Reviewerly/exHarmony huggingface dataset:
Syntactic, Semantic and Sentiment Analysis: The Joint Effect on Automated Essay Evaluation

Manual grading of essays by humans is time-consuming and likely to be susceptible to inconsistencies and inaccuracies.In recent years, an abundance of research has been done to automate essay evaluation processes, yet little has been done to take into consideration the syntax, semantic coherence and sentiments of the essay's text together.Our proposed system incorporates not just the rule-based grammar and surface level coherence check but also includes the semantic similarity of the sentences.We propose to use Graph-based relationships within the essay's content and polarity of opinion expressions.Semantic similarity is determined between each statement of the essay to form these Graph-based spatial relationships and novel features are obtained from it.Our algorithm uses 23 salient features with high predictive power, which is less than the current systems while considering every aspect to cover the dimensions that a human grader focuses on.Fewer features help us get rid of the redundancies of the data so that the predictions are based on more representative features and are robust to noisy data.The prediction of the scores is done with neural networks using the data released by the ASAP competition held by Kaggle.The resulting agreement between human grader's score and the system's prediction is measured using Quadratic Weighted Kappa (QWK).Our system produces a QWK of 0.793.
Asymmetry Hurts: Private Information Retrieval Under Asymmetric Traffic Constraints

We consider the classical setting of private information retrieval (PIR) of a single message (file) out of M messages from N distributed databases under the new constraint of asymmetric traffic from databases. In this problem, the ratios between the traffic from the databases are constrained, i.e., the ratio of the length of the answer string that the user (retriever) receives from the nth database to the total length of all answer strings from all databases is constrained to be τn. This may happen if the user's access to the databases is restricted due to database availability, channel quality to the databases, and other factors. For this problem, for fixed M, N, we develop a general upper bound C̅(τ), which generalizes the converse proof of Sun-Jafar, where database symmetry was inherently used. Our converse bound is a piece-wise affine function in the traffic ratio vector τ = (τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , · · · ,τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> ). For the lower bound, we explicitly show the achievability of (M+N-1/M) corner points. For the remaining traffic ratio vectors, we perform time-sharing between these corner points. The recursive structure of our achievability scheme is captured via a system of difference equations. The upper and lower bounds exactly match for M = 2 and M = 3 for any N and any τ. The results show strict loss of PIR capacity due to the asymmetric traffic constraints compared with the symmetric case of Sun-Jafar which implicitly uses τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> = N1for all n.
Large-Scale Multi-Object Rearrangement

This paper describes a new robotic tabletop rearrangement system, and presents experimental results. The tasks involve rearranging as many as 30 to 100 blocks, sometimes packed with a density of up to 40%. The high packing factor forces the system to push several objects at a time, making accurate simulation difficult, if not impossible. Nonetheless, the system achieves goals specifying the pose of every object, with an average precision of ± 1 mm and ± 2°. The system searches through policy rollouts of simulated pushing actions, using an Iterated Local Search technique to escape local minima. In real world execution, the system executes just one action from a policy, then uses a vision system to update the estimated task state, and replans. The system accepts a fully general description of task goals, which means it can solve the singulation and separation problems addressed in prior work, but can also solve sorting problems and spell out words, among other things. The paper includes examples of several solved problems, statistical analysis of the system's behavior on different types of problems, and some discussion of limitations, insights, and future work.
Predictive Collision Avoidance for the Dynamic Window Approach

Foresighted navigation is an essential skill for robots to rise from rigid factory floor installations to much more versatile mobile robots that partake in our everyday environment. The current state of the art that provides this mobility to some extent is the Dynamic Window Approach combined with a global start-to-target path planner. However, neither the Dynamic Window Approach nor the path planner are equipped to predict the motion of other objects in the environment. We propose a change in the Dynamic Window Approach-a dynamic collision model-that is capable of predicting future collisions with the environment by also taking into account the motion of other objects. We show in simulated experiments that our new way of computing the Dynamic Window Approach significantly reduces the number of collisions in a dynamic setting with nonholonomic vehicles while still being computationally efficient.
Fast User-Guided Video Object Segmentation by Interaction-And-Propagation Networks

