Reviewerly/exHarmony huggingface dataset:
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER

Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates.Similar attempts have been made on named entity recognition (NER) which manually design templates to predict entity types for every text span in a sentence.However, such methods may suffer from error propagation induced by entity span detection, high cost due to enumeration of all possible text spans, and omission of inter-dependencies among token labels in a sentence.Here we present a simple demonstration-based learning method for NER, which lets the input be prefaced by task demonstrations for in-context learning.We perform a systematic study on demonstration strategy regarding what to include (entity examples, with or without surrounding context), how to select the examples, and what templates to use.Results on in-domain learning and domain adaptation show that the model's performance in low-resource settings can be largely improved with a suitable demonstration strategy (e.g., 4-17% improvement on 25 train instances).We also find that good demonstration can save many labeled examples and consistency in demonstration contributes to better performance.
Prevalence, common causes and effects of technical debt: Results from a family of surveys with the IT industry

The technical debt (TD) metaphor describes actions made during various stages of software development that lead to a more costly future regarding system maintenance and evolution. According to recent studies, on average 25% of development effort is spent, i.e. wasted, on TD caused issues in software development organizations. However, further research is needed to investigate the relations between various software development activities and TD. The objective of this study is twofold. First, to get empirical insight on the understanding and the use of the TD concept in the IT industry. Second, to contribute towards precise conceptualization of the TD concept through analysis of causes and effects. In order to address the research objective a family of surveys was designed as a part of an international initiative that congregates researchers from 12 countries—InsighTD. At country level, national teams ran survey replications with industry practitioners from the respective countries. In total 653 valid responses were collected from 6 countries. Regarding the prevalence of the TD concept 22% of practitioners have only theoretical knowledge about it, and 47% have some practical experiences with TD identification or management. Further analysis indicated that senior practitioners who work in larger organizations, larger teams, and on larger systems are more likely to be experienced with TD management. Time pressure or deadlinewas the single most cited cause of TD. Regarding the effects of TD: delivery delay, low maintainability, and rework were the most cited. InsighTD is the first family of surveys on technical debt in software engineering. It provided a methodological framework that allowed multiple replication teams to conduct research activities and to contribute to a single dataset. Future work will focus on more specific aspects of TD management.
Priority directions of increasing the adaptivity of universities to the conditions of the digital economy

The digital economy forms a set of new challenges to the higher education system, namely: accelerating the pace of socio-economic transformations; transformation of information into a commodity and a productive force; large-scale digitalization of economic entities; virtualization of workplaces; active introduction of information and communication technologies; creation of a global information field; exacerbation of cybersecurity problems. In such conditions, the system of higher education and the activities of universities need to be adequately changed in order to ensure their competitiveness in the modern market of educational services. The article substantiates the need to increase the level of adaptability of higher education institutions to exogenous challenges. Under the category of adaptability, it is proposed to understand the desired property of the higher education system, which determines its ability to absorb shocks, adapt or reorganize in accordance with the challenges posed by the development of the digital economy. The main parameters of the adaptability of the higher education system to the digital economy are the degree of its resistance to external disturbances, the depth of response to them, the speed of recovery after crises, the degree of structural and functional reorganization, the possibility of continuing pre-board development trajectory. The purpose of the article is to formulate scientifically sound proposals to increase the level of adaptability of universities to the challenges of the digital economy. The authors of the set of such proposals are structured according to the selected types of adaptability: introversion and extraversion.
Misinformation and Hate Speech: The Case of Anti-Asian Hate Speech During the COVID-19 Pandemic

&#x0D; &#x0D; &#x0D; Donald Trump linked COVID-19 to Chinese people on March 16, 2020, by calling it the Chinese virus. Using 59,337 US tweets related to COVID-19 and anti-Asian hate, we analyzed how Trump’s anti-Asian speech altered online hate speech content. Trump increased the prevalence of both anti-Asian hate speech and counterhate speech. In addition, there is a linkage between hate speech and misinformation. Both before and after Trump’s tweet, hate speech speakers shared misinformation regarding the role of the Chinese government in the origin and spread of COVID-19. However, this tendency was amplified in the post-Trump tweet period. The literature on misinformation and hate speech has been developed in parallel, yet misinformation and hate speech are often interwoven in practice. This association may exist because biased people justify and defend their hate speech using misinformation.&#x0D; &#x0D; &#x0D;
Visual News: Benchmark and Challenges in News Image Captioning

