Reviewerly/exHarmony huggingface dataset:
Video Visual Relation Detection via Multi-modal Feature Fusion

Video visual relation detection is a meaningful research problem, which aims to build a bridge between dynamic vision and language. In this paper, we propose a novel video visual relation detection method with multi-model feature fusion. First, we detect objects on each frame densely with the state-of-the-art video object detection model, flow-guided feature aggregation (FGFA), and generate object trajectories by linking the temporally independent objects with Seq-NMS and KCF tracker. Next, we break the relation candidates, i.e., co-occurrent object trajectory pairs, into short-term segments and predict relations with spatial-temporal feature and language context feature. Finally, we greedily associate the short-term relation segments into complete relation instances. The experiment results show that our proposed method outperforms other methods by a large margin, which also earned us the first place in visual relation detection task of Video Relation Understanding Challenge (VRU), ACMMM 2019.
Towards data literacy competencies: Business students, workforce needs, and the role of the librarian

Companies are looking to harness the power of data, both big and small, to take their business to new levels. One major hurdle for companies seeking to become data-centric is facing a lack of data literate talent for hire in the current market of recent college graduates. This article establishes a conversation about data literacy in business education, discusses the role of the librarian in this work, and proposes a set of data literacy competencies that librarians could help incorporate into business school education, as has been similarly seen in other disciplines.
Straggler Mitigation at Scale

Runtime performance variability has been a major issue, hindering predictable and scalable performance in modern distributed systems. Executing requests or jobs redundantly over multiple servers have been shown to be effective for mitigating variability, both in theory and practice. Systems that employ redundancy has drawn significant attention, and numerous papers have analyzed the pain and gain of redundancy under various service models and assumptions on the runtime variability. This paper presents a cost (pain) vs. latency (gain) analysis of executing jobs of many tasks by employing replicated or erasure coded redundancy. The tail heaviness of service time variability is decisive on the pain and gain of redundancy and we quantify its effect by deriving expressions for cost and latency. Specifically, we try to answer four questions: 1) How do replicated and coded redundancy compare in the cost vs. latency tradeoff? 2) Can we introduce redundancy after waiting some time and expect it to reduce the cost? 3) Can relaunching the tasks that appear to be straggling after some time help to reduce cost and/or latency? 4) Is it effective to use redundancy and relaunching together? We validate the answers we found for each of these questions via simulations that use empirical distributions extracted from a Google cluster data.
Adaptive FOV Control of Laparoscopes With Programmable Composed Constraints

Field of view (FOV) is important for minimally invasive surgeries. However, manual laparoscope manipulation is laborious and requires a lot of training. Assistants' fatigue and misunderstanding during collaboration will also compromise the quality of FOV. To improve these drawbacks, we developed a new algorithm to automatically manipulate 2-D laparoscopes. The innovative aspect lies in the following ways: the algorithm designs adaptive laws for on-line estimation of depth information, which is difficult to calibrate accurately in advance; to follow moving tools and stabilize FOV around local operations at the same time, a region-based visual servoing method is proposed. We design a novel region factor to deal with regions of different shapes; Aside from the remote center of motion (RCM), the eye-hand coordination problems, especially misorientation, are also key issues for MIS. However, how to reduce the misorientation effects is still an open problem in the literature. We propose a new constraint called the intuitive virtual plane (IVP) constraint to minimize the misorientation; in contrast with the most related works assuming the optical axis and physical axis of a laparoscope are perfectly coincident to each other, our method can be extended to different kinds of laparoscopes without any assumptions on its optical and physical axis with null-space method to decouple image-based visual servoing (IBVS) and RCM-IVP constraints. Experiments are presented to validate the method. Moreover, user studies and interviews are conducted with medical professionals and inexperienced subjects, and the results show that our system is able to improve efficiency, eye-hand coordination, and safety for FOV control of laparoscopes.
FAIR Data Reuse – the Path through Data Citation

