Reviewerly/exHarmony huggingface dataset:
Deep Micro-Dictionary Learning and Coding Network

In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding Network (DDLCN). DDLCN has most of the standard deep learning layers (pooling, fully, connected, input/output, etc.) but the main difference is that the fundamental convolutional layers are replaced by novel compound dictionary learning and coding layers. The dictionary learning layer learns an over-complete dictionary for the input training data. At the deep coding layer, a locality constraint is added to guarantee that the activated dictionary bases are close to each other. Next, the activated dictionary atoms are assembled together and passed to the next compound dictionary learning and coding layers. In this way, the activated atoms in the first layer can be represented by the deeper atoms in the second dictionary. Intuitively, the second dictionary is designed to learn the fine-grained components which are shared among the input dictionary atoms. In this way, a more informative and discriminative low-level representation of the dictionary atoms can be obtained. We empirically compare the proposed DDLCN with several dictionary learning methods and deep learning architectures. The experimental results on four popular benchmark datasets demonstrate that the proposed DDLCN achieves competitive results compared with state-of-the-art approaches.
Development of Cognitive Training Program With EEG Headset

Recent research has shown that cognitive function can be improved with proper practices. To improve cognitive skills, we developed a series of training programs using the Unity 3D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> Game Engine, which connects to an EPOC+ headset from EMOTIV <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> to provide EEG data. In order to engage participants and maintain interest, we employed game concepts and developed each program as a game with specific rules and three levels of difficulty. The programs focus on such cognitive abilities as reaction speed, flexibility, attention span, memory and problem solving. We analyzed a user's performance with a detection system embedded in each program; the system automatically directs the user to a suitable level. To simplify software development, the interactive framework was designed as an application programming interface (API), which employs rich texts, static images, animations, and user interactions. We also investigated the effectiveness of using cognitive training with EPOC+ headsets to improve cognitive skills. The results are promising in some users.
Formal modelling and verifying elasticity strategies in cloud systems

Elasticity property allows cloud systems to adapt to their input workload by provisioning and deprovisioning resources as the demand grows and drops. However, due to the unpredictable nature of workload, providing accurate action plans to manage a cloud system's elasticity is a particularly challenging task. In this study, the authors propose a bigraphical reactive system-based approach to provide a formal modelling of cloud systems’ structure using bigraphs, and their elastic behaviours using bigraphical reaction rules. They introduce elasticity strategies to describe cloud systems’ auto-adaptation behaviours. One step further, they encode the bigraphical specifications into Maude language to enable an autonomic executability of the elastic behaviours and verify their correctness. Finally, they propose a queuing-based approach to discuss and analyse elasticity strategies in cloud systems through different simulated scenarios.
Destek Vektör Makineleri Kullanılarak Spam SMS Tespiti

Short Message Service (SMS) is the most important communication tool in recent decades. With the increased popularity of mobile devices, the usage rate of SMS will increase more and more in years. SMS is a practical method used to reach individuals directly. But this practical and easy method can cause SMS to be misused. The advertising or promotional SMS of the companies are an examples of this misuse. In this study, a spam SMS detection technique is proposed using SVM. SMSSpamCollection dataset, which is contain 747 spam SMS and 4827 ham SMS, is used. 10 fold cross-validation technique is used to evaluate prediction of Spam SMS in the dataset. Therefore, proposed approach achieved 98.33 % true positive rate and 0,087 false positive rate for SVM classification algorithm.
A Novel Approach on Visual Question Answering by Parameter Prediction using Faster Region Based Convolutional Neural Network

Visual Question Answering (VQA) is a stimulating process in the field of Natural Language Processing (NLP) and Computer Vision (CV).In this process machine can find an answer to a natural language question which is related to an image.Question can be open-ended or multiple choice.Datasets of VQA contain mainly three components; questions, images and answers.Researchers overcome the VQA problem with deep learning based architecture that jointly combines both of two networks i.e.Convolution Neural Network (CNN) for visual (image) representation and Recurrent Neural Network (RNN) with Long Short Time Memory (LSTM) for textual (question) representation and trained the combined network end to end to generate the answer.Those models are able to answer the common and simple questions that are directly related to the image's content.But different types of questions need different level of understanding to produce correct answers.To solve this problem, we use faster Region based-CNN (R-CNN) for extracting image features with an extra fully connected layer whose weights are dynamically obtained by LSTMs cell according to the question.We claim in this paper that a single R-CNN architecture can solve the problems related to VQA by modifying weights in the parameter prediction layer.Authors trained the network end to end by Stochastic Gradient Descent (SGD) using pretrained faster R-CNN and LSTM and tested it on benchmark datasets of VQA.
Color perception algorithm of medical images using density peak based hierarchical clustering

