Reviewerly/exHarmony huggingface dataset:
Fall Detection for the Elderly Based on 3-Axis Accelerometer and Depth Sensor Fusion with Random Forest Classifier

In this paper, we propose a new fall detection method that combines 3-axis accelerometer and depth sensors. By combining vision and acceleration-derived features we managed to minimize the false detection rate that is considerably higher when the decision is based on just one type of feature. Also, using machine learning has led to good generalization performance. In addition, we newly created fall database that are more realistic than previous ones. Experiment results show that the proposed method can efficiently detect falls.
Multi-stage Prediction Networks for Data Harmonization

In this paper, we introduce multi-task learning (MTL) to data harmonization (DH); where we aim to harmonize images across different acquisition platforms and sites. This allows us to integrate information from multiple acquisitions and improve the predictive performance and learning efficiency of the harmonization model. Specifically, we introduce the Multi Stage Prediction (MSP) Network, a MTL framework that incorporates neural networks of potentially disparate architectures, trained for different individual acquisition platforms, into a larger architecture that is refined in unison. The MSP utilizes high-level features of single networks for individual tasks, as inputs of additional neural networks to inform the final prediction, therefore exploiting redundancy across tasks to make the most of limited training data. We validate our methods on a dMRI harmonization challenge dataset, where we predict three modern platform types, from one obtained from an old scanner. We show how MTL architectures, such as the MSP, produce around 20% improvement of patch-based mean-squared error over current state-of-the-art methods and that our MSP outperforms off-the-shelf MTL networks. Our code is available [1].
Optimization of fuzzy controllers for autonomous mobile robots using the grey wolf optimizer

Through the advancement of science and technology, every day new methods or computational techniques are emerging that allow to solve problems in widely different areas, such as medicine, engineering, even in any industrial process. Optimization is of vital importance in applications and in industry, the main objective being to find the best possible solution to a particular problem of interest. In this work we propose to use the Grey Wolf Optimizer (GWO), which is a relatively new meta-heuristic, inspired by the hunting behavior and leadership hierarchy of grey wolves, for the optimization of fuzzy controllers for mobile autonomous robots. In addition to analyzing and explaining the proposed methodology based on GWO we are presenting simulation results for a two wheeled autonomous mobile robot to validate the efficiency of the proposed approach.
Usability and effects of text, image and audio feedback on exercise correction during augmented reality based motor rehabilitation

This paper aims to evaluate text, image and audio feedback regarding usability, exercise correction and user preferences when using Augmented Reality (AR) to guide movements execution in rehabilitation applications. For that, this study used an AR rehabilitation tool, named ARkanoidAR, which attempts to correct the wrong movement performed by a user through text, image and audio feedback, while he/she is playing a game controlled based on the therapy protocol defined by a physiotherapist for that particular user. The ARkanoidAR uses a Kinect sensor to track the human body and its movements, and it is completely configurable according to patient therapy needs. To evaluate feedback effects individually, this study collected quantitative data to measure success rate, which represents the amount of time that the user executed the movements correctly when using the application with each feedback individually activated. Experiments were composed of five phases: control, text, image, audio and control. The feedback sequence was randomized before the tests in order to avoid bias. Quantitative results show that users understood the instructions for performing the movements correctly since the success rate along time increased. Tests also included observation and qualitative analysis through questionnaires and a semi-structured interview to evaluate the usability perception of users. The qualitative data helped to detect different types of problems for each feedback based on users' answers. This study was carried out with healthy people, and did not predict the particularities and limitations of physiotherapy patients.
Label consistent locally linear embedding based cross-modal hashing

Hashing methods have gained widespread attention in cross-modal retrieval applications due to their efficiency and effectiveness. Many works have been done but they fail to capture the feature based similarity consistency or the discriminative semantics of label consistency. In addition, most of them suffer from large quantization loss, resulting in low retrieval performance. To address these issues, we propose a novel cross-modal hashing method named Label Consistent Locally Linear Embedding based Cross-modal Hashing (LCLCH). LCLCH preserves the non-linear manifold structure of different modality data by Locally Linear Embedding, and transforms heterogeneous data into a latent common semantic space to reduce the semantic gap and support cross-modal retrieval tasks. Therefore, it not only discovers the potential correlation of heterogeneous cross-modal data but also maintains label consistency. To further ensure the effectiveness of hash code learning, we utilize an iterative quantization method to handle the discrete optimization task and obtain the hash codes directly. We compare LCLCH with some advanced supervised and unsupervised methods on three datasets to evaluate its effectiveness.
Analyzing Echo Chambers: A Logic of Strong and Weak Ties

