Reviewerly/exHarmony huggingface dataset:
Collaborative and Competitive Futures for Virtual Reality Music and Sound

The histories of virtual reality systems draw heavily from foundational work in telepresence and robotics, cinema and gaming. Across each of these disciplines the roles of “player” and “audience” member vary significantly, as do the rules, affordances and experiential goals put forth by the systems themselves. As such, designers and developers of interactive virtual (here defined as including mixed and augmented) reality systems are faced with a fundamental choice: to create experiences that are inherently collaborative or competitive. While on the surface such a choice might seem a simple articulation of the core design principles for any one given project, the directions in which virtual reality systems are guided and the impacts these choices will have on societal acceptance of VR as a principal component of our technological futures should not be ignored. This paper discusses models of collaboration and competition as put forth within a series of interactive virtual experiences and proposes an ideal future for virtual reality in which interactivity and telepresence leverage collaboration as a core mechanic.
Matching Seqlets: An Unsupervised Approach for Locality Preserving Sequence Matching

In this paper, we propose a novel unsupervised approach for sequence matching by explicitly accounting for the locality properties in the sequences. In contrast to conventional approaches that rely on frame-to-frame matching, we conduct matching using sequencelet or seqlet, a sub-sequence wherein the frames share strong similarities and are thus grouped together. The optimal seqlets and matching between them are learned jointly, without any supervision from users. The learned seqlets preserve the locality information at the scale of interest and resolve the ambiguities during matching, which are omitted by frame-based matching methods. We show that our proposed approach outperforms the state-of-the-art ones on datasets of different domains including human actions, facial expressions, speech, and character strokes.
People Tracking by Cooperative Fusion of RADAR and Camera Sensors

Accurate 3D tracking of objects from monocular camera poses challenges due to the loss of depth during projection. Although ranging by RADAR has proven effective in highway environments, people tracking remains beyond the capability of single sensor systems. In this paper, we propose a cooperative RADAR-camera fusion method for people tracking on the ground plane. Using average person height, joint detection likelihood is calculated by back-projecting detections from the camera onto the RADAR Range-Azimuth data. Peaks in the joint likelihood, representing candidate targets, are fed into a Particle Filter tracker. Depending on the association outcome, particles are updated using the associated detections (Tracking by Detection), or by sampling the raw likelihood itself (Tracking Before Detection). Utilizing the raw likelihood data has the advantage that lost targets are continuously tracked even if the camera or RADAR signal is below the detection threshold. We show that in single target, uncluttered environments, the proposed method entirely outperforms camera-only tracking. Experiments in a real-world urban environment also confirm that the cooperative fusion tracker produces significantly better estimates, even in difficult and ambiguous situations.
Privacy Policy Specification Framework for Addressing End-Users’ Privacy Requirements

Privacy policies are a widely used approach in informing end-users about the processing of their data and collecting consent to such processing. These policies are defined by the service providers and end-users do not have any control over them. According to the General Data Protection Regulation of the European Union, service providers should make the data processing of end-users’ data transparent in a comprehensible way. Furthermore, service providers are obliged to provide the end-users with control over their data. Currently, end-users have to comprehend a lengthy textual policy in order to understand how their data is processed. Improved representations of policies have been proposed before, however these improvements do mostly not empower the end-users in controlling their data. This paper provides a conceptual model and a proof of concept for the privacy policy specification framework that empowers end-users’ when using online services. Instead of having to accept predefined privacy policies, end-users can define their privacy preferences and adjust the applied privacy policy for a specific service.
iX-BSP: Belief Space Planning through Incremental Expectation

Belief space planning (BSP) is a fundamental problem in robotics. Determining an optimal action quickly grows intractable as it involves calculating the expected accumulated cost (reward), where the expectation accounts for all future measurement realizations. State of the art approaches therefore resort to simplifying assumptions and approximations to reduce computational complexity. Importantly, while in robotics re-planning is essential, these approaches calculate each planning session from scratch. In this work we contribute a novel approach, iX-BSP, that is based on the key insight that calculations in consecutive planning sessions are similar in nature and can be thus re-used. Our approach performs incremental calculation of the expectation by appropriately re-using computations already performed in a precursory planing session while accounting for the information obtained in inference between the two planning sessions. The formulation of our approach considers general distributions and accounts for data association aspects. We evaluate iX-BSP in statistical simulation and show that incremental expectation calculations significantly reduce runtime without impacting performance.
Enhancing V-SLAM Keyframe Selection with an Efficient ConvNet for Semantic Analysis

