Reviewerly/exHarmony huggingface dataset:
AGV Navigation Based on AprilTags2 Auxiliary Positioning

Aiming at the problems of poor flexibility, complicated path maintenance and poor positioning performance in the current guidance technology of AGV, this paper designs and implements a new navigation system of automated guided vehicle (AGV) based on AprilTags2 auxiliary positioning. The system uses the Robot Operating System (ROS) as a platform to develop AGV navigation functions. The navigation system comprises two parts: the hardware and software layers. Firstly, hardware selection is performed after considering the actual requirements, performance, cost, and other factors in the hardware layer. Simultaneously, the AGV chassis and single-steering wheel walking mechanism are built to provide a stable and flexible operating platform for the software layer. Secondly, the software layer design includes two parts, namely the ROS navigation planning end and AprilTags2 detection. The ROS planning navigation end performs the design of four functional modules (i.e., AGV map construction, autonomous positioning, path planning, and path tracking), while the AprilTags2 detection part obtains the visual positioning pose of AGV by setting the AprilTags2 on each site and using the Kinect1 camera to detect. A more accurate AGV pose can be obtained by merging the former pose with the kinematic estimated pose. Finally, the two groups of positioning errors of the system before and after adding the AprilTags2 auxiliary positioning are tested and compared. The experimental results show that the navigation accuracy of the proposed system with AprilTags2 auxiliary positioning is significantly improved.
Simultaneous Heterogeneous Sensor Localization, Joint Tracking, and Upper Extremity Modeling for Stroke Rehabilitation

In stroke rehabilitation systems and applications, reliability, accuracy, and occlusion should be taken into consideration. Unfortunately, most existing approaches focus primarily on the first two issues. However, during the stroke rehabilitation process, occlusion leads to incorrect judgements even for medical staff. In order to tackle these three important issues simultaneously, we propose a heterogeneous sensor fusion framework composed of an RGB-D camera and a wearable device to consider occlusion and provide robust joint locations for rehabilitation. To fuse multiple sensor measurements when compensating for occlusion, we apply heterogeneous sensor simultaneous localization, tracking, and modeling to estimate the locations of joints and sensors and construct an upper extremity model for occlusion situation. Virtual measurements based on this model are used to estimate the joint's location during occlusion, and a virtual relative orientation technique is applied to relax system limitations regarding orientation. Experimental results using the proposed approach with synthetic data and data collected from ten subjects show a 4.6 cm error on average and about 15 cm error on average during occlusion. This constitutes a more robust approach for stroke patients which takes into account these three important issues.
An Unsupervised Deep Learning Method for Diffeomorphic Mono-and Multi-modal Image Registration

Different from image segmentation, developing a deep learning network for image registration is less straightforward because training data cannot be prepared or supervised by humans unless they are trivial. In this paper we present an unsupervised deep leaning model in which the deformation fields are self-trained by an image similarity metric and a regularization term. The latter consists of a smoothing constraint on the derivatives and a constraint on the determinant of the transformation in order to obtain spatially smooth and plausible solution. The proposed algorithm is first trained and tested on synthetic and real mono-modal images. The results show how it deals with large deformation registration problems and leads to a real time solution with no folding. It is then generalised to multi-modal images. Although any variational model may be used to work with our the deep learning algorithm, we present a new model using the reproducing Kernel Hilbert space theory, where an initial given pair of images, which are assumed non-linearly correlated, are first processed and optimized to serve the purpose of “intensity or edge correction” and to yield intermediate new images which are more strongly correlated and will be used for training the model. Experiments and comparisons with learning and non-learning models demonstrate that this approach can deliver good performance and simultaneously generate an accurate diffeomorphic transformation.
A Privacy-Preserving Context Ontology (PPCO) for Smart Connected Toys

Ubiquitous mobile technology like Smart Connected Toys (SCTs) have unique challenges of clearly defining context data elements due to unstructured, consistent, and persistent changes in the environment. SCTs interact with its context to achieve meaningful functionality while maintaining context data privacy. As SCTs become increasingly pervasive, the toys with their built-in features must be aware of and adapt to their changing contexts while providing a sense of privacy and security to contextual data processed to support its use. This paper presents a context profile through SCT Privacy-Preserving Context Ontology (PPCO) and examines the benefits of designing a context data model for SCT privacy goals. Our proposed data context model is an abstract model, which organizes elements of data and standardizes how they relate to one another. It organizes properties of related entries in SCT based on eXtensible Markup Language (XML) to depict and project how the SCT contextual information - related to the SCTs' environment - is assembled and maintained. Ultimately, the PPCO provides a structured description of the SCT context profile necessary to identify needed privacy controls to support SCT privacy goals.
Vision Based Topological State Recognition for Deformable Linear Object Untangling Conducted in Unknown Background

