Reviewerly/exHarmony huggingface dataset:
A New Formulation of Linear Discriminant Analysis for Robust Dimensionality Reduction

Dimensionality reduction is a critical technology in the domain of pattern recognition, and linear discriminant analysis (LDA) is one of the most popular supervised dimensionality reduction methods. However, whenever its distance criterion of objective function uses <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math></inline-formula> -norm, it is sensitive to outliers. In this paper, we propose a new formulation of linear discriminant analysis via joint <inline-formula><tex-math notation="LaTeX">$L_{2,1}$</tex-math></inline-formula> -norm minimization on objective function to induce robustness, so as to efficiently alleviate the influence of outliers and improve the robustness of proposed method. An efficient iterative algorithm is proposed to solve the optimization problem and proved to be convergent. Extensive experiments are performed on an artificial data set, on UCI data sets, and on four face data sets, which sufficiently demonstrates the efficiency of comparing to other methods and robustness to outliers of our approach.
End-to-end Audio Visual Scene-aware Dialog Using Multimodal Attention-based Video Features

In order for machines interacting with the real world to have conversations with users about the objects and events around them, they need to understand dynamic audiovisual scenes. The recent revolution of neural network models allows us to combine various modules into a single end-to-end differentiable network. As a result, Audio Visual Scene-Aware Dialog (AVSD) systems for real-world applications can be developed by integrating state-of-the-art technologies from multiple research areas, including end-to-end dialog technologies, visual question answering (VQA) technologies, and video description technologies. In this paper, we introduce a new data set of dialogs about videos of human behaviors, as well as an end-to-end Audio Visual Scene-Aware Dialog (AVSD) model, trained using this new data set, that generates responses in a dialog about a video. By using features that were developed for multimodal attention-based video description, our system improves the quality of generated dialog about dynamic video scenes.
Underwater Image Restoration Using Color-Line Model

Underwater images typically suffer from low visibility and severe colorcast due to scattering and absorption. In this letter, a novel method is proposed to handle the scattering and absorption problems of light with different wavelengths based on the color-line model. We filter out image patches that exhibit the characteristics of the color-line prior and recover the color line of the patches. Then, the local transmission for each patch is estimated based on the offsets of the color lines along the background-light vector from the origin. We also develop an optimization function to derive the local transmission and to obtain the solution in the underwater environment. Experimental results are presented to show that the proposed method can produce high-quality underwater images with relatively genuine colors, natural appearance, and improved contrast and visibility.
TERRA: A path planning algorithm for cooperative UGV–UAV exploration

In this paper, we consider the scenario of exploring a planetary surface with a system formed by an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV). The goal is to reach a set of target points minimizing the travelling distance. Some expected key problems in planetary explorations are the UGVs functionality constraints to reach some target points as a single robot system and the UAVs energy constraints to reach all the target points on its own. We present an approach based on the coordination of a hybrid UGV–UAV system, in which both robots work together for reaching all the target points. Our strategy proposes the UGV as a moving charging station to solve the UAV energy constraint problem, and the UAV as the robotic system in charge of reaching the target points to solve the UGV functionality constraints. To overcome this problem, we formulate a strategy merging combinatorial classic techniques and modern evolutionary approaches aiming to optimize the travelling distance. Our solution has been tested in several simulation runs with different target points distributions. The results demonstrate that our approach is able to generate a coordinated plan for optimizing the hybrid UGV–UAV system in the exploration scenario.
The Capacity of Private Computation

We introduce the problem of private computation, comprised of N distributed and non-colluding servers, K independent datasets, and a user who wants to compute a function of the datasets privately, i.e., without revealing which function he wants to compute, to any individual server. This private computation problem is a strict generalization of the private information retrieval (PIR) problem, obtained by expanding the PIR message set (which consists of only independent messages) to also include functions of those messages. The capacity of private computation, C, is defined as the maximum number of bits of the desired function that can be retrieved per bit of total download from all servers. We characterize the capacity of private computation, for N servers and K independent datasets that are replicated at each server, when the functions to be computed are arbitrary linear combinations of the datasets. Surprisingly, the capacity, C=(1+1/N+ ⋯ +1/N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-1</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , matches the capacity of PIR with N servers and K messages. Thus, allowing arbitrary linear computations does not reduce the communication rate compared to pure dataset retrieval. The same insight is shown to hold even for arbitrary non-linear computations when the number of datasets K → ∞.
HARD-DE: Hierarchical ARchive Based Mutation Strategy With Depth Information of Evolution for the Enhancement of Differential Evolution on Numerical Optimization

