Reviewerly/exHarmony huggingface dataset:
Deep Reinforced Attention Learning for Quality-Aware Visual Recognition

In this paper, we build upon the weakly-supervised generation mechanism of intermediate attention maps in any convolutional neural networks and disclose the effectiveness of attention modules more straightforwardly to fully exploit their potential. Given an existing neural network equipped with arbitrary attention modules, we introduce a meta critic network to evaluate the quality of attention maps in the main network. Due to the discreteness of our designed reward, the proposed learning method is arranged in a reinforcement learning setting, where the attention actors and recurrent critics are alternately optimized to provide instant critique and revision for the temporary attention representation, hence coined as Deep REinforced Attention Learning (DREAL). It could be applied universally to network architectures with different types of attention modules and promotes their expressive ability by maximizing the relative gain of the final recognition performance arising from each individual attention module, as demonstrated by extensive experiments on both category and instance recognition benchmarks.
What Developers (Care to) Recall: An Interview Survey on Smaller Systems

Developers spend most of their time with program comprehension, obtaining (or recovering) the knowledge they need to perform a task. Researchers have investigated the information needs of developers to understand what knowledge is important and to scope techniques, for example, to facilitate program comprehension, support knowledge recovery, or identify experts. Similarly, researchers analyzed developers' memory to understand how they forget, which essentially causes the need to recover knowledge. However, we are not aware of studies linking these research directions to investigate what knowledge developers aim to keep in their memory, allowing them to ask less and different questions during knowledge recovery. To address this gap, we conducted an interview survey with 17 experienced developers, in which we investigated 1) what knowledge developers consider important to remember; 2) whether developers can correctly recall knowledge about their (smaller) systems; and 3) how their self-assessment relates to their actual knowledge. Our results indicate, among others, that developers consider architecture and abstract code knowledge (e.g., its intent) as most important to remember, that the perceived importance relates to their ability to recall knowledge correctly, and that their self-assessment decreases while reflecting about their system. Based on these findings, we discuss research directions and practical implications for managing and recovering developers' knowledge.
Understanding Customer Sentiment: Lexical Analysis of Restaurant Reviews

Understanding customer's sentiment (satisfaction or dissatisfaction) is considered as valuable information for both the potential customers and restaurant authority. However, analyzing customer reviews (unstructured texts) one by one is a difficult task and also practically impossible when the number of reviews is enormous. Therefore, it seems conceivable to have a mechanism to analyze customer reviews automatically and provide the necessary information in a precise way. Here, we introduce a Natural Language Processing (NLP) based opinion mining methodology to analyze the customer opinion automatically. For that, first, a captive portal is used to collect customer's reviews. Then, the opinion mining technique is applied to analyze the reviews to explore customer sentiment about food quality, service, environment, etc. A data-driven experiment is conducted to evaluate the proposed methodology. The experiment result showed the effectiveness of the proposed method for retrieving and analyzing customer sentiment.
3D Mirrored Object Selection for Occluded Objects in Virtual Environments

There is an increasing demand for the manipulation of virtual objects in 3D virtual reality (VR) space, which begins with the user selecting a desired object.Existing selection methods aim to provide an intuitive and natural experience that minimizes user fatigue.Although typical ray-casting methods are effective when objects in the virtual space are sparsely or evenly placed, the selection becomes challenging when these objects are smaller or farther away, particularly when they overlap.In this study, we propose a method to address these challenges via an effective selection process.Based on 3D mirroring, the proposed method provides a new interaction metaphor that allows for a more efficient selection of occluded virtual objects.We conduct systematic experiments with 21 subjects and use various levels of target visibility determined by object size, spatial density, and degrees of occlusion.The results indicate that, in occluded 3D environments, the proposed 3D mirrored selection process outperforms traditional gaze-supported selection processes and offers better user satisfaction.
Hand with Sensing Sphere: Body-Centered Spatial Interactions with a Hand-Worn Spherical Camera

We propose a novel body-centered interaction system making use of a spherical camera attached to a hand. Its broad and unique field of view enables an all-in-one approach to sensing multiple pieces of contextual information in hand-based spatial interactions: (i) hand location on the body surface, (ii) hand posture, (iii) hand keypoints in certain postures, and (iv) the near-hand environment. The proposed system makes use of a deep-learning approach to perform hand location and posture recognition. The proposed system is capable of achieving high hand location and posture recognition accuracy, 85.0 % and 88.9 % respectively, after collecting sufficient data and training. Our result and example demonstrations show the potential of utilizing 360° cameras for vision-based sensing in context-aware body-centered spatial interactions.
A Distributed Blockchain-based Video Sharing System with Copyright, Integrity, and Immutability

