Reviewerly/exHarmony huggingface dataset:
Automated Perpendicular Parking System With Approximated Clothoid-Based Local Path Planning

This paper proposes an automated perpendicular parking system using approximated clothoid-based local path planning and a virtual towing method. Clothoid path planning provides smooth maneuvering, but we cannot obtain a closed-form solution. Further, it costs computational overhead. To cope with these problems, we present a novel vehicle control algorithm with approximated clothoid-based local path planning. We show that a parking control system could be presented as a closed-loop system by integrating path planning and control. The closed-loop system is represented as a discrete linear time-varying system and its stability is proved using the Lyapunov stability theorem. In the proposed system, we also see that a numerical singularity problem may incur, which could bring undesirable steering. To avoid numerical singularity, we also propose a virtual towing method. From numerical simulations, we show that the vehicle converges smoothly to the parking spot.
Single image dehazing based on fusion of sky region segmentation

Abstract In order to solve the problem of image sharpening with sky region, a single image dehazing method combining sky region segmentation was proposed. Firstly, a fusion sky segmentation method combining OTSU algorithm with Canny operator edge detection is proposed to divide foggy image into sky region and non-sky region. Secondly, a linear index was constructed for the sky region using the dark channel prior sky transmittance to modify the sky region transmittance. For the non-sky region, a fast guided filter was used to optimize the dark channel prior non-sky transmittance. The two were combined to obtain the final transmittance, and the atmospheric light value was optimized by means of average estimation. Finally, combined with the atmospheric scattering model, the image is reconstructed dehaze. The experimental results show that the proposed method can segment the sky more accurately, estimate the atmospheric light value and transmittance more accurately and efficiently, and has the advantages of high contrast, natural color and clear details.
Performance analysis of a hybrid agent for quantum-accessible reinforcement learning

In the last decade quantum machine learning has provided fascinating and fundamental improvements to supervised, unsupervised and reinforcement learning. In reinforcement learning, a so-called agent is challenged to solve a task given by some environment. The agent learns to solve the task by exploring the environment and exploiting the rewards it gets from the environment. For some classical task environments, such as deterministic strictly epochal environments, an analogue quantum environment can be constructed which allows to find rewards quadratically faster by applying quantum algorithms. In this paper, we analytically analyze the behavior of a hybrid agent which combines this quadratic speedup in exploration with the policy update of a classical agent. This leads to a faster learning of the hybrid agent compared to the classical agent. We demonstrate that if the classical agent needs on average $\langle J \rangle$ rewards and $\langle T \rangle_c$ epochs to learn how to solve the task, the hybrid agent will take $\langle T \rangle_q \leq \alpha \sqrt{\langle T \rangle_c \langle J \rangle}$ epochs on average. Here, $\alpha$ denotes a constant which is independent of the problem size. Additionally, we prove that if the environment allows for maximally $\alpha_o k_\text{max}$ sequential coherent interactions, e.g. due to noise effects, an improvement given by $\langle T \rangle_q \approx \alpha_o\langle T \rangle_c/4 k_\text{max}$ is still possible.
A Virtual Reality Cycling System Based On Multi-Sensor Fusion

Cycling is a popular sport, however, many enthusiasts can't go outside for the weather and traffic accidents. In order to solve this problem, we propose a virtual reality cycling system with multi-sensor fusion. Hall sensors, gyroscopes and other sensors are used to capture the cycling speed and rotation angle. Using Bluetooth to exchange realtime data with the mobile virtual reality scene, we can monitor the movement consumption and the speed and distance of the bicycle in real time. In order to simulate the natural wind effect during cycling and enhance the cycling experience, the real time speed of the bicycle is used to drive the motor to generate the natural wind from the fan. Finally, the virtual simulation scene of bicycle riding is built through the virtual reality environment of mobile phone, and the indoor VR cycling effect is realized.
Estimation of a regression function on a manifold by fully connected deep neural networks

Estimation of a regression function from independent and identically distributed data is considered. The L2 error with integration with respect to the distribution of the predictor variable is used as the error criterion. The rate of convergence of least squares estimates based on fully connected spaces of deep neural networks with ReLU activation function is analyzed for smooth regression functions. It is shown that in case that the distribution of the predictor variable is concentrated on a manifold, these estimates achieve a rate of convergence which depends on the dimension of the manifold and not on the number of components of the predictor variable.
Morphable Convolutional Neural Network for Biomedical Image Segmentation