In this paper, we propose a deep learning based method to recognize the topological state of a deformable linear object (DLO). The utilization of deep learning can ensure that topological state recognition is robust to background change. This feature is useful if applications of DLO manipulation in real environment. And this feature has never be realized. In addition, the proposed scheme is also applied to the situation when multiple DLOs exist. This situation has never been considered. By integrating the proposed topological state recognition method and DLO untangling strategy, rope untangling experiments are conducted for both the situations of containing a single DLO and double DLOs.
Analyzing the usage of character groups and keyboard patterns in password creation

Purpose Using passwords to keep account and data safe is very common in modern computing. The purpose of this paper is to look into methods for cracking passwords as a means of increasing security, a practice commonly used in penetration testing. Further, in the discipline of digital forensics, password cracking is often an essential part of a computer examination as data has to be decrypted to be analyzed. This paper seeks to look into how users that actively encrypt data construct their passwords to benefit the forensics community. Design/methodology/approach The study began with an automated analysis of over one billion passwords in 22 different password databases that leaked to the internet. The study validated the result with an experiment were passwords created on a local website was analyzed during account creation. Further a survey was used to gather data that was used to identify differences in password behavior between user that actively encrypt their data and other users. Findings The result of this study suggests that American lowercase letters and numbers are present in almost every password and that users seem to avoid using special characters if they can. Further, the study suggests that users that actively encrypt their data are more prone to use keyboard patterns as passwords than other users. Originality/value This paper contributes to the existing body of knowledge around password behavior and suggests that password-guessing attacks should focus on American letters and numbers. Further, the paper suggests that forensics experts should consider testing patterns-based passwords when performing password-guessing attacks against encrypted data.
Modeling influence of change readiness on knowledge acquisition process: A case study

Change readiness (CR) has important impact on the success of knowledge acquisition (KA). So it is necessary for managers to know how KA is shaped by CR elements. Review of the extant literature shows a paucity in this regard specially regarding intra-organizational rather than inter-organizational level. Therefore, to bridge this gap, this study aims to present a fuzzy cognitive map (FCM) model in which interactions among CR elements for KA are identified. To do so, first, elements for measuring CR for KA were extracted from the relevant literature. Then, the identified elements were screened through distributing the first designed questionnaire among the select sample of survey organization. Then, the second questionnaire was used to measure the select elements and find the relation between them, using FCM and Mental Modeler Software. To improve CR for KA, some scenarios are suggested by the managers and the impact of each scenario on the whole CR elements is identified using sensitive analysis of the FCM model. After comparing the scenarios, it is concluded improving more elements in one scenario does not necessarily result in better impact on the whole CR for KA. This is due to the interaction among the elements which sometimes could be negative. The proposed model might help managers of the survey organization evaluate their CR improvement plans for KA before taking any actions. In addition, this study can give an idea to other organizations and industries to apply this approach according to their own relevant criteria. This study is among the first in its kind which presents a model using FCM method in which the interactions among the CR elements influencing KA are considered.
Salient Object Detection Based on Unified Convex Surrogate for Non-Convex Schatten-$p$ Norm

Nuclear norm and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> norm are the common regularization in salient object detection. However, existing literatures show that these terms either 1) are very slow for large scale problems due to singular value decomposition (SVD) on full matrix in every iteration, or 2) over-penalize the large singular values. In this paper, we propose to use respectively the non-convex weighted Schatten- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> quasi-norm and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${l_{p}}$ </tex-math></inline-formula> -norm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(0 &lt; p &lt; 1)$ </tex-math></inline-formula> for characterizing background and salient object. By matrix factorization, the optimization process, associated with the alternating direction method of multiplier (ADMM), is based on a unified convex surrogate which is only required to handle some small size matrices. Simultaneously, the convergence of algorithm is analyzed and validated. Experimental results indicate the new method usually outperform the state-of-the-art methods.
Gradient Boosting with Extreme Learning Machines for the Optimization of Nonlinear Functionals

