Reviewerly/exHarmony huggingface dataset:
Safe and Efficient High Dimensional Motion Planning in Space-Time with Time Parameterized Prediction

In this work, we propose an algorithm that can plan safe and efficient robot trajectories in real time, given time-parameterized motion predictions, in order to avoid fast-moving obstacles in human-robot collaborative environments. Our algorithm is able to reduce the robot configuration space and the time domain significantly by constructing a Lazy Safe Interval Probabilistic Roadmap based on a pre-planned path. The algorithm then plans efficient obstacle-avoidance strategies within the space-time roadmap. We benchmarked our algorithm by evaluating the performance of a simulated 6-joint manipulator attempting to avoid a quickly moving human hand, using a dataset collected from human experiments. We compared our algorithm's performance with those of 8 variations of prior state-of-the-art planners. Results from this empirical evaluation indicate that our method generated safe plans in 97.5% of the evaluated situations, achieved a planning speed 30 times faster than the benchmarked methods that planned in the time domain without space reduction, and accomplished the minimal solution execution time among the benchmarked planners with a similar planning speed.
Mixed Reality in Art Education

In this paper both receptive and productive Augmented Reality (AR)/Virtual Realty (VR)-Concepts to teach art are presented and discussed. Receptive means learning about art as an (immersed) spectator while productive means offering opportunities for own mixed reality artistic creations. The prototypes presented are pioneer research in terms of VR and AR, so called Mixed Reality (MR), informed art education. One of the core aims is to exemplify art educational applications by exploring usage of MR beyond traditional. The three MR based art educational concepts presented here invite users to experience art in heterogenous spatial contexts. MR is used here to highlight semantics of historically and art historically relevant spaces offering immersive as well as creative and participatory ways of learning. Three prototypes of MR informed art education are presented. First, a AR-based art history city guide is described with integration of teacher-based evaluation. Art is taught here in an interdisciplinary approach as situated learning experience visiting Munich's art historical sites and architecture. Teachers' attitudes towards AR/VR-based art history learning were conducted after trial of one of the applications. According to the educator's feedback, motivation, attention but also spatial imagination and perception are estimated to be fostered. Next, an AR-based installation of a missing painting by Franz Marc is presented conveying complex meanings of both architecture in art historical context as well as the art work itself. Finally, an immersive VR-art educational concept integrating two paintings of Caspar David Friedrich is described, offering sequential ways to inform about the painting's iconography. When it comes to implementing MR-concepts in day-to-day practice, media competence as well es technology acceptance or media cultural negotiations are vital aspects beyond the questions of it-technological equipment and infrastructure.
A new approach to local navigation for autonomous driving vehicles based on the curvature velocity method

This paper presents an approach for a car navigation system to follow a road path consisting on a sequence of lanelets. The motion control is divided into high-level planning that produces the road path and lower-level reactive control that safely follows the path. The approach presented here is the lower-level reactive control that combines the simple pure pursuit method to obtain a reference curvature with the beam curvature method (BCM) that keeps the car in the center of the free space in the lane avoiding obstacles that can partially block the lane. The whole system has been applied to an autonomous vehicle aimed for elderly or disable people.
Using app reviews for competitive analysis: tool support

Play Store and App Store have a large number of apps that are in competition as they share a fair amount of common features. User reviews of apps contain important information such as feature evaluation, bug report and feature request, which is useful input for the improvement of app quality. Automatic extraction and summarization of this information could offer app developers opportunities for understanding the strengths and weaknesses of their app and/or prioritizing the app features for the next release cycle. To support these goals, we developed the tool REVSUM which automatically identifies developer-relevant information from reviews, such as reported bugs or requested features. Then, app features are extracted automatically from these reviews using the recently proposed rule based approach SAFE. Finally, a summary is generated that supports the application of the following three use cases: (1) view users' sentiments about app features in competing apps, (2) detect which summarized app features were mentioned in bug related reviews, and (3) identify new app features requested by users.
An Improved Data Hiding Scheme Through Image Interpolation

Hidden data communicationJana, Manasi throughJana, Biswapati image interpolation is a challenging research issue in the field of information hiding, which maintains the tradeoff between payload and quality. This paper presents an improved interpolation-based data hiding schemes. Tang et al. proposed a high-capacity steganographic scheme through multi-layer embedding (CRS) with average embedding capacity 1.79 bpp with nearly 34 dB PSNR. To improve the interpolation-based data hiding scheme, we proposed an advanced data hiding scheme which divides a fixed length sub-message into floor and ceiling values. Then one value is added and another is subtracted from two adjacent interpolated pixels to minimize the image distortion. Here, two interpolated pixels are calculated between each adjacent pixels to increase the embedding capacity. The experimental results show that the proposed scheme has better performance than other existing interpolation methods such as NMI, IMP, and CRS. Moreover, the suggested scheme has a low-time complexity and provide good visual quality.
A Convolutional Neural Network Vision System Approach to Indoor Autonomous Quadrotor Navigation

