Reviewerly/exHarmony huggingface dataset:
Image watermarking algorithm based on quaternion and chaotic Lorenz system

Quaternions are hyper-complex numbers applied in three-dimensional space, which are widely used in physics and image processing. In this paper, we utilize the features of quaternions for constructing a new substitution box (S-box) based on Lorenz system. The performance of the proposed S-box is investigated by S-box evaluation criterion. The results demonstrate that the constructed S-box has significant qualities for viable applications in security purposes. As well as, a new image watermarking mechanism is introduced which its embedding procedures based on the suggested Sbox. Experimental results demonstrate the efficiency of the presented image watermarking technique.
Dilated Light-Head R-CNN using tri-center loss for driving behavior recognition

Unsafe driving behavior causes many traffic accidents, resulting in serious casualties and property losses. It is observed that what the driver is doing can be revealed from the clues on the image, such as the hand with a cigarette. However, the existing behavior recognition approaches are not ideal for distinguishing driving behavior with only local differences. In this paper, we recognize driving behavior by detecting the action-specific parts and propose Dilated Light-Head R-CNN (DL-RCNN) approach, which uses dilated convolution to ensure image resolution for critical details. The technical novelty includes: a position-sensitive RoI alignment to improve the perception of small objects, and tri-center loss to enforce similarity between intra-class features and difference between features of distinct classes. We also adopt two strategies including online hard example mining and proper calibration of key parameters. The evaluation results on the public Kaggle-driving data set and self-built data set show that DL-RCNN achieves state-of-the-art performance in recognizing driving behavior.
Estimation of Vehicle Distance Based on Feature Points Using Monocular Vision

In this digital era safety and security have the highest precedence, the advanced driver assistance system is the latest trend and where many challenges are open for researchers. Vehicle to vehicle distance estimation is one of the most important challenges to provide the security and safety alerts for the driver. In order to achieve this, image of the front vehicle is captured using the single camera under monocular vision to estimate the vehicle distance. Then three key steps are designed to estimate the vehicle distance: extracting and locating the key features of the vehicle, characteristic triangle is drawn between those features to calculate pixel area and develop the measuring formula to calculate the distance. For efficient feature extraction and localizing of the feature position, conventional AdaBoost algorithm is utilized to find the strong features for scalable samples. Distance measurement formulation is used to derive the correlation between the pixel area and distance by considering the different parameters from the prototype of pinhole camera, camera standardization and plotting of area. Formula is developed to estimate the optimum moving distance between vehicles to vehicle. After the experimental analysis, the accuracy rate is improved and time complexity satisfies the precision.
Analyzing Effect of Ensemble Models on Multi-Layer Perceptron Network for Software Effort Estimation

Effort Estimation is a very challenging task in the software development life cycle. Inaccurate estimations may cause client dissatisfaction and thereby, decrease the quality of the product. Considering the problem of software cost and effort estimation, it is conceivable to call attention to that the estimation procedure considers the qualities present in the data set, as well as the aspects of the environment in which the model is embedded. Existing literature have the instances where machine learning techniques have been used to estimate the effort required to develop any software. Yet it is quite uncertain for any particular model to perform well with all the data sets. In this paper, Multi-Layer Perceptron (MLPNN) and its ensembles are explored in order to improve the performance of software effort estimation process. Firstly, MLPNN, Ridge-MLPNN, Lasso-MLPNN, Bagging-MLPNN, and AdaBoost-MLPNN models are developed and, then, the performance of these models are compared on the basis of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score to find the best model fitting this dataset. Results obtained from the study demonstrate that the R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of AdaBoost-MLPNN is 82.213%, which is highest among all the models.
Cannygan: Edge-Preserving Image Translation with Disentangled Features

The image-to-image translation task often associates with the problem of missing texture and edge information. In this paper, we proposed a framework to translate images while preserving more realistic textures and details. To this end, we disentangle the samples into shared content space and domain-specific style domain. Then, according to the blurred outlines and textures in the source domain, we introduce the classic canny edge detection algorithm to encode the boundary and edge information in the content latent space. We test the proposed method in the thermal to visible image translation scenario and the experimental results demonstrate that the proposed method outperforms several other state-of-the-art models.
Convolutional Neural Networks with Layer Reuse

