Reviewerly/exHarmony huggingface dataset:
Kernel-lasso feature expansion method: boosting the prediction ability of machine learning in heart attack

Abstract Heart attack needs to cause a high degree in modern society, which is now at the top of the list of most common diseases. One-third of all deaths in the world are caused by heart disease, and in our country, hundreds of thousands of people die of heart disease every year. If people can predict heart attack at an early period, they may prevent and cure the potential risk. Therefore, one novel and efficient prediction model is needed. Based on machine learning, we design one novel method in feature selection called kernel-lasso feature expansion to increase the prediction ability. This method considers the potential influence by the curse of dimension and lack of features, which can increase the dimension of the existed data and select the effective features by lasso regression. Compared with other feature selection methods such as Lasso and step regression, the novel method increases the predicted ability of gradient boosting machine, DNN, and SVM models, while SVM with kernel-lasso feature expansion achieves the best performances in Accuracy (0.84(%95Cl:0.78-0.89)).
Energy Efficient Geo-distributed Data Center Resizing Markov Chain Model

Big data contains large-volume, complex and growing data sets with multiple, autonomous sources. Big data processing is the explosive growth of demands on computation, storage and communication in data centers, which hence incurs considerable operational expenditure to data center providers. Therefore, to minimize the cost is one of the issues for the upcoming big data era. Using these three factors, i.e., task assignment, data placement and data routing, deeply influenced by the operational expenditure of geo-distributed data centers. In this paper, we are ambitious to study the cost minimization problem via a joint optimization of these three factors for big data processing in geo-distributed data centers. Proposed using n-dimensional Markov chain and procure average task completion time.
Highly Efficient Multimedia Image Retrieval using Slim Descriptor

Efficient multimedia image extraction with high precision compatible with diverse image datasets is an implicit requirement of current image retrieval systems. In this paper, a multimedia image descriptor is introduced to achieve high performance along with high accuracy. For this, Histograms of Oriented Gradients (HOG) are extracted from a dense grid partitioned image by taking edge intensity based orientation histograms as primitive feature vectors. We depleted these massive redundant candidates to linearly uncorrelated variables by applying orthogonal transformation to achieve Principal Components (PC) where succeeding component’s constraint dependent orthogonal variance based local descriptors are compact and robust to deformation. A distinctness of our proposed approach is the selection of a single coefficient having largest variance as image descriptor out of returned dimensionally reduced vectors which results in higher performance and less space and time consumption. Supervised learning using Support Vector Machine (SVM) is then applied on non-probabilistic binary linear classification of images. The experimental results show higher precision, low memory consumption and sufficient performance gain.
The Stability of Headphones

The main purpose of this study is to obtain the relationship between the stability of wearing headphones and the evaluation of users' subjective comfort in high-frequency scenarios. The experimental results show that when the user wears the headset to complete high-frequency actions, the “displacement” of the headset relative to the ear is one of the main reasons for the discomfort of wearing. It is found that there is a non-linear relationship between the stability of headphones and the evaluation of wearing comfort, which may be due to the fact that the stable headphones squeeze the users' ears in the headband force or ear cushion shape and size, which has a negative impact on the comfort evaluation.
Predicting Large-scaled, Cloud-hosted Virtual World Resource Demands for Automated Load Balancing

The complexity and computational resource demands of large-scale virtual world simulations are increasing with advancements in CPU and GPU computing power, availability of broadband internet connectivity, and reduction in hardware costs. From these areas of growth, cloud-based services are gaining in popularity for hosting simulations due to their cost-effectiveness, ease in resource management and scale, and data center availability in virtually every region in the world. The next phase of cloud-based simulation hosting will address the fallacies of over/under-utilization of compute resources by incorporating big data analytics and processing, software containerization, automated allocation schemes, and load-balancers. To meet these demands, this study performs the preliminary work of profiling virtual-world simulation loads and then determine which form of analysis (linear or non-linear methods) produces the lowest error prediction of resource needs. As a result of this study, future automated load-balancers will be able to estimate upcoming simulation demands and modify current resource allocations to better match actual needs.
Specific Designed Facial Expression Recognition System for Interactive Film Applications

