Web Events Research Articles

Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime

Malicious actors, specially trained professionals operating anonymously on the dark web (DW) platform to conduct cyber fraud, illegal drug supply, online kidnapping orders, CryptoLocker induction, contract hacking, terrorist recruitment portals on the online social network (OSN) platform, and financing are always a possibility in the hyperspace. The amount and variety of unlawful actions are increasing, which has prompted law enforcement (LE) agencies to develop efficient prevention tactics. In the current atmosphere of rapidly expanding cybercrime, conventional crime-solving methods are unable to produce results due to their slowness and inefficiency. The methods for accurately predicting crime before it happens "automated machine" to help police officers ease the burden on personnel while also assisting in preventing offense. To achieve and explain the results of a few cases in which such approaches were applied, we advise combining machine learning (ML) with computer vision (CV) strategies. This study's objective is to present dark web crime statistics and a forecasting model for generating alerts of illegal operations like drug supply, people smuggling, terrorist staffing and radicalization, and deceitful activities that are connected to gangs or organizations showing online presence using ML and CV to help law enforcement organizations identify, and accumulate proactive tactics for solving crimes.

Leveraging enterprise knowledge graph to infer web events’ influences via self-supervised learning

Knowledge graph (KG) techniques have achieved successful results in many tasks, especially in semantic web and natural language processing domains. In recent years, representation learning on KG has been successfully applied to e-business applications, such as event-driven automatic investment strategies. However, there is still limited research about learning events’ influence on KG for modern quantitative investment. In this paper, we propose a novel event influence learning framework to predict stock market trends, called ST-Trend, leveraging enterprise knowledge graph to represent company correlation relationships, for mining the deep background knowledge of web events, with three self-supervised learning tasks. In particular, we devise two jointly self-supervised tasks to identify the relations between web events and companies. The first task is for generating ground-truth event-company correlation labels based on the enterprise knowledge graph. The second task is used to train how to identify the correlated companies of an event based on the generated correlation labels, with the encoding of web events, company features, and technical sequential data. We then design the prediction network to infer an event’s influence on stock price trends of the identified correlated companies based on the enterprise KG. Finally, we perform extensive experiments on a massive real-life dataset to validate the effectiveness of our proposed framework, and the experimental results demonstrate its superior performance in predicting stock market trends via considering events’ influences with the enterprise knowledge graph.

ENVIRONMENTAL EDUCATION AND COVID-19: LEARNING, DIGITAL TECHNOLOGY, AND THE USE OF YOUTUBE AS A TEACHING PLATFORM IN PANDEMIC

Environmental education (EE) is an indispensable and urgent cross-cutting theme for society. Theisolation brought about by the Covid-19 pandemic has profoundly changed social relationshipsand daily routines. In the teaching context, digital technology and e-learning have gainedemphasis in academic activities and events. The objective was to identify and discuss howYouTube™ supported the expansion of higher education. The qualitative study examined 283videos on YouTube™ posted between February 2020 to February 2021, which went throughexploratory, bibliographic, and thematic analysis. Watching online videos is one of the mostperformed activities in Brazil, according to data from PNAD (2017), and, in the learning process,YouTube™ presents the most effective resources in supporting the dissemination of e-learningby university students through web events. In general, YouTube™ videos have widely served tomeet the interests of various institutions that sought success, ease, democratization, anddissemination of audiovisual content but urgently need to expand the digital accessibility of theirevents to people with visual and hearing disabilities

GeoTraPredict: A machine learning system of web spatio-temporal traffic flow

Traffic flow prediction is an important component for self-driving. Traffic flow is closely related to population distribution, and the traffic flow is not only related to the absolute number of human population but also to their concerns and interests. Accurate spatio-temporal web traffic flow prediction is critical in many applications, such as bandwidth allocation, anomaly detection, congestion control and admission control. Most existing traffic flow prediction methods use models based on time-series analysis and remain inadequate for many real-world applications. Web traffic flow is found to be strongly associated with the spatio-temporal distribution of the population. Increasingly, it is critical to understand and make decisions based on the relationship between population patterns and web traffic flow patterns. It has been proven that different people have different responses to web events. Due to the complexity of spatial data structures and the huge volume of web traffic flow log data, it is difficult to routinely find the relationship between web events and population distributions without an appropriate processing framework. In this paper, we propose an innovative framework named GeoTrafficPredict to support the accurate spatio-temporal prediction of web traffic flow. GeoTrafficPredict provides a machine learning platform to learn the spatio-temporal pattern of traffic flow and use the pattern to predict the trend in both spatial and temporal dimension. Also, GeoTrafficPredict provide data aggregation portal and cloud-based computation function. GeoTrafficPredict deploys a series of computational images in a cloud computing environment, and the implementation on China’s CSTNET illustrates the performance of our platform.

