Reviewerly/exHarmony huggingface dataset:
Boosting Document Retrieval with Knowledge Extraction and Linked Data

Given a document collection, Document Retrieval is the task of returning the most relevant documents for a specified user query. In this paper, we assess a document retrieval approach exploiting Linked Open Data and Knowledge Extraction techniques. Based on Natural Language Processing methods (e.g. , Entity Linking, Frame Detection), knowledge extraction allows disambiguating the semantic content of queries and documents, linking it to established Linked Open Data resources (e.g., DBpedia, YAGO) from which additional semantic terms (entities, types, frames, temporal information) are imported to realize a semantic-based expansion of queries and documents. The approach, implemented in the KE4IR system, has been evaluated on different state-of-the-art datasets, on a total of 555 queries and with document collections spanning from few hundreds to more than a million of documents. The results show that the expansion with semantic content extracted from queries and documents enables consistently outperforming retrieval performances when only textual information is exploited; on a specific dataset for semantic search, KE4IR outperforms a reference ontology-based search system. The experiments also validate the feasibility of applying knowledge extraction techniques for document retrieval – i.e., processing the document collection, building the expanded index, and searching over it – on large collections (e.g., TREC WT10g).
State Exchange with Quantum Side Information

We consider a quantum communication task between two users Alice and Bob, in which Alice and Bob exchange their respective quantum information by means of local operations and classical communication assisted by shared entanglement. Here, we assume that Alice and Bob may have quantum side information, not transferred, and classical communication is free. In this work, we derive general upper and lower bounds for the least amount of entanglement which is necessary to perfectly perform this task, called the state exchange with quantum side information. Moreover, we show that the optimal entanglement cost can be negative when Alice and Bob make use of their quantum side information. We finally provide conditions on the initial state for the state exchange with quantum side information which give the exact optimal entanglement cost.
Fast Montgomery modular multiplier for Rivest–Shamir–Adleman cryptosystem

A high-speed Montgomery modular multiplier is proposed in this study. This multiplier requires binary input and also generates a modular product in binary form. To speed up the process of multiplication, intermediate operands of the multiplier are kept in carry-save form. This multiplier employs a two-level carry-save adder architecture to keep intermediate operands in carry-save form. This multiplier also employs a look-ahead carry unit (LCU) for conversion of the final output from carry-save form to binary form. LCU is also utilised for pre-computation of intermediate operands. Format conversion and pre-computation require additional clock cycles. In general, each cycle of the Montgomery multipliers involves addition and shift operation followed by computation of the following quotient. The proposed multiplier adds and shifts as well as computes the following two quotients simultaneously to minimise the critical path delay. In addition, the proposed multiplier combines two iterations, which require additional intermediate operands. In this way, the execution time and the number of clock cycles required for multiplication are minimised significantly and extra clock cycles required for format conversion and operand pre-computation can be overlooked. Experimental results show that the proposed approach can achieve significant speed and throughput improvement as compared to previous multipliers.
Unicorn: Unified resource orchestration for multi-domain, geo-distributed data analytics

As the data volume increases exponentially over time, data-intensive analytics benefits substantially from multi-organizational, geographically-distributed, collaborative computing, where different organizations contribute various yet scarce resources, e.g., computation, storage and networking resources, to collaboratively collect, share and analyze extremely large amounts of data. By analyzing the data analytics trace from the Compact Muon Solenoid (CMS) experiment, one of the largest scientific experiments in the world, and systematically examining the design of existing resource management systems for clusters, we show that the multi-domain, geo-distributed, resource-disaggregated nature of this new paradigm calls for a framework to manage a large set of distributively-owned, heterogeneous resources, with the objective of efficient resource utilization, following the autonomy and privacy of different domains, and that the fundamental challenge for designing such a framework is: how to accurately discover and represent resource availability of a large set of distributively-owned, heterogeneous resources across different domains with minimal information exposure from each domain? Existing resource management systems are designed for single-domain clusters and cannot address this challenge. In this paper, we design Unicorn, the first unified resource orchestration framework for multi-domain, geo-distributed data analytics. In Unicorn, we encode the resource availability for each domain into resource state abstraction, a variant of the network view abstraction extended to accurately represent the availability of multiple resources with minimal information exposure using a set of linear inequalities. We then design a novel, efficient cross-domain query algorithm and a privacy-preserving resource information integration protocol to discover and integrate the accurate, minimal resource availability information for a set of data analytics jobs across different domains. In addition, Unicorn also contains a global resource orchestrator that computes optimal resource allocation decisions for data analytics jobs. We implement a prototype of Unicorn and present preliminary evaluation results to demonstrate its efficiency and efficacy. We also give a full demonstration of the Unicorn system at SuperComputing 2017.
Big graph classification frameworks based on Extreme Learning Machine

