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More than tools for managing physical and digital objects, museum collection management systems (CMS) serve as platforms for structuring, integrating, and making accessible the rich data embodied by natural history collections. Here we describe Arctos, a scalable community solution for managing and publishing global biological, geological, and cultural collections data for research and education. Specific goals are to: (1) Describe the core features and implementation of Arctos for a broad audience with respect to the biodiversity informatics principles that enable high quality research; (2) Highlight the unique aspects of Arctos; (3) Illustrate Arctos as a model for supporting and enhancing the Digital Extended Specimen concept; and (4) Emphasize the role of the Arctos community for improving data discovery and enabling cross-disciplinary, integrative studies within a sustainable governance model. In addition to detailing Arctos as both a community of museum professionals and a collection database platform, we discuss how Arctos achieves its richly annotated data by creating a web of knowledge with deep connections between catalog records and derived or associated data. We also highlight the value of Arctos as an educational resource. Finally, we present the financial model of fiscal sponsorship by a nonprofit organization, implemented in 2022, to ensure the long-term success and sustainability of Arctos. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Madagascar's children are taught that their ancestors destroyed the vast ancient forest, giving rise to modern grasslands, a misleading myth that continues to undermine people's relationship with nature. Replacing this myth with a more nuanced and accurate narrative around grassy ecosystems is fundamental to building a more positive relationship between Madagascar's people and ecosystems, a process that is necessary to build modern conservation practice and environmental governance. The data we present are consistent with a pre‐human grass flora in the region, suggesting the local grassy ecosystems should not be written off as fully anthropogenic and undesirable but are instead in need of recognition and further research attention. Summary: Grassy ecosystems cover over a quarter of dry land, with mosaics of old‐growth and recently formed anthropogenic systems appearing superficially similar to an uninformed observer. Grasses are part of a deep web of historic relationships between grassy ecosystems and local people.We collect and compile the first list of grass species (Poaceae) in north‐west Madagascar in order to make an initial evidence assessment for a pre‐human grass flora. Field surveys and voucher specimen collections were carried out. Specimen identification was performed using morphological character comparison with reference specimens, literature and personal knowledge of grasses from other parts of Madagascar.Seventy species are recorded and assigned to 44 genera. The species list is compared with regional grass checklists from central and south‐central Madagascar. Thirty‐five grasses are unique to Mahajanga, comprising 19% of the multi‐region species pool analysed; the regional species richness is comparable to that of the highlands.The main subfamilies represented are the Panicoideae (64%) and Chloridoideae (29%). Seven species are endemic to Madagascar, reflecting their evolutionary origins on the island. Many of the species are found in multiple types of habitat and substrate, while others are restricted to limited areas and habitats, and local species richness varies significantly with habitat and soil. The main disturbance regimes driving species occurrence include fire and grazing, possibly partly reflecting similar disturbances prior to human arrival. The patterns observed in the grass flora of north‐western Madagascar are overall congruent with a pre‐human grass flora. [ABSTRACT FROM AUTHOR]

The dark web is considered an expansion of the deep web, intentionally hidden from the surface web. It can only be accessed with a particular group of browsers that allow the user to stay anonymous while navigating the dark web. With the untraceable hidden layer of the Internet and the anonymity of the users associated with the dark web, several impressive cybercrimes have been reported. This paper aims to examine the impact of the dark web on society. The article systematically reviews relevant academic literature and books to understand how the dark web works and its societal effects. The study has found that the dark web is an enabler of several cybercrimes. Moreover, while governments and regulatory authorities have introduced strategic detection techniques on the dark web, cybercriminals are adaptive towards the strategies and, given time, will usually find ways to bypass such detection techniques. It is recommended that the regulatory authorities and cyber threat intelligence periodically review the detection techniques for effective monitoring. Furthermore, security agencies or forensic analysts should ensure that they are updated with the latest scientific knowledge on the safe management of the dark web by undertaking more training in cyber security. There is also a need for further research to focus on awareness campaigns about the dangers of the dark web. [ABSTRACT FROM AUTHOR]

Palmprint biometrics has a broad application prospect owing to non-intrusiveness, ease of image acquisition and stable textural pattern. Hand-crafted approaches are vulnerable to non-ideal palmprint images caused by uneven illumination, motion blur and noise contamination. Many researchers have designed excellent texture descriptors or/and advanced image pre-processing algorithms. Nevertheless, they are highly targeted at some specific data, less adaptable to the emerging data. In this paper, a semi-pretrained contactless palmprint recognition deep network is developed to achieve high accuracy and robustness. Semi-CPRN is composed of underlying network structure from ResNet-152 and the proposed assistant layers dedicated to high-performance palmprint recognition. The well-designed assistant layers enhance convolutional neural network to steadily extract the real palmprint features even from the degraded images without being deceived by the degradation factors. Besides, to better carry out the research on palmprint recognition in the open environment, we established a new contactless database HIT-NIST-V1 under natural scene. The comparative experiments on CASIA, IITD, PolyU3D/2D, Tongji and HIT-NIST-V1 illustrate that Semi-CPRN is comparable and superior to previously published state-of-the-art approaches. Simultaneously, CNN-based palmprint biometrics methods show significant robustness to motion blur, Gaussian noise, and salt and pepper noise. [ABSTRACT FROM AUTHOR]

