Reviewerly/exHarmony huggingface dataset:
Obstacle Avoidance for Swarm Robot Based on Self-Organizing Migrating Algorithm

This paper presents an obstacle avoidance algorithm for swarm robot in unknown environment based on self-organizing migrating algorithm in which the trajectory is divided into a set of points created by the SOMA that robot must pass through. During the move, obstacles will be detected by sensors on the robot, it only knows the target position without knowing the obstacles location until detecting them. The fitness function is based on the principle of attraction from the target and the repulsion of the obstacles, which helps robot find the trajectory for moving, and the ways leaving it to move safety away from the trapped area. Simulation results are also presented to show the feasibility of the algorithm.
Sparseness reduction in collaborative filtering using a nearest neighbour artificial immune system with genetic algorithms

In collaborative filtering, one of the main challenges that researchers face is sparseness in the data, which is caused by users rating fewer items as the number of items increase in the dataset. The effect is poor predictions and recommendations of items to users by expert and intelligent systems like the recommender system. This paper proposes a Nearest Neighbour Artificial Immune System with a Genetic Algorithm (NNAISGA) to perform fast data imputations to reduce sparseness. The main impact is sustained and reliable predictions as the number of missing data increases in the dataset. The second benefit is to help sustain or improve recommendations of items to users from common methods used in collaborative filtering such as the User-based, Item-based, Slope-one, Tendencies-based and Non-Negative Matrix Factorisation (NNMF) methods. We show that NNAISGA is the desired method for learning and imputation over the traditional genetic algorithm. The findings show that using the NNAISGA as a fast imputation method yields promising results. All of the methods, except for the User-based method, show significant improvements or sustained accuracies in terms of predictions and recommendations.
A new multiframe super-resolution based on nonlinear registration and a spatially weighted regularization

Increasing the size of an image is an extensively studied problem in image processing. In recent years, many studies have been conducted on image super-resolution (SR). Since the super-resolution techniques depend on the motion precision estimation, we investigate the use of a nonlinear elastic (called hyperelastic) image registration. Also, we propose a spatially weighted second order SR algorithm, which takes into account the distribution of the spatial information in different image areas. The hyperelastic image registration is used to handle the subpixel errors between the unregistered images, while the spatially weighted second order regularization allows to increase the robustness of the restoration step with respect to degradation factors (blur and noise). As a result, the registration model is more efficient and easier to implement and the proposed SR algorithm reduces artifacts in flat regions of the image and also preserves well sharp edges. The efficiency of the proposed model is demonstrated using simulated and real tests, while comparison with other competitive SR methods is achieved.
Scene Privacy Protection

Images shared on social media are routinely analysed by classifiers for content annotation and user profiling. These automatic inferences reveal to the service provider sensitive information that a naive user might want to keep private. To address this problem, we present a method designed to distort the image data so as to hinder the inference of a classifier without affecting the utility for social media users. The proposed approach is based on the Fast Gradient Sign Method (FGSM) and limits the likelihood that automatic inference can expose the true class of a distorted image. Experimental results on a scene classification task show that the proposed method, private FGSM, achieves a desirable trade-off between the drop in classification accuracy and the distortion on the private classes of the Places365-Standard dataset using ResNet50. The classifier is misled 94.40% of the times in the top-5 classes with only a small average reduction of three image quality measures (SSIM, PSNR, BRISQUE).
Low-resolution Visual Recognition via Deep Feature Distillation

Here we study the low-resolution visual recognition problem. Conventional methods are usually trained on images with large ROIs (regions of interest), while the regions and insider images are often small and blur in real-world applications. Therefore, deep neural networks learned on high-resolution images cannot be directly used for recognizing low-resolution objects. To overcome this challenging problem, we propose to use the teacher-student learning paradigm for distilling useful feature information from a pre-trained deep model on high-resolution visual data. In practice, a distillation loss is used to seek the perceptual consistency of low-resolution images and high-resolution images. By simultaneously optimizing the recognition loss and distillation loss, we formulate a novel low-resolution recognition approach. Experiments conducted on benchmarks demonstrate that the proposed method is capable to learn well-performed models for recognizing low-resolution objects, which is superior to the state-of-the-art methods.
Securing Distributed Gradient Descent in High Dimensional Statistical Learning

