Reviewerly/exHarmony huggingface dataset:
Misinformation Detection and Adversarial Attack Cost Analysis in Directional Social Networks

This paper develops a novel detection system of possibly fake accounts on public social media, called FADE, that uses features based on group behaviors to identify suspicious groups. The work is motivated by the prospect of mitigating misinformation campaigns on social media, where malicious entities on directional social networks pose as credible sources and coordinate the spreading of highly corroborated false information. Instead of account-level detection, this paper aims to detect the very group activity that underlies misinformation campaigns; namely, the coordinated spreading of messages to boost (misinformation) visibility. The existing group detection methods group users into two clusters (fake or not) and directly produce clusters of fake accounts. Conversely, we group users into many clusters based on information propagation patterns and user features and then classify them. The benefit of multiple clusters is that we can detect suspicious behavior more easily from cluster-wide statistics. In order to improve clustering accuracy, we analyze and select the most important features for clustering based on Bayesian optimization instead of using all the features. Accordingly, similarity metrics are defined that allow clustering of individually plausible accounts in a manner that enables one to detect suspicious clusters of activity. Cluster-level features are then used to decide if the cluster is benign. We further explore the cost of adversarial attacks on our detection model. Evaluation results on Twitter data sets demonstrate that our proposed approach outperforms state-of-the-art baselines in detecting accounts created for information manipulation campaigns. In addition, we show that the cost of subverting detection (without reducing the effectiveness of the attacker's campaign) is high.
Nonreversible Jump Algorithms for Bayesian Nested Model Selection

Nonreversible Markov chain Monte Carlo methods often outperform their reversible counterparts in terms of asymptotic variance of ergodic averages and mixing properties. Lifting the state-space is a generic technique for constructing such samplers. The idea is to think of the random variables we want to generate as position variables and to associate to them direction variables so as to design Markov chains which do not have the diffusive behavior often exhibited by reversible schemes. In this article, we explore the benefits of using such ideas in the context of Bayesian model choice for nested models, a class of models for which the model indicator variable is an ordinal random variable. By lifting this model indicator variable, we obtain nonreversible jump algorithms, a nonreversible version of the popular reversible jump algorithms. This simple algorithmic modification provides samplers which can empirically outperform their reversible counterparts at no extra computational cost. The code to reproduce all experiments is available online 1 .
A Non-iterative Radial Basis Function Based Quick Convolutional Neural Network

In the past few years, Convolutional Neural Networks (CNNs) have achieved surprisingly good results for objects classification in real world images. However, training a CNN from scratch for large datasets is still a nightmare, when it comes to time and resources. The main reason for this problem is long iterative training process used in CNN's fully connected layer which is also called a classification layer. Therefore, in this paper we propose a novel approach to make the convolutional neural network quicker and more efficient for image classification tasks. The proposed approach consists of a convolutional feature extraction layer and a non-iterative radial basis function-based classification layer. The proposed approach has been evaluated on three benchmark datasets such as CIFAR-10, MNIST and Digit. The experimental results have demonstrated that the proposed approach can achieve same or higher accuracy in lesser time than the standard CNN.
Line Follower Robot: Design and Hardware Application

Robots are specially designed machines used to accomplish dedicated and repetitive actions with high speed, precision and endurance. These actions are mainly controlled by using advance programming, manipulators and sensors etc. The sensor based black line follower robots are one of the most basic robots used to follow black line on white background or vice versa. These robots may be used to in various industrial and domestic applications such as to carry goods, floor cleaning, delivery services and transportation. In this paper, a simple and cost effective circuit design of black line follower robot and its practical implementation has been presented.
Poem Generation using Transformers and Doc2Vec Embeddings

Poems are sequences of words that express ideas and emotions in an imaginative style, some following strict literary syntax or form. They are artistic expressions, and their generation requires in-depth knowledge and mastery of language. As such, poem generation is considered a very challenging task in Natural Language Processing and has been attracting research interest in the recent decade. We propose a method of generating poems using transformers, coupled with doc2vec embeddings in order to assess the automatically generated poems. In this method, we first train a transformer and a doc2vec model using a poem dataset. Then the trained transformer takes an input text and produces several generated poems. To have an objective basis for assessing the generated poems, we present a preliminary attempt at measuring the quality of a machine-generated poem by computing the cosine similarity score, referenced to the trained doc2vec model. This score is used as a basis for choosing the final output poem. The results show that this method ensures good cohesion between the machine-generated poem and the given input text. We then also explore the implication of the transformer training to the doc2vec embeddings of its output poems, which are shown to be more similar to poems (documents) in the train set as training progresses. Finally, we demonstrate how transformers can learn some poetry styles by exposing them to poems of specific poets.
Analyzing the Components of Distributed Coevolutionary GAN Training

