Reviewerly/exHarmony huggingface dataset:
Compression artifacts reduction by improved generative adversarial networks

In this paper, we propose an improved generative adversarial network (GAN) for image compression artifacts reduction task (artifacts reduction by GANs, ARGAN). The lossy compression leads to quite complicated compression artifacts, especially blocking artifacts and ringing effects. To handle this problem, we choose generative adversarial networks as an effective solution to reduce diverse compression artifacts. The structure of “U-NET” style is adopted as the generative network in the GAN. A discriminator network is designed in a convolutional manner to differentiate the restored images from the ground truth distribution. This approach can help improve the performance because the adversarial loss aggressively encourages the output image to be close to the distribution of the ground truth. Our method not only learns an end-to-end mapping from input degraded image to corresponding restored image, but also learns a loss function to train this mapping. Benefit from the improved GANs, we can achieve desired results without hand-engineering the loss functions. The experiments show that our method achieves better performance than the state-of-the-art methods.
Robust Pervasive Detection for Adversarial Samples of Artificial Intelligence in IoT Environments

Nowadays, artificial intelligence technologies (e.g., deep neural networks) have been used widely in the Internet of Things (IoT) to provide smart services and sensing data processing. The evolving neural network even exceeds the human cognitive level. However, the accuracy of these structures depends to some extent on the accuracy of the training data. Some well-designed generated antagonistic disturbances are sufficient to deceive model when added to images. Such attacks cause the classifiers trained by the neural network to misidentify the object and thus completely fail. On the other hand, the various existing defensive methods that have been proposed suffer from two criticisms. The first thing that bears the brunt is unsatisfactory detection rate due to low robustness toward the adversarial sample. Second, the excessive dependence on the output of specific network structure layers hinders the emergence of universal schemes. In this paper, we propose the large margin cosine estimation (LMCE) detection scheme to overcome the above shortcomings, making the detection independent and universal. We illustrate the principle of our approach and demonstrate the significance and analysis of some important parameters. Moreover, we model various types of adversarial attacks and establish proposed defense mechanisms against them and evaluate our approach from different aspects. This method has been clearly validated on a range of standard datasets including MNIST, CIFAR-10, and SVHN. The assessment strongly reflects the robustness and pervasive of this approach in the face of various white and semi-white box attacks.
Improving face recognition by elman neural network using curvelet transform and HSI color space

In this paper, a suggested algorithm was proposed to increase the efficiency of the Elman neural algorithm in face recognition.The proposed algorithm was studied on the images of 20 students from the Department of Computer Science, Tikrit University.First step creates dataset of faces, second step convert color space to HSI and using saturation layer, image decomposition using curvelet transform, feature extraction using Principle component analysis, and final step face recognition using Elman neural network.after applying proposed algorithm, the rate of face recognition 94%.
Synthetic Gastritis Image Generation via Loss Function-Based Conditional PGGAN

In this paper, a novel synthetic gastritis image generation method based on a generative adversarial network (GAN) model is presented. Sharing medical image data is a crucial issue for realizing diagnostic supporting systems. However, it is still difficult for researchers to obtain medical image data since the data include individual information. Recently proposed GAN models can learn the distribution of training images without seeing real image data, and individual information can be completely anonymized by generated images. If generated images can be used as training images in medical image classification, promoting medical image analysis will become feasible. In this paper, we targeted gastritis, which is a risk factor for gastric cancer and can be diagnosed by gastric X-ray images. Instead of collecting a large amount of gastric X-ray image data, an image generation approach was adopted in our method. We newly propose loss function-based conditional progressive growing generative adversarial network (LC-PGGAN), a gastritis image generation method that can be used for a gastritis classification problem. The LC-PGGAN gradually learns the characteristics of gastritis in gastric X-ray images by adding new layers during the training step. Moreover, the LC-PGGAN employs loss function-based conditional adversarial learning so that generated images can be used as the gastritis classification task. We show that images generated by the LC-PGGAN are effective for gastritis classification using gastric X-ray images and have clinical characteristics of the target symptom.
Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection

