Reviewerly/exHarmony huggingface dataset:
Processing Simple Geometric Attributes with Autoencoders

Image synthesis is a core problem in modern deep learning, and many recent architectures such as autoencoders and generative adversarial networks produce spectacular results on highly complex data, such as images of faces or landscapes. While these results open up a wide range of new, advanced synthesis applications, there is also a severe lack of theoretical understanding of how these networks work. This results in a wide range of practical problems, such as difficulties in training, the tendency to sample images with little or no variability and generalization problems. In this paper, we propose to analyze the ability of the simplest generative network, the autoencoder, to encode and decode two simple geometric attributes: size and position. We believe that, in order to understand more complicated tasks, it is necessary to first understand how these networks process simple attributes. For the first property, we analyze the case of images of centered disks with variable radii. We explain how the autoencoder projects these images to and from a latent space of smallest possible dimension, a scalar. In particular, we describe both the encoding process and a closed-form solution to the decoding training problem in a network without biases and shows that during training, the network indeed finds this solution. We then investigate the best regularization approaches which yield networks that generalize well. For the second property, position, we look at the encoding and decoding of Dirac delta functions, also known as “one-hot” vectors. We describe a handcrafted filter that achieves encoding perfectly and show that the network naturally finds this filter during training. We also show experimentally that the decoding can be achieved if the dataset is sampled in an appropriate manner. We hope that the insights given here will provide better understanding of the precise mechanisms used by generative networks and will ultimately contribute to producing more robust and generalizable networks.
Vertical Locomotion in VR Using Full Body Gestures

Virtual Reality experiences today are majorly based on horizontal locomotion. In these experiences, movement in the virtual space is accomplished using teleportation, gaze input or tracking in physical space which is limited to a certain extent. Our work focuses on intuitive interactions for vertical locomotion involving both hands and feet. Such an instance of vertical locomotion is - ladder climbing. In this paper, we present an interaction technique for climbing a ladder in Virtual Reality (VR). This technique is derived from the natural motions of the limbs while climbing a ladder in reality, adhering to safe climbing practices. The developed interaction can be used in training experiences as well as gaming experiences. Preliminary evaluation of our interaction technique showed positive results across dimensions like - learnability, natural mapping, and intuitiveness.
Knowledge Capturing via Conceptual Reframing

In this work we describe a novel approach to goal-oriented knowledge capturing that, differently from the standard knowledge acquisition pipelines, employs a dynamic conceptual reframing mechanismrelying on a non monotonic reasoning procedure. This approachhas been implemented in a knowledge based system able to findthe solution to not directly satisfiable goals by recombining, in aninnovative way, at least two concepts of a given knowledge base(KB). The output of such combinatorial mechanism results, de facto, in an extension of the initial KB able to satisfy the original goal. The proposed approach has been tested in the task of goal-driven concept invention and has been compared with human responses.
Rate Distortion via Deep Learning

We explore the connections between rate distortion/lossy source coding and deep learning models, the Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs). We show that rate distortion is a function of the RBM log partition function and that RBM/DBN can be used to learn the rate distortion approaching posterior as in the Blahut-Arimoto algorithm. We propose an algorithm for lossy compressing of binary sources. The algorithm consists of two stages, a training stage that learns the posterior with training data of the same class as the source, and a compression/reproduction stage that is comprised of a lossless compression and a lossless reproduction. Theoretical results show that the proposed algorithm achieves the optimum rate distortion function for stationary ergodic sources asymptotically. Numerical experiments show that the proposed algorithm outperforms the reported best results.
Structured analysis of the high-dimensional FMR model

The finite mixture of regression (FMR) model is a popular tool for accommodating data heterogeneity. In the analysis of FMR models with high-dimensional covariates, it is necessary to conduct regularized estimation and identify important covariates rather than noises. In the literature, there has been a lack of attention paid to the differences among important covariates, which can lead to the underlying structure of covariate effects. Specifically, important covariates can be classified into two types: those that behave the same in different subpopulations and those that behave differently. It is of interest to conduct structured analysis to identify such structures, which will enable researchers to better understand covariates and their associations with outcomes. Specifically, the FMR model with high-dimensional covariates is considered. A structured penalization approach is developed for regularized estimation, selection of important variables, and, equally importantly, identification of the underlying covariate effect structure. The proposed approach can be effectively realized, and its statistical properties are rigorously established. Simulation demonstrates its superiority over alternatives. In the analysis of cancer gene expression data, interesting models/structures missed by the existing analysis are identified.
Effect of Dropout and Batch Normalization in Siamese Network for Face Recognition

