Reviewerly/exHarmony huggingface dataset:
Compositional data network analysis via lasso penalized D-trace loss

With the development of high-throughput sequencing techniques for 16S-rRNA gene profiling, the analysis of microbial communities is becoming more and more attractive and reliable. Inferring the direct interaction network among microbial communities helps in the identification of mechanisms underlying community structure. However, the analysis of compositional data remains challenging by the relative information conveyed by such data, as well as its high dimensionality.In this article, we first propose a novel loss function for compositional data called CD-trace based on D-trace loss. A sparse matrix estimator for the direct interaction network is defined as the minimizer of lasso penalized CD-trace loss under positive-definite constraint. An efficient alternating direction algorithm is developed for numerical computation. Simulation results show that CD-trace compares favorably to gCoda and that it is better than sparse inverse covariance estimation for ecological association inference (SPIEC-EASI) (hereinafter S-E) in network recovery with compositional data. Finally, we test CD-trace and compare it to the other methods noted above using mouse skin microbiome data.The CD-trace is open source and freely available from https://github.com/coamo2/CD-trace under GNU LGPL v3.Supplementary data are available at Bioinformatics online.
Sum-of-Squares Meets Program Obfuscation, Revisited

We develop attacks on the security of variants of pseudo-random generators computed by quadratic polynomials. In particular we give a general condition for breaking the one-way property of mappings where every output is a quadratic polynomial (over the reals) of the input. As a corollary, we break the degree-2 candidates for security assumptions recently proposed for constructing indistinguishability obfuscation by Ananth, Jain and Sahai (ePrint 2018) and Agrawal (ePrint 2018). We present conjectures that would imply our attacks extend to a wider variety of instances, and in particular offer experimental evidence that they break assumption of Lin-Matt (ePrint 2018). Our algorithms use semidefinite programming, and in particular, results on low-rank recovery (Recht, Fazel, Parrilo 2007) and matrix completion (Gross 2009).
Optimal mathematical programming and variable neighborhood search for k-modes categorical data clustering

The conventional k-modes algorithm and its variants have been extensively used for categorical data clustering. However, these algorithms have some drawbacks, e.g., they can be trapped into local optima and sensitive to initial clusters/modes. Our numerical experiments even showed that the k-modes algorithm could not identify the optimal clustering results for some special datasets regardless the selection of the initial centers. In this paper, we developed an integer linear programming (ILP) approach for the k-modes clustering, which is independent to the initial solution and can obtain directly the optimal results for small-sized datasets. We also developed a heuristic algorithm that implements iterative partial optimization in the ILP approach based on a framework of variable neighborhood search, known as IPO-ILP-VNS, to search for near-optimal results of medium and large sized datasets with controlled computing time. Experiments on 38 datasets, including 27 synthesized small datasets and 11 known benchmark datasets from the UCI site were carried out to test the proposed ILP approach and the IPO-ILP-VNS algorithm. The experimental results outperformed the conventional and other existing enhanced k-modes algorithms in literature, updated 9 of the UCI benchmark datasets with new and improved results.
A high speed and high efficiency superconducting photon number resolving detector

A series superconducting nanowire single-photon detector is a high performance nanowire array detector that has the potential to reach a high system detection efficiency (SQE), a high counting rate (CR) and photon number resolving functionality. Interconnected with a simplified readout, it shows promise for large scale expansion and has good prospects for future applications in single-photon imaging, quantum optics, quantum computing and deep-space communication. In this paper, we demonstrate a high SQE and CR series superconducting nanowire single-photon detector with an active area of 20 × 20 μm2. The detector shows a maximum SQE of 68% at 1550 nm with a dark count rate of 200 Hz and a wide response wavelength from 1100–2000 nm. It also achieves a CR over 300 MHz at single photon power level and has the capability of resolving photon numbers by up to six photons.
Robust object tracking via integration of particle filtering with deep detection

We propose a video object tracker (IDPF-RP) which is built upon the variable-rate color particle filtering with two innovations: (i) A deep region proposal network guided candidate BB selection scheme based on the dynamic prediction model of particle filtering is proposed to accurately generate the qualified object BBs. The introduced region proposal alignment scheme significantly improves the localization accuracy of tracking. (ii) A decision level fusion scheme that integrates the particle filter tracker and a deep detector resulting in an improved object tracking accuracy is formulated. This enables us to adaptively update the target model that improves robustness to appearance changes arising from high motion and occlusion. Performance evaluation reported on challenging VOT2018/2017/2016 and OTB-50 data sets demonstrates that IDPF-RP outperforms state-of-the-art trackers especially under size, appearance and illumination changes. Our tracker achieves comparable mean accuracy on VOT2018 while it respectively provides about 8%, 15%, and 30% higher success rates on VOT2016, VOT2017 and OTB-50 when IoU threshold is 0.5.
Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint

