Reviewerly/exHarmony huggingface dataset:
What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process

In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the increasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful — topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations.
Governance in Decentralised Networks

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The Pod People: Understanding Manipulation of Social Media Popularity via Reciprocity Abuse

Online Social Network (OSN) Users' demand to increase their account popularity has driven the creation of an underground ecosystem that provides services or techniques to help users manipulate content curation algorithms. One method of subversion that has recently emerged occurs when users form groups, called pods, to facilitate reciprocity abuse, where each member reciprocally interacts with content posted by other members of the group. We collect 1.8 million Instagram posts that were posted in pods hosted on Telegram. We first summarize the properties of these pods and how they are used, uncovering that they are easily discoverable by Google search and have a low barrier to entry. We then create two machine learning models for detecting Instagram posts that have gained interaction through two different kinds of pods, achieving 0.91 and 0.94 AUC, respectively. Finally, we find that pods are effective tools for increasing users' Instagram popularity, we estimate that pod utilization leads to a significantly increased level of likely organic comment interaction on users' subsequent posts.
Few-Shot Learning for New User Recommendation in Location-based Social Networks

The proliferation of GPS-enabled devices establishes the prosperity of location-based social networks, which results in a tremendous amount of user check-ins. These check-ins bring in preeminent opportunities to understand users’ preferences and facilitate matching between users and businesses. However, the user check-ins are extremely sparse due to the huge user and business bases, which makes matching a daunting task. In this work, we investigate the recommendation problem in the context of identifying potential new customers for businesses in LBSNs. In particular, we focus on investigating the geographical influence, composed of geographical convenience and geographical dependency. In addition, we leverage metric-learning-based few-shot learning to fully utilize the user check-ins and facilitate the matching between users and businesses. To evaluate our proposed method, we conduct a series of experiments to extensively compare with 13 baselines using two real-world datasets. The results demonstrate that the proposed method outperforms all these baselines by a significant margin.
Few-Sample and Adversarial Representation Learning for Continual Stream Mining

Deep Neural Networks (DNNs) have primarily been demonstrated to be useful for closed-world classification problems where the number of categories is fixed. However, DNNs notoriously fail when tasked with label prediction in a non-stationary data stream scenario, which has the continuous emergence of the unknown or novel class (categories not in the training set). For example, new topics continually emerge in social media or e-commerce. To solve this challenge, a DNN should not only be able to detect the novel class effectively but also incrementally learn new concepts from limited samples over time. Literature that addresses both problems simultaneously is limited. In this paper, we focus on improving the generalization of the model on the novel classes, and making the model continually learn from only a few samples from the novel categories. Different from existing approaches that rely on abundant labeled instances to re-train/update the model, we propose a new approach based on Few Sample and Adversarial Representation Learning (FSAR). The key novelty is that we introduce the adversarial confusion term into both the representation learning and few-sample learning process, which reduces the over-confidence of the model on the seen classes, further enhance the generalization of the model to detect and learn new categories with only a few samples. We train the FSAR operated in two stages: first, FSAR learns an intra-class compacted and inter-class separated feature embedding to detect the novel classes; next, we collect a few labeled samples belong to the new categories, utilize episode-training to exploit the intrinsic features for few-sample learning. We evaluated FSAR on different datasets, using extensive experimental results from various simulated stream benchmarks to show that FSAR effectively outperforms current state-of-the-art approaches.
Time Evaluation Of Different Cryptography Algorithms Using Labview

Abstract A new LabVIEW simulation design was applied to provide a comparison study among several types of the most common symmetric and asymmetric encryption algorithms. Also, the comparison with advanced encryption package program was made. The results showed that the new LabVIEW simulation design had advantages in several points, where the evaluated time required for the encryption and decryption processes will be lesser than it values using advanced encryption package program. In this work, the symmetric encryption algorithms were used: AES (Advanced Encryption Standard), DES (Data Encryption Standard), 3DES (Triple Data Encryption Standard) and RC2 (Rivest Cipher) algorithms, while the asymmetric encryption algorithm used was the RSA (Rivest-Shamir-Aldeman) algorithm. The comparison had been achieved using different sizes of text files with different key sizes. The final results proved that the LabVIEW simulation design is better than the Advanced Encryption Package because it increased the algorithm’s performance by 65% in terms of speed and throughput, and for the algorithms’ comparison, the final results showed the effectiveness of AES algorithm over the other algorithms in terms of speed and throughput for the encryption and decryption processes.
Modeling of Churn Process in Bitcoin Network

