Reviewerly/exHarmony huggingface dataset:
Spark Performance Optimization Analysis With Multi-Layer Parameter Using Shuffling and Scheduling With Data Serialization in Different Data Caching Options

As social networking services and e-commerce are growing rapidly, the number of online users also dynamically growing that facilitate contribution of huge contents to digital world. In such dynamic environment, meeting the demand of computing is very challenging special with existing computing model. Although Spark is recently introduced to alleviate the problems with concept of in-memory computing for big data analytic with many parameters configuration that allow to configure and improve its performance, still it has performance bottleneck which require to investigate performance improvement mechanism by focus on the combinations of Scheduling and Shuffle Manager with data serialization with intermediate data caching options. Standalone cluster computing model was selected as experimental methodology with submit command line for data submission. Three Spark application such as WorkCount, TeraSort and PageRank were selected and developed for experiment. As a result, 2.45% and 8.01% performance improvement are achieved in OFFHEAP and Memory Only Ser data caching option, respectively.
Digitalization of Industry and Education

The fourth industrial revolution, associated with the introduction of robotics, cyber-physical systems, artificial intelligence, neural networks, affects all spheres of human life. There is a need for specialists with the appropriate skills. The article discusses the elements of Industry 4.0: Internet of Things, robotization, PLM system. The impact of digitalization on the educational sector is also considered.
Overall of Personal Big Data Collection and Its Applications

The Internet of Things (IoT) era has advanced rapidly because of the network, communication, and sensor technologies. By using these technologies, people can gather a wide area of information automatically in real-time easily. Nowadays, the collected data has been used in many kinds of current services. We have named lifestyle authentication applies human life patterns, lifestyle patterns conducted by using life logs collected by IoT data. We conducted, to promote research on lifestyle authentication, a large-scale demonstrative experiment gathering lifestyle data from over 57,000 participants in 2017. We investigated the applicability of the data in the development of a lifestyle authentication technique.
Contrastive Cycle Consistency Learning for Unsupervised Visual Tracking

Unsupervised visual tracking has received increasing attention recently. Existing unsupervised visual tracking methods mainly exploit the cycle consistency of sequential images to learn an unsupervised representation for target objects. Due to the small appearance changes between consecutive images, existing unsupervised deep trackers compute the cycle consistency loss over a temporal span to reduce data correlation. However, this causes the learned unsupervised representation not robust to abrupt motion changes as the rich motion dynamics between consecutive frames are not exploited. To address this problem, we propose to contrastively learn cycle consistency over consecutive frames with data augmentation. Specifically, we first use a skipping frame scheme to perform step-by-step cycle tracking for learning unsupervised representation. We then perform unsupervised tracking by computing the contrastive cycle consistency over the augmented consecutive frames, which simulates the challenging scenarios of large appearance changes in visual tracking. This helps us make full use of the valuable temporal motion information for learning robust unsupervised representation. Extensive experiments on large-scale benchmark datasets demonstrate that our proposed tracker significantly advances the state-of-the-art unsupervised visual tracking algorithms by large margins.
Rock painting restoration method based on saliency detection and TV model

Traditional restoration algorithms are not effective enough to fully repair rock paintings. The barley lithology is used as an example in this work to demonstrate a novel repair algorithm based on saliency detection and total variation (TV). The damage detection area of the rock painting image is extracted by saliency detection, then the improved TV model is applied to complete the repair. Experiments show that the proposed algorithm can effectively improve the poor robustness and virtual boundary of the traditional TV model image restoration algorithm, and make the image smoother and achieve better repair results. At the same time, a set of solutions for the extraction and restoration of structural rock paintings has been formed.
A Robust End-to-end Method for Parametric Curve Tracing via Soft Cosine-similarity-based Objective Function

Parametric curve tracing enables wide applications, such as lane following in autonomous driving, volumetric reconstruction in seismic, single-molecule/protein tracking in microscopy. Most existing parametric curve tracing methods require several steps, including curve identification and parameterization. Such multi-step methods can lead to lengthy and complicated parameter optimization. Additionally, the performance of curve identification methods can be degraded by noisy or low-light images. To address these challenges, we present a novel single-step approach to trace curves parametrically via optimizing a self-defined non-linear objective function that describes several key properties of the curve. Under the assumption that signals along the curve resemble each other, our objective function will guide this pathfinding process from a seed point along the direction according to maximum cosine similarity. No pre- and post-processing step is required to measure the tangent or normal vectors. We visualize our objective function and conduct several numerical experiments. These empirical experiments demonstrate that our method outperforms other competing methods across image domains. It yields better accuracy even in low signal-to-noise ratio (SNR) conditions.
Deep Reinforcement Learning Control of White-Light Continuum Generation

