Miracle-dz/UniFAR-FTUs huggingface dataset:
Providing Security and Integrity of Public Data with Proficient User Revocation in the Cloud

The straightforward method, which allows an existing user to download the corresponding part of shared data and re-sign it during user revocation, is inefficient due to the large size of shared data in the cloud. In this paper, we propose the integrity of shared data with efficient user revocation in mind. By utilizing the idea of proxy re-signatures, we allow the cloud to re-sign blocks on behalf of existing users during user revocation, so that existing users do not need to download and re-sign blocks by themselves. In addition, a public verifier is always able to audit the integrity of shared data without retrieving the entire data from the cloud.
On Tree-structured Multi-stage Principal Component Analysis (TMPCA) for Text Classification

A novel sequence-to-vector (seq2vec) embedding method, called the tree-structured multi-stage principal component analysis (TMPCA), is proposed for the text classification problem in this paper. Unlike conventional word-to-vector embedding methods, the TMPCA method conducts dimension reduction at the sequence level without labeled training data. Furthermore, it can preserve the sequential structure of input sequences. We show that TMPCA is computationally efficient and able to facilitate sequence-based text classification tasks by preserving strong mutual information between its input and output mathematically. It is also demonstrated by experimental results that a dense (fully connected) network trained on the TMPCA preprocessed data achieves better performance than state-of-the-art fastText and other neural-network-based solutions.
Learned Experts' Assessment-based Reconstruction Network ("LEARN") for Sparse-data CT

Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view CT, tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly use regularizers in the CS framework. Currently, how to choose the parameters adaptively for regularization is a major open problem. In this paper, inspired by the idea of machine learning especially deep learning, we unfold a state-of-the-art"fields of experts"based iterative reconstruction scheme up to a number of iterations for data-driven training, construct a Learned Experts' Assessment-based Reconstruction Network ("LEARN") for sparse-data CT, and demonstrate the feasibility and merits of our LEARN network. The experimental results with our proposed LEARN network produces a competitive performance with the well-known Mayo Clinic Low-Dose Challenge Dataset relative to several state-of-the-art methods, in terms of artifact reduction, feature preservation, and computational speed. This is consistent to our insight that because all the regularization terms and parameters used in the iterative reconstruction are now learned from the training data, our LEARN network utilizes application-oriented knowledge more effectively and recovers underlying images more favorably than competing algorithms. Also, the number of layers in the LEARN network is only 12, reducing the computational complexity of typical iterative algorithms by orders of magnitude.
H.264 and MPEG-4 Video Compression: Video Coding for Next-Generation Multimedia

About the Author.Foreword. Preface.Glossary. 1. Introduction.2. Video Formats and Quality.3. Video Coding Concepts.4. The MPEG-4 and H.264 Standards.5. MPEG-4 Visual.6. H.264/MPEG-4 Part 10.7. Design and Performance.8. Applications and Directions.Bibliography. Index.
Synthesis of a Predictive Push-Recovery Controller: Simulation Results on the iCub Humanoid Robot

When balancing, a humanoid robot can be easily subjected to unexpected disturbances due to external pushes or modeling errors. In these circumstances, reactive movements become a necessary requirement in order to avoid potentially harmful falling states. In our previous work, we designed and implemented on the real platform, a strategy based on simple models. In this paper we conceive a Model Predictive Controller which enables the prediction of future evolutions of the robot, taking into account constraints switching when performing a step. The control inputs computed by this strategy, namely the desired contact wrenches, are directly obtained on the robot through the momentum-based whole-body torque controller implemented on iCub. The proposed approach is validated through simulations, revealing higher robustness and reliability when executing the recovery strategy.
Rx-Caffe: Framework for evaluating and training Deep Neural Networks on Resistive Crossbars