Today's video-sharing systems are platforms that have been developed to be a hub for the introduction and sharing of videos created by video creators from all over the world. These systems generate significant revenue through advertising or copyright transferring they receive from video viewers, which share only certain percentage with video creators. However, To make more profit for video creators, in this paper we proposed a distributed video sharing architecture with the possibility of performing financial transactions that works on the Ethereum. In this new architecture, the Blockchain network separated from storage; because video storage in Blockchain results to be costly for mining in the nodes in the network. Ethereum's smart contract is responsible for monitoring the validity, transparency, immutability, integrity, and security of transactions as well as controlling video access. On the other side of the architecture, there is an Ethereum-compatible distributed file storage called Swarm for video storage. Due to the lack of video storage in the Blockchain network, the integrity of the videos is questionable. To solve this problem in the proposed scheme, a Merkel tree of video hashes in the swarm is created and stored in the Blockchain to ensure the immutability and integrity of the videos and, to be transparent and valid to everyone. Finally, the performance of the proposed architecture in terms of security, structure distribution, downtime probability, fault tolerance, transparency, content integrity, immutability, and sustainability has been compared with similar systems and its performance has been proven.
Dynamics of Coordinate Ascent Variational Inference: A Case Study in 2D Ising Models

Variational algorithms have gained prominence over the past two decades as a scalable computational environment for Bayesian inference. In this article, we explore tools from the dynamical systems literature to study the convergence of coordinate ascent algorithms for mean field variational inference. Focusing on the Ising model defined on two nodes, we fully characterize the dynamics of the sequential coordinate ascent algorithm and its parallel version. We observe that in the regime where the objective function is convex, both the algorithms are stable and exhibit convergence to the unique fixed point. Our analyses reveal interesting discordances between these two versions of the algorithm in the region when the objective function is non-convex. In fact, the parallel version exhibits a periodic oscillatory behavior which is absent in the sequential version. Drawing intuition from the Markov chain Monte Carlo literature, we empirically show that a parameter expansion of the Ising model, popularly called the Edward–Sokal coupling, leads to an enlargement of the regime of convergence to the global optima.
Redis-Based Messaging Queue and Cache-Enabled Parallel Processing Social Media Analytics Framework

Abstract The extensive usage of social media polarity analysis claims the need for real-time analytics and runtime outcomes on dashboards. In data analytics, only 30% of the time is consumed in modeling and evaluation stages and 70% is consumed in data engineering tasks. There are lots of machine learning algorithms to achieve a desirable outcome in prediction points of view, but they lack in handling data and their transformation so-called data engineering tasks, and reducing its time remained still challenging. The contribution of this research paper is to encounter the mentioned challenges by presenting a parallelly, scalable, effective, responsive and fault-tolerant framework to perform end-to-end data analytics tasks in real-time and batch-processing manner. An experimental analysis on Twitter posts supported the claims and signifies the benefits of parallelism of data processing units. This research has highlighted the importance of processing mentioned URLs and embedded images along with post content to boost the prediction efficiency. Furthermore, this research additionally provided a comparison of naive Bayes, support vector machines, extreme gradient boosting and long short-term memory (LSTM) machine learning techniques for sentiment analysis on Twitter posts and concluded LSTM as the most effective technique in this regard.
Android-based augmented reality media and the curiosity about mathematics

Abstract The rapid development of technology has changed the paradigm of education, especially when technology is combined pedagogically, creating new opportunities to improve the quality of teaching and learning. One of the opportunities to improve the quality of teaching and learning is the use of Augmented Reality. Augmented Reality technology attracts student motivation and curiosity so that students are more active, creative, and have high enthusiasm for learning, specifically mathematics. This study reviews some of the literature regarding information about mobile augmented reality and its implementation in increasing students’ curiosity about mathematics.
SphericRTC