Motivation Studying the genetic makeup of viruses and phages through genome analysis is crucial for comprehending their function in causing diseases, progressing medicine, tracing their evolutionary history, monitoring the environment, and creating innovative biotechnologies. However, accessing the necessary data can be challenging due to a lack of dedicated comparative genomic tools and viral and phage databases, which are often outdated. Moreover, many wet bench experimentalists may not have the computational proficiency required to manipulate large amounts of genomic data. Results We have developed VAPEX (Virus And Phage EXplorer), a web server which is supported by a database and features a user-friendly web interface. This tool enables users to easily perform various genomic analysis queries on all natural viruses and phages that have been fully sequenced and are listed in the NCBI compendium. VAPEX therefore excels in producing visual depictions of fully resolved synteny maps, which is one of its key strengths. VAPEX has the ability to exhibit a vast array of orthologous gene classes simultaneously through the use of symbolic representation. Additionally, VAPEX can fully analyze user-submitted viral and phage genomes, including those that have not yet been annotated. Availability and implementation VAPEX can be accessed from all current web browsers such as Chrome, Firefox, Edge, Safari, and Opera. VAPEX is freely accessible at https://archaea.i2bc.paris-saclay.fr/vapex/. [ABSTRACT FROM AUTHOR]

As the center of the financial market, the stock market is popular with the public attention of investors. It is of great significance for investors that an effective analytic method of stock public opinion is proposed. As the main communication platform, the forum not only provides the investors with investment information but also comments related to the stock market. In view of the defects of text emotions and investment problems, this paper proposes a framework based on web crawler and deep learning technologies including one-dimensional convolutional neural networks (1DCNN) and long short-term memory (LSTM), to evaluate the stock market volatility. Among them, the extracted features include not only the stock price but also the text information. Firstly, we develop the crawler technology to grab large-scale text data from the internet and they are manually labeled their emotions by analyzing the relevant financial knowledge. Secondly, as the character-level text classification method, the 1DCNN is designed for text sentiment classification to detect the reliability of text annotation. Finally, considering the time sequence of price and the continuity of post influence, the emotional and technical features are combined to estimate the fluctuation of the stock market in different industries by the LSTM model. We test four evaluation indexes, the classification accuracy of the model is 74.38%, the accuracy rate is 76.83%, the recall rate is 70%, and the F1 value is 72.8%. The results show that the combination of characteristics of internet public opinion more effectively evaluates the changes in the stock market. [ABSTRACT FROM AUTHOR]

Phishing is a major problem on darknets. Phishing is the practice of attacking an unaware person by pretending to be someone else to steal their digital data. In anonymous platforms such as the dark web or deep web of Tor, detecting the attacker or phishing attacks is a much more complicated practice. Generic phishing attacks can be easy to spot. Today's challenge is detecting the various attacks in the anonymous network is very hard. The intelligent factor of attacks can bypass traditional detection solutions. To solve the problem of complications in the Tor Network, this work focuses on the development of automated detection of vulnerable attacks in phishing-based Tor hidden services. The proposed model initially divides the attack parameters into three categories into Class A, Class B, and Class C based on technical perspectives and some defined threshold values. Next, the class A attacks (i.e. top level domain and protocol similarity) attacks are detected by a random forest (RF) classifier. Then, the class B attacks can be identified by the convolutional neural network (CNN). Finally, the LSTM model is applied for the accurate classification of multiple attacks in the Tor network. The experimental validation of the proposed model is tested using the CIRCL and AIL datasets. The experimental values highlighted the promising performance of the proposed model over other methods with a maximum overall detection accuracy of 95.60% and 95.77% on CIRCL and AIL datasets respectively. Therefore, the proposed model effectively detects multiple attacks in the Tor network under dynamic and real-time environments. [ABSTRACT FROM AUTHOR]

The advent of metabarcoding (metaB) in aquatic ecology has provided a huge amount of information on plankton biodiversity worldwide. However, the large datasets obtained with that approach are still partially explored, especially for what concerns the study of trophic interactions and food webs. In this study, we analysed a metaB time series from the Long‐Term Ecological Research station MareChiara (LTER‐MC) in the Gulf of Naples, Mediterranean Sea, Italy, to describe the link between plankton diversity and food‐web structure. We derived co‐occurrence networks from metaB time series, identified putative trophic interactions among co‐occurrences based on biological information (body size and trophic habit) available for planktonic organisms detected by metaB, and converted co‐occurrence networks into conceptual models of food webs. The latter showed structural properties resembling ecological processes, because network modularity (the presence of semi‐independent sub‐networks) paralleled trophic hierarchy (the dimensional difference between predator and prey). We also analysed the role of planktonic organisms in maintaining network modularity. The largest predators occupied distinct modules, suggesting niche partitioning, whereas the smallest preys worked as fundamental connectors between larger predators (and different modules). Overall, the presence of trophic hierarchy and modularity shown herein supports the view of the high ecological resilience of plankton, pursued via food‐web rewiring, to environmental shifts. [ABSTRACT FROM AUTHOR]

