Reviewerly/exHarmony huggingface dataset:
Metrics for Graph Partition by Using Machine Learning Techniques

In our previous work, we explored the possibility of applying machine learning technique to graph partition. We use some metrics to describe the graph, rank the execution time of some graph algorithm and feed them into the machine learning models. We proved that decision tree and KNN and good models of this problem. In the paper, we go on to investigate more metrics to describe the graph after partitioning. We found that AverageDegreeNotCut is also an important metric. We improve the precision score of original machine learning models by 4.9 percent.
Design Prompts for Virtual Reality in Education

The Learning Innovation team at the Open University, the UK’s largest higher education institution, is leading a project to support the design and implementation of educational Virtual Reality (VR). Using an iterative design methodology enables the team to explore VR’s educational affordances, and the new opportunities these represent to educators over established media forms. The design and development of two VR applications utilizing IBM Watson’s Artificial Intelligence (AI), for language learning and phlebotomy, has resulted in design principles that reinforces understanding of implementing AI in VR. The design principles are part of a broader ‘suitability toolkit’ enabling a practical assessment of the appropriateness of VR in educational contexts early on in the design process.
Personalized Topic Graph Generation Method Using Image Labels in Image-Sharing SNS

Due to the rapid growth of social networking services (SNSs), many researchers have conducted research to find the users’ interests from them in order to recommend potential friends or contents related to their interests. However, it is very difficult to discover the users’ interests because of noisiness and sparsity of the SNS data. To overcome the difficulty, we propose a modified Latent Dirichlet Allocation (LDA) model named LDA-IL that utilizes relatively low-noise image data compared with text documents. In addition, it is important to find implicit interests as well as explicit interests of the users. To do so, we propose a method of generating the personalized topic graph that represents the users’ interests. To prove the validity of the LDA-IL model and the personalized topic graph, we developed an illustrative scenario and performed experiments.
Applying computer simulation using optical design software to multi-spectral geometrical calibration of stereoscopic measurement systems

Stereoscopic imagers are widely used tools for precise three-dimensional (3D) characterization of various objects in industrial and biomedical applications. Narrow-band spectral imaging significantly increases capabilities of these devices, i.e. allows to analyze spatial distribution of spectral properties as well as to achieve higher image contrast, lower optical aberrations and, therefore, to improve geometrical measurements accuracy. Using spectral stereoscopic images for 3D measurements requires a proper choice of a number, positions and width of spectral bands used for calibration and interpolation of the calculated parameters. The experimental determination of these parameters does not guarantee the optimal choice and may be difficult to implement and time-consuming for a large number of spectral bands. In this paper, we demonstrate that the optical design software can be effective for the computer simulation of calibration, comparison of mathematical models and assigning spectral calibration parameters. We show the possibility to optimize the parameters of multi-spectral geometrical calibration to ensure the required measurement accuracy provided by the stereoscopic system on the stage of its optical design before manufacturing via the design of self-developed prism-based imager. Computer simulation allowed us to compare two camera models and various spectral options (conventional white-light as well as arbitrary number, positions and width of spectral channels) applied to calibration procedure. The results of computer simulation are confirmed by multiple experiments. Proposed approach may be used for estimation of 3D measurements errors caused by image noise, tolerances of optical components, temperature variations and other factors.
Diameter quantification of through holes in pipelines hidden by protective layers of composite materials using instantaneous shearography simultaneously in three shearing directions

