基于信任链的骚扰电话预判技术研究

当前,骚扰电话乱象屡禁不止,严重影响了人民群众的正常生活,甚至威胁到个人财产安全,因此简单有效地识别骚扰电话技术成为亟待解决的问题。区别于现有的对电话号码进行黑白名单标记、大数据分析等识别方法,提出基于信任链的骚扰电话预判技术,从可信度量的角度计算来电号码的可靠程度,并针对高危受害群体的典型应用场景进行分析,通过对骚扰电话的预判,降低其受到电话骚扰甚至电话诈骗的可能性。     

融合信任和基于概率矩阵分解的推荐算法

针对推荐精度不准确、数据稀疏、恶意推荐的问题，提出融合信任基于概率矩阵分解（PMF）的新推荐模型。首先，通过建立基于信任的协同过滤模型（CFMTS）将改进的信任机制融入到协同过滤推荐算法中。信任值通过全局信任及局部信任计算获得，其中局部信任利用了信任传播机制计算用户的直接信任值和间接信任值得到，全局信任采用信任有向图的方式计算得到。然后，将信任值与评分相似度融合以解决数据稀疏、恶意推荐的问题。同时，将CFMTS融入到PMF模型中以建立新的推荐模型——融合信任基于概率矩阵分解模型（MPMFFT），通过梯度下降算法对用户特征向量和项目特征向量进行计算以产生预测评分值，进一步提高推荐系统的精准度。通过实验将提出的MPMFFT与经典的PMF、社交信息的矩阵分解（SocialMF）、社交信息的推荐（SoRec）、加权社交信息的推荐（RSTE）等模型进行了结果的对比和分析，在公开的真实数据集Epinions上MPMFFT的平均绝对误差（MAE）和均方根误差（RMSE）比最优的RSTE模型分别降低2.9%和1.5%，同时在公开的真实数据集Ciao上MPMFFT的MAE和RMSE比最优的SocialMF模型分别降低1.1%和1.8%，结果证实了模型能在一定程度上解决数据稀疏、恶意推荐问题，有效提高推荐质量。     

A multi-agent architecture for outsourcing SMEs manufacturing supply chain

In the present, global and competitive market customer's demands are very volatile. It is very difficult for small and medium size enterprises (SMEs) to satisfy their customer with quality product in reasonable price. Large firms are spending huge amount of their budget in generating technology development. To cope with this competitive market, usually, SMEs concentrate on their core expertise and outsource some non-core items or activities. The outsourcing decision (when, what, whom) is very crucial for small-scale industries. Realizing this scenario, in this article, an automated self-adaptive multi-agent system has been proposed. The proposed system will help SMEs to take an appropriate decision to mitigate the uncertainty in supply chain. The developed system exploits the “Outsourcing of operations” feature by its agents to conclude the manufacturing processes faster and reduce the idle time of certain machines with less human intervention. The communication protocol for agents has been described to give an insight of their communication. It was explained how various agents like outsourcing, material planning and supplier selection will come into action at different stages and take appropriate outsourcing decisions. Therefore, this multi-agent architecture will facilitate small scale manufacturing industries to execute their manufacturing processes and complex logistics issues efficiently. The execution of proposed architecture has been described in the simulated case study.     

Nonlinear model-based track guidance of user-defined points at the vehicle front

Preventive pedestrian protection systems are validated by means of fully automated driving tests reproducing safety-critical traffic situations on a proving ground. In order to assess these preventive safety systems, a precise and reproducible collision of a pedestrian dummy with a specific point at the vehicle front, e.g., the left corner of the vehicle, must be ensured. Hence, a track guidance of this specific point is required. Beyond the state of the art a new nonlinear model describing the lateral deviation of any point at the vehicle front to a predefined path is proposed in this paper. Based on this model the method of input–output linearization is used to design a flexible lateral guidance system for an easy application in different vehicles. Furthermore, the closed-loop stability is proven and experimental results are presented.     

Learning effectiveness of applying automated music composition software in the high grades of elementary school

The paper is mainly to explore the automated music composition experiences for the students in the high grades of elementary school who are unfamiliar with the professional music theory. The proposed Automated Composition for Music Education (ACME) software was developed to help the students to compose the rhythm and melody themselves, while chord configuration and arrangement are automatically generated. Subsequently, sixth-year students of a non-experimental school were recruited to use the software for the completion of its development. Analysis of learning effectiveness at the reaction level, analysis of learning-level learning effectiveness, correlation analysis of the reaction- and learning-levels learning effectiveness has been performed with a significant result. The correlations between each component of learning effectiveness at the reaction and learning levels were also found. Finally the students in the experimental group using the ACME teaching materials showed a better learning effectiveness at both reaction level and learning level, than those of the students in the comparison group.     

