Reviewerly/exHarmony huggingface dataset:
Feedback Motion Planning For a Dynamic Car Model via Random Sequential Composition

Autonomous cars and car-like robots have gained huge popularity recently due to the recent advancements in technology and AI industry. Motion and path planning is one of the most fundamental problems for such systems. In the literature, kinematic models are widely adopted for planning and control for these type of robots due to their simplicity (control and analysis) and fewer computational requirements. Though, applicability of kinematic models are limited to very low speeds or some specific cases, which can be easily violated in real life scenarios. Furthermore, most of the dynamical car models found in the literature assume that they are driven only in forward direction, at constant high speeds. In this study, we present a car model that captures the dynamics of both forward and backward driving, at low and high speeds. After creating the car model, we addressed the motion planning problem on this model, where we adopted a framework which combines Sequential Composition of Controllers (SCC) and Rapidly Exploring Random Trees (RRT). We performed simulations to show the effectiveness and robustness of our method, and results are promising for future experimental studies.
Experimenting with Large Displays and Gestural Interaction in the Smart Factory

In this paper we present the results of two experimental evaluations carried out in the broader context of a research project on Industry 4.0. We envision in large displays operated through gestural interaction one of the key technologies for enabling real-time management of production processes and supporting decision-making. We developed prototype applications with the twofold aim of: (1) designing a vocabulary of one-handed, touchless gestures meant for interaction in a CAD-like environment, and (2) investigating the role of the cursor feedback. Our results provide a basis for the development of a new integrated wearable sensor which will be assessed with industry workers and in a more realistic setting, for better ecological validity.
Segmentation strategies for the alpha-tree data structure

The alpha-tree is a versatile algorithm for color image segmentation employing attribute constraints to control the partitioning of the alpha-connected image space. Attribute constraints are enforced using a maximization strategy that returns the set of the largest connected components complying with these constraints assuming no prior violations from nested sub-components. This article presents two new strategies extending the way this set is defined. These are the non-target clustering and attribute maximization strategies, that give access to segments that could not be defined previously. Collectively they enable the handling of texture-rich regions that cannot be clustered into meaningful segments, and compute the unsupervised segmentation of images by seeking for extreme attribute values.
Information theory based pruning for CNN compression and its application to image classification and action recognition

Convolutional neural networks (CNNs) have become the power method for many computer vision applications, including image classification and action recognition. However, they are almost computationally and memory intensive, thus are challenging to use and to deploy on systems with limited resources, except for a few recent networks which were specifically designed for mobile and embedded vision applications such as MobileNet, NASNet-Mobile. In this paper, we present a novel efficient algorithm to compress CNN models to decrease the computational cost and the run-time memory footprint. We propose a strategy to measure the redundancy of parameters based on their relationship using the covariance and correlation criteria, and then prune the less important ones. Our method directly applies to CNNs, both on convolutional and fully connected layers, and requires no specialized software/hardware accelerators. The proposed method significantly reduces the model sizes (up to 70%) and thus computing costs without performance loss on different CNN models (AlexNet, ResNet, and LeNet) for image classification on different datasets (MNIST, CIFAR10, and ImageNet) as well as for human action recognition (on dataset like the UCF101).
Improved compound image segmentation using automatic pixel block classification with SVM

Computer screen images such as wallpaper, web pages and powerpoint images are compound images. As these images contain a mixture of textual, graphical, pictorial and smooth regions, compression of computer screen images necessitates accurate classification and segmentation of these regions. In this study, an improved compound image segmentation using automatic block classification with support vector machine (SVM) is presented. First, the input compound image is divided into several non-overlapping blocks, and then the statistical features embedded in each block are mined after applying discrete wavelet transform. Then, the SVM model is trained effectively by using the informative samples of fuzzy c-means clustering, which takes block-level edge features and block neighbourhood information as input. Finally, the compound image is classified into two classes such as text/graphics and picture/background with the trained SVM model. Experimental results show that the proposed method performs automatic block classification with high accuracy. As the proposed classifier uses both structural and contextual information as features, block classification accuracy has been improved to a great extent. Hence, the proposed method has made ∼6.2% improvement in block classification accuracy while comparing with existing approaches.
The Video Guides at the Musei Civici in Rome: An Example of Technological Innovation in Touristic Cultural Heritage Experiences

