Reviewerly/exHarmony huggingface dataset:
Discriminant locality preserving projections based on L2,-norm for image feature extraction and recognition

Conventional discriminant locality preserving projections (DLPP) is sensitive to outliers because the formulation of its objective function is based on L2-norm. Not only that, the learned features by DLPP are linear combinations of all the original features. So, it is hard to know which features play an important role in feature extraction, and the learned features often contain irrelevant information. In this paper, we propose a robust version of DLPP based on L2,p-norm with 0<p<1, termed DLPP-L2,p, for image feature extraction and recognition. DLPP-L2,p learns an optimal projection matrix by maximizing the L2,p-norm-based locality preserving between-class dispersion and minimizing the L2,p-norm-based locality preserving within-class dispersion simultaneously. Furthermore, by imposing an L2,p-norm penalty on the projection matrix to achieve row-sparsity, DLPP-L2,p can discard irrelevant features and transform relevant features simultaneously. Experimental results on several image datasets demonstrate the effectiveness and robustness of DLPP-L2,p.
Weighted least-squares phase-unwrapping algorithm based on the orientation coherence for discontinuous optical phase patterns

Phase unwrapping is one of the key steps of optical interferogram analysis, among which phase discontinuity is still a challenge. In this paper, we propose a new weighted least-squares phase-unwrapping algorithm for discontinuous optical phase patterns. In the proposed algorithm, the orientation coherence is introduced to define the new weighting coefficient, which can accurately show the wrapped phase quality. According to our proposed algorithm, the new weighting coefficient has a good performance on distinguishing the continuous regions and the discontinuous regions in wrapped phase patterns. This advantage of our algorithm can ensure a more reliable unwrapped result for discontinuous optical phase patterns. We test the proposed algorithm on the computer-simulated speckle phase images and two experimentally obtained phase images, respectively, and compare them with the other five widely used methods. The experimental results demonstrate the performance of our new weighted least-squares phase-unwrapping algorithm.
Cooperative Multi-agent Policy Gradient

Reinforcement Learning (RL) for decentralized partially observable Markov decision processes (Dec-POMDPs) is lagging behind the spectacular breakthroughs of single-agent RL. That is because assumptions that hold in single-agent settings are often obsolete in decentralized multi-agent systems. To tackle this issue, we investigate the foundations of policy gradient methods within the centralized training for decentralized control (CTDC) paradigm. In this paradigm, learning can be accomplished in a centralized manner while execution can still be independent. Using this insight, we establish policy gradient theorem and compatible function approximations for decentralized multi-agent systems. Resulting actor-critic methods preserve the decentralized control at the execution phase, but can also estimate the policy gradient from collective experiences guided by a centralized critic at the training phase. Experiments demonstrate our policy gradient methods compare favorably against standard RL techniques in benchmarks from the literature. Code related to this paper is available at: https://gitlab.inria.fr/gbono/coop-ma-pg .
Learning Robust Auto-Encoders With Regularizer for Linearity and Sparsity

Unsupervised feature learning via auto-encoders results in low-dimensional representations in latent space that capture the patterns of input data. The auto-encoders with robust regularization learn qualified features that are less sensitive to small perturbations of inputs. However, the previous robust auto-encoders highly depend on pre-defined structure settings and often learn full-connected networks that are easily prone to over-fitting. To solve the above limitations, we propose in this paper an explicitly regularized framework which improves the sparsity and flexibility of robust auto-encoders. First, our model encourages the activation functions to automatically adjust themselves between linear and non-linear ones. Second, the mapping functions of the encoder are constrained by group sparsity and exclusive sparsity to reduce the redundancy of parameters. The proximal gradient method is used to optimize our model since the objective function contains non-smooth components. We conduct experiments in single-layer and multiple-layer auto-encoders in the classification task. The numerical results show that our model achieves better accuracy than baseline models. Our method also shows better performance in denoising task.
Generation of superposition coherent states of microwave fields via dissipation of a superconducting qubit with broken inversion symmetry

We present an efficient approach for dissipative generation of quantum superposition states in the microwave resonator, which is coupled to a superconducting qubit. We investigate the situation where the inversion symmetry of potential energy of the qubit is broken, and the strong two-photon nonlinear coupling between qubit and resonator can be realized via the transverse and longitudinal couplings. According to the two-photon dissipation and driving process, the dissipation of a qubit can be utilized to steer the microwave field into a quantum superposition of distinct coherent states, i.e., the Schr\"odinger cat state. In addition, we also extend the method to produce an entangled coherent state of two spatially separated resonators. Our scheme is based on quantum reservoir engineering and turns detrimental noise into a resource, which makes it feasible in experimental implementation. The present result may have potential applications in the field of quantum information processing with circuit QED systems.
Optimal Shadowing Filter for a Positioning and Tracking Methodology with Limited Information

