Reviewerly/exHarmony huggingface dataset:
Quantum state transfer on unsymmetrical graphs via discrete-time quantum walk

Perfect state transfer can be achieved between two marked vertices of graphs like a star graph, a complete graph with self-loops and a complete bipartite graph, and two-dimensional Lattice by means of discrete-time quantum walk. In this paper, we investigate the quality of quantum state transfer between two marked vertices of an unsymmetrical graph like the butterfly network. Our numerical results support the conjecture that the fidelity of state transfer depends on the quantum state to be transferred dynamically. The butterfly network is a typical example studied in networking coding. Therefore, these results can provide a clue to the construction of quantum network coding schemes.
A data-driven computational semiotics: The semantic vector space of Magritte’s artworks

Abstract The rise of big digital data is changing the framework within which linguists, sociologists, anthropologists, and other researchers are working. Semiotics is not spared by this paradigm shift. A data-driven computational semiotics is the study with an intensive use of computational methods of patterns in human-created contents related to semiotic phenomena. One of the most promising frameworks in this research program is the Semantic Vector Space (SVS) models and their methods. The objective of this article is to contribute to the exploration of the SVS for a computational semiotics by showing what types of semiotic analysis can be accomplished within this framework. The study is applied to a unique body of digitized artworks. We conducted three short experiments in which we explore three types of semiotic analysis: paradigmatic analysis, componential analysis, and topic modelling analysis. The results reported show that the SVS constitutes a powerful framework within which various types of semiotic analysis can be carried out.
Designing AR enhanced art exhibitions

This paper describes a methodology for the creation of augmented reality experiences for artistic exhibitions, targeted at offering a smooth authoring path to designers with limited computer science skills. The work describes how to design a smooth and coherent experience for the visitors, in spite of the limitations of the current authoring environments, providing a cognitive and emotional impact without overwhelming the original exhibition concept. The methodology was experimented in occasion of the world famous Biennale Art Exhibition in Venice and showed to be effective in the creation of a meaningful user experience.
Complementing Logical Reasoning with Sub-symbolic Commonsense

Neuro-symbolic integration is a current field of investigation in which symbolic approaches are combined with deep learning ones. In this work we start from simple non-relational knowledge that can be extracted from text by considering the co-occurrence of entities inside textual corpora; we show that we can easily integrate this knowledge with Logic Tensor Networks (LTNs), a neuro-symbolic model. Using LTNs it is possible to integrate axioms and facts with commonsense knowledge represented in a sub-symbolic form in one single model performing well in reasoning tasks. In spite of some current limitations, we show that results are promising.
Challenging the Boundaries of Speech Recognition: The MALACH Corpus

There has been huge progress in speech recognition over the last several years. Tasks once thought extremely difficult, such as SWITCHBOARD, now approach levels of human performance. The MALACH corpus (LDC catalog LDC2012S05), a 375-Hour subset of a large archive of Holocaust testimonies collected by the Survivors of the Shoah Visual History Foundation, presents significant challenges to the speech community. The collection consists of unconstrained, natural speech filled with disfluencies, heavy accents, age-related coarticulations, un-cued speaker and language switching, and emotional speech - all still open problems for speech recognition systems. Transcription is challenging even for skilled human annotators. This paper proposes that the community place focus on the MALACH corpus to develop speech recognition systems that are more robust with respect to accents, disfluencies and emotional speech. To reduce the barrier for entry, a lexicon and training and testing setups have been created and baseline results using current deep learning technologies are presented. The metadata has just been released by LDC (LDC2019S11). It is hoped that this resource will enable the community to build on top of these baselines so that the extremely important information in these and related oral histories becomes accessible to a wider audience.
Geometric Algebra Representation and Ensemble Action Classification Method for 3D Skeleton Orientation Data

In this paper, we propose a novel human body posture representation based on Geometric Algebra to extract the angles and orientations of the most informative body joints to describe human body postures.As a motion usually consists of a number of postures, which are different even in the same type of motion.We treat the postures of a motion independently.For each posture, a new Geometric Algebra based skeleton posture descriptor is used to construct the feature vectors as the input for the Support Vector Machine classifier to decide its motion type.To get the type of the whole motion, we choose the most frequent class from the sequence of predictions of the motion postures using a simple voting scheme.We have tested the method on a public benchmark SYSU-3D-HIO and an in-house dataset of human exercises.The results have demonstrated the effectiveness of our method.
Inside Agile Family Software Development Methodologies