We propose a morphable convolution framework, which can be applied to irregularly shaped region of input feature map. This framework reduces the computational footprint of a regular CNN operation in the context of biomedical semantic image segmentation. The traditional CNN based approach has high accuracy, but suffers from high training and inference computation costs, compared to a conventional edge detection based approach. In this work, we combine the concept of morphable convolution with the edge detection algorithms resulting in a hierarchical framework, which first detects the edges and then generate a layer-wise annotation map. The annotation map guides the convolution operation to be run only on a small, useful fraction of pixels in the feature map. We evaluate our framework on three cell tracking datasets and the experimental results indicate that our framework saves ~30% and ~10% execution time on CPU and GPU, respectively, without loss of accuracy, compared to the baseline conventional CNN approaches.
Marking Audio Signals Using Fractal Gaussian Noise

In paper an algorithm for embedding a pseudo-random sequence (PRS) with specified statistical characteristics into the detail coefficients of discrete wavelet decomposition is proposed to protect copyright, authenticity and integrity of audio files. For this purpose, fractal Gaussian noise (FGN) with a given fractal dimension is embedded in each bit of the PRS by replacing the detailing coefficients of the selected wavelet decomposition level. The studies carried out show the high efficiency of the proposed algorithm. It is shown that the best results are observed when embedding the detail coefficients of the first decomposition level into the wavelet and using wavelets of the Simlet 4 type.
Research on Virtual Pottery Teaching System

This work is mainly based on the research of virtual pottery teaching environment based on gesture interaction. A set of virtual pottery gesture recognition library is developed through Leap Motion equipment research.
Approach to Improve the Performance Using Bit-level Sparsity in Neural Networks

This paper presents a convolutional neural network (CNN) accelerator that can skip zero weights and handle outliers, which are few but have a significant impact on the accuracy of CNNs, to achieve speedup and increase the energy efficiency of CNN. We propose an offline weight-scheduling algorithm which can skip zero weights and combine two non-outlier weights simultaneously using bit-level sparsity of CNNs. We use a reconfigurable multiplier-and-accumulator (MAC) unit for two purposes; usually used to compute combined two non-outliers and sometimes to compute outliers. We further improve the speedup of our accelerator by clipping some of the outliers with negligible accuracy loss. Compared to DaDianNao [7] and Bit-Tactical [16] architectures, our CNN accelerator can improve the speed by 3.34 and 2.31 times higher and reduce energy consumption by 29.3% and 30.2%, respectively.
A Multi Source Graph-Based Hybrid Recommendation Algorithm

Images that widely exist on e-commerce sites, social networks, and many other applications are one of the most important information resources integrated into the recently deployed image-based recommender systems. In the latest studies, researchers have jointly considered ratings and images to generate recommendations, many of which are still restricted to limited information sources, sources namely, ratings with another input data, or which require the pre-existence of domain knowledge to generate recommendations. In this paper, a new graph-based hybrid framework is introduced to generate recommendations and overcome these challenges. Firstly, a simple overview of the framework is provided and, then, two different information sources (visual images and numerical ratings) are utilized to describe how the proposed framework can be developed in practice. Furthermore, the users’ visual preferences are determined based on which item they have already purchased. Then, each user is represented as a visual feature vector. Finally, the similarity factors between the users or items are evaluated from the user visual-feature or item visual-feature matrices, to be included the proposed algorithm for more efficiency. The proposed hybrid recommendation method depends on a link prediction approach and reveals the significant potential for performance improvement in top-N recommendation tasks. The experimental results demonstrate the superior performance of the proposed appraoch using three quality measurements - hit-ratio, recall, and precision - on the three subsets of the Amazon dataset, as well as its flexibility to incorporate different information sources. Finally, it is concluded that hybrid recommendation algorithms that use the integration of multiple types of input data perform better than previous recommendation algorithms that only utilize one type of input data.
Enhancing Aspect Extraction for Hindi

Aspect extraction is not a well-explored topic in Hindi, with only one corpus having been developed for the task.In this paper, we discuss the merits of the existing corpus in terms of quality, size, sparsity, and performance in aspect extraction tasks using established models.To provide a better baseline corpus for aspect extraction, we translate the SemEval 2014 aspect-based sentiment analysis dataset and annotate the aspects in that data.We provide rigorous guidelines and a replicable methodology for this task.We quantitatively evaluate the translations and annotations using inter-annotator agreement scores.We also evaluate our dataset using state-of-the-art neural aspect extraction models in both monolingual and multilingual settings and show that the models perform far better on our corpus than on the existing Hindi dataset.With this, we establish our corpus as the gold-standard aspect extraction dataset in Hindi.
An Overall Architecture Design of a Hybrid Blockchain Technology that Solves the Separation of Basic Data and Business Data

