Reviewerly/exHarmony huggingface dataset:
XOR-ed visual secret sharing scheme with robust and meaningful shadows based on QR codes

Abstract Quick response (QR) codes are becoming increasingly popular in various areas of life due to the advantages of the error correction capacity, the ability to be scanned quickly and the capacity to contain meaningful content. The distribution of dark and light modules of a QR code looks random, but the content of a code can be decoded by a standard QR reader. Thus, a QR code is often used in combination with visual secret sharing (VSS) to generate meaningful shadows. There may be some losses in the process of distribution and preservation of the shadows. To recover secret images with high quality, it is necessary to consider the scheme’s robustness. However, few studies examine robustness of VSS combined with QR codes. In this paper, we propose a robust ( k , n )-threshold XOR-ed VSS (XVSS) scheme based on a QR code with the error correction ability. Compared with OR-ed VSS (OVSS), XVSS can recover the secret image losslessly, and the amount of computation needed is low. Since the standard QR encoder does not check if the padding codewords are correct during the encoding phase, we replace padding codewords by initial shadows shared from the secret image using XVSS to generate QR code shadows. As a result, the shadows can be decoded normally, and their error correction abilities are preserved. Once all the shadows have been collected, the secret image can be recovered losslessly. More importantly, if some conventional image attacks, including rotation, JPEG compression, Gaussian noise, salt-and-pepper noise, cropping, resizing, and even the addition of camera and screen noises are performed on the shadows, the secret image can still be recovered. The experimental results and comparisons demonstrate the effectiveness of our scheme.
Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in Cloud

Due to serving several purposes simultaneously, running scientific workflows on dynamic environments such as cloud computing, has become multi-objective scheduling.Among these purposes, Cost and Makespan are probably the most two primitive objectives.Another critical factor in a large-scale scientific workflow is tremendous amount of data during execution.Therefore, this work also includes Data Movement as an additional objective as it has a major impact on network utilization and energy consumption in network equipment in cloud data center.In considering these three objectives, this work proposes a framework for scheduling solutions which combines a new nodes clustering technique in Directed Acyclic Graph (DAG) model known as Multilevel Dependent Node Clustering (MDNC) and the multiobjective optimization, Extreme Nondominated Sorting Genetic Algorithm-III (E-NSGA-III).E-NSGA-III is the recent extension of Nondominated Sorting Genetic Algorithm (NSGA-III).Five well-known scientific workflows, CyberShake, Epigenomics, LIGO, Montage, and SIPHT are selected as testbeds, while the commonly known Hypervolume is chosen as the performance metric.In this work, MDNC is also experimented with both NSGA-III.Comparison among three approaches, E-NSGA-III alone, E-NSGA-III with Peer-to-Peer clustering and E-NSGA-III with MDNC are carried out.The superiority of the proposed framework among them and its limitation are discussed.
Deep Grammatical Multi-classifier for Continuous Sign Language Recognition

In this paper, we propose a novel deep architecture with multiple classifiers for continuous sign language recognition. Representing the sign video with a 3D convolutional residual network and a bidirectional LSTM, we formulate continuous sign language recognition as a grammatical-rule-based classification problem. We first split a text sentence of sign language into isolated words and n-grams, where an n-gram is a sequence of consecutive n words in a sentence. Then, we propose a word-independent classifiers (WIC) module and an n-gram classifier (NGC) module to identify the words and n-grams in a sentence, respectively. A greedy decoding algorithm is employed to integrate words and n-grams into the sentence based on the confidence scores provided by both modules. Our method is evaluated on a Chinese continuous sign language recognition benchmark, and the experimental results demonstrate its effectiveness and superiority.
Data-Driven Body-Machine Interface for Drone Intuitive Control through Voice and Gestures

Aerial drones can be used for a number of monitoring and control applications. Most of existing drone control platforms are quite primitive in terms of body-machine interface. They are usually a variation of a hand-held remote controller or ground control station. However, in a number of line-of-sight scenarios it would be more convenient to use the human gestures and voice for the drone control. In this work, we present an approach for instantaneous control of drones based on human voice and gestures. The proposed solution includes wearable sensors and embedded artificial intelligence. We use a microphone and an Inertial Measurement Unit (IMU) for capturing the human voice and the hand movements. Primary control is implemented by a voice recognition unit based on Recurrent Neural Network (RNN) while the secondary control is implemented by the gesture recognition system based on Convolutional Neural Network (CNN). For implementing the embedded intelligence, we use a low-power embedded system with a graphical processing unit able to run pre-trained neural networks on board of the drone. As a result, the system can perform different speech and gesture recognition tasks real-time.
Cerberus: A Blockchain-Based Accreditation and Degree Verification System

