Reviewerly/exHarmony huggingface dataset:
Loop-Net: Joint Unsupervised Disparity and Optical Flow Estimation of Stereo Videos With Spatiotemporal Loop Consistency

Most of existing deep learning-based depth and optical flow estimation methods require the supervision of a lot of ground truth data, and hardly generalize to video frames, resulting in temporal inconsistency. In this letter, we propose a joint framework that estimates disparity and optical flow of stereo videos and generalizes across various video frames by considering the spatiotemporal relation between the disparity and flow without supervision. To improve both accuracy and consistency, we propose a loop consistency loss which enforces the spatiotemporal consistency of the estimated disparity and optical flow. Furthermore, we introduce a video-based training scheme using the c-LSTM to reinforce the temporal consistency. Extensive experiments show our proposed methods not only estimate disparity and optical flow accurately but also further improve spatiotemporal consistency. Our framework outperforms the state-of-the-art unsupervised depth and optical flow estimation models on the KITTI benchmark dataset.
Rotated Faster R-CNN for Oriented Object Detection in Aerial Images

Object detection have been widely used in the field of remote sensing. Different from natural scene images, the aerial images acquired by satellite and UAV are taken from birdview perspective. Common object detection algorithms suffer from the poor performance of detecting oriented targets. In this paper, we propose a Rotated Faster R-CNN to detect arbitrary oriented ground targets. On the basis of Faster R-CNN, we add a regression branch to predict the oriented bounding boxes for ground targets. Instead of removing the branch of predicting the horizontal bounding boxes, we train both two branches as a multi-task problem to improve the accuracy of our algorithms. And balanced FPN is used to improve the performance of detecting small targets in high resolution aerial images. We conduct experiments on DOTA dataset. Our methods could achieve competitive results of mAP 74.56 and FPS 13.0. The experiments prove that our algorithms show better results than previous algorithms in terms of accuracy.
Development and application of an electronic treatment register: a system for enumerating populations and monitoring treatment during mass drug administration

We developed an electronic treatment register for the DeWorm3 Project, a cluster-randomised, controlled trial in Benin, India, and Malawi testing the feasibility of interrupting transmission of soil-transmitted helminths through community-wide mass drug administration. The electronic treatment register was designed in xlsform, deployed via the SurveyCTO mobile data collection platform, and implemented on smartphones running the Android operating system. The versatile system enables collection of census and treatment status information, facilitates data aggregation and visualisation, and permits real-time feedback loops during implementation of mass drug administration. Here we describe the system’s design and use within the DeWorm3 Project and key features, and by sharing the register here, we hope our readers will further explore its use within their research and disease-control activities.
High capacity reversible data hiding in encrypted image based on image encoding and POB

Reversible data-hiding in encrypted image (RDH-EI) can be used for protection of original image by image owner; data hider can embed secret data in the encrypted image without necessity of knowing the original image content. In order to enhance the capacity of embedding in encrypted image, a novel RDH-EI framework based on JPEG-LS and permutation ordered binary (POB) number system is proposed. In the proposed scheme, the original image is first encoded by JPEG-LS, and then the encoded image is encrypted by bit planes. In the phase of data hiding, the encrypted image is first shuffled, and then non-encoded encrypted pixels are totally replaced by the secret data. Lastly, the above generated image is divided into some non-overlapping 2 × 2 blocks, the block feature of 3-bits which is calculated from the pixels within the block and 5-bits secret data are embedded into the every pixel of the block, and then the pixels with 10-bits data in the block are transformed into the POB value. Experimental results show that the proposed scheme obtains the higher embedding rate compared with some of the state-of-the-art schemes, the weak robustness and performance of verification for tampered parts in the marked image are also achieved for the proposed scheme.
A semi-explicit short text retrieval method combining Wikipedia features

With the advantages such as openness, interactivity, immediacy, and simplicity, the large number of short text data appear in the Web information space. Considering the short length, little information, sparse features and irregular grammar, the traditional information analyzing and retrieval technologies cannot deal with short text effectively. In view of the above problems, in this paper a new short text retrieval method based on the current mainstream semantic knowledge source, Wikipedia, is proposed. To be specific, a semantic feature selection algorithm is proposed to return the top k most relevant Wikipedia concepts as the whole vector space for a given short text. Thus, by analyzing the topic information of the semantic features contained in Wikipedia concepts, we propose some formulas to determine the association coefficient list between different components of the corresponding positions in two different feature vectors. On this basis, a new semantic relatedness assessment method under this lower dimensional semantic space is designed. According to computing and sorting the semantic relatedness between user queries and the target short text, a novel semi-explicit short text retrieval method combining Wikipedia concept feature and the corresponding topic information is proposed. Lastly, based on the experimental results on twitter subsets, we verify that our proposal has advantages over other some current retrieval methods on MAP, [email protected] and R-Prec, and can return more valid results.
Fast Memory-efficient Extreme Events Prediction in Complex Time series