Graph data analysis is a hot topic in recent research area. Graph classification is one of the most important graph data analysis problems, which choose the most probable class labels of graphs using models based on the training dataset. It has wildly applications in protein group identification, chemical compounds classification and so on. Many existing research of graph learning suffer from high computation cost as large scale graph data are dramatically increased. In order to realize big graph classification with real-time learning ability and good scalability, efficient feature extraction approaches and ELM variants are utilized in this paper. To be specific, we present three frameworks of big graph classification based on ELMs: (1) a framework with a compression-based frequent subgraph mining method to reduce graph size; (2) an incremental framework to handle dynamic graphs; (3) a distributed framework with distributed ELMs to provide good scalability and easy implementation on cloud platforms. Extensive experiments are conducted on clusters with large real-world graph datasets. The experimental results demonstrate that our frameworks are efficient in big graph classification applications, and well suitable for dynamic networks. The results also validate that ELM and its variants have good classification performance on large-scale graphs.
Lower bounds for text indexing with mismatches and differences

In this paper we study lower bounds for the fundamental problem of text indexing with mismatches and differences.In this problem we are given a long string of length n, the "text", and the task is to preprocess it into a data structure such that given a query string Q, one can quickly identify substrings that are within Hamming or edit distance at most k from Q.This problem is at the core of various problems arising in biology and text processing.While exact text indexing allows linear-size data structures with linear query time, text indexing with k mismatches (or k differences) seems to be much harder: All known data structures have exponential dependency on k either in the space, or in the time bound.We provide conditional and pointer-machine lower bounds that make a step toward explaining this phenomenon.We start by demonstrating lower bounds for k = Θ(log n).We show that assuming the Strong Exponential Time Hypothesis, any data structure for text indexing that can be constructed in polynomial time cannot have O(n 1-δ ) query time, for any δ > 0. This bound also extends to the setting where we only ask for (1+ε)approximate solutions for text indexing.However, in many applications the value of k is rather small, and one might hope that for small k we can develop more efficient solutions.We show that this would require a radically new approach as using the current methods one cannot avoid exponential dependency on k either in the space, or in the time bound for all even 8 √ 3
Covariate Shift Adaptation on Learning from Positive and Unlabeled Data

The goal of binary classification is to identify whether an input sample belongs to positive or negative classes. Usually, supervised learning is applied to obtain a classification rule, but in real-world applications, it is conceivable that only positive and unlabeled data are accessible for learning, which is called learning from positive and unlabeled data (PU learning). Furthermore, in practice, data distributions are likely to differ between training and testing due to, for example, time variation and domain shift. The covariate shift is a dataset shift situation, where distributions of covariates (inputs) differ between training and testing, but the input-output relation is the same. In this paper, we address the PU learning problem under the covariate shift. We propose an importanceweighted PU learning method and reveal in which situations the importance-weighting is necessary. Moreover, we derive the convergence rate of the proposed method under mild conditions and experimentally demonstrate its effectiveness.
Practical designs for permutation-symmetric problem Hamiltonians on hypercubes

We present a method to experimentally realize large-scale permutation-symmetric Hamiltonians for continuous-time quantum protocols such as quantum walks and adiabatic quantum computation. In particular, the method can be used to perform an encoded continuous-time quantum search on a hypercube graph with ${2}^{n}$ vertices encoded into $2n$ qubits. We provide details for a realistically achievable implementation in Rydberg atomic systems. Although the method is perturbative, the realization is always achieved at second order in perturbation theory, regardless of the size of the mapped system. This highly efficient mapping provides a natural set of problems which are tractable both numerically and analytically, thereby providing a powerful tool for benchmarking quantum hardware and experimentally investigating the physics of continuous-time quantum protocols.
STiMR k-Means: An Efficient Clustering Method for Big Data

Big Data clustering has become an important challenge in data analysis since several applications require scalable clustering methods to organize such data into groups of similar objects. Given the computational cost of most of the existing clustering methods, we propose in this paper a new clustering method, referred to as STiMR [Formula: see text]-means, able to provide good tradeoff between scalability and clustering quality. The proposed method is based on the combination of three acceleration techniques: sampling, triangle inequality and MapReduce. Sampling is used to reduce the number of data points when building cluster prototypes, triangle inequality is used to reduce the number of comparisons when looking for nearest clusters and MapReduce is used to configure a parallel framework for running the proposed method. Experiments performed on simulated and real datasets have shown the effectiveness of the proposed method, with the existing ones, in terms of running time, scalability and internal validity measures.
Population-guided large margin classifier for high-dimension low-sample-size problems

