Reviewerly/exHarmony huggingface dataset:
ReQue

In this paper, we implement and publicly share a configurable software workflow and a collection of gold standard datasets for training and evaluating supervised query refinement methods. Existing datasets such as AOL and MS MARCO, which have been extensively used in the literature for this purpose, are based on the weak assumption that users' input queries improve gradually within a search session, i.e., the last query where the user ends her information seeking session is the best reconstructed version of her initial query. In practice, such an assumption is not necessarily accurate for a variety of reasons, e.g., topic drift. The objective of our work is to enable researchers to build gold standard query refinement datasets without having to rely on such weak assumptions. Our software workflow, which generates such gold standard query datasets, takes three inputs: (1) a dataset of queries along with their associated relevance judgements (e.g. TREC topics), (2) an information retrieval method (e.g., BM25), and (3) an evaluation metric (e.g., MAP), and outputs a gold standard dataset. The produced gold standard dataset includes a list of revised queries for each query in the input dataset, each of which effectively improves the performance of the specified retrieval method in terms of the desirable evaluation metric. Since our workflow can be used to generate gold standard datasets for any input query set, in this paper, we have generated and publicly shared gold standard datasets for TREC queries associated with Robust04, Gov2, ClueWeb09, and ClueWeb12. The source code of our software workflow, the generated gold datasets, and benchmark results for three state-of-the-art supervised query refinement methods over these datasets are made publicly available for reproducibility purposes.
Sliding Differential Evolution Scheduling for Federated Learning in Bandwidth-Limited Networks

Federated learning (FL) in a bandwidth-limited network with energy-limited user equipments (UEs) is under-explored. In this article, to jointly save energy consumed by the battery-limited UEs and accelerate the convergence of the global model in FL for the bandwidth-limited network, we propose the sliding differential evolution-based scheduling (SDES) policy. To this end, we first formulate an optimization that aims to minimize a weighted sum of energy consumption and model training convergence. Then, we apply the SDES with parallel differential evolution (DE) operations in several small-scale windows, to address the above proposed problem effectively. Compared with existing scheduling policies, the proposed SDES performs well in reducing energy consumption and the model convergence with lower computational complexity.
Video Activity Recognition With Varying Rhythms

Recognizing normal and anomalous events in long and complex videos with multiple sub-activities has received considerable attention in recent years. This task is more challenging than traditional action recognition in short and relatively homogeneous video clips. Other than the difficulty in recognizing activities in long videos, one other challenge is the varying activity rhythms. The rhythm of sub-actions in an activity can differ in nature and can pose additional challenges that affect the performance of activity recognition methods. In this article, five video activity recognition methods were evaluated using two publicly available video datasets, Breakfast and VIRAT, which consist of long and complex videos. Extensive experiments and analyses showed that among these methods, VideoGraph, was found to perform distinctly better than the other investigated methods while maintaining high accuracy even if the test videos were exposed to severe rhythm changes. The results indicated that VideoGraph is less sensitive to varying rhythms in contrast to other investigated methods. By changing some of the architecture parameters, we also observed performance improvements in VideoGraph.
Are You One of Us?

To better understand how we can broaden participation in computing, this exploratory study examines the interview process for elite internships at established technology companies. Through conducting 36 interviews with evaluators at technology companies, we find that in addition to technical competence, evaluators often assess internship applicants based on explicit and implicit signals of industrial fit, organizational fit, and individual fit. These evaluative criteria are reminiscent of prior literature linking biases in hiring to social class background. By reflecting on how our findings relate to previous studies, we suggest that evaluators? assessments of fit are potentially linked to social class background, and this might be an invisible factor contributing to hiring biases at technology companies. Given that hiring only culturally similar employees can have negative individual, organizational, and societal consequences, we propose strategies for evaluators to broaden their evaluation perspectives and to enact inclusive interviewing practices. We conclude with a call for further research on the role of social class background in the hiring process.
Automatic Short Answer Grading using Siamese Bidirectional LSTM Based Regression