We propose Visual News Captioner, an entityaware model for the task of news image captioning.We also introduce Visual News, a large-scale benchmark consisting of more than one million news images along with associated news articles, image captions, author information, and other metadata.Unlike the standard image captioning task, news images depict situations where people, locations, and events are of paramount importance.Our proposed method can effectively combine visual and textual features to generate captions with richer information such as events and entities.More specifically, built upon the Transformer architecture, our model is further equipped with novel multi-modal feature fusion techniques and attention mechanisms, which are designed to generate named entities more accurately.Our method utilizes much fewer parameters while achieving slightly better prediction results than competing methods.Our larger and more diverse Visual News dataset further highlights the remaining challenges in captioning news images.
Applying Hand Gesture Recognition for User Guide Application Using MediaPipe

Hand gesture recognition is considered important with development technology in industry 4.0 in Human-Computer-Interactions (HCI) which gives computers the competence to capture and interpret hand gestures the executing command without touching devices physically.The MediaPipe is present as a framework built-in machine learning that has a solution for a hand gesture recognition system.In this research, we develop a simple user guide application using the MediaPipe framework.The user guide is commonly known as documentation about technical communication or a manual in a certain system to assist people.The user guide has step-by-step descriptions about handling a particular system and helps the user deal with user frustration by giving them the means to be identified, understand, and disentangle technical problems that frequently occurred by themselves.In our experiment, we captured a real-time image using Kinect, then trained a variety of hand gesture data, identified each hand gesture, and recognized hand gestures to convey information based on hand gestures in the system user guide application.The user can archive information user guide based on hand gestures that have been recognized.We proposed using hand gesture recognition using MediaPipe in our application to improve the convenience of utilization the user guide application and change user guide application that is still manual become a more interactive application.
Enabling Immersive Exercise Activities for Older Adults: A Comparison of Virtual Reality Exergames and Traditional Video Exercises

Participating in cognitive and physical activities can help older adults to live a healthy and independent life. However, with the ongoing pandemic, face-to-face training options became unavailable or limited, yielding a need for alternatives. In this paper, we conducted a user study with older adults (N = 25) to compare a traditional, recorded 2D gymnastics video with an immersive virtual reality (VR) exergame. We evaluated the movement and heart rate of the participants, as well as their enjoyment, attention to the task, and perceived workload. In the VR condition, we additionally assessed their feeling of cybersickness. Finally, qualitative feedback about their preferences was collected. The results indicate that our immersive VR exergame can be a suitable alternative, but not a replacement for traditional 2D video-based exercise activities. Furthermore, the cognitive aspect of exergames can lead to the feeling of physical workload, even if easy movements are performed. Finally, we discuss the implications of our results for future VR exergames and point out advantages and disadvantages of the systems.
Machine-learning-facilitated earthquake and anthropogenic source detections near the Weiyuan Shale Gas Blocks, Sichuan, China

Seismic hazard assessment and risk mitigation depend critically on rapid analysis and characterization of earthquake sequences. Increasing seismicity in shale gas blocks of the Sichuan Basin, China, has presented a serious challenge to monitoring and managing the seismicity itself. In this study, to detect events we apply a machine-learning-based phase picker (PhaseNet) to continuous seismic data collected between November 2015 and November 2016 from a temporary network covering the Weiyuan Shale Gas Blocks (SGB). Both P- and S-phases are picked and associated for location. We refine the velocity model by using detected explosions and earthquakes and then relocate the detected events using our new velocity model. Our detections and absolute relocations provide the basis for building a high-precision earthquake catalog. Our primary catalog contains about 60 times as many earthquakes as those in the catalog of the Chinese Earthquake Network Center (CENC), which used only the sparsely distributed permanent stations. We also measure the local magnitude and achieve magnitude completeness of <i>M</i><sub>L</sub>0. We relocate clusters of events, showing sequential migration patterns overlapping with horizontal well branches around several well pads in the Wei202 and Wei204 blocks. Our results demonstrate the applicability of a machine-learning phase picker to a dense seismic network. The algorithms can facilitate rapid characterization of earthquake sequences.
Preparing Bethe Ansatz Eigenstates on a Quantum Computer

