Reviewerly/exHarmony huggingface dataset:
Barycenters of Natural Images - Constrained Wasserstein Barycenters for Image Morphing

Image interpolation, or image morphing, refers to a visual transition between two (or more) input images. For such a transition to look visually appealing, its desirable properties are (i) to be smooth; (ii) to apply the minimal required change in the image; and (iii) to seem "real", avoiding unnatural artifacts in each image in the transition. To obtain a smooth and straightforward transition, one may adopt the well-known Wasserstein Barycenter Problem (WBP). While this approach guarantees minimal changes under the Wasserstein metric, the resulting images might seem unnatural. In this work, we propose a novel approach for image morphing that possesses all three desired properties. To this end, we define a constrained variant of the WBP that enforces the intermediate images to satisfy an image prior. We describe an algorithm that solves this problem and demonstrate it using the sparse prior and generative adversarial networks.
Model Explanations with Differential Privacy

Using machine learning models in critical decision-making processes has given rise to a call for algorithmic transparency. Model explanations, however, might leak information about the sensitive data used to train and explain the model, undermining data privacy. We focus on black-box feature-based model explanations, which locally approximate the model around the point of interest, using potentially sensitive data. We design differentially private local approximation mechanisms, and evaluate their effect on explanation quality. To protect training data, we use existing differentially private learning algorithms. However, to protect the privacy of data which is used during the local approximation, we design an adaptive differentially private algorithm, which finds the minimal privacy budget required to produce accurate explanations. Both empirically and analytically, we evaluate the impact of the randomness needed in differential privacy algorithms on the fidelity of model explanations.
Gaze Control of a Robotic Head for Realistic Interaction With Humans

When there is an interaction between a robot and a person, gaze control is very important for face-to-face communication. However, when a robot interacts with several people, neurorobotics plays an important role to determine the person to look at and those to pay attention to among the others. There are several factors which can influence the decision: who is speaking, who he/she is speaking to, where people are looking, if the user wants to attract attention, etc. This article presents a novel method to decide who to pay attention to when a robot interacts with several people. The proposed method is based on a competitive network that receives different stimuli (look, speak, pose, hoard conversation, habituation, etc.) that compete with each other to decide who to pay attention to. The dynamic nature of this neural network allows a smooth transition in the focus of attention to a significant change in stimuli. A conversation is created between different participants, replicating human behavior in the robot. The method deals with the problem of several interlocutors appearing and disappearing from the visual field of the robot. A robotic head has been designed and built and a virtual agent projected on the robot's face display has been integrated with the gaze control. Different experiments have been carried out with that robotic head integrated into a ROS architecture model. The work presents the analysis of the method, how the system has been integrated with the robotic head and the experiments and results obtained.
Shannon Entropy as an Indicator for Sorting Processes in Hydrothermal Systems

Hydrothermal processes modify the chemical and mineralogical composition of rock. We studied and quantified the effects of hydrothermal processes on the composition of volcanic rocks by a novel application of the Shannon entropy, which is a measure of uncertainty and commonly applied in information theory. We show here that the Shannon entropies calculated on major elemental chemical composition data and short-wave infrared (SWIR) reflectance spectra of hydrothermally altered rocks are lower than unaltered rocks with a comparable primary composition. The lowering of the Shannon entropy indicates chemical and spectral sorting during hydrothermal alteration of rocks. The hydrothermal processes described in this study present a natural mechanism for transforming energy from heat to increased order in rock. The increased order is manifest as the increased sorting of chemical elements and SWIR absorption features of the rock, and can be measured and quantified by the Shannon entropy. The results are useful for the study of hydrothermal mineral deposits, early life environments and the effects of hydrothermal processes on rocks.
Identification of ISM Band Signals Using Deep Learning

Spectrum awareness is now becoming more and more important in recent years, which can be utilized in areas like spectrum resource allocation, spectrum management, inference control, and security protection. Deep learning (DL) models, including convolutional neural network models have been widely used for classification related tasks, such as modulation classification, medium access control protocol (MAC) classification, and spectrum sensing. In this paper, a pre-trained Inception V3 model (CNN-based) is used to classify industrial, scientific, and medical (ISM) radio band signals. Experimentation results demonstrate the effectiveness of deep learning in ISM band signal identification.
Scale-Aware Rolling Fusion Network for Crowd Counting