We present a deep learning method for the interactive video object segmentation. Our method is built upon two core operations, interaction and propagation, and each operation is conducted by Convolutional Neural Networks. The two networks are connected both internally and externally so that the networks are trained jointly and interact with each other to solve the complex video object segmentation problem. We propose a new multi-round training scheme for the interactive video object segmentation so that the networks can learn how to understand the user's intention and update incorrect estimations during the training. At the testing time, our method produces high-quality results and also runs fast enough to work with users interactively. We evaluated the proposed method quantitatively on the interactive track benchmark at the DAVIS Challenge 2018. We outperformed other competing methods by a significant margin in both the speed and the accuracy. We also demonstrated that our method works well with real user interactions.
Comparing of deep neural networks and extreme learning machines based on growing and pruning approach

Recently, the studies based on Deep Neural Networks and Extreme Learning Machines have become prominent. The models of parameters designed in these studies have been chosen randomly and the models have been designed in this direction. The main focus of this study is to determine the ideal parameters i.e. optimum hidden layer number, optimum hidden neuron number and activation function for Deep Neural Networks and Extreme Learning Machines architectures based on growing and pruning approach and to compare the performances of the models designed. The performances of the models are evaluated on two datasets; Parkinson and Self-Care Activities Dataset. Multi experiments have verified that the Deep Neural Networks architectures present a good prediction performance and this architecture outperforms the Extreme Learning Machines.
A 3D Spatially Weighted Network for Segmentation of Brain Tissue From MRI

The segmentation of brain tissue in MRI is valuable for extracting brain structure to aid diagnosis, treatment and tracking the progression of different neurologic diseases. Medical image data are volumetric and some neural network models for medical image segmentation have addressed this using a 3D convolutional architecture. However, this volumetric spatial information has not been fully exploited to enhance the representative ability of deep networks, and these networks have not fully addressed the practical issues facing the analysis of multimodal MRI data. In this paper, we propose a spatially-weighted 3D network (SW-3D-UNet) for brain tissue segmentation of single-modality MRI, and extend it using multimodality MRI data. We validate our model on the MRBrainS13 and MALC12 datasets. This unpublished model ranked first on the leaderboard of the MRBrainS13 Challenge.
The password is dead, long live the password – A laboratory study on user perceptions of authentication schemes

Password authentication is still ubiquitous although alternatives have been developed to overcome its shortcomings such as high cognitive load for users. Using an objective rating scheme Bonneau et al. (2012) demonstrated that replacing the password poses a quest that yet remains unsolved. To shine light on this intractable issue we turn towards subjective user perceptions that influence acceptance and actual use of authentication schemes. We first conducted an extensive rating of objective features of authentication schemes to inform our selection of schemes for this research. Building on the findings thereof, 41 users interacted with twelve different authentication schemes in a laboratory study. The participants’ ratings revealed that the password followed by fingerprint authentication scored highest in terms of preference, usability, intention to use and lowest in terms of expected problems and effort. Usability and effort seem to be important factors for users’ preference rating whereas security and privacy ratings were not correlated with preference. One reason for these factors to fall behind might be their opacity and the resulting difficulty to evaluate them from a user perspective. Further, security and usability perceptions deviated from objective factors and should therefore be carefully considered before making decisions in terms of authentication. Suggestions for making security and privacy features more tangible and to allow for an easier integration in the users’ decision process are discussed.
How Case-Based Reasoning Explains Neural Networks: A Theoretical Analysis of XAI Using Post-Hoc Explanation-by-Example from a Survey of ANN-CBR Twin-Systems

This paper proposes a theoretical analysis of one approach to the eXplainable AI (XAI) problem, using post-hoc explanation-by-example, that relies on the twinning of artificial neural networks (ANNs) with case-based reasoning (CBR) systems; so-called ANN-CBR twins. It surveys these systems to advance a new theoretical interpretation of previous work and define a road map for CBR’s further role in XAI. A systematic survey of 1,102 papers was conducted to identify a fragmented literature on this topic and trace its influence to more recent work involving deep neural networks (DNNs). The twin-systems approach is advanced as one possible coherent, generic solution to the XAI problem. The paper concludes by road-mapping future directions for this XAI solution, considering (i) further tests of feature-weighting techniques, (ii) how explanatory cases might be deployed (e.g., in counterfactuals, a fortori cases), and (iii) the unwelcome, much-ignored issue of user evaluation.
Spock: Exploiting Serverless Functions for SLO and Cost Aware Resource Procurement in Public Cloud