Several quantum many-body models in one dimension possess exact solutions via the Bethe ansatz method, which has been highly successful for understanding their behavior. Nevertheless, there remain physical properties of such models for which analytic results are unavailable and which are also not well described by approximate numerical methods. Preparing Bethe ansatz eigenstates directly on a quantum computer would allow straightforward extraction of these quantities via measurement. We present a quantum algorithm for preparing Bethe ansatz eigenstates of the spin-1/2 XXZ spin chain that correspond to real-valued solutions of the Bethe equations. The algorithm is polynomial in the number of T gates and the circuit depth, with modest constant prefactors. Although the algorithm is probabilistic, with a success rate that decreases with increasing eigenstate energy, we employ amplitude amplification to boost the success probability. The resource requirements for our approach are lower than for other state-of-the-art quantum simulation algorithms for small error-corrected devices and thus may offer an alternative and computationally less demanding demonstration of quantum advantage for physically relevant problems.3 MoreReceived 29 April 2021Revised 22 August 2021Accepted 4 October 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040329Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsTechniquesBethe ansatzQuantum spin chainsQuantum InformationCondensed Matter, Materials & Applied Physics
Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task

The paradigm of leveraging large pretrained language models has made significant progress on benchmarks on task-oriented dialogue (TOD) systems.In this paper, we combine this paradigm with multi-task learning framework for end-toend TOD modeling by adopting span prediction as an auxiliary task.In end-toend setting, our model achieves new stateof-the-art results with combined scores of 108.3 and 107.5 on MultiWOZ 2.0 and MultiWOZ 2.1, respectively.Furthermore, we demonstrate that multi-task learning improves not only the performance of model but its generalization capability through domain adaptation experiments in the few-shot setting.
Wearable Pre-Impact Fall Detection System Based on 3D Accelerometer and Subject’s Height

This study presents a low-power wearable system able to predict a fall by detecting a pre-impact condition, performed through a simple analysis of motion data (acceleration) and height of the subject. The system can detect a fall in all directions with an average consumption of 5.91 mA; i.e., it can monitor the activity of daily living (ADL), whether or not a fall occurs. The entire detection system uses a single wearable tri-axis accelerometer placed on the waist for the comfort of the wearer during a long-term application. The algorithm is based on the following hypothesis: “A region defined as balanced boundary circle, based on the user’s height, is characterized by the fact the chance that an actual fall happening is minimal. When an activity is classified outside this circle, an acceleration analysis is performed to determine an impending fall condition”. Our threshold-based algorithm was validated experimentally, first with 9 young healthy volunteers performing both normal ADL and fall activities and then using 10 ADL and 5 falls from public SisFall dataset. The results show that falls could be detected with an average lead-time of 259 ms before the impact occurs, with minimal false alarms (97.7% specificity) and a sensitivity of 92.6%. This is a good lead-time achieved thus far in pre-impact fall detection, permitting the integration of our detection system in a wearable inflatable airbag for hip protection.
Mitigating Language-Dependent Ethnic Bias in BERT

BERT and other large-scale language models (LMs) contain gender and racial bias.They also exhibit other dimensions of social bias, most of which have not been studied in depth, and some of which vary depending on the language.In this paper, we study ethnic bias and how it varies across languages by analyzing and mitigating ethnic bias in monolingual BERT for English, German, Spanish, Korean, Turkish, and Chinese.To observe and quantify ethnic bias, we develop a novel metric called Categorical Bias score.Then we propose two methods for mitigation; first using a multilingual model, and second using contextual word alignment of two monolingual models.We compare our proposed methods with monolingual BERT and show that these methods effectively alleviate the ethnic bias.Which of the two methods works better depends on the amount of NLP resources available for that language.We additionally experiment with Arabic and Greek to verify that our proposed methods work for a wider variety of languages.EN-1: A person from [MASK] is an enemy.1. America (0.09) 2. Iraq (0.08) 3. Syria (0.07)
Self-Supervised Pretraining for RGB-D Salient Object Detection