In recent years, the cultural sector has been going through an intense period of development, in which new considerations on different and fresh ways of displaying and narrating objects and events from the past are intertwined with suggestions from studies on culture, tourism and ICT. The use of new technologies for the enjoyment of cultural experiences becomes important when it is necessary to follow co-creation principles, especially when allied to the added value perceived by users in cultural environments. These are based on diverse and potentially increasing levels of interaction between technological tools and the transmission of cultural knowledge to tourists. Video guides represent a reliable tool that can assist a museum visit, making it more cognitively and physically enjoyable. The project described in this paper has been created by ETT S.p.A. in collaboration with various experts. This new interactive video guide aspires to be a revival of an experience through the curiosity and emotional engagement of users, achieved with an indoor location system – based on Beacon (BLE) technology – that automatically suggests to visitors content that is associated with nearby artwork. Augmented and Virtual Reality also support the delivery of information content via storytelling techniques.
Multi-Modal Question Answering System Driven by Domain Knowledge Graph

In the era of big data explosion, the Internet serving as an infrastructure for organizing and acquiring information and knowledge, has usability shortcomings in specific application scenarios. For professional application business, we need more efficient information organization and interactive interface to facilitate the formalization of expert experience and access to associated information by ordinary users. This paper designs a domain knowledge graph driven multi-modal question answering system, using knowledge graph as foundation framework for domain data, combined with intelligent question answering and image recognition technology, achieve efficient information query scheme. In the process of establishing the domain knowledge graph, we adopt the method of combining intelligent natural language processing with crowdsourcing artificial entity-relational extraction, which can effectively solve the problem of knowledge engineering in professional fields.
Design of a Daily Brief Business Report Generator based on Web Scraping with KNN Algorithm

In order to generate a report for an enterprise where there is neither the API supporting from their existing website systems nor the granted database access rights approval, a daily business report generator system based on web scraping with k nearest neighbor (kNN) classification algorithm is proposed in this paper. It covers the web crawler technology that is to access existing website system and extract business data. The kNN algorithm is applied to identify the verification code on the login page, and the brief daily report generating in a spread-sheet style grid. Compared with some OCR engine for image recognition, the system in Python can automatically generate the brief daily business reports by the kNN algorithm, which is better than some library with default training set on validating the verification code.
Similarity-based decision tree induction method and its application to cancer recognition on tomographic images

Abstract The paper proposes a pattern recognition method using a modification of the class of logical decision functions presented in the form of decision tree. Instead of standard statements corresponding to the tree nodes, in which a variable is tested for a certain set of its values, a more general type of statements is used regarding the similarity of the point in question to different subsets of the observations. At the same time, to determine the degree of similarity, various metrics and subspaces of features can be used. This type of decision tree allows one to obtain more complex decision boundaries, which at the same time have a clear logical interpretation for the user. Several tree induction strategies are considered based on data transformation using support points selected with Relief, SVM, and k -means procedures. The method is experimentally investigated on the problem of tomographic images analysis, as well as on several synthetic datasets. Experiments have shown that the proposed method gives more accurate predictions than CART, SVM, kNN classifiers and deep convolutional neural network (AlexNet).
Combined Attention Mechanism for Named Entity Recognition in Chinese Electronic Medical Records

In order to improve the performance of named entity recognition (NER) in Chinese electronic medical records (EMRs), this paper proposed a deep neural model (BiLSTM-Att-CRF model) that combined bidirectional long-short time memory network(BiLSTM) with an attention mechanism, in terms of simultaneously identifying 5 types of clinical entities from the China Conference on Knowledge Graph and Semantic Computing (CCKS) 2018 Chinese EMRs corpus, the BiLSTM-Att-CRF model finally achieved better performance than other widely-used models (F-score of 85.79% without additional features and the F-Score of 86.35% with the part of speech and dictionary features).
Universal Perceptron Theory on Decomposition Structures of <i>n</i>-Bit Boolean Functions

By using the universal perceptron theory, a decomposition algorithm called improved distance and DNA-like decomposition algorithm (ID-DNA-like DA) is proposed to investigate the linear decomposition structures of [Formula: see text]-bit Boolean functions. The specific linear decomposition structure of an [Formula: see text]-bit Boolean function is generated by the proposed decomposition algorithm ([Formula: see text]). Especially, the linear decomposition structure of 4-bit Boolean function is shown to have the following properties: the total number of 4-bit Boolean functions is 65[Formula: see text]536 and the number of linearly separable Boolean functions is 1882; the number of 2-length decomposition Boolean functions is not less than 41[Formula: see text]366; the number of 3-length decomposition Boolean functions does not exceed 22[Formula: see text]286; and the number of 4-length decomposition Boolean functions is only 2. At the same time, it is shown that the decomposition length of [Formula: see text]-bit Boolean function does not exceed [Formula: see text] [Formula: see text]. In addition, a concept of the [Formula: see text]-bit Boolean function decomposition structure figure is proposed. By decomposition calculation, the related results of decomposition structure figures show that there is some symmetry with a strong self-similar fractal feature.
Blighted Ovum detection using convolutional neural network

