Reviewerly/exHarmony huggingface dataset:
Getting to Know Your Neighbors (KYN). Explaining Item Similarity in Nearest Neighbors Collaborative Filtering Recommendations

The popular neighborhood-based Collaborative Filtering recommendation techniques are mostly characterized as black-box systems in which resulting outputs are not easy to interpret. In this work, our goal is to provide human-interpretable explanations of item-based collaborative filtering recommendations and to understand the underlying data distribution. We propose the Know Your Neighbors (KYN) algorithm - a novel model-agnostic approach to explaining similarity-based CF recommendations on both local and global levels. In this approach, a post-hoc explainer model is applied to reveal the most important descriptive features of items that explain the neighborhood function for two popular collaborative algorithms. Our algorithm is evaluated on two public recommendation datasets. The descriptions generated for both the datasets are consistent with our intuitive understanding of user behaviors, but they also reveal that each representation may be susceptible to different types of biases in the dataset.
Removing Dust By Metacrawler

Nowadays URLs collected by Search engine contain mirrored data. Some of the pages gathered by the crawler contain duplicated data. Different URLs with Similar Text are generally known as DUST. With the effect of DUST, the disk storage is wasted, quality rankings are degraded and lower user experiences. To avoid such problems, many kind of research has been recommended and the methods which are already available define only URL DUST removal and detection. The system which is going to be implemented can find and erase content DUST and URL DUST. The concept of the Metacrawler is introduced that crawl the documents and gets results from all the three search engines. The comparisons of content of every website with the another to eliminate mirrored data using k- gram paraphrase technique is defined in current method.
Vision-based reconstruction of laser projection with invariant composed of points and circle on 2D reference

Abstract A vision-based reconstruction method is conducted by the point-circle invariant and the planar laser. The planar laser is coplanar to the two-dimensional (2D) reference. The combination of a circle on the 2D reference, a point on the 2D reference and a random point on the laser stripe is considered as the invariant, which is impervious to the projection from the laser plane to the image. Therefore, the reconstruction model is achieved by the invariant, which is generated from the projections of the above geometric features. The experiments are performed to verify the performance and reconstruction error of the method. The minimum error is 0.473 mm for the camera-reference distance of 600 mm, the scaleplate-projector distance of 100 mm and the reference length of 30 mm. Besides, the maximum error is 4.960 mm, for the camera-reference distance of 900 mm, the scaleplate-projector distance of 400 mm and the reference length of 120 mm. The reconstruction error means of 0.891 mm, 1.365 mm, 2.578 mm and 3.767 mm are observed with respect to the test conditions of 600–900 mm, which proves the applicability of the reconstruction method with the point-circle invariant.
Research on Intrinsic-Safe Through-the-Earth Radio Communication System Technology with Large Depth

This paper studies transmission characteristics of low-frequency (LF) electromagnetic wave on penetrating into the stratum, and carries out research and design over Intrinsic-safe Through-the-earth Radio Communication System Technology with Large depth. Its creativity is divided into free space transmission and transmission loss over stratum medium, which lays the foundation for the design of Intrinsicsafe Radio Through-the-earth Communication System Technology with Large depth. It adopts magnetic antenna with high-sensitivity, MSK modulation and error-corrective encoding technique, suppression over complicated underground EMI, and proved to be successful in dualdirection underground communication with large depth through actual test and trial in mine shaft, which can serve as crucial communication means for safety in coal production and disaster relief.
Improvement of “Text Steganography Based on Unicode of Characters in Multilingual” by Custom Font with Special Properties

Abstract Since computer utilization is expanding, for both social and trade ranges, secure communications through channels got to be an exceptionally critical issue. Information hiding away could be a strategy to get a secure communication medium and securing the data amid transmission. Text documents have very less redundant information as compared to the images and audio, therefore, text steganography is most challenging. This paper aims to improve “text steganography based on Unicode of characters in multilingual” by design new font with special properties for purposes of hiding data. Furthermore, this method based on making the same glyphs for the multiple codes, the Set of High-Frequency Letters called SHFL in the English language was chosen for the embedding process. The hiding method replaces the code of English symbol with other code that has the same glyph exactly. Two bits are hidden at once, utilizing glyph1 for hiding 00 and utilizing glyph2, glyph3, and glyph4 for hiding 01, 10, and 11. The improvement increases the steganography capacity, transparency and improves the security and robustness of the text stego file.
Application and Innovation of Multimedia Information Technology in the Arrangement of Aerobics Music