Differential evolution is a famous and effective branch of evolutionary computation, which aims at tackling complex optimization problems. There are two aspects significantly affecting the overall performance of DE variants, one is trial vector generation strategy and the other is the control parameter adaptation scheme. Here in this paper, a new hierarchical archive-based trial vector generation strategy with depth information of evolution was proposed to get a better perception of landscapes of objective functions as well as to improve the candidate diversity of the trial vectors. Furthermore, novel adaptation schemes both for crossover rate Cr and for population size ps were also advanced in this paper, and consequently, an overall better optimization performance was obtained after these changes. The novel HARD-DE algorithm was verified under many benchmarks of the Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single-objective optimization as well as two benchmarks on real-world optimization from CEC2011 test suite, and the experiment results showed that the proposed HARD-DE algorithm was competitive with the other state-of-the-art DE variants.
High-speed three-dimensional shape measurement based on cyclic complementary Gray-code light

The binary defocusing technique has been widely used in high-speed three-dimensional (3D) shape measurement because it breaks the bottlenecks in high-speed fringe projection and the projector's nonlinear response. However, it is challenging for this method to realize a two- or multi-frequency phase-shifting algorithm because it is difficult to simultaneously generate high-quality sinusoidal fringe patterns with different periods under the same defocusing degree. To bypass this challenge, we proposed a high-speed 3D shape measurement technique for dynamic scenes based on cyclic complementary Gray-code (CCGC) patterns. In this proposed method, the projected phase-shifting sinusoidal fringes kept one same frequency, which is beneficial to ensure the optimum defocusing degree for binary dithering technique. The wrapped phase can be calculated by phase-shifting algorithm and unwrapped with the aid of complementary Gray-code (CGC) patterns in a simple and robust way. Then, the cyclic coding strategy further extends the unambiguous phase measurement range and improves the measurement accuracy compared with the traditional Gray-coding strategy under the condition of the same number of projected patterns. High-quality 3D results of three complex dynamic scenes-including a cooling fan and a standard ceramic ball with a free-falling table tennis, collapsing building blocks, and impact of the Newton's cradle-were demonstrated at a frame rate of 357 fps. This verified the proposed method's feasibility and validity.
"They Don't Leave Us Alone Anywhere We Go"

South Asia faces one of the largest gender gaps online globally, and online safety is one of the main barriers to gender-equitable Internet access [GSMA, 2015]. To better understand the gendered risks and coping practices online in South Asia, we present a qualitative study of the online abuse experiences and coping practices of 199 people who identified as women and 6 NGO staff from India, Pakistan, and Bangladesh, using a feminist analysis. We found that a majority of our participants regularly contended with online abuse, experiencing three major abuse types: cyberstalking, impersonation, and personal content leakages. Consequences of abuse included emotional harm, reputation damage, and physical and sexual violence. Participants coped through informal channels rather than through technological protections or law enforcement. Altogether, our findings point to opportunities for designs, policies, and algorithms to improve women's safety online in South Asia.
An iterative strategy for task assignment and path planning of distributed multiple unmanned aerial vehicles

In this paper, we propose an iterative strategy to enhance the performance of task assignment and path planning in applications of distributed multiple unmanned aerial vehicles (Multi-UAV). As an improvement of the conventional management of airborne computation and communication resources of UAVs, our strategy overcomes difficulties caused by the information coupling between task assignment and path planning. A distributed mission planning framework is presented with the strategy, in which the UAVs re-evaluate unreasonable assignment results and overvalued tasks during the planning process. The proposed strategy has advantages in algorithm stability and complexity, as it controls the task valuation error within a certain range via computation with limited complexity. Compared to the conventional methods, our strategy with the framework can achieve better performance of planning results and consume less computing resources. Simulation results show the effectiveness of the proposed strategy in terms of the computational efficiency and the mission execution reward.
Artificial intelligence and ambient intelligence