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
CMU-VP: Cooperative Multicast and Unicast With Viewport Prediction for VR Video Streaming in 5G H-CRAN

Virtual reality (VR) is commonly regarded as one of 5G killer-applications. Transmission efficiency and quality of experience (QoE) are the most concerning issues for VR video streaming in 5G networks. Several multicast approaches were proposed to address these issues regardless of variance of personal viewports. In this paper, we explore a novel scheme combining multicast and unicast sessions in heterogeneous cloud-radio access networks (H-CRAN), in which a basic version of the video is transmitted to all users through the g-NB in a multicast session, and tiles of enhanced-version are transmitted to each viewer in a unicast session through its stationed remote radio head (RHH). To ensure the real-time content delivery, a user's viewport is predicted using a method based on historical trajectories and similarity of motion behavior, and then the tiles of predicted viewport in a version dependent on the channel quality are sent to the user in the unicast session. The scheme is formulated into a mixed-integer nonlinear problem (MINLP), and two near-optimal solutions are proposed to solve it by applying greedy approach and approximate approach, respectively. The simulation results show that our proposed scheme ensures better QoE under constrained bandwidth, and the proposed near-optimal solutions can efficiently solve the problem with low complexity and comparable performance.
Competition Between Morphological Words and Multiword Expressions

Competition in morphology is generally viewed as a relation holding between words or word formation processes. This article, framed within Construction Morphology, explores another type of competition which is still largely neglected, namely the competition between morphological words (i.e., simple, derived and compound words) and multiword expressions. It shows that competition is at work between these two types of constructions and that it may lead to bidirectional blocking, thus suggesting a view of the mental lexicon where both words and multiword expressions are stored on a par with each other. Competition at different levels of abstraction (specific lexical items vs. patterns of formation) and along different dimensions (synchronic vs. diachronic) is also discussed. Two case-studies from Italian are offered that explore the synchronic competition between (i) the simile construction with color adjectives (rosso come il fuoco ‘red as the fire’) vs. the corresponding compound pattern (rosso fuoco ‘fire-like red’) and (ii) irreversible binomials (sano e salvo ‘safe and sound’) vs. coordinate compounds of the sordomuto ‘deaf-mute’ type. The findings show that even when competition occurs between specific lexical items belonging to different patterns, there is often differentiation at the more abstract level, with different patterns specializing for different functions, as a result of the struggle for existence theorized by Aronoff (Competition and the lexicon. In: Elia A, Iacobini C, Voghera M (eds) Livelli di analisi e fenomeni di interfaccia. Bulzoni, Roma, pp 39–52, 2016, Competitors and alternants in linguistic morphology. In: Rainer F, Gardani F, Dressler WU, Luschützky HC (eds) Competition in inflection and word-formation. Springer, Cham, pp 39–66, 2019).
Netflix Control Method Using Smartwatches and Continuous Gesture Recognition

This work proposes and evaluates a method for interaction and control of Netflix using continuous recognition of gestures in smartwatches. This method allows the user to pause, resume, rewind, move forward, increase and decrease the volume of the Netflix player using gestures similar to traditional icons for these tasks. The recognition of gestures is done using the algorithm of continuous recognition of gestures, so it is possible to recognize a gesture before it is finalized, in this way, it is possible to perform the action quickly thus improving the feedback to the user. We have developed a prototype for smartwatches that communicates with a communication platform that receives the actions and is responsible for executing them on Netflix. When performing a gesture, the first step is to recognize it, in sequence, the gesture is sent to the interaction platform and, finally, the action related to the gesture is performed. We performed an experiment with users and a usability and experience test, the results show that the gestures proposed in the method are intuitive and that it has the potential to be used in a pleasant and satisfactory way by the users.
Flujos de trabajo para el periodismo postindustrial: métodos y programas para una comunicación organizacional más agil y transversal

Media seek to survive in a scenario of constant and dramatic changes. News organizations introduce flexible workflows in their newsrooms to reduce uncertainty and to adapt to the environment. In this paper, we propose a conceptual model that identifies two groups of practices: 1) methods for designing and creating products, which allow to launch assets that continually evolve to meet the changing needs of users; and 2), agile methodologies to manage the daily work, which allow a more fluid and transversal organizational communication and, consequently, a better coordination between professionals and teams. In addition, we provide a specific set of examples of each type of workflow, and we also explore the relationship between both. Finally, we collect a varied range of technological tools that facilitate flexibility and communication.
Real-world applications for drones