In this work, we present an analytical model for the churning process in Bitcoin network. We model the churning process for ordinary (non-gateway) nodes with homogeneous sleep time using Continuous Time Markov Chain. Within performance results, we present mean synchronization time needed when nodes come back from sleep and block distribution time in presence of churning nodes. Our results indicate that sleep times of the order of several hours require synchronization times in the order of a minute. Also, a large number of churning nodes can significantly affect the distribution power of the Bitcoin network.
Historical Arabic Manuscripts Text Recognition Using Convolutional Neural Network

The Islamic heritage is rich of Arabic manuscripts that contain valuable knowledge of Islamic Sciences, such as Hadeethe, Tafseer and Akhidah. However, these manuscripts are hard to read and there is a need to convert them into a publishable form. Therefore, this paper proposes a method for recognizing the text in the images of these manuscripts and convert it into a readable text that can be copied and saved for further usage in other researches. The main steps of our algorithm are as follow: 1) enhancing the image (preprocessing); 2) dividing the manuscript image into lines and characters (segmentation);3) building the dataset of Arabic characters;4) recognizing the text (classification). In the classification stage, we apply Convolutional Neural Network CNN on three created datasets, and it provides an accuracy that ranges between 74.29% to 88.20%.
Co-occurrence of deep convolutional features for image search

Image search can be tackled using deep features from pre-trained Convolutional Neural Networks (CNN). The feature map from the last convolutional layer of a CNN encodes descriptive information from which a discriminative global descriptor can be obtained. We propose a new representation of co-occurrences from deep convolutional features to extract additional relevant information from this last convolutional layer. Combining this co-occurrence map with the feature map, we achieve an improved image representation. We present two different methods to get the co-occurrence representation, the first one based on direct aggregation of activations, and the second one, based on a trainable co-occurrence representation. The image descriptors derived from our methodology improve the performance in very well-known image retrieval datasets as we prove in the experiments.
Mammographic mass segmentation using multichannel and multiscale fully convolutional networks

Abstract Breast cancer is one of the leading causes of death among women worldwide. Mammographic mass segmentation is an important task in mammogram analysis. This process, however, poses a prominent challenge considering that masses can be obscured in images and appear with irregular shapes and low image contrast. In this study, a multichannel, multiscale fully convolutional network is proposed and evaluated for mass segmentation in mammograms. To reduce the impact of surrounding unrelated structures, preprocessed images with a salient mass appearance are obtained as the second input channel of the network. Furthermore, to jointly conduct fine boundary delineation and global mass localization, we incorporate more crucial context information by learning multiscale features from different resolution levels. The performance of our segmentation approach is compared with that of several traditional and deep‐learning‐based methods on the popular DDSM and INbreast datasets. The evaluation indices consist of the Dice similarity coefficient, area overlap measure, area undersegmentation measure, area oversegmentation measure, and Hausdorff distance. The mean values of the Dice similarity coefficient and Hausdorff distance of our proposed segmentation method are 0.915 ± 0.031 and 6.257 ± 3.380, respectively, on DDSM and 0.918 ± 0.038 and 2.572 ± 0.956, respectively, on INbreast, which are superior to those of the existing methods. The experimental results verify that our proposed multichannel, multiscale fully convolutional network can reliably segment masses in mammograms.
IStego100K: Large-Scale Image Steganalysis Dataset

