Reviewerly/exHarmony huggingface dataset:
Learning to Learn to be Right for the Right Reasons

Improving model generalization on held-out data is one of the core objectives in commonsense reasoning.Recent work has shown that models trained on the dataset with superficial cues tend to perform well on the easy test set with superficial cues but perform poorly on the hard test set without superficial cues.Previous approaches have resorted to manual methods of encouraging models not to overfit to superficial cues.While some of the methods have improved performance on hard instances, they also lead to degraded performance on easy instances.Here, we propose to explicitly learn a model that does well on both the easy test set with superficial cues and hard test set without superficial cues.Using a meta-learning objective, we learn such a model that improves performance on both the easy test set and the hard test set.By evaluating our models on Choice of Plausible Alternatives (COPA) and Commonsense Explanation, we show that our proposed method leads to improved performance on both the easy test set and the hard test set upon which we observe up to 16.5 percentage points improvement over the baseline. ModelEnsemble Data Easy Hard Overall SuperGlue Leaderboard Score: RoBERTa-large + ALBERT-xxLarge yes COPA --90.8RoBERTa-large yes COPA --90.6 RoBERTa-large no COPA 90.5 83.9 86.4 RoBERTa-large-adversarial no COPA 70.0 56.5 61.6 RoBERTa-large + Balanced data no B-COPA 90.0 88.1 88.8 RoBERTa-large-meta-learned (ours) no COPA 92.6 89.7 90.8 RoBERTa-base no COPA 80.0 71.3 74.6 RoBERTa-base-adversarial no COPA 76.3 57.0 64.4 RoBERTa-base no B-COPA 78.4 78.1 78.2 RoBERTa-base-meta-learned (ours) no COPA 87.9 78.4 82.0 RoBERTa-large no Cs-Ex 98.0 79.1 93.8 RoBERTa-large-adversarial no Cs-Ex 94.0 59.0 86.2 RoBERTa-large-meta-learned (ours) no Cs-Ex 98.9 87.1 96.2 RoBERTa-base no Cs-Ex 95.0 62.1 87.7 RoBERTa-base-adversarial no Cs-Ex 93.8 54.8 85.2 RoBERTa-base-meta-learned (ours) no Cs-Ex 97.6 78.6 93.4
f-Domain Adversarial Learning: Theory and Algorithms

Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain, and a related labeled dataset. In this paper, we introduce a novel and general domain-adversarial framework. Specifically, we derive a novel generalization bound for domain adaptation that exploits a new measure of discrepancy between distributions based on a variational characterization of f-divergences. It recovers the theoretical results from Ben-David et al. (2010a) as a special case and supports divergences used in practice. Based on this bound, we derive a new algorithmic framework that introduces a key correction in the original adversarial training method of Ganin et al. (2016). We show that many regularizers and ad-hoc objectives introduced over the last years in this framework are then not required to achieve performance comparable to (if not better than) state-of-the-art domain-adversarial methods. Experimental analysis conducted on real-world natural language and computer vision datasets show that our framework outperforms existing baselines, and obtains the best results for f-divergences that were not considered previously in domain-adversarial learning.
Action Recognition Network Using Stacked Short-Term Deep Features and Bidirectional Moving Average

Action recognition requires the accurate analysis of action elements in the form of a video clip and a properly ordered sequence of the elements. To solve the two sub-problems, it is necessary to learn both spatio-temporal information and the temporal relationship between different action elements. Existing convolutional neural network (CNN)-based action recognition methods have focused on learning only spatial or temporal information without considering the temporal relation between action elements. In this paper, we create short-term pixel-difference images from the input video, and take the difference images as an input to a bidirectional exponential moving average sub-network to analyze the action elements and their temporal relations. The proposed method consists of: (i) generation of RGB and differential images, (ii) extraction of deep feature maps using an image classification sub-network, (iii) weight assignment to extracted feature maps using a bidirectional, exponential, moving average sub-network, and (iv) late fusion with a three-dimensional convolutional (C3D) sub-network to improve the accuracy of action recognition. Experimental results show that the proposed method achieves a higher performance level than existing baseline methods. In addition, the proposed action recognition network takes only 0.075 seconds per action class, which guarantees various high-speed or real-time applications, such as abnormal action classification, human–computer interaction, and intelligent visual surveillance.
WBI at MEDIQA 2021: Summarizing Consumer Health Questions with Generative Transformers