Web event evolution trend prediction based on its computational social context

Predicting future trends of Web events can help significantly improve the quality of Web services, e.g., improving the user satisfaction of news websites. Existing approaches in this regard are based mainly on temporal patterns mined with the assumption that enough temporal data is available on hand. However, most Web events do not have a long lifecycle, but a burst property, which drastically reduces the performance of temporal patterns mining. Furthermore, these approaches overlook the influence of the social context surrounding the Web events. In this paper, we propose a novel method to predict future trends of Web events, based on their social contexts rather than temporal patterns. More specially, in the proposed method, a computational model for the social context is first built as a two-layer Association Linked Network considering its properties, such as the associative network property and the small world property. Then, the interaction between a Web event and the social context is simulated, based on the anchoring theory. Finally, an external force is defined and evaluated to quantify the influence of the social context on the evolution of Web events, which is used to predict future trends of Web events. Experiments show that the performance of the proposed method is better than that of the traditional time series-based approaches.

First observations of white sharks (Carcharodon carcharias) attacking a live humpback whale (Megaptera novaeangliae)

To date, white shark (Carcharodon carcharias) feeding events involving large whales have largely been described only in terms of observed scavenging events. Scavenging occurs in all ecosystems and is usually associated with stochastic feeding events. In marine ecosystems, whale carcasses commonly provide these community-wide food web events. Whale carcasses are the single largest source of carrion in marine ecosystems, to the extent that they are thought to constitute an important part of large white shark foraging ecology, shaping many aspects of the life history of the sharks, including adult migrations. However, to date, no part of this white shark feeding event has been described involving a live whale. To the best of our knowledge, here we provide the first published description of white sharks attacking and killing a live humpback whale (Megaptera novaeangliae). The attack displayed novel behaviours, including evidence of the ‘bite and spit’ tactic, rarely described in non-pinniped-related white shark feeding events before and all part of a tactical timeline of an attack that is precise, deliberate and effective.

Causal relation extraction and network construction of web events

The explosive increase of news data on the web has created a mass of causal knowledge, which explains the causal relation between two events that effect event will occur following the occurrence of cause event. Analysis of causal knowledge has gain lots of attentions due to its widespread applications, such as question answering, event prediction, generating future scenarios, and commonsense causal reasoning. However, few researches are based on Chinese news corpus, and no effective causal template is proposed for extracting Chinese causal relationship. Therefore, the method for extracting causal relation and building network of causal events from Chinese news corpus is proposed. First, we propose a method to obtain complete cue phrases set and present four common causal patterns to extract causal relations. And then we merge the same events by similarity calculation of causal events. At last, a network of causal events is constructed. Experiments on the datasets show the effectiveness of the proposed approach.

Causal relation extraction and network construction of web events

The explosive increase of news data on the web has created a mass of causal knowledge, which explains the causal relation between two events that effect event will occur following the occurrence of cause event. Analysis of causal knowledge has gain lots of attentions due to its widespread applications, such as question answering, event prediction, generating future scenarios, and commonsense causal reasoning. However, few researches are based on Chinese news corpus, and no effective causal template is proposed for extracting Chinese causal relationship. Therefore, the method for extracting causal relation and building network of causal events from Chinese news corpus is proposed. First, we propose a method to obtain complete cue phrases set and present four common causal patterns to extract causal relations. And then we merge the same events by similarity calculation of causal events. At last, a network of causal events is constructed. Experiments on the datasets show the effectiveness of the proposed approach.

Association link network based semantic coherence measurement for short texts of web events

As novel web social Media emerges on the web, large-scale short texts are springing up. Although these massive short texts contain rich information, their disorder nature makes users difficult to o...