We present the SphericRTC system for real-time 360-degree video communication. 360-degree video allows the viewer to observe the environment in any direction from the camera location. This more-immersive streaming experience allows users to more-efficiently exchange information and can be beneficial in the real-time setting. Our system applies a novel approach to select representations of 360-degree frames to allow efficient, content-adaptive delivery. The system performs joint content and bitrate adaptation in real-time by offloading expensive transformation operations to the GPU via CUDA. The system demonstrates that the multiple sub-components -- viewport feedback, representation selection, and joint content and bitrate adaptation -- can be effectively integrated within a single framework. Compared to a baseline implementation, views in SphericRTC have consistently higher visual quality. The median Viewport-PSNR of such views is 2.25 dB higher than views in the baseline system.
Analysis and Prediction of Deforming 3D Shapes Using Oriented Bounding Boxes and LSTM Autoencoders

For sequences of complex 3D shapes in time we present a general approach to detect patterns for their analysis and to predict the deformation by making use of structural components of the complex shape. We incorporate long short-term memory (LSTM) layers into an autoencoder to create low dimensional representations that allow the detection of patterns in the data and additionally detect the temporal dynamics in the deformation behavior. This is achieved with two decoders, one for reconstruction and one for prediction of future time steps of the sequence. In a preprocessing step the components of the studied object are converted to oriented bounding boxes which capture the impact of plastic deformation and allow reducing the dimensionality of the data describing the structure. The architecture is tested on the results of 196 car crash simulations of a model with 133 different components, where material properties are varied. In the latent representation we can detect patterns in the plastic deformation for the different components. The predicted bounding boxes give an estimate of the final simulation result and their quality is improved in comparison to different baselines.
Sea of Genes: A Reflection on Visualising Metagenomic Data for Museums

We examine the application of narrative strategies to present a complex and unfamiliar metagenomics dataset to the public in a science museum. Our dataset contains information about microbial gene expressions that scientists use to infer the behavior of microbes. This exhibit had three goals: to inform (the) public about microbes' behavior, cycles, and patterns; to link their behavior to the concept of gene expression; and to highlight scientists' use of gene expression data to understand the role of microbes. To address these three goals, we created a visualization with three narrative layers, each layer corresponding to a goal. This study presented us with an opportunity to assess existing frameworks for narrative visualization in a naturalistic setting. We present three successive rounds of design and evaluation of our attempts to engage visitors with complex data through narrative visualization. We highlight our design choices and their underlying rationale based on extant theories. We conclude that a central animation based on a curated dataset could successfully achieve our first goal, i.e., to communicate the aggregate behavior and interactions of microbes. We failed to achieve our second goal and had limited success with the third goal. Overall, this study highlights the challenges of telling multi-layered stories and the need for new frameworks for communicating layered stories in public settings.
A method of traffic police detection based on attention mechanism in natural scene

The complex and varied urban road conditions have always been a difficult and a pivotal component in the study of driverless technology especially at intersections. In China, it is necessary for driverless cars to understand the gestures of traffic police. To identify the traffic police gesture at the intersection, the key step is to detect the traffic police at the intersection. At present, the research on traffic police detection is still in its infancy, there exists common problems such as slow detection speed and other real time problems in this method, and there is not a standardized traffic police data set ether. For the real time problems, this paper introduces the attention mechanism, and proposes a new real-time detection method of traffic police based on attention mechanism. The method proposed in this paper has strong robustness and can quickly complete the target detection task. For the data set problem, this paper analyzes and discloses similar data sets published through research. In the meanwhile, this paper collects 24,530 video data on the actual road, and it extracts and saves 12,000 pictures containing traffic police at frame rate of 30FPS as traffic police detection training and validated data set.
Deep Learning Model with Transfer Learning to Infer Personal Preferences in Images

In this paper, we propose a deep convolutional neural network model with transfer learning that reflects personal preferences from inter-domain databases of images having atypical visual characteristics. The proposed model utilized three public image databases (Fashion-MNIST, Labeled Faces in the Wild [LFW], and Indoor Scene Recognition) that include images with atypical visual characteristics in order to train and infer personal visual preferences. The effectiveness of transfer learning for incremental preference learning was verified by experiments using inter-domain visual datasets with different visual characteristics. Moreover, a gradient class activation mapping (Grad-CAM) approach was applied to the proposed model, providing explanations about personal visual preference possibilities. Experiments showed that the proposed preference-learning model using transfer learning outperformed a preference model not using transfer learning. In terms of the accuracy of preference recognition, the proposed model showed a maximum of about 7.6% improvement for the LFW database and a maximum of about 9.4% improvement for the Indoor Scene Recognition database, compared to the model that did not reflect transfer learning.
Localizing and excluding quantum information; or, how to share a quantum secret in spacetime

