Reviewerly/exHarmony huggingface dataset:
Development of a Bi-level Web Connected Home Access System using Multi-Deep Learning Neural Networks

Home entrance is a vital entry point that should be secured at all times. A bi-level home access system was designed and developed using face authentication and hand gesture recognition. The system's mainframe runs on a Raspberry Pi 3 minicomputer. The board serves as the computing platform to process various deep learning algorithms for face authentication and hand gesture recognition. It also serves as a communication hub which allows registered users to communicate with the system remotely via mobile application. Home occupants may also register emergency contacts such as their neighbours' for quick response at their property. An Android mobile application was developed for remote user interface. Google Firebase platform was used to store user profile and historical data. The face authentication consists of two steps, namely face detection and face recognition. The Multitask Cascaded Convolutional Neural Network (MTCNN) was employed for face detection, while the Inception ResNet was used for face recognition. Upon successful face authentication, the system proceeds to read the user's hand gesture. First, the system detects the hand using Single Shot MultiBox Detector (SSD) that runs on a Convolutional Neural Network (CNN). Next, a sequence of hand pose is recognised using the conventional CNN method. Based on experiments, the average detection/recognition accuracy under normal operating conditions using real face and realvideo captured by the system is approximately 95.7%. An occupant needs approximately 7s to complete the process to enter the house.
Anomaly Prediction Based on Machine Learning for Memory-Constrained Devices

This paper proposes an anomaly prediction method based on k-means clustering that assumes embedded devices with memory constraints. With this method, by checking control system behavior in detail using k-means clustering, it is possible to predict anomalies. However, continuing clustering is difficult because data accumulate in memory similar to existing k-means clustering method, which is problematic for embedded devices with low memory capacity. Therefore, we also propose k-means clustering to continue clustering for infinite stream data. The proposed k-means clustering method is based on online k-means clustering of sequential processing. The proposed k-means clustering method only stores data required for anomaly prediction and releases other data from memory. Due to these characteristics, the proposed k-means clustering realizes that anomaly prediction is performed by reducing memory consumption. Experiments were performed with actual data of control system for anomaly prediction. Experimental results show that the proposed anomaly prediction method can predict anomaly, and the proposed k-means clustering can predict anomalies similar to standard k-means clustering while reducing memory consumption. Moreover, the proposed k-means clustering demonstrates better results of anomaly prediction than existing online k-means clustering.
Evaluating systematic transactional data enrichment and reuse

A library account-based recommender system was developed using machine learning processing over transactional data of 383,828 check-outs sourced from a large multi-unit research library. The machine learning process utilized the FP-growth algorithm [13] over the subject metadata associated with physical items that were checked-out together in the library. The purpose of this paper is to evaluate the results of systematic transactional data reuse in machine learning. The analysis herein contains a large-scale network visualization of 180,441 subject association rules and corresponding node metrics.
Feature Enhancement of Medical Ultrasound Scans Using Multifractal Measures

In the field of ultrasound image analysis, speckle artefact removal algorithms play an important role in improving the diagnostic value of the images. It is well known that speckle filtering algorithms generally induce blurring and other unwanted artefacts. It is therefore often required to have both speckle reduction and feature improvement to enhance the overall visual quality of medical ultrasound video frames. In this paper, we use a convolutional neural network for effective speckle reduction. We also propose a novel method based on multifractal measures to characterize texture features and use them to enhance the contrast, and also certain types of resolvable details, and structure in ultrasound video frames. Four different types of multifractal measures are used for comparative analysis. Ultrasound scans of multiple anatomical structures are used for experimental validation of the proposed methods. Subjective and objective evaluation by clinical experts and three quality metrics indicate that the proposed pipeline with the inverse-min multifractal measure is able to reduce speckle content effectively while preserving visual quality, information content, and improving features useful for better clinical interpretation.
Extraction of Causal and Performance Result Information Belong to Business Segments from Securities Reports

In this research, we propose a method of extracting business segments from securities reports and extracting sentences containing causal and result information concerning business performance for each extracted business segments. For example, our method extracts“ In the aluminum rolled products business, shipments of high purity foils for aluminum electrolytic capacitors for industrial equipment and automotive use increased and sales increased. ”as a sentence containing causal information concerning business performance. Moreover, our method estimates that the sentence belongs to business segment“ aluminum ”. Our method extracts“ As a result of this segment sales in this segment were 105,439 million yen, operating profit was 6,697 million yen. ”as a sentences containing performance result information belong to“aluminum”segment. We evaluated our method and the method of extracting sentences containing causal information attained 69.3% precision and 72.5% recall, and the method of extracting sentences containing performance result information attained 78.8% precision and 91.1% recall.
Dynamic Resource Shaping for Compute Clusters

