Reviewerly/exHarmony huggingface dataset:
Research on excellent teacher assessment system based on multiple assessment model

According to the existing education policy, this paper puts forward the research on the evaluation system of excellent teachers based on the multiple assessment model, reasonably arranges the scope of the assessment of excellent teachers, and mobilizes the enthusiasm of teachers. By optimizing the hardware structure of the system, SQL server is used The platform provides the system operation effect, improves the software operation mode, and provides the speed and effectiveness of teacher assessment. Finally, through experiments, it is proved that the excellent teacher assessment system based on multiple assessment is relatively better in the actual application process, which has a positive role in improving the teaching level and fully meets the research requirements.
International Workshop on Variability Management for Modern Technologies (VM4ModernTech 2021)

Variability is an inherent property of software systems that allows developers to deal with the needs of different customers and environments, creating a family of related systems. Variability can be managed in an opportunistic fashion, for example, using clone-and-own, or by employing a systematic approach, for instance, using a software product line (SPL). In the SPL community, variability management has been discussed for systems in various domains, such as defense, avionics, or finance, and for different platforms, such as desktops, web applications, or embedded systems. Unfortunately, other research communities---particularly those working on modern technologies, such as microservice architectures, cyber-physical systems, robotics, cloud computing, autonomous driving, or ML/AI-based systems---are less aware of the state-of-the-art in variability management, which is why they face similar problems and start to redeveloped the same solutions as the SPL community already did. With the International Workshop on Variability Management for Modern Technologies, we aim to foster and strengthen synergies between the communities researching variability management and modern technologies. More precisely, we aim to attract researchers and practitioners to contribute processes, techniques, tools, empirical studies, and problem descriptions or solutions that are related to reuse and variability management for modern technologies. By inviting different communities and establishing collaborations between them, we hope that the workshop can raise the interest of researchers outside the SPL community for variability management, and thus reduce the extent of costly redevelopments in research.
Reinforcement Symbolic Learning

Complex problem solving involves representing structured knowledge, reasoning and learning, all at once. In this prospective study, we make explicit how a reinforcement learning paradigm can be applied to a symbolic representation of a concrete problem-solving task, modeled here by an ontology. This preliminary paper is only a set of ideas while feasibility verification is still a perspective of this work.
Automatic Sweep Scan Path Planning for Five-Axis Free-Form Surface Inspection Based on Hybrid Swept Area Potential Field

Sweep scan is an emerging five-axis inspection technology of retrieving geometric data from free-form surfaces. Compared with the traditional surface inspection by coordinate measuring machine (CMM) that works in a point-by-point manner, sweep scan is able to make the stylus tip continuously sweep at high speed on the surface to inspect, whilst acquiring the 3-D point data accurately and efficiently. However, at present, for an arbitrary free-form surface, it still heavily depends on humans to manually generate a suitable sweep scan path. This paper presents a practical sweep scan path planning method, which can automatically generate an efficient sweep scan path for an arbitrary free-form surface. The resultant sweep scan path is able to cater to the shape of the surface to inspect and the unique kinematic characteristics of the typical five-axis inspection machine, so as to achieve a tremendous increase in inspection efficiency. The experiments performed by us show that when compared with some existing automatic sweep scan path generation algorithms such as the simple zigzag method, the proposed method is able to reduce the total inspection time by as much as seven times.
Immersive Analytics Lessons From the Electronic Visualization Laboratory: A 25-Year Perspective

This paper provides a 25-year-long perspective on immersive analytics through the lens of first-in-kind technological advancements introduced at the Electronic Visualization Laboratory, University of Illinois at Chicago, along with the challenges and lessons learned from multiple immersive analytics projects.
Compressing Inverted Indexes with Recursive Graph Bisection: A Reproducibility Study

Document reordering is an important but often overlooked preprocessing stage in index construction. Reordering document identifiers in graphs and inverted indexes has been shown to reduce storage costs and improve processing efficiency in the resulting indexes. However, surprisingly few document reordering algorithms are publicly available despite their importance. A new reordering algorithm derived from recursive graph bisection was recently proposed by Dhulipala et al., and shown to be highly effective and efficient when compared against other state-of-the-art reordering strategies. In this work, we present a reproducibility study of this new algorithm. We describe the implementation challenges encountered, and explore the performance characteristics of our clean-room reimplementation. We show that we are able to successfully reproduce the core results of the original paper, and show that the algorithm generalizes to other collections and indexing frameworks. Furthermore, we make our implementation publicly available to help promote further research in this space.
Geochemical and mineralogical maps, with interpretation, for soils of the conterminous United States

