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As network complexity increases because of the diversification of applications and services based on internet of autonomous things, it is difficult for humans to design the optimal control rule for software-defined networking controllers. Intelligence-defined networking, called IDN, is proposed to overcome this limitation through machine learning algorithms. Since the existing IDN approaches are mostly designed to optimize only the network quality of services, including throughput, jitter, and latency, the controllers don’t consider the importance of data in the applications and the services. This causes the controller to allocate insufficient resources to the crucial data flow, which leads critical problems, such as self-driving car accidents. To prevent this problem, we propose an importance-based IDN architecture that enables network controllers to manage network traffic with importance levels of data flows. Firstly, we devise an importance estimation scheme to set the importance level for the flows. Secondly, a dynamic resource allocation model of the controllers is developed by means of deep learning algorithms in order to make the optimal network resource. Additionally, an online learning mechanism based on weighted auto-labeling is adopted to continue enhancing the adaptability of the resource allocation model on runtime as the network conditions change. The evaluation results of the proposed architecture under various autonomous things scenarios show that the loss rate for data flows of higher importance is reduced by one-quarter compared to the case of a network controller without importance level and that the bandwidth waste ratio is reduced by ten percent compared to the rule-based model.

Multiple-input converters (MICs) and multiple-output converters (MOCs) are attractive solutions for interfacing various voltage levels. In order to reveal the intrinsic relationships among the diverse topologies and provide as many viable topologies as possible for practical applications, this paper aims to analyze the topology construction principles and propose a systematic approach to derive MICs and MOCs. To begin with, the general principle of the topology derivation is analyzed according to circuit network theory. Inspired by the idea of design controllable inductor power-flow loops (IPFLs), five construction types are proposed to create multiple power-flow network (MPN) in MICs and MOCs. Then, four basic switching cells for the topology synthesis are proposed and a flow diagram for the optimal design procedure is provided to guide the topology derivation. As one example, a family of viable and optimized MICs and MOCs with various characteristics are derived from typical Buck converter. Based on the analysis of the derived converters in continuous conduction mode (CCM) and discontinuous conduction mode (DCM), the number of magnetic components is not increased at all so that they are promising candidates for applications requiring compact size and high integration. Besides, topology comparison and selection among a family of MICs is also conducted in view of practical specifications. Finally, one derived dual-input converter is analyzed in detail and experimentally verified to demonstrate the theoretical results.

Transaksi jual beli online semakin meningkat terutama untuk jenis barang atau benda mati. Hal ini dikarenakan sarana penunjang logistic pengiriman tidak ada kendala kebijakan pengiriman. Berbeda dengan jual beli mahluk hidup seperti ikan hias yang membutuhkan penanganan proses logistik yang khusus seperti penyediaan media air beroksigen di dalam bungkus yang tidak boleh bocor dan membutuhkan waktu pengiriman yang singkat untuk menjaga agar ikan hias tersebut tetap hidup dan sehat. Permasalah lain yang dihadapi yaitu sulitnya memahami penjelasan lokasi penjual, lamanya proses pengiriman, ikan hias tidak sesuai dengan gambar dan deskripsinya, harga ikan tidak transparan dan dicantumkan, serta penjual atau pembeli sulit dihubungi. Oleh sebab itu kebutuhan pengembangan aplikasi android jual beli ikan hias online muncul pada komunitas penggemar ikan hias Mega Mendung Betta Cirebon, dengan tujuan membangun sistem yang dapat menghubungkan penjual dan pembeli, dan memfasilitasi kemudahan proses jual beli seperti memberikan lokasi penjual dan pembeli yang lebih akurat dan estimasi waktu pengiriman dari tempat penjual ke tempat pembeli, serta membantu mempromosikan produk ikan hiasnya lebih luas. Penelitian ini menggunakan metode analisis deskriptif dan metode pengembangan sistem prototipe. Sumber data yang digunakan adalah data primer sekunder, didapat dari hasil observasi, wawancara dan studi pustaka. Implementasi aplikasi menggunakan bahasa pemrograman PHP dan Java, serta menggunakan MySQL sebagai pengolah database. Perancangan sistem dimodelkan dengan menggunakan Data Flow Diagram (DFD) dan Entity Relationship Diagram (ERD). Purwarupa aplikasi android jual beli ikan hias online berhasil dibuat dan diuji menggunakan blackbox testing dengan hasil 23 dari 24 butir pengujian atau 95,83 % telah berhasil dan sesuai dengan yang diharapakan.