Automatic student assessment plays an important role in education - it provides instant feedback to learners, and at the same time reduces tedious grading workload for instructors. In this paper, we investigate new machine learning techniques for automatic short answer grading (ASAG). The ASAG problem mainly involves assessing short, natural language responses to given questions automatically. While current research in the field has focused either on feature engineering or deep learning, we propose a new approach which combines the advantages of both. More specifically, we propose a Siamese Bidirectional LSTM Neural Network based Regressor in conjunction with handcrafted features for ASAG. Extensive experiments using the popular Mohler ASAG dataset which contains training samples from Computer Science courses, have demonstrated that our system, despite being simpler, provides similar or better overall performance in terms of grading accuracy (measured with Pearson r, mean absolute error and root mean squared error) compared to state-of-the-art results.
Tolerant Markov Boundary Discovery for Feature Selection

Due to the interpretability and robustness, Markov boundary (MB) has received much attention and been widely applied to causal feature selection. However, enormous empirical studies show that, existing algorithms achieve outstanding performance only on the standard Bayesian network data. While on the real-world data, they could not identify some of the relevant features since the large conditioning set and the ignored multivariate dependence lead to performance degradation. In this paper, we propose a tolerant MB discovery algorithm (TLMB), which maps the feature space and target space to a reproducing kernel Hilbert space through the conditional covariance operator, to measure the causal information carried by a feature. Specifically, TLMB uses a score function to filter the redundant features first and then minimize the trace of the conditional covariance operator, where both of the score function and the optimization problem work in the reproducing kernel Hilbert space so that TLMB can select features with not only pairwise dependence but also multivariate dependence. Moreover, as a MB-based method, TLMB can automatically determine the number of selected features due to the property of MB.
The benefits of a feature model in banking

This experience report describes the surprisingly beneficial introduction of feature modeling at KfW, a government promotional bank. On behalf of the government and based on promotional directives, KfW grants retail loans to small and medium enterprises, business founders, self-employed professionals, municipalities and private individuals. The promotional directives, called programs, define mandatory and optional properties of these loans. We have now successfully built a feature model from these properties.
On-demand Influencer Discovery on Social Media

Identifying influencers on social media, such as Twitter, has played a central role in many applications, including online marketing and political campaigns. Compared with social media celebrities, domain-specific influencers are less expensive to hire and more engaged in spreading messages such as new treatment or timely prevention for HIV. However, most of the existing topic modeling based approaches fail to identify influencers who are dedicated to the rare yet important topics such as HIV and suicide. To alleviate this limitation, we investigate an on-Demand Influencer Discovery (DID) framework that is able to identify influencers on any subject depicted by a few user-specified keywords, regardless of its popularity on social media. The DID model employs an iterative learning process that integrates the language attention network as a subject filter and the influence convolution network built on user interactions. Comprehensive evaluations on Twitter datasets show that the DID model can reliably identify influencers even on rare subjects such as HIV and suicide, outperforming existing topic-specific influencer detection models.
Breast Cancer Classification Using Machine Learning Algorithms

Breast Cancer is a disease that is depicted as anomalous and unwanted cells that increase and affect regular cells in the body. Breast cancer occurs initially in the cells of the breast as malignant cells meet that can capture neighboring flesh or disperse to different areas of the body. As per the registered record provided by the World Health Organization (WHO), cancer affects 2.1 million women every single year. According to the latest report a year ago, 268,600 women expired from breast cancer which is approximately 13% of all deaths caused due to cancer. Different Machine Learning Algorithms are been handled for the detection and analysis of breast cancer and also to provide high classification and accuracy and effective diagnostic capabilities. The actual scope of the proposed design is to reach up the machine learning algorithm and to determine initial detection model for breast cancer which can predict the type of cancer, i.e., benign and malign, Doctor will examine patient based on physical examination process and then various input parameters are checked using the Random Forest algorithm and ensemble learning algorithm in which we have proposed bagging sampling and Decision Tree using CART. The Doctor will first examine breast cancer physically and secondly based on various input parameters. The data set here used will be UCI data-set and it will be dynamic.
Multi-modal Fusion Using Spatio-temporal and Static Features for Group Emotion Recognition