This paper proposes a generic memory-efficient framework for realtime stochastic extreme events prediction in complex time series systems such as intrusion detection, Internet of Things (IoT), social networks, stock markets etc. Ideally we exploit the expressiveness of deep neural networks and temporal nature of sequence-to-sequence structures (parallel Convolutional and recurrent neural networks) glued on Convolutional Quantile Loss and memory network to model explicitly extreme events. Convolutional Quantile Loss is used to predict future extreme events, while memory network is used to memorize extreme events in future observations. We show that the approach can capture long and short-term temporal effects as well as other non-linear dynamic patterns across multiple probabilistic time series with reliable principled uncertainty estimates. We demonstrate and validate empirically the effectiveness of the proposed framework via extensive experiments and rigorous evaluation on large-scale real world datasets. The experimental results showcase that the proposed method is fast, robust, accurate and has superior performance compared to the well-known prediction methods.
Extended partial key exposure attacks on RSA: Improvement up to full size decryption exponents

Partial key exposure attacks on RSA have been intensively studied by using lattice-based Coppersmith's methods. Ernst et al. (Eurocrypt'05) studied the problem by considering three attack scenarios; (1) the most significant bits (MSBs) of a secret exponent d known, (2) the least significant bits (LSBs) of d known, (3) both the MSBs and the LSBs of d known. The proposed attacks were valuable since they were the first results to handle full size exponents e. Takayasu and Kunihiro (SAC'14, Theoretical Computer Science'19) proposed improved attacks for (1) and (2) when d is sufficiently small, i.e., d<N0.5625 for (1) and d<N0.368 for (2), by utilizing a linearization technique. In this paper, we extend Takayasu-Kunihiro's attacks and improve Ernst et al.'s attack for (3). In particular, our attack contains Takayasu-Kunihiro's attacks for (1) and (2) as special cases when the amount of given LSBs and MSBs are zero, respectively. Furthermore, as opposed to Takayasu-Kunihiro's attacks, our improvement against Ernst et al.'s attack is not limited to small secret exponents such as d<N0.5625. Indeed, we are able to improve Ernst et al.'s attack almost up to full size decryption exponents, i.e., even when d is close to N. Technically, the extension is not straightforward. We first modify Takayasu-Kunihiro's lattice basis matrix for (2), so that it is compatible to embed the given MSBs. The modification is crucial for embedding both the MSBs and the LSBs simultaneously to the matrix.
NESTORE

Conversational agents are becoming widespread thanks to the development of natural language processing and deep learning techniques. NESTORE is a virtual coach designed to support seniors during their journey to wellbeing. This virtual coach interacts with the user through two different type of conversational agents: a text-based chatbot integrated in a mobile application and a tangible coach, which is a physical object based on vocal interaction and tangible interaction. This paper presents the design of each conversational agent interface developed to support seniors in enhancing their wellbeing. A cloud conversational agent manages the user interaction with the NESTORE virtual coach, which can occur through interfaces with different design, forms and capabilities, i.e., the chatbot and the tangible coach: two different intelligent interfaces with a shared mind.
RAHM: Relation augmented hierarchical multi-task learning framework for reasonable medication stocking

As an important task in digital preventive healthcare management, especially in the secondary prevention stage, active medication stocking refers to the process of preparing necessary medications in advance according to the predicted disease progression of patients. However, predicting preventive or even life-saving medicine for each patient is a non-trivial task. Existing models usually overlook the implicit hierarchical relation between patient’s predicted diseases and medications, and mainly focus on single tasks (medication recommendation or disease prediction). To tackle this limitation, we propose a relation augmented hierarchical multi-task learning framework, named RAHM. which is capable of learning multi-level relation-aware patient representation for reasonable medication stocking. Specifically, the framework first leverages the underlying structural relations of Electronic Health Record (EHR) data to learn the low-level patient visit representation. Then, it uses a regular LSTM to encode the historical temporal disease information for disease-level patient representation learning. Further, a relation-aware LSTM (R-LSTM) is proposed to handle the relations between diseases and medication in longitudinal patient records, which can better integrate the historical information into the medication-level patient representation. In the learning process, two pseudo residual structures are introduced to mitigate the error propagation and preserve the valuable relation information of EHRs. To validate our method, extensive experiments have been conducted based on the real-world clinical dataset. The results demonstrate a consistent superiority of our framework over several baselines in suggesting reasonable stock medication.
Token-Based Platforms and Speculators

