Background: The applying of third-party libraries is an integral part of many applications. But the libraries choosing is time-consuming even for experienced developers. The automated recommendation system for libraries recommendation is widely researched to help developers to choose libraries. Aim: from software engineering aspect, our research aims to give developers a reliable recommended list of third-party libraries at the early phase of software development lifecycle to help them build their development environment faster; and from technical aspect, our research aims to build a generalizable recommendation system framework which combines collaborative filtering and topic modeling techniques, in order to improve the performance of libraries recommendation significantly. Our works on this research: 1) we design a hybrid methodology to combine collaborative filtering and LDA text mining technology; 2) we build a recommendation system framework successfully based on the above hybrid methodology; 3) we make a well-designed experiment to validate the methodology and framework which use the data of 1,013 mobile application projects; 4) we do the evaluation for the result of the experiment. Conclusions: 1) hybrid methodology with collaborative filtering and LDA can improve the performance of libraries recommendation significantly; 2) based on the hybrid methodology, the framework works very well on the libraries recommendation for helping developers' libraries choosing. Further research is necessary to improve the performance of the libraries recommendation including: 1) use more accurate NLP technologies improve the correlation analysis; 2) try other similarity calculation methodology for collaborative filtering to rise the accuracy; 3) on this research, we just bring the time-series approach to the framework and make an experiment as comparative trial, the result shows that the performance improves continuously, so in further research we plan to use time-series data-mining as the basic methodology to update the framework.

As IT systems, including network systems using SDN/NFV technologies, become large-scaled and complicated, the cost of system management also increases rapidly. Network operators have to maintain their workflow in constructing and consistently updating such complex systems, and thus these management tasks in generating system update plan are desired to be automated. Declarative system update with state space search is a promising approach to enable this automation, however, the current methods is not enough scalable to practical systems. In this paper, we propose a novel heuristic approach to greatly reduce computation time to solve system update procedure for practical systems. Our heuristics accounts for structural bottleneck of the system update and advance search to resolve bottlenecks of current system states. This paper includes the following contributions: (1) formal definition of a novel heuristic function specialized to system update for A* search algorithm, (2) proofs that our heuristic function is consistent, i.e., A* algorithm with our heuristics returns a correct optimal solution and can omit repeatedly expansion of nodes in search spaces, and (3) results of performance evaluation of our heuristics. We evaluate the proposed algorithm in two cases; upgrading running hypervisor and rolling update of running VMs. The results show that computation time to solve system update plan for a system with 100 VMs does not exceed several minutes, whereas the conventional algorithm is only applicable for a very small system.

Traffic engineering refers to techniques to accommodate traffic efficiently by dynamically configuring traffic routes so as to adjust to changes in traffic. If traffic changes frequently and drastically, the interval of route reconfiguration should be short. However, with shorter intervals, obtaining traffic information is problematic. To calculate a suitable route, accurate traffic information of the whole network must be gathered. This is difficult in short intervals, owing to the overhead incurred to monitor and collect traffic information. In this paper, we propose a framework for traffic engineering in cases where only partial traffic information can be obtained in each time slot. The proposed framework is inspired by the human brain, and uses conditional probability to make decisions. In this framework, a controller is deployed to (1) obtain a limited amount of traffic information, (2) estimate and predict the probability distribution of the traffic, (3) configure routes considering the probability distribution of future predicted traffic, and (4) select traffic that should be monitored during the next period considering the system performance yielded by route reconfiguration. We evaluate our framework with a simulation. The results demonstrate that our framework improves the efficiency of traffic accommodation even when only partial traffic information is monitored during each time slot.

A phase locking value (PLV) in electrocorticography is an essential indicator for analysis of cognitive activities and detection of severe diseases such as seizure of epilepsy. The PLV computation requires a simultaneous pursuit of high-throughput and low-cost implementation in hardware acceleration. The PLV computation consists of bandpass filtering, Hilbert transform, and mean phase coherence (MPC) calculation. The MPC calculation includes trigonometric functions and divisions, and these calculations require a lot of computational amounts. This paper proposes an MPC calculation method that removes high-cost operations from the original MPC with mathematically identical derivations while the conventional methods sacrifice either computational accuracy or throughput. This paper also proposes a hardware implementation of MPC calculator whose latency is 21 cycles and pipeline interval is five cycles. Compared with the conventional implementation with the same standard cell library, the proposed implementation marks 2.8 times better hardware implementation efficiency that is defined as throughput per gate counts.