Distributed coevolutionary Generative Adversarial Network (GAN) training has empirically shown success in overcoming GAN training pathologies. This is mainly due to diversity maintenance in the populations of generators and discriminators during the training process. The method studied here coevolves sub-populations on each cell of a spatial grid organized into overlapping Moore neighborhoods. We investigate the impact on performance of two algorithm components that influence the diversity during coevolution: the performance-based selection/replacement inside each sub-population and the communication through migration of solutions (networks) among overlapping neighborhoods. In experiments on MNIST dataset, we find that the combination of these two components provides the best generative models. In addition, migrating solutions without applying selection in the sub-populations achieves competitive results, while selection without communication between cells reduces performance.
Jitter-Robust Video Retargeting With Kalman Filter And Attention Saliency Fusion Network

In this paper, we propose a novel framework for video retargeting with Kalman filter and Attention Saliency Fusion Network (ASFN). First, we estimate the importance map which accounts for a content-aware saliency map. The aspect ratio of a video is then resized by adaptive scaling function. Finally, the Kalman filter is applied to the image sequence for alleviating jittering and wavy effect of moving objects. Proposed method uses two consecutive images which can be implemented on real-time application. Experimental results validate the proposed framework can achieve content-preserving results while alleviating the jitter effect.
ImRP: A Predictive Partition Method for Data Skew Alleviation in Spark Streaming Environment

Spark Streaming is an extension of the core Spark engine that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. It treats stream as a series of deterministic batches and handles them as regular jobs. However, for a stream job responsible for a batch, data skew (i.e., the imbalance in the amount of data allocated to each reduce task), can degrade the job performance significantly because of load imbalance. In this paper, we propose an improved range partitioner (ImRP) to alleviate the reduce skew for stream jobs in Spark Streaming. Unlike previous work, ImRP does not require any pre-run sampling of input data and generates the data partition scheme based on the intermediate data distribution estimated by the previous batch processing, in which a prediction model EWMA (Exponentially Weighted Moving Average) is adopted. To lighten the data skew, ImRP presents a novel method of calculating the partition borders optimally, and a mechanism of splitting the border key clusters when the semantics of shuffle operators permit. Besides, ImRP considers the integrated partition size and heterogeneity of computing environments when balancing the load among reduce tasks appropriately. We implement ImRP in Spark-3.0 and evaluate its performance on four representative benchmarks: wordCount, sort, pageRank, and LDA. The results show that by mitigating the data skew, ImRP can decrease the execution time of stream jobs substantially compared with some other partition strategies, especially when the skew degree of input batch is serious.
SkipConv: Skip Convolution for Computationally Efficient Deep CNNs

Convolution operation in deep convolutional neural networks is the most computationally expensive as compared to other operations. Most of the model computation (FLOPS) in the deep architecture belong to convolution operation. In this paper, we are proposing a novel skip convolution operation that employs significantly fewer computation as compared to the traditional one without sacrificing model accuracy. Skip convolution operation produces structured sparsity in the output feature maps without requiring sparsity in the model parameters for computation reduction. The existing convolution operation performs the redundant computation for object feature representation while the proposed convolution skips redundant computation. Our empirical evaluation for various deep models (VGG, ResNet, MobileNet, and Faster R-CNN) over various benchmarked datasets (CIFAR-10, CIFAR-100, ImageNet, and MS-COCO) show that skip convolution reduces the computation significantly while preserving feature representational capacity. The proposed approach is model-agnostic and can be applied over any architecture. The proposed approach does not require a pretrained model and does train from scratch. Hence we achieve significant computation reduction at training and test time. We are also able to reduce computation in an already compact model such as MobileNet using skip convolution. We also show empirically that the proposed convolution works well for other tasks such as object detection. Therefore, SkipConv can be a widely usable and efficient way of reducing computation in deep CNN models.
Privacy-Preserving Verifiable Graph Intersection Scheme With Cryptographic Accumulators in Social Networks

Due to wealthy structure and semantic information expressed by a graph, the graph is frequently employed in numerous social applications to show social relationships. Among those important applications, the private graph intersection operation plays an important part in social networks. Because of the high cost of managing graph data and the computational difficulty of graph intersection operation, delegating the computations to the cloud server (CS) is an attractive alternative. However, when the CS is untrusted or compromised by some adversaries, the results that the CS returns can not be guaranteed to be correct. In such cases, it may have serious consequences for the application functionality. In this article, we present an efficient and privacy-preserving verifiable graph intersection scheme with cryptographic accumulators in social networks. Using the proposed scheme, we construct the framework to provide secure verifiable graph intersection operation in an untrusted cloud, and the requester can verify the correctness of the graph intersection result that the CS returns. Additionally, the data owners' graph data privacy and user authentication are well protected. The detailed correctness proof and performance analysis show that the proposed scheme is secure and feasible. Thus, our scheme is appropriate for many practical applications.
Generative Adversarial Stacked Autoencoders for Facial Pose Normalization and Emotion Recognition

