Reviewerly/exHarmony huggingface dataset:
Selective Complementary Features For Multi-Person Pose Estimation

Multi-person pose estimation is a fundamental yet challenging research topic for many computer vision applications. It is difficult to achieve accurate localization results due to occlusion and complex background. In this paper, we propose a novel multi-person pose estimation approach with information complement and attention refinement residual module. To recover occlusion, the complementary features with multi-scale semantics information are extracted by our proposed Information Complement Module (ICM). To effectively discover the channel relationship and selectively highlight task-related regions in the feature maps, we design an Attention Refinement Residual Bottleneck (ARRB) module, which is an extension of residual unit with attention mechanism. We conduct ablation studies to investigate the efficacy of our method and compare it with the state-of-the-art methods on the COCO keypoint benchmark. Experimental results demonstrate that the selective complementary features are effective for multi-person pose estimation.
Augmenting JPEG2000 With Wavelet Coefficient Prediction

This paper presents a novel modification to the Discrete Wavelet Transform used in the JPEG2000 standard. The modification utilises a learning-based method to predict the detail coefficients from the approximation coefficients at each level of the transform, allowing for higher compression rates while maintaining most of JPEG2000's appealing features. A coefficient prediction method based on single-image super resolution literature has been implemented and tested to assess the potential of the compression system. We have observed that the accuracy of the prediction is highly dependent on the content of the test image, with certain features being highly predictable and others not predictable at all. In the best performing test images, we see a quality gain of up to 1.5dB or equivalently a reduction in bit-rate of up to 14% over the JPEG2000 standard.
Deep Imitative Reinforcement Learning for Temporal Logic Robot Motion Planning with Noisy Semantic Observations

In this paper, we propose a Deep Imitative Q-learning (DIQL) method to synthesize control policies for mobile robots that need to satisfy Linear Temporal Logic (LTL) specifications using noisy semantic observations of their surroundings. The robot sensing error is modeled using probabilistic labels defined over the states of a Labeled Transition System (LTS) and the robot mobility is modeled using a Labeled Markov Decision Process (LMDP) with unknown transition probabilities. We use existing product-based model checkers (PMCs) as experts to guide the Q-learning algorithm to convergence. To the best of our knowledge, this is the first approach that models noise in semantic observations using probabilistic labeling functions and employs existing model checkers to provide suboptimal instructions to the Q-learning agent.
The effects of required security on software development effort

Problem: developers are increasingly adopting security practices in software projects in response to cyber threats. Despite the additional effort required to perform those practices, current cost models either do not consider security as an input or were not properly validated with empirical data. Hypothesis: increasing degrees of application of security practices and security features, motivated by security risks, lead to growing levels of added software development effort. Such an effort increase can be quantified through a parametric model that takes as input the usage degrees of security practices and requirements and outputs the additional software development effort. Contributions: the accurate prediction of secure software development effort will support the provision of a proper amount of resources to projects. We also expect that the quantification of the security effort will contribute to advance research on the cost-effectiveness of software security.
Recognition and Recall of Geographic Data In Cartograms

We investigate the memorability of two types of cartograms, both in terms of recognition of the visualization and recall of the data. A cartogram, or a value-by-area map, is a representation of a map in which geographic regions are modified to reflect a given statistic, such as population or income. Of the many different types of cartograms, the contiguous and Dorling types are among the most popular and most effective. With this in mind, we evaluate the memorability of these two cartogram types with a human-subjects study, using task-based experimental data and cartogram visualization tasks based on Bertin's map reading levels. In particular, our results indicate that Dorling cartograms are associated with better recall of general patterns and trends. This, together with additional significant differences between the two most popular cartogram types, has implications for the design and use of cartograms, in the context of memorability.
Interactivity-Based Quality Prediction of Conversations with Transmission Delay

While the experienced quality of a conversation transmitted over a telephone network is dependent on the parameters of the network and possible degradations, it has been shown that the sensitivity to these degradations is influenced by the type of the conversation and its contents. Especially for delayed speech transmission the conversational scenario and the adaption of turn-taking behavior of the interlocutors factor into the conversational quality rating of a specific conversation. Parametric Conversation Analysis has been proven to be a good method to extract parameters from a recorded conversation that are representable of the interactivity. While the narrowband version of a popular and standardized quality prediction model, the E-model, uses the interactivity to predict a conversational quality MOS for the given conditions, there are no attempts to predict the conversational quality of individual conversations so far. In this paper, we propose a model to predict the quality of a conversation under the influence of transmission delay based on the interactivity parameters extracted from that conversation. We evaluate which parameters are most suited for such a prediction, and compare our results to the parameter-based predictions of the E-model.
Composition of Transformations in the Registration of Sets of Points or Oriented Points

