Reviewerly/exHarmony huggingface dataset:
Vision-Based Measurement and Prediction of Object Trajectory for Robotic Manipulation in Dynamic and Uncertain Scenarios

Vision-based measurement and prediction (VMP) are the very important and challenging part for autonomous robotic manipulation, especially in dynamic and uncertain scenarios. However, due to the potential limitations of visual measurement in such an environment such as occlusion, lighting, and hardware limitations, it is not easy to acquire the accurate positions of an object as the observations. Moreover, manipulating a dynamic object with unknown or uncertain motion rules usually requires an accurate prediction of motion trajectory at the desired moment, which dramatically increases the difficulty. To address the problem, we propose a time granularity-based vision prediction framework whose core is an integrated prediction model based on multiple [i.e., long short-term memory (LSTM)] neural networks. At first, we use the vision sensor to acquire raw measurements and adopt the preprocessing method (e.g., data completion, error compensation, and filtering) to turn raw measurements into the standard trajectory data. Then, we devise a novel integration strategy based on time granularity boost (TG-Boost) to select appropriate base predictors and further utilize these history trajectory data to construct the high-precision prediction model. Finally, we use the simulation and a series of dynamic manipulation experiments to validate the proposed methodology. The results also show that our method outperforms the state-of-the-art prediction algorithms in terms of prediction accuracy, success rate, and robustness.
MultiCloud Tournament: A cloud federation approach to prevent Free-Riders by encouraging resource sharing

Organized in associations, Cloud Computing Providers became able to negotiate the acquisition of resources from other providers at more attractive prices, while allowing the marketing of their own idle resources to the other members of the respective Inter-Cloud. However, Inter-Cloud associations are characterized as resource-sharing environments, which allow for the presence of malicious providers called Free-Riders. Free-Riders have predatory behavior, seeking to serve only their own interests and consequently harming other providers within the environment. Mechanisms to avoid Free-Riders, as well as to keep resources supply × demand balanced, are needed in Inter-cloud environments such as Cloud Federations. For example, the absence of a resource consumption control mechanism can cause a Free-Rider to consume resources in a way that is disproportionate to that offered to Inter-Cloud, referring to the problem described previously. In view of the above, this paper presents a new Inter-Cloud called MultiClouds Tournament (MCT) focused on addressing the standalone cloud providers limitations. This Inter-Cloud is inspired by the format of a soccer tournament and based on the main features of Cloud Federations. We assessed the MCT using a real workload, showing it offers an architecture that adds to the characteristics of the federations, being effective in the management of CSPs and robust in relation to Free-Riders prevention and the main strategies CSPs.
Eye vs. Head: Comparing Gaze Methods for Interaction in Augmented Reality

Visualization in virtual 3D environments can provide a natural way for users to explore data. Often, arm and short head movements are required for interaction in augmented reality, which can be tiring and strenuous though. In an effort toward more user-friendly interaction, we developed a prototype that allows users to manipulate virtual objects using a combination of eye gaze and an external clicker device. Using this prototype, we performed a user study comparing four different input methods of which head gaze plus clicker was preferred by most participants.
How elementary school teachers use ICT-based learning media?

Abstract Integrated learning in elementary schools requires learning media as a means of communication. Research is needed on analysis of the media use of learning in elementary school learning. The purpose of this research is to examine how the elementary school teachers of perception and motivation to implement ICT-based learning media in learning activities. In this study using a quantitative approach to the type of survey, researchers used surveys, interviews and observations to collect data. The results of quantitative analysis of elementary school teachers are often categorized in learning media use. These results show that teachers have a good perception of the utilization of ICT in learning activities. However, to implement ICT-based media is not optimal.
Force-Based Algorithm for Motion Planning of Large Agent

This article presents a distributed, efficient, scalable, and real-time motion planning algorithm for a large group of agents moving in 2-D or 3-D spaces. This algorithm enables autonomous agents to generate individual trajectories independently with only the relative position information of neighboring agents. Each agent applies a force-based control that contains two main terms: 1) collision avoidance and 2) navigational feedback. The first term keeps two agents separate with a certain distance, while the second term attracts each agent toward its goal location. Compared with existing collision-avoidance algorithms, the proposed force-based motion planning (FMP) algorithm can find collision-free motions with lower transition time, free from velocity state information of neighboring agents. It leads to less computational overhead. The performance of proposed FMP is examined over several dense and complex 2-D and 3-D benchmark simulation scenarios, with results outperforming existing methods.
Dynamic array PIN:A novel approach to secure NFC electronic payment between ATM and smartphone