We consider unreliable distributed learning systems wherein the training data is kept confidential by external workers, and the learner has to interact closely with those workers to train a model. In particular, we assume that there exists a system adversary that can adaptively compromise some workers; the compromised workers deviate from their local designed specifications by sending out arbitrarily malicious messages.
Around the (Virtual) World

Virtual worlds are infinite environments in which the user can move around freely. When shifting from controller-based movement to regular walking as an input, the limitation of the real world also limits the virtual world. Tackling this challenge, we propose the use of electrical muscle stimulation to limit the necessary real-world space to create an unlimited walking experience. We thereby actuate the users` legs in a way that they deviate from their straight route and thus, walk in circles in the real world while still walking straight in the virtual world. We report on a study comparing this approach to vision shift - the state of the art approach - as well as combining both approaches. The results show that particularly combining both approaches yield high potential to create an infinite walking experience.
<i>I'm Sensing in the Rain</i>

Major virtual reality (VR) companies are trying to enhance the sense of immersion in virtual environments by implementing haptic feedback in their systems (e.g., Oculus Touch). It is known that tactile stimulation adds realism to a virtual environment. In addition, when users are not limited by wearing any attachments (e.g., gloves), it is even possible to create more immersive experiences. Mid-air haptic technology provides contactless haptic feedback and offers the potential for creating such immersive VR experiences. However, one of the limitations of mid-air haptics resides in the need for freehand tracking systems (e.g., Leap Motion) to deliver tactile feedback to the user's hand. These tracking systems are not accurate, limiting designers capability of delivering spatially precise tactile stimulation. Here, we investigated an alternative way to convey incongruent visual-tactile stimulation that can be used to create the illusion of a congruent visual-tactile experience, while participants experience the phenomenon of the rubber hand illusion in VR.
Design and Evaluation of a Social Media Writing Support Tool for People with Dyslexia

People with dyslexia face challenges expressing themselves in writing on social networking sites (SNSs). Such challenges come from not only the technicality of writing, but also the self-representation aspect of sharing and communicating publicly on social networking sites such as Facebook. To empower people with dyslexia-style writing to express them-selves more confidently on SNSs, we designed and implemented Additional Writing Help(AWH) - a writing assistance tool to proofread text produced by users with dyslexia before they post on Facebook. AWH was powered by a neural machine translation (NMT) model that translates dyslexia style to non-dyslexia style writing. We evaluated the performance and the design of AWH through a week-long field study with 19 people with dyslexia and received highly positive feedback. Our field study demonstrated the value of providing better and more extensive writing support on SNSs, and the potential of AI for building a more inclusive Internet.
Deep Neural Network Regularization for Feature Selection in Learning-to-Rank

Learning-to-rank is an emerging area of research for a wide range of applications. Many algorithms are devised to tackle the problem of learning-to-rank. However, very few existing algorithms deal with deep learning. Previous research depicts that deep learning makes significant improvements in a variety of applications. The proposed model makes use of the deep neural network for learning-to-rank for document retrieval. It employs a regularization technique particularly suited for the deep neural network to improve the results significantly. The main aim of regularization is optimizing the weight of neural network, selecting the relevant features with active neurons at the input layer, and pruning of the network by selecting only active neurons at hidden layer while learning. Specifically, we use group ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization in order to induce the group level sparsity on the network's connections. Set of outgoing weights from each hidden layer represents the group here. The sparsity of network is measured by the sparsity ratio and it is compared with learning-to-rank models, which adopt the embedded method for feature selection. An extensive experimental evaluation considers the performance of the extended ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization technique against classical regularization techniques. The empirical results confirm that sparse group ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization is able to achieve competitive performance while simultaneously making the network compact with less number of input features. The model is analyzed with respect to evaluating measures, such as prediction accuracy, NDCG@n, MAP, and Precision on benchmark datasets, which demonstrate improved results over other state-of-the-art methods.
From HCI to HCI-Amusement

Notions of what counts as a contribution to HCI continue to be contested as our field expands to accommodate perspectives from the arts and humanities. This paper aims to advance the position of the arts and further contribute to these debates by actively exploring what a "non-contribution" would look like in HCI. We do this by taking inspiration from Fluxus, a collective of artists in the 1950's and 1960's who actively challenged and reworked practices of fine arts institutions by producing radically accessible, ephemeral, and modest works of "art-amusement." We use Fluxus to develop three analogous forms of "HCI-amusements," each of which shed light on dominant practices and values within HCI by resisting to fit into its logics.
Sensory Alignment in Immersive Entertainment