Registration of point sets in medical imaging applications may in some cases benefit from application-specific rather than general models of deformation by which to transform the model point set to the target. Further, including orientation data with the points may improve accuracy. To facilitate this, we propose an algorithm to register sets of points or oriented points through arbitrarily composed sets of transformations, so as to allow the construction of context-specific deformation spaces. The algorithm is generic with respect to the choice of transformations, requiring only that each constituent has a known solution to a particular standard form of equation. Our approach is framed in the mixture model framework, and constitutes a generalized expectation maximization algorithm. We present experimental results for two models—a 2D model of a cardiac ventricle, and a 3D model of a bug—testing the algorithm's robustness to noise and outliers, and comparing the accuracy when using points or oriented points. The results suggest the algorithm is quite robust to both noise and outliers, with inclusion of orientation data consistently resulting in more accurate registrations.
That password doesn't sound right

Despite 2-factor authentication and other modern approaches, authentication by password is still the most commonly used method on the Internet. Unfortunately, as analyses show, many users still choose weak and easy-to-guess passwords. To alleviate the significant effects of this problem, systems often employ textual or graphical feedback to make the user aware of this problem, which often falls short on engaging the user and achieving the intended user reaction, i.e., choosing a stronger password.
HeteGraph: A Convolutional Framework for Graph Learning in Recommender Systems

With the explosive growth of online information, many recommendation methods have been proposed. This research direction is boosted with deep learning architectures, especially the recently proposed Graph Convolutional Networks (GCNs). GCNs have shown tremendous potential in graph embedding learning thanks to its inductive inference property. However, most of the existing GCN based methods focus on solving tasks in the homogeneous graph settings, and none of them considers heterogeneous graph settings. In this paper, we bridge the gap by developing a novel framework called HeteGraph based on the GCN principles. HeteGraph can handle heterogeneous graphs in the recommender systems. Specifically, we propose a sampling technique and a graph convolutional operation to learn high quality graph's node embeddings, which differs from the traditional GCN approaches where a full graph adjacency matrix is needed for the embedding learning. For evaluation, we design two models based on the HeteGraph framework to evaluate two important recommendation tasks, namely item rating prediction and diversified item recommendations. Extensive experiments show our HeteGraph's encouraging performance on the first task and state-of-the-art performance on the second task.
Subversion-Resistant Commitment Schemes: Definitions and Constructions

Recently, various news is reported about the subversion of trusted setup phase in mass-surveillance activities; strictly speaking about commitment schemes, recently it was discovered that the SwissPost-Scytl mix-net uses a trapdoor commitment scheme, that allows undetectably altering the votes and breaking users’ privacy, given the trapdoor [, ]. Motivated by such news and recent studies on subversion-resistance of various cryptographic primitives, this research studies the security of commitment schemes in the presence of a maliciously chosen commitment key. To attain a clear understanding of achievable security, we define a variety of current definitions called subversion hiding, subversion equivocality, and subversion binding. Then we provide both negative and positive results on constructing subversion-resistant commitment schemes, by showing that some combinations of notions are not compatible while presenting subversion-resistant constructions that can achieve other combinations.
A Stochastic-Performance-Distribution-Based Approach to Cloud Workflow Scheduling with Fluctuating Performance

The cloud computing paradigm is characterized by the ability to provide flexible provisioning patterns for computing resources and on-demand common services. As a result, building business processes and workflow-based applications on cloud computing platforms is becoming increasingly popular. However, since real-world cloud services are often affected by real-time performance changes or fluctuations, it is difficult to guarantee the cost-effectiveness and quality-of-service (Qos) of cloud-based workflows at real time. In this work, we consider that workflows, in terms of Directed Acyclic Graphs (DAGs), to be supported by decentralized cloud infrastructures are with time-varying performance and aim at reducing the monetary cost of workflows with the completion-time constraint to be satisfied. We tackle the performance-fluctuation workflow scheduling problem by incorporating a stochastic-performance-distribution-based framework for estimation and optimization of workflow critical paths. The proposed method dynamically generates the workflow scheduling plan according to the accumulated stochastic distributions of tasks. In order to prove the effectiveness of our proposed method, we conducted a large number of experimental case studies on real third-party commercial clouds and showed that our method was significantly better than the existing method.
Opportunistic Meta-Learning: A Case Study for Quality Assurance in Industry 4.0 Environments

