Reviewerly/exHarmony huggingface dataset:
Augmented reality display of neurosurgery craniotomy lesions based on feature contour matching

Traditional neurosurgical craniotomy primarily uses two-dimensional cranial medical images to estimate the location of a patient’s intracranial lesions. Such work relies on the experience and skills of the doctor and may result in accidental injury to important intracranial physiological tissues. To help doctors more intuitively determine patient lesion information and improve the accuracy of surgical route formulation and craniotomy safety, an augmented reality method for displaying neurosurgery craniotomy lesions based on feature contour matching is proposed. This method uses threshold segmentation and region growing algorithms to reconstruct a 3-D Computed tomography image of the patient’s head. The augmented reality engine is used to adjust the reconstruction model’s relevant parameters to meet the doctor’s requirements and determine the augmented reality matching method for feature contour matching. By using the mobile terminal to align the real skull model, the virtual lesion model is displayed. Using the designed user interface, doctors can view the patient’s personal information and can zoom in, zoom out, and rotate the virtual model. Therefore, the patient’s lesions information can be visualized accurately, which provides a visual basis for preoperative preparation.
3D Object Detection Based on Feature Pyramid Network

3D object detection aims to study how to perceive environmental information effectively, classify and locate interested objects accurately. In order to solve the problem that the object is easy to be lost in complex environments (such as partial occlusion, at long distance), we proposed a multi-scale 3D object detection method based on feature pyramid network (FPN). Firstly, input RGB image corresponding to the scene and bird's eye view (BEV) map into the pyramid feature extractor to construct multi-scale and strong semantic feature representations. Secondly, priori anchor boxes are applied to each layer of the feature pyramid, and the regional fusion features are obtained and input to the classifier and regressor. Finally, cross-scale detection is completed to obtain the best classification, dimensions and orientation estimation results. The experiments are conducted on the KITTI dataset and the results show that FPN-based feature extraction and cross-scale detection method can effectively improve the detection ability of partial occluded and long-distance objects.
Research on Fault Diagnosis Expert System of On-board Radio of a Certain Armored Vehicle Based on CLIPS

This article briefly introduces the concept and grammatical composition of the expert system language CLIPS. For a certain type of armored vehicle car radio failure occurrence mode, a failure analysis model is established using CLIPS, a knowledge base and inference engine are written, and CLIPS embedded programming is implemented in combination with VC++. This example is used to illustrate the application of CLIPS in armored vehicle fault analysis.
Evaluation model on uncertainty of the wind turbine state

It is beneficial to improve the production efficiency and quality of wind farm to accurately evaluate the state of wind turbines (WTs) and formulate operation and maintenance strategies. By analyzing the energy conversion relationship of WTs from wind energy to electric energy, the uncertainty factors affecting the state of WTs are studied, two comprehensive states of efficiency and performance in the production and operation of WTs are proposed, and the efficiency cloud model of wind energy conversion and the performance cloud model of electric energy production are accordingly established. Based on the cloud model eigenvalues, the deviation and stability index for evaluating the state of WTs are constructed, and the unified evaluation method for WTs is given. The problem of making direct comparison and evaluation between WTs with different capacities is solved. Through application of SCADA data of multiple WTs with different wind farms and different capacities in a whole year, it shows that the evaluation model in this paper has strong applicability and high accuracy. And it can be used not only to evaluate the running state of WTs, but also to select the WT type for new wind farm.
Applying Augmented Reality in the Italian Food and Dining Industry: Cultural Heritage Perspectives

This chapter explores the intention to use Augmented Reality (AR) technology within dining experiences and activities in the Italian food and dining industry by focusing on cultural heritage perspectives. Six case studies are presented drawing on state of art within the industry, catching insights and highlighting limits and opportunities for the future. From the results, it emerges that AR is considered as a potential means able to increase opportunities for the food and dining industry. At the same time, the industry is not quite culturally ready for the technology improvement of customer contacts. From the analysis emerges the intention of entrepreneurs to consider AR as a potential element of business improvement to enhance the connection between food value proposition and cultural heritage.
Dynamic Forest of Random Subsets‐based supervised hash signature scheme for secure user authentication in smart home environment

