Reviewerly/exHarmony huggingface dataset:
Research on Small Sample Image Recognition Based on Transfer Learning

Abstract With the improvement of electronic hardware configuration performance, high-speed computing of large and complex data can be realized, and artificial intelligence, which requires a large amount of data calculation has been rapidly developed. Nowadays, artificial intelligence has developed rapidly in image recognition, natural language processing and speech recognition, and also has achieved good results in these areas. Among them, the convolutional neural networks plays an important role in image recognition. Some excellent convolutional neural networks such as VggNet, GoogleNet and ResNet have reached more than 90% in image recognition accuracy. But these convolutional neural network models are based on big data, consume a lot of computing time, which is very costly. Therefore, when data is limited, hardware configuration is low, and time is tight, a research on small sample image recognition based on transfer learning becomes very necessary. In this paper, the inception-V3 model is used to transfer learning. The training nodes of the model are skillfully used to train the model on the data set of ten kinds of fruits. The model is tested by the training model and good accuracy is obtained. The advantage of the experiment is that the training time is short, the required data set is small, and the image recognition accuracy is high
Trade-offs between inference and learning in image segmentation

During end-to-end learning, application level performance metrics, in combination with large training sets, are used to optimize deep neural network pipelines for the task at hand. There are two main places where application level performance metrics are typically introduced: in energy functions that are minimized during inference and in loss functions that are minimized during training. Minimizing energy functions and minimizing loss functions are both hard problems in the general case and an application specific trade-off must be made between how much effort is spent in inference versus training. In this paper we explore this trade-off in the context of image segmentation. Specifically, we use a novel, computationally efficient, family of networks to investigate the trade-off between two traditional extremes. At one extreme are inference networks that minimize a correlation clustering energy function. At the other extreme are learning networks that minimize a Rand Error loss function.
Single Image Defogging Method Combined with Multi-exposure Fusion Approach Based on Parameter Dynamic Selection

A single image defogging method combined with multi-exposure fusion approach based on parameter dynamic selection is proposed in this paper. Gamma correction was used to improve the contrast of the haze source image, and spatial linear saturation stretch was used to enhance the color saturation. Then, multi-exposure fusion scheme based on dynamic parameter selection is used to collect the patches with the best contrast, saturation and texture structure from each image and fuse them into a single fog-free image. The experimental results show that the proposed method is effective in defogging.
An Approach for Handling Partially Visible Human Shapes in People Detection Systems

In this paper, we propose a novel approach for handling occlusion in people detection systems using deep learning technique. The occlusion is handled by applying a part-object detection classifier on top of a whole-object classifier for detections with low confidence in our proposed architecture. In contrast to the other approaches, the proposed framework uses only one part-object detection classifier that is capable of performing a multi-class inference on the proposed regions. Experimental results show that our approach is able to detect partially visible human shapes better than other state-of-the-art models like ResNet50, ResNet101 and MobileNet and maintain the same level of performance as Inception. Although the paper presents results for people detection, the model can be applied to other domains as well.
Construction of Cloud Platform for Mining Group Operation

Abstract The operation important for mining group management. The traditional operation is complicated and inefficient for many reasons. Information technologies such as cloud computing, big data, and business intelligence, are inevitable requirements for the quality and efficiency of the mining group. In this study, a cloud platform structure for mining group operation was established with virtualization resource layer, cloud computing platform layer, and service access layer. Then the business logic on cloud platform was designed by using business model, computing logic, business intelligence tools, mathematical model and analysis method, which consists of distributed data acquisition, multidimensional data processing and storage, and business intelligence oriented analysis and decision-making. Finally, a case study was taken out at a large mining group which significantly increases the efficiency of management. The study suggests that Cloud Platform for Mining Group Operation can greatly improve the efficiency of information circulation and provide reliable decision support.
On the effectiveness of MH-based joint-decoders for very short Tardos fingerprinting codes