This paper describes our contribution for the MEDIQA-2021 Task 1 question summarization competition.We model the task as conditional generation problem.Our concrete pipeline performs a finetuning of the large pretrained generative transformers PEGA-SUS (Zhang et al., 2020a) and BART (Lewis et al., 2020).We used the resulting models as strong baselines and experimented with (i) integrating structured knowledge via entity embeddings, (ii) ensembling multiple generative models with the generator-discriminator framework and (iii) disentangling summarization and interrogative prediction to achieve further improvements.Our best performing model, a fine-tuned vanilla PEGASUS, reached the second place in the competition with an ROUGE-2-F1 score of 15.99.We observed that all of our additional measures hurt performance (up to 5.2 pp) on the official test set.In course of a post-hoc experimental analysis which uses a larger validation set results indicate slight performance improvements through the proposed extensions.However, further analysis is need to provide stronger evidence.
Cascaded feature enhancement network model for real-time video monitoring of power system

The application of real-time monitoring has been widely used to detect the safety and stability of the electric power system. Traditional monitoring relies heavily on human judgment and is impossible to detect status in real-time. Recently, with the development of deep learning, the object detection algorithm based on the deep convolutional neural network becomes a great option for realizing real-time monitoring applications of the power system. However, in power system scenarios, failed or unreal-time detection of abnormal conditions may cause a hazardous accident. To apply and optimize the object detection algorithm, issues such as multi-scale objects, class imbalance, and difficulty in balance speed and accuracy need to be addressed to improve the detection performance. Thus, we present a cascaded feature enhancement network model that combining attention mechanism, feature fusion scheme, and Cascaded Refinement Scheme. Attention mechanism and feature fusion scheme can help extract more effective feature information of multi-scale objects. Cascaded Refinement Scheme can effectively solve the problem of class imbalance. The whole model can well balanced in detect speed and accuracy. Experiments are performed on two benchmarks: PSA_Datasets and PASCAL VOC. Our method gets an absolute gain of 1.6% (300×300 input), 2.6% (512×512 input) in terms of mAP result of PSA_Datasets and 1% (300×300 input), 1.6% (512×512 input) in PASCAL VOC Dataset, compared to the best results of other SOTA detectors.
FPGA Implementation of Q-RTS for Real-Time Swarm Intelligence Systems

We propose an architectural blueprint to implement Q-RTS, Q-Learning Real-Time Swarm Reinforcement Learning algorithm, on FPGA. The design solution is built on FPGA-based Centralized RL Processing Units (CRLPU). A CRLPU processes local and global state-action matrices and exchanges information frames with low-power Microcontroller-based Agents. The novel architecture implementation, for up to 32 Agents with up to 512 states, on a Xilinx Ultrascale device shows low resource requirements in terms of CLB (7%) and memory (2% FF and 22% BRAM). Performance metrics show that the required energy per generated action is always lower than 1 µJ.
Semi-supervised Interactive Intent Labeling