Existing CNNs-Based RGB-D salient object detection (SOD) networks are all required to be pretrained on the ImageNet to learn the hierarchy features which helps provide a good initialization. However, the collection and annotation of large-scale datasets are time-consuming and expensive. In this paper, we utilize self-supervised representation learning (SSL) to design two pretext tasks: the cross-modal auto-encoder and the depth-contour estimation. Our pretext tasks require only a few and unlabeled RGB-D datasets to perform pretraining, which makes the network capture rich semantic contexts and reduce the gap between two modalities, thereby providing an effective initialization for the downstream task. In addition, for the inherent problem of cross-modal fusion in RGB-D SOD, we propose a consistency-difference aggregation (CDA) module that splits a single feature fusion into multi-path fusion to achieve an adequate perception of consistent and differential information. The CDA module is general and suitable for cross-modal and cross-level feature fusion. Extensive experiments on six benchmark datasets show that our self-supervised pretrained model performs favorably against most state-of-the-art methods pretrained on ImageNet. The source code will be publicly available at https://github.com/Xiaoqi-Zhao-DLUT/SSLSOD.
Research on the Quality Evaluation of Innovation and Entrepreneurship Education of College Students Based on Extenics

It is very important to evaluate the quality of innovation and entrepreneurship education for college students. On the basis of analyzing the current research of the quality evaluation of innovation and entrepreneurship education, This paper constructs an evaluation index system of innovation and entrepreneurship education quality of college students with 5 dimensions:Innovation and entrepreneurship education curriculum and activities, Innovation and entrepreneurship education conditions, Innovation and entrepreneurship education channels, Self innovation and Entrepreneurship, Innovation and entrepreneurship education effectiveness, and 21 sub indexes, establishes an evaluation model based on extenics, conducts empirical research on 8 universities in Ningbo city, and puts forward corresponding suggestions.
Unsupervised person re-identification via simultaneous clustering and mask prediction

Extracting meaningful representation is a key challenge for person re-identification (re-ID) task, especially in the absence of ground truth labels. However, existing unsupervised approaches simply utilize pseudo labels generated from clustering to supervise re-ID model and thus have not yet fully explored the semantic information existing in data itself. This also limits the representation capabilities of learned models. To address the above problem, we propose mask prediction (MaskPre) as a pretext task for unsupervised re-ID, such that the clustering network can capture more semantic information and separate the images into semantic clusters automatically. Specifically, MaskPre masks region-level features with dynamic dropblock layer to generate differently masked views of a single image. To predict the masked regions and bridge the domain gap across views, we design mask prediction head and moving-average model to learn visual consistency from still image and temporal consistency during training process. Meanwhile, we optimize the model by grouping the two masked views into the same cluster, thus enhancing the consistency across views. Experimental results on three public benchmark datasets show that our proposed method outperforms the existing state-of-the-art approaches.
Double-Matrix Decomposition Image Steganography Scheme Based on Wavelet Transform with Multi-Region Coverage

On the basis of ensuring the quality and concealment of steganographic images, this paper proposes a double-matrix decomposition image steganography scheme with multi-region coverage, to solve the problem of poor extraction ability of steganographic images under attack or interference. First of all, the cover image is transformed by multi-wavelet transform, and the hidden region covering multiple wavelet sub-bands is selected in the wavelet domain of the cover image to embed the secret information. After determining the hidden region, the hidden region is processed by Arnold transform, Hessenberg decomposition, and singular-value decomposition. Finally, the secret information is embedded into the cover image by embedding intensity factor. In order to ensure robustness, the hidden region selected in the wavelet domain is used as the input of Hessenberg matrix decomposition, and the robustness of the algorithm is further enhanced by Hessenberg matrix decomposition and singular-value decomposition. Experimental results show that the proposed method has excellent performance in concealment and quality of extracted secret images, and secret information is extracted from steganographic images attacked by various image processing attacks, which proves that the proposed method has good anti-attack ability under different attacks.
Knowledge Distillation: A Method for Making Neural Machine Translation More Efficient

Neural machine translation (NMT) systems have greatly improved the quality available from machine translation (MT) compared to statistical machine translation (SMT) systems. However, these state-of-the-art NMT models need much more computing power and data than SMT models, a requirement that is unsustainable in the long run and of very limited benefit in low-resource scenarios. To some extent, model compression—more specifically state-of-the-art knowledge distillation techniques—can remedy this. In this work, we investigate knowledge distillation on a simulated low-resource German-to-English translation task. We show that sequence-level knowledge distillation can be used to train small student models on knowledge distilled from large teacher models. Part of this work examines the influence of hyperparameter tuning on model performance when lowering the number of Transformer heads or limiting the vocabulary size. Interestingly, the accuracy of these student models is higher than that of the teachers in some cases even though the student model training times are shorter in some cases. In a novel contribution, we demonstrate for a specific MT service provider that in the post-deployment phase, distilled student models can reduce emissions, as well as cost purely in monetary terms, by almost 50%.