Reviewerly/exHarmony huggingface dataset:
Toward Ambient Assistance: a Spatially-aware Virtual Assistant eNabled by Object Detection

Over the past few decades, the overall aging of the population has been unprecedentedly significant. As traditional care-giving methods are expensive financially and emotionally, Ambient-Assisted Living (AAL) appears to be a promising alternative to ensuring the convenience and safety of the elderly. This paper presents SVAN—a Spatially-aware Virtual Assistant eNabled by Object Detection that facilitates its users’ lives in household scenarios. With its ability to achieve real-time multi-object classification and localization, SVAN is able to serve as an spatially-aware intelligent assistant to the resident. Designed with the older adults in mind, SVAN could enhance their performance in daily activities, prevent potential harm and injuries, and offer emotional support as a companion. We conducted experiments to evaluate SVAN’s performance in different household scenarios, analyzed SVAN’s effectiveness based on its accuracy and efficiency, and highlighted methods to further improve the proposed system.
Affinity matrix with large eigenvalue gap for graph-based subspace clustering and semi-supervised classification

In the graph-based learning method, the data graph or similarity matrix reveals the relationship between data, and reflects similar attributes within a class and differences between classes. Inspired by Davis–Kahan Theorem that the stability of matrix eigenvector space depends on its spectral distance (i.e. its eigenvalue gap), in this paper, we propose a global local affinity matrix model with low rank subspace sparse representation (GLAM-LRSR) based on global information of eigenvalue gap and local distance between samples. This method approximate the similarity matrix with ideally diagonal block structure from the perspective of maximizing the eigenvalue gap, and the local distance between data is utilized as a regular term to prevent the eigenvalue gap from being too large to ensure the efficacy of similarity matrix. We have shown that the combination of subspace (LRSR) partitioning method such as Sparse Subspace Clustering(SSC) and the similarity matrix constructed by GLAM can improve the accuracy of subspace clustering, and that the similarity matrix constructed by GLAM-LRSR can be successfully applied to graph-based semi-supervised classification task. Our experiments on synthetic data as well as the real-world datasets for face clustering, face recovery and motion segmentation have clearly demonstrate the significant advantages of GLAM-LRSR and its effectiveness.
Outfit Completion and Clothes Recommendation

Recommending fashion outfits requires learning a concept of style and fashionability that is typically human. There has been an increasing research effort into creating Machine Learning models able to learn such concepts, in order to distinguish between compatible and incompatible clothes and to select an item that would complete an outfit. However, most of the work done in literature tackles this problem from a pure Machine Learning point of view, disregarding real-case scenarios and the human interaction with systems able to generate outfits. This work tries to move the problem of generating outfits to the Recommender Systems domain by presenting as its main contribution a novel algorithm for a fashion-specific Recommender System that generates fashionable outfits, able to scale its inference time to be useful in real use case scenarios, and applies such algorithm on public and industrial datasets. In addition to this, this work shows preliminary results on how this algorithm can be employed in a real scenario and reports as preliminary results the evaluations provided by three professional stylists on the outfits generated by such algorithms.
MaraVis: Representation and Coordinated Intervention of Medical Encounters in Urban Marathon

There is an increased use of Internet-of-Things and wearable sensing devices in the urban marathon to ensure effective response to unforeseen medical needs. However, the massive amount of real-time, heterogeneous movement and psychological data of runners impose great challenges on prompt medical incident analysis and intervention. Conventional approaches compile such data into one dashboard visualization to facilitate rapid data absorption but fail to support joint decision-making and operations in medical encounters. In this paper, we present MaraVis, a real-time urban marathon visualization and coordinated intervention system. It first visually summarizes real-time marathon data to facilitate the detection and exploration of possible anomalous events. Then, it calculates an optimal camera route with an arrangement of shots to guide offline effort to catch these events in time with a smooth view transition. We conduct a within-subjects study with two baseline systems to assess the efficacy of MaraVis.
Capturing, Preserving, and Digitizing Legacy Seismic Data from the Montserrat Volcano Observatory Analog Seismic Network, July 1995–December 2004