A Convolutional Neural Network (CNN) vision-based approach is demonstrated to enable autonomous flight of a stock unmodified quadrotor drone in hallway environments. The video stream from a monocular front-facing camera on-board a quadrotor drone is fed to Convolutional Neural Network (CNN) environment classifiers at a base station in order to detect upcoming intersections and dead-ends. Detecting these hallway structural features allows our control planning algorithms to take appropriate action in order to stop and turn at intersections or stop before colliding with dead-ends such as walls and doors. The use of CNNs permit intersections and dead-ends to be detected with a high degree of accuracy in a wide variety of indoor environments with varying contrasts, lighting conditions, obstructions, and many other conditions that prevent easy generalization of feature extraction. Overall, our approach allows for real-time navigation at high rates of speed approaching 2 m/s.
ClusterNav: Learning-Based Robust Navigation Operating in Cluttered Environments

Robust autonomous navigation is one of the most important aspects in the acceptance of social robots by elderly users. Traditional model-based navigation techniques provide a stable theoretical and practical foundation for autonomous operation in domestic environments, but fall short in achieving human-like, acceptable behaviour while still being able to robustly navigate cluttered environments. In this work, we propose ClusterNav, a novel learning-based technique for navigation. Our technique consists of teaching the robot how it should move in the environment in a human-like manner, capturing key features of this demonstration in a geometric representation of the environment. This representation is then used to generate new trajectories for execution, allowing the robot move in an acceptable manner. We have tested our technique in a real environment in an elderly care facility, comparing it with the traditional model-based approach. Tests involved both expert and non-expert users teleoperating the robot. Results show that ClusterNav is capable of navigating the environment, achieving better similarity with the reference trajectories and higher execution speed when compared to the model-based approach.
Decentralising scientific publishing: can the blockchain improve science communication?

We present a decentralised solution for managing scientific communication, based on distributed ledger technologies, also called blockchains. The proposed system aims to solve incentive problems displayed by traditional systems in scientific communication and publication. A minimal working model is presented, defining roles, processes, and expected results from the novel system. The proposed solution is viable, given the current status of blockchain technology, and should lead to a rethinking of current practices and their consequences for scientific communication.
On-Demand Video-Streaming Quality of Experience Maximization in Mobile Edge Computing

Mobile Edge Computing (MEC) has recently emerged as a promising paradigm to enhance mobile networks' performance by providing cloud-computing capabilities to the edge of the Radio Access Network (RAN) with the deployment of MEC servers right at the Base Stations (BSs). Meanwhile, in-network caching and video transcoding have become important complementary technologies to lower network cost and to enhance Quality of Experience (QoE) for video-streaming users. In this paper, we aim at optimizing the QoE for dynamic adaptive video streaming by taking into account the Distortion Rate (DR) characteristics of videos and the coordination among MEC servers. Specifically, a novel Video-streaming QoE Maximization (VQM) problem is cast as a Mixed-Integer Nonlinear Program (MINLP) that jointly determines the integer video resolution levels and video transmission data rates. Due to the challenging combinatorial and non-convex nature of this problem, the Dual-Decomposition Method (DDM) is employed to decouple the original problem into two tractable subproblems, which can be solved efficiently using standard optimization solvers. Real-time experiments on a wireless video streaming testbed have been performed on a FDD-downlink LTE emulation system to characterize the performance and computing resource consumption of the MEC server under various realistic conditions. Emulation results of the proposed strategy show significant improvement in terms of users' QoE over traditional approaches.
A Sentiment Augmented Deep Architecture to Predict Peer Review Outcomes

Peer review texts reflect the overall impression of the reviewers towards a candidate research paper and by far are the most important? artifact used by editors and program chairs to determine the prospective inclusion of a manuscript in a given journal or a conference. Here in this work, we study how we could make use of the sentiment information embedded within peer review texts to help editors or program chairs to make better editorial decisions. We design an efficient deep neural architecture that takes into account: the paper, the corresponding reviews, and sentiment polarity of the reviews to predict the recommendation score of reviewers and well as to anticipate the final decision. Our results show that we achieve significant improvement over the baselines (~ 29% error reduction) proposed in a recently released dataset of peer reviews.
How to model the neurocognitive dynamics of decision making: A methodological primer with ACT-R