When we use digital systems to stimulate the senses, we typically stimulate only a subset of users' senses, leaving other senses stimulated by the physical world. This creates potential for misalignment between senses, where digital and physical stimulation give conflicting signals to users. We synthesize knowledge from HCI, traditional entertainments, and underlying sensory science research relating to how senses work when given conflicting signals. Using this knowledge we present a design dimension of sensory alignment, and show how this dimension presents opportunities for a range of creative strategies ranging from full alignment of sensory stimulation, up to extreme conflict between senses.
Statistical distributions connected to motion of substance in a channel of a network

We discuss a model of motion of substance through the nodes of a channel of a network. The channel can be modeled by a chain of urns where each urn can exchange substance with the neighboring urns. In addition the urns can exchange substance with the network nodes and the extension of the model with respect to the previous research is that we consider also the possibility for exchange of substance among the urns and the environment of the network. We consider stationary regime of motion of substance through a finite channel (stationary regime of exchange of substance along the chain of urns) and obtain a class of statistical distributions for the substance in the channel. Our attention is concentrated on this class of distributions and we show that for the case of finite channel the class of distributions contains as particular cases truncated versions of the families of distributions of Katz, Ord, Kemp, etc. Several characteristics of the obtained class of distributions are calculated. The theory for the case of chain containing infinite number of urns is presented in the Appendix.
Learning Structural Representations via Dynamic Object Landmarks Discovery for Sketch Recognition and Retrieval

State-of-the-art methods on sketch classification and retrieval are based on deep convolutional neural network to learn representations. Although deep neural networks have the ability to model images with hierarchical representations by convolution kernels, they can not automatically extract the structural representations of object categories in a human-perceptible way. Furthermore, sketch images usually have large scale visual variations caused by the styles of drawing or viewpoints, which make it difficult to develop generalized representations using the fixed computational mode of convolutional kernel. In this paper, our aim is to address the problem of fixed computational mode in feature extraction process without extra supervision. We propose a novel architecture to dynamically discover the object landmarks and learn the discriminative structural representations. Our model is composed of two components: a representative landmark discovering module that localizes the key points on the object, and a category-aware representation learning module that develops the category-specific features. Specifically, we develop a structure-aware offset layer to dynamically localize the representative landmarks, which is optimized based on the category labels without extra supervision. After that, a diversity branch is introduced to extract the global discriminative features for each category. Finally, we employ a multi-task loss function to develop an end-to-end trainable architecture. At testing time, we fuse all the predictions with different number of landmarks to achieve the final results. Through extensive experiments, we compare our model with several state-of-the-art methods on two challenging datasets TU-Berlin and Sketchy for sketch classification and retrieval, and the experimental results demonstrate the effectiveness of our proposed model.
Hand Gesture Recognition and Implementation for Disables using CNN’S

Hand Gestures are playing the major role in today's industry. The development of the gesture recognition or detection will help people in different ways. Development of many automated technologies like machine learning, deep learning, neural networks and computer vision will help in using futuristic methods like gesture controlling and recognition. These gestures can be used in such a way it can help with people who have difficulty in controlling or operation systems or devices. Here the development of the model is in such a way it can help us to recognition and implementation. For such developments these models will be working with Convolution Neural Networks and Back Propagation methodologies which can help for the easy working of these models.
Multi-view clustering via joint feature selection and partially constrained cluster label learning

Real world data are often represented by multiple distinct feature sets, and some prior knowledge is provided, such as labels of some examples or pairwise constraints between several sample pairs. Accordingly, task of multi-view clustering arises from a complex information aggregation of multiple sources of feature sets and knowledge prior. In this paper, we propose to optimize the cluster indicator, which representing the class labels is an intuitive reflection of the clustering structure. Besides, the prior indicating the same level of semantics can be directly utilized guiding the learned clustering structure. Furthermore, feature selection is embedded into the above process to select views and features in each view, which leads to the most discriminative views and features chosen for every single cluster. To these ends, an objective is accordingly proposed with an efficient optimization strategy and convergence analysis. Extensive experiments demonstrate that our model performs better than the state-of-the-art methods.
The iterative convolution–thresholding method (ICTM) for image segmentation

