Reviewerly/exHarmony huggingface dataset:
Modeling for Three-Subset Division Property Without Unknown Subset

A division property is a generic tool to search for integral distinguishers, and automatic tools such as MILP or SAT/SMT allow us to evaluate the propagation efficiently. In the application to stream ciphers, it enables us to estimate the security of cube attacks theoretically, and it leads to the best key-recovery attacks against well-known stream ciphers. However, it was reported that some of the key-recovery attacks based on the division property degenerate to distinguishing attacks due to the inaccuracy of the division property. Three-subset division property (without unknown subset) is a promising method to solve this inaccuracy problem, and a new algorithm using automatic tools for the three-subset division property was recently proposed at Asiacrypt2019. In this paper, we first show that this state-of-the-art algorithm is not always efficient and we cannot improve the existing key-recovery attacks. Then, we focus on the feature of the three-subset division property without unknown subset and propose another new efficient algorithm using automatic tools. Our algorithm is more efficient than existing algorithms, and it can improve existing key-recovery attacks. In the application to Trivium, we show a 841-round key-recovery attack. We also show that a 855-round key-recovery attack, which was proposed at CRYPTO2018, has a critical flaw and does not work. As a result, our 841-round attack becomes the best key-recovery attack. In the application to Grain-128AEAD, we show that the known 184-round key-recovery attack degenerates to distinguishing attacks. Then, the distinguishing attacks are improved up to 189 rounds, and we also show the best key-recovery attack against 190 rounds.
Detecting Face2Face Facial Reenactment in Videos

Visual content has become the primary source of information, as evident in the billions of images and videos, shared and uploaded on the Internet every single day. This has led to an increase in alterations in images and videos to make them more informative and eye-catching for the viewers worldwide. Some of these alterations are simple, like copy-move, and are easily detectable, while other sophisticated alterations like reenactment based DeepFakes are hard to detect. Reenactment alterations allow the source to change the target expressions and create photo-realistic images and videos. While the technology can be potentially used for several applications, the malicious usage of automatic reenactment has a very large social implication. It is therefore important to develop detection techniques to distinguish real images and videos with the altered ones. This research proposes a learning-based algorithm for detecting reenactment based alterations. The proposed algorithm uses a multi-stream network that learns regional artifacts and provides a robust performance at various compression levels. We also propose a loss function for the balanced learning of the streams for the proposed network. The performance is evaluated on the publicly available FaceForen- sics dataset. The results show state-of-the-art classification accuracy of 99.96%, 99.10%, and 91.20% for no, easy, and hard compression factors, respectively.
Deep learning for biological age estimation

Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data mining approaches are still popular in aging research, they typically need feature engineering or feature extraction for robust performance. Explicit feature engineering represents a major challenge, as it requires significant domain knowledge. The latest advances in deep learning provide a paradigm shift in eliciting meaningful knowledge from complex data without performing explicit feature engineering. In this article, we review the recent literature on applying deep learning in biological age estimation. We consider the current data modalities that have been used to study aging and the deep learning architectures that have been applied. We identify four broad classes of measures to quantify the performance of algorithms for biological age estimation and based on these evaluate the current approaches. The paper concludes with a brief discussion on possible future directions in biological aging research using deep learning. This study has significant potentials for improving our understanding of the health status of individuals, for instance, based on their physical activities, blood samples and body shapes. Thus, the results of the study could have implications in different health care settings, from palliative care to public health.
Automatic Salient Object Extraction Based on Locally Adaptive Thresholding to Generate Tactile Graphics

Automatic extraction of salient regions is beneficial for various computer vision applications, such as image segmentation and object recognition. The salient visual information across images is very useful and plays a significant role for the visually impaired in identifying tactile information. In this paper, we introduce a novel saliency cuts method using local adaptive thresholding to obtain four regions from a given saliency map. First, we produced four regions for image segmentation using a saliency map as an input image and local adaptive thresholding. Second, the four regions were used to initialize an iterative version of the GrabCuts algorithm and to produce a robust and high-quality binary mask with a full resolution. Finally, salient objects’ outer boundaries and inner edges were detected using the solution from our previous research. Experimental results showed that local adaptive thresholding using integral images can produce a more robust binary mask compared to the results from previous works that make use of global thresholding techniques for salient object segmentation. The proposed method can extract salient objects with a low-quality saliency map, achieving a promising performance compared to existing methods. The proposed method has advantages in extracting salient objects and generating simple, important edges from natural scene images efficiently for delivering visually salient information to the visually impaired.
Weighted sigmoid gate unit for an activation function of deep neural network

