Reviewerly/exHarmony huggingface dataset:
Dwarf Mongoose Optimization Algorithm

This paper proposes a new metaheuristic algorithm called dwarf mongoose optimization algorithm (DMO) to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The DMO mimics the foraging behavior of the dwarf mongoose. The restrictive mode of prey capture (feeding) has dramatically affected the mongooses’ social behavior and ecological adaptations to compensate for efficient family nutrition. The compensatory behavioral adaptations of the mongoose are prey size, space utilization, group size, and food provisioning. Three social groups of the dwarf mongoose are used in the proposed algorithm, the alpha group, babysitters, and the scout group. The family forage as a unit, and the alpha female initiates foraging, determines the foraging path, the distance covered, and the sleeping mounds. A certain number of the mongoose population (usually a mixture of males and females) serve as the babysitters. They remain with the young until the group returns at midday or evening. The babysitters are exchanged for the first to forage with the group (exploitation phase). The dwarf mongooses do not build a nest for their young; they move them from one sleeping mound to another and do not return to the previously foraged site. The dwarf mongoose has adopted a seminomadic way of life in a territory large enough to support the entire group (exploration phase). The nomadic behavior prevents overexploitation of a particular area. It also ensures exploration of the whole territory because no previously visited sleeping mound is returned. The performance of the proposed DMO algorithm is compared with seven other algorithms to show its effectiveness in terms of different performance metrics and statistics. In most cases, the near-optimal solutions achieved by the DMO are better than the best solutions obtained by the current state-of-the-art algorithms. Matlab codes of DMO are available at https://www.mathworks.com/matlabcentral/fileexchange/105125-dwarf-mongoose-optimization-algorithm.
Blockchain for Supply Chain Traceability: Business Requirements and Critical Success Factors

We seek to guide operations management (OM) research on the implementation of supply chain traceability systems by identifying business requirements and the factors critical to successful implementation. We first motivate the need for implementing traceability systems in two very different industries—cobalt mining and pharmaceuticals—and present business requirements and critical success factors for implementation. Next, we describe how we carried out thematic analysis of practitioner and scholarly articles on implementing blockchain for supply chain traceability. Finally, we present our results pertaining to the needs of different stakeholders such as suppliers, consumers, and regulators. The business requirements for traceability systems are curbing illegal practices; improving sustainability performance; increasing operational efficiency; enhancing supply‐chain coordination; and sensing market trends. Critical success factors for implementation are companies’ capabilities; collaboration; technology maturity; supply chain practices; leadership; and governance of the traceability efforts. These findings provide a nascent measurement model for empirical work and a foundation for descriptive and normative research on blockchain applications for supply chain traceability.
A mayfly optimization algorithm

This paper introduces a new method called the Mayfly Algorithm (MA) to solve optimization problems. Inspired from the flight behavior and the mating process of mayflies, the proposed algorithm combines major advantages of swarm intelligence and evolutionary algorithms. To evaluate the performance of the proposed algorithm, 38 mathematical benchmark functions, including 13 CEC2017 test functions, are employed and the results are compared to those of seven state of the art well-known metaheuristic optimization methods. The MA’s performance is also assessed through convergence behavior in multi-objective optimization as well as using a real-world discrete flow-shop scheduling problem. The comparison results demonstrate the superiority of the proposed method in terms of convergence rate and convergence speed. The processes of nuptial dance and random flight enhance the balance between algorithm’s exploration and exploitation properties and assist its escape from local optima.
pixelNeRF: Neural Radiance Fields from One or Few Images

We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. The existing approach for constructing neural radiance fields [27] involves optimizing the representation to every scene independently, requiring many calibrated views and significant compute time. We take a step towards resolving these shortcomings by introducing an architecture that conditions a NeRF on image inputs in a fully convolutional manner. This allows the network to be trained across multiple scenes to learn a scene prior, enabling it to perform novel view synthesis in a feed-forward manner from a sparse set of views (as few as one). Leveraging the volume rendering approach of NeRF, our model can be trained directly from images with no explicit 3D supervision. We conduct extensive experiments on ShapeNet benchmarks for single image novel view synthesis tasks with held-out objects as well as entire unseen categories. We further demonstrate the flexibility of pixelNeRF by demonstrating it on multi-object ShapeNet scenes and real scenes from the DTU dataset. In all cases, pixelNeRF outperforms current state-of-the-art baselines for novel view synthesis and single image 3D reconstruction. For the video and code, please visit the project website:https://alexyu.net/pixelnerf.
Artificial Intelligence and Management: The Automation–Augmentation Paradox