Digital content piracy is nowadays really burden-some both for companies and professionals. To overcome this problem, digital data producers usually protect their documents from illegal distributions by leveraging digital fingerprinting techniques for marking each copy of their documents in order to uniquely identify it. Unfortunately, in the case that multiple malicious users jointly collaborate for illegally sharing a document copy, they may forge the embedded fingerprint by simply comparing their copies and modifying the fingerprint's bits that differ in their copies. In [1] an optimal fingerprinting schema has been defined for accusing a guilty user with very high probability under the assumption that the fingerprinting code is sufficiently long. To remove this assumption, several joint-decoding techniques have been recently proposed [2], [3]. In this work, we perform a deep experimental analysis of the Metropolis-Hastings based joint-decoding approach defined in [3], with the aim of assessing the sensitivity to various parameters of the model, thus making it usable in practical contexts.
Secure Encrypted Data Transmission Via Multiple Chaotic Maps

Encryption is carried out for private sharing of confidential data. It is used to avoid data breach. The process involves randomising the image pixels using three chaotic sequences namely masking, swapping and permutation sequence. XOR is done between the image pixels and the masking sequence. The masked sequence is then shuffled using the permutation sequence. This is then diffused using the swapping sequence. This encrypted data is more randomised using four different permutation sequence namely discrete chaos, continuous chaos, dynamic random growth and coupled map lattice. The image is divided into four parts, and each part is encrypted using XOR based on the chess-based horse move algorithm. The strength of the image encryption process is calculated using the different encryption metrics.
Active Two Phase Collaborative Representation Classifier for Image Categorization

In recent times, the Sparse Representation Classifier (SRC), the Collaborative Representation Classifier (CRC), and the Two Phase Test Sample Sparse Representation (TPTSSR) classifier were proposed as classification tools that exploit sparse representation. Inspired by active learning techniques, this paper proposes an active Collaborative Representation Classifier that can be exploited by these supervised frameworks. The introduced Active Two Phase Collaborative Representation Classifier (ATPCRC) begins by estimating the label of the available unlabeled samples. At testing stage, based on the TPTSSR framework any test sample will have two representations that are calculated separately by using two different dictionaries. The first dictionary is composed of all samples having original labels. The second dictionary contains the whole dataset samples (original and predicted labels). The two kinds of class-wise reconstruction error are then fused in order to infer the label of the test sample. The proposal is validated on four public image datasets. The results shoe that the introduced ATPCRC can outperform the classic TPTSSR as well as several state-of-the-art approaches that use label and unlabeled data samples.
A Real-time Algorithm for Regional Crowd Counting Based on Survival Analysis Theory

Abstract This paper proposes a real-time algorithm for regional crowd counting based on survival analysis theory. Our algorithm can accurately calculate regional people flow in real time over a long period, which considers tourists’ stopping time as a type of survival time, and discretizes its continuous distribution probability as a proportion of stopping people in a monitoring region. Compared to other crowd counting methods, our algorithm calculates regional population in real time using a mathematical statistical model, which greatly reduces the dependence on hardware devices. Experiments in two real scenarios verify that the average accuracy of our algorithm is over 91% in comparison with 60% of sensing devices.
Segmentation of speech on phonetic elements for systems of speech information protection

The article is devoted to the development of speech segmentation algorithm on phonetic elements for the synthesis of speech-like signals in speech information protection systems. The main attention is paid to establishing the boundaries of phonetic units of speech, taking into account the influence of this factor on the quality of the synthesized speech by the compilation method. The features of establishing the boundaries of phonemes for fused speech and the influence of this factor on the quality of synthesized speech on the basis of phonemes are considered. It is proposed to ensure the quality of synthesized speech beginning and ending phonemes at the segmentation set in the transition implementation of a signal through zero and in the synthesis of speech-like signals to use the spline function at the boundaries of segments phonemes.
Distributed Classification of Text Streams

Text stream classification is an important problem that is difficult to solve at scale. Batch processing systems, widely adopted for text classification tasks, cannot provide for low latency. Distributed stream processing systems can offer low latency, but do not support the same level of fault tolerance and determinism as the batch systems. In this work, we demonstrate how the distributed stream processing features can affect the results of a typical text classification data flow. Our analysis shows emerged trade-offs between fault tolerance and reproducibility on the one side, and performance on the other side. We outline potential ways to solve the revealed issues and to handle streaming features.
Search-Based Motion Planning for MAVs Decentralized Formation Control