The untraceable part of the Deep Web, also known as the Dark Web, is one of the most used "secretive spaces" to execute all sorts of illegal and criminal activities by terrorists, cybercriminals, spies, and offenders. Identifying actions, products, and offenders on the Dark Web is challenging due to its size, intractability, and anonymity. Therefore, it is crucial to intelligently enforce tools and techniques capable of identifying the activities of the Dark Web to assist law enforcement agencies as a support system. Therefore, this study proposes four deep learning architectures (RNN, CNN, LSTM, and Transformer)-based classification models using the pre-trained word embedding representations to identify illicit activities related to cybercrimes on Dark Web forums. We used the Agora dataset derived from the DarkNet market archive, which lists 109 activities by category. The listings in the dataset are vaguely described, and several data points are untagged, which rules out the automatic labeling of category items as target classes. Hence, to overcome this constraint, we applied a meticulously designed human annotation scheme to annotate the data, taking into account all the attributes to infer the context. In this research, we conducted comprehensive evaluations to assess the performance of our proposed approach. Our proposed BERT-based classification model achieved an accuracy score of 96%. Given the unbalancedness of the experimental data, our results indicate the advantage of our tailored data preprocessing strategies and validate our annotation scheme. Thus, in real-world scenarios, our work can be used to analyze Dark Web forums and identify cybercrimes by law enforcement agencies and can pave the path to develop sophisticated systems as per the requirements. [ABSTRACT FROM AUTHOR]

This letter is a comment on an article titled "A Framework for the Fair Pricing of Medicines." The author acknowledges that the article presents an interesting viewpoint but points out that it lacks practical application and does not consider how the theoretical framework could be applied to different health systems and global pharmaceutical pricing. The author suggests that the paper would have been more useful if it had been framed within the broader context of pharmaceutical policy. The letter also highlights the importance of considering the specific pharmaceutical policies and health systems of different countries when discussing fair pricing. Overall, the author emphasizes the need for further debate and consideration of these factors in a globalized world. [Extracted from the article]

Reconnaissance and monitoring activities of actors on the Internet are becoming indispensable security tools for governments around the world. The Deep Web as part of the Internet protects actors. However, cheap and reliable encryption and online identity protection have become attractive to state and non-state actors in the cyber domain. Unfortunately, part of the Deep Web, the Dark Web is now offering to training terrorists, and trading arms and illicit substances. The Dark Web represents a communication and information systems infrastructure where actors are anonymous and the services are adaptable to the demands of geopolitical circumstances. This paper analyzes the ways of using the Dark Web and its impact on the information environment since the global war on terrorism began, through a period when humanity struggled with Covid-19 pandemic to the war in Ukraine. [ABSTRACT FROM AUTHOR]

Enhanced richness and size of the data on the web paves the path for increased online services, supporting the sophisticated usage of heterogeneous complex tasks by users. As deep web online services are increasing, several efficient techniques to explore the location of deep web interfaces are also leveraged thereby providing better support in user information exploration on the web concerning queries and the user clicks on web-related search data. This searching process provides an improved experience to users using crawling websites and searches rank with titles and links. The main problem behind search data on the web is organizing user search information based on their preferences into dynamic query formations and data coming from associated web links in an automated way on web services on high-quality sparse data. Vast amounts of data with the Dynamic nature of web interfaces explore or achieve high efficiency and coverage of all deep web interfaces that turn out to be a challenging issue. To handle such issues, we propose and introduce a novel and efficient two-phase deep learning data crawler framework (NTPDCF). The first phase initiates in gathering accurate and highly relevant links using the search engine, and the second phase explores fast and in-site relevant website links using adaptive site ranking. The approach focuses on drilling relevant site data and top-k ranking with different relations based on dynamic features with user preferences in single- and multi-query formation with adaptive weight features. The method promises to visualize improved results with efficient data exploration over the traditional approach concerning real-time defense and e-commerce related to web-based services. [ABSTRACT FROM AUTHOR]

The metazoan parasite community and the stomach contents of the nursehound shark Scyliorhinus stellaris from the Gulf of Naples (central Mediterranean Sea) were studied. The nursehound shark harboured a poor parasite community composed of a species of gill monogenean (Hexabothrium appendiculatum) and three intestinal cestode taxa (Acanthobothrium coronatum and two unidentified species of the genera Yamaguticestus and Scyphophyllidium), all represented by adult stages. Hosts were mostly parasitized by individuals of A. coronatum, which was the most abundant species and contributed to almost 80% of the total number of parasites found. Conversely, other trophically transmitted parasites (i.e., Yamaguticestus sp. and Scyphophyllidium sp.) showed low prevalence and abundance. The parasite infracommunity was poor, showing low values of species richness, total mean abundance, and diversity indices. Overall, 52 prey items belonging to 13 taxa were identified in the stomach contents. Cephalopods were the most important prey items (represented by nine taxa) and the most diverse and abundant group. In the multivariate space provided by a principal component of mixed data (PCAmix), nursehound sharks distributed along two main axes, related to individual traits (first axis) and stomach contents (including empty ones, second axis). A logistic regression based on the first two axes of the PCAmix showed a significant influence of host individual traits and, to a lesser extent, of stomach contents, regarding the probability of being infected by A. coronatum. Alongside specific traits already associated with parasites transmission, our results highlight the importance of cephalopods in transferring cestode infections through trophic web interactions in the top‐predator nursehound shark. [ABSTRACT FROM AUTHOR]

The medical-patient relationship is facing pollution of information all over the internet, for physician and patients is becoming tougher to keep updated with the highest quality of information. During the last 20 years multiple evaluation tools have been developed trying to find the best tool to assess high-quality information, to date DISCERN tool represents the most widely spread. Information can be found on the surface internet and in the deep web, constituting the biggest chunk of the internet, informing and controlling the quality of information is a formidable task. PubMed and Google Scholar are the most important tools for a physician to find information, although multiple others are available; awareness must be raised over improving current strategies for data mining high-quality information for the patients and the healthcare community. [ABSTRACT FROM AUTHOR]

Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior developments in de novo drug design. However, data availability, deep data processing, and the lack of user-friendly DL tools and interfaces make it difficult to apply these DL techniques to drug design. We hereby present ReMODE (Receptor-based MOlecular DEsign), a new web server based on DL algorithm for target-specific ligand design, which integrates different functional modules to enable users to develop customizable drug design tasks. As designed, the ReMODE sever can construct the target-specific tasks toward the protein targets selected by users. Meanwhile, the server also provides some extensions: users can optimize the drug-likeness or synthetic accessibility of the generated molecules, and control other physicochemical properties; users can also choose a sub-structure/scaffold as a starting point for fragment-based drug design. The ReMODE server also enables users to optimize the pharmacophore matching and docking conformations of the generated molecules. We believe that the ReMODE server will benefit researchers for drug discovery. ReMODE is publicly available at http://cadd.zju.edu.cn/relation/remode/. [ABSTRACT FROM AUTHOR]

Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior developments in de novo drug design. However, data availability, deep data processing, and the lack of user-friendly DL tools and interfaces make it difficult to apply these DL techniques to drug design. We hereby present ReMODE (Receptor-based MOlecular DEsign), a new web server based on DL algorithm for target-specific ligand design, which integrates different functional modules to enable users to develop customizable drug design tasks. As designed, the ReMODE sever can construct the target-specific tasks toward the protein targets selected by users. Meanwhile, the server also provides some extensions: users can optimize the drug-likeness or synthetic accessibility of the generated molecules, and control other physicochemical properties; users can also choose a sub-structure/scaffold as a starting point for fragment-based drug design. The ReMODE server also enables users to optimize the pharmacophore matching and docking conformations of the generated molecules. We believe that the ReMODE server will benefit researchers for drug discovery. ReMODE is publicly available at http://cadd.zju.edu.cn/relation/remode/. [ABSTRACT FROM AUTHOR]

Due to remarkable development of advanced data, it has gotten drawn-out to recover applicable query items from the web crawler. The customary web search tool furnishes pertinent site pages however with the blend of unessential pages with less coverage rate and high slithering time. To overcome the issues, this paper proposes an Intelligent Multi-Agent design that utilizes Differential Evolution Algorithm (DEA) to extricate more pertinent site pages with high coverage rate and less slithering time. Three intelligent agents, specifically, Trajectory pattern mining agent, Query clustering agent and Web page classification agent are employed to track the relevancy of the pages, hidden web links and classify the searchable and non-searchable forms respectively. The extrication of relevant pages is optimized by the DEA algorithm. DEA calculation like selection, crossover and mutation are utilized for optimizing the extraction process. In any case, in Genetic Algorithm (GA) determination of chromosome depends on the wellness esteem, where the low quality chromosomes don't get by in the future. The Differential Evolutionary activity help the helpless chromosome to get by in the opposition and it will create the upgraded results. The learnt by experience of the intelligent agents help to work on the presentation of the framework. The exploration result shows that the proposed architecture gives the high precision and recall ratesover the current web crawlers. The creeping time for recovering the website pages is likewise diminished utilizing the proposed DEAIMA architecture. • Due to remarkable development of data, it has gotten drawn-out to recover applicable query items from the web crawler. • The customary web search tool furnishes pertinent site pages with less coverage rate and high slithering time. • The exploration result shows that the proposed engineering gives the preferred accuracy and review over the current web crawlers. [ABSTRACT FROM AUTHOR]

With the freedom offered by the Deep Web, people have the opportunity to express themselves freely and discretely, and sadly, this is one of the reasons why people carry out illicit activities there. In this work, a novel dataset for Dark Web active domains known as crawler-DB is presented. To build the crawler-DB, the Onion Routing Network (Tor) was sampled, and then a web crawler capable of crawling into links was built. The link addresses that are gathered by the crawler are then classified automatically into five classes. The algorithm built in this study demonstrated good performance as it achieved an accuracy of 85%. A popular text representation method was used with the proposed crawler-DB crossed by two different supervised classifiers to facilitate the categorization of the Tor concealed services. The results of the experiments conducted in this study show that using the Term Frequency-Inverse Document Frequency (TF-IDF) word representation with a linear support vector classifier achieves 91% of 5 folds cross-validation accuracy when classifying a subset of illegal activities from crawler-DB, while the accuracy of Naïve Bayes was 80.6%. The good performance of the linear SVC might support potential tools to help the authorities in the detection of these activities. Moreover, outcomes are expected to be significant in both practical and theoretical aspects, and they may pave the way for further research. [ABSTRACT FROM AUTHOR]

The last two decades have seen a marked increase in the illegal activities on the Dark Web. Prompt evolvement and use of sophisticated protocols make it difficult for security agencies to identify and investigate these activities by conventional methods. Moreover, tracing criminals and terrorists poses a great challenge keeping in mind that cybercrimes are no less serious than real life crimes. At the same time, computer security societies and law enforcement pay a great deal of attention on detecting and monitoring illegal sites on the Dark Web. Retrieval of relevant information is not an easy task because of vastness and ever-changing nature of the Dark Web; as a result, web crawlers play a vital role in achieving this task. Thereafter, data mining techniques are applied to extract useful patterns that would help security agencies to limit and get rid of cybercrimes. The aim of this paper is to present a survey for those researchers who are interested in this topic. We started by discussing the internet layers and the properties of the Deep Web, followed by explaining the technical characters of The Onion Routing (TOR) network, and finally describing the approaches of accessing, extracting and processing Dark Web data. Understanding the Dark Web, its properties and its threats is vital for internet servers; we do hope this paper be of help in that goal. [ABSTRACT FROM AUTHOR]