Ambient intelligence (AmI) is intrinsically and thoroughly connected with artificial intelligence (AI). Some even say that it is, in essence, AI in the environment. AI, on the other hand, owes its success to the phenomenal development of the information and communication technologies (ICTs), based on principles such as Moore’s law. In this paper we give an overview of the progress in AI and AmI interconnected with ICT through information-society laws, superintelligence, and several related disciplines, such as multi-agent systems and the Semantic Web, ambient assisted living and e-healthcare, AmI for assisting medical diagnosis, ambient intelligence for e-learning and ambient intelligence for smart cities. Besides a short history and a description of the current state, the frontiers and the future of AmI and AI are also considered in the paper.
Reduced Reference Quality Assessment of Light Field Images

Immersive media, such as free view point video and 360° video, are expected to be dominant as broadcasting services. The light field (LF) imaging is being considered as a next generation imaging technology offering the possibility to provide new services, including six degree-of-freedom video. The drawback of this technology is in the size of the generated content thus requiring novel compression systems and the design of ad-hoc methodologies for evaluating the perceived quality. In this paper, the relation between depth map quality and overall quality of LF image is studied. Next, a reduced reference quality assessment metric for LF images is presented. To predict the quality of distorted LF images, the measure of distortion in the depth map is exploited. To test and validate the proposed framework, a subjective experiment has been performed, and a LF image quality dataset has been created. The dataset is also used for evaluating the performance of state-of-the-art quality metrics, when applied to LF images. The achieved results evidence that the estimated quality score by the proposed framework has a significant correlation with subjective quality rating. Consequently, reference data can be delivered to the clients thus allowing the local estimation of the perceived quality of service.
Data scarcity, robustness and extreme multi-label classification

The goal in extreme multi-label classification (XMC) is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels. The distribution of training instances among labels in XMC exhibits a long tail, implying that a large fraction of labels have a very small number of positive training instances. Detecting tail-labels, which represent diversity of the label space and account for a large fraction (upto 80%) of all the labels, has been a significant research challenge in XMC. In this work, we pose the tail-label detection task in XMC as robust learning in the presence of worst-case perturbations. This viewpoint is motivated by a key observation that there is a significant change in the distribution of the feature composition of instances of these labels from the training set to test set. For shallow classifiers, our robustness perspective to XMC naturally motivates the well-known $$\ell _1$$ -regularized classification. Contrary to the popular belief that Hamming loss is unsuitable for tail-labels detection in XMC, we show that minimizing (convex upper bound on) Hamming loss with appropriate regularization surpasses many state-of-the-art methods. Furthermore, we also highlight the sub-optimality of the co-ordinate descent based solver in the LibLinear package, which, given its ubiquity, is interesting in its own right. We also investigate the spectral properties of label graphs for providing novel insights towards understanding the conditions governing the performance of Hamming loss based one-vs-rest scheme vis-à-vis label embedding methods.
A personalized clustering-based and reliable trust-aware QoS prediction approach for cloud service recommendation in cloud manufacturing

With the growing popularity of cloud manufacturing (CMfg), an increasing number of functionally equivalent cloud services are available on the Internet, which makes cloud service recommendation an important topic recently. Since the quality-of-service (QoS) varies largely in cloud environment, many Collaborative Filtering (CF) approaches are recently proposed to predict the uncertain QoS by utilizing the historical QoS records of similar users. In addition to the data sparsity problem, however, most existing approaches also ignore the task similarity among different users, while dissimilar tasks requested by similar users may lead to quite different QoS prediction results, and thus cannot achieve higher prediction accuracy and recommendation quality. To address this problem, we develop a novel clustering-based and trust-aware approach. Firstly, a set of similar users can be identified from the view of task similarity using clustering-based algorithm, where task similarity can be computed by incorporating both explicit textual information and rating information as well as implicit context information. Secondly, considering the fact that the QoS values may be contributed by unreliable users, we design a trust-aware CF approach by merging local and global trust values, to reconstruct trust network of the clustered users. Finally, we combine the clustering-based algorithm and trust-aware approach to make a more personalized QoS prediction and reliable cloud service recommendation for the active user in CMfg. Experimental results on two real-world data sets demonstrate the effectiveness of the proposed approach compared with other state-of-the-art approaches.
Governance, CO<sub>2</sub>emissions and inclusive human development in sub-Saharan Africa