In order to promote the rapid development of image steganalysis technology, in this paper, we construct and release a multivariable large-scale image steganalysis dataset called IStego100K. It contains 208,104 images with the same size of 1024*1024. Among them, 200,000 images (100,000 cover-stego image pairs) are divided as the training set and the remaining 8,104 as testing set. In addition, we hope that IStego100K can help researchers further explore the development of universal image steganalysis algorithms, so we try to reduce limits on the images in IStego100K. For each image in IStego100K, the quality factors is randomly set in the range of 75–95, the steganographic algorithm is randomly selected from three well-known steganographic algorithms, which are J-uniward, nsF5 and UERD, and the embedding rate is also randomly set to be a value of 0.1–0.4. In addition, considering the possible mismatch between training samples and test samples in real environment, we add a test set (DS-Test) whose source of samples are different from the training set. We hope that this test set can help to evaluate the robustness of steganalysis algorithms. We tested the performance of some latest steganalysis algorithms on IStego100K, with specific results and analysis details in the experimental part. We hope that the IStego100K dataset will further promote the development of universal image steganalysis technology (The description of IStego100K and instructions for use can be found here: https://github.com/YangzlTHU/IStego100K ).
Audio Adversarial Examples Generation with Recurrent Neural Networks

Previous methods of performing adversarial attacks against speech recognition systems often treat this problem as a solely optimization problem and require iterative updates to generate optimal solutions. Although they can achieve high success rate, the process is too computational heavy even with the help of GPU. In this paper, we introduce a new type of real-time adversarial attack methodology, which applies Recurrent Neural Networks (RNN) with a two-step training process to generate adversarial examples targeting a Keyword Spotting (KWS) system. We extend our attack to physical world by adding extra constraints in order to eliminate the distortions in real world. In the experiment, we launch a real-time adversarial attack on the KWS system both in digital and physical world. The experimental results of digital world show that the execution time of our attack is more than 400 times faster than the state-of-the-art attack (i.e., C&W attack) with the comparable attack success rate. In physical world, after adding extra constraints, the perturbation becomes more robust such that the average attack success rate increases from 40.3% to 84.3%.
Channel Boosted Convolutional Neural Network for Classification of Mitotic Nuclei using Histopathological Images

Development of a diagnostic model using deep learning techniques is one of the eminent areas of research in computer aided diagnostic (CAD) systems. CAD systems based on deep learning have been developed to automate the process of mitosis classification. However, mitosis classification usually suffers from class imbalance problem, moreover, high similarity between mitotic and non-mitotic nuclei as well as discrepancy in appearance and shape make classification of mitotic nuclei a challenging area of research. Quality of CAD systems that use medical images for diagnosis, highly depends on how image features are manipulated in order to get better results. This work suggests a new technique of Channel Boosted Convolutional Neural Network (CB-CNN) to classify breast cancer mitotic nuclei. In this technique, feature representation of a CNN is boosted by adding auxiliary feature channels along with original feature space (Red, Green and Blue channels) to increase the generalization of model for heterogeneous and sparse data. In the proposed method, initially (80x80) patches of mitotic and non-mitotic nuclei were extracted from histopathological images by performing histogram based binary thresholding. For the development of a CB-CNN, the potential of auxiliary feature learners was exploited to learn high-level feature representation. In this regard, an additional set of texture and gradient based feature channels were concatenated with original RGB features space of data. This boosted representation was assigned to a custom made deep CNN model. Learning capacity of the proposed CB-CNN is evaluated on TUPAC'16 challenge dataset. Channel Boosting based CNNs (0.53 for CB-CNN, 0.71 for CB-VGG and 0.54 for CB-ResNet) show improved performance in terms of F-score as compared to SVM (F-score: 0.42), and baseline CNN (0.47 for CNN, 0.55 for VGG and 0.44 for ResNet) classifier with and without transfer learning. The improved performance of CB-CNN suggests that the boosting of channel representation improves the generalization of a CNN by making feature space more versatile and flexible, thus making it robust towards heterogeneous data.
RDMN: A Relative Density Measure Based on MST Neighborhood for Clustering Multi-Scale Datasets