Public Sentiments Analysis Based on Fuzzy Logic for Text

Sentiment analysis from microblog platform has received an increasing interest from web mining community in recent years. Current sentiment analysis methods are mainly based on the hypothesis that each word expresses only one sentiment. However, human sentiment are prototyped and fuzzy-confined as declared in social psychology, which is conflicting with the hypothesis. This is one of the barriers that impede the computation of complex public sentiment of web events in microblog. Therefore, how to find a reasonable computational model, combining learning technology and human sentiment cognition theory, is a novel idea in event sentiment analysis of microblog. In this paper, a new sentiment computation approach, which is defined as public sentiments discriminator (PSD), considering both fuzzy logic and sentiment complexity, is proposed. Unlike traditional machine learning methods, PSD is based on the rational hypothesis that sentiments are correlated with each other. A three-level computing structure, sentiment-term level, microblog level and public sentiment level, is employed. Experiments show that the proposed approach, PSD, can achieve similar accuracy and [Formula: see text]1-measure but more cognitive results when compared with traditional well-known machine learning methods. These experimental studies have confirmed that PSD can generate an interpretable result with no restriction among sentiments.

The Mobile Media Based Emergency Management of Web Events Influence in Cyber-Physical Space

Event has been described as something unexpectedly-happened or a story, which is described by a series of text information sources in time sequence. In recent years, with the rapid development of the information society, the application services gradually become the main form of information dissemination and communication, such as social networking site, post bar, etc. The generated social impact by the application services propagation after an event occurrence is growing larger, while recently people pay much more attention to the research of the information dissemination model. In this paper, we proposed a Cyber-Physical Space Event Model (CPSEM) and an Event Influence Scope Detection Algorithm (EISDA) from Cyber-Physical Space, and made relevant experiments on the basis of formal description. The CPSEM analysed events from Cyber Space and Physical Space which effectively made up the sidedness only from one space. In addition, EISDA was proposed based on CPSEM in the further, which identified a hot event influence scope on the basis of two spaces. Experiments verified that CPSEM solved incompleteness and unreality of internet data, and EISDA identified scope and diffusion direction of event influence from a new perspective and have improved the experiment accuracy for business application.

Uncertainty Analysis for the Keyword System of Web Events

Webpage recommendations for hot Web events can assist people to easily follow the evolution of these Web events. At the same time, there are different levels of semantic uncertainty underlying the amount of Webpages for a Web event, such as recapitulative information and detailed information. Apparently, the grasp of the semantic uncertainty of Web events could improve the satisfactoriness of Webpage recommendations. However, traditional hit-rate-based or clustering-based Webpage recommendation methods have overlooked these different levels of semantic uncertainty. In this paper, we propose a framework to identify the different underlying levels of semantic uncertainty in terms of Web events, and then utilize these for Webpage recommendations. Our idea is to consider a Web event as a system composed of different keywords, and the uncertainty of this keyword system is related to the uncertainty of the particular Web event. Based on keyword association linked network Web event representation and Shannon entropy, we identify the different levels of semantic uncertainty, and construct a semantic pyramid (SP) to express the uncertainty hierarchy of a Web event. Finally, an SP-based Webpage recommendation system is developed. Experiments show that the proposed algorithm can significantly capture the different levels of the semantic uncertainties of Web events and it can be applied to Webpage recommendations.

RETRACTED: Crowd Sensing Based Burst Computing of Events Using Social Media

With the popularity of web, the internet is becoming a major information provider and poster of an event due to its real-time, open, and dynamic features. In this paper, crowd sensing based burst computation algorithm of a web event is developed in order to let the people know a web event clearly and help the social group or government process the events effectively. Different temporal features of web events are developed to provide the basics for the proposed computation algorithm. The burst power is presented to integrate the above temporal features of an event. Empirical experiments on real datasets including Google Zeitgeist and Google Trends show that the number of web pages and the average clustering coefficient can be used to detect events. The evaluations on real dataset show that the proposed function integrating the number of web pages and the average clustering coefficient can be used for event detection efficiently and correctly.

