Reviewerly/exHarmony huggingface dataset:
ACN: Occlusion-tolerant face alignment by attentional combination of heterogeneous regression networks

This paper presents the Attentional Combination Network (ACN), which is a highly accurate face alignment method that is tolerant of occlusion. The method combines a coordinate regression network and a heatmap regression network with a spatial attention. The coordinate regression generates the coordinates of facial landmark points directly such that they are fitted to the input face on the whole. The heatmap regression generates the heatmap of facial landmark points such that each channel provides good localization of the detail of its facial landmark point. These independent regressions compensate for each other complementarily such that the overall fitting tendency of the coordinate regression compensates for the inaccurate alignment of the heatmap regression due to missing local information, and the detailed localization of the heatmap regression compensates for the relatively inaccurate alignment of the coordinate regression. The proposed ACN uses coordinate-to-heatmap and the heatmap-to-coordinate conversion networks to combine two heterogeneous regressions, and to generate the final coordinates of the facial landmark points. The ACN use the spatial attention mechanism to effectively reject impeditive local features that are caused by the occlusion. In experiments on several benchmarks, the proposed ACN achieved state-of-the-art accuracy
A Novel Salient Feature Fusion Method for Ship Detection in Synthetic Aperture Radar Images

Ship detection of synthetic aperture radar (SAR) images is one of the research hotspots in the field of marine surveillance.Fusing salient features to detection network can effectively improve the precision of ship detection.However, how to effectively fuse the salient features of SAR images is still a difficult task.In this paper, to improve the ship detection precision, we design a novel one-stage ship detection network to fuse salient features and deep convolutional neural network (CNN) features.Firstly, a saliency map extraction algorithm is proposed.The algorithm is applied to generate saliency map by using multi-scale pyramid features and frequency domain features.Secondly, the backbone of the ship detection network contains a two-stream network.The upper-stream network uses the original SAR image as input to extract multi-scale deep CNN features.The lower-stream network uses the corresponding saliency map as input to acquire multi-scale salient features.Thirdly, for integrating the salient features to deep CNN features, a novel salient feature fusion method is designed.Finally, an improved bi-directional feature pyramid network is applied to the ship detection network for reducing the computational complexity and network parameters.The proposed methods are evaluated on the public ship detection dataset and the experimental results shows that it can make a significant improvement in the precision of SAR image ship detection.
A real‐time quadrotor trajectory planning framework based on B‐spline and nonuniform kinodynamic search

Abstract Autonomous navigation of quadrotor is required by many application scenarios, such as exploration, search, and rescue. The trajectory planning algorithm is the core of autonomous navigation, which can undoubtedly greatly enhance the safety of flight. In this paper, a trajectory planning framework based on B‐spline and kinodynamic search is proposed. This framework can be used for a limited‐sensing quadrotor to plan safe and dynamically feasible trajectories in unknown environments, and the flight is safe and effective along with these trajectories. First, a B‐spline based nonuniform kinodynamic (BNUK) search algorithm is proposed to generate dynamically feasible trajectories efficiently. The characteristics of nonuniform search make the generated trajectories safe and reasonable time‐allocation. Then, a trajectory optimization method based on control point optimization is proposed. The trajectory generated by BNUK is further optimized by solving a quadratically constrained quadratic programming problem. The smoothness of the trajectory is improved, and the control cost is reduced. Extensive analysis and comparative simulation experiments verify the advancedness of the proposed framework. We also integrated it into a vision‐based quadrotor autonomous navigation system, and multiple outdoor flight experiments show the effectiveness of the proposed framework.
Refactoring community smells in the wild

Community smells have been defined as sub-optimal organizational structures that may lead to social debt. Previous studies have shown that they are highly diffused in both open- and closed-source projects, are perceived as harmful by practitioners, and can even lead to the introduction of technical debt in source code. Despite the presence of this body of research, little is known on the practitioners' perceived prominence of community smells in practice as well as on the strategies adopted to deal with them. This paper aims at bridging this gap by proposing an empirical study in which 76 software practitioners are inquired on (i) the prominence of four well-known community smells, i.e., Organizational Silo, Black Cloud, Lone Wolf, and Radio Silence, in their contexts and (ii) the methods they adopted to "refactor" them. Our results first reveal that community smells frequently manifest themselves in software projects and, more importantly, there exist specific refactoring practices to deal with each of the considered community smells.
Learning Disentangled Representations with Latent Variation Predictability