Selecting relevant visual information from a video is a challenging task on its own and even more in robotics, due to strong computational restrictions. This work proposes a novel keyframe selection strategy based on image quality and semantic information, which boosts strategies currently used in Visual-SLAM (V-SLAM). Commonly used V-SLAM methods select keyframes based only on relative displacements and amount of tracked feature points. Our strategy to select more carefully these keyframes allows the robotic systems to make better use of them. With minimal computational cost, we show that our selection includes more relevant keyframes, which are useful for additional posterior recognition tasks, without penalizing the existing ones, mainly place recognition. A key ingredient is our novel CNN architecture to run a quick semantic image analysis at the onboard CPU of the robot. It provides sufficient accuracy significantly faster than related works. We demonstrate our hypothesis with several public datasets with challenging robotic data.
A Novel Multi-objective Binary Differential Evolution Algorithm for Multi-label Feature Selection

In machine learning, multi-label classification aims to assign labels of instances in a dataset which are associated to more than one class label. Feature selection as an important task in predictive model construction improves the performance of multi-label classification. Since feature selection task can be interpreted as optimizing multiple objectives in a massive search space, multi-objective evolutionary techniques can be applied to tackle this family of problems. In this paper, a binary multi-objective feature selection is proposed for multi-label data with considering number of features and classification accuracy as objectives. A binary differential evolution is proposed based on opposition-based learning concept and partially voting between two candidate solutions to decide about absence or presence of a feature in third randomly selected solution. Because feature selection is basically a binary optimization problem, proposing a binary operator improves the effectiveness of search process in evolutionary algorithms. The proposed operator is utilized in third version of Generalized Differential Evolution (GDE3) which is a multi-objective optimization algorithm to select best subset of multi-label features with minimum number of features. A benchmarking is conducted on eight multi-label datasets in terms of several multi-objective assessment metrics including the Hypervolume indicator, Pure Diversity, and Set-coverage. Experimental results show significant improvements for proposed method in comparison with the state-of-the-art multi-objective feature selection methods for multi-label classification, which are namely NSGA-II and PSO based approaches.
In case of emergency, order pizza

The biggest challenge for voice technologies is action recognition. This is partly because current approaches prioritize abstract context over practical action, and tend to ignore the detailed, sequential structure of talk by emulating scripted, often stereotypical dialogue. This provocation paper analyzes an urgent case of how a caller and a 911 dispatcher work together to achieve action recognition. We outline their 'seen but unnoticed' interactional methods and suggest how computational systems can learn from conversation analysis and use micro-analytic detail to recognize social actions.
Unconditionally Secure Computation Against Low-Complexity Leakage

We consider the problem of constructing leakage-resilient circuit compilers that are secure against global leakage functions with bounded output length. By global, we mean that the leakage can depend on all circuit wires and output a low-complexity function (represented as a multi-output Boolean circuit) applied on these wires. In this work, we design compilers both in the stateless (a.k.a. single-shot leakage) setting and the stateful (a.k.a. continuous leakage) setting that are unconditionally secure against $$\mathsf {AC}^0$$ leakage and similar low-complexity classes. In the stateless case, we show that the original private circuits construction of Ishai, Sahai, and Wagner (Crypto 2003) is actually secure against $$\mathsf {AC}^0$$ leakage. In the stateful case, we modify the construction of Rothblum (Crypto 2012), obtaining a simple construction with unconditional security. Prior works that designed leakage-resilient circuit compilers against $$\mathsf {AC}^0$$ leakage had to rely either on secure hardware components (Faust et al., Eurocrypt 2010, Miles-Viola, STOC 2013) or on (unproven) complexity-theoretic assumptions (Rothblum, Crypto 2012).
Assessing the effectiveness of goal-oriented modeling languages: A family of experiments

Several goal-oriented languages focus on modeling stakeholders’ objectives, interests or wishes. However, these languages can be used for various purposes (e.g., exploring system solutions or evaluating alternatives), and there are few guidelines on how to use these models downstream to the software requirements and design artifacts. Moreover, little attention has been paid to the empirical evaluation of this kind of languages. In a previous work, we proposed [email protected] as a specialization of the Goal Requirements Language (GRL) to specify stakeholders’ goals when dealing with early requirements in the context of incremental software development. This paper compares the [email protected] language with the i* language, with respect to the quality of goal models, the participants’ modeling time and productivity when creating the models, and their perceptions regarding ease of use and usefulness. A family of experiments was carried out with 184 students and practitioners in which the participants were asked to specify a goal model using each of the languages. The participants also filled in a questionnaire that allowed us to assess their perceptions. The results of the individual experiments and the meta-analysis indicate that the quality of goal models obtained with [email protected] is higher than that of i*, but that the participants required less time to create the goal models when using i*. The results also show that the participants perceived [email protected] to be easier to use and more useful than i* in at least two experiments of the family. [email protected] makes it possible to obtain goal models with good quality when compared to i*, which is one of the most frequently used goal-oriented modeling languages. It can, therefore, be considered as a promising emerging approach in this area. Several insights emerged from the study and opportunities for improving both languages are outlined.
Research on infrared image enhancement and segmentation of power equipment based on partial differential equation