Echo chamber is a widely used term describing a situation where certain information is reinforced within a closed network. To reason about echo chambers, we introduce a two-sorted hybrid logic of strong and weak ties based on a logic of positive and negative relations known from the literature. We show that some classical property definitions can be formalized and that a known claim from social network analysis is a validity. We also prove that the logic is axiomatizable, sound and strongly complete. We combine our results with research on homophily and social group formation to represent relations between similar agents. Lastly, we add a knowledge modality and dynamic operators to analyze change in these networks.
An Analytical Comparison of Mobile Application Development using Agile Methodologies

Purpose and Motivation: The rapid progress in mobile applications and the highly competitive environment increase the number of mobile application projects. Problem Statement: The purpose of the study is to find an appropriate approach that meets the requirement of mobile application systems. Methods: We will study agile processes to meet the requirements, making mobile application development simple and well-suited wit the characteristics of mobile devices. It was discovered that agile methodologies are best suited to the development of mobile applications as they are small time, need suppleness reduces misuse and promotion of time. Results: We have studied several agile approaches, but no research provides specific reasons to adopt Agile as necessary model for development of mobile applications. Conclusion: For mobile application development, we have proposed the modified Agile methodology that would the best Agile approach and that meets the strengths of each selected approach for mobile software engineering and removing the weakness.
What Are the Parameters that Affect the Construction of a Knowledge Graph?

A large number of datasets are made publicly available on a wide range of formats. Due to interoperability problems, the construction of RDF-based knowledge graphs (KG) using declarative mapping languages has emerged with the aim of integrating heterogeneous sources in a uniform way. Although the scientific community has actively contributed with several engines to solve the problem of knowledge graph construction, the lack of testbeds has prevented reproducible benchmarking of these engines. In this paper, we tackle the problem of evaluating knowledge graph creation, and analyze and empirically study a set of variables and configurations that impact on the behaviour of these engines (e.g. data size, data distribution, mapping complexity). The evaluation has been conducted on RMLMapper and the SDM-RDFizer, two state-of-the-art engines that interpret the RDF Mapping Language (RML) and transform (semi)-structured data into RDF knowledge graphs. The results allow us to discover unknown relations between these engines that cannot be observed in other configurations.
Choreographic Pose Identification using Convolutional Neural Networks

In this paper we present a deep learning scheme for classification of dance postures using Kinect II RGB data and Convolutional Neural Networks (CNN). This is achieved through the analysis of a data-set that includes three traditional Greek dances, where each dance was performed by 3 different dancers. The obtained data were processed and analyzed using a deep convolutional neural network, in order to identify the primitive postures that comprise the choreography. To enhance the classification performance, a background subtraction framework was utilized, while the CNN architecture was adapted to simulate a moving average behavior. The overall system can be used as an AI module for assessing the performance of users in a serious game for learning traditional dance choreographies.
THE WAR ON DRUGS 2.0: DARKNET FENTANYL'S RISE AND THE EFFECTS OF REGULATORY AND LAW ENFORCEMENT ACTION

U.S. overdose deaths attributed to synthetic opioids, such as fentanyl, have increased from under 3,000 in 2013 to nearly 20,000 in 2016, making up half of all opioid‐related overdose deaths. Using web scrapes of darknet markets from 2014 to 2016, I provide historical prices for fentanyl and its most popular analogues and find that fentanyl vendors priced fentanyl in 2014 at a 90% discount compared to an equivalent dose of heroin. Using regression discontinuity, I evaluate the effects of two major law enforcement and regulatory events. I find minimal lasting effects of U.S. legal actions intended to disrupt darknet markets, but there are statistically significant indications of a price increase corresponding with regulatory action in China. Despite these indications of some regulatory success, fentanyl prices remained approximately 90% cheaper than heroin. ( JEL I18, K42)
Time-varying Pedestrian Flow Models for Service Robots

