Reviewerly/exHarmony huggingface dataset:
On the use of Benford's law to detect GAN-generated images

The advent of Generative Adversarial Network (GAN) architectures has given anyone the ability of generating incredibly realistic synthetic imagery. The malicious diffusion of GAN-generated images may lead to serious social and political consequences (e.g., fake news spreading, opinion formation, etc.). It is therefore important to regulate the widespread distribution of synthetic imagery by developing solutions able to detect them. In this paper, we study the possibility of using Benford's law to discriminate GAN-generated images from natural photographs. Benford's law describes the distribution of the most significant digit for quantized Discrete Cosine Transform (DCT) coefficients. Extending and generalizing this property, we show that it is possible to extract a compact feature vector from an image. This feature vector can be fed to an extremely simple classifier for GAN-generated image detection purpose.
Adaptive Fusion CNN Features for RGBT Object Tracking

Thermal sensors play an important role in intelligent transportation system. This paper studies the problem of RGB and thermal (RGBT) tracking in challenging situations by leveraging multimodal data. A RGBT object tracking method is proposed in correlation filter tracking framework based on short term historical information. Given the initial object bounding box, hierarchical convolutional neural network (CNN) is employed to extract features. The target is tracked for RGB and thermal modalities separately. Then the backward tracking is implemented in the two modalities. The difference between each pair is computed, which is an indicator of the tracking quality in each modality. Considering the temporal continuity of sequence frames, we also incorporate the history data into the weights computation to achieve a robust fusion of different source data. Experiments on three RGBT datasets show the proposed method achieves comparable results to state-of-the-art methods.
Rethinking the Runway: Using Avant-Garde Fashion To Design a System for Wearables

Technology has become increasingly pervasive in the creative and experimental environment of the avant-garde fashion runway, particularly in relation to its garments. However, several disciplines are often necessary when exploring technologies for the construction of expressive garments (e.g. garments that respond to their environment), creating a barrier for fashion designers that has limited their ability to leverage new technologies. To help overcome this barrier, we designed and deployed Brookdale, a prototyping system for wearable technology consisting of new plug-and-play hardware that can be programmed using drag-and-drop software. Brookdale was created using a 24-week participatory design process with 17 novice fashion-tech designers. At the end of the 24 week process, designers showcased their Brookdale-enhanced garment collections at an avant-garde fashion-tech runway show in New York City. We report on the experiences, outcomes, and lessons learned throughout this process, and describe results from interviews with the fashion-tech designers 16 weeks after the fashion show, demonstrating the lasting positive impact of Brookdale.
Project Tasca: Enabling Touch and Contextual Interactions with a Pocket-based Textile Sensor

We present Project Tasca, a pocket-based textile sensor that detects user input and recognizes everyday objects that a user carries in the pockets of a pair of pants (e.g., keys, coins, electronic devices, or plastic items). By creating a new fabric-based sensor capable of detecting in-pocket touch and pressure, and recognizing metallic, non-metallic, and tagged objects inside the pocket, we enable a rich variety of subtle, eyes-free, and always-available input, as well as context-driven interactions in wearable scenarios. We developed our prototype by integrating four distinct types of sensing methods, namely: inductive sensing, capacitive sensing, resistive sensing, and NFC in a multi-layer fabric structure into the form factor of a jeans pocket. Through a ten-participant study, we evaluated the performance of our prototype across 11 common objects including hands, 8 force gestures, and 30 NFC tag placements. We yielded a 92.3% personal cross-validation accuracy for object recognition, 96.4% accuracy for gesture recognition, and a 100% accuracy for detecting NFC tags at close distance . We conclude by demonstrating the interactions enabled by our pocket-based sensor in several applications.
Migration and Mobility in HCI: Rethinking Boundaries, Methods, and Impact

Research on migration (both internal and external, voluntary and forced) has been an emergent domain in HCI and related disciplines over the past decade. However, as the number of migrants has been increasing over the last two decades, coupled with various growing global affairs, new challenges encountered by diverse types of migrants (e.g. international students, migrant workers) keep arising, and research on mobility gets entangled with many broader social and political issues. Hence, migration can no longer be considered as a 'special case' for some immigrant and refugee communities, but an everyday reality to hundreds of millions of people worldwide and across diverse socio-economic groups. Therefore, the objectives of this workshop are to (a) build a community with HCI researchers and practitioners involved in different domains, within and beyond migration, to share ideas and exchange expertise, (b) broaden the scope of HCI migration research and identify gaps within this field, and (c) provide a safe space for critical reflection on methodological approaches, research infrastructure, and space boundaries in relation with migration to achieve a better real-world impact.
An Improved Discriminative Model Prediction Approach to Real-Time Tracking of Objects With Camera as Sensors

