Cyber Enigmas? Passive detection and Pedagogical agents

The wildfire model for invasive weed management is a simple yet effective strategic guide framed on the cornerstones of prevention, early detection, timely control, and vegetation restoration. These guidelines are the same fundamental ingredients that make up this nation's formula for effective wildfire management. Just as early detection is critical for success in fighting fires, early detection is also essential for minimizing ecological effects and to control costs associated with new weed invasions. Both active and passive detection are important in wildfire management, and the same is true for invasive weed management. Passive weed detection—the chance discovery of new infestations by individuals pursuing other outdoor activities—can be helpful but is generally haphazard and incomplete. Passive detection alone will almost certainly fail to find many weed infestations until they have grown too large for true eradication. However, passive detection should neither be discouraged nor its value minimized because it can provide a meaningful way to inform and include additional people who otherwise would not be concerned or involved in weed management. Active weed detection consists of periodic field surveys conducted by trained individuals having clearly defined objectives and using sound search and mapping methods. Traditional methods of on-the-ground weed surveys remain important, but new technology such as remote sensing now offers additional options. Systematic field surveys and other early detection efforts are as important to successful weed management as the organized network of fire lookouts and aircraft patrols are to modern wildfire management. Any wildland invasive weed management program that does not include an effective early detection component will most likely fail.

Online chatbots (also known as pedagogical agents or virtual assistants) are becoming embedded into the fabric of technology, both in educational and commercial settings. Yet understanding of these technologies is inchoate and often untheorised, influenced by individuals' willingness to trust technologies, aesthetic appearance of the chatbot and technical literacy, among other factors. This paper draws upon data from two research studies that evaluated students' experiences of using pedagogical agents in education using responsive evaluation. The findings suggest that emotional connections with pedagogical agents were intrinsic to the user's sense of trust and therefore likely to affect levels of truthfulness and engagement. They also indicate that the topic of the pedagogical agent-student interaction is key to the student's experience. The implications of these studies are that truthfulness, personalisation and emotional engagement are all vital components in using pedagogical agents to enhance online learning.

Active and Passive Methods of Detecting Parkinson's Disease.

Bystander apathy has been a source of debate for decades. In the past half-century, psychologists developed theoretical frameworks to understand bystander activity, commonly referred to as bystander intervention models (BIMs). More recently, BIMs have been modified to facilitate initiatives to prevent various forms of online victimization. This chapter begins with a review of BIMs and recent applications of bystander intervention research to online environments. We also present several future directions for research along with applications for reducing technology-facilitated violence, including programming recommendations and theoretical development.

In Chap. 5, a particular subtarget detection technique in active hyperspectral target detection, called constrained energy minimization (CEM), was developed for its real-time and causal implementation. Rather than CEM, this chapter focuses on passive hyperspectral target detection and investigates a commonly used passive target detection technique, anomaly detection (AD), especially for real-time and causal processing capabilities that are developed for CEM in Chap. 5 and will also be derived in a similar manner for AD. To this end, this chapter can be considered a companion chapter of Chap. 5. Technically speaking, passive target detection must be carried out without any prior knowledge, specifically, a complete lack of availability of target knowledge. Owing to the nature of hyperspectral imaging sensors, which can uncover many unknown material substances, passive hyperspectral target detection is a major task in hyperspectral image analysis and has been studied extensively in the literature. Applications of passive target detection include surveillance and monitoring, where no knowledge is required a priori. Of particular interest in passive target detection is AD, which is generally performed in a completely blind environment. To suppress unknown backgrounds, AD makes use of the global sample correlation/covariance matrix R/K, referred to as R/K-AD, so as to enhance its detectability. In general, anomalies appear unexpectedly and cannot be detected by visual inspection. Most importantly, they vary with time and data sample vectors. Accordingly, developing real-time processing for R/K-AD on a timely basis sample by sample is crucial and critical in many real-world applications, for example, abnormalities in agriculture and forestry, environmental monitoring, combat vehicles on a battlefield, drug trafficking in law enforcement, and food inspection. As noted in Chap. 5, this real-time process requires that causality be included in an algorithm’s design. In analogy with real-time CEM, the concept of a causal sample correlation matrix (CSCRM), introduced in Chap. 5, is also applicable to AD for this purpose. However, in a broader sense, CSCRM is expanded into a causal sample correlation/covariance matrix (CSCRM/CSCVM) to replace R/K with CSCRM/CSCVM specified by R(r n )/K(r n ), respectively, where r n is the current data sample vector being processed. The capability for real-time processing is then derived from such a CSCRM/CSCVM because CSCRM/CSCVM varies with data sample vectors. Thus, a causal processing of K-AD/R-AD requires repeatedly calculating inverses of such sample-varying CSCRM/CSCVM and must be processed in a causal manner sample by sample, referred to as causal K-AD/R-AD (CR-K-AD/CR-AD). To further reduce computational complexity and computer processing time for CK-AD/CR-AD, the notion of innovation in a Kalman filter developed in Chap. 3 is once again used to derive recursive causal update equations to calculate CSCRM/CSCVM, which leads to recursive versions of K-AD/R-AD, to be called recursive CK-AD/CR-AD (R-CK-AD/R-CR-AD). In particular, when R-CK-AD/R-CR-AD is realized by real-time processing, it is referred to as RT CK-AD/RT-CR-AD in this chapter.

