The Implications of Understanding Contextual Hadith on Religious Radicalism (Case Study of Darus-Sunnah International Institute for Hadith Sciences)

In hadith studies, many conclusions state that textual reasoning towards hadith is the main cause of intolerance and radicalism. This makes some scholars such as Yusuf al-Qardhawi and Ali Mustafa Yakub offer a more complex understanding of hadith involving asbab wurud al-hadith, al-wahdah al-maudhu'iyyah fi al-hadith, ikhtilaf al-ahadith and so on. Hadith reasoning like this is considered by some as a contextualization of hadith that will prevent someone from religious radicalism. This study wants to answer the question, to what extent does contextual reasoning in hadith prevent a person from religious radicalism? Researchers will examine the formulation of contextual hadith reasoning initiated by Ali Mustafa Yaqub in al-Thuruq al-Shahihah fi Fahm al-Sunnah al-Nabawiyyah and standards of religious radicalism initiated by LIPI in the Strategy for Anticipating Radicalism and Religious Intolerance in Indonesia. Each will be used as an independent variable and dependent variable. This research is mixed research with the type of field research. The data processing technique used is a simple regression test using the SPSS 20 program. The research object of this study is the Mahasantri Darus-Sunnah International Institute for Hadith Sciences with a total of 32 people. While the sampling system in this study is a random sample. The results of this study indicate that contextual hadith reasoning has a sig. 0.008 which is less than 0.05 so it is said to have a significant effect between contextual hadith reasoning on religious radicalism. The magnitude of the influence of this hadith reasoning itself is 21.2%. The direction of the effect is negative with a magnitude of -.643 at a constant of 69.792. This means that there is a negative relationship between contextual hadith reasoning and religious radicalism. The more contextual a person's understanding of hadith is, the further away he is from radicalism.

Content Service Platform for Providing User-Centric Content in Smart Environments

Smart environments, such as smart cities and streets, contain various heterogeneous devices and content that provide information to users and interact with each other. In a smart environment, appropriate content should be provided based on the situations of users. Additionally, when a user is in motion, it is necessary to provide content in a seamless manner without interruption. A method for systematically controlling the delivery of such content is required. Therefore, we propose a content service platform to meet the needs discussed above. The content service platform supports the delivery of content and events between different devices, as well as the control of content. Context-aware technology can also be applied to support customized content. In this paper, we present an architectural model, a contextual reasoning process, and case study on applying content service platform to a smart street environment. The proposed content service platform applied as a base model to support the provision of user-specific content in smart environments.

Novel Machine learning approach for Self-Aware prediction based on the Contextual reasoning

Machine learning is compelling in solving various applied problems. Nevertheless, machine learning methods lack the contextual reasoning capabilities and cannot be fitted to utilize additional information about circumstances, environments, backgrounds, etc. Such information provides essential knowledge about possible reasons for particular actions. This knowledge could not be processed directly by either machine learning methods. This paper presents the context-aware machine learning approach for actor behavior contextual reasoning analysis and context-based prediction for threat assessment. Moreover, the proposed approach uses context-aware prediction to tackle the interaction between actors. An idea of the technique lies in the cooperative use of two classification methods when one way predicts an actor’s behavior. The second method discloses such predicted action (behavior) that is non-typical or unusual. Such integration of two-method allows the actor to make the self-awareness threat assessment based on relations between different actors where some multidimensional numerical data define the connections. This approach predicts the possible further situation and makes its threat assessment without any waiting for future actions. The suggested approach is based on the Decision Tree and Support Vector Method algorithm. Due to the complexity of context, marine traffic data was chosen to demonstrate the proposed approach capability. This technique could deal with the end-to-end approach for safe vessel navigation in maritime traffic with considerable ship congestion.

Data in Context: How Digital Transformation Can Support Human Reasoning in Cyber-Physical Production Systems

In traditional production plants, current technologies do not provide sufficient context to support information integration and interpretation. Digital transformation technologies have the potential to support contextualization, but it is unclear how this can be achieved. The present article presents a selection of the psychological literature in four areas relevant to contextualization: information sampling, information integration, categorization, and causal reasoning. Characteristic biases and limitations of human information processing are discussed. Based on this literature, we derive functional requirements for digital transformation technologies, focusing on the cognitive activities they should support. We then present a selection of technologies that have the potential to foster contextualization. These technologies enable the modelling of system relations, the integration of data from different sources, and the connection of the present situation with historical data. We illustrate how these technologies can support contextual reasoning, and highlight challenges that should be addressed when designing human–machine cooperation in cyber-physical production systems.