Higher cognitive functions are the product of a dynamic interplay of perceptual, mnemonic, and other cognitive processes. Modeling the interplay of these processes and generating predictions about both behavioral and neural data can be achieved with cognitive architectures. However, such architectures are still used relatively rarely, likely because working with them comes with high entry-level barriers. To lower these barriers, we provide a methodological primer for modeling higher cognitive functions and their constituent cognitive subprocesses with arguably the most developed cognitive architecture today—ACT-R. We showcase a principled method of generating individual response time predictions, and demonstrate how neural data can be used to refine ACT-R models. To illustrate our approach, we develop a fully specified neurocognitive model of a prominent strategy for memory-based decisions—the take-the-best heuristic—modeling decision making as a dynamic interplay of perceptual, motor, and memory processes. This implementation allows us to predict the dynamics of behavior and the temporal and spatial patterns of brain activity. Moreover, we show that comparing the predictions for brain activity to empirical BOLD data allows us to differentiate competing ACT-R implementations of take the best.
Indoor Object C1assification for Autonomous Navigation Assistance Based on Deep CNN Model

Indoor object classification is a key element for indoor navigation assistance systems. Indoor objects knowledge helps Visually Impaired People (VIP) in their indoor navigation and facilitates their daily life. This paper proposes a new classification system used especially for indoor object recognition based on Deep Convolutional Neural Network (DCNN) model which can be implemented on mobile embedded platforms. Experimental results obtained using natural images (with natural illumination) from the MCIndoor 20000 dataset show that the proposed approach achieves almost100% accuracy for indoor object classification.
Cross-database Micro-Expression Recognition with Deep Convolutional Networks

Micro-expression recognition (MER) is attracting more and more interests as it has important applications for analyzing human behaviors. Since the recognition ability for individual datasets has been improved greatly, few works have been devoted to the cross database task of MER, which is more challenging for capturing the subtle changes of micro-expressions from different environments. In this paper, we employ an end-to-end deep model for learning the representation and classifier automatically. In the deep model, the recurrent convolutional layers are utilized to exploit the learning ability with the optical flow fields of micro-expression sequences, which are enhanced by a motion magnification procedure. To ease the influence of samples from different datasets (environments), we present three normalization methods (i.e., sample-wise, subject-wise and dataset-wise methods) to restrain the variations of samples. The experiments are performed on the cross database of MERC2019 challenge, and achieve comparative performance than the baseline method.
Unsupervised domain adaptation: A multi-task learning-based method

This paper presents a new perspective to formulate unsupervised domain adaptation as a multi-task learning problem. This formulation removes the commonly used assumption in the classifier-based adaptation approach that a shared classifier exists for the same task in different domains. Specifically, the source task is to learn a linear classifier from the labelled source data and the target task is to learn a linear transform to cluster the unlabelled target data such that the original target data are mapped to a lower dimensional subspace where the geometric structure is preserved. The two tasks are jointly learned by enforcing the target transformation is close to the source classifier and the class distribution shift between domains is reduced in the meantime. Two novel classifier-based adaptation algorithms are proposed upon the formulation using Regularized Least Squares and Support Vector Machines respectively, in which unshared classifiers between the source and target domains are assumed and jointly learned to effectively deal with large domain shift. Experiments on both synthetic and real-world cross domain recognition tasks have shown that the proposed methods outperform several state-of-the-art unsupervised domain adaptation methods.
Youth Hackers and Adult Hackers in South Korea: An Application of Cybercriminal Profiling

Modern societies across the globe rely more on technology every day. While such advances have made some aspects of life easier, there is an increasing risk of cybercrime that can jeopardize national security and economic vitality. The purpose of this study is to build cybercriminal profiles in order to better predict future cyber-threats and create a more targeted prevention strategy for South Korea. The FBI's criminal profiling analysis is employed. Data were derived from court records and news media documents reporting computer hacking incidents in South Korea between 2010 and 2019. Our analysis shows that there are differences in motivations, attack methods, and accomplices in computer hacking incidents between youth and adult hackers. Policy implications are discussed.
Vision: Exploring Challenges and Opportunities for Usable Authentication in the Smart Home

In this work, we explore designs and possible threats of current smart home environments to support the design of novel authentication mechanisms. Our work is motivated by the ever increasing number of smart devices in people's homes that are meant to enhance users' daily life. Examples for such devices include, but are not limited to, smart TVs, cleaning robots, devices for health and well-being, as well as for cooking. Such devices are capable of collecting sensitive data and subsequently derive information, for example, whether a user is at home, how often they cook or watch TV, etc. To understand possible threats emerging from smart devices being capable of accessing sensitive data, we first chart a design and problem space. Our work is complemented by an in-depth discussion on future research directions and challenges as well as how threats can be mitigated by visionary novel authentication approaches.
Revisiting User Privacy for Certificate Transparency