Social network services for self-media, such as Weibo, Blog, and WeChat Public, constitute a powerful medium that allows users to publish posts every day. Due to insufficient information transparency, malicious marketing of the Internet from self-media posts imposes potential harm on society. Therefore, it is necessary to identify news with marketing intentions for life. We follow the idea of text classification to identify marketing intentions. Although there are some current methods to address intention detection, the challenge is how the feature extraction of text reflects semantic information and how to improve the time complexity and space complexity of the recognition model. To this end, this paper proposes a machine learning method to identify marketing intentions from large-scale We-Media data. First, the proposed Latent Semantic Analysis (LSI)-Word2vec model can reflect the semantic features. Second, the decision tree model is simplified by decision tree pruning to save computing resources and reduce the time complexity. Finally, this paper examines the effects of classifier associations and uses the optimal configuration to help people efficiently identify marketing intention. Finally, the detailed experimental evaluation on several metrics shows that our approaches are effective and efficient. The F1 value can be increased by about 5%, and the running time is increased by 20%, which prove that the newly-proposed method can effectively improve the accuracy of marketing news recognition.
An empirical assessment of baseline feature location techniques

Abstract Feature Location (FL) aims to locate observable functionalities in source code. Considering its key role in software maintenance, a vast array of automated and semi-automated Feature Location Techniques (FLTs) have been proposed. To compare FLTs, an open, standard set of non-subjective, reproducible “compare-to” FLT techniques (baseline techniques) should be used for evaluation. In order to relate the performance of FLTs compared against different baseline techniques, these compare-to techniques should be evaluated against each other. But evaluation across FLTs is confounded by empirical designs that incorporate different FL goals and evaluation criteria. This paper moves towards standardizing FLT comparability by assessing eight baseline techniques in an empirical design that addresses these confounding factors. These baseline techniques are assessed in twelve case studies to rank their performance. Results of the case studies suggest that different baseline techniques perform differently and that VSM-Lucene and LSI-Matlab performed better than other implementations. By presenting the relative performances of baseline techniques this paper facilitates empirical cross-comparison of existing and future FLTs. Finally, the results suggest that the performance of FLTs partially depends on system/benchmark characteristics, in addition to the FLTs themselves.
On Data Center Demand Response: A Cloud Federation Approach

The significantly high energy consumption of data centers constitutes a major load on the smart power grid.Data center demand response is a promising solution to incentivize the cloud providers to adapt their consumption to the power grid conditions.These policies not only mitigate the operational stability issues of the smart grid but also potentially decrease the electricity bills of cloud providers.Cloud providers can improve their contribution and reduce their energy cost by collaboratively managing their workload.Through cooperation in the form of cloud federations, providers can spatially migrate their workload to better utilize the benefits provided by demand response schemes over multiple locations.To this end, this work considers an interaction system between the independent cloud providers and the corresponding smart grid utilities in the context of a demand response program.Leveraging the cooperative game theory, this paper presents a federation formation among the cloud providers in the presence of a location-dependent demand response program.A distributed algorithm that is coupled with an optimal workload allocation problem is applied.The effect of the federation's formation on the clouds' profits and on the smart grid performance is analyzed through simulation.Simulation results show that cooperation increases the clouds' profits as well as the smart grid performance compared to the noncooperative case.
Learn to Detect: Improving the Accuracy of Earthquake Detection

Earthquake early warning system uses high-speed computer network to transmit earthquake information to population center ahead of the arrival of destructive earthquake waves. This short (10 s of seconds) lead time will allow emergency responses such as turning off gas pipeline valves to be activated to mitigate potential disaster and casualties. However, the excessive false alarm rate of such a system imposes heavy cost in terms of loss of services, undue panics, and diminishing credibility of such a warning system. At the current, the decision algorithm to issue an early warning of the onset of an earthquake is often based on empirically chosen features and heuristically set thresholds and suffers from excessive false alarm rate. In this paper, we experimented with three advanced machine learning algorithms, namely, K-nearest neighbor (KNN), classification tree, and support vector machine (SVM) and compared their performance against a traditional criterion-based method. Using the seismic data collected by an experimental strong motion detection network in Taiwan for these experiments, we observed that the machine learning algorithms exhibit higher detection accuracy with much reduced false alarm rate.
Dualityfree Methods for Stochastic Composition Optimization