In order to realize the automatic and non-inductive reimbursement of invoices and eliminate the various inconveniences and risks caused by artificial reimbursement, this article proposes to combine blockchain technology with electronic invoices to complete the consumption, reimbursement, and tax payment through the blockchain network system. The node paperless invoice interactive process, and realize the automatic processing of off-chain data through the Internet of Things technology, seamless connection with the on-chain, while using a dual-chain structure to ensure the subsequent identification and business separation, through the combination of theory and technical architecture, Sense the invoice reimbursement system, thereby reducing China's overall reimbursement costs and promoting the improvement of the taxation system.
New Real Time (M-Bug) Algorithm for Path Planning and Obstacle Avoidance In 2D Unknown Environment

This paper proposes a new sensor-based path planning and collision avoidance algorithm for nonholonomic robot travelling in unknown 2D environment named as (M-Bug) algorithm in order to reduce the overall of traveled path. It uses the difference between slope lines to find and choose the smallest angle located between slope of straight line from mobile robot to goal point and slope from mobile robot and two endpoints of edges for obstacle, by choosing the smallest one which refers to nearest leave point to reach goal point and repeat this action with other obstacles until mobile robot reach to goal point. this algorithm based on using on-board sensors to generate information about the environment and obstacle located in front of mobile robot. This algorithm creates better performance when compared to the other traditional algorithms. It provides real time obstacle avoidance, compatible with LIDAR sensor and fast enough to be implemented on embedded microcontrollers. The used algorithm is based on a modification for existing path planning and obstacle voidance algorithms (Bug1, Bug2, Tangent-Bug, K-Bug, Dist-Bug ......etc.), and The Vector Field Histogram (VFH) Algorithm in unknown environment. The main advantage of this algorithm is that it is not limited to the origin-destiny line and changes the main line every leave point. MATLAB 2018a is used in simulation to validate the effectiveness of the proposed algorithm, and it is implemented on multiple scenarios to show the ability of the mobile robot to follow a path, detect obstacles, and navigate around them to avoid collision.
Railway Components Wear: a Smart Platform for Full Traceability of Maintenance Activities

The railway components subjected to wear, e.g. ballast, sub-ballast, sleepers and general infrastructures, may generate off-design conditions which produce wear to the rail machines, as wagons and locomotives. The present work aims at describing a methodology which was followed to classify the infrastructure anomalies and analyse the possible induced effect on the rail machines. A platform was developed to include and manage the activities and data concerning the maintenance state of the components. In detail, the platform modules involved the spare part and storage management, the testing activities, the processing technologies for part restoring and used machines. The project was developed in collaboration with an Italian company which is daily involved in maintenance activities for rail machines and facilities.
DNFA: Differential No-Fault Analysis of Bit Permutation Based Ciphers Assisted by Side-Channel

Physical security of NIST lightweight cryptography competition candidates is gaining importance as the standardization process progresses. Side-channel attacks (SCA) are a well-researched topic within the physical security of cryptographic implementations. It was shown that collisions in the intermediate values can be captured by side-channel measurements to reduce the complexity of the key retrieval to trivial numbers. In this paper, we target a specific bit permutation vulnerability in the block cipher GIFT that allows the attacker to mount a key recovery attack. We present a novel SCA methodology called DCSCA - Differential Ciphertext SCA, which follows principles of differential fault analysis, but instead of the usage of faults, it utilizes SCA and statistical distribution of intermediate values. We simulate the attack on a publicly available bitslice implementation of GIFT, showing the practicality of the attack. We further show the application of the attack on GIFT-based AEAD schemes (GIFT-COFB, ESTATE, HYENA, and SUNDAE-GIFT) proposed for the NIST LWC competition. DCSCA can recover the master key with 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">13.39</sup> AEAD sessions, assuming 32 encryptions per session.
A Preliminary Path Planning Algorithm for the Last-mile Delivery by an Unmanned Aerial Vehicle in Urban Town Areas

The applications by means of drones or unmanned aerial vehicles (UAVs) are increasing in areas from military to industry, commercial, and civil. Examples of applications include surveillance, photography, agriculture, and parcel delivery. Increasing applications results in more people will experience drones in their daily lives. However, not all people are comfortable with the drones around them, this is especially true in terms of urban last mile (LM) drone delivery, people tend to get annoyed when noisy drones pass by their window. This paper presents a novel 3D path planning algorithm that is applicable to the last mile (LM) delivery in an urban environment. The algorithm is called the modified Jump Point Search (MJPS) algorithm. It is developed based on the original Jump Point Search (JPS) and combined with the A* search algorithm. The result of the MJPS algorithm shows an obstacle-free path with reduced residences’ perceived annoyance due to drone operation noise and operational risk. It can be used as a way to increase public acceptance for drone operations in close vicinity of their neighborhood.
Semantic Segmentation of Satellite Images using Deep Learning

