Miracle-dz/UniFAR-FTUs huggingface dataset:
A haptic device for wrist and elbow rehabilitation

Interest in the devices for rehabilitation applications has been increasing. And the devices before have proved that they might assist in and quantify the rehabilitation for upper limb disability caused by stroke. This paper is to introduce rehabilitation application of a haptic device based on virtual reality technology, which is compact, portable, and modular. The focus here is a device with force feedback designed to provide five degrees of freedom, which are rotation, opposition, translation, pitch, and yaw. With five degrees of freedom above, the device can help individuals with arm weakness do their exercise and make patients achieve favorable rehabilitation efficacy during their upper limb rehabilitation.
Moving Path Following Control of Constrained Underactuated Vehicles: A Nonlinear Model Predictive Control Approach

This paper addresses the design of a stabilizing continuous time sampled-data Nonlinear Model Predictive Control (NMPC) law to solve the Moving Path Following (MPF) motion control problem for constrained under-actuated robotic vehicles. In this scenario, the robotic vehicle is tasked to converge to a desired geometric path, expressed with respect to a moving frame of reference, while satisfying the actuation constraints. This control problem is addressed in the NMPC framework. Speciﬁcally, ﬁrst a suboptimal Lyapunov-based nonlinear auxiliary control law is designed to solve the MPF problem. Then, the latter is used for the design of a suitable terminal set and terminal cost of the MPC controller to enforce closed-loop guarantees. Exploiting the properties of the auxiliary control law, we show that for a suitable selection of the input constraints, the terminal set can be removed, resulting in a global region of attraction of the proposed controller. Simulation results are provided to illustrate the proposed control strategy.
Hybrid approach combining statistical and rule-based models for the automated indexing of bibliographic metadata in the area of planning and building construction

ICONDA$^{®}$ Bibliographic (International Construction Database) is a bibliographic database, which contains English-language documents in the area of planning and building construction. The documents are indexed with descriptors from controlled vocabularies (FINDEX thesauri, an authority list). The manual assignment of the descriptors is time-consuming and expensive. To solve this problem, an automated indexing system was developed. The indexing system combines a statistical classifier that is based on the vector space model with a rule-based classifier. In the statistical classifier, descriptor profiles are automatically trained from already indexed documents. The results provided by the statistical classifier will be improved with the rule based classifier that filters incorrect and adds missing descriptors. The rules can be created manually or automatically from already indexed documents. The hybrid approach is particularly useful when a descriptor cannot be successfully trained by the statistical classifier. In this case, the system can be easily fine-tuned by adding specific rules for the descriptor.
Artificial neural network-based highest surface temperature prediction method of secondary battery

The present invention relates to a secondary battery of the highest surface temperature prediction algorithm based on artificial neural networks, battery thermal management system belongs to the technical field. The secondary battery in high and low temperature test chamber, connected to the charge and discharge test machine; charging the battery is discharged; monitoring changes in the maximum temperature of the battery surface during the charging process; an input by setting Back-Propagation neural network model, output, number of neurons, layers, transfer function and training algorithm to either build the model; model training data for the prediction model can be applied; the maximum surface temperature of the battery charging process at ambient temperature by other models to predict. Model of the invention to apply it is simple, easy to control parameters, useful results; the maximum surface temperature of the battery at different ambient temperatures to predict, safe and effective to provide a guarantee for the working cell and a battery thermal management system.
Evolutionary Spatiotemporal Community Discovery in Dynamic Weighted Networks

Detecting evolving communities in dynamic weighted networks are significant for understanding the evolutionary patterns of complex networks. However, it is difficult and challenging for traditional approaches to extract evolving communities with notable significance from dense and large dynamic complex networks, because most of communities are still so dense and large that we could not observe directly the detailed evolving sub-structures. In this paper, a novel approach is proposed to extract overlapping evolutionary spatiotemporal communities in large, dense and dynamic weighted networks. Evolutionary spatiotemporal communities can not only show the evolutionary of nodes and edges in a certain period clearly, but also contain weight vectors with similar evolving trend. Experiments on the global trading network show that the proposed approach can discover more sophisticated evolving patterns and properties which hide in those seemingly stable community structures.
Vehicle dynamics prediction module

