Reviewerly/exHarmony huggingface dataset:
Hypercane

In the Dark and Stormy Archives (DSA) project, we focus on storytelling techniques to summarize collections of archived web pages. Since collections can have hundreds or even thousands of seeds (initial URLs) and each seed can be recrawled many times, with each version separately maintained, techniques that include information about all members of the collection can be overwhelming. The premise of storytelling is to focus on sampling exemplar pages from the collection, and present them in a social media interface familiar to users. We present Hypercane , the tool in the DSA suite responsible for selecting exemplar pages. Hypercane offers eight action statements that can be combined in various ways to customize the sample that is produced. Because of its modular design, Hypercane can also be used to analyze large web archive collections outside of the DSA suite.
New Metrics for Understanding Touch by People with and without Limited Fine Motor Function

Current performance measures with touch-based systems usually focus on overall performance, such as touch accuracy and target acquisition speed. But a touch is not an atomic event; it is a process that unfolds over time, and this process can be characterized to gain insight into users’ touch behaviors. To this end, our work proposes 13 target-agnostic touch performance metrics to characterize what happens during a touch. These metrics are: touch direction, variability, drift, duration, extent, absolute/signed area change, area variability, area deviation, absolute/signed angle change, angle variability, and angle deviation. Unlike traditional touch performance measures that treat a touch as a single (x, y) coordinate, we regard a touch as a time series of ovals that occur from finger-down to finger-up. We provide a mathematical formula and intuitive description for each metric we propose. To evaluate our metrics, we run an analysis on a publicly available dataset containing touch inputs by people with and without limited fine motor function, finding our metrics helpful in characterizing different fine motor control challenges. Our metrics can be useful to designers and evaluators of touch-based systems, particularly when making touch screens accessible to all forms of touch.
<scp>MetaFAIR</scp>: A Metadata Application Profile for Managing Research Data

Abstract This paper reports on the development of a metadata application profile (AP), MetaFAIR, designed to support research data management (RDM) to make research data findable, accessible, interoperable, and reusable. The development of MetaFAIR followed a three‐step process that included learning about the characteristics of datasets from researchers to establish their context and requirements, as well as iterative design and testing with researchers' feedback. Guided by the FAIR principles (Findability, Accessibility, Interoperability, and Reusability), MetaFAIR focuses on accommodating description needs particular to computational social science datasets while seeking to provide general enough elements to describe data collections across many different domains. In this paper, MetaFAIR is placed in the context of historical and recent developments in the areas of RDM and application profile creation; following this contextualization, the paper describes the central considerations and challenges of the MetaFAIR development process and discusses its significance for future work in RDM.
Automatic Scoring System for Handwritten Examination Papers Based on YOLO Algorithm

Written examination is an important way of measuring knowledge and ability, but manual scoring of papers is tedious and error-prone. Automated scoring is more fair, accurate and efficient. With the development of artificial intelligence, automatic scoring of papers through image recognition and object detection is becoming an achievable and promising technology today. The aim of this paper is to design an automatic scoring system for objective questions in the examination papers. The automatic scoring system uses YOLOv3 technique to detect and recognize handwritten numbers and characters on examination papers. It also addresses the problem of incorrect recognition due to scribbles. Compared to optical symbol recognition, it can recognize the handwritten answers without extra answer cards. In addition, there is no limit where the student can fill in the answer. The experiments show that the automatic scoring system performs satisfactorily and has good prospects for practical application in the future.
Infrared Target Detection Based on Deep Learning

With the development of artificial intelligence technology, computer vision has gradually entered people's daily lives. In recent years, target detection as an important branch of computer vision has attracted the attention of many scholars and has made some progress. In various application scenarios with target detection and recognition, such as night, haze and other severe weather conditions, the demand and application of target detection for infrared images are becoming more and more extensive. There are obvious differences between infrared images and visible light images. Infrared images have low imaging contrast, unobvious texture features, and more noise. These physical characteristics make infrared target detection always challenging. This paper is based on the one-stage target detection algorithm YOLOv3. In order to improve the detection ability of small targets, we improve the network structure and expand the network to 4 feature scales; by introducing GIOU, the loss function is improved, and the accuracy of network detection is improved; by merging the Batch Normalization layer and the convolutional layer, the speed of network inference is speeded up. Experimental results show that compared with the original YOLOv3 network, the improved YOLOv3-4GB network has improved detection accuracy and enhanced detection capabilities for small targets; this paper deploys the improved algorithm on the embedded platform to meet the requirements of real-time detection.
Product-oriented Machine Translation with Cross-modal Cross-lingual Pre-training