Pipelines usually transport hydrocarbons in the oil and gas industry. Small amounts of salt water may be present, which can cause corrosion from the inside to outside. Severe corrosion may produce through holes in the pipe wall. External protective layers of fiber reinforced plastics has been applied to postpone the need to stop production to perform a definitive repair, but do not stop the internal corrosion. It is very important to monitor the dimensions of through holes hidden by the protective layers of composite materials, which cannot exceed critical size and compromise pipe safety. Classical methods, such as ultrasound, do not give very reliable answers when composite materials and steel are combined. There is a great demand for a reliable and practical in field non-destructive inspection method. This work brings a hybrid and reliable solution, which combines a new configuration of a portable one-shot shearography system with finite element methods, resulting in a portable and easy to apply solution. The paper describes the principle of the portable one-shot shearography system that is able of simultaneously measuring in three shearing directions[1]. Considerations and modeling of finite element to determine the response of the composite material to the variation of the internal pressure of the duct are discussed. The report details how the experimental data and numerical model results are combined through an iterative process to determine the diameter of the hidden hole. Instead of integrating the experimental results of shearography, the authors differentiate the results of finite element in the same directions as the one-shot shearography system does simultaneously. The minimum of the square error results in an estimation of the hidden hole diameter. Experiments were performed with 150 mm diameter tubes with 20 to 50 mm diameter holes hidden by 6 to 24 mm thick glass fiber reinforced plastic protective layers. Smaller thickness and bigger diameters led to better results. Mean deviations of the order of 10% were found for the whole set of tests. Although the authors consider it is possible to improve the results, deviations of the order of up to 10% are very acceptable results and represent a significant improvement over the classic methods used today. The robustness of the optical system, the ease of use of the algorithm and the level of uncertainty already achieved make the authors believe that the techniques developed here achieve the requirements and will be very useful for the oil and gas industry demands.
Application of 3D Symbols as Tangible Input Devices

A new type of tangible input devices employing three dimensional (3D) symbols for smart input is considered. A 3D symbol is an object derived from a 2D symbol that can be physically touched and manipulated. In this paper, application of 3D symbols as general-purpose tangible input devices to control software is presented. A systematic method to design effective and useful 3D symbols is briefly proposed. An overview of reassuring results from studies with 3D symbols designed with the method is reported. Such 3D symbols are anticipated to take full advantage of human abilities to improve human-computer interaction.
Towards Peripheral Awareness of Remote Family Member's Context Using Self-mobile Robotic Avatars (demo)

Real-time remote interaction has become easier and richer powered by recent advances in mobile computing and communication. A number of research have been explored on enriching family interaction by augmenting an interaction channel with asynchronous communication [6] or additional sensory stimuli [5]. However, it is still far from achieving a sense of living together for family members involuntarily living apart, especially in context-aware impromptu interaction. For families living together, it is trivial to naturally perceive behavioral and situational contexts of the other and initiate a relevant interaction intuitively. For example, a wife starts a casual chat with asking her husband what he is going to cook when she sees him going to the kitchen or hears a simmering sound.
Optimizing Association Rule Mining Using Walk Back Artificial Bee Colony (WalkBackABC) Algorithm

Association rule mining is considered to be the major task in data mining by most of the researchers where the process will find interesting relationships among various items in itemsets of huge database or a dataset. In this process, Aproiri algorithm is considered to be the familiar algorithm for performing association rule mining for implementing frequent itemset generation by providing minimum threshold value and we have explored advantages and disadvantages of association rule mining. In this paper, we have proposed WalkBackABC framework that optimizes ARM by increasing the exploration area and optimizes association rules which is further compared individually with apriori, FP growth and ABC algorithms in our proposed results where the rules generated are simple and comprehensible.
Towards the Completion of a Domain-Specific Knowledge Base with Emerging Query Terms

Domain-specific knowledge bases play an increasingly important role in a variety of real applications. In this paper, we use the product knowledge base in the largest Chinese e-commerce platform, Taobao, as an example to investigate a completion procedure of a domain-specific knowledge base. We argue that the domain-specific knowledge bases tend to be incomplete, and are oblivious to their incompleteness, without a continuous completion procedure in place. The key component of this completion procedure is the classification of emerging query terms into corresponding properties of categories in existing taxonomy. Our proposal is that we use query logs to complete the product knowledge base of Taobao. However, the query driven completion usually faces many challenges including distinguishing the fine-grained semantic of unrecognized terms, handling the sparse data and so on. We propose a graph based solution to overcome these challenges. We first construct a lot of positive evidence to establish the semantical similarity between terms, and then run a shortest path or alternatively a random walk on the similarity graph under a set of constraints derived from a set of negative evidence to find the best candidate property for emerging query terms. We finally conduct extensive experiments on real data of Taobao and a subset of CN-DBpedia. The results show that our solution classifies emerging query terms with a good performance. Our solution is already deployed in Taobao, helping it find nearly 7 million new values for properties. The complete product knowledge base significantly improves the ratio of recognized queries and recognized terms by more than 25% and 32%, respectively.
An AETA Geo-sound Anomaly Detection Method Based on Baer Operator