Reduction of Escherichia coli O157:H7 and Salmonella Typhimurium DT 104 on fresh produce using an automated washer with near neutral electrolyzed (NEO) water and ultrasound

Automated produce washers can be a useful processing aid when treating fresh produce contaminated with pathogens. The use of near neutral pH electrolyzed (NEO) water as a wash or sanitizing solution has been shown to lead to significant reductions of Escherichia coli O157:H7 and Salmonella on fresh produce. To further enhance reported pathogen reductions, the effects of a combined NEO water (155 mg/L free chlorine, pH 6.5) and ultrasound wash protocol on lettuce and tomatoes inoculated with E. coli O157:H7 and S. Typhimurium DT 104 were studied. The effects of the pH of NEO water and washer agitation on pathogen reductions were also assessed. Inoculated tomatoes and lettuce leaves were treated with either chilled deionized water or NEO water, with or without 20 kHz ultrasound (130 W and 210 W). Tomatoes were treated for 1, 3 and 5 min while lettuce was treated for 5, 10 and 15 min. Ultrasound significantly increased the oxidation-reduction potential (ORP) of NEO water (p < 0.05) but did not affect the pH and free chlorine concentration (p > 0.05). Increased washing time and higher ultrasonic power led to significantly greater reductions of both pathogens on produce items (p < 0.05). NEO water combined with 210 W ultrasonication for 15 min led to 4.4 and 4.3 log reductions of E. coli O157:H7 and S. Typhimurium on lettuce, respectively, while 210 W ultrasound for 5 min completely inactivated both pathogens on tomatoes. Both pathogens were completely inactivated in NEO water solutions, suggesting that its use presents little chance of cross-contamination.     

Transferability of calibrated microsimulation model parameters for safety assessment using simulated conflicts

Several studies have investigated the relationship between field-measured conflicts and the conflicts obtained from micro-simulation models using the Surrogate Safety Assessment Model (SSAM). Results from recent studies have shown that while reasonable correlation between simulated and real traffic conflicts can be obtained especially after proper calibration, more work is still needed to confirm that simulated conflicts provide safety measures beyond what can be expected from exposure. As well, the results have emphasized that using micro-simulation model to evaluate safety without proper model calibration should be avoided. The calibration process adjusts relevant simulation parameters to maximize the correlation between field-measured and simulated conflicts.The main objective of this study is to investigate the transferability of calibrated parameters of the traffic simulation model (VISSIM) for safety analysis between different sites. The main purpose is to examine whether the calibrated parameters, when applied to other sites, give reasonable results in terms of the correlation between the field-measured and the simulated conflicts. Eighty-three hours of video data from two signalized intersections in Surrey, BC were used in this study. Automated video-based computer vision techniques were used to extract vehicle trajectories and identify field-measured rear-end conflicts. Calibrated VISSIM parameters obtained from the first intersection which maximized the correlation between simulated and field-observed conflicts were used to estimate traffic conflicts at the second intersection and to compare the results to parameters optimized specifically for the second intersection. The results show that the VISSIM parameters are generally transferable between the two locations as the transferred parameters provided better correlation between simulated and field-measured conflicts than using the default VISSIM parameters. Of the six VISSIM parameters identified as important for the safety analysis, two parameters were directly transferable, three parameters were transferable to some degree, and one parameter was not transferable.     

Automated visual inspection in the semiconductor industry: A survey

Automated visual inspection is an image-processing technique for quality control and production line automation. This paper reviews various optical inspection approaches in the semiconductor industry and categorize the previous literatures by the inspection algorithm and inspected products. The vision-based algorithms that had been adopted in the visual inspection systems include projection methods, filtering-based approaches, learning-based approaches, and hybrid methods. To discuss about the practical applications, the semiconductor industry covers the manufacturing and production of wafer, thin-film transistor liquid crystal displays, and light-emitting diodes. To improve the yield rate and reduce manufacturing costs, the inspection devices are widely installed in the design, layout, fabrication, assembly, and testing processes of production lines. To achieve a high robustness and computational efficiency of automated visual inspection, interdisciplinary knowledge between precision manufacturing and advanced image-processing techniques is required in the novel system design. This paper reviews multiple defect types of various inspected products which can be referenced for further implementations and improvements.     

Integrated Inspection of QoM,QoP, and QoS for AOI Industries in Metaverses

With the rapid development of information technologies such as digital twin, extended reality, and blockchain, the hype around "metaverse" is increasing at astronomical speed. However, much attention has been paid to its entertainment and social functions. Considering the openness and interoperability of metaverses, the market of quality inspection promises explosive growth. In this paper, taking advantage of metaverses, we first propose the concept of Automated Quality Inspection (AutoQI), which performs integrated inspection covering the entire manufacturing process, including Quality of Materials, Quality of Manufacturing (QoM), Quality of Products, Quality of Processes (QoP), Quality of Systems, and Quality of Services (QoS). Based on the scenarios engineering theory, we discuss how to perform interactions between metaverses and the physical world for virtual design instruction and physical validation feedback. Then we introduce a bottom-up inspection device development workflow with productivity tools offered by metaverses, making development more effective and efficient than ever. As the core of quality inspection, we propose Quality Transformers to complete detection task, while federated learning is integrated to regulate data sharing. In summary, we point out the development directions of quality inspection under metaverse tide.      

Factors that determine residents’ acceptance of smart city technologies

ABSTRACT

While some cities attempt to determine their residents’ demand for smart-city technologies, others simply move forward with smart-related strategies and projects. This study is among the first to empirically determine which factors most affect residents’ and public servants’ intention to use smart-city services. A Smart Cities Stakeholders Adoption Model (SSA), based on Unified Theory of Acceptance and Use of Technology (UTAUT2), is developed and tested on a mid-size U.S. city as a case study. A questionnaire was administered in order to determine the influence of seven factors – effort expectancy, self-efficacy, perceived privacy, perceived security, trust in technology, price value and trust in government – on behaviour intention, specifically the decision to adopt smart-city technologies. Results show that each of these factors significantly influenced citizen intention to use smart-city services. They also reveal perceived security and perceived privacy to be strong determinants of trust in technology, and price value a determinant of trust in government. In turn, both types of trust are shown to increase user intention to both adopt and use smart-city services. These findings offer city officials an approach to gauging residential intention to use smart-city services, as well as identify those factors critical to developing a successful smart-city strategy.