Translating e-commercial product descriptions, a.k.a product-oriented machine translation (PMT), is essential to serve e-shoppers all over the world. However, due to the domain specialty, the PMT task is more challenging than traditional machine translation problems. Firstly, there are many specialized jargons in the product description, which are ambiguous to translate without the product image. Secondly, product descriptions are related to the image in more complicated ways than standard image descriptions, involving various visual aspects such as objects, shapes, colors or even subjective styles. Moreover, existing PMT datasets are small in scale to support the research. In this paper, we first construct a large-scale bilingual product description dataset called Fashion-MMT, which contains over 114k noisy and 40k manually cleaned description translations with multiple product images. To effectively learn semantic alignments among product images and bilingual texts in translation, we design a unified product-oriented cross-modal cross-lingual model for pre-training and fine-tuning. Experiments on the Fashion-MMT and Multi30k datasets show that our model significantly outperforms the state-of-the-art models even pre-trained on the same dataset. It is also shown to benefit more from large-scale noisy data to improve the translation quality. We will release the dataset and codes at https://github.com/syuqings/Fashion-MMT.
SAP-SGD: Accelerating Distributed Parallel Training with High Communication Efficiency on Heterogeneous Clusters

Due to rapid product iterations and high prices, the phenomenon that GPUs in clusters have heterogeneous configurations is widespread. However, existing parallel training mechanisms perform poorly on heterogeneous clusters. The synchronous parallel mechanism can cause fast GPUs to wait for the slowest GPU for synchronization, thus wasting their computing power. The asynchronous parallel mechanism has communication bottlenecks and can exacerbate the straggler problem, causing increased training iterations and even incorrect convergence. In this paper, we introduce a communication-efficient semi-asynchronous parallel mechanism (SAP-SGD), which can take full advantage of the acceleration effect of asynchronous strategy on heterogeneous training and can constrain the straggler problem by using interval global synchronization. A novel weighted aggregation strategy is proposed to aggregate the model parameters with different versions. Experimental results show that our proposed strategy can achieve up to $6.74\times$ speedup on training time, with almost no accuracy decrease.
Rest Intervals during Virtual Reality Gaming Augments Standing Postural Sway Disturbance

Immersive virtual reality (VR) can cause acute sickness, visual disturbance, and balance impairment. Some manufacturers recommend intermittent breaks to overcome these issues; however, limited evidence examining whether this is beneficial exists. The aim of this study was to examine whether taking breaks during VR gaming reduced its effect on postural sway during standing balance assessments. Twenty-five people participated in this crossover design study, performing 50 min of VR gaming either continuously or with intermittent 10 min exposure/rest intervals. Standing eyes open, two-legged balance assessments were performed immediately pre-, immediately post- and 40 min post-exposure. The primary outcome measure was total path length; secondary measures included independent axis path velocity, amplitude, standard deviation, discrete and continuous wavelet transform-derived variables, and detrended fluctuation analysis. Total path length was significantly (p < 0.05) reduced immediately post-VR gaming exposure in the intermittent rest break group both in comparison to within-condition baseline values and between-condition timepoint results. Conversely, it remained consistent across timepoints in the continuous exposure group. These changes consisted of a more clustered movement speed pattern about a lower central frequency, evidenced by signal frequency content. These findings indicate that caution is required before recommending rest breaks during VR exposure until we know more about how balance and falls risk are affected.
Maximization of Solar Radiation on PV Panels With Optimal Intervals and Tilt Angle: Case Study of Yanbu, Saudi Arabia

A key factor in the performance of PV panels is the tilt angle, adjustable via various tracking systems. Fixed tilt angle PV panels miss out on most of the solar radiation each day whereas continuous tracking systems are not always cost-efficient, rather impractical in some cases. Therefore, adjusting the tilt angle using a limited number of periods per year can be a good, compromised solution. In this paper, a new approach is proposed to maximize the impact of solar radiation on PV panels by adjusting their tilt angles. Based on a limited number of periods or intervals per year, the optimal duration (number of days) of each period or interval along with the optimum tilt angle corresponding to each interval are determined by solving two interlinked optimization problems. These two problems are solved using the Most Valuable Player Algorithm (MVPA) combined with the Particle Swarm Optimization (PSO) algorithm. The case study for Yanbu, a western coastal city of Saudi Arabia has been investigated. The obtained results show that the enhanced solar power generation can be achieved by using optimal intervals and optimum tilt angle and provide a suitable benchmark for similar techniques to be used in the future to solve the problem of tilt angle adjustment for maximizing PV panels output.
Knowledge-Graph-Aware Recommendation in Movie Domain