In view of the multi-component seismic monitoring system AETA's geo-sound precursor data (GD data), a new method is proposed for anomaly detection. Baer operator is executed for GD data to figure out the Baer feature sequence. The sliding interquartile method (IQR) can compute the abnormal index of the Baer feature sequence, and then the anomaly of the geo-sound data can be detected. And the experiment result in the northern central area of the Longmenshan fault zone shows that 61.54% geo-sound anomalies occurred in the 7 days before a nearby earthquake or 7 days after the earthquake. Meanwhile, 84.62% earthquakes could find at least one nearby station with geo-sound anomalies within ±7 days. The result indicates that there is a certain correlation between earthquakes and geo-sound abnormalities detected by the method. In addition, we compared the result of sliding IQR method based on Baer feature with that based on amplitude. The comparison result shows that Baer feature has better anomaly feature picking ability. This method fills the gap in the GD data anomaly detecting method domain.
Ambient Cues of Kitchen Counter in Guiding Cooking Activities for Alzheimer's Patient

Loss of memory is among the first symptoms reported by patients suffering from Alzheimer's disease (AD). The individual pattern of impaired memory functions correlates with functional brain integrity will result in the inability of the patient to complete a particular task. This research focuses on designing the ambient cues in the kitchen counter to guide Alzheimer's patient to complete cooking activities based on the concept of Stimulus-Response Compatibility (SRC) and whereas the activity is focus on the sequential workflow of cooking activities based on the Hierarchical Task Analysis (HTA). Wizard-of-Oz is used to simulate the behavior of theoretical intelligent ambient cues. The results show with the aid of ambient cues in the kitchen, patient be able to complete the task until the end even if there is confusing or disruption in the middle of the activity. The effectiveness of ambient cues show that patient can easily understood the cues of guiding the cooking flow when mistakes happened.
Pipelined FPGA Implementation of a Wave-Front-Fetch Graph Cut System

The current mainstream method for stereo vision is to find corresponding points of two-dimensional images obtained from multiple cameras and restore three-dimensional information using the principle of triangulation. However, the occlusion problem often makes it difficult to search for corresponding points. Therefore, a new approach has been proposed in which the three-dimensional space is directly considered as a three-dimensional graph instead of searching for corresponding points of two images. In this approach, a 3D grid graph is constructed based on luminance values obtained from the left and right cameras, and a highly likely object surface is obtained by cutting this graph. This paper proposes a pipelined architecture for 3D grid graph cut, aiming at a real-time stereo vision system. The system uses Wave-Front-Fetch algorithm, which is oriented for parallel processing. We achieved processing time of about 21 ms for a graph of $$129 \times 129 \times 16$$ nodes, resulting in a frame rate of about 49 fps. Our approach was about 19 times faster than a well-known graph cut software library.
The Fine Plane Range Estimation From Point Cloud

This paper deals with the fine plane estimation. For a plane detection the level connected component labeling algorithm is used. The precise plane position is determined by the plane's points histogram in the scanning dimension. Up till now, the final plane's range has been selected manually by a parameter. Plane presence in an input point cloud represents the higher points' concentration in the scanning dimension, higher than average value. The concentration decreases on both sides from a local maximum value. This paper presents the algorithm of the elbows' position estimation for the optimal plane's points range expression. The experimental part shows practical results of several examples and gives recommendations for parameters setting.
Motion Detection in Digital Video Recording Format with Static Background

Nowadays, security of valuable and secret assets is very important for large organization. Need of an efficient and reliable security system arises due to limitation of human resources and manpower. In such systems, video surveillance systems have their own significance. Although some people don’t like the idea of being monitored, surveillance systems improve the public security, allowing the system to detect dangers. Multimedia data typically means digital imagesaudio, video, animation and graphics together with text data. The acquisition, generation, storage and processing of multimedia data in computers and transmission over networks have grown tremendously in the recent past. In video surveillance, motion detection refers to the capability of the surveillance system to detect motion, identify the activity and capture the events while preventing the negligible activities. Motion detection is usually a software-based monitoring algorithm which, when it detects motions will signal the surveillance camera to begin capturing the event. Also called activity detection. An advanced motion detection surveillance system can analyze the type of motion to see if it warrants an alarm. In this research, we propose a novel motion detection algorithm for digital video recording (DVR) system using a Fuzzy Rule Based System that detects the motion in the running video intelligently.
Development and Oculographic Research of the Website Design Concept for Inclusive Education