With the development of science and technology, image processing is applied more and more widely. In power system, a large number of power equipment always work in harsh environment, which easily leads to equipment damage, not only affects the normal operation of equipment, but also may lead to accidents. In order to ensure the normal operation of power equipment, it is necessary to monitor the operation status of power equipment in real time. The key of real-time monitoring is how to analyze the quality of infrared image. In order to improve the quality of infrared image of power equipment, this paper analyses the traditional image processing technology, mainly using partial differential equation to optimize and improve the image enhancement algorithm and segmentation method, so as to improve the quality of infrared image of power equipment. Firstly, in the aspect of infrared image enhancement, partial differential equation is used to improve the shortcomings of the traditional contrast enhancement method, enhance the texture details of the image while effectively improving the brightness visual effect of the image; then, in image segmentation, the stopping function of GAC model of classical partial differential equation is improved to improve the effect of infrared image segmentation of power equipment. Through the analysis of simulation experiments, this paper improves the traditional method by using partial differential equation in image enhancement and image segmentation, which can effectively improve the quality and segmentation effect of infrared image of power equipment.
A prediction error based histogram association and mapping technique for data embedment

A histogram association and mapping based embedding policy implants a group of bits, called message chunk, in a block. The strategy first calculates a distribution of pixel values of each block in the grayscale, called histogram. Each gray value acts as a bin of the histogram. Though the histogram makes up of 256 bins, only several bins hold these pixel values of a block. The implantation rules shift the pixel value containing bins as a single object to a new position in the histogram. The number of implanted bits is dependent on the range of pixel values of the working block. Implanting bits' number increases for a smaller value of range and vice versa. Being motivated, this research does the identical embedding task by the range of absolute-valued prediction errors. The research implements a predictor and measures the prediction errors in their absolute values. Computed errors' range is smaller than the range of the pixel values. The proposed method does the histogram-shifting task by the range of error values. The proposed method increases the number of embedded bits by a multiple of 1.31 to 2.04. In the second phase, it further applies the same predictor to the last calculated absolute-valued errors. The method yields a smaller range of absolute-valued errors by repeating the procedure for a number of times. The algorithm applies that smaller range in implementing HAM policy. The approach increases the number of implanted bits.
Big Data

This chapter gives information about the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by big data applications. Big data is one area where we can store, extract, and process a large amount of data. All these data are very often unstructured. Using big data, security functions are required to work over the heterogeneous composition of diverse hardware, operating systems, and network domains. A clearly defined security boundary like firewalls and demilitarized zones (DMZs), conventional security solutions, are not effective for big data as it expands with the help of public clouds. This chapter discusses the different concepts like characteristics, risks, life cycle, and data collection of big data, map reduce components, issues and challenges in big data, cloud secure alliance, approaches to solve security issues, introduction of cybercrime, YARN, and Hadoop components.
Supervector Extraction for Encoding Speaker and Phrase Information with Neural Networks for Text-Dependent Speaker Verification

In this paper, we propose a new differentiable neural network with an alignment mechanism for text-dependent speaker verification. Unlike previous works, we do not extract the embedding of an utterance from the global average pooling of the temporal dimension. Our system replaces this reduction mechanism by a phonetic phrase alignment model to keep the temporal structure of each phrase since the phonetic information is relevant in the verification task. Moreover, we can apply a convolutional neural network as front-end, and, thanks to the alignment process being differentiable, we can train the network to produce a supervector for each utterance that will be discriminative to the speaker and the phrase simultaneously. This choice has the advantage that the supervector encodes the phrase and speaker information providing good performance in text-dependent speaker verification tasks. The verification process is performed using a basic similarity metric. The new model using alignment to produce supervectors was evaluated on the RSR2015-Part I database, providing competitive results compared to similar size networks that make use of the global average pooling to extract embeddings. Furthermore, we also evaluated this proposal on the RSR2015-Part II. To our knowledge, this system achieves the best published results obtained on this second part.
Path-constrained trajectory time-optimization in a three-dimensional steady flow field