Credential fraud is a widespread practice that undermines investment and confidence in higher education systems and bears significant economic and social costs. Legacy credential verification systems are typically time-consuming, costly, and bureaucratic, and struggle against certain classes of credential fraud. In this article, we propose a comprehensive blockchain-based credential verification solution, Cerberus, which is considerably more efficient, easy, and intuitive to use, and effectively mitigates widespread manifestations of credential fraud. Cerberus also improves significantly upon other blockchain-based solutions in the research literature: it adheres closely to the existing credential verification ecosystem and addresses real-world fraud scenarios. Moreover, Cerberus uses on-chain smart contracts for credential revocation, and it does not entail students or employers to manage digital identities or cryptographic credentials to use the system. We prototype our solution and describe our attempt to design an online verification service with a rich feature set, including data privacy, transcript verification, and selective disclosure of data. We hope this effort contributes positively to alleviating the problem of fake credentials.
Guess where? Actor-supervision for spatiotemporal action localization

This paper addresses the problem of spatiotemporal localization of actions in videos. Compared to leading approaches, which all learn to localize based on carefully annotated boxes on training video frames, we adhere to a solution only requiring video class labels. We introduce an actor-supervised architecture that exploits the inherent compositionality of actions in terms of actor transformations, to localize actions. We make two contributions. First, we propose actor proposals derived from a detector for human and non-human actors intended for images, which are linked over time by Siamese similarity matching to account for actor deformations. Second, we propose an actor-based attention mechanism enabling localization from action class labels and actor proposals. It exploits a new actor pooling operation and is end-to-end trainable. Experiments on four action datasets show actor supervision is state-of-the-art for action localization from video class labels and is even competitive to some box-supervised alternatives.
An end-to-end pseudo relevance feedback framework for neural document retrieval

Pseudo relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches. While neural retrieval models have recently demonstrated promising results for ad-hoc document retrieval, combining them with PRF is not straightforward due to incompatibilities between existing PRF approaches and neural architectures. To bridge this gap, we propose an end-to-end neural PRF framework, coined NPRF, that enriches the representation of user information need from a single query to multiple PRF documents. NPRF can be used with existing neural IR models by embedding different neural models as building blocks. Three state-of-the-art neural retrieval models, including the unigram DRMM and KNRM models, and the position-aware PACRR model, are utilized to instantiate the NPRF framework. Extensive experiments on two standard test collections, TREC1-3 and Robust04, confirm the effectiveness of the proposed NPRF framework in improving the performance of three state-of-the-art neural IR models. In addition, analysis shows that integrating the existing neural IR models within the NPRF framework results in reduced training and validation losses, and consequently, improved effectiveness of the learned ranking functions.
Is an Affine Constraint Needed for Affine Subspace Clustering?

Subspace clustering methods based on expressing each data point as a linear combination of other data points have achieved great success in computer vision applications such as motion segmentation, face and digit clustering. In face clustering, the subspaces are linear and subspace clustering methods can be applied directly. In motion segmentation, the subspaces are affine and an additional affine constraint on the coefficients is often enforced. However, since affine subspaces can always be embedded into linear subspaces of one extra dimension, it is unclear if the affine constraint is really necessary. This paper shows, both theoretically and empirically, that when the dimension of the ambient space is high relative to the sum of the dimensions of the affine subspaces, the affine constraint has a negligible effect on clustering performance. Specifically, our analysis provides conditions that guarantee the correctness of affine subspace clustering methods both with and without the affine constraint, and shows that these conditions are satisfied for high-dimensional data. Underlying our analysis is the notion of affinely independent subspaces, which not only provides geometrically interpretable correctness conditions, but also clarifies the relationships between existing results for affine subspace clustering.
BPF++: A Unified Factorization model for predicting retweet behaviors