Building the Natural Language Understanding (NLU) modules of task-oriented Spoken Dialogue Systems (SDS) involves a definition of intents and entities, collection of task-relevant data, annotating the data with intents and entities, and then repeating the same process over and over again for adding any functionality/enhancement to the SDS.In this work, we showcase an Intent Bulk Labeling system where SDS developers can interactively label and augment training data from unlabeled utterance corpora using advanced clustering and visual labeling methods.We extend the Deep Aligned Clustering (Zhang et al., 2021) work with a better backbone BERT model, explore techniques to select the seed data for labeling, and develop a data balancing method using an oversampling technique that utilizes paraphrasing models.We also look at the effect of data augmentation on the clustering process.Our results show that we can achieve over 10% gain in clustering accuracy on some datasets using the combination of the above techniques.Finally, we extract utterance embeddings from the clustering model and plot the data to interactively bulk label the samples, reducing the time and effort for data labeling of the whole dataset significantly.
The Effect of Online Tutoring Applications on Student Learning Outcomes during the COVID-19 Pandemic

The number of schools and campuses closed due to the COVID-19 outbreak to reduce the spread has led to learning to be carried out from home online using the internet media. Along with this, the use of online learning applications itself has also increased. This study aims to see the impact on Scholar education in the course of this pandemic. From the research, it is hoped that the benefits of online learning applications widely available in smartphone applications will be known, and the adverse effects may also arise. The tool is a qualitative technique in this review the analysis of literature. Results have shown that learning online by remote means applies social distancing to avoid the Covid-19 epidemic spread chains, and students' online tutoring services are highly sought after because it makes students understand. Whether it's learning that students or students want to improve and add to their knowledge and insights has not been understood.
Hieroglyphic Semiotics of Emoji Signs in Digital Communication

This paper is a parcel of a series of studies carried out by the group of authors on the structural and systemic specifics of the artificial language emoji.In this study, this problem is considered through the prism of two linguistic dimensions of existence -natural language (Chinese) and artificial (emoji).This approach is related to the semiotic nature of languages -hieroglyphics (Chinese), iconicity (emoji), the structure of which implies an aletic (from the Greek άλήθεια -truth) sign in terms of content and expression.Taking into account the specifics of the use of hieroglyphic and iconic writing in digital communication, an empirical experiment was conducted with the method of transformation of hieroglyphic and iconic signs, the denotation of which is the "sun".In addition, the formalization of the form of signs and comparative analysis of the elements of the whole sign with consistent approbation of their integral form and content proved that the occasional is a rectangular form of the Chinese character "sun", its perception -completely usual rays in the form of lines around it, where the geometric figure of the circle is qualia.The results of the experiment allowed to fractalize the scheme as a complex implicative statement, in which the deductivization of the sign explains its main stages (ontological denotation, conceptual denotation, lingual denotation), which provided for the formalization of the content plane in the digital transformation of the expression plane, approbation of the emoji sign via the Lexonomy digital lexicographic system.In view of all the above, the phenomenological specificity of natural languages, as well as ICT allowed us to determine the hieroglyphic and iconic signs of the logos.The sample of the generation of the emoji sign is disclosed, which is based on the signs of the alternative model of digital presentation of fiction created with the help of the Emoji-Maker platform and tested by linguists-in-training.
Toward an Innovative Educational Method to Train Students to Agile Approaches in Higher Education: The A.L.P.E.S.

Introduced in 2013, the A.L.P.E.S. approach (AgiLe aPproaches in higher Education Studies) aims to apply agile practices to teaching. Agile approaches are project management practices for IT development. More pragmatic than traditional methods, they allow to be closer to the applicant and to involve him/her as much as possible. They offer a great reactivity and a good adaptation to best meet the needs. They are used today in a large part of IT companies. Largely inspired by agile approaches, the A.L.P.E.S. approach allows the teaching of project management in a transverse way to a main course. It makes teaching more flexible and more adapted to the students. In this article, we describe the approach. We describe the tools, the process of creating a course, and the process of running a course.
Random Fuzzy Granular Decision Tree

