Reviewerly/exHarmony huggingface dataset:
Dual pyramid network for salient object detection

Saliency detection is a fundamental yet challenging task in computer vision, aiming at identifying the most visually distinctive objects in an image. Despite promising results from recent deep learning based methods, existing works are still not sufficient to incorporate momentous global context to detect salient objects. In this work, we present a dual pyramid network (DPNet) for salient object detection by formulating self-attention mechanism into the sub-regions based contexts. Our key idea is to aggregate different sub-regions based contexts into the enhanced global context without sacrificing the resolution of deep features. To achieve this, we design the spatial pyramid module and channel-wise pyramid module. The proposed modules can enhance the global context by incorporating self-attention mechanism into the sub-regions based contexts in the spatial and channel-wise dimension, respectively. Compared with 23 state-of-the-art methods on six RGB saliency benchmark datasets, experimental results show that our method performs favorably over all the others, both quantitatively and visually. The superior performance of proposed DPNet than others on three RGB-D saliency benchmark datasets further demonstrates the powerful generalization ability of our network.
Enhancing Monte Carlo Tree Search for Playing Hearthstone

Hearthstone is a popular online collectible card game (CCG). Hearthstone imposes interesting challenges in developing a search algorithm for the game AI. As a CCG, it has a considerable amount of hidden information from each player’s private hand and deck. Moreover, the action space is full of stochastic actions compared to other similar games. That is, instead of a single move, each player is allowed to build a move sequence via various combinations of atomic actions. Therefore, when applying any heuristic search algorithm, the branching factor of the search space is extremely large. In this paper, we explore the use of Monte Carlo Tree Search (MCTS) with approaches to reduce the complexity of the search space and decide on the best strategy. First, we utilise state abstraction to present the search space as a Directed Acyclic Graph (DAG) and introduce a variant of Upper Confidence Bound for Trees (UCT) algorithm for the DAG. Next, we apply the sparse sampling algorithm to handle imperfect information and randomness and reduce the stochastic branching factor. This paper presents empirical evaluations of the proposed framework for Hearthstone and the experimental results suggest that our approach is well suited for developing a better AI agent.
Harnessing Side Information for Classification Under Label Noise

Practical data sets often contain the label noise caused by various human factors or measurement errors, which means that a fraction of training examples might be mistakenly labeled. Such noisy labels will mislead the classifier training and severely decrease the classification performance. Existing approaches to handle this problem are usually developed through various surrogate loss functions under the framework of empirical risk minimization. However, they are only suitable for binary classification and also require strong prior knowledge. Therefore, this article treats the example features as side information and formulates the noisy label removal problem as a matrix recovery problem. We denote our proposed method as “label noise handling via side information” (LNSI). Specifically, the observed label matrix is decomposed as the sum of two parts, in which the first part reveals the true labels and can be obtained by conducting a low-rank mapping on the side information; and the second part captures the incorrect labels and is modeled by a row-sparse matrix. The merits of such formulation lie in three aspects: 1) the strong recovery ability of this strategy has been sufficiently demonstrated by intensive theoretical works on side information; 2) multi-class situations can be directly handled with the aid of learned projection matrix; and 3) only very weak assumptions are required for model design, making LNSI applicable to a wide range of practical problems. Moreover, we theoretically derive the generalization bound of LNSI and show that the expected classification error of LNSI is upper bounded. The experimental results on a variety of data sets including UCI benchmark data sets and practical data sets confirm the superiority of LNSI to state-of-the-art approaches on label noise handling.
Differential privacy for sparse classification learning

In this paper, we present a differential privacy version of convex and nonconvex sparse classification approach. Based on alternating direction method of multiplier (ADMM) algorithm, we transform the solving of sparse problem into the multistep iteration process. Then we add exponential noise to stable steps to achieve privacy protection. By the property of the post-processing holding of differential privacy, the proposed approach satisfies the ϵ−differential privacy even when the original problem is unstable. Furthermore, we present the theoretical privacy bound of the differential privacy classification algorithm. Specifically, the privacy bound of our algorithm is controlled by the algorithm iteration number, the privacy parameter, the parameter of loss function, ADMM pre-selected parameter, and the data size. Finally we apply our framework to logistic regression with L1 regularizer and logistic regression with L1/2 regularizer. Numerical studies demonstrate that our method is both effective and efficient which performs well in sensitive data analysis.
Performance Prediction for Non-Factoid Question Answering