Recently, the prediction of retweet behaviors has attracted significant attention, as it can facilitate with a number of tasks, such as popular tweet prediction, personalized recommendation and business intelligence. However, in existing studies, two main problems exists in the prediction of retweet behaviors. (1) The relationship between users is extremely simple when social influences are used for prediction. (2) An effective framework that unifies the effects of both heterogeneous social relations of users and multidimensional similarities of tweets does not exist. Therefore, we propose a unified factorization model that incorporates social influence and tweet similarity into a traditional Bayesian Poisson factorization (BPF) model, named BPF++. Specifically, we utilize a variety of social influence and tweet similarity jointly to improve performance. Furthermore, we integrate trust strengths between users and degrees of similarity between tweets to the framework. We adopt an efficient coordinate ascent algorithm to learn the parameters of the BPF++ model. Extensive experiments are conducted to evaluate the performance of our model on the Sina Weibo dataset. Results demonstrate improvements of 113.64% and 116.28% in the [email protected] and [email protected] scores, respectively, compared with BPF.
Self-Adaptive Network Pruning

Deep convolutional neural networks have been proved successful on a wide range of tasks, yet they are still hindered by their large computation cost in many industrial scenarios. In this paper, we propose to reduce such cost for CNNs through a self-adaptive network pruning method (SANP). Our method introduces a general Saliency-and-Pruning Module (SPM) for each convolutional layer, which learns to predict saliency scores and applies pruning for each channel. Given a total computation budget, SANP adaptively determines the pruning strategy with respect to each layer and each sample, such that the average computation cost meets the budget. This design allows SANP to be more efficient in computation, as well as more robust to datasets and backbones. Extensive experiments on 2 datasets and 3 backbones show that SANP surpasses state-of-the-art methods in both classification accuracy and pruning rate.
Connectivity-based cylinder detection in unorganized point clouds

Cylinder detection is an important step in reverse engineering of industrial sites, as such environments often contain a large number of cylindrical pipes and tanks. However, existing techniques for cylinder detection require the specification of several parameters which are difficult to adjust because their values depend on the noise level of the input point cloud. Also, these solutions often expect the cylinders to be either parallel or perpendicular to the ground. We present a cylinder-detection technique that is robust to noise, contains parameters which require little to no fine-tuning, and can handle cylinders with arbitrary orientations. Our approach is based on a robust linear-time circle-detection algorithm that naturally discards outliers, allowing our technique to handle datasets with various density and noise levels while using a set of default parameter values. It works by projecting the point cloud onto a set of directions over the unit hemisphere and detecting circular projections formed by samples defining connected components in 3D. The extracted cylindrical surfaces are obtained by fitting a cylinder to each connected component. We compared our technique against the state-of-the-art methods on both synthetic and real datasets containing various densities and noise levels, and show that it outperforms existing techniques in terms of accuracy and robustness to noise, while still maintaining a competitive running time.
Multiple-image encryption base on compressed coded aperture imaging

A multiple-image encryption method based on compressed coded aperture imaging and pixel location scrambling operation is proposed. In the encryption process, each plaintext image is compressively encoded by its corresponding random matrix, which is generated via a row and column displacement transformation operation on an original random matrix (ORM) and some auxiliary keys. With the help of charge coupled device (CCD), the superimposed image are then generated by recording and grouping all the encoded images; finally, the ciphertext image is obtained by pixel location scrambling operation for the superimposed image. During decryption, the participant who possesses his/her correct key-group, can successfully reconstruct the corresponding plaintext image by location index key, auxiliary keys, scrambling operation, compressed coded aperture imaging retrieval algorithm. Theoretical analysis and numerical simulations validate the feasibility of the proposed method.
Edge-enabled Disaster Rescue

In the aftermath of earthquakes, floods, and other disasters, photos are increasingly playing more significant roles, such as finding missing people and assessing disasters, in rescue and recovery efforts. These disaster photos are taken in real time by the crowd, unmanned aerial vehicles, and wireless sensors. However, communications equipment is often damaged in disasters, and the very limited communication bandwidth restricts the upload of photos to the cloud center, seriously impeding disaster rescue endeavors. Based on edge computing, we propose Echo, a highly time-efficient disaster rescue framework. By utilizing the computing, storage, and communication abilities of edge servers, disaster photos are preprocessed and analyzed in real time, and more specific visuals are immensely helpful for conducting emergency response and rescue. This article takes the search for missing people as a case study to show that Echo can be more advantageous in terms of disaster rescue. To greatly conserve valuable communication bandwidth, only significantly associated images are extracted and uploaded to the cloud center for subsequent facial recognition. Furthermore, an adaptive photo detector is designed to utilize the precious and unstable communication bandwidth effectively, as well as ensure the photo detection precision and recall rate. The effectiveness and efficiency of the proposed method are demonstrated by simulation experiments.
Measurement, Analysis, and Enhancement of Multipath TCP Energy Efficiency for Datacenters

