Reviewerly/exHarmony huggingface dataset:
Path planning for terrain of steep incline using Bézier curves

In order to navigate rugged, sloped, slippery, or, in general, challenging terrains, a robot needs to occasionally adapt its path plan in order to accommodate particular circumstances. Steep inclines are a prime example of situations where a non-shortest path plan can dramatically increase a mobile platform's ability to operate. In this paper, we present a path planning method that produces smooth curves that are longer than a straight line to the goal, but are designed to be optimal (not longer than necessary) for a given maximum inclination that the robot platform is capable of, while simultaneously taking into consideration the obstacles in the field.
A Forensic Technique to Detect Copy-Move Forgery Based on Image Statistics

The proliferation of easy multimedia editing tools has ruined the trust in what we see. Forensic techniques are proposed to detect forgeries unnoticeable by naked human eyes. In this paper, we focus on a specific copy-move forgery attack that happens to alter portions within an image. It may be aimed to hide any sensitive information contained in a particular image portion or misguide the facts. Here, we propose to exploit the image's statistical properties, specifically, mean and variance, to detect the forged portions. A block-wise comparison is made based on these properties to localize the forged region called a prediction mask. Post-processing methods have been proposed to reduce false positives and improve the accuracy(F-score) of the prediction mask. This decrease in FPR in the final result is comes from post processing method of overlaying multiple masks with different values of threshold and block_size of the sliding window.
Adaptive Learning of Probabilistic Neural Network in Situation of Overlapping Classes in Classification Task

The adaptive probabilistic neural networks for classification task in situation of overlapping classes is proposed. This network is designed to solve data classification task when data are fed sequentially in the online mode, and forming classes are mutually overlapped - the fuzzy case. The distinct feature of the network is that the learning process of the pattern layer uses the sliding window. This allows us to keep the constant number of neurons in this layer. Another point of the learning process is the tuning ability of activation functions’ widespread parameters in online mode. The described advantage allows us to improve the classification quality. Last but not least is the ability to compute both the probability and membership levels of each observation to each of forming classes. The proposed adaptive probabilistic neural network with fuzzy interference is simple in numerical implementation and has high learning speed. The results of experiments confirmed the correctness of approach under consideration.
A Quantitative Assessment of the Incomplete Integral Contrast for Complex Images

Contrast is the key factor that determines the quality of the image and effectiveness of its visual perception. The purpose of this work is to improve the accuracy of quantifying the contrast of complex multi-element images using different metrics. To this end, this paper proposes a new approach to quickly quantifying the overall contrast of complex multi-element images. The proposed approach is based on assessing the distribution of brightness for the reference image, which has a maximum value of contrast for the used metric, and on the subsequent normalizing the magnitude of the given contrast estimate for the current image. Based on this approach, new definitions of incomplete integral contrast were proposed for linear and weighted contrast kernels. The proposed approach allows us to compare the contrast of different images based on estimates obtained using different metrics. The proposed approach provides improved the accuracy and reliability of the comparative analysis of the objective quality of different images using different metrics of contrast. The proposed approach allows us to more fully assess the relationship between various techniques of quantifying the overall contrast of images.
Bionics as the New Required Vector of Development

The article analyzes and summarizes the principles of architectural bionics applied to various construction and technical structures. The analysis was carried out in the course of studying the scientific literature on the topic “Bionics. Architectural structures». The purpose of this work is to study the principles of architectural bionics, to study the possibility and effectiveness of their application for solving engineering and technical problems. The main task of the work was to study the directions and principles of development of architectural bionics, to assess the effectiveness of their application for solving technical problems, to find the correspondence of biological systems to construction and technical structures and facilities, to analyze known architectural structures from the point of view of architectural bionics.
Data Distillation for Traffic Sign Detection

This work is devoted to the traffic sign detection on images using deep learning methods. We focus on the problem of detector transfer to new datasets with different road signs. We present an algorithm for distilling a set of unlabelled data to select the most informative images to be labeled. This method allows to significantly reduce the amount of data labeling with a small decline of detector performance.
Collaborative Business Model Structures for Wireless Ambient Assisted Living Systems