In this work, we propose a novel Generative Adversarial Stacked Autoencoder that learns to map facial expressions with up to ±60 degrees to an illumination invariant facial representation of 0 degrees. We accomplish this by using a novel convolutional layer that exploits both local and global spatial information, and a convolutional layer with a reduced number of parameters that exploits facial symmetry. Furthermore, we introduce a generative adversarial gradual greedy layer-wise learning algorithm designed to train Adversarial Autoencoders in an efficient and incremental manner. We demonstrate the efficiency of our method and report state-of-the-art performance on several facial emotion recognition corpora, including one collected in the wild.
Activity Normalization for Activity Detection in Surveillance Videos

A framework for activity detection in surveillance videos generally involves activity proposal generation and activity classification. An activity proposal is a spatial and temporal candidate region for an arbitrary activity, and an activity classifier identifies the activity class for activity proposals. One of the difficulties in activity classification is the variation in the number of activity appearances due to the diversity of the object moving directions and inter-object-positional relationships. To solve this problem, we propose an activity normalization method for rotating activity proposals so that the object-movement direction and inter-object-positional relationship are constant among all activity proposals before classification. The experimental results indicate that activity classification accuracy improves by adding our method to a general activity detection framework using the ActEV/VIRAT dataset.
Rate Distortion Optimization Over Large Scale Video Corpus With Machine Learning

We present an efficient codec-agnostic method for bitrate allocation over a large scale video corpus with the goal of minimizing the average bitrate subject to constraints on average and minimum quality. Our method clusters the videos in the corpus such that videos within one cluster have similar rate-distortion (R-D) characteristics. We train a support vector machine classifier to predict the R-D cluster of a video using simple video complexity features that are computationally easy to obtain. The model allows us to classify a large sample of the corpus in order to estimate the distribution of the number of videos in each of the clusters. We use this distribution to find the optimal encoder operating point for each R-D cluster. Experiments with AV1 encoder show that our method can achieve the same average quality over the corpus with 22% less average bitrate.
Planning, Learning and Reasoning Framework for Robot Truck Unloading

We consider the task of autonomously unloading boxes from trucks using an industrial manipulator robot. There are multiple challenges that arise: (1) real-time motion planning for a complex robotic system carrying two articulated mechanisms, an arm and a scooper, (2) decision-making in terms of what action to execute next given imperfect information about boxes such as their masses, (3) accounting for the sequential nature of the problem where current actions affect future state of the boxes, and (4) real-time execution that interleaves high-level decision-making with lower level motion planning. In this work, we propose a planning, learning, and reasoning framework to tackle these challenges, and describe its components including motion planning, belief space planning for offline learning, online decision-making based on offline learning, and an execution module to combine decision-making with motion planning. We analyze the performance of the framework on real-world scenarios. In particular, motion planning and execution modules are evaluated in simulation and on a real robot, while offline learning and online decision-making are evaluated in simulated real-world scenarios.
A Preliminary Semiotic-Conceptual Analysis of a Learning Management System

Semiotic-Conceptual Analysis (SCA) is a mathematical formalisation of semiotics that builds on a conceptual foundation. Its aim is to present a means for analysing the relationship between representations and their meanings. SCA can thus support “interactive linguistics”. The initial step of SCA is a qualitative analysis which provides an overview of the structures of an application domain. This initial step is followed by more formal analyses. In this paper, the initial step of SCA is applied to a learning management system as an example from the domain of educational software.
Ensuring Privacy of Occupancy Changes in Smart Buildings

Smart building management systems rely on sensors to optimize the operation of buildings. If an unauthorized user gains access to these sensors, a privacy leak may occur. This paper considers such a potential leak of privacy in a smart residential building, and how it may be mitigated by corrupting the measurements with additive Gaussian noise. This corruption is done in order to hide when the occupancy changes in an apartment. A lower bound on the variance of any estimator that estimates the change time is derived. The bound is then used to analyze how different model parameters affect the variance. It is shown that the signal to noise ratio and the system dynamics are the main factors that affect the bound. These results are then verified on a simulator of the KTH Live-In Lab Testbed, showing good correspondence with theoretical results.
T: A Heuristic Search Based Path Planning Algorithm for Temporal Logic Specifications