Positioning and tracking a moving target from limited positional information is a frequently-encountered problem. For given noisy observations of the target’s position, one wants to estimate the true trajectory and reconstruct the full phase space including velocity and acceleration. The shadowing filter offers a robust methodology to achieve such an estimation and reconstruction. Here, we highlight and validate important merits of this methodology for real-life applications. In particular, we explore the filter’s performance when dealing with correlated or uncorrelated noise, irregular sampling in time and how it can be optimised even when the true dynamics of the system are not known.
A Generic Human–Machine Annotation Framework Based on Dynamic Cooperative Learning

The task of obtaining meaningful annotations is a tedious work, incurring considerable costs and time consumption. Dynamic active learning and cooperative learning are recently proposed approaches to reduce human effort of annotating data with subjective phenomena. In this paper, we introduce a novel generic annotation framework, with the aim to achieve the optimal tradeoff between label reliability and cost reduction by making efficient use of human and machine work force. To this end, we use dropout to assess model uncertainty and thereby to decide which instances can be automatically labeled by the machine and which ones require human inspection. In addition, we propose an early stopping criterion based on inter-rater agreement in order to focus human resources on those ambiguous instances that are difficult to label. In contrast to the existing algorithms, the new confidence measures are not only applicable to binary classification tasks but also regression problems. The proposed method is evaluated on the benchmark datasets for nonnative English prosody estimation, provided in the INTERSPEECH computational paralinguistics challenge. In the result, the novel dynamic cooperative learning algorithm yields 0.424 Spearman's correlation coefficient compared to 0.413 with passive learning, while reducing the amount of human annotations by 74%.
Using technology to enhance and encourage dance-based exercise

This study investigated the role of Self-Service Technologies (SSTs) in dance-based exercise in order to begin exploring the motivations behind the use (or not) of SSTs by ordinary men and women in this context. The research approach employed interviews to gain insights into participants' use of SSTs and their exercise practices, in order to start establishing ways in which dance can be re/incorporated into people's lives through the design of appropriate SSTs. Findings from this study highlight the significant opportunity to further explore how the properties of music and dance can be integrated into the design of new SSTs. Literature suggests dance could be a beneficial exercise format for many people and self-service technology abounds for exercise but is often not used consistently. Our interviews asked participants about dance-based exercise and SSTs for exercise and showed that there is an opportunity to design SSTs to help people access dance-based exercise. SSTs should help people learn dance, build confidence, and dance alone or with others. SSTs could facilitate movement and increase engagement with physical activity whilst addressing issues around logistics, confidence and dance knowledge and experience.
An Efficient Multi-Path Multitarget Tracking Algorithm for Over-The-Horizon Radar

In target tracking environments using over-the-horizon radar (OTHR), one target may generate multiple detections through different signal propagation paths. Trackers need to jointly handle the uncertainties stemming from both measurement origin and measurement path. Traditional multitarget tracking algorithms suffer from high computational loads in such environments since they need to enumerate all possible joint measurement-to-track assignments considering the measurements paths unless they employ some approximations regarding the measurements and their corresponding paths. In this paper, we propose a novel algorithm, named multi-path linear multitarget integrated probabilistic data association (MP-LM-IPDA), to efficiently track multitarget in multiple detection environments. Instead of generating all possible joint assignments, MP-LM-IPDA calculates the modulated clutter measurement density for each measurement cell of each track. The modulated clutter measurement density considers the possibility that the measurement cells originate from the clutter as well as from other potential targets. By incorporating the modulated clutter measurement density, the single target tracking structure can be applied for multitarget tracking, which significantly reduces the computational load. The simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.
Search-based multi-vulnerability testing of XML injections in web applications

