Reviewerly/exHarmony huggingface dataset:
Attentive Group Equivariant Convolutional Networks

Although group convolutional networks are able to learn powerful representations based on symmetry patterns, they lack explicit means to learn meaningful relationships among them (e.g., relative positions and poses). In this paper, we present attentive group equivariant convolutions, a generalization of the group convolution, in which attention is applied during the course of convolution to accentuate meaningful symmetry combinations and suppress non-plausible, misleading ones. We indicate that prior work on visual attention can be described as special cases of our proposed framework and show empirically that our attentive group equivariant convolutional networks consistently outperform conventional group convolutional networks on benchmark image datasets. Simultaneously, we provide interpretability to the learned concepts through the visualization of equivariant attention maps.
Dynamic Water Strider Algorithm for Optimal Design of Skeletal Structures

In the present paper, a dynamic version of Water Strider Algorithm (WSA) is proposed. The WSA as well as the Dynamic Water Strider Algorithm (DWSA) are applied to minimize the weight of several skeletal structures. WSA is a nature-inspired metaheuristic that mimics the territorial behavior, intelligent ripple communication, mating style, feeding mechanisms, and succession of water strider insects. The efficiency of these algorithms is tested by optimizing different truss and frame structures subject to multiple loading conditions and constraints. Comparing the results obtained by DWSA with those of other methods it becomes evident that DWSA is a suitable technique for optimizing the structural design and minimizing the weight of structures while fulfilling all constraints.
Survey of Mobile Device Authentication Methods Based on RF Fingerprint

Radio frequency (RF) fingerprint is the physical layer nature of wireless communication devices. It is like different people have different fingerprints, different wireless devices also have different RF fingerprints, which can be used for identification and access authentication of wireless devices in mobile communication. This paper mainly reviews the research progress of RF fingerprint technology in the past ten years. According to the typical process of RF fingerprint authentication, the basic concept of RF fingerprint and the overall framework of identity authentication are introduced. Then, the paper expounds two kinds of RF fingerprint authentication models of "1 to 1" and "1 to N" respectively, and briefly describes its implementation process. Subsequently, this paper has reviewed the papers on RF fingerprint authentication in recent years, and we select representative papers for introduction. This paper also conducts experiments on 10 communication devices of the same model, and we divide them into legal devices and illegal devices. The RF fingerprint authentication model is used to classify legal devices and reject illegal devices.
Real-time matching strategy for rotary objects using digital image correlation

Real-time monitoring of structural health conditions for rotary objects is of importance for safety assessments. In this work, an efficient algorithm based on digital image correlation is presented to achieve accurate rotational matching in real time. The proposed algorithm measures rotation in object motion with an integer pixel search followed by a subpixel correlation refinement. In the integer pixel search, the reference subset is rotated inversely to facilitate the correlation computation between the reference and target subsets. Then an independent and global integer pixel search for each point of interest is performed by applying the particle swarm optimization algorithm. Finally, a modified iterative registration algorithm is introduced to refine the displacement in the subpixel level by considering both the rotation angle and displacement components. Simulation and rotation experiments demonstrate that the proposed method achieves rapid and accurate measurements and is an effective method for retrieving the rotation data of rotating structures.
A Novel Fitness Tracker Using Edge Machine Learning

Several characteristics of the human body turn into postural behavior, recognizable also during sport activities. The presence of differences between body types could lead to different behavior of wearable and fitness-devote products. A new wearable based on machine learning techniques for the exercise detection and repetitions count is described in this work. A proper dataset has been obtained in order to offline train the network. Eventually, the machine learning algorithm has been implemented inside an edge device for real-time test e verification.
Chaotic time series prediction using phase space reconstruction based conceptor network

The Conceptor network is a new framework of reservoir computing (RC), in addition to the features of easy training, global convergence, it can online learn new classes of input patterns without complete re-learning from all the training data. The conventional connection topology and weights of the hidden layer (reservoir) of RC are initialized randomly, and are fixed to be no longer fine-tuned after initialization. However, it has been demonstrated that the reservoir connection of RC plays an important role in the computational performance of RC. Therefore, in this paper, we optimize the Conceptor's reservoir connection and propose a phase space reconstruction (PSR) -based reservoir generation method. We tested the generation method on time series prediction task, and the experiment results showed that the proposed PSR-based method can improve the prediction accuracy of Conceptor networks. Further, we compared the PSR-based Conceptor with two Conceptor networks of other typical reservoir topologies (random connected, cortex-like connected), and found that all of their prediction accuracy showed a nonlinear decline trend with increasing storage load, but in comparison, our proposed PSR-based method has the best accuracy under different storage loads.
Improving Revocation for Group Signature with Redactable Signature