In this contribution we used the vehicle dynamics for creation a vehicle motion prediction module for autopilot tasks. A Vehicle Dynamics Prediction Module (VDPM) was created as an application for self-driving hardware assistance for high velocities maneuvering (above 40km/h). The dynamic characteristics of a vehicle have a complex mathematical mechanism, but have a regularity character. This fact allows us to create a basis with a limited number of pre-calculated maneuvers to describe the behavior of the vehicle dynamics for maneuvering in public roads.
Inductive voltage divider modeling in Matlab

Inductive voltage dividers have the most appropriate metrological characteristics on alternative current and are widely used for converting physical signals. The model of a double-decade inductive voltage divider was designed with the help of Matlab/Simulink. The first decade is an inductive voltage divider with balanced winding, the second decade is a single-stage inductive voltage divider. In the paper, a new transfer function algorithm was given. The study shows errors and differences that appeared between the third degree reduced model and a twenty degree unreduced model. The obtained results of amplitude error differ no more than by 7 % between the reduced and unreduced model.
Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.
Allocation dynamique de bande passante pour l’interconnexion RF d’un réseau sur puce

Avec l’augmentation du nombre de cœurs, les problemes de congestion sont commence avec les interconnexions conventionnelles. Afin de remedier a ces defis, WiNoCoD projet (Wired RF Network-on-Chip Reconfigurable-on-Demand) a ete initie par le financement de l’Agence Nationale de Recherche (ANR). Ce travail de these contribue a WiNoCoD projet. Une structure de controleur de RF est propose pour l’interconnexion OFDMA de WiNoCoD et plusieurs algorithmes d’allocation de bande passante efficaces (distribues et centralises) sont developpes, concernant les demandes et contraintes tres specifiques de l’environnement sur-puce. Un protocole innovante pour l’arbitrage des sous-porteuses pour des longueurs bimodales de paquets sur-puce, qui ne necessite aucun signalisation supplementaire est introduit. Utilisation des ordres de modulation eleves avec plus grande consommation d’energie est evaluee.
IMPLEMENT ONLINE BASED HUGE APPEARANCE SERVICES FOR TELECOMMUNICATION DATABASES

Important advance has in the previous endorse winging it of web telemedicine table arrangement dance. Particularly indexes as a meaningful category of the above-mentioned techniques robust regard in many studies. Web telemedicine bibliography techniques enable good capacity persistent shipment of victim data on any occasion basic. We initiate a mixed web data services that affirm effective comeback time for vast techniques of Tele-health index care. Our point up present table care by demand scenarios in industrious telemedicine arrangements to upgrade care admissions and weaken care difficulties. In our work, we unveil a three-fold manner and that exist the basis of data disjunction, index websites clustering farther original data distribution.
Correlated Differential Privacy Protection for Mobile Crowdsensing

Mobile CrowdSensing (MCS) is a new paradigm that leverages pervasive mobile devices to efficiently collect the big sensory data, enabling various large-scale applications. However, people’s concerns about the loss of individual privacy seriously hinder the prevalence of MCS applications. Differential privacy is widely focused owing to its rigorous definition and strong privacy guarantee, but the state-of-the-art studies still demonstrate its weakness on correlated data, resulting in compromising individual privacy. In this paper, we investigate the influence of sensing data correlation on differential privacy protection for MCS systems, and explore the perturbation mechanisms from two different perspectives. From a protector’s perspective, based on the Bayesian Network to model the probabilistic relationship among sensing data, we use the classical definition of differential privacy to deduce the scale parameter, and present one perturbation mechanism. From an adversary’s perspective, based on the Gaussian correlation model to describe the data correlation, we analyze the importance of the maximum correlated group to compute the Bayesian differential privacy leakage, and then provide another perturbation mechanism. Compared with the existing solutions, our mechanisms are applicable to arbitrary aggregate query function, and can avoid introducing too much noise. Moreover, we demonstrate the effectiveness of our mechanisms through extensive simulations.
Knowledge Workers: How Are They Different? (And Why Does It Matter?)

In this study, we integrate research in educational psychology, a field that partly has focused on the global effects of higher education on individuals to conceptualize and test a model that explains how knowledge workers can be thought to differ from other groups of workers. Conceptualizing a common constellation of personality dispositions associated with knowledge workers (i.e., ambiguity tolerance, need for closure, dogmatism, authoritarianism, and cognitive ability), we propose multiple outcome pathways by which individual-level education affects the five personality traits under study. Based on a nest5ed sample of 351 employees across 54 workgroups, the results were consistent with our predictions.
Assuring Student Learning Outcomes Achievement through Faculty Development: An Online University Example.