In this paper, we present a method which can generate color medical images based on multi-feature fusion and hierarchical density peak clustering. First, the proposed method extracts multi-feature to gain abundant feature information. Then, select the regional representative pixels utilizing the local density and neighborhood relationship of pixels. Next, color information is embedded to regional representative pixels. Finally, obtain the color information of each pixel according to the similarity of the representative pixel and non-representative pixel. The experiments on CT, MRI, PET, Ultrasound and DTI demonstrate that the color medical images generated by the proposed algorithm have exquisite details and clear texture information. Moreover, the complexity of the proposed method is O(MN), lower than O(N3) (DPDR). Compared with other corresponding methods, the proposed method has higher contrast, average gradient and lower information entropy.
Categorical combinatorics of scheduling and synchronization in game semantics

Game semantics is the art of interpreting types as games and programs as strategies interacting in space and time with their environment. In order to reflect the interactive behavior of programs, strategies are required to follow specific scheduling policies. Typically, in the case of a purely sequential programming language, the program (Player) and its environment (Opponent) will play one after the other, in a strictly alternating way. On the other hand, in the case of a concurrent language, Player and Opponent will be allowed to play several moves in a row, in a non-alternating way. In both cases, the scheduling policy is designed very carefully in order to ensure that the strategies synchronize properly and compose well when plugged together. A longstanding conceptual problem has been to understand when and why a given scheduling policy works and is compositional in that sense. In this paper, we exhibit a number of simple and fundamental combinatorial structures which ensure that a given scheduling policy encoded as synchronization template defines a symmetric monoidal closed (and in fact star-autonomous) bicategory of games, strategies and simulations. To that purpose, we choose to work at a very general level, and illustrate our method by constructing two template game models of linear logic with different flavors (alternating and non-alternating) using the same categorical combinatorics, performed in the category of small categories. As a whole, the paper may be seen as a hymn in praise of synchronization, building on the notion of synchronization algebra in process calculi and adapting it smoothly to programming language semantics, using a combination of ideas at the converging point of game semantics and of categorical algebra.
Engineering fidelity of the generalized Pauli channels via legitimate memory kernels

We analyze the fidelity of the generalized Pauli channels governed by memory kernel master equations. It is shown that by appropriate engineering of parameters of the corresponding memory kernel, the quantum evolution with nonlocal noise can have higher fidelity than the corresponding purely Markovian evolution governed by the Markovian semigroup. Similar engineering can substantially influence the evolution of quantum entanglement, entropy, and quantum coherence. Interestingly, by adding a nonlocal noise, one can protect the system against a sudden death of entanglement.
Long range quantum coherence, quantum &amp; classical correlations in Heisenberg XX chain

A comparative study of pairwise quantum coherence, quantum and classical correlations is addressed for non-nearest spin pairs of the 1D Heisenberg spin-12 XX chain. Following the Jordan–Wigner mapping, we diagonalize the hamiltonian of the chain and we check this procedure numerically as well. Using the “Pauli basis expansion” formalism we get the pairwise quantities studied in this work at any distance. We then, show the role of quantum correlations in revealing quantum phase transitions, the robustness of quantum discord to the temperature and the dominance of quantum correlations over their classical counterpart in the magnetic and thermal interval in quantum spin chains. We conclude the paper by shedding light from a resource-driven point of view on the new born quantity “quantum coherence” where we discuss its role in detecting quantum phase transitions being a long-range quantity, and how it outclasses the usual quantum correlations measures in the robustness against the temperature, which indicates potential uses in the framework of quantum information processing.
Re&lt;sup&gt;2&lt;/sup&gt;EMA: Regularized and Reinitialized Exponential Moving Average for Target Model Update in Object Tracking

Target model update plays an important role in visual object tracking. However, performing optimal model update is challenging. In this work, we propose to achieve an optimal target model by learning a transformation matrix from the last target model to the newly generated one, which results into a minimization objective. In this objective, there exists two challenges. The first is that the newly generated target model is unreliable. To overcome this problem, we propose to impose a penalty to limit the distance between the learned target model and the last one. The second is that as time evolves, we can not decide whether the last target model has been corrupted or not. To get out of this dilemma, we propose a reinitialization term. Besides, to control the complexity of the transformation matrix, we also add a regularizer. We find that the optimization formula’s solution, with some simplifications, degenerates to EMA. Finally, despite the simplicity, extensive experiments conducted on several commonly used benchmarks demonstrate the effectiveness of our proposed approach in relatively long term scenarios.
Automated Rule Base Completion as Bayesian Concept Induction