This study investigates the relevance of government quality in moderating the incidence of environmental degradation on inclusive human development in 44 sub-Saharan African countries for the period 2000–2012. Environmental degradation is measured with CO 2 emissions and the governance dynamics include: political stability, voice and accountability, government effectiveness, regulation quality, the rule of law and corruption-control. The empirical evidence is based on the generalised method of moments. Regulation quality modulates CO 2 emissions to exert a net negative effect on inclusive development. Institutional governance (consisting of corruption-control and the rule of law) modulates CO 2 emissions to also exert a net negative effect on inclusive human development. Fortunately, the corresponding interactive effects are positive, which indicates that good governance needs to be enhanced to achieve positive net effects. A policy threshold of institutional governance at which institutional governance completely dampens the unfavourable effect of CO 2 emissions on inclusive human development is established. Other policy implications are discussed.
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction

Geometric deep learning provides a principled and versatile manner for integration of imaging and non-imaging modalities in the medical domain. Graph Convolutional Networks (GCNs) in particular have been explored on a wide variety of problems such as disease prediction, segmentation, and matrix completion by leveraging large, multi-modal datasets. In this paper, we introduce a new spectral domain architecture for deep learning on graphs for disease prediction. The novelty lies in defining geometric ‘inception modules’ which are capable of capturing intra- and inter-graph structural heterogeneity during convolutions. We design filters with different kernel sizes to build our architecture. We show our disease prediction results on two publicly available datasets. Further, we provide insights on the behaviour of regular GCNs and our proposed model under varying input scenarios on simulated data.
DeepRadioID

Radio fingerprinting provides a reliable and energy-efficient IoT authentication strategy by leveraging the unique hardware-level imperfections imposed on the received wireless signal by the transmitter's radio circuitry. Most of existing approaches utilize hand-tailored protocol-specific feature extraction techniques, which can identify devices operating under a pre-defined wireless protocol only. Conversely, by mapping inputs onto a very large feature space, deep learning algorithms can be trained to fingerprint large populations of devices operating under any wireless standard.
Communicating and controlling robot arm motion intent through mixed-reality head-mounted displays

Efficient motion intent communication is necessary for safe and collaborative work environments with co-located humans and robots. Humans efficiently communicate their motion intent to other humans through gestures, gaze, and other non-verbal cues, and can replan their motions in response. However, robots often have difficulty using these methods. Many existing methods for robot motion intent communication rely on 2D displays, which require the human to continually pause their work to check a visualization. We propose a mixed-reality head-mounted display (HMD) visualization of the intended robot motion over the wearer’s real-world view of the robot and its environment. In addition, our interface allows users to adjust the intended goal pose of the end effector using hand gestures. We describe its implementation, which connects a ROS-enabled robot to the HoloLens using ROS Reality, using MoveIt for motion planning, and using Unity to render the visualization. To evaluate the effectiveness of this system against a 2D display visualization and against no visualization, we asked 32 participants to label various arm trajectories as either colliding or non-colliding with blocks arranged on a table. We found a 15% increase in accuracy with a 38% decrease in the time it took to complete the task compared with the next best system. These results demonstrate that a mixed-reality HMD allows a human to determine where the robot is going to move more quickly and accurately than existing baselines.
Context-Aware Visual Policy Network for Fine-Grained Image Captioning

With the maturity of visual detection techniques, we are more ambitious in describing visual content with open-vocabulary, fine-grained and free-form language, i.e., the task of image captioning. In particular, we are interested in generating longer, richer and more fine-grained sentences and paragraphs as image descriptions. Image captioning can be translated to the task of sequential language prediction given visual content, where the output sequence forms natural language description with plausible grammar. However, existing image captioning methods focus only on language policy while not visual policy, and thus fail to capture visual context that are crucial for compositional reasoning such as object relationships (e.g., "man riding horse") and visual comparisons (e.g., "small(er) cat"). This issue is especially severe when generating longer sequences such as a paragraph. To fill the gap, we propose a Context-Aware Visual Policy network (CAVP) for fine-grained image-to-language generation: image sentence captioning and image paragraph captioning. During captioning, CAVP explicitly considers the previous visual attentions as context, and decides whether the context is used for the current word/sentence generation given the current visual attention. Compared against traditional visual attention mechanism that only fixes a single visual region at each step, CAVP can attend to complex visual compositions over time. The whole image captioning model-CAVP and its subsequent language policy network-can be efficiently optimized end-to-end by using an actor-critic policy gradient method. We have demonstrated the effectiveness of CAVP by state-of-the-art performances on MS-COCO and Stanford captioning datasets, using various metrics and sensible visualizations of qualitative visual context.