In this paper, we consider the composition optimization with two expected-value functions in the form of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$({1}/{n})\sum _{i = 1}^{n} F_{i}\left({({1}/{m})\sum _{j = 1}^{m} G_{j}(x)}\right)+R(x)$ </tex-math></inline-formula> , which formulates many important problems in statistical learning and machine learning such as solving Bellman equations in reinforcement learning and nonlinear embedding. Full gradient- or classical stochastic gradient descent-based optimization algorithms are unsuitable or computationally expensive to solve this problem due to the inner expectation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$({1}/{m})\sum _{j = 1}^{m} G_{j}(x)$ </tex-math></inline-formula> . We propose a dualityfree-based stochastic composition method that combines the variance reduction methods to address the stochastic composition problem. We apply the stochastic variance reduction gradient- and stochastic average gradient algorithm-based methods to estimate the inner function and the dualityfree method to estimate the outer function. We prove the linear convergence rate not only for the convex composition problem but also for the case that the individual outer functions are nonconvex, while the objective function is strongly convex. We also provide the results of experiments that show the effectiveness of our proposed methods.
Entity-Centric Contextual Affective Analysis

While contextualized word representations have improved state-of-the-art benchmarks in many NLP tasks, their potential usefulness for social-oriented tasks remains largely unexplored.We show how contextualized word embeddings can be used to capture affect dimensions in portrayals of people.We evaluate our methodology quantitatively, on held-out affect lexicons, and qualitatively, through case examples.We find that contextualized word representations do encode meaningful affect information, but they are heavily biased towards their training data, which limits their usefulness to in-domain analyses.We ultimately use our method to examine differences in portrayals of men and women.
Constructivism-Based Methodology for Teaching Artificial Intelligence Topics Focused on Sustainable Development

This article proposes the creation of a course based on a series of practical sessions, where the students have to develop their practical knowledge about artificial intelligence techniques, specifically multilayer perceptron. The novelty of this paper is based on the constructivism methodology regarding artificial intelligence and sustainable development. Moreover, it can be implemented in different majors because of the flexibility in certain aspects. It is oriented to evaluate skills in the broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context. The proposal helps the students to turn theoretical concepts into more tangible objects where they can build their knowledge by programming their implementations in software. Then, programming codes for practicing the neural networks theory, finite impulse response, empirical mode decomposition and discrete wavelet transform are achieved to compare percentage classification between different techniques. Also, it measures the interaction between the student and the theoretical mathematics of artificial intelligence. The continuous evaluations at the end of the practical sessions corroborate the increase in the knowledge of the students. A study based on rubrics illustrates an increase in the average grade obtained by the students in the elaboration of each practice. Finally, a senior project is carried out by taking into account sustainable development issues and the usage of tools of artificial intelligence.
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics

A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over long time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary. This ensures eigenvalues with unit norm and thus stable dynamics and training. However this comes at the cost of reduced expressivity due to the limited variety of orthogonal transformations. We propose a novel connectivity structure based on the Schur decomposition and a splitting of the Schur form into normal and non-normal parts. This allows to parametrize matrices with unit-norm eigenspectra without orthogonality constraints on eigenbases. The resulting architecture ensures access to a larger space of spectrally constrained matrices, of which orthogonal matrices are a subset. This crucial difference retains the stability advantages and training speed of orthogonal RNNs while enhancing expressivity, especially on tasks that require computations over ongoing input sequences.
Machine Learning for Performance Prediction of Spark Cloud Applications

Big data applications and analytics are employed in many sectors for a variety of goals: improving customers satisfaction, predicting market behavior or improving processes in public health.These applications consist of complex software stacks that are often run on cloud systems.Predicting execution times is important for estimating the cost of cloud services and for effectively managing the underlying resources at runtime.Machine Learning (ML), providing black box solutions to model the relationship between application performance and system configuration without requiring in-detail knowledge of the system, has become a popular way of predicting the performance of big data applications.We investigate the cost-benefits of using supervised ML models for predicting the performance of applications on Spark, one of today's most widely used frameworks for big data analysis.We compare our approach with Ernest (an ML-based technique proposed in the literature by the Spark inventors) on a range of scenarios, application workloads, and cloud system configurations.Our experiments show that Ernest can accurately estimate the performance of very regular applications, but it fails when applications exhibit more irregular patterns and/or when extrapolating on bigger data set sizes.Results show that our models match or exceed Ernest's performance, sometimes enabling us to reduce the prediction error from 126-187% to only 5-19%.
A unified variance-reduced accelerated gradient method for convex optimization

We propose a novel randomized incremental gradient algorithm, namely, VAriance-Reduced Accelerated Gradient (Varag), for finite-sum optimization. Equipped with a unified step-size policy that adjusts itself to the value of the conditional number, Varag exhibits the unified optimal rates of convergence for solving smooth convex finite-sum problems directly regardless of their strong convexity. Moreover, Varag is the first accelerated randomized incremental gradient method that benefits from the strong convexity of the data-fidelity term to achieve the optimal linear convergence. It also establishes an optimal linear rate of convergence for solving a wide class of problems only satisfying a certain error bound condition rather than strong convexity. Varag can also be extended to solve stochastic finite-sum problems.
An Advanced Quantum Optimization Algorithm for Robot Path Planning