One of the key goals of the FAIR guiding principles is defined by its final principle – to optimize data sets for reuse by both humans and machines. To do so, data providers need to implement and support consistent machine readable metadata to describe their data sets. This can seem like a daunting task for data providers, whether it is determining what level of detail should be provided in the provenance metadata or figuring out what common shared vocabularies should be used. Additionally, for existing data sets it is often unclear what steps should be taken to enable maximal, appropriate reuse. Data citation already plays an important role in making data findable and accessible, providing persistent and unique identifiers plus metadata on over 16 million data sets. In this paper, we discuss how data citation and its underlying infrastructures, in particular associated metadata, provide an important pathway for enabling FAIR data reuse.
ShareAR

Augmented reality is an emerging application on mobile devices. However, there is a lack of understanding of the communication requirements and challenges of multi-user AR scenarios. In this position paper, we propose several important research issues that need to be addressed for low-latency, accurate shared AR experiences: (a) Systems tradeoffs of AR communication architectures used today in mobile AR platforms; (b) Understanding AR communication patterns and adapting the AR application layer to dynamically changing network conditions; and (c) Tools and methodologies to evaluate AR quality of experience in real time on mobile devices. We present preliminary measurements of off-the-shelf mobile AR platforms as well as results from our AR system, ShareAR, illustrating performance tradeoffs and indicating promising new research directions.
Positive And Unlabeled Learning Algorithms And Applications: A Survey

This paper will address the Positive and Unlabeled learning problem (PU learning) and its importance in the growing field of semi-supervised learning. In most real-world classification applications, well labeled data is expensive or impossible to obtain. We can often label a small subset of data as belonging to the class of interest. It is frequently impractical to manually label all data we are not interested in. We are left with a small set of positive labeled items of interest and a large set of unknown and unlabeled data. Learning a model for this is the PU learning problem. In this paper, we explore several applications for PU learning including examples in biological/medical, business, security, and signal processing. We then survey the literature for new and existing solutions to the PU learning problem.
Mobile Robot Navigation Using an Object Recognition Software with RGBD Images and the YOLO Algorithm

This work presents a vision system based on the YOLO algorithm to identify static objects that could be obstacles in the path of a mobile robot. In order to identify the objects and its distances, a Microsoft Kinect sensor was used. In addition, a Nvidia Jetson TX2 GPU was used to increase the image processing algorithm performance. Our experimental results indicate that the YOLO network has detected all the predefined obstacles for which it has been trained with good reliability and the calculus of the distance using the depth information returned by the Microsoft Kinect camera had an error below of 3,64%.
MASR: A Modular Accelerator for Sparse RNNs

Recurrent neural networks (RNNs) are becoming the de-facto solution for speech recognition. RNNs exploit long-term temporal relationships in data by applying repeated, learned transformations. Unlike fully-connected (FC) layers with single vector matrix operations, RNN layers consist of hundreds of such operations chained over time. This poses challenges unique to RNNs that are not found in convolutional neural networks(CNNs) or FC models, namely large dynamic activation. In this paper we present MASR, a principled and modular architecture that accelerates bidirectional RNNs for on-chip ASR. MASR is designed to exploit sparsity in both dynamic activations and static weights. The architecture is enhanced by a series of dynamic activation optimizations that enable compact storage, ensure no energy is wasted computing null operations, and maintain high MAC utilization for highly parallel accelerator designs. In comparison to current state-of-the-art sparse neural network accelerators (e.g., EIE), MASR provides 2×area 3×energy, and 1.6×performance benefits. The modular nature of MASR enables designs that efficiently scale from resource-constrained low-power IoT applications to large-scale, highly parallel datacenter deployments.
Face Video Deblurring Using 3D Facial Priors