In the context of self-adaptive and self-organising systems, the adaptation of spontaneously available knowledge is a key to improve a system's learning quality in detecting novelties or improving the accuracy in general. We introduce an opportunistic meta-learning architecture capable of evaluating and ranking knowledge sources at runtime and to adapt to the most promising one. Furthermore, we present experimental simulations in the domain of quality assurance and show that adaptive knowledge can stabilise the learning procedure.
Edge-Assisted Federated Learning: An Empirical Study from Software Decomposition Perspective

Federated learning is considered to be a privacy-preserving collaborative machine learning training method. However, due to the general limitation of the computing ability of the terminal device, the training efficiency becomes an issue when training some complex deep neural network models. On the other hand, edges, the nearby stationary devices with higher computational capacity, might serve as a help. This paper presents the design of a component-based federated learning framework, which facilitates the offloading of training layers to nearby edge devices while preserving the users’ privacy. We conduct an empirical study on a classic convolutional neural network to validate our framework. Experiments show that this method can effectively shorten the time cost for mobile terminals to perform local training in the federated learning process.
Cross-Domain Data Fusion On Distribution Network Voltage Estimation with D-S Evidence Theory

The Cyber-Physical system (CPS) is an emerging concept for realizing the system wholeness and the interplay of different network components in the electricity system with various embedded devices. However, the increasing penetration of embedded devices also brings severe data explosion and uncertainties to the voltage estimation process in practical power grid operations. Data fusion method has significant performance on improving the accuracy of state estimation on the deficient cross-domain dataset. This paper applies the data fusion method and D-S evidence theory to aggerate the information from various monitored devices in the distribution network and resolve the voltage estimation problem of the distribution network. Apart from conventional data fusion model, a two-stage D-S evidence data fusion framework is also proposed to improve the estimation accuracy and also quantify correlation factors between recorded parameters of monitored network devices and the overall network status of the whole distribution systems. This paper illustrates the feasibility and reliability of the proposed data fusion frameworks by a case study on an actual MV distribution system with deficient datasets and operation information of the main 33/11 kV transformer.
Anticipating the Start of User Interaction for Service Robot in the Wild

A service robot is expected to provide proactive service for visitors who require its help. In contrast to passive service, e.g., providing service only after being spoken to, proactive service initiates an interaction at an early stage, e.g., talking to potential visitors who need the robot’s help in advance. This paper addresses how to anticipate the start of user interaction. We propose an approach using only a single RGB camera that anticipates whether a visitor will come to the robot for interaction or just pass it by. In the proposed approach, we (i) utilize the visitor’s pose information from captured images incorporating facial information, (ii) train a CNN-LSTM–based model in an end-to-end manner with an exponential loss for early anticipation, and (iii) during the training, the network branch for facial keypoints acquired as the part of the human pose information is taught to mimic the branch trained with the face image from a specialized face detector with a human verification. By virtue of (iii), at the inference, we can run our model in an embedded system processing only the pose information without an additional face detector and typical accuracy drop.We evaluated the proposed approach on our collected real world data with a real service robot and publicly available JPL interaction dataset and found that it achieved accurate anticipation performance.
Enhancing Scatter-plots with Start-plots for Visualising Multi-dimensional Data

Scatter-plot visualisation techniques are useful for analysing the correlations of variables on the axes as well as revealing patterns or abnormality in multidimensional data sets. However, Scatter-plot techniques have a drawback that they are not effective in showing a high number of dimensions where each plot in two-dimensional space can show a pair-wise of two variables on the X- and Y-axis. Star-plots are suitable for showing small data set with a low number of dimensions thank to the compactness in the visualisation. This paper proposes a hybrid technique that integrates Start-plots with Scatter-plots in the visualisation to enable the greater capability of scatter-plots in showing more information on each individual Star-plot. Our visualisation provides both overall views of mapping variables on multiple Scatter-plots whilst we also utilise Star-plots for showing the selected attributes on individual items for better comparison among and within variables. We also demonstrate the effectiveness of this hybrid method through case studies on various data sets.
Hybrid algorithms for spectral noise removal in hyper spectral images

The image acquired from a sensor is always degraded by some form of noise. The noise can be estimated and removed by the process of denoising. Recently, the use of Hybrid Algorithms for denoising has gained popularity. The most commonly used transformation are Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). DCT has the property of more energy compaction and requires less resources for computational whereas DWT is a multi-resolution transformation. The proposed Hybrid Algorithms take advantage of both of the algorithms and this reduces the false contouring and blocking articrafts effectively. In this paper, the Hybrid Algorithms are evaluated for various images by differentiating with respect to Mean Square Error, Peak Signal to Noise Ratio, Coefficient of Variance, Structural Similarity Index and Mean Structural Similarity Index.
DeepSemanticHPPC: Hypothesis-based Planning over Uncertain Semantic Point Clouds