Deep Neural Networks (DNNs) are widely used to perform machine learning tasks in speech, image, video, and natural language processing. The high computation and storage demands of DNNs have led to a need for the energy-efficient implementations. Resistive crossbar systems have emerged as promising candidates due to their ability to compactly and efficiently realize the primitive DNN operation, viz., vectormatrix multiplication. However, in practice, the functionality of resistive crossbars may deviate considerably from the ideal abstraction due to the device and circuit level non-idealities such as driver resistance, sensing resistance, sneak paths, interconnect parasitics, non-linearities in the peripheral circuits, imperfect write operations, and process variations. Although DNNs are somewhat tolerant to errors in their computations, it is still essential to evaluate the impact of the errors introduced due to crossbar non-idealities on DNN accuracy. Unfortunately, device and circuit-level models are not feasible to use in the context of large-scale DNNs with 1.9-13.8 million neurons and 2.6-15.5 billion synaptic connections. In this work, we present a fast and accurate simulation framework to enable training and evaluation of large-scale DNNs on resistive crossbar based hardware fabrics. We first characterize the impact of non-idealities in resistive crossbars on the errors incurred in the realized vector-matrix multiplication operation. We show that the errors have significant data-dependence, i.e., they depend on the applied inputs and weights programmed into the crossbars. We propose a Fast Crossbar Model (FCM) that accurately captures the errors arising due to non-idealities while being four-to-five orders of magnitude faster than circuit simulation. Finally, we develop Rx-Caffe, an enhanced version of the popular Caffe machine learning software framework to train and evaluate DNNs on resistive crossbar systems. We used RxCaffe to evaluate a suite of large-scale DNNs (ResNet, GoogleNet, OverFeat, VGG, Network-in-Network, and AlexNet) developed for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Our experiments reveal that resistive crossbar nonidealities can lead to significant accuracy degradations of 9.6%32% for these large-scale DNNs. To the best of our knowledge, this work is the first evaluation of the accuracy of large-scale DNNs on resistive crossbar based hardware and highlights the need for further efforts to address the impact of non-idealities.
Why Johnny Can't Develop Mobile Crowdsourcing Applications with Location Privacy

Mobile crowdsourcing (MCS) relies on users’ devices as sensors to perform geo-located data collection. Crowdsourcing enables application developers to benefit from large and diverse datasets at a low cost, making MCS extremely popular. The collection of geo-located data, however, raises serious privacy concerns for users. Yet, despite the large research body on location privacy-preserving mechanisms (LPPMs), MCS developers implement little to no protection for data collection or publication. In this paper we investigate the reason behind this gap between research and reality. We study the performance of existing LPPMs on publicly available data from two mobile crowdsourcing projects. Our results show that well-established defenses, designed with location-based services (LBSs) in mind, are either not applicable or only offer some protection in MCS. Furthermore, they have a strong impact on the utility of the applications. This is because these LPPMs are optimized for utility functions based on users’ locations, while MCS utility functions depend on the values (e.g., measurements) associated with those locations. We hope that this work raises awareness in the community about an overlooked problem such that soon we have new LPPMs that can help MCS developers to protect the privacy of their users.
Making Concurrent Algorithms Detectable

Non-volatile memory (NVM) promises persistent main memory that remains correct despite loss of power. This has sparked a line of research into algorithms that can recover from a system crash. Since caches are expected to remain volatile, concurrent data structures must be redesigned to guarantee that they are left in a consistent state after a system crash (durability), and that the execution can be continued upon recovery (detectability). However, the prospect of redesigning every concurrent data structure before it can be used in NVM architectures is daunting. In this paper, we give a general construction to convert any concurrent algorithm into one that is both durable and detectable. This construction is easy to apply to any algorithm, not requiring any understanding of the algorithm's semantics, and guarantees that the algorithm's structure and correctness are preserved. We also provide an optimized transformation that works for any normalized lock-free data structure, thus allowing more efficient durable and detectable constructions for a large class of algorithms. Lastly, we present the first formal definition of detectability, and show that any detectable algorithm can be safely used inside our transformations.
Partially Observable Reinforcement Learning for Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as `Partially Detected Intelligent Transportation Systems'. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.
Deep Extrofitting: Specialization and Generalization of Expansional Retrofitting Word Vectors using Semantic Lexicons