Nowadays, data-centers are largely under-utilized because resource allocation is based on reservation mechanisms which ignore actual resource utilization. Indeed, it is common to reserve resources for peak demand, which may occur only for a small portion of the application life time. As a consequence, cluster resources often go under-utilized. In this work, we propose a mechanism that improves compute cluster utilization and their responsiveness, while preventing application failures due to contention in accessing finite resources such as RAM. Our method monitors resource utilization and employs a data-driven approach to resource demand forecasting, featuring quantification of uncertainty in the predictions. Using demand forecast and its confidence, our mechanism modulates cluster resources assigned to running applications, and reduces the turnaround time by more than one order of magnitude while keeping application failures under control. Thus, tenants enjoy a responsive system and providers benefit from an efficient cluster utilization.
A Case Study on the Fault Diagnostics for the Car Part

Sudden vehicle problems during its operation cause great damage to the driver. In this vein, it is necessary to monitor the conditions of critical vehicle parts and take the suitable actions in advance based on the analysis of the conditions. These days the development of ICT (Information Communication Technology) and IoT (Internet of Things) enables us to collect health status data and environmental data of vehicle parts, which leads to take much concern on Condition-Based Maintenance (CBM). In this study we will propose an approach to identify the fault status of the ignition plug in the vehicle’s engine part based on the gathered part status data.
Salience Guided Pooling in Deep Convolutional Networks

While deep learning approach has been prevalent for generating image features, conventional handcrafted salience features still have the strength of providing explicit domain knowledge and reflecting intuitive visual understanding. However, the existing usages of handcrafted features in deep network are lack in addressing the issues of parameter quality. In this research, we propose a novel pooling method that enriches the deep features by utilizing the injected salience shape features - Generic Edge Tokens and Curve Partitioning Points, to adjust the outputs of pooling layer. The model trained under the guidance of domain prior knowledge is able to produce deep representation embracing merits from both handcrafted features and deep network. The experimental results show its improved performance with reduced learning curve. The proposed novel pooling method is generic, ie. open to other handcrafted features and different deep network architectures.
An Empirical Study on Design of Career Path Mapping for Female Officers

This research is to design of Career path mapping for female officers. To complete the purpose, the research has firstly conducted a survey of 781(91%) graduates from KMOU. This consisted of descriptive statistics that aggregated the response results and presented the frequency. Through the survey, Career path mapping is drawn by dividing the having a boarding experience, boarding period, occupational type and so on. In regardless of whether or not they have boarding experience, it is necessary to draw up a road map combining the comprehensive solution of educational institutions necessary for female officers to manage their long-term career paths and the step-by-step implementation plan to engage in their own maritime sector. To fulfill these, further research needs that consideration should be given to new ways to upgrade the career path map and to coordinate with the various constituent parts of the maritime sector, training curriculum and promotion of more assistance and support with their career in maritime sector.
Hand Shape Optimisation for Geometry-Based Models Using EPD-Based Particle Swarm Optimisation

This chapter employs the proposed PSO $$+$$ EPD to find an accurate posture and shape for the proposed 3D hand model. A number of other experiments are done to verify and justify the type of optimisation and objective functions employed.
A Function Clustering Algorithm for Resource Utilization in Service Function Chaining

Virtualized service and network functions are deployed on virtual machines (VMs) to realize essential processing to realize service function chaining (SFC). Issues on SFC is SF allocation to a VM and to minimize the response time and number of function instances. In this paper, we propose an SF clustering-based scheduling algorithm, called "SF-clustering for utilizing virtual CPUs" (SFCUV), to solve the SF allocation and SF selection problems simultaneously. Experimental results show that SF-CUV can utilize vCPUs to minimize the response time.
Exploiting Large Unlabeled Data in Automatic Evaluation of Coherence in Czech

The paper contributes to the research on automatic evaluation of surface coherence in student essays. We look into possibilities of using large unlabeled data to improve quality of such evaluation. Particularly, we propose two approaches to benefit from the large data: (i) n-gram language model, and (ii) density estimates of features used by the evaluation system. In our experiments, we integrate these approaches that exploit data from the Czech National Corpus into the evaluator of surface coherence for Czech, the EVALD system, and test its performance on two datasets: essays written by native speakers (L1) as well as foreign learners of Czech (L2). The system implementing these approaches together with other new features significantly outperforms the original EVALD system, especially on L1 with a large margin.
Introduction to Data Visualization