In this paper, we propose a novel linear binary classifier, denoted by population-guided large margin classifier (PGLMC), applicable to any sorts of data, including high-dimensional low-sample-size (HDLSS). PGLMC is conceived with a projecting direction w given by the comprehensive consideration of local structural information of the hyperplane and the statistics of the training samples. Our proposed model has several advantages compared to those widely used approaches. First, it isn't sensitive to the intercept term b. Second, it operates well with imbalanced data. Third, it is relatively simple to be implemented based on Quadratic Programming. Fourth, it is robust to the model specification for various real applications. The theoretical properties of PGLMC are proven. We conduct a series of evaluations on the simulated and five realworld benchmark data sets, including DNA classification, medical image analysis and face recognition. PGLMC outperforms the state-of-theart classification methods in most cases, or obtains comparable results.
RANDOM MASK: Towards Robust Convolutional Neural Networks

Robustness of neural networks has recently been highlighted by the adversarial examples, i.e., inputs added with well-designed perturbations which are imperceptible to humans but can cause the network to give incorrect outputs. In this paper, we design a new CNN architecture that by itself has good robustness. We introduce a simple but powerful technique, Random Mask, to modify existing CNN structures. We show that CNN with Random Mask achieves state-of-the-art performance against black-box adversarial attacks without applying any adversarial training. We next investigate the adversarial examples which 'fool' a CNN with Random Mask. Surprisingly, we find that these adversarial examples often 'fool' humans as well. This raises fundamental questions on how to define adversarial examples and robustness properly.
User Expectations of Augmented Reality Experience in Indian School Education

Emerging technologies are lately being welcomed in Indian schools to enrich the quality of teaching and learning. Augmented Reality (AR) is one such technology that can be introduced in the classrooms. For a fruitful acceptance, it is required to design the AR interactions in schools as per the expectations of students, teachers and parents for a satisfactory user experience. In this paper, we present our study that was conducted with 47 participants belonging to three user groups of 6 parents, 7 teachers and 34 students. The broad goal was to understand the outlook of students toward technology as well as the expectations of the three user groups from an AR experience in Indian school education. Based on the mapping of the user stories, certain inferences were obtained which suggested the user requirements pertaining to AR experience in classrooms. We posit that these characteristic expectations of user experience can be used to develop AR applications for classrooms in future.
Generating open world descriptions of video using common sense knowledge in a pattern theory framework

The task of interpretation of activities as captured in video extends beyond just the recognition of observed actions and objects. It involves open world reasoning and constructing deep semantic connections that go beyond what is directly observed in the video and annotated in the training data. Prior knowledge plays a big role. Grenanderâs canonical pattern theory representation offers an elegant mechanism to capture these semantic connections between what is observed directly in the image and past knowledge in large-scale common sense knowledge bases, such as ConceptNet. We represent interpretations using a connected structure of basic detected (grounded) concepts, such as objects and actions, that are bound by semantics with other background concepts not directly observed, i.e., contextualization cues. Concepts are basic generators and the bonds are defined by the semantic relationships between concepts. Local and global regularity constraints govern these bonds and the overall connection structure. We use an inference engine based on energy minimization using an efficient Markov Chain Monte Carlo that uses the ConceptNet in its move proposals to find these structures that describe the image content. Using four different publicly available large datasets, Charades, Microsoft Visual Description Corpus (MSVD), Breakfast Actions, and CMU Kitchen, we show that the proposed model can generate video interpretations whose quality is comparable or better than those reported by state-of-the-art approaches, such as different forms of deep learning models, graphical models, and context-free grammars. Apart from the increased performance, the use of encoded common sense knowledge sources alleviate the need for large annotated training datasets and help tackle any imbalance in the data through prior knowledge, which is the bane of current machine learning approaches.
A Performance Comparison of Loss Functions for Deep Face Recognition

