Reviewerly/exHarmony huggingface dataset:
Multiviewpoint-Based Agglomerative Hierarchical Clustering

The cosine similarity is a similarity measure useful for document clustering. The cosine similarity between two points is determined by the angle between their corresponding vectors observed from the single reference viewpoint, the origin. Recently, Nguyen et al. [6] proposed a new similarity measure called MVS (MultiViewpoint-based Similarity) in which the vectors are observed from multiple viewpoints. They incorporated MVS into some non-hierarchical clustering algorithm and showed that MVS outperforms the original cosine similarity. This paper proposes an agglomerative hierarchical clustering which couples the average-link method with MVS. Despite MVS is more complex than the cosine similarity, our clustering algorithm achieves the same time complexity as the average-link method with the cosine similarity by computing the inter-cluster similarity smartly. Interestingly, our algorithm can be expanded to control the size fairness among clusters. Experimentally in document clustering, our algorithm outputs more accurate clustering results than the average-link method with the cosine similarity almost without lengthening the running time.
Less Than 10% of Library Service Users Ask For Help

User engagement and support is getting increasing attention in the world of institutional repositories as user-driven improvements are becoming the norm. As the Los Alamos National Laboratory Research Library prepared for an interface overhaul intended to modernize current online tools, we developed a pipeline to track and analyze user feedback. We focused our efforts on emails specific to one tool sent to the helpdesk, from its launch in March 2012 to November 2018. We analyzed both email volume and email contents and found that while email volume is stable over time, contents vary in correlation to changes in tool workflows, capabilities, and policies dictating tool use. Our developed methods offer insights into user behavior and guidance for future tool development.
Reliable Photovoltaic Generation Forecasting via Quantile Determination

Photovoltaic (PV) generation forecasting is critical in advancing the PV penetration level in power systems. However, the inherent uncertainties of PV generation can hardly be quantified by traditional point forecast methods as point forecasting methods can only provide one predicted value at each time step. Due to its superior ability of measuring uncertainties, probabilistic forecasting, which can be in the form of quantile forecasts, is drawing increasing attention. Nevertheless, due to the extensive stochastic nature of PV generation, the existing probabilistic forecasting methods are not sufficiently reliable to quantify the uncertainties of PV generation. This paper adapts quantile determination (QD) to improve the reliability of probabilistic PV generation forecasting. Based on the modified QD, a new method for probabilistic PV generation forecasting is proposed. A one-hour-ahead PV generation forecasting case study reveals that with the proposed QD, the reliability of probabilistic PV forecasting is significantly improved. Moreover, it is also shown that the proposed probabilistic PV generation forecasting method can conduct much more accurate and reliable quantile forecasts, compared with existing methods.
Combination of Positions and Angles for Hand Pose Estimation

This paper deals with the estimation of hand pose from a single depth image. We present a method that is based on a description of the hand pose via local rotations of bones trained discriminatively in an end-to-end fashion using a convolutional neural network. We compare our method with existing approach of hand pose estimation of 3D locations of hand joints. For this purpose, we collected precise ground-truth data with a passive marker-based optical motion capture technology. The results show, that the estimation of the hand pose formulated as a combination of local rotations of bones and relative locations of joints outperforms the direct estimation of 3D global joints locations.
Context-Aware GANs for Image Generation from Multimodal Queries

In this paper, we propose a novel model of context-aware generative adversarial networks (GANs) to generate images from a multimodal query: a pair of condition text and context image. In our study, context is defined as the objects and concepts that appear in the image but not in the text. We construct two object trees expressing the objects and the corresponding hierarchical relationships described in the input condition text and context image, respectively. We compare these two object trees to extract the context. Then, based on the extracted context, we generate parameters for the generator in context-aware GANs. To guarantee that the generated image is related to the multimodal query, i.e., both the condition text and context image, we also construct a context discriminator in addition to the condition discriminator, similar to that of conditional GANs. The experimental results reveal that the prepared model generates images with higher resolutions, containing more contextual information than previous models.
Empirical Analysis of Semantic Metadata Extraction from Video Lecture Subtitles

Video lectures improve the learning experiences considering individual's needs and learning styles. However, the large amount of educational content and their availability in a fragmented way turns difficult the tasks of accessing these resources and understanding the concepts under study. Extracting relevant information from video lectures can be useful for recommendation purposes and for helping the interpretation of a concept in an exact moment of a lecture. The extraction of semantic metadata from a video natural language subtitle involves challenges in dealing with informal aspects of language and the detection of semantic classes from the text. In this paper, we conduct an empirical analysis of semantic annotation approaches supported by ontologies in the extraction of relevant metadata from textual transcriptions of video lectures in Computer Science. The obtained results indicate that existing tools can be useful for the studied task and the video lecture semantic metadata extraction process is highly influenced by the underlying ontologies.
The preliminary results of Lindu software: a free seismological data processing using python framework