Deep Reinforcement Learning (deep RL) is a branch of Machine Learning dealing with the solution of optimization problems, usually formalized as Markov Decision Processes [1] . At discrete temporal steps, an agent takes an action on the system receiving back a reward that depends on the state reached by the system. The goal of the agent is to determine an optimal policy, i.e. a map between states and actions, to maximise the future rewards, by directly experiencing and sampling the environment without any a-priori knowledge of the system. Among the various optimization algorithms, deep RL is particularly versatile thanks to the underlying neural networks [2] , a powerful universal approximator.
Autoregressive Articulatory WaveNet Flow for Speaker-Independent Acoustic-to-Articulatory Inversion

In this paper we introduce a new speaker independent method for Acoustic-to-Articulatory Inversion. The proposed architecture, Speaker Independent-Articulatory WaveNet (SI-AWN), models the relationship between acoustic and articulatory features by conditioning the articulatory trajectories on acoustic features and then utilizes the structure for unseen target speakers. We evaluate the proposed SI-AWN on the Electro Magnetic Articulography corpus of Mandarin Accented English (EMA-MAE), using the pool of acoustic-articulatory information from 35 reference speakers and testing on target speakers that include male, female, native and non-native speakers. The results suggest that SI-AWN improves the performance of the acoustic-to-articulatory inversion process compared to the baseline Maximum Likelihood Regression-Parallel Reference Speaker Weighting (MLLR-PRSW) method by 21 percent. To the best of our knowledge, this is the first application of a WaveNet-like synthesis approach to the problem of Speaker Independent Acoustic-to-Articulatory Inversion, and results are comparable to or better than the best currently published systems.
An Empirical Study on Sentiment Analysis of Student Happiness Index via Twitter

The ubiquities of social media have changed the way how the general public consumes their daily news and information. This results in social media becoming an agent of influence in changing the public opinions towards an organization via social media. Thus, university institutions or organizations must perceive and monitor any issues regarding the university or organization on social media and the student's sentiment towards their university. This is because university reputation is one of the main aspects that heavily affected student enrollment decisions for universities This paper is an empirical study on sentiment analysis of student happiness. This project aims to investigate and analyze the sentiment classifier as the measurement in visualizing the student happiness index on Twitter.
Participatory Design: Apps from The Older Adults to The Older Adults

The use of technology to address health issues among older adults is becoming popular nowadays, but, in practice, there is very little systematic work on how to design and develop for older adults. This paper investigated participatory design in designing and developing two mobile apps to support community-living older adults to maintain their health. We examine 1) three older adults to individually participate in designing an app to self-monitor their fruit, vegetable and liquid intakes and 2) a group of four older adults participate in designing an app to address loneliness. In this paper, we present methodological insights of conducting participatory design with older adults. We focus more on the mutual learning between the researcher and the older adults as “designers”. We found that both methods provide rich data for developing the apps. However, when having a group of older adults together was found to stimulate the discussion among them easily, the participants were more open to critique the design suggestions, the moderator did not have to provoke often to guide the discussion, and in terms of time, although the session was slightly longer, it generates more data per participant. We acknowledge that the topic between the two groups was different, and each topic’s privacy was also a concern. We also acknowledge that the number of participants is low, and the participant’s technology background can be a concern. Either way, we recommend continuing to involve older adults in the technology design and development phase.
Techniques for Selecting the Optimal Parameters of One-Class Support Vector Machine Classifier for Reduced Samples

Usually, the One-Class Support Vector Machine (OC-SVM) requires a large dataset for modeling effectively the target class independently to other classes. For finding the OC-SVM model, the available dataset is subdivided into two subsets namely training and validation, which are used for training and validating the optimal parameters. This approach is effective when a large dataset is available. However, when training samples are reduced, parameters of the OC-SVM are difficult to find in absence of the validation subset. Hence, this paper proposes various techniques for selecting the optimal parameters using only a training subset. The experimental evaluation conducted on several real-world benchmarks proves the effective use of the new selection parameter techniques for validating the model of OC-SVM classifiers versus the standard validation techniques
Super-Resolution Algorithm Applied in the Zoning of Aerial Images

