People associate poor eye contact with deception. This perception may have hurt large-scale adoption of videoconferencing technology.
Crop disease diagnosis is an essential step in crop disease treatment and is a hot issue in agricultural research. However, in agricultural production, identifying only coarse-grained diseases of crops is insufficient because treatment methods are different in different grades of even the same disease. Inappropriate treatments are not only ineffective in treating diseases but also affect crop yield and food safety. We combine IoT technology with deep learning to build an IoT system for crop fine-grained disease identification. This system can automatically detect crop diseases and send diagnostic results to farmers. We propose a multidimensional feature compensation residual neural network (MDFC-ResNet) model for fine-grained disease identification in the system. MDFC-ResNet identifies from three dimensions, namely, species, coarse-grained disease, and fine-grained disease and sets up a compensation layer that uses a compensation algorithm to fuse multidimensional recognition results. Experiments show that the MDFC-ResNet neural network has better recognition effect and is more instructive in actual agricultural production activities than other popular deep learning models.
Steganography has long been regarded as a tool used for illicit and destructive purposes such as crime and warfare. Currently, digital tools are widely available to ordinary computer users also. Steganography software allows both illicit and legitimate users to hide messages so that they will not be detected in transit. This article provides a brief history of steganography, discusses the current status in the computer age, and relates this to forensic, security, and legal issues. The paper concludes with recommendations for digital forensics investigators, IT staff, individual users, and other stakeholders.
Information systems development projects are a significant expenditure of time, effort and money for many enterprises. Historically it has been estimated that 50-80% of projects fail to achieve their objectives for a variety of reasons. Researchers have identified numerous factors associated with system development failure. In this paper, we first synthesize the vast research regarding systems development risk factors and provide a framework that illustrates interactions between risk factors. The framework was used to develop an open-ended questionnaire that was answered by an inter-industry group of experienced systems development engineers and project managers. Analysis of their reports indicates that experienced professionals perceive that all risk factors (technical, resource, etc.) ultimately derive from organizationally-oriented factors, to be solved with organizational responses. This holistic viewpoint of risk assessment is counter to that of systems professionals more involved in day-to-day development decision making. For these developers, risks are more likely to be characterized as fitting into traditional discrete categories. This apparent dichotomy of risk importance was further investigated through an intra-organizational study which directly assessed how professionals recognize and treat risks in the development process. Results illustrate that a successful project environment may be characterised as one in which all systems professionals maintain a holistic view of organizational risk and that organizational culture, as opposed to experience, may predicate such an environment. Implications and future research directions are discussed.
Technology adoption by individuals has traditionally been regarded by information systems researchers as a choice between adoption and non-adoption of a single technology. With the current diversity of technology alternatives, the adoption decision may be more accurately specified as a choice between competing alternative technologies. The research question may no longer be simply whether technology is adopted, but rather which technology is adopted. The authors illustrate this with a simplified model of choice between two competing technologies, where the second technology is an enhanced version of the first. Their theoretical model is based on Expectancy Theory (ET). Results indicate that system characteristics can be successfully captured in the Valence Model of ET, and effort expectancy in the Force Model. Future research can expand on these results by including more factors in the Valence Model, and by comparing more than two alternative technologies.
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