Abstract An eruption of the Soufrière Hills Volcano (SHV) on the eastern Caribbean island of Montserrat began on 18 July 1995 and continued until February 2010. Within nine days of the eruption onset, an existing four-station analog seismic network (ASN) was expanded to 10 sites. Telemetered data from this network were recorded, processed, and archived locally using a system developed by scientists from the U.S. Geological Survey (USGS) Volcano Disaster Assistance Program (VDAP). In October 1996, a digital seismic network (DSN) was deployed with the ability to capture larger amplitude signals across a broader frequency range. These two networks operated in parallel until December 2004, with separate telemetry and acquisition systems (analysis systems were merged in March 2001). Although the DSN provided better quality data for research, the ASN featured superior real-time monitoring tools and captured valuable data including the only seismic data from the first 15 months of the eruption. These successes of the ASN have been rather overlooked. This article documents the evolution of the ASN, the VDAP system, the original data captured, and the recovery and conversion of more than 230,000 seismic events from legacy SUDS, Hypo71, and Seislog formats into Seisan database with waveform data in miniSEED format. No digital catalog existed for these events, but students at the University of South Florida have classified two-thirds of the 40,000 events that were captured between July 1995 and October 1996. Locations and magnitudes were recovered for ∼10,000 of these events. Real-time seismic amplitude measurement, seismic spectral amplitude measurement, and tiltmeter data were also captured. The result is that the ASN seismic dataset is now more discoverable, accessible, and reusable, in accordance with FAIR data principles. These efforts could catalyze new research on the 1995–2010 SHV eruption. Furthermore, many observatories have data in these same legacy data formats and might benefit from procedures and codes documented here.
Interactive Parallel Coordinates for Parametric Design Space Exploration

We present an interactive visualization based on parallel coordinates that enables comparison, generation, and modification of multiple parametric design alternatives. Such capabilities are lacking in existing tools. Initial evaluation suggests that our proposal improves usability over existing tools, has novel parameter space exploration capabilities, and also reveals a space for designing direct interactions with visualizations to support parametric exploration.
Evolutionary Computation and AI Safety

Recent developments in artificial intelligence and machine learning have spurred interest in the growing field of AI safety, which studies how to prevent human-harming accidents when deploying AI systems. This paper thus explores the intersection of AI safety with evolutionary computation, to show how safety issues arise in evolutionary computation and how understanding from evolutionary computational and biological evolution can inform the broader study of AI safety.
GIS Android mobile based Software Development for Tourism Objects, Public Places list, Transportations list, and Culinary Places list Using Usability Measurement

Abstract Android is a mobile operating system used for touch screen based smart phones and computer tablets. The software is built using Java Android Programming language and using eclipse as the development tool. The purpose of this research is to produce a Geographic Application for tourism guidance in Palembang city consisting of tourism objects list, public places list, transportations list, culinary places list and also other helpful features that will help tourists to find the most effective and efficient tourism destinations, The measurement of the software are using usability matrix where the usability criteria used are Learnability , Efficiency , memorability , few error and satisfaction , with Nielsen’s usability measurement so that the software is produced with quality and accuracy to get relevant information about tourist attraction in Palembang City.
Trust through Digital Technologies

This paper examines the concept of trust in an increasingly digital society on the one hand and how it can be established with regard to digital documentation of online help services on the other. Trust is particularly important in the sector of digital services sector because requests and offers for help, and thus highly sensitive data, are offered, processed, and used on various online channels. With the advent of blockchain technology, there is a new central opportunity to create trust on digital platforms such as those used for online help services. After clarification of relevant concepts and terms, diverse forms of the blockchain technology are explained on the basis of the individually specific configuration of a blockchain. The main emphasis lies on federated blockchains which provide the central advantages of blockchain technology while avoiding the risk of passing on sensitive data. This results in a possible use of the technology for the area of digital services.
Low-light Image Enhancement with Deep Blind Denoising

Visual processing technology based on the high-quality image has been widely applied to maritime supervision. However, maritime accidents easily occur in a low-light environment. The images captured by the monitor often suffer from low visibility and noise, which will directly affect the rescue efficiency and accident investigation. To improve the quality of low-light images, we propose a low-light enhancement method based on the Retinex theory and deep blind denoising. In particular, the coarse illumination map is estimated via finding the maximum in the three channels of the original images. Then, we adopt the Guided filter to refine the coarse illumination map and adjust it by Gamma correction. Based on the Retinex theory, the reflectance map can be easily obtained by the original image and the refined illumination map. Meanwhile, the convolutional neural network is introduced to blindly remove the noise of the reflectance map. Finally, we can obtain the enhanced image by the adjusted illumination and denoised reflectance maps. Experiments have been implemented on realistic and synthetic low-light images to verify the effectiveness of our method. Experimental results have illustrated that it is superior to several popular low-light enhancement methods.
Effects of and Defenses Against Adversarial Attacks on a Traffic Light Classification CNN