Holistic assistance systems can enable people with physical or mental disabilities to stay longer in their own homes and also relieve the burden on the nursing sector. Interdisciplinary partners are increasingly taking up this topic in order to jointly search for suitable solutions. Smart sensors are generally used in these complex systems. Data is transferred and managed in a portal using minimally invasive sensors and wirelessly. Holistic assistance systems can include, for example, reminder functions, the recording of vital data, fall recognition and video communication options. Despite the high potential assessment, only a few Ambient Assisted Living (AAL) systems have been able to establish themselves on the market in the past. One reason for this is the lack of collaborative business models. However, existing business model structures are primarily suitable for describing the business model structure for a single company and therefore less suitable for a business model structure of a collaboration of companies. This type of cooperation therefore requires the development of new business model structures or the adaptation of existing structures. For this reason this paper presents first results on the development of business model structures for collaborations in the AAL sector. Furthermore, different business model variants with their respective advantages and disadvantages are presented.
Circular Local Directional Pattern for Texture Analysis

This paper presents a novel texture feature extraction method, Circular Local Directional Pattern (CILDP), that is inspired by Local Binary pattern (LBP) and Local Directional Pattern (LDP). This method relies on circular shape to compute the directional edge responses based on Kirsch Masks using different radiuses. The performance of the proposed method is evaluated using five classifiers on textures from the Kylberg dataset. Results achieved establish that the proposed method consistently outperforms LBP and LDP when different radiuses are considered.
Effective Sparsity-Prior Image Denoising Algorithm for CMOS Image Sensor in Ultra-Low Light Imaging Applications

An effective algorithm is designed for incorporating in a 3D stacked CMOS image sensor for image denoising in ultra-low light conditions. The algorithm originates from sparsity-prior of image and non-locally clustered sparse representation. The simulation results of the CIS image signal processing (ISP) demonstrate high-performance at intense noise level of ultra-low light images, which reveal a great potential of the CIS design in various applications such as night shot, security monitoring, machine-state inspection, medical imaging, biophysics detection, etc.
Zone based Indexing Model for Database Identification in Search Query Processing

In this paper, authors have proposed zone based indexing framework to store data into databases and to retrieve relevant web pages from databases. Web pages are gathered from registered website using web crawlers and relevant web pages are accessed from stored web pages for search queries placed by different users. Response of a search query depends on network framework and indexing structure of data. Orientation of web pages in databases plays a major role to provide minimum delay and maximum accuracy in response of a search query. In our approach, keywords in each web page and search query are identified and mapped with configured zones. Then, association of keywords with zones facilitates to identify databases from available databases to store web pages and to fetch relevant URLs of web pages for processing of search queries. Placement of web pages into databases and processing of search queries are performed with identification of zones using zone based indexing framework to provide accuracy. Comparative analysis has been done to exhibit superior performance of proposed approach over existing framework with respect to response time.
Mathematical Modeling and Prediction of Neural Network Training based on RC Circuits

A recent study [1] and our experimental results show that a Neural Network(NN) often learns the most important features early during training. This results in accuracy during training to follow similar curves in each case. The magnitude of weight updates are the highest during the early iterations and weight updates stabilize with continued training of the NN. In this paper, we examine the training curve of a neural network with respect to an Iteration Weight value of τ. Using τ, we can predict the approximate maximum accuracy and the required number of training examples to reach that specific accuracy level. The model resembles the well understood Resistance-Capacitor(RC) Charging Circuit and appears to act accordingly. Our motivation stems from the fact that predicting the number of training iterations for a model to reach a desired level of accuracy has not been attained. As such, our work should be useful for researchers in their efforts to improve their training designs.
Real noise in quantum link

In this paper, for the first time, we analyze real noise in a 143 km intercity quantum line which is based on the original technical solution for subcarrier wave quantum key distribution system. The result of fundamental statistical analysis of the quantum bit error rate is presented. We draw initial conclusions on the stability and security of quantum communications and provide recommendations on online noise monitoring to increase communication security.
Tumor Budding Detection in H&amp;E-Stained Images Using Deep Semantic Learning

Tumor buddings (TB), a special formation of cancerous cells that bud from the tumor front, are fast becoming the key indicator in modern clinical applications where they play a significant role in prognostic and evaluation of colorectal cancers in histopathological images. Recently, computational methods have been rapidly evolving in the domain of digital pathology, yet the literature lacks computerized approaches to automate the localization and segmentation of TBs in hematoxylin and eosin (H&E)-stained images. This research addresses this very challenging task of tumor budding detection in H&E images by presenting different deep learning architectures designed for semantic segmentation. The proposed design for a new Convolutional Neural Network (CNN) incorporates convolution filters with different factors of dilations. Multiple experiments based on a newly constructed colorectal cancer histopathological image collection provided promising performances. The best average intersection over union (IOU) for TB of 0.11, IOU for non-TB of 0.86, mean IOU of 0.49 and weighted IOU of 0.83 were observed.
Comprehensive Detail Refinement Network for Vehicle Re-identification

