Reviewerly/exHarmony huggingface dataset:
Multi-class joint subspace learning for cross-modal retrieval

Most existing supervised subspace learning methods use the label information for high-level semantic exploration and learn one couple of common mapping matrices for all classes in the retrieval task. However, there are different semantic distributions among different classes and thus we propose to learn different mapping matrices for different classes in this paper, which facilitates learning more discriminative subspace. In addition, semantic overlap usually exists among different classes, which is reflected through common samples in different classes. Therefore, the multi-class joint subspace learning algorithm (MJSL) is proposed to distinct the different classes and mine the potential shared information of semantic overlap as much as possible. Specifically, the MJSL method considers exploring high-level semantic, keeping pair-wised closeness and selecting optimal features to obtain the most discriminative subspace for each class. Meanwhile, the trace-norm based joint learning is used for exploring the potential shared information of semantic overlap among different classes. Since the optimal mapping matrices have been learned via an iterative joint optimization algorithm with fast convergence, a linear SVM classifier is trained to establish the mapping relationship between multi-modal data and their potential semantic classes. Thus, the most related mapping matrices can be identified for each query adaptively and the retrieval performance can be promoted. Extensive experiments on two popular public datasets demonstrate that our algorithm outperforms several state-of-the-art cross-modal retrieval algorithms.
Two Hand Gesture Based 3D Navigation in Virtual Environments

Natural interaction is gaining popularity due to its simple, attractive, and realistic nature, which realizes direct Human Computer Interaction (HCI).In this paper, we presented a novel two hand gesture based interaction technique for 3 dimensional (3D) navigation in Virtual Environments (VEs).The system used computer vision techniques for the detection of hand gestures (colored thumbs) from real scene and performed different navigation (forward, backward, up, down, left, and right) tasks in the VE.The proposed technique also allow users to efficiently control speed during navigation.The proposed technique is implemented via a VE for experimental purposes.Forty (40) participants performed the experimental study.Experiments revealed that the proposed technique is feasible, easy to learn and use, having less cognitive load on users.Finally gesture recognition engines were used to assess the accuracy and performance of the proposed gestures.kNN achieved high accuracy rates (95.7%) as compared to SVM (95.3%).kNN also has high performance rates in terms of training time (3.16 secs) and prediction speed (6600 obs/sec) as compared to SVM with 6.40 secs and 2900 obs/sec.
Perturbative expansion of entanglement negativity using patterned matrix calculus

Negativity is an entanglement monotone frequently used to quantify entanglement in bipartite states. Because negativity is a nonanalytic function of a density matrix, existing methods used in the physics literature are insufficient to compute its derivatives. To this end we develop techniques in the calculus of complex, patterned matrices and use them to conduct a perturbative analysis of negativity in terms of arbitrary variations of the density operator. The result is an easy-to-implement expansion that can be carried out to all orders. On the way we provide convenient representations of the partial transposition map appearing in the definition of negativity. Our methods are well suited to study the growth and decay of entanglement in a wide range of physical systems, including the generic linear growth of entanglement in many-body systems, and have broad relevance to many functions of quantum states and observables.
Are screening methods useful in feature selection? An empirical study

Filter or screening methods are often used as a preprocessing step for reducing the number of variables used by a learning algorithm in obtaining a classification or regression model. While there are many such filter methods, there is a need for an objective evaluation of these methods. Such an evaluation is needed to compare them with each other and also to answer whether they are at all useful, or a learning algorithm could do a better job without them. For this purpose, many popular screening methods are partnered in this paper with three regression learners and five classification learners and evaluated on ten real datasets to obtain accuracy criteria such as R-square and area under the ROC curve (AUC). The obtained results are compared through curve plots and comparison tables in order to find out whether screening methods help improve the performance of learning algorithms and how they fare with each other. Our findings revealed that the screening methods were useful in improving the prediction of the best learner on two regression and two classification datasets out of the ten datasets evaluated.
GPU-accelerated feature tracking for 3D reconstruction

3D reconstruction based on structure from motion is one of the most techniques to produce sparse point-cloud model and camera parameter. However, this technique heavily relies on feature tracking method to obtain feature correspondences, then resulting in a heavy computation burden. To speed up 3D reconstruction, in this paper, we design a novel GPU-accelerated feature tracking (GFT) method for large-scale structure from motion (SFM)-based 3D reconstruction. The proposed GFT method consists of GPU-based Gaussian of image (DOG) keypoint detector, RootSIFT descriptor extractor, k nearest matching, and outlier removing. Firstly, our GPU-based DOG implementation can detect thousands of keypoints in real-time, whose speed is 30 times faster than that of the CPU version. Secondly, our GPU-based RootSIFT descriptor can compute thousands of descriptors in real-time. Thirdly, our GPU-based descriptor matching is 10 times faster than that of the state-of-the-art methods. Finally, we conduct thorough experiments on different datasets to evaluate the proposed method. Experimental results demonstrate the effectiveness and efficiency of the proposed method.
ROMIR: Robust Multi-View Image Re-Ranking