Taking three recent business books on artificial intelligence (AI) as a starting point, we explore the automation and augmentation concepts in the management domain. Whereas automation implies that machines take over a human task, augmentation means that humans collaborate closely with machines to perform a task. Taking a normative stance, the three books advise organizations to prioritize augmentation, which they relate to superior performance. Using a more comprehensive paradox theory perspective, we argue that, in the management domain, augmentation cannot be neatly separated from automation. These dual AI applications are interdependent across time and space, creating a paradoxical tension. Overemphasizing either augmentation or automation fuels reinforcing cycles with negative organizational and societal outcomes. However, if organizations adopt a broader perspective comprising both automation and augmentation, they could deal with the tension and achieve complementarities that benefit business and society. Drawing on our insights, we conclude that management scholars need to be involved in research on the use of AI in organizations. We also argue that a substantial change is required in how AI research is currently conducted in order to develop meaningful theory and to provide practice with sound advice.
Optimizing Federated Learning on Non-IID Data with Reinforcement Learning

The widespread deployment of machine learning applications in ubiquitous environments has sparked interests in exploiting the vast amount of data stored on mobile devices. To preserve data privacy, Federated Learning has been proposed to learn a shared model by performing distributed training locally on participating devices and aggregating the local models into a global one. However, due to the limited network connectivity of mobile devices, it is not practical for federated learning to perform model updates and aggregation on all participating devices in parallel. Besides, data samples across all devices are usually not independent and identically distributed (IID), posing additional challenges to the convergence and speed of federated learning. In this paper, we propose Favor, an experience-driven control framework that intelligently chooses the client devices to participate in each round of federated learning to counterbalance the bias introduced by non-IID data and to speed up convergence. Through both empirical and mathematical analysis, we observe an implicit connection between the distribution of training data on a device and the model weights trained based on those data, which enables us to profile the data distribution on that device based on its uploaded model weights. We then propose a mechanism based on deep Q-learning that learns to select a subset of devices in each communication round to maximize a reward that encourages the increase of validation accuracy and penalizes the use of more communication rounds. With extensive experiments performed in PyTorch, we show that the number of communication rounds required in federated learning can be reduced by up to 49% on the MNIST dataset, 23% on FashionMNIST, and 42% on CIFAR-10, as compared to the Federated Averaging algorithm.
A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective

Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough labeled data. Second, unlike traditional machine learning, deep learning techniques automatically generate features, which saves feature engineering costs, but in return may require larger amounts of labeled data. Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data. In this survey, we perform a comprehensive study of data collection from a data management point of view. Data collection largely consists of data acquisition, data labeling, and improvement of existing data or models. We provide a research landscape of these operations, provide guidelines on which technique to use when, and identify interesting research challenges. The integration of machine learning and data management for data collection is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research.
Multiscale Vision Transformers

We present Multiscale Vision Transformers (MViT) for video and image recognition, by connecting the seminal idea of multiscale feature hierarchies with transformer models. Multiscale Transformers have several channel-resolution scale stages. Starting from the input resolution and a small channel dimension, the stages hierarchically expand the channel capacity while reducing the spatial resolution. This creates a multiscale pyramid of features with early layers operating at high spatial resolution to model simple low-level visual information, and deeper layers at spatially coarse, but complex, high-dimensional features. We evaluate this fundamental architectural prior for modeling the dense nature of visual signals for a variety of video recognition tasks where it outperforms concurrent vision transformers that rely on large scale external pre-training and are 5-10× more costly in computation and parameters. We further remove the temporal dimension and apply our model for image classification where it outperforms prior work on vision transformers. Code is available at: https://github.com/facebookresearch/SlowFast.
ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks

As designing appropriate Convolutional Neural Network (CNN) architecture in the context of a given application usually involves heavy human works or numerous GPU hours, the research community is soliciting the architecture-neutral CNN structures, which can be easily plugged into multiple mature architectures to improve the performance on our real-world applications. We propose Asymmetric Convolution Block (ACB), an architecture-neutral structure as a CNN building block, which uses 1D asymmetric convolutions to strengthen the square convolution kernels. For an off-the-shelf architecture, we replace the standard square-kernel convolutional layers with ACBs to construct an Asymmetric Convolutional Network (ACNet), which can be trained to reach a higher level of accuracy. After training, we equivalently convert the ACNet into the same original architecture, thus requiring no extra computations anymore. We have observed that ACNet can improve the performance of various models on CIFAR and ImageNet by a clear margin. Through further experiments, we attribute the effectiveness of ACB to its capability of enhancing the model's robustness to rotational distortions and strengthening the central skeleton parts of square convolution kernels.
Recent Progress on Generative Adversarial Networks (GANs): A Survey

Generative adversarial network (GANs) is one of the most important research avenues in the field of artificial intelligence, and its outstanding data generation capacity has received wide attention. In this paper, we present the recent progress on GANs. First, the basic theory of GANs and the differences among different generative models in recent years were analyzed and summarized. Then, the derived models of GANs are classified and introduced one by one. Third, the training tricks and evaluation metrics were given. Fourth, the applications of GANs were introduced. Finally, the problem, we need to address, and future directions were discussed.
Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges

There is a growing interest in the wireless communications community to complement the traditional model-driven design approaches with data-driven machine learning (ML)-based solutions. While conventional ML approaches rely on the assumption of having the data and processing heads in a central entity, this is not always feasible in wireless communications applications because of the inaccessibility of private data and large communication overhead required to transmit raw data to central ML processors. As a result, decentralized ML approaches that keep the data where it is generated are much more appealing. Due to its privacy-preserving nature, federated learning is particularly relevant for many wireless applications, especially in the context of fifth generation (5G) networks. In this article, we provide an accessible introduction to the general idea of federated learning, discuss several possible applications in 5G networks, and describe key technical challenges and open problems for future research on federated learning in the context of wireless communications.
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

Many real-world cyber-physical systems (CPSs) are engineered for mission-critical tasks and usually are prime targets for cyber-attacks. The rich sensor data in CPSs can be continuously monitored for intrusion events through anomaly detection. On one hand, conventional supervised anomaly detection methods are unable to exploit the large amounts of data due to the lack of labelled data. On the other hand, current unsupervised machine learning approaches have not fully exploited the spatial-temporal correlation and other dependencies amongst the multiple variables (sensors/actuators) in the system when detecting anomalies. In this work, we propose an unsupervised multivariate anomaly detection method based on Generative Adversarial Networks (GANs), using the Long-Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) as the base models (namely, the generator and discriminator) in the GAN framework to capture the temporal correlation of time series distributions. Instead of treating each data stream independently, our proposed Multivariate Anomaly Detection with GAN (MAD-GAN) framework considers the entire variable set concurrently to capture the latent interactions amongst the variables. We also fully exploit both the generator and discriminator produced by the GAN, using a novel anomaly score called DR-score to detect anomalies through discrimination and reconstruction. We have tested our proposed MAD-GAN using two recent datasets collected from real-world CPSs: the Secure Water Treatment (SWaT) and the Water Distribution (WADI) datasets. Our experimental results show that the proposed MAD-GAN is effective in reporting anomalies caused by various cyber-attacks inserted in these complex real-world systems.
Deep Leakage from Gradients

Exchanging model updates is a widely used method in the modern federated learning system. For a long time, people believed that gradients are safe to share: i.e., the gradients are less informative than the training data. However, there is information hidden in the gradients. Moreover, it is even possible to reconstruct the private training data from the publicly shared gradients. This chapter discusses techniques that reveal information hidden in gradients and validate the effectiveness on common deep learning tasks. It is important to raise people’s awareness to rethink the gradient’s safety. Several possible defense strategies have also been discussed to prevent such privacy leakage.
OM Forum—Distributed Ledgers and Operations: What Operations Management Researchers Should Know About Blockchain Technology