Summary The advent of information and communication technology has revolutionized the application of smart home through which the residents remotely establish superior control over the smart devices. The users and the smart devices interact over an insecure communication channel that may help an illegitimate user to gain access on the data disseminated by the smart devices, resulting in high exploiting attacks like privileged insider attack, smart device and gateway node impersonation attack, and smart node capture attack in the smart home. Thus, a secure remote authentication scheme that permits only legitimate users to gain access to the smart devices based on supervised learning becomes essential. In this article, Dynamic Forest of Random Subsets‐based supervised hash signature (DFORS‐SHS) scheme is proposed for robust user authentication in smart homes. This proposed DFORS‐SHS scheme concurrently performs the signed generation of random subsets such that each element of a signature is used for the generation of successive random subset elements for authentication. It utilized the strength of revealed secret keys through chaining approach that bundles the random subset generation and signature. This supervised learning approach only permits the signer of the original message to potentially generate a random subset and prevents the malicious intruder from gaining the access of the smart devices. The formal verification and the security investigation of the proposed DFORS‐SHS scheme are conducted using the Burrows‐Abadi‐Needham (BAN) and AVIPSA toolkit for determining communication cost, communication time, computation time, and energy consumptions incurred during the authentication process. It can be stated that, the communication cost and storage costs of the proposed DFORS‐SHS scheme was also identified to be comparatively minimized than the baseline smart home authentication schemes.
Application of the Digital Adaptive Platform for Learning Programming with the Teamwork Skills Forming Function

Research is carried out on approaches for the digital adaptive platform for learning programming with the teamwork skills forming function application. The work environment interface and key components that make the platform adaptable are demonstrated. The platform services responsible for information security functions are detailed. An example of the presented task is given.
A Hybrid Obstacle Avoidance Strategy Based on PSO in Source Location

This paper focuses on obstacle avoidance in the source location problem, in which robots capture the signal strength and find the signal source in an unknown environment. This work proposes a particle swarm optimizer (PSO) with a hybrid obstacle avoidance strategy to solve the problem. The signal strength is considered as the fitness function for PSO to guide robots. During moving, artificial potential fields are adopted to make robots avoid obstacles and each other. A deadlock escaping strategy is put forward to deal with the constraints of concave obstacles. The weighted average velocity of a robot is employed to check whether it is stuck by an obstacle. If so, a tabu area is set to push robots out of the area and prevent them from searching the same place again. These tabu areas offer robots key information about obstacles in an unknown environment and improve robots' ability of obstacle avoidance. The proposed algorithm is adaptive in unknown environments, meaning that no prior knowledge is needed. Simulation tests verify the effectiveness of the developed algorithm, showing satisfactory performance when dealing with concave obstacles.
Towards Reducing Labeling Efforts in IoT-based Machine Learning Systems

The number of Internet-of-Things (IoT) and edge devices has exploded in recent years. Coupled with recent advances in learning methodologies, these can make the vision of smart building a reality and transform how people interact with their environment. Deep learning-driven systems have been already successful in many IoT application areas of pervasive sensing and ubiquitous computing including human activity monitoring and occupancy detection. However, the robustness of such systems at scale is dependent on the availability of a large amount of data labeled by a human expert. Acquiring human annotation involves high effort, high cost, and is often error-prone. Therefore, learning to execute tasks in a machine learning system without manual inspection is of prime interest as it can open up opportunities for continual learning in the long-term deployment of IoT systems. In this work, we propose deep neural network-based approaches for segmentation and classification tasks in sensor streams with no or limited labels. We focus on reducing human labeling efforts in machine learning systems by 1) leveraging temporal features learned through deep neural networks for downstream tasks and 2) learning robust features in dynamic environments by utilizing multiple sensing sources and semi-supervised learning (e.g. small amount of labeled data).
Constraint Satisfaction for Motion Feasibility Checking

Task and motion planning (TAMP) is a key research field for robotic manipulation tasks. The goal of TAMP is to generate motion-feasible task plan automatically. Existing methods for checking motion feasibility of task plan skeletons have some limitations of semantic-free pose candidate sampling, weak search heuristics, and early value commitment. In order to overcome these limitations, we propose a novel constraint satisfaction framework for checking motion feasibility of task plan skeletons. Our framework provides (1) a semantic pose candidate sampling method, (2) novel variable and constraint ordering heuristics based on intra- and inter-action dependencies in a task plan skeleton, and (3) an efficient search strategy using constraint propagation. Based upon these techniques, our framework can improve the efficiency of motion feasibility checking for TAMP. From experiments using the humanoid robot PR2, we show that the motion feasibility checking in our framework is 1.4x to 6.0x faster than previous ones.
Mismatches Filtering of Unmanned Aerial Vehicle Images Based on Motion Vector Consistency

