Reviewerly/exHarmony huggingface dataset:
Professional English Teaching Model Based on Wireless Network Communication and Multimedia

The integration of multimedia technology and foreign language teaching has gradually become a contemporary key research topic under the trend of the integration of modern information technology and education. As an emerging method, multimedia-assisted learning method can not only create a more realistic language learning environment but also make contributions in stimulating students’ willingness to learn and improving educational effects. However, sometimes it is used improperly, this method can easily lead to the teaching form being larger than the content, and the teaching effect may not be satisfactory. This article focuses on the characteristics of the professional English teaching model based on wireless network communication and multimedia, discusses the practical application of wireless network communication and multimedia technology in professional English teaching, and conducts a simple research on the professional English teaching model. Taking the professional English teaching model and the traditional multimedia teaching model of English as the research objects, two natural students are selected as the research objects, and they are divided into the experimental group and the control group. Statistical software is used to analyze the data in detail. The test results of the experimental class and the control class were analyzed before and after the experiment. The experimental results show that the professional English teaching model based on wireless network and multimedia has a significant teaching effect compared with the traditional English teaching model. Comparing the experimental group and the control group, English scores improved by 11%. The gap between the two groups is very obvious. As far as the scores of each part of the English test paper are concerned, the scores of each part of the experimental group are also higher than those of the control group. The professional English teaching model based on wireless network communication and multimedia turns out to be effective and popular among students.
Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos

Multimodal self-supervised learning is getting more and more attention as it allows not only to train large networks without human supervision but also to search and retrieve data across various modalities. In this context, this paper proposes a self-supervised training framework that learns a common multimodal embedding space that, in addition to sharing representations across different modalities, enforces a grouping of semantically similar instances. To this end, we extend the concept of instance-level contrastive learning with a multimodal clustering step in the training pipeline to capture semantic similarities across modalities. The resulting embedding space enables retrieval of samples across all modalities, even from unseen datasets and different domains. To evaluate our approach, we train our model on the HowTo100M dataset and evaluate its zero-shot retrieval capabilities in two challenging domains, namely text-to-video retrieval, and temporal action localization, showing state-of-the-art results on four different datasets.
Research on blood glucose data prediction based on I-GWO-KELM algorithm

Predicting future blood glucose (BG) levels in diabetic patients can help reduce high and low blood glucose events. The blood glucose prediction method in this article is as follows: the improved grey wolf optimizer is combined with the kernel extreme learning machine, called as I-GWO-KELM. The data set of patients was obtained by continuous glucose monitoring. Initially, the Kalman filter method was used to reduce the errors during data collection. The grey wolf optimizer algorithm optimizes the parameters of the kernel extreme learning machine, including regularization coefficients and kernel parameters. Then KELM was used to predict blood glucose, and mean absolute error (MAE) and root mean square error were (RMSE) used to evaluate the model performance. Experiments prove that BG prediction model based on I3-GWO-KELM outperformed other models, with MAE and RMSE are 12.127 mg/dL and 16.544 mg/dL for prediction horizon (PH) 30 minutes. I3-GWO-KELM has strong robustness and is suitable for the prediction of time series data.
Lyapunov-stable neural-network control

Deep learning has had a far reaching impact in robotics. Specifically, deep reinforcement learning algorithms have been highly effective in synthesizing neural-network controllers for a wide range of tasks. However, despite this empirical success, these controllers still lack theoretical guarantees on their performance, such as Lyapunov stability (i.e., all trajectories of the closed-loop system are guaranteed to converge to a goal state under the control policy). This is in stark contrast to traditional model-based controller design, where principled approaches (like LQR) can synthesize stable controllers with provable guarantees. To address this gap, we propose a generic method to synthesize a Lyapunov-stable neural-network controller, together with a neural-network Lyapunov function to simultaneously certify its stability. Our approach formulates the Lyapunov condition verification as a mixed-integer linear program (MIP). Our MIP verifier either certifies the Lyapunov condition, or generates counter examples that can help improve the candidate controller and the Lyapunov function. We also present an optimization program to compute an inner approximation of the region of attraction for the closed-loop system. We apply our approach to robots including an inverted pendulum, a 2D and a 3D quadrotor, and showcase that our neural-network controller outperforms a baseline LQR controller. The code is open sourced at \url{https://github.com/StanfordASL/neural-network-lyapunov}.
A Framework for Implementing Prediction Algorithm over Cloud Data as a Procedure for Cloud Data Mining