• A tailor-made steel reinforcing method was found effective in shear strength improvement. • Fibre adoption enhanced crack-opening resistance, ultimate shear strength and shear deformation. • It is essential to provide sufficient web reinforcement above and below web openings. • The simplified analytical model yielded safe predictions. Application of fibre-reinforced concrete or novel web reinforcement detailing could be an effective way to enhance structural behaviour of continuous deep beams with web openings. However, there are very few experimental works studying the synergistic effects of these two methods. Therefore, an experimental programme is introduced in this paper to study synergistic effects of shear reinforcement and hybrid fibres on shear strength of nine double-span deep beams with unsymmetrical web openings (denoted as pierced span). The test specimens were designed to consist of a pierced beam span with a web opening and an adjacent solid beam span. The spans with openings were designed with different shear reinforcement configurations. It was experimentally shown that provision of sufficient web steels above and below web openings has the most important and beneficial effect on ultimate shear strength of the pierced spans. Besides, a tailored steel reinforcing method was also proposed that is capable of improving strut capacity near the web opening. In addition, adoption of hybrid fibre reinforcement led to enhanced crack-opening resistance, ultimate shear strength and shear deformation. Based on the research outcomes, it was recommended to adopt the proposed detailing method or fibre reinforcement dosage for shear strength enhancement of continuous deep beams with web openings. Furthermore, an analytical model was adopted to the shear strength of continuous deep beams accounting for web openings, steel stirrups, and hybrid fibres, which yielded safe predictions. [ABSTRACT FROM AUTHOR]

The authors discuss this special edition that emerged from a selection of papers presented at the "Dark Social Spaces" symposium held at Deakin University in Melbourne, Victoria in 2019. Topics covered include the dark Web, digital life via the concept of the social, and various presentations of identity on social media.

Targeted advertising lies at the heart of digital economic models and has been scrutinized with respect to the potential pathologies of discriminatory job and housing advertising along with concerns about harmful forms of manipulation and the invasive character of online data harvesting. This article takes as its focus the non-transparent forms of cultural association reproduced by targeted advertising. We call into question the ready distinction between editorial content and advertising that treats news content as a public interest good and advertising as primarily a matter of private interest and personal taste. In online media environments, it has become difficult to interrogate advertising due to their 'dark' nature (they are only visible to those to whom they are targeted) allowing them to avoid public scrutiny. This project puts forward a prototype for a tool that may be used to advance accountability discussions further by providing insight into how targeted advertising takes place across demographic categories. This article describes the pilot test of such a tool – one that can be deployed to reveal and illuminate patterns that emerge from targeted and dark advertising practices. [ABSTRACT FROM AUTHOR]

Using Reddit as a case study, this article blurs the boundary between the 'dark' and 'clear' web, arguing that in between these spaces there is what I describe as a 'brackish web', inhabited by 'brackish social spaces'. Reddit, I argue, sits in a murky space between the dark and clear, with its technological rationality, politics, and technological affordances exhibiting elements of both the clear and dark infrastructures. I study these similarities through examining Reddit's relationship to surveillance, arguing that it exhibits similarities to the dark web through a politics of anonymity and its practices of content moderation. Reddit, however, has slowly moved away from some of its 'dark' elements, in particular through a growing reliance on advertising revenue. This has pushed the platform to engage in more surveillance of its users, pushing it away from its dark web politics. This article challenges the dark and clear web binary, further nuancing our understandings of the politics, economics and technological rationality that underpins online digital platforms. [ABSTRACT FROM AUTHOR]

This article contrasts the dark with the electrified light of datafied life to bring into relief the impact of data surveillance upon social transformation. Our argument is that darkness remains fundamental to creative social experimentation and thus to a society's capacity for change and sustained transformation. This argument might seem antithetical to the age of information. Yet, we find darkness encourages exploratory forms of engagement, intercourse and exchange, that electrified light forbids. We draw on Rancière's considerations of politics and the police to suggest that the policing by digitally-electrified light extinguishes the politics of experimentation that test the make-up and limits of social interaction. Our work is structured by first explaining the baseline of the digital electric policing through the conceptualization of Facebook's social graph, then switches to antithetical examples of politics enabled in the dark. We use the examples of the Dark Web, end to end encryption, and trustless infrastructures like Craigslist to illustrate generative tensions that are the signatures of what we understand as the dark social. We conclude that the dark social involves a decentralized capacity to experiment and transform what is social. [ABSTRACT FROM AUTHOR]

Newly emerging technologies for digital communication facilitate rapid data collection, storage and processing whereby subsequent interactions can be unpredictable. This creates a 'darker turn' in neo-communicative practices. That which is 'dark' is understood as communication that has either limited distribution, is not open to all users – closed groups by way of example – or is veiled. Dark social spaces are, however, indistinct requiring further study. This is giving rise to new work in what I call 'dark social studies'. To further explore the nature and use of dark social spaces, a digital (auto)ethnographic study of 'dark' social connection was undertaken. The analysis specifically focused on Mastodon, Galaxy3 and 8Kun's '.onion' available over Tor (The Onion Router). This article concludes that for a number, virtual 'dark' spaces provide affirmative, intimate and vital zones of connection to others and peer collaboration. Further, the interconnected and interactive capacity of dark social spaces facilitates user expectations for dark connection while exposing simultaneously the limitations of our intimate machines. [ABSTRACT FROM AUTHOR]