A convolutional layer in a Convolutional Neural Network (CNN) consists of many filters which apply convolution operation to the input, capture some special patterns and pass the result to the next layer. If the same patterns also occur at the deeper layers of the network, why wouldn't the same convolutional filters be used also in those layers? In this paper, we propose a CNN architecture, Layer Reuse Network (LruNet), where the convolutional layers are used repeatedly without the need of introducing new layers to get a better performance. This approach introduces several advantages: (i) Considerable amount of parameters are saved since we are reusing the layers instead of introducing new layers, (ii) the Memory Access Cost (MAC) can be reduced since reused layer parameters can be fetched only once, (iii) the number of nonlinearities increases with layer reuse, and (iv) reused layers get gradient updates from multiple parts of the network. The proposed approach is evaluated on CIFAR-10, CIFAR-100 and Fashion-MNIST datasets for image classification task, and layer reuse improves the performance by 5.14%, 5.85% and 2.29%, respectively. The source code and pretrained models are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A Learning-Based Prediction Model for Baby Accidents

According to the statistics in the United Kingdom, more than two million babies and toddlers experienced accidents every year. Despite the places where accidents happened, most of the accidents could've been predicted and prevented. In order to avoid causing injuries by accident, a temporal-pyramid long short-term memory (TP-LSTM) network along with the temporal attention mechanism is proposed to predict whether an accident will happen in the future or not. The proposed network is capable of capturing important information of the video at different temporal resolution and selecting crucial frames that contribute to the accident most. Moreover, the proposed early exponential loss (EEL) function is incorporated to achieve better prediction. The baby video dataset (BVD) containing 670 videos is collected from several video-sharing websites. 320 of which are with accidents and the others are without accidents. The experimental results show that the proposed network attains average precision of 61.13% and the accidents are foreseen 4.196 seconds before the occurrence with 80% recall.
Shortening the Libert-Peters-Yung Revocable Group Signature Scheme by Using the Random Oracle Methodology

At EUROCRYPT 2012, Libert, Peters and Yung (LPY) proposed the first scalable revocable group signature (R-GS) scheme in the standard model which achieves constant signing/verification costs and other costs regarding signers are at most logarithmic in N, where N is the maximum number of group members. However, although the LPY R-GS scheme is asymptotically quite efficient, this scheme is not sufficiently efficient in practice. For example, the signature size of the LPY scheme is roughly 10 times larger than that of an RSA signature (for 160-bit security). In this paper, we propose a compact R-GS scheme secure in the random oracle model that is efficient not only in the asymptotic sense but also in practical parameter settings. We achieve the same efficiency as the LPY scheme in an asymptotic sense, and the signature size is nearly equal to that of an RSA signature (for 160-bit security). It is particularly worth noting that our R-GS scheme has the smallest signature size compared to those of previous R-GS schemes which enable constant signing/verification costs. Our technique, which we call parallel Boneh-Boyen-Shacham group signature technique, helps to construct an R-GS scheme without following the technique used in LPY, i.e., we directly apply the Naor-Naor-Lotspiech framework without using any identity-based encryption.
Context-Anchors for Hybrid Resolution Face Detection

Despite the positive trends in the development of face detection, open challenges still exist, such as the detection of degraded faces caused by small-size, defocus blur and occlusion in surveillance video. When utilizing anchor-based methods, the anchors are the basic units of training samples, and their ranges are proportional to the ranges of the original label boxes (ground truth). This paper argues that the selected range of an anchor is crucial for a detection task and proposes a face detection model CAHR (context-anchors for hybrid-resolution model) to balance the image resolution and the spatial context range for the purposes of locating small faces. In the training phase, specific size of spatial context is introduced for each anchor, and an image pyramid is employed for a dual CNNs model. In experiments, the indepth analysis of amplification ratio of the anchor and the detection rate is revealed. The detection rate of the small faces is improved by using the proposed model. It is also validated with a massively face datasets (WIDER FACE), demonstrating its superiority to the original hybrid-resolution model (HR) and some other advanced methods.
Multi-center variable-scale search algorithm for combinatorial optimization problems with the multimodal property

Combinatorial optimization problems (COPs) are discrete problems arising from aerospace, bioinformatics, manufacturing, and other fields. One of the classic COPs is the scheduling problem. Moreover, these problems are usually multimodal optimization problems with a quantity of global and local optima. As a result, many search algorithms can easily become trapped into local optima. In this article, we propose a multi-center variable-scale search algorithm for solving both single-objective and multi-objective COPs. The algorithm consists of two distinct points. First, the multi-center strategy chooses several individuals with better performance as the only parents of the next generation, which means that there are a number of separate searching areas around the searching center. Second, the next generation of the population is produced by a variable-scale strategy with an exponential equation based on the searching center. The equation is designed to control the neighborhood scale, and adaptively realize the large-scale and small-scale searches at different search stages to balance the maintenance of diversity and convergence speed. In addition, an approach of adjusting centers is proposed concerning the number and distribution of centers for solving multi-objective COPs. Finally, the proposed algorithm is applied to three COPs, including the well-known flexible job shop scheduling problem, the unrelated parallel machine scheduling problem, and the test task scheduling problem. Both the single-objective optimization algorithm and the multi-objective optimization algorithm demonstrate competitive performance compared with existing methods.
Layout and Context Understanding for Image Synthesis with Scene Graphs