Group signature is a major cryptographic tool allowing anonymous access to a service. However, in practice, access to a service is usually granted for some periods of time, which implies that the signing rights must be deactivated the rest of the time. This requirement thus calls for complex forms of revocation, reminiscent of the concept of time-bound keys. However, schemes implementing this concept are rare and only allow revocation with limited granularity. That is, signing keys are associated with an expiry time and become definitively useless once the latter has passed.In this paper, we revisit the notion of group signatures with time-bound keys with several contributions. Firstly, we extend this notion to allow high granularity revocation: a member’s signing key can in particular be deactivated at some moments and then be automatically reinstated. Secondly, we show that this complex property is actually simple to achieve using redactable signature. In particular, we consider in this context a recent redactable signature scheme from PKC 20 that we improve by dramatically reducing the size of the public key. The resulting construction is of independent interest.
Deep learning to combat phishing

With the tremendous increase in our dependence on online services for daily needs, cyber threats and related crime have also increased at an alarming rate. Phishing is one of the most common cyber threats used these days to steal secure information like personal details and financial credentials that are often misused for impersonation and committing cyber fraud. Although numerous solutions exist for detection and prevention from phishing scenarios, no solution can combat phishing attacks in real-time efficiently. This paper is focussed on studying the real-time security aspects of providing safe online services to every user. The primary aim of this paper is to propose an anti-phishing framework that could be implemented by Internet Security Providers (ISPs) to handle phishing attacks at their own level and providing secure connections to end users irrespective of the client machine configurations. The proposed framework utilizes the advantages of deep learning classification implemented as the backend for identifying phishing websites at the end of ISPs. This approach is capable of handling phishing attacks efficiently as compared to other client-specific approaches as the ISPs proactively can validate and flag the new domains added for HTTP/HTTPS as well as SMTP and prevent millions of users from getting affected.
Attention-Guided Generative Adversarial Network to Address Atypical Anatomy in Synthetic CT Generation

Recently, interest in MR-only treatment planning using synthetic CTs (synCTs) has grown rapidly in radiation therapy. However, developing class solutions for medical images that contain atypical anatomy remains a major limitation. In this paper, we propose a novel spatial attention-guided generative adversarial network (attention-GAN) model to generate accurate synCTs using Tl-weighted MRI images as the input to address atypical anatomy. Experimental results on fifteen brain cancer patients show that attention-GAN outperformed existing synCT models and achieved an average MAE of 85.22±12.08, 232.41±60.86, 246.38±42.67 Hounsfield units between synCT and CT-SIM across the entire head, bone and air regions, respectively. Qualitative analysis shows that attention-GAN has the ability to use spatially focused areas to better handle outliers, areas with complex anatomy or post-surgical regions, and thus offer strong potential for supporting near real-time MR-only treatment planning.
How to Model the Bribery Attack: A Practical Quantification Method in Blockchain

Due to substantial profit gain and economic rewards, decentralized cryptocurrency systems have become primary targets for attackers. Double-spending is one of the most rudimentary and collective risks. Even without high hash power, attackers can still increase the probability of double-spending by bribing other miners to subvert the consensus agreement. This kind of attack is called bribery attack and a number of bribery attack models have been proposed during last few years. The evaluation and comparison of bribery attack models remain problematic due to the lack of systematic methods to quantify them. In particular, the costs and benefits of attackers are rarely considered which influenced by many factors. We propose a quantitative analysis method for previous bribery attack models. For further exploration, we design a bribery attack model and introduce profit formulations based on our analysis method. We experimentally prove that our model can reduce costs and increase benefits of bribery attacks compared with comparable models. The result shows our quantitative method is instructive both for bribery attack designing and analyzing.
Detection of Small Aerial Object Using Random Projection Feature With Region Clustering

