Menyerupai Allah: Manusia yang Mengaktualisasikan Diri

This paper is a study of the question of how can human beings carry out a call that 'becoming like God'? To answer this question, a construction model of ideas referred to as 'self-actualizing' is mentioned in this paper. It will be presented in two main theses. First, Aquinas's epistemology-metaphysical concept can be a solution to the unclear in understanding the idea of 'becoming like God'. Second, with the participation of Heidegger's thinking on the concept of dasein, mankind can find concrete steps in trying to become like God, namely by actualizing all the potential they have. This paper uses a literature study by examining and analyzing the literature related to the relevant topic. The conclusion in this paper is that the potential that humans have is always in a continuous process to pursue their actuality. This is similar to God Himself who is a pure act.AbstrakTulisan ini merupakan sebuah telaah atas pertanyaan bagaimana manusia dapat melaksanakan panggilan 'menyerupai Allah'? Untuk menjawab pertanyaan tersebut, sebuah model konstruksi gagasan yang disebut sebagai 'mengaktualisasikan diri' diajukan dalam tulisan ini. Hal ini akan dipaparkan dalam dua tesis utama, yaitu, pertama, bahwa konsep epistemologi-metafisika tradisi Aquinas dapat menjadi solusi atas kekaburan dalam memahami gagasan tentang 'menyerupai Tuhan'. Kedua, bahwa dengan partisipasi pemikiran Heidegger mengenai konsep dasein, maka manusia dapat menemukan langkah konkret dalam berupaya untuk Menyerupai Allah, yakni dengan mengaktualisasikan segala potensi yang ia miliki. Tulisan ini menggunakan studi pustaka dengan memeriksa dan menganalisis literatur terkait topik yang relevan. Kesimpulan dalam tulisan ini yaitu potensi yang manusia miliki selalu berada dalam sebuah proses terus-menerus untuk mengejar aktualitasnya. Hal tersebut serupa dengan Allah sendiri yang adalah pure act.

APPLICATION OF THE APPROACH TO CREATE AN INFORMATION CONSTRUCTION MODEL AT THE PRE-DESIGN STAGE OF INVESTMENT AND CONSTRUCTION PROJECTS

At the present stage of development of the construction industry, steps to improve the efficiency of investment and construction projects can no longer be considered outside the context of construction information modeling technologies. Today, one of the main criteria for optimizing work on investment and construction projects is to reduce the time spent searching, working out and agreeing on spatial, financial, and temporal information about the construction object, therefore, the issue of the correct organization of information modeling of a construction site is quite relevant. In most cases, the design assignment is drawn up in the form given in Appendix B DBN A 2.2-2-2014 and is formal in nature. The presence of a design assignment, drawn up using the tools proposed below, reflects the thoroughness of the customer's elaboration of an investment and construction project, and reduces the risks of its implementation. The proposed methodology is based on three components of counter design, such as disaggregation, aggregation, and decomposition, from which its name – DAD –approach comes from. Allocation of structural elements in the structure of a building by disaggregation (analysis of alternative options) allows to create its model in the form of a hierarchical structure consisting of detailed and mutually synchronized parts - the configuration of the building. In accordance with the configuration of the construction site, hierarchical numbering is further reflected at all stages of design and in all its sections, incl. stamps of drawings and local estimates. This creates a unified system of interconnection and control of project sections. The creation of a construction site configuration in the form of a tree model with hierarchically numbered elements at the pre-design stage allows it to be applied at subsequent stages of the life cycle of a construction object in information modeling technologies. The use of the DAD – approach in counter-design is intended to increase the efficiency of the implementation of investment and construction projects.

Music and philosophical naturalism

<p>The philosophical and scientific explication of music is a cutting-edge field in contemporary academia. This thesis develops a naturalistic framework for theorising about music. The following novel philosophical positions are motivated and defended: a polysemy analysis of “sound”, conceptual pluralism about music, a pluralistic framework for approaching the science of music, and a fictionalist account of Western musical artworks. The adaptation/ by-product framework for couching discussion about the evolution of music is critiqued. A novel, co-evolutionary, niche construction model of the foundations of musicality and the origins, expansion and stabilisation of music is developed, couched in the general context of hominin evolution and prehistory. Conceptual and methodological reflection accompanies the evolutionary scenario developed.</p>