Multipath TCP (MPTCP) has recently been suggested as a promising transport protocol to boost the utilization of underlaying datacenter networks, yet it also increases the host CPU power consumption. It remains unclear whether datacenters can indeed benefit from using MPTCP from the perspective of energy efficiency. Through realworld measurement of MPTCP, we show that the energy efficiency of MPTCP is largely related to the flow completion time and the existence of link-sharing subflows. In particular, we find that the link-sharing subflows in MPTCP will significantly elevate the CPUs' power consumption on hosts. To make the matter worse, it will also reduce the transmission efficiency for both throughput-sensitive long flows and latency-sensitive short flows. To address such a problem, we present MPTCP-D, an energy-efficient enhancement of MPTCP in datacenter networks. MPTCP-D incorporates a novel congestion control algorithm that improves energy efficiency by minimizing the flow completion time. It also has a build-in subflow elimination mechanism that precludes link-sharing subflows from increasing the host CPU power consumption. We implement MPTCP-D in the Linux kernel, analyze the parameter selection in the algorithm and study its performance through packet-level simulation and on Amazon EC2. Our results show that, without degrading the performance of the long flow throughput and the short flow completion time, MPTCP-D reduces the long flow energy consumption by up to 72% compared to DCTCP for data transfers, and reduces the short flow power consumption by up to 46% compared to MPTCP with link-sharing subflows.
Object Detection with Low Capacity GPU Systems Using Improved Faster R-CNN

Object detection in remote sensing images has been frequently used in a wide range of areas such as land planning, city monitoring, traffic monitoring, and agricultural applications. It is essential in the field of aerial and satellite image analysis but it is also a challenge. To overcome this challenging problem, there are many object detection models using convolutional neural networks (CNN). The deformable convolutional structure has been introduced to eliminate the disadvantage of the fixed grid structure of the convolutional neural networks. In this study, a multi-scale Faster R-CNN method based on deformable convolution is proposed for single/low graphics processing unit (GPU) systems. Weight standardization (WS) is used instead of batch normalization (BN) to make the proposed model more efficient for a small batch size (1 img/per GPU) on single GPU systems. Experiments were conducted on the publicly available 10-class geospatial object detection (NWPU-VHR 10) dataset to evaluate the object detection performance of the proposed model. Experiment results show that our model achieved a 92.3 mAP. This is a 1.7% mAP increase when compared to the best results in the models using the same dataset.
Optimisation based material parameter identification using full field displacement and temperature measurements

A material parameter identification is presented for a fully thermo-mechanically coupled material model based on full field displacement and temperature measurements. The basic theory of the inverse problem is recapitulated, focusing on the choice of the objective function, proposing a new formulation, and explaining in detail the necessary numerical treatment of experimental data during the pre-processing of an identification. This includes the handling of the intrinsically different data sets of displacement (Lagrangian type) and temperature (Eulerian type). Experimental data is obtained by means of a Digital-Image-Correlation (DIC) as well as by a thermography system and three algorithmic boxes are provided for the necessary pre-processing. The experimental setup is discussed, measured data presented and analysed. From this setup, a successive approach to the identification process is motivated. Based on the experimental observations, a thermo-mechanically coupled material model is chosen, the required constitutive relations summarised and the material parameters interpreted. For the fixed choice of model and experiments, the inverse problem is solved. A very good fit was obtained for both the displacement and the temperature field. Results are interpreted and remaining errors discussed.
Performance Evaluation of a Real-Time Seismic Detection System Based on CFAR Detectors

In this research work, we present a novel application specific Ordered cell averaging-CFAR (OCA-CFAR) detector for anthropogenic seismic events. We investigate the use of geophones and real-time hardware for the detection of moving targets such as vehicle, human footsteps and activities such as hammering on the ground. We present here energy assisted variants of Constant False Alarm Rate (CFAR) detector implemented on a real-time embedded platform to obtain detection cues of the ground seismic source. The OCA-CFAR algorithm has been modified in terms of window length and position of Cell Under Test (CUT), suitably adapted to the nature of seismic events engendered from moving vehicles and humans. The proposed detector has been compared with the popularly known variants such as Cell Averaging-CFAR, Least Of-CFAR, Greatest Of-CFAR and Ordered Statistics-CFAR for probabilities of false alarm ranging from 1e-1 to 1e-5. The proposed method shows an increased detection rate with minimized false alarms on the field trials and a dataset containing the seismic signature of human activities and some of civilian vehicles.