First posted April 25, 2019 For additional information, contact: Director, Geology, Geophysics, and Geochemistry Science CenterU.S. Geological SurveyBox 25046, MS 973Denver, CO 80225 Between 2007 and 2013, the U.S. Geological Survey conducted a low-density (1 site per 1,600 square kilometers, 4,857 sites) geochemical and mineralogical survey of soils in the conterminous United States. The sampling protocol for the national-scale survey included, at each site, a sample from a depth of 0 to 5 centimeters, a composite of the soil A horizon, and a deeper sample from the soil C horizon or, if the top of the C horizon was at a depth greater than 1 meter, a sample from a depth of approximately 80–100 centimeters. The <2-millimeter fraction of each sample was analyzed for a suite of 45 major and trace elements by methods that yield the total or near-total elemental concentration. The major mineralogical components in the samples from the soil A and C horizons were determined by a quantitative X-ray diffraction method using Rietveld refinement. This report presents all the maps and statistical information for each determined element and mineral along with an interpretive section discussing the possible processes that caused the observed national-scale geochemical and mineralogical patterns. Most often, the geochemical and mineralogical patterns reflect the composition of the underlying soil parent material with some modifications caused by leaching of the more mobile elements (for example, calcium and sodium) in the humid areas of the country.
Coupled ensembles of neural networks

We investigate in this paper the architecture of deep convolutional networks. Building on existing state of the art models, we propose a reconfiguration of the model parameters into several parallel branches at the global network level, with each branch being a standalone CNN. We show that this arrangement is an efficient way to significantly reduce the number of parameters while at the same time improving the performance. The use of branches brings an additional form of regularization. In addition to splitting the parameters into parallel branches, we propose a tighter coupling of these branches by averaging their log-probabilities. The tighter coupling favours the learning of better representations, even at the level of the individual branches, as compared to when each branch is trained independently. We refer to this branched architecture as “coupled ensembles”. The approach is generic and can be applied to almost any neural network architecture. With coupled ensembles of DenseNet-BC and parameter budget of 25 M, we obtain error rates of 2.92%, 15.68% and 1.50% on CIFAR-10, CIFAR-100 and SVHN, respectively. For the same parameter budget, DenseNet-BC has an error rate of 3.46%, 17.18%, and 1.8%, respectively. With ensembles of coupled ensembles, of DenseNet-BC networks, with 50M total parameters, we obtain error rates of 2.72%, 15.13% and 1.42%, respectively on these tasks.
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization

For SGD based distributed stochastic optimization, computation complexity, measured by the convergence rate in terms of the number of stochastic gradient calls, and communication complexity, measured by the number of inter-node communication rounds, are two most important performance metrics. The classical data-parallel implementation of SGD over $N$ workers can achieve linear speedup of its convergence rate but incurs an inter-node communication round at each batch. We study the benefit of using dynamically increasing batch sizes in parallel SGD for stochastic non-convex optimization by charactering the attained convergence rate and the required number of communication rounds. We show that for stochastic non-convex optimization under the P-L condition, the classical data-parallel SGD with exponentially increasing batch sizes can achieve the fastest known $O(1/(NT))$ convergence with linear speedup using only $\log(T)$ communication rounds. For general stochastic non-convex optimization, we propose a Catalyst-like algorithm to achieve the fastest known $O(1/\sqrt{NT})$ convergence with only $O(\sqrt{NT}\log(\frac{T}{N}))$ communication rounds.
Label-specific feature selection and two-level label recovery for multi-label classification with missing labels

In multi-label learning, each instance is assigned by several nonexclusive labels. However, these labels are often incomplete, resulting in unsatisfactory performance in label related applications. We design a two-level label recovery mechanism to perform label imputation in training sets. An instance-wise semantic relational graph and a label-wise semantic relational graph are used in this mechanism to recover the label matrix. These two graphs exhibit a capability of capturing reliable two-level semantic correlations. We also design a label-specific feature selection mechanism to perform label prediction in testing sets. The local and global feature–label connection are both exploited in this mechanism to learn an inductive classifier. By updating the matrix that represents the relevance between features and the predicted labels, the label-specific feature selection mechanism is robust to missing labels. At last, intensive experimental results on nine datasets under different domains are presented to demonstrate the effectiveness of the proposed approach.
Celebrity Profiling