In a dynamic model of cryptocurrencies and tokens, users hold tokens for transactions and speculators hold tokens for returns. Speculation is procyclical, i.e., high in a bull and low in a bear market. Speculators affect users via two opposing effects, adverse crowding-out and benign liquidity provision, and their token investments are static substitutes but dynamic complements. A dual token structure with a governance token and stablecoin attenuates the crowding-out effect, harnesses speculator sentiment, and stimulates adoption. The model has both empirical implications regarding cryptocurrency usage and speculation, and normative implications for the optimal design of token-based or decentralized finance platforms.
A Locality Sensitive Hashing Based Approach for Federated Recommender System

The recommender system is an important application in big data analytics because accurate recommendation items or high-valued suggestions can bring high profit to both commercial companies and customers. To make precise recommendations, a recommender system often needs large and fine-grained data for training. In the current big data era, data often exist in the form of isolated islands, and it is difficult to integrate the data scattered due to privacy security concerns. Moreover, privacy laws and regulations make it harder to share data. Therefore, designing a privacy-preserving recommender system is of paramount importance. Existing privacy-preserving recommender system models mainly adapt cryptography approaches to achieve privacy preservation. However, cryptography approaches have heavy overhead when performing encryption and decryption operations and they lack a good level of flexibility. In this paper, we propose a Locality Sensitive Hashing (LSH) based approach for federated recommender system. Our proposed efficient and scalable federated recommender system can make full use of multiple source data from different data owners while guaranteeing preservation of privacy of contributing parties. Extensive experiments on real-world benchmark datasets show that our approach can achieve both high time efficiency and accuracy under small privacy budgets.
Cryptocurrency valuation and ethics: a text analytic approach

A recent and potentially profound innovation is the creation of cryptocurrencies and the underlying technology that is essential for their use in various financial transactions. Given the anonymity of a user of a cryptocurrency, such digital currencies may be used for many different types of both lawful and illicit activities. The main purpose of this paper is to examine the extent to which ethical considerations associated with the use of cryptocurrencies affect the valuations attached to such currencies. The examination is based on a text analytic approach that involves measuring the extent to which ethical and unethical words are used in a discussion related to Bitcoin on Twitter to determine if there is a connection between ethics and cryptocurrency valuations. We find the frequency of an unethical discussion about Bitcoin is negatively associated with its price. In contrast, the frequency of an ethical discussion is positively associated with its price.
Fast Secure and Anonymous Key Agreement Against Bad Randomness for Cloud Computing

In cloud computing, resources are usually in cloud service provider’s network and typically accessed remotely by the cloud users via public channels. Key agreement enables secure channel establishment over a public channel for the secure communications between a cloud user and a cloud service provider. Existing key agreement protocols for cloud computing suffer from some challenges, e.g., realizing low connection delay, eliminating certificate management problem, enhancing user privacy and avoiding bad randomness. To tackle these challenges, we propose a certificateless 0-RTT anonymous AKA protocol against bad randomness for secure channel establishment in cloud computing. As a 0-RTT protocol, it significantly speeds up the efficiency of the secure channel establishment process. Further, our protocol does not need for the certificates to bind a public key with an entity’s identity and hence solves the certificate management problem. Finally, concrete security analysis of the protocol is also proposed. The protocol not only satisfies the traditional security attributes (e.g., known-key security, unknown key-share), but also strong security guarantees, i.e., user privacy and bad randomness resistance.
An efficient framework for generating robust adversarial examples

Recent studies show that deep neural networks (DNNs) suffer adversarial examples. That is, attackers can mislead the output of a DNN by adding subtle perturbation to a benign input image. In addition, researchers propose new generation of technologies to produce robust adversarial examples. Robust adversarial examples can consistently fool DNN models under predefined hyperparameter space, which can break through some defenses against adversarial examples or even generate physical adversarial examples against real-world applications. Behind these achievements, expectation over transformation (EOT) algorithm plays as the backbone framework for generating robust adversarial examples. Though EOT framework is powerful, we know little about why such a framework can generate robust adversarial examples. To address this issue, we do the first work to explain the principle behind robust adversarial examples. Then, based on the findings, we point out that traditional EOT framework has a performance problem and propose an adaptive sampling algorithm to overcome such a problem. By modeling the sampling process as classic Coupon Collector Problem, we prove that our new framework reduces the cost from O ( n ∗ log ⁡ ( n ) ) to O ( n ), where n denotes the number of sampling points. Under the view of computational complexity, the algorithm is optimal for this problem. The experimental results show that our algorithm can save up to 23% overhead in average. This is significant for black-box attack, where the cost is charged by the amount of queries.
A bivariate logistic regression model based on latent variables