Density based clustering techniques discover the intrinsic clusters by separating the regions present in the dataset as high- and low-density regions based on their neighborhood information. They are popular and effective because they identify the clusters of arbitrary shapes and automatically detect the number of clusters. However, the distribution patterns of clusters are natural and complex in the datasets generated by different applications. Most of the existing density based clustering algorithms are not suitable to identify the clusters of complex pattern with large variation in density because they use fixed global parameters to compute the density of data points. Minimum spanning tree (MST) of a complete graph easily captures the intrinsic neighborhood information of different characteristic datasets without any user defined parameters. We propose a new Relative Density measure based on MST Neighborhood graph (RDMN) to compute the density of data points. Based on this new density measure, we propose a clustering technique to identify the clusters of complex patterns with varying density. The MST neighborhood graph is partitioned into dense regions based on the density level of data points to retain the shape of clusters. Finally, these regions are merged into actual clusters using MST based clustering technique. To the best of our knowledge, the proposed RDMN is the first MST based density measure for capturing the intrinsic neighborhood without any user defined parameter. Experimental results on synthetic and real datasets demonstrate that the proposed algorithm outperforms other popular clustering techniques in terms of cluster quality, accuracy, and robustness against noise and detecting the outliers.
Morphological and compositional analysis of alluvial gold: The Fresnedoso gold placer (Spain)

The Fresnedoso creek gold placer (FGP) in the Moraleja Basin (W Iberian Massif) has been investigated in terms of morphological, textural and compositional evolution of gold particles. A mixture of two populations has been identified being coherent with primary sources located at a proximal (2.5–10 km), and distal (20–50 km) range. Shape and compositional evidences suggest recycling of paleoplacers (tertiary) has to be considered to some extend. Primary laminar morphologies point to lode deposits in small-flat veins hosted in the metasediments of the Schist Greywacke Complex. All these features suggest the Fresnedoso creek gold placer is a deposit constituted by with mono- and polycyclic particles. Transport distance – Flattening indexes (CFI, Shilo) models have been tested and result in useful information for exploration of undiscovered deposits, even with a limited dataset. Compositional analysis of gold particle morphotypes has revealed that the Fresnedoso gold is a AuAg bimetallic alloy. Textural and compositional groups include: type 1 (Au1 = Au88-94Ag12-6), type 2 (Au2 = Au99 Ag1) and type 3 (Au3 ~ Au99Ag1). Variations in composition of type 1 (Particle's cores) could reflect differences in primary composition, spatial dispersion of sources or secondary processes, but fit within the compositional range of orogenic gold deposits. Secondary processes have been explored to explain compositional heterogeneity in the particles. Gold type 2 (rim) and 3 (micro-aggregates) represents two different de-alloying stages, from initial Ag-leaching at the rim and/or through grain-boundaries and microfractures (Au2), to complete resetting of primary chemical imprint, pervasive porous texture and Au re-precipitation with Fe-OOH and clays (Au3). A model is proposed which combines: Ag de-alloying enhanced in a Cl-Fe rich environment (lateritic) and the deformation/recrystallization processes related to mechanical cold-work in the river load-bed during transport.
Theory of Ising Machines and a Common Software Platform for Ising Machines

Ising machines are a new type of non-Neumann computer that specializes in solving combinatorial optimization problems efficiently. The input form of Ising machines is the energy function of the Ising model or quadratic unconstrained binary optimization form, and Ising machines operate to search for a condition to minimize the energy function. We describe the theory of Ising machines and the present status of the Ising machines, software for Ising machines, and applications using Ising machines.
Object Detection Using Deep Learning: Single Shot Detector with a Refined Feature-fusion Structure

In order to improve the detection accuracy of objects at different scales, the most recent studies applied multilayer architecture. However, the extracted low-level feature in the shallow layers may not work perfectly on the detection performance due to its less semantic information, especially for small objects. In this paper, we propose a refined feature-fusion structure to be integrated with single shot detector (SSD). To obtain the rich representation ability for feature mapping, in the fusion block, the deconvolution operation is basically applied to fuse high-level semantic features and low-level semantic features. It is noteworthy that in the proposed framework, the feature pyramid network is modified to better describe the features by the skip connection. An adaptive weighted connection is designed at the feature-fusion block, which further enhances the performance of the detection. On PASCAL VOC2007 test set, the experimental results show that the mAP of the proposed network is higher than SSD and deconvolutional single shot detector (DSSD) by 2.03% and 0.63%, respectively. Meanwhile, the speed of our method is as 2.2 times fast as the DSSD. Furthermore, the mAP of our refined feature-fusion structure SSD is 6.2% higher than SSD on the small object test set of PASCAL VOC2007, which verifies the effectiveness of the proposed model.