We are witnessing the emergence of elastic web services which are hosted in public cloud infrastructures. For reasons of cost-effectiveness, it is crucial for the elasticity of these web services to match the dynamically-evolving user demand. Traditional approaches employ clusters of virtual machines (VMs) to dynamically scale resources based on application demand. However, they still face challenges such as higher cost due to over-provisioning or incur service level objective (SLO) violations due to under-provisioning. Motivated by this observation, we propose Spock, a new scalable and elastic control system that exploits both VMs and serverless functions to reduce cost and ensure SLO for elastic web services. We show that under two different scaling policies, Spock reduces SLO violations of queries by up to 74% when compared to VM-based resource procurement schemes. Further, Spock yields significant cost savings, by up to 33% compared to traditional approaches which use only VMs.
Knowledge-enhanced neural networks for sentiment analysis of Chinese reviews

Sentiment analysis aims to extract structured opinions from unstructured reviews and determine their sentiment polarities. However, existing sentiment analysis systems fail to identify aspect-opinion pairs and perform poorly on small training corpora. To address these issues, we propose a novel framework to model aspect-opinion pair identification and aspect-level sentiment classification as a joint text classification task. Moreover, we incorporate external knowledge into neural networks to compensate for the lack of training data. In our approach, context features extracted from review sentences and external knowledge retrieved from a sentiment knowledge graph are used to identify aspect-opinion pairs and determine their sentiment polarities. In this way, our model is able to provide more detailed sentiment analysis results and achieve better performance with limited training corpora. We evaluate our approach using a Chinese car review dataset. Experimental results show that the knowledge-enhanced neural networks consistently outperform the conventional models.
Generating Adversarial Examples By Makeup Attacks on Face Recognition

Deep Learning models have been developed rapidly and achieved great success in computer vision and natural language processing. In this paper, we propose to generate adversarial examples to attack well-trained face recognition models by applying makeup effect to face images. It consists of two generative adversarial networks (GANs) based subnetworks, Makeup Transfer Sub-network and Adversarial Attack Sub-network. Makeup Transfer Sub-network transfers the non-makeup face images to makeup faces. Adversarial Attack Sub-networks hides attack information within makeup effect. The generated face images make the well-trained face recognition models misclassified as dodge attack or target attack. The experimental results demonstrate that our method can generate high-quality face makeup images and achieve higher error rates on various face recognition models compared to the existing attack methods.
Unsupervised Anomaly Detection Using Variational Auto-Encoder based Feature Extraction

Anomaly detection is a key task in Prognostics and Health Management (PHM) system. Specially, in most practical applications, the lack of labels often exists which makes the unsupervised anomaly detection very meaningful. Furthermore, unsupervised anomaly detection is also considered as a challenging task due to the diversity and information-lack of data. Variational Auto-Encoder (VAE) is a stochastic generative model which is designed to reconstruct input data as close as possible. In this paper, VAE is applied to extract valuable features for the unsupervised anomaly detection tasks. Comparison experiments are conducted on KDD CUP 99 dataset and MNIST dataset. Results show that features obtained by VAE can make unsupervised anomaly detection approaches perform better. Auto-Encoder (AE) and Kernel Principle Component Analysis (KPCA) were applied as comparisons. The result demonstrates that VAE gets best performance among them.
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and numerous other critical tasks. As the deployment of artificial intelligence technologies becomes ubiquitous, it is unsurprising that adversaries have begun developing methods to manipulate machine learning models to their advantage. While the visual analytics community has developed methods for opening the black box of machine learning models, little work has focused on helping the user understand their model vulnerabilities in the context of adversarial attacks. In this paper, we present a visual analytics framework for explaining and exploring model vulnerabilities to adversarial attacks. Our framework employs a multi-faceted visualization scheme designed to support the analysis of data poisoning attacks from the perspective of models, data instances, features, and local structures. We demonstrate our framework through two case studies on binary classifiers and illustrate model vulnerabilities with respect to varying attack strategies.
Data Acquisition for Argument Search: The args.me Corpus

Argument search is the study of search engine technology that can retrieve arguments for potentially controversial topics or claims upon user request. The design of an argument search engine is tied to its underlying argument acquisition paradigm. More specifically, the employed paradigm controls the trade-off between retrieval precision and recall and thus determines basic search characteristics: Compiling an exhaustive argument corpus offline benefits precision at the expense of recall, whereas retrieving arguments from the web on-the-fly benefits recall at the expense of precision. This paper presents the new corpus of our argument search engine args.me, which follows the former paradigm. We freely provide the corpus to the community. With 387 606 arguments it is one of the largest argument resources available so far. In a qualitative analysis, we compare the args.me corpus acquisition paradigm to that of two other argument search engines, and we report first empirical insights into how people search with args.me.
Interactive Correction of Mislabeled Training Data

