Reviewerly/exHarmony huggingface dataset:
Emerging Urban Challenge: RPAS/UAVs in Cities

An emerging urban challenge will be the proliferation of Remotely Piloted Aircraft Systems (RPAS) or drones, as their usage grows and drones fill the urban skies. Urban airspace will include many more Uninhabited Aerial Vehicles (UAVs), and related accidents and mishaps will also increase. Applications of UAVs in urban environments include photography and film-making, security monitoring, real estate, construction, property and infrastructure inspections, leisure, public safety (fires, natural disasters, investigations), traffic, and much more. Emerging UAV applications include last-mile drone delivery services. This paper discusses urban scenarios where drones are more ubiquitous. Monitoring and related safety of these UAVs will be increasingly important. Though multi-modal purpose-built drone tracking and monitoring systems will be the most effective solution for detection and tracking of these RPAS, during the transition to more regular drone use in urban areas and in everyday urban applications, a more rapid-deployment and agile detection system is needed that does not require installation of hardware. In this paper, sensory substitution is presented as an approach to use ambient and pre-existing microphones and AI techniques to detect the presence of RPAS. Preliminary results show promise for this agile IoT method as a key solution to this emerging urban challenge.
A Preliminary Study to Evaluate Graphical Passwords for Older Adults

Picture-based authentication offers visual cues (e.g. images) that helps users recalling their passwords. Human facial images were utilized in making graphical passwords, as humans are better at remembering human faces than other types of images. Prior research found that users performed better with the partitioned facial images than with the full-face portrait images; nevertheless, age-related differences were not considered. This present research is a preliminary study to assess older adults' understanding toward Passface and overall experimental procedure, by examining two levels of facial image presentation: full-face portrait image, and partitioned facial image. The results showed that the partitioned facial image aided memory and recall better than the full-face portrait image, which was reflected by registration time, authentication time and success rate. In general, majority of older adults were able to understand Passface and overall experimental procedure.
Adaptive Incentive Allocation for Influence-Aware Proactive Recommendation

Most recommendation systems are designed for seeking users’ demands and preferences, whereas impotent to affect users’ decisions for realizing the system-level objective. In this light, we intend to propose a generic concept named ‘proactive recommendation’, which focuses on not only maintaining users’ satisfaction but also realizing system-level objectives. In this paper, we claim the proactive recommendation is crucial for the scenario where the system objectives are required to realize. To realize proactive recommendation, we intend to affect users’ decision-making by providing incentives and utilizing social influence between users. We design an approach for discovering the influential users in an unknown network, and a dynamic game-based mechanism that allocates incentives to users dynamically. The preliminary experimental results show the effectiveness of the proposed approach.
Subtitle Region Selection of S3D Images in Consideration of Visual Discomfort and Viewing Habit

Subtitles, serving as a linguistic approximation of the visual content, are an essential element in stereoscopic advertisement and the film industry. Due to the vergence accommodation conflict, the stereoscopic 3D (S3D) subtitle inevitably causes visual discomfort. To meet the viewing experience, the subtitle region should be carefully arranged. Unfortunately, very few works have been dedicated to this area. In this article, we propose a method for S3D subtitle region selection in consideration of visual discomfort and viewing habit. First, we divide the disparity map into multiple depth layers according to the disparity value. The preferential processed depth layer is determined by considering the disparity value of the foremost object. Second, the optimal region and coarse disparity value for S3D subtitle insertion are chosen by convolving the selective depth layer with the mean filter. Specifically, the viewing habit is considered during the region selection. Finally, after region selection, the disparity value of the subtitle is further modified by using the just noticeable depth difference (JNDD) model. Given that there is no public database reported for the evaluation of S3D subtitle insertion, we collect 120 S3D images as the test platform. Both objective and subjective experiments are conducted to evaluate the comfort degree of the inserted subtitle. Experimental results demonstrate that the proposed method can obtain promising performance in improving the viewing experience of the inserted subtitle.
Smart Interconnected Infrastructure for Security and Safety in Public Places

In this paper, we present work in progress on the development of an intelligent interconnected infrastructure for public security and protection domain. In contrast to other Internet of Things (IoT) frameworks, the proposed system aims to effectively combine device and human awareness to achieve situational awareness, so as to provide a protection and security environment for citizens. The emphasis is placed on tourists, by creating the appropriate infrastructure to address a set of urgent situations, such as health-related problems and missing children in overcrowded environments, supporting smart links between humans and entities on the basis of goals, and adapting device operation to comply with human objectives, profiles and privacy. The framework effectively combines state-of-the-art technologies on IoT data collection and analytics, knowledge representation and interoperability, crowdsourcing, data fusion and decision-making.
Prediction of Students' Course Failure Based on Campus Card Data