Variational methods, which have been tremendously successful in image segmentation, work by minimizing a given objective functional. The objective functional usually consists of a fidelity term and a regularization term. Because objective functionals may vary from different types of images, developing an efficient, simple, and general numerical method to minimize them has become increasingly vital. However, many existing methods are model-based, converge relatively slowly, or involve complicated techniques. In this paper, we develop a novel iterative convolution–thresholding method (ICTM) that is simple, efficient, and applicable to a wide range of variational models for image segmentation. In ICTM, the interface between two different segment domains is implicitly represented by the characteristic functions of domains. The fidelity term is usually written into a linear functional of the characteristic functions, and the regularization term is approximated by a functional of characteristic functions in terms of heat kernel convolution. This allows us to design an iterative convolution–thresholding method to minimize the approximate energy. The method has the energy-decaying property, and thus the unconditional stability is theoretically guaranteed. Numerical experiments show that the method is simple, easy to implement, robust, and applicable to various image segmentation models.
Career Mentoring in Online Communities

Although people frequently seek mentoring or advice for their career, most mentoring is performed in person. Little research has examined the nature and quality of career mentoring online. To address this gap, we study how people use online Q&A forums for career advice. We develop a taxonomy of career advice requests based on a qualitative analysis of posts in a career-related online forum, identifying three key types: best practices, career threats, and time-sensitive requests. Our quantitative analysis of responses shows that both requesters and external viewers value general information, encouragement, and guidance, but not role modeling. We found no relation between the type of requests and features of responses, nor differences in responses valued by requesters versus external viewers. We present design recommendations for supporting online career advice exchange.
AILA

Document labeling is a critical step in building various machine learning applications. However, the step can be time-consuming and arduous, requiring a significant amount of human efforts. To support an efficient document labeling environment, we present a system called Attentive Interactive Labeling Assistant (AILA). In its core, AILA uses Interactive Attention Module (IAM), a novel module that visually highlights words in a document that labelers may pay attention to when labeling a document. IAM utilizes attention-based Deep Neural Networks which not only support a prediction of which words to highlight but also enable labelers to indicate words that should be assigned a high attention weight while labeling to improve the future quality of word prediction.We evaluated the labeling efficiency and the accuracy by comparing the conditions with and without IAM in our study. The results showed that participants' labeling efficiency increased significantly under the condition with IAM than the condition without IAM, while the two conditions maintained roughly the same labeling accuracy.
VelociWatch

Virtual keyboard typing is typically aided by an auto-correct method that decodes a user's noisy taps into their intended text. This decoding process can reduce error rates and possibly increase entry rates by allowing users to type faster but less precisely. However, virtual keyboard decoders sometimes make mistakes that change a user's desired word into another. This is particularly problematic for challenging text such as proper names. We investigate whether users can guess words that are likely to cause auto-correct problems and whether users can adjust their behavior to assist the decoder. We conduct computational experiments to decide what predictions to offer in a virtual keyboard and design a smartwatch keyboard named VelociWatch. Novice users were able to use the features of VelociWatch to enter challenging text at 17 words-per-minute with a corrected error rate of 3%. Interestingly, they wrote slightly faster and just as accurately on a simpler keyboard with limited correction options. Our finding suggest users may be able to type difficult words on a smartwatch simply by tapping precisely without the use of auto-correct.
A Study of Image Upsampling and Downsampling Filters

In this paper, a set of techniques used for downsampling and upsampling of 2D images is analyzed on various image datasets. The comparison takes into account a significant number of interpolation kernels, their parameters, and their algebraical form, focusing mostly on linear interpolation methods with symmetric kernels. The most suitable metrics for measuring the performance of upsampling and downsampling filters’ combinations are presented, discussing their strengths and weaknesses. A test benchmark is proposed, and the obtained results are analyzed with respect to the presented metrics, offering explanations about specific filter behaviors in general, or just in certain circumstances. In the end, a set of filters and parameters recommendations is offered based on extensive testing on carefully selected image datasets. The entire research is based on the study of a large set of research papers and on a solid discussion of the underlying signal processing theory.