A rule-based argumentation framework for distributed contextual reasoning in dynamic environments

In this study, we tackled the problem of distributed reasoning in environments in which agents may have incomplete and inconsistent knowledge. Conflicts between agents are resolved through defeasible argumentation-based semantics with a preference function. Support for dynamic environments, where agents constantly enter and leave the system, was achieved by means of rules whose premises can be held by arbitrary agents. Moreover, we presented a formalism that enables agents to share information about their current situation or focus when issuing queries to other agents. This is necessary in environments where agents have a partial view of the world and must be able to cooperate with one another to reach conclusions. Hence, we presented the formalization of a multi-agent system and the argument construction and semantics that define its reasoning approach. Using example scenarios, we demonstrated that our system enables the modeling of a broader range of scenarios than related work.

Contextual and Possibilistic Reasoning for Coalition Formation

In multi-agent systems, agents often need to cooperate and form coalitions to fulfil their goals, for example by carrying out certain actions together or by sharing their resources. In such situations, some questions that may arise are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents’ abilities to carry out certain tasks? In this article, we address the question of how to identify and evaluate the potential agent coalitions, while taking into consideration the uncertainty around the agents’ actions. Our methodology is the following: We model multi-agent systems as Multi-Context Systems, by representing agents as contexts and the dependencies among agents as bridge rules. Using methods and tools for contextual reasoning, we compute all possible coalitions with which the agents can fulfil their goals. Finally, we evaluate the coalitions using appropriate metrics, each corresponding to a different requirement. To demonstrate our approach, we use an example from robotics.

An Efficient Framework for Dense Video Captioning

Dense video captioning is an extremely challenging task since an accurate and faithful description of events in a video requires a holistic knowledge of the video contents as well as contextual reasoning of individual events. Most existing approaches handle this problem by first proposing event boundaries from a video and then captioning on a subset of the proposals. Generation of dense temporal annotations and corresponding captions from long videos can be dramatically source consuming. In this paper, we focus on the task of generating a dense description of temporally untrimmed videos and aim to significantly reduce the computational cost by processing fewer frames while maintaining accuracy. Existing video captioning methods sample frames with a predefined frequency over the entire video or use all the frames. Instead, we propose a deep reinforcement-based approach which enables an agent to describe multiple events in a video by watching a portion of the frames. The agent needs to watch more frames when it is processing an informative part of the video, and skip frames when there is redundancy. The agent is trained using actor-critic algorithm, where the actor determines the frames to be watched from a video and the critic assesses the optimality of the decisions taken by the actor. Such an efficient frame selection simplifies the event proposal task considerably. This has the added effect of reducing the occurrence of unwanted proposals. The encoded state representation of the frame selection agent is further utilized for guiding event proposal and caption generation tasks. We also leverage the idea of knowledge distillation to improve the accuracy. We conduct extensive evaluations on ActivityNet captions dataset to validate our method.

A Contextual Review on the Evolution of Corporate Social Responsibility

This article attempts the perilous tasks of reviewing corporate social responsibility. Reviewing those literatures is a notorious challenge because corporate social responsibility has developed inconsistently. Authors that insist a precise definition are often disappointed because corporate social responsibility is a relative concept. It has never assumed a stagnated role. To encaptivate this review, this article peruses corporate social responsibility from a contextual approach. It reviews the development of corporate social responsibility at every stage of its evolution by addressing three contextual conundrums. Firstly, it peruses the motivational construct at every stage of development. This provides a critical insight on why corporate social responsibility was fashioned as such by analysing them contextually. Secondly, this review examines stakeholder inclusiveness at each epoch of development. This again critically exposes the category of beneficiaries included in each stage of progress categorising the evolution of their beneficiaries. Lastly, this work examines the extent of instutionalisation of corporate social responsibility illustrating the pattern in which the concept received legal and social acclamation. By addressing these three scopes, this article hopes to protrude categorically the contextual influence on corporate social responsibility so that reader(s) might understand at a deeper level the contextual reasoning and deduction on how the concept is shaped and reshaped.