We present a human-centric spatiotemporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples' routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.
On Autonomous Systems: From Reflexive, Imperative and Adaptive Intelligence to Autonomous and Cognitive Intelligence

Autonomous systems underpinned by cognitive intelligence represent advanced forms of artificial intelligence studied in intelligence science, systems science, and computational intelligence. Traditional theories and technologies of autonomous systems put emphases on human-system interactions and humans in-the-loop. This paper explores the intelligence and system foundations of autonomous systems. It focuses on what structural and behavioral properties constitute the intelligence power of autonomous systems. It explains how system intelligence aggregates from reflexive, imperative, adaptive intelligence to autonomous and cognitive intelligence. A Hierarchical Intelligence Model (HIM) is introduced to elaborate the evolution of human and system intelligence as an inductive process. A set of properties of system autonomy is formally analyzed towards a wide range of autonomous system applications in computational intelligence and systems engineering.
Techniques in Complex Semantic Fieldwork

The main goal of semantic fieldwork is to accurately capture the contribution of natural language expressions to truth conditions and to pragmatic felicity conditions, by interacting with native speakers of the language under investigation. Most semantic fieldwork tasks (including, for example, acceptability judgment tasks, elicited production tasks, and translation tasks) require the researcher to present a discourse context to the consultant. The important questions then become how to present that context to consultants and how to best ensure that the consultant and the researcher have the same context in mind. We argue that phenomena which rely on controlling for interlocutor beliefs are particularly well suited for the storyboard elicitation methodology. This includes “out-of-the-blue” scenarios, which we treat as a special type of discourse context that must also be controlled for. We illustrate these claims by presenting novel storyboards targeting the de re/ de dicto ambiguity and verum marking.
Multi-attribute Categorization of MOOC Forum Posts and Applications to Conversational Agents

Discussion forums are among the most common interaction tools offered by MOOCs. Nevertheless, due to the high number of students enrolled and the relatively small number of tutors, it is virtually impossible for instructors to effectively monitor and moderate them. For this reason, teacher-guided instructional scaffolding activities may be very limited, even impossible in such environments. On the other hand, students who seek to clarify concepts may not get the attention they need, and lack of responsiveness often favors abandonment. In order to mitigate these issues, we propose in this work a multi-attribute text categorization tool able to automatically detect useful information from MOOC forum posts including intents, topics covered, sentiment polarity, level of confusion and urgency. Extracted information may be used directly by instructors for moderating and planning their interventions as well as input for conversational software agents able to engage learners in guided, constructive discussions through natural language. The results of an experiment aimed at evaluating the performance of the proposed approach on an existing dataset are also presented, as well as the description of an application scenario that exploits the extracted information within a conversation agents’ framework.
A Blockchain-based Model for Cloud Service Quality Monitoring

This paper introduces a novel blockchain-based decentralized federation model that embodies quality verification for cloud providers who lease computing resources from each other. The blockchain structure removes the barriers of a traditional centralized federation and offers a fully distributed and transparent administration by enforcing the involved agents to maintain consensus on the data. For a blockchain-based federation, it is vital to avoid blind-trust on the claimed SLA guarantees and monitor the quality of service which is highly desirable considering the multi-tenancy characteristic of cloud services. Due to the fact that the blockchain network is unable to access the outside world, it cannot handle, by its own, providers misbehavior in terms of SLA violations. Thus, we introduce oracle as a verifier agent to monitor the quality of the service and report to the smart contract agents deployed on the blockchain. Oracle is a trusted third-party agent who can communicate with the outside world of the blockchain network. The interaction between cloud service providers (either providing a service or requesting it from another provider) and the oracle through smart contracts comprises a system of autonomous and utility maximizer agents. Cloud requesters seek to receive high quality services with constant monitoring at cheap prices or even with no charge, while cloud providers aim to have a balanced work-load with less preserved capacity, and the oracle tends to charge higher for their monitoring services. Therefore, to model this conflicting situation, we formulate a dynamic Stackelberg differential game to optimize the cost of using the oracle and maximize the profit of the agents with the role of provider agent as a leader, and the requester and verifier agents as followers. Our designed Stackelberg differential game can seize the dynamicity of users' demand and resource provisioning in a competitive cloud market. We implemented our proposed decentralized model using the Solidity language in the remix IDE on the Ethereum network. We further evaluated the optimal controls and agents' profit with real-world data simulated for three concrete cloud providers. The results revealed that the requester agent initiates most of the quality verification requests at the beginning to the middle time of the contract. Thus, the provider agent could reserve less computing resources considering the fact that it could share the workload among other customers' computing resources during the peak-time. Moreover, imposing a higher penalty on the provider agent increased the capacity and decreased the number of requests for quality verification at the equilibrium. The evaluation also disclosed that the impact of timing in the dynamic pricing strategy of the verifier agent is very minimal, and the provisioning capacity of the provider is strongly correlated with the monitoring price.
The Impact of Software Security Practices on Development Effort: An Initial Survey