The objective of this course is to provide students, young researchers, and practitioners without a formal education in data visualization with an introduction and overview of the field. In addition to introducing basic concepts, the course will present diverse visualizations for different kinds of data (e.g., categorical, numeric, hierarchic, network, temporal, and spatial data); and for different kinds of visualization tasks and goals (e.g., retrieve value, filter, compute derived value, find extremum, sort, determine range, characterize distribution, find anomalies, cluster, and correlate). We will also discuss the role of data stories to convey data-driven insights.
On Computational Complexity of Pipe Puzzles

In this paper, we investigate computational complexity of pipe puzzles. A pipe puzzle is a kind of tiling puzzle; the input is a set of cards, and a part of a pipe is drawn on each card. For a given set of cards, we arrange them and connect the pipes. We have to connect all pipes without creating any local loop. While ordinary tiling puzzles, like jigsaw puzzles, ask to arrange the tiles with local consistency, pipe puzzles ask to join all pipes. We first show that the pipe puzzle is NP-complete in general even if the goal shape is quite restricted. We also investigate restricted cases and show some polynomial-time algorithms.
A Tale of Two Communities: An Analysis of Three Decades of Case-Based Reasoning Research

We analyse three decades of case-based reasoning (CBR) research to better understand the health of CBR and its relationship to adjacent research fields. We identify two largely separate CBR communities, one based on the research published at mainstream CBR venues (ICCBR, ECCBR etc.), the other encompassing CBR work with no direct connection to these venues. We analyse their scale, impact, and focus, and the potential to bring them closer together in the future.
Showcasing Deeply Supervised Multimodal Attentional Translation Embeddings: a Demo for Visual Relationship Detection

We address the task of Visual Relationship Detection, i.e. the detection of <; Subject, Predicate, Object> triplets in an image, introducing Multimodal Attentional Translation Embeddings (ICIP 2019 paper, id 3642). Motivated by the need of visualization and interpretation of the results, as well as the lack of other tools for online predictions on this task, we design and implement the first architecture for live inference of visual relationships on video streams and wild images, including research and engineering extensions, ablation models and a lightweight CPU-version. The code is available at https://bitbucket.org/deeplabai/vrd.
Research on Engineering Testing Model of Mechatronics Products

Engineering test of electromechanical products is an important part of product quality management and an important means to improve product quality and reliability. Engineering testing models are usually used to programme engineering tests and generate engineering test curves. The two most commonly used engineering test models are PM2 model (Planning Model Based on Projection Methodology) and CE model (an extended planning model of Crow). By analyzing the sensitivity of the parameters of the two models, the results show that these two models can not accurately describe engineering tests sometimes, because of the defects of the models themselves. Therefore, this paper presents an equation to express the required engineering test time by fix effectiveness factor and management strategy. Furthermore, a new engineering planning model based on the equation is proposed. Based on the case study, the proposed engineering planning curve matched well with the actual phased growth curves, which shows the proposed engineering testing planning model is reasonable and credible.
A Motion Planning Algorithm Based on Trajectory Optimization with Workspace Goal Region

We consider a motion planning problem with workspace goal region in obstacle environments and the task is to move the end-effector into a specific goal region without posture constraints. In order to improve the quality of the planning trajectory, we present a goal region constraint algorithm based on Gaussian process motion planning. We construct a distance field for the irregular goal region in advance. The closest distance and direction from any location of the workspace to the specific goal region can be easily computed. Combined with the original method, we define a goal-region-constrained likelihood which specifies the probability that the position of end-effector is within the specific goal region and move the end-effector to a better position by numerical optimization. Finally, multiple simulation experiments are carried out and the results show that the proposed algorithm can quickly plan an obstacle avoidance trajectory in joint space and the quality of the trajectory is improved effectively compared to randomly specifying a goal configuration.
American Sign Language Recognition: Algorithm and Experimentation System

The objective of this paper is an attempt to take part in the fast development of computer science, especially machine learning, in the field of finding the tools for supporting the disabled people. Speech-and–hearing impaired people are the part of our society and it would be a great convenience both for them and for speaking people, who would have an opportunity for a better communication using computer technology. In this paper, the results of the research concerning recognition of sign language have been provided. The research includes experimenting with the images transformations and the usage of different learning and feature detecting algorithms to obtain the best quality of signs recognition. In addition, the recommendations based on results of experiments can be applied in practical issues, for example, in determining hands rotations used in sign language, which can improve the accuracy of recognition.
Semantic Information Retrieval based on Topic Modeling and Community Interests Mining