This paper presents our approach for Audio-video Group Emotion Recognition sub-challenge in the EmotiW 2020. The task is to classify a video into one of the group emotions such as positive, neutral, and negative. Our approach exploits two different feature levels for this task, spatio-temporal feature and static feature level. In spatio-temporal feature level, we adopt multiple input modalities (RGB, RGB difference, optical flow, warped optical flow) into multiple video classification network to train the spatio-temporal model. In static feature level, we crop all faces and bodies in an image with the state-of the-art human pose estimation method and train kinds of CNNs with the image-level labels of group emotions. Finally, we fuse all 14 models result together, and achieve the third place in this sub-challenge with classification accuracies of 71.93% and 70.77% on the validation set and test set, respectively.
Augmented and Virtual Reality Web Applications for Music Stage Performance

In this paper, it is explored how augmented and virtual reality web applications can contribute to stage performance in audio-visual arts. As outcome, application case study is presented: augmented reality loop sampler interface integrated with virtual reality song visualizer. Moreover, a modelling tool leveraging metamodel and code generation for rapid creation of custom augmented/virtual reality applications is introduced. According to the achieved results, the proposed approach significantly speeds up the development of such applications.
Topology-Aware Joint Graph Filter and Edge Weight Identification for Network Processes

Data defined over a network have been successfully modelled by means of graph filters.However, although in many scenarios the connectivity of the network is known, e.g., smart grids, social networks, etc., the lack of well-defined interaction weights hinders the ability to model the observed networked data using graph filters.Therefore, in this paper, we focus on the joint identification of coefficients and graph weights defining the graph filter that best models the observed input/output network data.While these two problems have been mostly addressed separately, we here propose an iterative method that exploits the knowledge of the support of the graph for the joint identification of graph filter coefficients and edge weights.We further show that our iterative scheme guarantees a non-increasing cost at every iteration, ensuring a globally-convergent behavior.Numerical experiments confirm the applicability of our proposed approach.
A completed local shrinkage pattern for texture classification

Visual texture classification plays a critical role in computer vision and pattern recognition. As one of the most popular texture descriptors, local binary pattern(LBP) has achieved extensive development and applications due to its simplicity and high efficiency. However, it is hard for most LBP-based methods to represent completed local texture information efficiently as they either only encode the sign of local difference or fuse low-discriminative features with high dimensionality. To alleviate these problems, this paper groundbreaking introduces a new insight in analyzing completed local texture information, called completed local shrinkage pattern(CLSP), which achieves a high tradeoff between discriminativeness and dimensionality. First, we map the completed local difference to a new encoding space through a shrinkage function and present the local shrinkage pattern. As the important supplement of local texture information, the center pixel is also encoded to build the local center pattern. Finally, the two sub-features are combined to generate the completed local shrinkage pattern. Moreover, we design a multi-information integration texture classification framework, which utilizes the original texture images and the gradient texture images to enrich the diversity of texture information. Experimental results on four popular texture databases demonstrate that the proposed CLSP descriptor achieves superior classification performance with low dimensionality.
Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor

Abstract The mechanical properties of composite fiber materials are highly dependent on the orientation of the fibers. Micro-CT enables acquisition of high-resolution 3D images, where individual fibers are visible. However, manually extracting orientation information from the samples is impractical due to the size of the 3D images. In this paper, we use a Structure Tensor to extract orientation information from a large 3D image of non-crimp glass fiber fabric. We go through the process of segmenting the image and extracting the orientation distribution step-by-step using structure tensor and show the results of the analysis of the studied non-crimp fabric. The Jupyter notebooks and Python code used for the data-analysis are publicly available, detailing the process and allowing the reader to use the method on their own data. The results show that structure tensor analysis works well for determining fiber orientations, which has many useful applications.
Fall Detection in Elderly Care System Based on Group of Pictures

Fall detection is a serious problem in elder people. Constant inspection is important for this fall identification. Currently, numerous methods associated with fall detection are a significant area of research for safety purposes and for the healthcare industries. The objective of this paper is to identify elderly falls. The proposed method introduces keyframe based fall detection in elderly care system. Experiments were conducted on University of Rzeszow (UR) Fall Detection dataset, Fall Detection Dataset and MultiCam dataset. It is substantially proved that the proposed method achieves higher accuracy rate of 99%, 98.15% and 99% for UR Fall detection dataset, Fall Detection Dataset and MultiCam dataset, respectively. The performance of the proposed method is compared with other methods and proved to have higher accuracy rate than those methods.
Identification and Classification of Architecturally Significant Functional Requirements