The fundamental path planning problem for a mobile robot involves generating a trajectory for point-to-point navigation while avoiding obstacles. Heuristic-based search algorithms like A* have been shown to be efficient in solving such planning problems. Recently, there has been an increased interest in specifying complex path planning problem using temporal logic. In the state-of-the-art algorithm, the temporal logic path planning problem is reduced to a graph search problem, and Dijkstra's shortest path algorithm is used to compute the optimal trajectory satisfying the specification.The A* algorithm, when used with an appropriate heuristic for the distance from the destination, can generate an optimal path in a graph more efficiently than Dijkstra's shortest path algorithm. The primary challenge for using A* algorithm in temporal logic path planning is that there is no notion of a single destination state for the robot. We present a novel path planning algorithm T* that uses the A* search procedure opportunistically to generate an optimal trajectory satisfying a temporal logic query. Our experimental results demonstrate that T* achieves an order of magnitude improvement over the state-of-the-art algorithm to solve many temporal logic path planning problems in 2-D as well as 3-D workspaces.
A Method for Semantic Knee Bone and Cartilage Segmentation with Deep 3D Shape Fitting Using Data from the Osteoarthritis Initiative

We present a multistage method for deep semantic segmentation of bone structures based on a landmark-based shape regression and subsequent local segmentation of relevant areas. Our solution covers the entire pipeline from 2D-based pre-segmentation, a method for fast deep 3D shape regression and subsequent patch-based 3D semantic segmentation for final segmentation. Since we perform landmark regression using a statistical shape model, our method is able to fit an arbitrary number of landmarks without increase in model complexity. The algorithm is evaluated on the OAI-ZIB dataset, for which we use the binary masks to generate sets of corresponding landmarks and build a deep statistical shape model. By employing our proposed deep shape fitting, our method achieves the performance of existing high-precision approaches in terms of segmentation accuracy while at the same time drastically reducing computational complexity and improving runtime by a large margin.
Deep Echo State Networks with Multi-Span Features for Nonlinear Time Series Prediction

Nonlinear time-series prediction is one of the challenging topics in machine learning due to complex non-stationarity in the temporal dynamics. Many recurrent neural network models have been proposed for enhancing the prediction accuracy in time-series prediction tasks. Echo state networks (ESNs) are a variant of recurrent neural networks, which have great potential for addressing machine learning tasks with a very low learning cost. However, the existing ESN-based models have used only single-span features to our best knowledge. In this study, we propose two deep ESN models incorporating multi-span features to improve the prediction performance. We show that the two deep ESN models yield better prediction performance compared to the other state-of-the-art ESN-based methods in benchmark time-series prediction tasks with three models: the Lorenz system, the Mackey-Glass system, and the NARMA-10 system. Our analyses illustrate that deeper structures decrease the multicollinearity of the extracted features and thus contribute to improved performance. The presented results suggest that the proposed models contribute to the development of artificial intelligence for temporal information processing.
Feature Extraction to Heterogeneous Cross Project Defect Prediction

Cross Project Defect Prediction (CPDP) predicts faults in a target project (which has deficient faulty data) by defect prediction models learned from another project's fault data. Nonetheless, these studies have a prevalent problem that needs uniform metrics, i.e. to describe themselves; distinct projects must have similar features. This article emphasizes heterogeneous CPDP (HCPDP) modeling that does not require the same set of metrics between two applications, and also builds model of defect prediction based on matched heterogeneous metrics that show comparable distribution in their values for a given pair of datasets. HCPDP modeling consists of three main phase feature ranking and feature selection, metric matching, and finally, the binary classification of unlabeled instances in the target application is performed as clean or buggy instances using an appropriate classification. This paper empirically and theoretically evaluates the effect of an additional modeling phase i.e. extraction of features on performance of the HCPDP model. Selection of features is to weed out obsolete or redundant features from your dataset. The key difference between selection and extraction of features is that selection of features preserves a subset of the original features while extraction of features also produces new ones. This paper compares the performance of the proposed HCPDP model on 13 benchmarked datasets of three source project groups AEEEM, ReLink& SOFTLAB with and without applying feature extraction phase using three machine learning classifiers. We have used Chi Square Test (CST) to pick features, and Principle Components Analysis (PCA) method to extract brand features of the dataset. Results show that for prediction pairs (JDT, ar1) & (Safe, ar3), prediction accuracies were significantly improved when we employed feature extraction phase in model. The comparative analysis among all three classifier demonstrates that GBM performs best among all for both prediction pairs.