Considerable attention has recently been devoted to the problem of automatically extending knowledge bases by applying some form of inductive reasoning. While the vast majority of existing work is centred around so-called knowledge graphs, in this paper we consider a setting where the input consists of a set of (existential) rules. To this end, we exploit a vector space representation of the considered concepts, which is partly induced from the rule base itself and partly from a pre-trained word embedding. Inspired by recent approaches to concept induction, we then model rule templates in this vector space embedding using Gaussian distributions. Unlike many existing approaches, we learn rules by directly exploiting regularities in the given rule base, and do not require that a database with concept and relation instances is given. As a result, our method can be applied to a wide variety of ontologies. We present experimental results that demonstrate the effectiveness of our method.
Bootstrap Estimated Uncertainty of the Environment Model for Model-Based Reinforcement Learning

Model-based reinforcement learning (RL) methods attempt to learn a dynamics model to simulate the real environment and utilize the model to make better decisions. However, the learned environment simulator often has more or less model error which would disturb making decision and reduce performance. We propose a bootstrapped model-based RL method which bootstraps the modules in each depth of the planning tree. This method can quantify the uncertainty of environment model on different state-action pairs and lead the agent to explore the pairs with higher uncertainty to reduce the potential model errors. Moreover, we sample target values from their bootstrap distribution to connect the uncertainties at current and subsequent time-steps and introduce the prior mechanism to improve the exploration efficiency. Experiment results demonstrate that our method efficiently decreases model error and outperforms TreeQN and other stateof-the-art methods on multiple Atari games.
Active Sampling for Open-Set Classification without Initial Annotation

Open-set classification is a common problem in many real world tasks, where data is collected for known classes, and some novel classes occur at the test stage. In this paper, we focus on a more challenging case where the data examples collected for known classes are all unlabeled. Due to the high cost of label annotation, it is rather important to train a model with least labeled data for both accurate classification on known classes and effective detection of novel classes. Firstly, we propose an active learning method by incorporating structured sparsity with diversity to select representative examples for annotation. Then a latent low-rank representation is employed to simultaneously perform classification and novel class detection. Also, the method along with a fast optimization solution is extended to a multi-stage scenario, where classes occur and disappear in batches at each stage. Experimental results on multiple datasets validate the superiority of the proposed method with regard to different performance measures.
Better Fine-Tuning via Instance Weighting for Text Classification

Transfer learning for deep neural networks has achieved great success in many text classification applications. A simple yet effective transfer learning method is to fine-tune the pretrained model parameters. Previous fine-tuning works mainly focus on the pre-training stage and investigate how to pretrain a set of parameters that can help the target task most. In this paper, we propose an Instance Weighting based Finetuning (IW-Fit) method, which revises the fine-tuning stage to improve the final performance on the target domain. IW-Fit adjusts instance weights at each fine-tuning epoch dynamically to accomplish two goals: 1) identify and learn the specific knowledge of the target domain effectively; 2) well preserve the shared knowledge between the source and the target domains. The designed instance weighting metrics used in IW-Fit are model-agnostic, which are easy to implement for general DNN-based classifiers. Experimental results show that IW-Fit can consistently improve the classification accuracy on the target domain.
Adaptive Sparse Confidence-Weighted Learning for Online Feature Selection

In this paper, we propose a new online feature selection algorithm for streaming data. We aim to focus on the following two problems which remain unaddressed in literature. First, most existing online feature selection algorithms merely utilize the first-order information of the data streams, regardless of the fact that second-order information explores the correlations between features and significantly improves the performance. Second, most online feature selection algorithms are based on the balanced data presumption, which is not true in many real-world applications. For example, in fraud detection, the number of positive examples are much less than negative examples because most cases are not fraud. The balanced assumption will make the selected features biased towards the majority class and fail to detect the fraud cases. We propose an Adaptive Sparse Confidence-Weighted (ASCW) algorithm to solve the aforementioned two problems. We first introduce an `0-norm constraint into the second-order confidence-weighted (CW) learning for feature selection. Then the original loss is substituted with a cost-sensitive loss function to address the imbalanced data issue. Furthermore, our algorithm maintains multiple sparse CW learner with the corresponding cost vector to dynamically select an optimal cost. We theoretically enhance the theory of sparse CW learning and analyze the performance behavior in F-measure. Empirical studies show the superior performance over the stateof-the-art online learning methods in the online-batch setting.
Structured Bayesian Networks: From Inference to Learning with Routes