Behaviors of an object-oriented system can be visualized as reverse-engineered sequence diagrams from execution traces. This approach is a valuable tool for program comprehension tasks. However, owing to the massiveness of information contained in an execution trace, a reverse-engineered sequence diagram is often afflicted by a scalability issue. To address this issue, many trace summarization techniques have been proposed. Most of the previous techniques focused on reducing the vertical size of the diagram. To cope with the scalability issue, decreasing the horizontal size of the diagram is also very important. Nonetheless, few studies have addressed this point; thus, there is a lot of needs for further development of horizontal summarization techniques. We present in this paper a method for identifying core objects for trace summarization by analyzing reference relations and dynamic properties. Visualizing only interactions related to core objects, we can obtain a horizontally compactified reverse-engineered sequence diagram that contains system's key behaviors. To identify core objects, first, we detect and eliminate temporary objects that are trivial for a system by analyzing reference relations and lifetimes of objects. Then, estimating the importance of each non-trivial object based on their dynamic properties, we identify highly important ones (i.e., core objects). We implemented our technique in our tool and evaluated it by using traces from various open-source software systems. The results showed that our technique was much more effective in terms of the horizontal reduction of a reverse-engineered sequence diagram, compared with the state-of-the-art trace summarization technique. The horizontal compression ratio of our technique was 134.6 on average, whereas that of the state-of-the-art technique was 11.5. The runtime overhead imposed by our technique was 167.6% on average. This overhead is relatively small compared with recent scalable dynamic analysis techniques, which shows the practicality of our technique. Overall, our technique can achieve a significant reduction of the horizontal size of a reverse-engineered sequence diagram with a small overhead and is expected to be a valuable tool for program comprehension.

An important step for improving software analyzability is applying refactorings during the maintenance phase to remove bad smells, especially the long method bad smell. Long method bad smell occurs most frequently and is a root cause of other bad smells. However, no research has proposed an approach to repeating refactoring identification, suggestion, and application until all long method bad smells have been removed completely without reducing software analyzability. This paper proposes an effective approach to identifying refactoring opportunities and suggesting an effective refactoring set for complete removal of long method bad smell without reducing code analyzability. This approach, called the long method remover or LMR, uses refactoring enabling conditions based on program analysis and code metrics to identify four refactoring techniques and uses a technique embedded in JDeodorant to identify extract method. For effective refactoring set suggestion, LMR uses two criteria: code analyzability level and the number of statements impacted by the refactorings. LMR also uses side effect analysis to ensure behavior preservation. To evaluate LMR, we apply it to the core package of a real world java application. Our evaluation criteria are 1) the preservation of code functionality, 2) the removal rate of long method characteristics, and 3) the improvement on analyzability. The result showed that the methods that apply suggested refactoring sets can completely remove long method bad smell, still have behavior preservation, and have not decreased analyzability. It is concluded that LMR meets the objectives in almost all classes. We also discussed the issues we found during evaluation as lesson learned.

Irreversible thermal paints or temperature sensitive paints are a kind of special temperature sensor which can indicate the temperature grad by judging the color change and is widely used for off-line temperature measurement during aero engine test. Unfortunately, the hot gases flow within the engine during measuring always make the paint color degraded, which means a serious saturation reduction and contrast loss of the paint colors. This phenomenon makes it more difficult to interpret the thermal paint test results. Present contrast enhancement algorithms can significantly increase the image contrast but can't protect the hue feature of the paint images effectively, which always cause color shift. In this paper, we propose a color restoration method for thermal paint image. This method utilizes the atmospheric scattering model to restore the lost contrast and saturation information, so that the hue can be protected and the temperature can be precisely interpreted based on the image.

To efficiently use network resources, internet service providers need to conduct traffic engineering that dynamically controls traffic routes to accommodate traffic change with limited network resources. The performance of traffic engineering (TE) depends on the accuracy of traffic prediction. However, the size of traffic change has been drastically increasing in recent years due to the growth in various types of network services, which has made traffic prediction difficult. Our approach to tackle this issue is to separate traffic into predictable and unpredictable parts and to apply different control policies. However, there are two challenges to achieving this: dynamically separating traffic according to predictability and dynamically controlling routes for each separated traffic part. In this paper, we propose a macroflow-based TE scheme that uses different routing policies in accordance with traffic predictability. We also propose a traffic-separation algorithm based on real-time traffic analysis and a framework for controlling separated traffic with software-defined networking technology, particularly OpenFlow. An evaluation of actual traffic measured in an Internet2 network shows that compared with current TE schemes the proposed scheme can reduce the maximum link load by 34% (at the most congested time) and the average link load by an average of 11%.

