Conclusion

Hardware Trojan detection techniques have been studied extensively. However, to develop reliable and effective defenses, it is important to figure out how hardware Trojans are implemented in practical scenarios. The authors attempt to make a review of the hardware Trojan design and implementations in the last decade and also provide an outlook. Unlike all previous surveys that discuss Trojans from the defender's perspective, for the first time, the authors study the Trojans from the attacker's perspective, focusing on the attacker's methods, capabilities, and challenges when the attacker designs and implements a hardware Trojan. First, the authors present adversarial models in terms of the adversary's methods, adversary's capabilities, and adversary's challenges in seven practical hardware Trojan implementation scenarios: in‐house design team attacks, third‐party intellectual property vendor attacks, computer‐aided design tools attacks, fabrication stage attacks, testing stage attacks, distribution stage attacks, and field‐programmable gate array Trojan attacks. Second, the authors analyse the hardware Trojan implementation methods under each adversarial model in terms of seven aspects/metrics: hardware Trojan attack scenarios, the attacker's motivation, feasibility, detectability (anti‐detection capability), protection and prevention suggestions for the designer, overhead analysis, and case studies of Trojan implementations. Finally, future directions on hardware Trojan attacks and defenses are also discussed.

Single-phase grounding fault of neutral un-effectual grounding systems have been a major concern of the public safety in distribution system. Under the background of the high-quality development of energy internet promoted by State Grid Corporation of China, the construction of distribution internet of things brings new technologies and opportunities for intelligent monitoring, automation, and protection control of distribution network. This paper presents a new method to identify the fault location of neutral un-effectual grounding systems by collecting and calculating measurement data from a smart distribution transformer terminal. Through the simulation of medium-voltage and low-voltage combined distribution systems in MATLAB software, it is verified that the proposed method has good robustness and accuracy. Monitoring the status information of the medium-voltage distribution network through the smart distribution transformer terminal to realize the fault location of neutral un-effectual grounding systems has great potential in practical application and implementation.

Understanding students’ psychological pressure and bad emotional reaction can solve psychological problems as soon as possible and avoid affecting students’ normal study life. With the improvement of global scientific and technological strength, and the step-by-step in-depth research on deep learning and computational intelligence optimization. Now, we have enough conditions to build a psychological and emotional data set for the field of education, and build a mental health stress detection model with emotional analysis function. In addition, a variety of experimental methods are used for comparison, which shows the superior performance of the model in practical application scenarios. The results show that: (1) the data set constructed for the model is reasonable. Psychological stress test shows that the tested college students are in good health and have no positive performance. Schools need to pay special attention to obsessive–compulsive disorder and interpersonal sensitivity, and the average values of both indicators are higher than 0.9. (2) For the optimization of ant colony algorithm (ACO) computational intelligence, both the stability and the average execution time of the algorithm are obviously higher than those of other algorithms. This model has obvious performance advantages after using this algorithm. (3) Using loss function value to measure the difference between simulated emotion analysis and real value. The difference of most emotion tests is less than 3%; the accuracy difference between sadness and fear is about 7%. Although the final results prove the feasibility of this method, there are still some shortcomings to be optimized.

Mental fatigue in drivers is one of the leading causes that give rise to traffic accidents. Electroencephalography (EEG) based driving fatigue studies showed promising performance in fatigue monitoring. However, complex methodologies are not suitable for practical implementation. In our simulation based setup that retained the constraints of real driving, we took a step closer to fatigue estimation in a practical scenario. We adopted a pre-processing pipeline with low computational complexity, which can be easily and practically implemented in real-time. Moreover, regression-based continuous fatigue estimation was achieved using power spectral features in conjunction with time as the fatigue label. We sought to compare three regression models and three time windows to demonstrate their effects on the performance of fatigue estimation. Dynamic time warping was proposed as a new measure for evaluating the performance of fatigue estimation. The results derived from the validation of the proposed framework on 19 subjects showed that our proposed framework was promising towards practical implementation. Fatigue estimation by the support vector regression with radial basis function kernel and 5-second window length achieved the best performance. We also provided a comprehensive analysis on the spatial distribution of channels and frequency bands mostly contributing to fatigue estimation, which can inform the feature and channel reduction for real-time fatigue monitoring in practical driving. After reducing the number of electrodes by 75%, the proposed framework retained comparable performance in fatigue estimation. This study demonstrates the feasibility and adaptability of our proposed framework in practical implementation of mental fatigue estimation.

