Reviewerly/exHarmony huggingface dataset:
Hough Forest With Optimized Leaves for Global Hand Pose Estimation With Arbitrary Postures

Vision-based hand pose estimation is important in human-computer interaction. While many recent works focus on full degree-of-freedom hand pose estimation, robust estimation of global hand pose remains a challenging problem. This paper presents a novel algorithm to optimize the leaf weights in a Hough forest to assist global hand pose estimation with a single depth camera. Different from traditional Hough forest, we propose to learn the vote weights stored at the leaf nodes of a forest in a principled way to minimize average pose prediction error, so that ambiguous votes are largely suppressed during prediction fusion. Experiments show that the proposed method largely improves pose estimation accuracy with optimized leaf weights on both synthesis and real datasets and performs favorably compared to state-of-the-art convolutional neural network-based methods. On real-world depth videos, the proposed method demonstrates improved robustness compared to several other recent hand tracking systems from both industry and academy. Moreover, we utilize the proposed method to build virtual/augmented reality applications to allow users to manipulate and examine virtual objects with bare hands.
Error Correcting Input and Output Hashing

Most learning-based hashing algorithms leverage sample-to-sample similarities, such as neighborhood structure, to generate binary codes, which achieve promising results for image retrieval. This type of methods are referred to as instance-level encoding. However, it is nontrivial to define a scalar to represent sample-to-sample similarity encoding the semantic labels and the data structure. To address this issue, in this paper, we seek to use a class-level encoding method, which encodes the class-to-class relationship, to take the semantic information of classes into consideration. Based on these two encodings, we propose a novel framework, error correcting input and output (EC-IO) coding, which does class-level and instance-level encoding under a unified mapping space. Our proposed model contains two major components, which are distribution preservation and error correction. With these two components, our model maps the input feature of samples and the output code of classes into a unified space to encode the intrinsic structure of data and semantic information of classes simultaneously. Under this framework, we present our hashing model, EC-IO hashing (EC-IOH), by approximating the mapping space with the Hamming space. Extensive experiments are conducted to evaluate the retrieval performance, and EC-IOH exhibits superior and competitive performances comparing with popular supervised and unsupervised hashing methods.
Conditional adversarial synthesis of 3D facial action units

Employing deep learning-based approaches for fine-grained facial expression analysis, such as those involving the estimation of Action Unit (AU) intensities, is difficult due to the lack of a large-scale dataset of real faces with sufficiently diverse AU labels for training. In this paper, we consider how AU-level facial image synthesis can be used to substantially augment such a dataset. We propose an AU synthesis framework that combines the well-known 3D Morphable Model (3DMM), which intrinsically disentangles expression parameters from other face attributes, with models that adversarially generate 3DMM expression parameters conditioned on given target AU labels, in contrast to the more conventional approach of generating facial images directly. In this way, we are able to synthesize new combinations of expression parameters and facial images from desired AU labels. Extensive quantitative and qualitative results on the benchmark DISFA dataset demonstrate the effectiveness of our method on 3DMM facial expression parameter synthesis and data augmentation for deep learning-based AU intensity estimation.
AM-FED+: An Extended Dataset of Naturalistic Facial Expressions Collected in Everyday Settings

Public datasets have played a significant role in advancing the state-of-the-art in automated facial coding. Many of these datasets contain posed expressions and/or videos recorded in controlled lab conditions with little variation in lighting or head pose. As such, the data do not reflect the conditions observed in many real-world applications. We present AM-FED+ an extended dataset of naturalistic facial response videos collected in everyday settings. The dataset contains 1,044 videos of which 545 videos (263,705 frames or 21,859 seconds) have been comprehensively manually coded for facial action units. These videos act as a challenging benchmark for automated facial coding systems. All the videos contain gender labels and a large subset (77 percent) contain age and country information. Subject self-reported liking and familiarity with the stimuli are also included. We provide automated facial landmark detection locations for the videos. Finally, baseline action unit classification results are presented for the coded videos. The dataset is available to download online: https://www.affectiva.com/facial-expression-dataset/.
A Hybrid Level Set With Semantic Shape Constraint for Object Segmentation