The development of an extremely efficient packet inspection algorithm for lookup engines is important in order to realize high throughput and to lower energy dissipation. In this paper, we propose a new lookup engine based on a combination of a mismatch detection circuit and a linked-list hash table. The engine has an automatic rule registration and deletion function; the results are that it is only necessary to input rules, and the various tables included in the circuits, such as the Mismatch Table, Index Table, and Rule Table, will be automatically configured using the embedded hardware. This function utilizes a match/mismatch assessment for normal packet inspection operations. An experimental chip was fabricated using 40-nm 8-metal CMOS process technology. The chip operates at a frequency of 100MHz under a power supply voltage of VDD =1.1V. A throughput of 100Mpacket/s (=51.2Gb/s) is obtained at an operating frequency of 100MHz, which is three times greater than the throughput of 33Mpacket/s obtained with a conventional lookup engine without a mismatch detection circuit. The measured energy dissipation was a 1.58pJ/b·Search.

This paper presents past and recent trends of optical networks and addresses the future directions. First, we describe path networks with the historical backgrounds and trends. path networks have advanced by using various multiplexing technologies. They include time-division multiplexing (TDM), asynchronous transfer mode (ATM), and wavelength-division multiplexing (WDM). ATM was later succeeded to multi-protocol label switching (MPLS). Second, we present generalized MPLS technologies (GMPLS). In GMPLS, the label concept of MPLS is extended to other labels used in TDM, WDM, and fiber networks. GMPLS enables network operators to serve networks deployed by different technologies with a common protocol suite of GMPLS. Third, we describe multi-layer traffic engineering and a path computation element (PCE). Multi-layer traffic engineering designs and controls networks considering resource usages of more than one layer. This leads to use network resources more efficiently than the single-layer traffic engineering adopted independently for each layer. PCE is defined as a network element that computes paths, which are used for traffic engineering. Then, we address software-defined networks, which put the designed network functions into the programmable data plane by way of the management plane. We describe the evaluation from GMPLS to software defined networking (SDN) and transport SDN. Fifth, we describe the advanced devices and switches for optical networks. Finally, we address advances in networking technologies and future directions on optical networking.

Systematic research on electromagnetic compatibility (EMC) in Japan started in 1977 by the establishment of a technical committee on “environmental electromagnetic engineering” named EMCJ, which was founded both in the Institute of Electronics and Communication Engineers or the present IEICE (Institute of Electronics, Information and Communication Engineers) and in the Institute of Electrical Engineers of Japan or the IEEJ. The research activities have been continued as the basic field of interdisciplinary study to harmonize even in the electromagnetic (EM) environment where radio waves provide intolerable EM disturbances to electronic equipment and to that environment itself. The subjects and their outcomes which the EMCJ has dealt with during about 40 years from the EMCJ establishment include the evaluation of EM environment, EMC of electric and electronic equipment, and EMC of biological effects involving bioelectromagnetics and so on. In this paper, the establishment history and structure of the EMCJ are reviewed along with the change in activities, and topics of the technical reports presented at EMCJ meetings from 2006 to 2016 are surveyed. In addition, internationalization and its related campaign are presented in conjunction with the EMCJ research activities, and the status quo of the EMCJ under the IEICE is also discussed along with the prospects.

Telecommunication networks have evolved from telephony networks to the Internet, and they sustainably support the development of a secured, safe, and comfortable society. The so-called “switching technology” including the evolved “network system technology” is one of the main infrastructure technologies used for realizing information communication services. On the occasion of completion of 100 years since the establishment of the IEICE, we summarize the history of network system technologies and present their future direction for the next generation. We mainly focus on a series of technologies that evolved through the discussions of the IEICE technical committees on switching engineering, launched 50 years ago, switching systems engineering, and network systems in action.

This article describes an approximate model of a group of cells in the wireless 4G network with implemented load balancing mechanism. An appropriately modified model of Erlang's Ideal Grading is used to model this group of cells. The model makes it possible to take into account limited availability of resources of individual cells to multi-rate elastic and adaptive traffic streams generated by Erlang and Engset sources. The developed solution allows the basic traffic characteristics in the considered system to be determined, i.e. the occupancy distribution and the blocking probability. Because of the approximate nature of the proposed model, the results obtained based on the model were compared with the results of a digital simulation. The present study validates the adopted assumptions of the proposed model.

The goal of software testing should go beyond simply finding defects. Ultimately, testing should be focused on increasing customer satisfaction. Defects that are detected in areas of the software that the customers are especially interested in can cause more customer dissatisfaction. If these defects accumulate, they can cause the software to be shunned in the marketplace. Therefore, it is important to focus on reducing defects in areas that customers consider valuable. This article proposes a value-driven V-model (V2 model) that deals with customer values and reflects them in the test design for increasing customer satisfaction and raising test efficiency.