Abstract Bivariate observations of binary and ordinal data arise frequently and require a bivariate modeling approach in cases where one is interested in aspects of the marginal distributions as separate outcomes along with the association between the two. We consider methods for constructing such bivariate models based on latent variables with logistic marginals and propose a model based on the Ali‐Mikhail‐Haq bivariate logistic distribution. We motivate the model as an extension of that based on the Gumbel type 2 distribution as considered by other authors and as a bivariate extension of the logistic distribution, which preserves certain natural characteristics. Basic properties of the obtained model are studied and the proposed methods are illustrated through analysis of two data sets: a basic science cognitive experiment of visual recognition and awareness and a clinical data set describing assessments of walking disability among multiple sclerosis patients.
Research on Task Scheduling Strategy Optimization Based onACO in Cloud Computing Environment

Cloud computing, as a general IT service model, effectively schedules various types of request, which is very important in terms of saving resources, improving the service quality and response speed of cloud data centers. For this purpose, the cloudlet scheduling strategy of the cloud data center is studied based on the tasks sent by users as the granularity to economize the amount of virtual machines, while taking the overall work execution time into consideration. First, the principle of ACO(Ant Colony algorithm), the main process and steps of the algorithm are introduced; secondly, the scheduling strategy aiming at reducing virtual machines and task make-span is introduced. The method in this paper makes use of the advantages of ACO. By lessening running virtual machines, the data center resource utilization is improved, while taking into account the service quality of task execution time. Experiments on CloudSim have proved the effectiveness of the algorithm. Compared with traditional Greedy algorithms, it makes large decrease on virtual machine amount, lower the cost of the data center, and find a balance on make-span and service quality as well.
Generalized Optimal Control for Autonomous Mine Countermeasures Missions

This article presents a computational framework for planning mine countermeasures (MCM) search missions by autonomous vehicles. It employs generalized optimal control (GenOC), a model-based trajectory optimization approach, to maximize the expected search performance of vehicle-sensor pairs in different minehunting scenarios. We describe each element of the proposed framework and adapt it to solve real-world MCM motion planning problems. A key contribution of this article develops sensor models that are more tunable than conventional ones based on lateral range curves. The proposed models incorporate engineering parameters and 3-D geometry to compute mine detection probability as a function of sonar design and search vehicle trajectories. Specific examples for various forward-looking and sidescan sonar systems deployed by unmanned vehicles are included. Objective computations utilize these sonar detection models during optimization to minimize the risk that candidate search trajectories fail to detect mines in an area of interest. Simulation results highlight the flexibility of our proposed GenOC framework and confirm that optimal trajectories outperform conventional search patterns under time or resource constraints. We conclude by identifying some of the practical considerations of this approach, and suggest ways that numerical analysis of GenOC solutions can be used for MCM mission planning and decision aid development.
Location Privacy-Preserving Method Based on Historical Proximity Location

With the rapid development of Internet services, mobile communications, and IoT applications, Location-Based Service (LBS) has become an indispensable part in our daily life in recent years. However, when users benefit from LBSs, the collection and analysis of users’ location data and trajectory information may jeopardize their privacy. To address this problem, a new privacy-preserving method based on historical proximity locations is proposed. The main idea of this approach is to substitute one existing historical adjacent location around the user for his/her current location and then submit the selected location to the LBS server. This method ensures that the user can obtain location-based services without submitting the real location information to the untrusted LBS server, which can improve the privacy-preserving level while reducing the calculation and communication overhead on the server side. Furthermore, our scheme can not only provide privacy preservation in snapshot queries but also protect trajectory privacy in continuous LBSs. Compared with other location privacy-preserving methods such as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>k</mml:mi></mml:math>-anonymity and dummy location, our scheme improves the quality of LBS and query efficiency while keeping a satisfactory privacy level.
Focusing on the Recovery Aspects of Cyber Resilience

Advances in technology and emerging cyber security tactics, techniques, and procedures (TTPs) are pillars for the `social good' on the one hand. On the other hand, they have advanced the landscape for adversarial purposes, e.g., the increasing number of cyber incidents and data breaches. This realisation that cyber incidents cannot be completely avoidable has made cyber resilience an extremely important preposition of any comprehensive and reliable cyber security strategy. Despite the importance, very little contributions exist on cyber recovery <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> - a core aspect of the cyber resilience, and cyber security standard. In this paper, we develop and present a comprehensive cyber recovery operational framework. An adaptive, robust framework that can be used as it is, or adapted by an organisation to create its own cyber recovery manual or operative. Each aspects of the framework are discussed thoroughly. Further, we show how a cutdown version of the framework is implemented, mindful that not all organisations are of the same size.