Reviewerly/exHarmony huggingface dataset:
Vehicle Detection Under Unmanned Aerial Vehicle Based on Improved YOLOv3

Under the unmanned aerial vehicle (UAV), the ground vehicle target is generally small in size, and the background information is more prominent. Therefore, we propose an improved detection model based on YOLOv3. First, in order to make full use of network feature information, we add connections between the front and back layers of the network to improve the information flow in the deep network. These layers are constructed as a loop and alternately updated structure, which reduces the depth of the network and improve the reuse of features. In addition, on the one hand, considering that the vehicle target is small in the UAV scene, the imbalance between the foreground and the background category is extremely serious. We improve the loss function and reduce the weight of the easily discernible background confidence loss, so that the model pays more attention to the hard samples. On the other hand, the improved IoU index is used as the loss function of regression task to improve the accuracy of small target detection. Experiments on the vehicle dataset of UAV show that the detection accuracy of the proposed algorithm is improved without reducing the rate.
Variants of Cuckoo Search with Levy Flight and Dynamic Strategy Based Cuckoo Search (DSBCS)

Cuckoo algorithm which is inspired from the breeding strategy of cuckoo bird is a type of meta-heuristics, which is widely used in solving NP-hard problems. Furthermore, CS has only one input parameter i.e., discovery probability. So, it is generally applicable to vast set of problems as compared to other multi parameter algorithms. Moreover, Cuckoo search uses Levy flight which is supposed to be global random walk as compared to other distribution schemes. In this paper, we have deeply studied Cuckoo Search Algorithm and Levy flight with important variants of both. We reviewed and compared Binary cuckoo search, Improved (Dynamic)cuckoo search, as variants of CS while Mantegna, Gaussian, and Cauchy distribution as variants of Levy walk. Furthermore, on the behalf of this we have proposed Dynamic Strategy based cuckoo search (DSBCS) which is supposed to give better results due to dynamic discovery probability, best global walk, good neighbor strategy after discovery and solution preserving strategy for replacement.
Any Curve Path Following of Snake-like Robots

Because of redundant freedoms, designing a robust any path following controller for snake-like robots is very challenging. In this paper, a novel path following controller, combining the robust following controller used in the unicycle robot and the fiber bundle model of snake-like robots, is proposed to follow any planar curve. More specifically, firstly, the kinematic model is established based on the fiber bundle theory which connects snake-like robots and unicycle robots. Then the robust path following controller for the unicycle robots is modified to be applied to the sake-like robots. Finally, to obtain a feasible controller, an estimation method for the orientation angle of the robot in the fiber bundle is proposed. To validate the proposed method, numerical simulations and experiments are performed. The results demonstrate that the proposed following controller is robust and valid.
Daily Locomotor Movement Recognition with a Smart Insole and a Pre-defined Route Map: Towards Early Motor Dysfunction Detection

Motor dysfunction, a well-known early sign of neurodegenerative diseases, is occurring to seniors at a growing rate and affects their physical capability of independent living if not treated effectively. The symptoms of motor dysfunction are hard to notice at early stages and can deteriorate in the long term. Thus, it is desirable to detect motor function changes in daily life in a noninvasive manner. This paper aims to accomplish this goal by proposing a method to auto-recognize nine types of daily activities from continuous movements with the use of a smart insole and a pre-designed route map. The route map creates a semi-controlled environment to help the subjects take actions comfortably and behave in experiments as they do in real life. The nine types of highly similar activities are selected from the motor examination and the balance evaluation system test. Preliminary experiments were done with four subjects with controlled and uncontrolled data collection. Four supervised machine learning classifiers are evaluated and compared for classification performance with a 2s window and different overlaps. With regards to the performance and robustness of classifiers, the Random Forest classifier trained with Mix Dataset shows the best results with an averaged classification accuracy of 98.19% in model training, 92.67% in cross-validation and 83.87% in prediction. The results show that it is feasible to recognize these nine activities from daily locomotor movement and further extract parameters of interest from activity periods for early motor dysfunction detection.
Detection and Avoidance of Input Validation Attacks in Web Application Using Deterministic Push Down Automata