Generic person tracking is a basic task in visual surveillance by using camera as sensors. Many deep learning-based trackers have obtained outstanding performance. Among them, trackers based on Siamese networks have drawn great attention and are promising. These training methods are competitive but training data can be more effectively augmented to improve their person tracking performance. Many other trackers use only one layer to extract semantic features, likely hindering their discriminative learning. In this paper, we propose an enhanced discriminative model prediction method with efficient data augmentation and robust feature fusion. Specifically, we propose to implement an effective data augmentation strategy (e.g., color jitter and motion blur) to unleash the greater potential of original training data. We also adopt a multi-layer feature fusion to obtain a more discriminative feature map. Thus, the proposed tracker can discriminate an object in complicated scenarios in real time. We conduct extensive experiments on two datasets, i.e., VOT2018 and UAV123. Objective evaluation on VOT2018 demonstrates that with its expected average overlap value of 0.430, it outperforms a state-of-the-art tracker by 4.88%. On UAV123, it does so by 4.5% in success rate and 4.4% in precision rate. In addition, our further experimental results reveal that our algorithm can reach a speed that is high enough to meet the real-time tracking requirement when camera are used as sensors.
Aerial scene understanding in the wild: Multi-scene recognition via prototype-based memory networks

Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to categorize an aerial image into one scene-level label, while in real-world scenarios, there often exist multiple scenes in a single image. Therefore, in this paper, we propose to take a step forward to a more practical and challenging task, namely multi-scene recognition in single images. Moreover, we note that manually yielding annotations for such a task is extraordinarily time- and labor-consuming. To address this, we propose a prototype-based memory network to recognize multiple scenes in a single image by leveraging massive well-annotated single-scene images. The proposed network consists of three key components: 1) a prototype learning module, 2) a prototype-inhabiting external memory, and 3) a multi-head attention-based memory retrieval module. To be more specific, we first learn the prototype representation of each aerial scene from single-scene aerial image datasets and store it in an external memory. Afterwards, a multi-head attention-based memory retrieval module is devised to retrieve scene prototypes relevant to query multi-scene images for final predictions. Notably, only a limited number of annotated multi-scene images are needed in the training phase. To facilitate the progress of aerial scene recognition, we produce a new multi-scene aerial image (MAI) dataset. Experimental results on variant dataset configurations demonstrate the effectiveness of our network. Our dataset and codes are publicly available.
Driver Identification Using Vehicular Sensing Data: A Deep Learning Approach

Driver identification plays a pivotal role in the design of advanced driver assistant systems. The continued development of in-vehicle networking systems, CAN-bus technology, and the ubiquitous presence of smartphones as well as the broad range of state-of-the-art sensors have paved the way to collect huge amount of data from both vehicles and drivers. This paper addresses the necessity of having a large volume of labeled data for driver identification and presents a novel methodology to identify drivers based on their driving behavior analysis. The proposed architecture benefits from triplet loss training for driving time series in an unsupervised approach. An encoder architecture based on exponentially dilated causal convolutions is employed to obtain the representations. An SVM classifier is then trained on top of the representations to predict the person behind the wheel. The experiment results demonstrated higher performance of the proposed methodology when compared to benchmark methods.
Detection of Cyberbullying on Social Media Using Machine learning

Cyberbullying is a major problem encountered on internet that affects teenagers and also adults. It has lead to mishappenings like suicide and depression. Regulation of content on Social media platorms has become a growing need. The following study uses data from two different forms of cyberbullying, hate speech tweets from Twittter and comments based on personal attacks from Wikipedia forums to build a model based on detection of Cyberbullying in text data using Natural Language Processing and Machine learning. Three methods for Feature extraction and four classifiers are studied to outline the best approach. For Tweet data the model provides accuracies above 90% and for Wikipedia data it gives accuracies above 80%.
Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier

To prevent disasters and to control and supervise crowds, automated video surveillance has become indispensable. In today’s complex and crowded environments, manual surveillance and monitoring systems are inefficient, labor intensive, and unwieldy. Automated video surveillance systems offer promising solutions, but challenges remain. One of the major challenges is the extraction of true foregrounds of pixels representing humans only. Furthermore, to accurately understand and interpret crowd behavior, human crowd behavior (HCB) systems require robust feature extraction methods, along with powerful and reliable decision-making classifiers. In this paper, we describe our approach to these issues by presenting a novel Particles Force Model for multi-person tracking, a vigorous fusion of global and local descriptors, along with a robust improved entropy classifier for detecting and interpreting crowd behavior. In the proposed model, necessary preprocessing steps are followed by the application of a first distance algorithm for the removal of background clutter; true-foreground elements are then extracted via a Particles Force Model. The detected human forms are then counted by labeling and performing cluster estimation, using a K-nearest neighbors search algorithm. After that, the location of all the human silhouettes is fixed and, using the Jaccard similarity index and normalized cross-correlation as a cost function, multi-person tracking is performed. For HCB detection, we introduced human crowd contour extraction as a global feature and a particles gradient motion (PGD) descriptor, along with geometrical and speeded up robust features (SURF) for local features. After features were extracted, we applied bat optimization for optimal features, which also works as a pre-classifier. Finally, we introduced a robust improved entropy classifier for decision making and automated crowd behavior detection in smart surveillance systems. We evaluated the performance of our proposed system on a publicly available benchmark PETS2009 and UMN dataset. Experimental results show that our system performed better compared to existing well-known state-of-the-art methods by achieving higher accuracy rates. The proposed system can be deployed to great benefit in numerous public places, such as airports, shopping malls, city centers, and train stations to control, supervise, and protect crowds.
Beyond Universal Person Re-Identification Attack

Deep learning-based person re-identification (Re-ID) has made great progress and achieved high performance recently. In this paper, we make the first attempt to examine the vulnerability of current person Re-ID models against a dangerous attack method, i.e., the universal adversarial perturbation (UAP) attack, which has been shown to fool classification models with a little overhead. We propose a more universal adversarial perturbation (MUAP) method for both image-agnostic and model-insensitive person Re-ID attack. Firstly, we adopt a list-wise attack objective function to disrupt the similarity ranking list directly. Secondly, we propose a model-insensitive mechanism for cross-model attack. Extensive experiments show that the proposed attack approach achieves high attack performance and outperforms other state of the arts by large margin in cross-model scenario. The results also demonstrate the vulnerability of current Re-ID models to MUAP and further suggest the need of designing more robust Re-ID models.
Global-Local Graph Convolutional Network for cross-modality person re-identification

Visible-thermal person re-identification (VT-ReID) is an important task for retrieving pedestrian between visible and thermal modality. It makes up for the drawbacks of single modality person re-identification in night surveillance applications. Most of the existing methods extract the features of different images/parts independently which ignore the potential relationship between them. In this paper, we propose a novel Global-Local Graph Convolutional Network (GLGCN) to learn discriminative feature representation by modeling the relation through graph convolutional network. The local graph module builds the potential relation of different body parts within each modality to extract discriminative part-level features. The global module constructs the contextual relation of same identity across two modalities to reduce the modality discrepancy. By training the two modules jointly, the robustness of the model can be further improved. The experiment results on the SYSU-MM01 and RegDB datasets demonstrate that our model outperforms the state-of-the-art methods.
Human Action Recognition using Machine Learning in Uncontrolled Environment

Video based Human Action Recognition (HAR) is an active research field of Machine Learning (ML) and human detection in videos is the most important step in action recognition. Recently, several techniques and algorithms have been proposed to increase the accuracy of HAR process, but margin of improvement still exists. Detection and classification of human actions is a challenging task due to random changes in human appearance, clothes, illumination, and background. In this article, an efficient technique to classify human actions by utilizing steps like removing redundant frames from videos, extracting Segments of Interest (SoIs), feature descriptor mining through Geodesic Distance (GD), 3D Cartesian-plane Features (3D-CF), Joints MOCAP (JMOCAP) and n-way Point Trajectory Generation (nPTG). A Neuro Fuzzy Classifier (NFC) is used at the end for the classification purpose. The proposed technique is tested on two publicly available datasets including HMDB-51 and Hollywood2, and achieved an accuracy of 82.55% and 91.99% respectively. These efficient results prove the validity of proposed model.
Combining GANs and AutoEncoders for efficient anomaly detection

In this work, we propose CBiGAN - a novel method for anomaly detection in images, where a consistency constraint is introduced as a regularization term in both the encoder and decoder of a BiGAN. Our model exhibits fairly good modeling power and reconstruction consistency capability. We evaluate the proposed method on MVTec AD - a real-world benchmark for unsupervised anomaly detection on high-resolution images - and compare against standard baselines and state-of-the-art approaches. Experiments show that the proposed method improves the performance of BiGAN formulations by a large margin and performs comparably to expensive state-of-the-art iterative methods while reducing the computational cost. We also observe that our model is particularly effective in texture-type anomaly detection, as it sets a new state of the art in this category. Our code is available at https://github.com/fabiocarrara/cbigan-ad/.
HIDE: Short IDs for Robust and Anonymous Linking of Users Across Multiple Sessions in Small HCI Experiments