In this paper we investigate the use of the Extreme Learning Machine (ELM) paradigm for the approximate minimization of a general class of functionals which arise routinely in operations research, optimal control and statistics problems. The ELM and, in general, neural networks with random hidden weights, have proved to be very efficient tools for the optimization of costs typical of machine learning problems, due to the possibility of computing the optimal outer weights in closed form. Yet, this feature is possible only when the cost is a sum of squared terms, as in regression, while more general cost functionals must be addressed with other methods. Here we focus on the gradient boosting technique combined with the ELM to address important instances of optimization problems such as optimal control of a complex system, multistage optimization and maximum likelihood estimation. Through the application of a simple gradient boosting descent algorithm, we show how it is possible to take advantage of the accuracy and efficiency of the ELM for the approximate solution of this wide family of optimization problems.
A Geo-indistinguishable Location Privacy Preservation Scheme for Location-Based Services in Vehicular Networks

In vehicular networks, the location-based services (LBSs) are very popular and essential for most vehicular applications. However, large number of location information sharing may raise location privacy leakage of in-vehicle users. Since the existing privacy protection mechanisms ignore the trajectory information, so that the location privacy of in-vehicle users and the trade-off between privacy and quality of service (QoS) cannot be effectively solved. In order to provide satisfactory privacy and QoS for in-vehicle users, in this paper, we propose an improved geo-indistinguishable location privacy protection scheme (GLPPS). Specifically, we first select an area of service retrieval (ASR) instead of user's real location to send to LBS, which can protect location privacy while avoiding the leakage of trajectory. Secondly, we establish an income model based on Stackelberg between in-vehicle users and attackers, and design an IM-ASR algorithm based on the Iterative method and Maximin theorem, to achieve optimal trade-off between privacy and QoS. Finally, we prove that GLPPS satisfies $$\alpha $$-differential privacy. Moreover, the simulation results demonstrate that GLPPS can reduce loss of QoS while improving location privacy compared to other methods.
Enhancing IDEA Algorithm using Circular Shift and Middle Square Method

Data security is an important issue and cryptographic algorithms are essential parts in security. IDEA is a symmetric block cipher and the fundamental innovation in the design is the use of operations from three different algebraic groups. The interaction of different group operations contributes to the confusion required for a secure cipher. However, IDEA has weak key classes which hindered in the faultless security of the data. Some keys containing a large number of 0 bits produce weak encryption. The encryption process of the algorithm is not enough to ensure the security against brute force attack. Circular shift and middle square method are proposed to enhance the algorithmic complexity of IDEA. The proposed enhancement will cover the number of rounds, bit adjustment, shifting, during the encryption and decryption phase. Combining middle square method and circular shift improves the randomness and promotes good diffusion.
Frontal View Synthesis Based on a Novel GAN with Global and Local Discriminators

Although researchers have achieved a relatively high accuracy in face recognition, pose variations are still a big problem in the real life. This paper proposes a novel Generative Adversarial Network with global and local discriminators for frontal view synthesis. Two discriminators are designed to deal with global features and local pixels respectively. This paper also introduces a combined loss function with L1 loss, blur loss, and identity preserving loss. The method is effective for facial analysis tasks with synthesized faces. Experiments show the method can preserve both identities and image quality.
Image Semantic Segmentation Method Based on Atrous Algorithm and Convolution CRF

For semantic image segmentation, much of the work is done using the conditional random field probability graph model as the backend processing for CNN. Among the more successful work, DCNN introduces atrous convolution to extend the perceptual domain without increasing the number of parameters and the amount of computation; capturing objects and contexts of images from multiple scales through the atrous spatial pyramid pool; by combining DCNN and Probability Graph model to enhance the positioning of object boundaries. However, conditional random fields have the disadvantage of slow training and reasoning and difficulty in learning internal parameters. We try to introduce convolutional CRF, solve the problem of CRF through convolution operation, and can be effectively implemented on the GPU. Our proposed algorithm has achieved good results in the PASCAL VOC-2012 semantic image segmentation task.
Processing graphs with barrierless asynchronous parallel model on shared-memory systems

Graph analytics systems rely on parallel models to realize parallel processing of large-scale graphs, in which the most extensively used models are the bulk synchronous parallel (BSP) and the asynchronous parallel (AP) model. The BSP model is simple and deterministic but has difficulties in expressing some graph processing algorithms, while the AP model is flexible to handle various kinds of algorithms but behaves inefficiently on memory-intensive algorithms due to higher scheduling overhead and internal global barriers. Additionally, although the matrix computation has demonstrated its high-performance characteristic in graph processing, most matrix-based systems only support the BSP model. To improve efficiency of the AP model for graph analytics, this paper proposes a barrierless AP model which uses matrix operations to process graphs and removes the global barrier in each superstep of the AP model to accelerate the execution progress of algorithms. The proposed model is evaluated by comparing it with both AP and BSP-based systems including GraphLab, GRACE, and GraphMat. Experimental results show that our model behaves better than the asynchronous systems, and even outperforms the BSP-based system on some memory-intensive graph algorithms as well as computation-intensive graph algorithms.
Short Text Embedding for Clustering Based on Word and Topic Semantic Information