Latent traversal is a popular approach to visualize the disentangled latent representations. Given a bunch of variations in a single unit of the latent representation, it is expected that there is a change in a single factor of variation of the data while others are fixed. However, this impressive experimental observation is rarely explicitly encoded in the objective function of learning disentangled representations. This paper defines the variation predictability of latent disentangled representations. Given image pairs generated by latent codes varying in a single dimension, this varied dimension could be closely correlated with these image pairs if the representation is well disentangled. Within an adversarial generation process, we encourage variation predictability by maximizing the mutual information between latent variations and corresponding image pairs. We further develop an evaluation metric that does not rely on the ground-truth generative factors to measure the disentanglement of latent representations. The proposed variation predictability is a general constraint that is applicable to the VAE and GAN frameworks for boosting disentanglement of latent representations. Experiments show that the proposed variation predictability correlates well with existing ground-truth-required metrics and the proposed algorithm is effective for disentanglement learning.
BERT-hLSTMs: BERT and hierarchical LSTMs for visual storytelling

Visual storytelling is a creative and challenging task, aiming to automatically generate a story-like description for a sequence of images. The descriptions generated by previous visual storytelling approaches lack coherence because they use word-level sequence generation methods and do not adequately consider sentence-level dependencies. To tackle this problem, we propose a novel hierarchical visual storytelling framework which separately models sentence-level and word-level semantics. We use the transformer-based BERT to obtain embeddings for sentences and words. We then employ a hierarchical LSTM network: the bottom LSTM receives as input the sentence vector representation from BERT, to learn the dependencies between the sentences corresponding to images, and the top LSTM is responsible for generating the corresponding word vector representations, taking input from the bottom LSTM. Experimental results demonstrate that our model outperforms most closely related baselines under automatic evaluation metrics BLEU and CIDEr, and also show the effectiveness of our method with human evaluation.
A Systematic Review of Rapid Prototyping Tools for Augmented Reality

The creation of low fidelity prototypes for Augmented Reality (AR) applications can be more challenging compared to the others technologies. Due to the unique characteristics of AR applications (combine the real and virtual world) developers and designers can face a lack of tools that helps in the conception of low fidelity prototypes, reducing the possibility for testing new ideas and iterations. Based on an analysis of 39 different resources from research and industry, we discuss a set of artifacts (tools, frameworks, and software) for rapid prototyping for AR. We found 30 artifacts for high and low fidelity prototypes and provide a list of tools that can facilitate the design of AR applications for HMD and smartphone devices in the early stages of design, discussing their benefits and limitations. Paper prototyping and Wizard of Oz techniques were the most cited artifacts.
Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter Optimization

With the exponential growth of the presence of sport in the media, the need for effective classification of sports images has become crucial. The traditional approaches require carefully hand-crafted features, which make them impractical for massive-scale data and less accurate in distinguishing images that are very similar in appearance. As the deep learning methods can automatically extract deep representation of training data and have achieved impressive performance in image classification, our goal was to apply them to automatic classification of very similar sports disciplines. For this purpose, we developed a CNN-TL-DE method for image classification using the fine-tuning of transfer learning for training a convolutional neural network model with the use of hyper-parameter optimization based on differential evolution. Through the automatic optimization of neural network topology and essential training parameters, we significantly improved the classification performance evaluated on a dataset composed from images of four similar sports—American football, rugby, soccer, and field hockey. The analysis of interpretable representation of the trained model additionally revealed interesting insights into how our model perceives images which contributed to a greater confidence in the model prediction. The performed experiments showed our proposed method to be a very competitive image classification method for distinguishing very similar sports and sport situations.
Key Frame Proposal Network for Efficient Pose Estimation in Videos

Human pose estimation in video relies on local information by either estimating each frame independently or tracking poses across frames. In this paper, we propose a novel method combining local approaches with global context. We introduce a light weighted, unsupervised, key frame proposal network (K-FPN) to select informative frames and a learned dictionary to recover the entire pose sequence from these frames. The K-FPN speeds up the pose estimation and provides robustness to bad frames with occlusion, motion blur, and illumination changes, while the learned dictionary provides global dynamic context. Experiments on Penn Action and sub-JHMDB datasets show that the proposed method achieves state-of-the-art accuracy, with substantial speed-up.
Considerations about the efficiency and sufficiency of the utilization of the Scrum methodology: A survey for analyzing results for development teams