An activation function has a crucial role in a deep neural network. A simple rectified linear unit (ReLU) is widely used for the activation function. In this paper, a weighted sigmoid gate unit (WiG) is proposed as the activation function. The proposed WiG consists of a multiplication of inputs and the weighted sigmoid gate. It is shown that the WiG includes the ReLU and the same activation functions as a special case. Many activation functions have been proposed to overcome the ReLU. In the literature, the performance is mainly evaluated with an object recognition task. The proposed WiG is evaluated with the object recognition task and the image restoration task. Then, the experimental comparisons demonstrate the proposed WiG overcomes the existing activation functions including the ReLU.
Blockchain as a Trust Builder in the Smart City Domain: A Systematic Literature Review

The revolution of the Internet of Things (IoT) is transforming many concepts, making them ''Smart''.It has revolutionized many areas of real life.Smart City is one of the key concepts of this revolution.Although cities are being digitally transformed, it still has obstacles along the way.In this paper, we have analyzed different publications for our Systematic Literature Review (SLR).This study highlights the areas where the blockchain is used and determines the benefits of using blockchain.The main contribution is to explore and identify the barriers and hurdles in Smart City Domain and how these hurdles are mitigated by the blockchain technology.This Systematic Study also addresses different challenges and issues such as security, immutability, interoperability, decentralization, privacy, and trust in the development of Smart Cities.An overview of the systematic research is also presented that would help identify the most and least studied concerns addressed in this study.This paper aims at analyzing how blockchain technology is used in different smart city business models, what are the features of blockchain that could consider it to be used beyond crypto currencies.We hope that this study can inspire interest in theory and exercise to stand-in discussions in this area adhere to these limits.
Self-Supervised Dynamic CT Perfusion Image Denoising With Deep Neural Networks

Dynamic computed tomography perfusion (CTP) imaging is a promising approach for acute ischemic stroke diagnosis and evaluation. Hemodynamic parametric maps of cerebral parenchyma are calculated from repeated CT scans of the first pass of iodinated contrast through the brain. It is necessary to reduce the dose of CTP for routine applications due to the high radiation exposure from the repeated scans, where image denoising is necessary to achieve a reliable diagnosis. In this article, we proposed a self-supervised deep learning method for CTP denoising, which did not require any high-dose reference images for training. The network was trained by mapping each frame of CTP to an estimation from its adjacent frames. Because the noise in the source and target was independent, this approach could effectively remove the noise. Being free from high-dose training images granted the proposed method easier adaptation to different scanning protocols. The method was validated on both simulation and a public real dataset. The proposed method achieved improved image quality compared to conventional denoising methods. On the real data, the proposed method also had improved spatial resolution and contrast-to-noise ratio compared to supervised learning which was trained on the simulation data.
A dynamic ensemble outlier detection model based on an adaptive k-nearest neighbor rule

Ensembles of outlier detectors are drawing increasing attentions recently, in spite of the difficulty on developing ensembles in the framework of unsupervised learning. We have noted that existing outlier ensembles often use certain fusion rules (e.g. majority voting) to aggregate individual learners. Theoretically, these individuals are assumed to be error-independent so that single models can be outperformed by the ensemble. However, it is of great difficulty to satisfy this assumption in practical applications. By dynamic selecting more competent individual(s) for each test pattern, this problem can be alleviated effectively. Inspired by this idea, this paper proposes a dynamic ensemble outlier detection model using one-class classifiers as base learners. As the competences of base detectors are estimated totally on data points in the validation set, its impact on the selection is significant. In order to achieve an efficient selection, we propose an adaptive k-nearest neighbor (KNN) rule, instead of traditional KNN algorithm, to constitute the validation set for each test pattern. Our adaptive KNN rule firstly uses algorithm support vector data description (SVDD) to mine the local area where class conditional probabilities are not constant in terms of the corresponding test pattern. Competences estimated with neighbor patterns in this area should thus be more accurate than that by traditional KNN rule. A probabilistic model that uses posterior probabilities of one-class classifiers is used then to estimate classifier competences. We present experimental evidence of the detection performance improvement over single models and over a variety of static ensemble models, by using data sets from UCI repository.
"Transport Me Away": Fostering Flow in Open Offices through Virtual Reality