Existing face deblurring methods only consider single frames and do not account for facial structure and identity information. These methods struggle to deblur face videos that exhibit significant pose variations and misalignment. In this paper we propose a novel face video deblurring network capitalizing on 3D facial priors. The model consists of two main branches: i) a face video deblurring sub-network based on an encoder-decoder architecture, and ii) a 3D face reconstruction and rendering branch for predicting 3D priors of salient facial structures and identity knowledge. These structures encourage the deblurring branch to generate sharp faces with detailed structures. Our method not only uses low-level information (i.e., image intensity), but also middle-level information (i.e., 3D facial structure) and high-level knowledge (i.e., identity content) to further explore spatial constraints of facial components from blurry face frames. Extensive experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
B-UAVC: Buffered Uncertainty-Aware Voronoi Cells for Probabilistic Multi-Robot Collision Avoidance

This paper presents B-UAVC, a distributed collision avoidance method for multi-robot systems that accounts for uncertainties in robot localization.In particular, Buffered Uncertainty-Aware Voronoi Cells (B-UAVC) are employed to compute regions where the robots can safely navigate.By computing a set of chance constraints, which guarantee that the robot remains within its B-UAVC, the method can be applied to non-holonomic robots.A local trajectory for each robot is then computed by introducing these chance constraints in a receding horizon model predictive controller.The method guarantees, under the assumption of normally distributed position uncertainty, that the collision probability between the robots remains below a specified threshold.We evaluate the proposed method with a team of quadrotors in simulations and in real experiments.
Single underwater image restoration by decomposing curves of attenuating color

Underwater images exhibit poor visibility, such as color distortion, low contrast and blur, which results from exponential attenuation that the propagated light suffers in water. The degraded underwater images pose challenges to underwater target detection and further analysis. To address these problems, this paper presents a hybrid underwater image processing approach which consists of an underwater image restoration algorithm and a color balance algorithm. In our work, we design a novel scoring formula to accurately locate background region. Meanwhile, we innovatively decompose the attenuation curves on RGB channels to calculate transmission maps. The proposed algorithm can effectively estimate the transmission maps and the background light from a single underwater image, which can be applied to restore the underwater image. Moreover, a color balance algorithm is introduced to output an enhanced image with natural appearance. Ultimately, comprehensive tests are implemented to evaluate performance of our method. Experiments on images captured in different underwater scenes manifest that the proposed method outperforms several prominent underwater image enhancement and restoration approaches.
Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people's requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.
Resource Characterisation of Personal-Scale Sensing Models on Edge Accelerators

Edge accelerator is a class of brand-new purpose-built System On a Chip (SoC) for running deep learning models efficiently on edge devices. These accelerators offer various benefits such as ultra-low latency, sensitive data protection, and high availability due to their locality and are opening up interminable opportunities for building sensory systems in the real world. Naturally, in the context of sensory awareness systems, e.g., IoT, wearables, and other sensory devices, the emergence of edge accelerators is pushing us to rethink how we design these systems at a personal-scale. To this end, in this paper we take a closer look at the performance of a set of edge accelerators in running a collection of personal-scale sensory deep learning models. We benchmark eight different models with varying architectures and tasks (i.e., motion, audio, and vision) across seven platform configurations with three different accelerators including Google Coral, NVidia Jetson Nano, and Intel Neural Compute Stick. We report on their execution performance concerning latency, memory, and power consumption while discussing their current workflows and limitations. The results and insights lay an empirical foundation for the development of sensory systems on edge accelerators.
Refractive Two-View Reconstruction for Underwater 3D Vision

