Reviewerly/exHarmony huggingface dataset:
MetaPix: Few-Shot Video Retargeting

We address the task of unsupervised retargeting of human actions from one video to another. We consider the challenging setting where only a few frames of the target is available. The core of our approach is a conditional generative model that can transcode input skeletal poses (automatically extracted with an off-the-shelf pose estimator) to output target frames. However, it is challenging to build a universal transcoder because humans can appear wildly different due to clothing and background scene geometry. Instead, we learn to adapt – or personalize – a universal generator to the particular human and background in the target. To do so, we make use of meta-learning to discover effective strategies for on-the-fly personalization. One significant benefit of meta-learning is that the personalized transcoder naturally enforces temporal coherence across its generated frames; all frames contain consistent clothing and background geometry of the target. We experiment on in-the-wild internet videos and images and show our approach improves over widely-used baselines for the task.
Anytime Recognition with Routing Convolutional Networks

Achieving an automatic trade-off between accuracy and efficiency for a single deep neural network is highly desired in time-sensitive computer vision applications. To achieve anytime prediction, existing methods only embed fixed exits to neural networks and make the predictions with the fixed exits for all the samples (refer to the “latest-all” strategy). However, it is observed that the latest exit within a time budget does not always provide a more accurate prediction than the earlier exits for testing samples of various difficulties, making the “latest-all” strategy a sub-optimal solution. Motivated by this, we propose to improve the anytime prediction accuracy by allowing each sample to adaptively select its own optimal exit within a specific time budget. Specifically, we propose a new Routing Convolutional Network (RCN). For any given time budget, it adaptively selects the optimal layer as exit for a specific testing sample. To learn an optimal policy for sample routing, a Q-network is embedded into the RCN at each exit, considering both potential information gain and time-cost. To further boost the anytime prediction accuracy, the exits and the Q-networks are optimized alternately to mutually boost each other under the cost-sensitive environment. Apart from applying to whole image classification, RCN can also be adapted to dense prediction tasks, e.g., scene parsing, to achieve the pixel-level anytime prediction. Extensive experimental results on CIFAR-10, CIFAR-100, and ImageNet classification benchmarks, and Cityscapes scene parsing benchmark demonstrate the efficacy of the proposed RCN for anytime recognition.
Scene classification of high-resolution remote sensing images based on IMFNet

Currently, due to the limited amount of data and the difficulty of designing a network, there are few papers on constructing a new convolutional neural network for scene classification using the publicly available datasets of high-resolution remote sensing images. Considering the existing problems, the current scene classification methods of high-resolution remote sensing images are summarized, and the IMFNet model is constructed to classify scenes of high-resolution remote sensing images in this paper. The IMFNet is an end-to-end network, which can learn features from data automatically. The main characteristic of the IMFNet network structure is that the Inception module is used to extract the details of remote sensing images and the multifeature fusion strategy is proposed to ensure the integrity of information. In addition, optimization methods are adopted to improve the classification accuracy. In order to verify the effectiveness of the method proposed in this paper, the two benchmark datasets—the UC Merced dataset and the SIRI-WHU dataset were adopted for experiments. The classification accuracy of the two datasets reaches 92.14% and 90.43%, respectively. Experimental results show that the method proposed has certain advantages over the classification methods based on low-level and middle-level visual features and even some classification methods based on high-level visual features.
Autonomous UAV Navigation Using Reinforcement Learning

Over the last few years, UAV applications have grown immensely from delivery services to military use.Major goal of UAV applications is to be able to operate and implement various tasks without any human aid.To best of our knowledge, in the existing works for autonomous navigation for UAV's, ideal environments (e.g., 2D) are considered instead of realistic or special hardware are used (e.g., nine range sensors) to navigate through an ideal environment.Therefore, in this thesis, we aim to overcome the limitations of the existing works by proposing a model for navigating a drone in an unknown environment without any human help or aid.The goal of this research is to navigate from location A to location B in unknown terrain without having any prior knowledge about the terrain using default drone sensors only.We present a model which is compatible with almost every off-the-shelf drone available in the market.Our methodology utilizes only standard drone sensors which are attached to almost every drone.These include a camera, GPS, IMU, magnetometer, and barometer.Our methodology uses 3D, POMDP, and continuous environment.It also takes into account environmental factors such as winds and rain.Our main contributions are: 1) We are using realistic environment model including factors like rain and wind.2) We are only using onboard computing resources to run our model instead of some external server.3) We were able to fly the achieve complete autonomous flight using only standard drone sensors.
Boolean satisfiability in quantum compilation

