Reviewerly/exHarmony huggingface dataset:
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings

Image translation refers to the task of mapping images from a visual domain to another. Given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving semantic content shared across the two domains. We introduce xgan, a dual adversarial auto-encoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions. We exploit ideas from the domain adaptationDomain adaptation literature and define a semantic consistency loss which encourages the learned embedding to preserve semantics shared across domains. We report promising qualitative results for the task of face-to-cartoon translation. The cartoon dataset we collected for this purpose, "CartoonSet", is also publicly available as a new benchmark for semantic style transferStyle transfer at https://google.github.io/cartoonset/index.html .
Faster computation of isogenies of large prime degree

Let $\mathcal{E}/\mathbb{F}_q$ be an elliptic curve, and $P$ a point in $\mathcal{E}(\mathbb{F}_q)$ of prime order $\ell$. Vélu's formulae let us compute a quotient curve $\mathcal{E}' = \mathcal{E}/\langle{P}\rangle$ and rational maps defining a quotient isogeny $ϕ: \mathcal{E} \to \mathcal{E}'$ in $\tilde{O}(\ell)$ $\mathbb{F}_q$-operations, where the $\tilde{O}$ is uniform in $q$.This article shows how to compute $\mathcal{E}'$, and $ϕ(Q)$ for $Q$ in $\mathcal{E}(\mathbb{F}_q)$, using only $\tilde{O}(\sqrt{\ell})$ $\mathbb{F}_q$-operations, where the $\tilde{O}$ is again uniform in $q$.As an application, this article speeds up some computations used in the isogeny-based cryptosystems CSIDH and CSURF.
Deep Residual Flow for Out of Distribution Detection

The effective application of neural networks in the real-world relies on proficiently detecting out-of-distribution examples. Contemporary methods seek to model the distribution of feature activations in the training data for adequately distinguishing abnormalities, and the state-of-the-art method uses Gaussian distribution models. In this work, we present a novel approach that improves upon the state-of-the-art by leveraging an expressive density model based on normalizing flows. We introduce the residual flow, a novel flow architecture that learns the residual distribution from a base Gaussian distribution. Our model is general, and can be applied to any data that is approximately Gaussian. For out of distribution detection in image datasets, our approach provides a principled improvement over the state-of-the-art. Specifically, we demonstrate the effectiveness of our method in ResNet and DenseNet architectures trained on various image datasets. For example, on a ResNet trained on CIFAR-100 and evaluated on detection of out-of-distribution samples from the ImageNet dataset, holding the true positive rate (TPR) at 95%, we improve the true negative rate (TNR) from 56.7% (current state of-the-art) to 77.5% (ours).
Small Target Tracking in Satellite Videos Using Background Compensation

Through the use of video technology, satellites can detect dynamic targets and analyze their motion characteristics. Target tracking can extract dynamic information about key ground targets for target monitoring and trajectory prediction by satellite video. Tracking algorithms are affected by target motion characteristics, such as velocity and direction, as well as background characteristics, such as illumination changes, occlusion, and background similarities with the target. However, these problems are seldom studied with satellite video cameras. Current algorithms are unsuitable for satellite video because of the poor texture and color features of the target in satellite video. Therefore, in this article, we enhance target tracking for satellite video technology using two aspects: 1) sample training strategy and 2) sample characterization. We establish a filter training mechanism for the target and background to improve the discrimination ability of the tracking algorithm. We then build a target feature model using a Gabor filter to enhance the contrast between the target and background. Moreover, we propose a tracking state evaluation index to avoid tracking drift. Tracking experiments using nine sets of Jilin-1 satellite videos show that the proposed approach can accurately locate a target under weak feature attributes. Therefore, this article contributes to more robust tracking using satellite video technology.
A scale-adaptive object-tracking algorithm with occlusion detection

Abstract The methods combining correlation filters (CFs) with the features of convolutional neural network (CNN) are good at object tracking. However, the high-level features of a typical CNN without residual structure suffer from the shortage of fine-grained information, it is easily affected by similar objects or background noise. Meanwhile, CF-based methods usually update filters at every frame even when occlusion occurs, which degrades the capability of discriminating the target from background. A novel scale-adaptive object-tracking method is proposed in this paper. Firstly, the features are extracted from different layers of ResNet to produce response maps, and then, in order to locate the target more accurately, these response maps are fused based on AdaBoost algorithm. Secondly, to prevent the filters from updating when occlusion occurs, an update strategy with occlusion detection is proposed. Finally, a scale filter is used to estimate the target scale. The experimental results demonstrate that the proposed method performs favorably compared with several mainstream methods especially in the case of occlusion and scale change.
Review of video compression techniques based on fractal transform function and swarm intelligence