A novel comprehensive detail refinement network, called the CDRNet, to learn robust and diverse features from vehicle images is proposed. There are three modules in the proposed algorithm: the global attention, the detail, and the local feature refinement modules. The global attention module extracts crucial global characteristics while the detail and local refinement modules retrieve important minor features. Experiments on benchmark datasets, VeRi-776 and VehicleID, show that the proposed network outperforms state-of-the-art approaches and is very helpful for vehicle re-identification.
Monocular 3D Object Detection Using Feature Map Transformation: Towards Learning Perspective-Invariant Scene Representations

In this paper we propose to use a feature map transformation network for the task of monocular 3D object detection. Given a monocular camera image, the transformation network encodes features of the scene in an abstract, perspective-invariant latent representation. This latent representation can then be decoded into a bird's-eye view representation to estimate objects' position and rotation in 3D space. In our experiments on the Kitti object detection dataset we show that our model is able to learn to estimate objects' 3D position from a monocular camera image alone without having any explicit geometric model or other prior information on how to perform the transformation. While performing slightly worse than networks which are purpose-built for this task, our approach allows feeding the same bird's-eye view object detection network with input data from different sensor modalities. This can increase redundancy in a safety-critical environment. We present additional experiments to gain insight into the properties of the learned perspective-invariant abstract scene representation.
Optimal single-shot discrimination of optical modes

Retrieving classical information encoded in optical modes is at the heart of many quantum information processing tasks, especially in the field of quantum communication and sensing. Yet, despite its importance, the fundamental limits of optical mode discrimination have been studied only in few specific examples. Here we present a toolbox to find the optimal discrimination of any set of optical modes. The toolbox uses linear and semi-definite programming techniques, which provide rigorous (not heuristic) bounds, and which can be efficiently solved on standard computers. We study both probabilistic and unambiguous single-shot discrimination in two scenarios: the "channel-discrimination scenario", typical of metrology, in which the verifier holds the light source and can set up a reference frame for the phase; and the "source-discrimination scenario", more frequent in cryptography, in which the verifier only sees states that are diagonal in the photon-number basis. Our techniques are illustrated with several examples. Among the results, we find that, for many sets of modes, the optimal state for mode discrimination is a superposition or mixture of at most two number states; but this is not general, and we also exhibit counter-examples.
Survey of Crisis Identification Using Social Media Interaction

Social media have played integral roles in many crises around the world. Thailand faced severe floods between July 2011 and January 2012, when more than 13.6 million people were affected. This 7-month disaster provides a great opportunity to understand the use of social media for managing a crisis project before, during, and after its occurrence. However, current literature lacks a theoretical framework on investigating the relationship between social media and crisis management from the project management perspective. The paper adopts a social media-based crisis management framework and the structuration theory in investigating and analyzing social media. The results suggest that social media should be utilized to meet different information needs in order to achieve the success of managing a future crisis project.
U-Net Segmentation Achieve Clinically of HT29 Colon-Cancer Cell to Analyze Variations Morphology in Mitotic Defects and Micro Nuclei

Highly regarded techniques task to analyze mitotic defects and micro nuclei often used to identify cancer cells metastasize on the basis of a medical pathology evaluation. However, the above-mentioned fragmented proliferative of cancer cells during mitosis also reveals error-prone, even trained hands or clinicians. The segmentation task required to minimize error-prone might well be accomplished through several medical analyses. This approach is typically complicated and requires the assistance of powerful computational tools. The experimental approach is tested with HT-29 colon cancer cell datasets. The U-Net segmentation approach significantly improves metric segmentation performance. The outcomes obtained from the data analysis is IoU 94.30, Dice Coefficient 87.84, Precision 90.58, Reca1191.81, Accuracy 94.51, Loss 16.65, and Fl-Score 91.19.
Webqual and Importance Performance Analysis Method: The Evaluation of Tegal City's Public Service Information System Web Quality