Quantum compilation is the task of translating a quantum algorithm implemented in a high-level quantum programming language into a technology-dependent instructions flow for a physical quantum computer. To tackle the large gap between the quantum program and the low-level instructions, quantum compilation is split into a multi-stage flow consisting of several layers of abstraction. Several different individual tasks have been proposed for the layers in the flow, many of them are NP-hard. In this article, we will describe the flow and we will propose algorithms based on Boolean satisfiability, which is a good match to tackle such computationally complex problems. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.
MuffNet: Multi-Layer Feature Federation for Mobile Deep Learning

The increasing industrial demands to deploy deep neural networks on resource constrained mobile device motivates recent researches of efficient structure for deep learning. One popular approach is to densify network connectivity by sharing feature maps between layers. A side effect of this approach is that the volume of the feature maps and the convolution computation will exponentially blow up. In this work, we propose a novel structure, named Multi-Layer Feature Federation Network (MuffNet), to address this issue. The MuffNet is a densely connected network but consumes much less memory and computation at inference. The key idea of the MuffNet is to elaborately split the feature maps of one layer to different groups. Each feature map group is then shared only once with the other layer. In this way we maintain the network computation within bud-get while keeping the topology density of the network. On the theoretical side, we show that under the same computational budget, MuffNet is a better universal approximator for functions containing high frequency components. We validate the superiority of MuffNet on popular image classification and object detection benchmark datasets. The extensive experiments show that MuffNet is more efficient especially for small models under 45 MFLOPs.
The Awareness Behaviour of Students on Cyber Security Awareness by Using Social Media Platforms: A Case Study at Central University of Technology

The internet is not a secure place because of limited regulations. The unawareness of users about threats that can face them in cyberspace, can cause the successful execution of such threats. Users should establish a culture of awareness before entering the workforce. Therefore, academic institutions should engage in the process to enhance cyber security awareness (CSA) among students. In order to communicate effectively on CSA, the medium of communication should be familiar to the user and the user has to engage with this medium on a regular basis. Students at a higher academic institution reveal that they engage with social media platforms at least once a week with Facebook and YouTube the most popular. They also use communication media like websites to pursue material about CSA. This study found that there is a lack among students to engage with CSA initiatives that are available. It is suggested that academic institutions can contribute to the awareness of students by providing CSA material on a regular basis to them. Institutions can make use of social media platforms (Facebook and YouTube) and also communication mediums (institutional website and e-mails) to communicate CSA material with the students.
Experimental realization of state transfer by quantum walks with two coins

Quantum state transfer between different sites is a significant problem for quantum networks and quantum computers. By selecting quantum walks with two coins as the basic model and two coin spaces as the communication carriers, we successfully implement quantum state transfer on various graphs (EPL, \textbf{124} (2018) 60009) \cite{Shang_2019}. Here, we demonstrate the experimental implementation of this scheme using IBM quantum experience platform. In particular, we show the transfer of Bell state, GHZ state and W state on complete graph on the quantum device. Also, we observe that our protocol has high fidelity by preforming quantum state tomography.
Coverage path planning for a flock of aerial vehicles to support autonomous rovers through traversability analysis

In rough terrains, such as landslides or volcanic eruptions, it is extremely complex to plan safe trajectories for an Unmanned Ground Vehicle (UGV), since both robot stability and path execution feasibility must be guaranteed. In this paper, we present a complete solution for the autonomous navigation of ground vehicles in the mentioned scenarios. The proposed solution integrates three different aspects. The first is the coverage path planning for the definition of UAV trajectories for aerial imagery acquisition. The collected images are used for the photogrammetric reconstruction of the considered area. The second aspect is the adoption of a flock of UAVs to implement the coverage in a parallel way. In fact, when non-coverable zones are present, decomposition of the whole area to survey is performed. A solution to assign the different regions among the flying vehicles composing the team is presented. The last aspect is the path planning of the ground vehicle by means of a traversability analysis performed on the terrain 3D model. The computed paths are optimal in terms of the difficulty of moving across the rough terrain. The results of each step within the overall approach are shown.
Developing Universal Dependencies for Wolof

This paper presents work on the creation of a Universal Dependency (UD) treebank for Wolof as the first UD treebank within the Northern Atlantic branch of the Niger-Congo languages.The paper reports on various issues related to word segmentation for tokenization and the mapping of PoS tags, morphological features and dependency relations to existing conventions for annotating Wolof.It also outlines some specific constructions as a starting point for discussing several more general UD annotation guidelines, in particular for noun class marking, deixis encoding, and focus marking.
The Need for Trust and Ethics in the Digital Age – Sunshine and Shadows in the FinTech World