In this paper, we propose the Data Flow Interface (DFI) as a way to make it easier for data processing systems to exploit high-speed networks without the need to deal with the complexity of RDMA. By lifting the level of abstraction, DFI factors out much of the complexity of network communication and makes it easier for developers to declaratively express how data should be efficiently routed to accomplish a given distributed data processing task. As we show in our experiments, DFI is able to support a wide variety of data-centric applications with high performance at a low complexity for the applications.

Multiple-port DC-DC converters are characterized by a variety of kinds and a large number of circuit topologies. This paper aims to reveal the intrinsic relationship among the topologies of multiple-port DC-DC converters and propose the topology derivation method. Firstly, voltage- and current-type ports are summarized from basic DC-DC converters, and the approach of converting current-type ports into voltage-type ports is discussed. Then, according to the Kirchhoffs laws, four types of multiple-input multiple-output converters named input-port-series output-port-series (IPSOPS), input-port-series output-port-parallel (IPSOPP), input-port-parallel output-port-series (IPPOPS) and input-port-parallel output-port-parallel (IPPOPP), are presented. Secondly, the topology derivation method of multiple-port bidirectional DC-DC converters based on voltage-type ports is studied in terms of power flow paths in various operation modes, and then the topology optimization method is proposed. Particularly, a flow diagram for the optimal design procedure is given to guide the topology derivation for some specific requirements. Based on the proposed approach, a family of multiple-port DC-DC converters can be derived, which provides lots of viable candidates for practical engineering. Furthermore, one derived converter named the parallel-type three-port bidirectional Buck converter (Buck-TPC) is analyzed in three operation modes to demonstrate the topology derivation. Finally, the effectiveness of the above theoretical analysis is verified by those experimental results.

Service activities carried out by the Academic and Student Administration Bureau (BAAK) STMIK Widuri for students who will carry out field placement program (KKP) are currently still carried out conventionally, this makes the process of making KKP cover letters and also assignments for supervisors takes time, which is quite long and less efficient. To overcome these problems, it is necessary to create a web-based application system that can simplify and speed up the activities of student KKP administration services. In this study, observations were made on the current system modeled using Business Process Modeling Notation (BPMN) and Data Flow Diagrams (DFD). The problem analysis process uses the Root Cause Analysis (RCA) method. The system design runs with UML diagrams, namely Use Case, Activity and Deployment Diagrams. Making a prototype with a high fidelity concept using the PHP programming language, HTML 5, CSS, jQuery, Bootstrap and DataTables frameworks and the Chart.js library. This research resulted in a prototype that describes the overall functionality of the Online MPA system, interactive and has an attractive interface.

Low-latency data service is an increasingly critical challenge for data center applications. In the modern distributed storage systems, proper data placement helps reduce the data movement delay, which can contribute to the service latency reduction tremendously. Existing data placement solutions have often assumed the prior distribution of data requests or discovered it via trace analysis. However, data placement is a difficult online decision-making problem faced with dynamic network conditions and time-varying user request patterns. The conventional static model-based solutions are less effective to handle the dynamic system. With an overall consideration of data movement and analytical latency, we develop a reinforcement learning-based framework DataBot+, automatically learning the optimal placement policies. DataBot+ adopts neural networks, trained with a variant of Q-learning, whose input is the real-time data flow measurements and whose output is a value function estimating the near-future latency. For instantaneous decision making, DataBot+ is decoupled into two asynchronous production and training components, ensuring that the training delay will not introduce extra overheads to handle the data flows. Evaluation results driven by real-world traces demonstrate the effectiveness of our design.