Face recognition is one of the most widely publicized feature in the devices today and hence represents an important problem that should be studied with the utmost priority. As per the recent trends, the Convolutional Neural Network (CNN) based approaches are highly successful in many tasks of Computer Vision including face recognition. The loss function is used on the top of CNN to judge the goodness of any network. In this paper, we present a performance comparison of different loss functions such as Cross-Entropy, Angular Softmax, Additive-Margin Softmax, ArcFace and Marginal Loss for face recognition. The experiments are conducted with two CNN architectures namely, ResNet and MobileNet. Two widely used face datasets namely, CASIA-Webface and MS-Celeb-1M are used for the training and benchmark Labeled Faces in the Wild (LFW) face dataset is used for the testing.
Monitoring Indicators of the Flipped Classroom Learning Process based on Data Mining – Taking the Course of “Virtual Reality Technology” as an Example

In recent years, due to the superiority of its teaching effect, the flipped classroom has attracted more and more attention from colleges and universities. In the information age, MOOC and micro-lectures have gradually entered the college classroom and become new ways of learning and teaching. Considering the further requirement on the quality of teaching, it is not enough to monitor merely the learning results. Instead, the curriculum reform requires more attention to the learning process. Data mining was combined with MOOC and micro-lectures in this study for the case study of the course of “Virtual Reality Technology”. Academic performance, learning input and learning behavior were selected as the indicators to monitor the process of learning “Virtual Reality Technology’. Relevant data were collected and analyzed. The analysis results were used as the basis to provide ideas and approaches for the implementation of reliable and effective teaching intervention methods in the process of teaching “Virtual Reality Technology”, so as improve the quality of teaching and provide reference for other related courses.
Max-margin Class Imbalanced Learning with Gaussian Affinity

Real-world object classes appear in imbalanced ratios. This poses a significant challenge for classifiers which get biased towards frequent classes. We hypothesize that improving the generalization capability of a classifier should improve learning on imbalanced datasets. Here, we introduce the first hybrid loss function that jointly performs classification and clustering in a single formulation. Our approach is based on an `affinity measure' in Euclidean space that leads to the following benefits: (1) direct enforcement of maximum margin constraints on classification boundaries, (2) a tractable way to ensure uniformly spaced and equidistant cluster centers, (3) flexibility to learn multiple class prototypes to support diversity and discriminability in feature space. Our extensive experiments demonstrate the significant performance improvements on visual classification and verification tasks on multiple imbalanced datasets. The proposed loss can easily be plugged in any deep architecture as a differentiable block and demonstrates robustness against different levels of data imbalance and corrupted labels.
Application of Artificial Neural Network for Image Noise Level Estimation in the SVD domain

The blind additive white Gaussian noise level estimation is an important and a challenging area of digital image processing with numerous applications including image denoising and image segmentation. In this paper, a novel block-based noise level estimation algorithm is proposed. The algorithm relies on the artificial neural network to perform a complex image patch analysis in the singular value decomposition (SVD) domain and to evaluate noise level estimates. The algorithm exhibits the capacity to adjust the effective singular value tail length with respect to the observed noise levels. The results of comparative analysis show that the proposed ANN-based algorithm outperforms the alternative single stage block-based noise level estimating algorithm in the SVD domain in terms of mean square error (MSE) and average error for all considered choices of block size. The most significant improvements in MSE levels are obtained at low noise levels. For some test images, such as “Car” and “Girlface”, at σ = 1 , these improvements can be as high as 99% and 98.5%, respectively. In addition, the proposed algorithm eliminates the error-prone manual parameter fine-tuning and automates the entire noise level estimation process.
Semantic Representation

In recent years, there has been renewed interest in the NLP community in genuine language understanding and dialogue. Thus the long-standing issue of how the semantic content of language should be represented is reentering the communal discussion. This paper provides a brief opinionated survey of broad-coverage semantic representation (SR). It suggests multiple desiderata for such representations, and then outlines more than a dozen approaches to SR-some longstanding, and some more recent, providing quick characterizations, pros, cons, and some comments on implementations.
Function Private Predicate Encryption for Low Min-Entropy Predicates

In this work, we propose new constructions for zero inner-product encryption (ZIPE) and non-zero inner-product encryption (NIPE) from prime-order bilinear pairings, which are both attribute and function private in the public-key setting. Our constructions are inspired by the linear FE constructions of Agrawal et al. [Crypto’16] and the simulation secure ZIPE of Wee [TCC’17]. In our ZIPE scheme, we show a novel way of embedding two different hard problem instances in a single secret key - one for unbounded collusion-resistance and the other for function privacy. For NIPE, we introduce new techniques for simultaneously achieving attribute and function privacy. We further show that the two constructions naturally generalize to a wider class of predicate encryption schemes such as subspace membership, subspace non-membership and hidden-vector encryption.