Recovering 3D geometry from cameras in underwater applications involves the Refractive Structure-from-Motion problem where the non-linear distortion of light induced by a change of medium density invalidates the single viewpoint assumption. The pinhole-plus-distortion camera projection model suffers from a systematic geometric bias since refractive distortion depends on object distance. This leads to inaccurate camera pose and 3D shape estimation. To account for refraction, it is possible to use the axial camera model or to explicitly consider one or multiple parallel refractive interfaces whose orientations and positions with respect to the camera can be calibrated. Although it has been demonstrated that the refractive camera model is well-suited for underwater imaging, Refractive Structure-from-Motion remains particularly difficult to use in practice when considering the seldom studied case of a camera with a flat refractive interface. Our method applies to the case of underwater imaging systems whose entrance lens is in direct contact with the external medium. By adopting the refractive camera model, we provide a succinct derivation and expression for the refractive fundamental matrix and use this as the basis for a novel two-view reconstruction method for underwater imaging. For validation we use synthetic data to show the numerical properties of our method and we provide results on real data to demonstrate its practical application within laboratory settings and for medical applications in fluid-immersed endoscopy. We demonstrate our approach outperforms classic two-view Structure-from-Motion method relying on the pinhole-plus-distortion camera model.
A Hitchhiker’s Guide On Distributed Training Of Deep Neural Networks

Deep learning has led to tremendous advancements in the field of Artificial Intelligence. One caveat, however, is the substantial amount of compute needed to train these deep learning models. Training a benchmark dataset like ImageNet on a single machine with a modern GPU can take up to a week and distributing training on multiple machines has been observed to drastically bring this time down. Recent work has brought down ImageNet training time to as low as 4 min by using a cluster of 2048 GPUs. This paper surveys the various algorithms and techniques used in distributed training and presents the current state of the art for a modern distributed training framework. More specifically, we explore the synchronous and asynchronous variants of distributed Stochastic Gradient Descent, various All Reduce gradient aggregation strategies and best practices for obtaining higher throughput and lower latency over a cluster such as mixed precision training, large batch training, and gradient compression.
A hybrid personalized scholarly venue recommender system integrating social network analysis and contextual similarity

Rapidly developing academic venues throw a challenge to researchers in identifying the most appropriate ones that are in-line with their scholarly interests and of high relevance. Even a high-quality paper is sometimes rejected due to a mismatch between the area of the paper, and the scope of the journal attempted to. Recommending appropriate academic venues can, therefore, enable researchers to identify and take part in relevant conferences and to publish in impactful journals. Although a researcher may know a few leading high-profile venues for her specific field of interest, a venue recommender system becomes particularly helpful when one explores a new field or when more options are needed. We propose DISCOVER: A Diversified yet Integrated Social network analysis and COntextual similarity-based scholarly VEnue Recommender system. Our work provides an integrated framework incorporating social network analysis, including centrality measure calculation, citation and co-citation analysis, topic modeling based contextual similarity, and key-route identification based main path analysis of a bibliographic citation network. The paper also addresses cold start issues for a new researcher and a new venue along with a considerable reduction in data sparsity, computational costs, diversity, and stability problems. Experiments based on the Microsoft Academic Graph (MAG) dataset show that the proposed DISCOVER outperforms state-of-the-art recommendation techniques using standard metrics of [email protected], [email protected], accuracy, MRR, F−measuremacro, diversity, stability, and average venue quality.
Introduction: new directions in cybercrime research

The Internet, computers, cell phones, and other forms of technology have revolutionized every aspect of human life over the last several decades, including how we communicate, bank, shop, obtain th...
Multi-source domain adaptation with joint learning for cross-domain sentiment classification

Cross-domain sentiment classification uses knowledge from source domain tasks to enhance the sentiment classification of the target task. It can reduce the workload of data annotations in the new domain, and significantly improve the utilization of labeled resources in the source domains. Available approaches generally use knowledge from a single-source domain and hard parameter sharing methods, which are likely to ignore the differences among domain-specific features. We propose a novel framework with multi-source domain adaptation and joint learning for multi-source cross-domain sentiment classification tasks This framework uses bi-directional gated recurrent units and convolutional neural networks for deep feature extraction and soft parameter sharing for information transfer across tasks. Furthermore, it minimizes distance constraints for deep domain fusion. Multi-source domain adaptation involves multiple concurrent task learning, and the gradients are simultaneously back propagated. We validate the proposed framework on multi-source cross-domain sentiment classification datasets in Chinese and English. The experimental results demonstrate that the proposed method is more effective than state-of-the-art methods in improving accuracy and generalization capability.