Reviewerly/exHarmony huggingface dataset:
Deep RGB-D Saliency Detection Without Depth

The existing saliency detection models based on RGB colors only leverage appearance cues to detect salient objects. Depth information also plays a very important role in visual saliency detection and can supply complementary cues for saliency detection. Although many RGB-D saliency models have been proposed, they require to acquire depth data, which is expensive and not easy to get. In this paper, we propose to estimate depth information from monocular RGB images and leverage the intermediate depth features to enhance the saliency detection performance in a deep neural network framework. Specifically, we first use an encoder network to extract common features from each RGB image and then build two decoder networks for depth estimation and saliency detection, respectively. The depth decoder features can be fused with the RGB saliency features to enhance their capability. Furthermore, we also propose a novel dense multiscale fusion model to densely fuse multiscale depth and RGB features based on the dense ASPP model. A new global context branch is also added to boost the multiscale features. Experimental results demonstrate that the added depth cues and the proposed fusion model can both improve the saliency detection performance. Finally, our model not only outperforms state-of-the-art RGB saliency models, but also achieves comparable results compared with state-of-the-art RGB-D saliency models.
Countering Distrust in Illicit Online Networks: The Dispute Resolution Strategies of Cybercriminals

The core of this article is a detailed investigation of the dispute resolution system contained within Darkode, an elite cybercriminal forum. Extracting the dedicated disputes section from within the marketplace, where users can report bad behavior and register complaints, we carry out content analysis on these threads. This involves both descriptive statistics across the data set and qualitative analysis of particular posts of interest, leading to a number of new insights. First, the overall level of disputes is quite high, even though members are vetted for entry in the first instance. Second, the lower ranked members of the marketplace are the most highly represented category for both the plaintiffs and defendants. Third, vendors are accused of malfeasance far more often than buyers, and their “crimes” are most commonly either not providing the product/service or providing a poor one. Fourth, the monetary size of the disputes is surprisingly small. Finally, only 23.1% of disputes reach a clear outcome.
Deep active learning for interictal ictal injury continuum EEG patterns

Seizures and seizure-like electroencephalography (EEG) patterns, collectively referred to as “ictal interictal injury continuum” (IIIC) patterns, are commonly encountered in critically ill patients. Automated detection is important for patient care and to enable research. However, training accurate detectors requires a large labeled dataset. Active Learning (AL) may help select informative examples to label, but the optimal AL approach remains unclear. We assembled >200,000 h of EEG from 1,454 hospitalized patients. From these, we collected 9,808 labeled and 120,000 unlabeled 10-second EEG segments. Labels included 6 IIIC patterns. In each AL iteration, a Dense-Net Convolutional Neural Network (CNN) learned vector representations for EEG segments using available labels, which were used to create a 2D embedding map. Nearest-neighbor label spreading within the embedding map was used to create additional pseudo-labeled data. A second Dense-Net was trained using real- and pseudo-labels. We evaluated several strategies for selecting candidate points for experts to label next. Finally, we compared two methods for class balancing within queries: standard balanced-based querying (SBBQ), and high confidence spread-based balanced querying (HCSBBQ). Our results show: 1) Label spreading increased convergence speed for AL. 2) All query criteria produced similar results to random sampling. 3) HCSBBQ query balancing performed best. Using label spreading and HCSBBQ query balancing, we were able to train models approaching expert-level performance across all pattern categories after obtaining ∼7000 expert labels. Our results provide guidance regarding the use of AL to efficiently label large EEG datasets in critically ill patients.
Spoken Corpora Data, Automatic Speech Recognition, and Bias Against African American Language

Recent work has revealed that major automatic speech recognition (ASR) systems such as Apple, Amazon, Google, IBM, and Microsoft perform much more poorly for Black U.S. speakers than for white U.S. speakers. Researchers postulate that this may be a result of biased datasets which are largely racially homogeneous. However, while the study of ASR performance with regards to the intersection of racial identity and language use is slowly gaining traction within AI, machine learning, and algorithmic bias research, little to nothing has been done to examine the data drawn from the spoken corpora which are commonly used in the training and evaluation of ASRs in order to understand whether or not they are actually biased, this study seeks to begin addressing this gap in the research by investigating spoken corpora used for ASR training and evaluation for a grammatical linguistic feature of what the field of linguistics terms African American Language (AAL), a systematic, rule-governed, and legitimate linguistic variety spoken by many (but not all) African Americans in the U.S. This grammatical feature, habitual 'be', is an uninflected form of 'be' that encodes the characteristic of habituality, as in "I be in my office by 7:30am", paraphrasable as "I am usually in my office by 7:30" in Standardized American English. This study utilizes established corpus linguistics methods on the transcribed data of four major spoken corpora -- Switchboard, Fisher, TIMIT, and LibriSpeech -- to understand the frequency, distribution, and usage of habitual 'be' within each corpus as compared to a reference corpus of spoken AAL -- the Corpus of Regional African American Language (CORAAL). The results find that habitual 'be' appears far less frequently, is dispersed in far fewer transcribed texts, and is surrounded by a much less diverse set of word types and parts of speech in the four ASR corpora as compared with CORAAL. This work provides foundational evidence that spoken corpora used in the training and evaluation of widely used ASR systems are, in fact, biased against AAL and likely contribute to poorer ASR performance for Black users.
Incentive Mechanism Design for Federated Learning: A Two-stage Stackelberg Game Approach