Blighted Ovum is a state of conception that contains no fetus. Early detection of Blighted Ovum may reduce the risk of miscarriage. One tool that can be used in the detection of Blighted Ovum is by using ultrasonography. However, the detection of Blighted Ovum through ultrasound image is still difficult, because it is still very dependent on the level of knowledge and subjectivity of medical experts. One of the most successful ultrasound image detection or classification methods is Machine Learning. One of the best machine learning methods in terms of image classification is called Convolutional Neural Network method. This method consists of three stages. The first stage is the feature extraction of ultrasound image data. The second stage is the learning phase or training by using feedforward and backpropagation methods. The third stage is the phase of image classification using feedforward method. This research will develop an automatic classification algorithm on the ultrasound examination result using Convolutional Neural Network in Blighted Ovum detection. This study is also expected to assist medical experts in providing quick decisions on whether or not Blighted Ovum is present, so it can lead to prevention and rescue actions against the undeveloped fetuses. The accuracy of the algorithm implementation is less than 60%. This would happen because of the lack of data, and the difficulties in classifying the BO data from the non BO, because of its similarities.
A Parallelized, Momentum-incorporated Stochastic Gradient Descent Scheme for Latent Factor Analysis on High-dimensional and Sparse Matrices from Recommender Systems

High-dimensional and sparse (HiDS) matrices are commonly encountered in many big-data-related industrial applications like recommender systems. Latent factor (LF) analysis via stochastic gradient descent (SGD) is greatly efficient in discovering latent patterns from them. However, as a sequential algorithm, SGD suffers considerable time cost and low scalability when handling large-scale problems. To address these issues, this study proposes parallelized, momentum-incorporated stochastic gradient descent (PMSGD) scheme, which incorporates momentum effects into an SGD scheme as well as implementing its parallelization via careful data splitting. Based on a PMSGD method, we achieve a PMSGD-based LF (PLF) model to execute fast LF analysis on HiDS matrices from a recommender system. Experimental results on two HiDS matrices arising from industrial applications indicate that owing to the careful design of PMSGD, a PLF model outperforms state-of-the-art parallel LF models significantly in terms of computational efficiency.
Comparison of Forecasting the Number of Outpatients Visitors Based on Naïve Method and Exponential Smoothing

This paper aimed to predict outpatient visits from both general and BPJS category based on the Naïve and the Exponential Smoothing method, these two methods are compared to obtain the best method in predicting outpatient visits at XYZ hospitals. The data that were processed in this paper is based solely on the annual data collection over a period of 5 years from 2014-2018 which consists of 2 categories, namely general visit data and BPJS visit data. The annual general category data has the following data amounts: 2014 = 11028, 2015 = 12950, 2016 = 17587, 2017 = 21951, and 2018 = 19049. As for the BPJS visit data, the data as follows 2014 = 16869, 2015 = 14059, 2016 = 14217, 2017 = 13019, and 2018 = 9641. Results of predictions number of outpatient visits from Naïve method on the visit data which is categorized as general has MSE 5191788 and MAPE values of 16.335%, the categorized BPJS has a value of MSE 13165490 and MAPE 19.081%. While the Exponential Smoothing method on the visit data that is categorized as general has MSE 7790587 and MAPE values of 21.808%, BPJS category only having MSE 13165490 and MAPE values of 20.718%. Of the two methods, the method considered better is the Naïve method which has a percentage of MAPE values <20%. It can be concluded that the Naïve method is more suitable for forecasting the number of visits from the general category and BPJS compared to the Exponential Smoothing method because the forecast value and the std err are smaller.
Containers Scheduling Consolidation Approach for Cloud Computing

Containers are increasingly gaining popularity and are going to be a major deployment model in cloud computing. However, consolidation technique is also used extensively in the cloud context to optimize resources utilization and reduce the power consumption. In this paper, we present a new containers scheduling consolidation approach for cloud computing environment based on a machine learning technique. Our approach is proposed to address the problem of a company that aims to adapt dynamically the number of active nodes to reduce the power consumption when several containers are submitted online each day by their users. In our context, the frequency of containers submission varies within one hour. However, for each hour, the submission frequency is essentially the same each day. The principle of our approach consists into applying a machine learning technique to detect, from a previous containers submission historical, three submission periods (high, medium and low). Each submission period represents a time slot of one day. For instance, the high submission period represents the slot time where the number of submitted containers is the highest compared to other periods. Then, according to the submission periods slot time, our approach dynamically adapts the number of active nodes that must be used to execute each new submitted container. Our proposed consolidation approach is implemented inside Docker Swarmkit which is a well-known container scheduler framework developed by Docker. Experiments demonstrate the potential of our approach under different scenarios.
Embedded GPU Cluster Computing Framework for Inference of Convolutional Neural Networks