Human brucellosis (HB) remains a serious public health concern owing to its resurgence across the globe and specifically in China. The timely detection of this disease is the key to its prevention and control. We sought to describe the differences in the demographics of high-risk populations with detected cases of HB contracted from active versus passive sources. We collected data from a large sample population from January to December 2018, in Jining City, China. We recruited patients that were at high-risk for brucellosis from three hospitals and Centers of Disease Control and Prevention (CDCs). These patients were classified into two groups: the active detection group was composed of individuals receiving brucellosis counseling at the CDCs; the passive detection group came from hospitals and high-risk HB groups. We tested a total of 2,247 subjects and 13.3% (299) presented as positive for HB. The positive rates for active and passive detection groups were 20.5% (256/1,249) and 4.3% (43/998), respectively (p < 0.001). The detection rate of confirmed HB cases varied among all groups but was higher in the active detection group than in the passive detection group when controlled for age, sex, ethnicity, education, career, and contact history with sheep or cattle (p < 0.05). Males, farmers, those with four types of contact history with sheep or cattle, and those presenting fever, hyperhidrosis and muscle pain were independent factors associated with confirmed HB cases in multivariate analysis of the active detection group. Active detection is the most common method used to detect brucellosis cases and should be applied to detect HB cases early and avoid misdiagnosis. We need to improve our understanding of brucellosis for high-risk populations. Passive HB detection can be supplemented with active detection when the cognitive changes resulting from brucellosis are low. It is important that healthcare providers understand and emphasis the timely diagnosis of HB.

Active magnetic detection is an effective method for detecting metal targets. Its practical application is confirmed in many previous studies, and its advantages over passive detection have been widely recognized. Most importantly, it can detect weak magnetic or non-magnetic metal targets, unlike traditional magnetic detection. However, the secondary magnetic field generated by the eddy current on the target excited by the active emission primary magnetic field is always weak, and it has a fast attenuation in space. The target signal measured by the sensor is typically several orders of magnitude smaller than the background interference, which makes it challenging to extract the target signal during detection. This study mitigates these challenges by constructing the secondary magnetic field characteristic of the target from the perspective of signal energy. A set of 13 orthonormal basis functions is further derived to linearly describe this characteristic, which improves the signal-to-noise ratio (SNR) of the signal. The main contribution of this study is the proposal of an eddy current magnetic field orthonormal basis function (ECMF-OBF) method to effectively characterize the secondary magnetic field of magnetic targets in active magnetic detection. The SNR is considerably improved by the proposed method, and this is independent of the target magnetic moment orientation. Our experiment is performed in different environments including indoors, lake, and sea verify the method&#x2019;s performance. Therefore, it has excellent value in the practical application of active magnetic detection.

Reflections on adapting group model building scripts into online workshops

Intelligent Personal Assistants (IPAs) such as Apple's Siri, Google Now, and Amazon Alexa are becoming an increasingly important class of web application. In contrast to previous keyword-oriented search applications, IPAs support a rich query interface that allows user interaction through images, audio, and natural language queries. However, modern IPAs rely heavily on compute-intensive machine-learning inference. To achieve acceptable performance, ML-driven IPAs increasingly depend on specialized hardware accelerators (e.g. GPUs, FPGAs or TPUs), increasing costs for IPA service providers. For end-users, IPAs also present considerable privacy risks given the sensitive nature of the data they capture.We present PAIGE, a hybrid edge-cloud architecture for privacy-preserving Intelligent Personal Assistants. PAIGE's design is founded on the assumption that recent advances in low-cost hardware for machine-learning inference offer an opportunity to offload compute-intensive IPA ML tasks to the network edge. To allow privacy-preserving access to large IPA databases for less compute-intensive pre-processed queries, PAIGE leverages trusted execution environments at the server side. PAIGE's hybrid design allows privacy-preserving hardware acceleration of compute-intensive tasks, while avoiding the need to move potentially large IPA question-answering databases to the edge. As a step towards realising PAIGE, we present a first systematic performance evaluation of existing edge accelerator hardware platforms for a subset of IPA workloads, and show they offer a competitive alternative to existing data-center alternatives.

Objective – The aim of this study is to assess and compare the metacognitive awareness of university students in online and in-person learning environments. Secondly, the study aims to explore the students’ preferences for online and in-person learning to compare themetacognitive awareness of students based on their preferences. Methods – A total of 79 university students completed the Metacognitive Awareness Inventory, providing self-reported data on their metacognitive awareness. The study used a paired sample t-test to compare mean scores of metacognitive awareness between online and in-person classroom settings. Additionally, participants evaluated their experiences with online and in-person learning using a pairwise comparison as a scaling method. Results – The t-test results indicate statistically significant differences in metacognitive awareness between online and in-person classroom settings. Specifically, significant differences were found in declarative knowledge, procedural knowledge, conditional knowledge, planning, information management strategies, monitoring, debugging strategies, and evaluation of learning. Students showed higher levels of metacognitive awareness across all facets in traditional in-person learning setting. Descriptive analysis of scaling pairwise comparison revealed that online learning was strongly preferred for comfort, while in-person learning was preferred modality for motivation to study and active participation. In oppose to t-test results, ANOVA did not reveal significant differences in metacognitive awareness based on students’ preferences in online nor in traditional learning settings. Conclusions – The results provide valuable information on the metacognitive awareness and preferences of the students in two different learning environments.