Convolutional Neural networks (CNNs) have become more and more prevalent in applications such as image recognition and autonomous driving. However, recent literature shows that a lot of these deep-learning algorithms are susceptible to small perturbations to their input known as adversarial attacks. In this project, adversarial attack algorithms and defenses were implemented and evaluated using real traffic lights data and model. Using the spatial, one-pixel, Carlini & Wagner (C&W), and boundary attacks, a traffic-light classification neural network's robustness was evaluated. It was found that the particular model architecture was only really susceptible to the spatial attack (56% decrease in accuracy on perturbed images) and the C&W attack (0% accuracy on perturbed images). Subsequently, two defense strategies were proposed to increase the model's resilience to these attacks. First, the model was trained on a dataset composed of both perturbed and unperturbed images. This significantly helped against the spatial attack (22.5% decrease in success rate). Second, since the C&W attack maintained a 100% success rate, defensive distillation was then implemented. However, this only made the model more susceptible to the C&W attack (same success rate) and the spatial attack (13% increase in success rate). This shows that there is a lot of work that needs to be done to increase the robustness of applied neural networks against adversarial attacks. In summary, for the implemented traffic light CNN, only the spatial and C&W attacks can fool it. The implemented two defensive strategies were either marginally successful or completely failed against the attacks.
Exploring Spatial Scale Perception in Immersive Virtual Reality for Risk Assessment in Interior Design

One of the utilities of Virtual Reality is to provide its users with new perspectives, which is a promising application for architectural and interior design. In this paper, we investigate the effects of varying spatial scale perception (SSP), the perception of risks, and the ability to detect them in the virtual environment. We conducted a user study where participants experienced four unique perspectives, that of a two-year-old child, an eight-year-old child, an adult and a person in a wheelchair by manipulating their virtual inter-pupillary distance and eye height. We found that varying SSP had significant impacts on the perceived level of risk, heights of the identified risk, and the number of risks discovered. The results yielded empirical evidence to support that experiencing different SSP, can potentially help identify issues during an architectural design process for various groups of users.
Designing Sparse Reliable Pose-Graph SLAM: A Graph-Theoretic Approach

In this paper, we aim to design sparse D-optimal (determinantoptimal) pose-graph SLAM problems through the synthesis of sparse graphs with the maximum weighted number of spanning trees. Characterizing graphs with the maximum number of spanning trees is an open problem in general. To tackle this problem, several new theoretical results are established in this paper, including the monotone log-submodularity of the weighted number of spanning trees. By exploiting these structures, we design a complementary pair of near-optimal efficient approximation algorithms with provable guarantees. Our theoretical results are validated using random graphs and a publicly available pose-graph SLAM dataset.
Approximate Quantiles for Datacenter Telemetry Monitoring

Datacenter systems require real-time troubleshooting so as to minimize downtimes. In doing so, datacenter operators employ streaming analytics for collecting and processing datacenter telemetry over a temporal window. Quantile computation is key to this telemetry monitoring since it can summarize the typical and abnormal behavior of the monitored system. However, computing quantiles in real-time is resource-intensive as it requires processing hundreds of millions of events in seconds while providing high accuracy. To address these challenges, we propose AOMG, an efficient and accurate quantile approximation algorithm that capitalizes insights from our workload study. AOMG improves performance through two-level hierarchical windowing while offering small value errors in a wide range of quantiles by taking into account the density of underlying data distribution. Our evaluations show that AOMG estimates the exact quantiles with less than 5% relative value error for a variety of use cases while providing high throughput.
A Reservoir Computing Scheme for Multi-class Classification

A recent addition to the field of neural networks is a framework for recurrent neural networks (RNNs) called reservoir computing. Reservoir computing has proven successful in predicting dynamical systems, but little attention has been given to its possible use as a classification method. Literature has been written specifically in regard to reservoir computing for binary classification, but few papers have specifically been written about multi-class classification. This article aims to provide a generic scheme for multi-class classification based on reservoir computing. The comparable performance has shown that our proposed scheme as an alternative classifier can catch up with and even outstrip the performance of some traditional classifiers such as naïve Bayesian, decision trees, random forest, and neural network over several data sets.
Detecting Early Signs of Cyberbullying in Social Media

Nowadays, the amount of users’ activities on online social media is growing dramatically. These online environments provide excellent opportunities for communication and knowledge sharing. However, some people misuse them to harass and bully others online, a phenomenon called cyberbullying. Due to its harmful effects on people, especially youth, it is imperative to detect cyberbullying as early as possible before it causes irreparable damages to victims. Most of the relevant available resources are not explicitly designed to detect cyberbullying, but related content, such as hate speech and abusive language. In this paper, we propose a new approach to create a corpus suited for cyberbullying detection. We also investigate the possibility of designing a framework to monitor the streams of users’ online messages and detects the signs of cyberbullying as early as possible.
Multilevel medical image encryption for secure communication

