Supporting tactical intelligence using collaborative environments and social networking

The performance of image classification technology based on deep network has been greatly improved, making computer vision enter the stage of industrialization and be gradually applied to many aspects of human work and life. As a typical classification task in computer vision, human behavior recognition has immeasurable potential value in medical, family, transportation, and other scenarios. At the same time, in the field of competitive sports, the integration of artificial intelligence technology and sports technical and tactical analysis is undoubtedly an important way to innovate and improve the technical and tactical level. Taking karate as an example, the study of athletes’ training and competition videos is an important means and method for technical and tactical analysis in competitive sports. Traditional tactical intelligence analysis methods have many shortcomings, such as high labor cost, serious data loss, long delay, and low accuracy. Therefore, based on the convolutional neural network, this paper establishes a new graph convolution model for automatic intelligent analysis of karate athletes’ technical action recognition, action frequency statistics, and trajectory tracking. The technology effectively makes up for the disadvantages of traditional tactical intelligence analysis methods. The research results show that the new topology map construction method has a significant effect on improving the accuracy of behavior recognition and also lays a foundation for technical and tactical analysis.

Competitive Intelligence (CI) can be described as a process of gathering, analysing and interpreting internal and external intelligence on competitors and the competitive environment to guide a company’s strategy, planning and tactical decision making in its commercial operations. Intelligence provision is done through the CI cycle, which includes identification of key intelligence topics and questions, intelligence collection, analysis, interpretation and dissemination. CI can bring value to the company if it is delivered regularly and if no elements of the CI cycle are overlooked.

The aim of this study is to reveal Open Education related social media usage in Turkey through social network analyses. To this end, the most widely used social media network in Turkey, Facebook, was chosen. All the pages and groups created on Facebook related to Open Education were found. A total of 207 groups and 521 pages were accessed and entered into the social network analysis. Within the scope of this study, the density and centrality features of the network were analyzed for groups whereas the density, centrality, and degrees features of the network were analyzed for pages. The study's results revealed that while pages and groups related to programs emphasizing current job opportunities and student support services were more actively used, commercial accounts attempted to exploit students' exam anxiety instead of generating actual content. The findings also revealed that well structured institutional pages appeal to students and have the potential to reach high levels of popular in short periods of time.

Intelligence analysis is not a passive process, but proactive and reactive. Analysts have the opportunity to request collection of further intelligence to improve the analysis. The identification and selection of collection opportunities is a human led process requiring an understanding of the value of a collection to be balanced against the cost and risks of making the collection. Argumentation frameworks are a mathematical formalization to describe arguments and conflicts between such arguments. Argumentation frameworks can be used to represent intelligence analyses, with observations, hypotheses and assumptions represented as arguments and conflicts between these represented by attacks. A value of information can be defined over an argumentation framework based on the change in the result of evaluation of the framework, according to argumentation semantics, when arguments are added or removed. When accompanied by a representation of collection opportunities, the value of information can be used to assess which collection opportunity is most beneficial. In this paper we define a framework for calculating the net benefit over a closed set of potential collection opportunities. This framework is demonstrated against an example intelligence analysis on maritime situational awareness scenario.

for many users nowadays social media plays a big role in their world, as it helps to connect with new people while keeping the old bonds intact. The drawback is that we can’t able to see that, the data is being generated every minute of a second and that data is kept secure in the hands of a few tech giants, who are using it and selling it as they see fit and using it in a manner as they see profitable and with this paper I’d like to propose that shifting social media from host-centric to the information-centric network on the blockchain would not only be safer yet convenient and secured.In peer-to-peer system, cryptocurrency is used to pay for and secure content acquisition.The blockchain lets individuals acquire ownership over their Data, Property and Assets. In social networking, a decentralised approach to blockchain technology protects privacy and allows for e-commerce, ledger based crowd funding, smart apps, and smart contracts. Platforms like Facebook, LinkedIn, Instagram, Twitter, Reddit, and other digital social network media platforms offer free for all access, which is convenient in the name of connecting and socialising but they are collecting are data and using it to sell it to other third party buyers for the price of our privacySome of the most important difficulties confronting social media network business period, profit is focused on advertising and analytics. False propaganda based information on (Facebook), rampant bullying and harassment (Twitter), single party controlled censorship, and demonetization are some of the most serious issues facing social networking (YouTube).

Social media has become a constant presence in the daily lives of young people. As a result, many educational procedures and processes have been influenced by the widespread use of social networks. The number of people using social media has continuously increased. Students’ perceptions on how social media affects their learning is a prominent subject of research. Students’ school achievement has been significantly impacted by their interaction with professors, peers, and online knowledge-sharing behaviors. Several aspects are involved in this review, which aims to shed light on how students use social media in higher education. Findings reveal that the usage of online social media for cooperative learning encourages students to be innovative, dynamic, and research oriented. In other words, it is a matter of academic competence. This study gathered and analyzed social media data in higher education for computing disciplines. Digital libraries from EBSCO, IEEE, and ACM were utilized for data gathering. All of the above traits were found to be positively influenced by social networks, which suggest that their usage in higher education should be broadened. Teachers and educational institutions have not taken full use of internet social networks. Keywords: social media; social networks; higher institutions education

Navigating Social Media in #Ophthalmology

A novel intelligent food consumption data collection and analysis system is presented in this paper. Information collection devices and an analysis system are the two important constituent part of this system which are introduced in detail. We design this system to solve the problems such as statistical and administration departments having difficulty to acquire actual data in resident food consumption, people having demand of healthy diet with the improvement of living standard, and the demand of food safety regulation. The design combines applied techniques including smartphones, intelligent wearable devices and big data. Cloud service is utilized to provide information service after customers' food data are collected.