Advancements on text-to-image synthesis generate remarkable images from textual descriptions. However, these methods are designed to generate only one object with varying attributes. They face difficulties with complex descriptions having multiple arbitrary objects since it would require information on the placement and sizes of each object in the image. Recently, a method that infers object layouts from scene graphs has been proposed as a solution to this problem. However, their method uses only object labels in describing the layout, which fail to capture the appearance of some objects. Moreover, their model is biased towards generating rectangular shaped objects in the absence of ground-truth masks. In this paper, we propose an object encoding module to capture object features and use it as additional information to the image generation network. We also introduce a graph-cuts based segmentation method that can infer the masks of objects from bounding boxes to better model object shapes. Our method produces more discernible images with more realistic shapes as compared to the images generated by the current state-of-the-art method.
Extreme learning machine with local connections

This paper is concerned with the sparsification of the input-hidden weights of ELM (extreme learning machine). For ordinary feedforward neural networks, the sparsification is usually done by introducing certain regularization technique into the learning process of the network. However, this strategy cannot be applied for ELM, since the input-hidden weights of ELM are supposed to be randomly chosen rather than iteratively learned. To this end, we propose a modified ELM, called ELM-LC (ELM with local connections), which is designed for the sparsification of the input-hidden weights as follows: The hidden nodes and the input nodes are divided respectively into several corresponding groups, and each input node group is fully connected with its corresponding hidden node group, but is not connected with any other hidden node group. As in the usual ELM, the input-hidden weights are randomly given, and the hidden-output weights are obtained through a least square learning. In the numerical simulations on some benchmark problems, the new ELM-LC behaves better than the traditional ELM and the ELM with normal sparse input-hidden weights.
Effects of Likeness and Synchronicity on the Ownership Illusion over a Moving Virtual Robotic Arm and Hand

In this study we investigated body ownership over a virtual hand and arm as a function of their visual appearance (likeness) and synchronicity of visuo-tactile stimulation with a virtual electric toothbrush and a vibrotactile glove. In all conditions, participants controlled the movement of arm and fingers, maintaining synchronicity in motor-proprioceptive-visual signals. While the effects of varying likeness and temporal synchronicity of visual and haptic stimuli on the ownership illusion have both been investigated individually before, their relative contribution is still unknown. We find that likeness should be complete: making only the hand robotic reduces the subjective ownership illusion to same level as that of a full robotic arm and hand. Visuo-tactile synchronicity is not a hard prerequisite for an ownership illusion to occur: a high degree of agency with congruent motor-proprioceptive-visual cues and an arm/hand layout similar to one's own body can be sufficiently strong to overrule incongruent visuo-tactile cues. This work is part of a larger study on the relative contribution of factors such as likeness, viewing mode, tactile stimulation and degree of agency on the body ownership illusion. The results may contribute to the enhancement of dexterous performance in remote telemanipulation tasks.
Cogni-Net: Cognitive Feature Learning Through Deep Visual Perception

Can we ask computers to recognize what we see from brain signals alone? Our paper seeks to utilize the knowledge learnt in the visual domain by popular pre-trained vision models and use it to teach a recurrent model being trained on brain signals to learn a discriminative manifold of the human brain's cognition of different visual object categories in response to perceived visual cues. For this we make use of brain EEG signals triggered from visual stimuli like images and leverage the natural synchronization between images and their corresponding brain signals to learn a novel representation of the cognitive feature space. The concept of knowledge distillation has been used here for training the deep cognition model, CogniNet <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , by employing a student-teacher learning technique in order to bridge the process of inter-modal knowledge transfer. The proposed novel architecture obtains state-of-the-art results, significantly surpassing other existing models. The experiments performed by us also suggest that if visual stimuli information like brain EEG signals can be gathered on a large scale, then that would help to obtain a better understanding of the largely unexplored domain of human brain cognition.
Survey on Neural Machine Translation for multilingual translation system

