Reviewerly/exHarmony huggingface dataset:
Avoid Zoom Fatigue, Be Present and Learn

This paper explores plausible reasons why some students report having more difficulty learning online, predominantly in Zoom synchronous classes, and suggests strategies that students can do to optimize their learning. During anonymous classroom observations, approximately 80% of 350 college students polled indicated it was harder to focus their attention and stay present while taking classes online. They also reported experiencing more isolation, anxiety, and depression compared to face-to-face classes, although much of this may be due to COVID-19 social isolation. Students often appear nonresponsive when attending online synchronous Zoom classes that negatively impacts the nonverbal dynamics of student–instructor interactions. Communication issues includes internet challenges, lack of facial expressions, body appearance, and movement. Students also report that it is more challenging to maintain attention, especially when they are multitasking. Suggested strategies are to optimize learning that includes arranging the camera so that you are visible, using active facial and body responses as if you are communicating to just one person face-to-face, configuring your body and environment (sitting upright and creating unique cues for each specific task), reducing multitasking and notifications, and optimizing arousal and vision regeneration.
Frontline encounters of the AI kind: An evolved service encounter framework

Artificial intelligence (AI) is radically transforming frontline service encounters, with AI increasingly playing the role of employee or customer. Programmed to speak or write like a human, AI is poised to usher in a frontline service revolution. No longer will frontline encounters between customer and employee be simply human-to-human; rather, researchers must consider an evolved paradigm where each actor could be either human or AI. Further complicating this 2 × 2 framework is whether the human, either customer or employee, recognizes when they are interacting with a non-human exchange partner. Accordingly, we develop an evolved service encounter framework and, in doing so, introduce the concept of counterfeit service, interspecific service (AI-to-human), interAI service (AI-to-AI), and offer a research agenda focused on the implementation of AI in dyadic service exchanges.
Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning

Racial equality is an important theme of international human rights law, but it has been largely obscured when the overall face recognition accuracy is pursued blindly. More facts indicate racial bias indeed degrades the fairness of recognition system and the error rates on non-Caucasians are usually much higher than Caucasians. To encourage fairness, we introduce the idea of adaptive margin to learn balanced performance for different races based on large margin losses. A reinforcement learning based race balance network (RL-RBN) is proposed. We formulate the process of finding the optimal margins for non-Caucasians as a Markov decision process and employ deep Q-learning to learn policies for an agent to select appropriate margin by approximating the Q-value function. Guided by the agent, the skewness of feature scatter between races can be reduced. Besides, we provide two ethnicity aware training datasets, called BUPT-Globalface and BUPT-Balancedface dataset, which can be utilized to study racial bias from both data and algorithm aspects. Extensive experiments on RFW database show that RL-RBN successfully mitigates racial bias and learns more balanced performance.
Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.
Recommender systems in the healthcare domain: state-of-the-art and research issues

Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future.
CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation

Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to extract rich features for cellular estimation and following diagnosis as well as treatment. While it still remains challenging because the wide existence of nuclei clusters, along with the large morphological variances among different organs make nuclei instance segmentation susceptible to over-/under-segmentation. Additionally, the inevitably subjective annotating and mislabeling prevent the network learning from reliable samples and eventually reduce the generalization capability for robustly segmenting unseen organ nuclei. To address these issues, we propose a novel deep neural network, namely Contour-aware Informative Aggregation Network (CIA-Net) with multi-level information aggregation module between two task-specific decoders. Rather than independent decoders, it leverages the merit of spatial and texture dependencies between nuclei and contour by bi-directionally aggregating task-specific features. Furthermore, we proposed a novel smooth truncated loss that modulates losses to reduce the perturbation from outliers. Consequently, the network can focus on learning from reliable and informative samples, which inherently improves the generalization capability. Experiments on the 2018 MICCAI challenge of Multi-Organ-Nuclei-Segmentation validated the effectiveness of our proposed method, surpassing all the other 35 competitive teams by a significant margin.
Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos

The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos. Existing studies have adopted strategies of sliding window over the entire video or exhaustively ranking all possible clip-sentence pairs in a presegmented video, which inevitably suffer from exhaustively enumerated candidates. To alleviate this problem, we formulate this task as a problem of sequential decision making by learning an agent which regulates the temporal grounding boundaries progressively based on its policy. Specifically, we propose a reinforcement learning based framework improved by multi-task learning and it shows steady performance gains by considering additional supervised boundary information during training. Our proposed framework achieves state-of-the-art performance on ActivityNet’18 DenseCaption dataset (Krishna et al. 2017) and Charades-STA dataset (Sigurdsson et al. 2016; Gao et al. 2017) while observing only 10 or less clips per video.
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality

Previous studies in multimodal sentiment analysis have used limited datasets, which only contain unified multimodal annotations.However, the unified annotations do not always reflect the independent sentiment of single modalities and limit the model to capture the difference between modalities.In this paper, we introduce a Chinese single-and multimodal sentiment analysis dataset, CH-SIMS, which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations.It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis.Furthermore, we propose a multi-task learning framework based on late fusion as the baseline.Extensive experiments on the CH-SIMS show that our methods achieve state-of-the-art performance and learn more distinctive unimodal representations.
Explanation of machine learning models using shapley additive explanation and application for real data in hospital

When using machine learning techniques in decision-making processes, the interpretability of the models is important. In the present paper, we adopted the Shapley additive explanation (SHAP), which is based on fair profit allocation among many stakeholders depending on their contribution, for interpreting a gradient-boosting decision tree model using hospital data.For better interpretability, we propose two novel techniques as follows: (1) a new metric of feature importance using SHAP and (2) a technique termed feature packing, which packs multiple similar features into one grouped feature to allow an easier understanding of the model without reconstruction of the model. We then compared the explanation results between the SHAP framework and existing methods using cerebral infarction data from our hospital.The interpretation by SHAP was mostly consistent with that by the existing methods. We showed how the A/G ratio works as an important prognostic factor for cerebral infarction using proposed techniques.Our techniques are useful for interpreting machine learning models and can uncover the underlying relationships between features and outcome.
A Survey on Long-Range Attacks for Proof of Stake Protocols

Despite common arguments about the prevalence of blockchain technology, in terms of security, privacy, and immutability, in reality, several attacks can be launched against them. This paper provides a systematic literature review on long-range attacks for proof of stake protocols. If successful, these attacks may take over the main chain and partially, or even completely, rewrite the history of transactions that are stored in the blockchain. To this end, we describe how proof of stake protocols work, their fundamental properties, their drawbacks, and their attack surface. After presenting long-range attacks, we discuss possible countermeasures and their applicability.
A novel enhanced whale optimization algorithm for global optimization

One of the main issues with heuristics and meta-heuristics is the local optima stagnation phenomena. It is often called premature convergence, which refers to the assumption of a locally optimal solution as the best solution for an optimization problem and failure in finding the global optimum. Whale Optimization Algorithm (WOA) has demonstrated its merits in the optimization area. Though WOA is an effective algorithm, it may suffer from a low exploration of the search space. In this work, an enhanced WOA (WOAmM) is proposed. The mutualism phase from Symbiotic Organisms Search (SOS) is modified and integrated with WOA to alleviate premature convergence's inherent drawback. The addition of a modified mutualism phase with WOA makes the algorithm a balanced one to explore search space more extensively and avoid wasting computational resources in excessive exploitation. The proposed WOAmM method is tested on 36 benchmark functions and IEEE CEC 2019 function suite. It is compared with a wide range of algorithms, including improved WOAs and other meta-heuristics. Statistical analyses and convergence analysis are performed as well to examine its effectiveness and convergence speed. In addition, six real-world engineering optimization problems are solved by the proposed method. The performance is compared with a wide range of existing algorithms to inspect the problem-solving capability of WOAmM. The results demonstrate the performance improvement of WOAmM and its superiority over different algorithms.
Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery

Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic monitoring and disaster management. The huge variation in object scale, orientation, category, and complex backgrounds, as well as the different camera sensors pose great challenges for current algorithms. In this work, we propose a new method consisting of a novel joint image cascade and feature pyramid network with multi-size convolution kernels to extract multi-scale strong and weak semantic features. These features are fed into rotation-based region proposal and region of interest networks to produce object detections. Finally, rotational non-maximum suppression is applied to remove redundant detections. During training, we minimize joint horizontal and oriented bounding box loss functions, as well as a novel loss that enforces oriented boxes to be rectangular. Our method achieves 68.16% mAP on horizontal and 72.45% mAP on oriented bounding box detection tasks on the challenging DOTA dataset, outperforming all published methods by a large margin ( $$+6$$ % and $$+12$$ % absolute improvement, respectively). Furthermore, it generalizes to two other datasets, NWPU VHR-10 and UCAS-AOD, and achieves competitive results with the baselines even when trained on DOTA. Our method can be deployed in multi-class object detection applications, regardless of the image and object scales and orientations, making it a great choice for unconstrained aerial and satellite imagery.
Correlated charge noise and relaxation errors in superconducting qubits

The central challenge in building a quantum computer is error correction. Unlike classical bits, which are susceptible to only one type of error, quantum bits (qubits) are susceptible to two types of error, corresponding to flips of the qubit state about the X and Z directions. Although the Heisenberg uncertainty principle precludes simultaneous monitoring of X- and Z-flips on a single qubit, it is possible to encode quantum information in large arrays of entangled qubits that enable accurate monitoring of all errors in the system, provided that the error rate is low1. Another crucial requirement is that errors cannot be correlated. Here we characterize a superconducting multiqubit circuit and find that charge noise in the chip is highly correlated on a length scale over 600 micrometres; moreover, discrete charge jumps are accompanied by a strong transient reduction of qubit energy relaxation time across the millimetre-scale chip. The resulting correlated errors are explained in terms of the charging event and phonon-mediated quasiparticle generation associated with absorption of γ-rays and cosmic-ray muons in the qubit substrate. Robust quantum error correction will require the development of mitigation strategies to protect multiqubit arrays from correlated errors due to particle impacts. Cosmic-ray particles and γ-rays striking superconducting circuits can generate qubit errors that are spatially correlated across several millimetres, hampering current error-correction approaches.
7.7 LNPU: A 25.3TFLOPS/W Sparse Deep-Neural-Network Learning Processor with Fine-Grained Mixed Precision of FP8-FP16

Recently, deep neural network (DNN) hardware accelerators have been reported for energy-efficient deep learning (DL) acceleration [1–6]. Most prior DNN inference accelerators are trained in the cloud using public datasets; parameters are then downloaded to implement AI [1–5]. However, local DNN learning with domain-specific and private data is required meet various user preferences on edge or mobile devices. Since edge and mobile devices contain only limited computation capability with battery power, an energy-efficient DNN learning processor is necessary. Only [6] supported on-chip DNN learning, but it was not energy-efficient, as it did not utilize sparsity which represents 37%-61% of the inputs for various CNNs, such as VGG16, AlexNet and ResNet-18, as shown in Fig. 7.7.1. Although [3–5] utilized the sparsity, they only considered the inference phase with inter-channel accumulation in Fig. 7.7.1, and did not support intra-channel accumulation for the weight-gradient generation (WG) step of the learning phase. Also, [6] adopted FP16, but it was not energy optimal because FP8 is enough for many input operands with 4× less energy than FP16.
Readmission prediction using deep learning on electronic health records

Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. In order to reduce readmissions and curb the cost of care, it is important to initiate targeted intervention programs for patients at risk of readmission. This requires identifying high-risk patients at the time of discharge from hospital. Here, using real data from over 7500 CHF patients hospitalized between 2012 and 2016 in Sweden, we built and tested a deep learning framework to predict 30-day unscheduled readmission. We present a cost-sensitive formulation of Long Short-Term Memory (LSTM) neural network using expert features and contextual embedding of clinical concepts. This study targets key elements of an Electronic Health Record (EHR) driven prediction model in a single framework: using both expert and machine derived features, incorporating sequential patterns and addressing the class imbalance problem. We evaluate the contribution of each element towards prediction performance (ROC-AUC, F1-measure) and cost-savings. We show that the model with all key elements achieves higher discrimination ability (AUC: 0.77; F1: 0.51; Cost: 22% of maximum possible savings) outperforming the reduced models in at least two evaluation metrics. Additionally, we present a simple financial analysis to estimate annual savings if targeted interventions are offered to high risk patients.
Deep Neural Network Initialization With Decision Trees

In this paper, a novel, automated process for constructing and initializing deep feedforward neural networks based on decision trees is presented. The proposed algorithm maps a collection of decision trees trained on the data into a collection of initialized neural networks with the structures of the networks determined by the structures of the trees. The tree-informed initialization acts as a warm-start to the neural network training process, resulting in efficiently trained, accurate networks. These models, referred to as "deep jointly informed neural networks" (DJINN), demonstrate high predictive performance for a variety of regression and classification data sets and display comparable performance to Bayesian hyperparameter optimization at a lower computational cost. By combining the user-friendly features of decision tree models with the flexibility and scalability of deep neural networks, DJINN is an attractive algorithm for training predictive models on a wide range of complex data sets.
MapReduce-based big data classification model using feature subset selection and hyperparameter tuned deep belief network

In recent times, big data classification has become a hot research topic in various domains, such as healthcare, e-commerce, finance, etc. The inclusion of the feature selection process helps to improve the big data classification process and can be done by the use of metaheuristic optimization algorithms. This study focuses on the design of a big data classification model using chaotic pigeon inspired optimization (CPIO)-based feature selection with an optimal deep belief network (DBN) model. The proposed model is executed in the Hadoop MapReduce environment to manage big data. Initially, the CPIO algorithm is applied to select a useful subset of features. In addition, the Harris hawks optimization (HHO)-based DBN model is derived as a classifier to allocate appropriate class labels. The design of the HHO algorithm to tune the hyperparameters of the DBN model assists in boosting the classification performance. To examine the superiority of the presented technique, a series of simulations were performed, and the results were inspected under various dimensions. The resultant values highlighted the supremacy of the presented technique over the recent techniques.
Resilient Chatbots

Text-based conversational systems, also referred to as chatbots, have grown widely popular. Current natural language understanding technologies are not yet ready to tackle the complexities in conversational interactions. Breakdowns are common, leading to negative user experiences. Guided by communication theories, we explore user preferences for eight repair strategies, including ones that are common in commercially-deployed chatbots (e.g., confirmation, providing options), as well as novel strategies that explain characteristics of the underlying machine learning algorithms. We conducted a scenario-based study to compare repair strategies with Mechanical Turk workers (N=203). We found that providing options and explanations were generally favored, as they manifest initiative from the chatbot and are actionable to recover from breakdowns. Through detailed analysis of participants' responses, we provide a nuanced understanding on the strengths and weaknesses of each repair strategy.
Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems

Engineering design problems are usually large-scale constrained optimization problems, and metaheuristic algorithms are vital for solving such complex problems. Therefore, this paper introduces a new nature-inspired metaheuristic algorithm: the gannet optimization algorithm (GOA). The GOA mathematizes the various unique behaviors of gannets during foraging and is used to enable exploration and exploitation. GOA’s U-shaped and V-shaped diving patterns are responsible for exploring the optimal region within the search space, with sudden turns and random walks ensuring better solutions are found in this region. In order to verify the ability of the GOA to find the optimal solution, we compared it with other comparison algorithms in multiple dimensions of 28 benchmark functions. We found that the GOA has a shorter running time in high dimensions and can provide a better solution. Finally, we apply the GOA to five engineering optimization problems. The experimental results show that the GOA is suitable for many constrained engineering design problems and can provide better solutions in most cases.