Problem definition: Blockchain is a form of distributed ledger technology. While it has grown in prominence, its full potential and possible downsides are not fully understood yet, especially with respect to operations management (OM). Academic/practical relevance: This article fills this gap. Methodology: After briefly reviewing the technical foundations, we explore multiple business and policy aspects. Results: We identify five key strengths, the corresponding five main weaknesses, and three research themes of applying blockchain technology to OM. The key strengths are (1) visibility, (2) aggregation, (3) validation, (4) automation, and (5) resiliency. The corresponding weaknesses are (1) lack of privacy, (2) lack of standardization, (3) garbage in, garbage out, (4) black box effect, and (5) inefficiency. The three research themes are (1) information, (2) automation, and (3) tokenization. Managerial implications: We illustrate these research themes with multiple promising research problems, ranging from classical inventory management, to new areas of ethical OM, and to questions of industrial organization.
PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

Recent advances in image-based 3D human shape estimation have been driven by the significant improvement in representation power afforded by deep neural networks. Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images. We argue that this limitation stems primarily form two conflicting requirements; accurate predictions require large context, but precise predictions require high resolution. Due to memory limitations in current hardware, previous approaches tend to take low resolution images as input to cover large spatial context, and produce less precise (or low resolution) 3D estimates as a result. We address this limitation by formulating a multi-level architecture that is end-to-end trainable. A coarse level observes the whole image at lower resolution and focuses on holistic reasoning. This provides context to an fine level which estimates highly detailed geometry by observing higher-resolution images. We demonstrate that our approach significantly outperforms existing state-of-the-art techniques on single image human shape reconstruction by fully leveraging 1k-resolution input images.
LSTM-CNN Architecture for Human Activity Recognition

In the past years, traditional pattern recognition methods have made great progress. However, these methods rely heavily on manual feature extraction, which may hinder the generalization model performance. With the increasing popularity and success of deep learning methods, using these techniques to recognize human actions in mobile and wearable computing scenarios has attracted widespread attention. In this paper, a deep neural network that combines convolutional layers with long short-term memory (LSTM) was proposed. This model could extract activity features automatically and classify them with a few model parameters. LSTM is a variant of the recurrent neural network (RNN), which is more suitable for processing temporal sequences. In the proposed architecture, the raw data collected by mobile sensors was fed into a two-layer LSTM followed by convolutional layers. In addition, a global average pooling layer (GAP) was applied to replace the fully connected layer after convolution for reducing model parameters. Moreover, a batch normalization layer (BN) was added after the GAP layer to speed up the convergence, and obvious results were achieved. The model performance was evaluated on three public datasets (UCI, WISDM, and OPPORTUNITY). Finally, the overall accuracy of the model in the UCI-HAR dataset is 95.78%, in the WISDM dataset is 95.85%, and in the OPPORTUNITY dataset is 92.63%. The results show that the proposed model has higher robustness and better activity detection capability than some of the reported results. It can not only adaptively extract activity features, but also has fewer parameters and higher accuracy.
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation

Convolutional neural networks have been widely deployed in various application scenarios. In order to extend the applications' boundaries to some accuracy-crucial domains, researchers have been investigating approaches to boost accuracy through either deeper or wider network structures, which brings with them the exponential increment of the computational and storage cost, delaying the responding time. In this paper, we propose a general training framework named self distillation, which notably enhances the performance (accuracy) of convolutional neural networks through shrinking the size of the network rather than aggrandizing it. Different from traditional knowledge distillation - a knowledge transformation methodology among networks, which forces student neural networks to approximate the softmax layer outputs of pre-trained teacher neural networks, the proposed self distillation framework distills knowledge within network itself. The networks are firstly divided into several sections. Then the knowledge in the deeper portion of the networks is squeezed into the shallow ones. Experiments further prove the generalization of the proposed self distillation framework: enhancement of accuracy at average level is 2.65%, varying from 0.61% in ResNeXt as minimum to 4.07% in VGG19 as maximum. In addition, it can also provide flexibility of depth-wise scalable inference on resource-limited edge devices. Our codes have been released on github.