Neural Machine Translation (NMT) has provided promising results in the field of machine translation in recent times. As compared to the previously used rule-based methods or Statistical Machine Translation (SMT), NMT outperforms SMT in quite a few linguist categories. In this paper, we analyze various models, approaches and frameworks used in NMT to find an efficient method to create a translation system and to achieve accurate translation between Indian languages and English.
Pose and position trajectory tracking for aerial transportation of a rod-like object

This paper focuses on a pose tracking problem for a system composed of two connected rigid bodies, namely an aerial vehicle – with hovering capabilities – and a rod-like rigid body. The rod-like rigid body has an axis of axial symmetry, and the joint connecting the aerial vehicle and the rod lies along that axis. The aerial vehicle is meant to transport that object, which can swing/slung with respect to the vehicle: we refer to this as generalized slung load transportation because the object being transported is a rod-like object, as opposed to standard slung load transportation where the object is a point mass with no moment of inertia. Given this system, we consider two tracking problems. Firstly, we assume that a torque input on the joint is available, and, for this scenario, we formulate a semi-pose tracking problem, requiring the rod object to track a desired pose trajectory, apart from rotations around its axis of axial symmetry (and thus the semi qualifier). Secondly, we assume that no such torque input is available, and, for this scenario, we formulate a position tracking problem, requiring a specific point along the axis of axial symmetry of the rod object to track a desired position trajectory. Our approach for solving both these problems lies in finding a state and an input transformations, such that the vector field in the new coordinates is of a known form for which controllers are found in the literature, and which we leverage in this paper. Simulations are presented that validate the proposed algorithms.
Flooding Along Oda River Due to the Western Japan Heavy Rain in 2018

The heavy rain that hit Western Japan in July 2018 triggered the worst rain-related disaster in the Heisei era, with the total of dead and missing persons exceeding 230, mainly in the Hiroshima and Okayama Prefectures. At several locations along Oda River (of the Takahashi river system) and its tributaries, dikes were breached due to large-scale flood, leaving 51 persons dead. This paper aims to shed light on the scale of inundation along Oda River and its tributaries and identify the characteristics of and critical factors for human damage. Field surveys were conducted to measure flood marks in flooded areas and river channels, and gauge the extent of damage to people and property. The surveys found that a large area was inundated on the north side of Oda River, with an inundation depth exceeding 5 m for 1 km in the south-north direction and 3.5 km in the east-west direction, which made vertical evacuation of residents difficult. The findings that about 80% of the dead were found on the first floor of their houses, with those who had lived in a one-story house and those who had lived in a two-story house accounting for 50% each of the deceased, indicate how difficult even vertical evacuation was. The findings appear to be related to the considerable inundation depth and high rate of water level increase, along with the fact that the majority of the deceased were elderly people.
A Short Review of Age and Gender Recognition Based on Speech

In the speech research area, with the continuous advancement of intelligence, the speaker's age and gender recognition of have been widely used in security, e-commerce and many other fields. Meanwhile, the speaker's age and gender have a certain influence on the speaker recognition, emotion recognition and the speaker's intention recognition. This paper investigates the feature extraction, model selection, and corpus usage, summarizes the current main research results, and points out the key issues and prospects of the research.
Challenges in Requirement Engineering: Could Design Thinking Help?

Despite its many successes, requirement engineering (RE) still presents a series of challenges and limitations. Many of the challenges identified almost a decade ago are still present; in some cases, they are accentuated by the increasing complexity, extension and depth of software systems. Design Thinking (DT) is a process that emerged from design areas; due to its effectiveness in solving certain types of problems (wicked problems), its use has spread to other domains. The RE has historically been enriched by the contribution of other domains, theoretical models and frameworks. The contribution of this paper is its reflections on the potential benefits of DT techniques in solving current challenges in the practice of RE. Our contribution is based mainly on our experience as practitioners who train companies in DT, and in the analysis on recent research on the use of DT in RE.
Soft Tissue Removal in X-Ray Images by Half Window Dark Channel Prior

Soft tissue in X-ray images obscures the bone structure such that the details on bones are not clear. Conventional methods simultaneously enhance the image contrast for the soft tissue and the bones. Here we propose to remove all soft tissue in X-ray images, making the bone structure clear. For this purpose, we first propose a half window dark channel prior and a half window guided filter. Then, we apply this prior and filter on X-ray images. After processing, the bone details in X-ray images become clear and sharp. Several experiments confirm the effectiveness and efficiency of our method. Our method can be used for bone segmentation, classification, recognition, and clinical diagnosis.