Asynchronous discussions in the online teaching and learning environment signiﬁcantly contributes to the achievement of student learning outcomes, which is dependent upon qualiﬁed and engaged faculty members. The discourse within this article addresses how an online university conducted faculty development through its unique Robust Learning Model (RLM) and its associated unique pedagogy and learning management system, which is also utilized by the university's students. The results revealed that shared engagement between faculty members in the faculty development activity similar to the university's students, honed faculty members' teaching skills that lend to assuring student learning outcomes achievement in the online learning environment.
CrowdOptim: A Crowd-driven Neural Network Hyperparameter Optimization Approach to AI-based Smart Urban Sensing

AI-based smart urban sensing (ASUS) has emerged as a scalable and pervasive application paradigm in smart city planning and management that aims to automatically assess the physical status of the urban environments by leveraging AI techniques and massive urban sensing data. In this paper, we focus on a crowd-driven neural network (NN) hyperparameter optimization problem in ASUS applications. Our goal is to utilize the human intelligence from crowdsourcing systems to identify the optimal NN hyperparameter configuration for an ASUS model. Our work is motivated by the observation that the hyperparameters of current ASUS models are often manually configured by the AI specialists, which is known to be both error-prone and suboptimal. Two key technical challenges exist in solving our problem: i) it is challenging to effectively translate the highly complex NN hyperparameter optimization problem in AI to a simplified problem that can be solved by crowd workers without extensive AI expertise; ii) it is difficult to identify the optimal hyperparameter configuration in the large hyperparameter search space given the blackbox nature of the AI model. To address the above challenges, we develop CrowdOptim, a crowd-AI collaborative learning framework that integrates the techniques from crowdsourcing, hyperparameter optimization, and estimation theory to address the crowd-driven NN hyperparameter optimization problem in ASUS applications. The evaluation results from two real-world ASUS applications (i.e., smart city infrastructure monitoring (SCIM) and urban environment cleanliness assessment (UECA)) show that CrowdOptim consistently outperforms the state-of-the-art deep convolutional networks, crowd-AI, and hyperparameter optimization baselines in achieving the ASUS application objectives under various evaluation scenarios.
Matching for Peer Support: Exploring Algorithmic Matching for Online Mental Health Communities

Online mental health communities (OMHCs) have emerged in recent years as an effective and accessible way to obtain peer support, filling crucial gaps of traditional mental health resources. However, the mechanisms for users to find relationships that fulfill their needs and capabilities in these communities are highly underdeveloped. Using a mixed-methods approach of user interviews and behavioral log analysis on 7Cups.com, we explore central challenges in finding adequate peer relationships in online support platforms and how algorithmic matching can alleviate many of these issues. We measure the impact of using qualities like gender and age in purposeful matching to improve member experiences, with especially salient results for users belonging to vulnerable populations. Lastly, we note key considerations for designing matching systems in the online mental health context, such as the necessity for better moderation to avoid potential harassment behaviors exacerbated by algorithmic matching. Our findings yield key insights into current user experiences in OMHCs as well as design implications for building matching systems in the future for OMHCs.
The Impact of Airbnb on Residential Property Values and Rents: Evidence from Portugal

Short-term rentals have facilitated the upraise trend in tourism growth in several cities around the world. However, concerns for the negative effects that such home-sharing platforms may have on the housing market and traditional markets have driven community groups and housing advocates to intensely react against them. Whether or not shortterm rentals increase housing prices and rents for local residents is an empirical question. We quantify the causal effects of Airbnb's short-term rentals on urban housing affordability in Portugal by estimating quarterly housing rents and prices as a function of Airbnb concentration. We take advantage of the 2014 regulatory reform and employ a difference-in-differences (DiD) empirical strategy. We estimate an overall increase in property values of 34% and 10.9% for rents due to the short-term lease regulatory reform. We also find that these effects are particularly localized to the historical centers and areas attractive to tourists in the cities of Lisbon and Porto. A better understanding of the effects of shortterm home rentals on housing markets and of the magnitude of its impact on residential property prices and rents are crucial information to determine whether it needs to be regulated and how proper regulation should be designed.
Design optimisation of reluctance synchronous machines: a motor and generator study