This work concerns a specific indirect method to solve an optimal control problem with state constraints in which the dynamics are driven by an ordinary differential equation involving a three-dimensional steady flow field. A moving object within this field is considered subject to a path constraint given by a cylinder. The given control system is linear, but the vector flow field may exhibit essential nonlinearity. An indirect numerical method is proposed based on the maximum principle in Gamkrelidze's form. This form of optimality conditions uses the concept of the extended Hamilton-Pontryagin function. The extended Hamilton-Pontryagin function differs from the conventional one in view of an additional term associated with the measure Lagrange multiplier. The proposed computational method essentially relies on the continuity of the measure multiplier, which is guaranteed under certain regularity conditions. Moreover, this property, together with the regularity condition, allows us to obtain an explicit expression for the measure multiplier. The application of the maximum principle yields a two-point boundary-value problem, which is solved by a variant of the shooting method.
Evaluating Teacher Avatar Appearances in Educational VR

We present a pilot study of four teacher avatars for an educational virtual field trip. The avatars consist of a depth-video-based mesh of a real person, a game-style human model, a robot model, and the robot with its head replaced by a video feed of the teacher's face. Multiple avatars were developed to consider alternatives to the mesh representation that required high-bandwidth networks and a non-immersive teacher interface. The pilot study presents a random avatar to the participant at each of 4 educational stations, and follows up with a subjective questionnaire. Most notably, we find positive affinity for the plain robot model to be similar to that of the video mesh, which was previously shown to provide high co-presence and good results for education. Results are guiding a larger study that will measure the educational efficacy of revised avatars.
Ensemble Classifier Optimization by Reducing Input Features and Base Classifiers

Ensemble classifier approaches either exploit the input feature space also known as the dataset attributes, or exploit the data sample space, for example Random Forest (RaF) exploits input features whereas Bagging exploits data sample space. Very few ensemble classifier approaches exist that exploit the both. In this paper we propose an ensemble classifier approach that first reduces input feature space by selecting only the significant features from input data that can maximize the classification performance of the ensemble and then optimize the base classifier pool by incorporating an evolutionary algorithm. The proposed approach is evaluated on benchmark datasets from UCI repository. The results are compared with single classifier approaches and existing state of the art ensemble classifier approaches.
Image steganography algorithm based on key matrix generated by quantum walks

This paper is to present a new image steganography technique based on quantum walks (QWs). In the proposed technique, the secret image is a gray-scale and the cover is a color image. The main contribution of QWs is to select one channel from (RGB) of the cover image that will host the secret bits. The performed analysis provides the proposed approach a strong base in the field of image steganography. The mean values of the results obtained based on analysis are as follows: PSNR is 48.5978, SSIM is 0.9964, UIQ is 0.9727, NCC is 0.996, and the capacity is 2-bit/pixel.
Risk-Based Static Authentication in Web Applications with Behavioral Biometrics and Session Context Analytics

In order to improve the security of password-based authentication in web applications, it is a common industry practice to profile users based on their sessions context, such as IP ranges and Browser type. On the other hand, behavioral dynamics such as mouse and keyword features have been proposed in order to improve authentication, but have been shown most effective only in continuous authentication scenarios. In this paper we propose to combine both fingerprinting and behavioral dynamics (for mouse and keyboard) in order to increase security of login mechanisms. We do this by using machine learning techniques that aim at high accuracy, and only occasionally raise alarms for manual inspection. Our combined approach achieves an AUC of 0.957. We discuss the practicality of our approach in industrial contexts.
Evolutionary Design of Recurrent Neural Network Architecture for Human Activity Recognition

Recurrent Neural Networks (RNNs) are a major class of artificial neural networks and one of the most effective deep learning models for human activity recognition (HAR). However designing the architecture of an RNN together with suitable hyper-parameters for any learning task can be very time-consuming and requires expert domain knowledge. This paper focuses on exploring a Genetic Algorithm (GA) based method to automatically design suitable architectures of Long Short Term Memory (LSTM)-based RNNs in a fully automated manner. An encoding strategy is proposed to encode the architectures and hyper-parameters of LSTM, for the sake of easy operation by GA. As a variant of RNN, LSTM is selected for this work due to its special feature to handle long-term dependencies. For verification and evaluation, three real-world benchmark datasets are used. We study three models of operation for the evolved deep LSTM-RNN, i.e., unidirectional, bidirectional and cascaded. The results of our experiments show that the RNN architectures automatically designed by our GA method can outperform state-of-the-art RNN systems and machine learning systems for HAR.