The cloud has become an important phrase in data storage for many reasons. Cloud services and applications are widespread in many industries including healthcare due to easy access. The limitless quantity of data available on the clouds has triggered the interest of many researchers in the recent past. It has forced us to deploy machine learning for analyzing the data to get insights as well as model building. In this paper, we have built a service on Heroku Cloud which is a cloud platform as a service (PaaS) and has 15 thousand records with 25 features. The data belongs to healthcare and is related to post-surgery complications. The boost prediction algorithm was applied for analysis and implementation was done in python. The results helped us to determine and tune some of the hyperparameters which have correlations with complications and the reported accuracy of training and testing was found to be 91% and 88% respectively.
Distributional Robustness Loss for Long-Tail Learning

Real-world data is often unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes. To address unbalanced data, most studies try balancing the data, the loss, or the classifier to reduce classification bias towards head classes. Far less attention has been given to the latent representations learned with unbalanced data. We show that the feature extractor part of deep networks suffers greatly from this bias. We propose a new loss based on robustness theory, which encourages the model to learn high-quality representations for both head and tail classes. While the general form of the robustness loss may be hard to compute, we further derive an easy-to-compute upper bound that can be minimized efficiently. This procedure reduces representation bias towards head classes in the feature space and achieves new SOTA results on CIFAR100-LT, ImageNet-LT, and iNaturalist long-tail benchmarks. We find that training with robustness increases recognition accuracy of tail classes while largely maintaining the accuracy of head classes. The new robustness loss can be combined with various classifier balancing techniques and can be applied to representations at several layers of the deep model.
Stability Analysis and Control of Decision-Making of Miners in Blockchain

To maintain blockchain-based services with ensuring its security, it is an important issue how to decide a mining reward so that the number of miners participating in the mining increases. We propose a dynamical model of decision-making for miners using an evolutionary game approach and analyze the stability of equilibrium points of the proposed model. The proposed model is described by the 1st-order differential equation. So, it is simple but its theoretical analysis gives an insight into the characteristics of the decision-making. Through the analysis of the equilibrium points, we show the transcritical bifurcations and hysteresis phenomena of the equilibrium points. We also design a controller that determines the mining reward based on the number of participating miners to stabilize the state that all miners participate in the mining. Numerical simulation shows that there is a trade-off in the choice of the design parameters.
The Multi-Output Quantum Pulse Gate: a Novel High-Dimensional QKD Decoder

With the development of quantum computers, classical cryptography for secure communication is in danger of becoming obsolete. Quantum cryptography, however, can exploit the laws of quantum mechanics to guarantee unconditional security independently of the computational power of a potential eavesdropper. An example is quantum key distribution (QKD), which allows two parties to encrypt a message through a random secret key encoded in the degrees of freedom of quantum particles, typically photons.
Investigate, Identify and Estimate the Technical Debt: A Systematic Mapping Study

Context: Technical Debt (TD) is a metaphor that refers to short-term solutions in software development that may affect the cost to the software development life cycle. Objective: To explore and understand TDrelated to the software industry as well as an overview on the current state of TD research. Forty-three TD empirical studies were collected for classification and analyzation. Goals: Classify TD types, find the indicators used to detect TD, find the estimators used to quantify the TD, evaluate how researchers investigate TD. Method: By performing a systematic mapping study to identify and analyze the TD empirical studies which published between 2014 and 2017. Results: We present the most common indicators and evaluators to identify and evaluate the TD, and we gathered thirteen types of TD. We showed some ways to investigate the TD, and used tools in the selected studies. Conclusion: The outcome of our systematic mapping study can help researchers to identify interestand future in TD.
A Novel Remedy for Image Encryption and Data Hiding

With the progression of privacy protection and cloud storage, the concept known as Reversible Data Hiding in Encrypted Images (RDHEI) has earned recognition for secure communication, because it sustains the remarkable property of the initial image being retrieved losslessly after the data embedded in it is obtained while ensuring the image content’s confidentiality. Many encryption algorithms which usually use the concept of chaotic systems have been introduced to preserve digital images against cryptographic attacks. In our paper, we propose an image encryption algorithm which is based on the principle of Rubik’s Cube and RDHEI scheme based on Most Significant Bit (MSB) Steganography. In this method, the image is initially scrambled using the Rubik’s cube principle, and encryption is accomplished using two encryption keys that are randomly generated. Later we analyze the MSB bits of the encrypted image into which the information to be hidden is embedded using a data-hiding key, as per the individual content owner’s requirement. Using MSB Steganography leads to the prevention of hidden data being exposed to cyber-attacks and from being easily cracked. Experimental results prove that the outcome obtained with this method is much better than many of the conventional techniques, taking into consideration both the embedding capacity and reconstructed image quality. We have executed our system on several images, and every time the resulting image has striking similarity to the initial image considering terms such as SSIM and PSNR.
Breast Cancer Histopathological Image Classification using EfficientNet Architecture