SCRUM is an important strategy used for software development. However, developers may face specific issues considered relevant that might fail to lead to the best solutions implemented or result in longer development times. Literature has a significant number of ”best practices” to guide development teams; however, sometimes are very theoretical and may or may not be relevant to a particular environment or organization. This study evaluated these best practices in terms of if they were properly followed and how effective they were within various organizations. Aiming to gather information as to how tasks are actually performed as compared to SCRUM standard practices, the study performed a survey to software developers, product owners, and others. The survey focused on gathering and presenting data on the perceptions, needs, activities and issues from the various Scrum traditional roles involved in software development. A questionnaire was designed that included 15 multiple-choice questions and 2 open ended questions, Questions related to effectiveness of collecting development features/requirements, daily meetings duration, overall efficiency of the Scrum structure, and interactions with Product Owners all were distributed within multiple organizations. Overall, the study compared the implementation and use of SCRUM effectiveness based on traditional methodology SCRUM methodology. The study was designed to aid all roles involved in scrum when implementing such a framework within an organization. This study focused on specific features considered relevant for successfully developing systems using SCRUM as methodology. Among these features, the focus were on: (1) is the ticket allocation strategy effectiveness for the development processes? (2) are the daily stand-up meetings efficient and do they represent an important activity during the process? (3) is the timeline defined for each sprint appropriate? and (4) are the team members improving their capabilities on designing, coding and testing activities as they might be allocated to perform a variety of front or back-end development activities? As with other best practices, whether software development related or in other industries, theoretical guidelines should only serve as a basis for implementation as each organization has its unique characteristics, varying number of software developers, and local requirements.
Content-Based Collaborative Filtering using Word Embedding

The lack of sufficient ratings will reduce effectively modeling user reference and finding trustworthy similar users in collaborative filtering (CF)-based recommendation systems, also known as a cold-start problem. To solve this problem and improve the efficiency of recommendation systems, we propose a new content-based CF approach based on item similarity. We apply the model in the movie domain and extract features such as genres, directors, actors, and plots of the movies. We use the Jaccard coefficient index to covert the extracted features such as genres, directors, actors to the vectors while the plot feature is converted to the semantic vectors. Then, the similarity of the movies is calculated by soft cosine measure based on vectorized features. We apply the word embedding model (i.e., Word2Vec) for representing the plots feature as semantic vectors instead of using traditional models such as a binary bag of words and a TF-IDF vector space. Experiment results show the superiority of the proposed system in terms of accuracy, precision, recall, and F1 scores in cold-start conditions compared to the baseline systems.
FPGA-Based Optimized Design of Montgomery Modular Multiplier

This brief introduces FPGA-based optimized implementation of Montgomery Modular Multiplier (MMM) architecture. The novel architecture of the proposed design enhanced the maximum frequency of the design and also the occupied area on the targeted FPGA. A Xilinx Virtex-6 FPGA implementation of the proposed architecture comparing with other related designs revealed that, our design occupies the smallest area, and the efficiency is enhanced in the range between 1.2 to 11.7 times the efficiency of other relevant designs. The proposed design is implemented as a modular multiplier for lightweight elliptic curve cryptography (ECC) over general GF(p). The proposed architecture is targeted the hardware implementation of lightweight cryptographic modules used on the System on Chip (SoC) and Internet of Things (IoT) devices.
Minimizing cybersickness in head-mounted display systems: causes and strategies review

Virtual reality has become popular in games and training applications because of the mass use of head-mounted displays for audiences in various segments such as education, military, entertainment, and games. Despite such devices producing a high sense of immersion and presence, they also cause discomfort to users in uncomfortable sensations called cybersickness (CS). We reviewed the literature on theories of discomfort manifestations usually attributed to virtual reality environments in this work. Additionally, we reviewed existing strategies aimed at minimizing CS problems by dividing them into 10 groups of causes: locomotion, acceleration, a field of view, depth of field, rotational movements, exposure time, static rest frames, postural instability, latency lag, and degree of control. Additionally, we discuss how the CS measurement has been conducted based on subjective, objective, and users profile data. Our review facilitates researchers to identify the leading causes for most discomfort situations in virtual reality environments and associate the most recommended strategies to minimize such discomfort.
Development of a hyperspectral imaging technique using LA-ICP-MS to show the spatial distribution of elements in soil cores

Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is a versatile analytical technique that can create qualitative images delineating the spatial distribution of elements in different samples. However, there is no demonstration of this technique in imaging the spatial distribution of elements in soil cores. Herein, we developed a hyperspectral imaging method using LA-ICP-MS to visualize the spatial distribution of selected elements in soil cores as a proof of concept. Soil cores were collected in plastic liners, frozen at −20°C; cut uniformly in halves using a band saw. The surface of the intact cores was ablated using a 213 nm laser optimized with the following parameters: spot size 100 µm, laser energy of 7 mJ, and scan speed of 220 µms−1. Two-dimensional images of the soil cores were created for Ca, Mg, P, K, Na, Zn, Fe, Co, and Mn using the iolite software. The new imaging method was very effective in showing that different crop management systems and fertilizer applications altered the levels and spatial distribution of Ca, Mg, P, K, Na, Zn, Fe, Co, and Mn in the evaluated soil cores. The results show that the developed method has great potential in multi elemental imaging of intact soil cores. This could have huge implications in environmental impact assessments, soil resource evaluation, agriculture crop production and the effectiveness of land use or land management systems in modulating the spatial distribution of elements within the soil.
Guideline and Tool for Designing an Assembly Task Support System Using Augmented Reality