The retrofitting techniques, which inject external resources into word representations, have compensated the weakness of distributed representations in semantic and relational knowledge between words. Implicitly retrofitting word vectors by expansional technique showed that the method outperforms retrofitting in word similarity task with generalization. In this paper, we propose deep extrofitting: in-depth stacking of extrofitting. We first stack extrofitting for word vector generalization. Next, we combine extrofitting with retrofitting, finding new vector space on specialization that prevents retrofitting from converging in a few iterations. When experimenting with GloVe, we show that our methods outperform the previous methods on most of word similarity task while requiring only synonyms as external resources. We also report further analysis on the effect of word vector specialization and word vector generalization in text classification task.
Multitask Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies

We introduce a new RL problem where the agent is required to execute a given subtask graph which describes a set of subtasks and their dependency. Unlike existing approaches that explicitly describe what the agent should do, our problem only describes properties of subtasks and relationships among them, which requires the agent to perform a complex reasoning to find the optimal subtask to execute. To solve this problem, we propose a neural subtask graph solver (NSS) which encodes the subtask graph using a recursive neural network. To overcome the difficulty of training, we propose a novel non-parametric gradient-based policy to pre-train our NSS agent and further finetune it through actor-critic method. The experimental results on two 2D visual domains show that our agent can perform a complex reasoning to find the optimal way of executing the subtask graph and generalize well to the unseen subtask graphs. In addition, we compare our agent with a Monte-Carlo tree search (MCTS) method showing that our method is much more efficient than MCTS, and the performance of NSS can be further improved by combining it with MCTS.
CT-GAN: Conditional Transformation Generative Adversarial Network for Image Attribute Modification

We propose a novel, fully-convolutional conditional generative model capable of learning image transformations using a light-weight network suited for real-time applications. We introduce the conditional transformation unit (CTU) designed to produce specified attribute modifications and an adaptive discriminator used to stabilize the learning procedure. We show that the network is capable of accurately modeling several discrete modifications simultaneously and can produce seamless continuous attribute modification via piece-wise interpolation. We also propose a task-divided decoder that incorporates a refinement map, designed to improve the network’s coarse pixel estimation, along with RGB color balance parameters. We exceed state-of-the-art results on synthetic face and chair datasets and demonstrate the model’s robustness using real hand pose datasets. Moreover, the proposed fully-convolutional model requires significantly fewer weights than conventional alternatives and is shown to provide an effective framework for producing a diverse range of real-time image attribute modifications.
The Influence of Rapid Serial Visual Presentation on Performance in a Video Game

The military has investigated the use of hand held devices and video games for training and mission planning. The purpose of this study was to continue previous research investigating the influence of rapid serial information presentation (RSVP) of intelligence reporting on task speed and reading comprehension. Fifty participants moved through a computer game to find a target as quickly as possible based on intelligence reports. There were two presentation styles (RSVP or traditional) and two intelligence formats (content words or full sentences). There were no between group differences in terms of search task performance within the game. Individuals scored higher on reading comprehension tests in the traditional text presentation group when shown content words and individuals in the traditional text presentation group when shown full sentences. We are conducting further analyses to address specific differences in performances and reading comprehension between groups. The results of this study suggest researchers and trainers should continue to investigate rapid serial information presentation to determine if it can be employed during training for multiple types of tasks.
Cyber-Physical Microservices and IoT-based Framework: The case of Evolvable Assembly Systems

The manufacturing industry is facing the challenge to address the individual requirements of customers that lead to increased product variety and volume reduction. Evolvable assembly systems (ASs) have been defined during the past years to enable quick adaptation of manufacturing assets to respond to the evolving market requirements. Meanwhile, the manufacturing era is changing due to the fourth industrial revolution, i.e., Industry 4.0, that will change the traditional manufacturing environment to an IoT-based one. In this context, this paper presents a cyber-physical microservices and IoT-based framework for ASs with the intention to exploit the benefits of the microservices and the IoT technologies but also to utilize the existing huge investment based on traditional technologies in this domain. The framework provides a solid basis for the establishment of a common vocabulary for assembly system experts and IoT experts, as well as to capture domain knowledge that is exploited by a model-driven engineering approach to semi-automate the development and operation of ASs. A case study for the assembly of an everyday life product was adopted to demonstrate the approach even to nonexperts of this domain.
Human Contour Extraction Using Level Set