Tourism is growing very rapidly with the development of technology, especially information technology .Through information technology people can easily get access to information about various destinations and tourist attractions as well as hotel by online media communication. Therefore, this study was trying to use social media such as facebook and twitter, where both social media were widely used by companies engaged in tourism in the world as well as in Bali. Internet consists of several websites, but the most effective one to conduct the research on tourism such as webseries 2.0 or commonly called as social networking media. Social medias - Facebook and Twitter - both were wildly used by the companies running in the sector of tourism in Bali, such as Hotels, Villas, Restaurants, Spas, Travel Agents, Airlines, and Tourist Attraction Managements, to advertised their products and services in their respective field. Through those social media, tourists will be able to access the comments or reviews about the companies. The problem raised in this research is how Bali’s tourism data in social networking media were used and considered, and how was the image of Bali’s tourism in their point of viewbased on the research. The method used here was descriptive qualitative method which describes this phenomenon descriptively by analyzing the obtained comments on social networking media such as facebook and twitter, and then classified in the form of comments positive, negative, and unidentified comment. Afterwards comments were re-analyzed through 4A Approach which consist of Attraction, amenities, Ancillary, and Accessibility. Data which has been obtained re-analyzed via the data obtained from the homepage of (TripAdvisor and Agoda.com). In addition, to strengthen the research data obtained from the homepage of TripAdvisor and Agoda.com , then it has been elaborated on the point rating system which indicatedthat the company were registered in both of those social networking media such as Hotel, Villa, SPA, restaurants, travel agents, airlines, media, tourist attraction management has a good rating result, and it has represented that the image of Bali tourism has a good rank in the eyes of local and international tourists. This data was also strengthened by the existence of empirical studies where there were the data from a hundred tourists came to Bali both locally and internationally has filled out the questionnaire about Bali in order to obtain maximum results on the image of Bali tourism. The results showed that, social networking media were effectively used in this study. Many companies in Bali used facebook and twitter. In addition, Bali Tourism image placed in positive rank on both of social networking sites, and it was gained by the results of questionnaires spread towards the visitor.

Disruption of a terrorist attack depends on having information facilitating the identification and location of those involved in supporting, planning, and carrying out the attack. Such information arises from myriad sources, such as human or instrument surveillance by intelligence or law enforcement agencies, a variety of documents concerning transactions, and tips from a wide range of occasional observers. Given the enormous amount of information available, a method is needed to cull and analyze only that which is relevant to the task, confirm its validity, and eliminate the rest. The risk‐based methodology for scenario tracking, intelligence gathering, and analysis for countering terrorism builds on the premise that in planning, supporting, and carrying out a terrorist plot, those involved will conduct a series of related activities for which there may be some observables and other acquirable evidence. Those activities taken together constitute a threat scenario . Information consistent with a realistic threat scenario may be useful in thwarting an impending attack. Information not consistent with any such scenario is irrelevant. Thus, the methodology requires a comprehensive set of realistic threat scenarios that would form a systemic process for collecting and analyzing information. It also requires a process for judging the validity and usefulness of such information. The key questions for intelligence gathering and analysis are: how to produce a comprehensive set of threat scenarios, how to winnow that set to a subset of most likely scenarios, what supplementary intelligence is worth pursuing, how to judge the relevance of available information, and how to validate and analyze the information. The methodology presented in this paper can serve as a vehicle with which to enable the intelligence community to better: (a) assess the intent and capabilities of terrorist groups, (b) develop and compare terrorist scenarios from different sources and aggregate the set that should guide decisions on intelligence collection, (c) assess the possible distributions of responsibility for intelligence gathering and analysis across various homeland security agencies at the federal, state, and local levels, and (d) establish effective collection priorities to meet the demands of counterterrorism. Some of the critical issues addressed in this paper include: (1) how to create a reasonably complete set of scenarios and filter it down to a more manageable set to establish intelligence collection priorities, (2) how to integrate the wide variety of intelligence sources associated with monitoring for terrorism and analytically account for the corresponding disparities in information reliability, and (3) how to incorporate these new methodologies into existing information management efforts related to protecting our nation's critical infrastructures. © 2003 Wiley Periodicals, Inc. Syst Eng 6: 152–169, 2003 7

Intelligence is judged by its ability to avoid surprises. Unfolding the dry logic of uncertainty shows how intelligence failures caused by surprises are unavoidable. This is done by the systematic study of the general conditions that trigger the different kinds of surprise, categorized according to their different epistemological nature. Five conceptual categories of surprise are analyzed: lack of expectations; insufficient evidence between two or more alternative courses of action; false justified beliefs; incomplete theory to ground a belief, and an utterly false expectation produced by our (usually reliable) mindset. Intelligence serves as a countermeasure to opponents’ actions at strategic and tactical levels. However, intelligence analysis can only minimize the risk; it cannot eliminate it altogether.

