Reviewerly/exHarmony huggingface dataset:
Ensemble Teaching for Hybrid Label Propagation

Label propagation aims to iteratively diffuse the label information from labeled examples to unlabeled examples over a similarity graph. Current label propagation algorithms cannot consistently yield satisfactory performance due to two reasons: one is the instability of single propagation method in dealing with various practical data, and the other one is the improper propagation sequence ignoring the labeling difficulties of different examples. To remedy above defects, this paper proposes a novel propagation algorithm called hybrid diffusion under ensemble teaching (HyDEnT). Specifically, HyDEnT integrates multiple propagation methods as base "learners" to fully exploit their individual wisdom, which helps HyDEnT to be stable and obtain consistent encouraging results. More importantly, HyDEnT conducts propagation under the guidance of an ensemble of "teachers". That is to say, in every propagation round the simplest curriculum examples are wisely designated by a teaching algorithm, so that their labels can be reliably and accurately decided by the learners. To optimally choose these simplest examples, every teacher in the ensemble should comprehensively consider the examples' difficulties from its own viewpoint, as well as the common knowledge shared by all the teachers. This is accomplished by a designed optimization problem, which can be efficiently solved via the block coordinate descent method. Thanks to the efforts of the teachers, all the unlabeled examples are logically propagated from simple to difficult, leading to better propagation quality of HyDEnT than the existing methods. Experiments on six popular datasets reveal that HyDEnT achieves the highest classification accuracy when compared with six state-of-the-art propagation methodologies such as harmonic functions, Fick's law assisted propagation, linear neighborhood propagation, semisupervised ensemble learning, bipartite graph-based consensus maximization, and teaching-to-learn and learning-to-teach.
Dual Implicit Mining-Based Latent Friend Recommendation

The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user-user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen-Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.
Enhancing batch normalized convolutional networks using displaced rectifier linear units: A systematic comparative study

A substantial number of expert and intelligent systems rely on deep learning methods to solve problems in areas such as economics, physics, and medicine. Improving the accuracy of the activation functions used by such methods can directly and positively impact the overall performance and quality of the mentioned systems at no cost whatsoever. In this sense, enhancing the design of such theoretical fundamental blocks is of great significance as it immediately impacts a broad range of current and future real-world deep learning based applications. Therefore, in this paper, we turn our attention to the interworking between the activation functions and the batch normalization, which is practically a mandatory technique to train deep networks currently. We propose the activation function Displaced Rectifier Linear Unit (DReLU) by conjecturing that extending the identity function of ReLU to the third quadrant enhances compatibility with batch normalization. Moreover, we used statistical tests to compare the impact of using distinct activation functions (ReLU, LReLU, PReLU, ELU, and DReLU) on the learning speed and test accuracy performance of standardized VGG and Residual Networks state-of-the-art models. These Convolutional Neural Networks were trained on CIFAR-100 and CIFAR-10, the most commonly used deep learning computer vision datasets. The results showed DReLU speeded up learning in all models and datasets. Besides, statistical significant performance assessments (p < 0.05) showed DReLU enhanced the test accuracy presented by ReLU in all scenarios. Furthermore, DReLU showed better test accuracy than any other tested activation function in all experiments with one exception, in which case it presented the second best performance. Therefore, this work demonstrates that it is possible to increase performance replacing ReLU by an enhanced activation function.
Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots

We present the design and evaluation of an integrated problem solving environment for cancer therapy analysis. The environment intertwines a statistical martingale model and a K Nearest Neighbor approach with visual encodings, including novel interactive nomograms, in order to compute and explain a patient's probability of survival as a function of similar patient results. A coordinated views paradigm enables exploration of the multivariate, heterogeneous and few-valued data from a large head and neck cancer repository. A visual scaffolding approach further enables users to build from familiar representations to unfamiliar ones. Evaluation with domain experts show how this visualization approach and set of streamlined workflows enable the systematic and precise analysis of a patient prognosis in the context of cohorts of similar patients. We describe the design lessons learned from this successful, multi-site remote collaboration.
Integrated Rough Fuzzy Clustering for Categorical data Analysis