Abstract Aerobics is a body exercise combining gymnastics, dance and music. In aerobics training and competitions, music runs through. Based on multimedia information technology, this article introduces the software and hardware of aerobics computer music production, aerobics music arrangement steps and operation methods, and provides a certain reference for aerobics music production and research.
The Comparison of Cloud Migration Effort on Platform as a Service

Abstract Platform as a Service offers users to easily and quickly deploy applications in the cloud environment without thinking about the availability of supporting resources for developing the application. However, migrating on-premise applications to cloud vendors still has risks ranging from the compatibility of application technology with the cloud platform to the efforts needed. One obstacle in the adoption of cloud technology is the lack of visibility in planning migration efforts. So this study aims to measure and compare the feasibility of PaaS cloud vendors based on the calculation of the effort required during the migration process through an empirical approach. Specifically, an experiment was carried out by migrating on-premise application to Amazon Elastic Beanstalk, Azure App Service, and Google App Engine. Empirical results show that Azure App Service and Amazon Elastic Beanstalk have similar complexity of effort, while Google App Engine has the highest complexity of effort.
The chief project officer: a new executive role for turbulent times

Purpose While it is common for most C-suite executives to have substantial project responsibilities, many do not have a strong understanding of project management leading to significant failures. As projects are the main mechanisms for implementing changes, project performance has disproportional impact on the competitiveness and viability of organizations. This paper aims to attempt to raise awareness of the Chief Project Officer (CPO) role and lay out important skills and capabilities that are needed for managers to ascend to this role as well as key topics of concern when preparing the mindset to be a successful CPO. Design/methodology/approach The authors have based this research on reviewing publications from the project management journals and publications, interviews of project management professionals and drawing from our industry experience in the field of project management. Findings Most organizations have project managers, either formal or informal, to lead projects. As project environment intensifies across industries, larger organizations rely on a project executives and project management office to oversee projects and performance of C-suite tasks. However, these projects and project management office (PMO) managers often lacks the authority and legitimacy to fully carry out the expected function. This is a clear gap in organizational management, and the gap is growing as more resources are dedicated to projects. Many organizations are equipped with the human resource to develop a CPO, the authors identify five main skills and 20 key topics that are crucial to the success of this vital role. Originality/value While the awareness of the CPO is still in its infancy, most organizations have equivalent or emerging senior project executive roles that may evolve and become CPOs. Today, organizations are increasingly pushed to pursue project-oriented structures to a rapidly changing environment, global market and fast-paced technological advances. It is likely that the CPO role will grow and become a crucial component in top management teams in the coming years to help organizations in moving forward to achieve their strategic goals and objectives.
Dense Disparity Computing Method Based on Mesh Aggregation and Snake Optimization for Stereo Vision

In order to generate continuous and dense disparity images, a stereo matching method based on mesh aggregation and Snake optimization is proposed in this article. First, the reference pixels are obtained, so as to improve the suppression effect of the brightness difference in Census transform and improve the accuracy of initial matching cost calculation. Second, the image is divided by SLIC super pixel segmentation method, and the neighborhood pixels are searched according to the mesh search in the region, and the matching cost of these pixels are aggregated together according to the corresponding weight to complete cost aggregation of the pixels to be matched. Third, the Snake algorithm is used in optimizing the boundary of the disparity region. Eight classes of images on the Middlebury platform are selected as the test images, and the four algorithms on the Middlebury platform are selected as reference algorithms to carry out the experimental research. The experimental results show that proportion to bad pixels is low and disparity is continuous and dense on the disparity image calculated by the algorithm proposed in this article. Performance of the proposed method is close to LocalExp algorithm which is the best on the Middlebury platform, and the proposed method can be better applied in the stereo vision.
Intelligent image recognition system for detecting abnormal features of scenic spots based on deep learning

To monitor the scene anomaly in real-time through video and image and identify the emergencies, try to respond quickly at the beginning of the emergency and reduce the loss. This paper mainly focuses on the realization of the image recognition system for the anomalous characteristics of tourism emergencies. The problem is to study the number of people in the scenic spot based on scenic spot monitoring. The video-based population anomaly monitoring method has improved the AUC index of the W-SFM method by 0.423, and the AUC has increased by 0.0844 compared with the optical flow method; Degree-enhanced algorithm (BCOF), by grasping the micro-blog data related to the scenic spot, comprehensively predicts the overall comfort of the current tourists in the scenic spot, and establishes a tourist state expression model. Compared with the BN algorithm and the NEG algorithm, the BCOF algorithm is the accuracy and the recall rate of tourists in the scenic spots was improved by 14% and 18% respectively. The image recognition system of tourism emergency anomaly was established, and the early warning model of tourism emergency based on group intelligence perception was used to implement early warning on scenic spots. Monitoring, can achieve an overall accuracy of 83.33%, the model has a strong predictive ability, and achieves a scenic spot Real-time monitoring of events.
Deep-learning Object Detection for Resource Recycling