Nowadays, multiple applications based on images and unmanned aerial vehicles (UAVs), such as autonomous flying, precision agriculture, and zoning for territorial planning, are possible thanks to the growing development of machine learning and the evolution of convolutional and adversarial networks. Nevertheless, this type of application implies a significant challenge because even though the images taken by a high-end drone are very accurate, it is not enough since the level of detail required for most precision agriculture and zoning applications is very high. So, it is necessary to further improve the images by implementing different techniques to recognize small details. Hence, an alternative to follow is the super-resolution method, which allows constructing an image with the information from multiple images. An efficient tool can be obtained by combining drones’ advantages with different image processing techniques. This article proposes a method to improve the quality of images taken on board in a drone by increasing information obtained from multiple images that present noise, vibration-induced displacements, and illumination changes. These higher resolution images, called super-resolution images, allow supervised training processes to perform different zoning methods better. In this study, GAN-type networks show the best results to recognize visually differentiated ones on an aerial image automatically. The quality measure of the super-resolution image obtained by different methods was defined using sharpness and entropy metrics, and a semantic confusion matrix measures the accuracy of the following semantic segmentation network. Finally, the results show that the super-resolution algorithm’s implementation and the automatic segmentation provide an acceptable accuracy according to the defined metrics.
Towards All-optical Stochastic Computing Using Photonic Crystal Nanocavities

Stochastic computing allows a drastic reduction in hardware complexity using serial processing of bit streams. While the induced high computing latency can be overcome using integrated optics technology, the design of realistic optical stochastic computing architectures calls for energy efficient switching devices. Photonics Crystal (PhC) nanocavities are μm 2 scale devices offering 100fJ switching operation under picoseconds-scale switching speed. Fabrication process allows controlling the Quality factor of each nanocavity resonance, leading to opportunities to implement architectures involving cascaded gates and multi-wavelength signaling. In this paper, we investigate the design of cascaded gates architecture using nanocavities in the context of stochastic computing. We propose a transmission model considering key nanocavity device parameters, such as Quality factors, resonance wavelength, and switching efficiency. The model is calibrated with experimental measurements. We propose the design of XOR gate and multiplexer. We illustrate the use of the gates to design an edge detection filter. System-level exploration of laser power, bit-stream length and bit-error rate is carried out for the processing of gray-scale images. The results show that the proposed architecture leads to 8.5nJ/pixel energy consumption and 512ns/pixel processing time.
Convolution Tells Where to Look

Many attention models have been introduced to boost the representational power of convolutional neural networks (CNNs). Most of them are self-attention models which generate an attention mask based on current features, like spatial attention and channel attention model. However, these attention models may not achieve good results when the current features are the low-level features of CNNs. In this work, we propose a new lightweight attention unit, feature difference (FD) model, which utilizes the difference between two feature maps to generate the attention mask. The FD module can be integrated into most of the state-of-the-art CNNs like ResNets and VGG just by adding some shortcut connections, which does not introduce any additional parameters and layers. Extensive experiments show that the FD model can help improve the performance of the baseline on four benchmarks, including CIFAR10, CIFAR100, ImageNet-1K, and VOC PASCAL. Note that ResNet44 (6.10% error) with FD model achieves better results than ResNet56 (6.24% error), while the former has fewer parameters than the latter one by 29%.
eXtreme Programming, Agile Methodologies, Software Project Management, Customer Role, Rigorous Testing

Agile software development methodologies are attracting attention from academics and practitioners for planning and managing software projects. The eXtreme Programming (XP) challenges conformist wisdom regarding software system development processes and practices as agile methodologies. To work efficiently in the current software development practice, characterized by requirements fuzziness, XP moves away from document-centric operations into people-centric management. In the XP-based software project, the customers play an essential role, having multiple responsibilities such as driving the project, gathering requirements (‘user stories'), and exercising quality control (or acceptance testing). Besides, the customers must liaise with external project stakeholders (e.g., funding authorities, end-users) while maintaining the development team's trust and the wider business. The success of such software project management practices relies on the quality result of each stage of development obtained through rigorous testing. This chapter describes three characteristics of XP project management: customer role, software testing feedback, and learning.
Operationalizing the Benefit of Information Radiators from an Awareness Point of View