The proper validation of input and sanitization is critical issue in developing web applications. Errors and flaws in validation operations resulting in malicious behavior in web application can be easily exploited by attackers. Since attackers are rapidly developing their skills and abilities they focus on exploring vulnerabilities in the web applications and try to compromise confidentiality, integrity and availability of information system. Input Validation Attacks (IVAs) are the attacks where a hacker sends malicious inputs (cheat codes) to confuse a web application in order to have access or destroy back end of application without knowledge of users. Input validation serves as the first line of defense for such attacks. Examples of input validation attacks include Cross Site Scripting (XSS), SQL Injection Attack (SQLIA), buffer overflow and directory traversal. Using Input validation attacks hackers can steal the sensitive data which decrease organization market value. In this project, we investigate the problem of detection and removal of validation bugs both at the client-side and the server-side code by using our approach. In this paper we proposed new idea that makes it possible to detect and prevent input validation attack using static and dynamic analysis.
El aprendizaje digital desde la visión Transdigital

The emergence of digital technology for personal use has revolutionized societies since its appearance in the 1980s. One of the scopes that has been promisingly transformed is the education. The influence of the digital content has manifested itself in different aspects of the educational structure, such as the curriculum, the socio-educational context, the didactic environments, the teaching-learning agents and the institutional organization. However, the students have manifested an itinerant conduct of search, selection, analysis, understanding and reflection of content on the network, which occurs in a complementary way to their academic training. The purpose of this work is to make a documentary review of digital learning from three approaches: historical (epistolary, audiovisual, virtual, web 2.0), descriptive (learning landscape, educational environments, networking) and analytical (student, teacher, learning context). The possibility of a digital-human convergence research line called Transdigital is also explored, as a result of the reflection regarding the irruption of technology in the educational field.
PAIC: Parallelised Attentive Image Captioning

Most encoder-decoder architectures generate the image description sentence based on the recurrent neural networks (RNN). However, the RNN decoder trained by Back Propagation Through Time (BPTT) is inherently time-consuming, accompanied by the gradient vanishing problem. To overcome these difficulties, we propose a novel Parallelised Attentive Image Captioning Model (PAIC) that purely employs the optimised attention mechanism to decode natural sentences without using RNNs. At each decoding phase, our model can precisely localise different areas of image utilising the well-defined spatial attention module, meanwhile capturing the word sequence powered by the well-attested multi-head self-attention model. In contrast to the RNNs, the proposed PAIC can efficiently exploit the parallel computation advantages of GPU hardware for training, and further facilitate the gradient propagation. Extensive experiments on MS-COCO demonstrate that the proposed PAIC significantly reduces the training time, while achieving competitive performance compared to conventional RNN-based models.
Bridging the Gap between ISO 26262 and Machine Learning: A Survey of Techniques for Developing Confidence in Machine Learning Systems

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Machine Learning (ML) based technologies are increasingly being used to fulfill safety-critical functions in autonomous and advanced driver assistance systems (ADAS). This change has been spurred by recent developments in ML and Artificial Intelligence techniques as well as rapid growth of computing power. However, demonstrating that ML-based systems achieve the necessary level of safety integrity remains a challenge. Current research and development work focused on establishing safe operation of ML-based systems presents individual techniques that might be used to gain confidence in these systems. As a result, there is minimal guidance for supporting a safety standard such as ISO 26262 - Road Vehicles - Functional Safety, to enable the development of ML-based systems. This paper presents a survey of recent ML literature to identify techniques and methods that can contribute to meeting ISO 26262 requirements. The surveyed literature is mapped onto the system development lifecycle V-model and the applicability of individual techniques and methods are discussed for each major phase of development.&lt;/div&gt;&lt;/div&gt;
Phase-modulation combined deflectometry for small defect detection

Deflectometry has been widely used to detect defects on specular surfaces. However, it is still very challenging to detect defects on semispecular or diffuse surfaces because of the low contrast and low signal-to-noise ratio. To address this challenge, we proposed a phase-modulation combined method for accurate defect detection. Based on the phase and modulation of captured fringes, a dual-branch convolutional neural network is employed to simultaneously extract geometric and photometric features from the phase-shifting pattern sequence and modulation, which improves the defect detection performance significantly. Compared to state-of-the-art methods, we believe the results demonstrated the proposed method’s effectiveness and capability to reduce false positives.
Orthotropic artifacts suppression for THz and x-ray images using guided filtering

The paper presents a novel method for suppression of the orthotropic stripe artifacts typical for sensitive optical detector arrays. The algorithm is based on the guided filtering technique where the guidance image is constructed from the input frame in a way that removes artifacts from local contrast structures while disregarding the low-frequency distortions. The artifact suppression procedure was applied to the images of human faces taken with the IR -- THz camera in the diagnosis of psycho-emotional states. In this case, the presence of orthotropic artifacts prevents digital image stabilization. We also demonstrated that adaptation of the alg
Futuristic Finger and its Modern Day Applications