Student's learning attitude is one of the focuses in higher education at present. Warning of course failure can be given to students timely during the term, so as to urge students to pay more attention to certain subject and also to provide information reference and decision support for relevant teachers and educational administration departments as well. Catering and library data from campus card data of college students is extracted in this paper and adopted as characteristics for C4.5, logistic regression, SVM and Adaboost to predict the failure of courses.
Real Time Hand Movement Trajectory Tracking for Enhancing Dementia Screening in Ageing Deaf Signers of British Sign Language

Real time hand movement trajectory tracking based on machine learning approaches may assist the early identification of dementia in ageing Deaf individuals who are users of British Sign Language (BSL), since there are few clinicians with appropriate communication skills, and a shortage of sign language interpreters. Unlike other computer vision systems used in dementia stage assessment such as RGB-D video with the aid of depth camera, activities of daily living (ADL) monitored by information and communication technologies (ICT) facilities, or X-Ray, computed tomography (CT), and magnetic resonance imaging (MRI) images fed to machine learning algorithms, the system developed here focuses on analysing the sign language space envelope (sign trajectories/depth/speed) and facial expression of deaf individuals, using normal 2D videos. In this work, we are interested in providing a more accurate segmentation of objects of interest in relation to the background, so that accurate real-time hand trajectories (path of the trajectory and speed) can be achieved. The paper presents and evaluates two types of hand movement trajectory models. In the first model, the hand sign trajectory is tracked by implementing skin colour segmentation. In the second model, the hand sign trajectory is tracked using Part Affinity Fields based on the OpenPose Skeleton Model [1, 2]. Comparisons of results between the two different models demonstrate that the second model provides enhanced improvements in terms of tracking accuracy and robustness of tracking. The pattern differences in facial and trajectory motion data achieved from the presented models will be beneficial not only for screening of deaf individuals for dementia, but also for assessment of other acquired neurological impairments associated with motor changes, for example, stroke and Parkinson's disease.
An iterative high dynamic range image processing approach adapted to overexposure 3D scene

In order to solve the problem of local overexposure caused by uneven surface reflectance of the target object under strong light background, this paper proposes a high-efficient iterative illumination method called iterative high dynamic range (IHDR), which is based on the object surface depth information acquisition, iterative self-adaptive illumination, high dynamic range algorithm and non-reference image quality assessment. In experiments, Fourier Transform Profilometry (FTP) based on elliptic filter is designed to build the correspondence between the projection and the target object by means of projector-camera system. A rapid iterative illumination based on novel inversion algorithm is proposed to eliminate overexposure areas. It is proved that IHDR can recover detail information after only two iterations. Due to its high efficiency, the IHDR could be used in on-line production image acquisition system.
Interaction Design for Domestic Sound Zones

Sound zone systems have actively been developed for more than two decades. Building on this, we explore four different interaction design approaches for domestic sound zone systems: Tangible representation, light projection, familiar objects, and handhelds. These four approaches were conceived through a scenario-based workshop with HCI and IS experts responding to the functional challenges, opportunities, and requirements of interactive sound zones. The work presented in this paper contributes to development of interaction design for sound zone systems which is an essential parallel to the technical development.
Data-Driven Model-Free Adaptive Control Based on Error Minimized Regularized Online Sequential Extreme Learning Machine

Model-free adaptive control (MFAC) builds a virtual equivalent dynamic linearized model by using a dynamic linearization technique. The virtual equivalent dynamic linearized model contains some time-varying parameters, time-varying parameters usually include high nonlinearity implicitly, and the performance will degrade if the nonlinearity of these time-varying parameters is high. In this paper, first, a novel learning algorithm named error minimized regularized online sequential extreme learning machine (EMREOS-ELM) is investigated. Second, EMREOS-ELM is used to estimate those time-varying parameters, a model-free adaptive control method based on EMREOS-ELM is introduced for single-input single-output unknown discrete-time nonlinear systems, and the stability of the proposed algorithm is guaranteed by theoretical analysis. Finally, the proposed algorithm is compared with five other control algorithms for an unknown discrete-time nonlinear system, and simulation results show that the proposed algorithm can improve the performance of control systems.
A knowledge domain structure to enable system wide reasoning and decision making