Data processing with multiple domains is an important concept in any platform; it deals with multimedia and textual information. Where textual data processing focuses on a structured or unstructured way of data processing which computes in less time with no compression over the data, multimedia data are processing deals with a processing requirement algorithm where compression is needed. This involve processing of video and their frames and compression in short forms such that the fast processing of storage as well as the access can be performed. There are different ways of performing compression, such as fractal compression, wavelet transform, compressive sensing, contractive transformation and other ways. One way of performing such a compression is working with the high frequency component of multimedia data. One of the most recent topics is fractal transformation which follows the block symmetry and archives high compression ratio. Yet, there are limitations such as working with speed and its cost while performing proper encoding and decoding using fractal compression. Swarm optimization and other related algorithms make it usable along with fractal compression function. In this paper, we review multiple algorithms in the field of fractal-based video compression and swarm intelligence for problems of optimization.
Attacking vision-based perception in end-to-end autonomous driving models

Recent advances in machine learning, especially techniques such as deep neural networks, are enabling a range of emerging applications. One such example is autonomous driving, which often relies on deep learning for perception. However, deep learning-based perception has been shown to be vulnerable to a host of subtle adversarial manipulations of images. Nevertheless, the vast majority of such demonstrations focus on perception that is disembodied from end-to-end control. We present novel end-to-end attacks on autonomous driving in simulation, using simple physically realizable attacks: the painting of black lines on the road. These attacks target deep neural network models for end-to-end autonomous driving control. A systematic investigation shows that such attacks are easy to engineer, and we describe scenarios (e.g., right turns) in which they are highly effective. We define several objective functions that quantify the success of an attack and develop techniques based on Bayesian Optimization to efficiently traverse the search space of higher dimensional attacks. Additionally, we define a novel class of hijacking attacks, where painting lines on the road cause the driverless car to follow a target path. Through the use of network deconvolution, we provide insights into the successful attacks, which appear to work by mimicking activations of entirely different scenarios. Our code is available on https://github.com/xz-group/AdverseDrive
From Empowerment Dynamics to Team Adaptability: Exploring and Conceptualizing the Continuous Agile Team Innovation Process

To foster their innovation teams’ adaptability, organizations are increasingly relying on agile teams. While research on the adoption of agile methods and practices has grown tremendously in the past decade, little is currently known about the human side of agile teams and how it contributes toward the emergence of adaptability. While the Agile Manifesto states that individuals and interactions are more important for agile product development than tools and processes, research on how these interactions unfold is still in its infancy. To shed light on the human side of adaptability, 44 semi‐structured, in‐depth interviews were conducted with team members and leaders from various teams at three organizations (i.e., two German and one multinational European firm). The inductive analysis identified empowerment as a focal human factor for adaptability emergence. A model of the continuous agile team innovation process is developed and uncovers the importance of dynamic empowerment states and their temporary equilibria for team adaptability. The underlying findings demonstrate that empowerment is not a static state, but rather emerges through the interactions between various actors. Specifically, the team and its leader engage in both empowerment‐enhancing and empowerment‐reducing activities. These activities are further influenced by the agile team’s immediate context: Two‐fold customer influences, that is, supporting and hindering empowerment interactions, and the organizational environment, that is, undergoing an agile transformation and supportive top management behaviors, play an important role in affecting the empowerment dynamics that result in team adaptability. As such, this study contributes to the innovation and management literatures by revealing the dynamic role of the empowerment and adaptability constructs for agile innovation processes and the importance of various actors and the organizational environment for fostering adaptability. Practical insights are offered to management, teams, and team members on how to create conditions for empowerment dynamics and consequently adaptability to unfold.
EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition

Abstract Despite the lack of invariance problem (the many‐to‐many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side‐stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real‐world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two‐layer network that borrows one element from ASR, long short‐term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human‐like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR.
Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking

The success of DNNs has driven the extensive applications of person re-identification (ReID) into a new era. However, whether ReID inherits the vulnerability of DNNs remains unexplored. To examine the robustness of ReID systems is rather important because the insecurity of ReID systems may cause severe losses, e.g., the criminals may use the adversarial perturbations to cheat the CCTV systems. In this work, we examine the insecurity of current best-performing ReID models by proposing a learning-to-mis-rank formulation to perturb the ranking of the system output. As the cross-dataset transferability is crucial in the ReID domain, we also perform a back-box attack by developing a novel multi-stage network architecture that pyramids the features of different levels to extract general and transferable features for the adversarial perturbations. Our method can control the number of malicious pixels by using differentiable multi-shot sampling. To guarantee the inconspicuousness of the attack, we also propose a new perception loss to achieve better visual quality. Extensive experiments on four of the largest ReID benchmarks (i.e., Market1501, CUHK03, DukeMTMC, and MSMT17) not only show the effectiveness of our method, but also provides directions of the future improvement in the robustness of ReID systems. For example, the accuracy of one of the best-performing ReID systems drops sharply from 91.8% to 1.4% after being attacked by our method. Some attack results are shown in Fig. 1. The code is available at: https://github.com/whj363636/Adversarial-attack-on-Person-ReID-With-Deep-Mis-Ranking.
Cooling Effect on the Floating Solar PV: Performance and Economic Analysis on the Case of West Java Province in Indonesia

Solar photovoltaic technology is one of the most well established new and renewable energy technologies. Many researchers have undertaken wide research and development in this sector, including material and system design. To protect the exhaustion of global terrestrial land and to avoid the occupation of extensive farmlands, solar photovoltaic (PV) developers, as well as policymakers, have pursued various solutions, including the development of floating solar PV (FPV). This study consists of a technological and economic perspective to analyze the floating solar PV system. The authors utilize remote sensing results to predict FPV efficiency and measure energy yield from the system while also developing an economics analysis on an FPV project by comparison with ground-based solar PV (GPV). The results from the remote sensing method found that the lake has a cooler temperature than the ground, with an annual difference of around 8 °C. FPV efficiency was also shown to be around 0.61% higher than GPV in terms of the prediction. FPV economic parameter comparison also resulted in 3.37 cents/kWh lower levelized cost of electricity (LCOE), and 6.08% higher internal rate of return (IRR) compared to GPV in the base scenario.
Automated Machine Learning: The New Wave of Machine Learning

With the explosion in the use of machine learning in various domains, the need for an efficient pipeline for the development of machine learning models has never been more critical. However, the task of forming and training models largely remains traditional with a dependency on domain experts and time-consuming data manipulation operations, which impedes the development of machine learning models in both academia as well as industry. This demand advocates the new research era concerned with fitting machine learning models fully automatically i.e., AutoML. Automated Machine Learning(AutoML) is an end-to-end process that aims at automating this model development pipeline without any external assistance. First, we provide an insights of AutoML. Second, we delve into the individual segments in the AutoML pipeline and cover their approaches in brief. We also provide a case study on the industrial use and impact of AutoML with a focus on practical applicability in a business context. At last, we conclude with the open research issues, and future research directions.
I-It, I-Thou, I-Robot: The Perceived Humanness of AI in Human-Machine Communication

As artificial intelligence (AI) technologies become more common and capable interaction partners (human-machine communication; HMC), understanding how people perceive and interact with them becomes increasingly important to study. This essay argues that one important avenue for this study is the application of relevant interpersonal and computer-mediated communication (CMC) theories. The paper suggests that these theories are relevant because the Computers as Social Actors (CASA) approach has shown that people tend to respond to technologies as they do to other people. It summarizes some theories that may be especially useful for future study in this field. Finally, a case is made that the study of AI and HMC may also be important for greater understanding of the human-human communication process as well.
Deep Transfer Hashing for Image Retrieval

