IMPLEMENTASI MANAJEMEN DANA PADA BANK SYARIAH

Tulisan ini bertujuan untuk mengkaji impelementasi manajemen dana pada bank syariah. Pengelolaan dana Bank Syariah merupakan upaya untuk mengarahkan posisi dana yang diterima bank syariah dari kegiatan mengumpulkan dana menyalurkan dana dalam bentuk pembiayaan sehingga bank syariah tetap mampu memenuhi kriteria-kriteria likuiditas, rentabilitas dan solvabilitas. Sumber pendanaan bank syariah diantaranya berasal dari dana sendiri, Dana Pihak Ketiga (DPK) dan dana pinjaman. Dana bank syariah bisa berasal dari modal yaitu modal inti (core capital), kuasi ekuitas (Mudharabah Account), dan dana titipan (wadiah). Sementara itu penggunaan dana pada bank syariah terdiri atas pengeluaran untuk Earning Assests dan Non Earning Assets. Metode yang digunakan bank syariah didalam mengalokasikan dananya dibedakan menjadi dua pendekatan dengan mempertimbangan sumber dana yang diperoleh bank syariah yaitu Pool of fund approach dan Asset allocation approach.

Multi-UAV Cooperative Transportation Using Dynamic Control Allocation and a Reinforcement Learning Compensator

This paper proposes a new trajectory tracking method for a 6 degree-of-freedom (DOF) cable-suspended payload controlled by a team of quadrotors. Using the modeling convention of reconfigurable cable-driven parallel robots (RCDPRs) the coupled dynamics of the payload and the quadrotors are derived. Based on this dynamic model, a new dynamic control allocation approach is introduced to optimally distribute the virtual control input (i.e., the wrench to be exerted on the payload) among the cables and generate reference positions for the quadrotors on-line, while avoiding collisions between quadrotors and accounting for cable tension constraints. Furthermore, a new reinforcement-learning (RL) compensator is proposed to reduce tracking errors caused by the constraints in the quadrotors’ thrusts. Numerical simulations are conducted to validate the proposed approach.

Axiomatic and Dynamic Processes for a Symmetric Allocation Rule

It has recently become imperative to analyze relevant issues to improve the efficiency of resource allocation by means of different perspectives and ways of thinking. There exist numerous decisive factors, such as changes in forms of allocation, reactive behavior, and the interaction and functional effectiveness of strategies, that need to be complied. In contrast to expert meetings, rules of thumb, or other existing concepts, this article aims to offer a different and efficient resource allocation approach by applying game-theoretical methods to resource-allocation situations. Our major investigative procedures are as follows: (1) after comparing our method with pre-existing allocation rules from pre-existing allocation rules, a symmetric allocation rule is proposed that considers both units and their energy grades; (2) based on the properties of grade completeness, criterion for models, unmixed equality symmetry, grade synchronization, and consistency, some axiomatic outcomes are used to examine the mathematical accuracy and the applied rationality of this symmetric allocation rule; (3) based on a symmetrical revising function, a dynamic process is applied to show that this symmetric allocation rule can be reached by units that start from an arbitrary grade completeness situation; and (4) these axiomatic and dynamic results and related meanings are applied to show that this symmetric allocation rule can present an optimal alternative guide for resource-allocation processes. Related applications, comparisons, and statements are also offered throughout this article.

Topic2features: a novel framework to classify noisy and sparse textual data using LDA topic distributions

In supervised machine learning, specifically in classification tasks, selecting and analyzing the feature vector to achieve better results is one of the most important tasks. Traditional methods such as comparing the features’ cosine similarity and exploring the datasets manually to check which feature vector is suitable is relatively time consuming. Many classification tasks failed to achieve better classification results because of poor feature vector selection and sparseness of data. In this paper, we proposed a novel framework, topic2features (T2F), to deal with short and sparse data using the topic distributions of hidden topics gathered from dataset and converting into feature vectors to build supervised classifier. For this we leveraged the unsupervised topic modelling LDA (latent dirichlet allocation) approach to retrieve the topic distributions employed in supervised learning algorithms. We made use of labelled data and topic distributions of hidden topics that were generated from that data. We explored how the representation based on topics affect the classification performance by applying supervised classification algorithms. Additionally, we did careful evaluation on two types of datasets and compared them with baseline approaches without topic distributions and other comparable methods. The results show that our framework performs significantly better in terms of classification performance compared to the baseline(without T2F) approaches and also yields improvement in terms of F1 score compared to other compared approaches.