This paper presents a search-based motion planning method with motion primitives to solve the Decentralized Formation Control problems of Micro Aerial Vehicles (MAVs) in unknown environment. Given a team of $$ N $$ MAVs, our algorithm finds trajectories that navigate MAVs from their start positions to non-interchangeable goal positions (the labeled MAVs planning problem) in a collision-free manner. We use Lifelong Planning $$ A^{ * } $$ ( $$ LPA^{ * } $$ ) algorithm to leverage incremental search techniques for dynamic environment, and an additional graph pruning process is necessary to maintain the correctness and optimality of the planning results for searching with motion primitives. The proposed planner is shown to be efficient and its resolution complete and optimal. We demonstrate the feasibility of the generated formation control algorithm in simulations and real-world experiments.
Model-Based Extraction of Knowledge about the Effect of Cloud Application Context on Application Service Cost and Quality of Service

With the increased usage of cloud computing in production environments, both for scientific workflows and industrial applications, the focus of application providers shifts towards service cost optimisation. One of the ways to achieve minimised service execution cost is to optimise the placement of the service in the resource pool of the cloud data centres. An increasing number of research approaches is focusing on using machine learning algorithms to deal with dynamic cloud workloads by allocating resources to services in an adaptive way. Many of such solutions are intended for cloud infrastructure providers and deal only with specific types of cloud services. In this paper, we present a model-based approach aimed at the providers of applications hosted in the cloud, which is applicable in early phases of the service lifecycle and can be used for any cloud application service. Using several machine learning methods, we create models to predict cloud service cost and response times of two cloud applications. We also explore how to extract knowledge about the effect that the cloud application context has on both service cost and quality of service so that the gained knowledge can be used in the service placement decision process. The experimental results demonstrate the ability of providing relevant information about the impact of cloud application context parameters on service cost and quality of service. The results also indicate the relevance of our approach for applications in preproduction phase since application providers can gain useful insights regarding service placement decision without acquiring extensive training datasets.
Collecting Type Information Using Unit Tests for Customizing JavaScript Virtual Machines

To use JavaScript for Internet of Things (IoT), it is essential to reduce the size of the virtual machine (VM). Although operators in JavaScript are polymorphic, many applications apply operators to only a limited set of datatypes. Thus, customizing a VM to include only implementations of operators of datatypes used can reduce the size of the VM. In this research, we determine the set of operand datatypes that are possibly given to each operator for individual applications. Because JavaScript is a dynamically typed programming language, it is difficult to infer types accurately. Instead, as a practical approach, we obtain type information from executions of unit tests of the application. We implemented a prototype system to record datatypes that are given to each operator in executions of unit tests for Jasmine. The system does not record operands of operators executed by the Jasmine's code but only those by the application's code. We applied this system to two small applications and found that it determined type information with high accuracy.
Digital Literacies in the Classroom

In efforts to improve students' digital literacies on a STEM-focused campus, one university created a digital literacies initiative to support both faculty and students. Faculty development programming supported the development of assignment parameters, detailed assessment rubrics, and scaffolding activities. A campus tutoring center was piloted to support students' acquisition of digital literacies. This chapter offers examples from three faculty members who participated in the digital literacies initiative and implemented digital literacy assignments in their courses. The researchers offer best practices for campuses interested in developing digital literacy initiatives.
Optimal Trajectory Planning of Eye-head Gaze Shifts for Bionic Robot Vision System on Orchard Environment Perception

Abstract To make orchard robots obtain environmental information quickly and smoothly, this paper studies the neuro-mechanism of primates’ gaze shifts. Meanwhile, according to the optimal control theory, this paper considers time and energy as factors of formulating cost function and then analyses kinematic relationship between eye and head during gaze shifts to find constraint conditions. Solving the two-point boundary value differential equations of optimal control, this paper can extract the best control signal of coordinated eye and head movements. By building a multi-degree-of-freedom robot vision system corresponding to the biological model, this paper simulates primates’ gaze shifts and verifies the feasibility and validity of the method proposed.
An Improved Algorithm Inspired by Natural Biology for Image Enhancement Used in Space Station

Abstract In the space station, the uneven illumination of image brings significant challenges for image enhancement in vision system due to high dynamic range of light intensity and the light reflection of metal facilities. In order to solve this problem in monitoring, unattended operation and robot operation, Retinex algorithm was proposed to estimate the luminosity. However, the effectiveness of this method is highly dependent on the selected path and consumes huge computational cost. This paper proposes an algorithm inspired by the Archimedes spiral in natural environment to improve the processing time and enhance the images quality. First, the structure and mechanism of Human Visual System (HVS) are analyzed. Second, the original image is decomposed into the brightness and color layers based on the structural human eyes mechanism. Third, the brightness layer is processed by the improved Retinex algorithm to enhance the original image by merging it with the color layer. The proposed algorithm is validated with the experimental results.
Estimation of Speed and Distance of Surrounding Vehicles from a Single Camera