Measuring the veracity of web event via uncertainty

Web events, whose data occur as one kind of big data, have attracted considerable interests during the past years. However, most existing related works fail to measure the veracity of web events. In this research, we propose an approach to measure the veracity of web event via its uncertainty based on its features distribution on different kind of confident websites. Firstly, the proposed approach mines various event features from the data of web event which may influence on the measuring process of uncertainty. Secondly, one computational model is introduced to simulate the influence process of the above features on the evolution process of web event. Thirdly, matrix operations are managed to facilitate practice. Finally, experiments are made based on the analysis above, and the results proved that the proposed uncertainty measuring algorithm is promising to measure the veracity of web event for big data.

Web Event Topic Analysis by Topic Feature Clustering and Extended LDA Model

To analyze topics of a large number of web events, we proposed an event topic analysis approach by topic feature clustering and extended LDA (latent dirichlet allocation) model. The extended LDA model is dimension LDA (DLDA) which integrates topic probability of LDA model. We represent an event as a multi-dimensions vector and use DLDA model to select topic feature words in events. We aggregate events which have a common topic by topic feature clustering. In clustering process we use dynamic K-means method to automatically select suitable number of clusters. In this paper a topic term generating rule is proposed to compose topic terms by clustered topic feature words. We accurately detect a common topic from lots of different events and analyze topic terms for events. Experiments on dataset results show that the web event topic analysis approach has high accuracy.

Endovascular WEB Flow Disruption in Middle Cerebral Artery Aneurysms

The endovascular treatment of middle cerebral artery (MCA) aneurysms with unfavorable anatomy (wide neck, unfavorable morphology) is frequently challenging. Flow disruption with the WEB is a potentially interesting endovascular treatment for this type of aneurysm. To report in a multicenter series the preliminary treatment experience of MCA aneurysms with flow disruption by the WEB. Thirty-three patients with 34 MCA aneurysms were treated with the WEB in 5 European centers. The ability to successfully deploy the WEB, procedure- and device-related adverse events, morbidity and mortality of the treatment, and short-term angiographic follow-up results were analyzed. Most treated aneurysms were unruptured (85.3%) and were between 5 and 10 mm (85.3%) with a neck size ≥ 4 mm (88.2%). The treatment failed in 1 of the 34 aneurysms (2.9%) owing to a lack of appropriate device size. Treatment was performed exclusively with the WEB in 29 of 33 aneurysms (87.9%). Additional treatment (coiling and/or stenting) was used in 4 of 33 aneurysms (12.1%). Mortality of the treatment was 0.0% and morbidity was 3.1% (intraoperative rupture with modified Rankin Scale score of 3 at the 1-month follow-up). In short-term follow-up (range, 2-12 months), adequate occlusion (total occlusion or neck remnant) was observed in 83.3% of aneurysms. WEB flow disruption seems to be a promising technique for the treatment of complex MCA aneurysms, particularly those with a wide neck or unfavorable dome-to-neck ratio.

Comments: HSE Now

Editor's column This month, SPE will launch a new web- and app-based publication focusing on the health, safety, security, environment, and social responsibility (HSSE-SR) discipline. With news, updates, and technical information needed by oil and gas professionals working in this discipline, HSE Now will be updated continually and will be available on the web and mobile devices such as tablets and smartphones. It will be free to all SPE members. Unlike other SPE publications, HSE Now will include articles, reports, government documents, and other material from both SPE and non-SPE resources around the globe. The amount of information in this area has exploded in the past decade as the oil and gas industry is being held to higher standards in areas such as environ-mental responsibility, sustainability, and workplace safety. The occurrence of high-profile accidents such as the US Gulf of Mexico Macondo explosion has heightened public scrutiny of industry standards and practices. The new periodical also reflects an outreach by SPE to a group within the industry that does not have an obvious professional home. Last year, SPE launched a print publication, Oil and Gas Facilities, targeted to the increasingly important projects, facilities, and construction discipline and the publication has been very well received. The primary objective is to make it easier for SPE members working in this discipline to find critical information they need to do their jobs. Because the subtopics in this discipline are so diverse, there is no single source of information on the collective topic, and the SPE vehicle will serve as a curator of information, bringing together high-quality SPE and non-SPE information on the discipline. An editorial advisory board will help HSE Now plan and develop content. Its members are Roland Moreau, ExxonMobil, chairman and SPE Technical Director for Health, Safety, Security, Environment, and Social Responsibility; Tom Knode, Halliburton; Emmanuel Garland, Total; Herman Vacca, EP Energy; Davide Scotti, Saipem; Dave McLeod, Halliburton; Mark Whittenberg, Schlumberger; Chris Deepak, Halliburton; Clarion Johnson, ExxonMobil; Andrew Wojtanowicz, Louisiana State University. Moreau describes the increasing importance of HSSE-SR in the industry in this month’s Guest Editorial. Operators, service companies, and others involved in producing oil and gas to meet growing global demand are challenged today to do so safely and in a way that is compatible with the environmental and economic needs of local communities. SPE is also meeting the demand for more information and dialogue in this area through a growing number of regional HSSE-SR conferences around the world, in addition to workshops and on-demand web events. This month, an SPE Americas HSSR-SR conference will take place in Galveston, Texas, and similar conferences are scheduled this year in London, Lima, and Doha. The SPE International Conference on Health, Safety, and Environment will be held in March 2014 in Long Beach, California.