When can quantum information be localized to each of a collection of spacetime regions, while also excluded from another collection of regions? We answer this question by defining and analyzing the localize-exclude task, in which a quantum system must be localized to a collection of authorized regions while also being excluded from a set of unauthorized regions. This task is a spacetime analogue of quantum secret sharing, with authorized and unauthorized regions replacing authorized and unauthorized sets of parties. Our analysis yields the first quantum secret sharing scheme for arbitrary access structures for which the number of qubits required scales polynomially with the number of authorized sets. We also study a second related task called state-assembly, in which shares of a quantum system are requested at sets of spacetime points. We fully characterize the conditions under which both the localize-exclude and state-assembly tasks can be achieved, and give explicit protocols. Finally, we propose a cryptographic application of these tasks which we call party-independent transfer.
RealHePoNet: a robust single-stage ConvNet for head pose estimation in the wild

Human head pose estimation in images has applications in many fields such as human–computer interaction or video surveillance tasks. In this work, we address this problem, defined here as the estimation of both vertical (tilt/pitch) and horizontal (pan/yaw) angles, through the use of a single Convolutional Neural Network (ConvNet) model, trying to balance precision and inference speed in order to maximize its usability in real-world applications. Our model is trained over the combination of two datasets: ‘Pointing’04’ (aiming at covering a wide range of poses) and ‘Annotated Facial Landmarks in the Wild’ (in order to improve robustness of our model for its use on real-world images). Three different partitions of the combined dataset are defined and used for training, validation and testing purposes. As a result of this work, we have obtained a trained ConvNet model, coined RealHePoNet, that given a low-resolution grayscale input image, and without the need of using facial landmarks, is able to estimate with low error both tilt and pan angles ( $$~4.4^{\circ }$$ average error on the test partition). Also, given its low inference time (6 ms per head), we consider our model usable even when paired with medium-spec hardware (i.e. GTX 1060 GPU). Code available at: https://github.com/rafabs97/headpose_final Demo video at: https://www.youtube.com/watch?v=2UeuXh5DjAE .
Performance Evaluation of the Impact of NUMA on One-sided RDMA Interactions

Remote direct memory access (RDMA) and non-uniform memory access (NUMA) are critical technologies of modern high-performance computing platforms. RDMA allows nodes to directly access memory on remote machines. Multiprocessor architectures implement NUMA to scale up memory access performance. When paired together, these technologies exhibit performance penalties under certain configurations. This paper is the first study to explore these configurations to provide quantitative findings on the impact of NUMA for RDMA-based systems. One of the consequences of ultra-fast networks is that known implications of NUMA locality now constitute a higher relative impact on the performance of RDMA-enabled distributed systems. Our study quantifies its role and uncovers unexpected behavior. In summary, poor NUMA locality of remotely accessible memory can lead to an automatic 20% performance degradation. Additionally, local workloads operating on remotely accessible memory can lead to 300% performance gap depending on memory locality. Surprisingly, configurations demonstrating this result contradict the presumed impact of NUMA locality. Our findings are validated using two generations of RDMA cards, a synthetic benchmark, and the popular application Memcached ported for RDMA.
Semantic Fusion Infrastructure for Unmanned Vehicle System Based on Cooperative 5G MEC

Since local sensing system is inherently limited, it is a trend to combine Cooperative Vehicle Infrastructure System (CVIS) and autonomous driving technologies to address the limitations of the vehicle-centric perception. However, problems such as high vehicle cost and perception limitations still exist. In this paper, from the perspective of distributed cooperative processing, a scalable 5G multi-access edge computing (MEC) driven vehicle infrastructure cooperative system is proposed. Based on the edge offload capability of 5G MEC, this system supports mapping sensor observations into a semantic description of the vehicle's environment. Through interactive perception fusion, it provides environment awareness of high-precision maps for autonomous driving. The experiment confirms that the cooperative sensing network can achieve 33 fps and improve the detection precision by around 10% as opposed to the typical detection method. In addition, compared with single viewpoint perception, the accuracy of the fusion scheme is further improved. In particular, over the 5G telecommunication network, the cooperative system can be more scalable to connect distributed sensors and is expected to lead to efficient autonomous driving.