Knowledge-graph-aware recommendation often overlooks the importance of unstructured information, such as text information and picture information. In order to address the problem, we propose KGMR (Knowledge Graph for Movie Recommendation) algorithm which uses a large amount of heterogeneous information provided by the multi-modal knowledge graph to design the semantic types and semantic relations in the movie field. Constructed a knowledge graph in the film domain, and visualized it with Neo4j graph database. The text information and picture information are respectively represented by Doc2Vec and Convolutional Auto-Encode (CAE) algorithms, combined with Bayesian Personalized Ranking (BPR) to construct a recommendation system. Using the MovieLens-latest-small data set for testing, the results show that KGMR has a good improvement in the evaluation value, which proves that the knowledge graph of movies is integrated into the recommendation algorithm, and movies can be accurately recommended to target users.
Method of Robot Episode Cognition Based on Hippocampus Mechanism

This paper proposes a method to realize the robot&#x2019;s self-learning of environment by using an episode cognition model inspired by the hippocampus. The episode cognition map is suitable for robot navigation in an unknown environment, which solves the problem of robustness of the robot&#x2019;s perception in complex and dynamic environments. The model called the hippocampus episode cognitive network (HEC), is based on the hippocampus CA1, CA3, and the dentate gyrus, and combined with the adaptive resonance theory (ART) network. It extract new events through the incremental generation of cognitive neurons, and encode the events into episodes through space-time connections. The episode nodes can be connected to generate an episode cognition map. This method can learn, store, and update information for autonomous mobile robots in an unknown environment. Based on the episode cognitive map, the path trajectory can be predicted through the playback of the episode neuron. The experimental results on the mobile robot show that this method can effectively improve the robot&#x2019;s adaptability for location and mapping in a complex and dynamic environment.
Research data management and sharing awareness, attitude, and behavior of academic researchers

This study assesses the research data management (RDM) awareness, attitude, practices, and behaviors of Pakistan's academic researchers. By using an internationally designed structured questionnaire as a data collection instrument. Quantitative survey research method was opted to meet the research objectives and data was collected from academicians and researchers of four premier universities of Pakistan. The study reveals used and produced data file formats, data acquisition sources, data storage patterns, metadata and tagging practices, data sharing patterns, RDM awareness, attitude, and behavior of the respondents by investigating the self-opinion of respondents on extensive sets of structured questionnaire items. It is a comprehensive assessment of the phenomenon from a developing country's perspective where research data management policies are absent at national and institutional level. The findings have theoretical implications for researchers and practical implications for policymakers, university administrators, university library administrators, and educational trainers.
Learning Panoptic Segmentation from Instance Contours

Panoptic Segmentation aims to provide an understanding of background (stuff) and instances of objects (things) at a pixel level. It combines the separate tasks of semantic segmentation (pixel level classification) and instance segmentation to build a single unified scene understanding task. Typically, panoptic segmentation is derived by combining semantic and instance segmentation tasks that are learned separately or jointly (multi-task networks). In general, instance segmentation networks are built by adding a foreground mask estimation layer on top of object detectors or using instance clustering methods that assign a pixel to an instance center. In this work, we present a fully convolution neural network that learns instance segmentation from semantic segmentation and instance contours (boundaries of things). Instance contours along with semantic segmentation yield a boundary aware semantic segmentation of things. Connected component labeling on these results produces instance segmentation. We merge semantic and instance segmentation results to output panoptic segmentation. We evaluate our proposed method on the CityScapes dataset to demonstrate qualitative and quantitative performances along with several ablation studies. Our overview video can be accessed from https://youtu.be/wBtcxRhG3e0.
Better Learning Shot Boundary Detection via Multi-task

Shot boundary detection (SBD) plays an important role in video understanding, since most recent works take the shot as minimal granularity instead of frames for upstream tasks. However, the large variations of hard-cut and gradual-change transitions within shots significantly limit the performance of SBD. To deal with the variations, we propose a multi-task architecture called Transnet++. Transnet++ disentangles the two types of transition and adopts two separate branches to predict them respectively. Two branches share the same video knowledge space and their results are fused for final prediction. Moreover, we propose a spatial attention module (SAM) to enhance the feature representations which suffers from redundant padding region. Meanwhile, a temporal attention module (TAM) is applied to capture the long-term information of the video for alleviating the over-segmentation problem. Experimental results (91.16% f1-score) on Tencent AVS Dataset demonstrate the effectiveness and superiority of Transnet++ for SBD.
A 3D Mesh-Based Lifting-and-Projection Network for Human Pose Transfer