When learning sequence representations, traditional pattern-based methods often suffer from the data sparsity and high-dimensionality problems while recent neural embedding methods often fail on sequential datasets with a small vocabulary. To address these disadvantages, we propose an unsupervised method (named Sqn2Vec) which first leverages sequential patterns (SPs) to increase the vocabulary size and then learns low-dimensional continuous vectors for sequences via a neural embedding model. Moreover, our method enforces a gap constraint among symbols in sequences to obtain meaningful and discriminative SPs. Consequently, Sqn2Vec produces significantly better sequence representations than a comprehensive list of state-of-the-art baselines, particularly on sequential datasets with a relatively small vocabulary. We demonstrate the superior performance of Sqn2Vec in several machine learning tasks including sequence classification, clustering, and visualization.
Efficient conic fitting with an analytical Polar-N-Direction geometric distance

Fitting conics from images is a preliminary step for its plentiful applications. It is a common sense that geometric distance based fitting methods are better than algebraic distance based ones. However, for a long time, there has not been a geometric distance between a point and a general conic that allows easy computation and achieves high accuracy simultaneously. Though Sampson distance is widely accepted, it is only a first-order approximation. For other geometric distances, the computations are too complex to be popular in practice. In this paper, we derive a new geometric distance between a point and a general conic, called Polar-N-Direction distance. The distance can be adapted to a projective transformation because it is computed along the normal direction of the polar line of the point, making conic fitting more robust. Moreover, Polar-N-Direction distance is accurate and simultaneously still analytical in an explicit representation, which is quite easy to be implemented. Then, based on the distance, a new cost function is constructed. The conic fitting optimization by minimizing this cost function has all the merits of the geometric distance based methods and simultaneously avoids their limitations. Experiments show that the conic fitting method is greatly efficient.
A novel approach to reconstruction based saliency detection via convolutional neural network stacked with auto-encoder

Visual saliency detection, aimed at the simulation of human visual system (HVS), has drawn wide attention in recent decades. Reconstruction based saliency detection models are established methods for saliency detection, which predict unexpected regions via linear combination or auto-encoder network. However, these models are ineffective in dealing with images due to the loss of spatial information caused by the conversion from images to vectors. In this paper, a novel approach is proposed to solve this problem. The core is a deep reconstruction model, i.e., convolutional neural network for reconstruction stacked with auto-encoder (CNNR for short). On the one hand, the use of CNN is able to directly take two-dimensional data as input rather than having to convert the matrix to a series of vectors as in conventional reconstruction based saliency detection methods. On the other hand, the training process of CNN is augmented with the initialization obtained by an unsupervised learning process of convolutional auto-encoder (CAE). By this way, our CNNR model can be trained on limited labeled data, with the weights of the CNN being meaningfully initialized by CAE instead of random initialization. Performance evaluations are conducted through comprehensive experiments on four benchmark datasets and the comparisons with eight state-of-the-art saliency detection models show that our proposed deep reconstruction model outperforms most of the eight state-of-the-art saliency detection models.
Accelerating Sequential Minimal Optimization via Stochastic Subgradient Descent

Sequential minimal optimization (SMO) is one of the most popular methods for solving a variety of support vector machines (SVMs). The shrinking and caching techniques are commonly used to accelerate SMO. An interesting phenomenon of SMO is that most of the computational time is wasted on the first half of iterations for building a good solution closing to the optimal. However, as we all know, the stochastic subgradient descent (SSGD) method is extremely fast for building a good solution. In this paper, we propose a generalized framework of accelerating SMO through SSGD for a variety of SVMs of binary classification, regression, ordinal regression, and so on. We also provide a deep insight about why SSGD can accelerate SMO. Experimental results on a variety of datasets and learning applications confirm that our method can effectively speed up SMO.
An Experience-Based Scheme for Energy-SLA Balance in Cloud Data Centers