The security of any medical image can be characterized as its integrity, confidentiality, and authentication. These security vulnerabilities restrict the development of mobile healthcare applications, which intend to improve the efficiency of medical image communication. To address the vulnerabilities associated with medical images, we propose a multilevel chaotic encryption technique for medical images. This technique utilizes multiple chaotic maps viz.: sine, cubic, and logistic maps for enhancing the key space, security, and robustness of medical images. The security, speed, and reliability of the proposed technique for medical images are illustrated via experiments for key sensitivity, performance, and statistical analysis. The proposed technique offers a large key space, pixel diffusion, and confusion. Security analysis shows a high sensitive dependence of the encryption and decryption techniques to any subtle change in the secret key, the plain medical image and the encrypted image. Also, the proposed technique has a large enough key space to see off attacks like brute force. Therefore considering the security during transmission of medical images over the communication network, the proposed work becomes a potential choice.
Comparison of Public, Private Schools and Deeni Madaris Students Regarding Respect as Moral Adjustment in Society

Respect means due regard for the rights of others, wishes and feelings on the behalf of their achievements, qualities and abilities. The study was cross-sectional in nature. The population comprised of principals, teachers, parents and students of all Public, Private schools and Deena Madras of Southern districts of Khyber Pakhtunkhwa, Pakistan. The key purpose of the study was to compare Public, Private schools and Deeni Madaris students regarding respect as a moral adjustment in society. The total numbers of (n=1500) respondents including “Principals, Teachers, Parents and Students” were taken as a sample through L.R. Gay Size rule of thumb. Convenient, stratified, simple random and disproportionate sampling techniques were used to gather data from the respondents. A statement regarding respect was developed on the six points following Likert type scale. Data was statistically analyzed through SPSS by using frequency and one-way ANOVA that provide important information for policymakers and the stakeholders. Results and conclusions were drawn. It was concluded that respect.
InvaliDB: Scalable Push-Based Real-Time Queries on Top of Pull-Based Databases

Traditional databases are optimized for pull-based queries, i.e. they make information available in direct response to client requests. While this access pattern is adequate for mostly static domains, it requires inefficient and slow workarounds (e.g. periodic polling) when clients need to stay up-to-date. Acknowledging reactive and interactive workloads, modern real-time databases such as Firebase, Meteor, and RethinkDB proactively deliver result updates to their clients through push-based real-time queries. However, current implementations are only of limited practical relevance, since they are incompatible with existing technology stacks, fail under heavy load, or do not support complex queries to begin with. To address these issues, we propose the system design InvaliDB which combines linear read and write scalability for real-time queries with superior query expressiveness and legacy compatibility. We compare InvaliDB against competing system designs to emphasize the benefits of our approach that has been serving customers at the Database-as-a-Service company Baqend since July 2017.
Restoration and Reuse Design of Industrial Heritage based on Virtual Reality Technology

Abstract Based on the practical work of revitalizing the old industrial heritage of Zhengzhou No.2 Abrasive Wheel Factory, the method and technical feasibility of the restoration and reuse design of industrial heritage based on virtual reality technology are discussed in the paper. The research is based on MARS virtual reality technology platform, which is compatible with traditional modeling software, such as SKETCHUP and 3DMAX, and can directly use related software for modeling and import, reduce repetitive work and greatly shorten working time. In addition, the platform has good post-scene simulation function and is suitable for restoration and creative conceptual design in complex environment. The paper is divided into three parts. The first part is a brief introduction of the project background and task. The second part discusses the virtual reality technology solution proposed according to the project needs, and further analyzes the implementation process and technical characteristics of the solution. The final part evaluates the role of virtual reality technology in the restoration and reuse design of industrial heritage, so as to provide some inspiration for relevant designs.
Optimal Motion Planning for Differential Drive Mobile Robots based on Multiple-Interval Chebyshev Pseudospectral Methods

SUMMARY This paper presents a Chebyshev Pseudospectral (PS) method for solving the motion planning problem of nonholonomic mobile robots with kinematic and dynamic constraints. The state and control variables are expanded in the Chebyshev polynomial of order N , and Chebyshev–Gauss–Lobatto (CGL) nodes are provided for approximating the system dynamics, boundary conditions, and performance index. For the lack of enough nodes nearby the obstacles, the interpolation of trajectory may violate the obstacles and the multiple-interval strategy is proposed to deal with the violation. Numerical examples demonstrate that multiple-interval strategy yields more accurate results than the single-interval Chebyshev PS method.