Breast cancer is the most common type of cancer affecting women. The formation of lumps in the breast is one of the first signs of the presence of this disease. These tumors can either be cancerous or benign and hence a breast tissue biopsy is conducted to determine their nature. Advancements in the field of vision-based Deep Learning have facilitated the wide adoption of automated diagnostic systems in hospitals, for tasks such as cancer and COVID detection from lung X-ray scans, diabetic retinopathy detection from retinal fundus images, brain MRI segmentation, etc. Moving forward, reduction in training, validation and development times, and efficient usage of training resources for these models will be more in focus. The EfficientNet architecture proposed by Google has recently outperformed prior state-of-the-art architectures such as DenseNet and ResNet on the ImageNet classification task while using fewer parameters and epochs to converge faster. In this paper, we compare the performance of the EfficientNetB3 architecture with the above-mentioned architectures for the tasks of binary and multinomial tumor classification on the benchmark BreakHis dataset, which consists of around 8000 breast histopathology images of varying magnification. Our results show that under similar training conditions, the EfficientNetB3 can converge faster and outperform the previous benchmark models by a significant margin. Our best models achieved 100% sensitivity and accuracy on certain binary classification tasks and a sensitivity of 95.45% and precision of 95.15% on 8-ary classification tasks.
Wearable Technology and Robotics for a Mobile World

This chapter discusses the mechanical engineering technology academic program director focused on health and human performance along with Brittany Partridge, an applied informaticist. Mobile computing has multiple dimensions, many of which are largely unknown. Generally speaking, wearable tech is anything on, in, or around a living thing that performs a function such as mirroring or enhancing capabilities or improving efficiencies. The concept of wearable robotics never held a prominent place in William’s mind until he found himself in a scenario where the associated technology became a central theme toward the improvement of his quality of life. Thinking about the elements that healthcare professionals and patients might consider as wearable robotics, the technology associated with sensing, diagnostics, and mobility is the perfect place to start. Integrating with human cognitive function, motor function, and sensing such that the technology functions intuitively enables users to perform tasks that exceed normal human capacity.
Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators

Recent advances in reinforcement learning (RL) have led to a growing interest in applying RL to classical planning domains or applying classical planning methods to some complex RL domains. However, the long-horizon goal-based problems found in classical planning lead to sparse rewards for RL, making direct application inefficient. In this paper, we propose to leverage domain-independent heuristic functions commonly used in the classical planning literature to improve the sample efficiency of RL. These classical heuristics act as dense reward generators to alleviate the sparse-rewards issue and enable our RL agent to learn domain-specific value functions as residuals on these heuristics, making learning easier. Correct application of this technique requires consolidating the discounted metric used in RL and the non-discounted metric used in heuristics. We implement the value functions using Neural Logic Machines, a neural network architecture designed for grounded first-order logic inputs. We demonstrate on several classical planning domains that using classical heuristics for RL allows for good sample efficiency compared to sparse-reward RL. We further show that our learned value functions generalize to novel problem instances in the same domain. The source code and the appendix are available at github.com/ibm/pddlrl and arxiv.org/abs/2109.14830.
Counting Queries over ELHI⊥ Ontologies

While ontology-mediated query answering most often adopts (unions of) conjunctive queries as the query language, some recent works have explored the use of counting queries coupled with DL-Lite ontologies. The aim of the present paper is to extend the study of counting queries to Horn description logics outside the DL-Lite family. Through a combination of novel techniques, adaptations of existing constructions, and new connections to closed predicates, we achieve a complete picture of the data and combined complexity of answering counting conjunctive queries (CCQs) and cardinality queries (a restricted class of CCQs) in ELHI⊥ and its various sublogics. Notably, we show that CCQ answering is 2EXP-complete in combined complexity for ELHI⊥ and every sublogic that extends EL or DL-Lite-pos-H. Our study not only provides the first results for counting queries beyond DL-Lite, but it also closes some open questions about the combined complexity of CCQ answering in DL-Lite.
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision

Federated Learning (FL) is transforming the ML training ecosystem from a centralized over-the-cloud setting to distributed training over edge devices in order to strengthen data privacy. An essential but rarely studied challenge in FL is label deficiency at the edge. This problem is even more pronounced in FL compared to centralized training due to the fact that FL users are often reluctant to label their private data. Furthermore, due to the heterogeneous nature of the data at edge devices, it is crucial to develop personalized models. In this paper we propose self-supervised federated learning (SSFL), a unified self-supervised and personalized federated learning framework, and a series of algorithms under this framework which work towards addressing these challenges. First, under the SSFL framework, we demonstrate that the standard FedAvg algorithm is compatible with recent breakthroughs in centralized self-supervised learning such as SimSiam networks. Moreover, to deal with data heterogeneity at the edge devices in this framework, we have innovated a series of algorithms that broaden existing supervised personalization algorithms into the setting of self-supervised learning. We further propose a novel personalized federated self-supervised learning algorithm, Per-SSFL, which balances personalization and consensus by carefully regulating the distance between the local and global representations of data. To provide a comprehensive comparative analysis of all proposed algorithms, we also develop a distributed training system and related evaluation protocol for SSFL. Our findings show that the gap of evaluation accuracy between supervised learning and unsupervised learning in FL is both small and reasonable. The performance comparison indicates the representation regularization-based personalization method is able to outperform other variants.
Paying down metadata debt: learning the representation of concepts using topic models

We introduce a data management problem called metadata debt, to identify the mapping between data concepts and their logical representations. We describe how this mapping can be learned using semisupervised topic models based on low-rank matrix factorizations that account for missing and noisy labels, coupled with sparsity penalties to improve localization and interpretability. We introduce a gauge transformation approach that allows us to construct explicit associations between topics and concept labels, and thus assign meaning to topics. We also show how to use this topic model for semisupervised learning tasks like extrapolating from known labels, evaluating possible errors in existing labels, and predicting missing features. We show results from this topic model in predicting subject tags on over 25,000 datasets from Kaggle.com, demonstrating the ability to learn semantically meaningful features.
ActiveCrowds: A Human-in-the-Loop Machine Learning Framework

A widespread practice in machine learning solutions is the continuous use of human intelligence to increase their quality and efficiency. A common problem in such solutions is the requirement of a large amount of labeled data. In this paper, we present a practical implementation of the human-in-the-loop computing practice, which includes the combination of active and transfer learning for sophisticated data sampling and weight initialization respectively, and a cross-platform mobile application for crowdsourcing data annotation tasks. We study the use of the proposed framework to a post-event building reconnaissance scenario, where we utilized the implementation of an existing pre-trained computer vision model, an image binary classification solution built on top of it, and max entropy and random sampling as uncertainty sampling methods for the active learning step. Multiple annotations with majority voting as quality assurance are required for new human-annotated images to be added on the train set and retrain the model. We provide the results and discuss our next steps.
Geological and Geochemical Prospecting for Gold Mineralization in Bode-Saadu Axis, Southwestern Nigeria

Geological and geochemical studies of gold mineralization in Bode-Saadu axis, Southwestern Basement Complex, Nigeria have been conducted in order to study their mode of occurrence, structural settings, pattern of distribution and potential. Geological studies reveal that granite and gneiss granite are the prominent rocks in the area and exist in the north-eastern, north-western and southwestern part of the area whereas mica and amphibole schist occur as low-lying exposure. Petrographic studies reveal spatial association between gold mineralization and the fractured zones. Geochemical studies show that the gold concentration in rocks and stream sediments from the region is low with the exception of a few areas with high values ​​and is related to the occurrence of fissure in the northeast-west direction. The distribution pattern of gold in the region is skewed NE-SW, indicating that gold mineralization is structurally controlled. Keywords: Geochemical prospecting; Geological studies; Gold mineralization; Structurally controlled.
Plenum a la Mode - Augmented Reality Fashions

Inspired by ideas portrayed in science fiction, the authors sought to develop a set of augmented reality fashions that showcased scenes from a science fiction novel recently published by the principal author. The development team included artists and designers, a programmer, and the writer. Significant technical challenges needed to be overcome for success, including fabric construction and manipulation, image enhancement, robust image recognition and tracking capabilities, and the management of lighting and suitable backgrounds. Viewing geometries were also a non-trivial problem. The final solution permitted acceptable but not perfect real-time tracking of the fashion models and the visualization of both static and dynamic 3D elements overlaid onto the physical garments.