Reviewerly/exHarmony huggingface dataset:
The transition to remote education format in medical university: opinion of students about transformation of education process at the outset of COVID-19 pandemic

The COVID-19 pandemic came out as an imperative of large-scale transformations in higher education system, including higher medical school. At that, introduction of self-isolation regime in the spring 2020 highlighted the task of organizing education in remote format with maintaining education quality. The article presents results of sociological survey with purpose of studying perception of students of medical university of transition to remote learning format at the COVID-19 pandemic outset. The majority of respondents perceived remote learning format as an emergency measure to maintain continuity of education process, that, however, cannot completely replace full-time practice-oriented teaching "at the bedside of the patient". Understanding the special tasks of training medical specialist required new solutions from higher school and additional efforts from all subjects of educational process - restructuring organizational infrastructure ensuring communication of teaching staff and students, solving organizational problems of access to educational content, adapting to practice of digital medical education, expanding use of AR, VR, mixed reality and simulation learning technologies. The students, highly assessing quality of digital content, noted difficulties of self-organization in "home" format due to a lot of distracting factors (family, domestic life, etc.) that had key impact on motivation of their learning activities. Nevertheless, among respondents of medical university, a number of students formed subgroup that considering personal experience of remote learning assumed that this format is actual and effective and allows one to fully master university curriculum. The transition to remote learning format in university as emergency measure during pandemic, will obviously be consolidated as an invariant standard of organizing educational process.
Control of amplitude homogeneity in coherent Ising machines with artificial Zeeman terms

Abstract A coherent Ising machine (CIM) is an open-dissipative Ising solver using optical pulses generated from a degenerate optical parametric oscillator as analog magnetizations. When solving real-world optimization problems with CIM, this solver has two difficulties: mutual coupling induced amplitude inhomogeneity and absence of natural way to implement Zeeman terms. For the approximate Gaussian formulation of CIMs with amplitude control feedback, we add artificial Zeemam terms using the target amplitude information. Here we show, for 16-spin CIM with Zeeman terms, the amplitude control increases the performance, particularly when Zeeman terms are competing against mutual coupling coefficients.
Minimax Demographic Group Fairness in Federated Learning

Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minimax group fairness in federated learning scenarios where different participating entities may only have access to a subset of the population groups during the training phase. We formally analyze how our proposed group fairness objective differs from existing federated learning fairness criteria that impose similar performance across participants instead of demographic groups. We provide an optimization algorithm – FedMinMax – for solving the proposed problem that provably enjoys the performance guarantees of centralized learning algorithms. We experimentally compare the proposed approach against other state-of-the-art methods in terms of group fairness in various federated learning setups, showing that our approach exhibits competitive or superior performance.
Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning

With the rapid development of society and economy, people’s living standards are improving day by day, and increasingly attention is paid to physical health, which has set off a fitness upsurge. The purpose of this paper was to analyze the impact of bodybuilding exercise on physical fitness based on deep learning. It provides a reference for fitness enthusiasts to choose scientific and targeted exercise methods, and provides a theoretical basis for the promotion of bodybuilding and fitness. This paper first gives a general introduction to deep learning and adds image segmentation technology to design experiments for bodybuilding and fitness. The experiment was divided into groups A and B, and control group C. In this paper, recurrent neural network and gated recurrent neural network are introduced to compare and analyze the data, and the stability of data processing with different activation functions is compared. The data results show that under the scientific and reasonable arrangement of exercise conditions, bodybuilding and fitness exercises have a corresponding positive effect on the body shape and posture of the subjects. It is more practical to choose a combination of aerobic and anaerobic exercise. In this paper, based on the deep learning algorithm, compared with the recurrent neural network, the gated recurrent neural network is more suitable for processing sequence problems. In the experimental analysis part, this paper compares and analyzes the experimental results of the data under different activation functions, sigmoid function, and tanh function. It is found that the tanh activation function and the gated recurrent neural network are more stable for data processing. The highest AUC value of the traditional recurrent neural network differs by 0.78 from the highest AUC value of the gated recurrent neural network. The data analysis results are in line with the actual situation.
Vegetation Evolution: An Optimization Algorithm Inspired by the Life Cycle of Plants