Architecturally Significant Functional Requirements (ASFRs) are those functional requirements that have a significant impact on the architecture of the software system. ASFRs contain comprehensive information to aid architectural decisions; however, their architectural impact is often not explicitly stated in software requirement specification documents. ASFRs are therefore hard to detect, and if missed, can result in expensive refactoring efforts in later stages of software development. Identification and classification of ASFRs using traditional machine learning algorithms have been reported in the past. In this paper, we present our experiments with a deep learning-based model for performing the same task. Our approach is based on a Bidirectional Long Short-Term Memory Network (Bi-LSTM) to capture the context information for each word in the software requirements text, followed by an Attention model to aggregate useful information from these words in order to get the final classification. For ASFR identification, we obtained an f-score of 0.86 and for ASFR classification, we obtained an average f-score of 0. 83.
JustCorrect

Correcting errors in entered text is a common task but usually difficult to perform on mobile devices due to tedious cursor navigation steps. In this paper, we present JustCorrect, an intelligent post hoc text correction technique for smartphones. To make a correction, the user simply types the correct text at the end of their current input, and JustCorrect will automatically detect the error and apply the correction in the form of an insertion or a substitution. In this way, manual navigation steps are bypassed, and the correction can be committed with a single tap. We solved two critical problems to support JustCorrect: (1) Correction Algorithm: we propose an algorithm that infers the user's correction intention from the last typed word. (2) Input Modalities: our study revealed that both tap and gesture were suitable input modalities for performing JustCorrect. Based on our findings, we integrated JustCorrect into a soft keyboard. Our user studies show that using JustCorrect reduces the text correction time by 12.8% over the stock Android keyboard and by 9.7% over the "Type, then Correct" text correction technique by Zhang et al. (2019). Overall, JustCorrect complements existing post hoc text correction techniques, making error correction more automatic and intelligent.
Iteratively Adapting Avatars using Task-Integrated Optimisation

Virtual Reality allows users to embody avatars that do not match their real bodies. Earlier work has selected changes to the avatar arbitrarily and it therefore remains unclear how to change avatars to improve users' performance. We propose a systematic approach for iteratively adapting the avatar to perform better for a given task based on users' performance. The approach is evaluated in a target selection task, where the forearms of the avatar are scaled to improve performance. A comparison between the optimised and real arm lengths shows a significant reduction in average tapping time by 18.7%, for forearms multiplied in length by 5.6. Additionally, with the adapted avatar, participants moved their real body and arms significantly less, and subjective measures show reduced physical demand and frustration. In a second study, we modify finger lengths for a linear tapping task to achieve a better performing avatar, which demonstrates the generalisability of the approach.
Universal stego post-processing for enhancing image steganography

It is well known that the designing or improving embedding cost becomes a key issue for current steganographic methods. Unlike existing works, we propose a novel framework to enhance the steganography security via post-processing on the embedding units (i.e., pixel values and DCT coefficients) of stego directly. In this paper, we firstly analyze the characteristics of STCs (Syndrome-Trellis Codes), and then design the rule for post-processing to ensure the correct extraction of hidden message. Since the steganography artifacts are typically reflected on image residuals, we try to reduce the residual distance between cover and the modified stego in order to enhance steganography security. To this end, we model the post-processing as a non-linear integer programming, and implement it via heuristic search. In addition, we carefully determine several important issues in the proposed post-processing, such as the candidate embedding units to be modified, the direction and amplitude of post-modification, the adaptive filters for getting residuals, and the distance measure of residuals. Extensive experimental results evaluated on both hand-crafted steganalytic features and deep learning based ones demonstrate that the proposed method can effectively enhance the security of most modern steganographic methods both in spatial and JPEG domains.
Towards Temporal Knowledge Graph Embeddings with Arbitrary Time Precision

Acknowledging the dynamic nature of knowledge graphs, the problem of learning temporal knowledge graph embeddings has recently gained attention. Essentially, the goal is to learn vector representation for the nodes and edges of a knowledge graph taking time into account. These representations must preserve certain properties of the original graph, so as to allow not only classification or clustering tasks, as for classical graph embeddings, but also approximate time-dependent query answering or link predictions over knowledge graphs. For instance, "who was the leader of Germany in 1994?'' or "when was Bonn the capital of Germany?''