Design optimisation of Reluctance Synchronous Machines: A Motor and Generator Study E. Howard Department of Electrical Engineering, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa. Dissertation: PhdEng (Elec) August 2017 This study focuses on the design and optimisation of the recently popular synchronous reluctance machine. The increase of this machine’s popularity is because of its inherent high efficiency, with industry pushing for an ever higher efficiency machine drive package. The study proposes design techniques that can be implemented in the design process to minimise or remove the inherent weaknesses. Machines designed by implementing the study techniques are manufactured and tested, with proposed design techniques validated through testing. Finally, the developed machine model is implemented in a design study in the high power range for wind generator application. It was found, assuming mechanical feasibility, that the type of machine can be implemented in the very high power range, with competitive efficiency and power factor values achieved. In order to even further improve machine performance and thus its competitiveness, an assisted reluctance synchronous machine model is proposed and optimised by implementing a retrofit rotor design and pre-designed machine stator. Results of this proposed machine indicate highly competitive machine performance, comparable with currently implemented machines in the field, thus warranting further investigation. ii Stellenbosch University https://scholar.sun.ac.za
Uncovering the scholarly use of Twitter in the academia: Experiences in a British University

The potential of social media such as Twitter has been dubbed transformational. However, most of these effects have been studied in the political, social or technological sphere. Its impact on scholarship remains under-researched. The paper explores how academics use Twitter as part of their scholarly activities. A qualitative approach based on an interpretative methodology was adopted for the study, and data gathered through 28 semi-structured interviews with academic staff who regularly use Twitter for academic and non-academic purposes. The findings uncover the rich benefits that enthusiasts gain from use of Twitter as an information source for scholarly communication – in the context of creation, usage and sharing of scholarly information. This was manifested in academics’ information-seeking behaviour in with links to networking and collaboration as part of scientific practice.
Online Casinos: The Addiction Under Control Role of Web 2.0 and re-documentarisation

—In this work, we will consider how to design and implement tools or Information System based and enriched by a semantic search engines to the forums in the world of online gambling (articles, tutorials, etc.) supporting both textual and semantic expressions. On the basis of this new approach, based on neuro-economic field, we will look on addiction using neuroscience and game theory in order to construct an efficient Information System that takes into account the expectations and attitudes of players.
Disaster privacy/privacy disaster

Privacy expectations during disasters differ significantly from non-emergency situations. Recent scandals, such as inappropriate disclosures from FEMA to contractors, illustrate that tradeoffs between emergencies and privacy must be made carefully. Increased use of social technologies to facilitate communication and support first responders provide more opportunities for privacy infringements, despite increased regulation of disaster information flows to government agencies and with trusted partners of the government. This paper specifically explores the actual practices followed by popular disaster apps. Our empirical study compares content analysis of privacy policies and government agency policies, structured by the contextual integrity (CI) framework, with static and dynamic app analysis documenting the personal data they send. We identify substantive gaps between regulation and guidance, privacy policies, and information flows generated by apps/platforms, resulting from ambiguities and exploitation of exemptions. Results also indicate gaps between governance and practice, including: (1) many apps ignore transmission principles self-defined in policy; (2) while some policies state they “might” access location data under certain conditions, those conditions are not met as 12 apps included in our study capture location immediately upon download; and (3) not all third parties data recipients are identified in policy, including instances that violate expectations of trusted third parties. We visually map disaster information flows during disasters and around third party and government apps within the disaster response domain, and emphasize information exchanges between specific actors and the differences between actual flows of personal information and regulatory and policy specifications.
Recruiting for school improvement: the relationship between teacher-centric school quality factors and school improvement designations in Kentucky

In 2018, Kentucky began the implementation of a new school accountability system. Like other states across the nation, Kentucky’s new system was designed to bring state policy into alignment with federal changes brought about by the Every Student Succeeds Act (ESSA) of 2015. In addition to the creation of new school improvement labels, Kentucky’s system also reports on a variety of teacher-centric school quality factors. A statistical analysis was conducted to determine if a relationship exists between these school quality factors and the school’s improvement designation. The study found that schools identified as needing the greatest improvement (Comprehensive Support and Improvement [CSI]) are statistically more likely to have higher teacher turnover rates, higher percentages of new teachers, and employ teachers with lower rates of advanced education in the year prior to identification than their counterparts with either the Targeted Support and Improvement (TSI) or Other designations. These findings should inform future policy making and elevate teacher recruitment and retention as a school improvement priority.