Cognitive Radio is an enabling technology that is expected to address the problem of scarcity of spectrum by intelligent access, mobility and management of spectrum and pave way for efficient usage of the same. Increasingly, it is seen that most research is directed towards building theoretic models for spectrum access and management. Though some of these are backed by simulations, certainty of any method can be proven only by practical implementation on hardware testbeds in real-world environments. Sometimes, assumptions that are made to satisfy simulation requirements are difficult to realize in practical scenarios, thereby, their implementation in real-world is even more challenging. In this paper, taking cue from our earlier implementation work on simple Snapshot ED and CUSUM based algorithms for single node spectrum sensing, we explain the implementation of Sequential change detection algorithm in co-operative mode, the Dual-CUSUM. Single node sensing is not reliable, especially, when the nodes are subjected to shadow fading due to obstacles (hidden node problem). This is mitigated by using co-operative sensing that exploits the inherent spatial diversity and it is seen that with certain fusion rules, Cooperative CUSUM (aka Dual-CUSUM) is better than its counterpart Co-operative Snapshot ED, especially, under low SNR regimes.

On October 27, 2005, a service-oriented architecture (SOA) provider consortium, comprised of Sonic Software, Systinet, AmberPoint, and BearingPoint released a new SOA Maturity Model to the public. The SOA MM has five levels, each representing an increasing degree of business value. The consortium’s objective is to provide guidance to IT leaders and decision makers who need to associate (and communicate) the long-term investments of SOA with real business value. In this report, we provide an overview of the SOA MM, share our insights, and point out some relevant industry immaturities.

We demonstrate the practical implementation of a schema for Smart Grid Last Mile (SGLM) traffic management in leased Internet access networks, based on well-known queue management and traffic marking algorithms such as Random Early Drop and Token Bucket. Internet access links are often over-provisioned, with much higher installed capacity than user needs, so domestic applications do not compete for bandwidth. In this scenario the additional load posed by SGLM traffic is negligible. On some occasions, however, Internet access links are moderately stressed and traffic management techniques are required for SGLM traffic to coexist seamlessly with user traffic. In a previous paper, we formulated a theoretical model for SGLM traffic in general networks, and a management solution for its coexistence with user traffic in the particular case of leased Internet access networks. In the present paper, we demonstrate the feasibility of that solution in a practical scenario. We have implemented the traffic management policies on real hardware and employed SGLM traffic generators to evaluate our schema in a real network. The results indicate that our proposal achieves low-latency SGLM communications in a scenario with high domestic user traffic.

Guided wave testing has been widely used as an effective way to inspect and monitor structural integrity. Evaluating the performance of damage detection methods is critical for the practical implementation of such methods, especially for long term monitoring systems under complex environmental and operational conditions (EOC). Although damage can be applied to test structures in a laboratory environment to evaluate the performance, it is often prohibitively expensive to comprehensively investigate the sensitivity of damage detection on different damage types, sizes, and locations under different EOC. In this paper, we propose a cost-effective methodology to evaluate the performance and estimate the sensitivity of damage detection methods by synthesizing undamaged records collected under varying EOCs and simulated artificial damage signals. We produce the receiver operating characteristics (ROC) of damage detection methods on different synthetic datasets to predict the performance in practical scenarios. doi: 10.12783/SHM2015/243

Globalization and liberalization of the world economy has leveled the field for all competitors around the globe. In this high competition environment all the industries try to lower the manufacturing cost of product and to increase their profit without compromising quality of product. Industries are in the race to search the ways to reduce the manufacturing cost of product. All over the world industries implement many techniques to sustain in this competitive world. In this environment Maintenance practices’ become the essential and basic need of industries. The main objective of maintenance practices is to keep machines running all the time and increasing the availability of resources. Like this maintenance practices have become one of the most important functions in an organization. In India large industries implemented maintenance practices successfully and receiving their benefits. But a very little information is available regarding the success role of maintenance practices in small and medium enterprises. To face global competition successfully, SMEs have to successfully implement the maintenances practices. The purpose of this paper is to review the current scenario of various maintenance practices being implemented in SMEs. The paper will have great value for managers of SMEs in understanding the benefits accrued by implementing these maintenance practices. The paper will also cover the direct and indirect benefits of maintenance practices in Indian SMEs.

Indoor high precision three-dimensional positioning system based on visible light communication using modified genetic algorithm

Development of a Building Information Modelling Asset (BIMAsset) value realisation model

Social media along with different web forums contribute to a huge amount of data, i.e. opinions, feedbacks, reviews generated on a daily basis. Sentiment analysis is the identification of polarity (positive, negative or neutral) of the data to analyse the opinion of users on various platforms on a range of topics including those that have real business value, i.e. polarity of users on a product. With the advent of platforms like Twitter, people freely express their opinion on almost everything and that generates a huge amount of data which cannot be processed or analysed manually; therefore, we have various NLP and machine learning techniques which can effectively analyse and predict the polarity of the data which enables us to capture the sentiment of the people regarding a particular issue. In this paper, we intend to analyse the different machine learning algorithms for sentiment analysis of Twitter data and compare the algorithms’ performance on different datasets with the help of metrics like precision, accuracy, F-measure and recall.