Record linking is needed to analyze observations across multiple sessions. However, recent privacy legislature such as the General Data Protection Regulations (GDPR) restricts the storage of information that identify individuals. Obtaining permissions to store information about individuals can be bureaucratic and time-consuming. Anonymous schemes such as self-generated ids and machine generated ids have been proposed. However, self-generated linking ids demand effort from the participants, while machine assisted schemes typically generate long and incomprehensible ids. Consequently, there is a risk that students and researchers will limit their research to single-session experiments to avoid privacy issues. To simplify the administration of small multi-session experiments, the HIDE procedure is proposed for generating short human readable ids for linking participants across multiple sessions while maintaining anonymity and being robust to input errors. The approach is different from previous approaches in that the goal is to minimize the length of the linking ids. First, the procedure converts the participant's name into a phonetic representation. Next, this phonetic representation is hashed, and a truncated snippet of the hash is used as the linking id. HIDE is initialized by searching for a salt (a random data added to the hash input) that minimizes the id lengths. Experiments show that the procedure is capable of coding small experiments with 20 participants using two digits, and experiments of around 200 participants with four digits. An implementation of the procedure has been made available through a simple web interface. It is hoped that the procedure can help students and HCI researchers collect more comprehensive data by following participants over time, while protecting their privacy.
Fast Multi-view Clustering via Prototype Graph

Multi-view clustering attracts considerable attention due to its effectiveness in unsupervised learning. However, previous multi-view spectral clustering methods include two separated steps: 1) Obtaining a spectral embedding; 2) Performing classical clustering methods. Although these methods have achieved promising performance, there is still some limitations. First, in computing spectral embedding, multi-view spectral clustering approaches exist high computational complexity since they usually need eigenvalue decomposition on laplacian matrix L. Its computational complexity is O(n<sup>3</sup>) where n is the number of samples; Second, in constructing similarity matrices, previous methods need to compute similarity between any two samples; Third, the two-stage approach only can obtain sub-optimal solution; Fourth, treating equally all views is unreasonable. To address these issues, we propose a Fast Multi-view Clustering via Prototype Graph (FMVPG) method. Specifically, the prototype graph is firstly constructed, and then simultaneously perform spectral embedding to obtain the real matrix and spectral rotation to get the indicator matrix. In addition, the Alternating Direction Method of Multipliers (ADMM) is used to solve the joint optimization problem. Further, we conduct extensive experiments to evaluate the proposed FMVPG approach. These experimental results show the comparable or even better clustering performance than the state-of-the-art approaches.
The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications

Given the overwhelming impact of machine learning on the last decade, several libraries and frameworks have been developed in recent years to simplify the design and training of neural networks, providing array-based programming, automatic differentiation and user-friendly access to hardware accelerators.None of those tools, however, was designed with native and transparent support for Cloud Computing or heterogeneous High-Performance Computing (HPC).The DeepHealth Toolkit is an open source Deep Learning toolkit aimed at boosting productivity of data scientists operating in the medical field by providing a unified framework for the distributed training of neural networks, which is able to leverage hybrid HPC and cloud environments in a transparent way for the user.The toolkit is composed of a Computer Vision library, a Deep Learning library, and a front-end for non-expert users; all of the components are focused on the medical domain, but they are general purpose and can be applied to any other field.In this paper, the principles driving the design of the DeepHealth libraries are described, along with details about the implementation and the interaction between the different elements composing the toolkit.Finally, experiments on common benchmarks prove the efficiency of each separate component and of the DeepHealth Toolkit overall.
Privacy and Security in Cognitive Cities: A Systematic Review

The emerging paradigm of the cognitive city, which augments smart cities with learning and behavioral change capabilities, is gaining increasing attention as a promising solution to the challenges of future mega-cities. Cognitive cities are built upon artificial learning and behavioral analysis techniques founded on the exploitation of human-machine collective intelligence. Hence, cognitive cities rely on the sharing of citizens’ daily-life data, which might be considered sensitive personal data. In this context, privacy and security of the shared information become critical issues that have to be addressed to guarantee the proper deployment of cognitive cities and the fundamental rights of people. This article provides a thorough literature review using the recommendations for systematic reviews proposed by Vom Brocke et al. and the PRISMA statement. We analyze peer-reviewed publications indexed in ACM Digital Library, IEEE Xplore, Scopus, and Web of Science until July 2020. We identify the main challenges on privacy and information security within cognitive cities, and the proposals described in the literature to address them. We conclude that many challenges remain open and we suggest several research lines that will require further examination in the years to come.