Open offices are cost-effective and continue to be popular. However, research shows that these environments, brimming with distractions and sensory overload, frequently hamper productivity. Our research investigates the use of virtual reality (VR) to mitigate distractions in an open office setting and improve one's ability to be in flow. In a lab study, 35 participants performed visual programming tasks in four combinations of physical (open or closed office) and virtual environments (beach or virtual office). While participants both preferred and were in flow more in a closed office without VR, in an open office, the VR environments outperformed the no VR condition in all measures of flow, performance, and preference. Especially considering the recent rapid advancements in VR, our findings illustrate the potential VR has to improve flow and satisfaction in open offices.
Reliable Data Distillation on Graph Convolutional Network

Graph Convolutional Network (GCN) is a widely used method for learning from graph-based data. However, it fails to use the unlabeled data to its full potential, thereby hindering its ability. Given some pseudo labels of the unlabeled data, the GCN can benefit from this extra supervision. Based on Knowledge Distillation and Ensemble Learning, lots of methods use a teacher-student architecture to make better use of the unlabeled data and then make a better prediction. However, these methods introduce unnecessary training costs and a high bias of student model if the teacher's predictions are unreliable. Besides, the final ensemble gains are limited due to limited diversity in the combined models. Therefore, we propose Reliable Data Distillation, a reliable data driven semi-supervised GCN training method. By defining the node reliability and edge reliability in a graph, we can make better use of high quality data and improve the graph representation learning. Furthermore, considering the data reliability and data importance, we propose a new ensemble learning method for GCN and a novel Self-Boosting SSL Framework to combine the above optimizations. Finally, our extensive evaluation of Reliable Data Distillation on real-world datasets shows that our approach outperforms the state-of-the-art methods on semi-supervised node classification tasks.
2019 Evolutionary Algorithms Review

Evolutionary algorithm research and applications began over 50 years ago. Like other artificial intelligence techniques, evolutionary algorithms will likely see increased use and development due to the increased availability of computation, more robust and available open source software libraries, and the increasing demand for artificial intelligence techniques. As these techniques become more adopted and capable, it is the right time to take a perspective of their ability to integrate into society and the human processes they intend to augment. In this review, we explore a new taxonomy of evolutionary algorithms and resulting classifications that look at five main areas: the ability to manage the control of the environment with limiters, the ability to explain and repeat the search process, the ability to understand input and output causality within a solution, the ability to manage algorithm bias due to data or user design, and lastly, the ability to add corrective measures. These areas are motivated by today’s pressures on industry to conform to both societies concerns and new government regulatory rules. As many reviews of evolutionary algorithms exist, after motivating this new taxonomy, we briefly classify a broad range of algorithms and identify areas of future research.
Craftspeople as Technical Collaborators: Lessons Learned through an Experimental Weaving Residency

While craft has had increasing influence on HCI research, HCI researchers tend to engage craft in limited capacities, often focusing on the juxtapositions of "traditional" craft and "innovative" computing. In this paper, we describe the structure and results of a six-week "experimental weaving residency" to show how HCI practitioners, engineers, and craftspeople perform similar work and can productively collaborate to envision new technological interfaces at early stages of development. We address both social and technical challenges of residencies and critically reflect on biases about technical and craft labor that we held prior to the residency. We share our experiences and lessons learned in the hopes of supporting future collaborations with craftspeople and broadening the techniques we use to address design challenges.
Automatic Annotation Synchronizing with Textual Description for Visualization

In this paper, we propose a technique for automatically annotating visualizations according to the textual description. In our approach, visual elements in the target visualization, along with their visual properties, are identified and extracted with a Mask R-CNN model. Meanwhile, the description is parsed to generate visual search requests. Based on the identification results and search requests, each descriptive sentence is displayed beside the described focal areas as annotations. Different sentences are presented in various scenes of the generated animation to promote a vivid step-by-step presentation. With a user-customized style, the animation can guide the audience's attention via proper highlighting such as emphasizing specific features or isolating part of the data. We demonstrate the utility and usability of our method through a user study with use cases.
Distort, Extort, Deceive and Exploit: Exploring the Inner Workings of a Romance Fraud