When organizations first harnessed information technology to solve business problems, automate processes and provide information, it could take many months, even years, to deploy an application. This situation changed two decades ago when Software-as-a-Service products, hosted in the cloud, became available. Today, technology has matured sufficiently, and it is possible to install an IT platform to support a global business process in a matter of weeks and to stitch multiple applications together into a reliable and agile architectural foundation. While legacy technology still poses a major problem, technology for new investments is no longer the bottleneck that it once was.This is likely one of the reasons why it is widely acknowledged that achieving digital transformation ambitions is less about technology deployment and more about the ability of the organization to adopt it and adapt to it, and to ultimately create real business value. But here is the conundrum: while the time to implement technology has been significantly compressed, achieving the organizational changes needed to reap benefits still require months and even years to achieve.A weakness in mainstream project management literature, especially when applied to technology-enabled business investments, is the assumption that the project is complete once the software is released, stabilized, and "accepted" by the project's sponsor. Sometimes, a window of time is allocated to "finish" the project; an after-action or lessons learned report is produced, the project team disbands, and victory is declared. When considering the success or failure of projects, discussions usually revolve around the budget, schedule, the software and its acceptance, but generally not about the changes that the organization has experienced as a result of improved processes or enhanced capabilities or whether the expected benefits were delivered. Despite recognizing that benefits come from organizational changes, project management is still premised around scope, resources and time. This can be because benefits have not yet happened; the nature of most IT projects is that benefits only emerge many weeks and months after "go-live." Victory is often declared prematurely.To promote the achievement of benefits from IT investments, the concept of Benefits Management was introduced in the 1990's. Its aim was to focus on what business benefits were expected to be delivered from the investment and to accelerate the realization of these benefits from harnessing the capabilities of technology. This was achieved by identifying the organizational changes necessary to release these benefits, as well as tracking the benefits realized throughout the entire initiation-to-realization cycle. It also advocated that expected benefits should be aligned to key strategic drivers.Since the original work on Benefits Management was undertaken, the context for IT investments has changed dramatically. The initial research was focused on improving the performance outcomes from large enterprise system investments; these systems had an internal organizational focus and took considerable time to implement. Back then, how systems were built was also different, due primarily to the constraints imposed by technology, development frameworks and dominant practices. Technologies like AI and analytics pose particular challenges for managing benefits in that it is difficult to specify them prior to technology deployment. Moreover, today, technology is a competitive necessity; it is shaping business models and customers expect to engage with an organization in a digital way; and systems can extend outside the organizational boundary to ecosystem partners. Speed, innovation, and agility are critical determinants of success in a digital-first world, fundamentally changing how an organization competes. All of these changes impact the nature of projects and how they are set up and run.Although benefits management is an established concept in the project management and technology literatures, it is not well-known as an organizational practice. In this paper, we revisit the benefits management concept, discuss some of the adoption barriers, and suggest a number of ways to adapt benefits management to digital transformation programs.

A swift execution from idea to market has become a key competitive advantage for software companies to enable them to survive and grow in turbulent business environments. To combat this challenge, companies are using hackathons. A hackathon is a highly engaging, continuous event in which people in small groups produce working software prototypes in a limited amount of time. F-Secure, a software product company, views hackathons as a possible solution to the fundamental business problem of how to make revenue from an idea, spanning the phases from creating the idea to producing a software prototype. However, hackathons pose the challenge of how to transform those promising prototypes into finalized products that create revenue and real business value.

There are a number of situations where modeling of mineral composition (as opposed to rock/lithology) adds real business value, for example; complex mineralogy leading to variable grain density means that more accurate estimation of porosity could be achieved through mineral volume analysis; in some reservoirs certain minerals may control permeability (e.g. diagenetic cements); the geomechanical properties of certain reservoirs may be controlled by abundance of key minerals; and mineral volume interpretations may help with well to well correlation. The mineralogical complexity of mudstone reservoirs has led to the increased usage of multi-mineral optimizing petrophysical models in recent times for estimating porosity, water and hydrocarbon volumes. A key uncertainty in these models is the log response parameter assigned for each log equation related to each volumetric variable. Default parameter values are commonly used and often need to be modified by taking into account subjective local knowledge or intuition in order to achieve a result that is considered acceptable. This paper describes the methods developed at Chevron for calibration of mineral log response parameters using core data (referred to as BestRock). Mineral log response parameters are controlled by the major and trace element chemistry of the individual minerals in the formation rock matrix. BestRock uses a non-linear approach to optimize whole rock chemistry with mineralogy to calculate individual mineral structural formulae and trace element associations from which log response parameters can be determined. Accurate quantitative phase analysis by X-ray diffraction (QXRD) and rigorous sample preparation are especially critical steps in the process. QXRD in combination with aliquot whole rock elemental analyses are processed using Chevron's BestRock optimization software to provide refined quantities of the mineral species present in the formation, their structural formulae, and their predicted wireline log responses. Calibrated petrophysical models are built from the information obtained from the QXRD and BestRock results. The method described herein provides an independent and robust method for determining petrophysical parameters that is independent of the interpreter, quick to implement, and is supported by real data. The application of this method leads to improved mineral volume predictions from petrophysical models.