In multi-view re-ranking, multiple heterogeneous visual features are usually projected onto a low-dimensional subspace, and thus the resulting latent representation can be used for the subsequent similarity-based ranking. Albeit effective, this standard mechanism underplays the intrinsic structure underlying the latent subspace and does not take into account the substantial noise in the original spaces. In this paper, we propose a robust multi-view image re-ranking strategy. Due to the dramatic variability in image visual appearance, it is necessary to uncover the shared components underlying those query-related instances that are visually unlike for improving the re-ranking accuracy. Consequently, it is reasonable to assume the latent subspace enjoys the low-rank property and thus the subspace recovery can be achieved via the low-rank modeling accordingly. In addition, since the real-world data are usually partially contaminated, we employ `2;1-norm based sparsity constraint to appropriately model the sample-specific mapping noise for enhancing the model robustness. In order to produce discriminative representations, we encode a similarity preserving term in our multi-view embedding framework. As a result, the sample separability is maximally maintained in the latent subspace with sufficient discriminative power. The extensive evaluations on public landmark benchmarks demonstrate the efficacy and superiority of the proposed method.
Generalized Convolution Spectral Mixture for Multitask Gaussian Processes

Multitask Gaussian processes (MTGPs) are a powerful approach for modeling dependencies between multiple related tasks or functions for joint regression. Current kernels for MTGPs cannot fully model nonlinear task correlations and other types of dependencies. In this article, we address this limitation. We focus on spectral mixture (SM) kernels and propose an enhancement of this type of kernels, called multitask generalized convolution SM (MT-GCSM) kernel. The MT-GCSM kernel can model nonlinear task correlations and dependence between components, including time and phase delay dependence. Each task in MT-GCSM has its GCSM kernel with its number of convolution structures, and dependencies between all components from different tasks are considered. Another constraint of current kernels for MTGPs is that components from different tasks are aligned. Here, we lift this constraint by using inner and outer full cross convolution between a base component and the reversed complex conjugate of another base component. Extensive experiments on two synthetic and three real-life data sets illustrate the difference between MT-GCSM and previous SM kernels as well as the practical effectiveness of MT-GCSM.
Improved Fault-Tolerant Aggregate Signatures

Fault-tolerant aggregate signatures allow the verification algorithm to recognize and verify all the valid individual signatures in an aggregate signature. However, in ordinary aggregate signature schemes, if there is a single faulty individual signature in the valid aggregate, the whole aggregate will be invalid. This will make great difficulties in many applications including secure logging and batch verification in vehicular ad hoc network et al. In this paper, inspired by the finite set theory called uniform (k,n)-set proposed by Cao, we put forward a novel fault-tolerant aggregate signature scheme. Our new scheme is more efficient compared with previous fault-tolerant aggregate signature scheme based on cover-free family. It is noted that our scheme is quite easy to be implemented in the real scenario.
On the decision problem for MELL

In this short paper I will exhibit several mistakes in the recent attempt by Bimbó [3] to prove the decidability of the multiplicative exponential fragment of linear logic (MELL). In fact, the main mistake is so serious that there is no obvious fix, and therefore the decidability of MELL remains an open problem. As a side effect, this paper contains a complete (syntactic) proof of the decidability of the relevant version of MELL (called RMELL in this paper), that is the logic obtained from MELL by replacing the linear logic contraction rule by a general unrestricted version of the contraction rule. This proof can also be found (with a small error) in [3], and a semantic proof can be found in [35].
Exploring the link between employee commitment, recruitment process, and performance of internal supply chain of manufacturing firms in UAE

This paper investigates the impact of employee commitment as well as the recruitment process on the performance of internal supply chain.Additionally, the current study is intended to investigate the mediating role of the recruitment process on the relationship between employee commitment and firm supply chain performance.The main objective behind any recruitment process is to recruit committed employees to help a firm enhance the performance of the organizations.Therefore, the current study is carried out to fill a gap by exploring the relationship between recruitment processes, employee commitment, and firm supply chain performance.To achieve the research objective, the study has analyzed the data gathered from 284 mangers of manufacturing organizations of United Arab Emirates.284 questionnaires out of 550 are found useful, so the response rate is 52 percent.The structural equation modelling using AMOS is used to analyze the data.The results of the study show an agreement with the results of the hypotheses.The direct relationship between commitment types and firms' internal supply chain performance, and between recruitment process and firms' internal supply chain performance are found positive and significant.The results of the study will be helpful for HR practitioners, operation managers, researchers, and policy makers in understanding the impact of employee commitments and recruitment process on internal supply chains' performance of manufacturing firms.
Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases

Background: On a tide of big data, machine learning is coming to its day. Referring to huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correlations between phenotypes and modifications in histone or genes, accelerating the screen of lead compounds targeting epigenetics diseases and many other aspects around the study on epigenetics, which consequently realizes the hope of precision medicine. Methods: In this minireview, we will focus on reviewing the fundamentals and applications of machine learning methods which are regularly used in epigenetics filed and explain their features. Their advantages and disadvantages will also be discussed. Results: Machine learning algorithms have accelerated studies in precision medicine targeting epigenetics diseases. Conclusion: In order to make full use of machine learning algorithms, one should get familiar with the pros and cons of them, which will benefit from big data by choosing the most suitable method(s).
Data-driven Indoor Scene Modeling from a Single Color Image with Iterative Object Segmentation and Model Retrieval

We propose a new method for modeling the indoor scene from a single color image. With our system, the user only needs to drag a few semantic bounding boxes surrounding the objects of interest. Our system then automatically finds the most similar 3D models from the ShapeNet model repository and aligns them with the corresponding objects of interest. To achieve this, each 3D model is represented as a group of view-dependent representations generated from a set of synthesized views. We iteratively conduct object segmentation and 3D model retrieval, based on the observation that good segmentation of the objects of interest can significantly improve the accuracy of model retrieval and make it robust to cluttered background and occlusions, and in turn, the retrieved 3D models can be used to assist with object segmentation. Segmentation of all objects of interest is achieved simultaneously under a unified multilabeling framework which fully utilizes the correspondences between the objects of interest and retrieved model images. Besides, we propose a new method to estimate the scene layout of the input image with the segmentation masks, which helps compose the resulting scene and further improves the modeling result remarkably. We verify the effectiveness of our approach through experimenting with a variety of indoor images and comparing against the relevant methods.
Analysis of Facebook Interaction as Basis for Synthetic Expanded Social Graph Generation

Social networks have long been the subject of scientific researches, frequently hindered by the unavailability of representative datasets. The advent of online social networks (OSNs), which store data about interactions between billions of people, has greatly alleviated this problem. Since user interaction on the OSNs can correspond to their real-life relationships, OSN datasets quickly became a highly sought-after resource for social network research. However, enabling open access to such data entails serious security and privacy risks, especially after the introduction of the European General Data Protection Regulation. Some researchers mitigate this problem through anonymization, while others argue for the creation of synthetic datasets. We consider synthetic datasets preferable since they circumvent the security and privacy issues. Existing synthetic dataset generators produce a social graph containing only information whether a pair of nodes are connected. However, interpersonal relationships are much more complex. Because of that, our research considers the possibility of generating a synthetic expanded social graph which, in addition to the information about the existence of a connection between a pair of users, also provides information about the types and intensities of users’ interactions. As Facebook is the leading OSN today, we performed an extensive analysis of Facebook users’ interaction records with the aim of getting an insight into real-life interaction patterns. In this paper, we present results of this analysis at ego-user level and offer the conceptual solution for synthetic expanded social graph generation, which uses conducted analysis results as its basis.
Determinantal Reinforcement Learning

We study reinforcement learning for controlling multiple agents in a collaborative manner. In some of those tasks, it is insufficient for the individual agents to take relevant actions, but those actions should also have diversity. We propose the approach of using the determinant of a positive semidefinite matrix to approximate the action-value function in reinforcement learning, where we learn the matrix in a way that it represents the relevance and diversity of the actions. Experimental results show that the proposed approach allows the agents to learn a nearly optimal policy approximately ten times faster than baseline approaches in benchmark tasks of multi-agent reinforcement learning. The proposed approach is also shown to achieve the performance that cannot be achieved with conventional approaches in partially observable environment with exponentially large action space.
Misalignment correction for free-form surface in non-null interferometric testing