In this study, the classification problem is solved from the view of granular computing. That is, the classification problem is equivalently transformed into the fuzzy granular space to solve. Most classification algorithms are only adopted to handle numerical data; random fuzzy granular decision tree (RFGDT) can handle not only numerical data but also nonnumerical data like information granules. Measures can be taken in four ways as follows. First, an adaptive global random clustering (AGRC) algorithm is proposed, which can adaptively find the optimal cluster centers and maximize the ratio of interclass standard deviation to intraclass standard deviation, and avoid falling into local optimal solution; second, on the basis of AGRC, a parallel model is designed for fuzzy granulation of data to construct granular space, which can greatly enhance the efficiency compared with serial granulation of data; third, in the fuzzy granular space, we design RFGDT to classify the fuzzy granules, which can select important features as tree nodes based on information gain ratio and avoid the problem of overfitting based on the pruning algorithm proposed. Finally, we employ the dataset from UC Irvine Machine Learning Repository for verification. Theory and experimental results prove that RFGDT has high efficiency and accuracy and is robust in solving classification problems.
CuSP

Graph analytics systems must analyze graphs with billions of vertices and edges which require several terabytes of storage. Distributed-memory clusters are often used for analyzing such large graphs since the main memory of a single machine is usually restricted to a few hundreds of gigabytes. This requires partitioning the graph among the machines in the cluster. Existing graph analytics systems use a built-in partitioner that incorporates a particular partitioning policy, but the best policy is dependent on the algorithm, input graph, and platform. Therefore, built-in partitioners are not sufficiently flexible. Stand-alone graph partitioners are available, but they too implement only a few policies. CuSP is a fast streaming edge partitioning framework which permits users to specify the desired partitioning policy at a high level of abstraction and quickly generates highquality graph partitions. For example, it can partition wdc12, the largest publicly available web-crawl graph with 4 billion vertices and 129 billion edges, in under 2 minutes for clusters with 128 machines. Our experiments show that it can produce quality partitions 6× faster on average than the state-of-theart stand-alone partitioner in the literature while supporting a wider range of partitioning policies.
Rider Optimization Algorithm (ROA): An optimization solution for engineering problem

Optimization is a process and instrument applied to solve an engineering problem. Rider optimization algorithm (ROA) is a new optimization technique used to find the target location through different components like bypass rider, follower, overtake, and attackers’ parameters. An optimization problem can be solved through an objective function. The inspirational ROA development is based on the riders who proceed towards target locations to win the race. Any optimization problem is either nature-inspired or artificial intelligence techniques. Some of the nature-inspired algorithms are firefly algorithm (FA), ant colony optimization (ACO), particle swarm optimization (PSO), grey wolf optimization (GWO), spider monkey optimization (SMO), and so on. For solving engineering problems, multi-objective optimizations are used for engineering design, scientific experiments and decision making, etc. This paper introduces ROA, its application in industries, and usability in engineering and industrial problem. The purpose of this paper is to identify the importance and understanding of the optimization-related problem.
Practical Approaches: UX and XR in the Real World

This chapter will examine the UX design process for an experimental XR product in development. How are the established UX methods playing out for a digital experience, bridging between two emerging technologies, XR and the blockchain? How practical are user empathy maps, and how to transition from the touch points of a user journey to a spatial experience? What tools or practices can be utilized to be more efficient, when transitioning from low-fidelity to high-fidelity prototype? XR gives designers more problems to solve, but it also opens doors to new methods in user research and data acquisition for testing, for example, when using WebVR-based user interaction. Along with exploring these new approaches, this chapter will wrap up with fundamental thoughts on where this era of the human-machine interface (HMI) will take us as a society, as individuals and, in particular, as designers, balancing powerful opportunities with new responsibilities.
What Did They Mean by That? Young Adults' Interpretations of 105 Common Emojis

DATA REPORT article Front. Psychol., 31 May 2021Sec. Human-Media Interaction https://doi.org/10.3389/fpsyg.2021.655297
Privacy-Preserving Attribute-Based Keyword Search with Traceability and Revocation for Cloud-Assisted IoT