Approximate computing (AC) is an effective energy-efficient method for applications that have intrinsic error resilience. Early research efforts select noncritical portion of the computation, operations that have little impact on the accuracy of the results, for approximation. They ignore the runtime information and result in either under-approximation, which fails to reach the full potential of energy saving, or over-approximation, which causes unacceptable errors in the computation. A recently proposed runtime approach first estimates the noncritical portion for given input values and then performs accurate computation only on the critical portion. However, its complicated estimation process brings large runtime overhead and may not be suitable for real time embedded software. In this paper, we solve this problem by proposing a Runtime Machine Learning based Identification Model (RMLIM) to locate the noncritical portion in the data flow graph representation of any software program. RMLIM is trained offline by carefully generated training data set and then applied at runtime for each input. This reduces the runtime complexity of identifying noncritical parts. Our experiments show that, compared with the existing runtime AC method, our machine learning based approach can maintain similar energy efficiency and computation accuracy, but reduces the execution time by 40%-60%.

A distributed computing platform is preferred to meet out the current challenges of cloud computing paradigm. Hadoop manages distribution of large volume of big data and data is stored using cluster analysis of data. Hadoop Distributed File System (HDFS) is used to manage the problems in handling big data. HDFS can easily store large terabytes of data using commodity servers. The proposed system provides efficient data integrity verification service for big data management based on HDFS. HDFS has data integrity commitment, since it handles large volumes of data. Hadoop HDFS framework uses CRC-32C checksum verification on data node of content blocks to identify data corruption. A data aware module is implemented in Hadoop which provides more clustering process and reduces the computing performances of server. A broad range of data types is analyzed and clustered. The proposed research work uses balanced and proxy encryption technique using Cloud me tool and gives optimized query time and resource usage.

As an important part of the evaluation reform, online evaluation system can effectively improve the efficiency of evaluation work, which has been paid attention by teaching institutions. The online evaluation system needs to support the safe and stable transmission of information between the client and the server, and socket protocol establishes the connection through the listening port, which can easily carry out the message transmission and process control. Because it can well meet the construction requirements of online evaluation system, it is applied in our study. The building of online evaluation system based on socket protocol includes the function design of students and teachers, data flow design, evaluation difficulty grading design and system implementation. The system uses Java language and MVC mode for development, which has good scalability and platform-independence. It realizes the paperless examination process and greatly reduces the workload of teachers. The contribution of this paper is mainly reflected in two aspects. One is to explore the construction of an online evaluation system based on the socket protocol, and it provide an Asynchronous IO technical solution for the network communication between the student and the server, which provides a reference for the development of similar systems. The second is to give the realization method of the difficulty classification of the evaluation, and classify the difficulty of the test questions, which lays the foundation for carrying out personalized testing and evaluation.

In this fast-paced era, the need for precise, fast, and accurate information is something that must be done. Effectiveness and efficiency should be carried out according to needs so that the information can be relevant to its users. Thus, the registration information system, which has been manual in agencies or organizations, can be changed to online with accurate and timely data. Likewise, the Tamansari Religious Affairs Office in Bogor Regency. The current marriage registration system at the Tamansari Bogor Regency Office of Religious Affairs is no longer in line with the current development. The current system does not use computers as data processing tools, the results are still not well structured. Especially for processing the data of the prospective bride and groom. In this system, what is processed is registration data, bridal data, guardian data, witness data, admin data, and other data. The problem that occurs at this time is that the processed data feels slow, the resulting report has to pass data collection several times. In the information system to fulfill this final project, we will explain how to design and build a web-based marriage registration information system. The tools used in designing this system are MySQL, Entity Relational Diagrams, and Data Flow Diagrams, while for data collection using observation, interviews, and literature study. Meanwhile, database application development tools use MySQL and the programming language uses PHP. From this application, the benefits of data processing of brides, guardians, witnesses, etc. will be automatically obtained. Reports can also help by showing what is needed to process data quickly and easily. Keyword: Sistem Informasi, PHP My Admin, MySQL, Website, Entity Relational Diagram dan Data Flow Diagram