Unmanned vehicles have a number of compelling real-world use cases.
Sample Complexity Bounds for the Linear Quadratic Regulator

Author(s): Tu, Stephen Lyle | Advisor(s): Recht, Benjamin | Abstract: Reinforcement learning (RL) has demonstrated impressive performance in various domains such as video games, Go, robotic locomotion, and manipulation tasks. As we turn towards RL to power autonomous systems in the physical world, a natural question to ask is, how do we ensure that the behavior observed in the laboratory reflects the behavior that occurs when systems are deployed in the real world? How much data do we need to collect in order to learn how to control a system with a high degree of confidence?This thesis takes a step towards answering these questions by establishing the Linear Quadratic Regulator (LQR) as a baseline for comparison of RL algorithms. LQR is a fundamental problem in optimal control theory for which the exact solution is efficiently computable with perfect knowledge of the underlying dynamics. This makes LQR well suited as a baseline for studying the sample complexity of RL algorithms which learn how to control from observing repeated interactions with the system.The first part of this thesis focuses on model-based algorithms which estimate a model of the underlying system, and then build a controller based on the estimated dynamics. We show that the classic certainty equivalence controller, which discards confidence intervals surrounding the estimated dynamics, is efficient in regimes of low uncertainty. For regimes of moderate uncertainty, we propose a new model-based algorithm based on robust optimization, and show that it is also sample efficient.The second part studies model-free algorithms which learn intermediate representations instead, or directly search for the parameters of the optimal controller. We first look at the classical least-squares policy iteration algorithm, and establish an upper bound on its sample complexity. We then use tools from asymptotic statistics to characterize the asymptotic behavior of both the certainty equivalence controller and the popular policy gradient method on a particular family of LQR instances, which allows us to directly compare the bounds. This comparison reveals that the model-free policy gradient method has polynomial in state/input dimension and horizon length worse sample complexity than the model-based certainty equivalence controller. Our experiments corroborate this finding and show that model-based algorithms are more sample efficient than model-free algorithms for LQR.
Deep Learning for Learning Graph Representations

Mining graph data has become a popular research topic in computer science and has been widely studied in both academia and industry given the increasing amount of network data in the recent years. However, the huge amount of network data has posed great challenges for efficient analysis. This motivates the advent of graph representation which maps the graph into a low-dimension vector space, keeping original graph structure and supporting graph inference. The investigation on efficient representation of a graph has profound theoretical significance and important realistic meaning, we therefore introduce some basic ideas in graph representation/network embedding as well as some representative models in this chapter.
Trigraph Regularized Collective Matrix Tri-Factorization Framework on Multiview Features for Multilabel Image Annotation

Due to the explosive growth of image data, image annotation has been one of the most popular research directions in computer vision.It has been widely used in image retrieval, image analysis and understanding.Because traditional manual image annotation is time consuming, more advanced automatic annotation methods are needed.A major challenge in developing an efficient image annotation method is how to effectively use all available information contained in the data.To this end, this paper proposes a novel image annotation framework that uses multiple information from data.It employs nonnegative matrix tri-factorization (NMTF) to simultaneously factorize image-to-label, image-to-feature, and feature-tolabel relation matrices using their intertype relationships and incorporates the intratype information through manifold regularizations.This method can be referred to as the trigraph regularized collective matrix trifactorization framework (TG-CMTF).TG-CMTF captures the correlations among different labels, different images and different features.By taking advantage of these relations from images, features and labels, TG-CMTF can achieve better annotation performance than most state-of-the-art methods.The promising experimental results on three standard benchmarks have shown the effectiveness of this information.Furthermore, we show the annotation process as a precise optimization problem and solve it by an iterative algorithm, which proves the correctness of the proposed method from the mathematical theory.
Metric Learning for Dynamic Text Classification