Modern web applications often interact with internal web services, which are not directly accessible to users. However, malicious user inputs can be used to exploit security vulnerabilities in web services through the application front-ends. Therefore, testing techniques have been proposed to reveal security flaws in the interactions with back-end web services, e.g., XML Injections (XMLi). Given a potentially malicious message between a web application and web services, search-based techniques have been used to find input data to mislead the web application into sending such a message, possibly compromising the target web service. However, state-of-the-art techniques focus on (search for) one single malicious message at a time. Since, in practice, there can be many different kinds of malicious messages, with only a few of them which can possibly be generated by a given front-end, searching for one single message at a time is ineffective and may not scale. To overcome these limitations, we propose a novel co-evolutionary algorithm (COMIX) that is tailored to our problem and uncover multiple vulnerabilities at the same time. Our experiments show that COMIX outperforms a single-target search approach for XMLi and other multi-target search algorithms originally defined for white-box unit testing.
An Energy Efficiency Optimization and Control Model for Hadoop Clusters

The majority of large-scale data intensive applications designed by MapReduce model are deployed and executed on a large-scale distributed Hadoop system.Running such application on a large cluster requires a large amount of energy.Therefore improving energy efficiency and minimizing energy consumption when executing each MapReduce job is a critical concern for data centers.We propose a control model based on model prediction control (MPC) for improving energy efficiency of Hadoop cluster while satisfying performance goal.The controller can dynamically scale CPU frequency in facing the workloads changes.The wavelet neural network is utilized to build the prediction model, which can accurately estimate energy efficiency ratio based on the features extracting from the variation of Hadoop workloads.With the help of a wavelet neural network, the controller can guarantee the energy efficiency ratio converges to an ideal set point within a finite settling time.We perform the extensive experiments on a Hadoop cluster to compare our controller with the previous MPC controller.The results demonstrate that our controller outperforms the baseline on energy efficiency control and adapts to workloads of Hadoop cluster.
Geochemical mapping and estimation of background concentrations in soils of Carajás mineral province, eastern Amazonian Craton, Brazil

Companhia de Pesquisa de Recursos Minerais (CPRM, the Brazilian geological survey) has carried out geochemical surveys focused on the Carajas region, the largest mineral province in Brazil. One of these surveys covered the area of Canaa dos Carajas, where 257 soil samples, including 32 duplicate samples, were collected from the B-horizon of soils within an area of c. 3500 km2. The
An Improved BPDF Filter for High Density Salt and Pepper Denoising

The BPDF (based on pixel density filter) is an effective filter to remove the salt-and-pepper noise, but it only can work on low and medium noise levels. In this paper we propose an improved version of the BPDF to remove the high-density salt-and-pepper noise. Our proposed method can work effectively on high and very high noise levels (above 90%). In the experiment, we compare the denoising result of the proposed method with the denoising results by BPDF filter and DAMF filter to prove effectiveness of the proposed method. The denoising quality is assessed by the peak signal-to-noise ratio and the structure similarity metrics.
Adversarial Imitation Learning from State-only Demonstrations

Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions. The lack of action information both distinguishes IfO from most of the literature in imitation learning, and also sets it apart as a method that may enable agents to learn from a large set of previously inapplicable resources such as internet videos. In this paper, we propose a new IfO approach based on generative adversarial networks called generative adversarial imitation from observation (GAIfO). We demonstrate that our approach performs comparably to classical imitation learning approaches (which have access to the demonstrator's actions) and significantly outperforms existing imitation from observation methods in high-dimensional simulation environments.
Weighted Densely Connected Convolutional Networks for Reinforcement Learning

A weighted densely connected convolution network (W-DenseNet) is proposed for reinforcement learning in this work. The W-DenseNet can maximize the information flow between all layers in the network by cross layer connection, which can reduce the phenomenon of gradient vanishing and degradation, and greatly improves the speed of training convergence. The weight coefficient introduced in W-DenseNet, the current layer received all the previous layers’ feature maps with different initial weights, which can extract feature information of different layers more effectively according to tasks. According to the weight adjusted by learning, the cross-layer connection is pruned to remove the cross-layer connection with smaller weight, so as to reduce the number of cross-layer. In this work, GridWorld and FlappyBird games are used for simulation. The simulation results of deep reinforcement learning based on W-DenseNet are compared with the traditional deep reinforcement learning algorithm and reinforcement learning algorithm based on DenseNet. The simulation results show that the proposed W-DenseNet method can make the results more convergent, reduce the training time, and obtain more stable results.
ObSecure Logging (OSLo): A Framework to Protect and Evaluate the Web Search Privacy in Health Care Domain