In recent times, advanced data mining research has been mostly focusing on clustering of categorical data, where a natural ordering in attribute values is missing. To address this fact the Rough Fuzzy K-Modes clustering technique has been recently developed in order to handle imperfect information, i.e. indiscernibility (coarseness) and vagueness within the dataset. However, it has been observed that the said technique suffers from the problem of local optima due to the random choice of initial cluster modes. Hence, in this paper, we have proposed an integrated clustering technique using multi-phase learning. In this regard, first, Simulated Annealing based Rough Fuzzy K-Modes and Genetic Algorithm based Rough Fuzzy K-Modes are proposed in order to perform the clustering better by considering clustering as an underlying optimization problem. These clustering methods individually produce clusters having set of central and peripheral points. Thereafter, for each case, final improved clustering results are obtained by assigning peripheral points to a particular crisp cluster using Random Forest, where central points are used as training set. Second, the varying cardinality of the training and testing sets produced by each clustering method further motivated us to propose a generalized technique called Integrated Rough Fuzzy Clustering using Random Forest, where, results of three aforementioned clustering techniques are used to compute the roughness measure. Based on this measure, three different sets namely best central points, semi-best central points and pure peripheral points are determined. Thereafter, using multi-phase learning, best central points are used to classify the semi-best central points and then using both of them, pure peripheral points are classified by Random Forest. Experimental results are reported quantitatively and visually to demonstrate the effectiveness of the proposed methods in comparison with well-known state-of-the-art methods for six synthetic and five real-life datasets. Finally, statistical significance tests are conducted to establish the superiority of the results produced by the proposed methods.
Robust Finite-Horizon Filtering for 2-D Systems With Randomly Varying Sensor Delays

This paper is concerned with the robust finite-horizon filter design problem for a class of two-dimensional (2-D) time-varying systems with norm-bounded parameter uncertainties and incomplete measurements. The incomplete measurements cover randomly occurring sensor delays and missing measurements that are presented in a unified form by resorting to a stochastic Kronecker delta function. The occurrences of the sensor delays and missing measurements are governed by stochastic variables with known probability distributions. The main aim of the addressed problem is to design a recursive filter with appropriate gain parameters that ensure the local minimum of certain upper bound on the estimation error variance at each time instant. With the aid of the inductive approach and the 2-D Riccati-like difference equations, one of the first few attempts is made to tackle the robust filter design problem for 2-D uncertain systems with random sensor delays over a finite horizon. Sufficient conditions are provided for the existence of an upper bound on the estimation error variance, an algorithm is then developed to derive such an upper bound, and finally the desired filter is designed to minimize the obtained upper bound. The filter design procedure is of a recursive form that facilitates the online calculation. A numerical simulation is carried out to show the effectiveness of the developed filtering scheme.
Top- Durable Graph Pattern Queries on Temporal Graphs

Graphs offer a natural model for the relationships and interactions among entities, such as those occurring among users in social and cooperation networks, and proteins in biological networks. Since most such networks are dynamic, to capture their evolution over time, we assume a sequence of graph snapshots where each graph snapshot represents the state of the network at a different time instance. Given this sequence, we seek to find the top- <inline-formula> <tex-math notation="LaTeX">$k$</tex-math></inline-formula> <i>most durable matches</i> of an input graph pattern query, that is, the matches that exist for the longest period of time. The straightforward way to address this problem is to apply a state-of-the-art graph pattern matching algorithm at each snapshot and then aggregate the results. However, for large networks and long sequences, this approach is computationally expensive, since all matches have to be generated at each snapshot, including those appearing only once. We propose a new approach that uses a compact representation of the sequence of graph snapshots, appropriate time indexes to prune the search space, and strategies to determine the duration of the seeking matches. Finally, we present experiments with real datasets that illustrate the efficiency and effectiveness of our approach.
Zero-shot Hashing with orthogonal projection for image retrieval

Hashing has been widely used in large-scale image retrieval. Supervised information such as semantic similarity and class label, and Convolutional Neural Network (CNN) has greatly improved the quality of hash codes and hash functions. However, due to the explosive growth of web data, existing hashing methods cannot well perform on emerging images of new classes. In this paper, we propose a novel hashing method based on orthogonal projection of both image and semantic attribute, which constrains the generated binary codes in orthogonal space should be orthogonal with each other when they belong to different classes, otherwise be same. This constraint guarantees that the generated hash codes from different categories have equal Hamming distance, which also makes the space more discriminative within limited code length. To improve the performance, we also extend our method with a deep model. Experiments of both our linear and deep model on three popular datasets show that our method can achieve competitive results, specially, the deep model can outperform all the listed state-of-the-art approaches.
Group Anomaly Detection Using Deep Generative Models

Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points. In particular, we perform group anomaly detection (GAD) with an emphasis on irregular group distributions (e.g. irregular mixtures of image pixels). GAD is an important task in detecting unusual and anomalous phenomena in real-world applications such as high energy particle physics, social media and medical imaging. In this paper, we take a generative approach by proposing deep generative models: Adversarial autoencoder (AAE) and variational autoencoder (VAE) for group anomaly detection. Both AAE and VAE detect group anomalies using point-wise input data where group memberships are known a priori. We conduct extensive experiments to evaluate our models on real world datasets. The empirical results demonstrate that our approach is effective and robust in detecting group anomalies. Code related to this paper is available at: https://github.com/raghavchalapathy/gad , https://www.cs.cmu.edu/~lxiong/gad/gad.html , https://github.com/jorjasso/SMDD-group-anomaly-detection , https://github.com/cjlin1/libsvm .
I/O Efficient Core Graph Decomposition: Application to Degeneracy Ordering

Core decomposition is a fundamental graph problem with a large number of applications. Most existing approaches for core decomposition assume that the graph is kept in memory of a machine. Nevertheless, many real-world graphs are too big to reside in memory. In this paper, we study I/O efficient core decomposition following a semi-external model, which only allows node information to be loaded in memory. We propose a semi-external algorithm and an optimized algorithm for I/O efficient core decomposition. To handle dynamic graph updates, we firstly show that our algorithm can be naturally extended to handle edge deletion. Then, we propose an I/O efficient core maintenance algorithm to handle edge insertion, and an improved algorithm to further reduce I/O and CPU cost. In addition, based on our core decomposition algorithms, we further propose an I/O efficient semi-external algorithm for degeneracy ordering, which is an important graph problem that is highly related to core decomposition. We also consider how to maintain the degeneracy order. We conduct extensive experiments on 12 real large graphs. Our optimal core decomposition algorithm significantly outperforms the existing I/O efficient algorithm in terms of both processing time and memory consumption. They are very scalable to handle web-scale graphs. As an example, we are the first to handle a web graph with 978.5 million nodes and 42.6 billion edges using less than 4.2 GB memory. We also show that our proposed algorithms for degeneracy order computation and maintenance can handle big graphs efficiently with small memory overhead.
An improved linear kernel for complementary maximal strip recovery: Simpler and smaller

We study the Complementary Maximal Strip Recovery problem (CMSR), where the given are two strings S1 and S2 of distinct letters, each of which appears either in the positive form or the negative form. The question is whether there are k letters whose deletion results in two matched strings. String S1 matches string S2 if there are partitions of S1 and S2 such that each component of the partitions contains at least two letters and, moreover, for each component S1i of the partition of S1, there is a unique component S2j in the partition of S2 which is either equal to S1i or can be obtained from S1i by firstly reversing the order of the letters and then negating the letters. The CMSR problem is known to be NP-hard and fixed-parameter tractable with respect to k. In particular, a linear kernel of size 74k+4 was developed based on 8 reduction rules. Very recently, by imposing 3 new reduction rules to the previous kernelization, the linear kernel has been improved to 58k. We aim to simplify the kernelization, yet obtain an improved kernel. In particular, we study 7 reduction rules which lead to a linear kernel of size 42k+24.
Stacked Robust Adaptively Regularized Auto-Regressions for Domain Adaptation

Domain adaptation is the situation for supervised learning in which the training data are sampled from the source domain while the test data are sampled from the target domain that follows a different distribution. The key to solving such a problem is to reduce effects of the discrepancy between the training data and test data. Recently, deep learning methods that employ stacked denoising auto-encoders (SDAs) to learn new representations for both domains have been successfully applied in domain adaptation. And, remarkable performance on multi-domain sentiment analysis datasets has been reported, making deep learning a promising approach to domain adaptation problems. In this paper, a deep learning method called Stacked Robust Adaptively Regularized Auto-regressions (SRARAs) is proposed to learn useful representations for domain adaptation problems. Each layer of SRARAs contains two steps: a linear transformation step, which is based on robust adaptively regularized auto-regression, and a non-linear squashing transformation step. The first step aims at reducing the discrepancy between the training data and test data, and the second step is to introduce non-linearity and control the range of the elements in the outputs. The experimental results on text and image datasets demonstrate that the proposed method is very effective.
Refinement-Based Hierarchical Modeling and Correctness Verification of Cross-Organization Collaborative Emergency Response Processes