To architect and design a system, the stakeholder needs have to be satisfied by technical solutions, for which decisions on trade-offs have to be made. A trend is that the number of functions, components, and interfaces in systems increase, often by an order of magnitude or more, such that reasoning about the impact of a decision becomes increasingly hard and tracing its impact throughout the system is crucial. Therefore, we decompose a system in areas of knowledge and information, which we call knowledge domains. Architecting means taking decisions, for which the impact on knowledge domains and their explicit relations are required. Existing approaches that reason across systems either do not make explicit relations between knowledge domains, or perform a quantitative computation instead of reasoning, where for both it is difficult to trace the impact of decisions. In this paper, we present an architecture reasoning structure with which knowledge domains from different disciplines can be explicitly related, which enables system wide reasoning and decision making. With just eight language elements, a system can be described in an information structure. By applying a knowledge domain pattern, the essential information of knowledge domains is captured. Via relations, both qualitative and quantitative reasoning can be performed to trace the impact of decisions. An example is used to illustrate the approach, for which the tension for a decision is shown by tracing its impact via quantitative and qualitative relations. The approach was investigated and validated in the industrial context of Océ professional printing systems.
Advanced energy management system based on PV and load forecasting for load smoothing and optimized peak shaving of islanded power systems

Photovoltaic (PV) systems constitute one of the most promising renewable energy sources, especially for warm and sunny regions like the southern-European islands. In such isolated systems, it is important to utilize clean energy in an optimal way in order to achieve high renewable penetration. In this operational strategy, a Battery Energy Storage System (BESS) is most often used to transfer an amount of the stored renewable energy to the peak hours. This study presents an integrated energy management methodology for a PV-BESS energy system targeting to make the load curve of the conventional fuel based units as smooth as possible. The presented methodology includes prediction modules for short-term load and PV production forecasting using artificial neural, and a novel, optimized peak shaving algorithm capable of performing each day’s maximum amount of peak shaving and smoothing level simultaneously. The algorithm is coupled with the overall system model in the Modelica environment, on the basis of which dynamic simulations are performed. The simulation results are compared with the previous version of the algorithm that had been developed in CERTH, and it is revealed that the system’s performance is drastically improved. The overall approach proves that in such islanding systems, a PV-BESS is a suitable option to flatten the load of the conventional fuel based units, achieve steadier operation and increase the share of renewable energy penetration to the grid.
Conversational User Interface Integration in Controlling IoT Devices Applied to Smart Agriculture: Analysis of a Chatbot System Design

Smart Agriculture is a considerably novel approach which attempts to encounter the climate change challenges as well as the increased global nutritional needs through the adoption of automated and ICT directed innovative technologies. Since the latest solutions for Smart Agriculture employ cloud services and advanced interconnectivity methods which consolidate the Internet of Things (IoT) technology features along with the ubiquity and mobility attributes granted by smart devices, the necessity of extreme customization, enabling the remote interaction of the objects, is creating the need for sophisticated interfaces. Moreover, in order to achieve the maximum possible penetration of IoT technologies in the agricultural sector it is essential to establish interaction methods among users, applications and systems through interfaces which are simple and friendly in end-usage. Herewith, in this paper an attempt is made to encounter this issue through the integration of Conversational User Interfaces (CUI) in controlling IoT devices for Smart Agriculture. The proposed approach introduces the design and indicative usage of a chatbot system which employs a messenger service platform environment in natural language so as to provide an efficient, secure and user-friendly framework of interaction with the IoT devices deployed for agricultural purposes.
Vision: Shining Light on Smart Homes – Supporting Informed Decision-Making of End Users

Smart Home technologies are promising to enhance quality of life, autonomous living of elderly people and home security. Yet, uncertainty in terms of personal benefits, functionality, cost, privacy and security features influences potential users' decision processes. The vision of this research thus is to support end users' informed decision-making in terms of Smart Home technologies. We developed and tested a prototype of an interactive Smart Home configurator that provides users with targeted information according to their interests. It visualizes Smart Home processes and implications for privacy and security to increase transparency and reduce uncertainty in the decision-process. Ideas for further improving and extending the concept of the Smart Home configurator are discussed.
Facial Soft Biometrics Detection on Low Power Devices