With the rapid development of cloud computing and Internet of Things (IoT) technology, it is becoming increasingly popular for source-limited devices to outsource the massive IoT data to the cloud. How to protect data security and user privacy is an important challenge in the cloud-assisted IoT environment. Attribute-based keyword search (ABKS) has been regarded as a promising solution to ensure data confidentiality and fine-grained search control for cloud-assisted IoT. However, due to the fact that multiple users may have the same retrieval permission in ABKS, malicious users may sell their private keys on the Internet without fear of being caught. In addition, most of existing ABKS schemes do not protect the access policy which may contain privacy information. Towards this end, we present a privacy-preserving ABKS that simultaneously supports policy hiding, malicious user traceability, and revocation. Formal security analysis shows that our scheme can not only guarantee the confidentiality of keywords and access policies but also realize the traceability of malicious users. Furthermore, we provide another more efficient construction for public tracing.
Reviewing a Decade of Human-Computer Interaction for Development (HCI4D) Research, as One of Best's "Grand Challenges"

The human-computer interaction for development (HCI4D) field emerged at the intersection of the fields of information and communication technology for development (ICT4D) and human-computer interaction (HCI). In 2010, Michael Best nominated HCI4D as one of ICT4D's "grand challenges". This HCI4D field is now entering its second decade, and it is important to reflect on the research that has been conducted, and to consider how HCI4D researchers have addressed the challenge that constitutes the raison d'etre of HCI4D's existence. Best provided four guidelines to inform researchers embracing this challenge. This study commences by identifying the primary HCI4D-specific themes, and then carries out a systematic literature review of the HCI4D literature to build a corpus to support the analysis. The corpus is analysed to reflect on how well the field's practices align with Best's guidelines. The overall finding is that HCI4D researchers have largely been following Best's guidelines and that the HCI4D field is demonstrating encouraging signs of emerging maturity.
Probing Contextual Language Models for Common Ground with Visual Representations

The success of large-scale contextual language models has attracted great interest in probing what is encoded in their representations.In this work, we consider a new question: to what extent contextual representations of concrete nouns are aligned with corresponding visual representations?We design a probing model that evaluates how effective are text-only representations in distinguishing between matching and non-matching visual representations.Our findings show that language representations alone provide a strong signal for retrieving image patches from the correct object categories.Moreover, they are effective in retrieving specific instances of image patches; textual context plays an important role in this process.Visually grounded language models slightly outperform text-only language models in instance retrieval, but greatly under-perform humans.We hope our analyses inspire future research in understanding and improving the visual capabilities of language models.
Using Machine Learning Algorithms to Recognize Shuttlecock Movements

Shuttlecock is an excellent traditional national sport in China. Because of its simplicity, convenience, and fun, it is loved by the broad masses of people, especially teenagers and children. The development of shuttlecock sports into a confrontational event is not long, and it takes a period of research to master the tactics and strategies of shuttlecock sports. Based on this, this article proposes the use of machine learning algorithms to recognize the movement of shuttlecock movements, aiming to provide more theoretical and technical support for shuttlecock competitions by identifying features through actions with the assistance of technical algorithms. This paper uses literature research methods, model methods, comparative analysis methods, and other methods to deeply study the motion characteristics of shuttlecock motion, the key algorithms of machine learning algorithms, and other theories and construct the shuttlecock motion recognition based on multiview clustering algorithm. The model analyzes the robustness and accuracy of the machine learning algorithm and other algorithms, such as a variety of performance comparisons, and the results of the shuttlecock motion recognition image. For the key movements of shuttlecock movement, disk, stretch, hook, wipe, knock, and abduction, the algorithm proposed in this paper has a good movement recognition rate, which can reach 91.2%. Although several similar actions can be recognized well, the average recognition accuracy rate can exceed 75%, and even through continuous image capture, the number of occurrences of the action can be automatically analyzed, which is beneficial to athletes. And the coach can better analyze tactics and research strategies.