Estimating the quality of a result list, often referred to as query performance prediction (QPP), is a challenging and important task in information retrieval. It can be used as feedback to users, search engines, and system administrators. Although predicting the performance of retrieval models has been extensively studied for the ad-hoc retrieval task, the effectiveness of performance prediction methods for question answering (QA) systems is relatively unstudied. The short length of answers, the dominance of neural models in QA, and the re-ranking nature of most QA systems make performance prediction for QA a unique, important, and technically interesting task. In this paper, we introduce and motivate the task of performance prediction for non-factoid question answering and propose a neural performance predictor for this task. Our experiments on two recent datasets demonstrate that the proposed model outperforms competitive baselines in all settings.
Blockchain Principles, Qualities, and Business Applications

The two most interesting qualities of the blockchain are decentralization and immutability. A traditional network structure is the “client-server” structure where there is a centralized server and everyone who wants to connect with the server can send a query to get the required information. In order for this type of network system to work, it is critical that the server function continuously. Since servers handle a lot of sensitive material, anyone can hack the server and get those pieces of information. Blockchain technology relies on peer-to-peer network architecture as shown in Fig. 3.1.
General Board Game Playing for Education and Research in Generic AI Game Learning

We present a new general board game (GBG) playing and learning framework. GBG defines the common interfaces for board games, game states and their AI agents. It allows one to run competitions of different agents on different games. It standardizes those parts of board game playing and learning that otherwise would be tedious and repetitive parts in coding. GBG is suitable for arbitrary 1-, 2-, ...,N-player board games. It makes a generic TD(λ)-n-tuple agent for the first time available to arbitrary games. On various games, TD(λ)-n-tuple is found to be superior to other generic agents like MCTS. GBG aims at the educational perspective, where it helps students to start faster in the area of game learning. GBG aims as well at the research perspective by collecting a growing set of games and AI agents to assess their strengths and generalization capabilities in meaningful competitions. Initial successful educational and research results are reported.
A Novel Hybrid Model of WRF and Clearness Index-Based Kalman Filter for Day-Ahead Solar Radiation Forecasting

Day-ahead forecasting of solar radiation is essential for grid balancing, real-time unit dispatching, scheduling and trading in the solar energy utilization system. In order to provide reliable forecasts of solar radiation, a novel hybrid model is proposed in this study. The hybrid model consists of two modules: a mesoscale numerical weather prediction model (WRF: Weather Research and Forecasting) and Kalman filter. However, the Kalman filter is less likely to predict sudden changes in the forecasting errors. To address this shortcoming, we develop a new framework to implement a Kalman filter based on the clearness index. The performance of this hybrid model is evaluated using a one-year dataset of solar radiation taken from a photovoltaic plant located at Maizuru, Japan and Qinghai, China, respectively. The numerical results reveal that the proposed hybrid model performs much better in comparison with the WRF-alone forecasts under different sky conditions. In particular, in the case of clear sky conditions, the hybrid model can improve the forecasting accuracy by 95.7% and 90.9% in mean bias error (MBE), and 42.2% and 26.8% in root mean square error (RMSE) for Maizuru and Qinghai sites, respectively.
A survey on clone refactoring and tracking

Code clones, identical or nearly similar code fragments in a software system's code-base, have mixed impacts on software evolution and maintenance. Focusing on the issues of clones researchers suggest managing them through refactoring, and tracking. In this paper we present a survey on the state-of-the-art of clone refactoring and tracking techniques, and identify future research possibilities in these areas. We define the quality assessment features for the clone refactoring and tracking tools, and make a comparison among these tools considering these features. To the best of our knowledge, our survey is the first comprehensive study on clone refactoring and tracking. According to our survey on clone refactoring we realize that automatic refactoring cannot eradicate the necessity of manual effort regarding finding refactoring opportunities, and post refactoring testing of system behaviour. Post refactoring testing can require a significant amount of time and effort from the quality assurance engineers. There is a marked lack of research on the effect of clone refactoring on system performance. Future investigations in this direction will add much value to clone refactoring research. We also feel the necessity of future research towards real-time detection, and tracking of code clones in a big-data environment.
Data Encoding Methods for Byzantine-Resilient Distributed Optimization