Unlike rotational symmetric surfaces, free-form surfaces have sophisticated degrees of freedom, therefore, are more difficult to be aligned. In this work, a practical and accurate correction method is proposed for the misalignment removal of the free-form surface in a non-null interferometric testing system based on computer modeling. The misalignment aberrations, introduced by axial location error, rotation error and non-axial attitude error, are modeled and corrected step by step. The axial and non-axial positions of the free-form surface in the computer model are adjusted from the ideal position to the misaligned one, matching the actual positions in experiment for the correction. Since the modeled position are tuned to be consistent with the actual one, all the misalignment aberrations can be removed from the test wavefront with the reconstruction algorithm. Computer simulations are displayed to verify the accuracy of the proposed method. Experimental results after alignment show basic consistency with the results of Taylor Hobson Profilometer, in which the peak-to-valley (PV) value error of the profile line is better than 1/30λ.
Image Classification Using Low-Rank Regularized Extreme Learning Machine

Extreme learning machine (ELM), a least-square-based learning algorithm, is a competitive machine learning method and provides efficient unified learning solutions for the applications of classification and regression. However, most existing models do not consider the relationship between features. In this paper, a low-rank regularized extreme learning machine is proposed by imposing low-rank constraints with the extracted features that are related on ELM. By this method, our model preserves the global geometric structure and simultaneously encodes discriminant information of data. Meanwhile, it can attain the solution with minimum norm, which is very important for ELM model. Extensive experiments on four widely used face datasets illustrate that the proposed model achieves better performance than ELM algorithms with other regularization terms for image classification.
Siamese Networks for Semantic Pattern Similarity

Semantic Pattern Similarity is an interesting, though not often encountered NLP task where two sentences are compared not by their specific meaning, but by their more abstract semantic pattern (e.g., preposition or frame). We utilize Siamese Networks to model this task, and show its usefulness in determining SQL patterns for unseen questions in a database-backed question answering scenario. Our approach achieves high accuracy and contains a built-in proxy for confidence, which can be used to keep precision arbitrarily high.
Reasoning over Assumption-Based Argumentation Frameworks via Direct Answer Set Programming Encodings

Focusing on assumption-based argumentation (ABA) as a central structured formalism to AI argumentation, we propose a new approach to reasoning in ABA with and without preferences. While previous approaches apply either specialized algorithms or translate ABA reasoning to reasoning over abstract argumentation frameworks, we develop a direct approach by encoding ABA reasoning tasks in answer set programming. This significantly improves on the empirical performance of current ABA reasoning systems. We also give new complexity results for reasoning in ABA+, suggesting that the integration of preferential information into ABA results in increased problem complexity for several central argumentation semantics.
Improving Domain-Independent Planning via Critical Section Macro-Operators

Macro-operators, macros for short, are a well-known technique for enhancing performance of planning engines by providing “short-cuts” in the state space. Existing macro learning systems usually generate macros from most frequent sequences of actions in training plans. Such approach priorities frequently used sequences of actions over meaningful activities to be performed for solving planning tasks.&#x0D; This paper presents a technique that, inspired by resource locking in critical sections in parallel computing, learns macros capturing activities in which a limited resource (e.g., a robotic hand) is used. In particular, such macros capture the whole activity in which the resource is “locked” (e.g., the robotic hand is holding an object) and thus “bridge” states in which the resource is locked and cannot be used. We also introduce an “aggressive” variant of our technique that removes original operators superseded by macros from the domain model. Usefulness of macros is evaluated on several stateof-the-art planners, and a wide range of benchmarks from the learning tracks of the 2008 and 2011 editions of the International Planning Competition.
Complexity of Inconsistency-Tolerant Query Answering in Datalog+/– under Cardinality-Based Repairs

Querying inconsistent ontological knowledge bases is an important problem in practice, for which several inconsistencytolerant query answering semantics have been proposed, including query answering relative to all repairs, relative to the intersection of repairs, and relative to the intersection of closed repairs. In these semantics, one assumes that the input database is erroneous, and the notion of repair describes a maximally consistent subset of the input database, where different notions of maximality (such as subset and cardinality maximality) are considered. In this paper, we give a precise picture of the computational complexity of inconsistencytolerant (Boolean conjunctive) query answering in a wide range of Datalog± languages under the cardinality-based versions of the above three repair semantics.
An Abstraction-Based Method for Verifying Strategic Properties in Multi-Agent Systems with Imperfect Information

We investigate the verification of Multi-agent Systems against strategic properties expressed in Alternating-time Temporal Logic under the assumptions of imperfect information and perfect recall. To this end, we develop a three-valued semantics for concurrent game structures upon which we define an abstraction method. We prove that concurrent game structures with imperfect information admit perfect information abstractions that preserve three-valued satisfaction. Further, we present a refinement procedure to deal with cases where the value of a specification is undefined. We illustrate the overall procedure in a variant of the Train Gate Controller scenario under imperfect information and perfect recall.