Federated Learning (FL) is a newly-emerging distributed ML model, where a server can coordinate multiple workers to cooperatively train a learning model by using their private datasets, while ensuring these datasets not to be revealed to others. In this paper, we focus on the incentive mechanism design for FL systems. Taking the incentives into consideration, we first design two utility functions for the server and workers, respectively. Then, we model the corresponding utility optimization problem as a two-stage Stackelberg game by seeing the server as a leader and the workers as some followers. Next, we derive an optimal Equilibrium solution for the both stages of the whole game. Based on this solution, we design an incentive mechanism that can ensure the server to achieve the optimal utility, while stimulating workers to do their best to train the ML model. Finally, we conduct extensive simulations to demonstrate the significant performance of the proposed mechanism.
Dog Nose-Print Identification Using Deep Neural Networks

Recently, there has been rapid growth in the number of people who own companion pets (cats and dogs) due to low birth rates, an increasingly aging population, and an increasing number of single-person households.This trend has resulted in a growing interest in problems requiring solutions, such as missing pets and false insurance claims.Traditional non-biometric-based methods cannot address these problems.This paper proposes a novel deep-learning model that can extract discriminative features through dog nose-print patterns for individual identification.We present a robust baseline for how individual dogs can be identified.The proposed dog nose network (DNNet) is a convolutional neural network (CNN)-based Siamese network structure comprising feature extraction and self-attention modules.Moreover, there is no need for a separate scanning device because it uses popular mobile devices to acquire the dataset.Besides high recognition performance, the proposed method also ensures simplicity and efficiency.The proposed method achieves better recognition performance than state-of-the-art methods for the collected dog nose-print dataset.It achieves recognition performance superior to state-of-the-art methods for the collected dog nose-print dataset.Using multiple datasets through cross-validation, we acquired an average identification accuracy of 98.972% with the Rank-1 approach.Additional performance benefits were demonstrated through the receiver operating characteristic (ROC) curve, t-distributed stochastic neighbor embedding (t-SNE), and confusion matrix.
Tensor product‐based model transformation approach to cart position modeling and control in pendulum‐cart systems

Abstract The paper presents the application of the tensor product (TP)‐based model transformation technique to model and control the cart position of single‐input multi‐output pendulum‐cart systems (PCSs). The modeling is first carried out. The derived TP model, the nonlinear model of PCS, and the laboratory equipment are tested in the same open‐loop scenario, and their corresponding outputs are collected, measured, and compared. The parallel distributed compensation (PDC) technique is next applied to TP‐based cart position controller design. A linear state feedback control design is also designed in order to conduct a comparative analysis. The two proposed control system structures are experimentally validated and tested in the same scenarios on the laboratory equipment, and their performance indices are analyzed and compared.
Inductive Relation Prediction by BERT

Relation prediction in knowledge graphs is dominated by embedding based methods which mainly focus on the transductive setting. Unfortunately, they are not able to handle inductive learning where unseen entities and relations are present and cannot take advantage of prior knowledge. Furthermore, their inference process is not easily explainable. In this work, we propose an all-in-one solution, called BERTRL (BERT-based Relational Learning), which leverages pre-trained language model and fine-tunes it by taking relation instances and their possible reasoning paths as training samples. BERTRL outperforms the SOTAs in 15 out of 18 cases in both inductive and transductive settings. Meanwhile, it demonstrates strong generalization capability in few-shot learning and is explainable. The data and code can be found at https://github.com/zhw12/BERTRL.
DMN4: Few-Shot Learning via Discriminative Mutual Nearest Neighbor Neural Network