Emotion Artificial Intelligence (AI) is a novel technology for advanced human machine interaction in real-world applications. In interactive films, variant contents can be adjusted and displayed in the film based on audiences’ interaction such as voice, hand gesture or body movement, etc. In this paper, a specific emotion detection system is designed and implemented that can detect emotion continuously through the audience’s facial expression and give the feedback to the film immediately. Then the film can change its contents accordingly. In this system, A pre-trained convolutional neural network is used for feature extraction from video frames and then the emotion is predicted by a support vector regression model. The environmental noise is reduced in the pre-processing stage and the final prediction is smoothed in the post-processing. A database is recorded for this particular scene and the proposed system is trained on it. The experimental results demonstrate the effectiveness of the system and the built interactive film “RIOT” has been exhibited on several occasions with good performance.
Segmentation-Based Seam Cutting for High-Resolution 360-Degree Video Stitching

We present a novel segmentation-based seam cutting algorithm to generate visually plausible high-resolution 360-degree video efficiently. While the demand for an efficient video stitching algorithm for generating immersive videos has increased, it has received limited attention in the literature. Furthermore, stitched videos often suffer from distorted objects, temporal inconsistency and time constraints. Thus, in this paper, we propose an efficient seam finding algorithm that preserves objects from distortion, minimizes temporal inconsistency, and reduces processing time. One of the fundamental steps in image and video stitching is the estimation of seam boundary. To do this, the proposed algorithm leverages a convolutional neural networks-based instance segmentation algorithm that provides more accurate object regions. It computes energy surfaces considering the regions and then estimates seam boundary by discovering a minimal energy path with minimal computations. We validate the proposed algorithm using real-world high-resolution 360-degree sequences. The experimental results verify that the proposed algorithm can produce seam boundaries that avoid objects with better temporary consistency. The proposed algorithm reduces the number of pixels passed through objects by approximately 30% on average compared to the existing algorithms. The qualitative comparisons furthermore demonstrate that the proposed algorithm consistently produces more perceptually pleasing results.
FPGA Hardware Implementation of a SHA384 Accelerator for Internet of Things Applications

The abundance of the IoT devices surrounding us brings new opportunities and challenges.IoT technology enables remote monitoring and control of cyber-physical systems on a global scale.One key aspect of IoT technology is the security which is usually neglected by manufacturers.Because of IoT based security breaches, IoT devices need cryptographic functions to provide confidentiality, integrity and authentication capabilities in modern applications.However, the limited computational power available in the processors used in IoT systems imposes the development and use of hardware peripherals dedicated for performing cryptographic operations.One of the most popular cryptographic functions used in the IoT applications is the secure hash algorithms.They are extensively used for data integrity and authentication applications.In this work, we have designed, verified, and implemented a hardware IP core of the SHA-384 algorithm.In addition, we have also integrated the SHA-384 hardware module with a synthesizable processor as an AXI4 peripheral to enable in-application testing using custom software.Our design can operate up to 170 MHz and occupies only 982 CLB slices and one BRAM on a Xilinx Artix-7 FPGA device.The estimated total power consumption is 223 mW when the module is integrated with a minimal configuration Microblaze processor system.
An improved generative adversarial network for remote sensing image denoising

Existing methods for remote sensing image denoising typically suffer from a common drawback of fuzzy edge information. In this paper, we proposed a Generative Adversarial Network(GAN) based on the residual learning and perceptual loss for image denoising. The proposed GAN is designed with the two parts: The generator network takes the high-frequency layer of noisy image as the input and outputs a clean image after training. In order to eliminate noise better while retaining more edges and details, three residual blocks are embedded in the generator and a perceptual loss function is added to learn the perceptual differences between the denoised images and the ground truth images. The discriminator network based on 70×70 PatchGAN can discern between the denoised image and the clean image through a confidence value. The experiments show that our proposed network achieves superior performances and preserve majority the edge contours and fine details from low-quality observations.
A new variable selection algorithm for LSTM neural network

This paper proposes an accurate and reliable input variable selection algorithm by embedding a nonnegative garrote (NNG) algorithm into long short term memory (LSTM) neural network to perform data-driven modeling on a highly nonlinear and dynamic time-delay dataset. Firstly, an LSTM deep neural network is trained, and a well-trained LSTM network is obtained by optimizing the parameters of LSTM through a grid search algorithm. Secondly, the initial input weights of LSTM are compressed accurately by the NNG algorithm, and block cross-validation is applied to the optimization calculation process to achieve input variable selection. Finally, the performance of the algorithm is verified by the improved Friedman time-delay artificial datasets. Simulation results show that the algorithm could construct a more simplified and better predictive model than other traditional algorithms.
Decoupled Instance-label Extreme Multi-label Classification with Skew Coordinate Feature Space