Human pose transfer has typically been modeled as a 2D image-to-image translation problem. This formulation ignores the human body shape prior in 3D space and inevitably causes implausible artifacts, especially when facing occlusion. To address this issue, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lifting-and-projection</i> framework to perform pose transfer in the 3D mesh space. The core of our framework is a foreground generation module, that consists of two novel networks: a lifting-and-projection network (LPNet) and an appearance detail compensating network (ADCNet). To leverage the human body shape prior, LPNet exploits the topological information of the body mesh to learn an expressive visual representation for the target person in the 3D mesh space. To preserve texture details, ADCNet is further introduced to enhance the feature produced by LPNet with the source foreground image. Such design of the foreground generation module enables the model to better handle difficult cases such as those with occlusions. Experiments on the iPER and Fashion datasets empirically demonstrate that the proposed lifting-and-projection framework is effective and outperforms the existing image-to-image-based and mesh-based methods on human pose transfer task in both self-transfer and cross-transfer settings.
Robot Action Diagnosis and Experience Correction by Falsifying Parameterised Execution Models

When faced with an execution failure, an intelligent robot should be able to identify the likely reasons for the failure and adapt its execution policy accordingly. This paper addresses the question of how to utilise knowledge about the execution process, expressed in terms of learned constraints, in order to direct the diagnosis and experience acquisition process. In particular, we present two methods for creating a synergy between failure diagnosis and execution model learning. We first propose a method for diagnosing execution failures of parameterised action execution models, which searches for action parameters that violate a learned precondition model. We then develop a strategy that uses the results of the diagnosis process for generating synthetic data that are more likely to lead to successful execution, thereby increasing the set of available experiences to learn from. The diagnosis and experience correction methods are evaluated for the problem of handle grasping, such that we experimentally demonstrate the effectiveness of the diagnosis algorithm and show that corrected failed experiences can contribute towards improving the execution success of a robot.
GATE: Gradual Effect Types

Two recent exciting trends in programming languages are gradual types and algebraic effect handlers. Several steps are required to bring algebraic effect handlers to wider use, one of the most important being the development of a suitable gradual type system.
Workflow on CNN utilization and inference in FPGA for embedded applications: 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2021)

The operation of devices such as wearables, hearables, mobile telephones and their accessories can be challenging due to the limitations of embedded systems restrictions in size and computational power. Design scalability and flexibility are necessary factors to better support the variation in utilization of applications. Convolutional Neural Networks (CNN) are able to address the plurality of requirements with their specialization in multi-dimensional, grid like topologies. At the time of proceeding with this study, Field Programmable Gate Arrays (FPGA) have demonstrated as most suitable for latency-sensitive, hardware acceleration and real-time inference jobs. In this work, we present the workflow utilizing a custom CNN model that is small enough to fit and operate in low cost FPGA, such as the Zynq7020 device available to the ZedBoard. The end to end steps to reproduce this work are available at https://github.com/ECSAlab/dnndk-zedboard.
Dual Alignment Self-Supervised Incomplete Multi-View Subspace Clustering Network

Incomplete multi-view clustering has attracted much attention in decade years. To date, most of the remarkable achievements, however, exploit shallow models to learn shared feature representations based on incomplete views. Although some deep learning methods have been proposed to solve this issue, the existing ones still have the following problems: 1) The consistency between views is ignored, which will have serious negative impacts on incomplete multi-view learning. 2) The learned features do not have sufficient cluster-friendliness, that is, the tightness within clusters and the repulsiveness between clusters are not fully considered. To tackle the above shortcomings, we propose a Dual Alignment Self-supervised Incomplete Multi-view Subspace Clustering network (DASIMSC) in this paper. Specifically, the manifold alignment constraint and consistency alignment constraint are integrated with the autoencoder to preserve the compact inherent local structure within the view and the consistency semantics between incomplete views, respectively. Moreover, a self-expression layer coupled with a spectral clustering module is designed to naturally separate different types of data, leveraging the current clustering results to supervise subspace learning, which excludes inter-cluster. Experimental results on several datasets show that our algorithm outperforms all compared state-of-the-arts.