Celebrities are among the most prolific users of social media, promoting their personas and rallying followers.This activity is closely tied to genuine writing samples, which makes them worthy research subjects in many respects, not least profiling.With this paper we introduce the Webis Celebrity Corpus 2019.For its construction the Twitter feeds of 71,706 verified accounts have been carefully linked with their respective Wikidata items, crawling both.After cleansing, the resulting profiles contain an average of 29,968 words per profile and up to 239 pieces of personal information.A crossevaluation that checked the correct association of Twitter account and Wikidata item revealed an error rate of only 0.6%, rendering the profiles highly reliable.Our corpus comprises a wide cross-section of local and global celebrities, forming a unique combination of scale, profile comprehensiveness, and label reliability.We further establish the state of the art's profiling performance by evaluating the winning approaches submitted to the PAN gender prediction tasks in a transfer learning experiment.They are only outperformed by our own deep learning approach, which we also use to exemplify celebrity occupation prediction for the first time.
Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System

Research on the automatic generation of poetry, the treasure of human culture, has lasted for decades.Most existing systems, however, are merely model-oriented, which input some user-specified keywords and directly complete the generation process in one pass, with little user participation.We believe that the machine, being a collaborator or an assistant, should not replace human beings in poetic creation.Therefore, we proposed Jiuge, a humanmachine collaborative Chinese classical poetry generation system.Unlike previous systems, Jiuge allows users to revise the unsatisfied parts of a generated poem draft repeatedly.According to the revision, the poem will be dynamically updated and regenerated.After the revision and modification procedure, the user can write a satisfying poem together with Jiuge system collaboratively.Besides, Jiuge can accept multi-modal inputs, such as keywords, plain text or images.By exposing the options of poetry genres, styles and revision modes, Jiuge, acting as a professional assistant, allows constant and active participation of users in poetic creation.
Linguistic summarization of in-home sensor data

With the increase in the population of older adults around the world, a significant amount of work has been done on in-home sensor technology to aid the elderly age independently. However, due to the large amounts of data generated by the sensors, it takes a lot of effort and time for the clinicians to makes sense of this data. In this work, we develop a system to help make this data more useful by presenting it in the form of natural language. We start by identifying important attributes in the sensor data that are relevant to the health of the elderly. We then develop algorithms to extract these important health related features from the sensor parameters and summarize them in natural language. We focus on making the natural language summaries to be informative, accurate and concise. We designed multiple surveys using real and synthetic data to validate the summaries produced by our algorithms. We show that the algorithms produce meaningful results comparable to human subjects. We also implemented our linguistic summarization system to produce summaries of data leading to health alerts derived from the sensor data. The system is running live in 110 apartments currently. By the means of retrospective case studies, we illustrate that the linguistic summaries are able to make the connection between changes in the sensor data and the health of the elderly. We present a system that extracts important clinically relevant features from in-home sensor data generated in the apartments of the elderly and summarize those features in natural language. The preliminary testing of our summarization system shows that it has the potential to help the clinicians utilize this data effectively.
Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition

We propose Human Pose Models that represent RGB and depth images of human poses independent of clothing textures, backgrounds, lighting conditions, body shapes and camera viewpoints. Learning such universal models requires training images where all factors are varied for every human pose. Capturing such data is prohibitively expensive. Therefore, we develop a framework for synthesizing the training data. First, we learn representative human poses from a large corpus of real motion captured human skeleton data. Next, we fit synthetic 3D humans with different body shapes to each pose and render each from 180 camera viewpoints while randomly varying the clothing textures, background and lighting. Generative Adversarial Networks are employed to minimize the gap between synthetic and real image distributions. CNN models are then learned that transfer human poses to a shared high-level invariant space. The learned CNN models are then used as invariant feature extractors from real RGB and depth frames of human action videos and the temporal variations are modelled by Fourier Temporal Pyramid. Finally, linear SVM is used for classification. Experiments on three benchmark human action datasets show that our algorithm outperforms existing methods by significant margins for RGB only and RGB-D action recognition.
Multi-Scale Vehicle Detection for Foreground-Background Class Imbalance with Improved YOLOv2