Abstract Recent years have seen a growing concern over global warming, as well as environmental pollution and protection issues. Resource recycling helps the effective reduction of greenhouse gases and environmental pollution, and improves the quality of life for many people. This paper proposes a deep-learning object detection system for resource recycling. The resource recycling of the objects including paper cups, plastic bottles, and aluminum cans was conducted by artificial intelligence. Single shot multibox detector (SSD) and faster region-based convolutional neural network (Faster R-CNN) models were utilized for the training of the deep-learning object detection. With regard to data set images and training time, the accuracy, training steps, and loss function of the SSD and Faster R-CNN models were studied. The accuracy and loss characteristics of the deep-learning object detection system for resource recycling were demonstrated. The system exhibits good potential for the applications of resource recycling and environmental protection.
Leveraging undergraduate federal work study student skill sets to support an R2 university libraries’ research data services

This article discusses creative approaches to establishing a research data services (RDS) at an R2 (high research activity) institution with a limited budget. Seton Hall University Libraries conducted a study of faculty data practices to inform services. The findings indicated that there was a need to support student, faculty, and administrative offices with data related questions such as obtaining data and performing data analysis. To support the findings, undergraduate Federal Work Students (FWS) were hired to build the RDS program in the Seton Hall University Libraries. This model makes it possible for medium sized institutions with limited resources to offer RDS.
A LITERATURE REVIEW AND CLASSIFICATION OF BOOK RECOMMENDATION RESEARCH

The act of reading has benefits for individuals and societies, which can be a long-term commitment. While the overload of books information and readers’ specific needs make book recommendation (BR) in demand, BR is receiving great attention from the research community with different perspectives. The increasing amount of research conducted with BR calls for a classification methodology regarding trends and distribution in this field. This paper presents a study of recommender systems in the domain of BR. The main goal of this work is to provide authors with insights on the trends of academic literature reviews in the proposed context and to present a comparison of different research approaches. The authors searched for up-to-date research papers related to recommender systems for BR within a time period of eighteen years, from 2000 to 2018. Starting from 2000, a significant amount of research related to the subject field of recommender systems was conducted, which led to the first ACM Conference on Recommender Systems. After the filtering process, 39 papers were finally selected from journals, conferences and theses in five different academic databases (i.e. IEEE, ACM, Science Direct, Springer and ProQuest). The general classification is presented in this work, in order to describe the recommendation approaches for BR. This work can be extended in the future to include novel methodologies and trends of recommender systems for BR or other fields.
Learning Weighted Top-k Support Vector Machine

Top-k error ratio is a popular performance measure for multi-category classification in which the number of categories is large. With the aim of obtaining the multi-category classifier minimizing the top-k error, Lapin et al. has developed the top-k support vector machine (top-k SVM) which is trained with the top-k hinge loss. Although top-k hinge is designed to be suitable for the top-k error, another loss or the top-k' hinge loss with k' ≠ k often yields a smaller top-k error ratio than the top-k hinge loss. This suggests that the top-k hinge loss is not always the optimal choice for the top-k error, which motivates us to explore variants of the top-k hinge loss. In this paper, we studied a weighted variant of the top-k hinge loss, and refer to the learning machine as the weighted top-k SVM. We developed a new optimization algorithm based on the Frank-Wolfe algorithm that requires no step size, enjoys the clear stopping criterion, and is never solicitous for computational instability. The Frank-Wolfe algorithm repeats the direction finding step and the line search step. The discoveries in this study are that both the steps can be given in a closed form. By smoothing the loss function, geometrical convergence can be achieved. Experimental results reveal that the weighted top-k SVM often achieved the better pattern recognition performance compared to the unweighted top-k SVM.
Multiverse: Dynamic VM Provisioning for Virtualized High Performance Computing Clusters