Few-shot learning (FSL) aims to classify images under low-data regimes, where the conventional pooled global feature is likely to lose useful local characteristics. Recent work has achieved promising performances by using deep descriptors. They generally take all deep descriptors from neural networks into consideration while ignoring that some of them are useless in classification due to their limited receptive field, e.g., task-irrelevant descriptors could be misleading and multiple aggregative descriptors from background clutter could even overwhelm the object's presence. In this paper, we argue that a Mutual Nearest Neighbor (MNN) relation should be established to explicitly select the query descriptors that are most relevant to each task and discard less relevant ones from aggregative clutters in FSL. Specifically, we propose Discriminative Mutual Nearest Neighbor Neural Network (DMN4) for FSL. Extensive experiments demonstrate that our method outperforms the existing state-of-the-arts on both fine-grained and generalized datasets.
Cryptospace Invertible Steganography with Conditional Generative Adversarial Networks

Deep neural networks have become the foundation of many modern intelligent systems. Recently, the author has explored adversarial learning for invertible steganography (ALIS) and demonstrated the potential of deep neural networks to reinvigorate an obsolete invertible steganographic method. With the worldwide popularisation of the Internet of things and cloud computing, invertible steganography can be recognised as a favourable way of facilitating data management and authentication due to the ability to embed information without causing permanent distortion. In light of growing concerns over cybersecurity, it is important to take a step forwards to investigate invertible steganography for encrypted data. Indeed, the multidisciplinary research in invertible steganography and cryptospace computing has received considerable attention. In this paper, we extend previous work and address the problem of cryptospace invertible steganography with deep neural networks. Specifically, we revisit a seminal work on cryptospace invertible steganography in which the problem of message decoding and image recovery is viewed as a type of binary classification. We formulate a general expression encompassing spatial, spectral, and structural analyses towards this particular classification problem and propose a novel discrimination function based on a recurrent conditional generative adversarial network (RCGAN) which predicts bit-planes with stacked neural networks in a top-down manner. Experimental results evaluate the performance of various discrimination functions and validate the superiority of neural-network-aided discrimination function in terms of classification accuracy.
AN EFFECTIVE DEEP LEARNING APPROACH FOR IMPROVING OFF-LINE ARABIC HANDWRITTEN CHARACTER RECOGNITION

Developing systems in the computer vision domain persuade researchers in many applications. The main goal in computer vision applications is to enable the computers to imitate humans in their vision system. Various systems are developed for classifying and recognizing the different types of images. This paper introduces an effective approach towards designing a system for recognizing an isolated handwritten Arabic character based on a deep learning approach. The deep learning approach is based on the convolutional neural network (CNNs).  CNN plays an important role in every single application of the computer vision domain. The CNN model is developed and trained with Arabic handwritten characters in offline mode using three Arabic handwritten character recognition datasets. In order to validate the proposed system various experiments were conducted using the AHCR, AHCD, Hijja datasets. The AHCR dataset consists of 28000 images, the AHCD consists of 16800 images, and the Hijja dataset consists of 47434 images. Testing the proposed system yields excellent recognition results in both training and testing. The result shows that the performance of the proposed system achieves a superior accuracy of 89.8, 95.4%, and 92.5% using the AHCR, AHCD, and Hijja datasets respectively.
Classifying Model-View-Controller Software Applications Using Self-Organizing Maps

The new era of information and the needs of our society require continuous change in software and technology. Changes are produced very quickly and software systems require evolving at the same velocity, which implies that the decision-making process of software architectures should be (semi-)automated to satisfy changing needs and to avoid wrong decisions. This issue is critical since suboptimal architecture design decisions may lead to high cost and poor software quality. Therefore, systematic and (semi-)automated mechanisms that help software architects during the decision-making process are required. Architectural patterns are one of the most important features of software applications, but the same pattern can be implemented in different ways, leaving to results of different quality. When an application requires to evolve, knowledge extracted from similar applications is useful for driving decisions, since quality pattern implementations can be reproduced in similar applications to improve specific quality attributes. Therefore, clustering methods are especially suitable for classifying similar pattern implementations. In this paper, we apply a novel unsupervised clustering technique, based on the well-known artificial neural network model Self-Organizing Maps, to classify Model-View-Controller (MVC) pattern from a quality point of view. Software quality is analyzed by 24 metrics organized into the categories of Count/Size, Maintainability, Duplications, Complexity, and Design Quality. The main goal of this work is twofold: to identify the quality features that establish the similarity of MVC applications without software architect bias, and to classify MVC applications by means of Self-Organizing Maps based on quality metrics. To that end, this work performs an exploratory study by conducting two analyses with a dataset of 87 Java MVC applications characterized by the 24 metrics and two attributes that describe the technology dimension of the application. The stated findings provide a knowledge base that can help in the decision-making process for the architecture of Java MVC applications.
Sampling Kaczmarz-Motzkin method for linear feasibility problems: generalization and acceleration