Deep supervised hashing has emerged as an influential solution to large-scale semantic image retrieval problems in computer vision. In the light of recent progress, image label is the common way to define whether two images belong to the same category, but it contains little supervised information. The one-hot label can't accurately define the similarity of two images, which is important for image retrieval. In this paper, we propose an effective method, Deep Transfer Hashing(DTH) which uses the knowledge from teacher model as the supervised information. Inspired by knowledge distillation for model compression and deep hashing for fast image retrieval, we transfer the knowledge from a complex convolutional neural network(teacher) to a small neural network(student) which is used for fast image retrieval. The distance of the knowledge from teacher model can indicate the similarity of images. By minimizing the hashing codes distribution between the hashing layers of teacher model and student model, we can improve the retrieval performance. And we also evaluate the performance of the compressed model at inference stage. We test our method on widely used datasets CIFAR-10 and NUS-WIDE and we compare our method with other state-of-the-art methods in image retrieval domain. The experimental results show that our method can improve the image retrieval baseline by a large margin and better than other methods.
Optimized multi-UAV cooperative path planning under the complex confrontation environment

As an emerging technology, multi-UAV collaboration is widely used in military and civil applications, including regional surveillance, remote sensing, target strike, etc. As a key step in the implementation of multi-UAV cooperative missions, path planning aims to generate near-optimal paths that satisfy certain constraints, ensure that each UAV can reach the mission area quickly and reduce the probability of being captured and destroyed by the antagonism side. In this paper, we design an optimized multi-UAV cooperative path planning method under the complex confrontation environment. Firstly, the threat model is designed based on the actual situation. Combining the threat and fuel consumption criteria, under the constraints of time and space, a multi-constraint objective optimization model is established. Following this, an improved grey wolf optimizer algorithm is used to solve the optimization model. Based on the characteristics of the multi-UAV cooperative path planning, the algorithm is improved in three aspects: population initialization, decay factor updating, and individual position updating. The simulation results demonstrate that the proposed algorithm is effective in generating paths for multi-UAV cooperative path planning and has the advantages of a lower path cost and faster convergence speed as compared to the other algorithms tested in this work.
Estimating Generalized Gaussian Blur Kernels for Out-of-Focus Image Deblurring

Out-of-focus blur is a common image degradation phenomenon that occurs in case of lens defocusing. The out-of-focus blur kernel is usually modeled as a Gaussian function or a uniform disk in previous work. In this paper, we propose that it can be more accurately depicted using the generalized Gaussian (GG) function. This is motivated by the theoretical analysis of the out-of-focus blur and the practical observation of real blur kernels. We show that as the out-of-focus blur kernels are of specific shapes, the GG function can be further simplified to a single-parameter model. We estimate the parameter of the GG blur kernel from image patches containing step edges, and obtain the clear image by non-blind image deblurring. Experimental results validate that the proposed GG blur kernel estimation algorithm outperforms the state-of-the-art ones deploying either parametric (disk and Gaussian) or nonparametric kernels, and consequently benefits the image deblurring process.
Image Captioning: A Comprehensive Survey

The primary purpose of image captioning is to generate a caption for an image. Image captioning needs to identify objects in image, actions, their relationship and some silent feature that may be missing in the image. After identification the next step is to generate a most relevant and brief description for the image that must be syntactically and semantically correct. It uses both computer vision concepts for identification of objects and natural language processing methods for description. It's difficult for a machine to imitate human brain ability however researches in this field have shown a great achievement. Deep learning techniques are enough capable to handle such problems using CNN and LSTM. It can be used in many intelligent control systems and IOT based devices. In this survey paper, we are presenting different approaches of image captioning such as retrieval based, template based and deep learning based as well as different evaluation techniques.
Interactive machine teaching: a human-centered approach to building machine-learned models

Modern systems can augment people’s capabilities by using machine-learned models to surface intelligent behaviors. Unfortunately, building these models remains challenging and beyond the reach of non-machine learning experts. We describe interactive machine teaching (IMT) and its potential to simplify the creation of machine-learned models. One of the key characteristics of IMT is its iterative process in which the human-in-the-loop takes the role of a teacher teaching a machine how to perform a task. We explore alternative learning theories as potential theoretical foundations for IMT, the intrinsic human capabilities related to teaching, and how IMT systems might leverage them. We argue that IMT processes that enable people to leverage these capabilities have a variety of benefits, including making machine learning methods accessible to subject-matter experts and the creation of semantic and debuggable machine learning (ML) models. We present an integrated teaching environment (ITE) that embodies principles from IMT, and use it as a design probe to observe how non-ML experts do IMT and as the basis of a system that helps us study how to guide teachers. We explore and highlight the benefits and challenges of IMT systems. We conclude by outlining six research challenges to advance the field of IMT.