IntroductionThis paper presents an innovative Intelligent Robot Sports Competition Tactical Analysis Model that leverages multimodal perception to tackle the pressing challenge of analyzing opponent tactics in sports competitions. The current landscape of sports competition analysis necessitates a comprehensive understanding of opponent strategies. However, traditional methods are often constrained to a single data source or modality, limiting their ability to capture the intricate details of opponent tactics.MethodsOur system integrates the Swin Transformer and CLIP models, harnessing cross-modal transfer learning to enable a holistic observation and analysis of opponent tactics. The Swin Transformer is employed to acquire knowledge about opponent action postures and behavioral patterns in basketball or football games, while the CLIP model enhances the system's comprehension of opponent tactical information by establishing semantic associations between images and text. To address potential imbalances and biases between these models, we introduce a cross-modal transfer learning technique that mitigates modal bias issues, thereby enhancing the model's generalization performance on multimodal data.ResultsThrough cross-modal transfer learning, tactical information learned from images by the Swin Transformer is effectively transferred to the CLIP model, providing coaches and athletes with comprehensive tactical insights. Our method is rigorously tested and validated using Sport UV, Sports-1M, HMDB51, and NPU RGB+D datasets. Experimental results demonstrate the system's impressive performance in terms of prediction accuracy, stability, training time, inference time, number of parameters, and computational complexity. Notably, the system outperforms other models, with a remarkable 8.47% lower prediction error (MAE) on the Kinetics dataset, accompanied by a 72.86-second reduction in training time.DiscussionThe presented system proves to be highly suitable for real-time sports competition assistance and analysis, offering a novel and effective approach for an Intelligent Robot Sports Competition Tactical Analysis Model that maximizes the potential of multimodal perception technology. By harnessing the synergies between the Swin Transformer and CLIP models, we address the limitations of traditional methods and significantly advance the field of sports competition analysis. This innovative model opens up new avenues for comprehensive tactical analysis in sports, benefiting coaches, athletes, and sports enthusiasts alike.

: The Central Intelligence Agency (CIA) is charged with the responsibility for collection and analysis of intelligence and its distribution to the president and his staff. In recent years this responsibility has been expanded to include Congress and other government departments and agencies. As policymakers deal with more complicated and time-sensitive issues they look for clear and complete intelligence information that will inform and guide their decision-making efforts. Policymakers work with ambiguity and complexity. There is a natural tendency for them to seek intelligence information which supports their own view. They consider intelligence information which support their position to be a potential political threat. Some will ignore unfavorable intelligence, while others may attempt to discredit it through the political process (Gates, 218-221). It is also generally acknowledge that in a politically charged environment some policymakers selectively use intelligence to influence public debate or policy. A small number disingenuously label intelligence they dislike as too soft, biased, slanted or cooked to destroy its legitimacy (Gates, 219).

With the advent of social network services such as Twitter, Facebook, Tumbler, and Google+, the research on social network and media analysis has been greatly advanced. In recent years, the interactions among people, sharing of knowledge and experiences, community activities in social network services increase greatly, which would make the research on social networks more important. Furthermore, as social media contents within social network services are rapidly being produced and consumed, the social media contents now account for the majority of content published on the world wide web. Social media is differentiated from traditional media in many aspects such as its frequency, quality, usability, immediacy, and permanence, which leads to significant potential to the social media analysis research.

We aimed to investigate if social networks measures can be used as indicators of training tasks' adjustment level to soccer players’ tactical skills. Twenty-four U17 male soccer players (16.89 ± 0.11 years) participated in this study. The System of Tactical Assessment in Football (FUT-SAT) was used to identify players’ tactical level and to organize them into three groups: Higher tactical level (Group 01), Intermediate tactical level (Group 02) and Lower tactical level (Group 03). Then, the players performed three High difficulty Small-Sided and Conditioned Games (HD-SSCG) and three Low difficulty Small-Sided and Conditioned Games (LD-SSCG). Teams’ interaction patterns and players’ prominence were analysed based on macro (Density – D and Clustering coefficient – CC) and micro networks (Indegree, Outdegree, Total links and Eigenvector) measures. We found that Group 01 presented higher D (p = .004 and ES = 1.189) and CC (p =.004 and ES = .785) at HD-SSCG than Group 03, whereas Group 03 presented higher values of D (p = .003 and ES = 1.200) and CC (p = .037 and ES = 1.180) at LD-SSCG than Group 01. When training tasks difficulty were adjusted to players’ tactical level, teams played more collectively and players were more actively engaged in ball circulation. We concluded that macro and micro networks measures can be applied in training context as indicators of training tasks adjustment to players' tactical level.