We consider distributed gradient computation, where both data and computation are distributed among m worker machines, t of which can be Byzantine adversaries, and a designated (master) node computes the model/parameter vector for generalized linear models, iteratively, using proximal gradient descent (PGD), of which gradient descent (GD) is a special case. The Byzantine adversaries can (collaboratively) deviate arbitrarily from their gradient computation. To solve this, we propose a method based on data encoding and (real) error correction to combat the adversarial behavior. We can tolerate up to t ≤ [m-1/2] corrupt worker nodes, which is information-theoretically optimal. Our method does not assume any probability distribution on the data. We develop a sparse encoding scheme which enables computationally efficient data encoding. We demonstrate a trade-off between the number of adversaries tolerated and the resource requirement (storage and computational complexity). As an example, our scheme incurs a constant overhead (storage and computational complexity) over that required by the distributed PGD algorithm, without adversaries, for t ≤ m/3 . Our encoding works as efficiently in the streaming data etting as it does in the
A Concentration of Measure Approach to Database De-anonymization

In this paper, matching of correlated high-dimensional databases is investigated. A stochastic database model is considered where the correlation among the database entries is governed by an arbitrary joint distribution. Concentration of measure theorems such as typicality and laws of large numbers are used to develop a database matching scheme and derive necessary conditions for successful matching. Furthermore, it is shown that these conditions are tight through a converse result which characterizes a set of distributions on the database entries for which reliable matching is not possible. The necessary and sufficient conditions for reliable matching are evaluated in the cases when the database entries are independent and identically distributed as well as under Markovian database models.
Comparison of Temporal and Non-Temporal Features Effect on Machine Learning Models Quality and Interpretability for Chronic Heart Failure Patients

Chronic diseases are complex systems that can be described by various heteroscedastic data that varies in time. The goal of this work is to determine whether historical data helps to improve machine learning predictive models or is it more efficient to use the latest data describing the disease in particular moment in time. For simplicity we call features from the first group dynamic and features from the second one – static. We study the way both groups affect predictions quality and its interpretation. We set the experiments on data of chronic heart patients from Almazov Medical Research Center. From this data we extracted more than 300 features from patient comorbidity, anamnesis, analysis, etc. In terms of Chronic Heart Failure (CHF) modelling three different tasks have been selected: CHF identification as main diagnosis, CHF stage classification and diastolic blood pressure prediction. For each task several machine learning algorithms on three groups of features: static, dynamic and the whole feature set. The results show that, in general, models perform better on combination of temporal and non-temporal features.
Teaching Machines to Recognize Neurodynamic Correlates of Team and Team Member Uncertainty

We describe efforts to make humans more transparent to machines by focusing on uncertainty, a concept with roots in neuronal populations that scales through social interactions. To be effective team partners, machines will need to learn why uncertainty happens, how it happens, how long it will last, and possible mitigations the machine can supply. Electroencephalography-derived measures of team neurodynamic organization were used to identify times of uncertainty in military, health care, and high school problem-solving teams. A set of neurodynamic sequences was assembled that differed in the magnitudes and durations of uncertainty with the goal of training machines to detect the onset of prolonged periods of high level uncertainty, that is, when a team might require support. Variations in uncertainty onset were identified by classifying the first 70 s of the exemplars using self-organizing maps (SOM), a machine architecture that develops a topology during training that separates closely related from desperate data. Clusters developed during training that distinguished patterns of no uncertainty, low-level and quickly resolved uncertainty, and prolonged high-level uncertainty, creating opportunities for neurodynamic-based systems that can interpret the ebbs and flows in team uncertainty and provide recommendations to the trainer or team in near real time when needed.
A Benchmark of Four Methods for Generating 360<sup>∘</sup> Saliency Maps from Eye Tracking Data

Modeling and visualization of user attention in Virtual Reality (VR) is important for many applications, such as gaze prediction, robotics, retargeting, video compression, and rendering. Several methods have been proposed to model eye tracking data as saliency maps. We benchmark the performance of four such methods for 360 ∘ images. We provide a comprehensive analysis and implementations of these methods to assist researchers and practitioners. Finally, we make recommendations based on our benchmark analyses and the ease of implementation.
MDS Symbol-Pair Repeated-Root Constacylic Codes of Prime Power Lengths Over $\mathbb F_{p^m}+ u \mathbb F_{p^m}$

