Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

A research survey: review of flexible job shop scheduling techniques

Background: College students are increasingly reporting common mental health problems, such as depression and anxiety, and they frequently encounter barriers to seeking traditional mental health treatments. Digital mental health interventions, such as those delivered via the Web and apps, offer the potential to improve access to mental health treatment. Objective: This study aimed to review the literature on digital mental health interventions focused on depression, anxiety, and enhancement of psychological well-being among samples of college students to identify the effectiveness, usability, acceptability, uptake, and adoption of such programs. Methods: We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (registration number CRD42018092800), and the search strategy was conducted by a medical research librarian in the following databases: MEDLINE (Ovid), EMBASE (Elsevier), PsycINFO (EbscoHost), the Cochrane Library (Wiley), and Web of Science (Thomson Reuters) from the date of inception to April 2019. Data were synthesized using a systematic narrative synthesis framework, and formal quality assessments were conducted to address the risk of bias. Results: A total of 89 studies met the inclusion criteria. The majority of interventions (71/89, 80%) were delivered via a website, and the most common intervention was internet-based cognitive behavioral therapy (28, 31%). Many programs (33, 37%) featured human support in the form of coaching. The majority of programs were either effective (42, 47%) or partially effective (30, 34%) in producing beneficial changes in the main psychological outcome variables. Approximately half of the studies (45, 51%) did not present any usability or acceptability outcomes, and few studies (4, 4%) examined a broad implementation of digital mental health interventions on college campuses. Quality assessments revealed a moderate-to-severe risk of bias in many of the studies. Conclusions: Results suggest that digital mental health interventions can be effective for improving depression, anxiety, and psychological well-being among college students, but more rigorous studies are needed to ascertain the effective elements of these interventions. Continued research on improving the user experience of, and thus user engagement with, these programs appears vital for the sustainable implementation of digital mental health interventions on college campuses.

/pdf/digital-mental-health-interventions-for-depression-anxiety-4huewt627e.pdf

Digital Mental Health Interventions for Depression, Anxiety, and Enhancement of Psychological Well-Being Among College Students: Systematic Review

Intelligence has been considered as the major challenge in promoting economic potential and production efficiency of precision agriculture. In order to apply advanced deep-learning technology to complete various agricultural tasks in online and offline ways, a large number of crop vision datasets with domain-specific annotation are urgently needed. To encourage further progress in challenging realistic agricultural conditions, we present the CropDeep species classification and detection dataset, consisting of 31,147 images with over 49,000 annotated instances from 31 different classes. In contrast to existing vision datasets, images were collected with different cameras and equipment in greenhouses, captured in a wide variety of situations. It features visually similar species and periodic changes with more representative annotations, which have supported a stronger benchmark for deep-learning-based classification and detection. To further verify the application prospect, we provide extensive baseline experiments using state-of-the-art deep-learning classification and detection models. Results show that current deep-learning-based methods achieve well performance in classification accuracy over 99%. While current deep-learning methods achieve only 92% detection accuracy, illustrating the difficulty of the dataset and improvement room of state-of-the-art deep-learning models when applied to crops production and management. Specifically, we suggest that the YOLOv3 network has good potential application in agricultural detection tasks.

CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture.

The impact of Internet of Things has been revolutionized in all fields of life, but its impact on the healthcare system has been significant due to its cutting edge transition. The role of Internet of Things becomes more dominant when it is supported by the features of mobile computing. The mobile computing extends the functionality of IoT in healthcare environment by bringing a massive support in the form of mobile health (m-health). In this research, a systematic literature review protocol is proposed to study how mobile computing assists IoT applications in healthcare, contributes to the current and future research work of IoT in the healthcare system, brings privacy and security in health IoT devices, and affects the IoT in the healthcare system. Furthermore, the intentions of the paper are to study the impacts of mobile computing on IoT in healthcare environment or smart hospitals in light of our systematic literature review protocol. The proposed study reports the papers that were included based on filtering process by title, abstract, and contents, and a total of 116 primary studies were included to support the proposed research. These papers were then analysed for research questions defined for the proposed study.

/pdf/internet-of-things-for-healthcare-using-effects-of-mobile-4ojig1c50j.pdf

Internet of Things for Healthcare Using Effects of Mobile Computing: A Systematic Literature Review

This work brings together the emerging virtual reality techniques and the natural user interfaces to offer new possibilities in the field of rehabilitation. We have designed a rehabilitation game based on a low cost device (Microsoft KinectTM) connected to a personal computer. It provides patients having Parkinson's Disease (PD) with a motivating way to perform several motor rehabilitation exercises to improve their rehabilitation. The experiment was tested on seven Parkinson's Disease patients and results demonstrated significant improvements in completion time score and in the 10 Meters Walk Test score. Nevertheless, additional research is needed to determine if this type of training has a long-term impact. Both the device and protocol were well accepted by subjects, being safe and easy to use. We conclude that our work provides a simple and suitable tool resulting in a more enriching rehabilitation process where motivation is highly encouraged in PD patients. Feedback coming from participants corroborate the hypothesis that the system can be applied not only in clinical rehabilitation centers but at home.

A Kinect-Based System for Lower Limb Rehabilitation in Parkinson's Disease Patients: a Pilot Study

Jorge J. Gomez-Sanz Facultad de Informatica Universidad Complutense de Madrid 28040 Madrid, Spain jjgomez@sip.ucm.es Ruben Fuentes Facultad de Informatica Universidad Complutense de Madrid 28040 Madrid, Spain ruben@sip.ucm.es Juan Pavon Facultad de Informatica Universidad Complutense de Madrid 28040 Madrid, Spain jpavon@sip.ucm.es Ivan Garcia-Magarino Facultad de Informatica Universidad Complutense de Madrid 28040 Madrid, Spain ivan_gmg@fdi.ucm.es

/pdf/ingenias-development-kit-a-visual-multi-agent-system-14i5iv4hb0.pdf

INGENIAS Development Kit: a visual Multi-Agent System development environment (Demo Paper)

Unmanned aerial vehicles (UAVs) can support surveillance even in areas without network infrastructure. However, UAV networks raise security challenges because of its dynamic topology. This paper proposes a technique for maintaining security in UAV networks in the context of surveillance, by corroborating information about events from different sources. In this way, UAV networks can conform peer-to-peer information inspired by the principles of blockchain, and detect compromised UAVs based on trust policies. The proposed technique uses a secure asymmetric encryption with a pre-shared list of official UAVs. Using this technique, the wrong information can be detected when an official UAV is physically hijacked. The novel agent based simulator ABS-SecurityUAV is used to validate the proposed approach. In our experiments, around 90% of UAVs were able to corroborate information about a person walking in a controlled area, while none of the UAVs corroborated fake information coming from a hijacked UAV.

/pdf/security-in-networks-of-unmanned-aerial-vehicles-for-1tntvinpjk.pdf

Iván García-Magariño

Papers

Internet of Things for Healthcare Using Effects of Mobile Computing: A Systematic Literature Review

A Kinect-Based System for Lower Limb Rehabilitation in Parkinson's Disease Patients: a Pilot Study

INGENIAS Development Kit: a visual Multi-Agent System development environment (Demo Paper)

Security in networks of unmanned aerial vehicles for surveillance with an agent-based approach inspired by the principles of blockchain

Deep learning-based urban big data fusion in smart cities: Towards traffic monitoring and flow-preserving fusion