Near Field Communication (NFC) technology has been used recently for electronic payment between an Automated Teller Machine (ATM) and a Smartphone. It is threatened by several attacks that can steal the user personal data like the password or the Personal Identification Number (PIN). In this paper, we present Dynamic Array PIN (DAP), a novel approach for user authentication on a Smartphone that uses NFC electronic payment with an ATM. Our analysis and experimentation prove that this technique protects against thirteen different attacks and is cost-effective in terms of required hardware, authentication time, computing power and storage space.
Sky Optimization: Semantically aware image processing of skies in low-light photography

The sky is a major component of the appearance of a photograph, and its color and tone can strongly influence the mood of a picture. In nighttime photography, the sky can also suffer from noise and color artifacts. For this reason, there is a strong desire to process the sky in isolation from the rest of the scene to achieve an optimal look. In this work, we propose an automated method, which can run as a part of a camera pipeline, for creating accurate sky alpha-masks and using them to improve the appearance of the sky. Our method performs end-to-end sky optimization in less than half a second per image on a mobile device. We introduce a method for creating an accurate sky-mask dataset that is based on partially annotated images that are inpainted and refined by our modified weighted guided filter. We use this dataset to train a neural network for semantic sky segmentation. Due to the compute and power constraints of mobile devices, sky segmentation is performed at a low image resolution. Our modified weighted guided filter is used for edge-aware upsampling to resize the alpha-mask to a higher resolution. With this detailed mask we automatically apply post-processing steps to the sky in isolation, such as automatic spatially varying white-balance, brightness adjustments, contrast enhancement, and noise reduction.
A Multitask Learning Approach for Diacritic Restoration

In many languages like Arabic, diacritics are used to specify pronunciations as well as meanings. Such diacritics are often omitted in written text, increasing the number of possible pronunciations and meanings for a word. This results in a more ambiguous text making computational processing on such text more difficult. Diacritic restoration is the task of restoring missing diacritics in the written text. Most state-of-the-art diacritic restoration models are built on character level information which helps generalize the model to unseen data, but presumably lose useful information at the word level. Thus, to compensate for this loss, we investigate the use of multi-task learning to jointly optimize diacritic restoration with related NLP problems namely word segmentation, part-of-speech tagging, and syntactic diacritization. We use Arabic as a case study since it has sufficient data resources for tasks that we consider in our joint modeling. Our joint models significantly outperform the baselines and are comparable to the state-of-the-art models that are more complex relying on morphological analyzers and/or a lot more data (e.g. dialectal data).
Recipe Retrieval with Visual Query of Ingredients

Recipe retrieval is a representative and useful application of cross-modal information retrieval. Recent studies have proposed frameworks for retrieving images of cuisines given textual ingredient lists and instructions. However, the textual form of ingredients easily causes information loss or inaccurate description, especially for novices of cookery who are often the main users of recipe retrieval systems. In this paper, we revisit the task of recipe retrieval by taking images of ingredients as input queries, and retrieving cuisine images by incorporating visual information of ingredients through a deep convolutional neural network. We build an image-to-image recipe retrieval system to validate the effect of ingredient image queries. We further combine the proposed solution with a state-of-the-art cross-modal recipe retrieval model to improve the overall performance of the recipe retrieval task.
Smarttext: Learning To Generate Harmonious Textual Layout Over Natural Image

Automatic typography is important because it helps designers avoid highly repetitive tasks and amateur users achieve high-quality textual layout designs. However, there are often many parameters that need to be adjusted in automatic typography work. In this paper, we propose an efficient content-aware learning-based framework to generate harmonious textual layout over natural image. Our method incorporates both semantic features and visual perception principles. First, we combine a semantic visual saliency detection network with diffusion equations and a text-region proposal algorithm to generate candidate text anchors with various positions and sizes. Second, we develop a deep scoring network to assess the aesthetic quality of the candidate results. We design multiple evaluations to compare our method with several baselines and a commercial poster design tool. The results demonstrate that our method can generate harmonious textual layout in various actual scenarios with better performance.
Non-adaptive adaptive sampling on turnstile streams

Adaptive sampling is a useful algorithmic tool for data summarization problems in the classical centralized setting, where the entire dataset is available to the single processor performing the computation. Adaptive sampling repeatedly selects rows of an underlying matrix A∈ℝ n× d , where n≫ d, with probabilities proportional to their distances to the subspace of the previously selected rows. Intuitively, adaptive sampling seems to be limited to trivial multi-pass algorithms in the streaming model of computation due to its inherently sequential nature of assigning sampling probabilities to each row only after the previous iteration is completed. Surprisingly, we show this is not the case by giving the first one-pass algorithms for adaptive sampling on turnstile streams and using space poly(d,k,logn), where k is the number of adaptive sampling rounds to be performed.
Artificial Intelligence Fights Crime and Terrorism at a New Level