Fast and efficient mismatches filtering is significant for unmanned aerial vehicle (UAV) image matching. The traditional geometric model-based filtering is time-consuming and may deteriorate when nonrigid transform exists. To extract more reliable correspondences with less running time, filtering mismatches under the guidance of a subset of initial seed matches derived from putative matches is proposed. The method includes three steps: 1) obtain a subset of initial seed matches based on information entropy and range searching; 2) construct a seed kd-tree with initial seed matches to determine the remaining matches according to the consistency of k-nearest neighbors, and then add the true matching point to the seed kd-tree; and 3) repeat step 2 until all remaining putative matches are confirmed. Extensive experiments prove that the proposed method is competitive compared with state-of-the-art methods.
Real-time Behavior Analysis of an Inattentive Driver

Driver distraction is defined as an activity performed by a driver that diverts attention away from the driver’s primary ones and that can be the leading cause of motor vehicle accidents. The driver is the most important participant in vehicle control, including steering, throttling, braking, maneuvering, and other operations. These primary tasks must be accomplished safely for all traffic participants. This project proposes a driver distraction detection system that identifies various types of distractions through a camera observing the driver. Our goal is to build a high accuracy that captures the live movement of the driver and to distinguish whether the driver is driving safely or conducting a particular kind of distraction. Based on these activities, the system will classify them accordingly using a proper Machine Learning technique and instruct the driver about the distraction.
Research on Encryption and Anti-counterfeiting Combining One Thing a Code and Variable Digital Printing

Two-dimension code is widely used in the fields of anti-counterfeiting, anti-channeling and product traceability, but its anti-counterfeiting level is increasingly unable to meet the needs of consumers and brands. This paper proposes a method of combining variable data printing with one thing a code, and realizes random variable pattern printing through digital printing, and the system automatically associates the unique QR code of the product (one thing a code). Mutual authentication, using product packaging (or label) patterns and QR code, realizes double anti-counterfeiting. And through the RSA algorithm for encryption processing, by setting different permissions, products can be anti-counterfeit and traceable. The result proves that combined anti-counterfeiting technology include one thing a code and one object one picture, effectively solve the problem of QR code being maliciously modified and copied, therefore achieve the purpose of anti-counterfeiting and anti-channeling. In addition, this method can effectively solve the problem of data transmission and association, and variable data anti-counterfeit labels can be mass produced in digital printing equipment.
AES Based Enhanced Technique with Reduced Time Complexity

Lately, security has been emerging as the main issue for a plethora of web-based and other applications. The level of vulnerability of data is growing at two different levels, server security, and data transfer security respectively. While server security is handled by the firewall and other technologies, data transfer security is handled by using various cryptographic techniques. Symmetric data encryption and non-symmetric data encryption are two prime techniques used in cryptography. In the proposed research, an enhanced symmetric key is used for encryption purposes to ensure the integrity of data. Furthermore, this paper has proposed an enhanced AES-based algorithm that is capable of enhancing throughput without tampering with the security parameters of the existing technique. It has been found after implementation of the proposed system that the computation time for the proposed system has improved by 25% and 14% respectively for the mentioned test cases compared to the existing AES algorithm.
Solving logistics on the basis of fuzzy package

Abstract The article discusses an approach to solving a logistic problem based on fuzzy set theory. The classic fuzzy control module is developed at the node of the transport network, as well as when solving the problem of modeling the traffic control strategy in the network. An ambiguous rule base was developed for defining traffic control parameters at a transport network node, and rules were proposed for mapping a control parameter to a specific class and fuzzy rule base.
Automatic Keyword Extraction From Text Documents

Keyword indexing is the problem of assigning keywords to text documents. It is an important task as keywords play crucial roles in several information retrieval tasks. The problem is also challenging as the number of text documents is increasing, and such documents come in different forms (i.e., scientific papers, online news articles, and microblog posts). This chapter provides an overview of keyword indexing and elaborates on keyword extraction techniques. The authors provide the general motivations behind the supervised and the unsupervised keyword extraction and enumerate several pioneering and state-of-the-art techniques. Feature engineering, evaluation metrics, and benchmark datasets used to evaluate the performance of keyword extraction systems are also discussed.
IDIgles: 3D-printed Customized Goggles based on Scanned Faces

We present IDIgles, 3D-printed customized goggles based on scanned faces, to address the problem of sustained wearing of mass-produced, ill-fitting goggles, which can cause discomfort and soreness due to pressure on the user's face. To produce IDIgles, we obtain a digital model of user's face through 3D scanning, estimate a proper head pose using a neural network, and generate a 3D goggle model that fits the scanned face. The 3D model is then printed and post-processed to give the user a pair of customized goggles. Experiments showed that our method is able to generate closely-fitting customized goggles and improve sustained wearing experience.
New Loss Function for Multiclass, Single-Label Classification