Background: Software projects are facing the need to adopt security practices during the software development life cycle (SDLC). Nevertheless, the amount of effort to be invested in order to achieve a certain level of software security is not clear yet. Aims: The goal of this study is to get an overview of the application of software security practices in the industry and to identify the impact of the introduction of such activities in software development projects in terms of effort/cost. Method: We conducted a survey on a software security group of a professional social network by applying a random sampling strategy to establish a representative set of participants. Results: The questionnaire was fully answered by 110 participants, from the 808 profiles that were invited from the sampling frame. The results show that security practices have been applied thoroughly in the projects and revealed high variability in secure software development effort across the participants' projects. Further research is needed to understand the different professionals' perspectives regarding security effort in projects. As lessons learned, we found that the professional social network offered a demographically diverse sampling frame, but this comes with hurdles that need to be overcome. Conclusions: The experiences of the participants showed that security is a factor that drives effort in software projects, and security practices need to be taken into account when planning software development initiatives. Our findings about the current state of practices and adoptions can help practitioners and researchers in future endeavors.
Assessing the Effectiveness of the Augmented Reality Courseware for Starry Sky Exploration

Augmented reality tools and applications have been shown to have powerfully impelled the development of the field of education. In this article, the authors designed and developed an augmented reality technology-based courseware “Starry Sky Exploration—Eight Planets in the Solar System” and explored how AR can bring an immersive learning experience to students and improve students' learning effectiveness. This article presents and evaluates AR courseware applicable for the geography curriculum in secondary schools in China. In this study, 36 students from Shanghai secondary vocational school were invited to participate in the experiment, the authors use reliability analysis, regression analysis and brainwave analysis to evaluate the effectiveness of the AR course. The authors found that students have higher learning satisfaction and behavioral willingness in AR-based experiential learning activities. It can be seen that AR helps to stimulate students' interest in learning.
Image Classification Based on transfer Learning of Convolutional neural network

Aiming at the issue of timeliness and lack of partial image data in life, an algorithm, transfer learning which based on convolutional neural network (CNN) is proposed, combining image histogram of oriented gradient (HOG) feature extraction method and support vector machine (SVM) pre-classification method. Firstly, the HOG features of the training sample similar to the attributes of the samples which to be classified are extracted, then the hog features of the training samples are imported into the SVM classifier to get the pre-classification results. Finally, the pre-classification results are used as training samples to train the transfer network of CNN for getting new transfer learning model, this model can be used to classify similar pre-classification samples. The experimental results show that the classification accuracy of the five categories of elephants and dinosaurs used in this paper is effectively improved, and the overall classification accuracy can reach 95%, compared with the traditional classifier algorithm and convolutional neural network algorithm. The classification accuracy has been improved by about 5%.
Performance Optimization Techniques for ReactJS

React is one of the popular web frameworks that has gained importance over other frameworks such as Angular, Vue, etc.. This is because of its implementation of Virtual DOM, whose primary objective is to enhance the overall performance of the application. However, there are certain things that one has to keep in mind before designing the applications. Failing to anticipate the problems that may occur component hierarchy will lead to performance degradation. Some of the commonly faced problems are component re-rendering, application lag due to background computations being run, lag due to processing large data sets in a single stretch, etc. This paper will describe some of the practical ways of overcoming such problems within the application, thus enhancing the performance of the ReactJS App in a production environment. The paper will also describe a time-efficient search algorithm that can be used for searching objects in a large data set.