding features to reduce the overhead and complexity while maintaining high estimation accuracy. Based on extensive numerical analysis, we validate that the derived features successfully replace unknown knowledge of retention time, and the proposed feature selection algorithm precisely adjusts the trade-off between overhead/complexity and estimation accuracy. Furthermore, the simulation and analysis results show that the proposed framework not only outperforms the conventional estimation schemes but also achieves the near-optimal frame error rate while sustaining low latency performance.
Smart Spaces Middleware: A Requirement-Oriented Overview

The progress of Internet of Things (IoT) leads to appearance of diverse networked environments where specific class of service-oriented applications–-smart spaces–-are becoming developed and deployed. The deployment is based on certain software platforms, widely accepted as smart spaces middleware. This paper overviews this type of middleware with focus on the requirements that the middleware supports in IoT environments. We analyze and systemize the middleware requirements. The systemized requirements are mapped to the particular existing middleware solutions, leading to identification and comparison of basic approaches in the smart spaces development problem.
Regulation of Innate Immune Responses by Autophagy: A Goldmine for Viruses

Autophagy is a lysosomal degradation pathway for intracellular components and is highly conserved across eukaryotes. This process is a key player in innate immunity and its activation has anti-microbial effects by directly targeting pathogens and also by regulating innate immune responses. Autophagy dysfunction is often associated with inflammatory diseases. Many studies have shown that it can also play a role in the control of innate immunity by preventing exacerbated inflammation and its harmful effects toward the host. The arms race between hosts and pathogens has led some viruses to evolve strategies that enable them to benefit from autophagy, either by directly hijacking the autophagy pathway for their life cycle, or by using its regulatory functions in innate immunity. The control of viral replication and spread involves the production of anti-viral cytokines. Controlling the signals that lead to production of these cytokines is a perfect way for viruses to escape from innate immune responses and establish successful infection. Published reports related to this last viral strategy have extensively grown in recent years. In this review we describe several links between autophagy and regulation of innate immune responses and we provide an overview of how viruses exploit these links for their own benefit.
The Effect and Technique in Search Engine Optimization

In this modern era, we can find for anything anytime, anywhere on the internet. Most people will find it on Google. When we look for something, we will go to Google search engines. Google has more than 35 trillion Web Pages on the internet and if we have a website how can people notice your website that is buried with other 35 trillion websites? The question is how to make our website can be in the top position of search results. The answer is simple, that is by optimizing search engines on your website. In this paper we will inform SEO techniques such as onsite optimization and offsite optimization along with the effects of the two techniques which also allows your website to be on the top search of results. Finally, we also discussed the benefits of the website being to be the top in search engine.
Opcode n-gram based Malware Classification in Android

Smartphone's pervasive presence has offered new possibilities to life experiences, with its power to compute, sense, connect and to be mobile. Android OS since its release in 2008, has grown as the most preferred choice in the market and thus popular among app developers. Detecting nature of an application (benign or malicious) is an ever-evolving research problem. Detection and containment of malicious apps by hosting platforms is an important security consideration [8], [10], [13]. Malware like Geinimi and DroidDream pursuits to collect personal identifiable information, privilege escalation and perform financial operations. In this paper we propose a opcode based approach for malware familial classification. It uses bloom filter for similarity calculation among n-gram based attributes.
The Response of an Intertidal Concholepas concholepas (Gastropoda) Population to Protection from Man in Southern Chile and the Effects on Benthic Sessile Assemblages

In this paper we document the changes experienced by the carnivorous Concholepas concholepas snail population after the establishment of a marine reserve in May 1978. Our data indicate that the release from human predation results in a striking change of the Concholepas population structure, due to the increment of individuals larger than 40-50 mm peristomal length. This evidence strongly indicates that the usual absence of larger size classes from the intertidal is not a consequence of physiological limitations of the muricid, as was previously thought. Significant density differences were detected only in 1984, but they were due to substantial reduction of the snails in the harvested control area.
Aggregated demand analysis and forescasting methodology for the Iberian Electricity Market

This paper introduces an aggregated demand short-medium-term forecasting methodology based on calendar and temperature corrections that returns a very low relative error prediction when tested against actual demand values in the Iberian Electricity Market. The work introduces the market structure and the revealed importance of the demand on the electricity price, according to past correlations. Then, it introduces some of the most extended metrics in the literature being used for comparison among methodologies, as well as the databases consulted to obtain the required information. The performance of the proposed forecasting method is analysed against real data and compared with other simple proposal providing good results.