Abstract In this paper, we present a grounded theory research for the benefit information radiators provided using the concept of awareness. Information radiators are ubiquitous installations that disseminate information that is likely to improve awareness in semi-public environments. We discuss potential awareness benefits of information radiators and how these can be provided. Furthermore, we examine how to address several awareness facets that correlate with the benefits to contribute to this goal. The results aim to close the research gap about the benefit of information radiators considered from an awareness point of view.
Clothing Attribute Recognition with Semi-supervised Learning

Clothing attribute recognition is a challenging task in the field of computer vision and multimedia. In this paper, we propose a semi-supervised method for clothing attribute prediction, which can utilize unsupervised and supervised data together. There are two parts in the proposed model, i.e., the supervised part for training clothing attribute recognition and the unsupervised part for learning the clues of the images themselves. Specifically, we introduce image transformation, i.e., projective transform, as the unsupervised part, and the MSE loss is used to regress the parameters of the transform coefficients. To explore the effectiveness of the proposed semi-supervised method, we design different scales of the unsupervised data to verify it. And the experimental results show the semi-supervised data can obtain good performance and alleviate human labor.
The Application of Big Data Technology in University Library Management Service

At present, the amount of information stored in university libraries is increasing, and readers’ needs for information and document types are becoming more and more detailed, personalized and complex. In order to better manage university libraries and provide readers with better services, it is necessary to vigorously develop personalized information services. This is also a service currently being carried out by various university libraries in our country. Based on this, the purpose of this article is to study the application of big data technology in university library management services. This article first summarizes the basic theory of big data, and then extends its core technologies such as data mining. Combining with the current situation of university library management in our country, analyze the existing problems and shortcomings. On this basis, data mining and other technologies are used for application research. This article systematically expounds the composition and design of the library management service system based on data mining and other technologies, and uses comparative analysis and observation methods to study the subject of this article. Experimental research shows that compared with the traditional university library management service system, the university library management service system based on data mining technology studied in this paper is 5.9% higher in the new book recommendation module, 11.4% higher in the resource link module, and course reference book recommendation. The module is 4.8% higher, and the recommended classic book module is 5.8% higher. Fully reflects the feasibility of the subject of this article.
Research on Information System Model for Movable Cultural Relics through Blockchain Technology

The management of movable cultural relics is significant for cultural relics' work. Unified and normalized management has great social and economic benefits in strengthening the management of cultural relics, improving the ability of risk prevention, strengthening the supervision of cultural relics transfer, and standardizing the legal collection of cultural relics. To manage movable cultural relics scientifically and efficiently, a movable cultural relics management system based on blockchain is constructed based on the current situation of China's movable cultural relics management and the advantages of blockchain in multi-party cooperation, immutability, whole-process traceability, and time stamp.
NSRL: Named Entity Recognition with Noisy Labels via Selective Review Learning

Named entity recognition (NER) is a task of identifying both types and spans in the sentences. Previous works always assume that the NER datasets are correctly annotated. However, not all samples help with generalization. There are many noisy samples from a variety of sources (e.g., weak, pseudo, or distant annotations). Meanwhile existing methods are prone to cause error propagation in self-training process because of ignoring the overfitting, and becomes particularly challenging. In this paper, we propose a robust Selective Review Learning (NSRL) framework for NER task with noisy labels. Specifically, we design a Status Loss Function (SLF) which helps the model review the previous knowledge continuously when learning new knowledge, and prevents model from overfitting noisy samples in self-training process. In addition, we propose a novel Confidence Estimate Mechanism (CEM), which utilizes the difference between logit values to identify positive samples. Experiments on four distant supervision datasets and two real-world datasets show that the NSRL significantly outperforms previous methods.
Artificial Potential Field Method for Area Coverage of Multi Agricultural Robots

The robot area full coverage problem is a particular path planning problem, which requires robots to cover all parts of the area except obstacles. It has been used in the actual production of agriculture, such as weeding, fertilization, spraying. In this paper, an artificial potential field method of multi-robot full coverage path planning algorithm is used to make several agricultural robots spontaneous organization and coordination, to achieve the full coverage of farmland, greenhouse and other agricultural scenes. According to robots' repulsion force to obstacles in the artificial potential field method, the repulsion force between robots is increased. The goal points are generated to lead the robots to move to the direction that is not covered, and the re-coverage of the area is minimized as far as possible. Simulations show that the algorithm can achieve more than 95% full coverage of the task area with good performance, adaptability and robustness.