Augmented reality (AR) systems support complex tasks like assembly by overlaying task-related content onto the real world. In recent years, the effort of designing and developing assembly task support systems in AR decreased with the availability of high potential head-mounted displays and provision of integrated development environments. Nevertheless, problems still arise when companies craft an effective AR task support system, particularly in the difficulty of selecting appropriate techniques and information-presentation methods, and the requirements that vary with each use case. In this study, we formulated a corresponding guideline, developed a selection aid tool that incorporates filtering based on the categorization of subtasks and the degree of freedom of available tracking, and evaluated their effectiveness in two experiments. First, to confirm effects on system design, we asked 18 participants to perform the design action of the AR system with the guideline for two tasks (PC assembly and rope work). Consequently, to verify the quality of the designed AR systems from Experiment 1, we asked another set of 20 participants to perform the same tasks with those systems. The results confirm that using the guideline can considerably lower efforts creating media and alleviate the error for a specific process. We envision our guideline and tool to be accessible as an online web page, assisting AR assembly task support system designer/developers worldwide.
Decentralized Lightweight Detection of Eclipse Attacks on Bitcoin Clients

Clients of permissionless blockchain systems, like Bitcoin, rely on an underlying peer-to-peer network to send and receive transactions. It is critical that a client is connected to at least one honest peer, as otherwise the client can be convinced to accept a maliciously forked view of the blockchain. In such an eclipse attack, the client is unable to reliably distinguish the canonical view of the blockchain from the view provided by the attacker. The consequences of this can be catastrophic if the client makes business decisions based on a distorted view of the blockchain transactions. In this paper, we investigate the design space and propose two approaches for Bitcoin clients to detect whether an eclipse attack against them is ongoing. Each approach chooses a different trade-off between average attack detection time and network load. The first scheme is based on the detection of suspicious block timestamps. The second scheme allows blockchain clients to utilize their natural connections to the Internet (i.e., standard web activity) to gossip about their blockchain views with other clients through contacted servers. Our proposals improve upon previously proposed eclipse attack countermeasures without introducing any dedicated infrastructure or changes to the Bitcoin protocol and network. We demonstrate the effectiveness of the gossip-based schemes through real-world implementation. The results of our experiments indicate that the protocol incurs a negligible overhead, detects eclipse attacks rapidly with high probability, and is well-suited for practical deployment.
Segment spatial-temporal representation and cooperative learning of convolution neural networks for multimodal-based action recognition

The multimodal based human action recognition is an attracting increasing topic since the different modalities can provide complementary information. However, it is difficult to improve the recognition performance due to the limitation of the ability to learn spatial-temporal features. In this paper, we propose a novel approach for multimodal human action recognition by learning complementary features from RGB-D sequence. Firstly, a segmented rank pooling method is proposed to compress the entire RGB-D sequence into dynamic images as inputs to the Convolutional Networks (ConvNets) for capturing spatial-temporal information. Secondly, a Segment Cooperative ConvNets (SC-ConvNets) is designed to learn the complementary features from RGB-D modalities. Different from the ConvNets-based approaches that learn multimodal features with multiple separate networks, the proposed SC-ConvNets enhance the recognition performance through joint optimization learning of single ConvNets. Then a single loss function is optimized to narrow the variance between RGB and depth modalities. In order to verify the effectiveness of the proposed method, we evaluate the SC-ConvNets on four public benchmark multimodal datasets, including NTU RGB+D 60, NTU RGB+D 120, SYSU 3D HOI, and PKU-MMD datasets. The proposed method achieves an accuracy of 89.4% and 91.2% for cross-subject and cross-view on NTU RGB+D 60, 86.9% and 87.7% for cross-subject and cross-setup on NTU RGB+D 120, 92.1% and 93.2% for cross-subject and cross-view on PKU-MMD, which are the state-of-the-art, and the accuracy of 84.2% and 82.9% for setting-1 and setting-2 on SYSU 3D HOI, which are comparable to the state-of-the-art. The experimental results demonstrate that the proposed segmented rank pooling can represent discriminative spatial-temporal information from the entire RGB and depth sequence, and the proposed SC-ConvNets can enhance recognition performance by learning complementary features from different modalities.