Deep neural networks can perform complex transformations for classification and automatic feature extraction. Their training can be time consuming and require a large number of numerical calculations. Therefore, it is important to choose the good initial learning settings. Results depend, inter alia, on a loss function. The paper proposes a new loss function for multiclass, single-label classification. Experiments were conducted with convolutional neural networks trained on several popular data sets. Tests with multilayer perceptron were also carried out. The obtained results indicate that the proposed loss may be a good alternative to the categorical cross-entropy.
Privacy-Preserving System for Enriched-Integrated Service

In this study, we propose a secure data-providing system by using a verifiable attribute-based keyword search (VABKS), which also has the functions of privacy preservation and feedback to providers with IP anonymous server. We give both theoretic and experimental result, which show that our proposed system is a secure system with real-time property. One potential application of the system is to Integrated Broadcast-Broadband (IBB) services, which acquire information related to broadcast programs via broadband networks. One such service is a recommendation service that delivers recommendations matching user preferences (such as to TV programs) determined from the user's viewing history. We have developed a real-time system outsourcing data to the cloud and performing keyword searches on it by dividing the search process into two stages and performing heavy processing on the cloud side.
Analysis of Information Compression of Medical Images for Survival Models

In this paper, we investigate the methods for extraction of significant information from medical breast cancer images for survival analysis. In breast cancer diagnostics as well as in the analysis of other medical images it is still common to employ manual assessment by medical staff, which needs a lot of prior professional knowledge. Unfortunately, such feature engineering is difficult to manage, reproduce and it also depends on each specific task and expert experience. In this study, we analyze how high-level features can be used for survival models. We create unsupervised learning models for information compression to bottlenecks via convolutional neural networks (CNN) and autoencoders (AE) to obtain the informative covariates. Then we use these image-related covariates in Cox proportional hazards regression. We demonstrate that unsupervised methods allow extracting meaningful covariates that are significant for survival analysis without using explicit feature engineering or image labeling. We run the experiments on TCGA (The Cancer Genome Atlass) breast cancer dataset.
Correction: Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data

This article details a correction to the article: Task Group on Data Citation Standards and Practices, C.-I., 2013. Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data. Data Science Journal, 12, pp. CIDCR1–CIDCR7.
Formation of professional competencies as a basis of professional self-determination of students of agricultural universities

The article presents an analysis of the problem of the formation of professional competencies as the basis of students’ professional self-determination. Attention is paid to comprehending the aspects of the competence-based approach in professional education, which was developed in foreign practice and introduced into the Russian educational paradigm and acquires specificity in the space of interaction with Russian scientific psychological and pedagogical traditions. The article describes the components of professional competence, comprehends the important points of professional self-determination of students, and presents the main tasks of psychological and pedagogical support of the process of professional self-determination of students. A person in the reflexive-value space of his professional self-determination can become the author of personal self-development and consciously transform his professional biography. However, it is important to understand that this process is largely determined by the external circumstances of life, economic, political, social conditions. In this regard, it is necessary to note the importance of the educational space, the activity of teachers, who by their activity create certain psychological and pedagogical conditions for the professional self-determination of students.
Remote Torque Control of a Water-Driven Robot with Air-Hydraulic Servo Booster

Water-hydraulic system is environmentally friendly technology, and its application to robot manipulators is eagerly awaited. However, they are still expensive compared to oil-hydraulic systems because of the technical problems related to fast sliding motions between the solid materials contacting each other with the very small gap in the pumps or valves. To avoid these difficulties, we proposed an alternative solution, which lead to a pump-less hydraulic system called AHSB. The system utilizes an air-hydraulic booster to control the low-to-middle range water pressure by pneumatic servo valves. In this paper, we analyze the water-hydraulic dynamics and develop a robust pressure-based joint torque controller using Sliding Mode Control. The controller is implemented on a single-axis robot arm for the remote torque control application, which is crucially important for future tele-operated field robots. The experiments under quasi-static condition demonstrates the first successful torque control results on the water-driven robot with 10 kg payload, which is connected with 20 meter-long hydraulic hoses.
Detecting sparse heterogeneous mixtures in a two-sample problem

We consider the problem of detecting sparse heterogeneous mixtures in a two-sample setting from a nonparametric perspective, where the effect manifests itself as a positive shift. We suggest a two-sample higher criticism test, and show that it is first-order comparable to the likelihood ratio test for the generalized Gaussian mixture models in all sparsity regimes.