Abstract LINDU software is developed to solve integrated earthquake data processing. It is GUI based software that fulfil the needed for user friendly type of software. The Python framework is used for computation and visualization and integrates the common programs for earthquake data processing, such as GAD.exe, JHD.exe, and HypoDD.exe. It is also integrates the common procedure of routine data processing in earthquake seismology and works in local and regional scale. In this paper, we shows the preliminary results of LINDU software for several functions. To identify arrival time of P-wave we employ Akaike Information Criterion (AIC), MER (Modified Energy Ratio) and S/L Kurt’s method. The results of these method will be considered as guided – auto picking. However, the results also can be treated as reference for picking manually with Seisgram2k.jar. Geiger’s method is employed to locate the event location. The events can be relocated and 1D velocity can be updated by employing Joint Hypocenter Determination (JHD). The next method to relocate the event location is Double Difference (DD) algorithm. The precision result of Lindu software has been tested using IRIS and real data available which run seamlessly.
Weighted-$A^*$ Based Energy Efficient Resource Allocation in G.Fast

One of the key challenges in G.fast is to minimize the power consumption at distribution points. G.fast standards define Discontinuous Operation modes that provide avenues for power reduction by allowing intermittent transmission of users along time slots. In this paper, we formulate a power efficiency problem as a user-slot assignment problem, where we schedule users to time slots during discontinuous operation such that the total power consumption is minimized. Since the general user-slot assignment is NP hard, we propose a weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A^{*}$ </tex-math></inline-formula> algorithm based solution that achieves reasonable performance with limited computational resources. Further, the proposed user-slot assignment is also G.fast standards compliant and therefore can be implemented in practice. We also explore different precoding as well as user grouping strategies that can be employed while performing user-slot allocation. Finally, the main insight of this work is that on using our user-slot assignment algorithm and by only precoding during the normal operations, we achieve energy efficiency levels comparable to those achieved when precoding is applied during the discontinuous operations without the suggested user-slot allocation.
Exploration of Large Omnidirectional Images in Immersive Environments

Navigation is a major challenge in exploring data within immersive environments, especially of large omnidirectional spherical images. We propose a method of auto-scaling to allow users to navigate using teleportation within the safe boundary of their physical environment with different levels of focus. Our method combines physical navigation with virtual teleportation. We also propose a “peek then warp” behavior when using a zoom lens and evaluate our system in conjunction with different teleportation transitions, including a proposed transition for exploration of omnidirectional and 360-degree panoramic imagery, termed Envelop, wherein the destination view expands out from the zoom lens to completely envelop the user. In this work, we focus on visualizing and navigating large omnidirectional or panoramic images with application to GIS visualization as an inside-out omnidirectional image of the earth. We conducted two user studies to evaluate our techniques over a search and comparison task. Our results illustrate the advantages of our techniques for navigation and exploration of omnidirectional images in an immersive environment.
Orogenic Gold Metallogenic System in North Margin of Yangtze Block: Preliminary Study on Tectonic Evolution and Mineralization

Abstract The northern margin of the Yangtze massif is mainly composed of Longmenshan orogenic belt, Hannan-Micangshan uplift and Beidabashan arcuate tectonic belt. The strata are composed of sedimentary caprock and basement. The basement is Archaean-Lower Proterozoic and Mesoproterzoic volcanic rocks. Continental collision between North China plate and Yangtze plate, the three secondary tectonic units have very high similarities in geological structure, geological evolution, mineralization and deposit characteristics. Therefore, we believe that the northern margin of the Yangtze block can be classified as a metallogenic belt. The orogenic gold metallogenic belt on the northern margin of the Yangtze massif has geological conditions for forming large-scale gold deposits. However, the formation time of the gold deposit is younger, lower crustal uplift, and the degree of erosion of the sedimentary caprock is weak. The deep ore-rich body has not exposed the surface. It is suggested to carry out systematic comprehensive aeromagnetic, voyage weight, avionics and other geophysical exploration work in the gold mineralization belt on the northern margin of the Yangtze block. Finally, a favorable target area is delineated.
Unsupervised facial image occlusion detection with deep autoencoder

