Exploiting Cognitive Structure for Adaptive Learning

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19

Cultures of conflict:Protests, violent repression, and community values

What are the cultural origins of societal conflicts that revolve around democratization, women’s rights, and modern libertarian values? We propose that deep-seated differences in community-based collective values (at the micro-level) may be related to why people support anti-government protest and why they support repression of such protests (at the macro-level). The hypothesis was examined among residents of Turkey (N = 500). Cultural values, measured at the individual level and community level with the community collectivism scale, correlated with political orientation and emotions, as well as with subsequent support for anti-governmental protest or its repression. The main conclusions are that both support for protest and support for repression are related to the cultural values people hold and their subsequent political orientations and emotions. Micro-level cultural values in local communities may thus play a role in explaining macro-level socio-political divides

Volumetric laser endomicroscopy and its application to Barrett's esophagus: results from a 1,000 patient registry.

Volumetric laser endomicroscopy (VLE) uses optical coherence tomography (OCT) for real-time, microscopic cross-sectional imaging. A US-based multi-center registry was constructed to prospectively collect data on patients undergoing upper endoscopy during which a VLE scan was performed. The objective of this registry was to determine usage patterns of VLE in clinical practice and to estimate quantitative and qualitative performance metrics as they are applied to Barrett's esophagus (BE) management. All procedures utilized the NvisionVLE Imaging System (NinePoint Medical, Bedford, MA) which was used by investigators to identify the tissue types present, along with focal areas of concern. Following the VLE procedure, investigators were asked to answer six key questions regarding how VLE impacted each case. Statistical analyses including neoplasia diagnostic yield improvement using VLE was performed. One thousand patients were enrolled across 18 US trial sites from August 2014 through April 2016. In patients with previously diagnosed or suspected BE (894/1000), investigators used VLE and identified areas of concern not seen on white light endoscopy (WLE) in 59% of the procedures. VLE imaging also guided tissue acquisition and treatment in 71% and 54% of procedures, respectively. VLE as an adjunct modality improved the neoplasia diagnostic yield by 55% beyond the standard of care practice. In patients with no prior history of therapy, and without visual findings from other technologies, VLE-guided tissue acquisition increased neoplasia detection over random biopsies by 700%. Registry investigators reported that VLE improved the BE management process when used as an adjunct tissue acquisition and treatment guidance tool. The ability of VLE to image large segments of the esophagus with microscopic cross-sectional detail may provide additional benefits including higher yield biopsies and more efficient tissue acquisition. Clinicaltrials.gov NCT02215291

Prevalence of HIV, Herpes Simplex Virus-2, and Syphilis in male sex partners of pregnant women in Peru

<p>Abstract</p> <p>Background:</p> <p>Sexually active heterosexual men may represent an important risk factor for HIV infection and STI transmission to their female partners and unborn children, though little is known about the prevalence of STIs in this population. We sought to determine the prevalence of HIV, herpes simplex virus type 2 (HSV-2), and syphilis infection and associated risk behaviors among male sex partners of pregnant women in Peru.</p> <p>Methods:</p> <p>Survey and seroprevalence data were collected from 1,835 male partners of pregnant women in four cities in Peru. Serum was tested for antibodies to HIV, HSV-2, and syphilis.</p> <p>Results:</p> <p>Among the 1,835 male participants, HIV prevalence was 0.8% (95% CI = 0.5–1.4%), HSV-2 16.0% (95% CI = 14.3–17.8%), and syphilis 1.6% (95% CI = 1.0–2.2%). Additionally, 11.0% reported a lifetime history of intercourse with men, and 37.1% with female sex workers. Unprotected intercourse with men during the previous year was reported by 0.9% and with female sex workers by 1.2%.</p> <p>Conclusion:</p> <p>Pregnant women's sex partners reported lifetime sexual contact with core risk groups, had an elevated prevalence of HSV-2, and demonstrated the potential to spread HIV and other STIs to their partners. Though the prevalence of HIV in the population was not significantly higher than observed in other samples of heterosexuals in Peru, the risk of HIV transmission to their female partners may be exacerbated by their increased prevalence of HSV-2 infection. Further study of heterosexual populations is necessary to fully understand the epidemiology of HIV/STIs in Latin America.</p

Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems

We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observation. The encoding is useful for sharing local visual observations with other agents under communication resource constraints. The actor-encoder encodes the raw images and chooses an action based on local observations and messages sent by the other agents. The machine learning agent generates not only an actuator command to the physical device, but also a communication message to the other agents. We formulate a reinforcement learning problem, which extends the action space to consider the communication action as well. The feasibility of the reinforcement learning framework is demonstrated using a 3D simulation environment with two collaborating agents. The environment provides realistic visual observations to be used and shared between the two agents.Comment: AIAA SciTech 201

Signals in the Soil: Underground Antennas

Antenna is a major design component of Internet of Underground Things (IOUT) communication system. The use of antenna, in IOUT, differs from traditional communication in that it is buried in the soil. Therefore, one of the main challenges, in IOUT applications, is to establish a reliable communication. To that end, there is a need of designing an underground-specific antenna. Three major factors that can impact the performance of a buried antenna are: (1) effect of high soil permittivity changes the wavelength of EM waves, (2) variations in soil moisture with time affecting the permittivity of the soil, and (3) difference in how EM waves propagate during aboveground (AG) and underground (UG) communications. For the third challenge above, it is to be noted that lateral waves are dominant component in EM during UG2UG communication and suffer lowest attenuation as compared to other, direct and reflected, components. Therefore, antennas used for over-the-air (OTA) communication will not be suitable for UG communication because of impedance mismatch. This chapter focuses on developing a theoretical model for understanding the impact of soil on antenna by conducting experiments in different soil types (silty clay loam, sandy, and silt loam soil) and indoor testbed. The purpose of the model is to predict UG antenna resonance for designing efficient communication system for IOUT. Based on the model a wideband planar antenna is designed considering soil dispersion and soil–air interface reflection effect which improves the communication range five times from the antennas designed only for the wavelength change in soil. Furthermore, it also focuses on developing an impedance model to study the effect of changing wavelength in underground communication. It is also discussed how soil–air interface and soil properties effect the return loss of dipole antenna

Current Advances in Internet of Underground Things

The latest developments in Internet of Underground Things are covered in this chapter. First, the IOUT Architecture is discussed followed by the explanation of the challenges being faced in this paradigm. Moreover, a comprehensive coverage of the different IOUT components is presented that includes communications, sensing, and system integration with the cloud. An in-depth coverage of the applications of the IOUT in various disciplines is also surveyed. These applications include areas such as decision agriculture, pipeline monitoring, border control, and oil wells

Internet of Things in Water Management and Treatment

The goal of the water security IoT chapter is to present a comprehensive and integrated IoT based approach to environmental quality and monitoring by generating new knowledge and innovative approaches that focus on sustainable resource management. Mainly, this chapter focuses on IoT applications in wastewater and stormwater, and the human and environmental consequences of water contaminants and their treatment. The IoT applications using sensors for sewer and stormwater monitoring across networked landscapes, water quality assessment, treatment, and sustainable management are introduced. The studies of rate limitations in biophysical and geochemical processes that support the ecosystem services related to water quality are presented. The applications of IoT solutions based on these discoveries are also discussed