Due to wide application prospects and various challenges such as large scale variation, inter-occlusion between crowd people and background noise, crowd counting is receiving increasing attention. In this paper, we propose a scale-aware rolling fusion network (SRF-Net) for crowd counting, which focuses on dealing with scale variation in highly congested noisy scenes. SRF-Net is a two-stage architecture that consists of a band-pass stage and a rolling guidance stage. Compared with the existing methods, SRF-Net achieves better results in retaining appropriate multi-level features and capturing multi-scale features, thus improving the quality of density estimation maps in crowded scenarios with large scale variation. We evaluate our method on three popular crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QNRF), and extensive experiments show its outperformance over the state-of-the-art approaches.
Automatic Semantic Description Extraction from Social Big Data for Emergency Management

Emergency events are unexpected and dangerous situations which the authorities must manage and respond to as quickly as possible. The main objectives of emergency management are to provide human safety and security, and Social Big Data (SBD) offers an important information source, created directly from eyewitness reports, to assist with these issues. However, the manual extraction of hidden meaning from SBD is both time-consuming and labor-intensive, which are major drawbacks for a process that needs accurate information to be produced in real-time. The solution is an automatic approach to knowledge discovery, and we propose a semantic description technique based on the use of triple store indexing for named entity recognition and relation extraction. Our technique can discover hidden SBD information more effectively than traditional approaches, and can be used for intelligent emergency management.
Effects of mobile news interface design features on users’ gaze behaviours and behavioural performance: evidence from China

The increasing growth in the use of mobile news apps has raised questions on how their interface design features affect users' gaze behaviours and behavioural performance. To tackle these issues, two experiments (visual browse and search tasks) were designed to investigate the impact of interface design features (colour and layout) on users' gaze behaviours (fixation count, fixation time ratio and first fixation duration) and behavioural performance (task completion time and search accuracy) with a portable eye tracker. Twenty-four participants were recruited to browse and search for news in different mobile news interfaces with Chinese language. The results showed that mobile news apps with white interfaces attracted more attention and participants needed more time to comprehend the information of white interfaces in the visual browse task. Furthermore, participants achieved higher search efficiency in the visual search task by using interfaces with red keyword and LT-RP (Left Text-Right Picture). In addition, it is noticeable that participants paid more attention to the text than the pictures of mobile news interfaces, and most participants first looked at text and later observed pictures. The findings provide valuable and interesting insights for better understanding users' gaze behaviours and behavioural performance of mobile news apps.
Information sharing and the bane of information leakage: a multigroup analysis of contract versus noncontract

Purpose This study investigates information quality, information security technology and information sharing with moderation by information security culture and information leakage and how they all play out to influence supply chain performance for contract suppliers (Contract), noncontract suppliers (Noncontract) and pooled suppliers (Contract and Noncontract combined). Design/methodology/approach Multigroup analysis was deployed to compare the impact on Contract and Noncontract. Findings The finding on pooled suppliers confirmed the hypothesis that, in the multigroup analysis, information security culture negatively impacted the information quality–information sharing relationship of Contract. Practical implications The practical learning point is that Noncontract could still share information and perform and in some instances better than Contract. Noncontract suppliers are still workable. Originality/value Information security culture motivated Noncontract to share and perform better than Contract. This result presents a dilemma.
A Graph Auto-encoder Model of Derivational Morphology

There has been little work on modeling the morphological well-formedness (MWF) of derivatives, a problem judged to be complex and difficult in linguistics (Bauer, 2019).We present a graph auto-encoder that learns embeddings capturing information about the compatibility of affixes and stems in derivation.The auto-encoder models MWF in English surprisingly well by combining syntactic and semantic information with associative information from the mental lexicon.
Data-Efficient Semi-Supervised Learning by Reliable Edge Mining

Learning powerful discriminative features is a challenging task in Semi-Supervised Learning, as the estimation of the feature space is more likely to be wrong with scarcer labeled data. Previous methods utilize a relation graph with edges representing 'similarity' or 'dissimilarity' between nodes. Similar nodes are forced to output consistent features, while dissimilar nodes are forced to be inconsistent. However, since unlabeled data may be wrongly labeled, the judgment of edges may be unreliable. Besides, the nodes connected by edges may already be well fitted, thus contributing little to the model training. We propose Reliable Edge Mining (REM), which forms a reliable graph by only selecting reliable and useful edges. Guided by the graph, the feature extractor is able to learn discriminative features in a data-efficient way, and consequently boosts the accuracy of the learned classifier. Visual analyses show that the features learned are more discriminative and better reveals the underlying structure of the data. REM can be combined with perturbation-based methods like Pi-model, TempEns and Mean Teacher to further improve accuracy. Experiments prove that our method is data-efficient on simple tasks like SVHN and CIFAR-10, and achieves state-of-the-art results on the challenging CIFAR-100.
MGHCNET: A Deep Multi-Scale Granular and Holistic Channel Feature Generation Network for Image Super Resolution