Users around the world send queries to the Web Search Engine (WSE) to retrieve data from the Internet. Users usually take primary assistance relating to medical information from WSE via search queries. The search queries relating to diseases and treatment is contemplated to be the most personal facts about the user. The search queries often contain identifiable information that can be linked back to the originator, which can compromise the privacy of a user. In this work, we are proposing a distributed privacy-preserving protocol (OSLo) that eliminates limitation in the existing distributed privacy-preserving protocols and a framework, which evaluates the privacy of a user. The OSLo framework asses the local privacy relative to the group of users involved in forwarding query to the WSE and the profile privacy against the profiling of WSE. The privacy analysis shows that the local privacy of a user directly depends on the size of the group and inversely on the number of compromised users. We have performed experiments to evaluate the profile privacy of a user using a privacy metric Profile Exposure Level. The OSLo is simulated with a subset of 1000 users of the AOL query log. The results show that OSLo performs better than the benchmark privacy-preserving protocol on the basis of privacy and delay. Additionally, results depict that the privacy of a user depends on the size of the group.
Broadening the Discussion of Ethics in the Interaction Design and Children Community

Interaction Design and Children (IDC) as an academic field, and as a community, has a responsibility to engage with the many and diverse ethical challenges that arise from work that concerns the creation of digital technology for and with children -- both in terms of research and industry contexts. This panel builds on a short history of similar events at previous conferences and aims to foster and strengthen the debate about ethical conduct and moral responsibilities in IDC. In this year's panel, we seek to broaden the discussion by collecting ethical concerns, issues or dilemmas from within the community to be discussed at the conference. To this end, we will issue an open call for input that will be publicised via the usual channels. The organisers then will synthesise the responses and facilitate the discussion and debate at the panel.
Eating and Drinking Recognition in Free-Living Conditions for Triggering Smart Reminders

The increasingly aging society in developed countries has raised attention to the role of technology in seniors’ lives, namely concerning isolation-related issues. Independent seniors that live alone frequently neglect meals, hydration and proper medication-taking behavior. This work aims at eating and drinking recognition in free-living conditions for triggering smart reminders to autonomously living seniors, keeping system design considerations, namely usability and senior-acceptance criteria, in the loop. To that end, we conceived a new dataset featuring accelerometer and gyroscope wrist data to conduct the experiments. We assessed the performance of a single multi-class classification model when compared against several binary classification models, one for each activity of interest (eating vs. non-eating; drinking vs. non-drinking). Binary classification models performed consistently better for all tested classifiers (k-NN, Naive Bayes, Decision Tree, Multilayer Perceptron, Random Forests, HMM). This evidence supported the proposal of a semi-hierarchical activity recognition algorithm that enabled the implementation of two distinct data stream segmentation techniques, the customization of the classification models of each activity of interest and the establishment of a set of restrictions to apply on top of the classification output, based on daily evidence. An F1-score of 97% was finally attained for the simultaneous recognition of eating and drinking in an all-day acquisition from one young user, and 93% in a test set with 31 h of data from 5 different unseen users, 2 of which were seniors. These results were deemed very promising towards solving the problem of food and fluids intake monitoring with practical systems which shall maximize user-acceptance.
Construction of Residue Number System Using Hardware Efficient Diagonal Function

The residue number system (RNS) is a non-positional number system that allows one to perform addition and multiplication operations fast and in parallel. However, because the RNS is a non-positional number system, magnitude comparison of numbers in RNS form is impossible, so a division operation and an operation of reverse conversion into a positional form containing magnitude comparison operations are impossible too. Therefore, RNS has disadvantages in that some operations in RNS, such as reverse conversion into positional form, magnitude comparison, and division of numbers are problematic. One of the approaches to solve this problem is using the diagonal function (DF). In this paper, we propose a method of RNS construction with a convenient form of DF, which leads to the calculations modulo 2 n , 2 n − 1 or 2 n + 1 and allows us to design efficient hardware implementations. We constructed a hardware simulation of magnitude comparison and reverse conversion into a positional form using RNS with different moduli sets constructed by our proposed method, and used different approaches to perform magnitude comparison and reverse conversion: DF, Chinese remainder theorem (CRT) and CRT with fractional values (CRTf). Hardware modeling was performed on Xilinx Artix 7 xc7a200tfbg484-2 in Vivado 2016.3 and the strategy of synthesis was highly area optimized. The hardware simulation of magnitude comparison shows that, for three moduli, the proposed method allows us to reduce hardware resources by 5.98–49.72% in comparison with known methods. For the four moduli, the proposed method reduces delay by 4.92–21.95% and hardware costs by twice as much by comparison to known methods. A comparison of simulation results from the proposed moduli sets and balanced moduli sets shows that the use of these proposed moduli sets allows up to twice the reduction in circuit delay, although, in several cases, it requires more hardware resources than balanced moduli sets.