Short text clustering is used in various applications and becomes a significant problem, while it also is a challenging task due to the sparsity problem of traditional short text representations. Early methods either cause waste of space or ignore the order of word sequence. To tackle these problems, a self-taught convolutional neural network model is proposed to construct short text representations. However, it extracts the semantic information only from the word context without any other unsupervised features and ignores the different contributions of textual content in clustering. In this paper, we propose an effective short text embedding method for clustering based on word and topic semantic information (STE-WT). Taking advantage of the topic semantic information and capturing the differences in the contributions of the content by an attention mechanism, our proposed model successfully constructs much better short text representations for clustering. Extensive experimental results on real datasets demonstrate the effectiveness and superiority of our framework compared with state-of-the-art methods.
Customized Interpretable Conformal Regressors

Interpretability is recognized as a key property of trustworthy predictive models. Only interpretable models make it straightforward to explain individual predictions, and allow inspection and analysis of the model itself. In real-world scenarios, these explanations and insights are often needed for a specific batch of predictions, i.e., a production set. If the input vectors for this production set are available when generating the predictive model, a methodology called oracle coaching can be used to produce highly accurate and interpretable models optimized for the specific production set. In this paper, oracle coaching is, for the first time, combined with the conformal prediction framework for predictive regression. A conformal regressor, which is built on top of a standard regression model, outputs valid prediction intervals, i.e., the error rate on novel data is bounded by a preset significance level, as long as the labeled data used for calibration is exchangeable with production data. Since validity is guaranteed for all conformal predictors, the key performance metric is the size of the prediction intervals, where tighter (more efficient) intervals are preferred. The efficiency of a conformal model depends on several factors, but more accurate underlying models will generally also lead to improved efficiency in the corresponding conformal predictor. A key contribution in this paper is the design of setups ensuring that when oracle coached regression trees, that per definition utilize knowledge about production data, are used as underlying models for conformal regressors, these remain valid. The experiments, using 20 publicly available regression data sets, demonstrate the validity of the suggested setups. Results also show that utilizing oracle-coached underlying models will generally lead to significantly more efficient conformal regressors, compared to when these are built on top of models induced using only training data.
Comparison Of The Performance Of Eigenface And Fisherface Algorithm In The Face Recognition Process

Based on previous research that has been conducted by other researchers about the Eigenface and Fisherface algorithms, where face image recognition uses eigenfaces with various conditions of variation from expression to face assessors has success rate until 75%. Adaboost face recognition, Eigenface PCA and MySQL produce 80% of various different conditions. Face recognition with fisherfaces by 73 face trials of different expressions and different positions, 93% success. From the previous research, the author wanted to know which algorithm has a level of speed in terms of the accuracy of time, distance and light sensitivity in the face recognition process. From the trials of the two algorithms, it produces success percentages and accuracy charts. However, this research shows us how to implement of Ficherface and Eigenface Algorithm by experimental, then show us the time process of Fisherface Algorithm and time process of Eigenface Algorithm for face recognition. The results of this study are Time Performance The Fisherface algorithm is faster than the eigenface algorithm and the accuracy of the Eigenface Algorithm is 96% and the accuracy of the fisherface algorithm is 97%.
News Recommendation Model Based on Improved Label Propagation Algorithm

Facing the ever-expanding scale of news information, how to filter and redundant information in complex and diverse data to accurately recommend information to users has become an important challenge. News recommendations have become a powerful tool for dealing with information overload. Scholars have done a lot of research on personalized news recommendation. However, due to the sparseness of user data, the traditional collaborative filtering algorithm is not only too time-consuming but also less accurate. Therefore, aiming at the characteristics of academic social networks, we propose a news recommendation model based on community detection, which builds a friend relationship community by using the users friend relationship and coherent neighborhood propinquity algorithm, and then integrates the collaborative filtering algorithm to recommend news to users. Through verification on the dataset of the academic social network SCHOLAT, we can prove that the recommendation model can achieve good accuracy while improving recommendation efficiency.