Stability-featured dynamic multi-path routing (SDMR) based on the existing Traffic engineering eXplicit Control Protocol (TeXCP) is proposed and evaluated for traffic engineering in terrestrial networks. SDMR abandons the sophisticated stability maintenance mechanisms of TeXCP, whose load balancing scheme is also modified in the proposed mechanism. SDMR is proved to be able to converge to a unique equilibria state, which has been corroborated by the simulations.

Designing a backbone IP network, especially to support both unicast and multicast traffic under delay constraints, is a difficult problem. Real network design must consider cost, performance and reliability. Therefore, a simulator can help a network designer to test the functionality of the network before the implementation. This paper proposes a heuristic design algorithm called D-MENTOR, and the algorithm was developed by programming based on Mesh Network Topological Optimization and Routing Version 2 (MENTOR-II) to integrate as a new module of DElite tool. The simulation results show that, in almost all test cases, the proposed algorithm yields lower installation cost.

For the maintenance of a business system, developers must understand the business rules implemented in the system. One type of business rules defines computational business rules; they represent how an output value of a feature is computed from the valid inputs. Unfortunately, understanding business rules is a tedious and error-prone activity. We propose a program-dependence analysis technique tailored to understanding computational business rules. Given a variable representing an output, the proposed technique extracts the conditional statements that may affect the computation of the output. To evaluate the usefulness of the technique, we conducted an experiment with eight developers in one company. The results confirm that the proposed technique enables developers to accurately identify conditional statements corresponding to computational business rules. Furthermore, we compare the number of conditional statements extracted by the proposed technique and program slicing. We conclude that the proposed technique, in general, is more effective than program slicing.

Phishing attacks target financial returns by luring Internet users to exposure their sensitive information. Phishing originates from e-mail fraud, and recently it is also spread by social networks and short message service (SMS), which makes phishing become more widespread. Phishing attacks have drawn great attention due to their high volume and causing heavy losses, and many methods have been developed to fight against them. However, most of researches suffered low detection accuracy or high false positive (FP) rate, and phishing attacks are facing the Internet users continuously. In this paper, we are concerned about feature engineering for improving the classification performance on phishing web pages detection. We propose a novel anti-phishing framework that employs feature engineering including feature selection and feature extraction. First, we perform feature selection based on genetic algorithm (GA) to divide features into critical features and non-critical features. Then, the non-critical features are projected to a new feature by implementing feature extraction based on a two-stage projection pursuit (PP) algorithm. Finally, we take the critical features and the new feature as input data to construct the detection model. Our anti-phishing framework does not simply eliminate the non-critical features, but considers utilizing their projection in the process of classification, which is different from literatures. Experimental results show that the proposed framework is effective in detecting phishing web pages.

In this paper, we propose a true random number generator (TRNG) exploiting jitter and the chaotic behavior in cross ring oscillators (CROs). We make a further study of the feedback ring architecture and cross-connect the XOR gates and inverters to form an oscillator. The CRO utilizes totally digital logic circuits, and gains a high and robust entropy rate, as the jitter in the CRO can accumulate locally between adjacent stages. Two specific working modes of CRO in which the CRO can work in a consistent state and a free-running state respectively are introduced and analyzed both theoretically and experimentally. Finally, different stage lengths of cross ring true random number generators (CRTRNGs) are tested in different Field Programmable Gate Arrays (FPGAs) and test results are analyzed and compared. Especially, random data achieved from a design of 63-stage CRTRNG in Altera Cyclone IV passes both the NIST and Diehard test suites at a rate as high as 240Mbit/s.

Traffic is a key aspect of everyday life. Its study, as it happens with other complex phenomena, has found in simulation a basic tool. However, the use of simulations faces important limitations. Building them requires considering different aspects of traffic (e.g. urbanism, car features, and individual drivers) with their specific theories, that must be integrated to provide a coherent model. There is also a variety of simulation platforms with different requirements. Many of these problems demand multi-disciplinary teams, where the different backgrounds can hinder the communication and validation of simulations. The Model-Driven Engineering (MDE) of simulations has been proposed in other fields to address these issues. Such approaches develop graphical Modelling Languages (MLs) that researchers use to model their problems, and then semi-automatically generate simulations from those models. Working in this way promotes communication, platform independence, incremental development, and reutilisation. This paper presents the first steps for a MDE framework for traffic simulations. It introduces a tailored extensible ML for domain experts. The ML is focused on human actions, so it adopts an Agent-Based Modelling perspective. Regarding traffic aspects, it includes concepts commonly found in related literature following the Driver-Vehicle-Environment model. The language is also suitable to accommodate additional theories using its extension mechanisms. The approach is supported by an infrastructure developed using Eclipse MDE projects: the ML is specified with Ecore, and a model editor and a code generator tools are provided. A case study illustrates how to develop a simulation based on a driver's behaviour theory for a specific target platform using these elements.