This paper presents a hybrid level set method for object segmentation. The method deconstructs segmentation task into two procedures, i.e., shape transformation and curve evolution, which are alternately optimized until convergence. In this framework, only one shape prior encoded by shape context is utilized to estimate a transformation allowing the curve to have the same semantic expression as shape prior, and curve evolution is driven by an energy functional with topology-preserving and kernelized terms. In such a way, the proposed method is featured by the following advantages: 1) hybrid paradigm makes the level set framework possess the ability of incorporating other shape-related techniques about shape descriptor and distance; 2) shape context endows one single prior with semanticity, and hence leads to the competitive performance compared to the ones with multiple shape priors; and 3) additionally, combining topology-preserving and kernelization mechanisms together contributes to realizing a more reasonable segmentation on textured and noisy images. As far as we know, we propose a hybrid level set framework and utilize shape context to guide curve evolution for the first time. Our method is evaluated with synthetic, healthcare, and natural images, as a result, it shows competitive and even better performance compared to the counterparts.
Horizon scanning in policy research database with a probabilistic topic model

National governments take advantage of collective intelligence when conducting foresight processes. They grasp emerging issues through expert reviews as well as public opinions. It raises national agendas and affects policy-making process. Therefore, by examining policy papers which contain societal issues, we can perceive past, current, and future environments. In this study, we exploit policy research database of Republic of Korea, which is a unique source that automatically collects all policy papers written by national research institutes, to extract latent topics and their trends over 10 years through a probabilistic topic model. Detected topics fairly correspond to expert-selected future drivers in national foresight report, implying that public discourse and policy agenda are coupled. We suggest to utilize open government data and text mining methods for building open foresight framework that various actors exchange their opinions on societal issues.
Word sense induction using word embeddings and community detection in complex networks

Word Sense Induction (WSI) is the ability to automatically induce word senses from corpora. The WSI task was first proposed to overcome the limitations of manually annotated corpus that are required in word sense disambiguation systems. Even though several works have been proposed to induce word senses, existing systems are still very limited in the sense that they make use of structured, domain-specific knowledge sources. In this paper, we devise a method that leverages recent findings in word embeddings research to generate context embeddings, which are embeddings containing information about the semantical context of a word. In order to induce senses, we modeled the set of ambiguous words as a complex network. In the generated network, two instances (nodes) are connected if the respective context embeddings are similar. Upon using well-established community detection methods to cluster the obtained context embeddings, we found that the proposed method yields excellent performance for the WSI task. Our method outperformed competing algorithms and baselines, in a completely unsupervised manner and without the need of any additional structured knowledge source.
Real-Time Fault-Tolerant Moving Horizon Air Data Estimation for the RECONFIGURE Benchmark

This paper proposes a real-time fault-tolerant estimation approach for combined sensor fault diagnosis and air data reconstruction. Due to simultaneous influence of winds and latent faults on monitored sensors, it is challenging to address the tradeoff between robustness to wind disturbances and sensitivity to sensor faults. As opposed to conventional fault-tolerant estimators that do not consider any constraints, we propose a constrained fault-tolerant estimator using moving horizon estimation (MHE). By exploiting wind bounds according to the weather or flight conditions, this approach improves fault sensitivity without sacrificing disturbance robustness. This improvement is attributed to active inequality constraints caused by faults, as shown in sensitivity analysis of the formulated MHE problem. The challenge of real-time nonlinear MHE is addressed by adopting an efficient structure-exploiting algorithm within a real-time iteration scheme. In order to facilitate the industrial validation and verification, the algorithm is implemented using an Airbus graphical symbol library to be compliant with the actual flight control computer, and its feasibility of real-time computation has been validated. The simulation results on the RECONFIGURE benchmark, which is a high-fidelity Airbus simulator, over a wide range of the flight envelop show the efficacy of the proposed approach.
Long-Short-Term Features for Dynamic Scene Classification

Dynamic scene classification has been extensively studied in computer vision due to its widespread applications. The key to dynamic scene classification lies in jointly characterizing spatial appearance and temporal dynamics to achieve informative representation, which remains an outstanding task in the literature. In this paper, we propose a unified framework to extract spatial and temporal features for dynamic scene representation. More specifically, we deploy two variants of deep convolutional neural networks to encode spatial appearance and short-term dynamics into short-term deep features (STDF). Based on STDF, we propose using the autoregressive moving average model to extract long-term frequency features (LTFF). By combining STDF and LTFF, we establish the long-short-term feature (LSTF) representations of dynamic scenes. The LSTF characterizes both spatial and temporal patterns of dynamic scenes for comprehensive and information representation that enables more accurate classification. Extensive experiments on three-dynamic scene classification benchmarks have shown that the proposed LSTF achieves high performance and substantially surpasses the state-of-the-art methods.
Learning and Selecting Confidence Measures for Robust Stereo Matching