The proliferation of cloud computing has resulted in the establishment of large-scale data centers containing thousands of computing nodes and consuming enormous amounts of electrical energy.However, the low-cost and high-efficiency slogans are getting louder and louder, and the IT industry is also striving for this pursuit.Therefore, it is vital to minimizing the energy consumption for cloud providers while ensuring the quality of service for cloud users.In this paper, we propose several heuristic strategies to optimize these two metrics based on a two-level management model under a heterogeneous cloud environment.First, to detect whether a physical node is continuously overloaded, we propose an empirical forecast algorithm, which predicts the future state of the host by statistically analyzing the historical utilization data of the host.Second, we propose a weighted priority virtual machine (VM) selection algorithm.For each VM on the overloaded host, we weight several utilization factors and calculate its migration priority.Then, we simulate the proposed approach and compare it with the existing overloaded hosts detection algorithms with different VM selection policies under different workloads.
Foregrounding backstage activities for engaging children in a FabLab for STEM education

To tackle the worldwide shortage of graduates with a STEM diploma, governments are allocating funds to STEM education. This article focuses on the potential of FabLabs in relation to STEM education for non-expert users; more specifically, children of 6–16 years old. We describe two case studies – 'Wa Make?' and 'Making Things!' – that are part of a long-term process of engaging local non-expert users in FabLab Genk. Although the goal of the two cases was the same (i.e. getting children acquainted with a FabLab), their setups differed completely. By comparing the two cases, we address the importance of backstage activities for engaging non-expert users in STEM-related activities in FabLabs and report on the different front- and backstage activities that we undertook in both case studies. We discuss how focusing on these backstage activities resulted in us moving away from the '30-minute workshop model' to engage non-expert users in FabLab Genk in the long-term. We also report on how it urged us to thoroughly reflect upon the roles that adults (i.e. the researchers, youth workers, teachers, etc.) take on in STEM-related activities in FabLab. With our article we aim to provide handlebars for engaging children in a FabLab for STEM educational purposes, through foregrounding backstage activities. By doing so, this paper advocates to consider FabLabs as more than merely technical infrastructures and to foreground the importance of backstage activities in processes to engage non-expert users in STEM education.
Towards Learning a Realistic Rendering of Human Behavior

Realistic rendering of human behavior is of great interest for applications such as video animations, virtual reality and gaming engines. Commonly animations of persons performing actions are rendered by articulating explicit 3D models based on sequences of coarse body shape representations simulating a certain behavior. While the simulation of natural behavior can be efficiently learned, the corresponding 3D models are typically designed in manual, laborious processes or reconstructed from costly (multi-)sensor data. In this work, we present an approach towards a holistic learning framework for rendering human behavior in which all components are learned from easily available data. To enable control over the generated behavior, we utilize motion capture data and generate realistic motions based on user inputs. Alternatively, we can directly copy behavior from videos and learn a rendering of characters using RGB camera data only. Our experiments show that we can further improve data efficiency by training on multiple characters at the same time. Overall our approach shows a new path towards easily available, personalized avatar creation.
Direct generation of level of service maps from images using convolutional and long short-term memory networks

Congestion in transport stations could result in stampede development and deadly crush situations. Closed circuit television (CCTV) cameras enable station managers to monitor the crowd and reduce overcrowding risks. However, identifying congestion conditions is a very laborious task for a human operator who has to monitor multiple locations at the same time. This paper presents a new approach to automated image-based identification of congestion as measured by level of service (LOS), which is the most widely accepted standard for measuring congestion. Existing methods for measuring LOS based on crowd density estimation from images have the disadvantages that, crowd density cannot be estimated accurately. In addition, the calculation of flow parameters involves a complex process, and consequently these parameters are not indicative of congestion in real-time. This paper proposes a novel method based on machine learning to directly classify LOS without calculating flow parameters. In the proposed method, visual features extracted by a deep convolutional neural network are classified using a support vector machine classifier and the classification results are further refined by using a long short-term memory network. A second contribution of this research is to develop a web-based LOS map visualization platform to monitor pedestrian distribution and variation of distribution in real-time. Experimental evaluation at Flinders Street Station in Melbourne shows that this method can achieve an accuracy of 81.9% and efficiency of 0.40 seconds per frame in LOS classification using CCTV images.
Quantitative Verification of Masked Arithmetic Programs Against Side-Channel Attacks