In early 2020, the COVID-19 pandemic forced educators to transition to online teaching almost overnight. This paper focuses on students’ perceptions of communication skills in a practice course in the spring of 2020. The beginning of the course was held face-to-face and conducted online at the end. We sought information regarding students’ experiences of online course in a skill-based course. Relying on knowledge about online communication, we selected three areas to focus on in online course. First, feedback is essential in the process of learning communication skills and is included in the course learning objectives. Second involves eliciting conversations in an online environment. The third area is the connection between the audience and the speaker in public speaking. Our goal was to develop the course according to student’s perceptions. At the end of the course, 26 students answered open-ended questions about the two forms of implementation. Using thematic analysis, five different themes were constructed: (1) positive perceptions of the course, (2) neutral perceptions of online course, (3) perceptions of the challenges in online course, (4) perceptions of public speaking, and (5) perceptions of feedback. During the course, the students learned much-appreciated computer-mediated communication. They believed that the good learning results were due to careful organization and connection to other students. While uncommon, few students perceived online course as neither good nor bad, just neutral. Nevertheless, online course was not without challenges; students identified several problems concerning, for example, conversations and non-verbal communication. Furthermore, students considered feedback to be successful; however, online public speaking was perceived as different from speaking to a physical audience. The results of this study indicate that in a skill-based course, the online format can be just as effective as the face-to-face format. However, especially because public speaking was regarded as different and participants appreciated practicing communication in the online setting, we suggest that students should have the opportunity to practice communication skills both online and face-to-face. Additionally, although conversations received special attention, it caused the most significant challenges in the online environment; therefore, we propose that solutions to address this problem must be investigated further.

Multi grid-connected inverters islanding detection methods include two categories: passive islanding detection and active islanding detection. Firstly, an active islanding detection method based on negative sequence current disturbance is analyzed. Aiming at the shortcomings of this method, a novel passive islanding detection method is proposed in this paper. The proposed islanding detection method uses wavelet transform to extract signal features, and then according to the extracted signal features adopts the neural network to judge whether or not the islanding occurs. The simulation results show that the passive multi grid-connected inverters islanding detection method has good islanding detection performance, and does not affect the stability and power quality of the system.

Lofargram (or spectral gram) is widely used in sonar for detection of narrowband tonal signals and/or wideband transient signals. Such signals at a level comparable to the noise level are difficult to detect using snapshots of the signal spectrum, as they look like the noise. However, as noise is random in time while the signals are persistent over time, they can be detected by line-association (often referred to as eyeball integration) using the Lofargram. This concept for passive signal detection is carried forward to active signal detection using the Dopplergram. In an active system, the challenge is to detect a target echo from the noise and/or the reverberation signals. In a noise-limited situation, there is only so much power one can put out due to hardware and environmental (marine mammal) limitations. In a reverberation-limited environment, the problem is the high probability of false alarms due to reverberations. Active detections using a Dopplergram are rid of these problems in a natural way. [Work supported by ONR.]

Themis: A passive-active hybrid framework with in-network intelligence for lightweight failure localization

Objective To explore the distribution of the positive rate of HIV antibody in a comprehensive hospital and the characteristics of the newly diagnosed HIV infected persons, so as to provide a theoretical basis for the development of the targeted HIV prevention and control measures. Methods During 2011 and 2016, the patients who went to HIV counseling and Testing Center (VCT model) , the provider initiate testing counseling (PITC model) , or the preoperative HIV testing (conventional passive detection model) for medical service were selected. The differences of the efficiency of HIV detection and trend of change were compared, and the characteristics of the newly diagnosed HIV infected persons were analyzed. Results The positive detection rates of HIV antibody VCT model, PITC model and conventional passive detection model were 4.7% (77/1 649) , 0.9% (28/3 222) and 0.05% (60/109 170) , respectively. The differences had statistical significance (χ2=2 514.673, P<0.01) . From 2011 to 2016, the rate of HIV detected by conventional passive detection mode had gradually increased (χ2trend=20.287, P<0.01) . At the time of diagnosis, the CD4+T lymphocyte counted in VCT model was (423±56) /μL, which was higher than those in PITC model and conventional passive detection model (F=25.086, P<0.01) . Moreover, the proportion of WHO stage Ⅲ and Ⅳ in VCT model is lower than PITC model and conventional passive detection model (χ2=37.393, P<0.01) . Conclusions The combination of three HIV detection models of active testing, passive testing and routine testing can greatly improve the efficiency of HIV detection in comprehensive hospitals. Key words: HIV; Voluntary Counseling; Active detection; Passive detection