Deep Learning requires huge amount of data with related labels, that are necessary for proper training. Thanks to modern videogames, which aim at photorealism, it is possible to easily obtain synthetic dataset by extracting information directly from the game engine. The intent is to use data extracted from a videogame to obtain a representation of various scenarios and train a deep neural network to infer the information required for a specific task. In this work we focus on computer vision aids for automotive applications and we target to estimate the distance and speed of the surrounding vehicles by using a single dashboard camera. We propose two network models for distance and speed estimation, respectively. We show that training them by using synthetic images generated by a game engine is a viable solution that turns out to be very effective in real settings.
Blockchain in building logistics: emerging knowledge, and related actors in Sweden

Large building projects involve complex on-site logistics regarding materials and subsystems, often encompassing hundreds of vehicles handling incoming and outcoming goods and requiring precise timing and space handling. Such a material flow is generally decoupled from the respective economic flow; however, the integration of the two could, among others, foster a holistic overview of the full construction project production, facilitate the collaboration of the supply chain stakeholders, and optimize constructability. Blockchain technologies can enable an integration of these flows by using the distributed ledger facility inherent in a decentralized blockchain network, as well as smart contracts. This paper aims at reviewing the emerging knowledge on blockchain in construction and identifying different constellations of companies and flows in digital building logistics. Theoretically, the paper draws on a sociotechnical approach, which views the development of digitalization as an intertwined social and technical process, where technology is co-shaped with practice. Building on a literature review and interviews and dialogues with actors active in building logistics within Sweden, at least three digital building logistics constellations with the potential to implement blockchain solutions are identified: (1) large contractors integrating building logistics internally, to overcome transaction challenges and maintain power over business-critical supply processes, (2) clients employing independent third-party logistics consultants acting as convenors of different interests in the building logistics setup, and (3) other thirdparty actors such as construction equipment suppliers, offering customised digital building logistics solutions.
A Novel Method for Reconstructing Dynamic Bayesian Network

Abstract Upon data renewal, a Bayesian network might need reconstruction. In this paper, a novel method, which is based on a proposition, is proposed for such reconstruct work of a kind of symptom--disease Bayesian network scored by a K2 soring function. The proof of the proposition for the method is given firstly, and later the performance of the method is illustrated with numerical examples.
Lucid Peninsula, a Physical Narrative Art Installation Comprising Interactive 360° Virtual Reality Components

This article describes the design of the Lucid Peninsula, a Physical Narrative Art Installation, developed to engage a wide variety of audiences with a possible future, in which the practice of lucid dreaming plays an important role. In this article, the authors present and discuss the results from the in-the-wild evaluation of the audience reception of the artwork. Of particular concern is the assessment of the role of the virtual reality (VR) interfaces designed to deepen involvement and interaction with the art piece. This article reports on the conception and production of the installation as well as its evaluation and derived insights. The study provides evidence of the positive role of the 360° mobile VR assets in strengthening the narrative and the artist's intent in the Lucid Peninsula artistic installation. Results show how the VR interface supported engagement, positive affect, and reflections on the physical narrative scenario.
Applicability of controlled natural languages for architecture analysis and documentation

Formal approaches are a prerequisite for automated quality control. However, such approaches are often difficult to learn and apply and thus have not found their way into mainstream software development. Controlled natural languages (CNLs) seek to overcome the drawbacks of formal approaches by hiding their formality behind a restricted set of natural language, which is intended to be easier to use. In this paper, we analyze the applicability of CNLs for automated architecture analysis and as a means for architecture documentation in an industrial case study. We evaluated the CNL with 12 experienced practitioners in a focus group. The results show that practitioners perceive CNLs as an appropriate and an understandable means for formalizing and documenting architectural rules. Even without verifying rules automatically, a project can benefit from such languages, since they allow to find a common sense about architecture concepts and relations and to use those concepts and relations consistently throughout the development and evolution phase.