Power side-channel attacks, which can deduce secret data via statistical analysis, have become a serious threat. Masking is an effective countermeasure for reducing the statistical dependence between secret data and side-channel information. However, designing masking algorithms is an error-prone process. In this paper, we propose a hybrid approach combing type inference and model-counting to verify masked arithmetic programs against side-channel attacks. The type inference allows an efficient, lightweight procedure to determine most observable variables whereas model-counting accounts for completeness. In case that the program is not perfectly masked, we also provide a method to quantify the security level of the program. We implement our methods in a tool QMVerif and evaluate it on cryptographic benchmarks. The experiment results show the effectiveness and efficiency of our approach.
XMSS and Embedded Systems

We describe a software-hardware co-design for the hash-based post-quantum signature scheme XMSS on a RISC-V embedded processor. We provide software optimizations for the XMSS reference implementation for SHA-256 parameter sets and several hardware accelerators that allow to balance area usage and performance based on individual needs. By integrating our hardware accelerators into the RISC-V processor, the version with the best time-area product generates a key pair (that can be used to generate $$2^{10}$$ signatures) in 3.44 s, achieving an over $$54 \times $$ speedup in wall-clock time compared to the pure software version. For such a key pair, signature generation takes less than 10 ms and verification takes less than 6 ms, bringing speedups of over $$42 \times $$ and $$17 \times $$ respectively. We tested and measured the cycle count of our implementation on an Intel Cyclone V SoC FPGA. The integration of our XMSS accelerators into an embedded RISC-V processor shows that it is possible to use hash-based post-quantum signatures for a large variety of embedded applications.
Facial cues to age perception using three-dimensional analysis

To clarify cues for age perception, the three-dimensional head and face forms of Japanese women were analyzed. It is known that age-related transformations are mainly caused by changes in soft tissue during adulthood. A homologous polygon model was created by fitting template meshes to each study participant to obtain three-dimensional data for analyzing whole head and face forms. Using principal component analysis of the vertices coordinates of these models, 26 principal components were extracted (contribution ratios >0.5%), which accounted for more than 90% of the total variance. Among the principal components, five had a significant correlation with the perceived ages of the participants (p < 0.05). Transformations with these principal components in the age-related direction produced aged faces. Moreover, the older the perceived age, the larger the ratio of age-manifesting participants, namely participants who had one or more age-related principal component score greater than +1.0 σ in the age-related direction. Therefore, these five principal components were regarded as aging factors. A cluster analysis of the five aging factors revealed that all of the participants fell into one of four groups, meaning that specific combinations of factors could be used as cues for age perception in each group. These results suggest that Japanese women can be classified into four groups according to age-related transformations of soft tissue in the face.
What We Vote for? Answer Selection from User Expertise View in Community Question Answering

Answer selection is an important problem in community question answering (CQA), as it enables the distilling of reliable information and knowledge. Most existing approaches tackle this problem as a text matching task. However, they ignore the influence of the community in voting the best answers. Answer quality is highly correlated with semantic relevance and user expertise in CQA. In this paper, we formalize the answer selection problem from the user expertise view, considering both the semantic relevance in question-answer pair and user expertise in question-user pair. We design a novel matching function, explicitly modeling the influence of user expertise in community acceptance. Moreover, we introduce latent user vectors into the representation learning of answer, capturing the implicit topic interests in learned user vectors. Extensive experiments on two datasets from real world CQA sites demonstrate that our model outperforms state-of-the-art approaches for answer selection in CQA. Furthermore, the user representations learned by our model provide us a quantitative way to understand both the authority and topic-sensitive interests of users.
Learning tractable Bayesian networks in the space of elimination orders

The computational complexity of inference is now one of the most relevant topics in the field of Bayesian networks. Although the literature contains approaches that learn Bayesian networks from high dimensional datasets, traditional methods do not bound the inference complexity of the learned models, often producing models where exact inference is intractable. This paper focuses on learning tractable Bayesian networks from data. To address this problem, we propose strategies for learning Bayesian networks in the space of elimination orders. In this manner, we can efficiently bound the inference complexity of the networks during the learning process. Searching in the combined space of directed acyclic graphs and elimination orders can be extremely computationally demanding. We demonstrate that one type of elimination trees, which we define as valid, can be used as an equivalence class of directed acyclic graphs and elimination orders, removing redundancy. We propose methods for incrementally compiling local changes made to directed acyclic graphs in elimination trees and for searching for elimination trees of low width. Using these methods, we can move through the space of valid elimination trees in polynomial time with respect to the number of network variables and in linear time with respect to treewidth. Experimental results show that our approach successfully bounds the inference complexity of the learned models, while it is competitive with other state-of-the-art methods in terms of fitting to data.