Gesture regulated systems controlled by smart wearables have spearheaded the next epoch in human machine interaction. Gestures being intuitive and expressive are a more convenient way of communication. However, for developing a gesture controlled system, we need to accurately detect fingertips. In this paper, we present a fingertip detection system that can be efficiently used by smart wearables. This approach is free of markers and centroid-based techniques which are traditionally used to detect fingertips.The system's functionality is controlled by the number of fingertips in the frame. We collated a customized dataset, `1-2-3-4 Hands', which contained the images of different hands gesturing using one to four fingers. Using Faster RCNN with Inception v2 module, we trained this dataset to build a model capable of recognizing any of the first four fingertips, excluding the thumb. The count of fingertips is used to perform an action in real-time gesture controlled systems. Finally, we have implemented finger-control solutions such as AirWriting, AirDrawing, and Gaming Controls and enlisted their benefits.
Software smell detection based on machine learning and its empirical study

As an important maintenance measure, software reconfiguration is the key to detect the unreasonable part of the code module, namely code smell. Traditional detection methods rely on the experience of engineers, and the location efficiency of reconfiguration points is low. The existing automatic detection tools identify code smell with limited accuracy. Aiming at the problem that the number of reconstructed points in software system is huge and various, and the automation of reconstructed activities is low and difficult to optimize, the research framework of software smell prediction based on machine learning is studied and designed. Taking four common code smells as the research object, the classification algorithm and detection model of the best code smell are established, and the dimension reduction method of feature extraction is further improved. The highest accuracy rate is 89.8%, which can improve the automation level of software smell detection.
Deep learning self-calibration from planes

Many applications in computer vision require calibrated cameras, but identifying camera calibration parameters is a tedious task. Common methods require custom-built calibration patterns from which many images from different perspectives have to be taken. This research introduces a novel auto calibration method to reduce the work to a minimum. The method utilizes a neural network framework and learns the parameters through backpropagation and gradient descent. Three views of the same arbitrarily textured flat surface are used as an input. Two of the views are transformed to match the third reference view by plane homographies. Feature maps are extracted and the views are compared with their help. In- and extrinsic, as well as distortion parameters can then be learned by maximizing the similarity between the transformed views and the reference view. The results show that the method is able to find the calibration parameters of artificially distorted images. Results with real camera images are comparable to common methods that require planar calibration patterns, which makes the proposed method a quick alternative.
A calibration method for automotive augmented reality head-up displays using a chessboard and warping maps

We propose a calibration method for automotive augmented reality head-up displays (AR-HUD) using a chessboard pattern and warping maps. The HUD is modeled as a pinhole camera whose intrinsic parameters are determined by employing a stereo method. We select several viewpoints within the driver’s eye box and place a smartphone at each of them in sequence, whose position is sensed by a head tracker. By automatically shifting 2D points on the HUD virtual image to 3D chessboard corners within the view of the smartphone camera, we obtain a group of 2D–3D correspondences and then compute view-dependent extrinsic parameters. Using these parameters, we reproject the chessboard corners back to the virtual image. Comparing the results with measured virtual points, we acquire 2D distributions of biases, from which we reconstruct a series of warping maps as a tool for compensating optical distortions. For any other uninvolved viewpoint in the eye box, we obtain its corresponding extrinsic parameters and warping maps through interpolation. Our method outperforms the existing ones in terms of modeling complexity as well as experimental workload. The reprojection errors at 7.5 m distance fall within a few millimeters, which indicates a high augmentation accuracy. Besides, we calibrate the head tracker by utilizing the acquired extrinsic parameters and viewpoint tracking results.
Monospaced font detection using character segmentation and Fourier transform

In this paper, we propose a new method to detect monospaced font in text line images. Although many authors address more complex problems of text recognition or font recognition, this problem is still challenging when dealing with camera-captured images of identity documents. However, such a font characteristic can be useful in document authentication. These images usually contain complex backgrounds and various distortions. Our approach is based on a segmentation neural network and Fourier Transform for detecting “strong” periodic components in the segmentor output. The experimental results show that the combination of neural network and Fourier Transform deals with the task of monospaced font detection more effectively than the same Fourier analysis applied to the results of an image processing method for segmentation. The main advantage of the neural network is that its output does not depend on background, font and characters characteristics directly.
Continuous signed distance computation for polygonal robots in 3D