Search engines or localized software systems developed for information searching, play an important role in knowledge discovery. Proliferation of data in the web and social media has posed significant challenges in finding relevant information efficiently even using those search engines or other software systems. Moreover, those systems or engines tend to collect large number of data, which could be useful for end users in various ways but have overlooked the meaning of the search phrases, hence generate irrelevant search results. A unit level searching i.e. searching information within a website or page is also not effective as they follow exact keyword matching techniques and ignore the semantic level matching of search phrases. In order to address those deficiencies, this research proposes a hybrid approach which use the semantics of data, community preferences as well as collaborative filtering techniques for semantic information retrieval. More specifically, Topic modeling based on Latent Dirichlet Allocation together with topic-driven based community detection methods are applied for identifying personalized search results generation and hence improve the relatedness of the research results. Based on the proposed hybrid approach a framework for semantic search that can easily be integrated to a software application has been implemented. The evaluation results confirm the effectiveness of search results which outperform benchmark approaches that follow traditional keyword search algorithms.
An Improved Matrix Approximation for Recommender Systems Based on Context-Information and Transfer Learning

As we all known, transfer learning is an effective way to alleviate the sparsity problem in recommender systems by transferring the shared knowledge cross multiple related domains. However, additional related domain is not always available, and auxiliary data may be noisy and this leads to negative transfer. In this paper, we suppose that different parts of one domain also have the shared knowledge and put forward a novel in-domain collaborative filtering framework, which utilizes contextual information to divide an original user-item interaction matrix into some smaller sub-matrices and regards the selected sub-matrices as “multiple domains” to establish transfer learning. The proposed framework no longer needs additional domain information and has a better adaptive ability. Also, considering more actual situation that users may have multiple personalities and items may have diverse attributes, we resort to Rating-Matrix Generative Model (RMGM) to generate the shared cluster-level rating pattern. Experiments on dataset Douban with three different categories demonstrate that the proposed framework can improve the prediction accuracy as well as the top-N recommendation performance.
Non-Local Hierarchical Residual Network for Single Image Super-Resolution

Recently, deep convolutional neural networks (CNNs) have been demonstrated excellent performance on single image super-resolution (SISR). However, most deep learning based methods lack the ability to distinguish features in network. For image super-resolution, it is important to design an effective prior to learn the correlation of various feature. To solve this problem, we propose a non-local hierarchical residual network (NHRN) for SISR. Specifically, we introduce a non-local module to measure the self-similarity between each pixels in the feature map, and obtain a weight matrix guiding the deep network to find more precise relationship between L-R and HR images. Thus our method reconstruct images with a sharper edge. In addition, we employ group convolutions to build a hierarchical residual structure, which enable the network extract image features hierarchically. It can reduce the executive time while ensuring reconstruct image quality. Extensive experiments show that our NHRN achieves better accuracy and speed against state-of-the-art methods.
Learning Dense Correspondences for Video Objects

We introduce a learning based method for extracting distinctive features on video objects. Based on the extracted features, we are able to derive dense correspondences between the object in the current video frame and the reference template, and then use the correspondences to identify the grasping points on the object. We train a deep-learning model to predict dense feature maps using the training data collected via solving simultaneous localization and mapping (SLAM). Further, a new feature-aggregation technique based on the optical flow of consecutive frames is applied to the integration of multiple feature maps for alleviating uncertainties. We also use the optical flow information to assess the reliability of feature matching. The experimental results show that our approach effectively reduces unreliable correspondences and thus improves the matching accuracy.
Multi-Image Blind Deconvolution Using Low-Rank Representation

Blind deconvolution is a restoration process of an image which is blurred by an unknown point spread function (PSF) (a.k.a. blur kernel). Some previous works [1], [2], [3], [4] have shown that multiple blurred captures of the same scene can improve the quality of blind deconvolution result. However, the previous multi-image blind deconvolution methods are prone to inconsistencies between observations such as moving objects, illumination change or mis-alignment. In this paper, we present a new multi-image blind deconvolution algorithm using low-rank representation which utilizes similar components of the multiple images for collaboration. Our framework alternatively solves a Schatten-0 norm low-rank approximation and a MAP-based L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm blind deconvolution for finding the true latent images and their corresponding PSFs and warping parameters. The experimental results show that our approach can recover high quality images in the presence of possible corruptions on both static and moving scenes and outperforms the state-of-the-art results.