MDS codes have the highest possible error-detecting and error-correcting capability among codes of given length and size. Let p be any prime, and s, m be positive integers. Here, we consider all constacyclic codes of length p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> over the ring R = F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> m + uF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> m (u <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0). The units of the ring R are of the form α + uβ and γ, where α, β, γ ∈ F* <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> m, which provides p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> (p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> - 1) constacyclic codes. We acquire that the (α + uβ)-constacyclic codes of ps length over R are the ideals 〈(α <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> x - 1) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sup> 〉, 0 ≤ j ≤ 2 p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> , of the finite chain ring R[x]/〈x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ps</sup> - (α + uβ)〉 and the γ-constacyclic codes of ps length over R are the ideals of the ring R[x]/〈x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ps</sup> - γ〉 which is a local ring with the maximal ideal 〈u, x - γ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> 〉, but it is not a chain ring. In this paper, we obtain all MDS symbol-pair constacyclic codes of length ps over R. We deduce that the MDS symbol-pair constacyclic codes are the trivial ideal (1) and the Type 3 ideal of γ-constacyclic codes for some particular values of p and s. We also present several parameters including the exact symbol-pair distances of MDS constacyclic symbol-pair codes for different values of p and s.
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems

Question Answering (QA) systems are used to provide proper responses to users' questions automatically. Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification (PI) problem. Given a question, the aim of the task is to find the most similar question from a QA knowledge base. In this paper, we propose a Multi-task Sentence Encoding Model (MSEM) for the PI problem, wherein a connected graph is employed to depict the relation between sentences, and a multi-task learning model is applied to address both the sentence matching and sentence intent classification problem. In addition, we implement a general semantic retrieval framework that combines our proposed model and the Approximate Nearest Neighbor (ANN) technology, which enables us to find the most similar question from all available candidates very quickly during online serving. The experiments show the superiority of our proposed method as compared with the existing sentence matching models.
An ensemble method for inverse reinforcement learning

In inverse reinforcement learning (IRL), a reward function is learnt to generalize experts’ behavior. This paper proposes a model-free IRL algorithm based on an ensemble method, where the reward function is regarded as a parametric function of expected features. In other words, the parameters are updated based on a weak classification method. The IRL is formulated as a problem of a boosting classifier, akin to the renowned Adaboost algorithm for classification, feature expectations from experts’ demonstration, and the trajectory induced by an agent's current policy. The proposed approach takes individual feature expectation as attractor or expeller, depending on the sign of the residuals of the state trajectories between expert's demonstration and the one induced by RL with the currently approximated reward function, so as to tackle its central challenges of accurate inference, generalizability, and correctness of prior knowledge. Then, the proposed method is applied further to approximate an abstract reward function from observations of more complex behavior composed of several basic actions. The results of the simulations in a labyrinth are shown to validate the proposed algorithm. Furthermore, behaviors composed of a set of primitive actions on a soccer robot field are examined for the applicability of the proposed method.
Towards Image Classification with Machine Learning Methodologies for Smartphones

Recent developments in machine learning engendered many algorithms designed to solve diverse problems. More complicated tasks can be solved since numerous features included in much larger datasets are extracted by deep learning architectures. The prevailing transfer learning method in recent years enables researchers and engineers to conduct experiments within limited computing and time constraints. In this paper, we evaluated traditional machine learning, deep learning and transfer learning methodologies to compare their characteristics by training and testing on a butterfly dataset, and determined the optimal model to deploy in an Android application. The application can detect the category of a butterfly by either capturing a real-time picture of a butterfly or choosing one picture from the mobile gallery.
Efficient Auction Mechanism for Edge Computing Resource Allocation in Mobile Blockchain

Blockchain technique has been widely applied in various fields, such as finance, Internet of Things, law, etc. However, it is a challenge to apply blockchain technique for mobile applications, as the mobile devices cannot afford computing resources required by the mining processes. This paper proposes a mechanism based on a combinatorial double auction to offload the mining process of miners to the edge servers. The mechanism is formulated as a resource allocation problem. The corresponding allocation algorithms and payment scheme are proposed to allocate resources and calculate trade prices, respectively. Moreover, this paper proves that the proposed mechanism is efficient in terms of computation, and it satisfies three properties of economic auction which are budget balance, individual rationality and truthfulness. Experimental results show that the proposed mechanism is able to yield higher total utility, together with good scalability. For the case of 900 miners in the auction, the total utility of the proposed mechanism is higher than two existing works by 39.2% and 569.9% on average, respectively.