Face recognition techniques have been developed significantly in recent years. However, recognizing faces with partial occlusion is still a challenging problem. Although there are many works to solve the problem of obscuring the face, the occlusion is still a challenge in face recognition. To overcome this issue, firstly we should detect the occlusion position in the facial images. We construct a robust self-encoding machine to solve the occlusion detection problem in face images and uses synthetic occlusion data for training. We evaluated our method under various synthetic occlusion face images. Experiments show that our method can effectively detect various types of occlusion masks in an unsupervised manner and has better robustness to the occlusion categories.
Implementation of AdaBoost Face Detection Using Vivado HLS

For the problem that Adaptive Boosting (AdaBoost) face detection algorithm is slowly implemented on the embedded platform by software, this paper adopts the method of the full hardware acceleration. The intellectual property (IP) core of AdaBoost algorithm is designed by Vivado high-level synthesis (HLS), which may reduce the development difficulty and shorten the development cycle. The design adopts the serial–parallel structure to accelerate face detection and uses several methods of optimizing hardware resource. The face detection algorithm is implemented on the Zedboard platform and achieves the purpose of real-time detection.
Implementing Phenotypic Plasticity with an Adaptive Generative Representation

This study compares an adaptive and a nonadaptive representation for finding long walks on obstructed grids. This process models adaption of a simple plant to an environment where the plant's ability to grow is impeded by obstructions such as resource poor areas like bare rock. The intent of the adaptive representation is to model the biological phenomenon of phenotypic plasticity in which gene regulation is at least partially in response to environmental cues, in this case the obstructions. The adaptive representation is found to have a substantial advantage, with the greatest level of advantage at intermediate levels of obstruction. Agents are asked to solve multiple problem instances simultaneously (i.e. using the same chromosome). The advantage of the adaptive representation is also found to be higher when more boards are used in fitness evaluation.
Research on Modeling of Maritime Wireless Communication Based on Bayesian Linear Regression

Maritime wireless communication is in the stage of continuous development. Medium and short wave communication has always been the main method of maritime communications. Realizing rapid, efficient, and reliable signal transmission is the urgent need for the development of current situation. In this paper, aiming at MF, HF, or VHF radio waves emitted by land, Bayesian linear regression model is used to solve the reflection problem of calm sea surface, and then EM (expectation maximization) algorithm is further used to solve the complex Bayesian model to solve the reflection problem of rough sea surface. Furthermore, the advantages of the Bayesian linear regression model over other models, such as the Longley-Rice model, are obtained.
Toward Robust Online Adaptive Visual Tracking via Pyramidal Features Extraction

Visual object tracking in a complex situation is a challenging problem. In recent years, some high-quality trackers of convolutional neural networks (CNN) have received extensive attention. However, they still suffer from the model drift and limited appearance representations. In this paper, we design a more robust online adaptation tracking framework, called OPT, which consists of pyramidal feature extraction and domain-specific semantic branches. Specifically, we extract hierarchical features from a pre-trained backbone CNN to construct the subsequent pyramidal feature representations. Meanwhile, each domain-specific semantic branch for each sequence from tracking dataset is integrated into the proposed framework to implement the end-to-end training process. To obtain the robust tracking result during the test stage, we introduce an extra domain-specific semantic branch only based on the more distinct shallow features to adaptively update parameters online for the specific tracking target. Additionally, for the same input, the multiple different sample strategies are adopted to form a hard negative balanced sample distribution to achieve sufficient training on the limited data. Finally, extensive experiments on the OTB benchmarks demonstrate the effectiveness and robustness of our proposed tracking framework.
A 2-Dimensional Technology and Real-World Interaction View Approach

The rise of computing technology has increased its influence on societies. As computing technology penetrates into everyday life, we witness more interactions and integration issues with technology and societies In this paper, the author coins two dimensional models which reflects today’s technological landscape with the reality. Penetration to physical world and social aspects of human life creates increased border interaction for computing technologies. The author presents implications from these dimensional models.
Improving resolution images detail features for Generate Network

Update of deep network framework to super-resolution reconstruction has been greatly improved, but there are great problems of loss of texture information and decreased image detail quality. In this paper, we have constructed network focus on texture features structure, which can generate SR images by taking full advantage of low resolution images and improve the efficiency of generation. In our method, we first adopt extract detail texture information by kernel diversity network (KDN)which is a combined with residual network to extensive extract various feature of low dimensional images. Particularly, KDN is derived from the processing of the original image and has the ability to prevent information loss and its operation according to certain combination mode by convolution operations with different properties. Furthermore, we design pyramid amplification networks that improving generation speed and image quality to maximizing utilization information of the original image. Our final results show that an SR network with KDN and pyramid networks can generate more natural and clear texture in comparison to state-of-the-art methods.
Visualizing Commonalities and Variabilities in a Software Product Family