Structured Bayesian networks (SBNs) are a recently proposed class of probabilistic graphical models which integrate background knowledge in two forms: conditional independence constraints and Boolean domain constraints. In this paper, we propose the first exact inference algorithm for SBNs, based on compiling a given SBN to a Probabilistic Sentential Decision Diagram (PSDD). We further identify a tractable subclass of SBNs, which have PSDDs of polynomial size. These SBNs yield a tractable model of route distributions, whose structure can be learned from GPS data, using a simple algorithm that we propose. Empirically, we demonstrate the utility of our inference algorithm, showing that it can be an order-ofmagnitude more efficient than more traditional approaches to exact inference. We demonstrate the utility of our learning algorithm, showing that it can learn more accurate models and classifiers from GPS data.
Improving Search with Supervised Learning in Trick-Based Card Games

In trick-taking card games, a two-step process of state sampling and evaluation is widely used to approximate move values. While the evaluation component is vital, the accuracy of move value estimates is also fundamentally linked to how well the sampling distribution corresponds the true distribution. Despite this, recent work in trick-taking card game AI has mainly focused on improving evaluation algorithms with limited work on improving sampling. In this paper, we focus on the effect of sampling on the strength of a player and propose a novel method of sampling more realistic states given move history. In particular, we use predictions about locations of individual cards made by a deep neural network — trained on data from human gameplay — in order to sample likely worlds for evaluation. This technique, used in conjunction with Perfect Information Monte Carlo (PIMC) search, provides a substantial increase in cardplay strength in the popular trick-taking card game of Skat.
Enhanced Random Forest Algorithms for Partially Monotone Ordinal Classification

One of the factors hindering the use of classification models in decision making is that their predictions may contradict expectations. In domains such as finance and medicine, the ability to include knowledge of monotone (nondecreasing) relationships is sought after to increase accuracy and user satisfaction. As one of the most successful classifiers, attempts have been made to do so for Random Forest. Ideally a solution would (a) maximise accuracy; (b) have low complexity and scale well; (c) guarantee global monotonicity; and (d) cater for multi-class. This paper first reviews the state-of-theart from both the literature and statistical libraries, and identifies opportunities for improvement. A new rule-based method is then proposed, with a maximal accuracy variant and a faster approximate variant. Simulated and real datasets are then used to perform the most comprehensive ordinal classification benchmarking in the monotone forest literature. The proposed approaches are shown to reduce the bias induced by monotonisation and thereby improve accuracy.
Deriving Subgoals Autonomously to Accelerate Learning in Sparse Reward Domains

Sparse reward games, such as the infamous Montezuma’s Revenge, pose a significant challenge for Reinforcement Learning (RL) agents. Hierarchical RL, which promotes efficient exploration via subgoals, has shown promise in these games. However, existing agents rely either on human domain knowledge or slow autonomous methods to derive suitable subgoals. In this work, we describe a new, autonomous approach for deriving subgoals from raw pixels that is more efficient than competing methods. We propose a novel intrinsic reward scheme for exploiting the derived subgoals, applying it to three Atari games with sparse rewards. Our agent’s performance is comparable to that of state-of-the-art methods, demonstrating the usefulness of the subgoals found.
Ontology-Mediated Query Answering over Log-Linear Probabilistic Data

Large-scale knowledge bases are at the heart of modern information systems. Their knowledge is inherently uncertain, and hence they are often materialized as probabilistic databases. However, probabilistic database management systems typically lack the capability to incorporate implicit background knowledge and, consequently, fail to capture some intuitive query answers. Ontology-mediated query answering is a popular paradigm for encoding commonsense knowledge, which can provide more complete answers to user queries. We propose a new data model that integrates the paradigm of ontology-mediated query answering with probabilistic databases, employing a log-linear probability model. We compare our approach to existing proposals, and provide supporting computational results.
A method for fuzzy quantified querying over fuzzy Resource Description Framework graph

Vagueness (or imprecision) naturally arises in real-world Resource Description Framework (RDF) data. The way we query RDF data is a crucial subject because of the vagueness and the wide connectivity of such data. Fuzzy quantified queries have been studied over several types of data and allow to sum up large volumes of data in a very intuitive manner. However, there are a few works that have been led on RDF graph database. In this study, we dealt with a specific type of fuzzy quantified queries in the context of a (fuzzy) RDF graph database. We first show how these queries can be expressed and interpreted in a quantified graph pattern, which is an extension of graph patterns by supporting linguistic quantifier on edges. A query processing strategy based on subgraph matching queries is also proposed. Then, we develop an algorithm for evaluating quantified RDF graph patterns, that is, enumerating all RDF isomorphism from the given RDF graph patterns into the data graph. The algorithm is based on a backtracking strategy, which incrementally finds partial solutions by adding joinable candidate vertices or abandoning them when it determines they cannot be completed. Finally, we perform some experiments to study its performances. The results of these experiments show that the method of dealing with fuzzy quantification in a query is feasible.