Bidirectional in recent years, Deep learning performance in natural scene image processing has improved its use in remote sensing image analysis. In this paper, we used the semantic segmentation of remote sensing images for deep neural networks (DNN). To make it ideal for multi-target semantic segmentation of remote sensing image systems, we boost the Seg Net encoder-decoder CNN structures with index pooling &amp; U-net. The findings reveal that the segmentation of various objects has its benefits and drawbacks for both models. Furthermore, we provide an integrated algorithm that incorporates two models. The test results indicate that the integrated algorithm proposed will take advantage of all multi-target segmentation models and obtain improved segmentation relative to two models. Keywords: A Satellite Image, Deep Neural Network, U-net, SigNet.
Blockchain or Not? Optimal Decisions for Food Retailers Considering Consumer Expertise

Abstract This paper studies the benefit of the blockchain food traceability system (BFTS). Based on game theory and the willingness-to-pay model, pricing models are formulated considering important factors like the proportion of consumer with high expertise in traceability, risk attitude to doubtful traceability information and perceived convenience of traceability information checking. By compared the optimal total welfare under the BFTS and that under the traditional food traceability system in valuation analysis, conditions where applying the BFTS is more valuable than applying the TFTS are figured out. Finally, insightful management implications are given.
Blur-Specific No-Reference Image Quality Assesment for Microscopic Hyperspectral Image Focus Quantification

Hyperspectral (HS) imaging is a novel technique that allows for better understanding of materials, being an improvement in multiple applications. However, one of its main drawbacks is the focus assessment. This issue has already been covered for RGB images. Thus, in this study, it is going to be revised several no reference RGB image quality assessment algorithms (NR-IQA). To this aim, a HS image database was created by capturing different images of the same specimen at different working distances. NR-IQA algorithms were tested over monochromatic images extracted from the HS images. Additionally, a study through each independent wavelength was carried out. Results showed that some algorithms perform better for calibration samples and another ones for biological samples. Furthermore, focus differences were found in the initial and final wavelengths. In conclusion, HS image results are similar to the one obtained for RGB images but, there is still room for improvement.
A strongly complete axiomatization of intuitionistic temporal logic

Abstract In this paper, we consider the logic ${\textsf{ITL}}^{e}$, a variant of intuitionistic linear temporal logic that is interpreted over the class of dynamic Kripke frames. These are bi-relational structures of the form $ \langle{W, \preccurlyeq , f}\rangle $ where $\preccurlyeq $ is a partial order on $W$ and $f: W \to W$ is a $\preccurlyeq $-monotone function. Our main result answers a question recently raised by Boudou et al. (2017, A decidable intuitionistic temporal logic. In Computer Science Logic 2017, pp. 14:1–14:17. Vol. 82 of LIPIcs) about axiomatizing this logic. We provide an axiomatization of ${\textsf{ITL}}^{e}$ and prove its strong completeness with respect to the class of all dynamic Kripke frames. The proposed axiomatization is infinitary; it has two derivation rules with countably many premises and one conclusion. It should be mentioned that ${\textsf{ITL}}^{e}$ is semantically non-compact, so no finitary proof system for this logic could be strongly complete.
Taming the Interaction Jungle

Conversational interfaces have grown in popularity in recent years, thanks in large part to improvements to the underlying technology that drives them, a large and growing number of capabilities and an aggressive marketing effort by several large technology companies. Despite these advances, usage of conversational interfaces is low compared to competing interaction modalities, most notably smartphones and desktop computing. What is missing is a frank and detailed discussion of the embodiment of these interfaces from the get-go, rather than designers applying embodiment conservatively to their conversational interfaces as an afterthought. The potential benefits of embracing an embodiment first strategy to designing CUIs include improving the discoverability, memorability and enjoyment in using them.
Building Machine Learning Model for Predicting Breast Cancer Using different Regression Techniques

Abstract Breast cancer is one of the major life-threatening diseases in the world today. But early diagnosis would ensure timely treatment and even recovery. Apart from the medical procedures that help to identify cancer, techniques such as machine learning can be applied to predict the occurrence of the disease in patients by analyzing their cell data. In this work, an attempt is made to apply three different regression techniques Multiple, Ridge, and Polynomial regression techniques. The performance metrics reveal that Multiple regression has a better performance compared to the other models.
Applications of Genetic Algorithm in Water Resources Management and Optimization

It is the need of current scenario that water resource is to be retained and maintained. Different types of models have been applied for the optimization of Water Resource Management. Genetic Algorithm is one of the evolutionary algorithms, those are the part of Soft Computing, have been applied and different researches have done for the use of genetic algorithm in the water resource optimization. This paper is to review the different applications applied so far, their usefulness and also for their scope in the development of future models for the water resource planning and optimization.