We present a robust approach for computing disparity maps with a supervised learning-based confidence prediction. This approach takes into consideration following features. First, we analyze the characteristics of various confidence measures in the random forest framework to select effective confidence measures depending on the characteristics of the training data and matching strategies, such as similarity measures and parameters. We then train a random forest using the selected confidence measures to improve the efficiency of confidence prediction and to build a better prediction model. Second, we present a confidence-based matching cost modulation scheme, based on predicted confidence values, to improve the robustness and accuracy of the (semi-) global stereo matching algorithms. Finally, we apply the proposed modulation scheme to popularly used algorithms to make them robust against unexpected difficulties that could occur in an uncontrolled environment using challenging outdoor datasets. The proposed confidence measure selection and cost modulation schemes are experimentally verified from various perspectives using the KITTI and Middlebury datasets.
Empirical software engineering: From discipline to interdiscipline

Empirical software engineering has received much attention in recent years and coined the shift from a more design-science-driven engineering discipline to an insight-oriented, and theory-centric one. Yet, we still face many challenges, among which some increase the need for interdisciplinary research. This is especially true for the investigation of human-centric aspects of software engineering. Although we can already observe an increased recognition of the need for more interdisciplinary research in (empirical) software engineering, such research configurations come with challenges barely discussed from a scientific point of view. In this position paper, we critically reflect upon the epistemological setting of empirical software engineering and elaborate its configuration as an Interdiscipline. In particular, we (1) elaborate a pragmatic view on empirical research for software engineering reflecting a cyclic process for knowledge creation, (2) motivate a path towards symmetrical interdisciplinary research, and (3) adopt five rules of thumb from other interdisciplinary collaborations in our field before concluding with new emerging challenges. This shall support stopping to treating empirical software engineering as a developing discipline moving towards a paradigmatic stage of normal science, but as a configuration of symmetric interdisciplinary teams and research methods.
Redundancy Avoidance for Big Data in Data Centers: A Conventional Neural Network Approach

As the innovative data collection technologies are applying to every aspect of our society, the data volume is skyrocketing. Such phenomenon poses tremendous challenges to data centers with respect to enabling storage. In this paper, a hybrid-stream big data analytics model is proposed to perform multimedia big data analysis. This model contains four procedures, i.e., data pre-processing, data classification, data recognition and data load reduction. Specifically, an innovative multi-dimensional Convolution Neural Network (CNN) is proposed to assess the importance of each video frame. Thus, those unimportant frames can be dropped by a reliable decision-making algorithm. In order to ensure video quality, minimal correlation and minimal redundancy (MCMR) are combined to optimize the decision-making algorithm. Simulation results show that the amount of processed video is significantly reduced, and the quality of video is preserved due to the addition of MCMR. The simulation also proves that the proposed model performs steadily and is robust enough to scale up to accommodate the big data crush in data centers.
An efficient design for reversible Wallace unsigned multiplier

Today, reversible logic can be used for designing low-power CMOS circuits, optical data processing, DNA computations, biological researches, quantum circuits and nanotechnology. Sometimes using of reversible logic is inevitable such as build quantum computers. Reversible logic circuits structure is much more complicated than irreversible logic circuits. Multiplication operation is considered as one of the most important operations in the ALU unit. In this paper, we have proposed two 4 × 4 reversible unsigned multiplier circuits in which Wallace tree method is used to reduce the depth of circuits. In first design, the partial products circuit is designed using TG and FG gates so that TG is used to produce the partial products and FG for fan-out. In the second design, TG and PG gates are used to produce the partial products and no fan-out is required. Moreover, we have used PG gate and Feynman' block as reversible half-adder (HA) and full-adder (FA) in the summation network, respectively. In the first design, the main purpose is to decrease the depth of the circuit and increase the circuit speed. In the second design we would attempt to improve quantum parameters the number of garbage outputs, constant inputs and quantum cost. The evaluation results show that the first design, in terms of delay, is the fastest circuit. Also, the second design in terms of the number of constant inputs, garbage outputs and quantum cost is better than other designs.
Multi-Cloud Performance and Security Driven Federated Workflow Management