Planning in unstructured environments is challenging - it relies on sensing, perception, scene reconstruction, and reasoning about various uncertainties. We propose DeepSemanticHPPC, a novel uncertainty-aware hypothesis-based planner for unstructured environments. Our algorithmic pipeline consists of: a deep Bayesian neural network which segments surfaces with uncertainty estimates; a flexible point cloud scene representation; a next-best-view planner which minimizes the uncertainty of scene semantics using sparse visual measurements; and a hypothesis-based path planner that proposes multiple kinematically feasible paths with evolving safety confidences given next-best-view measurements. Our pipeline iteratively decreases semantic uncertainty along planned paths, filtering out unsafe paths with high confidence. We show that our framework plans safe paths in real-world environments where existing path planners typically fail.
Weakly Supervised Organ Localization with Attention Maps Regularized by Local Area Reconstruction

Fully supervised methods with numerous dense-labeled training data have achieved accurate localization results for anatomical structures. However, obtaining such a dedicated dataset usually requires clinical expertise and time-consuming annotation process. In this work, we tackle the organ localization problem under the setting of image-level annotations. Previous Class Activation Map (CAM) and its derivatives have proved that discriminative regions of images can be located with basic classification networks. To improve the representative capacity of attention maps generated by CAMs, a novel learning-based Local Area Reconstruction (LAR) method is proposed. Our weakly supervised organ localization network, namely OLNet, can generate high-resolution attention maps that preserve fine-detailed target anatomical structures. Online generated pseudo ground-truth is utilized to impose geometric constraints on attention maps. Extensive experiments on In-house Chest CT Dataset and Kidney Tumor Segmentation Benchmark (KiTS19) show that our approach can provide promising localization results both in saliency map and semantic segmentation perspectives.
A Novel Solar Tracker Driven by Waves: From Idea to Implementation

Traditional solar trackers often adopt motors to automatically adjust the attitude of the solar panels towards the sun for maximum power efficiency. In this paper, a novel design of solar tracker for the ocean environment is introduced. Utilizing the fluctuations due to the waves, electromagnetic brakes are utilized instead of motors to adjust the attitude of the solar panels. Compared with the traditional solar trackers, the proposed one is simpler in hardware while the harvesting efficiency is similar. The desired attitude is calculated out of the local location and time. Then based on the dynamic model of the system, the angular acceleration of the solar panels is estimated and a control algorithm is proposed to decide the release and lock states of the brakes. In such a manner, the adjustment of the attitude of the solar panels can be achieved by using two brakes only. Experiments are conducted to validate the acceleration estimator and the dynamic model. At last, the feasibility of the proposed solar tracker is tested on the real water surface. The results show that the system is able to adjust 40° in two dimensions within 28 seconds.
Improving iForest for Hydrological Time Series Anomaly Detection

With the increasing number of installed hydrological sensors, the data from these sensors usually contain a variety of abnormal values due to network congestion, equipment failure, or environmental influence. To deal with the anomaly on a larger scale of hydrological sensor data, a series of algorithms have been proposed. However, they are usually based on the ideas of distance or classification, which usually bring pretty high time complexity. To solve this problem, a detection algorithm called AR-iForest is proposed. It is an algorithm for hydrological time series anomaly detection based on the isolation forest. Firstly, the features of hydrological data are extracted and mapped it to a high-dimensional space. Before using the isolation forest in high-dimensional space for anomaly detection, the Auto-Regressive model is used first to predict the current data and calculate the confidence interval. Only the data not in the confidence interval needs to be detected. Secondly, a measure of the effectiveness of trees in the isolation forest is proposed. This method selects the tree with the best classification effect through continuous iteration. Finally, the proposed algorithm is integrated into the window of the big data platform Flink to give a performance evaluation. The experimental results show that the proposed algorithm increases the AUC value from 90.60% to 96.72%, and the detection time is reduced by 52.23%.
Collision-free Navigation of Human-centered Robots via Markov Games

We exploit Markov games as a framework for collision-free navigation of human-centered robots. Unlike the classical methods which formulate robot navigation as a single-agent Markov decision process with a static environment, our framework of Markov games adopts a multi-agent formulation with one primary agent representing the robot and the remaining auxiliary agents form a dynamic or even competing environment. Such a framework allows us to develop a path-following type adversarial training strategy to learn a robust decentralized collision avoidance policy. Through thorough experiments on both simulated and real-world mobile robots, we show that the learnt policy outperforms the state-of-the-art algorithms in both sample complexity and runtime robustness.