Abstract Romance fraud is a crime where the fraudster must strike a balance between the romantic and financial aspects of the communication for their criminal intent to remain hidden. This discourse analytic research examines the setup of information early in the interaction, the use of visceral language and isolation as key tactics of exploitation enabling the distortion of reality and manipulation of power. With demands shrouded in a health narrative, and secrecy urged for the preservation of the relationship and the victim’s happiness, this research reveals how the language of this financially and emotionally devastating crime involves grooming strategies akin to coercive control and domestic violence and abuse and exposes the inaccuracies of popular narratives surrounding victims and in awareness-raising and crime prevention strategies.
Automating Exploratory Data Analysis via Machine Learning: An Overview

Exploratory Data Analysis (EDA) is an important initial step for any knowledge discovery process, in which data scientists interactively explore unfamiliar datasets by issuing a sequence of analysis operations (e.g. filter, aggregation, and visualization). Since EDA is long known as a difficult task, requiring profound analytical skills, experience, and domain knowledge, a plethora of systems have been devised over the last decade in order to facilitate EDA.
A framework with efficient extraction and analysis of Twitter data for evaluating public opinions on transportation services

Public opinion is a valuable source for evaluating the performance of transportation services, which can guide management and policy adjustment. In contrast to traditional surveying methods, which are often inefficient and high-cost, social media has become a popular channel to collect public opinion due to its large data volume and accessibility characteristics. However, existing research lack efficient methods to extract and interpret Twitter data for transportation services evaluation. Therefore, this paper presents a comprehensive framework that enables high-efficient extraction and analysis of public opinions on transportation services from Twitter. The transportation system of Miami-Dade County is chosen as the case study to describe the framework development and validation process. First, Twitter data in a defined area over a certain period are collected and preprocessed to clean erroneous and redundant information. Then, the Twitter data that relate to personal opinions on transportation services (POTS) are filtered hieratically using text classification models trained by manually labeled dataset. Next, timeline analysis using Post Intensity (PI) and Average Sentiment Value (ASV) is implemented on the selected Twitter data to explore the public perceptions towards the transportation-related events. Topic modeling and tokenization techniques are also adopted to extract relevant semantic content needed for data analysis. Significantly, the developed framework can improve the efficiency of Twitter data extraction and analysis. Many manual steps are involved in the development process of the framework while can be avoided when generalized to other applications. The framework can be used as a tool for stakeholders to enable a holistic understanding of public opinions on transportation services and increase the degree of public participation in transportation management.
A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata

Copy Move Forgery (CMF) is a type of digital image forgery in which an image region is copied and pasted to another location within the same image with malicious intent to misrepresent its meaning. To prevent misinterpretation of an image content, several Copy Move Forgery Detection (CMFD) methods have been proposed in the past. However, the existing methods show limited robustness on images altered with post-processing attacks such as noise addition, compression, blurring etc. In this paper, we propose a robust method for detecting copy-move forgeries under different post-processing attacks. We use Discrete Cosine Transform (DCT) to extract features from each block. Next, Cellular Automata is employed to construct feature vectors based on the sign information of the DCT coefficients. Finally, feature vectors are matched using the kd-tree based nearest-neighbor searching method to find the duplicated areas in the image. Experimental results show that the proposed method performs exceptionally well relative to the other state-of-the-art methods from the literature even when an image is heavily affected by the post-processing attacks, in particular, JPEG compression and additive white Gaussian noise. Furthermore, experiments confirm the robustness of the proposed method against the range of combined attacks.
Living Bits

There are trillions of living biological "computers" on, inside, and around the human body: microbes. Microbes have the potential to enhance human-computer interaction (HCI) in entirely new ways. Advances in open-source biotechnology have already enabled designers, artists, and engineers to use microbes in redefining wearables, games, musical instruments, robots, and more. "Living Bits", inspired by Tangible Bits, is an attempt to think beyond the traditional boundaries that exist between biological cells and computers for integrating microorganism in HCI. In this work we: 1) outline and inspire the possibility for integrating organic and regenerative living systems in HCI; 2) explore and characterize human-microbe interactions across contexts and scales; 3) provide principles for stimulating discussions, presentations, and brainstorms of microbial interfaces. We aim to make Living Bits accessible to researchers across HCI, synthetic biology, biotechnology, and interaction design to explore the next generation of biological HCI.