Traditionally, HPC workloads have been deployed in bare-metal clusters; but the advances in virtualization have led the pathway for these workloads to be deployed in virtualized clusters. However, HPC cluster administrators/providers still face challenges in terms of resource elasticity and virtual machine (VM) provisioning at large-scale, due to the lack of coordination between a traditional HPC scheduler and the VM hypervisor (resource management layer). This lack of interaction leads to low cluster utilization and job completion throughput. Furthermore, the VM provisioning delays directly impact the overall performance of jobs in the cluster. Hence, there is a need for effectively provisioning virtualized HPC clusters, which can best-utilize the physical hardware with minimal provisioning overheads.Towards this, we propose Multiverse, a VM provisioning framework, which can dynamically spawn VMs for incoming jobs in a virtualized HPC cluster, by integrating the HPC scheduler along with VM resource manager. We have implemented this framework on the Slurm scheduler along with the vSphere VM resource manager. In order to reduce the VM provisioning overheads, we use instant cloning which shares both the disk and memory with the parent VM, when compared to full VM cloning which has to boot-up a new VM from scratch. Measurements with real-world HPC workloads demonstrate that, instant cloning is 2.5× faster than full cloning in terms of VM provisioning time. Further, it improves resource utilization by up to 40%, and cluster throughput by up to 1.5×, when compared to full clone for bursty job arrival scenarios.
Three-stage Junction Detection in Document Images

Engineering drawings contain many curves that intersect at a complex junction. Detection of these junctions is vital for correct recognition of line drawings. In this paper we study the detection of junctions in document images. A threestage junction detection is proposed to detect junctions in document images. The first stage works at the pixel level. Any pixel with more than two neighbors is labeled as low-level-junction. The second stage works at connected component level. All low-level-junctions adjacent to other low-level-junctions are merged into mid-level-junctions. The third stage combines mid-level-junction at a distance less than a threshold into a high-level-junction. Experimental results on real scanned images of mechanical engineering drawings show a precision rate of 82.38% and a recall rate of 97.29%.
Exploring Everyday Sounds in Dementia: Practical Guidelines for Interactive Workshops

Motivated by the successes of music in dementia, researchers and designers in HCI are exploring the potential benefits of non-musical sounds from everyday life for people with dementia. Research in the field of building acoustics and psychology is suggesting that re-presenting everyday sounds as soundscapes in the care space can influence emotional states, relieve boredom, and reduce stress. However, as the responses to sound are personal, and each person experiences dementia differently, an understanding is needed on how people with dementia individually react to sound. Consequently, participatory approaches are needed to take into account individual needs, preferences, and relationships in the process of identifying meaningful sounds. In this chapter, we reflect on our exploratory study, consisting of a series of interactive workshops involving people with dementia at a day-care facility to explore their personal responses to sound. We adopted a design research approach by using the dementia soundboard, a design artifact to facilitate re-experiencing of everyday sounds in the workshop space. Based on our experiences, we provide a critical reflection on our research protocol and articulate practical guidelines to enable the involvement of people with dementia and to promote social engagement and meaningful activity in the process of identifying affective audio content.
Soft subdivision motion planning for complex planar robots

The design and implementation of theoretically-sound robot motion planning algorithms is challenging. Within the framework of resolution-exact algorithms, it is possible to exploit soft predicates for collision detection. The design of soft predicates is a balancing act between their implementability and their accuracy/effectivity. In this paper, we focus on the class of planar polygonal rigid robots with arbitrarily complex geometry. We exploit the remarkable decomposability property of soft collision-detection predicates of such robots. We introduce a general technique to produce such a decomposition. If the robot is an m-gon, the complexity of this approach scales linearly in m. This contrasts with the O(m3) complexity known for exact planners. It follows that we can now routinely produce soft predicates for any rigid polygonal robot. This results in resolution-exact planners for such robots within the general Soft Subdivision Search (SSS) framework. This is a significant advancement in the theory of sound and complete planners for planar robots. We implemented such decomposed predicates in our open-source Core Library. The experiments show that our algorithms are effective, perform in real time on non-trivial environments, and can outperform many sampling-based methods.
Reconstruction of Cross-Correlations between Heterogeneous Trackers Using Deterministic Samples

The exploitation of dependencies between state estimates from distributed trackers plays a vital role in so-called track-to-track fusion and has been extensively studied for state estimates with the same state space. In contrast, dependencies are often neglected when considering heterogeneous state estimates referring to different state spaces, since the necessary transformations make the analytic calculation complex or infeasible. This paper aims to develop an overarching framework for the reconstruction of cross-covariances between state estimates obtained in heterogeneous state spaces. The proposed method uses a set of deterministic samples to calculate dependent information. Thus, it allows for a distributed track-keeping of correlations that also encodes the transformation into the local subsystems. To highlight the algorithm, we use a linear problem with heterogeneous trackers only and discuss the correlation problem in detail. The results show superior performance compared to neglecting the correlations.