Usually, the software development companies have a lot of variants of the same product to different clients.The engineering of Software Product Line (SPL) decrease development process effort compared to traditional methodology. However, the migration from existing variant systems to one SPL can be a costly task. Various approaches have been proposed to perform this process. This work, in turn, proposes support companies by a semi-automatic method to commonalities and variabilities identification and visualization for a set of products variants. We evaluate our proposed method on variant information systems of a software development company. The proposed method helped the volunteers identify the commonalities and variabilities among the variant information systems, thus representing a fundamental support to SPL adoption in a software development company.
Virtual Rotation with Visuo-Haptics

Redirected walking has been proposed as a means of making a narrow space feel wide in a virtual space. We examined the effect of visuo-haptics on the detection threshold of rotation gain when participants walked around a wall. We found that the threshold was affected by visuo-haptics only when they walked around the outside of the wall but was not affected when they walked around the inside of the wall.
Multi-task Learning for Pedestrian Body Parts Detection and Multi-attribute Classification

Pedestrian analysis plays a vital role in intelligent video surveillance and security-centric computer vision systems. Despite that the deep convolutional neural networks (DCNNs) achieved remarkable performance in computer vision, learning fine-grained features of pedestrian attribute tasks is a challenging task in complex surveillance scenarios. In this paper, we proposed a new DCNNs framework for pedestrian analysis, with the main idea of integrating different learning tasks of pedestrian body parts detection and pedestrian attribute classification, which called Hyper-pedestrian convolutional neural network (HP-CNN). Additionally, the proposed HP-CNN bring some advantages: 1) Squeeze-and-excitation block (SE-block) strengthens the representational power of networks by selectively emphasising informative features; 2) Multi-scale feature fusion concatenates more fine-grained information from both the low-level and high-level and enhances the contextual information from different convolutional layers. Experimental results conducted on the largest pedestrian attributes dataset show that the proposed HP-CNN obtained better pedestrian analysis results of both body parts detection and attribute classification, compared to the tested state-of-the-art methods. Especially, the auxiliary task of body parts detection can dramatically boost the performance of attribute classification.
A Signal Processing Perspective on Human Gait: Decoupling Walking Oscillations and Gestures

This study focuses on gesture recognition in mobile interaction settings, i.e. when the interacting partners are walking. This kind of interaction requires a particular coordination, e.g. by staying in the field of view of the partner, avoiding obstacles without disrupting group composition and sustaining joint attention during motion. In literature, various studies have proven that gestures are in close relation in achieving such goals. Thus, a mobile robot moving in a group with human pedestrians, has to identify such gestures to sustain group coordination. However, decoupling of the inherent -walking- oscillations and gestures, is a big challenge for the robot. To that end, we employ video data recorded in uncontrolled settings and detect arm gestures performed by human-human pedestrian pairs by adopting a signal processing approach. Namely, we exploit the fact that there is an inherent oscillatory motion at the upper limbs arising from the gait, independent of the view angle or distance of the user to the camera. We identify arm gestures as disturbances on these oscillations. In doing that, we use a simple pitch detection method from speech processing and assume data involving a low frequency periodicity to be free of gestures. In testing, we employ a video data set recorded in uncontrolled settings and show that we achieve a detection rate of 0.80.
Bank Account Abnormal Transaction Recognition Based on Relief Algorithm and BalanceCascade

With the rapid development of the banking industry, the number of transactions is exponential growth.At the same time, abnormal transactions are also increasing, causing immeasurable losses and risks.In terms of how to accurately identify suspicious transactions from massive customer information and bank account transaction data, this paper adopts the BalanceCascade algorithm based on Relief to solve the problem of unbalanced data in the identification of abnormal transactions in bank accounts, and proposes an effective abnormal transaction identification model.At the same time, the AUC and K-S index as the unbalanced data classification standards of performance evaluation, and finally to Kaggle data platform of bank accounts abnormal transaction data set, the results show that the proposed identification model of performance evaluation index of AUC 0.90 KS value at the same time also is as high as 0.64, shows that the model in as much as possible to reduce the rate of false positives and has high ability of classification and recognition, the method of bank accounts abnormal transaction identification has a certain reference value, enhance rapid response and improve the level of customer service for Banks have certain effect.