The paper focuses on maximizing feature extraction and classification using one-shot learning (meta-learning). The present work discusses how to maximize the performance of the Siamese Neural Network using various regularization and normalization techniques for very low epochs. In this paper we perform multi-class Face Recognition. A unique pairing of face images helps us to understand the generalization capacity of our network which is scrutinized on AT&T-ORL face databases. We performed experiments to see how learning can be made to converge within a few epochs, and the approach has also made a telling performance on unseen test data which is about 96.01%. Besides, we discuss the ways to speed up learning particularly for a Siamese network and achieve convergence within 5 epochs. We found one of the better regularization techniques for fast reduction of the loss function. It is apparent from our findings that only normalization is the effective approach while working within less epochs. Also, Dropout After Batch Normalization configuration results in smooth loss reduction.
Ellipsoid Fitting Using Variable Sample Consensus and Two-Ellipsoid-Bounding-Counting for Locating Lingwu Long Jujubes in a Natural Environment

Locating Lingwu Long Jujubes is a key step for the automatic picking of the jujubes which can lower labour costs. In this paper, a method for detecting Lingwu Long Jujubes in a natural environment with a three-dimensional (3D) point cloud is proposed. First, the jujubes are preliminarily extracted in two-dimensional (2D) image. Then, the points are fitted to ellipsoid by least square (LS) in the sample consensus framework. The model scores are different when sample size is changed. The variable sample consensus (VARSAC) is proposed to get higher score than random sample consensus (RANSAC). The normal vector and the distance need to be calculated in score calculation of the RANSAC. However, this score calculation method is complex and time-consuming. Thus, a new method called two-ellipsoid-bounding-counting (TEBC) is proposed. The TEBC produces two auxiliary ellipsoids that are obtained by scaling semiaxis of the model. The points, which is bounded by two auxiliary ellipsoids, are regarded as inliers. The functional value of every candidate point is calculated to select the inliers. The valid ellipsoids are determined by the prior information and the invariants. Finally, the centre, size and the attitude angle of the jujubes are solved using eigenvalues and eigenvectors. Experiments are carried out on synthetic and real datasets. The experimental results show that the proposed method can faster and more accurately detect jujube. The speed of the VARSAC+TEBC is approximately 4 times faster than that of the RANSAC in the real dataset.
Start Your ENGINEs: Dynamically Loadable Contemporary Crypto

Software ever-increasingly relies on building blocks implemented by security libraries, which provide access to evolving standards, protocols, and cryptographic primitives. These libraries are often subject to complex development models and long decision-making processes, which limit the ability of contributors to participate in the development process, hinder the deployment of scientific results and pose challenges for OS maintainers. In this paper, focusing on OpenSSL as a de-facto standard, we analyze these limits, their impact on the security of modern systems, and their significance for researchers. We propose the OpenSSL ENGINE API as a tool in a framework to overcome these limits, describing how it fits in the OpenSSL architecture, its features, and a technical review of its internals. We evaluate our methodology by instantiating libsuola, a new ENGINE providing support for emerging cryptographic standards such as X25519 and Ed25519 for currently deployed versions of OpenSSL, performing benchmarks to demonstrate the viability and benefits. The results confirm that the ENGINE API offers (1) an ideal architecture to address wide-ranging security concerns; (2) a valuable tool to enhance future research by easing testing and facilitating the dissemination of novel results in real-world systems; and (3) a means to bridge the gaps between research results and currently deployed systems.
Cognitive Infotainment Systems for Intelligent Vehicles