In the wake of the international financial crisis, digital financial and FinTech services have emerged as part of the fourth industrial revolution.These services, which are generally supported and welcomed by consumers, have now reached a stage where they are able to disrupt traditional financial structures.In the past, the role of FinTech startups was considered marginal and risk-free both by banks, which were busy restoring trust and optimising their operations, and by regulators and supervisors.However, as they have spread at a revolutionary pace, FinTech now entails several types of risk to financial stability and represents a serious threat.In order to address potential problems relating to financial stability, the need has emerged to regulate FinTech at the national and international levels, including the management of the body of data accumulated and used by FinTech companies.A situation has evolved in which three requirements should be met internationally and nationally: support for digital FinTech processes; a level playing field for incumbent banks and FinTech/BigTech companies; and the regulation of FinTechtype services at the international and national levels.Except for the first, these requirements have not been met to date, and expectations and codes of ethics and trust have gained prominence as workarounds.For that reason, the focus of our analysis is the importance of the requirements for trust and ethics in the banking sector, and the extent to which these requirements are enforced in the FinTech world.We present why it is becoming necessary to introduce codes of conduct, ethical standards and the exercise of due care in behaviour in order to build trust.
Bluethunder: A 2-level Directional Predictor Based Side-Channel Attack against SGX

Software Guard Extension (SGX) is a hardware-based trusted execution environment (TEE) implemented in recent Intel commodity processors. By isolating the memory of security-critical applications from untrusted software, this mechanism provides users with a strongly shielded environment called enclave for executing programs safely. However, recent studies have demonstrated that SGX enclaves are vulnerable to side-channel attacks. In order to deal with these attacks, several protection techniques have been studied and utilized.In this paper, we explore a new pattern history table (PHT) based side-channel attack against SGX named Bluethunder, which can bypass existing protection techniques and reveal the secret information inside an enclave. Comparing to existing PHT-based attacks (such as Branchscope [ERAG+18]), Bluethunder abuses the 2-level directional predictor in the branch prediction unit, on top of which we develop an exploitation methodology to disclose the input-dependent control flow in an enclave. Since the cost of training the 2-level predictor is pretty low, Bluethunder can achieve a high bandwidth during the attack. We evaluate our attacks on two case studies: extracting the format string information in the vfprintf function in the Intel SGX SDK and attacking the implementation of RSA decryption algorithm in mbed TLS. Both attacks show that Bluethunder can recover fine-grained information inside an enclave with low training overhead, which outperforms the latest PHT-based side channel attack (Branchscope) by 52×. Specifically, in the second attack, Bluethunder can recover the RSA private key with 96.76% accuracy in a single run.
Structural Decompositions of Epistemic Logic Programs

Epistemic logic programs (ELPs) are a popular generalization of standard Answer Set Programming (ASP) providing means for reasoning over answer sets within the language. This richer formalism comes at the price of higher computational complexity reaching up to the fourth level of the polynomial hierarchy. However, in contrast to standard ASP, dedicated investigations towards tractability have not been undertaken yet. In this paper, we give first results in this direction and show that central ELP problems can be solved in linear time for ELPs exhibiting structural properties in terms of bounded treewidth. We also provide a full dynamic programming algorithm that adheres to these bounds. Finally, we show that applying treewidth to a novel dependency structure—given in terms of epistemic literals—allows to bound the number of ASP solver calls in typical ELP solving procedures.
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees

Algorithms typically come with tunable parameters that have a considerable impact on the computational resources they consume. Too often, practitioners must hand-tune the parameters, a tedious and error-prone task. A recent line of research provides algorithms that return nearly-optimal parameters from within a finite set. These algorithms can be used when the parameter space is infinite by providing as input a random sample of parameters. This data-independent discretization, however, might miss pockets of nearly-optimal parameters: prior research has presented scenarios where the only viable parameters lie within an arbitrarily small region. We provide an algorithm that learns a finite set of promising parameters from within an infinite set. Our algorithm can help compile a configuration portfolio, or it can be used to select the input to a configuration algorithm for finite parameter spaces. Our approach applies to any configuration problem that satisfies a simple yet ubiquitous structure: the algorithm's performance is a piecewise constant function of its parameters. Prior research has exhibited this structure in domains from integer programming to clustering.
Multi-View Consistency for Relation Extraction via Mutual Information and Structure Prediction