Soft biometric traits have been proven to enhance person identification accuracy, when used complementary to primary biometric traits. They present a series of advantages such as compliance to the human language, robustness to low quality data, non-intrusive and consent free acquisition, and privacy preservation, increasing their applicability in realistic conditions. They can be extracted from a variety of individual modalities, with the human face being considered as the most informative source of attributes, as it provides rich geometrical and texture features. Recent advances in computer vision have allowed the accurate detection of such features under varying, non-ideal capturing conditions, with this increase in detection capacity, however, coming at the cost of high computational complexity. Meanwhile, the research and market interest has shifted towards the implementation of such methods on low power devices (i.e mobile phones), with data security concerns favoring on-device offline computation instead of cloud-based services. Towards this end, and taking into consideration recent advances in computationally efficient CNN design and multitask learning, we propose a novel CNN architecture, suitable for real time implementation on low power devices, which simultaneously performs gender, age, race, eyes state, eyewear, smile, beard and moustache estimation from unconstrained face images. The architecture employs the Mobilenet architecture and exploits the correlation between the individual biometric features, performing comparably to three state-of-the-art face analysis systems, while requiring significantly lower computational resources.
Road Anomaly Detection with Group Intelligence Perception

We consider a collection of the actual combat training of the troops that continues to deepen, and the use of training vehicles is becoming more frequent. We study the problem of detecting abnormal road surface, which may lead to traffic safety accidents and damage to the troops and officers and soldiers. In order to grasp the road situation in time, we design a road quality monitoring system based on group intelligence perception. The system adopts the concept of group intelligence perception, develops the data acquisition software under the smartphone Android environment, and places the mobile phone as the sensing terminal in the vehicle, collects the acceleration, speed, latitude and longitude data generated during the driving process of the vehicle, and collects the collected data. We establish the SVM support vector machine and processing application layer in detecting anomalies, further, we perform the experiments of system on the actual road environment, which can automatically detect the road anomaly.
Estimation of Hourly Solar Radiation on Horizontal Surface Using GAMF (Genetic Algorithm Modified Fuzzy) (Case Study in Surabaya)

Abstract The biggest renewable energy sources in Indonesia is solar energy. The installed capacity of Solar Power System in 2017 is still very far from the target. Solar radiation is very infulental on the photovoltaic performance in generating energy. The need for solar radiation estimation has become important in the design of photovoltaic system. Previous research has been done using Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy (ANFIS) methods. In this research, Genetic Algorithm Modified Fuzzy (GAMF) method used to estimate the solar radiation. Meteorogical dates used in this research are temperature, humidity, wind velocity and wind direction. There are 2 types of datas that are BMKG data and measurement data. Three experimental variations of input variation were performed for each data. For BMKG data the best estimation result is obtained when using humidity, temperature and wind velocity as variation inputs with RMSE and MAE of 145.19 and 72. While for the best result estimation result data obtained when applying humidity, temperature, wind speed and wind direction as variation inputs with values of RMSE and MAE were 1.44 and 0.65.
The Sepedi Helper Writing Assistant: A User Study

This article reports on an evaluation of the Sepedi Helper (http://www.sepedihelper.co.za/), an online writing assistant. The focus of the evaluation is on the verbal moods in Sepedi (Northern Sotho). The evaluation takes the form of a user study, and data collection was done by means of a questionnaire, administered to 81 first- and second-year students at a tertiary institution who do not have Sepedi as their strongest/home language. Analysis of the data includes identification of the most problematic moods and constructions and typical errors made by the students, and an evaluation of the contribution made by the Sepedi Helper. This writing assistant is a work in progress, with the developers’ ultimate aim being to compile a complete online grammar for Sepedi. The results of the evaluation indicate that the Sepedi Helper meets its stated aims, namely to confirm correct responses, correct errors, and identify the most common errors made by learners of Sepedi.
Benchmarking NLP Toolkits for Enterprise Application

Natural Language Processing (NLP) is an important technology that motivates the form of AI applications today. Many NLP libraries are available for researchers and developers to perform standard NLP tasks (such as segmentation, tokenization, lemmatization, POS tagging, and NER) without the need to develop from scratch. However, there are some challenges in selecting the most suitable library such as data type, performance, and the compatibility. In this paper, we assessed five popular NLP libraries for performing the standard processing tasks on datasets crawled from different online news sources in Malaysia. The obtained results are analysed and differences of those libraries are listed. The goal of this study is to provide a clear view for users to select the suitable NLP library for their text analysis task.
Unifying Computational Entropies via Kullback–Leibler Divergence

We introduce hardness in relative entropy, a new notion of hardness for search problems which on the one hand is satisfied by all one-way functions and on the other hand implies both next-block pseudoentropy and inaccessible entropy, two forms of computational entropy used in recent constructions of pseudorandom generators and statistically hiding commitment schemes, respectively. Thus, hardness in relative entropy unifies the latter two notions of computational entropy and sheds light on the apparent "duality" between them. Additionally, it yields a more modular and illuminating proof that one-way functions imply next-block inaccessible entropy, similar in structure to the proof that one-way functions imply next-block pseudoentropy (Vadhan and Zheng, STOC '12).