In this paper, we develop a visual analysis method for interactively improving the quality of labeled data, which is essential to the success of supervised and semi-supervised learning. The quality improvement is achieved through the use of user-selected trusted items. We employ a bi-level optimization model to accurately match the labels of the trusted items and to minimize the training loss. Based on this model, a scalable data correction algorithm is developed to handle tens of thousands of labeled data efficiently. The selection of the trusted items is facilitated by an incremental tSNE with improved computational efficiency and layout stability to ensure a smooth transition between different levels. We evaluated our method on real-world datasets through quantitative evaluation and case studies, and the results were generally favorable.
Dynamic Searchable Symmetric Encryption with Forward and Stronger Backward Privacy

Dynamic Searchable Symmetric Encryption (DSSE) enables a client to perform updates and searches on encrypted data which makes it very useful in practice. To protect DSSE from the leakage of updates (leading to break query or data privacy), two new security notions, forward and backward privacy, have been proposed recently. Although extensive attention has been paid to forward privacy, this is not the case for backward privacy. Backward privacy, first formally introduced by Bost et al., is classified into three types from weak to strong, exactly Type-III to Type-I. To the best of our knowledge, however, no practical DSSE schemes without trusted hardware (e.g. SGX) have been proposed so far, in terms of the strong backward privacy and constant roundtrips between the client and the server. In this work, we present a new DSSE scheme by leveraging simple symmetric encryption with homomorphic addition and bitmap index. The new scheme can achieve both forward and backward privacy with one roundtrip. In particular, the backward privacy we achieve in our scheme (denoted by Type-I $$^-$$ ) is stronger than Type-I. Moreover, our scheme is very practical as it involves only lightweight cryptographic operations. To make it scalable for supporting billions of files, we further extend it to a multi-block setting. Finally, we give the corresponding security proofs and experimental evaluation which demonstrate both security and practicality of our schemes, respectively.
Signed Graph Attention Networks

Graph or network data is ubiquitous in the real world, including social networks, information networks, traffic networks, biological networks and various technical networks. The non-Euclidean nature of graph data poses the challenge for modeling and analyzing graph data. Recently, Graph Neural Network (GNN) is proposed as a general and powerful framework to handle tasks on graph data, e.g., node embedding, link prediction and node classification. As a representative implementation of GNNs, Graph Attention Networks (GAT) is successfully applied in a variety of tasks on real datasets. However, GAT is designed to networks with only positive links and fails to handle signed networks which contain both positive and negative links. In this paper, we propose Signed Graph Attention Networks (SiGAT), generalizing GAT to signed networks. SiGAT incorporates graph motifs into GAT to capture two well-known theories in signed network research, i.e., balance theory and status theory. In SiGAT, motifs offer us the flexible structural pattern to aggregate and propagate messages on the signed network to generate node embeddings. We evaluate the proposed SiGAT method by applying it to the signed link prediction task. Experimental results on three real datasets demonstrate that SiGAT outperforms feature-based method, network embedding method and state-of-the-art GNN-based methods like signed graph convolutional networks (SGCN).
Towards Continuous Authentication on Mobile Phones using Deep Learning Models

Smartphones have become essential objects for our daily lives. Besides their original purpose of use, people use these devices as their personal assistants. Additionally, smartphones provide large internal storage which enables users to store their private information, such as personal photos, contact details, call histories, etc. On the other hand, because of their small sizes, these devices could easily get lost or stolen. Therefore, providing the security and privacy of smartphone users against unauthorized access is a significant and crucial area of research. One of the solutions is the use of behavioral biometrics, which tracks and identify users’ interaction patterns with the device. In this paper, we investigate the impact of using both touchscreen-based and sensor-based features in an authentication model using deep learning methods. Mainly, we train a three-layer deep network on the combined feature-sets and applied classification for revealing the behavioral characters of users for building an authentication model. We use HMOG dataset that includes data from 100 users over 24 sessions. We train different networks with different combinations of input data, namely only touch-screen data, only sensor data, and their combination. Our results show that we can achieve 88% accuracy and 15% EER values considering binary classification when different types of data are used together.
Deep Learning Approach to Determine the Impact of Socio Economic Factors on Bitcoin Price Prediction

Investment in cryptocurrency has been in trend from last many years. Bitcoin is one of the most popular and valuable cryptocurrency. Many studies have been done on bitcoin price prediction using various parameters which includes bitcoin factors, social media etc. In this paper, a comparative study of the various parameters affecting bitcoin price prediction is done based on Root Mean Square Error (RMSE) using various deep learning models like Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). We have studied the effect of Gold price on the price of bitcoin.