In this paper, we have observed that different types of plants in nature can use their own survival mechanisms to adapt to various living environments. A new population-based vegetation evolution (VEGE) algorithm is proposed to solve optimization problems by interactively simulating the growth and maturity periods of plants. In the growth period, individuals explore their local areas and grow in potential directions, while individuals generate many seed individuals and spread them as widely as possible in the maturity period. The main contribution of our proposed VEGE is to balance exploitation and exploration from a novel perspective, which is to perform these two periods in alternation to switch between two different search capabilities. To evaluate the performance of the proposed VEGE, we compare it with three well-known algorithms in the evolutionary computation community: differential evolution, particle swarm optimization, and enhanced fireworks algorithm — and run them on 28 benchmark functions with 2-dimensions (2D), 10D, and 30D with 30 trial runs. The experimental results show that VEGE is efficient and promising in terms of faster convergence speed and higher accuracy. In addition, we further analyze the effects of the composition of VEGE on performance, and some open topics are also given.
SPCS: a spatial pyramid convolutional shuffle module for YOLO to detect occluded object

Abstract In crowded scenes, one of the most important issues is that heavily overlapped objects are hardly distinguished from each other since most of their pixels are shared and the visible pixels of the occluded objects, which are used to represent their features, are limited. In this paper, a spatial pyramid convolutional shuffle (SPCS) module is proposed to extract refined information from the limited visible pixels of the occluded objects and generate distinguishable representations for the heavily overlapped objects. We adopt four convolutional kernels with different sizes and dilation rates at each location in the pyramid features and adjacently recombine their fused outputs spatially using a pixel shuffle module. In this way, four distinguishable instance predictions corresponding different convolutional kernels can be produced for each location in the pyramid feature. In addition, multiple convolutional operations with different kernel sizes and dilation rates at the same location can generate refined information for the corresponding regions, which is helpful to extract features for the occluded objects from their limited visible pixels. Extensive experimental results demonstrate that SPCS module can effectively boost the performance in crowded human detection. YOLO detector with SPCS module achieves 94.11% AP, 41.75% MR, 97.75% Recall on CrowdHuman, 93.04% AP, and 98.45% Recall on WiderPerson, which are the best compared with previous state-of-the-art models.
A Blockchain-based Customizable Document Registration Service for Third Parties

Blockchain has been relevant in the document management process, serving as a storage solution with the potential to guarantee the relevant requirements needed for any document storage and validation solution. However, due to the distributed nature of blockchain, we may face implementation difficulties and high operational costs, for example. To facilitate this process, we propose a customizable blockchain-based document registration service that makes it possible to create different types of gen-eralized documents for various application domains and store them in one or more blockchains integrated in an Application Programming Interface (API).
Prediction time of breast cancer tumor recurrence using Machine Learning

An in-depth study using the database from GLOBOCAN, CDC, and WHO health repository highlights the lethality of breast cancer, taking thousands of lives each year. However, a timely prediction of cancer can help patients to consult the doctor on time. In the past, various studies have successfully predicted the nature of the tumor to be benign or malignant and if the breast cancer tumor will reoccur or not but, no time-based models have been studied. With the help of Machine Learning, this study shows various prediction models that can be used to predict tumor reoccurrence time as accurately as 1 year. Among the 198 patients analyzed, 40% of the total patients were predicted to have breast cancer tumors reoccurring within 1st year of the diagnosis. The proposed machine learning techniques use various classification models such as Spectral clustering, DBSCAN, and k-means along with prediction models like Support Vector Machines (SVM), Decision trees, and Random Forest. The results demonstrate the ability of the model to predict the time taken by the tumor to reoccur or the time taken by the patient for full recovery with the best accuracy of 78.7% using SVM. This population-based study performed on multivariate real attributed characteristics data can therefore provide the patients a reasonable estimate about their recovery time or the time before which they should consult the doctor.
Fuzzy Dynamic Obstacle Avoidance Algorithm for Basketball Robot Based on Multi-Sensor Data Fusion Technology

This paper uses multi-sensor data fusion technology to design and develop a multi-sensor based basketball robot’s target recognition and positioning system in an unknown environment. We establish the movement model of the basketball robot, and combine the hardware configuration of the basketball robot, analyse the methods of speed control, position control, and direction control, propose a fuzzy obstacle avoidance strategy in the basketball robot movement process, and simulate the obstacle avoidance strategy. The simulation results show that the proposed fuzzy obstacle avoidance strategy is effective. Carry out the experiment of basketball robot target calibration, build the basketball robot monitoring system, and realize data transmission and real-time image processing. Experiments show that the basketball robot can achieve target recognition and positioning tasks in an unknown environment (obstacle comparison rules). It proves the effectiveness and robustness of the proposed following algorithm. It illustrates the effectiveness of the system designed in this paper.
Divide-and-Regroup Clustering for Domain Adaptive Person Re-identification