Human contour information is important for human activity understanding and human motion capture. Traditional human contour extraction methods are based on image morphological operation, but they fail when the contour information is dim and discontinuous. In this paper, a novel method using level set technique for human contour extraction in video frames with still background is proposed. Firstly the human profile is detected via image difference with background. After appropriate thresholding and morphological operation, the possible human profile region is then found out according to the image projection using the detected region boundary as the initial evolving curve, the accurate human contour is extracted using a modified level set method, experiments show that this method is faster and more efficient than traditional level set methods
Improving Frame Semantic Parsing with Hierarchical Dialogue Encoders

Conversational Language Understanding (CLU) is a key component of goal oriented dialogue systems that would parse user utterances into semantic frame representations. Traditionally CLU does not utilize the dialogue history beyond the previous system turn and contextual ambiguities are resolved by the downstream components. In this paper, we explore novel approaches for modeling dialogue context in a recurrent neural network (RNN) based language understanding system. We propose the Hierarchical Dialogue Encoder Network, that allows encoding context from the dialogue history in chronological order. We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history. Experiments with a multi-domain dialogue dataset demonstrate that the proposed architecture results in reduced semantic frame error rates.
Worst-Case Coalitions in Routing Games

Recently, there has been a growing interest in network scenarios where selfish agents are able to communicate, bargain and form coalitions, i.e., play a cooperative game, in order to overcome the inefficiency of noncooperative equilibria. We study such coalitional games under worst-case conditions in the fundamental load-balancing setting of routing over parallel links. Specifically, we investigate fundamental solution concepts of the considered cooperative game, namely the (Inner) Core and the min-max fair Nucleolus. Most notably we prove that under certain conditions they contain the system optimum.
Online Dictionary Matching with One Gap

The Online Dictionary Matching with Gaps Problem (DMG), is the following. Preprocess a dictionary $D$ of $d$ gapped patterns $P_1,\ldots,P_d$ such that given a query online text $T$ presented one character at a time, each time a character arrives we must report the subset of patterns that have a match ending at this character. A gapped pattern $P_i$ is of the form $P_{i,1}\{\alpha_i,\beta_i\}P_{i,2}$, where $P_{i,1},P_{i,2}\in \Sigma^*$ and $\{\alpha_i,\beta_i\}$ matches any substring with length between $\alpha_i$ and $\beta_i$. Finding efficient solutions for DMG has proven to be a difficult algorithmic challenge. Little progress has been made on this problem and to date, no truly efficient solutions are known. In this paper, we give a new DMG algorithm and provide compelling evidence that no substantially faster algorithm exists, based on the Integer3SUM conjecture.
On the Robustness of Deep Convolutional Neural Networks for Music Classification

Deep neural networks (DNN) have been successfully applied for music classification including music tagging. However, there are several open questions regarding generalisation and best practices in the choice of network architectures, hyperparameters and input representations. In this article, we investigate specific aspects of neural networks to deepen our understanding of their properties. We analyse and (re-)validate a large music tagging dataset to investigate the reliability of training and evaluation. We perform comprehensive experiments involving audio preprocessing using different time-frequency representations, logarithmic magnitude compression, frequency weighting and scaling. Using a trained network, we compute label vector similarities which is compared to groundtruth similarity. The results highlight several import aspects of music tagging and neural networks. We show that networks can be effective despite of relatively large error rates in groundtruth datasets. We subsequently show that many commonly used input preprocessing techniques are redundant except magnitude compression. Lastly, the analysis of our trained network provides valuable insight into the relationships between music tags. These results highlight the benefit of using data-driven methods to address automatic music tagging.
Developing an edge analytics platform for analyzing real-time transit data streams

The Internet of Mobile Things encompasses stream data being generated by sensors, network communications that pull and push these data streams, as well as running processing and analytics that can effectively leverage actionable information for planning, management, and business advantage. Edge computing emerges as a new paradigm that decentralizes the communication, computation, control and storage resources from the cloud to the edge of the Internet. This paper proposes an edge computing platform where mobile fog nodes are physical devices where descriptive analytics is deployed to analyze real-time transit data streams. An application experiment is used to evaluate the advantages and disadvantages of our proposed platform to run descriptive analytics at the mobile fog node and support transit managers with actionable information.