Lately, the automotive world is witnessing a trend related to the deployment and extensive use of Intelligent Transportation Systems (ITS) inside vehicles. Such systems generate large volumes of real-time data that need to be managed, communicated, interpreted, aggregated, and analyzed in order to support real-time decision-making capabilities. Furthermore, automotive industries have introduced In-Vehicle-Infotainment (IVI) systems in supporting drivers/passengers with a varying set of functions. The main purpose of this study is to present an IVI cognitive functionality that automatically and dynamically proposes the optimum music genre to the drivers /users when they want to travel with their vehicles. The proposed system utilizes (i) driver/user's profile data and his/her current situation, (ii) driver/user's personal preferences, (iii) external environment information obtained from sensor measurements, and (iv) previous knowledge, turned into experience, in an automated manner. Knowledge is obtained through the exploitation of a machine learning technique based on the Bayesian networking principles. Indicative simulation results showcase the behavior of the aforementioned IVI cognitive functionality in proactively identifying the optimal music genre and accordingly notifying the driver/user.
Single RGB-D Fitting: Total Human Modeling with an RGB-D Shot

Existing single shot based human modeling methods generally cannot model the complete pose details (e.g., head and hand positions) without non-trivial interactions. We explore the merits of both RGB and depth images and propose a new method called Single RGB-D Fitting (SRDF) to generate a realistic 3D human model with a single RGB-D shot from a consumer-grade depth camera. Specifically, the state-of-the-art deep learning techniques for RGB images are incorporated into SRDF, so that: 1) A compound skeleton detection method is introduced to obtain accurate 3D skeletons with refined hands based on the combination of depth and RGB images; and 2) an RGB image segmentation assisted point cloud pre-processing method is presented to obtain smooth foreground point clouds. In addition, several novel constraints are also introduced into the energy minimization model, including the shape continuity constraint, the keypoint-guided head pose prior constraint, and the penalty-enforced point cloud prior constraint. The energy model is optimized in a two-pass way so that a realistic shape can be estimated from coarse to fine. Through extensive experiments and comparisons with the state of the art methods, we demonstrate the effectiveness and efficiency of the proposed method.
High Precision Target Positioning Method for RSU in Cooperative Perception

Vehicle-road cooperative perception system can greatly improve the perception ability of intelligent vehicles by making use of perception information from road side units (RSU). This paper focuses on the target positioning of static camera for vehicle-road cooperation. A low-cost camera calibration method is proposed to complete the accurate mapping between the image plane and the 3D world space. Precise location of interested targets are achieved by an efficient tracking strategy. Real test scenarios show that our algorithm can effectively locate vehicles, pedestrians, non-motor vehicles and other targets with high accuracy. Our algorithm won the Monocular Static Camera Positioning and Ranging Competition for Autonomous Driving championship in 2019.
Secure Delegation of Isogeny Computations and Cryptographic Applications

We address the problem of speeding up isogeny computation for supersingular elliptic curves over finite fields using untrusted computational resources like third party servers or cloud service providers (CSPs). We first propose new, efficient and secure delegation schemes. This especially enables resource-constrained devices (e.g. smart cards, RFID tags, tiny sensor nodes) to effectively deploy post-quantum isogeny-based cryptographic protocols. To the best of our knowledge, these new schemes are the first attempt to generalize the classical secure delegation schemes for group exponentiations and pairing computation to an isogeny-based post-quantum setting. Then, we apply these secure delegation subroutines to improve the performance of supersingular isogeny-based zero-knowledge proofs of identity. Our experimental results show that, at the 128-bit quantum-security level, the proving party only needs about 3% of the original protocol cost, while the verifying party's effort is fully reduced to comparison operations. Lastly, we also apply our delegation schemes to decrease the computational cost of the decryption step for the NIST postquantum standardization candidate SIKE.
A Resume Recommendation Algorithm Based on K-means++ and Part-of-speech TF-IDF

This paper proposes a personalized resume recommendation algorithm in order to help job seekers find the right job and enterprises find the right talents. Firstly, we extracted resume feature and used K-means++ algorithm to cluster positions. The use of clustering results is beneficial for speedy recommendation. Afterward, we also constructed text vector and combined TF-IDF and part-of-speech weights to recommend resumes. At last, it is proved that the recommendation result of the proposed algorithm is better than the word frequency vector after a lot of manual evaluations.
CorpusVis – Visualizing Software Metrics at Scale