Traditional text classifiers are limited to predicting over a fixed set of labels. However, in many real-world applications the label set is frequently changing. For example, in intent classification, new intents may be added over time while others are removed. We propose to address the problem of dynamic text classification by replacing the traditional, fixed-size output layer with a learned, semantically meaningful metric space. Here the distances between textual inputs are optimized to perform nearest-neighbor classification across overlapping label sets. Changing the label set does not involve removing parameters, but rather simply adding or removing support points in the metric space. Then the learned metric can be fine-tuned with only a few additional training examples. We demonstrate that this simple strategy is robust to changes in the label space. Furthermore, our results show that learning a non-Euclidean metric can improve performance in the low data regime, suggesting that further work on metric spaces may benefit low-resource research.
Deep Learning Approach for Receipt Recognition

Inspired by the recent successes of deep learning on Computer Vision and Natural Language Processing, we present a deep learning approach for recognizing scanned receipts. The recognition system has two main modules: text detection based on Connectionist Text Proposal Network and text recognition based on Attention-based Encoder-Decoder. We also proposed pre-processing to extract receipt area and OCR verification to ignore handwriting. The experiments on the dataset of the Robust Reading Challenge on Scanned Receipts OCR and Information Extraction 2019 demonstrate that the accuracies were improved by integrating the pre-processing and the OCR verification. Our recognition system achieved 71.9% of the F1 score for detection and recognition task.
Almost in the Wild: Student Search Behaviors When Librarians Aren't Looking

Academic libraries offer a variety of tools for students to find information, including discovery systems and traditional library databases. This study contributes to the growing body of knowledge on student information-seeking behaviors by comparing how upper-level students majoring in Social and Behavioral Sciences use these two categories of search tools. Student search behavior and the use of search features, facets in particular, are quantified for each tool. The authors explore with statistical analyses whether these practices aid or hinder students in their search for high quality information. Qualitative data from student interviews is selectively employed to aid in explaining the results. Key findings include the differential use of search features in the discovery system versus the traditional database, and the relationships between the use of certain facets and the quality of sources chosen by students. Implications for instruction, search interface configuration, and default settings are discussed.
Semantic Tensor Product for Image Captioning

In this paper, we proposed a Semantic Tensor Product (STP) model aimed at finding a more grammatically correct caption for a given image. We tried two kinds of ways to integrate semantic concepts (i.e. tags) into Tensor Product Representation (TPR) in the process of image caption generation, where Tensor Product Representation has the advantage of decoding a role embedding using a corresponding unbinding vector in any position directly, while semantic concepts could provide crucial clues for internal TPR prediction. We qualitatively analyze the two proposed STP based models, and quantitatively evaluate them on benchmark dataset: MSCOCO. Experiment result shows that our results outperform the other models, and generated captions are more natural.
Benchmarking Deep Learning Infrastructures by Means of TensorFlow and Containers

Ever growing interest and usage of deep learning rises a question on the performance of various infrastructures suitable for training of neural networks. We present here our approach and first results of tests performed with TensorFlow Benchmarks which use best practices for multi-GPU and distributed training. We pack the Benchmarks in Docker containers and execute them by means of uDocker and Singularity container tools on a single machine and in the HPC environment. The Benchmarks comprise a number of convolutional neural network models run across synthetic data and e.g. the ImageNet dataset. For the same Nvidia K80 GPU card we achieve the same performance in terms of processed images per second and similar scalability between 1-2-4 GPUs as presented by the TensorFlow developers. We therefore do not obtain statistically significant overhead due to the usage of containers in the multi-GPU case, and the approach of using TF Benchmarks in a Docker container can be applied across various systems.
An Open Dataset of Abbreviations and Expansions

We present a data set of abbreviations and expansions, derived from a set of five open source systems, for use by the research and development communities.
Gradient $\ell_1$ Regularization for Quantization Robustness

We analyze the effect of quantizing weights and activations of neural networks on their loss and derive a simple regularization scheme that improves robustness against post-training quantization. By training quantization-ready networks, our approach enables storing a single set of weights that can be quantized on-demand to different bit-widths as energy and memory requirements of the application change. Unlike quantization-aware training using the straight-through estimator that only targets a specific bit-width and requires access to training data and pipeline, our regularization-based method paves the way for ``on the fly'' post-training quantization to various bit-widths. We show that by modeling quantization as a $\ell_\infty$-bounded perturbation, the first-order term in the loss expansion can be regularized using the $\ell_1$-norm of gradients. We experimentally validate our method on different vision architectures on CIFAR-10 and ImageNet datasets and show that the regularization of a neural network using our method improves robustness against quantization noise.