Small aerial object detection plays an important role in numerous computer vision tasks, including remote sensing, early warning systems, and visual tracking. Despite existing moving object detection techniques that can achieve reasonable results in normal size objects, they fail to distinguish the small objects from the dynamic background. To cope with this issue, a novel method is proposed for accurate small aerial object detection under different situations. Initially, the block segmentation is introduced for reducing frame information redundancy. Meanwhile, a random projection feature (RPF) is proposed for characterizing blocks into feature vectors. Subsequently, a moving direction estimation based on feature vectors is presented to measure the motions of blocks and filter out the major directions. Finally, variable search region clustering (VSRC), together with the color feature difference, is designed for extracting pixelwise targets from the remaining moving direction blocks. The comprehensive experiments demonstrate that our approach outperforms the level of state-of-the-art methods upon the integrity of small aerial objects, especially on the dynamic background and scale variation targets.
Mission Impossible Spaces: Using Challenge-Based Distractors to Reduce Noticeability of Self-Overlapping Virtual Architecture

Impossible spaces make it possible to maximize the area of virtual environments that can be explored on foot through self-overlapping virtual architecture. This paper details a study exploring how users’ ability to detect overlapping virtual architecture is affected when the virtual environment includes distractors that impose additional cognitive load by challenging the users. The results indicate that such distractors both increase self-reported task load and reduce users’ ability to reliably detect overlaps between adjacent virtual rooms. That is, rooms could overlap by up to 68% when distractors were presented, compared to 40% when no distractors were present.
Pretrained Language Models for Dialogue Generation with Multiple Input Sources

Large-scale pretrained language models have achieved outstanding performance on natural language understanding tasks.However, it is still under investigating how to apply them to dialogue generation tasks, especially those with responses conditioned on multiple sources.Previous work simply concatenates all input sources or averages information from different input sources.In this work, we study dialogue models with multiple input sources adapted from the pretrained language model GPT2.We explore various methods to fuse multiple separate attention information corresponding to different sources.Our experimental results show that proper fusion methods deliver higher relevance with dialogue history than simple fusion baselines.
Learning to Fuse Sentences with Transformers for Summarization

The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts. However, to date, summarizers can fail on fusing sentences. They tend to produce few summary sentences by fusion or generate incorrect fusions that lead the summary to fail to retain the original meaning. In this paper, we explore the ability of Transformers to fuse sentences and propose novel algorithms to enhance their ability to perform sentence fusion by leveraging the knowledge of points of correspondence between sentences. Through extensive experiments, we investigate the effects of different design choices on Transformer’s performance. Our findings highlight the importance of modeling points of correspondence between sentences for effective sentence fusion.
Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology

Recent works have shown that gradient descent can find a global minimum for over-parameterized neural networks where the widths of all the hidden layers scale polynomially with $N$ ($N$ being the number of training samples). In this paper, we prove that, for deep networks, a single layer of width $N$ following the input layer suffices to ensure a similar guarantee. In particular, all the remaining layers are allowed to have constant widths, and form a pyramidal topology. We show an application of our result to the widely used LeCun's initialization and obtain an over-parameterization requirement for the single wide layer of order $N^2.$
An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking

In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection. Moreover, a self-adaptive gain modification coefficient is defined to solve the low accuracy problem caused by the sigma point reduction, and the problem of particle degradation is solved by modifying the weights calculation method. In addition, a new multi-sensor fusion model is proposed, which better integrates radar and infrared sensors. Simulation results show that IUPF effectively improves real-time performance while ensuring the tracking accuracy compared with other algorithms. Besides, compared with the traditional distributed fusion architecture, the proposed new architecture makes better use of the advantages of radar and an infrared sensor and improves the tracking accuracy.
Solar Energy Application in Drainage Pumping Stations to Save Water and Reducing CO2 Emission