Code reuse brings vulnerabilities in third-party library to many Internet of Things (IoT) devices, opening them to attacks such as distributed denial of service. Program-wide binary diffing technology can help detect these vulnerabilities in IoT devices whose source codes are not public. Considering the architectures of IoT devices may vary, we propose a data-aware program-wide diffing method across architectures and optimization levels. We rely on the defined anchor functions and call relationship to expand the comparison scope within the target file, reducing the impact of different architectures on the diffing result. To make the diffing result more accurate, we extract the semantic features that can represent the code by data flow dependence analysis. Earth mover distance is used to calculate the similarity of functions in two files based on semantic features. We implemented a proof-of-concept DAPDiff and compared it with baseline BinDiff, TurboDiff and Asm2vec. Experiments showed the availability and effectiveness of our method across optimization levels and architectures. DAPDiff outperformed BinDiff in recall and precision by 41.4% and 9.2% on average when making diffing between standard third-party library and the real-world firmware files. This proves that DAPDiff can be applicable for the vulnerability detection in IoT devices.

Data-Flow Integrity (DFI) is a well-known approach to effectively detecting a wide range of software attacks. However, its real-world application has been quite limited so far because of the prohibitive performance overhead it incurs. Moreover, the overhead is enormously difficult to overcome without substantially lowering the DFI criterion. In this work, an analysis is performed to understand the main factors contributing to the overhead. Accordingly, a hardware-assisted parallel approach is proposed to tackle the overhead challenge. Simulations on SPEC CPU 2006 benchmark show that the proposed approach can completely enforce the DFI defined in the original seminal work while reducing performance overhead by 4x, on average., 24 pages, 16 figures, published in ACM Transactions on Design Automation of Electronic Systems

Due to the sensitive nature of diabetes-related data, preventing them from being easily shared between studies, and the wide discrepancies in their data processing pipeline, progress in the field of glucose prediction is hard to assess. To address this issue, we introduce GLYFE (GLYcemia Forecasting Evaluation), a benchmark of machine learning-based glucose predictive models. We present the accuracy and clinical acceptability of nine different models coming from the literature, from standard autoregressive to more complex neural network-based models. These results are obtained on two different datasets, namely UVA/Padova Type 1 Diabetes Metabolic Simulator (T1DMS) and Ohio Type-1 Diabetes Mellitus (OhioT1DM), featuring artificial and real type 1 diabetic patients respectively. By providing extensive details about the data flow as well as by providing the whole source code of the benchmarking process, we ensure the reproducibility of the results and the usability of the benchmark by the community. Those results serve as a basis of comparison for future studies. In a field where data are hard to obtain, and where the comparison of results from different studies is often irrelevant, GLYFE gives the opportunity of gathering researchers around a standardized common environment.

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose underground neutrino experiment. About 18000 20-inch photomultipliers (PMTs) are instrumented in the Central Detector to detect the photons, and the signals will be captured by high-speed high-resolution waveform full sampling technique. In addition, about 25000 3-inch PMTs are also instrumented to capture T/Q Hits information. This work builds a DAQ readout prototype system for JUNO, which can be used to test the full readout chain for 20-inch and 3-inch PMTs. The system is designed to continuously readout the data flow from multiple electronics channels, check the raw data, and finally save them into disk. Meanwhile, the graphical user interface (GUI) provides a real time display of the sampled waveforms. The system is developed with the open source Qt platform. The design and performance of the system have been verified in detail, particularly by performance test based on Gigabit Ethernet and 10-Gigabit Ethernet. A DAQ readout prototype system has been developed for JUNO, which is successfully applied to electronics testing system in Italy and PMT testing system in Guangdong.

With the constant flow of data across jurisdictions, issues regarding conflicting laws and the protection of rights arise. This article considers the EU–US data transfer relationship in the aftermath of the decision in Data Protection Commissioner v Facebook Ireland and Maximillian Schrems where the Court of Justice of the European Union (CJEU) invalidated an EU–US data transfer agreement for the second time in just five years. This judgment continues the line of cases emphasising the high value the Court places on securing EU personal data in accordance with EU data protection standards and fundamental rights. This article assesses the implications of the ruling for the vulnerable EU–US data transfer relationship.