Drawing on Raymond William's concept of the 'magic system,' this article argues that advertising on the Dark Web is a 'dark magic system'. The article first defines 'Dark Web' and then analyzes over 300 banner advertisements appearing on Tor onion service search engines. The advertisements are categorized into navigation, individual vendors, services, and markets. Next, the article traces the associations the advertisements make between advertised objects and values. The predominant values the advertisements invoke include navigation, OPSEC politics, and a justification to exploit the openness of others. The article then traces the limits of the dark magic system, including the system's inability to offer metrics, the problems of 'onion cloners,' and the constant threat of scams. The article concludes with an argument that the dark magic system is incapable of addressing the sort of anonymized political communication the Dark Web might afford. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including Dark Web advertising, social media, and electrified social.

The unrivaled growth in e‐commerce of animals and plants presents an unprecedented opportunity to monitor wildlife trade to inform conservation, biosecurity, and law enforcement. Using the internet to quantify the scale of the wildlife trade (volume and frequency) is a relatively recent and rapidly developing approach that lacks an accessible framework for locating relevant websites and collecting data. We produced an accessible guide for internet‐based wildlife trade surveillance. We detailed a repeatable method involving a systematic internet search, with search engines, to locate relevant websites and content. For data collection, we highlight web‐scraping technology as an efficient way to collect data in an automated fashion at regularly timed intervals. Our guide is applicable to the multitude of trade‐based contexts because researchers can tailor search keywords for specific taxa or derived products and locations of interest. We provide information for working with the diversity of websites used in wildlife trade. For example, to locate relevant content on social media (e.g., posts or groups), each social media platform should be examined individually via the site's internal search engine. A key advantage of using the internet to study wildlife trade is the relative ease of access to an increasing amount of trade‐related data. However, not all wildlife trade occurs online and it may occur on unobservable sections of the internet. Article Impact Statement: The internet is a vast source of wildlife trade data; our generalizable framework allows researchers to explore new contexts of the trade. [ABSTRACT FROM AUTHOR]

This article describes STEM education with artificial intelligence (AI) learning, particularly for non-engineering undergraduate students. In the designed three-week learning activities, students were encouraged to put their ideas about AI into practice through two hands-on activities, utilizing a provided deep learning-based web service. This study designed pre-test and post-test surveys to investigate the performance of students in different aspects of AI. With 328 students involved in these learning activities, we discovered from the surveys that the proposed learning method can effectively improve AI literacy among non-engineering students. This study also found that students' AI literacy correlated significantly with their awareness of AI ethical issues and that the STEM-based AI curriculum increased the awareness of AI ethical issues among low-AI-literate learners. This article discusses the association between learning activities and different aspects of AI learning. The proposed method can be used by teachers who want to introduce AI knowledge into general education courses. [ABSTRACT FROM AUTHOR]

Our objective was to examine conflicts of interest between the UK's health-focused All-Party Parliamentary Groups (APPGs) and the pharmaceutical industry between 2012 and 2018. APPGs are informal cross-party groups revolving around a particular topic run by and for Members of the UK's Houses of Commons and Lords. They facilitate engagement between parliamentarians and external organisations, disseminate knowledge, and generate debate through meetings, publications, and events. We identified APPGs focusing on physical or mental health, wellbeing, health care, or treatment and extracted details of their payments from external donors disclosed on the Register for All-Party Parliamentary Groups. We identified all donors which were pharmaceutical companies and pharmaceutical industry-funded patient organisations. We established that sixteen of 146 (11%) health-related APPGs had conflicts of interest indicated by reporting payments from thirty-five pharmaceutical companies worth £1,211,345.81 (16.6% of the £7,283,414.90 received by all health-related APPGs). Two APPGs (Health and Cancer) received more than half of the total value provided by drug companies. Fifty APPGs also had received payments from patient organisations with conflicts of interest, indicated by reporting 304 payments worth £986,054.94 from 57 (of 84) patient organisations which had received £27,883,556.3 from pharmaceutical companies across the same period. In total, drug companies and drug industry-funded patient organisations provided a combined total of £2,197,400.75 (30.2% of all funding received by health-related APPGs) and 468 (of 1,177–39.7%) payments to 58 (of 146–39.7%) health-related APPGs, with the APPG for Cancer receiving the most funding. In conclusion, we found evidence of conflicts of interests through APPGs receiving substantial income from pharmaceutical companies. Policy influence exerted by the pharmaceutical industry needs to be examined holistically, with an emphasis on relationships between actors potentially playing part in its lobbying campaigns. We also suggest ways of improving transparency of payment reporting by APPGs and pharmaceutical companies. [ABSTRACT FROM AUTHOR]

This article focuses on the prominence of dirt and excrements in online male subcultures. It offers an understanding of both the computer nerd cultures of the 4chan forum and Incel (acronym for "Involuntary celibate") forums that is grounded in their displays of anal sexuality. The article traces the development of this sexuality from the sadistic-aggressive acts of discharge characteristic of 4chan to the masochistic displays of self-deprecation in Incel forums. Whereas the former serve to draw boundaries between the subcultural sphere and that of the cultural mainstream, the latter take the provocative performance of sexual immaturity towards a point of catastrophic loss of control. These performances have political implications, in which the fantasy of being out of control in particular moves Incel culture close to right extremism in that, it is argued, it anticipates a fatalistic act of vengeance against those who are given the blame for one's castrated state. It is in this respect, I argue, that Incel culture must be seen as an extreme variation of online male subcultures. [ABSTRACT FROM AUTHOR]