Federated multi-cloud resource allocation for data-intensive application workflows is generally performed based on performance or quality of service (i.e., QSpecs) considerations. At the same time, end-to-end security requirements of these workflows across multiple domains are considered as an afterthought due to lack of standardized formalization methods. Consequently, diverse/heterogenous domain resource and security policies cause inter-conflicts between application's security and performance requirements that lead to sub-optimal resource allocations. In this paper, we present a joint performance and security-driven federated resource allocation scheme for data-intensive scientific applications. In order to aid joint resource brokering among multi-cloud domains with diverse/heterogenous security postures, we first define and characterize a data-intensive application's security specifications (i.e., SSpecs). Then we describe an alignment technique inspired by Portunes Algebra to homogenize the various domain resource policies (i.e., RSpecs) along an application's workflow lifecycle stages. Using such formalization and alignment, we propose a near optimal cost-aware joint QSpecs-SSpecs-driven, RSpecs-compliant resource allocation algorithm for multi-cloud computing resource domain/location selection as well as network path selection. We implement our security formalization, alignment, and allocation scheme as a framework, viz., “OnTimeURB” and validate it in a multi-cloud environment with exemplar data-intensive application workflows involving distributed computing and remote instrumentation use cases with different performance and security requirements.
Unsupervised Machine Learning on Encrypted Data

In the context of Fully Homomorphic Encryption, which allows computations on encrypted data, Machine Learning has been one of the most popular applications in the recent past. All of these works, however, have focused on supervised learning, where there is a labeled training set that is used to configure the model. In this work, we take the first step into the realm of unsupervised learning, which is an important area in Machine Learning and has many real-world applications, by addressing the clustering problem. To this end, we show how to implement the $$K$$ -Means-Algorithm. This algorithm poses several challenges in the FHE context, including a division, which we tackle by using a natural encoding that allows division and may be of independent interest. While this theoretically solves the problem, performance in practice is not optimal, so we then propose some changes to the clustering algorithm to make it executable under more conventional encodings. We show that our new algorithm achieves a clustering accuracy comparable to the original $$K$$ -Means-Algorithm, but has less than $$5\%$$ of its runtime.
Secure Data Group Sharing and Dissemination with Attribute and Time Conditions in Public Cloud

Cloud computing has become increasingly popular among users and businesses around the world. Although cryptographic techniques can provide data protection for users in public cloud, several issues also remain problematic, such as secure data group dissemination and fine-grained access control of time-sensitive data. In this paper, we propose an identity-based data group sharing and dissemination scheme in public cloud, in which data owner could broadcast encrypted data to a group of receivers at one time by specifying these receivers' identities in a convenient and secure way. In order to achieve secure and flexible data group dissemination, we adopt attribute-based and timed-release conditional proxy re-encryption to guarantee that only data disseminators whose attributes satisfy the access policy of encrypted data can disseminate it to other groups after the releasing time by delegating a re-encryption key to cloud server. The re-encryption conditions are associated with attributes and releasing time, which allows data owner to enforce fine-grained and timed-release access control over disseminated ciphertexts. The theoretical analysis and experimental results show our proposed scheme makes a tradeoff between computational overhead and expressive dissemination conditions.
Combining background subtraction algorithms with convolutional neural network

Accurate and fast extraction of foreground object is a key prerequisite for a wide range of computer vision applications such as object tracking and recognition. Thus, enormous background subtraction methods for foreground object detection have been proposed in recent decades. However, it is still regarded as a tough problem due to a variety of challenges such as illumination variations, camera jitter, dynamic backgrounds, shadows, and so on. Currently, there is no single method that can handle all the challenges in a robust way. In this letter, we try to solve this problem from a new perspective by combining different state-of-the-art background subtraction algorithms to create a more robust and more advanced foreground detection algorithm. More specifically, an encoder-decoder fully convolutional neural network architecture is trained to automatically learn how to leverage the characteristics of different algorithms to fuse the results produced by different background subtraction algorithms and output a more precise result. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that the proposed method outperforms all the considered single background subtraction algorithm. And we show that our solution is more efficient than other combination strategies.
Exact master equation and general non-Markovian dynamics in open quantum systems

Investigations of quantum and mesoscopic thermodynamics force one to answer two fundamental questions associated with the foundations of statistical mechanics: (i) how does macroscopic irreversibility emerge from microscopic reversibility? (ii) how does the system relax in general to thermal equilibrium with its environment? The answers to these questions rely on a deep understanding of nonequilibrium dynamics of systems interacting with their environments. Decoherence is also a main concern in developing quantum information technology. In the past two decades, many theoretical and experimental investigations have devoted to this topic, most of these investigations take the Markov (memory-less) approximation. These investigations have provided a partial understanding to several fundamental issues, such as quantum measurement and the quantum-to-classical transition, etc. However, experimental implementations of nanoscale solid-state quantum information processing makes strong non-Markovian memory effects unavoidable, thus rendering their study a pressing and vital issue. Through the rigorous derivation of the exact master equation and a systematical exploration of various non-Markovian processes for a large class of open quantum systems, we find that decoherence manifests unexpected complexities. We demonstrate these general non-Markovian dynamics manifested in different open quantum systems.