High expansion of urban population and infrastructure complemented by recent geopolitical world events, triggered an increasingly alarming number of threats. Law enforcement authorities are now challenged to redesign societal security concepts. Although current technological advances eased the information access, e.g., via video surveillance cameras, satellite data, drones, wearables, manual analysis of such big and diverse data to extract strategic knowledge is not a solution anymore. There is the critical need for automatic solutions. Artificial Intelligence (AI) and the breakthrough of deep neural networks opened a new perspective for providing such solutions with a human-grade accuracy. Here, we provide a snapshot of our AI research for counter terrorism on automatic person and object identification, retrieval of speech intelligence, and dissimulated behavior analysis. The research was carried out during the UEFISCDI SPIA-VA research project at CAMPUS Research Center, University “Politehnica” of Bucharest, with the participation of UTI Grup and of the Military Equipment and Technologies Research Agency (ACTTM), having as public beneficiary the Protection Guard Service, Romania (SPP).
Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open Problem

This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images.We consider the practical scenario where a high capacity server interacts with a myriad of resourcelimited edge devices, imposing strong requirements on scalability and local data privacy.We aim to address this challenge within the continual learning paradigm and provide a novel Dual User-Adaptation framework (DUA) to explore the problem.This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints.First, on the server, we introduce incremental learning of task-specific expert models, subsequently aggregated using a concealed unsupervised user prior.Aggregation avoids retraining, whereas the user prior conceals sensitive raw user data, and grants unsupervised adaptation.Second, local user-adaptation incorporates a domain adaptation point of view, adapting regularizing batch normalization parameters to the user data.We explore various empirical user configurations with different priors in categories and a tenfold of transforms for MIT Indoor Scene recognition, and classify numbers in a combined MNIST and SVHN setup.Extensive experiments yield promising results for data-driven local adaptation and elicit user priors for server adaptation to depend on the model rather than user data.Hence, although user-adaptation remains a challenging open problem, the DUA framework formalizes a principled foundation for personalizing both on server and user device, while maintaining privacy and scalability.
LSTPD: Least Slack Time-Based Preemptive Deadline Constraint Scheduler for Hadoop Clusters

Big data refers to numerous forms of complex and large datasets which need distinctive computational platforms in order to be analyzed.Hadoop is one of the popular frameworks for analytics of big data.In Hadoop, a big job is split into multiple small tasks and then they are distributed to worker nodes in a parallel way using MapReduce to speed up computational processes.In this aspect, it is important how to improve throughput performance.MapReduce jobs require quick responses from the worker nodes to complete them under their deadlines.The existing scheduling schemes for Hadoop such as FIFO, fair, and capacity schedulers cannot guarantee the quick response requirement satisfying a prior deadline.Thus, Hadoop system needs to improve response time and completion time for the heterogeneous MapReduce jobs.In this paper, we propose an efficient preemptive deadline constraint scheduler based on least slack time and data locality.In order for better allocation of tasks and load balancing, we first analyze the task scheduling behaviors of the Hadoop platform.Based on that, we propose a novel preemptive approach which considers the remaining execution time of the job being executed in deciding preemption.The experimental results show that the proposed scheme significantly reduces the job execution time and queue waiting time, compared to existing schemes.
Click-Through Rate Prediction of Online Banners Featuring Multimodal Analysis

As the online advertisement industry continues to grow, it is predicted that online advertisement will account for about 45% of global advertisement spending by 2020. a Thus, predicting the click-through rates (CTRs) of advertisements is increasingly crucial for the advertisement industry. Many studies have already addressed the CTR prediction. However, most studies tried to solve the problem using only metadata such as user id, URL of the landing page, business category, device type, etc., and did not include multimedia contents such as images or texts. Using these multimedia features with deep learning techniques, we propose a method to effectively predict CTRs for online banners, a popular form of online advertisements. We show that multimedia features of advertisements are useful for the task at hand. In our previous work [ 1 ], we proposed a CTR prediction model, which outperformed the state-of-the-art method that uses the three features mentioned above, and also we introduced an attention network for visualizing how much each feature affected the prediction result. In this work, we introduce another text analysis technique and more detailed metadata. As a result, we have achieved much better performance as compared to our previous work. Besides, for better analyzing of our model, we introduce another visualization technique to show regions in an image that make its CTR better or worse. Our prediction model gives us useful suggestions for improving design of advertisements to acquire higher CTRs.
Session-based Recommendation with Hierarchical Leaping Networks