According to Indonesian Law no. 14 of 2008 concerning public information disclosure states that every state organizer and public institution is encouraged to conduct public information disclosure. To carry out the transparency, the Tegal City Government has launched a service information system website at http://ppid.tegalkota.go.id as an online media in order to obtain and to provide Tegal City's public information services for the public. The interview result with the website service manager stated that the website has never been assessed against user satisfaction. In fact, the public information system is the image of the local government towards the seriousness of providing facilities for the public to be accessed easily. This study used WebQual 4.0 and Importance Performance Analysis (IPA) methods with 4 (four) instruments, there are usability quality, information quality, quality of service interaction, and visual quality with a total of 24 indicators presented in questionnaires. This type of research is descriptive with a quantitative approach and data is processed using SPSS software. The results of this study state that Tegal City public service information system website is good in general, but there are top priority improvements to be made immediately such as the attributes of providing accurate information and the ease for people to understand the information. The results of this research are expected to contribute positively to the development of Tegal city public service information system website in order to be better and qualified, and also improving competitiveness in realizing good governance.
Research on how to prevent online counterfeiting with blockchain-based cross border data sharing

While processed goods change ownership from manufacturers to customers, the complexity of distribution makes end-customers hardly tell genuineness and fakeness of purchased items. Product data are poorly shared between systems thus producers as well as costumers barely know what happened to their products. From this, we suggest how to prevent counterfeit goods by sharing product data end to end in crossing border circumstance with blockchain. Our suggestion is a conceptual construct applied for tracing any manufactured and processed goods since the project has just begun. We hope our construct contributes to solve real world problems.
A Mispronunciation Detection Method of Confusing Vowel Pair for Chinese Students

Abstract This paper presents a discriminative features based mispronunciation detection method for confusing vowel pair /i/ vs /I/ that are frequently mispronounced by Chinese learners of English. Firstly, the mean of the 39-dimensional Mel Frequency Cepstral Coefficients (MFCC) feature vector over all the frames of the current phoneme segment is employed as features to characterize the phoneme. Secondly, many specific acoustic features that can effectively capture the crucial properties of the long and short vowels are extracted. Finally, the Support Vector Machine (SVM) classifier is used for discrimination between confusing vowels /i/ and /I/ by using the discriminative features extracted from each phoneme. The experimental results show that, the proposed method can produce higher accuracy than the traditional Automatic Speech Recognition (ASR) based methods. In addition, the combination of spectral features with specific acoustic features can achieve better performance than using individual features.
Commentary: An SLA Perspective on Learner Corpus Research

There have been repeated calls over the years for greater interaction between Second Language Acquisition (SLA) research and Learner Corpus Research (LCR), including by myself and the editors of this volume, as well as many others (e.g. Granger et al. 2015; Lozano & Díaz-Negrillo 2019; Lozano & Mendikoetxea 2010; McEnery et al. 2019). In a range of publications (Myles 2005, 2007, 2008, 2015; Myles & Mitchell 2004), I have outlined, from an SLA perspective, what I perceive to be the gaps to be bridged in order for the two fields to be engaged in useful collaboration. My task with this commentary is to assess how far these gaps have narrowed, on the basis of the chapters in this volume.
Group Working Differential Evolution

Cooperative Coevolution (CC) is a method of improving the efficiency of evolutionary computation. Decision variables are divided into several species and the optimization is performed in each species. Search does not work in CC without properly dividing decision variables. In this paper, we combine the evaluation method of CC that can be searched without properly dividing the decision variables with Differential Evolution. The effectiveness of the search performance was evaluated using four benchmark functions, and the effectiveness was confirmed in the functions that have a dependency between variables.
Efficient Joined Pyramid Network Applied to Semantic Segmentation for GPU Embedded System

Fully Convolutional Networks (FCN) are the best methods for semantic segmentation. However, these networks are implemented in computers with high processing capabilities since they are computationally complex. For this reason, many strategies have emerged in the literature to propose novel efficient FCN for embedded applications. In order to contribute with this effort, we propose a network called Efficient Joined Pyramid Network (EJPNet), which is an efficient FCN that reduces the number of activations maps with pointwise convolutions in the encoder and also reduces the number of parameters with an Efficient Joint Pyramid Upsampling (JPU) decoder. EJPNet and other four state of the art efficient semantic segmentation methods were implemented on a Graphic Processing Unit (GPU) embedded system to obtain the performance in precision, number of parameters, memory footprint, Floating Point Operations per Second (FLOPS), and time processing. According to the results, EJPNet not only has the best number of parameters, FLOPS, and time processing, but also this network has one of the best precisions. Then, considering these results, EJPNet is a feasible semantic segmentation method for embedded systems applications.