In this paper, a new robot path planning algorithm based on Quantum-inspired Evolutionary Algorithm (QEA) is proposed. QEA is an advanced evolutionary computing scheme with the quantum computing features such as qubits and superposition. It is suitable for solving large scale optimization problems. The proposed QEA algorithm works in the discretized environment, and approximates the optimal robot planing path in a highly computationally efficient fashion. The simulation results indicate that the proposed QEA algorithm is suitable for both complex static and dynamic environment and considerably outperforms the conventional genetic algorithm (GA) for solving the robot path planning problem. Our algorithm runs in only about 2[Formula: see text]s, which demonstrates that it can well tackle the optimization problem in robot path planning.
Balancing Straight-Line Programs

We show that a context-free grammar of size m that produces a single string w of length n (such a grammar is also called a string straight-line program) can be transformed in linear time into a context-free grammar for w of size O(m), whose unique derivation tree has depth O(log n). This solves an open problem in the area of grammar-based compression, improves many results in this area and greatly simplifies many existing constructions. Similar results are stated for two formalisms for grammar-based tree compression: top dags and forest straight-line programs. These balancing results can be all deduced from a single meta theorem stating that the depth of an algebraic circuit over an algebra with a certain finite base property can be reduced to O(log n) with the cost of a constant multiplicative size increase. Here, n refers to the size of the unfolding (or unravelling) of the circuit. In particular, this results applies to standard arithmetic circuits over (non-commutative) semirings.A long version of the paper can be found in [1].
A Survey of Various Frameworks and Solutions in all Branches of Digital Forensics with a Focus on Cloud Forensics

Digital forensics is a class of forensic science interested with the use of digital information produced, stored and transmitted by various digital devices as source of evidence in investigations and legal proceedings.Digital forensics can be split up to several classes such as computer forensics, network forensics, mobile forensics, cloud computing forensics, and IoT forensics.In recent years, cloud computing has emerged as a popular computing model in various areas of human life.However, cloud computing systems lack support for computer forensic investigations.The main goal of digital forensics is to prove the presence of a particular document in a given digital device.This paper presents a comprehensive survey of various frameworks and solutions in all classes of digital forensics with a focus on cloud forensics.We start by discussing different forensics classes, their frameworks, limitations and solutions.Then we focus on the methodological aspect and existing challenges of cloud forensics.Moreover, the detailed comparison discusses drawbacks, differences and similarities of several suggested cloud computing frameworks providing future research directions.
Differential Privacy of Aggregated DC Optimal Power Flow Data

We consider the problem of privately releasing aggregated network statistics obtained from solving a DC optimal power flow (OPF) problem. It is shown that the mechanism that determines the noise distribution parameters are linked to the topology of the power system and the monotonicity of the network. We derive a measure of “almost” monotonicity and show how it can be used in conjunction with a linear program in order to release aggregated OPF data using the differential privacy framework.
Preemptive Toxic Language Detection in Wikipedia Comments Using Thread-Level Context

We address the task of automatically detecting toxic content in user generated texts.We focus on exploring the potential for preemptive moderation, i.e., predicting whether a particular conversation thread will, in the future, incite a toxic comment.Moreover, we perform preliminary investigation of whether a model that jointly considers all comments in a conversation thread outperforms a model that considers only individual comments.Using an existing dataset of conversations among Wikipedia contributors as a starting point, we compile a new large-scale dataset for this task consisting of labeled comments and comments from their conversation threads.
Live Video Analytics with FPGA-based Smart Cameras

Analyzing video feeds from large camera networks requires enormous compute and bandwidth. Edge computing has been proposed to ease the burden by bringing resources to the proximity of data. However, the number of cameras keeps growing and the associated computing resources on edge will again fall in short. To fundamentally solve the resource scarcity problem and make edge-based live video analytics scalable, we present an FPGA-based smart camera design that enables efficient in-situ streaming processing to meet the stringent low-power, energy-efficient, low-latency requirements of edge vision applications. By leveraging FPGA's intrinsic properties of architecture efficiency and exploiting its hardware support for parallelism, we demonstrate a 49x speedup over CPU and 6.4x more energy-efficiency than GPU, verified using a background subtraction algorithm.