Evidence-based STI (science, technology, and innovation) policy making requires accurate indicators of innovation in order to promote economic growth. However, traditional indicators from patents and questionnaire-based surveys often lack coverage, granularity as well as timeliness and may involve high data collection costs, especially when conducted at a large scale. Consequently, they struggle to provide policy makers and scientists with the full picture of the current state of the innovation system. In this paper, we propose a first approach on generating web-based innovation indicators which may have the potential to overcome some of the shortcomings of traditional indicators. Specifically, we develop a method to identify product innovator firms at a large scale and very low costs. We use traditional firm-level indicators from a questionnaire-based innovation survey (German Community Innovation Survey) to train an artificial neural network classification model on labelled (product innovator/no product innovator) web texts of surveyed firms. Subsequently, we apply this classification model to the web texts of hundreds of thousands of firms in Germany to predict whether they are product innovators or not. We then compare these predictions to firm-level patent statistics, survey extrapolation benchmark data, and regional innovation indicators. The results show that our approach produces reliable predictions and has the potential to be a valuable and highly cost-efficient addition to the existing set of innovation indicators, especially due to its coverage and regional granularity. [ABSTRACT FROM AUTHOR]

Microbial planktonic communities are the basis of food webs in aquatic ecosystems since they contribute substantially to primary production and nutrient recycling. Network analyses of DNA metabarcoding data sets emerged as a powerful tool to untangle the complex ecological relationships among the key players in food webs. In this study, we evaluated co‐occurrence networks constructed from time‐series metabarcoding data sets (12 months, biweekly sampling) of protistan plankton communities in surface layers (epilimnion) and bottom waters (hypolimnion) of two temperate deep lakes, Lake Mondsee (Austria) and Lake Zurich (Switzerland). Lake Zurich plankton communities were less tightly connected, more fragmented and had a higher susceptibility to a species extinction scenario compared to Lake Mondsee communities. We interpret these results as a lower robustness of Lake Zurich protistan plankton to environmental stressors, especially stressors resulting from climate change. In all networks, the phylum Ciliophora contributed the highest number of nodes, among them several in key positions of the networks. Associations in ciliate‐specific subnetworks resembled autecological species‐specific traits that indicate adaptions to specific environmental conditions. We demonstrate the strength of co‐occurrence network analyses to deepen our understanding of plankton community dynamics in lakes and indicate biotic relationships, which resulted in new hypotheses that may guide future research in climate‐stressed ecosystems. [ABSTRACT FROM AUTHOR]

In the era of industrial digitalization, people are increasingly investing in solutions that allow their process for data collection, data analysis and performance improvement. In this paper, advancing web scale knowledge extraction and alignment by integrating few sources by exploring different methods of aggregation and attention is considered in order focusing on image information. The main aim of data extraction with regards to semistructured data is to retrieve beneficial information from the web. The data from web also known as deep web is retrievable but it requires request through form submission because it cannot be performed by any search engines. As the HTML documents start to grow larger, it has been found that the process of data extraction has been plagued with lengthy processing time. In this research work, we propose an improved model namely wrapper extraction of image using document object model (DOM) and JavaScript object notation data (JSON) (WEIDJ) in response to the promising results of mining in a higher volume of image from a various type of format. To observe the efficiency of WEIDJ, we compare the performance of data extraction by different level of page extraction with VIBS, MDR, DEPTA and VIDE. It has yielded the best results in Precision with 100, Recall with 97.93103 and F-measure with 98.9547. [ABSTRACT FROM AUTHOR]

• A total of sixteen deep beam specimens were tested by considering the beam depth (i.e., 300 mm ∼ 1200 mm) and the shear-span ratio (i.e., 0.8, 1.3 and 1.8) as test variables. • Twelve beams were reinforced with BFRP bars while the other four beams were reinforced with steel bars to act as controls. • The corresponding size effect of BFRP-RC deep beams was quantitatively studied, and the applicability of current design codes and scientific calculation methods were discussed. • The size effect of FRP-RC deep beams is not reasonably considered in current design codes, while, Jin's theoretical model can scientifically reflect the effect of beam depth and shear-span ratio on the nominal shear strength. Basalt Fiber Reinforced Polymer (BFRP) bars appear effective in reducing corrosion-related damage and are gradually used as substitute for Steel bars in engineering practices. However, most of available efforts are focused on the size effect of BFRP-reinforced concrete (BFRP-RC) deep beams without web reinforcement, while less efforts have been studied on BFRP-RC deep beams with web reinforcement. Moreover, the quantitative influence of the shear-span ratio on the shear capacity of BFRP-RC deep beams is always in dispute. The purpose of the work is to investigate the quantitative influence of beam depth and shear-span ratio on the shear behavior of BFRP-RC deep beams with web reinforcement. A total of sixteen specimens were tested by considering the beam depth (i.e., 300 mm ∼ 1200 mm) and the shear-span ratio (i.e., 0.8, 1.3 and 1.8) as test variables. Twelve beams were reinforced with BFRP bars while the other four beams were reinforced with steel bars to act as controls. The corresponding size effect of BFRP-RC deep beams with web reinforcement was quantitatively studied, and the applicability of current design codes and scientific calculation methods were discussed. The results indicate that: 1) the increase of shear-span ratio reduces the nominal ultimate shear strength but has an ignorable influence on the size effect; 2) the nominal ultimate shear strength of BFRP-RC deep beams decline by about 46% as the beam depth increases from 300 mm to 1200 mm (both horizontal and vertical web reinforcement ratio are 0.51%); 3) the size effect of FRP-RC deep beams is not reasonably considered in current design codes, while, Jin's theoretical model can scientifically reflect the effect of beam depth and shear-span ratio on the nominal shear strength. [ABSTRACT FROM AUTHOR]