Extreme multi-label classification predicts labels that instances can have for a dataset with a massive number of labels. State-of-the-art approaches consider it more critical to accurately predict related labels than non-relevant labels, so they evaluate only the top k label candidates. However, the top k results are not suitable for applications, such as a drug repositioning, which aims to assign additional labels to instances. In this work, we propose DilXML, an extreme multi-label classifier that suggests a reasonable number of labels for each instance. DilXML overcomes the absence of negative data and the poverty of instances by decoupling instances and labels. Also, we propose three criteria for conceptual distance formulas considering a hierarchical structure between features. Through this, a skew coordinate feature space better reflects the relatedness between points. DilXML is the first extreme multi-label classification that conducts example-based evaluations. We compare over five state-of-the-art approaches: AnnexML, Bonsai, DiSMEC, FastXML, and ProXML. DilXML is the only one that achieves the best performance for all metrics and outperforms by 10% except for one data. For the targeted medical data, DilXML is 58% better on all four evaluation metrics than other methods. Besides, we conduct a literature review on drug repositioning candidates and confirm that newly obtained labels are significantly related to the instance.
Word2Mouth - An eLearning platform catered for Low-Income Countries

The United Nation's 4th Sustainable Development Goal aims to provide quality education to all. Unfortunately, currently it is too often the case that learners from Low-Income Countries (LICs) cannot access relevant educational resources. Low bandwidth, low literacy and different cultural backgrounds often cause barriers of entry to access to education for learners in LICs.Conversely, learning environments have changed dramatically in the last decade. With the rise of eLearning platforms such as Coursera and edX, a learner can now stream high quality Open Educational Resources (OERs) right from their own home, as long as they have a stable Internet connection. With over 100 million participants worldwide, eLearning platforms provide alternatives to conventional classroom education, democratising education for everyone. But do these new learning environments really improve access to education in LICs?Research shows that unforeseen differences in Internet access, language, literacy, and cultural backgrounds between learners from High-Income and Low-Income Countries (HICs/LICs) mean that eLearning is often inaccessible for people in LICs. In this paper, we describe Word2Mouth, an eLearning platform that has been designed specifically to overcome the barriers of entry to eLearning for LICs, educating learners in LICs in a way that current eLearning platforms fail to do.
Smart and Fully Functional Mobile-Robot Navigation System

Autonomous navigation systems of Wheeled Mobile Robot (WMR) include several interrelated concepts, techniques, and algorithms, such as perception, localization, mapping, planning, and path execution but mapping and path planning are at the core of all of them. Autonomous navigation remains one of the primary challenges in the mobile robot industry; many control algorithms and techniques have been recently developed that aim to overcome this challenge. In this research, we successfully proposed a smart fully functional mobile-robot navigation system (SFMRNS) for WMR, by developing and integrating many self-developed novel system and algorithms. The mapping system integrates two interrelated, self-developed novel components: (1) 2BEDM (Brightness-Based Enhancement for Disparity Map), and (2) I3MS (Intelligent Multi-Sensor Multi-Baseline Mapping System). For path-planning algorithm, we used a self-developed algorithm called AntStar (Ant system Star). For navigation, we used self-developed navigation system based on the behavior of the robots and obstacle. In additional, we used the dead-reckoning technique for localization, and a self-developed navigate system is used to navigate the robot to the target points. To ensure the success of all developed algorithms and systems, we test them individually, in as many experiments as possible, before integrating these components into a fully functional mobile-robot navigation system. The successful integration and interaction of all the proposed systems and algorithms demonstrates a high degree of applicability.
15‐1: High Resolution Display Data Transmission using Weak Sub‐color DWT

As a resolution of display increases, it is necessary to process video data more efficiently and economically. In this paper, an advanced technique is proposed to reduce its data bandwidth in real time without any noticeable degradation in its image quality. By analyzing the frequency characteristics of an input image data with respect to its sub‐colors, a sub‐color data having relatively smaller proportion in high frequency which can be regarded as a ‘Weak Sub‐color data’ can be converted and reduced using the Discrete Wavelet Transformation (DWT). Some of the DWT‐transformed data is transmitted with the other non‐processed original sub‐color data to reduce its data size. This technique can provide a robust video system interface that reduces its transmission bandwidth up to 25% or more with improved image quality.
An Operational Heterogeneous Network Generation Method Considering Information Fusion Entities

In modern joint operations, information fusion is of significant military value. Information fusion is the direct embodiment of orientation in the theory of Observation, Orientation, Decision and Action loop (OODA). Orientation can determine the correctness of decision-making and improve the speed of OODA tempo. In the existing operational heterogeneous network (OHN) model, the entities executing orientation and decision are usually regarded as the same node, which cannot reflect the importance of orientation. In view of this, this paper proposes an OHN model including information fusion node, and gives the generation method of the model. Experiments show that the OHN model generated by our method is in line with the reality of war, which provides a data basis for the study of modern warfare.
Implicit Action Inference for Natural Language Navigation Instruction Translation