POTENTIAL ENERGY OF OCEAN CURRENT FOR ELECTRIC POWER GENERATIONIN COASTAL AREAS OF EAST FLORES, NTT

<p>The electrical energy is one of the important needs for coastal communities particularly in small islands that has not reached by national electricity network. In order to fulfill this particular electricity needs, we do variety diversification efforts, such as the utilization of the ocean currents. The purpose of this survey is to recognize the morphology of seafloor and characteristic of hydro-oceanography as a fixed reference location in using the current energy. The study area is in the Larantuka Strait between Flores and Adonara islands which is part of the Flores Strait, East Nusatenggara Province. The methods of this study are current measurements, tidal and meteorological parameter observations, condition of coastal morphology and seafloor of the study area. The result show that, the location for turbine position is in area with relatively gentle slope morphology at a 20 meters water depth and it is close to local community. Based on the analysis of ADCP mobile current measurement, the minimum current velocity is 0.004 m/sec and maximum 3.68 m/sec. Whereas, from the ADCP static measurements the current velocity is from 0.002 m/sec to 2.83 m/sec. This condition is closely related to the semi-diurnal tide type in the study area, with two pairs of flood and web events within 24 hours. Therefore, the results of ocean current energy analysis indicate that the study area is very potential for using as a power plant location.</p><p>Keywords: potential energy of ocean currents, power plants, energy diversification, Larantuka Strait, East Flores</p>

POTENTIAL ENERGY OF OCEAN CURRENT FOR ELECTRIC POWER GENERATIONIN COASTAL AREAS OF EAST FLORES, NTT

The electrical energy is one of the important needs for coastal communities particularly in small islands that has not reached by national electricity network. In order to fulfill this particular electricity needs, we do variety diversification efforts, such as the utilization of the ocean currents. The purpose of this survey is to recognize the morphology of seafloor and characteristic of hydro-oceanography as a fixed reference location in using the current energy. The study area is in the Larantuka Strait between Flores and Adonara islands which is part of the Flores Strait, East Nusatenggara Province. The methods of this study are current measurements, tidal and meteorological parameter observations, condition of coastal morphology and seafloor of the study area. The result show that, the location for turbine position is in area with relatively gentle slope morphology at a 20 meters water depth and it is close to local community. Based on the analysis of ADCP mobile current measurement, the minimum current velocity is 0.004 m/sec and maximum 3.68 m/sec. Whereas, from the ADCP static measurements the current velocity is from 0.002 m/sec to 2.83 m/sec. This condition is closely related to the semi-diurnal tide type in the study area, with two pairs of flood and web events within 24 hours. Therefore, the results of ocean current energy analysis indicate that the study area is very potential for using as a power plant location.Keywords: potential energy of ocean currents, power plants, energy diversification, Larantuka Strait, East Flores

UIC Student branch fans the "flames"

The University of Illinois at Chicago (UIC) adopted the nickname the Flames in reference to the Great Chicago Fire of 1871, so it's only fitting that the university's IEEE Student Branch (SB) possesses the ability to rebuild quickly. The SB was looking to boost its profile and by keeping its Web site and events simple and remaining steadfast in networking, participation has increased in less than a year. That's no small task for a school with over 25,000 students, most of whom are commuters. With 30 active SB members, UIC has reached out to those nomadic students by going back to basics.