Freshwater resources in Egypt are very limited. Additionally, drains are important unconventional water resources. The objectives of this paper are divided into two goals. The first goal is to save freshwater by reducing the evaporation from the drainage channels. Secondly, to reduce the CO2 emission by replacing the traditional fuel of the drainage’s pump station by renewable energy. Many drainage pumps in Egypt are old and cause CO2 emission that is estimated by tons. Solar cell power represents the alternative fuel resource for these drainage pumps, it also reduces the emissions. The pump station on the Algharak drain in Egypt was studied as a sample for the drainage systems. Climate data such as solar irradiation, evaporation, air temperature, specific humidity, and wind speed were collected. The pump station data such as pump discharge, number of units, and the head were collected. Two scenarios of the pumping operation of the pump stations on Algharak drain were studied. In each scenario, the solar pumping power, area of solar cells, evaporation loss, and the value of reduction of CO2 were estimated. The results of this research could be used all over Egypt to help save a number of unconventional water resources.
Generating Random Parameters in Feedforward Neural Networks with Random Hidden Nodes: Drawbacks of the Standard Method and How to Improve It

The standard method of generating random weights and biases in feedforward neural networks with random hidden nodes selects them both from the uniform distribution over the same fixed interval. In this work, we show the drawbacks of this approach and propose new methods of generating random parameters. These methods ensure the most nonlinear fragments of sigmoids, which are most useful in modeling target function nonlinearity, are kept in the input hypercube. A new method generating sigmoids with uniformly distributed slope angles demonstrated the best performance on the illustrative examples.
Microscopy Image Restoration with Deep Wiener-Kolmogorov Filters

Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise. In this work, we propose a unifying framework of algorithms for Gaussian image deblurring and denoising. These algorithms are based on deep learning techniques for the design of learnable regularizers integrated into the Wiener-Kolmogorov filter. Our extensive experimentation line showcases that the proposed approach achieves a superior quality of image reconstruction and surpasses the solutions that rely either on deep learning or on optimization schemes alone. Augmented with the variance stabilizing transformation, the proposed reconstruction pipeline can also be successfully applied to the problem of Poisson image deblurring, surpassing the state-of-the-art methods. Moreover, several variants of the proposed framework demonstrate competitive performance at low computational complexity, which is of high importance for real-time imaging applications .
Ultra-sensitive phase measurement based on an SU(1,1) interferometer employing external resources and substract intensity detection

A new scheme has been proposed to realize the enhancement of phase sensitivity based on an SU(1,1) interferometer. Compared with the classical Mach-Zehnder interferometer, the SU(1,1) interferometer is splitted and recombined by an optical parametric amplifier and the phase sensitivity can beat shot noise limit by adjusting the parametric strength. In this model, the inputs of the SU(1,1) interferometer are bright entangled twin beams generated from four wave mixing and the detection method is substract intensity difference with one of the twin beams entering into the interferometer. The detection efficiency of the detector is taken into consideration. This scheme also proves that when one of the inputs of an SU(1,1) interferometer is an vacuum beam, the phase sensitivity can beat shot noise limit by employing substract intensity detection and external resources.
Keypoint based comprehensive copy‐move forgery detection

Verifying the authenticity of a digital image has been challenging problem. The simplest of the image tampering tricks is the copy-move forgery. In copy-move forgery copied portion of the image is pasted on another part of the same image. Geometrical transformations are used on the copied portions of the image before pasting it for the tampered image to look realistic and visually convincing. To make it more complex, other processing approaches may also be applied in the forged region for hiding traces of forgery. These processings are the scale, rotation, JPEG compression, and AWGN. In this paper, an approach based on features of the CenSurE keypoint detector and FREAK descriptor is proposed. This combination has novelty in itself as it has never been used for this purpose before to the best of authors' literature studies. CenSurE detectors are fast and give stable and accurate output even in the case of rotated images, which we club with binary descriptor FREAK. Hierarchical clustering and Neighbourhood search is applied in such a way that it can locate and detect multiple copy-move forgeries. The authors are hopeful that the proposed approach may be used in real-time image authentication and copy-move forgery detection.
PerSeVerE: persistency semantics for verification under ext4

Although ubiquitous, modern filesystems have rather complex behaviours that are hardly understood by programmers and lead to severe software bugs such as data corruption. As a first step to ensure correctness of software performing file I/O, we formalize the semantics of the Linux ext4 filesystem, which we integrate with the weak memory consistency semantics of C/C++. We further develop an effective model checking approach for verifying programs that use the filesystem. In doing so, we discover and report bugs in commonly-used text editors such as vim, emacs and nano.