Identity Recognition Using Biological Electroencephalogram Sensors

Brain wave signal is a bioelectric phenomenon reflecting activities in human brain. In this paper, we firstly introduce brain wave-based identity recognition techniques and the state-of-the-art work. We then analyze important features of brain wave and present challenges confronted by its applications. Further, we evaluate the security and practicality of using brain wave in identity recognition and anticounterfeiting authentication and describe use cases of several machine learning methods in brain wave signal processing. Afterwards, we survey the critical issues of characteristic extraction, classification, and selection involved in brain wave signal processing. Finally, we propose several brain wave-based identity recognition techniques for further studies and conclude this paper

Design, Synthesis, and In vitro Antitumor Activity Evaluation of Novel 4‐pyrrylamino Quinazoline Derivatives

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/88050/1/j.1747-0285.2011.01234.x.pd

Physical and mental health impairments experienced by operating surgeons and camera-holder assistants during laparoscopic surgery: a cross-sectional survey

IntroductionSurgeons may experience physical and mental health problems because of their jobs, which may lead to chronic muscle damage, burnout, or even withdrawal. However, these are often ignored in camera-holder assistants during laparoscopic surgery. We aimed to analyze the differences between operating surgeons and camera-holder assistants.MethodsFrom January 1, 2022, to December 31, 2022, a cross-sectional survey was conducted to evaluate the muscle pain, fatigue, verbal scolding, and task load for operating surgeons and camera-holder assistants. The Nordic Musculoskeletal Questionnaire, the Space Administration Task Load Index, and the Surgical Task Load Index (SURG-TLX) were combined in the questionnaire.Results2,184 operations were performed by a total of 94 operating surgeons and 220 camera assistants. 81% of operating surgeons and 78% of camera-holder assistants reported muscle pain/discomfort during the procedure. The most affected anatomic region was the shoulders for operating surgeons, and the lower back for camera-holder assistants. Intraoperative fatigue was reported by 41.7% of operating surgeons and 51.7% of camera-holder assistants. 55.2% of camera-holder assistants reported verbal scolding from the operating surgeons, primarily attributed to lapses in laparoscope movement coordination. The SURG-TLX results showed that the distributions of mental, physical, and situational stress for operating surgeons and camera-holder assistants were comparable.ConclusionLike operating surgeons, camera-holder assistants also face similar physical and mental health impairments while performing laparoscopic surgery. Improvements to the working conditions of the camera-holder assistant should not be overlooked

ADOPTING GAME-CHANGING CONSTRUCTION TECHNOLOGIES TO IMPROVE CONSTRUCTION PRODUCTIVITY FROM SINGAPORE CONTRACTORS � PERSPECTIVE: AN INSTITUTIONAL ANALYSIS

Bachelor'sBACHELOR OF SCIENCE (PROJECT AND FACILITIES MANAGEMENT

API recommendation for Mashup creation based on neural graph collaborative filtering

With the increase of open APIs appeared on the Web, reusing or combining these APIs to develop novel applications (e.g. Mashups) has attracted great interest from developers. However, to quickly find a suitable one among a huge number of APIs to meet a developer’s requirement is basically a non-trivial issue. Therefore, a high-quality API recommendation system is desirable. Although a number of collaborative filtering methods have been proposed for API recommendation, their recommendation accuracy is limited and needs to be further improved. Based on the neural graph collaborative filtering technique, this paper proposes an API recommendation method that exploits the high-order connectivity between APIs and API users. To evaluate the proposed method, extensive experiments are conducted on a real API dataset and the results show that the proposed method outperforms the state-of-the-art methods in API recommendation

Availability/Network-aware MapReduce over the Internet

International audienceMapReduce offers an ease-of-use programming paradigm for processing large datasets. In our previous work, we have designed a MapReduce framework called BitDew-MapReduce for desktop grid and volunteer computing environment, that allows nonexpert users to run data-intensive MapReduce jobs on top of volunteer resources over the Internet. However, network distance and resource availability have great impact on MapReduce applications running over the Internet. To address this, an availability and network-aware MapReduce framework over the Internet is proposed. Simulation results show that the MapReduce job response time could be decreased by 40.05%, thanks to Weighted Naive Bayes Classifier-based availability prediction and landmark-based network estimation. The effectiveness of the new MapReduce framework is further proved by performance evaluation in a real distributed environment

Logisticschain: A Blockchain-Based Secure Storage Scheme for Logistics Data

With the rapid development of information technology, logistics systems are developing towards intelligence. The Internet of Things (IoT) devices throughout the logistics network could provide strong support for smart logistics. However, due to the limited computing and storage resources of IoT devices, logistics data with user sensitive information are generally stored in a centralized cloud center, which could easily cause privacy leakage. In this paper, we propose Logisticschain, a blockchain-based secure storage scheme for logistics data. In this scheme, the sensing data from IoT devices should be encrypted for fine-grained access control, and a customized blockchain structure is proposed to improve the storage efficiency of systems. Also, an efficient consensus mechanism is introduced to improve the efficiency of the consensus process in the blockchain. Specific to the logistics process, the sensing data generated from IoT devices will be encrypted and aggregated into the blockchain to ensure data security. Moreover, the stored logistics records can be securely audited by leveraging the blockchain network; both IoT data and logistics demands cannot be deleted or tampered to avoid disputes. Finally, we analyze the security and privacy properties of our Logisticschain and evaluate its performance in terms of computational costs by developing an experimental platform

Composition pattern-aware web service recommendation based on depth factorisation machine

Web service composition has become a prevalent software development method that enables developing powerful Mashups by effectively combining Web services with different functions. However, as the number of Web services increases, it becomes challenging for developers to select appropriate services to develop Web applications that satisfy functional requirements. In order to recommend Web services considering user's preferences, a composition pattern-aware Web service recommendation method called EWACP-DeepFM is proposed, which combines the composition patterns between Web services and Mashups and the co-occurrence and popularity of Web services. By constructing a multi-dimensional feature matrix, which is further trained by the depth factorisation machine (DeepFM) model to learn potential link relationships between Web services and Mashup applications, and recommend Top-N best services for the target Mashup application. Experiments performed using the real datasets from ProgrammableWeb show that the proposed method outperforms others with better recommendation effectiveness