Residual blocks use skip connections in order to facilitate the flow of information in the network and thus, provide a good network performance. As different objects in a generic image appear at different scales, employing a multi-scale feature generation module in a residual block for image super resolution can further improve the network performance. In this paper, a new residual block that generates features at multiple scales is proposed for the task of image super resolution. In order to enhance the representational capability of the network while keeping its complexity low, the proposed residual block uses two different feature generation techniques, namely, multi-scale granular channel feature generation and uni-scale holistic channel feature generation, and fuses their output feature maps. It is shown that the network using the proposed residual block outperforms the state-of-the-art lightweight super resolution networks on four benchmark datasets with various scaling factors.
User-Inspired Posterior Network for Recommendation Reason Generation

Recommendation reason generation, aiming at showing the selling points of products for customers, plays a vital role in attracting customers' attention as well as improving user experience. A simple and effective way is to extract keywords directly from the knowledge-base of products, i.e., attributes or title, as the recommendation reason. However, generating recommendation reason from product knowledge doesn't naturally respond to users' interests. Fortunately, on some E-commerce websites, there exists more and more user-generated content (user-content for short), i.e., product question-answering (QA) discussions, which reflect user-cared aspects. Therefore, in this paper, we consider generating the recommendation reason by taking into account not only the product attributes but also the customer-generated product QA discussions. In reality, adequate user-content is only possible for the most popular commodities, whereas large sums of long-tail products or new products cannot gather a sufficient number of user-content. To tackle this problem, we propose a user-inspired multi-source posterior transformer (MSPT), which induces the model reflecting the users' interests with a posterior multiple QA discussions module, and generating recommendation reasons containing the product attributes as well as the user-cared aspects. Experimental results show that our model is superior to traditional generative models. Additionally, the analysis also shows that our model can focus more on the user-cared aspects than baselines.
Defending Black Box Facial Recognition Classifiers Against Adversarial Attacks

Defending adversarial attacks is a critical step towards reliable deployment of deep learning empowered solutions for biometrics verification. Current approaches for defending Black box models use the classification accuracy of the Black box as a performance metric for validating their defense. However, classification accuracy by itself is not a reliable metric to determine if the resulting image is "adversarial-free". This is a serious problem for online biometrics verification applications where the ground-truth of the incoming image is not known and hence we cannot compute the accuracy of the classifier or know if the image is "adversarial-free" or not. This paper proposes a novel framework for defending Black box systems from adversarial attacks using an ensemble of iterative adversarial image purifiers whose performance is continuously validated in a loop using Bayesian uncertainties. The proposed approach is (i) model agnostic, (ii) can convert single step black box defenses into an iterative defense and (iii) has the ability to reject adversarial examples. This paper uses facial recognition as a test case for validating the defense and experimental results on the MS-Celeb dataset show that the proposed approach can consistently detect adversarial examples and purify/reject them against a variety of adversarial attacks with different ranges of perturbations.
Multi-Modal Summary Generation using Multi-Objective Optimization

Significant development of communication technology over the past few years has motivated research in multi-modal summarization techniques. A majority of the previous works on multi-modal summarization focus on text and images. In this paper, we propose a novel extractive multi-objective optimization based model to produce a multi-modal summary containing text, images, and videos. Important objectives such as intra-modality salience, cross-modal redundancy and cross-modal similarity are optimized simultaneously in a multi-objective optimization framework to produce effective multi-modal output. The proposed model has been evaluated separately for different modalities, and has been found to perform better than state-of-the-art approaches.
Multi Level Directional Cross Binary Patterns