In door robot is widely used to inspect equipment in distribution power network system. Generally, the indoor navigation instructions sent to robot are based on natural language due to the lack of absolute position information. Translating natural language navigation instructions to action sequences that can be executed by machines requires not only language knowledge, but also knowledge related to space, action and perception. This paper studied the implicit action inference technology for natural language navigation instruction translation, and proposed a method to infer implicit actions in natural language instructions. Field test showed that proposed method can improve the probability of reaching the desired destination.
TourVis: Narrative Visualization of Multi‐Stage Bicycle Races

Abstract There are many multiple‐stage racing competitions in various sports such as swimming, running, or cycling. The wide availability of affordable tracking devices facilitates monitoring the position along with the race of all participants, even for non‐professional contests. Getting real‐time information of contenders is useful but also unleashes the possibility of creating more complex visualization systems that ease the understanding of the behavior of all participants during a simple stage or throughout the whole competition. In this paper we focus on bicycle races, which are highly popular, especially in Europe, being the Tour de France its greatest exponent. Current visualizations from TV broadcasting or real‐time tracking websites are useful to understand the current stage status, up to a certain extent. Unfortunately, still no current system exists that visualizes a whole multi‐stage contest in such a way that users can interactively explore the relevant events of a single stage (e.g. breakaways, groups, virtual leadership…), as well as the full competition. In this paper, we present an interactive system that is useful both for aficionados and professionals to visually analyze the development of multi‐stage cycling competitions.
QUALITATIVE ANALYSIS OF UNDERWATER IMAGE ENHANCEMENT

In underwater imaging, the visual quality of both video and image is degraded, influencing the recognition of many marine resources. Backscattering and attenuation are the main reasons in degradation of quality in underwater images. This paper aims at study and comparison of recent algorithms used to improve the visual quality of underwater images. Firstly, cause for the image degradation is analyzed. Secondly the algorithms, formulated are broadly categorized into Data Driven and Non - Data Driven approaches. Afterward the results of all the algorithms are tabulated. Finally, pros and cons are analyzed to formulate the effectiveness of each algorithm and paving the way for future research.
Interactive Analytic DBMSs: Breaching the Scalability Wall

Analytic DBMSs optimized for query interactivity commonly push the computation down to storage nodes, thus avoiding large network transfers and keeping query execution wall-time to a minimum. In these systems, data is sharded and stored locally by cluster nodes, which must all participate in query execution. As the system scales-out, hardware failures and other non-deterministic sources of tail latency start to dominate, to a point where query latency and success ratio increasingly violate the system's SLA. We refer to this tipping point as the system's scalability wall, when sharding data between more nodes only worsens the problem.This paper describes how an analytic DBMS optimized for low-latency queries can breach the scalability wall by sharding different tables to different subsets of cluster nodes - a strategy we call partial sharding - and reduce the query fan-out. Because partial sharding requires the DBMS to implement many tedious and complex shard management tasks, such as shard mapping, load balancing and fault tolerance, this paper describes how a database system can leverage an external general-purpose shard management service for such tasks. We present a case study based on Cubrick, an in-memory analytic DBMS developed at Facebook, highlighting the integration points with a shard management framework called Shard Manager. Finally, we describe the many design decisions, pitfalls and lessons learned during this process, which eventually allowed Cubrick to scale to thousands of nodes.
Solar Power Forecasting With Weather Factors Using Support Vector Regression

Solar power forecasting is very important in ensuring correct and reliable operation of solar power grids. In forecasting the solar power output, it is not very easy to determine which parameters are crucial. In this work, the effect of using temperature at noon as well as cloud percentage at noon were used as inputs and solar power output was considered as the output. Effects of using different kernels were studied. It is noticed that when root mean square error (RMSE) and mean absolute error (MAE) values were considered linear and radial kernels produced relatively better results. Moreover having temperature data together with cloud percentage data produced more accurate modelling and enabled to determine more accurate solar power output with relatively lower RMSE and MAE values.
The Emergence of Blockchain Technology: Implications for Operations and Supply Chain Management

In this article, we review the possibilities arising from the emergence of blockchain technology on operations and supply chain management (OSCM). This is the third part in a series of articles investigating rising digital technologies and their impact to operations and supply chain management. A short history and an introduction to blockchain technology are provided followed by a review of the current and future applications where this technology can change manufacturing and operations management. Moreover, we discuss the obstacles faced by this technology and how those can be overcome.