Relation Extraction (RE) is one of the fundamental tasks in Information Extraction. The goal of this task is to find the semantic relations between entity mentions in text. It has been shown in many previous work that the structure of the sentences (i.e., dependency trees) can provide important information/features for the RE models. However, the common limitation of the previous work on RE is the reliance on some external parsers to obtain the syntactic trees for the sentence structures. On the one hand, it is not guaranteed that the independent external parsers can offer the optimal sentence structures for RE and the customized structures for RE might help to further improve the performance. On the other hand, the quality of the external parsers might suffer when applied to different domains, thus also affecting the performance of the RE models on such domains. In order to overcome this issue, we introduce a novel method for RE that simultaneously induces the structures and predicts the relations for the input sentences, thus avoiding the external parsers and potentially leading to better sentence structures for RE. Our general strategy to learn the RE-specific structures is to apply two different methods to infer the structures for the input sentences (i.e., two views). We then introduce several mechanisms to encourage the structure and semantic consistencies between these two views so the effective structure and semantic representations for RE can emerge. We perform extensive experiments on the ACE 2005 and SemEval 2010 datasets to demonstrate the advantages of the proposed method, leading to the state-of-the-art performance on such datasets.
A Dataset for Low-Resource Stylized Sequence-to-Sequence Generation

Low-resource stylized sequence-to-sequence (S2S) generation is in high demand. However, its development is hindered by the datasets which have limitations on scale and automatic evaluation methods. We construct two large-scale, multiple-reference datasets for low-resource stylized S2S, the Machine Translation Formality Corpus (MTFC) that is easy to evaluate and the Twitter Conversation Formality Corpus (TCFC) that tackles an important problem in chatbots. These datasets contain context to source style parallel data, source style to target parallel data, and non-parallel sentences in the target style to enable the semi-supervised learning. We provide three baselines, the pivot-based method, the teacher-student method, and the back-translation method. We find that the pivot-based method is the worst, and the other two methods achieve the best score on different metrics.
Filling Conversation Ellipsis for Better Social Dialog Understanding

The phenomenon of ellipsis is prevalent in social conversations. Ellipsis increases the difficulty of a series of downstream language understanding tasks, such as dialog act prediction and semantic role labeling. We propose to resolve ellipsis through automatic sentence completion to improve language understanding. However, automatic ellipsis completion can result in output which does not accurately reflect user intent. To address this issue, we propose a method which considers both the original utterance that has ellipsis and the automatically completed utterance in dialog act and semantic role labeling tasks. Specifically, we first complete user utterances to resolve ellipsis using an end-to-end pointer network model. We then train a prediction model using both utterances containing ellipsis and our automatically completed utterances. Finally, we combine the prediction results from these two utterances using a selection model that is guided by expert knowledge. Our approach improves dialog act prediction and semantic role labeling by 1.3% and 2.5% in F1 score respectively in social conversations. We also present an open-domain human-machine conversation dataset with manually completed user utterances and annotated semantic role labeling after manual completion.
Sentiment Classification based on Linguistic Patterns in Citation Context

When citations occur, authors tend to express their emotions implicitly, which makes it difficult to identify the sentiment of citation context.However, authors will still use specific linguistic patterns to express their emotions in citation context.This article explores the linguistic patterns of emotional expression in citation context, and on this basis recognizes the sentimental polarity of citation context.Conditional random fields (CRF) model is introduced to annotate the logical relationship between syntactic structure and vocabularies in linguistic patterns.By analysing the effect of the generated CRF templates in classifying the subjective/objective sentences and the positive/negative emotional polarity in citation context, the role of linguistic patterns in classifying the citation sentiment is discussed.Experimental results show that the CRF model based on linguistic patterns is superior to the commonly used support vector machine (SVM) model in both subjective/objective and emotional polarity classification tasks.In the SVM model, the contextual information of citation context is considered by introducing one deep learning model of Word2vec.It shows that extracting linguistic patterns from the citation context helps reflect the way in which an author organizes his/her language in expressing his/her emotions.Extracting these linguistic patterns helps improve the performance of sentiment classification of citation context.
Differentially Private Learning with Small Public Data

Differentially private learning tackles tasks where the data are private and the learning process is subject to differential privacy requirements. In real applications, however, some public data are generally available in addition to private data, and it is interesting to consider how to exploit them. In this paper, we study a common situation where a small amount of public data can be used when solving the Empirical Risk Minimization problem over a private database. Specifically, we propose Private-Public Stochastic Gradient Descent, which utilizes such public information to adjust parameters in differentially private stochastic gradient descent and fine-tunes the final result with model reuse. Our method keeps differential privacy for the private database, and empirical study validates its superiority compared with existing approaches.