Randomized Kaczmarz (RK), Motzkin Method (MM) and Sampling Kaczmarz Motzkin (SKM) algorithms are commonly used iterative techniques for solving a system of linear inequalities (i.e., $Ax \leq b$). As linear systems of equations represent a modeling paradigm for solving many optimization problems, these randomized and iterative techniques are gaining popularity among researchers in different domains. In this work, we propose a Generalized Sampling Kaczmarz Motzkin (GSKM) method that unifies the iterative methods into a single framework. In addition to the general framework, we propose a Nesterov type acceleration scheme in the SKM method called as Probably Accelerated Sampling Kaczmarz Motzkin (PASKM). We prove the convergence theorems for both GSKM and PASKM algorithms in the $L_2$ norm perspective with respect to the proposed sampling distribution. Furthermore, we prove sub-linear convergence for the Cesaro average of iterates for the proposed GSKM and PASKM algorithms.From the convergence theorem of the GSKM algorithm, we find the convergence results of several well-known algorithms like the Kaczmarz method, Motzkin method and SKM algorithm. We perform thorough numerical experiments using both randomly generated and real-world (classification with support vector machine and Netlib LP) test instances to demonstrate the efficiency of the proposed methods. We compare the proposed algorithms with SKM, Interior Point Method (IPM) and Active Set Method (ASM) in terms of computation time and solution quality. In the majority of the problem instances, the proposed generalized and accelerated algorithms significantly outperform the state-of-the-art methods.
A Survey of Machine Learning-Based System Performance Optimization Techniques

Recently, the machine learning research trend expands to the system performance optimization field, where it has still been proposed by researchers based on their intuitions and heuristics. Compared to conventional major machine learning research areas such as image or speech recognition, machine learning-based system performance optimization fields are at the beginning stage. However, recent papers show that this approach is promising and has significant potential. This paper reviews 11 machine learning-based system performance optimization approaches from nine recent papers based on well-known machine learning models such as perceptron, LSTM, and RNN. This survey provides a detailed design and summarizes model, input, output, and prediction method of each approach. This paper covers various system performance areas from the data structure to essential system components of a computer system such as index structure, branch predictor, sort, and cache management. The result shows that machine learning-based system performance optimization has an important potential for future research. We expect that this paper shows a wide range of applicability of machine learning technology and provides a new perspective for system performance optimization.
Short-Term Solar Power Forecasts Considering Various Weather Variables

Solar generation has been developed rapidly in recent years. The output of solar generation systems is affected by various uncertain factors, such as different weather variables. If a large number of solar power systems are connected to the grid, the stability of the power system would be reduced. Therefore, we must pay attentions to solar power forecasting to avoid system instability. One of the important factors that may affect solar power generation is the weather condition, but the meteorological data have considerable uncertainty. Therefore, the main purpose of this paper is to identify important weather variables that affect solar power forecasting. That is, the inputs used in this work to predict solar power generation focuses on numerical weather prediction (NWP) data, which includes meteorological data such as radiation, precipitation, wind speed, and temperature. In addition, this work also considers different time series of input data to explore the relation among data sequences. Finally, this work used various deep learning models for solar power forecasting.
A Game-Theoretic Approach for Robust Federated Learning

Federated Learning enables aggregating models trained over a large number of clients by sending these models to a central server, while data privacy is preserved since only the models are sent. Federated learning techniques are considerably vulnerable to poisoning attacks. In this paper, we explore the threat of poisoning attacks and introduce a game-based robust federated averaging algorithm to detect and discard bad updates provided by the clients. We model the aggregating process with a mixed-strategy game that is played between the server and each client. The valid actions of the clients are to send good or bad updates while the server can accept or ignore these updates as its valid actions. By employing the Nash Equilibrium property, the server determines the probability of providing good updates by each client. The experimental results show that our proposed game-based aggregation algorithm is significantly more robust to faulty and noisy clients in comparison with the most recently presented methods. According to these results, our algorithm converges after a maximum of 30 iterations and can detect 100% of the bad clients for all the investigated scenarios. In addition, the accuracy of the proposed algorithm is at least 15.8% and 2.3% better than state of the art for flipping and noisy scenarios, respectively.