Clustering is important for domain adaptive person re-identification(re-ID). A majority of unsupervised domain adaptation (UDA) methods conduct clustering on the target domain and then use the generated pseudo labels for adaptive training. Albeit important, the clustering pipeline adopted by current literature is quite standard and lacks consideration for two characteristics of re-ID, i.e., 1) a single person has various feature distribution in multiple cameras. 2) a person’s occurrence in the same camera are usually temporally continuous. We argue that the multi-camera distribution hinders clustering because it enlarges the intra-class distances. In contrast, the temporal continuity prior is beneficial, because it offers clue for distinguishing some look-alike person (who are temporally far away from each other). These two insight motivate us to propose a novel Divide-And-Regroup Clustering (DARC) pipeline for re-ID UDA. Specifically, DARC divides the unlabeled data into multiple camera-specific groups and conducts local clustering within each camera. Afterwards, it regroups those local clusters potentially belonging to the same person into a unity. Through this divide-and-regroup pipeline, DARC avoids directly clustering across multiple cameras and focuses on the feature distribution within each individual camera. Moreover, during the local clustering, DARC uses the temporal continuity prior to distinguish some look-alike person and thus reduces false positive pseudo labels. Consequentially, DARC effectively reduces clustering errors and improves UDA. Importantly, we show that DARC is compatible to many pseudo label-based UDA methods and brings general improvement. Based on a recent UDA method, DARC advances the state of the art (e.g, 85.1% mAP on MSMT-to-Market and 83.1% mAP on PersonX-to-Market).
An Auto Encoder-based Dimensionality Reduction Technique for Efficient Entity Linking in Business Phone Conversations

An entity linking system links named entities in a text to their corresponding entries in a knowledge base. In recent years, building an entity linking system that leverages the transformer architecture has gained lots of attention. However, deploying a transformer-based neural entity linking system in industrial production environments in a limited resource setting is a challenging task. In this work, we present an entity linking system that leverages a transformer-based BERT encoder (the BLINK model) to connect the product and organization type entities in business phone conversations to their corresponding Wikipedia entries. We propose a dimensionality reduction technique via utilizing an auto encoder that can effectively compress the dimension of the pre-trained BERT embeddings to 256 from the original size of 1024. This allows our entity linking system to significantly optimize the space requirement when deployed in a resource limited cloud machine while reducing the inference time along with retaining high accuracy.
Ask the Users

Just-in-Time Adaptive Interventions (JITAIs) are envisioned to harness rich data on users' contexts. However, many JITAIs fall short in leveraging the value of the data while sculpting the interventions. Investigating the literature reveals a lack of user-centered design (UCD) methods in designing JITAIs. Our case study of applying a UCD process revealed that even without deploying a JITAI, UCD could uncover user interactions that inform critical design decisions of a JITAI's components. We reflect on our experiences engaging in a user-centered JITAI design process and urge the broader human-computer interaction (HCI) community to devise concrete design guidelines for JITAIs. We take the first step toward that goal by proposing a checklist of key design considerations for future JITAIs. Together, this case study contributes insights on applying UCD to the design of all components of a JITAI, which can help capture users' needs and provide actionable interventions.
MM-Rec: Visiolinguistic Model Empowered Multimodal News Recommendation

News representation is critical for news recommendation. Most existing methods learn news representations only from news texts while ignoring the visual information of news. In fact, users may click news not only due to the interest in news titles but also the attraction of news images. Thus, images are useful for representing news and predicting news clicks. Pretrained visiolinguistic models are powerful in multi-modal understanding, which can represent news from both textual and visual contents. In this paper, we propose a multimodal news recommendation method that can incorporate both textual and visual information of news to learn multimodal news representations. We first extract region-of-interests (ROIs) from news images via object detection. We then use a pre-trained visiolinguistic model to encode both news texts and image ROIs and model their inherent relatedness using co-attentional Transformers. In addition, we propose a crossmodal candidate-aware attention network to select relevant historical clicked news for the accurate modeling of user interest in candidate news. Experiments validate that incorporating multimodal news information can effectively improve the performance of news recommendation.
Looking for haptics. Touch digitalization business strategies in luxury and fashion during COVID-19 and beyond

