Abstract. This paper used Business process reengineering (BPR) for improve operation of computer service processes in the computer center. Change of service processes were conducted by implementing four-phase: 1) Finding of current process 2) Analysis current process 3) Redesign Process 4) Applying new process and realization framework to support redesign decision making. The framework based on policies and rules of organization. Finally, the new process was implemented at the computer center. The results showed that the new process was better than the current process, time of service decrease and reduce workload.
Vulnerability detection is commonly been executed during the testing phase of software development. Current methods are not able to detect system or software security vulnerabilities of certain types of attacks during the early stages of software development. These attacks include both the ones we did not anticipate as well as the ones unknown during the design phase. In this paper we propose a method to detect the security vulnerabilities during the design phase of software development. This approach simulates attacks according to the misuse patterns using model testing method. With this approach, we are able to analyze system security vulnerabilities during the design stage of the system development. The practical example provides evidences the feasibility of our method.
Foreign exchange (Forex) rate forecasting is presently pursued by many researchers as it plays an important role in financial technology and business. The challenge of Forex research lies in its characteristics, fluctuation, non-linearity, and random walk phenomena. Several related studies generate forecasting signals using fundamental data (FD) and technical indicator data (TI) to support Forex. FD is an indicator of country economic conditions, while TI shows the price pattern-based signal. However, these two indicators pose two major limitations on their deployment. First, modeling a sequential neural network causes gradient vanishing and information loss. Second, FD exerts a significant impact on currency price upon its quarterly update and release. The second limitation is known as FD releasing problem. Moreover, Forex forecasting using FD and TI is typically conducted in an equal aggregation manner, resulting in inaccurate prediction due to unequal data changing frequency. In this work, we propose BERTFOREX, a cascading model for Forex time-series forecasting. The proposed technique uses deep learning Bidirectional Encoder Representations from Transformer (BERT) based on FD and TI data characteristics. The technique first applies FD to extract the hidden patterns over the designated period. Then, these extracted hidden patterns of FD are aggregated as additional weights to TI since FD frequency changes slower than that of TI. This yields a combined aggregated pattern of FD and TI. BERT again applies the aggregated pattern to discover underlying patterns within TI and FD over other influencing days. We demonstrate the efficiency of BERTFOREX aggregated representation using a simple neural network in forecasting. The proposed method outperforms other methods in terms of percentage of correct signals, sensitivity, specificity, precision, and negative predictive value.
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