When an emergency occurs, one of the important challenges is how to form an effective and timely response. An emergency disposal plan is usually organized as a series of emergency response processes manipulated by one emergency command center and several subordinate emergency organizations. Moreover, these subordinate organizations are usually geographically dispersed and need to collaborate with each other. In this case, designing and verifying such cross-organizational collaborative emergency response processes are complicated and time-consuming. To address this problem, we propose a hierarchical modeling and correctness verification approach. A general framework for hierarchical modeling and correctness verification of such processes is first introduced. Then, a top-level model and two kinds of bottom-level models (complex and simple bottom-level ones) are proposed to model such processes and collaboration patterns from different abstraction levels. Next, Petri net refinement operation is adopted to refine the top-level model by using its corresponding bottom-level models to obtain the refined model. Finally, the correctness of the refined model is verified based on reachability graph. A typical running case of cross-organization collaborative fire emergency response processes is given to validate our proposed method.
A characterization of linearized polynomials with maximum kernel

We provide sufficient and necessary conditions for the coefficients of a $q$-polynomial $f$ over $\mathbb{F}_{q^n}$ which ensure that the number of distinct roots of $f$ in $\mathbb{F}_{q^n}$ equals the degree of $f$. We say that these polynomials have maximum kernel. As an application we study in detail $q$-polynomials of degree $q^{n-2}$ over $\mathbb{F}_{q^n}$ which have maximum kernel and for $n\leq 6$ we list all $q$-polynomials with maximum kernel. We also obtain information on the splitting field of an arbitrary $q$-polynomial. Analogous results are proved for $q^s$-polynomials as well, where $\gcd(s,n)=1$.
AccountTrade: Accountability Against Dishonest Big Data Buyers and Sellers

In this paper, a set of accountable protocols denoted as AccountTrade is proposed for big data trading among dishonest consumers. For achieving a secure big data trading environment, AccountTrade achieves book-keeping ability and accountability against dishonest consumers throughout the trading (i.e., buying and selling) of datasets. We investigate the consumers' responsibilities in the dataset trading, then we design AccountTrade to achieve accountability against dishonest consumers that are likely to deviate from the responsibilities. Specifically, a uniqueness index is defined and proposed, which is a new rigorous measurement of the data uniqueness for this purpose. Furthermore, several accountable trading protocols are presented to enable data brokers to blame the misbehaving entities when misbehavior is detected. The accountability of AccountTrade is formally defined, proved, and evaluated by an automatic verification tool as well as extensive simulation with real-world datasets. Our evaluation shows that AccountTrade incurs at most 10-kB storage overhead per file, and it is capable of 8-1000 concurrent data upload requests per server.
Utility-preserving privacy protection of nominal data sets via semantic rank swapping

Personal data are of great interest for research but, at the same time, they pose a serious privacy risk. Therefore, appropriate data protection measures should be undertaken by the data controller before making personal data available for secondary use. Also, such data protection should be done in a way that data are still useful for analysis. In the last years, a plethora of data protection mechanisms have been proposed. Among them, rank swapping is considered one of the best with respect to disclosure risk minimization and data utility preservation. Because rank swapping is based on sorting input data to swap values that are close to each other, in principle, it is a method restricted to numerical and ordinal categorical data. However, a significant amount of personal data currently compiled and used in data analysis are nominal, and their utility depends on the semantics they convey. To properly cope with this type of data, in this paper, we present rank swapping methods capable of protecting nominal data from a semantic perspective. Specifically, by exploiting ontologies, our methods are able to protect nominal data while properly preserving their semantics and, thus, their analytical utility. For that, we provide a suitable binary relation to semantically sort nominal data. Our proposal is capable of managing both independent individual attributes and non-independent multivariate data sets, being the latter especially relevant for data analysis. Empirical experiments carried on real clinical records and using a standard medical ontology show that our methods are able to preserve the semantic features of nominal data significantly better than standard permutation mechanisms.