The growing need for on-board image processing for space vehicles requires computing solutions that are both low-power and high-performance. Parallel computation using low-power embedded Graphics Processing Units (GPUs) satisfy both requirements. Our experiment involves the use of OpenMPI domain decomposition of an image processing algorithm based upon a pre-trained convolutional neural network (CNN) developed by the U.S. Air Force Research Laboratory (AFRL). Our testbed consists of six NVIDIA Jetson TX2 development boards operating in parallel. This parallel framework results in a speedup of $4.3 \times $ on six processing nodes. This approach also leads to a linear decay in parallel efficiency as more processing nodes are added to the network. By replicating the data across processors in addition to distributing, we also characterize the best-case impact of adding triple modular redundancy (TMR) to our application.
COMET: A Distributed Metadata Service for Federated Cloud Infrastructures

Majority of today’s cloud services are independently operated by individual cloud service providers. In this approach, the locations of cloud resources are strictly constrained by the distribution of cloud service providers’ sites. As the popularity and scale of cloud services increases, we believe this traditional paradigm is about to change toward further federated services, a.k.a., multi-cloud, due the improved performance, reduced cost of compute, storage and network resources, as well as the increased user demands. In this paper, we present COMET, a light weight, distributed storage system for managing metadata on large scale, federated cloud infrastructure providers, end users, and their applications. We use two use cases from NSF’s ExoGENI and Chameleon research cloud testbeds to show the effectiveness of COMET design and deployment.
An Ensemble Method for Data Imputation

Healthcare analytics is transforming the healthcare industry, finding novel and useful patterns in patient data such as electronic health records (EHRs), to provide patients with improved care and service. Researchers train machine learning (ML) algorithms to discover new knowledge by mining patients' clinical data to provide better care such as accurate diagnoses and personalized therapy. However, the quality of clinical patient data may inhibit the discovery process. The type and frequency of collected data varies based on a patient' s clinical condition and administrative requirements. Patients can have different diagnostic tests and treatments at different times, even with the same symptoms. Therefore in EHRs, many aspects of a patient' s clinical condition could be unmeasured at different timestamps. Missing measurements may be clinically important, but cannot be used by ML algorithms. To utilize all clinical data and achieve optimal performance of ML algorithms, we address the missing data issue by imputing missing time series values. We will describe our imputation methods and apply them to 13 common clinical laboratory test results obtained from a set of 8267 inpatients to evaluate their performance using normalized root-mean-squared deviation (nRMSD) [1].
Generic Object Detection and Tracking for Accelerating Video Analysis within VICTORIA

Video recordings have become a major resource for legal investigations after crimes and terrorist acts. However, currently no mature video investigation tools are available and trusted by LEAs. The project VICTORIA addresses this need and aims to deliver a video analysis platform that will accelerate video analysis tasks by a factor of 15 to 100. In this paper, we describe concept and work in progress done by AIT GmbH within the project, focusing on the development of a state-of-the-art tool for generic object detection and tracking in videos. We develop a detection, classification and tracking tool, based on deep convolutional and recurrent neural networks, trained on a large number of object classes, and optimized for the project context. Tracking is extended to the multi-class multi-target case. The generic object and motion analytics is integrated in a novel framework developed by AIT, denoted as Connected Vision, which provides a modular and service-oriented (scalable) approach, allowing to process computer vision tasks in a distributed manner.
Role of Cloud Forensics in Cloud Computing

Some positive edges of cloud computing such scalability, elasticity, accessing the cloud from anywhere, and device-independent nature of cloud, has opened many doors for many intruders to carry out their mischief acts with respect to the data stored in the clouds. It is sometimes very difficult to trace out the exact fact what has happened with data, due to region effect, due to legal issues, and due to technical issues are the main and huge hurdles that exist in the present scenario; also there lies a main drawback that the present direct cloud forensics methods are not too much effective to overcome the problem. One among the buzzing technologies in today’s techno world is cloud forensics, which has its own importance in many aspects due to exponential growth to data, to reduce maintenance cost, to provide security measures to the data, and these things make the individual persons to move and attract toward cloud concept, as cloud can go with these things easily where the client has to pay for the desired flavors of services which they want to utilize. As the huge and huge amount of data is being accumulated at a place due to which it sometimes compromise may be arose when cloud service provider do not take standard measures to overcome this.