Session-based recommendation aims to predict the next item that users will interact based solely on anonymous sessions. In real-life scenarios, the user's preferences are usually various, and distinguishing different preferences in the session is important. However previous studies focus mostly on the transition modeling between items, ignoring the mining of various user preferences. In this paper, we propose a Hierarchical Leaping Network (HLN) to explicitly model the users' multiple preferences by grouping items that share some relationships. We first design a Leap Recurrent Unit (LRU) which is capable of skipping preference-unrelated items and accepting knowledge of previously learned preferences. Then we introduce a Preference Manager (PM) to manage those learned preferences and produce an aggregated preference representation each time LRU reruns. The final output of PM which contains multiple preferences of the user is used to make recommendations. Experiments on two benchmark datasets demonstrate the effectiveness of HLN. Furthermore, the visualization of explicitly learned subsequences also confirms our idea.
Hardware-Aware Transformable Architecture Search with Efficient Search Space

While Neural Architecture Search (NAS) discovers the optimal topology structure, Transformable Architecture Search (TAS) aims to search for the best width and depth, which is more challenging due to the larger search space. Since FLOPs is inconsistent with the actual latency, hardware-aware TAS uses the inference latency to evaluate the efficiency. However, most existing work focuses on the search strategy, ignoring the critical role of the search space in affecting the actual efficiency. Motivated by it, we study hardware-aware TAS by considering the search space, to the best of our knowledge, for the first time. We propose a hardware-aware transformable architecture search (HTAS) framework to discover the optimal architecture for different hardware. The core of our method is a novel hardware-aware search space, which provides efficient channel choices for the search strategy to sample efficient architectures. Experiments on CIFAR datasets demonstrate the superiority of HTAS over the state-of-the-art method.
Enabling children to design for others with expanded proxy design

We explore the question of how child designers can provide design ideas for technology that might reduce the marginalisation that can be experienced by some of their peers. To do this, we introduce the idea of Expanded Proxy Design that moves beyond the notion of "proxies as people" in design, to guide methods for engaging children into thinking about design ideas for a group that exists at some distance from their own experience. We outline three case studies where we made use of such methods. First, we consider expanded proxies in the context of technology and newly immigrant children who are unable to speak in English. Second, we consider the case of designing technology for children with and without visual impairments. Finally, we consider designing playful experiences for children with different temperaments. We reflect on the extent to which this expanded notion of proxies can be used as a meaningful vehicle for overcoming marginalisation and exclusion when children with different abilities design for each other. And we suggest ways to characterise, develop and refine expanded proxy design methods in this broader sense.
Web-Supervised Network for Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) is a tough task due to its high annotation cost of the fine-grained subcategories. To build a large-scale dataset at low manual cost, straightforwardly learning from web images for FGVC has attracted broad attention. However, there exist two characteristics in the need of concerning for the web dataset: 1) Noisy images; 2) A large proportion of hard examples. In this paper, we propose a simple yet effective approach to deal with noisy images and hard examples during training. Our method is a pure web-supervised method for FGVC. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is much superior to the state-of-the-art web-supervised methods. The data and source code of this work have been posted available at: https://github.com/NUST-Machine-Intelligence-Laboratory/WSNFG.
Fantastic Answers and Where to Find Them: Immersive Question-Directed Visual Attention

While most visual attention studies focus on bottom-up attention with restricted field-of-view, real-life situations are filled with embodied vision tasks. The role of attention is more significant in the latter due to the information overload, and attention to the most important regions is critical to the success of tasks. The effects of visual attention on task performance in this context have also been widely ignored. This research addresses a number of challenges to bridge this research gap, on both the data and model aspects. Specifically, we introduce the first dataset of top-down attention in immersive scenes. The Immersive Question-directed Visual Attention (IQVA) dataset features visual attention and corresponding task performance (i.e., answer correctness). It consists of 975 questions and answers collected from people viewing 360° videos in a head-mounted display. Analyses of the data demonstrate a significant correlation between people's task performance and their eye movements, suggesting the role of attention in task performance. With that, a neural network is developed to encode the differences of correct and incorrect attention and jointly predict the two. The proposed attention model for the first time takes into account answer correctness, whose outputs naturally distinguish important regions from distractions. This study with new data and features may enable new tasks that leverage attention and answer correctness, and inspire new research that reveals the process behind decision making in performing various tasks.