The problem of finding new ways of web-design and usability research is topical. The article describes the approach to the redesign of the site for inclusive education with the research of usability at the design concept stage. The results of user testing of the new design concept of the site by the method of eye-tracking are presented. Eye Tracker equipment of an Eye Tribe model and the software OGAMA was used in the oculographic study process.
New approach to allocation planning of many‐task workflows on clouds

Summary Experience has shown that a priori created static resource allocation plans are vulnerable to runtime deviations and hence often become uneconomic or highly exceed a predefined soft deadline. The assumption of constant task execution times during allocation planning is even more unlikely in a cloud environment where virtualized resources vary in performance. Revising the initially created resource allocation plan at runtime allows the scheduler to react on deviations between planning and execution. Such an adaptive rescheduling of a many‐task application workflow is only feasible, when the planning time can be handled efficiently at runtime. In this paper, we present the static low‐complexity resource allocation planning algorithm (LCP) applicable to efficiently schedule many‐task scientific application workflows on cloud resources of different capabilities. The benefits of the presented algorithm are benchmarked against alternative approaches. The benchmark results show that LCP is not only able to compete against higher complexity algorithms in terms of planned costs and planned makespan but also outperforms them significantly by magnitudes of 2 to 160 in terms of required planning time. Hence, LCP is superior in terms of practical usability where low planning time is essential such as in our targeted online rescheduling scenario.
Characterization of the topography fidelity of 3D optical microscopy

The topography fidelity TF<sub>i</sub> indicates the accuracy of the estimation of the real surface, describes the instrument influenced deviation of a measured topography image and depends on the interaction of the surface topography with the instrument. To understand and investigate the topography fidelity of optical surface measurement instruments, and interference microscopes in particular, an analytical model based on the fraction of the total illumination criteria and a numerical model based on Richards-Wolf theory are used to characterize the topography fidelity of 3D optical microscopy. As reference artefacts step-like micro-structures with varying spatial frequency and therefore different aspect ratios are numerically investigated with the aforementioned models. To testify the feasibility of the numerical analysis, a commercial white light interference microscope has been employed to measure these reference artefacts. The relationship between the measured heights and the spatial frequency of the samples under investigation are detailed in this paper. The aspect ratio influences on measurement results predicted by the simulation models and the agreements with the experimental results are investigated and reported in detail.
A Modified Multi-size Convolution Neural Network for Winner Prediction Based on Time Serial Datasets

Researches of AI planning in Real-Time Strategy (RTS) games have been widely applied to human behavior modeling and combat simulation. Winner prediction is an important research area for AI planning, which ensures the decision accuracy. Convolution neural network has proved effective in predicting winner for RTS games. This paper focuses on modify the neural network to handle the time serial datasets. Experiments show that the modified evaluating algorithm can effectively improve the accuracy of winner prediction for time serial data in RTS games.
Vibration Analysis of Cyclic Symmetrical Systems by Quantum Algorithms

Quantum computers have provided exponentially faster solutions to several physical and engineering problems over existing classical solutions. In this paper we present two quantum algorithms to analyze the forced vibration of a mechanical system with cyclic symmetry. Our main algorithm solves the equation of motion of an undamped and nonelastic rotating system with cyclic symmetry consisting of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:math> sectors, by encoding the displacements of each sector at time <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:math> in a quantum state. The runtime of this algorithm is polylog in both <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:math>, thus exponentially faster than the analogous classical algorithm. Also we consider damped and elastic systems with cyclic symmetry and present another quantum algorithm to solve it in runtime polylog in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:math> and polynomial in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:math>.
Feedback cascade regression model for face alignment

Face alignment has made great progress in recent years and the cascade regression framework is one of the main contributors. However, the performance of this framework is unsatisfactory on heavily occluded faces or those far from the frontal pose. This is because regression is sensitive to hidden landmarks and unified initialisation can often lead to the method falling into local minima. The authors propose a new pipeline of salient‐to‐inner‐to‐all to progressively compute the locations of landmarks. Additionally, a feedback process is utilised to improve the robustness of regression. They bring out a pose‐invariant shape retrieval method to generate the discriminative initialisation. Experiments are performed on two benchmarks, and the experimental results demonstrate that the proposed method has a considerable improvement on the cascade regression model, and achieves favourable results compared with the state‐of‐the‐art deep learning‐based methods.