Public key infrastructure (PKI) based on certificate authorities is one of the cornerstones of secure communication over the internet.Certificates issued as part of this PKI provide authentication of web servers among others.Yet, the PKI ecosystem is susceptible to certificate misissuance and misuse attacks.To prevent those attacks, Certificate Transparency (CT) facilitates auditing of issued certificates and detecting certificates issued without authorization.Users that want to verify inclusion of certificates on CT log servers contact the CT server directly to retrieve inclusion proofs.This direct contact with the log server creates a privacy problem since the users' browsing activities could be recorded by the log server owner.Lueks and Goldberg (FC 2015) suggested the use of Private Information Retrieval (PIR) in order to protect the users' privacy in the CT ecosystem.With the immense amount of certificates included on CT log servers, their approach runs into performance issues, however.Nevertheless, we build on this approach and extend it using multi-tier Merkle trees, and render it practical using multi-server PIR protocols based on distributed point functions (DPFs).Our approach leads to a scalable design suitable to handle the increasing number of certificates and is, in addition, generic allowing instantiations using secure accumulators and PIRs.We implement and test this mechanism for privacy-preserving membership proof retrieval and show that it can be integrated without disrupting existing CT infrastructure.Most importantly, even for larger CT logs containing 2 31 certificates, our approach using sub-accumulators can provide privacy with a performance overhead of less than 9 milliseconds in total.3 https://www.section.io/blog/chrome-ct-compliance/ 4Precertificates are certificates provided to the log before the issuance of the actual certificate.They contain a special critical poison extension that renders the certificate unusable in TLS connections.
Knowledge Enhanced Neural Networks

We propose Knowledge Enhanced Neural Networks (KENN), an architecture for injecting prior knowledge, codified by a set of logical clauses, into a neural network. In KENN clauses are directly incorporated in the structure of the neural network as a new layer that includes a set of additional learnable parameters, called clause weights. As a consequence, KENN can learn the level of satisfiability to impose in the final classification. When training data contradicts a constraint, KENN learns to ignore it, making the system robust to the presence of wrong knowledge. Moreover, the method returns learned clause weights, which gives us informations about the influence of each constraint in the final predictions, increasing the interpretability of the model. We evaluated KENN on two standard datasets for multi-label classification, showing that the injection of clauses automatically extracted from the training data sensibly improves the performances. Furthermore, we apply KENN to solve the problem of finding relationship between detected objects in images by adopting manually curated clauses. The evaluation shows that KENN outperforms the state of the art methods on this task.
Dynamically Spatiotemporal Regularized Correlation Tracking

Recently, due to the high performance, spatially regularized strategy has been widely applied to addressing the issue of boundary effects existed in correlation filter (CF)-based visual tracking. Specifically, it introduces a spatially regularized term to penalize the coefficients of the CFs to be learned depending on their spatial locations. However, the regularization weights are often formed as a fixed Gaussian function, and hence may cause the learned model degenerate due to the inflexible constraints on the ever-changing CFs to be learned over time during tracking. To address this issue, in this paper, we develop a dynamically spatiotemporal regularization model to constrain the CFs to be learned with the ever-changing regularization weights learned from two consecutive frames. The proposed method jointly learns the CFs along with the dynamically spatiotemporal constraint term, which can be efficiently solved in the Fourier domain by the alternative direction method. Extensive evaluations on the popular data sets OTB-100 and VOT-2016 demonstrate that the proposed tracker performs favorably against the baseline tracker and several recently proposed state-of-the-art methods.
Development of a practical system for computerized evaluation of descriptive answers of middle school level students

Assessment plays an important role in education. Recently proposed machine learning-based systems for answer grading demand a large training data which is not available in many application areas. Creation of sufficient training data is costly and time-consuming. As a result, automatic long answer grading is still a challenge. In this paper, we propose a practical system for long or descriptive answer grading that can assess in a small class scenario. The system uses an expert-written reference answer and computes the similarity of a student answer with it. For the similarity computation, it uses several word level and sentence level similarity measures including TFIDF, Latent Semantic Indexing, Latent Dirichlet Analysis, TextRank summarizer, and neural sentence embedding-based InferSent. The student answer might contain certain facts that do not occur in the model answer. The system identifies such sentences, examine their relevance and correctness, and assigns extra marks accordingly. In the final phase, the system uses a clustering-based confidence analysis. The system is tested on an assessment of school-level social science answer books. The experimental results demonstrate that the system evaluates the answer books with high accuracy, the best root mean square error value is 0.59 on a 0–5 scoring scale.