We propose a novel method adaptive subdivision (AS) to evaluate the distance function for moving general polygonal models. The distance function can have a positive and a negative value, each of which corresponds to the Euclidean distance and penetration depth, respectively. In our approach, the distance between a pair of objects can be evaluated along any time interval of the object’s trajectory; therefore it is called “continuous”, and a minimum of the continuous distance (MCD) is determined for collision avoidance. In order to compute a MCD for general polygonal models, we calculate the upper and lower bounds of the distance in the time interval and abandons the time intervals that cannot realize the MCD. We have implemented our distance evaluation method, and have experimentally validated the proposed methods to effectively and accurately find the MCDs to generate a collision-free motion for the HRP-2 humanoid robot.
Morpheme Segmentation for Russian: Evaluation of Convolutional Neural Network Models

This paper is aimed at evaluating the performance of existing models of morphemic analysis for Russian based on convolutional neural networks. The models were trained on a relatively small amount of annotated training data (38,368 words). We tuned the hyperparameters to accommodate the harder task setting, which helped improve the accuracy of the model. In addition to testing 15 different configurations on the available test set, a new sample of 800 words containing roots that are missing in the training sample (e.g. neologisms and recent loan words) was manually created and annotated for morphemic structure (the new dataset is made available to the community). The effectiveness of the models was evaluated on this sample, and it turned out that the performance of the CNN models was much worse on this set (an almost 30% drop in word accuracy). We performed a classification of errors made by the best model both on the standard test set and the new one.
MTab: Matching Tabular Data to Knowledge Graph using Probability Models.

This paper presents the design of our system, namely MTab, for Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2019). MTab combines the voting algorithm and the probability models to solve critical problems of the matching tasks. Results on SemTab 2019 show that MTab obtains promising performance for the three matching tasks.
Redefining the value proposition through green promotions and green corporate image in the era of Industrial Revolution 4.0: a study of Gen Y green consumers in Malaysia

Industrial Revolution 4.0 is shaping the future of purchasing behaviour due to digital disruption. Digital disruption ends conventional business structures. This study aimed to analyse the relationship between green promotion (GP) and Gen Y green purchasing decisions (GPD) in Malaysia. It also aimed to incorporate a new strategy by introducing green corporate image as a variable to identify if it mediates the relationship between GP and GPD and explore green promotion and green corporate image as perceived by Gen Y consumers. A qualitative approach was used in which focus groups were interviewed to refine the questionnaire. Secondly, interviewing data was analysed by using structural equation modelling to determine the significance of green promotions and green corporate image on Gen Y purchasing behaviour. The findings of this study will help manufacturers to modify their promotional strategies and assist policymakers to align the 11th Malaysia Plan with Industrial Revolution 4.0 goals.
Hybrid Intra-hour Solar PV Power Forecasting using Statistical and Skycam-based Methods

We propose and test a hybrid solar PV power forecasting model which optimally combines statistical and skycam-based forecasts. We show our model’s capability to produce accurate forecasts seamlessly from 10-s to 10-min ahead using high-frequency measurements in Canberra, Australia. The hybrid model relies on an empirical clear-sky model for solar power and the identification of three condition variables, which are able to separate and model characteristic events associated with them. It significantly overperforms both its statistical component and its skycam component alone, achieving a relative RMSE reduction (forecast skill) of 19% against persistence of clear-sky index at 5-min ahead.
On the contributions of non-technical stakeholders to describing UX requirements by applying proto-persona

Context: Requirements elicitation is a software development phase that should investigate both functional and user experience (UX) requirements. Proto-persona is a technique that encourages the attention on the needs of a group of users. Usually, its elaboration is conducted by software specialists, technical stakeholders. However, non-technical stakeholders usually know more about target users and frequently do not take part in proto-persona elaboration. Objective: This work has the goal of investigating the contribution of non-technical stakeholders in the specification of UX requirements by using the proto-persona technique. For this, we explored the construction of the proto-personas and the use of these to the prototyping of solutions. Method: We carried out an empirical study in two rounds from which we analyzed and compared the contribution that technical and non-technical stakeholders had on the specification of UX requirements. In the first, 8 non-technical and 5 technical stakeholders built proto-personas. Afterwards, 18 pairs of software developers created low fidelity prototypes by using the information of proto-personas.~For the two rounds, we conducted a qualitative analysis exploring which UX requirements were described and used. Results: Our results revealed that both stakeholders have written up details of UX requirements on the artifact, however, throughout different and complementary perspectives. We also could observe that proto-personas produced by both were used on the prototyping activity. Conclusion: Our paper contributed to demonstrate that non-technical stakeholders were able to contribute to the specification of UX requirements and that proto-persona can boost such activity.