The fate of organic matter (OM) in the deep ocean remains enigmatic, with little understood regarding the flux and its utilization by deep food webs. We used compound‐specific nitrogen stable isotope ratios of source amino acids measured in particle size classes and deep zooplankton (700–1500 m) to determine the contribution of small (0.7–53 μm) vs. large particles (> 53 μm) to their diet at four sites in the North Pacific. Our results show that small particles constitute between 9% and 98% of zooplankton diets, being the contribution higher at sites with lower flux regimes. The contribution of small particles to the diet of deep zooplankton was also higher when biomass of vertical migrators, and therefore actively transported OM, was lower. Climate‐driven changes in primary production and export are expected to shift particle fluxes to smaller size classes, and thus their importance in midwater food webs may become more widespread. [ABSTRACT FROM AUTHOR]

Ear recognition technology has long been dominated by (local) descriptor-based techniques due to their formidable recognition performance and robustness to various sources of image variability. While deep-learning-based techniques have started to appear in this field only recently, they have already shown potential for further boosting the performance of ear recognition technology and dethroning descriptor-based methods as the current state of the art. However, while recognition performance is often the key factor when selecting recognition models for biometric technology, it is equally important that the behavior of the models is understood and their sensitivity to different covariates is known and well explored. Other factors, such as the train- and test-time complexity or resource requirements, are also paramount and need to be consider when designing recognition systems. To explore these issues, we present in this paper a comprehensive analysis of several descriptor- and deep-learning-based techniques for ear recognition. Our goal is to discover weak points of contemporary techniques, study the characteristics of the existing technology and identify open problems worth exploring in the future. We conduct our analysis through identification experiments on the challenging Annotated Web Ears (AWE) dataset and report our findings. The results of our analysis show that the presence of accessories and high degrees of head movement significantly impacts the identification performance of all types of recognition models, whereas mild degrees of the listed factors and other covariates such as gender and ethnicity impact the identification performance only to a limited extent. From a test-time-complexity point of view, the results suggest that lightweight deep models can be equally fast as descriptor-based methods given appropriate computing hardware, but require significantly more resources during training, where descriptor-based methods have a clear advantage. As an additional contribution, we also introduce a novel dataset of ear images, called AWE Extended (AWEx), which we collected from the web for the training of the deep models used in our experiments. AWEx contains 4104 images of 346 subjects and represents one of the largest and most challenging (publicly available) datasets of unconstrained ear images at the disposal of the research community. [ABSTRACT FROM AUTHOR]

A reprint of the article "OD Gets Wired" by Maya Townsend, Barbara Christian, Jo-Ann Hague, Deb Peck, Michael Ray and Bauback Yeganeh, which appeared in the 2008 issue of "OD Practitioner," is presented. Topics include facts of life and the world; and that people are radically connected, and the importance of collaboration.

In the globalized and digitalized society of the twenty-first century, it is of great interest to study the relation between bitcoin and Criminal Law. First, the challenge is to delineate conceptually and legally this virtual currency and elucidate whether we face a sophisticated pyramid scheme. Second, an analysis follows on the reasons why using bitcoins favors the commission of crimes like employing ransomware or laundering money through cryptocurrencies. Finally, it is highlighted the need to reinforce the mechanisms of self-protection of potential victims. [ABSTRACT FROM AUTHOR]

Tor (The Onion Router) is one of the most famous anonymous networks in the Deep Web. It provides a wide range of legal and illegal hidden services to the user. Recognizing such illicit domains is a challenging task for Cyber Security and Law Enforcement Agencies. However, doing it manually, based only on the officers' experience, is slow and prone to errors. Therefore, in this paper, we propose an automatic method based on perceptual hashing to recognize services on the Tor network only by means of their snapshots. Firstly, we introduce and make publicly available DUSI-2K (Darknet Usage Service Images-2K), an image dataset which contains snapshots from active Tor service domains. We also present a new, efficient, robust and discriminative image hashing method, named F-DNS, built by incorporating the Dominant Neighborhood Structure (DNS) map and the Global Neighborhood Structure (GNS) texture energy map extracted from the discrete cosine transform of the image. In order to evaluate the efficiency of our hashing method, we carry out intra- and inter- tests using images from some state-of-the-art datasets subject to various content-preserving operations. The high correlation coefficient values that our method obtains, indicates that F-DNS performs better than other state-of-the-art methods, especially in the case of rotation. Additionally, we assess F-DNS for recognizing the category of Tor domains based on their snapshots using the DUSI-2K dataset. We compare its performance with three typical image classification methods, i.e. Bag of Visual Words (BoVW) and features obtained from ResNet50 and Inception-ResNet-v2. F-DNS outperforms all of them, with an accuracy of 98.75%, against 31.39%, 82.70% and 85.19%, respectively. Fine-tuning ResNet50 and Inception-Resnet-v2 for DUSI-2K does not improve the result, attaining 37.12% and 79.15%, respectively. [ABSTRACT FROM AUTHOR]