The pattern recognition and computer vision have experienced a prominent progress in feature extraction techniques, judged by the extensive proposed methods in the literature. A big part of these works was devoted to enhance the texture classification performance, regarding the important role of textural analysis in various real-world and challenging applications. Developing discriminant feature extractors requires solid knowledge in machine learning and applied mathematics. However, Local Binary Patterns (LBP) offered much more space to develop enhanced handcrafted descriptors thanks to its simplicity and flexibility. In this paper we introduce a brand new LBP variant referred to as Multi Level Directional Cross Binary Patterns (MLD-CBP). The proposed representation is training-free, low-dimensional, yet discriminative and robust handcrafted operator for texture description. The concept of the proposed MLD-CBP descriptor is based on encoding the most informative directions contained within multi radiuses, which helps in detecting the gray level variations that may occur in different directions. Moreover, the proposed MLD-CBP handcrafted is combined with an automated SVM classifier based on the RBF Kernel, where the γ parameter is calculated automatically according to the training images. Conducted experiments on 15 well known and challenging databases of the literature, demonstrate prominent performance and stability compared to the results achieved by 30 recent and most powerful descriptors of the state-of-the-art. This paper provides also a comparative study on the effect of γ parameter to show the benefits of automatically tuning this parameter value considering the nature of the database and its size.
Generating microwave photon Fock states in a circuit QED via invariant-based shortcuts to adiabaticity

Optimal generation of photon Fock states is of significance to quantum science and technology. Here we develop a theoretical scheme for fast generating microwave photon Fock states in a circuit quantum electrodynamics (QED) via invariant-based shortcuts to adiabaticity. A superconducting transmon qubit is dispersively coupled to a cavity field of transmission line resonator. Two classical drivings are applied to a composite system, consisting of a transmon qubit and cavity field. In the two-photon resonance with a large intermediate-level detuning, a single-photon Fock state can be fast created by inversely engineering Rabi couplings. With the assistance of auxiliary driving, qubit state can be initialized for inducing multi-photon Fock states in the shortcut manner. Due to negligible leakage effects and high robustness, our scheme offers a potential way to rapidly prepare photon Fock states with a superconducting qubit in circuit QED.
Waveform Recognition in Multipath Fading using Autoencoder and CNN with Fourier Synchrosqueezing Transform

In this paper the problem of recognizing radar waveforms is addressed for multipath fading channels. Waveform classification is needed in spectrum sharing, radar-communications coexistence, cognitive radars, spectrum monitoring and signal intelligence. Different radar waveforms exhibit different properties in time-frequency domain. We propose a deep learning method for waveform classification. The received signal is first equalized to mitigate the effect of multipath fading channels by using a denoising auto-encoder (DAE). Then, the equalized signal is processed with Fourier synchrosqueezing transform that has excellent properties in revealing time-varying behavior, rate of, strength and number of oscillatory components in signals. The resulting time-frequency description is represented as a bivariate image that is fed into a convolutional neural network. The proposed method has superior performance over the widely used the Choi-Williams distribution (CWD) method in distinguishing among different radar waveforms even at low signal-to-noise ratio regime.
Smart Assistant for Deaf and Dumb Using Flexible Resistive Sensor : Implemented on LabVIEW Platform

The people with hearing and speaking difficulties use sign language to converse with each other. They have difficulties while conversing with people who do not know sign language. We have addressed this issue in our proposed project. This project proposes a model of Smart gloves and flex sensors for Sign language recognition. The proposed approach is based on detection of the finger movements and hand gestures to identify the gesture using signal processing kit in LabVIEW software and a data acquisition device (NI USB 6008 DAQ card). The signal is processed and is used to identify the sign shown and concatenate the letters into suitable word and also present the word in audio format. In our work we aim to provide a solution for the trainers in deaf and dumb schools to display letters, with its associated visual display, along with audio output, making learning very interactive and encouraging. It can be used to help treat patients suffering from speech-loss and partial paralysis patient with minimal movement of their fingers. In this project we have implemented the code in LabVIEW platform for twenty-six letters and concatenation of letters up to 6 letters according to American Sign Language. Algorithm can be customized to build the required word length.
MetaGen: An academic Meta-review Generation system

Peer reviews form an essential part of scientific communications. Research papers and proposals are reviewed by several peers before they are finally accepted or rejected. The procedure followed requires experts to review the research work. Then the area/program chair/ editor writes a meta-review summarizing the review comments and taking a call based on the reviewers' decisions. In this paper, we present MetaGen, a novel meta-review generation system which takes the peer reviews as input and produces an assistive meta-review. This meta-review generation can help the area/program chair writing a meta-review and taking the final decision on the paper/proposal. Thus it can also help to speed up the review process for conference/journals where a large number of submissions need to be handled within a stipulated time. Our approach first generates an extractive draft and then uses fine-tuned UniLM (Unified Langauge Model) for predicting the acceptance decision and making the final meta-review in an abstractive manner. To the best of our knowledge, this is the first work in the direction of meta-review generation. Evaluation based on ROUGE score shows promising results and comparison with few state-of-the-art summarizers demonstrates the effectiveness of the system.