Music and philosophical naturalism

<p>The philosophical and scientific explication of music is a cutting-edge field in contemporary academia. This thesis develops a naturalistic framework for theorising about music. The following novel philosophical positions are motivated and defended: a polysemy analysis of “sound”, conceptual pluralism about music, a pluralistic framework for approaching the science of music, and a fictionalist account of Western musical artworks. The adaptation/ by-product framework for couching discussion about the evolution of music is critiqued. A novel, co-evolutionary, niche construction model of the foundations of musicality and the origins, expansion and stabilisation of music is developed, couched in the general context of hominin evolution and prehistory. Conceptual and methodological reflection accompanies the evolutionary scenario developed.</p>

Where was the Toaster? Interplay of Episodic Memory Traces and Semantic Knowledge during Scenario Construction

Experiences that are retrieved from memory are often not accurate, but prone to biases. The interplay of already existing semantic knowledge and recently generated episodic memory traces might explain some of the underlying mechanisms. The scenario construction model postulates that during encoding, only the gist of an episode is stored in the episodic memory trace and during retrieval, any needed information that is missing from that trace is constructed from semantic knowledge. The current study aimed to investigate semantic construction in a realistic, yet controlled setting. Using a desktop virtual reality (VR) participants navigated through a flat in which some household items appeared in unexpected rooms, creating conflicts between the experienced episode and semantic expectations. During recall after two separate retrieval delays (one day vs one week) we were able to identify influences from semantic knowledge. To manipulate salience, some objects were task-relevant, and some were irrelevant to the sequence of actions. We used spatial and temporal recall measures. Both congruency and task-relevance, but not time, predicted correct episodic memory retrieval. In the spatial memory retrieval, semantic construction was more likely than guessing in cases of episodic memory failure and occurred more frequently for task-irrelevant objects. In the temporal recall at the second retrieval delay we could show that object-pairs belonging to the same semantic room-category were temporally clustered together compared to object-pairs from different semantic room-categories. Taken together, our findings support the predictions of the scenario construction model, as we found influences of semantic knowledge on both spatial and temporal memory recall. The new VR-paradigm appears to be a promising tool for investigating semantic construction.

Deep-insight visible neural network (DI-VNN) for improving interpretability of a non-image deep learning model by data-driven ontology

We aimed to provide a framework that organizes internal properties of a convolutional neural network (CNN) model using non-image data to be interpretable by human. The interface was represented as ontology map and network respectively by dimensional reduction and hierarchical clustering techniques. The applicability is to implement a prediction model either to classify categorical or to estimate numerical outcome, including but not limited to that using data from electronic health records. This pipeline harnesses invention of CNN algorithms for non-image data while improving the depth of interpretability by data-driven ontology. However, the DI-VNN is only for exploration beyond its predictive ability, which requires further explanatory studies, and needs a human user with specific competences in medicine, statistics, and machine learning to explore the DI-VNN with high confidence. The key stages consisted of data preprocessing, differential analysis, feature mapping, network architecture construction, model training and validation, and exploratory analysis.

Classification of sponge city construction modes based on regional features

In order to make the sponge city construction model more consistent with the inherent characteristics of the city, this paper constructs classification of the sponge city construction index system. We carry out multivariate statistical and cluster analysis based on GIS and classify the different regions of mainland China, put forward key points of construction for different types of regions. The results show that climatic factor, hydrological factor and soil factor are the main factors affecting sponge city construction, followed by city scale and level of urban economic development, the third is underlying surface type and geomorphological type. The cities are classified into ten clusters, and they present a continuously zonal or flaky distribution from northeast to southwest on the whole, more than 80% of cities obviously present a continuously zonal or flaky distribution. Each of the nine clusters has at least one pilot city, which can be used as a reference for construction.

Construction of Multimedia-Assisted English Teaching Mode in Big Data Network Environment

Oral English teaching is the weakest link in multimedia English teaching at this stage. English teachers are constantly exploring effective approaches to improve oral English Teaching in their own educational practice. The big data multimedia English teaching mode conforms to embark on the historical stage. Firstly, this paper constructs the big data architecture of English teaching model mining and divides the construction of the teaching model into three parts: data mining, teaching model evaluation, and improvement optimization. Data mining uses the advanced DBN network to send data into the DBN-DELM network, which significantly improves the accuracy of the multimedia assisted English construction model. The simulation results show that teaching mode construction based on big data can effectively improve students’ interest in English learning; attitude; and oral English level including pronunciation, pronunciation and intonation, dialogue and communication, and oral expression and improve students’ group cooperation and communication ability, autonomous learning ability, evaluation consciousness, and ability.