The sense of touch is central to fashion and luxury, because dress - garments and accessories - is experienced with and on the body. But on e-commerce websites, dress is dematerialized, and touch sensation simulated using audio-visual means. The growth of digital fashion communication during the COVID-19 pandemic has spotlighted this issue. However, developments in haptic technologies suggest that new modes of tactile interaction could be available in the online domain. Through interviews with fourteen fashion and luxury executives in Italy and Switzerland, this article investigates touch digitalization strategies during COVID-19 and the potential which haptics may hold to fill the online tactile void, using two surface haptic technologies as prompts. By doing so, this research fills a gap in the study of digital fashion and haptics, to the benefit of the academic and practitioner communities. Results indicate that executives deployed a host of visualization tactics during lockdown, reinforcing their conviction that still and moving images furnish an acceptable surrogate for touch sensation in the online domain. In fact, participants questioned the value of mediated tactile feedback provided by surface haptic devices. However, these experts also concurred that the technology holds promise, envisioning future use of haptic technologies in digital business-to-business and end-consumer markets. These could include haptic data integration within virtual prototyping, haptically enriched digital content for customer engagement, multisensory brand promotions in mixed or virtual reality, and phygital experiences.
Keyword-Driven Service Recommendation Via Deep Reinforced Steiner Tree Search

Developers need to reuse web services and create mashups suitable for various scenarios. Currently, it relies on the developer’s adequate domain knowledge to be able to find services and verify their compatibility. Although service recommendation systems already exist to assist them, inexperienced developers may not be able to adequately express their requirements, resulting in inappropriate and incompatible recommendations. To tackle this problem, we define a service-keyword correlation graph (SKCG) to capture the relationship between services and keywords, and the compatibility among services. Then, we propose keyword-based deep reinforced Steiner tree search (K-DRSTS) to recommend services for mashup creation. K-DRSTS models the task of service discovery as a Steiner tree search problem against SKCG. Leveraging deep reinforcement learning, K-DRSTS provides an efficient solution for solving the NP-hard search problem of the Steiner tree. Extensive experiments on real-world data sets have shown the effectiveness of K-DRSTS.
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation

When directly using existing text generation datasets for controllable generation, we are facing the problem of not having the domain knowledge and thus the aspects that could be controlled are limited.A typical example is when using CNN/Daily Mail dataset for controllable text summarization, there is no guided information on the emphasis of summary sentences.A more useful text generator should leverage both the input text and the control signal to guide the generation, which can only be built with a deep understanding of the domain knowledge.Motivated by this vision, our paper introduces a new text generation dataset, named MReD.Our new dataset consists of 7,089 meta-reviews and all its 45k meta-review sentences are manually annotated with one of the 9 carefully defined categories, including abstract, strength, decision, etc.We present experimental results on start-of-the-art summarization models, and propose methods for structurecontrolled generation with both extractive and abstractive models using our annotated data.By exploring various settings and analyzing the model behavior with respect to the control signal, we demonstrate the challenges of our proposed task and the values of our dataset MReD.Meanwhile, MReD also allows us to have a better understanding of the meta-review domain.
Decoding Knowledge Transfer for Neural Text-to-Speech Training

Neural end-to-end text-to-speech (TTS) is superior to conventional statistical methods in many ways. However, the exposure bias problem, that arises from the mismatch between the training and inference process in autoregressive models, remains an issue. It often leads to performance degradation in face of out-of-domain test data. To address this problem, we study a novel decoding knowledge transfer strategy, and propose a multi-teacher knowledge distillation (MT-KD) network for Tacotron2 TTS model. The idea is to pre-train two Tacotron2 TTS teacher models in teacher forcing and scheduled sampling modes, and transfer the pre-trained knowledge to a student model that performs free running decoding. We show that the MT-KD network provides an adequate platform for neural TTS training, where the student model learns to emulate the behaviors of the two teachers, at the same time, minimizing the mismatch between training and run-time inference. Experiments on both Chinese and English data show that MT-KD system consistently outperforms the competitive baselines in terms of naturalness, robustness and expressiveness for in-domain and out-of-domain test data. Furthermore, we show that knowledge distillation outperforms adversarial learning and data augmentation in addressing the exposure bias problem.