Vehicle detection is a challenging task in computer vision. In recent years, numerous vehicle detection methods have been proposed. Since the vehicles may have varying sizes in a scene, while the vehicles and the background in a scene may be with imbalanced sizes, the performance of vehicle detection is influenced. To obtain better performance on vehicle detection, a multi-scale vehicle detection method was proposed in this paper by improving YOLOv2. The main contributions of this paper include: (1) a new anchor box generation method Rk-means++ was proposed to enhance the adaptation of varying sizes of vehicles and achieve multi-scale detection; (2) Focal Loss was introduced into YOLOv2 for vehicle detection to reduce the negative influence on training resulting from imbalance between vehicles and background. The experimental results upon the Beijing Institute of Technology (BIT)-Vehicle public dataset demonstrated that the proposed method can obtain better performance on vehicle localization and recognition than that of other existing methods.
General Type-2 Fuzzy Sugeno Integral for Edge Detection

A type-2 fuzzy edge detection method is presented in this paper. The general process consists of first obtaining the image gradients in the four directions—horizontal, vertical, and the two diagonals—and this technique is known as the morphological gradient. After that, the general type-2 fuzzy Sugeno integral (GT2 FSI) is used to integrate the four image gradients. In this second step, the GT2 FSI establishes criteria to determine at which level the obtained image gradient belongs to an edge during the process; this is calculated assigning different general type-2 fuzzy densities, and these fuzzy gradients are aggregated using the meet and join operators. The gradient integration using the GT2 FSI provides a methodology for achieving more robust edge detection, even more if we are working with blurry images. The experimental evaluations are performed on synthetic and real images, and the accuracy is quantified using Pratt’s Figure of Merit. The results values demonstrate that the proposed edge detection method outperforms other existing algorithms.
SELF-ATTENTION WITH FUNCTIONAL TIME REPRESENTATION LEARNING

Sequential modelling with self-attention has achieved cutting edge performances in natural language processing. With advantages in model flexibility, computation complexity and interpretability, self-attention is gradually becoming a key component in event sequence models. However, like most other sequence models, self-attention does not account for the time span between events and thus captures sequential signals rather than temporal patterns. Without relying on recurrent network structures, self-attention recognizes event orderings via positional encoding. To bridge the gap between modelling time-independent and time-dependent event sequence, we introduce a functional feature map that embeds time span into high-dimensional spaces. By constructing the associated translation-invariant time kernel function, we reveal the functional forms of the feature map under classic functional function analysis results, namely Bochner's Theorem and Mercer's Theorem. We propose several models to learn the functional time representation and the interactions with event representation. These methods are evaluated on real-world datasets under various continuous-time event sequence prediction tasks. The experiments reveal that the proposed methods compare favorably to baseline models while also capture useful time-event interactions.
ImageCLEF 2019: Multimedia Retrieval in Medicine, Lifelogging, Security and Nature

This paper presents an overview of the ImageCLEF 2019 lab, organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2019, the 17th edition of ImageCLEF runs four main tasks: (i) a medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data, (ii) a lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (iii) a new security task addressing the problems of automatically identifying forged content and retrieve hidden information, and (iv) a new coral task about segmenting and labeling collections of coral images for 3D modeling. The strong participation, with 235 research groups registering, and 63 submitting over 359 runs, shows an important interest in this benchmark campaign.
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge

Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge.They often assume that writing knowledge can be acquired from the training data alone.However, when people are writing, they not only rely on the data but also consider related knowledge.In this paper, we enhance neural data-totext models with external knowledge in a simple but effective way to improve the fidelity of generated text.Besides relying on parallel data and text as in previous work, our model attends to relevant external knowledge, encoded as a temporary memory, and combines this knowledge with the context representation of data before generating words.This allows the model to infer relevant facts which are not explicitly stated in the data table from an external knowledge source.Experimental results on twenty-one Wikipedia infoboxto-text datasets show our model, KBAtt, consistently improves a state-of-the-art model on most of the datasets.In addition, to quantify when and why external knowledge is effective, we design a metric, KBGain, which shows a strong correlation with the observed performance boost.This result demonstrates the relevance of external knowledge and sparseness of original data are the main factors affecting system performance.