We do not know fully understand how software violates metrics based principles, particularly in large systems. Systems are restricted by structural and static deficiencies that we can aim to reduce by providing developers with effective visualizations of their code. We developed CorpusVis a widget-based application to explore software metrics of Java software systems from the Qualitas Corpus. Through an evaluation of the visualization techniques we identified what visualizations were effective and which ones did not scale well for large software systems. Our application helps to reduce the structural and static deficiencies in developers code which enables developers to spend less time maintaining legacy systems and learn to develop more effective code for future systems.
Virtual Reality in the Art Museum

With unique affordances of immersion and interaction, virtual reality (VR) technologies have been increasingly utilised in the art museum, offering new opportunities to update and enrich the museum practice whilst bringing issues and concerns in the meantime. The complexity of the situation is demonstrated widely in debates in existing literature, posing urgent needs for a systematic, theoretical approach to shed light on the issues involved and inform art museum professionals in their decision-making process. Based on a review of debates and investigations in related fields, this paper proposes the notion of presence as an appropriate theoretical lens of inspection that signifies critical salience in both fields of VR and art museums. It further argues that current studies on presence have not demonstrated a holistic approach to address this intersectional context, hence an integrated understanding of presence is to be developed. In doing so, this paper poses a taxonomy of presence in four dimensions as an initial attempt to bridge current findings, addressing aspects of human, content, interaction and socialising respectively in the context of art museums using VR.
Architecting dietary intake monitoring as a service combining NLP and IoT

Currently there exist many tools that support monitoring and encouragement of healthy nutrition habits in the context of wellness promotion. In this domain, interfaces based on natural language provide more flexibility for nutritional self-reporting than traditional form-based applications, allowing the users to provide richer and spontaneous descriptions. Nonetheless, in certain circumstances, natural language records may miss some important aspects, such as the quantity of food eaten, which results in incomplete recordings. In the Internet-of-Things (IoT) paradigm, smart home appliances can support and complement the recording process so as to make it more accurate. However, in order to build systems that support the semantic analysis of nutritional self-reports, it is necessary to integrate multiple inter-related components, possibly within complex e-health platforms. For this reason, these components should be designed and encapsulated avoiding monolithic approaches that derive in rigidity and dependency of particular technologies. Currently, there are no models or architectures that serve as a reference for developers towards this objective. In this paper, we present a service-based architecture that helps to contrast and complement the descriptions of food intakes by means of connected smart home devices, coordinating all the stages during the process of recognizing food records provided in natural language. Additionally, we aim to identify and design the essential services that are required to automate the recording and subsequent processing of natural language descriptions of nutritional intakes in association with smart home devices. The functionalities provided by each of these services are ready to work in isolation, just out of the box, or in downstream pipeline processes, bypassing the inconveniences of monolithic architectures.
Towards Understanding Creative Language in Tweets

Extracting fine-grained information from social media is traditionally a challenging task, since the language used in social media messages is usually informal, with creative genre-specific terminology and expression.How to handle such a challenge so as to automatically understand the opinions that people are communicating has become a hot subject of research.In this paper, we aim to show that leveraging the pre-learned knowledge can help neural network models understand the creative language in Tweets.In order to address this idea, we present a transfer learning model based on BERT.We fine-turned the pre-trained BERT model and applied the customized model to two downstream tasks described in SemEval-2018: Irony Detection task and Emoji Prediction task of Tweets.Our model could achieve an F-score of 38.52 (ranked 1/49) in Emoji Prediction task and 67.52 (ranked 2/43) and 51.35 (ranked 1/31) in Irony Detection subtask A and subtask B. The experimental results validate the effectiveness of our idea.
Interactive User Interface for Recognizing Online Handwritten Mathematical Expressions and Correcting Misrecognition

This paper presents an interactive user interface for the recognition of online handwritten mathematical expressions (HMEs). Since the recognition results may have errors, our objective is to make a user interface by which the users could verify and edit the recognition result of an HME easily without rewriting it. Multiple editing gestures are proposed to correct recognition errors in symbol recognition, symbol segmentation, and structure analysis. We implemented prototypes and conducted a user study to compare two variations for the user interface and a few variations for gestures. The results show that participants prefer the user interface showing the recognition result of each symbol apart from its bounding box to the one which embeds the recognition result into its bounding box. Moreover, the user study confirmed that our correcting gestures enable the users to modify the recognition result as they expected.